Data processing method and device based on data acquisition platform, equipment and medium

Abstract

The invention relates to the technical field of data processing, and relates to a data processing method, device and equipment based on a data acquisition platform and a storage medium, and the data processing method based on the data acquisition platform comprises the following steps: responding to a selection operation of a data entry mode of the data acquisition platform; acquiring initial business data of a target business object based on the data entry mode; performing multi-dimensional verification on the initial service data; and if the initial business data passes the multi-dimensional verification, determining the initial business data as target business data, and storing the target business data to a preset database. The method can be applied to business scenes such as financial science and technology, medical health, old-age care and the like, automation and intelligence of data processing can be realized, manual errors and repeated labor are effectively reduced, and the efficiency and accuracy of data processing are greatly improved.

Description

Technical Field

[0001] The present application relates to the fields of data processing and artificial intelligence technology, and can be applied to the fields of financial technology, medical health and elderly care, and in particular to a data processing method, device, computer equipment and computer-readable storage medium based on a data acquisition platform. Background Art

[0002] In many online businesses, such as insurance agent user services, banking and securities finance services, online medical service consulting services, and healthcare and elderly care services, enterprise data management faces numerous challenges and shortcomings. Data re-entry primarily relies on traditional manual methods, which are not only inefficient but also prone to errors, resulting in inconsistent data quality and hindering subsequent data analysis and decision support.

[0003] Therefore, how to improve the efficiency and accuracy of data processing has become an urgent problem to be solved. Summary of the Invention

[0004] The present application provides a data processing method, apparatus, computer equipment and computer-readable storage medium based on a data acquisition platform, which can realize automation and intelligence of data processing, effectively reduce manual errors and duplication of work, and greatly improve the efficiency and accuracy of data processing.

[0005] In a first aspect, the present application provides a data processing method based on a data acquisition platform, wherein the data acquisition platform is used to collect business data of at least one business object, and the method includes:

[0006] In response to a selection operation of a data entry mode of the data acquisition platform, obtaining initial business data of a target business object based on the data entry mode, the target business object being at least one of the business objects in the data acquisition platform;

[0007] Performing multi-dimensional verification on the initial business data, wherein the multi-dimensional verification includes at least one of data format verification, data content verification, and organizational structure path verification;

[0008] If the initial business data passes the multi-dimensional verification, the initial business data is determined as target business data, and the target business data is stored in a preset database.

[0009] In a second aspect, the present application further provides a data processing device, comprising:

[0010] a data entry module, configured to, in response to a selection operation of a data entry mode of the data acquisition platform, obtain initial business data of a target business object based on the data entry mode, wherein the target business object is at least one of the business objects in the data acquisition platform;

[0011] A multi-dimensional verification module, configured to perform multi-dimensional verification on the initial business data, wherein the multi-dimensional verification includes at least one of data format verification, data content verification, and organizational structure path verification;

[0012] A data storage module is used to determine the initial business data as target business data if the initial business data passes the multi-dimensional verification, and store the target business data in a preset database.

[0013] In a third aspect, the present application further provides a computer device, comprising a memory and a processor;

[0014] The memory is used to store computer programs;

[0015] The processor is used to execute the computer program and implement the data processing method based on the data acquisition platform as described above when executing the computer program.

[0016] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the data processing method based on the data acquisition platform as described above.

[0017] The present application discloses a data processing method, device, computer equipment and computer-readable storage medium based on a data acquisition platform, the method comprising: in response to a selection operation of a data entry mode of the data acquisition platform, obtaining initial business data of a target business object based on the data entry mode, the target business object being at least one of the business objects in the data acquisition platform; performing multi-dimensional verification on the initial business data, the multi-dimensional verification including at least one of data format verification, data content verification and organizational structure path verification; if the initial business data passes the multi-dimensional verification, the initial business data is determined as target business data, and the target business data is stored in a preset database. The embodiment of the present application obtains the initial business data of the target business object based on the data entry mode, performs multi-dimensional verification on the initial business data, and if the initial business data passes the multi-dimensional verification, the initial business data is determined as target business data, and the target business data is stored in a preset database, which can realize the automation and intelligence of data processing, eliminate the need for manual verification of the initial business data, effectively reduce manual errors and duplication of work, and greatly improve the efficiency and accuracy of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present application;

[0020] Figure 2 This is a schematic flow chart of a data processing method based on a data acquisition platform provided in an embodiment of the present application;

[0021] Figure 3 This is a schematic flowchart of desensitizing target business data provided by an embodiment of the present application;

[0022] Figure 4 This is a schematic diagram of storing business data in a database provided by an embodiment of the present application;

[0023] Figure 5 This is a functional diagram of a data acquisition platform provided in an embodiment of the present application;

[0024] Figure 6 This is a schematic block diagram of a data processing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0026] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0027] It should be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0028] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0029] Currently, the field of enterprise data management faces numerous challenges and shortcomings. First, data re-entry relies primarily on traditional manual methods, which are not only inefficient but also prone to errors, resulting in uneven data quality and affecting subsequent data analysis and decision support. Second, because data is stored in separate independent business systems or departments and lacks a unified management and integration mechanism, data silos are a serious problem, making cross-departmental data sharing and collaboration difficult, limiting the overall operational efficiency of the enterprise. Furthermore, manually processing large amounts of data is not only time-consuming and labor-intensive, but also increases the operating costs of the enterprise. Furthermore, it is difficult to ensure the real-time and accuracy of the data, making it difficult to meet the needs of modern enterprises for rapid data response.

[0030] To this end, embodiments of the present application provide a data processing method, data processing apparatus, computer equipment, and computer-readable storage medium based on a data acquisition platform. The data processing method based on the data acquisition platform can be applied to a computer device, and by acquiring initial business data of a target business object based on data entry, performing multi-dimensional verification on the initial business data, and if the initial business data passes the multi-dimensional verification, determining the initial business data as the target business data, and storing the target business data in a preset database, can achieve automated and intelligent data processing, eliminating the need for manual verification of the initial business data, effectively reducing manual errors and duplication of work, and greatly improving the efficiency and accuracy of data processing.

[0031] Exemplarily, the computer device can be a server or a terminal. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal can be an electronic device such as a smartphone, tablet computer, laptop computer, and desktop computer. The computer device is deployed with a data collection platform, which is used to collect and centrally manage business data of various business systems or business departments. The data collection platform adopts a simple and clear user interface and operation process, allowing users to easily get started and quickly complete data entry. At the same time, the data collection platform also provides rich help documents and online support services to help users solve problems encountered during use, so that users can use the data collection platform to complete data re-entry more conveniently.

[0032] See also Figure 1 , Figure 1 1 is a schematic diagram of a computer device according to an embodiment of the present invention. The computer device may include a processor and a memory, wherein the processor and the memory may be connected via a bus, which may be any suitable bus such as an Inter-Integrated Circuit (I2C) bus.

[0033] The memory may include a storage medium and an internal memory. The storage medium may be either a non-volatile storage medium or a volatile storage medium. The storage medium may store an operating system and a computer program, while the internal memory provides an environment for executing the computer program in the storage medium. The computer program includes program instructions that, when executed, cause the processor to execute the data processing method based on the data acquisition platform described in any embodiment.

[0034] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0035] The processor may be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or any conventional processor.

[0036] In one embodiment, the processor is configured to execute a computer program stored in the memory to implement the following steps:

[0037] In response to the selection operation of the data entry method of the data acquisition platform, the initial business data of the target business object is obtained based on the data entry method, and the target business object is at least one business object in the data acquisition platform; the initial business data is multi-dimensionally verified, and the multi-dimensional verification includes at least one of data format verification, data content verification and organizational structure path verification; if the initial business data passes the multi-dimensional verification, the initial business data is determined as the target business data, and the target business data is stored in a preset database.

[0038] In one embodiment, when implementing multi-dimensional verification of initial business data, the processor is configured to implement:

[0039] Perform data format verification on the data table in the initial business data to obtain data to be deleted that does not conform to the preset format, the data to be deleted including one or more of multiple headers, table header slashes, spaces, blank rows and blank columns; delete the data to be deleted from the initial business data.

[0040] In one embodiment, when implementing multi-dimensional verification of initial business data, the processor is configured to implement:

[0041] Obtain a preset configuration table, which includes a pre-set standard organizational structure path; perform path verification on the organizational structure path in the initial business data based on the standard organizational structure path; if the organizational structure path in the initial business data does not match the standard organizational structure path, output a prompt message indicating an organizational structure path error.

[0042] In one embodiment, after performing multi-dimensional verification on the initial business data, the processor is further configured to:

[0043] If the initial business data fails the multi-dimensional verification, a data modification prompt is output, where the data modification prompt is used to instruct the user to modify the initial business data.

[0044] In one embodiment, when the processor implements storing the target business data in a preset database, it is configured to implement:

[0045] The target business data is stored in a preset temporary data table, and it is determined whether the scheduling identifier of the target business data in the temporary data table is a preset field; if the scheduling identifier of the target business data in the temporary data table is not a preset field, the target business data is stored in a database, and the scheduling identifier of the target business data in the temporary data table is modified to a preset field.

[0046] In one embodiment, the processor is further configured to implement:

[0047] When a user's operation on a menu page in a data collection platform is detected, the target role to which the user belongs is determined; if the uniform resource locator of the menu page mounted by the target role is queried, it is determined that the user has the permission to edit the menu page, and the operation behavior is responded to; if the uniform resource locator of the menu page not mounted by the target role is queried, it is determined that the user does not have the permission to edit the menu page, and the operation behavior is intercepted, and a prompt message is output to indicate that the user does not have the permission to edit the menu page.

[0048] In one embodiment, the processor is further configured to implement:

[0049] When a user's operation behavior on a menu page in the data collection platform is detected, the user's operation behavior information is recorded, and the operation behavior information includes one or more of the operation type, operation time, and operation user.

[0050] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features of the embodiments can be combined with each other. Figure 2 , Figure 2 This is a schematic flow chart of a data processing method based on a data acquisition platform provided in an embodiment of the present application. Figure 2 As shown, the data processing method based on the data acquisition platform may include steps S101 to S103.

[0051] Step S101 : in response to a selection operation of a data entry mode of a data collection platform, initial business data of a target business object is acquired based on the data entry mode, where the target business object is at least one business object in the data collection platform.

[0052] The data processing method based on a data acquisition platform provided in the embodiments of the present application can be applied to a data acquisition platform, which is used to collect business data of at least one business object, thereby enabling the collection and centralized management of the business data of each business object. Business objects may include various business systems or business departments, where business systems may include but are not limited to insurance claims systems, bank settlement systems, online shopping transaction systems, financial technology business systems, medical care and elderly care management program systems, and the like.

[0053] For example, the data processing method based on the data acquisition platform provided in the embodiments of the present application can be applied in office scenarios such as financial technology, medical care, health care and elderly care.

[0054] For example, in a fintech scenario, users can enter fintech business data through a data collection platform or use the data collection platform to collect business data from different fintech business systems, thereby achieving centralized management of business data from different fintech business systems.

[0055] For example, in the medical, health and elderly care scenario, users can enter business data about medical and health through the data collection platform or use the data collection platform to collect business data from different medical, health and elderly care management program systems, thereby realizing centralized management of business data from different medical, health and elderly care management program systems.

[0056] In order to solve the problems of data silos and inconsistent data formats, the data acquisition platform in the embodiment of the present application has formulated unified data standards and format specifications. All business data entered into the data acquisition platform must be processed and stored in accordance with established standards, ensuring the interoperability and consistency of business data. This not only facilitates cross-departmental data sharing and collaboration, improves the overall operational efficiency of the enterprise, but also lays a solid foundation for subsequent data analysis and mining. In addition, the data acquisition platform adopts a centralized data management architecture, which centralizes data originally stored in various business systems or departments onto a unified platform for management. In this way, not only is centralized storage and unified access control of data achieved, but it also facilitates centralized monitoring and maintenance of data, improving the security and reliability of data.

[0057] Exemplarily, in response to the selection of the data entry method of the data collection platform, the initial business data of the target business object is obtained based on the data entry method, and the target business object is at least one business object in the data collection platform. It should be noted that when the user uses the data collection platform to collect data from various business systems or departments, the user can first select the data entry method so that the data collection platform collects the initial business data of the target business object based on the data entry method. Among them, the data entry method includes manual entry, batch import or interface docking. Manual entry refers to the user manually entering data into the data collection platform; batch import refers to the data collection platform batch importing qualified data from at least one business system or department based on the key fields or information set by the user; interface docking refers to importing data through the interface of the data collection platform and the interface of at least one business system or department. The target business object can also be specified by the user. The initial business data can be the business data filled in by the user on the data collection platform, or it can be the business data imported from the business system or department.

[0058] For example, if the user selects batch import as the data entry method, the data collection platform can batch import business data from the target business object and record the imported business data as the initial business data. For another example, if the user selects interface docking as the data entry method, the data collection platform can import business data from the target business object's interface based on the interface and record the imported business data as the initial business data.

[0059] Step S102: Perform multi-dimensional verification on the initial business data, where the multi-dimensional verification includes at least one of data format verification, data content verification, and organizational structure path verification.

[0060] For example, after obtaining the initial business data of the target business object through data entry, in order to improve the processing efficiency and accuracy of the data in the initial business data in subsequent processing, it is necessary to perform multi-dimensional verification on the initial business data. Multi-dimensional verification refers to verifying the initial business data from multiple dimensions, for example, the initial business data can be verified from dimensions such as data format, data content, and organizational structure path.

[0061] Exemplarily, multi-dimensional verification may include at least one of data format verification, data content verification, and organizational structure path verification. For example, data format verification and data content verification may be performed on the initial business data. For another example, data format verification, data content verification, and organizational structure path verification may be performed on the initial business data.

[0062] In some embodiments, performing multi-dimensional verification on the initial business data may include: performing data format verification on the data table in the initial business data to obtain data to be deleted that does not conform to the preset format, the data to be deleted including one or more of multiple headers, table header slashes, spaces, blank rows and blank columns; deleting the data to be deleted from the initial business data.

[0063] In an embodiment of the present application, in order to improve the clarity and readability of the initial business data, the data table in the initial business data should be made into a concise two-dimensional table to avoid using a complex three-dimensional table. In the data table, do not merge cells to avoid affecting the sorting and filtering functions of the data. At the same time, avoid using multiple titles and slash headers to maintain the clarity of the data; the title should be concise and clear, cannot be empty or repeated, and avoid using numbers as titles to prevent confusion. In addition, do not insert spaces in cells for typesetting, which will affect the alignment and formatting of the data. Finally, avoid blank rows and columns in the data table to keep the data table neat and efficient. Following these rules can greatly improve the readability and practicality of the initial business data.

[0064] Exemplarily, multiple titles, table header slashes, spaces, blank rows and empty columns in the data table and data to be deleted can be deleted from the initial business data.

[0065] The above embodiment can greatly improve the readability and practicality of the initial business data by performing data format verification on the data table in the initial business data and deleting multiple titles, table header slashes, spaces, blank rows and blank columns in the data table from the initial business data.

[0066] In some embodiments, performing multi-dimensional verification on the initial business data may include: obtaining a preset configuration table, the configuration table including a pre-set standard organizational structure path; performing path verification on the organizational structure path in the initial business data according to the standard organizational structure path; if the organizational structure path in the initial business data does not match the standard organizational structure path, outputting a prompt message indicating an organizational structure path error.

[0067] In this embodiment of the present application, the configuration table is a separate menu that is configured and maintained by the user to ensure the accuracy and timeliness of the standard organizational structure path in the configuration table. The organizational structure path is used to display the hierarchical structure and interpersonal relationships within an organization or institution, usually presented in the form of a tree diagram or hierarchical diagram, showing the relationships and hierarchies between different departments, positions, posts, and personnel.

[0068] Exemplarily, when performing organizational structure path verification on initial business data, the organizational structure path in the initial business data is verified against the standard organizational structure path. If the organizational structure path in the initial business data does not match the standard organizational structure path, a prompt message indicating an organizational structure path error is output. If the organizational structure path in the initial business data matches the standard organizational structure path, the initial business data is determined to have passed organizational structure path verification. After the prompt message indicating an organizational structure path error is output, the user can modify the organizational structure path in the initial business data based on the prompt message.

[0069] The above embodiment verifies the organizational structure path in the initial business data according to the standard organizational structure path, and outputs a prompt message indicating that the organizational structure path is wrong when the organizational structure path in the initial business data does not match the standard organizational structure path. This can promptly remind the user to modify the organizational structure path in the initial business data based on the prompt message, thereby effectively improving the data quality of the data collection platform and the convenience of user operations.

[0070] In an embodiment of the present application, in addition to performing data format verification and organizational structure path verification on the initial business data, data content verification can also be performed on the initial business data. For example, it is possible to detect whether the text and numerical values ​​of the data table in the initial business data are placed in different cells respectively, to avoid mixing text and numerical values ​​in the same cell, so as to preserve the clarity and operability of the data. In addition, regular expressions can be used to verify the date format to ensure that the input date format meets the specifications; if the input date format does not meet the specifications, the data collection platform will prompt the user with specific error information, such as "xx field + value" should be in "xx" format, to help users quickly locate and correct errors.

[0071] Step S103: If the initial business data passes the multi-dimensional verification, the initial business data is determined as the target business data, and the target business data is stored in a preset database.

[0072] For example, upon confirming that the initial business data has passed multi-dimensional verification, the initial business data may be determined as target business data, and the target business data may be stored in a preset database. The preset database may be a database within the data collection platform or a database external to the data collection platform. The preset database may be an open source object-relational database or other type of database, and this application does not limit this.

[0073] The above embodiment, by performing multi-dimensional verification on the initial business data, adopts unified data standards and format specifications to process and store all business data entered into the data collection platform in accordance with established standards, thereby solving the problems of data silos and inconsistent data formats, ensuring the interoperability and consistency of business data, thereby facilitating cross-departmental data sharing and collaborative work, and improving the overall operational efficiency of the enterprise.

[0074] In some embodiments, after performing multi-dimensional verification on the initial business data, it may also include: if the initial business data fails the multi-dimensional verification, outputting a data modification prompt, where the data modification prompt is used to instruct the user to modify the initial business data.

[0075] Exemplarily, when it is confirmed that the initial business data has not passed the multi-dimensional verification, a data modification prompt is used to instruct the user to modify the initial business data, so that the user can modify the initial business data based on the data modification prompt.

[0076] See also Figure 3 , Figure 3 This is a schematic flow chart of desensitizing target business data provided by an embodiment of the present application. Figure 3 As shown, the data processing method based on the data acquisition platform may include steps S201 to S203.

[0077] Step S201: Determine sensitive data in target business data.

[0078] In the embodiments of this application, the data collection platform uses encryption technology when uploading data to ensure data security during transmission. The database uses encrypted storage, so even if the data is illegally accessed, sensitive information cannot be directly read. When users query or download data, the data collection platform automatically decrypts it, ensuring that users can access and use the data normally.

[0079] For example, data involving user privacy in the target business data may be determined as sensitive data. For example, data such as a user's mobile phone number, ID number, bank account number, etc. may be determined as sensitive data.

[0080] Step S202: Based on the encryption algorithm, the sensitive data in the target business data is desensitized to obtain the desensitized target business data.

[0081] For example, the encryption algorithm may include, but is not limited to, a message-digest algorithm, an SM3 hash algorithm, an AES (Advanced Encryption Standard) algorithm, a Base64 encryption algorithm, and the like. For example, sensitive data in the target business data may be desensitized based on the Base64 encryption algorithm to obtain desensitized target business data. Desensitization refers to encrypting sensitive data or replacing it with other characters.

[0082] Step S203: store the desensitized target business data in the database.

[0083] For example, after the sensitive data in the target business data is desensitized, the desensitized target business data can be stored in the database.

[0084] The above embodiment, by using advanced encryption algorithms to desensitize sensitive data in the target business data, can ensure the security of business data during storage and transmission, reduce the risk of data leakage, and thus effectively improve the data security and user privacy protection level of the data collection platform.

[0085] In some embodiments, storing the target business data in a preset database may include: storing the target business data in a preset temporary data table, and determining whether the scheduling identifier of the target business data in the temporary data table is a preset field; if the scheduling identifier of the target business data in the temporary data table is not a preset field, storing the target business data in the database, and modifying the scheduling identifier of the target business data in the temporary data table to a preset field.

[0086] In an embodiment of the present application, to avoid repeatedly storing the same business data in a database, which would otherwise occupy an excessive amount of database storage space, the target business data may be stored in a temporary data table before being stored in a preset database. A dispatch flag for the target business data in the temporary data table is used to determine whether the target business data should be stored in the database. The dispatch flag is used to determine whether the target business data has already been stored in the database. If it is determined that the target business data has already been stored in the database, there is no need to store the target business data from the temporary data table in the database.

[0087] See also Figure 4 , Figure 4 This is a schematic diagram of storing business data in a database provided by an embodiment of the present application. Figure 4As shown, the target business data can be stored in a preset temporary data table, and it is determined whether the dispatch identifier of the target business data in the temporary data table is a preset field. The preset field can be set according to actual conditions, and the specific content is not limited. For example, the preset field can be Y, and it can be detected whether the dispatch identifier of the target business data is the preset field Y; if the dispatch identifier in the target business data is the preset field Y, it means that the target business data already exists in the database, and the target business data does not need to be stored in the database; if the dispatch identifier in the target business data is not the preset field Y, the target business data is stored in the data, and the dispatch identifier of the target business data is modified to the preset field Y in the temporary data table.

[0088] In the above embodiment, the target business data is first stored in a temporary data table, and it is determined whether the scheduling identifier of the target business data in the temporary data table is a preset field. When the scheduling identifier in the target business data is not a preset field, the scheduling identifier of the target business data in the temporary data table is modified to a preset field, thereby avoiding repeated storage of the same business data in the database. This not only saves resources and ensures consistency of business data, but also the incremental storage can improve computing efficiency and improve the concurrency performance of the database.

[0089] In the embodiment of the present application, the data collection platform also provides a complete access control mechanism. Only authorized users can access relevant business data, effectively preventing the risk of business data leakage and illegal access. The access control mechanism of the data collection platform will be described in detail below.

[0090] In some embodiments, the data processing method based on the data collection platform provided in the embodiments of the present application also includes: when detecting the user's operation behavior on the menu page in the data collection platform, determining the target role to which the user belongs; if the uniform resource locator of the target role's mounted menu page is queried, determining that the user has the authority to edit the menu page, and responding to the operation behavior; if the uniform resource locator of the target role's unmounted menu page is queried, determining that the user does not have the authority to edit the menu page, intercepting the operation behavior, and outputting a prompt message indicating that the user does not have the authority to edit the menu page.

[0091] Exemplarily, the operation behavior may include but is not limited to query, download, upload, modify, delete, schedule, etc.

[0092] It should be noted that the data collection platform adopts a strict permission control mechanism to ensure that all operations on each page, including query, download, upload, modification, deletion, scheduling and other operations, are configured with a corresponding URL (UnifommResource Locator) to achieve precise control of specific operations. After the user applies for an account on the data collection platform, he must first create a role corresponding to the user. A user can have multiple roles, and each role should be mounted with a corresponding menu URL to implement permission allocation for different roles. For operation requests that do not have the corresponding permissions, the data collection platform will automatically intercept and return them to the user, clearly informing them that they have no permissions, to ensure the security of the data collection platform and the compliance of operations. Following these rules can effectively improve the security of the data collection platform and user experience.

[0093] The above embodiment can ensure the security of the data collection platform and the compliance of operations by determining the target role to which the user belongs when detecting the user's operation behavior on the menu page in the data collection platform, and querying whether the target role mounts the uniform resource locator of the menu page, and then determining whether the user has the authority to edit the menu page.

[0094] In the embodiments of this application, the data collection platform also has powerful auditing and tracking capabilities, capable of recording and tracking all user operations on data, including data entry, modification, and deletion. This provides strong support for the security management and compliance review of business data. In the event of a data leak or security issue, the source of the problem can be quickly located and appropriate measures can be taken.

[0095] In some embodiments, the data processing method based on the data acquisition platform provided in the embodiments of the present application also includes: when detecting the user's operation behavior on the menu page in the data acquisition platform, recording the user's operation behavior information, the operation behavior information includes one or more of the operation type, operation time, and operation user.

[0096] In the embodiments of this application, the data collection platform also has powerful auditing and tracking capabilities, capable of recording and tracking all user operations on data, including type, time, and user. This provides strong support for the security management and compliance review of business data. In the event of a data leak or security issue, the source of the problem can be quickly located and appropriate measures can be taken.

[0097] For example, the data collection platform establishes a dedicated log table for all menu page operations, recording in detail the type of each operation, including data entry, modification, and deletion, to ensure transparency and traceability. At the same time, the log table contains records of user information and operation time, ensuring that every operation is clearly recorded for easy management and query. The log table adheres to the principles of data security and privacy protection, ensuring that the recorded information does not leak sensitive user information while meeting compliance and audit requirements. Through these measures, the security of the data collection platform and the transparency of operations can be effectively improved.

[0098] The above embodiment records the user's operation behavior information when detecting the user's operation behavior on the menu page in the data collection platform, which not only records and tracks the user's operation behavior, but also effectively improves the security of the data collection platform and the transparency of operations.

[0099] See also Figure 5 , Figure 5 This is a functional diagram of a data acquisition platform provided in an embodiment of the present application. Figure 5 As shown, the data collection platform mainly includes two modules: data import and data processing. Among them, the data import module can include sub-modules such as organizational structure, workflow, process management, supplementary recording, plan request, and system management. Data processing can include table structure sorting, addition, deletion, modification, and query, data cleaning, etc. The organizational structure includes organizations, positions, employees, companies, etc. Workflow is used to implement automatic transfer, process initiation, to-do list, participation process, copy process, etc. Process management is used to implement process design, process deployment, process monitoring, approval configuration, etc. Supplementary recording is used to implement report list entry. Plan request includes task details, execution records, etc. System management includes user management and function management.

[0100] like Figure 5 As shown, when the user needs to enter business data on the data collection platform, he can first download the supplementary recording template, fill in the business data on the supplementary recording template, and upload the filled-in initial business data to the system (i.e., the data collection platform); the data collection platform caches the initial business data uploaded by the user as temporary data, and then performs multi-dimensional verification on the initial business data. If the initial business data passes the multi-dimensional verification, it will be stored in the database; if the initial business data fails the multi-dimensional verification, a data modification prompt will be output, and the data modification prompt is used to instruct the user to modify the initial business data.

[0101] See also Figure 6 , Figure 6The embodiment of the present application further provides a schematic block diagram of a data processing device 1000, which is used to execute the aforementioned data processing method based on the data acquisition platform. The data processing device 1000 can be configured in a computer device.

[0102] like Figure 6 As shown, the data processing device 1000 includes: a data entry module 1001, a multi-dimensional verification module 1002 and a data storage module 1003.

[0103] The data entry module 1001 is used to obtain initial business data of a target business object based on the data entry method in response to a selection operation of a data entry method of the data acquisition platform. The target business object is at least one business object in the data acquisition platform.

[0104] The multi-dimensional verification module 1002 is used to perform multi-dimensional verification on the initial business data, where the multi-dimensional verification includes at least one of data format verification, data content verification, and organizational structure path verification.

[0105] The data storage module 1003 is configured to determine the initial business data as target business data if the initial business data passes the multi-dimensional verification, and store the target business data in a preset database.

[0106] It should be noted that those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0107] A computer-readable storage medium is also provided in an embodiment of the present application. The computer-readable storage medium stores a computer program. The computer program includes program instructions. The processor executes the program instructions to implement any data processing method based on the data acquisition platform provided in the embodiment of the present application.

[0108] For example, when the program is loaded by the processor, the following steps may be performed:

[0109] In response to the selection operation of the data entry method of the data acquisition platform, the initial business data of the target business object is obtained based on the data entry method, and the target business object is at least one business object in the data acquisition platform; the initial business data is multi-dimensionally verified, and the multi-dimensional verification includes at least one of data format verification, data content verification and organizational structure path verification; if the initial business data passes the multi-dimensional verification, the initial business data is determined as the target business data, and the target business data is stored in a preset database.

[0110] The computer-readable storage medium may be an internal storage unit of the computer device in the aforementioned embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital card (SD card), a flash memory card, etc. equipped on the computer device.

[0111] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the blockchain node, etc.

[0112] The blockchain referred to in this application is a new application model for computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Blockchain is essentially a decentralized database, a series of data blocks generated using cryptographic methods. Each data block contains information about a batch of network transactions, which is used to verify the validity of this information (to prevent counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, the platform product service layer, and the application service layer.

[0113] The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A data processing method based on a data acquisition platform, characterized in that: The data collection platform is used to collect business data of at least one business object; including: In response to a selection operation of a data entry mode of the data acquisition platform, obtaining initial business data of a target business object based on the data entry mode, the target business object being at least one of the business objects in the data acquisition platform; Performing multi-dimensional verification on the initial business data, wherein the multi-dimensional verification includes at least one of data format verification, data content verification, and organizational structure path verification; If the initial business data passes the multi-dimensional verification, the initial business data is determined as target business data, and the target business data is stored in a preset database.

2. The data processing method based on the data acquisition platform according to claim 1, characterized in that: The multi-dimensional verification of the initial business data includes: Performing data format verification on a data table in the initial business data to obtain data to be deleted that does not conform to a preset format, wherein the data to be deleted includes one or more of multiple headers, table header slashes, spaces, blank rows, and blank columns; The data to be deleted is deleted from the initial service data.

3. The data processing method based on the data acquisition platform according to claim 1, characterized in that: The multi-dimensional verification of the initial business data includes: Obtaining a preset configuration table, wherein the configuration table includes a preset standard organizational structure path; Performing path verification on the organizational structure path in the initial business data according to the standard organizational structure path; If the organizational structure path in the initial business data does not match the standard organizational structure path, a prompt message indicating that the organizational structure path is wrong is output.

4. The data processing method based on the data acquisition platform according to claim 1, characterized in that: After performing multi-dimensional verification on the initial business data, the method further includes: If the initial business data fails the multi-dimensional verification, a data modification prompt is output, where the data modification prompt is used to instruct the user to modify the initial business data.

5. The data processing method based on the data acquisition platform according to claim 1, characterized in that: The storing of the target business data in a preset database includes: Storing the target service data in a preset temporary data table, and determining whether the scheduling identifier of the target service data in the temporary data table is a preset field; If the scheduling identifier of the target business data in the temporary data table is not a preset field, the target business data is stored in the database, and the scheduling identifier of the target business data in the temporary data table is modified to the preset field.

6. The data processing method based on the data acquisition platform according to claim 1, characterized in that: The method further comprises: When detecting a user's operation behavior on a menu page in the data collection platform, determining the target role to which the user belongs; If the uniform resource locator of the menu page mounted by the target role is found, it is determined that the user has the authority to edit the menu page, and the operation behavior is responded to; If the query finds that the target role does not mount the uniform resource locator of the menu page, it is determined that the user does not have the authority to edit the menu page, and the operation behavior is intercepted, and a prompt message is output to indicate that the user does not have the authority to edit the menu page.

7. The data processing method based on the data acquisition platform according to claim 1, characterized in that: The method further comprises: When the user's operation behavior on the menu page in the data collection platform is detected, the user's operation behavior information is recorded, and the operation behavior information includes one or more of the operation type, operation time, and operation user.

8. A data processing device, characterized in that: include: a data entry module, configured to, in response to a selection operation of a data entry mode of the data acquisition platform, obtain initial business data of a target business object based on the data entry mode, wherein the target business object is at least one of the business objects in the data acquisition platform; A multi-dimensional verification module, configured to perform multi-dimensional verification on the initial business data, wherein the multi-dimensional verification includes at least one of data format verification, data content verification, and organizational structure path verification; A data storage module is used to determine the initial business data as target business data if the initial business data passes the multi-dimensional verification, and store the target business data in a preset database.

9. A computer device, characterized in that: The computer device includes a memory and a processor; The memory is used to store computer programs; The processor is used to execute the computer program and implement the data processing method based on the data acquisition platform according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the data processing method based on the data acquisition platform according to any one of claims 1 to 7.

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