1. An information processing method characterized by comprising:

receiving an information processing request, and further acquiring corresponding to-be-processed client information;

acquiring social credit data to perform labeling classification on the client information to be processed to generate a corresponding classification label;

determining target customer information to be processed based on the classification label and the customer information to be processed;

determining customer registration capital and a customer location corresponding to the target customer information to be processed, further determining a distance between the customer location and a preset target place, and clustering the target customer information to be processed based on the distance and the customer registration capital to generate a clustering cluster;

and determining the central point numerical value of each cluster, and sequencing the target to-be-processed client information based on the central point numerical value to obtain and output target client information through screening.

2. The method of claim 1, wherein generating the corresponding classification label comprises:

determining each legal representative person information corresponding to the to-be-processed client information and each corresponding contact way from a client basic information table of the social credit data, and determining the number of client information with different legal representative person information and same contact way in the to-be-processed client information;

and generating an agent account opening label to label the client information with different information of the legal representatives and the same contact way in the client information to be processed in response to the fact that the number of the client information is larger than a preset threshold value.

3. The method of claim 1, wherein generating the corresponding classification label comprises:

determining each coordinate information corresponding to the customer information to be processed from a customer coordinate information table in the social credit data, calling preset self-trade area coordinate range data to match with each coordinate information, and determining the self-trade area customer information in the customer information to be processed, wherein the customer information is located in the preset self-trade area coordinate range data;

a self trade area tag is generated to label the self trade area customer information.

4. The method of claim 1, wherein determining target pending customer information based on the category label and the pending customer information comprises:

and determining the client information with the classification label being empty in the client information to be processed as target client information to be processed.

5. The method of claim 1, wherein clustering the target pending customer information based on the distance and the customer registered capital, generating a cluster, comprises:

and clustering the target customer information to be processed according to a k-means clustering algorithm based on the distance and the customer registered capital respectively to generate distance cluster clusters and customer registered capital cluster clusters respectively.

6. The method of claim 5, wherein the sorting the target pending customer information based on the central point value to filter target customer information comprises:

determining the distance center point numerical value of each distance cluster, sequencing the distance center point numerical values from large to small, and assigning the numerical values from small to large respectively to generate the assignment of each distance cluster;

determining the numerical value of the client registered capital central point of each client registered capital cluster, sequencing the numerical values of the client registered capital central points from small to large, and assigning the numerical values from small to large respectively to generate the assignment of each client registered capital cluster;

and respectively calculating the sum of the distance cluster assignment corresponding to each piece of customer information in the to-be-processed customer information and the customer registered capital cluster assignment, sequencing the sum of the distance cluster assignment corresponding to each piece of customer information and the customer registered capital cluster assignment from large to small, and screening a preset amount of customer information to determine the customer information as target customer information.

7. The method of claim 1, wherein prior to said clustering said target pending customer information based on said distance and said customer registered capital, said method further comprises:

and according to an isolated forest algorithm, determining abnormal customer information in the target customer information to be processed, removing the abnormal customer information from the target customer information to be processed, and updating the target customer information to be processed.

8. An information processing apparatus characterized by comprising:

the receiving unit is configured to receive the information processing request and further acquire corresponding to-be-processed client information;

the classification label generating unit is configured to acquire social credit data so as to perform labeling classification on the to-be-processed customer information and generate a corresponding classification label;

a target pending customer information determination unit configured to determine target pending customer information based on the classification tag and the pending customer information;

the cluster generating unit is configured to determine customer registration capital and a customer location corresponding to the target customer information to be processed, further determine a distance between the customer location and a preset target place, and cluster the target customer information to be processed based on the distance and the customer registration capital to generate a cluster;

and the screening unit is configured to determine a central point numerical value of each cluster, and then sort the target to-be-processed client information based on the central point numerical value so as to screen and obtain the target client information, and output the target client information.

9. The apparatus of claim 8, wherein the classification label generating unit is further configured to:

determining each legal representative person information corresponding to the to-be-processed client information and each corresponding contact way from a client basic information table of the social credit data, and determining the number of client information with different legal representative person information and same contact way in the to-be-processed client information;

and generating an agent account opening label to label the client information with different information of the legal representatives and the same contact way in the client information to be processed in response to the fact that the number of the client information is larger than a preset threshold value.

10. The apparatus of claim 8, wherein the classification label generating unit is further configured to:

determining each coordinate information corresponding to the customer information to be processed from a customer coordinate information table in the social credit data, calling preset self-trade area coordinate range data to match with each coordinate information, and determining the self-trade area customer information in the customer information to be processed, wherein the customer information is located in the preset self-trade area coordinate range data;

a self trade area tag is generated to label the self trade area customer information.

11. An information processing electronic device characterized by comprising:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.

12. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.

Background

At present, in the face of a multi-source public account opening marketing business opportunity, training, testing and predicting are generally carried out by adopting an active client identification model and a marketing response client identification model, the matching degree of client asset grade scores and marketing activities is calculated, and the client marketing value is calculated by combining the active activity probability, the marketing response probability, the asset grade and the marketing activity matching degree, so that clients with high marketing value are screened out. But often the screening result is inaccurate, marketing cost is high, marketing success rate is low.

In the process of implementing the present application, the inventor finds that at least the following problems exist in the prior art:

the method has the advantages of inaccurate screening result, high marketing cost and low marketing success rate for the customers with high marketing value.

Disclosure of Invention

In view of this, the embodiments of the present application provide an information processing method and apparatus, which can solve the problems of inaccurate screening result, high marketing cost, and low marketing success rate of the existing client with high marketing value.

To achieve the above object, according to an aspect of an embodiment of the present application, there is provided an information processing method including:

receiving an information processing request, and further acquiring corresponding to-be-processed client information;

acquiring social credit data to perform labeling classification on client information to be processed to generate corresponding classification labels;

determining target customer information to be processed based on the classification label and the customer information to be processed;

determining customer registration capital and a customer location corresponding to the target customer information to be processed, further determining a distance between the customer location and a preset target place, and clustering the target customer information to be processed based on the distance and the customer registration capital to generate a cluster;

and determining the central point numerical value of each cluster, sequencing the target to-be-processed client information based on the central point numerical value, screening to obtain target client information, and outputting the target client information.

Optionally, generating a corresponding classification label includes:

determining each legal representative person information corresponding to the client information to be processed and each corresponding contact way from a client basic information table of the social credit data, and determining the number of client information with different legal representative person information and same contact way in the client information to be processed;

and generating an agent account opening label to label the client information with different information of legal representatives and the same contact way in the client information to be processed in response to the fact that the number of the client information is larger than the preset threshold value.

Optionally, generating a corresponding classification label includes:

determining each coordinate information corresponding to the customer information to be processed from a customer coordinate information table in the social credit data, calling preset self-trade area coordinate range data to be matched with each coordinate information, and determining the self-trade area customer information in the preset self-trade area coordinate range data in the customer information to be processed;

a self trade area tag is generated to label the self trade area customer information.

Optionally, determining the target pending customer information based on the classification label and the pending customer information includes:

and determining the client information with the classification label being empty in the client information to be processed as the target client information to be processed.

Optionally, clustering target pending customer information based on the distance and the customer registration capital, and generating a cluster, including:

and clustering target customer information to be processed according to a k-means clustering algorithm based on the distance and the customer registration capital respectively to generate distance cluster clusters and customer registration capital cluster clusters respectively.

Optionally, sorting the target to-be-processed client information based on the central point value to obtain target client information by screening, including:

determining the distance center point numerical value of each distance cluster, sequencing the distance center point numerical values from large to small, and assigning the numerical values from small to large respectively to generate the assignment of each distance cluster;

determining the numerical value of the client registered capital central point of each client registered capital cluster, sequencing the numerical values of the client registered capital central points from small to large, and assigning the numerical values from small to large respectively to generate the assignment of each client registered capital cluster;

respectively calculating the sum of the distance cluster assignment corresponding to each piece of customer information in the to-be-processed customer information and the customer registered capital cluster assignment, sequencing the sum of the distance cluster assignment corresponding to each piece of customer information and the customer registered capital cluster assignment from large to small, and screening a preset amount of customer information to determine the customer information as target customer information.

Optionally, before clustering the target pending customer information based on distance and customer registered capital, the method further comprises:

and determining abnormal customer information in the target customer information to be processed according to an isolated forest algorithm, removing the abnormal customer information from the target customer information to be processed, and updating the target customer information to be processed.

In addition, the present application also provides an information processing apparatus including:

the receiving unit is configured to receive the information processing request and further acquire corresponding to-be-processed client information;

the classification label generating unit is configured to acquire social credit data so as to perform labeling classification on the client information to be processed and generate a corresponding classification label;

a target pending customer information determination unit configured to determine target pending customer information based on the classification tag and the pending customer information;

the clustering generation unit is configured to determine client registration capital and client location corresponding to the target client information to be processed, further determine the distance between the client location and a preset target place, and cluster the target client information to be processed based on the distance and the client registration capital to generate clustering clusters;

and the screening unit is configured to determine a central point value of each cluster, and then sort the target to-be-processed client information based on the central point value so as to screen the target client information and output the target client information.

Optionally, the classification label generating unit is further configured to:

determining each legal representative person information corresponding to the client information to be processed and each corresponding contact way from a client basic information table of the social credit data, and determining the number of client information with different legal representative person information and same contact way in the client information to be processed;

and generating an agent account opening label to label the client information with different information of legal representatives and the same contact way in the client information to be processed in response to the fact that the number of the client information is larger than the preset threshold value.

Optionally, the classification label generating unit is further configured to:

determining each coordinate information corresponding to the customer information to be processed from a customer coordinate information table in the social credit data, calling preset self-trade area coordinate range data to be matched with each coordinate information, and determining the self-trade area customer information in the preset self-trade area coordinate range data in the customer information to be processed;

a self trade area tag is generated to label the self trade area customer information.

Optionally, the target pending customer information determination unit is further configured to:

and determining the client information with the classification label being empty in the client information to be processed as the target client information to be processed.

Optionally, the cluster generating unit is further configured to:

and clustering target customer information to be processed according to a k-means clustering algorithm based on the distance and the customer registration capital respectively to generate distance cluster clusters and customer registration capital cluster clusters respectively.

Optionally, the screening unit is further configured to:

determining the distance center point numerical value of each distance cluster, sequencing the distance center point numerical values from large to small, and assigning the numerical values from small to large respectively to generate the assignment of each distance cluster;

determining the numerical value of the client registered capital central point of each client registered capital cluster, sequencing the numerical values of the client registered capital central points from small to large, and assigning the numerical values from small to large respectively to generate the assignment of each client registered capital cluster;

respectively calculating the sum of the distance cluster assignment corresponding to each piece of customer information in the to-be-processed customer information and the customer registered capital cluster assignment, sequencing the sum of the distance cluster assignment corresponding to each piece of customer information and the customer registered capital cluster assignment from large to small, and screening a preset amount of customer information to determine the customer information as target customer information.

Optionally, the cluster generating unit is further configured to:

before clustering target to-be-processed customer information based on distance and customer registration capital, determining abnormal customer information in the target to-be-processed customer information according to an isolated forest algorithm, further removing the abnormal customer information from the target to-be-processed customer information, and updating the target to-be-processed customer information.

In addition, the present application also provides an information processing electronic device including: one or more processors; a storage device for storing one or more programs which, when executed by one or more processors, cause the one or more processors to implement the information processing method as described above.

In addition, the present application also provides a computer readable medium, on which a computer program is stored, which when executed by a processor implements the information processing method as described above.

One embodiment of the above invention has the following advantages or benefits: the method comprises the steps of receiving an information processing request, and further obtaining corresponding client information to be processed; acquiring social credit data to perform labeling classification on client information to be processed to generate corresponding classification labels; determining target customer information to be processed based on the classification label and the customer information to be processed; determining customer registration capital and a customer location corresponding to the target customer information to be processed, further determining a distance between the customer location and a preset target place, and clustering the target customer information to be processed based on the distance and the customer registration capital to generate a cluster; and determining the central point numerical value of each cluster, sequencing the target to-be-processed client information based on the central point numerical value, screening to obtain target client information, and outputting the target client information. Therefore, the method and the device cluster the target to-be-processed client information based on the distance between the location of the client and the destination (namely, the bank business outlets) and the client registration capital to generate the cluster, sort the cluster according to the central point value of the cluster, and screen the target client information, so that the target client information screening result can meet the conditions that the marketing distance is the shortest, the expected marketing benefit is the largest and the data processing burden is the smallest.

Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.

Drawings

The drawings are included to provide a further understanding of the application and are not to be construed as limiting the application. Wherein:

fig. 1 is a schematic diagram of a main flow of an information processing method according to a first embodiment of the present application;

fig. 2 is a schematic diagram of a main flow of an information processing method according to a second embodiment of the present application;

fig. 3 is a schematic view of an application scenario of an information processing method according to a third embodiment of the present application;

fig. 4 is a schematic diagram of main blocks of an information processing apparatus according to an embodiment of the present application;

FIG. 5 is an exemplary system architecture diagram to which embodiments of the present application may be applied;

fig. 6 is a schematic structural diagram of a computer system suitable for implementing the terminal device or the server according to the embodiment of the present application.

Detailed Description

The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

Fig. 1 is a schematic diagram of a main flow of an information processing method according to a first embodiment of the present application, and as shown in fig. 1, the information processing method includes:

step S101, receiving an information processing request, and further acquiring corresponding to-be-processed client information.

In this embodiment, an execution subject (for example, a server) of the information processing method may receive an information processing request by a wired connection or a wireless connection, where the information processing request may be a request for performing a filtering process on the customer information in the marketing target pool.

In some optional implementation manners of this embodiment, the execution subject may obtain the to-be-processed client information in the corresponding marketing target pool according to the identifier in the received information processing request, which may specifically be a marketing target pool number. The pending customer information may be derived from feedback information of the bank marketing campaign. The method is suitable for the situation that a plurality of marketing target pools exist.

As another implementation manner, when only one marketing target pool exists, the to-be-processed client information corresponding to the feedback information of the bank marketing activity is placed in the marketing target pool, and the execution main body triggers a process of acquiring the to-be-processed client information from the marketing target pool when receiving an information processing request, so as to process the to-be-processed client information.

Step S102, social credit data are obtained to perform labeling classification on the client information to be processed, and corresponding classification labels are generated.

In this embodiment, the social credit data may include a client basic information table (specifically, an enterprise basic information table). For example, the enterprise basic information table may specifically include data of a legal representative corresponding to each enterprise, a contact telephone of the legal representative, each enterprise registration address, enterprise coordinate information, and the like.

In some optional implementations of this embodiment, generating the corresponding classification label includes:

determining each legal representative person information corresponding to the client information to be processed and each corresponding contact way from a client basic information table of the social credit data, and determining the number of client information with different legal representative person information and same contact way in the client information to be processed; and generating an agent account opening label to label the client information with different information of legal representatives and the same contact way in the client information to be processed in response to the fact that the number of the client information is larger than the preset threshold value.

For example, the executive body may determine the number of enterprises with different legal representatives and the same contact telephone number in the enterprise basic information table based on the social credit data, and mark the enterprises with different legal representatives and the same contact telephone number with an agent account opening label when the determined number of enterprises is greater than or equal to a preset value. The preset value may be 5, or may be other values, which is not limited in this application. Exemplary, pseudo code for a particular implementation may be as follows:

select name of enterprise from basic information of enterprise

(contact telephone) in

(

Select contact telephone

From(

select distinting contact telephone, name of legal person

from Enterprise basic information Table t1

) group by contact telephone changing count (1) > -5

)

In some optional implementations of this embodiment, generating a corresponding classification label further includes:

determining each coordinate information corresponding to the customer information to be processed from a customer coordinate information table in the social credit data, calling preset self-trade area coordinate range data to be matched with each coordinate information, and determining the self-trade area customer information in the preset self-trade area coordinate range data in the customer information to be processed; a self trade area tag is generated to label the self trade area customer information. For example, the executing agent may label the enterprise with a "self-trade area" label based on social credit data with the enterprise registration address coordinate drop point within the identified self-trade area coordinate range. Exemplary, pseudo code for a particular implementation may be as follows:

select Enterprise name

from Enterprise coordinate information t1

where

Exists(select 1

from trade area coordinate range t2

where

T1, longitude between t 2. Minimum longitude and t 2. Maximum longitude

And T1. dimension between t 2. Smallest dimension and t 2. Maximum dimension

)

The executive body can judge whether to carry out preferential marketing on the enterprise customers through the generated classification labels.

And step S103, determining target customer information to be processed based on the classification label and the customer information to be processed.

In some optional implementations of this embodiment, determining the target to-be-processed client information based on the classification label and the to-be-processed client information includes:

and determining the client information with the classification label being empty in the client information to be processed as the target client information to be processed.

Specifically, the executing agent may only select the pre-judged enterprise with poor marketing effect to generate the "proxy account opening" label and the "self trade area" label. For example, the enterprise generating the "agent opening" tag may be an opening enterprise which is facilitated by some agent companies, and if a call is made to the enterprise for marketing, the enterprise may not receive the call by the enterprise legal person, so that the marketing effect is not ideal, so that the tag is generated for marking and the enterprises are divided into a group with the lowest marketing priority. For example, the enterprises generating the "self-trade area" tags may be some vacant companies to which there is a risk of making a call to cause poor marketing effect, so the tags are generated for marking and the enterprises are also classified into a group with the lowest marketing priority.

For example, the executing entity may determine, as the target pending client information (specifically, the target pending business), the client information (specifically, the business) of the pending client information for which the classification tag is not generated, that is, the classification tag is empty. The enterprise corresponding to the target to-be-processed client information is indicated to be neither an enterprise opening an account by an agent nor a self-trade area enterprise (i.e., a vacant company), but a normal enterprise with a better pre-judged marketing effect, and then the executive subject can determine the normal enterprise with the better pre-judged marketing effect (i.e., the client information) as the target to-be-processed enterprise (i.e., the target to-be-processed client information).

And step S104, determining customer registration capital and a customer location corresponding to the target customer information to be processed, further determining the distance between the customer location and a preset target area, and clustering the target customer information to be processed based on the distance and the customer registration capital to generate a cluster.

Specifically, prior to clustering the target pending customer information based on distance and customer registered capital, the method further comprises:

and determining abnormal customer information in the target customer information to be processed according to an isolated forest algorithm, removing the abnormal customer information from the target customer information to be processed, and updating the target customer information to be processed.

Illustratively, according to two factors mainly considered when the bank user marketing customer makes an account: customer location distance from bank outlet (Di), customer registered capital (Ci). And calculating the distance (Di) between the location of the customer and the bank business network according to the address coordinates of the business network and the registered address coordinates of the customer. And carrying out abnormal point detection on the Di and the Ci by using an isolated forest algorithm. Firstly, selecting clients entering an account opening marketing pool in about 1 month in a reasonable region range as samples (i clients), setting the number of binary trees according to i/3, and using the algorithm as follows:

clf＝IsolationForest(max_samples＝i/3*2,random_state＝rng)

clf.fit(X_train)

y_pred_train＝clf.predict(X_train)

y_pred_test＝clf.predict(X_test)

y_pred_outliers＝clf.predict(X_outliers)

and determining and recording abnormal points of Di and Ci measured by the algorithm, and excluding the abnormal points during K-means clustering. For example, an outlier may be customer information for which Di is too large (i.e., too far away) and Ci is too large (i.e., high in registered capital). Alternatively, the outlier may be customer information corresponding to Di being too small (i.e., too close in distance) but Ci being too small (i.e., registration capital being small).

The executive body can cluster the target customer information to be processed after the abnormal points are removed according to the distance (Di) between the customer location and the bank business outlet and the customer registration capital (Ci) respectively to generate corresponding cluster clusters respectively. Specifically, Di and Ci excluding the outlier are respectively set to K_DN and K_CAnd (3) carrying out K-means clustering (K-means clustering algorithm) by using Python, and further generating a clustering cluster corresponding to the distance (Di) and a clustering cluster corresponding to the client registration capital (Ci).

And S105, determining the central point numerical value of each cluster, sequencing the target to-be-processed client information based on the central point numerical value to obtain and output the target client information through screening.

In this embodiment, after the cluster is generated, the execution subject may sort the clustered cluster according to the central point value. In an example, the cluster clusters corresponding to the distance Di are sorted from large to small according to the central point numerical values, then a group with the largest central point numerical value is assigned with 1, a group with the second largest central point numerical value is assigned with 2, a group with the third largest central point numerical value is assigned with 3, … …, and a group with the smallest central point numerical value is assigned with n, so that Di is converted from a specific value to one of 1 to n. And sorting the registered capital Ci according to the central point numerical values from small to large, assigning a group with the smallest central point numerical value to 1, assigning a group with the second smallest central point numerical value to 2, assigning a group with the third smallest central point numerical value to 3, … …, and assigning a group with the largest central point numerical value to m, so that the specific value of Ci is converted into a certain numerical value from 1 to m. The executing agent can respectively convert Di and Ci of the outliers measured by the isolated forest algorithm into corresponding numbers from 1 to n (corresponding to the conversion assignment result of Di of the outliers) and corresponding numbers from 1 to m (corresponding to the conversion assignment result of Ci of the outliers) according to the comparison with the central point value of each corresponding classification cluster. Thereby obtaining a normalized value (Di ') of the distance from the bank business network to each customer site, and a normalized value (Ci') of the customer registered capital. Therefore, the data clustering and sorting of the distance (Di) between the customer location and the bank business outlet and the customer registration capital (Ci) are completed.

Finally, the executive body can sort the clients according to the sum of Di '+ Ci' of the single client from large to small, and the recommendation result meets the requirements of the three aspects of the shortest marketing distance, the maximum expected marketing benefit and the minimum data processing load.

The embodiment further obtains the corresponding client information to be processed by receiving the information processing request; acquiring social credit data to perform labeling classification on client information to be processed to generate corresponding classification labels; determining target customer information to be processed based on the classification label and the customer information to be processed; determining customer registration capital and a customer location corresponding to the target customer information to be processed, further determining a distance between the customer location and a preset target place, and clustering the target customer information to be processed based on the distance and the customer registration capital to generate a cluster; and determining the central point numerical value of each cluster, sequencing the target to-be-processed client information based on the central point numerical value, screening to obtain target client information, and outputting the target client information. Therefore, the method and the device cluster the target to-be-processed client information based on the distance between the location of the client and the destination (namely, the bank business outlets) and the client registration capital to generate the cluster, sort the cluster according to the central point value of the cluster, and screen the target client information, so that the target client information screening result can meet the conditions that the marketing distance is the shortest, the expected marketing benefit is the largest and the data processing burden is the smallest.

Fig. 2 is a schematic main flow diagram of an information processing method according to a second embodiment of the present application, and as shown in fig. 2, the information processing method includes:

step S201, receiving an information processing request, and further acquiring corresponding to-be-processed client information.

Step S202, social credit data are obtained to perform labeling classification on the client information to be processed, and corresponding classification labels are generated.

Step S203, target customer information to be processed is determined based on the classification label and the customer information to be processed.

Step S204, determining customer registration capital and a customer location corresponding to the target customer information to be processed, further determining a distance between the customer location and a preset target area, and clustering the target customer information to be processed based on the distance and the customer registration capital to generate a cluster.

The principle of step S201 to step S204 is similar to that of step S101 to step S104, and is not described here again.

Specifically, step S204 can also be implemented by step S2041:

step S2041, clustering target customer information to be processed according to a k-means clustering algorithm based on the distance and the customer registered capital respectively, and generating distance cluster clusters and customer registered capital cluster clusters respectively.

For example, the K-means clustering algorithm may be to select K randomly first_DThe distance (Di) between the location of n customers and the bank business outlet is used as the initial distance clustering center and K is selected_CAs an initial registered capital clustering center, m customer registered capital (Ci).

The executive may then calculate the distance between each object in the customer's location and the bank outlet's distance (Di) and the respective initial distant cluster center, assigning each object to the cluster center closest to it. The cluster centers and the objects assigned to them represent a cluster. Once all objects are assigned, the cluster center for each cluster is recalculated based on the objects existing in the cluster. This process will be repeated until some termination condition is met. The termination condition may be any one of the following: no (or minimum number) objects are reassigned to different clusters; no (or minimal) cluster center recurrence change; the sum of squared errors is locally minimal. And finally generating a clustering cluster corresponding to the distance (Di). The generation manner of the cluster corresponding to the client registration capital (Ci) is the same, and is not described here again.

Step S205, determining the central point value of each cluster, and further sorting the target to-be-processed client information based on the central point value to obtain and output the target client information through screening.

The principle of step S205 is similar to that of step S105, and is not described here again.

Specifically, step S205 can also be realized by step S2051 to step S2053:

step S2051, determining the distance center point value of each distance cluster, sorting the distance center point values from large to small, and assigning values from small to large, respectively, to generate an assignment for each distance cluster.

And step S2052, determining the numerical value of the client registered capital central point of each client registered capital cluster, sequencing the numerical values of the client registered capital central points from small to large, and assigning the numerical values from small to large respectively to generate the value assignment of each client registered capital cluster.

Step S2053 is to calculate the sum of the distance cluster assignment corresponding to each piece of customer information in the to-be-processed customer information and the customer registered capital cluster assignment, sort the sum of the distance cluster assignment corresponding to each piece of customer information and the customer registered capital cluster assignment from large to small, and further screen a preset amount of customer information to determine the customer information as the target customer information.

Specifically, the executive agent ranks the distance center point numerical values of the distance cluster from large to small and respectively assigns numerical values from small to large, ranks the registered capital center point numerical values of each client from small to large and respectively assigns numerical values from small to large, so as to determine the client information with the closest marketing distance, the maximum expected marketing benefit and the minimum data processing burden according to the sum of the ranked and assigned distance standard value (Di ') of each client and the registered customer standard value (Ci') of each client, and realize the quick and accurate determination of the high-quality client with the closest distance and the high registered customer so as to realize accurate marketing, and the marketing success rate is improved.

Specifically, the executive body classifies and orders the businesses entering the open marketing objective pool by utilizing social credit data. The bank user can arrange an account opening marketing sequence and marketing key points according to the classification labels and the sequencing result so as to determine a high-quality client which is closest to the bank business outlet (Di) and has the highest client registration capital (Ci) to perform accurate marketing, thereby saving marketing cost and improving marketing success rate.

Fig. 3 is a schematic view of an application scenario of an information processing method according to a third embodiment of the present application. The information processing method can be applied to the scenes of judging high-quality account opening business opportunities and carrying out marketing sequencing on the account opening business opportunities in the face of multi-source account opening business opportunities. As shown in fig. 3, the server 303 receives the information processing request 301, and further obtains corresponding to-be-processed client information 302. The server 303 obtains the social credit data 304 to perform tagged classification on the client information 302 to be processed, and generates a corresponding classification tag 305. The server 303 determines target pending client information 306 based on the classification tag 305 and the pending client information 302. The server 303 determines the customer registered capital 307 and the customer site 308 corresponding to the target pending customer information 306, further determines a distance 310 between the customer site 308 and a preset target site 309, so as to cluster the target pending customer information 306 based on the distance 310 and the customer registered capital 307, and generates a cluster 311. The server 303 determines the central point value 312 of each cluster 311, and then sorts the target to-be-processed client information 306 based on the central point value 312 to obtain and output target client information 313 by screening.

Fig. 4 is a schematic diagram of main blocks of an information processing apparatus according to an embodiment of the present application. As shown in fig. 4, the information processing apparatus includes a receiving unit 401, a classification label generating unit 402, a target pending customer information determining unit 403, a cluster generating unit 404, and a filtering unit 405.

The receiving unit 401 is configured to receive the information processing request, and further obtain corresponding to-be-processed client information.

A classification label generating unit 402 configured to acquire social credit data to perform labeling classification on the customer information to be processed, and generate a corresponding classification label.

A target pending customer information determination unit 403 configured to determine target pending customer information based on the classification tag and the pending customer information.

And a cluster generating unit 404 configured to determine a customer registration capital and a customer location corresponding to the target to-be-processed customer information, and further determine a distance between the customer location and a preset target place, so as to cluster the target to-be-processed customer information based on the distance and the customer registration capital, and generate a cluster.

And the screening unit 405 is configured to determine a central point value of each cluster, and then sort the target to-be-processed client information based on the central point value to screen the target client information, and output the target client information.

In some embodiments, the classification label generation unit 402 is further configured to: determining each legal representative person information corresponding to the client information to be processed and each corresponding contact way from a client basic information table of the social credit data, and determining the number of client information with different legal representative person information and same contact way in the client information to be processed; and generating an agent account opening label to label the client information with different information of legal representatives and the same contact way in the client information to be processed in response to the fact that the number of the client information is larger than the preset threshold value.

In some embodiments, the classification label generation unit 402 is further configured to: determining each coordinate information corresponding to the customer information to be processed from a customer coordinate information table in the social credit data, calling preset self-trade area coordinate range data to be matched with each coordinate information, and determining the self-trade area customer information in the preset self-trade area coordinate range data in the customer information to be processed; a self trade area tag is generated to label the self trade area customer information.

In some embodiments, the target pending customer information determination unit 403 is further configured to: and determining the client information with the classification label being empty in the client information to be processed as the target client information to be processed.

In some embodiments, cluster generation unit 404 is further configured to: and clustering target customer information to be processed according to a k-means clustering algorithm based on the distance and the customer registration capital respectively to generate distance cluster clusters and customer registration capital cluster clusters respectively.

In some embodiments, the screening unit 405 is further configured to: determining the distance center point numerical value of each distance cluster, sequencing the distance center point numerical values from large to small, and assigning the numerical values from small to large respectively to generate the assignment of each distance cluster; determining the numerical value of the client registered capital central point of each client registered capital cluster, sequencing the numerical values of the client registered capital central points from small to large, and assigning the numerical values from small to large respectively to generate the assignment of each client registered capital cluster; respectively calculating the sum of the distance cluster assignment corresponding to each piece of customer information in the to-be-processed customer information and the customer registered capital cluster assignment, sequencing the sum of the distance cluster assignment corresponding to each piece of customer information and the customer registered capital cluster assignment from large to small, and screening a preset amount of customer information to determine the customer information as target customer information.

In some embodiments, cluster generation unit 404 is further configured to: before clustering target to-be-processed customer information based on distance and customer registration capital, determining abnormal customer information in the target to-be-processed customer information according to an isolated forest algorithm, further removing the abnormal customer information from the target to-be-processed customer information, and updating the target to-be-processed customer information.

In the present application, the information processing method and the information processing apparatus have a corresponding relationship in the details of implementation, and therefore, the description of the details will not be repeated.

Fig. 5 shows an exemplary system architecture 500 to which the information processing method or the information processing apparatus of the embodiment of the present application can be applied.

As shown in fig. 5, the system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505. The network 504 serves to provide a medium for communication links between the terminal devices 501, 502, 503 and the server 505. Network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The client can use the terminal devices 501, 502, 503 to interact with a server 505 over a network 504 to receive or send messages, etc. The terminal devices 501, 502, 503 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).

The terminal devices 501, 502, 503 may be various electronic devices having information processing screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 505 may be a server that provides various services, such as a background management server (for example only) that supports information processing requests submitted by clients using the terminal devices 501, 502, 503. The background management server can receive the information processing request and further acquire corresponding to-be-processed client information; acquiring social credit data to perform labeling classification on client information to be processed to generate corresponding classification labels; determining target customer information to be processed based on the classification label and the customer information to be processed; determining customer registration capital and a customer location corresponding to the target customer information to be processed, further determining a distance between the customer location and a preset target place, and clustering the target customer information to be processed based on the distance and the customer registration capital to generate a cluster; and determining the central point numerical value of each cluster, sequencing the target to-be-processed client information based on the central point numerical value, screening to obtain target client information, and outputting the target client information. The method realizes that the target customer information screening result meets the requirements of the shortest marketing distance, the maximum expected marketing benefit and the minimum data processing burden.

It should be noted that the information processing method provided in the embodiment of the present application is generally executed by the server 505, and accordingly, the information processing apparatus is generally disposed in the server 505.

It should be understood that the number of terminal devices, networks, and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use in implementing a terminal device of an embodiment of the present application. The terminal device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the computer system 600 are also stored. The CPU601, ROM602, and RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output section 607 including a signal processing section such as a Cathode Ray Tube (CRT), a liquid crystal credit authorization inquiry processor (LCD), and the like, and a speaker and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.

In particular, according to embodiments disclosed herein, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments disclosed herein include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The above-described functions defined in the system of the present application are executed when the computer program is executed by the Central Processing Unit (CPU) 601.

It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a receiving unit, a classification tag generating unit, a target pending client information determining unit, a cluster generating unit, and a filtering unit. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.

As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs, and when the one or more programs are executed by one device, the device receives an information processing request, and further acquires corresponding to-be-processed client information; acquiring social credit data to perform labeling classification on client information to be processed to generate corresponding classification labels; determining target customer information to be processed based on the classification label and the customer information to be processed; determining customer registration capital and a customer location corresponding to the target customer information to be processed, further determining a distance between the customer location and a preset target place, and clustering the target customer information to be processed based on the distance and the customer registration capital to generate a cluster; and determining the central point numerical value of each cluster, sequencing the target to-be-processed client information based on the central point numerical value, screening to obtain target client information, and outputting the target client information.

According to the technical scheme of the embodiment of the application, the target customer information screening result can meet the requirements of being closest in marketing distance, largest in expected marketing benefit and smallest in data processing burden.

The above-described embodiments should not be construed as limiting the scope of the present application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.