A data analysis method, device and medium for basic-level jurisdiction

Abstract

This application discloses a data parsing method, device, and medium for grassroots jurisdiction, belonging to the field of data governance technology. It addresses the problem of low data parsing efficiency. The method includes: determining the grassroots jurisdiction of a grid worker; receiving a grassroots data query request from the grid worker and verifying the grid worker's identity; if the grid worker passes authentication, invoking a brute-force recursive method; based on a preset grassroots database, performing nested data parsing on the grassroots jurisdiction using the brute-force recursive method to generate multi-level nested target grassroots data for the grassroots jurisdiction; and displaying the target grassroots data to the grid worker. This improves the data parsing efficiency for grassroots jurisdictions.

Description

Technical Field

[0001] This application relates to the field of data governance technology, and in particular to a data parsing method, device and medium for grassroots jurisdiction. Background Technology

[0002] Currently, the "grid-based management and team-based service" approach is being implemented, forming a community-centric urban social governance system. Grid-based management establishes a target responsibility system that combines rigidity and flexibility, and its effective implementation is ensured through performance evaluation. However, the data management and permissions of grid members require effective division of their jurisdiction, thus employing numerous parent-child nested relationships, and even deeper nesting relationships.

[0003] Currently, to handle irregularly nested information, data is traversed manually or parsed multiple times using if statements. For example, if the data starts at the city level, it needs to be manually filtered sequentially from city to district (county) to street to community to building number to unit number to room number. Following this method, excessive nesting requires filtering data at each level in descending order, leading to low data parsing efficiency when there is a large amount of basic data. Summary of the Invention

[0004] This application provides a data parsing method, device, and medium for grassroots jurisdiction, which solves the problem of low data parsing efficiency when there is a large amount of grassroots data.

[0005] The embodiments of this application adopt the following technical solutions:

[0006] On one hand, this application provides a data parsing method for grassroots jurisdiction, which includes: determining the grassroots jurisdiction of a grid worker; receiving a grassroots data query request from the grid worker and verifying the grid worker's identity; if the grid worker passes the identity verification, invoking a brute-force recursive method; according to a preset grassroots database, performing data nesting parsing on the grassroots jurisdiction using the brute-force recursive method to generate multi-level nested target grassroots data of the grassroots jurisdiction; and displaying the target grassroots data to the grid worker.

[0007] In one example, the step of using the brute-force recursive method to perform nested data parsing on the basic-level jurisdiction to generate multi-level nested target basic-level data of the basic-level jurisdiction specifically includes: performing multi-level administrative division parsing on the basic-level jurisdiction to obtain multiple basic-level jurisdiction sub-scopes corresponding to each hierarchical structure; traversing the basic-level jurisdiction sub-scopes of each hierarchical structure using the brute-force recursive method until the child nodes of the hierarchical nodes of the hierarchical structure are empty, at which point the traversal of the basic-level data for the hierarchical structure stops; and calling the hierarchical parsing method to extract the basic-level data of the basic-level jurisdiction sub-scopes of the hierarchical structure from the basic-level database to generate multi-level nested target basic-level data of the basic-level jurisdiction.

[0008] In one example, the step of traversing the basic jurisdiction sub-scope of each hierarchical structure using the brute-force recursive method until the child nodes of the hierarchical nodes are empty, and then stopping the traversal of the hierarchical structure, specifically includes: traversing the basic jurisdiction sub-scope of each hierarchical structure using the brute-force recursive method; if the child nodes of the current level node of the hierarchical structure are not empty, then using the child nodes of the current level node as input to the brute-force recursive method, and calling the brute-force recursive method again; using the brute-force recursive method to determine whether the child nodes of the child node's level are empty; if so, then stopping the traversal of the hierarchical structure.

[0009] In one example, the step of performing multi-level administrative division analysis on the basic-level jurisdiction to obtain multiple hierarchical structures corresponding to the basic-level jurisdiction sub-scopes specifically includes: performing multi-level administrative division analysis on the basic-level jurisdiction to determine the administrative division information starting from the province, belonging to the province, and with the administrative division level decreasing sequentially, so as to obtain the hierarchical structure corresponding to the province; if the basic-level jurisdiction includes multiple hierarchical structures, then the basic-level jurisdiction sub-scopes corresponding to each of the multiple hierarchical structures are obtained.

[0010] In one example, the authentication of the grid worker specifically includes: determining the grid worker's location information; if the authentication method is face recognition, acquiring the grid worker's face image; recognizing the face image to obtain the grid worker's face image feature information; determining the corresponding regional face database based on the location information; comparing the face image feature information with the similarity in the regional face database; if the similarity is greater than a preset threshold, determining that the grid worker has passed authentication; if the similarity is less than or equal to the preset threshold, determining that the grid worker has failed authentication.

[0011] In one example, if the authentication method is facial recognition, before obtaining the grid worker's facial image, the method further includes: obtaining the grid worker's historical authentication information within a preset historical time period; inputting the historical authentication information into a pre-built authentication method preference neural network model to output the grid worker's authentication method preferences at different times of the day; determining the recommended authentication method to the grid worker based on the current authentication time and the authentication method preference, and calling the authentication method interface to display the corresponding authentication operation interface to the grid worker.

[0012] In one example, the method further includes: if the grid worker fails authentication, obtaining the grid worker's historical authentication failure information within a preset historical time period; determining whether the historical authentication failure information includes an auxiliary authentication user; the auxiliary authentication user having a direct family relationship with the grid worker; if so, confirming with the grid worker whether to send auxiliary authentication information to the auxiliary authentication user; if so, sending the auxiliary authentication information to the auxiliary authentication user; if the authentication information returned by the auxiliary authentication user conforms to the authentication rules, updating the authentication result and determining that the grid worker has passed authentication.

[0013] In one example, determining the grassroots jurisdiction of a grid worker specifically includes: obtaining the grid worker's unique identifier; retrieving the grid worker's unique identifier from a pre-built grassroots jurisdiction mapping table; and finding the grid worker's grassroots jurisdiction based on the unique identifier.

[0014] On the other hand, embodiments of this application provide a data parsing device for grassroots jurisdiction, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: determine the grassroots jurisdiction of a grid worker; receive a grassroots data query request from the grid worker and authenticate the grid worker; if the grid worker passes authentication, invoke a brute-force recursive method; perform nested data parsing of the grassroots jurisdiction using the brute-force recursive method based on a preset grassroots database to generate multi-level nested target grassroots data of the grassroots jurisdiction; and display the target grassroots data to the grid worker.

[0015] On the other hand, embodiments of this application provide a non-volatile computer storage medium for data parsing of grassroots jurisdiction, storing computer-executable instructions, wherein the computer-executable instructions are configured to: determine the grassroots jurisdiction of a grid worker; receive a grassroots data query request from the grid worker and authenticate the grid worker; if the grid worker passes authentication, invoke a brute-force recursive method; based on a preset grassroots database, perform nested data parsing of the grassroots jurisdiction using the brute-force recursive method to generate multi-level nested target grassroots data of the grassroots jurisdiction; and display the target grassroots data to the grid worker.

[0016] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects:

[0017] Once the grid worker's identity verification is successful and their security is confirmed, a brute-force recursive method is used to perform nested data parsing of the grassroots jurisdiction. Utilizing recursion reduces redundant judgments and loop code, facilitating development iterations and improving processing efficiency. Furthermore, under conditions that the data conforms to the solution method, dynamic multi-level data can be processed without requiring developers to handle data for different scenarios, improving code utilization. This enables rapid parsing of data within the grassroots governance jurisdiction, thus enhancing parsing efficiency. Attached Figure Description

[0018] To more clearly illustrate the technical solution of this application, some embodiments of this application will be described in detail below with reference to the accompanying drawings, in which:

[0019] Figure 1 A flowchart illustrating a data parsing method for grassroots jurisdiction provided in this application embodiment;

[0020] Figure 2 A schematic diagram illustrating the parsing process of a brute-force recursion method provided in an embodiment of this application;

[0021] Figure 3 A schematic diagram illustrating the parent-child relationship of basic data provided in this application embodiment;

[0022] Figure 4 This is a schematic diagram of the structure of a data parsing device for grassroots jurisdiction provided in an embodiment of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Some embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0025] Figure 1 This is a flowchart illustrating a data parsing method for grassroots jurisdiction, provided as an embodiment of this application. This method can be applied to different business domains, and certain input parameters or intermediate results in the process can be manually adjusted to help improve accuracy.

[0026] The analysis method involved in the embodiments of this application can be implemented by a terminal device or a server, and this application does not impose any special limitations on it. For ease of understanding and description, the following embodiments are all described in detail using a server as an example.

[0027] It should be noted that the server can be a single device or a system composed of multiple devices, i.e., a distributed server. This application does not make any specific limitations on this.

[0028] Figure 1 The process may include the following steps:

[0029] S101: Determine the grassroots jurisdiction of grid workers.

[0030] In some embodiments of this application, after logging into the data parsing system, the grid worker first obtains their unique identifier, then retrieves the unique identifier from a pre-built grassroots jurisdiction mapping table, and finally finds the grid worker's grassroots jurisdiction based on the unique identifier. It should be noted that the grassroots jurisdiction mapping table includes the grassroots jurisdiction corresponding to different unique identifiers.

[0031] S102: Receive the grassroots data query request from the grid worker and verify the identity of the grid worker.

[0032] In some embodiments of this application, in order to prevent the illegal use of grassroots data, the grid worker is authenticated when querying grassroots data.

[0033] Specifically, the location information of the grid worker can be determined, for example, by obtaining the location information of the data parsing device that the grid worker is logging into, and using that data parsing device location information as the grid worker's location information.

[0034] It should be noted that there are various methods of identity verification, such as facial recognition, fingerprint recognition, and mobile phone verification code recognition. Different users have different recognition habits. To make it more convenient for users, the recognition interface can be automatically brought up based on the user's recognition habits, without requiring the user to select a recognition method.

[0035] Specifically, the system first obtains the grid worker's historical authentication information within a preset historical time period. Then, it inputs this historical authentication information into a pre-built neural network model of authentication method preferences, outputting the grid worker's authentication method preferences for different time periods each day. Finally, based on the current authentication time and authentication method preferences, it determines the recommended authentication method to the grid worker and calls the authentication method interface to display the corresponding authentication operation interface to the grid worker.

[0036] It should be noted that when constructing the neural network model of identity verification method preference, the sample identity verification information of the sample grid workers is first obtained. The initial neural network structure is then trained in a supervised manner using the identity verification method preferences of the sample grid workers at different time periods as labels, thus obtaining the neural network model of identity verification method preference.

[0037] Therefore, if the authentication method is facial recognition, the grid worker's facial image is obtained through facial recognition, and the facial image feature information is obtained by recognizing the grid worker's face. Then, based on the location information, the corresponding regional facial database is determined. Finally, the facial image feature information is compared for similarity in the regional facial database.

[0038] If the similarity is greater than a preset threshold, the grid worker is determined to have passed authentication. If the similarity is less than or equal to the preset threshold, the grid worker is determined to have failed authentication.

[0039] Furthermore, if a facial recognition fails due to special circumstances, such as lighting conditions or system errors, but the grid worker is indeed authentic, then an auxiliary verification method is used.

[0040] Specifically, if a grid worker fails authentication, the system retrieves the grid worker's historical authentication failure information within a preset historical timeframe. Then, it determines whether the historical authentication failure information has any supporting user authentication information.

[0041] It should be noted that, in order to effectively prevent collusion between auxiliary verification personnel and illegal grid workers, auxiliary verification users must have a direct family relationship with the grid workers.

[0042] If yes, confirm with the grid worker whether to send auxiliary verification information to the user who needs to assist in verification. If no, send an authentication failure message to the grid worker.

[0043] If sent, auxiliary verification information will be sent to the auxiliary verification user.

[0044] If the verification information provided by the assisting user conforms to the verification rules, the identity verification result is updated to confirm that the grid worker has passed the identity verification.

[0045] If the verification information provided by the assisting user does not conform to the verification rules, an identity verification failure message is sent to the grid worker.

[0046] It should be noted that the identity verification method recommended to grid workers can also be fingerprint recognition or other methods. The specific verification process can be set according to actual needs, and will not be described here.

[0047] S103: If the grid worker passes the authentication, then the brute-force recursive method is invoked.

[0048] The main characteristics of brute-force recursion are that it transforms a problem into a subproblem of the same kind with a reduced scale, has a decision-making process after obtaining the result of the subproblem, and has clear conditions under which recursion is no longer necessary.

[0049] S104: Based on the preset grassroots database, the data nesting of the grassroots jurisdiction is parsed using the brute-force recursive method to generate multi-level nested target grassroots data of the grassroots jurisdiction.

[0050] In some embodiments of this application, the basic jurisdiction is first analyzed by administrative division at multiple levels to obtain multiple hierarchical structures corresponding to the basic jurisdiction sub-scopes.

[0051] Among them, the administrative divisions of the grassroots jurisdiction are analyzed at multiple levels to determine the division information starting from the province, belonging to the province, and with the administrative division level decreasing sequentially, so as to obtain the hierarchical structure corresponding to the province.

[0052] If the jurisdiction of a grassroots unit includes multiple hierarchical structures, then the grassroots jurisdiction sub-scopes corresponding to each hierarchical structure are obtained. For example, Province A, City B, District C is one hierarchical structure, and Province D, City E, District F, Town G, Village H is another hierarchical structure.

[0053] Then, a brute-force recursive method is used to traverse the basic-level jurisdiction of each hierarchical structure until the child nodes of the hierarchical nodes are empty, at which point the traversal of the basic-level data of the hierarchical structure stops.

[0054] In other words, when traversing the basic jurisdiction of each hierarchical structure using a brute-force recursive method, if the child nodes of the current level node in the hierarchical structure are not empty, then the child nodes of the current level node are used as input to the brute-force recursive method, and the brute-force recursive method is called again; the brute-force recursive method is used to determine whether the child nodes of the child node's level are empty; if so, then the traversal of the hierarchical structure is stopped.

[0055] Finally, the hierarchical resolution method is invoked to extract the grassroots data of the grassroots jurisdiction sub-scopes in the grassroots database to generate multi-level nested target grassroots data of the grassroots jurisdiction.

[0056] In other words, the grassroots data of each hierarchical sub-scope constitutes the target grassroots data of the grassroots jurisdiction.

[0057] It should be noted that AOP and reflection techniques are used to convert parsed data into list and map objects for rapid backend development.

[0058] Clearly, by using a brute-force recursive method (simulating Java's dynamic programming algorithm) to extract common subproblems, we can simplify complex problems. Complex problems (with many recursive calls) are decomposed into smaller subproblems. By using the subproblem extraction method, the creation and modification of objects can be stored in the subproblems, which enhances the effective use of data objects and reduces unnecessary space waste.

[0059] S105: Display the target grassroots data to the grid worker.

[0060] It should be noted that, although the embodiments in this application are based on... Figure 1 Steps S101 to S105 will be described sequentially, but this does not mean that steps S101 to S105 must be performed in a strict order. The reason this embodiment follows this order is... Figure 1 The order in which steps S101 to S105 are described is provided to facilitate understanding of the technical solutions of the embodiments of this application by those skilled in the art. In other words, the order of steps S101 to S105 can be appropriately adjusted according to actual needs.

[0061] pass Figure 1This method, after verifying the grid worker's identity and security, uses a brute-force recursive approach to perform nested data parsing of the grassroots jurisdiction. By using recursion, redundant judgments and loop code are reduced, facilitating development iterations and improving processing efficiency. Furthermore, under the condition that the data conforms to the solution method, dynamic multi-level data can be processed without requiring developers to handle data for different situations, improving code utilization. This enables rapid parsing of data within the grassroots governance jurisdiction, thus improving parsing efficiency.

[0062] More intuitively, Figure 2 This is a schematic diagram illustrating the parsing process of a brute-force recursive method provided in an embodiment of this application.

[0063] In some embodiments of this application, the basic conditions for the target grassroots data within the grassroots jurisdiction to satisfy the brute-force recursive algorithm are as follows: each level of data in the grassroots has the same data characteristics, and the analysis of these characteristics can be extracted to become the recursive parsing data; during the recursive process, when the level itself, i.e., children = 0, the recursion ends; the data is connected at previous levels, so the data can be processed completely.

[0064] like Figure 2 As shown, the jsonarray represents the basic scope information, i.e., the scope of jurisdiction at the basic level, mainly including the parsed permission range. `children` represents the lower-level information contained in this level, also stored in jsonarray format, and serves as the condition for the current recursive relationship. `fact()` is the main recursive method, traversing and processing the input jsonarray. `fn()` is the main level parsing method, focusing on processing the data logic at each level.

[0065] Based on this, the method begins by inputting the basic jurisdiction to be parsed, calling the `fact()` method with a `jsonarray` parameter. If the `children` data for this level is empty, the `fn()` method is called directly, and the result is returned, ending the method. If the `children` data for this level is not empty, the `children` data needs to be converted to a `jsonarray` as input, and `fact()` is called again. `fact()` checks if the `children` data for the current level is empty again; if it is, it returns to the current level, and so on. This process can transform data into a simple tree structure or enable rapid data comparison.

[0066] Using the methods described above, brute-force recursion can quickly and efficiently parse basic data. The data extraction using `fn()` simplifies the previously complex decision-making process. Whether `children` is zero is a clear condition for terminating the recursion. The parent-child relationships between data points ensure thorough data parsing.

[0067] More intuitively, Figure 3 This is a schematic diagram of a parent-child relationship in basic data provided in an embodiment of this application.

[0068] like Figure 3 As shown, the number of children of the city-level node is 3, and the child nodes of the city-level node include district-level 1, district-level 2, and district-level 3.

[0069] District-level 1 has 2 children. District-level 1's child nodes include two streets, and both streets have 0 children.

[0070] District 2 has 3 children. District 2's child nodes include 3 streets, one of which has 3 children and its child nodes include 3 communities.

[0071] If the number of children for district level 3 is 0, then child nodes are not included.

[0072] Based on the same idea, some embodiments of this application also provide devices and non-volatile computer storage media corresponding to the above methods.

[0073] Figure 4 A schematic diagram of a data parsing device for grassroots jurisdiction provided in this application embodiment includes:

[0074] At least one processor; and,

[0075] A memory communicatively connected to the at least one processor; wherein,

[0076] The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to:

[0077] Determine the grassroots jurisdiction of grid workers;

[0078] Receive the grassroots data query request from the grid worker and verify the grid worker's identity;

[0079] If the grid worker passes authentication, then the brute-force recursive method is invoked;

[0080] Based on the preset grassroots database, the data nesting parsing of the grassroots jurisdiction is performed using the brute-force recursive method to generate multi-level nested target grassroots data of the grassroots jurisdiction.

[0081] The target grassroots data is then displayed to the grid worker.

[0082] Some embodiments of this application provide a non-volatile computer storage medium for data parsing within a basic jurisdiction, storing computer-executable instructions, wherein the computer-executable instructions are configured as follows:

[0083] Determine the grassroots jurisdiction of grid workers;

[0084] Receive the grassroots data query request from the grid worker and verify the grid worker's identity;

[0085] If the grid worker passes authentication, then the brute-force recursive method is invoked;

[0086] Based on the preset grassroots database, the data nesting parsing of the grassroots jurisdiction is performed using the brute-force recursive method to generate multi-level nested target grassroots data of the grassroots jurisdiction.

[0087] The target grassroots data is then displayed to the grid worker.

[0088] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.

[0089] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0090] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0091] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0092] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0093] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0094] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0095] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0096] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0097] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0098] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the technical principles of this application should fall within the protection scope of this application.

Claims

1. A data parsing method for grassroots jurisdiction, characterized in that, The method includes: Determine the grassroots jurisdiction of grid workers; Receive the grassroots data query request from the grid worker and verify the grid worker's identity; If the grid worker passes authentication, then the brute-force recursive method is invoked; Based on the preset grassroots database, the data nesting parsing of the grassroots jurisdiction is performed using the brute-force recursive method to generate multi-level nested target grassroots data of the grassroots jurisdiction. The target grassroots data is displayed to the grid worker; The step of parsing the data nesting of the grassroots jurisdiction using the brute-force recursive method to generate multi-level nested target grassroots data of the grassroots jurisdiction specifically includes: The basic jurisdiction is analyzed by administrative division at multiple levels to obtain multiple hierarchical structures corresponding to the basic jurisdiction sub-scopes. The brute-force recursive method is used to traverse the basic jurisdiction sub-scope of each hierarchical structure until the child nodes of the hierarchical nodes are empty, at which point the traversal of the basic data of the hierarchical structure stops. The hierarchical resolution method is invoked, and through the hierarchical resolution method, the grassroots data of the grassroots jurisdiction sub-scope of the hierarchical structure is extracted from the grassroots database to generate multi-level nested target grassroots data of the grassroots jurisdiction. The step of traversing the basic jurisdiction sub-scope of each hierarchical structure using the brute-force recursive method until the child nodes of the hierarchical nodes are empty, and then stopping the traversal of the hierarchical structure, specifically includes: The brute-force recursive method is used to traverse the basic jurisdiction sub-scope of each hierarchical structure. If the child nodes of the current level node of the hierarchical structure are not empty, the child nodes of the current level node are used as the input of the brute-force recursive method, and the brute-force recursive method is called again. The brute-force recursive method is used to determine whether the child nodes at the child node level are empty; If so, then stop traversing the hierarchy. The process of performing multi-level administrative division analysis on the basic-level jurisdiction to obtain multiple hierarchical structures corresponding to different basic-level jurisdiction sub-scopes specifically includes: The administrative division of the grassroots jurisdiction is analyzed at multiple levels to determine the administrative division information starting from the province, belonging to the province, and with the administrative division level decreasing sequentially, so as to obtain the hierarchical structure corresponding to the province. If the basic jurisdiction includes multiple hierarchical structures, then the basic jurisdiction sub-scopes corresponding to each hierarchical structure are obtained.

2. The method according to claim 1, characterized in that, The authentication of the grid worker specifically includes: Determine the location information of the grid worker; If the authentication method is facial recognition, then obtain the facial image of the grid worker; The facial image is identified to obtain the facial feature information of the grid worker; Based on the location information, determine the corresponding regional face database; The facial image feature information is compared for similarity in the regional facial database; If the similarity is greater than a preset threshold, then the grid worker is determined to have passed identity verification. If the similarity is less than or equal to a preset threshold, it is determined that the grid worker has not passed the authentication.

3. The method according to claim 2, characterized in that, If the authentication method is facial recognition, before obtaining the grid worker's facial image, the method further includes: Obtain the grid worker's historical identity verification information within a preset historical time period; The historical authentication information is input into a pre-built neural network model of authentication method preference, which outputs the grid worker's authentication method preference at different times of the day. Based on the current verification time and the preferred verification method, determine the recommended verification method to the grid worker, and call the verification method interface to display the corresponding verification operation interface to the grid worker.

4. The method according to claim 1, characterized in that, The method further includes: If the grid worker fails to pass authentication, obtain the grid worker's historical authentication failure information within a preset historical time period; Determine whether the historical authentication failure information includes an auxiliary verification user; the auxiliary verification user has a direct family relationship with the grid worker; If so, confirm with the grid worker whether to send auxiliary verification information to the auxiliary verification user; If so, the auxiliary verification information is sent to the auxiliary verification user; If the verification information provided by the assisting verification user conforms to the verification rules, the identity verification result is updated to confirm that the grid worker has passed the identity verification.

5. The method according to claim 1, characterized in that, The determination of the grassroots jurisdiction of grid workers specifically includes: Obtain the unique identifier of the grid worker; Retrieve the unique identifier of the grid worker from the pre-built grassroots jurisdiction mapping table; Based on the unique identifier, locate the grassroots jurisdiction of the grid worker.

6. A data parsing device for grassroots jurisdiction, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to: perform a data parsing method for a grassroots jurisdiction as described in any one of claims 1-5.

7. A non-volatile computer storage medium for data parsing within a grassroots jurisdiction, storing computer-executable instructions, characterized in that, The computer-executable instructions are configured to execute a data parsing method for grassroots jurisdiction as described in any one of claims 1-5.

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