Enterprise full-data dynamic integration method based on intelligent algorithm

Abstract

The invention provides an enterprise full-data dynamic integration method based on an intelligent algorithm, and relates to the technical field of data processing. Historical enterprise data and department architecture of a target enterprise are obtained, and a data dynamic integration network is established; the output end of the data cross-department verification window is connected with an enterprise data uploading interface of the data dynamic integration network, data integration reliability verification is conducted on the newly-added enterprise data to obtain a verification result, if the verification result is passed, the newly-added enterprise data is synchronized to the data dynamic integration network to execute data dynamic integration, and otherwise, the newly-added enterprise data is synchronized to the data dynamic integration network to execute data dynamic integration. And according to a verification result, extracting abnormal enterprise data in a data dynamic integration network in a backtracking manner, and marking an abnormal department. The technical problems that in the prior art, enterprise data is not effectively managed, so that the data quality is low, and the working efficiency and the decision-making quality are reduced are solved, and the technical effects of more efficient data integrated management, data quality ensuring, data utilization rate improving, decision-making quality improving and potential risk reducing are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method for dynamically integrating all enterprise data based on an intelligent algorithm. Background Art

[0002] As the scale of enterprises expands and the business becomes more complex, data interaction between departments within the enterprise is indeed becoming more frequent, and cross-departmental data flow has become the norm. This makes the management and integration of enterprise data particularly important. However, due to the rapid development of enterprise informatization and networking, enterprise data has become diverse and frequently circulated. The traditional static and decentralized data management method can no longer meet the needs of modern enterprises. Moreover, this traditional management method not only affects data quality and leads to low data utilization, but also may bring potential risks to the decision-making and operation of the enterprise.

[0003] There is a technical problem in the existing technology that enterprise data is not effectively managed, resulting in low data quality, reduced work efficiency and decision-making quality. Summary of the invention

[0004] The present application provides a method for dynamic integration of all enterprise data based on intelligent algorithms, which is used to solve the technical problem in the prior art that enterprise data is not effectively managed, resulting in low data quality, reduced work efficiency and decision-making quality.

[0005] In view of the above problems, the present application provides a method for dynamic integration of all enterprise data based on an intelligent algorithm, the method comprising: interactively obtaining historical enterprise data and department structure of the target enterprise; establishing a data dynamic integration network based on the historical enterprise data and the department structure; pre-building a data cross-department verification window, and connecting the output end of the data cross-department verification window to the enterprise data upload interface of the data dynamic integration network; newly added enterprise data enters the data cross-department verification window to perform data integration reliability verification to obtain a verification result; if the verification result is passed, synchronizing the newly added enterprise data to the data dynamic integration network to perform data dynamic integration; if the verification result is failed, backtracking and extracting abnormal enterprise data in the data dynamic integration network and marking abnormal departments according to the verification result.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The method provided by the embodiment of the present application obtains the historical enterprise data and department structure of the target enterprise through interaction, establishes a data dynamic integration network based on the historical enterprise data and department structure, pre-builds a data cross-department verification window, connects the output end of the data cross-department verification window to the enterprise data upload interface of the data dynamic integration network, and the newly added enterprise data enters the data cross-department verification window to perform data integration reliability verification to obtain the verification result, and judges the verification result. If the verification result is passed, the newly added enterprise data is synchronized to the data dynamic integration network to perform data dynamic integration. If the verification result is not passed, the abnormal enterprise data is back-traced and extracted in the data dynamic integration network according to the verification result, and the abnormal department is marked. A more efficient data integration management is achieved, ensuring the quality of data, improving data utilization and decision-making quality, and reducing the technical effect of potential risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A flowchart of a method for dynamic integration of all enterprise data based on an intelligent algorithm is provided for this application.

[0010] Figure 2 A schematic diagram of the process of establishing a data dynamic integration network in a method for dynamic integration of all enterprise data based on an intelligent algorithm provided in this application.

[0011] Figure 3 A flowchart of a pre-built data cross-department verification window in a method for dynamic integration of all enterprise data based on an intelligent algorithm provided in this application. DETAILED DESCRIPTION

[0012] This application provides a method for dynamic integration of all enterprise data based on intelligent algorithms, which is used to solve the technical problem in the prior art that enterprise data is not effectively managed, resulting in low data quality, reduced work efficiency and decision-making quality. It achieves more efficient data integration management, ensures data quality, improves data utilization and decision-making quality, and reduces potential risks.

[0013] Below, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all of them.

[0014] like Figure 1 As shown, the present application provides a method for dynamic integration of all enterprise data based on an intelligent algorithm, the method comprising:

[0015] Interactively obtain the historical enterprise data and department structure of the target enterprise.

[0016] Specifically, historical enterprise data and departmental organizational structure information are obtained through the target enterprise's official website, target enterprise information query platform, target enterprise reports, etc. Historical enterprise data refers to the data information in various departments of the enterprise, such as sales data of the sales department, financial data of the finance department, human resources data of the human resources department, etc. Departmental structure refers to the hierarchical relationship between departments in the target enterprise.

[0017] A data dynamic integration network is established based on the historical enterprise data and the department structure.

[0018] Specifically, based on the acquired historical enterprise data and department structure, the data of each department is integrated into a unified platform, and the data is allowed to flow and update between different departments as needed, to build a data dynamic integration network, in which each department should have one or more data integration points, which are responsible for recording and managing the enterprise data uploaded by the department. By centrally managing the data of all departments and allowing data to circulate between departments as needed, not only can work efficiency be improved, but decision-making can also be more timely and accurate.

[0019] A data cross-department verification window is pre-constructed, and an output end of the data cross-department verification window is connected to an enterprise data upload interface of the data dynamic integration network.

[0020] Specifically, when there are problems with the data, the decisions made based on the data will also have problems. Therefore, on the basis of the data dynamic integration network, the quality of the data also needs to be ensured. In order to ensure that the data is accurate and reliable, a data verification window is set, namely the data cross-department verification window. The data cross-department verification window is used to check whether the data entering the network meets the standards and conditions for cross-departmental circulation, including judging the integrity, consistency and accuracy of the data. Through this verification window, it is possible to ensure timely and accurate detection of abnormal data and improve overall operational efficiency.

[0021] Next, the output end of the data cross-department verification window is connected to the enterprise data upload interface of the data dynamic integration network, which refers to the data interface that receives data input from various departments of the target enterprise. By connecting the output end of the data cross-department verification window to the enterprise data upload input interface of the data dynamic integration network, whenever data is ready to enter the network through the upload interface, it will first enter the verification window for verification, and only the verified data will be allowed to enter the data dynamic integration network for subsequent processing and circulation, realizing automatic quality inspection of data before uploading to the data dynamic integration network, ensuring that only data that meets the standards can circulate in the network, thereby improving the overall quality of data and the reliability of the network.

[0022] Newly added enterprise data enters the data cross-department verification window to perform data integration reliability verification to obtain verification results.

[0023] Specifically, newly added enterprise data refers to new data records generated by various departments of the target enterprise in daily operations, including but not limited to sales records, financial statements, inventory changes, customer information, market research data, etc. When new enterprise data in a department is ready to be added to the data dynamic integration network, it will first enter the data cross-department verification window. In the data cross-department verification window, the new enterprise data will be verified to evaluate its accuracy and reliability. After the verification is completed, the window will generate a verification result indicating whether the data has passed these quality standards.

[0024] If the verification result is passed, the newly added enterprise data is synchronized to the data dynamic integration network to perform data dynamic integration.

[0025] If the verification result is not passed, then the abnormal enterprise data is back-traced and extracted in the data dynamic integration network according to the verification result, and the abnormal department is marked.

[0026] Specifically, if the verification result is passed, indicating that the data is accurate and reliable, then these newly added enterprise data will be synchronized to the data dynamic integration network, which is responsible for integrating these data into the existing data flow to ensure that they can be correctly circulated and used throughout the enterprise. If the verification result shows that the data fails the quality check, then these data will not be added to the integration network. At the same time, a backtracking mechanism is started based on the verification result, backtracking to the data dynamic integration network, extracting those abnormal enterprise data that caused the verification failure, and marking the department to which the abnormal data belongs. Marking abnormal departments can identify which departments have problems in the data submission process, which may be data entry errors, improper data processing or other reasons that cause the data to fail to meet quality standards, which helps the enterprise management and technical team quickly locate the problem for further investigation and correction. By integrating and verifying the entire data in the target enterprise, it is ensured that the enterprise data has undergone strict quality control before entering the integration network, while improving the efficiency of problem discovery and problem solving, and improving work efficiency and decision-making quality.

[0027] In one embodiment, Figure 2 As shown, a data dynamic integration network is established based on the historical enterprise data and the department architecture, and the method also includes: obtaining multiple discrete departments based on the department architecture, and configuring multiple enterprise data integration nodes for the multiple discrete departments; building a stable communication link between the multiple enterprise data integration nodes based on the department architecture to obtain a standard dynamic integration architecture; obtaining a flow terminal data set by calling the historical enterprise data, and optimizing the standard dynamic integration architecture based on the flow terminal data set to obtain the data dynamic integration network.

[0028] Specifically, first, obtain and analyze the departmental structure of the target enterprise, identify departments and sub-departments at different levels, and the relationship and responsibilities between departments. Based on the analysis results, divide the target enterprise into multiple discrete departments, which are multiple independent departments or business units. Each department or business unit has its specific data processing requirements and data output. Then, for each discrete department, assign one or more enterprise data integration nodes, which will serve as the management and transmission center of department data and be responsible for collecting, storing and forwarding all data generated by the department.

[0029] Next, based on the departmental structure of the target enterprise, a stable communication link is established between each data integration node. The communication link is a wireless connection method to ensure that data can be efficiently and reliably transmitted between departments according to needs. When building the communication link, all data is encrypted during the transmission process. Through the construction of the communication link of the data integration node, a standard dynamic integration architecture is formed, which can support the integration, sharing and exchange of enterprise data, and can be flexibly adjusted and optimized as needed.

[0030] Finally, extract the data set of the final point of enterprise data flow from the historical enterprise data, that is, the end-of-flow data set. The data in the end-of-flow data set records the flow of data between various links and departments within the enterprise. Analyze the extracted end-of-flow data sets to analyze how these data sets flow within the target enterprise, including which departments the data has passed through and which processing steps the data has undergone. Use the end-of-flow data set as a reference to optimize the standard dynamic integration architecture, including adjusting the configuration of data integration nodes, optimizing the performance of communication links, etc., to improve the efficiency, accuracy and reliability of the data integration network, and then form an optimized data dynamic integration network, which can ensure more effective support for data integration and circulation, and improve the value and efficiency of data use.

[0031] Furthermore, a flow terminal data set is obtained by calling the historical enterprise data, and the standard dynamic integration architecture is optimized according to the flow terminal data set to obtain the data dynamic integration network. The method also includes: taking the multiple discrete departments as constraints to disassemble multiple data flow links in the flow terminal data set to obtain multiple data flow sequences; taking the multiple discrete departments as multiple topological nodes, using the multiple data flow sequences to perform topological connection to obtain a department topology network; performing connection feature analysis on the department topology network to obtain channel optimization reference information; using the channel optimization reference information to map new channels in the standard dynamic integration architecture to obtain the data dynamic integration network.

[0032] Specifically, the discrete departments of the enterprise are determined as the constraints of the analysis, and the multiple data flow links in the flow terminal data set are disassembled to analyze how the data flows between different departments, and a series of ordered data flow sequences are obtained, each of which represents the complete path of the data from the source to the destination. Each discrete department in the enterprise is regarded as a topological node, which represents the data processing center within the enterprise. The multiple data flow sequences obtained are used for topological connection, that is, the department nodes in the data flow sequence are connected to form a topological network that can reflect the data flow path, that is, the department topological network.

[0033] Then, the connection characteristics between nodes in the department topology network are studied, and key characteristics such as the strength, stability, and delay of the data flow are analyzed. Channel optimization reference information is formed based on the results of the connection characteristic analysis, and the channel optimization reference information is used to guide the subsequent optimization of the standard dynamic integration architecture.

[0034] Finally, according to the optimization reference information obtained from the connection feature analysis, the channel is mapped, added or adjusted in the standard dynamic integration architecture. After the mapping is completed, an optimized data dynamic integration network is obtained. The optimized data dynamic integration network not only reflects the data flow within the enterprise, but also optimizes according to historical data and actual needs, which can better support the enterprise's data management and improve the efficiency and reliability of data flow.

[0035] Furthermore, connection feature analysis is performed on the department topology network to obtain channel optimization reference information. The method also includes: presetting an out-degree connection frequency threshold and an in-degree connection frequency threshold; traversing the department topology network with the out-degree connection frequency threshold as a constraint to obtain first simplex communication information; traversing the department topology network with the in-degree connection frequency threshold as a constraint to obtain second simplex communication information; aggregating the first simplex communication information and the second simplex communication information to obtain channel optimization reference information, wherein the channel optimization reference information includes duplex communication reference information and simplex communication reference information.

[0036] Specifically, the preset out-degree connection frequency threshold refers to setting the connection frequency threshold of a node in the department topology network, that is, the minimum frequency of a department corresponding to a node sending data to other departments. Similarly, the in-degree connection frequency threshold refers to the minimum frequency of a department receiving data from other departments. The out-degree connection frequency threshold and the in-degree connection frequency threshold are set according to the actual situation, actual needs and expert experience of the enterprise.

[0037] Furthermore, the out-degree connection frequency threshold is used as a constraint, and the department topology network is traversed to find the out-degree connection, that is, the node department and data link whose data transmission frequency exceeds the threshold. When the data transmission frequency exceeds the threshold, it means that the department connection is relatively stable and reliable, and the department information exceeding the preset out-degree connection frequency threshold is used as the first simplex communication information, and the first simplex communication information mainly focuses on the simplex communication situation in data transmission. Similarly, the in-degree connection frequency threshold is used as a constraint, and the department topology network is traversed to find the in-degree connection, that is, the node department and data link whose data reception frequency exceeds the threshold, and the department information exceeding the preset in-degree connection frequency threshold is used as the second simplex communication information, and the second simplex communication information mainly focuses on the simplex communication situation in data reception.

[0038] Finally, the first simplex communication information and the second simplex communication information are aggregated to obtain channel optimization reference information including duplex communication reference information and simplex communication reference information, wherein the duplex communication reference information refers to communication departments and data links that are stable and reliable both in terms of sending (out-degree connection) and receiving (in-degree connection), and the simplex communication reference information refers to communication departments and data links that are stable and reliable only in terms of sending or receiving. Based on the department topology network and the preset threshold to obtain channel optimization reference information, it is possible to identify which departments are the key nodes of the data flow, as well as the main direction and strength of the data flow, and then through the optimization of the channel, the data transmission speed and reliability between departments can be improved, thereby improving the overall business operation efficiency.

[0039] In one embodiment, Figure 3 As shown, a data cross-department verification window is pre-constructed, including: constructing an associated department identification layer based on the NLP tool and the multiple discrete departments; constructing a data flow analysis layer using the multiple data flow links and the multiple data flow sequences as training data; extracting multiple sample comparison file groups from the multiple data flow links based on the multiple discrete departments, and using the multiple sample comparison file groups to train a pre-introduced data structure analyzer to obtain multiple file comparison branches; connecting the multiple file comparison branches in parallel to generate a data cross-department verification layer, wherein the multiple file comparison branches are mapped to the multiple discrete departments; cascading the associated department identification layer, the data flow analysis layer and the data cross-department verification layer, and introducing a data scheduling engine between the data flow analysis layer and the data cross-department verification layer to complete the construction of the data cross-department verification window.

[0040] Specifically, NLP tools use natural language processing technology to parse and understand text data, such as department names, relationships, and job descriptions within an enterprise. NLP tools are used to analyze text descriptions between multiple discrete departments in an enterprise, identify potential connections between them, and form an associated department identification layer that can identify and analyze the connections and dependencies between departments. Data flow sequence refers to the historical record of data flow, including specific sources, destinations, etc. Using a neural network model, multiple data flow links and multiple data flow sequences are used as training data to build a model that can analyze data flow conditions as a data flow analysis layer. The data flow analysis layer is used to analyze how data flows between different departments, including the starting point of the data, the specific time, the department of the route, and the destination, so as to better understand the patterns and laws of data flow.

[0041] Next, based on multiple discrete departments, multiple sample comparison file groups are extracted from multiple data flow links. These file groups contain samples of data flowing between different departments for comparison and verification. These sample comparison file groups are used to train the pre-introduced data structure analysis, and multiple branches specifically used for file comparison between different departments are obtained. Each branch corresponds to a discrete department. The data structure analyzer is a pre-introduced model used to analyze the structure and content of the file. All file comparison branches are connected in parallel to form a data cross-department verification layer, which can verify and compare the flow of data between different departments.

[0042] Finally, in cascading order, the output interface of the associated department identification layer is connected to the input interface of the data flow analysis layer, and then the output interface of the data flow analysis layer is connected to the input interface of the data cross-department verification layer to ensure that all interface connections are correct and data can flow smoothly between various levels. A data scheduling engine is introduced between the data flow analysis layer and the data cross-department verification layer. The data scheduling engine is responsible for coordinating and managing the flow and verification process of data to ensure the smooth operation of the entire system. Based on this, a complete data cross-department verification window is finally formed. The data cross-department verification window can automatically identify the relationship between departments, analyze the rationality of data flow, and verify the consistency and accuracy of cross-department data, improve the efficiency and accuracy of internal data management of the company, and provide strong support for the company's decision-making and business operations.

[0043] Furthermore, the newly added enterprise data enters the data cross-department verification window to perform data integration reliability verification to obtain verification results. The method also includes: using the associated department identification layer of the data cross-department verification window to perform data flow identification on the newly added enterprise data to obtain data flow analysis results; if the data flow analysis result is a single department, then according to the single department, the target data integration node is located in the data dynamic integration network; the data cross-department verification window uses the target data integration node and the newly added enterprise data as output data, and inputs them into the data dynamic integration network to perform data dynamic integration.

[0044] Specifically, the associated department identification layer in the data cross-department verification window is used to perform data flow identification on the newly added enterprise data, and compared with the known department association network to determine the data flow path within the target enterprise and the departments that may be involved, and obtain the data flow analysis results. According to the identification results, the data flow analysis results are obtained to determine whether the newly added data only involves a single department or crosses multiple departments. If the data flow analysis results show that the newly added data only involves a single department, that is, there is no need for cross-departmental flow, the next step is performed. According to the information of this single department, the corresponding target data integration node is found and confirmed in the data dynamic integration network. The data cross-department verification window will find the corresponding target data integration node and the newly added enterprise data as output data, and input the target data integration node and the newly added enterprise data as guidance into the data dynamic integration network. In the data dynamic integration network, the target data integration node receives the newly added enterprise data and performs data dynamic integration operations according to the preset rules and processes, ensuring that the newly added enterprise data performs effective data flow identification and data integration reliability verification in the data cross-department verification window, thereby improving the efficiency and quality of data integration. At the same time, it can automatically identify the departments where data flows, reducing the possibility of manual intervention and errors.

[0045] Furthermore, the associated department identification layer of the data cross-department verification window is used to perform data flow identification on the newly added enterprise data to obtain data flow analysis results. After that, the method also includes: if the data flow analysis result is a department set, the data flow analysis layer in the data cross-department verification window uses the data flow analysis result as an analysis constraint to analyze the newly added enterprise data to obtain a real-time data flow sequence and a real-time flow time series, wherein the real-time data flow sequence includes K real-time data flow departments and K data flow time nodes, wherein K is a positive integer greater than or equal to 2; the data scheduling engine interacts with the data dynamic integration network according to the real-time data flow sequence and the real-time flow time series to perform integrated data calls to obtain K historical integrated data; according to the K historical integrated data, K file comparison branches are mapped and activated in the data cross-department verification layer; the K file comparison branches are used to analyze the K historical integrated data and the newly added enterprise data to obtain the verification result.

[0046] Specifically, when the data flow analysis results show that the newly added data involves multiple departments and is a department set across multiple departments, the data flow analysis layer in the data cross-department verification window uses the data flow analysis results as constraints. Based on the department set constraints, the data flow analysis layer will further analyze the newly added enterprise data to obtain a real-time data flow sequence and a real-time flow time series. The real-time flow time series refers to the specific time length of the data in each flow department. The real-time data flow sequence includes K real-time data flow departments and K data flow time nodes, where K is a positive integer greater than or equal to 2, representing the different departments and corresponding time points through which the data flows.

[0047] Then, the data scheduling engine introduced between the data flow analysis layer and the data cross-department verification layer interacts with the data dynamic integration network according to the real-time data flow sequence and the real-time flow time series, and calls K historical integrated data corresponding to the real-time data flow sequence. The historical integrated data is data information related to the departments and time nodes in the real-time data flow sequence, which is used for comparison and verification with the new data.

[0048] Furthermore, based on the K historical integrated data obtained, K file comparison branches are mapped and activated in the data cross-department verification layer, where each file comparison branch corresponds to a historical integrated data for subsequent data comparison and analysis. Using these K file comparison branches, the K historical integrated data and the newly added enterprise data are compared and analyzed to obtain a verification result, which is a pass or fail mark or contains more detailed comparison difference information. Through the circulation relationship of the enterprise's newly added data, the accuracy of data across departments is verified, which can timely and accurately locate the deviation data and deviation department, ensuring that the newly added data has undergone strict quality control before entering the integrated network, while improving the efficiency of discovering and solving problems, improving work efficiency and decision-making quality.

[0049] Furthermore, the K file comparison branches are used to analyze the K historical integrated data and the newly added enterprise data to obtain the verification result. The method also includes: splitting the newly added enterprise data according to the K real-time data flow departments to obtain K real-time local newly added data; synchronizing the K real-time local newly added data and the K historical integrated data to the K file comparison branches to perform data structure consistency analysis to obtain K data structure analysis results; if the K data structure analysis results are all consistent, the verification result is passed; if the Mth data analysis result among the K data structure analysis results is inconsistent, locating the Mth data flow time node and the Mth real-time data flow department in the real-time data flow sequence based on the Mth data analysis result, wherein the Mth data analysis result is any one of the K data structure analysis results, and M is a positive integer less than or equal to K; according to the Mth data flow time node and the Mth real-time data flow department, backtracking and extracting abnormal enterprise data in the data dynamic integration network and marking abnormal departments.

[0050] Specifically, according to the K real-time data circulation departments, the newly added enterprise data is split into K real-time local newly added data. Each of the K real-time local newly added data corresponds to a real-time data circulation department and contains the data part related to the department. Then, the K real-time local newly added data and the K historical integrated data are synchronized to K file comparison branches, and data structure consistency analysis is performed on each file comparison branch to compare the structural differences between the real-time local newly added data and the historical integrated data. Based on the comparison results, multiple data structure analysis results are obtained, and each analysis result corresponds to the data consistency of a real-time data circulation department. The K data structure analysis results are judged. When the K structure analysis results are all consistent, it means that the new enterprise data is completely matched with the historical data in structure, and the verification result is passed, and there is no abnormal data or abnormal department.

[0051] When it is determined that the Mth data analysis result in the data structure analysis results in K is inconsistent, it means that there is a problem that the structure of the newly added enterprise data does not match the historical data, and further processing is required. The Mth data analysis result is inconsistent, which means that the data has structural abnormalities in the Mth data, and M is a positive integer less than or equal to K. For the problem of inconsistent Mth data structure, based on the inconsistent data analysis results, the corresponding Mth data flow time node and the Mth real-time data flow department are found in the real-time data flow sequence, that is, the data flow time node when the Mth data is abnormal and the department to which the Mth data belongs. Finally, the abnormal enterprise data related to the Mth data flow time node and the Mth real-time data flow department are retroactively extracted in the data dynamic integration network, and the department that caused the problem is marked. The abnormal enterprise data is data that contains errors or structural changes that do not meet expectations. The marking of abnormal departments helps enterprises find and repair problematic data. After determining the department to which the abnormal data belongs, the corresponding discrete data integration points are dynamically integrated and updated based on the circulation relationship, ensuring the consistency and accuracy of the data in the entire network, effectively handling the verification process of new enterprise data, and promptly discovering and handling abnormal situations, which helps to improve data quality and reduce errors and inconsistencies, thereby optimizing the entire data integration management process of the target enterprise and providing more reliable data support for decision-making.

[0052] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0053] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A method for dynamic integration of all enterprise data based on intelligent algorithms, characterized in that: The method comprises: Interactively obtain historical enterprise data and departmental structure of the target enterprise; Establishing a data dynamic integration network based on the historical enterprise data and the department structure; Pre-constructing a data cross-department verification window, and connecting the output end of the data cross-department verification window to the enterprise data upload interface of the data dynamic integration network; Newly added enterprise data enters the data cross-department verification window to perform data integration reliability verification to obtain verification results; If the verification result is passed, the newly added enterprise data is synchronized to the data dynamic integration network to perform data dynamic integration; If the verification result is not passed, then the abnormal enterprise data is back-traced and extracted in the data dynamic integration network according to the verification result, and the abnormal department is marked.

2. The method according to claim 1, characterized in that Establishing a data dynamic integration network based on the historical enterprise data and the department structure, the method further includes: Decomposing the department structure to obtain multiple discrete departments, and configuring multiple enterprise data integration nodes for the multiple discrete departments; Building a stable communication link between the plurality of enterprise data integration nodes according to the department architecture to obtain a standard dynamic integration architecture; The end-of-circulation data set is obtained by calling the historical enterprise data, and the standard dynamic integration architecture is optimized according to the end-of-circulation data set to obtain the data dynamic integration network.

3. The method according to claim 2, characterized in that The method further comprises: obtaining a data set of end-of-flow data from the historical enterprise data, optimizing the standard dynamic integration architecture according to the data set of end-of-flow data, and obtaining the data dynamic integration network. Taking the plurality of discrete departments as constraints, disassembling a plurality of data flow links in the flow terminal data set to obtain a plurality of data flow sequences; The plurality of discrete departments are taken as a plurality of topological nodes, and the plurality of data flow sequences are used for topological connection to obtain a departmental topological network; Performing connection characteristic analysis on the department topology network to obtain channel optimization reference information; The channel optimization reference information is used to map a newly added channel in the standard dynamic integration architecture to obtain the data dynamic integration network.

4. The method according to claim 3, characterized in that Performing connection feature analysis on the department topology network to obtain channel optimization reference information, the method further comprising: Preset out-degree connection frequency threshold and in-degree connection frequency threshold; Traversing the department topology network with the out-degree connection frequency threshold as a constraint to obtain first simplex communication information; Traversing the department topology network with the in-degree connection frequency threshold as a constraint to obtain second simplex communication information; The first simplex communication information and the second simplex communication information are aggregated to obtain channel optimization reference information, wherein the channel optimization reference information includes duplex communication reference information and simplex communication reference information.

5. The method according to claim 3, characterized in that Pre-building a data cross-department verification window, the method further includes: Building a related department identification layer based on the NLP tool and the plurality of discrete departments; The plurality of data flow links and the plurality of data flow sequences are used as training data to construct and generate a data flow analysis layer; Extracting multiple sample comparison file groups from the multiple data flow links based on the multiple discrete departments, and using the multiple sample comparison file groups to train a pre-introduced data structure analyzer to obtain multiple file comparison branches; Connecting the plurality of file comparison branches in parallel to generate a data cross-department verification layer, wherein the plurality of file comparison branches are mapped to the plurality of discrete departments; The associated department identification layer, the data flow analysis layer and the data cross-department verification layer are cascaded, and a data scheduling engine is introduced between the data flow analysis layer and the data cross-department verification layer to complete the construction of the data cross-department verification window.

6. The method according to claim 5, characterized in that The newly added enterprise data enters the data cross-department verification window to perform data integration reliability verification to obtain a verification result, and the method further includes: Using the associated department identification layer of the data cross-department verification window to perform data flow identification on the newly added enterprise data to obtain data flow analysis results; If the data flow analysis result is a single department, then locate the target data integration node in the data dynamic integration network according to the single department; The data cross-department verification window uses the target data integration node and the newly added enterprise data as output data, and inputs them into the data dynamic integration network to perform dynamic data integration.

7. The method according to claim 6, characterized in that The associated department identification layer of the data cross-department verification window is used to perform data flow identification on the newly added enterprise data to obtain data flow analysis results. After that, the method further includes: If the data flow analysis result is a department set, the data flow analysis layer in the data cross-department verification window uses the data flow analysis result as an analysis constraint, analyzes the newly added enterprise data to obtain a real-time data flow sequence and a real-time flow time sequence, wherein the real-time data flow sequence includes K real-time data flow departments and K data flow time nodes, wherein K is a positive integer greater than or equal to 2; The data scheduling engine interacts with the data dynamic integration network according to the real-time data flow sequence and the real-time flow time sequence to perform integrated data calls and obtain K historical integrated data; Mapping and activating K file comparison branches at the data cross-department verification layer according to the K historical integrated data; The K file comparison branches are used to analyze the K historical integration data and the newly added enterprise data to obtain the verification result.

8. The method according to claim 7, characterized in that The K file comparison branches are used to analyze the K historical integrated data and the newly added enterprise data to obtain the verification result, and the method further includes: Splitting the newly added enterprise data according to the K real-time data flow departments to obtain K real-time local newly added data; Synchronize the K real-time local newly added data and the K historical integrated data to the K file comparison branches to perform data structure consistency analysis to obtain K data structure analysis results; If the K data structure analysis results are all consistent, the verification result is passed; If the Mth data analysis result among the K data structure analysis results is inconsistent, locating the Mth data flow time node and the Mth real-time data flow department in the real-time data flow sequence based on the Mth data analysis result, wherein the Mth data analysis result is any one of the K data structure analysis results, and M is a positive integer less than or equal to K; According to the Mth data flow time node and the Mth real-time data flow department, the data dynamic integration network is retrospectively extracted with abnormal enterprise data and abnormal departments are marked.

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