1. A method of monitoring integrated data of a multi-agent, the method comprising:

acquiring an intelligent data monitoring instruction, wherein the intelligent data monitoring instruction is used for indicating that intelligent data are monitored in a specified intelligent data monitoring range;

acquiring a to-be-selected intelligent data set indicated by intelligent data monitoring, wherein the to-be-selected intelligent data set comprises first classified intelligent data and second classified intelligent data; the first classification intelligent data is intelligent data with a monitoring frequency standard, and the second classification intelligent data is intelligent data for screening monitoring positions from monitoring data acquired in real time;

acquiring monitoring range parameters of each intelligent data in the intelligent data set to be selected indicated by the intelligent data monitoring;

the monitoring range parameter is used for indicating a coefficient for monitoring that the corresponding intelligent data is sent to the specified intelligent data monitoring range; the monitoring range parameter of the first classified intelligent data is obtained by processing state data of the first classified intelligent data through a target feature training model; the target feature training model is obtained by performing reinforcement learning search on a sample integrated environment, wherein the sample integrated environment is composed of intelligent data in an intelligent data set to be selected, which is monitored and indicated by at least two pieces of reference intelligent data;

acquiring target intelligent data based on the monitoring range parameters of the intelligent data;

and sending the target intelligent data to the designated intelligent data monitoring range for monitoring.

2. The method of claim 1, wherein the status data comprises at least one of intelligent data group data, global data, and monitoring angle feature data; the intelligent data group data comprises:

at least one of a label of the corresponding intelligent data, a label of the corresponding intelligent data monitoring range, a monitored quantity of the corresponding intelligent data, a monitored quantity standard of the corresponding intelligent data, a monitoring range of the corresponding intelligent data and a monitoring quantity upper limit of the corresponding intelligent data;

the global data includes: at least one of a global breakage rate of the first classified intelligent data in the system, an average breakage rate of the second classified intelligent data in the system, and average real-time acquired monitoring data of the second classified intelligent data in the system;

the monitoring angle characteristics include: at least one of range data associated with the corresponding intelligent data monitoring indication, coordinate data associated with the corresponding intelligent data monitoring indication, and integrated data associated with the corresponding intelligent data monitoring indication.

3. The method of claim 1 or 2, wherein prior to obtaining the intelligent data monitoring indication, further comprising:

acquiring state data of each appointed sample intelligent data in the intelligent data set to be selected, which is indicated by the reference intelligent data monitoring;

the specified sample intelligent data is the first classified intelligent data in the intelligent data set to be selected indicated by the reference intelligent data monitoring;

processing the state data of the intelligent data of each designated sample through a first feature training model to obtain sample monitoring feature training;

the sample monitoring characteristic training is used for indicating target sample intelligent data in the intelligent data set to be selected indicated by the reference intelligent data monitoring;

updating state data of intelligent data in the sample integration environment through the sample monitoring feature training;

acquiring correction parameters based on the state data of the intelligent data in the sample integration environment before and after updating;

updating a parameter modification model based on the modification parameters;

updating the first characteristic training model according to the evaluation result of the parameter correction model on the sample monitoring characteristic training;

and acquiring the target feature training model based on the updated first feature training model.

4. The method of claim 3, wherein obtaining the revised parameters based on the state data of the intelligent data in the sample integration environment before and after the updating comprises:

acquiring a floating parameter based on state data of the intelligent data in the sample integration environment before and after updating, wherein the floating parameter comprises at least one of a range parameter of a global breakage rate of the first classified intelligent data in the sample integration environment, a range parameter of an average breakage rate of the first classified intelligent data in the sample integration environment, and a range parameter of monitoring data acquired in real time on average of the second classified intelligent data in the sample integration environment;

and acquiring the correction parameter based on the floating parameter.

5. The method of claim 4, wherein said obtaining the correction parameter based on the floating parameter comprises:

carrying out weight processing on each range parameter in the floating parameters to obtain a weight processing result; and acquiring the correction parameters based on the weight processing result.

6. The method of claim 4, wherein the first feature training model comprises a priority feature training model and a simulated feature training model; the sample monitoring feature training comprises priority monitoring feature training output by the priority feature training model and simulation monitoring feature training output by the simulation feature training model; the prior monitoring feature training is feature training for preferentially selecting the target sample intelligent data from the first classified intelligent data; the simulation monitoring feature training is feature training for performing mixed sequencing on the first classification intelligent data and the second classification intelligent data based on the monitoring range parameters and selecting the target sample intelligent data; the obtaining floating parameters based on the state data of the intelligent data in the sample integration environment before and after the updating includes:

acquiring a first floating parameter based on first state data, wherein the first state data is state data before and after updating state data of intelligent data in the sample integrated environment through the priority monitoring feature training;

acquiring a second floating parameter based on second state data, wherein the second state data is state data before and after updating the state data of the intelligent data in the sample integrated environment through the simulation monitoring feature training;

the obtaining the correction parameter based on the floating parameter includes: acquiring the increasing ratio of the second floating parameter relative to the first floating parameter; and acquiring the correction parameter based on the increase ratio.

7. The method of claim 6, wherein obtaining the target feature training model based on the updated first feature training model comprises:

and obtaining the simulated feature training model in the updated first feature training model as the target feature training model.

8. The method of claim 4, wherein updating the parametric rework model based on the rework parameters comprises:

acquiring correction parameters corresponding to n continuous reference intelligent data monitoring indications; wherein n is more than or equal to 2 and is an integer;

superposing correction parameters corresponding to n continuous reference intelligent data monitoring indications to obtain superposed correction parameters; and updating the parameter correction model based on the superposition correction parameters.

9. A cloud server, comprising:

a memory for storing a computer program;

a processor coupled to the memory for executing the computer program stored by the memory to implement the method of any of claims 1-7.

10. A computer-readable storage medium, in which a computer program is stored which, when running, performs the method of any one of claims 1 to 7.

Background

With the continuous development of information technology, information is an important resource, the security authorization of the information is concerned more and more, and the management and security monitoring of the information resource are an important direction. Sensitive information of countries, enterprises and public institutions and personal secrets is involved, so that the problems of leakage of the national, the enterprises and public institutions and the personal information are solved. Therefore, strict monitoring of the integration data is required, however, there are some drawbacks in the monitoring technology of the related integration data.

Disclosure of Invention

In view of this, the present application provides a method for monitoring integrated data of multiple agents and a cloud server.

In a first aspect, there is provided a method of monitoring integration data of a multi-agent, the method comprising:

acquiring an intelligent data monitoring instruction, wherein the intelligent data monitoring instruction is used for indicating that intelligent data are monitored in a specified intelligent data monitoring range;

acquiring a to-be-selected intelligent data set indicated by intelligent data monitoring, wherein the to-be-selected intelligent data set comprises first classified intelligent data and second classified intelligent data; the first classification intelligent data is intelligent data with a monitoring frequency standard, and the second classification intelligent data is intelligent data for screening monitoring positions from monitoring data acquired in real time;

acquiring monitoring range parameters of each intelligent data in the intelligent data set to be selected indicated by the intelligent data monitoring;

the monitoring range parameter is used for indicating a coefficient for monitoring that the corresponding intelligent data is sent to the specified intelligent data monitoring range; the monitoring range parameter of the first classified intelligent data is obtained by processing state data of the first classified intelligent data through a target feature training model; the target feature training model is obtained by performing reinforcement learning search on a sample integrated environment, wherein the sample integrated environment is composed of intelligent data in an intelligent data set to be selected, which is monitored and indicated by at least two pieces of reference intelligent data;

acquiring target intelligent data based on the monitoring range parameters of the intelligent data;

and sending the target intelligent data to the designated intelligent data monitoring range for monitoring.

Further, the state data comprises at least one of intelligent data group data, global data and monitoring angle characteristic data; the intelligent data group data comprises:

at least one of a label of the corresponding intelligent data, a label of the corresponding intelligent data monitoring range, a monitored quantity of the corresponding intelligent data, a monitored quantity standard of the corresponding intelligent data, a monitoring range of the corresponding intelligent data and a monitoring quantity upper limit of the corresponding intelligent data;

the global data includes: at least one of a global breakage rate of the first classified intelligent data in the system, an average breakage rate of the second classified intelligent data in the system, and average real-time acquired monitoring data of the second classified intelligent data in the system;

the monitoring angle characteristics include: at least one of range data associated with the corresponding intelligent data monitoring indication, coordinate data associated with the corresponding intelligent data monitoring indication, and integrated data associated with the corresponding intelligent data monitoring indication.

Further, before the obtaining of the intelligent data monitoring indication, the method further includes:

acquiring state data of each appointed sample intelligent data in the intelligent data set to be selected, which is indicated by the reference intelligent data monitoring;

the specified sample intelligent data is the first classified intelligent data in the intelligent data set to be selected indicated by the reference intelligent data monitoring;

processing the state data of the intelligent data of each designated sample through a first feature training model to obtain sample monitoring feature training;

the sample monitoring characteristic training is used for indicating target sample intelligent data in the intelligent data set to be selected indicated by the reference intelligent data monitoring;

updating state data of intelligent data in the sample integration environment through the sample monitoring feature training;

acquiring correction parameters based on the state data of the intelligent data in the sample integration environment before and after updating;

updating a parameter modification model based on the modification parameters;

updating the first characteristic training model according to the evaluation result of the parameter correction model on the sample monitoring characteristic training;

and acquiring the target feature training model based on the updated first feature training model.

Further, the obtaining a correction parameter based on the state data of the intelligent data in the sample integration environment before and after the updating includes:

acquiring a floating parameter based on state data of the intelligent data in the sample integration environment before and after updating, wherein the floating parameter comprises at least one of a range parameter of a global breakage rate of the first classified intelligent data in the sample integration environment, a range parameter of an average breakage rate of the first classified intelligent data in the sample integration environment, and a range parameter of monitoring data acquired in real time on average of the second classified intelligent data in the sample integration environment;

and acquiring the correction parameter based on the floating parameter.

Further, the obtaining the correction parameter based on the floating parameter includes:

carrying out weight processing on each range parameter in the floating parameters to obtain a weight processing result; and acquiring the correction parameters based on the weight processing result.

Further, the first feature training model comprises a priority feature training model and a simulation feature training model; the sample monitoring feature training comprises priority monitoring feature training output by the priority feature training model and simulation monitoring feature training output by the simulation feature training model; the prior monitoring feature training is feature training for preferentially selecting the target sample intelligent data from the first classified intelligent data; the simulation monitoring feature training is feature training for performing mixed sequencing on the first classification intelligent data and the second classification intelligent data based on the monitoring range parameters and selecting the target sample intelligent data; the obtaining floating parameters based on the state data of the intelligent data in the sample integration environment before and after the updating includes:

acquiring a first floating parameter based on first state data, wherein the first state data is state data before and after updating state data of intelligent data in the sample integrated environment through the priority monitoring feature training;

acquiring a second floating parameter based on second state data, wherein the second state data is state data before and after updating the state data of the intelligent data in the sample integrated environment through the simulation monitoring feature training;

the obtaining the correction parameter based on the floating parameter includes: acquiring the increasing ratio of the second floating parameter relative to the first floating parameter; and acquiring the correction parameter based on the increase ratio.

Further, the obtaining the target feature training model based on the updated first feature training model includes:

and obtaining the simulated feature training model in the updated first feature training model as the target feature training model.

Further, the updating the parameter modification model based on the modification parameter includes:

acquiring correction parameters corresponding to n continuous reference intelligent data monitoring indications; wherein n is more than or equal to 2 and is an integer;

superposing correction parameters corresponding to n continuous reference intelligent data monitoring indications to obtain superposed correction parameters; and updating the parameter correction model based on the superposition correction parameters.

In a second aspect, a cloud server is provided, including: a memory for storing a computer program; a processor coupled to the memory for executing the computer program stored by the memory to implement the above-described method.

In a third aspect, a computer-readable storage medium has stored thereon a computer program which, when executed, performs the above-described method.

The monitoring method for integrated data of multiple intelligent agents and the cloud server provided by the embodiment of the application, a target feature training model is obtained through reinforcement learning by a sample integrated environment composed of two kinds of intelligent data of different classifications, when an intelligent data monitoring instruction is subsequently received, state data of first classification intelligent data in an intelligent data set to be selected is processed through the target feature training model, monitoring range parameters of the first classification intelligent data obtained according to the target feature training model and monitoring range parameters of second classification intelligent data in the intelligent data set to be selected, target intelligent data selected from each intelligent data, namely, one intelligent data monitoring instruction, one intelligent data selected from the two kinds of intelligent data are monitored through a feature training model obtained through reinforcement learning, therefore, the hybrid control of the two kinds of classified intelligent data is realized, the intelligent data monitoring position in the system can be completely monitored, and the accuracy of model calculation of intelligent data monitoring is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 is a flowchart of a method for monitoring integrated data of multiple agents according to an embodiment of the present application.

Fig. 2 is a block diagram of a monitoring apparatus for integrated data of multiple agents according to an embodiment of the present application.

Fig. 3 is an architecture diagram of a multi-agent data-integrated monitoring system according to an embodiment of the present application.

Detailed Description

In order to better understand the technical solutions, the technical solutions of the present application are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.

In order to improve the technical problems described in the background art, the inventor innovatively provides a monitoring method of integrated data of multiple intelligent agents and a cloud server, the scheme can obtain a target feature training model through reinforcement learning in two different classified sample integration environments, when an intelligent data monitoring instruction is subsequently received, the target feature training model is used for processing state data of first classified intelligent data in an intelligent data set to be selected, target intelligent data selected according to monitoring range parameters of the first classified intelligent data obtained by the target feature training model and monitoring range parameters of second classified intelligent data in the intelligent data set to be selected is used for monitoring one intelligent data monitoring instruction, one intelligent data is selected from two different classified intelligent data as the target intelligent data through a feature training model obtained by reinforcement learning for monitoring, the hybrid control of the two kinds of classified intelligent data is realized, so that the intelligent data monitoring position in the system can be completely monitored, and the accuracy of model calculation of intelligent data monitoring is improved.

Referring to fig. 1, a method for monitoring integrated data of a multi-agent, which can be applied to a system for preventing an account at risk from invading and identifying is shown, and the method can include the technical solutions described in the following steps 100-600.

Step 100, obtaining an intelligent data monitoring instruction, wherein the intelligent data monitoring instruction is used for instructing to monitor intelligent data in a specified intelligent data monitoring range.

For example,

step 200, obtaining a candidate intelligent data set indicated by the intelligent data monitoring, wherein the candidate intelligent data set comprises first classification intelligent data and second classification intelligent data.

For example, the first classification intelligent data is intelligent data with a monitoring frequency standard, and the second classification intelligent data is intelligent data for screening a monitoring position from monitoring data collected in real time.

And 300, acquiring monitoring range parameters of each intelligent data in the intelligent data set to be selected indicated by the intelligent data monitoring.

For example,

and step 400, the monitoring range parameter is used for indicating a coefficient for monitoring that the corresponding intelligent data is sent to the specified intelligent data monitoring range.

For example, the monitoring range parameter of the first classification intelligent data is obtained by processing state data of the first classification intelligent data through a target feature training model; the target feature training model is obtained by performing reinforcement learning search on a sample integration environment, wherein the sample integration environment is composed of intelligent data in an intelligent data set to be selected, which is indicated by monitoring of at least two pieces of reference intelligent data.

And 500, acquiring target intelligent data based on the monitoring range parameters of the intelligent data.

For example,

and step 600, sending the target intelligent data to the designated intelligent data monitoring range for monitoring.

For example,

it can be understood that, in the implementation of the technical solutions described in the above steps 100 to 600, a target feature training model is obtained through reinforcement learning in a sample integration environment composed of two different classes of intelligent data, and when an intelligent data monitoring instruction is subsequently received, the state data of the first class of intelligent data in the to-be-selected intelligent data set is processed by the target feature training model, and the target intelligent data selected from the intelligent data sets is monitored according to the monitoring range parameter of the first class of intelligent data obtained by the target feature training model and the monitoring range parameter of the second class of intelligent data in the to-be-selected intelligent data set, that is, for an intelligent data monitoring instruction, one intelligent data is selected from the two different classes of intelligent data as the target intelligent data through a feature training model obtained through reinforcement learning to monitor, therefore, the hybrid control of the two kinds of classified intelligent data is realized, the intelligent data monitoring position in the system can be completely monitored, and the accuracy of model calculation of intelligent data monitoring is improved.

In an alternative embodiment, the inventors have discovered that the status data includes at least one of intelligent data group data, global data, and monitoring angle feature data; the intelligent data group data specifically comprises the technical scheme described in the following steps q 1-q 3.

And q1, at least one of the label of the corresponding intelligent data, the label of the monitoring range of the corresponding intelligent data, the monitored quantity of the corresponding intelligent data, the monitoring quantity standard of the corresponding intelligent data, the monitoring range of the corresponding intelligent data and the monitoring quantity upper limit of the corresponding intelligent data.

Step q2, the global data comprising: at least one of a global breakage rate of the first classified intelligent data in the system, an average breakage rate of the second classified intelligent data in the system, and average real-time acquired monitoring data of the second classified intelligent data in the system.

Step q3, the monitoring angle characteristics comprising: at least one of range data associated with the corresponding intelligent data monitoring indication, coordinate data associated with the corresponding intelligent data monitoring indication, and integrated data associated with the corresponding intelligent data monitoring indication.

It can be understood that when the technical scheme described in the step q 1-step q3 is executed, the specific situation analysis is performed on each state data, and the integrity of the state data is effectively improved.

Based on the above basis, before the intelligent data monitoring indication is obtained, the following technical solutions described in steps w 1-w 9 may also be included.

And step w1, acquiring the state data of each appointed sample intelligent data in the intelligent data set to be selected indicated by the reference intelligent data monitoring.

Step w2, the specified sample intelligent data is the first classified intelligent data in the candidate intelligent data set indicated by the reference intelligent data monitoring.

And step w3, processing the state data of each designated sample intelligent data through the first feature training model to obtain sample monitoring feature training.

And step w4, the sample monitoring feature is trained to indicate the target sample intelligent data in the candidate intelligent data set indicated by the reference intelligent data monitoring.

And w5, updating the state data of the intelligent data in the sample integration environment through the sample monitoring feature training.

And step w6, acquiring correction parameters based on the state data of the intelligent data in the sample integration environment before and after updating.

And step w7, updating the parameter correction model based on the correction parameters.

And w8, updating the first feature training model according to the evaluation result of the parameter correction model on the sample monitoring feature training.

And step w9, acquiring the target feature training model based on the updated first feature training model.

It can be understood that when the technical scheme described in the above steps w 1-w 9 is executed, the error range is effectively reduced by performing model calculation on the state data, and the calculated structure is fed back to the model, so that the accuracy of the model calculation is improved.

In an alternative embodiment, the inventor finds that, based on the state data of the intelligent data in the sample integration environment before and after the update, there is a problem that the range caused by the floating of the relevant data is inaccurate, so that it is difficult to accurately obtain the correction parameter, and in order to improve the above technical problem, the step of obtaining the correction parameter based on the state data of the intelligent data in the sample integration environment before and after the update described in step w6 may specifically include the technical solutions described in the following steps w6a1 and w6a 2.

Step w6a1, acquiring a floating parameter based on the state data of the intelligent data in the sample integration environment before and after updating, wherein the floating parameter comprises at least one of a range parameter of a global breakage rate of the first classification intelligent data in the sample integration environment, a range parameter of an average breakage rate of the first classification intelligent data in the sample integration environment, and a range parameter of monitoring data acquired in real time on average of the second classification intelligent data in the sample integration environment.

And a step w6a2, acquiring the correction parameter based on the floating parameter.

It can be understood that, when the technical solutions described in the above steps w6a1 and w6a2 are performed, based on the state data of the smart data in the sample integration environment before and after updating, the problem of inaccurate range caused by related data floating is avoided, so that the correction parameters can be accurately obtained.

In an alternative embodiment, the inventor finds that, based on the floating parameter, there is a problem that the weight of the floating parameter is not accurate, so that it is difficult to accurately obtain the correction parameter, and in order to improve the above technical problem, the step of obtaining the correction parameter based on the floating parameter described in step w6a2 may specifically include the technical solution described in the following step w6a2 a.

Step w6a2a, performing weight processing on each range parameter in the floating parameters to obtain a weight processing result; and acquiring the correction parameters based on the weight processing result.

It can be understood that when the technical solution described in the above step w6a2a is executed, the problem of inaccurate floating parameter weight is avoided based on the floating parameter, so that the correction parameter can be accurately obtained.

In an alternative embodiment, the inventors have discovered that the first feature training model comprises a priority feature training model and a simulated feature training model; the sample monitoring feature training comprises priority monitoring feature training output by the priority feature training model and simulation monitoring feature training output by the simulation feature training model; the prior monitoring feature training is feature training for preferentially selecting the target sample intelligent data from the first classified intelligent data; the simulation monitoring feature training is feature training for performing mixed sequencing on the first classification intelligent data and the second classification intelligent data based on the monitoring range parameters and selecting the target sample intelligent data; the step of obtaining the floating parameter based on the state data of the intelligent data in the sample integration environment before and after the update may specifically include the technical solutions described in the following steps e1 to e 3.

And e1, acquiring a first floating parameter based on first state data, wherein the first state data is the state data before and after the state data of the intelligent data in the sample integrated environment is updated through the priority monitoring feature training.

And e2, acquiring a second floating parameter based on second state data, wherein the second state data is the state data before and after the state data of the intelligent data in the sample integrated environment is updated through the simulation monitoring feature training.

Step e3, the obtaining the correction parameter based on the floating parameter includes: acquiring the increasing ratio of the second floating parameter relative to the first floating parameter; obtaining the correction parameter based on the increased proportion

It can be understood that when the technical solutions described in the above steps e 1-e 3 are executed, the first floating parameter is obtained by the first state data, and the second floating parameter is obtained by the second state data, so as to perform multidimensional analysis, and the correction parameter can be accurately obtained according to the floating parameter.

In an alternative embodiment, the inventors have found that, in order to improve the above technical problem, the step of obtaining the target feature training model based on the updated first feature training model described in step w9 may specifically include the technical solution described in the following step w9a1, because there is a problem that the updating is inaccurate, and thus it is difficult to accurately obtain the target feature training model.

And w9a1, obtaining the simulated feature training model in the updated first feature training model as the target feature training model.

It can be understood that when the technical solution described in the above step w9a1 is executed, the problem of inaccurate update is avoided based on the updated first feature training model, so that the target feature training model can be accurately obtained.

In an alternative embodiment, the inventor finds that, when the parameter modification model is updated based on the modification parameters, there are a plurality of reference intelligent data monitoring indications corresponding to the modification parameters, so that it is difficult to accurately update the parameter modification model, and in order to improve the above technical problem, the step of updating the parameter modification model based on the modification parameters described in step w7 may specifically include the technical solutions described in the following steps w7a1 and w7a 2.

Step w7a1, acquiring correction parameters corresponding to n continuous reference intelligent data monitoring indications; wherein n is not less than 2 and n is an integer.

Step w7a2, superposing correction parameters corresponding to n continuous reference intelligent data monitoring instructions to obtain superposed correction parameters; and updating the parameter correction model based on the superposition correction parameters.

It can be understood that when the technical solutions described in the above steps w7a1 and w7a2 are executed, the parameter correction model is updated based on the correction parameters, so that the problem that a plurality of reference intelligent data monitors indicate corresponding correction parameters is avoided, and the parameter correction model can be accurately updated.

In an alternative embodiment, the inventor finds that there is a technical problem of a break in superimposing correction parameters corresponding to n consecutive reference intelligent data monitoring indications, so that it is difficult to accurately obtain superimposed correction parameters, and in order to improve the technical problem, the step of superimposing correction parameters corresponding to n consecutive reference intelligent data monitoring indications to obtain superimposed correction parameters described in step w7a2 may specifically include the technical solution described in step f1 below.

And f1, superposing correction parameters corresponding to the n continuous reference intelligent data monitoring instructions based on the specified breakage coefficient to obtain the superposed correction parameters.

It can be understood that when the technical solution described in step f1 is executed, the correction parameters corresponding to n consecutive reference intelligent data monitoring indications are superimposed, so as to avoid the technical problem of breakage, and thus the superimposed correction parameters can be accurately obtained.

On the basis, please refer to fig. 2 in combination, there is provided a multi-agent integrated data monitoring apparatus 200, applied to a cloud server, the apparatus including:

an intelligent data indication module 210, configured to obtain an intelligent data monitoring indication, where the intelligent data monitoring indication is used to indicate that intelligent data is monitored in a specified intelligent data monitoring range;

a monitoring data obtaining module 220, configured to obtain a to-be-selected intelligent data set indicated by the intelligent data monitoring, where the to-be-selected intelligent data set includes first classification intelligent data and second classification intelligent data; the first classification intelligent data is intelligent data with a monitoring frequency standard, and the second classification intelligent data is intelligent data for screening monitoring positions from monitoring data acquired in real time;

a range parameter obtaining module 230, configured to obtain a monitoring range parameter of each intelligent data in the to-be-selected intelligent data set indicated by the intelligent data monitoring;

a monitoring coefficient indicating module 240, configured to use the monitoring range parameter to indicate a coefficient for monitoring that the corresponding intelligent data is sent to the specified intelligent data monitoring range; the monitoring range parameter of the first classified intelligent data is obtained by processing state data of the first classified intelligent data through a target feature training model; the target feature training model is obtained by performing reinforcement learning search on a sample integrated environment, wherein the sample integrated environment is composed of intelligent data in an intelligent data set to be selected, which is indicated by at least two reference intelligent data monitoring instructions;

an intelligent data obtaining module 250, configured to obtain target intelligent data based on the monitoring range parameter of each intelligent data;

and the intelligent data monitoring module 260 is configured to send the target intelligent data to the specified intelligent data monitoring range for monitoring.

On the basis of the above, please refer to fig. 3, which shows a multi-agent integrated data monitoring system 300, which includes a processor 310 and a memory 320, which are communicated with each other, wherein the processor 310 is used for reading the computer program from the memory 320 and executing the computer program to implement the above method.

The application provides a cloud server, includes: a memory for storing a computer program; a processor coupled to the memory for executing the computer program stored by the memory to implement the method described above.

On the basis of the above, there is also provided a computer-readable storage medium on which a computer program is stored, which when executed implements the above-described method.

In summary, based on the above solution, a target feature training model is obtained through reinforcement learning in a sample integration environment composed of two different types of intelligent data, when an intelligent data monitoring instruction is subsequently received, state data of a first type of intelligent data in an intelligent data set to be selected is processed through the target feature training model, target intelligent data selected from the intelligent data is obtained according to a monitoring range parameter of the first type of intelligent data obtained by the target feature training model and a monitoring range parameter of a second type of intelligent data in the intelligent data set to be selected, that is, for one intelligent data monitoring instruction, one intelligent data is selected from the two different types of intelligent data through a feature training model obtained through reinforcement learning as the target intelligent data for monitoring, thereby realizing mixed control of the two types of intelligent data, therefore, the intelligent data monitoring position in the system can be completely monitored, and the accuracy of model calculation of intelligent data monitoring is improved.

It should be appreciated that the system and its modules shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the numbers allow for adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.

The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.