A method and device for processing approval data across systems

Abstract

The application provides a processing method and device for approval data across systems, which comprises the following steps: obtaining approval process information received by a first system; obtaining an approval behavior tag of an approver corresponding to the approval process information; obtaining an approval node mapping table according to the corresponding relationship between the approval nodes of the first system and a second system; obtaining a target approval page according to the approval process information, the approval behavior tag and the approval node mapping table; converting the approval process information into target approval data of the second system according to the corresponding relationship between the approval data of the first system and the second system; determining a permission corresponding model according to the corresponding relationship between the approval permissions of the first system and the second system; obtaining an approval result according to the target approval page, the target approval data and the permission corresponding model, and feeding back to the first system. The application can improve the connection efficiency, data flow and sharing level, switching automation and smoothness, office efficiency and experience of cross-system approval.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data information processing, in particular to a cross-system approval data processing method and device. BACKGROUND

[0002] In today's digital office environment, the operation of enterprises highly depends on various office automation (OA) systems. Many enterprises use third-party OA systems to collaborate, and some enterprises also build their own internal OA systems according to the diversified needs of business segments. However, this coexistence of multiple systems brings a series of problems. The internal OA systems developed by different enterprises and the OA systems provided by various third-party vendors have significant differences in technical architecture, data format, and operation logic. For example, some OA systems focus on document management, while others excel in process approval. This makes employees need to frequently switch between different OA systems to complete various tasks in their daily work, greatly reducing work efficiency. At the same time, data between systems is difficult to share and interact, forming an information island, hindering the smooth progress of the overall business process of the enterprise.

[0003] Taking the project management process of an enterprise as an example, the project team may need to submit a project plan in the internal OA system, assign tasks and track progress in the third-party OA system, and handle expense reimbursement and other matters in the financial-related OA system. Employees need to switch between multiple systems, manually re-enter information, not only wasting time, but also prone to information inconsistency and omission. In addition, the operation and maintenance of multiple systems also bring a heavy burden to the IT department of the enterprise, increasing management costs and technical risks.

[0004] Currently, although there are some solutions in the market that attempt to solve the problem of system integration, most of them have limitations. Some solutions can only realize data connection between a limited number of systems, and cannot meet the comprehensive needs of enterprise multi-system unified access; some solutions require user manual intervention when switching systems, which is cumbersome to operate, difficult to achieve true seamless automatic switching, and cannot fundamentally solve the problem of low office efficiency faced by enterprises. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a cross-system approval data processing method and device. It can improve the connection efficiency of cross-system approval, data flow and sharing level, switching automation and smoothness, office efficiency and experience.

[0006] To solve the above technical problems, the technical solution of the present application is as follows:

[0007] A cross-system approval data processing method, comprising:

[0008] obtaining approval process information received by a first system;

[0009] obtaining an approval behavior tag of an approver corresponding to the approval process information;

[0010] obtaining an approval node mapping table according to a corresponding relationship between approval nodes of the first system and a second system;

[0011] obtaining a target approval page according to the approval process information, the approval behavior tag and the approval node mapping table;

[0012] converting the approval process information into target approval data of the second system according to a corresponding relationship between approval data of the first system and the second system;

[0013] determining a permission corresponding model according to a corresponding relationship between approval permissions of the first system and the second system;

[0014] obtaining an approval result according to the target approval page, the target approval data and the permission corresponding model, and feeding back to the first system.

[0015] Optionally, the obtaining of the approval process information received by the first system comprises:

[0016] establishing a data exchange channel according to an interface between the first system and the second system, and obtaining the approval process information through the data exchange channel.

[0017] Optionally, the obtaining of the approval behavior tag of the approver corresponding to the approval process information comprises:

[0018] obtaining historical approval time and historical approval results of the approver corresponding to the approval process information;

[0019] obtaining the approval behavior tag according to the historical approval time and the historical approval results.

[0020] Optionally, the obtaining of the approval behavior tag of the approver corresponding to the approval process information comprises:

[0021] obtaining an operation path of the approver corresponding to the approval process information;

[0022] performing cluster analysis on the operation path to obtain the approval behavior tag.

[0023] Optionally, the obtaining of the approval node mapping table according to the corresponding relationship between the approval nodes of the first system and the second system comprises:

[0024] obtaining a functional similarity of the approval nodes between the first system and the second system;

[0025] According to the functional similarity, a corresponding relationship of an approval node between the first system and the second system is determined;

[0026] According to the corresponding relationship, an approval node mapping table is obtained.

[0027] Optionally, according to the approval process information, the approval behavior label and the approval node mapping table, a target approval page is obtained, including:

[0028] According to the approval process information, the approval behavior label and the approval node mapping table, an approval page is set in a preset time, a preset preference and a preset node through the second system, and a target approval page is obtained.

[0029] Optionally, according to the corresponding relationship of the approval data between the first system and the second system, the approval process information is converted into target approval data of the second system, including:

[0030] According to a data format corresponding relationship of the approval data between the first system and the second system, a data conversion rule is obtained.

[0031] According to the data conversion rule, data conversion is performed to obtain target approval data.

[0032] Optionally, according to the corresponding relationship of the approval authority between the first system and the second system, an authority corresponding model is determined, including:

[0033] According to a corresponding relationship of an authority type, an authority level and an authority allocation mode of the approval authority between the first system and the second system, a standardization mapping rule is formulated to determine the authority corresponding model.

[0034] Optionally, according to the target approval page, the target approval data and the authority corresponding model, an approval result is obtained and fed back to the first system, including:

[0035] According to the target approval data obtained, the second system obtains an approval result in a corresponding approval authority range queried by the authority corresponding model under the target approval page, and feeds back to the first system.

[0036] Embodiments of the application also provide a processing device for approval data across systems, including:

[0037] An acquisition module is configured to acquire approval process information received by a first system and an approval behavior label of an approver corresponding to the approval process information.

[0038] The processing module is used for obtaining an approval node mapping table according to the correspondence between the approval nodes of the first system and the second system, obtaining a target approval page according to the approval process information, the approval behavior label and the approval node mapping table, converting the approval process information into target approval data of the second system according to the correspondence between the approval data of the first system and the second system, determining a permission correspondence model according to the correspondence between the approval permissions of the first system and the second system, obtaining an approval result according to the target approval page, the target approval data and the permission correspondence model, and feeding back to the first system.

[0039] The above technical solutions of the present application have at least the following technical effects:

[0040] The above cross-system approval data processing method of the present application can improve the connection efficiency, data flow and sharing level, switching automation and smoothness, office efficiency and experience of cross-system approval. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 Fig. 1 is a flowchart of the cross-system approval data processing method of the present application;

[0042] Figure 2 Fig. 2 is a schematic diagram of the cross-system approval data processing device of the present application. DETAILED DESCRIPTION

[0043] The exemplary embodiments of the present application will be described in detail with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided so that the present application can be more thoroughly understood and the scope of the present application can be accurately conveyed to those skilled in the art.

[0044] As shown in Fig. 1, the embodiment of the present application proposes a cross-system approval data processing method, which comprises: Figure 1

[0045] ​Step S1, obtaining approval process information received by a first system; here, the first system can be a third-party office software or a preset target organization office software, the second system can be a third-party office software used by the target organization in parallel with the first system, the second system and the first system are different office software, and the first system and the second system can be multiple; the approval process information is, for example, contract approval, data business process approval, order process approval, etc., and the approval process needs to pass at least one approval node for processing in the first system;

[0046] Step S2, obtaining the approval behavior tag of the approver corresponding to the approval process information; here, the approval behavior tag of the approver reflects the timeliness of the approval and the commonly used approval language when the approval node processes the corresponding approval process information;

[0047] Step S3, obtaining an approval node mapping table according to the corresponding relationship between the approval nodes of the first system and the second system; here, the corresponding relationship can be established according to the functions of the approval nodes of the first system and the second system to form the approval node mapping table;

[0048] Step S4, obtaining a target approval page according to the approval process information, the approval behavior tag and the approval node mapping table;

[0049] Step S5, converting the approval process information into target approval data of the second system according to the corresponding relationship between the approval data of the first system and the second system;

[0050] Step S6, determining a permission corresponding model according to the corresponding relationship between the approval permissions of the first system and the second system;

[0051] Step S7, obtaining an approval result according to the target approval page, the target approval data and the permission corresponding model, and feeding back to the first system.

[0052] In this embodiment, as Figure 1As shown, in the processing method of the approval data across the systems, the underlying specifications of the interfaces of each OA system are comprehensively sorted out, so that the approval information can be smoothly transferred between the OA systems through the OA system interfaces; the historical approval time, the historical approval result, the historical approval page and the historical message notification strategy and other information of the approver are analyzed and processed, the behavior mode of the approver is described, the operation marks of the approver in different OA systems are comprehensively collected and analyzed, the approval behavior tag library is established, the enterprise can deeply understand the work habits and preferences of the employees, and data support is provided for subsequent collaborative process optimization; the corresponding relationship of the approval nodes between the cross-office systems is established, so that the approval nodes can be connected and transferred across the platforms; according to the approval behavior tag library, the behavior habits of the approver, the cross-platform approval information of each process at the current approval node is efficiently and accurately pushed to the user in advance by presetting the time, the interface and the message notification, so that the approver can timely enter the target approval page, and the office process approval efficiency is improved; the cross-office system approval data conversion rules are established, so as to ensure that the approval data is accurately and timely transferred between the office systems; the cross-office system permission corresponding model is established, so as to guarantee the legality and security of the approval process when the approval process is transferred across the platforms, and the approver can perform the process approval across the office systems within the scope of the permission corresponding model according to the obtained target approval data under the target approval page, so as to realize the process approval across the systems, realize the seamless automatic switching algorithm between the multiple platforms, greatly improve the office efficiency, optimize the office experience of the employees, reduce the operation and maintenance cost of the enterprise, and enhance the competitiveness of the enterprise in the field of digital office.

[0053] The scheme of the present application generates an adaptive module by automatically analyzing the interface document of the third-party OA system, supports multiple interface technologies such as RESTful API and WebService, realizes fast and flexible access to different OA systems, has interface monitoring and self-healing capabilities, and guarantees the stability of the connection; introduces a visual process arrangement tool and a rule engine, supports enterprise users to customize cross-system business processes, realizes flexible adjustment and rapid deployment of business logic; constructs a user behavior mode model through multi-dimensional behavior collection, behavior sequence analysis and group behavior comparison, combines platform habit adaptation learning, accurately captures user operation preferences, and provides a basis for automatic switching; comprehensively considers process priority, platform load, user context and other factors, introduces reinforcement learning and artificial intervention threshold mechanism, realizes the intelligentization and precision of switching decision.

[0054] In an optional embodiment of the present application, in step S1, the approval process information received by the first system is obtained, including:

[0055] In step S11, a data exchange channel is established according to the interface between the first system and the second system; specifically,

[0056] acquire a first interface feature of the first system and a second interface feature of the second system;

[0057] establish a data exchange channel between the first interface and the second interface according to the first interface feature and the second interface feature, for example, using RESTful API, WebService, or other general standards to establish the data exchange channel;

[0058] In step S12, the approval process information is acquired through the data exchange channel. Specifically,

[0059] determine a first data format of the first system according to the first interface feature;

[0060] convert the approval process information in the first data format into approval process information in a standard data format;

[0061] transmit the approval process information in the standard data format to the second system through the data exchange channel, so that the second system acquires the specific content of the approval process information;

[0062] Similarly, if the approval process information is initiated by the second system, a second data format of the second system is determined according to the second interface feature, the approval process information in the second data format is converted into approval process information in a standard data format, and the approval process information in the standard data format is transmitted to the first system through the data exchange channel, so that the first system acquires the specific content of the approval process information;

[0063] In this embodiment, a unified cross-system access architecture is used, and an adaptive interface module is developed for the data format and interface specification of different office systems. RESTful API, WebService, and other general standards are used to establish a data exchange channel to ensure that the data of each system can be accurately and efficiently connected. In this way, the data structure of various office systems can be clearly aligned, avoiding the generation of data islands, and providing a solid foundation for cross-system process collaboration and information sharing. The data accessed from different OA systems is cleaned, integrated, and stored. Data cleaning is used to ensure the accuracy and integrity of the data. Through data integration technology, OA data operation change records from different systems are stored and updated in real time on other connected platforms. A unified operation interface is provided for enterprise employees, and employees can access the functions of all connected OA systems through the interface. Responsive design is used to ensure that the interface can be displayed and operated well on different devices (such as computers, tablets, and mobile phones).

[0064] Specifically, for third-party OA systems such as DingTalk and WeChat Work, information such as interface types (e.g., RESTful, WebService, RPC), request methods (GET / POST / PUT, etc.), parameter formats (JSON / XML / FormData), authentication methods (Token / OAuth2.0 / API key), field naming rules, and data return formats were collected through methods such as crawling official API documentation, analyzing interface call logs, and conducting developer interviews. An interface feature extraction tool was developed to automatically identify the core elements of the interface, such as the "Approval Form ID" field in DingTalk's approval interface and the "Document Number" field in WeChat Work, and to form an interface feature library.

[0065] Establish data exchange channels and standard data formats, and define a unified interface metadata standard, including elements such as interface name, function description, request parameters (name, type, length, constraints), return parameters, and error codes. Map the interface fields of each OA system to the unified metadata standard. For example, map DingTalk's "spId" (approval form ID) and WeChat Work's "billNo" (document number) to the unified field "approvalId," and mark the field type differences in the mapping table (such as the conversion rules when DingTalk uses strings and WeChat Work uses numbers).

[0066] Establish a formula for calculating the adaptation coefficient for each interface:

[0067] The compatibility coefficient = (field matching degree × 0.4) + (data type compatibility × 0.3) + (authentication method consistency × 0.3);

[0068] Among them, field matching degree is the proportion of the number of successfully mapped fields out of the total number of fields; data type compatibility is assigned a value based on the difficulty of type conversion (e.g., 0.8 for strings and numbers, 0.3 for arrays and objects); authentication method consistency is 1 (same) or 0.5 (conversion required). When the adaptation coefficient is ≥0.7, it is determined that it can be directly adapted; otherwise, customized conversion rules are required.

[0069] The adaptation coefficient is a key parameter used to adjust and optimize data processing in the implementation of data interface layer functions. It can adjust the data processing method according to different scenarios and needs, and play a role in adjusting data format conversion, adjusting data precision, adapting data volume, and optimizing system compatibility, so as to ensure the accuracy, consistency and compatibility of data.

[0070] In an optional embodiment of the present invention, step S2, obtaining the approval behavior tag of the approver corresponding to the approval process information, includes:

[0071] Step S21: Obtain the historical approval time and historical approval results of the approver corresponding to the approval process information;

[0072] Step S22, obtaining an approval behavior label according to the historical approval time and the historical approval result.

[0073] In this embodiment, when the approval form corresponding to the approval process information is created in the enterprise internal OA system, a globally unique tracking ID (for example, UUID) is generated. This ID will accompany the approval form in the flow between different OA systems. The tracking ID is added to the metadata of the approval form to ensure that the entire collaborative process of the approval form can be identified and associated in each system. Data collection scripts are implanted in the approval modules of each OA system (Dingding, WeChat for Enterprise, etc.). When the approver operates, the script records the operation time, the type of the document being approved, the approval result (pass / reject), and other basic information. For example, in the approval processing page of Dingding, when the approver A clicks the "pass" button, the collection script immediately records the timestamp and the contract document identifier to which the approval form belongs.

[0074] In each OA system, when the approval form enters or leaves a specific link (such as submission, start of approval, end of approval), a timestamp with accurate time is recorded. For example, in Dingding, when the approver A starts the approval, the start time T1 is recorded; when the approval is completed, the end time T2 is recorded. When the approval form is passed to WeChat for Enterprise, the WeChat for Enterprise system records the receiving time T3 and the viewing time T4 of the approver B, and other key time points. Collect all the timestamp data (i.e., historical approval time), analyze the time consumption of the approval form in different systems and different links, and pre-load in advance according to the preset time.

[0075] The calculation formula of the pre-loading trigger score is:

[0076]

[0077] wherein, TS is the pre-loading score, is the probability weight, is the current approval link state sequence, is the next approval link state sequence, represents the probability of transferring from state to state, is the interval weight, represents the average interval time of transferring from state to state.

[0078] When the preloading score is greater than a preset threshold, triggering target platform resource preloading. For example, the average approval time of approver A is T2-T1=X minutes, and the average interval time of the approval sheet from Dingding to Enterprise WeChat is T3-T2=Y minutes. According to the analysis result, the switching time in subsequent cross-platform flow is optimized. For example, it is found that B will process usually within 30 minutes after A approval, and the system can prepare for switching to Enterprise WeChat 25 minutes in advance after A approval is completed, including data synchronization, notification push, etc.

[0079] By accurately tracking the behavior data in the collaboration link, the enterprise can clearly understand the flow efficiency of the approval process between different platforms, find out potential time-consuming nodes, and optimize them accordingly, thereby speeding up the overall approval process and improving collaboration efficiency.

[0080] The approval result is analyzed by natural language processing (NLP) technology. If the approver often uses words such as "urgent priority processing", the system identifies his preference for urgent matters.

[0081] According to the collected data, a behavior tag is established for each approver. For example, if approver A handles approvals mostly at 10-11 am on Monday, a "Monday morning active" tag is established; if he often prioritizes handling of urgent contract documents, an "urgent contract preference" tag is established. These tags are stored in a unified database to form a cross-platform user behavior tag library for subsequent analysis and use.

[0082] By comprehensively collecting and analyzing the operation marks of the approver in different OA systems, the enterprise can deeply understand the work habits and preferences of the employees, and provide data support for subsequent collaboration process optimization, such as pushing approval tasks at appropriate time nodes to improve approval efficiency.

[0083] In an optional embodiment of the present application, another implementation of step S2 includes:

[0084] Step S23, obtaining the operation path and message receiving mode of the approver corresponding to the approval process information;

[0085] Step S24, clustering analysis is performed on the operation path to obtain the approval behavior tag.

[0086] In this embodiment, a log analysis tool is used to record the operation path of the user in the OA system. For example, in Dingding, the series of operation steps and click order of approver A from logging into the system to entering the "to-do list" page to handle the approval are recorded.

[0087] A large amount of operation path data is clustered and analyzed to find the most common operation habit patterns of users. According to the analysis results, a personalized operation habit configuration file is generated for each user.

[0088] The formula for operation path data clustering analysis is:

[0089]

[0090] C represents the strategy, which determines the action to be taken in a given state, is the learning rate, z is the current page, z' is the next page, and R is the reward, is the discount factor, and Q is the expected future cumulative reward, is the click operation, is the next click operation.

[0091] For example, approver A is used to handling approvals directly on the "To-Do" page in DingTalk. The path information is recorded in the configuration file. These configuration files are stored in the cloud, ensuring that users can access their personalized settings when logging in from different devices or at different times. When a user switches from one OA system to another, the system automatically guides the user to the operation page they commonly use based on their personalized configuration file. For example, when approver B switches from an internal OA system to WeChat for Work, the system automatically opens the "Workbench-Approval Center" entry in WeChat for Work. This reduces the user's operation adaptation cost when switching between different OA systems, improves the convenience and efficiency of user operations, allows users to quickly find the required functions, and enhances user experience, thereby promoting the smoothness of cross-platform collaboration.

[0092] Message receiving habit analysis: In OA systems such as WeChat for Work, user behavior analysis tools are used to collect data such as viewing time, click frequency, and reply frequency for different types of messages (such as "application messages", "work notifications", and "group messages") received by users. By analyzing these data, the message receiving habits of each user are determined. For example, it is found that approver B spends an average of 2 minutes viewing "application messages" and 10 minutes viewing "work notifications", indicating that he prefers to view "application messages".

[0093] Message notification strategy development: Based on the message receiving habits of users, personalized message notification strategies are developed for each user. For example, for approver B, when an approval form is transferred from DingTalk to WeChat for Work, the system will prioritize sending a reminder in the form of an "application message". In the message content, key information such as the document number, subject, and urgency level is included to attract the user's attention and prompt them to handle it quickly.

[0094] Real-time adjustment and optimization, continuous tracking of user feedback on messages, such as timely viewing, whether to handle approval, etc. If it is found that the response rate of users to a certain message notification method decreases, re-analyze user behavior and adjust the message notification strategy. For example, if it is found that the processing speed of the approver B for the "application message" has slowed down recently, the system analyzes the possible reasons, such as too many messages leading to neglect, and then tries to adjust the mode of sending "work notification" and "application message" at the same time, and observes the effect.

[0095] By precisely matching the message receiving habits of users in different OA systems, the reach rate and response efficiency of the approval reminder are improved, ensuring that the approver can timely obtain the approval task, speeding up the approval process, and improving the timeliness of cross-platform collaboration.

[0096] In an optional embodiment of the present application, in step S3, an approval node mapping table is obtained according to the correspondence between the approval nodes of the first system and the second system, comprising:

[0097] In step S31, the functional similarity between the approval nodes of the first system and the second system is obtained;

[0098] In step S32, the correspondence between the approval nodes of the first system and the second system is determined according to the functional similarity;

[0099] In step S33, an approval node mapping table is obtained according to the correspondence.

[0100] In this embodiment, all nodes of the business process in each OA system are combed in detail, such as the "approval initiation", "first-level approval", "second-level approval" nodes in Dingding, and the corresponding nodes in WeChat for enterprise.

[0101] A node mapping table is established to clearly define the correspondence between nodes with the same or similar functions in different OA systems. For example, the cosine similarity of node vector A (x1, x2, … x n ) and node vector B (y1, y2, … y n ) is calculated, and the calculation formula is as follows:

[0102]

[0103] Where Sim(A, B) represents the cosine similarity of vectors A and B. The greater the cosine similarity, the higher the similarity of the two vectors. The cosine similarity measures the similarity from the node function description, which is more suitable for text similarity measurement;

[0104] For example, the "approval initiation" node in Dingding is associated with the "to-be-approved receiving" node in WeChat for enterprise.

[0105] Field difference annotation, for each process node, analyze its field settings in different OA systems. For example, the "approval opinion" field in Dingding is "approval note" in WeChat for enterprise.

[0106] Record these field differences in the node mapping table, and develop field conversion rules to ensure that data can be correctly matched and converted when flowing between different systems.

[0107] Automatic triggering and data synchronization, when the approval form completes an operation in a node in one OA system, the system automatically triggers the corresponding associated node in another OA system according to the node mapping table. For example, after the "first-level approver" A completes the "first-level approval" in Dingding, the system automatically triggers the "to-be-approved receiver" node of the "second-level approver" B in WeChat for enterprise.

[0108] According to the field conversion rule, the approval form data is synchronized from the approval node of the first system to the corresponding approval node of the second system in the approval node mapping table. For example, the "approval opinion" field data in Dingding is synchronized to the "approval note" field in WeChat for enterprise.

[0109] The seamless connection between different OA systems is realized, the continuity and accuracy of the approval process are ensured when flowing across platforms, data loss or process interruption caused by node and field differences is avoided, and the reliability of cross-platform collaboration is improved.

[0110] In an optional embodiment of the application, in step S4, the target approval page is obtained according to the approval process information, the approval behavior label and the approval node mapping table, comprising:

[0111] In step S41, the target approval page is obtained by setting the approval page in the second system under the preset time, the preset preference, the preset node according to the approval process information, the approval behavior label and the approval node mapping table.

[0112] In this embodiment, the approval process information of the first system is transmitted to the second system through the interface specification mapping model, the habits and preferences of the approver such as approval time, approval result, operation path and message receiving mode are obtained through the approval behavior label, the approval nodes of the approval process in the second system are obtained through the approval node mapping table, the second system sets the approval page according to the obtained preset time, preset preference and preset node, obtains the target approval page, and actively pushes the target approval page to the approver.

[0113] In an optional embodiment of the application, in step S5, the approval process information is converted into the target approval data of the second system according to the corresponding relationship between the approval data of the first system and the second system, comprising:

[0114] Step S51, obtain data conversion rules according to the data format correspondence relationship of the approval data between the first system and the second system;

[0115] Step S52, perform data conversion according to the data conversion rules to obtain target approval data.

[0116] In this embodiment, the formats of approval forms and related data in various OA systems are comprehensively sorted out, including data structures (such as JSON, XML, database table structures), data types (such as strings, numbers, dates), and data encodings (such as UTF-8, GBK), etc.

[0117] A data format knowledge base is established to record the format characteristics and specifications of various types of data in each OA system. For example, the attachment list in Dingding approval forms is stored in JSON format, and each attachment contains fields such as file name, file size, and file type. The supported attachment information in WeChat Enterprise is an array, and each element contains file name, file path, etc.

[0118] According to the data format analysis results, detailed rules are formulated for data conversion between different OA systems. For complex data structures such as attachment lists, recursive conversion algorithms are developed.

[0119] For example, when converting the JSON format attachment list of Dingding to the array type attachment information of WeChat Enterprise, a conversion function is written to traverse the JSON list, extract the corresponding information such as file name and file path, and construct the array elements required by WeChat Enterprise.

[0120] For example, the attachment list of Dingding is in JSON format, with a structure similar to [{"name":"report.docx","path":" / company / reports / 2024 / ","size":102400},{"name":"presentation.pptx","path":" / company / presentations / ","size":256000}]. When converting it to the array type attachment information required by WeChat Enterprise, a conversion function is written to traverse the JSON list, extract the corresponding information such as file name and file path, and construct the array elements required by WeChat Enterprise. The converted format is as follows: [{"name":"report.docx","url":" / company / reports / 2024 / report.docx","type":"file"},{"name":"presentation.pptx","url":" / company / presentations / presentation.pptx","type":"file"}].

[0121] Develop the data conversion engine using a suitable programming language (such as Java, Python). The engine implements the functions of data reading, conversion according to conversion rules, and data writing. For example, in Java, use the JSON processing library to read the JSON data of the Dingding approval form, convert the data such as the attachment list according to the conversion rules, and then write the converted data into a format file or interface that can be received by WeChat Enterprise.

[0122] Before and after conversion, check the integrity and accuracy of the data. For example, compare the number of attachments, file name, file content hash value before and after conversion to ensure that the data is not lost or damaged.

[0123] Conduct a large number of simulation tests, use different types and different sizes of approval form data, and perform conversion tests between various OA system combinations to verify the stability and reliability of the conversion engine.

[0124] It can completely solve the problem of data format difference between different OA systems, realize lossless conversion of approval form data in cross-platform circulation, ensure that the approver can accurately view and process data in the target OA system, avoid data errors or unreadable due to incompatible data formats, and ensure the accuracy and integrity of cross-platform collaboration data.

[0125] In an optional embodiment of the present application, in step S6, the permission correspondence model is determined according to the correspondence between the approval permissions of the first system and the second system, comprising:

[0126] Step S61, according to the correspondence between the permission type, permission level and permission allocation method of the approval permissions between the first system and the second system, formulate a standardized mapping rule to determine the permission correspondence model.

[0127] In this embodiment, the permission management system of each OA system (such as Dingding and WeChat Enterprise) is deeply studied, including permission type (such as approval permission, viewing permission, editing permission), permission level (such as ordinary, senior, administrator) and permission allocation method (based on role, based on user) and the like.

[0128] A unified permission model is established, and the permission systems of various OA systems are mapped into the model for unified management and comparison. Specifically, first, the permission systems of the internal OA and various third-party OA systems are deeply deconstructed, and core elements such as role definition, permission level, and operation permission are sorted out. By formulating standardized mapping rules, the permission concepts in different systems are accurately matched with the permission units in the unified model, such as matching the "project approver" role in a third-party system to the "intermediate approval permission group" in the unified model. At the same time, a dynamic permission mapping mechanism is introduced. When the permission structure of an OA system changes, the system can automatically trigger the verification and adjustment process to ensure the real-time and consistency of the permission management. In addition, through the visual permission comparison tool, the differences in permission configuration of each system are intuitively displayed, providing data support for permission optimization and risk control, and finally realizing centralized and refined management of cross-system permissions.

[0129] When the approval sheet is transferred between different OA systems and involves switching of the approver's permissions, the system first checks the target approver's permissions in the target OA system according to the unified permission model. For example, when the approval sheet is transferred from Dingding to WeChat for approver B, the system checks the "contract approval permission" of approver B in WeChat. If the approver's actual permissions are insufficient, the next operation is triggered.

[0130] If the approver's permissions are insufficient, the system automatically pops up a permission application prompt box to inform the approver of the insufficient permissions and the required permission type. For example, it prompts approver B: "You are missing the contract approval permission in WeChat. Do you want to apply for it?"

[0131] After the approver clicks on the application, the system sends an application notification to the relevant permission administrator according to the pre-set permission application process, and records the application record. After the permission administrator handles the application, the system updates the approver's permission information in a timely manner to ensure the smooth transfer of the approval sheet.

[0132] This can effectively solve the problem of mismatched approver permissions caused by differences in the permission systems of different OA systems, ensure the legality and security of the approval process when it is transferred across platforms, avoid hindering the collaboration process due to permission problems, and improve the smoothness and stability of cross-platform collaboration.

[0133] In an optional embodiment of the present application, in step S7, the approval result is obtained according to the target approval page, the target approval data, and the permission corresponding model, and is fed back to the first system, comprising:

[0134] In step S71, the second system obtains the approval result within the corresponding approval permission range queried in the permission corresponding model according to the obtained target approval data under the target approval page, and feeds back to the first system.

[0135] In this embodiment, on the target approval page, the approver checks the approval authority based on the obtained target approval data, performs the approval operation on the approval process within the scope of the approver's authority, obtains the approval result, and feeds back the approval result to the first system.

[0136] This invention's solution automatically parses third-party OA system interface documents to generate an adaptation module. Combined with support for various interface technologies such as RESTful API and WebService, it enables rapid and flexible integration with different OA systems. It also features interface monitoring and self-healing capabilities to ensure stable integration. A visual process orchestration tool and rule engine are introduced, allowing enterprise users to customize cross-system business processes, enabling flexible adjustments and rapid deployment of business logic. Through multi-dimensional behavior collection, behavior sequence analysis, and group behavior comparison, a user behavior pattern model is constructed. Combined with platform habit adaptation learning, it accurately captures user operation preferences, providing a basis for automatic switching. Considering multiple factors such as process priority, platform load, and user context, a reinforcement learning and manual intervention threshold mechanism are introduced to achieve intelligent and precise switching decisions.

[0137] The solution of this invention enables efficient and unified access to multiple OA systems: it breaks through the limitations of interface differences between different OA systems, can quickly access internal enterprise and multiple third-party OA systems, breaks down information silos, realizes the free flow and sharing of data, and provides enterprises with an integrated office data environment.

[0138] Achieve seamless automatic switching across platforms: This solves the problems of cumbersome and inefficient switching between multiple OA systems, enabling automatic and smooth switching between systems. It can also adapt to the operating habits and business scenarios of different users, greatly improving the office efficiency and operating experience of employees.

[0139] Improve business process collaboration efficiency: By analyzing and optimizing the correlation of cross-system business processes, the automated flow and collaborative processing of business processes are realized, shortening the business processing cycle and improving the overall operational efficiency of the enterprise, especially in cross-enterprise collaboration scenarios.

[0140] Ensuring data security and system stability: During data processing and system switching, multiple encryption, access control, interface monitoring, and self-healing technologies were adopted to ensure data security and system stability, reducing the company's operational risks and costs.

[0141] like Figure 2 As shown, embodiments of the present invention also provide a cross-system approval data processing apparatus 20, comprising:

[0142] The acquisition module 21 is configured to acquire the approval process information received by the first system and an approval behavior tag of an approver corresponding to the approval process information.

[0143] The processing module 22 is configured to obtain an approval node mapping table according to a corresponding relationship of approval nodes between the first system and the second system, obtain a target approval page according to the approval process information, the approval behavior tag and the approval node mapping table, convert the approval process information into target approval data of the second system according to a corresponding relationship of approval data between the first system and the second system, determine a permission corresponding model according to a corresponding relationship of approval permissions between the first system and the second system, obtain an approval result according to the target approval page, the target approval data and the permission corresponding model, and feed back to the first system.

[0144] Optionally, the acquisition of the approval process information received by the first system comprises the following steps.

[0145] A data exchange channel is established according to an interface between the first system and the second system, and the approval process information is acquired through the data exchange channel.

[0146] Optionally, the acquisition of the approval behavior tag of the approver corresponding to the approval process information comprises the following steps.

[0147] The historical approval time and the historical approval result of the approver corresponding to the approval process information are acquired.

[0148] The approval behavior tag is obtained according to the historical approval time and the historical approval result.

[0149] Optionally, the acquisition of the approval behavior tag of the approver corresponding to the approval process information comprises the following steps.

[0150] The operation path of the approver corresponding to the approval process information is acquired.

[0151] The operation path is subjected to cluster analysis to obtain the approval behavior tag.

[0152] Optionally, the obtaining of the approval node mapping table according to the corresponding relationship of approval nodes between the first system and the second system comprises the following steps.

[0153] The functional similarity of the approval nodes between the first system and the second system is acquired.

[0154] The corresponding relationship of the approval nodes between the first system and the second system is determined according to the functional similarity.

[0155] The approval node mapping table is obtained according to the corresponding relationship.

[0156] Optionally, according to the approval process information, the approval behavior label and the approval node mapping table, a target approval page is obtained, including:

[0157] According to the approval process information, the approval behavior label and the approval node mapping table, an approval page is set by a second system under a preset time, a preset preference and a preset node, and a target approval page is obtained.

[0158] Optionally, according to a corresponding relationship of approval data between the first system and the second system, the approval process information is converted into target approval data of the second system, including:

[0159] According to a data format corresponding relationship of approval data between the first system and the second system, a data conversion rule is obtained.

[0160] According to the data conversion rule, data conversion is performed, and target approval data is obtained.

[0161] Optionally, according to a corresponding relationship of approval permissions between the first system and the second system, a permission corresponding model is determined, including:

[0162] According to a corresponding relationship of a permission type, a permission level and a permission allocation mode of approval permissions between the first system and the second system, a standardized mapping rule is formulated, and the permission corresponding model is determined.

[0163] Optionally, according to the target approval page, the target approval data and the permission corresponding model, an approval result is obtained, and is fed back to the first system, including:

[0164] According to the target approval data obtained, the second system obtains an approval result in a corresponding approval permission range queried by the permission corresponding model under the target approval page, and feeds back to the first system.

[0165] It should be noted that all implementation manners in the above method embodiments are applicable to the device embodiments, and can achieve the same technical effects.

[0166] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software mode depends on the specific application and design constraints of the technical solutions. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0167] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the system, device and unit described above can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0168] In the embodiments provided by the present application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are merely schematic, for example, the division of units is only a logical function division, and there can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or in other forms.

[0169] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0170] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0171] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the present application that essentially contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method of each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a ROM, a RAM, a magnetic disk or an optical disk, and various program code storage media.

[0172] Moreover, it is pointed out that in the device and method of the present application, obviously, the components or steps can be decomposed and / or recombined. These decompositions and / or recombination should be considered as equivalent solutions of the present application. Also, the steps of performing the above series of processes can naturally be executed in time sequence according to the order of description, but do not need to be necessarily executed in time sequence. Some steps can be executed in parallel or independently of each other. It can be understood by those skilled in the art that all or any steps or components of the method and device of the present application can be implemented in hardware, firmware, software or a combination thereof in any computing device (including processors, storage media, etc.) or network of computing devices, using the basic programming skills of those skilled in the art upon reading the description of the present application.

[0173] Therefore, the object of the present application can also be achieved by running a program or a set of programs on any computing device. The computing device can be a commonly known general-purpose device. Therefore, the object of the present application can also be achieved only by providing a program product containing program code for implementing the method or device. That is, such a program product also constitutes the present application, and a storage medium storing such a program product also constitutes the present application. Obviously, the storage medium can be any commonly known storage medium or any storage medium developed in the future. It is also pointed out that in the device and method of the present application, obviously, the components or steps can be decomposed and / or recombined. These decompositions and / or recombination should be considered as equivalent solutions of the present application. Also, the steps of performing the above series of processes can naturally be executed in time sequence according to the order of description, but do not need to be necessarily executed in time sequence. Some steps can be executed in parallel or independently of each other.

[0174] The above is the preferred embodiment of the present application. It should be pointed out that for those skilled in the art, several improvements and refinements can be made without departing from the principles of the present application, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A method for processing approval data across systems, characterized in that, The method comprises the following steps: obtaining the approval process information received by the first system; obtaining the approval behavior label of the approver corresponding to the approval process information; obtaining the approval node mapping table according to the correspondence between the approval nodes of the first system and the second system; obtaining the target approval page according to the approval process information, the approval behavior label and the approval node mapping table; converting the approval process information into the target approval data of the second system according to the correspondence between the approval data of the first system and the second system; determining the permission corresponding model according to the correspondence between the approval permissions of the first system and the second system; obtaining the approval result according to the target approval page, the target approval data and the permission corresponding model, and feeding back to the first system; wherein, obtaining the approval behavior label of the approver corresponding to the approval process information comprises: obtaining the historical approval time and the historical approval result of the approver corresponding to the approval process information; obtaining the approval behavior label according to the historical approval time and the historical approval result; or obtaining the operation path of the approver corresponding to the approval process information; obtaining the approval behavior label by clustering analysis on the operation path; wherein, the formula of operation path data clustering analysis is: C represents the policy, which determines the action to take in a given state, for the learning rate, z is the current page, z' is the next page, and R is the reward, for the discount factor, Q is the expected future cumulative reward, for the click operation, for the next click operation; wherein, all historical approval times are collected, the time consumption of the approval sheet in different systems and different links is analyzed, and the time is preloaded in advance; the calculation formula of the preloading trigger score is: wherein TS is a preloading score, is a probability weight, is a current approval link state sequence, is a next step approval link state sequence, denotes a probability of transition from state to state, is an interval weight, denotes an average interval time of transition from state to state; when the preloading score is greater than the preset threshold, triggering the target platform resource preloading; wherein, obtaining the approval node mapping table according to the correspondence between the approval nodes of the first system and the second system comprises: Obtaining the functional similarity of the approval nodes between the first system and the second system; the cosine similarity of the node vectors A (x1, x2, … x n ) and B (y1, y2, … y n ), the calculation formula is as follows: determining the correspondence between the approval nodes of the first system and the second system according to the function similarity; obtaining the approval node mapping table according to the correspondence; wherein, obtaining the target approval page according to the approval process information, the approval behavior label and the approval node mapping table comprises: setting the approval page through the second system under the preset time, the preset preference and the preset node according to the approval process information, the approval behavior label and the approval node mapping table, to obtain the target approval page.

2. The method of claim 1, wherein, obtaining the approval process information received by the first system comprises: establishing a data exchange channel between the interfaces of the first system and the second system; obtaining the approval process information through the data exchange channel.

3. The method of claim 1, wherein, converting the approval process information into the target approval data of the second system according to the correspondence between the approval data of the first system and the second system comprises: obtaining the data conversion rule according to the data format correspondence between the approval data of the first system and the second system; carrying out data conversion according to the data conversion rule to obtain the target approval data.

4. The method of claim 1, wherein the processing of the approval data across systems is further based on a user's role. determining the permission corresponding model according to the correspondence between the approval permissions of the first system and the second system comprises: formulating the standardization mapping rule according to the correspondence between the permission type, the permission level and the permission allocation mode of the approval permissions of the first system and the second system, to determine the permission corresponding model.

5. The method of claim 1, wherein the method further comprises: According to the target approval page, the target approval data and the permission corresponding model, an approval result is obtained and fed back to the first system, including: According to the target approval data obtained, the second system obtains an approval result within the corresponding approval permission range queried by the permission corresponding model under the target approval page and feeds back to the first system.

6. A cross-system approval data processing device, characterized in that, Including: The acquisition module is configured to acquire the approval process information received by the first system and the approval behavior tag of the approver corresponding to the approval process information; The processing module is configured to obtain an approval node mapping table according to the corresponding relationship between the approval nodes of the first system and the second system, obtain a target approval page according to the approval process information, the approval behavior tag and the approval node mapping table, and convert the approval process information into target approval data of the second system according to the corresponding relationship between the approval data of the first system and the second system; According to the corresponding relationship between the approval permissions of the first system and the second system, a permission corresponding model is determined; According to the target approval page, the target approval data and the permission corresponding model, an approval result is obtained and fed back to the first system; wherein the approval behavior tag of the approver corresponding to the approval process information is obtained, including: The historical approval time and the historical approval result of the approver corresponding to the approval process information are acquired, and the approval behavior tag is obtained according to the historical approval time and the historical approval result; or the operation path of the approver corresponding to the approval process information is acquired, the operation path is subjected to cluster analysis, and the approval behavior tag is obtained; wherein the formula for operation path data cluster analysis is: C represents the policy, which determines the action to take in a given state, for the learning rate, z is the current page, z' is the next page, and R is the reward, for the discount factor, Q is the expected future cumulative reward, for the click operation, for the next click operation; Wherein, all historical approval times are collected, the time consumption of the approval sheet in different systems and different links is analyzed, and preloading is performed in advance according to the preset time; the calculation formula of the preloading trigger score is: wherein TS is a preloading score, is a probability weight, is a current approval link state sequence, is a next step approval link state sequence, denotes a probability of transition from state to state, is an interval weight, denotes an average interval time of transition from state to state; When the preloading score is greater than the preset threshold, the target platform resource preloading is triggered; According to the corresponding relationship between the approval nodes of the first system and the second system, the approval node mapping table is obtained, including: Obtaining the functional similarity of the approval nodes between the first system and the second system; the cosine similarity of the node vectors A (x1, x2, … x n ) and B (y1, y2, … y n ), and the calculation formula is as follows: According to the function similarity, the corresponding relationship between the approval nodes of the first system and the second system is determined; According to the corresponding relationship, the approval node mapping table is obtained; According to the approval process information, the approval behavior tag and the approval node mapping table, the target approval page is obtained, including: According to the approval process information, the approval behavior tag and the approval node mapping table, the target approval page is obtained by setting the approval page under the preset time, the preset preference and the preset node through the second system.

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