1. A migration mode determination method comprises the following steps:

acquiring interest plane data of a traffic junction in a region, wherein the region is divided into a plurality of sub-regions;

collecting positioning data of migrating individuals going out across subareas;

matching the positioning data with the interest surface data, and determining the interest surface data in which the positioning data falls;

and determining the migration mode of the migration individual according to the interest plane data in which the positioning data falls.

2. The method of claim 1, wherein the acquiring of the interest plane data of the transportation junctions in the preset area comprises:

and if the traffic hub is a toll station, outwards extending a preset length by taking the central point of the toll station as a circle center to generate the interest plane data of the toll station.

3. The method of claim 1, wherein after the acquiring positioning data for migrating individuals who are traveling across sub-regions, further comprising:

sequencing the positioning data according to a time sequence;

and deleting the positioning data with the positioning time less than the starting time and/or greater than the ending time.

4. The method of claim 1, wherein the matching the positioning data with the area of interest data to determine the area of interest data into which the positioning data falls comprises:

and obtaining the intersection of the positioning data and the interest plane data by adopting a hive script.

5. The method of claim 1, wherein after determining the migration manner of the migrating individual according to the plane-of-interest data into which the positioning data falls, further comprising:

if the migration individual adopts multiple migration modes, sequencing the multiple migration modes according to the weight;

and determining the migration mode with the maximum weight as the migration mode of the migration individual.

6. A migration manner determination device, comprising:

a first acquisition module configured to acquire area-of-interest data of a transportation junction within an area, wherein the area is divided into a plurality of sub-areas;

the second acquisition module is configured to acquire positioning data of an individual migrating across the sub-area;

the matching module is configured to match the positioning data with the interest plane data and determine the interest plane data in which the positioning data falls;

the first determination module is configured to determine a migration mode of the migrating individual according to the interest plane data in which the positioning data falls.

7. The apparatus of claim 6, wherein the first acquisition module is further configured to:

and if the traffic hub is a toll station, outwards extending a preset length by taking the central point of the toll station as a circle center to generate the interest plane data of the toll station.

8. The apparatus of claim 6, wherein the apparatus further comprises:

a first ordering module configured to order the positioning data in chronological order;

and the deleting module is configured to delete the positioning data with the positioning time less than the starting time and/or greater than the ending time.

9. The apparatus of claim 6, wherein the matching module is further configured to:

and obtaining the intersection of the positioning data and the interest plane data by adopting a hive script.

10. The apparatus of claim 6, wherein the apparatus further comprises:

the second sequencing module is configured to sequence the plurality of migration modes according to the weight if the migration individual adopts a plurality of migration modes;

and the second determination module is configured to determine the migration mode with the largest weight as the migration mode of the migration individual.

11. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

12. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-5.

13. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-5.

Background

Migration refers to the act of an individual moving from one sub-area to another within an area, which may be by aircraft, train, self-driving, bus, etc., on a transportation means, which may be collectively referred to as a migration means.

The research on the migration modes across the sub-areas is less, and the main reason is that the data volume of each migration mode is closed and opaque, and the specific data volume cannot be obtained. Currently, research on migration patterns generally takes the form of questionnaires, that is, they are used in cross-sub-area traffic by issuing questionnaires to migrating individuals.

Disclosure of Invention

The disclosure provides a migration mode determination method, apparatus, device, storage medium, and program product.

According to a first aspect of the present disclosure, a migration method determination method is provided, including: acquiring interest surface data of a traffic junction in a region, wherein the region is divided into a plurality of sub-regions; collecting positioning data of migrating individuals going out across subareas; matching the positioning data with the interest surface data, and determining the interest surface data in which the positioning data falls; and determining the migration mode of the migration individual according to the interest plane data in which the positioning data falls.

According to a second aspect of the present disclosure, there is provided a migration manner determination apparatus, including: a first acquisition module configured to acquire area-of-interest data of a transportation junction within an area, wherein the area is divided into a plurality of sub-areas; the second acquisition module is configured to acquire positioning data of an individual migrating across the sub-area; the matching module is configured to match the positioning data with the interest area data and determine the interest area data in which the positioning data falls; the first determining module is configured to determine a migration mode of the migrating individual according to the interest plane data in which the positioning data falls.

According to a third aspect of the present disclosure, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described in any one of the implementations of the first aspect.

According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method as described in any one of the implementations of the first aspect.

According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method as described in any of the implementations of the first aspect.

The method combines space-time positioning data and map interest plane data, analyzes a specific migration mode adopted by the method by researching the behavior of migration individual positioning, and generates data covering sub-areas in any areas, so that the generated data is not influenced by artificial subjective will and is more comprehensive and accurate.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

fig. 1 is a flow chart of one embodiment of a migration approach determination method according to the present disclosure;

fig. 2 is a flow chart of yet another embodiment of a migration approach determination method according to the present disclosure;

fig. 3 is a scenario diagram of a migration manner determination method that may implement an embodiment of the present disclosure;

FIG. 4 is a diagram showing the results of a migration pattern of an individual;

fig. 5 is a schematic structural diagram of an embodiment of a migration manner determining apparatus according to the present disclosure;

fig. 6 is a block diagram of an electronic device for implementing a migration manner determination method according to an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. 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 disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 1 shows a flow 100 of an embodiment of a migration approach determination method according to the present disclosure. The migration mode determination method comprises the following steps:

step 101, data of interest planes of traffic hubs in an area are collected.

In this embodiment, the execution main body of the migration manner determination method may acquire the interest plane data of the transportation hub in the area.

Generally, AOI (Area Of Interest) data for individual transit hubs can be mapped by sending mapping vehicles out to individual transit hubs within an Area on an indefinite basis. A region may be a region within an arbitrary range, which may be divided into a plurality of sub-regions. For example, a region may be a country and a sub-region may be a city. As another example, a region may be a province and a sub-region may be a city. As another example, a region may be the world and a sub-region may be a country. The transportation hub can be an important component of a regional transportation system, and is an intersection of transportation lines of a transportation network of different transportation modes, including but not limited to airports, train stations, passenger stations, toll stations, service areas and the like. The interest plane data may refer to a geographic entity in the map data as an area. The regional geographic entity here is a transportation hub.

And 102, acquiring positioning data of the migrating individual going out of the sub-area.

In this embodiment, the execution subject may collect positioning data of an individual who migrates while traveling across the sub-area.

Wherein, the migrating individuals going out of the subareas are the individuals moving from one subarea to another subarea in the area. The positioning data is the positioning data of the migrating individual moving from one sub-area to another. The positioning data comprises positioning time of the starting point and the ending point in addition to the position data of the starting point and the ending point of the migrating individual.

And 103, matching the positioning data with the interest area data, and determining the interest area data in which the positioning data falls.

In this embodiment, the execution subject may match the positioning data with the interest plane data, and determine the interest plane data in which the positioning data falls.

Typically, the positioning data comprises position data of a starting point and an end point of the migrating individual, and the surface-of-interest data comprises geographical range data of the transportation hub. Matching the positioning data with the interest surface data, wherein if the position data of the starting point and/or the end point of the migrating individual falls into the geographic range data of the transportation junction, the matching is successful, and the positioning data falls into the interest surface data of the transportation junction; otherwise, the matching fails, and the positioning data does not fall into the interest plane data of the transportation junction.

And step 104, determining the migration mode of the migration individual according to the interest plane data in which the positioning data falls.

In this embodiment, the executing main body may determine the migration manner of the migrating individual according to the data of the interest plane in which the positioning data falls.

Generally, according to the data of the interest plane in which the positioning data falls, the migration mode of the migrating individual can be determined. For example, when the positioning data of the migrating individual is located at an airport, the adopted migrating mode is judged as an airplane; when the positioning data of the migrating individual falls on the train station, the adopted migrating mode is judged as the train; when the positioning data of the migrating individual falls on the bus stop, the adopted migrating mode is judged as the bus; and when the positioning data of the migrating individual falls into the service area or the toll station, the adopted migrating mode is judged to be self-driving.

In some embodiments, after obtaining the migration pattern of the migrating individual, the executing main body may further analyze the migration pattern between the sub-areas point to point. The migration mode between sub-areas has great value in the traffic field. From the perspective of the government, traffic departments in various regions can be guided to directly optimize the traffic facility configuration of the city, and prospective layout can be performed on the local traffic facility configuration in a targeted manner. From the social point of view, the closeness and the concrete way of the connection between the two sub-areas can be analyzed, or the travel habits of residents in a certain sub-area can be analyzed.

The method combines space-time positioning data and map interest plane data, analyzes a specific migration mode adopted by the method by researching the behavior of migration individual positioning, and generates data covering sub-areas in any areas, so that the generated data is not influenced by artificial subjective will and is more comprehensive and accurate. First, large spatiotemporal data is based on positioning data, on the order of hundreds of millions. Secondly, the large space-time data has considerable objectivity and is not influenced by artificial subjective will. Finally, the space-time big data has wide coverage, so the effect of point-to-point analysis of the migration mode between the sub-areas can be achieved.

Continuing to refer to fig. 2, a flow 200 of yet another embodiment of a migration approach determination method according to the present disclosure is shown. The migration mode determination method comprises the following steps:

step 201, collecting interest plane data of a traffic junction in an area.

In this embodiment, the execution main body of the migration manner determination method may acquire the interest plane data of the transportation hub in the area.

Generally, AOI data for individual transit hubs can be mapped by sending out mapping vehicles on an indefinite basis to individual transit hubs within an area. A region may be a region within an arbitrary range, which may be divided into a plurality of sub-regions. The transportation hub can be an important component of a regional transportation system, and is an intersection of transportation lines of a transportation network of different transportation modes, including but not limited to airports, train stations, passenger stations, toll stations, service areas and the like. For airport, railway station, passenger station and service area, as the migrating individuals all enter the geographical range, the geographical range data can be directly used as the interest area data. For the case that the transportation junction is a toll station, since the migrating individual does not enter the geographic range of the migrating individual, but enters the vicinity of the geographic range of the migrating individual, the data of the interest plane of the toll station can be generated by extending a preset length (for example, 50 meters) outwards with the center point of the toll station as the center of the circle. So that suitable interest plane data can be generated for the toll booth.

Step 202, collecting positioning data of the migrating individual going out across the subareas.

In this embodiment, the specific operation of step 202 has been described in detail in step 102 in the embodiment shown in fig. 1, and is not described herein again.

Step 203, the positioning data is sorted according to the time sequence.

In this embodiment, the execution body may sort the positioning data in a chronological order. For example, the positioning data is sorted from front to back in chronological order.

And step 204, deleting the positioning data with the positioning time less than the starting time and/or greater than the ending time.

In this embodiment, the execution body may delete the positioning data whose positioning time is less than the start time and/or greater than the end time.

Here, for the accuracy of the positioning data, the positioning data may be denoised, and the positioning data not in the migration time period may be deleted. And the positioning data with the positioning time less than the starting time and/or greater than the ending time is the positioning data which is not in the migration time period.

Step 205, obtaining the intersection of the positioning data and the interest plane data by using the hive script.

In this embodiment, the executing entity may obtain an intersection of the positioning data and the interest plane data by using a hive script, so as to determine the interest plane data into which the positioning data falls.

The hive is a data warehouse tool based on Hadoop, is used for data extraction, transformation and loading, and is a mechanism capable of storing, querying and analyzing large-scale data stored in Hadoop. The hive data warehouse tool can map the structured data file into a database table, provide SQL query function and convert SQL sentences into MapReduce tasks for execution. Hive has the advantages that the learning cost is low, rapid MapReduce statistics can be realized through similar SQL sentences, MapReduce is simpler, and a special MapReduce application program does not need to be developed. hive is well suited for statistical analysis of data warehouses.

And step 206, determining the migration mode of the migration individual according to the interest plane data in which the positioning data falls.

In this embodiment, the specific operation of step 206 has been described in detail in step 104 in the embodiment shown in fig. 1, and is not described herein again.

And step 207, if the migration individual adopts multiple migration modes, sequencing the multiple migration modes according to the weight.

In this embodiment, if the migration individual adopts multiple migration modes, the execution main body may rank the multiple migration modes according to the weight.

Generally, an individual may adopt a single migration mode or multiple migration modes in a single migration behavior. If the positioning data fall into the interest plane data of one transportation junction, a migration mode is adopted, and if the positioning data fall into the interest plane data of a plurality of transportation junctions, a multiple migration mode is adopted. For example, the positioning data falls into an airport and a train station, and the airplane and train migration mode is adopted.

For a single migration behavior adopting a multiple migration mode, the multiple migration modes need to be ordered according to the weight. Different weights are set for different migration modes. Generally, the weights thereof are getting smaller and smaller in the order of airplane, train, passenger car and self-driving. For example, for a migrating individual, the migrating mode of an airplane and a train is adopted in one migration behavior, and the sequence is that the airplane is in front and the train is behind.

And step 208, determining the migration mode with the maximum weight as the migration mode of the migration individual.

In this embodiment, the execution main body may determine the migration manner with the largest weight as the migration manner of the migrating individual.

And for the first migration behavior adopting the multiple migration modes, sequencing the multiple migration modes according to the weight. And then selecting the migration behavior ranked earlier from the first migration behavior. For example, if the individual uses airplane and train migration methods in one migration, the individual will be determined as airplane. The repulsion method ensures that only one migration mode of the migrating individual is ensured in one migration process, and the method accords with the cognition of people. For example, one migrating individual goes from the corridor to the Shenzhen, sits the train to the Beijing first, and then sits the airplane to the Shenzhen from the Beijing. At this time, he adopted two modes of migration, train and airplane. However, he often only says to sit on the airplane when describing to others how he is going from the corridor to Shenzhen, and does not say to sit on the train before sitting on the airplane.

As can be seen from fig. 2, compared with the embodiment corresponding to fig. 1, the migration method determining method in the embodiment highlights a positioning data denoising step, a positioning data and interest plane data matching step, and a migration method determining step. Therefore, the positioning data is denoised by the scheme described in the embodiment, and the positioning data not in the migration time period is deleted, so that the accuracy of the positioning data can be improved. The intersection of the positioning data and the interesting surface data is obtained by using the hive script, so that the learning cost is low. And for the first migration behavior adopting the multiple migration mode, sequencing the multiple migration modes according to the weight, and selecting the migration behavior with the sequencing advanced from the multiple migration modes to meet the cognition of people.

Further referring to fig. 3, a scene diagram of a migration manner determination method that may implement the embodiments of the present disclosure is shown. As shown in fig. 3, a migration manner research method based on space-time big data mainly relies on data capable of positioning and interest plane data generated by map application, and the main method steps are as follows:

step 1: AOI data of major transportation hubs throughout the country are collected. Transportation hubs include, but are not limited to, airports, train stations, passenger stations, toll booths, service areas, and the like.

step 2: and collecting the positioning data of the migrating individuals who go out across cities.

step 3: and obtaining the intersection of the positioning data and the interest plane data by adopting the hive script.

step 4: and determining the migration mode of the migration individual according to the interest plane data in which the positioning data falls. When the positioning data of the migrating individual falls into the airport, the adopted migrating mode is judged as the airplane; when the positioning data of the migrating individual falls on the train station, the adopted migrating mode is judged as the train; when the positioning data of the migrating individual falls on the bus stop, the adopted migrating mode is judged as the bus; and when the positioning data of the migrating individual falls into the service area or the toll station, the adopted migrating mode is judged to be self-driving.

Among them, step1 and step2 mainly play a role of data acquisition. step3 is the data computation logic. step4 is produced in a migration mode.

Further reference is made to fig. 4, which shows a schematic diagram of the results of the migration pattern of the migrating individual. As shown in fig. 4, after the migration pattern of the migrating individual is obtained, the migration pattern situation between cities can be analyzed point to point. Including the heat elimination of the migration between any two cities and the proportion of each migration mode.

With further reference to fig. 5, as an implementation of the methods shown in the above diagrams, the present disclosure provides an embodiment of a migration manner determination apparatus, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 1, and the apparatus may be specifically applied to various electronic devices.

As shown in fig. 5, the migration manner determining apparatus 500 of this embodiment may include: a first acquisition module 501, a second acquisition module 502, a matching module 503, and a first determination module 504. The first acquisition module 501 is configured to acquire interest plane data of a traffic junction in an area, wherein the area is divided into a plurality of sub-areas; a second acquisition module 502 configured to acquire positioning data of an individual migrating on a trip across sub-areas; the matching module 503 is configured to match the positioning data with the interest plane data, and determine the interest plane data in which the positioning data falls; the first determining module 504 is configured to determine a migration manner of the migrating individual according to the data of the interest plane in which the positioning data falls.

In this embodiment, in the migration manner determination apparatus 500: the specific processing and the technical effects of the first acquisition module 501, the second acquisition module 502, the matching module 503 and the first determination module 504 can refer to the related descriptions of step 101 and step 104 in the corresponding embodiment of fig. 1, which are not described herein again.

In some optional implementations of this embodiment, the first acquisition module 501 is further configured to: if the traffic hub is a toll station, the central point of the toll station is used as the center of a circle, the preset length is extended outwards, and interest plane data of the toll station is generated.

In some optional implementations of this embodiment, the migration manner determining apparatus 500 further includes: a first ordering module configured to order the positioning data in chronological order; and the deleting module is configured to delete the positioning data with the positioning time less than the starting time and/or greater than the ending time.

In some optional implementations of this embodiment, the matching module 503 is further configured to: and obtaining the intersection of the positioning data and the interest plane data by adopting the hive script.

In some optional implementations of this embodiment, the migration manner determining apparatus 500 further includes: the second sequencing module is configured to sequence the plurality of migration modes according to the weight if the migration individual adopts the plurality of migration modes; and the second determination module is configured to determine the migration mode with the largest weight as the migration mode of the migrating individual.

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

FIG. 6 illustrates a schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 6, the apparatus 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The calculation unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 601 performs the respective methods and processes described above, such as the migration manner determination method. For example, in some embodiments, the migration approach determination method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the migration manner determination method described above may be performed. Alternatively, in other embodiments, the calculation unit 601 may be configured to perform the migration manner determination method by any other suitable manner (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.