CN117009435A - Data synchronization method, equipment and storage medium based on identity recognition - Google Patents

Abstract

The application discloses a data synchronization method, equipment and storage medium based on identity recognition, wherein the data synchronization method based on the identity recognition comprises the following steps: carrying out hierarchical aggregation processing on the acquired personnel information data to obtain data summaries of a plurality of data levels; the method comprises the steps that personnel information data are sent to a target client, and the target client conducts hierarchical aggregation processing on the basis of the received personnel information data to obtain data summaries to be matched of all data levels; sequentially matching the data abstract with the data abstract to be matched returned by the target client in the hierarchical order of the data abstract to obtain a matching result; if the matching result shows that the target data abstract is inconsistent with the data abstract to be matched returned by the target client, sending personnel information data corresponding to the data level of the target data abstract to the target client for data synchronization. By the scheme, the consistency of data synchronization can be ensured, and the efficiency of data synchronization is improved.

Description

Data synchronization method, equipment and storage medium based on identity recognition

Technical field

This application relates to the field of data transmission technology, and in particular to a data synchronization method, device and storage medium based on identity recognition.

Background technique

With the development of Internet technology, identity recognition technology is used in many scenarios. For example, the monitoring platform server sends personnel information to several identity recognition terminals (clients). The terminals will collect face images and received Person information is matched to achieve identity recognition.

However, if there are too many identities to be identified, the amount of data involved is naturally difficult to estimate, and the data on the monitoring platform server and the identity recognition terminal may be inconsistent, and the data synchronization efficiency between the server and the client is low, making it impossible to A large amount of data needs to be confirmed in a timely manner, resulting in the terminal being unable to effectively perform identification.

Contents of the invention

This application at least provides a data synchronization method, device, equipment and computer-readable storage medium based on identity recognition.

The first aspect of this application provides a data synchronization method based on identity recognition, which includes: performing hierarchical aggregation processing on the acquired personnel information data to obtain data summaries of multiple data levels. The data levels include data rows, data Clusters and data tables, the data table includes at least one data cluster, the data cluster includes at least one data row, the data summary includes a row summary corresponding to the data row, a cluster summary corresponding to the data cluster and the Table summary corresponding to the data table; send the personnel information data to the target client, and the target client performs hierarchical aggregation processing based on the received personnel information data to obtain a summary of the data to be matched at each data level; use the data rows , the hierarchical order of data clusters and data tables is to sequentially combine the row summary corresponding to the data row, the cluster summary corresponding to the data cluster, and the table summary corresponding to the data table with the data summary to be matched returned by the target client. The corresponding to-be-matched row summary, to-be-matched cluster summary, and to-be-matched table summary are matched to obtain a matching result; if the target data summary in the matching result is inconsistent with the to-be-matched data summary returned by the target client, then The personnel information data corresponding to the data level where the target data summary is located is sent to the target client for data synchronization.

In one embodiment, the personnel information data includes an identity data table and a face image that matches the identity data in the identity data table. The obtained personnel information data is hierarchically aggregated to obtain multiple The step of data summary at the data level includes: aggregating the identity data in the identity data table to obtain at least one data cluster, the data cluster including at least one row of the identity data; Each row of identity data and the face image corresponding to each row of identity data are subjected to data summary processing to obtain at least one row summary and an image summary corresponding to the row summary; based on the row summary in the data cluster and the An image digest determines a cluster digest for the data cluster, and a table digest for the identity data table is determined based on the cluster digest for each data cluster.

In one embodiment, the row summary corresponding to the data row, the cluster summary corresponding to the data cluster and the table summary corresponding to the data table are sequentially arranged in the hierarchical order of the data row, data cluster and data table. The step of performing matching processing with the corresponding to-be-matched row summary, to-be-matched cluster summary or to-be-matched table summary in the to-be-matched data summary returned by the target client to obtain the matching result includes: if the target client returns to-be-matched row summary, then determine whether the row summary and the to-be-matched row summary match, and obtain the matching result; if the to-be-matched cluster summary returned by the target client is received, then determine whether the cluster summary and the to-be-matched row summary are Whether the summary of the cluster to be matched matches, the matching result is obtained; if the summary of the table to be matched returned by the target client is received, it is judged whether the table summary and the summary of the table to be matched match, and the matching result is obtained result.

In one embodiment, the step of performing data summary processing on each row of identity data in the data cluster and the face image corresponding to each row of identity data includes: obtaining a random number corresponding to each row of identity data. String; perform data summary processing on the corresponding identity data based on the random string to obtain the row summary corresponding to each row of identity data; perform data summary processing on the corresponding face image based on the random string to obtain the The image summary corresponding to the face image is described.

In one embodiment, the method further includes: if it is detected that the current running state and/or the running state of the target client is an idle state, sending a random summary confirmation request to the target client so that the target client In response to the received random summary confirmation request, the client returns the to-be-matched data summary corresponding to the random summary confirmation request in the target client; in order of the hierarchical order of the data rows, data clusters and data tables, The row summary corresponding to the data row, the cluster summary corresponding to the data cluster, and the table summary corresponding to the data table are matched with the received data summary to be matched returned by the target client to obtain a random matching result; Data synchronization is performed on the target client based on the random matching result.

In one embodiment, the method is applied to the target client. The method includes: based on the received personnel information data sent by the server, performing hierarchical aggregation of the personnel information data to obtain multiple data levels to be processed. Matching data summary, the data level includes data rows, data clusters and data tables, the data table includes at least one data cluster, the data cluster includes at least one data row, the data summary to be matched includes the data row corresponding to The row summary to be matched, the cluster summary to be matched corresponding to the data cluster, and the table summary to be matched corresponding to the data table; the row summary to be matched, the cluster summary to be matched, or the table to be matched in the data summary to be matched The summary is sent to the server, and the server sequentially sends the row summary corresponding to the data row, the cluster summary corresponding to the data cluster, and all the data in the server in the hierarchical order of the data row, data cluster, and data table. The table summary corresponding to the data table is matched with the corresponding row summary to be matched, the cluster summary to be matched, and the table summary to be matched in the data summary to be matched received by the server to obtain a matching result; if the service is received If there is a target data summary in the matching result sent by the server that is inconsistent with the data summary to be matched received by the server, the data will be processed based on the personnel information data corresponding to the data level at which the target data summary sent by the server is received. Synchronize.

In one embodiment, the person information includes an identity data table and a face image that matches the identity data in the identity data table. Based on the person information data sent by the received server, the person information is The step of performing hierarchical aggregation of data to obtain multiple data levels of data summaries to be matched includes: aggregating the identity data in the identity data table to obtain at least one data cluster, and the data cluster includes at least one row of The identity data corresponds to the face image; perform data summary processing on each row of identity data in the data cluster and the face image corresponding to each row of identity data to obtain at least one row to be matched. summary and the to-be-matched image summary corresponding to the to-be-matched row summary; determine the to-be-matched cluster summary of the data cluster based on the to-be-matched row summary and the to-be-matched image summary in the data cluster, and based on each data The cluster digest of the cluster to be matched determines the table digest of the identity data table to be matched.

In one embodiment, the step of sending the to-be-matched row summary, the to-be-matched cluster summary, and the to-be-matched table summary in the to-be-matched data summary to the server includes: if a message sent by the server is received. For a row of identity data in the identity data table, the summary of the row to be matched of the row of identity data is sent to the server for matching; if the identity data of all rows in a cluster of data clusters is received, the summary of the row to be matched is sent to the server. The summary of the cluster to be matched of a cluster of data clusters is sent to the server for matching; if the identity data of all clusters in the identity data table is received, the summary of the table to be matched is sent to the service. end to match.

The second aspect of this application provides a data synchronization device based on identity recognition. The device is provided on the server and includes: a first hierarchical aggregation module for hierarchically aggregating the acquired personnel information data to obtain Data summary of multiple data levels, the data level includes data rows, data clusters and data tables, the data table includes at least one data cluster, the data cluster includes at least one data row, the data summary includes the data The row summary corresponding to the row, the cluster summary corresponding to the data cluster and the table summary corresponding to the data table; the first data sending module is used to send the personnel information data to the target client, and the target client is based on The received personnel information data is subjected to hierarchical aggregation processing to obtain the data summary to be matched at each data level; the first data matching module is used to sequentially match the data rows corresponding to the data rows, data clusters and data tables in the hierarchical order. The row summary, the cluster summary corresponding to the data cluster, and the table summary corresponding to the data table are compared with the corresponding row summary to be matched, the cluster summary to be matched, and the table summary to be matched in the data summary to be matched returned by the target client. Match processing to obtain a matching result; the first data synchronization module is used to, if there is a target data summary in the matching result that is inconsistent with the data summary to be matched returned by the target client, corresponding to the data level where the target data summary is located The personnel information data is sent to the target client for data synchronization.

This application also provides a data synchronization device based on identity recognition. The device is provided on the client and includes: a second hierarchical aggregation module for synchronizing the personnel based on the received personnel information data sent by the server. The information data is hierarchically aggregated to obtain a summary of the data to be matched at multiple data levels. The data levels include data rows, data clusters and data tables. The data tables include at least one data cluster, and the data clusters include at least one data row, the summary of the data to be matched includes the summary of the row to be matched corresponding to the data row, the summary of the cluster to be matched corresponding to the data cluster, and the summary of the table to be matched corresponding to the data table; the second data sending module is used to The to-be-matched row summary, the to-be-matched cluster summary, or the to-be-matched table summary in the to-be-matched data summary are sent to the server, and the server sequentially sends the to-be-matched row summary, the to-be-matched cluster summary, and the to-be-matched table summary in the hierarchical order of the data rows, data clusters, and data tables. The row summary corresponding to the data row in the server, the cluster summary corresponding to the data cluster, and the table summary corresponding to the data table are the same as the corresponding row summary to be matched and the row summary to be matched in the data summary to be matched received by the server. The matching cluster summary and the summary of the table to be matched are matched to obtain the matching result; the second data synchronization module is used to if there is a target data summary in the matching result sent by the server and the summary of the target data received by the server to be matched. If the data summaries are inconsistent, data synchronization is performed based on the received personnel information data corresponding to the data level at which the target data summary is sent by the server.

The third aspect of this application provides an electronic device, including a memory and a processor. The processor is used to execute program instructions stored in the memory to implement the above-mentioned data synchronization method based on identity recognition.

The fourth aspect of this application provides a computer-readable storage medium on which program instructions are stored. When the program instructions are executed by a processor, the above-mentioned data synchronization method based on identity recognition is implemented.

In the above scheme, the server performs hierarchical aggregation of the acquired personnel information data to obtain data summaries at multiple data levels, thereby simplifying the personnel information data; the server sends the personnel information data to the client, and obtains the personnel information from the client. The summary of data to be matched returned after hierarchical aggregation of information data; the server matches the summary of data at all levels on the server and the summary of data at all levels to be matched returned by the client in sequence according to the hierarchical order of the data hierarchy, so as to realize the matching between the server and the client. Consistency verification of data between the two parties; the server sends the target data summary to the client, so that the client can perform data synchronization processing on the data summary that does not match the server based on the target data summary, thus realizing the communication between the server and the client. Data synchronization between devices ensures the consistency of data synchronization and improves the efficiency of data synchronization.

It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and do not limit the present application.

Description of the drawings

The accompanying drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present application, and together with the description, are used to explain the technical solutions of the present application.

Figure 1 is a schematic flowchart of an exemplary embodiment of the application's identity recognition-based data synchronization method applied to the server;

Figure 2 is a schematic structural diagram of hierarchical aggregation processing of personnel information data in this application;

Figure 3 is a schematic flow chart of random data confirmation in this application;

Figure 4 is a schematic flowchart of an exemplary embodiment of applying the identity recognition-based data synchronization method of the present application to a target client;

Figure 5 is a block diagram of a data synchronization device based on identity recognition provided on the server side according to an exemplary embodiment of the present application;

Figure 6 is a block diagram of an identity recognition-based data synchronization device provided on a target client according to an exemplary embodiment of the present application;

Figure 7 is a schematic structural diagram of an embodiment of the electronic device of the present application;

Figure 8 is a schematic structural diagram of an embodiment of a computer-readable storage medium of the present application.

Detailed ways

The solutions of the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

In the following description, specific details such as specific system structures, interfaces, technologies, etc. are provided for the purpose of explanation and not limitation, so as to provide a thorough understanding of the present application.

The term "and/or" in this article is just an association relationship that describes related objects, indicating that three relationships can exist. For example, A and/or B can mean: A exists alone, A and B exist simultaneously, and they exist alone. B these three situations. In addition, the character "/" in this article generally indicates that the related objects are an "or" relationship. In addition, "many" in this article means two or more than two. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, and C, which can mean including from A, Any one or more elements selected from the set composed of B and C.

Please refer to FIG. 1 , which is a schematic flowchart of an exemplary embodiment in which the identity recognition-based data synchronization method of the present application is applied to the server. Specifically, it can include the following steps:

Step S110: Perform hierarchical aggregation processing on the acquired personnel information data to obtain data summaries of multiple data levels. The data levels include data rows, data clusters and data tables. The data tables include at least one data cluster, and the data clusters include at least one data cluster. Data rows, data summaries include row summaries corresponding to data rows, cluster summaries corresponding to data clusters, and table summaries corresponding to data tables.

Personnel information data refers to data used for identity recognition, including the person's identity data table and the person's face image. The identity data in the identity data table matches the face image. It should be noted that in the identity recognition scenario , each row of identity data in the identity data table usually has a unique identifier.

Hierarchical aggregation refers to grouping or classifying personnel information data, and aggregating the grouped or classified personnel information data separately to form data with multiple hierarchical relationships. The aggregation processing includes data summary processing. It refers to turning messages of any length into short messages (digests) of fixed length, such as Hash function, MD5, SHA1 and Base64 encoding, etc., which can not only encrypt personnel information data, but also simplify personnel information data.

For example, reference can be made to Figure 2, which is a schematic structural diagram of hierarchical aggregation processing of personnel information data in this application. Each row of identity data in the identity data table can be grouped based on a preset grouping threshold, such as a grouping threshold. is 10, it means that every 10 rows of identity data in the identity data table is a group, and a group of data is equivalent to a cluster of data. That is, every 10 rows of identity data detected in the identity data table are grouped and aggregated into a data cluster. ; After grouping all the rows of identity data in the identity data table, multiple data levels are formed, including data rows, data clusters and data tables. The data table includes data clusters, the data clusters include data rows, and one data row represents a row. Identity data; performing summary processing on each row of identity data will obtain the row summary of the data row corresponding to each row of identity data; performing summary processing on the face image corresponding to each row of identity data will obtain the summary of the face image corresponding to each row of identity data. Image summary; perform preset summary splicing calculations on the row summaries and image summaries corresponding to all data rows in a data cluster, and then obtain the cluster summary of the data cluster; combine the cluster summaries of the data clusters to which all identity data in the identity data table belong After the calculation is completed, summary splicing calculation is performed based on the cluster summaries of all data clusters to obtain the table summary of the identity data table.

It should be noted that if the identity data is grouped until the last cluster, and the number of remaining identity data is less than the preset first grouping threshold, or data loss occurs during the identity data grouping process, resulting in The number of row data in the data cluster is less than the first grouping threshold. In order to ensure the integrity of the summary calculation process, the part with missing data in the data cluster can be filled with preset fixed data to replace the row summary of the data row at that position. .

It should also be noted that the cluster summary of the data cluster can be further streamlined by defining a multi-level cluster summary; for the convenience of explanation, the row summary based on the data row and the image summary of the face image are summarized and spliced to calculate The cluster summary of data clusters is defined as the first-order cluster summary; based on the preset second grouping threshold, the first-order cluster summaries of the number of data clusters corresponding to the second grouping threshold are summarized and spliced to obtain the second-order cluster summary; similarly , can be extended to the definition of third-order cluster summary and fourth-order cluster summary, which will not be described in detail here; and the first grouping threshold and the second grouping threshold are only different in naming, the first grouping threshold and the second grouping threshold They can be the same or different, and are not limited here. However, to achieve the definition of multi-order cluster summaries, the second grouping threshold should be smaller than the number of first-order cluster summaries.

Step S120: Send the personnel information data to the target client. The target client performs hierarchical aggregation processing based on the received personnel information data to obtain a summary of the data to be matched at each data level.

It should be noted that the process of hierarchical aggregation of personnel information data by the server does not conflict with the process of sending personnel information data to the target client by the server. Therefore, the execution order of steps S120 and S110 is not limited. Consider The server needs to verify the summary of data to be matched returned by the target client. Preferably, step S110 can be performed first.

It can be understood that in order to facilitate the confirmation of data consistency between the server and the target client, the hierarchical aggregation processing and data summary processing of personnel information data by the server and the target client should be the same. Among them, The target client can be preset with the same processing method as the server, or the server can send its processing method to the target client, or the target client can obtain it from other preset databases or servers. No limitation is made here.

Step S130: In order of the hierarchical order of data rows, data clusters and data tables, the row summary corresponding to the data row, the cluster summary corresponding to the data cluster and the table summary corresponding to the data table are combined with the corresponding data summary to be matched returned by the target client. The summary of the row to be matched, the summary of the cluster to be matched, and the summary of the table to be matched are matched to obtain the matching result.

It is understandable that because the hierarchical aggregation of personnel information data is processed in the same way between the target client and the server, the summary of the data to be matched returned by the target client naturally includes the summary of the row to be matched, the summary of the cluster to be matched, and the summary of the table to be matched. .

For example, you can continue to refer to the data hierarchy shown in Figure 2, and sequentially combine the row summary, cluster summary and table summary with the row summary to be matched and the row summary to be matched returned by the target client according to the hierarchical order of data rows, data clusters and data tables. The cluster summary and the summary of the table to be matched are processed correspondingly to obtain the matching result.

It should be noted that, combined with the foregoing embodiments, it can be seen that if a face recognition scenario is involved, the person information data includes face image data; performing hierarchical aggregation processing on the person information data can obtain the face image corresponding to each row of identity data. Face summary, and the face summary and row summary match each other and belong to the same data level; and the client can naturally obtain the face summary to be matched after hierarchical aggregation of the person information data; therefore, in the row summary When matching with the row summary to be matched, the face summary and the face summary to be matched can also be matched accordingly; when it is determined that the row summary of the same identity data is consistent with the row summary to be matched and the same face image corresponding to the identity data Only after the face summary of the face summary is consistent with the face summary to be matched, the person information data representing the data row will be consistent.

Step S140: If the target data summary in the matching result is inconsistent with the data summary to be matched returned by the target client, the personnel information data corresponding to the data level where the target data summary is located is sent to the target client for data synchronization.

Specifically, if a certain target data summary calculated by the server is inconsistent with a certain to-be-matched data summary returned by the target client, it means that the personnel information data corresponding to the data summary is inconsistent; therefore, the data level where the target data summary is located is The corresponding personnel information data is sent to the target client, so that the target client updates the personnel information data corresponding to the data level where the received target data summary is located to the corresponding data storage location in the target client to achieve data synchronization.

It can be seen that in this application solution, the server performs hierarchical aggregation of the acquired personnel information data to obtain data summaries at multiple data levels, thereby simplifying the personnel information data; the server sends the personnel information data to the client, Obtain the data summary to be matched returned by the client after hierarchically aggregating the personnel information data; the server matches the data summaries at all levels of the server with the data summaries to be matched at all levels returned by the client in sequence according to the hierarchical order of the data hierarchy to achieve matching. Data consistency verification between the server and the client; the server sends the target data summary to the client, so that the client can synchronize the data summary that does not match the server based on the target data summary, thus enabling the service to be realized Data synchronization between the client and the client ensures the consistency of data synchronization and improves the efficiency of data synchronization.

On the basis of the above embodiments, the embodiments of this application describe the steps of performing hierarchical aggregation processing on the acquired personnel information data to obtain data summaries of multiple data levels. The personnel information data includes identity data tables and identity data. Face images that match the identity data in the table. Specifically, the method of this embodiment includes the following steps:

Aggregate the identity data in the identity data table to obtain at least one data cluster, which includes at least one row of identity data; perform data summary processing on each row of identity data in the data cluster and the face image corresponding to each row of identity data. , obtain at least one row summary and an image summary corresponding to the row summary; determine the cluster summary of the data cluster based on the row summary and image summary in the data cluster, and determine the table summary of the identity data table based on the cluster summary of each data cluster.

It is understandable that in face recognition-related scenarios (such as attendance check-in, face unlocking, etc.), verification of personnel information data will involve matching of identity data and face image matching.

Described in conjunction with the foregoing embodiments, the identity data is grouped with a preset grouping threshold, and each grouped identity data is aggregated into a data cluster. The data cluster should include at least one row of identity data; for each data cluster, Each row of identity data and the face image corresponding to each row of identity data are processed separately to obtain a row summary corresponding to each row of identity data and an image summary corresponding to the face image. The row summary and image summary are based on the identity data and the face image. Match each other based on the corresponding relationship between the face images; perform a preset summary splicing calculation on the row summary and image summary in each data cluster to obtain the cluster summary of each data cluster, where the summary splicing calculation can be to combine each data cluster in the data cluster. The strings of row summaries and image summaries are spliced; it is also possible to splice the image summaries corresponding to each row summary and row summary first to obtain the initial splicing summary, perform data summarization processing on the initial splicing summary, and obtain the intermediate summary, and then All intermediate summaries in the data cluster are spliced to obtain the cluster summary of the data cluster. There are no limitations here on the specific summary splicing calculation method. You can refer to the general summary splicing calculation method in this field. In the same way, the identity data is The cluster summaries of all data clusters corresponding to the table are summarily spliced to obtain the table summary of the data table.

It can be seen that in this embodiment, a certain amount of identity data forms data clusters by aggregating the identity data in the identity data table, and the identity data in all data clusters exactly correspond to the identity data in the identity data table. Therefore, Multiple data levels of rows, clusters, and tables are formed to facilitate the management and call of personnel information data; by determining the summary of the data in each level.

Based on the above embodiment, this embodiment sequentially compares the row summary corresponding to the data row, the cluster summary corresponding to the data cluster, and the table summary corresponding to the data table with the target client in the hierarchical order of data rows, data clusters, and data tables. The corresponding row summary to be matched, cluster summary to be matched, or table summary to be matched in the returned data summary to be matched is matched, and the steps to obtain the matching result are described below. Specifically, the steps of this embodiment include:

If the summary of the row to be matched is received from the target client, it is determined whether the row summary and the summary of the row to be matched match, and the matching result is obtained; if the summary of the cluster to be matched is received from the target client, the cluster summary and the cluster to be matched are determined. Whether the summary matches, the matching result is obtained; if the summary of the table to be matched returned by the target client is received, it is judged whether the table summary and the summary of the table to be matched match, and the matching result is obtained.

It should be noted that the process of the target client returning the summary of the data to be matched is based on the data level. Therefore, the server will also perform the matching of the data summary and the summary of the data to be matched in sequence. For example, if the target client receives When a complete row of identity data and the face image corresponding to the identity data are sent to the server, the target client calculates the row summary of the identity data to be matched and the image summary of the face image to be matched, and then adds the identity data corresponding to the The summary of the row to be matched and the summary of the image to be matched are returned to the server for summary matching; if the target client receives complete identity data of all rows in a data cluster and the face image corresponding to the identity data, based on all identities in the data cluster The data and the corresponding face image calculate the summary of the cluster to be matched for the data cluster, and then return the summary of the cluster to be matched to the server for summary matching; if the target client receives all the identity data in the identity data table and the corresponding face image, after calculating the cluster summaries to be matched of the data clusters corresponding to all identity data, based on the cluster summaries of all data clusters to be matched, determine the table summary to be matched of the identity data table received by the target client, and then Return the table summary of the identity data table to be matched to the server for summary matching; that is, the summary matching process is performed by the target client in sequence according to the hierarchical order of the data level of the received data.

It can be seen from this that by sequentially processing the data summaries to be matched returned by the client according to the order of the data levels, it can quickly confirm whether the data summaries to be matched calculated by the client and the data summaries of the server are consistent, and the data summaries of different data levels can be matched separately. , making the summary matching process more rigorous and improving the accuracy of data consistency verification.

Based on the above embodiment, this embodiment explains the steps of performing data summary processing on each row of identity data in the data cluster and the face image corresponding to each row of identity data. Specifically, the steps of this embodiment include:

Obtain the random string corresponding to each row of identity data; perform data summary processing on the corresponding identity data based on the random string, and obtain the row summary corresponding to each row of identity data; perform data summary processing on the corresponding face image based on the random string, Get the image summary corresponding to the face image.

Random strings refer to randomly generated strings, which can be used for data summary processing of identity data, face images, data clusters, and identity data tables to streamline these data and improve their privacy. Among them, random characters There may be one or more strings. For specific methods of performing data summary processing on data based on random strings, please refer to common summary algorithms in the field, which will not be described in detail here.

It should be noted that the random string generated by the server based on each row of identity data will also be sent to the target client accordingly, so that the target client can use the same random string as the server to identify the person received by the target client. The information data undergoes data summary processing to ensure consistency between the server and the target client in the data processing process.

Preferably, in order to ensure the privacy and security of the data, different random strings can be used when performing data digest processing on different identity data; for example, a random string is generated according to each row of identity data in the identity data table, and the random character string is used. The identity data corresponding to the random string is subjected to data summary processing to obtain the row summary corresponding to the identity data; because the identity data corresponds to the face image, the corresponding face image is summarized through the random string to obtain the face image. Image summary.

Based on the above embodiments, as shown in Figure 3, which is a schematic flow chart of random data confirmation in this application, the data synchronization method based on identity recognition in this application also includes:

Step S310: If it is detected that the current running status and/or the running status of the target client is idle, a random summary confirmation request is sent to the target client, so that the target client responds to the received random summary confirmation request and returns to the target client. Neutralize the data digest to be matched corresponding to the random digest confirmation request.

The random summary confirmation request refers to the server randomly selecting one or more personnel information data, and sending a request to the target client to confirm whether the data digests of these personnel information data are consistent, and then determine whether the data summaries of these personnel information data in the server are consistent. Are the personnel information data in the target client consistent?

It should be noted that after the server fully confirms the consistency of the server's personnel information data and the target client's personnel information data, the server can also initiate random data confirmation on the target client to ensure subsequent data consistency.

Specifically, the random data confirmation process can be performed when the server is idle and/or when the target client is idle, so as to avoid affecting the normal operation of the server and the client; if the server detects the current operating status and/or the target client's If the running status is idle, a random summary confirmation request is sent to the target client, so that the target client randomly selects one or more rows of identity data from the personnel information data stored in the target client, obtains random confirmation identity data, and returns these random identity data. Confirm that one or more data summaries in the row summary to be matched, the image summary to be matched, the cluster summary to be matched, and the table summary to be matched corresponding to the identity data are sent to the server for summary matching to confirm data consistency.

Step S320, sequentially combine the row summary corresponding to the data row, the cluster summary corresponding to the data cluster, and the table summary corresponding to the data table with the to-be-matched data summary returned by the target client received in the hierarchical order of data rows, data clusters, and data tables. Perform matching processing to obtain random matching results.

It can be understood that during the random data confirmation process, the same data summary processing method and data summary matching method as in the previous embodiment can still be referred to, and details will not be described again here.

Optionally, the process of the server sending a random summary confirmation request to the target client can also be: randomly selecting from the personnel information data on the server, obtaining random personnel information data, and generating random summary confirmation based on the random personnel information data. The new random string of the process, based on the new random string, recalculates the data row summary, image summary, cluster summary of the data cluster where the random personnel information data is located, and the table summary of the data table where the random personnel information data is located based on the new random string. ; Send the new random string and the personnel serial number of the random personnel information data to the target client, so that the target client can search for the personnel information data in the target client based on the received personnel serial number, and similarly based on the received new personnel serial number The random string of the found personnel information data is recalculated to obtain the summary of the data row to be matched corresponding to the found personnel information data, the summary of the image to be matched, the summary of the cluster to be matched in the data cluster where the found personnel information data is located, and The summary of the to-be-matched table of the data table where the found personnel information data is located is returned to the server in turn. The server then matches the summary of the data rows corresponding to the random personnel information data and the summary of the data rows to be matched, and matches the summary of the random personnel information data to the corresponding rows of the data to be matched. Match the image summary with the image summary to be matched, match the cluster summary of the data cluster where the random person information data is located with the cluster summary to be matched, and match the table summary of the data table where the random person information data is with the table summary to be matched, to achieve the server-side target Random digest acknowledgment by the client further ensures data consistency between the server and the target client.

Step S330: Perform data synchronization on the target client based on the random matching results.

Specifically, if the target data summary in the random matching result is inconsistent with the data summary to be matched returned by the target client, the personnel information data corresponding to the data level where the target data summary is located is sent to the target client for data synchronization.

It can be seen that through the method of random data confirmation in this embodiment, data consistency between the server and the client is ensured without affecting the normal operation of the server and the client.

Based on the above embodiments, embodiments of the present application also include:

If the server receives an update instruction for personnel information data, where the update instruction includes but is not limited to data addition, data deletion, data modification and other operation instructions; the server will update the personnel information data corresponding to the instruction and the corresponding personnel information data. random strings are jointly sent to the target client; similarly, the target client will also perform data summary processing on the personnel information data corresponding to the update instruction, and return the corresponding summary to be matched to the server; the server will then based on these to be matched The summary is matched with the data summary of the personnel information data corresponding to the update instruction to complete the data consistency confirmation.

In one embodiment, as shown in Figure 4, Figure 4 is a schematic flow chart of an exemplary embodiment in which the data synchronization method based on identity recognition of the present application is applied to a target client. The data synchronization method based on identity recognition of the present application is applied to When targeting clients, specific steps include:

Step S410: Based on the received personnel information data sent by the server, perform hierarchical aggregation of the personnel information data to obtain multiple data levels of to-be-matched data summaries. The data levels include data rows, data clusters and data tables. The data tables include At least one data cluster, the data cluster includes at least one data row, and the data summary to be matched includes a row summary to be matched corresponding to the data row, a cluster summary to be matched corresponding to the data cluster, and a table summary to be matched corresponding to the data table.

Personnel information data refers to data used for identity recognition, including the person's identity data table and the person's face image. The identity data in the identity data table matches the face image. It should be noted that in the identity recognition scenario , each row of identity data in the identity data table usually has a unique identifier.

Hierarchical aggregation refers to grouping or classifying personnel information data, and aggregating the grouped or classified personnel information data separately to form data with multiple hierarchical relationships. The aggregation processing includes data summary processing. It refers to turning messages of any length into short messages (digests) of fixed length, such as Hash function, MD5, SHA1 and Base64 encoding, etc., which can not only encrypt personnel information data, but also simplify personnel information data.

Specifically, reference can be made to the description of step S110 and the schematic diagram of the effect of hierarchical aggregation processing shown in Figure 2. Similarly, the target client performs hierarchical aggregation processing based on the received personnel information data to obtain multiple data levels to be matched. Data summary; in addition, the target client will also receive a random string sent by the server to perform data summary processing on the personnel information data.

It should be noted that the target client performs hierarchical aggregation processing of personnel information data and the data summary processing method is the same as the server side performs hierarchical aggregation processing and data summary processing of personnel information data. Among them, the target client The client's processing method may be preset on the target client, may be sent by the server to the target client, or may be obtained by the target client from other preset databases or servers, which is not limited here.

Step S420: Send the to-be-matched row summary, the to-be-matched cluster summary, or the to-be-matched table summary in the to-be-matched data summary to the server. The server will sequentially send the data rows in the server in the hierarchical order of data rows, data clusters, and data tables. The corresponding row summary, the cluster summary corresponding to the data cluster, and the table summary corresponding to the data table are matched with the corresponding row summary to be matched, the cluster summary to be matched, and the table summary to be matched in the data summary to be matched received by the server, and we get Matching results.

It is understandable that because the hierarchical aggregation of personnel information data is processed in the same way between the target client and the server, the summary of the data to be matched returned by the target client naturally includes the summary of the row to be matched, the summary of the cluster to be matched, and the summary of the table to be matched. .

For example, you can continue to refer to the data hierarchy shown in Figure 2, and return the summary of the row to be matched, the summary of the cluster to be matched, and the summary of the table to be matched to the server in sequence according to the hierarchical order of data rows, data clusters, and data tables, so that the service The client performs corresponding summary matching processing on the row summary to be matched, the cluster summary to be matched, and the table summary to be matched, to obtain a matching result.

It should be noted that, combined with the foregoing embodiments, it can be seen that if a face recognition scenario is involved, the person information data includes face image data; performing hierarchical aggregation processing on the person information data can obtain the face image corresponding to each row of identity data. The face summary to be matched, and the face summary to be matched and the row summary to be matched match each other and belong to the same data level; therefore, when the client returns the data summary to be matched, the row summary to be matched and the row summary to be matched can also be The corresponding face summary to be matched is returned synchronously, so that when the server matches the row summary and the row summary to be matched, it can also match the face summary and the face summary to be matched accordingly; the same identity data is determined on the server Only when the row summary and the row summary to be matched are consistent and the face summary of the same face image corresponding to the identity data is consistent with the face summary to be matched can the person information data representing the data row be consistent.

Step S430, if there is a target data summary in the matching result received from the server that is inconsistent with the data summary to be matched received by the server, then the process is performed based on the personnel information data corresponding to the data level at which the target data summary sent by the server is located. data synchronization.

Specifically, if a certain target data summary calculated by the server is inconsistent with a certain to-be-matched data summary returned by the target client, it means that the personnel information data corresponding to the data summary is inconsistent; therefore, the target client will receive the service The target client can update the personnel information data corresponding to the data level of the target data summary sent by the client to the corresponding data storage location to achieve data synchronization.

It should be noted that there are many identical implementation methods for the hierarchical aggregation processing process, data summary processing process, summary matching process and data synchronization process of personnel information data on the server side and the target client. Therefore, the data based on identity recognition will be For specific descriptions of the implementation process of applying the synchronization method to the target client, please refer to the aforementioned specific descriptions of applying the identity recognition-based data synchronization method to the server.

It can be seen from the above that in this application, the target client performs hierarchical aggregation of the acquired personnel information data to obtain data summaries at multiple data levels, thereby simplifying the personnel information data; after the target client hierarchically aggregates the personnel information data The summary of the data to be matched is returned to the server, so that the server can sequentially match the data summaries of all levels on the server with the data summaries of all levels to be matched returned by the target client according to the hierarchical order of the data hierarchy, so as to realize the matching between the server and the target client. Consistency check of data between them; the target client synchronizes the data summary that does not match the server based on the received target data summary sent by the server, thus enabling data between the server and the client Synchronization improves the efficiency of data synchronization.

On the basis of the above embodiment, this embodiment explains the steps of hierarchically aggregating the personnel information data based on the received personnel information data sent by the server to obtain multiple data levels of to-be-matched data summaries. The personnel information Includes an identity data table and a face image that matches the identity data in the identity data table. Specifically, the steps of this embodiment include:

Aggregate the identity data in the identity data table to obtain at least one data cluster. The data cluster includes at least one row of identity data, and the identity data corresponds to the face image; each row of identity data and each row of identity data in the data cluster correspond to Perform data summary processing on the face image to obtain at least one row summary to be matched and an image summary to be matched corresponding to the row summary to be matched; determine the cluster summary to be matched in the data cluster based on the row summary to be matched and the image summary to be matched in the data cluster , and determine the to-be-matched table summary of the identity data table based on the to-be-matched cluster summary of each data cluster.

Described in conjunction with the foregoing embodiments, the identity data is grouped with a preset grouping threshold, and each grouped identity data is aggregated into a data cluster. The data cluster should include at least one row of identity data; for each data cluster, Each row of identity data and the face image corresponding to each row of identity data are processed separately, and the summary of the row to be matched corresponding to each row of identity data and the summary of the image to be matched corresponding to the face image are obtained, where the summary of the row to be matched and the summary of the row to be matched are obtained. The matching image summaries match each other based on the correspondence between the identity data and the face image; the row summaries to be matched and the image summaries to be matched in each data cluster are subjected to preset summary splicing calculations to obtain the clusters to be matched for each data cluster. Summary, where the summary splicing calculation can be to splice the strings of each row summary to be matched and the image summary to be matched in the data cluster; it can also be to match the summary of each row to be matched and the image to be matched corresponding to the row summary to be matched. The abstracts are first spliced to obtain the initial splicing summary to be matched. The initial splicing summary to be matched is subjected to data summary processing to obtain the intermediate summary to be matched. Then all the intermediate summaries to be matched in the data cluster are spliced to obtain the cluster to be matched for the data cluster. Abstract; For the specific method of summary splicing calculation, there is no limitation here. You can refer to the summary splicing calculation method commonly used in this field. In the same way, the summary of the clusters to be matched of all data clusters corresponding to the identity data table is summarized and spliced to obtain the Summary of the data table to be matched.

Based on the above embodiment, this embodiment describes the steps of sending the summary of the row to be matched, the summary of the cluster to be matched, and the summary of the table to be matched in the summary of the data to be matched to the server. Specifically, the steps of this embodiment include:

If a row of identity data in the identity data table sent by the server is received, the summary of the row to be matched for the row of identity data is sent to the server for matching; if the identity data of all rows in a data cluster is received, a row summary of the identity data is sent to the server for matching. The summary of the cluster to be matched is sent to the server for matching; if the identity data of all clusters in the identity data table is received, the summary of the table to be matched is sent to the server for matching.

In order to improve data processing efficiency and avoid excessive accumulation of data summaries to be matched received by the server, the target client returns the data summaries to be matched to the server in sequence based on the data hierarchy; for example, for the target client In terms of The corresponding summary of rows to be matched and summary of images to be matched are returned to the server for summary matching; if complete identity data of all rows in a data cluster and face images corresponding to the identity data are received, based on all identity data in the data cluster Calculate the summary of the cluster to be matched with the corresponding face image, and then return the summary of the cluster to be matched to the server for summary matching; if all the identity data in the identity data table and the corresponding face image are received , then after calculating the cluster summaries to be matched for the data clusters corresponding to all identity data, based on the cluster summaries to be matched of all data clusters, determine the table summary to be matched for the identity data table received by the target client, and then add the identity data table to the The summary of the table to be matched is returned to the server for summary matching.

It can be seen from this that the summary of the data to be matched is returned to the server in sequence according to the order of the data level. The server can quickly confirm whether the summary of the data to be matched is consistent with the data summary of the server, and match the data summaries of different data levels respectively, so that The summary matching process is more rigorous, ensuring the consistency of data synchronization and improving the accuracy of data consistency verification.

It should be further noted that the execution subject of the identity recognition-based data synchronization method may be an identity recognition-based data synchronization device provided on the server and/or an identity recognition-based data synchronization device provided on the client, for example, an identity recognition-based data synchronization device. The data synchronization method can be executed by a terminal device or a server or other processing device, where the terminal device can be a user equipment (User Equipment, UE), a computer, a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital processing device ( Personal Digital Assistant (PDA), handheld devices, computing devices, vehicle-mounted devices, wearable devices, etc.; the server can be a general-purpose server, a dedicated server, etc. In some possible implementations, the identity recognition-based data synchronization method can be implemented by the processor calling computer-readable instructions stored in the memory.

Figure 5 is a block diagram of a data synchronization device based on identity recognition provided on the server side according to an exemplary embodiment of the present application. As shown in FIG. 5 , the exemplary identity recognition-based data synchronization device 500 includes: a first hierarchical aggregation module 510 , a first data sending module 520 , a first data matching module 530 and a first data synchronization module 540 . specifically:

The first hierarchical aggregation module 510 is used to perform hierarchical aggregation processing on the acquired personnel information data to obtain data summaries of multiple data levels. The data levels include data rows, data clusters and data tables. The data tables include at least one data Cluster, the data cluster includes at least one data row, and the data summary includes the row summary corresponding to the data row, the cluster summary corresponding to the data cluster, and the table summary corresponding to the data table.

The first data sending module 520 is used to send personnel information data to the target client. The target client performs hierarchical aggregation processing based on the received personnel information data to obtain a summary of data to be matched at each data level.

The first data matching module 530 is used to sequentially match the row summary corresponding to the data row, the cluster summary corresponding to the data cluster, and the table summary corresponding to the data table with the data to be returned by the target client in the hierarchical order of data rows, data clusters, and data tables. The corresponding row summary to be matched, the cluster summary to be matched, and the table summary to be matched in the matching data summary are matched to obtain a matching result.

The first data synchronization module 540 is used to send the personnel information data corresponding to the data level where the target data summary is located to the target client for data synchronization if the target data summary in the matching result is inconsistent with the data summary to be matched returned by the target client. .

In this exemplary data synchronization device based on identity recognition, the server performs hierarchical aggregation of the acquired personnel information data to obtain data summaries of multiple data levels, thereby simplifying the personnel information data; the server performs hierarchical aggregation of the personnel information data. The data is sent to the client, and the summary of the data to be matched returned by the client after hierarchical aggregation of the personnel information data is obtained; the server combines the summaries of the data at all levels on the server and the summaries of the data to be matched at all levels returned by the client according to the hierarchical order of the data hierarchy. Matching is performed in turn to achieve consistency verification of data between the server and the client; the server sends a target data summary to the client, so that the client can synchronize data that does not match the data summary with the server based on the target data summary. , thus enabling data synchronization between the server and the client, ensuring the consistency of data synchronization and improving the efficiency of data synchronization.

For the functions of each module, please refer to the embodiment of the data synchronization method based on identity recognition, which will not be described again here.

FIG. 6 is a block diagram of a data synchronization device based on identity recognition provided on a client according to an exemplary embodiment of the present application. As shown in FIG. 6 , the exemplary identity recognition-based data synchronization device 600 includes: a second hierarchical aggregation module 610 , a second data sending module 620 and a second data synchronization module 630 . specifically:

The second hierarchical aggregation module 610 is used to hierarchically aggregate the personnel information data based on the received personnel information data sent by the server to obtain multiple data levels of to-be-matched data summaries. The data levels include data rows and data clusters. and a data table. The data table includes at least one data cluster. The data cluster includes at least one data row. The data summary to be matched includes a row summary to be matched corresponding to the data row, a cluster summary to be matched corresponding to the data cluster and a table to be matched corresponding to the data table. Summary.

The second data sending module 620 is used to send the to-be-matched row summary, the to-be-matched cluster summary, or the to-be-matched table summary in the to-be-matched data summary to the server. The server is sequentially arranged in the hierarchical order of data rows, data clusters, and data tables. Compare the row summary corresponding to the data row, the cluster summary corresponding to the data cluster, and the table summary corresponding to the data table in the server with the corresponding row summary to be matched, the cluster summary to be matched, and the table to be matched in the data summary to be matched received by the server. The abstracts are matched and the matching results are obtained.

The second data synchronization module 630 is used to, if the target data summary in the matching result received from the server is inconsistent with the data summary to be matched received by the server, based on the data level of the target data summary sent by the server. Corresponding personnel information data is synchronized.

In this exemplary data synchronization device based on identity recognition, the target client performs hierarchical aggregation of the acquired personnel information data to obtain data summaries of multiple data levels, thereby simplifying the personnel information data; the target client will The summary of the data to be matched after the hierarchical aggregation of the personnel information data is returned to the server, so that the server can match the summary of data at all levels on the server with the summaries of the data to be matched at all levels returned by the target client in sequence according to the hierarchical order of the data hierarchy. Verify the consistency of data between the server and the target client; the target client synchronizes the data summary that does not match the server based on the received target data summary sent by the server, thus enabling the server to Data synchronization with the client ensures the consistency of data synchronization and improves the efficiency of data synchronization.

For the functions of each module, please refer to the embodiment of the data synchronization method based on identity recognition, which will not be described again here.

Please refer to FIG. 7 , which is a schematic structural diagram of an embodiment of the electronic device of the present application. The electronic device 700 includes a memory 701 and a processor 702. The processor 702 is configured to execute program instructions stored in the memory 701 to implement the steps in any of the above identity recognition-based data synchronization method embodiments. In a specific implementation scenario, the electronic device 700 may include but is not limited to: a microcomputer and a server. In addition, the electronic device 700 may also include mobile devices such as notebook computers and tablet computers, which are not limited here.

Specifically, the processor 702 is used to control itself and the memory 701 to implement the steps in any of the above identity recognition-based data synchronization method embodiments. The processor 702 may also be called a CPU (Central Processing Unit). The processor 702 may be an integrated circuit chip with signal processing capabilities. The processor 702 can also be a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field-Programmable Gate Array, FPGA), or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. In addition, the processor 702 may be implemented by an integrated circuit chip.

In the above scheme, the server performs hierarchical aggregation of the acquired personnel information data to obtain data summaries at multiple data levels, thereby simplifying the personnel information data; the server sends the personnel information data to the client, and obtains the personnel information from the client. The summary of data to be matched returned after hierarchical aggregation of information data; the server matches the summary of data at all levels on the server and the summary of data at all levels to be matched returned by the client in sequence according to the hierarchical order of the data hierarchy, so as to realize the matching between the server and the client. Consistency verification of data between the two parties; the server sends the target data summary to the client, so that the client can perform data synchronization processing on the data summary that does not match the server based on the target data summary, thus realizing the communication between the server and the client. Data synchronization between devices ensures the consistency of data synchronization and improves the efficiency of data synchronization.

Please refer to FIG. 8 , which is a schematic structural diagram of an embodiment of a computer-readable storage medium of the present application. The computer-readable storage medium 810 stores program instructions 811 that can be executed by the processor. The program instructions 811 are used to implement the steps in any of the above-mentioned identity recognition-based data synchronization method embodiments.

In the above scheme, the server performs hierarchical aggregation of the acquired personnel information data to obtain data summaries at multiple data levels, thereby simplifying the personnel information data; the server sends the personnel information data to the client, and obtains the personnel information from the client. The summary of data to be matched returned after hierarchical aggregation of information data; the server matches the summary of data at all levels on the server and the summary of data at all levels to be matched returned by the client in sequence according to the hierarchical order of the data hierarchy, so as to realize the matching between the server and the client. Consistency verification of data between the two parties; the server sends the target data summary to the client, so that the client can perform data synchronization processing on the data summary that does not match the server based on the target data summary, thus realizing the communication between the server and the client. Data synchronization between devices ensures the consistency of data synchronization and improves the efficiency of data synchronization.

In some embodiments, the functions or modules provided by the device provided by the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments. For specific implementation, refer to the description of the above method embodiments. For the sake of brevity, here No longer.

The above description of various embodiments tends to emphasize the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described again here.

In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device implementation described above is only illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other divisions, for example, units or components may be combined or integrated. to another system, or some features can be ignored, or not implemented. On the other hand, the coupling or direct coupling or communication connection between each other shown or discussed may be through some interfaces, and the indirect coupling or communication connection of the devices or units may be in electrical, mechanical or other forms.

In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or software functional units. Integrated units may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as independent products. Based on this understanding, the technical solution of the present application is essentially or contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including a number of instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the various implementation methods of this application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code. .

Claims (10)

1. The data synchronization method based on identity recognition is characterized by being applied to a server, and comprises the following steps:

carrying out hierarchical aggregation processing on the acquired personnel information data to obtain data summaries of a plurality of data levels, wherein the data levels comprise data rows, data clusters and data tables, the data tables comprise at least one data cluster, the data clusters comprise at least one data row, and the data summaries comprise row summaries corresponding to the data rows, cluster summaries corresponding to the data clusters and table summaries corresponding to the data tables;

the personnel information data are sent to a target client, and the target client performs hierarchical aggregation processing on the received personnel information data to obtain data abstracts to be matched of all data levels;

sequentially carrying out matching processing on the row digests corresponding to the data row, the cluster digests corresponding to the data cluster and the table digests corresponding to the data table and the row digests to be matched, the cluster digests to be matched and the table digests to be matched corresponding to the data digests to be matched returned by the target client in the hierarchical order of the data row, the data cluster and the data table to obtain a matching result;

And if the matching result has inconsistent target data abstract and the data abstract to be matched returned by the target client, sending personnel information data corresponding to the data level of the target data abstract to the target client for data synchronization.

2. The method according to claim 1, wherein the personnel information data includes an identity data table and a face image matched with the identity data in the identity data table, and the step of hierarchically aggregating the acquired personnel information data to obtain the data digests of a plurality of data levels includes:

aggregating the identity data in the identity data table to obtain at least one cluster of data clusters, wherein the data clusters comprise at least one row of the identity data;

respectively carrying out data summarization processing on each line of identity data in the data cluster and the face image corresponding to each line of identity data to obtain at least one line of line summary and an image summary corresponding to the line summary;

determining a cluster digest of the data cluster based on the row digest and the image digest in the data cluster, and determining a table digest of the identity data table based on the cluster digest of each data cluster.

3. The method according to claim 2, wherein the step of sequentially matching, in the hierarchical order of the data row, the data cluster, and the data table, the row digest corresponding to the data row, the cluster digest corresponding to the data cluster, and the table digest corresponding to the data table with the row digest to be matched, the cluster digest to be matched, or the table digest to be matched corresponding to the data digest to be matched returned by the target client, to obtain a matching result includes:

if a line abstract to be matched returned by the target client is received, judging whether the line abstract is matched with the line abstract to be matched or not, and obtaining the matching result;

if a cluster abstract to be matched returned by the target client is received, judging whether the cluster abstract is matched with the cluster abstract to be matched or not, and obtaining the matching result;

and if the table abstract to be matched returned by the target client is received, judging whether the table abstract to be matched is matched with the table abstract to be matched or not, and obtaining the matching result.

4. The method according to claim 2, wherein the step of performing data summarization processing on each line of identity data in the data cluster and the face image corresponding to each line of identity data respectively includes:

Acquiring a random character string corresponding to each row of identity data;

performing data summarization processing on the corresponding identity data based on the random character strings to obtain a row summary corresponding to each row of identity data;

and carrying out data summarization processing on the corresponding face image based on the random character string to obtain an image summary corresponding to the face image.

5. The method according to claim 1, wherein the method further comprises:

if the current running state and/or the running state of the target client are/is detected to be in an idle state, a random abstract confirmation request is sent to the target client, so that the target client returns a data abstract to be matched, corresponding to the random abstract confirmation request, in the target client in response to the received random abstract confirmation request;

sequentially matching the row abstracts corresponding to the data rows, the cluster abstracts corresponding to the data clusters and the table abstracts corresponding to the data tables with the received data abstracts to be matched returned by the target client in the hierarchical order of the data rows, the data clusters and the data tables to obtain a random matching result;

and carrying out data synchronization on the target client based on the random matching result.

6. A data synchronization method based on identity recognition, wherein the method is applied to a target client, and the method comprises the following steps:

based on personnel information data sent by a received server, carrying out hierarchical aggregation on the personnel information data to obtain to-be-matched data summaries of a plurality of data levels, wherein the data levels comprise data rows, data clusters and data tables, the data tables comprise at least one data cluster, the data clusters comprise at least one data row, and the to-be-matched data summaries comprise to-be-matched row summaries corresponding to the data rows, to-be-matched cluster summaries corresponding to the data clusters and to-be-matched table summaries corresponding to the data tables;

the method comprises the steps that row summaries to be matched, cluster summaries to be matched or table summaries to be matched in the data summaries to be matched are sent to a server side, and the server side sequentially carries out matching processing on row summaries corresponding to the data rows, cluster summaries corresponding to the data clusters and table summaries corresponding to the data tables in the server side and corresponding row summaries to be matched, cluster summaries to be matched and table summaries to be matched in the data summaries to be matched received by the server side according to the hierarchical sequence of the data rows, the data clusters and the data tables, so that matching results are obtained;

If the target data abstract is inconsistent with the data abstract to be matched received by the server side in the matching result sent by the server side, carrying out data synchronization based on the received personnel information data corresponding to the data level where the target data abstract is located.

7. The method according to claim 6, wherein the personnel information includes an identity data table and a face image matched with the identity data in the identity data table, and the step of hierarchically aggregating the personnel information data based on the personnel information data sent by the received server to obtain the data digests to be matched of a plurality of data levels includes:

aggregating the identity data in the identity data table to obtain at least one cluster of data clusters, wherein the data clusters comprise at least one row of the identity data, and the identity data corresponds to the face image;

respectively carrying out data summarization processing on each line of identity data in the data cluster and the face image corresponding to each line of identity data to obtain at least one line of line summary to be matched and an image summary to be matched corresponding to the line summary to be matched;

Determining a to-be-matched cluster abstract of the data cluster based on the to-be-matched row abstract and the to-be-matched image abstract in the data cluster, and determining a to-be-matched table abstract of the identity data table based on the to-be-matched cluster abstract of each data cluster.

8. The method of claim 7, wherein the step of sending the row to be matched digest, the cluster to be matched digest, and the table to be matched digest in the data to be matched digest to the server includes:

if one line of identity data in the identity data table sent by the server is received, sending a line abstract to be matched of the one line of identity data to the server for matching;

if the identity data of all rows in a cluster of data clusters are received, sending a cluster abstract to be matched of the cluster of data clusters to the server for matching;

and if the identity data of all clusters in the identity data table is received, sending a table abstract to be matched of the identity data table to the server for matching.

9. An electronic device comprising a memory and a processor for executing program instructions stored in the memory to implement the method of any one of claims 1 to 5 or the method of any one of claims 6-8.

10. A computer readable storage medium having stored thereon program instructions, which when executed by a processor, implement the method of any of claims 1 to 5 or the method of any of claims 6-8.