Engineering Data Bilateral Authentication Method and System Based on Interface Fusion Technology

Abstract

The present invention discloses an engineering data bilateral authentication method and system based on interface fusion technology, including the following steps: S1. Perform interface management on the audit platform and configure the native data approval link and the twin data approval link in combination with the business type; S2. Configure a first authentication policy for the native data approval link and a second authentication policy for the twin data approval link; S3. Obtain the abnormal approval nodes of the native data approval link according to the approval results in combination with the triggering conditions of the threshold trigger mechanism, and determine the target twin abnormal approval nodes in the twin data approval link according to the abnormal approval nodes and the second authentication policy; S4. Determine the correlation degree values of each approval node in the twin data approval link according to the target twin abnormal approval nodes; determine the abnormal approval node troubleshooting strategy according to the correlation degree values. The solution analyzes and processes the interface data, and fully considers the correlation between approval projects, significantly improving the comprehensiveness and accuracy of abnormal troubleshooting.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and specifically, to an engineering data bilateral authentication method and system based on interface fusion technology. Background Art

[0002] With the continuous expansion of the scale and the improvement of the complexity of engineering projects, the approval of engineering approval data, as a key link to ensure project compliance, effective utilization of funds, and engineering quality, has become increasingly prominent. However, in the current management of engineering approval data, engineering approval data involves data in multiple fields, including finance, contracts, material procurement, construction progress, etc. These data are scattered in different systems with diverse formats. Traditional methods for approving engineering approval data often struggle to effectively integrate this data. Moreover, due to the lag of approval standards and the neglect of the correlation between approval items, it is difficult for approval personnel to quickly and accurately locate abnormal nodes when faced with a large amount of data, and they are even less able to effectively formulate targeted investigation strategies. This not only increases the difficulty of the approval work but also reduces the credibility of the approval results.

[0003] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present application, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0004] The object of the present invention is to solve the problem that the lack of analysis of the source and correlation of engineering approval data leads to the lack of correctness and comprehensiveness of the data approval results during engineering data approval. An engineering data bilateral authentication method and system based on interface fusion technology are proposed. By managing the interfaces of the audit platform and configuring the native data approval link and the twin data approval link according to the business type, after configuring the authentication strategy for the two data links and initially identifying the abnormal approval nodes in the native data approval link, further obtain the target twin abnormal approval nodes in the twin data approval link, and determine the final abnormal investigation strategy based on the correlation between the target twin abnormal approval nodes and the abnormal approval nodes. The solution significantly improves the comprehensiveness and accuracy of abnormal investigation and enhances the credibility of the approval results by analyzing and processing the interface data and fully considering the correlation between approval items.

[0005] In a first aspect, a technical solution provided in an embodiment of the present invention is an engineering data bilateral authentication method based on interface fusion technology, including the following steps:

[0006] S1. Perform interface management on the audit platform to obtain standard data tuples, and configure the native data approval link and the twin data approval link according to the business type of the standard data tuples;

[0007] S2. Configure the first authentication policy for the native data approval link according to the first data interface; configure the second authentication policy for the twin data approval link according to the second data interface and the first authentication policy.

[0008] S3. Determine the approval result of the native data approval link according to the first authentication policy. When the approval result meets the triggering condition of the threshold trigger mechanism, obtain the abnormal approval node of the native data approval link, and determine the target twin abnormal approval node in the twin data approval link according to the abnormal approval node and the second authentication policy.

[0009] S4. Determine the correlation degree value of each approval node in the twin data approval link according to the target twin abnormal approval node; determine the abnormal approval node troubleshooting strategy according to the correlation degree value.

[0010] In this solution, by managing the interfaces of the audit platform, standard data tuples are obtained, and the native data approval link and the twin data approval link are flexibly configured according to the business type, which not only ensures the standardized processing of data but also provides adaptability for different business scenarios. Further, the first authentication policy and the second authentication policy are respectively configured for the native data approval link and the twin data approval link, forming a two-layer authentication mechanism, effectively improving the security and accuracy of approval data troubleshooting. Further, when the approval result of the native data approval link triggers the threshold mechanism, the abnormal approval node can be automatically identified, and the corresponding target twin abnormal approval node can be located in the twin data approval link, realizing the rapid response and accurate positioning of abnormal situations. By calculating the correlation degree value of each approval node in the twin data approval link, the influence range of the abnormal approval node can be analyzed more comprehensively, and then a more effective abnormal approval node troubleshooting strategy can be formulated. The solution realizes standardized processing, two-layer security verification, intelligent exception handling, and abnormal security troubleshooting strategy in engineering approval data through interface integration and bilateral authentication technology, significantly improving the efficiency, security, and intelligent level of approval data processing.

[0011] Preferably, the interfaces of the audit platform are managed to obtain standard data tuples, and the native data approval link and the twin data approval link are configured according to the business type of the standard data tuples, including the following steps:

[0012] S11. The audit platform extracts the source features and content features of the interface data, takes the source features as the Key values of the standard data tuples, and the content features as the Value values of the standard data tuples.

[0013] S12. Determine the business type according to the Key value of the standard data tuple, and match the corresponding approval nodes and approval paths for the business type; construct the native data approval link and the twin data approval link of the corresponding interface data according to the approval nodes, approval paths, and Value values.

[0014] In this solution, the audit platform can extract the source features and content features of the interface data, and use them as the Key value and Value value of the standard data tuple respectively. This step realizes the standardization processing of the data, providing a basis for subsequent data processing and approval; according to the Key value of the standard data tuple, the audit platform can accurately identify the business type and match the corresponding approval nodes and approval paths for each business type, ensuring the close association between the approval link and the business logic, and improving the accuracy and pertinence of the approval; based on the approval nodes, approval paths and Value value, the audit platform can construct the native data approval link and the twin data approval link corresponding to the interface data; realizing the dual-track parallel operation of the approval link, not only retaining the approval track of the original data, but also creating twin data for comparison approval, enhancing the reliability and security of the approval; in summary, through steps such as feature extraction, business type identification and matching, and approval link construction, the solution realizes the refined management and dual-track parallel approval of the interface data, improves the accuracy and pertinence of the approval, enhances the reliability and security of the approval, and provides strong support for the enterprise's data management.

[0015] Preferably, the interface data includes data to be approved and approval specification data. Configuring the first authentication policy for the native data approval link according to the first data interface includes the following steps:

[0016] S201. Configure the first data interface for the data to be approved, and extract the Value-1 value of the data to be approved; the Value-1 value includes at least the file source address and the initial approval specification;

[0017] S202. Match the initial approval specification to the corresponding approval nodes in the native data approval link in sequence to obtain the first authentication policy.

[0018] In this solution, through steps such as configuring a data interface for the data to be approved, extracting key information, and matching the approval specification to the approval nodes, the configuration of the first authentication policy for the native data approval link is realized; not only improving the accuracy and standardization of the approval, but also providing strong support for the enterprise's data security management.

[0019] Preferably, configuring the second authentication policy for the twin data approval link according to the second data interface and the first authentication policy includes the following steps:

[0020] S211. Configure the second data interface for the approval specification data, and extract the Value-2 value of the approval specification data; the Value-2 value includes at least the file source address and the updated approval specification;

[0021] S212. The initial approval specifications of each approval node in the first authentication policy are sequentially replaced through updated approval specifications to obtain a second authentication policy belonging to the twin data approval link.

[0022] In this solution, a second data interface is configured for the approval specification data, and the Value-2 value of the approval specification data is extracted through this interface. The Value-2 value at least includes the file source address and the updated approval specification, which provides a necessary information basis for the subsequent update of the authentication policy; through the extracted updated approval specifications, the initial approval specifications of each approval node in the first authentication policy are sequentially replaced, thereby obtaining a second authentication policy belonging to the twin data approval link; realizing the flexible update of the approval policy, ensuring that the twin data approval link can be adjusted in a timely manner following the changes in the approval specifications, and improving the adaptability and accuracy of the approval.

[0023] Preferably, the first authentication policy and the second authentication policy at least include file source verification and approval verification;

[0024] The file source verification at least includes file compliance verification and file integrity verification;

[0025] The approval verification at least includes approval qualification verification, approval data verification, and approval logic verification.

[0026] In this solution, when the data to be approved and the approval specification data are connected to the audit platform through their respective data interfaces, first, the Value-1 value of the data to be approved is extracted, including the file source address and the initial approval specification, to configure a first authentication policy for the native data approval link. This policy at least covers two major aspects: file source verification and approval verification, ensuring the compliance and integrity of the file itself, as well as the accuracy of the qualifications, data, and logic in the approval process. Subsequently, by extracting the Value-2 value of the approval specification data, including the file source address and the updated approval specification, this technical solution further configures a second authentication policy for the twin data approval link; this policy also includes file source verification and approval verification, but it is configured based on the updated approval specifications, ensuring that the twin data approval link can be adjusted in a timely manner following the changes in the approval specifications; specifically, the file source verification ensures the compliance and integrity of the file to be approved, preventing illegal or damaged files from entering the approval process; while the approval verification comprehensively checks the qualifications of the approval personnel, the accuracy of the approval data, and the rationality of the approval logic, ensuring the fairness and accuracy of the approval process, and providing a more reliable and powerful guarantee for the enterprise's data security management.

[0027] Preferably, the approval result of the native data approval link is determined according to the first authentication policy. When the approval result meets the triggering condition of the threshold triggering mechanism, the abnormal approval node of the native data approval link is obtained; the method includes the following steps:

[0028] S301. Determine the approval result type according to the verification items in the first authentication policy. The approval result type includes the file source verification result and the approval verification result; set the first threshold triggering mechanism belonging to the file source verification result and the second threshold triggering mechanism belonging to the approval verification result;

[0029] S302. When the file source verification result meets the triggering condition of the first threshold triggering mechanism, the audit platform performs interface management to regenerate the standard data tuple and execute S1;

[0030] S303. When the approval verification result meets the triggering condition of the second threshold triggering mechanism, obtain the abnormal approval node of the native data approval link.

[0031] In this solution, when the native data approval link completes the approval according to the first authentication policy, a corresponding approval result will be generated; the solution first determines the type of the approval result according to the verification items in the first authentication policy, which includes the file source verification result and the approval verification result. At the same time, the first threshold triggering mechanism and the second threshold triggering mechanism are set for these two results respectively, so as to respond in time when the approval result is abnormal; if the file source verification result meets the triggering condition of the first threshold triggering mechanism, it means that there may be problems with the compliance or integrity of the approved file; at this time, the audit platform will perform interface management, regenerate the standard data tuple, and execute the approval process from the beginning (that is, execute S1) to ensure the accuracy and reliability of the approval; if the approval verification result meets the triggering condition of the second threshold triggering mechanism, it means that there may be problems such as non-compliance of qualifications, data errors, or unreasonable logic during the approval process. At this time, this technical solution will obtain the abnormal approval node in the native data approval link for subsequent targeted investigation and handling; in summary, this technical solution realizes the comprehensive monitoring and exception handling of the native data approval link by setting the threshold triggering mechanism and processing different types of approval results; it not only improves the accuracy and reliability of the approval, but also provides a more perfect guarantee for the enterprise's data security management; when the approval result is abnormal, it can respond in time and take corresponding handling measures to ensure the compliance and security of the data.

[0032] Preferably, the step of setting the first threshold triggering mechanism belonging to the file source verification result and the second threshold triggering mechanism belonging to the approval verification result; includes the following steps:

[0033] Determine the file source verification items based on the file source address extracted from the interface data and the list of approval documents; set the file compliance verification scope and file integrity verification scope corresponding to the file source verification items; use the non-compliance with the file compliance verification scope or / and the file integrity verification scope as the triggering condition of the first threshold triggering mechanism;

[0034] Determine the approval verification items based on the approval qualifications, approval data, and approval logic extracted from the interface data; set the file approval qualification verification scope, approval data verification scope, and approval logic verification sequence corresponding to the approval verification items; use the non-compliance with the file approval qualification verification scope or / and the approval data verification scope or / and the approval logic verification sequence as the triggering condition of the second threshold triggering mechanism.

[0035] In this solution, by setting the first threshold triggering mechanism and the second threshold triggering mechanism in detail, the accurate monitoring and management of the approval results of the native data approval link are realized. When the approval result is abnormal, it can respond in time and take corresponding handling measures, so as to ensure the compliance and security of the data.

[0036] Preferably, determining the twin abnormal approval nodes in the twin data approval link according to the abnormal approval nodes and the second authentication strategy; includes the following steps:

[0037] S311. Determine the primary twin abnormal approval nodes according to the node position of the abnormal approval nodes in the twin data approval link;

[0038] S312. Re-verify the primary twin abnormal approval nodes in combination with the approval verification items in the second authentication strategy to determine the secondary twin abnormal approval nodes; use the twin abnormal approval nodes as the target twin abnormal approval nodes.

[0039] In this solution, first, according to the node position of the exception approval node in the native data approval link, the corresponding primary twin exception approval node can be determined in the twin data approval link. By using the mapping relationship between the native data approval link and the twin data approval link, it is ensured that the node corresponding to the exception node in the native data approval link can be accurately found in the twin data approval link. Secondly, in order to further confirm whether the primary twin exception approval node actually has an exception, the solution re-verifies it in combination with the approval verification items in the second authentication strategy. By comparing the verification items in the second authentication strategy with the relevant data of the primary twin exception approval node, it is verified whether the node has an exception. Finally, after re-verification, if it is determined that the primary twin exception approval node actually has an exception, then this node will be determined as the secondary twin exception approval node and used as the target twin exception approval node for subsequent processing. To sum up, this technical solution searches for twin exception approval nodes in the twin data approval link through the mapping relationship and combines the second authentication strategy for re-verification, realizing the accurate search and confirmation of exception nodes in the twin data approval link; not only improving the accuracy and reliability of approval, but also providing more comprehensive protection for the enterprise's data security management. When an exception occurs in the native data approval link, the corresponding exception node can be found and processed in the twin data approval link in a timely manner, thus ensuring the compliance and security of the data.

[0040] Preferably, the relevance values of each approval node in the twin data approval link are determined according to the target twin exception approval node; and an exception approval node troubleshooting strategy is determined according to the relevance values; the method includes the following steps:

[0041] S41. Obtain the approval verification items corresponding to the target twin exception approval node; the approval verification items include the file approval qualification verification scope, the approval data verification scope, and the approval logic verification order;

[0042] S42. Construct triple data according to the numerical characteristics in the approval verification items; calculate the sum of the relevance between each target twin exception approval node and the primary twin exception approval node in the twin data approval link according to the triple data;

[0043] S43. Sort the target twin exception approval nodes from largest to smallest according to the sum of the relevance to obtain an exception approval node troubleshooting list, and execute the exception approval node troubleshooting strategy according to the exception approval node troubleshooting list in combination with the corresponding approval verification items.

[0044] In this solution, first, the technical solution will obtain the approval verification items corresponding to the target twin abnormal approval nodes. These verification items include the verification scope of document approval qualifications, the verification scope of approval data, and the verification logic verification order. These verification items are important bases for judging whether the approval nodes are normal. Next, according to the numerical characteristics in the approval verification items, the technical solution will construct triple data and use this data to calculate the sum of the association degrees between each target twin abnormal approval node and the primary twin abnormal approval node in the twin data approval link. The sum of the association degrees reflects the degree of association between each approval node and the abnormal node. The higher the association degree, the closer the relationship between the node and the abnormal node. Finally, the target twin abnormal approval nodes are sorted from largest to smallest according to the sum of the association degrees to obtain an abnormal approval node investigation table. This investigation table provides the approval personnel with the priority order for investigating abnormal nodes, enabling them to conduct more targeted investigation work. At the same time, in combination with the corresponding approval verification items, the approval personnel can execute the abnormal approval node investigation strategy to investigate and process the abnormal nodes one by one. To sum up, this technical solution realizes the efficient investigation and processing of abnormal nodes by calculating the association degree values of each approval node in the twin data approval link and determining the abnormal approval node investigation strategy.

[0045] Preferably, constructing triple data according to the numerical characteristics in the approval verification items; calculating the sum of the association degrees between each target twin abnormal approval node and the primary twin abnormal approval node in the twin data approval link according to the triple data; includes the following steps:

[0046] S421. Extract the numerical characteristics in the approval verification items to obtain the verification item values. Among them, the numerical characteristics include numerical features and non-numerical features. The verification item values corresponding to the numerical features include the verification scope of document approval qualifications and the verification scope of approval data. The verification item value corresponding to the non-numerical feature is the approval logic verification order. Specifically, when the approval logic verification order corresponding to the approval node is correct, it is "1", and when it is wrong, it is "0".

[0047] S422. Construct a triple data set M corresponding to the target twin abnormal approval node and a triple data set N corresponding to the primary twin abnormal approval node according to the approval node, the approval verification item, and the verification item value respectively.

[0048] S423. Calculate the sum of the association degrees between each element in the verification item triple data set M and all elements in the triple data set N according to the correlation formula.

[0049] In this solution, first, the technical solution extracts the numerical characteristics in the approval verification items to obtain verification item values. These numerical characteristics include numerical features and non-numerical features. The verification item values corresponding to numerical features, such as the approval qualification verification range and the approval data verification range of documents, can be quantified by specific numerical values. The verification item value corresponding to the non-numerical feature is the approval logic verification order, which indicates whether the verification order of approval nodes is correct logically. If it is correct, it is "1"; if it is incorrect, it is "0". Next, according to the approval nodes, approval verification items, and verification item values, the technical solution constructs the triple data set M corresponding to the target twin abnormal approval node and the triple data set N corresponding to the primary twin abnormal approval node respectively. These two sets respectively contain all relevant information of the target twin abnormal approval node and the primary twin abnormal approval node. Finally, the solution uses a relevance calculation formula (which can be the Pearson correlation coefficient method and the Spearman rank correlation coefficient method) to calculate the sum of the correlation degrees between each element in the verification item triple data set M and all elements in the triple data set N. This sum of correlation degrees reflects the degree of association between the target twin abnormal approval node and the primary twin abnormal approval node. By comparing the sums of correlation degrees of different target twin abnormal approval nodes, the approval personnel can conduct more targeted investigation work and give priority to processing the nodes with higher correlation degrees. To sum up, by constructing triple data and calculating the sum of correlation degrees, the accurate quantification of the degree of association between abnormal nodes and primary abnormal nodes in the twin data approval link is realized, which not only improves the efficiency and accuracy of approval, but also provides more refined guarantee for the enterprise's data security management.

[0050] In a second aspect, a technical solution provided in an embodiment of the present invention is an engineering data bilateral authentication system, including:

[0051] An approval link construction module: manages the interfaces of the audit platform to obtain standard data tuples, and configures the native data approval link and the twin data approval link according to the business types of the standard data tuples;

[0052] An authentication module: configures a first authentication policy for the native data approval link according to the first data interface; configures a second authentication policy for the twin data approval link according to the second data interface and the first authentication policy;

[0053] An abnormal determination module: determines the approval result of the native data approval link according to the first authentication policy. When the approval result meets the triggering conditions of the threshold trigger mechanism, it obtains the abnormal approval nodes of the native data approval link, and determines the target twin abnormal approval nodes in the twin data approval link according to the abnormal approval nodes and the second authentication policy;

[0054] Execution module: Determine the correlation values of each approval node in the twin data approval link according to the target twin exception approval node; Determine the exception approval node troubleshooting strategy according to the correlation values.

[0055] In a third aspect, a technical solution provided in an embodiment of the present invention is an electronic device, including a memory and a processor. When the processor calls the computer program stored in the memory, the steps of the engineering data bilateral authentication method based on the interface fusion technology are implemented.

[0056] In a fourth aspect, a technical solution provided in an embodiment of the present invention is a storage medium. When the computer executable instructions stored in the storage medium are loaded and executed by a processor, the steps of the engineering data bilateral authentication method based on the interface fusion technology are implemented.

[0057] The present invention at least has the following substantial technical effects:

[0058] The following are three substantial technical effects selected and elaborated in detail from the above technical solutions:

[0059] (1) The solution obtains standard data tuples through interface management, and configures the native data approval link and the twin data approval link accordingly, realizing dual-track parallel approval of data; When the approval result of the native data approval link triggers the threshold mechanism, it can automatically identify the abnormal approval node, and locate the corresponding target twin abnormal approval node through the mapping relationship in the twin data approval link; Through dual-track parallel approval, not only the approval track of the original data is retained, but also the reliability and security of the approval are enhanced through twin data.

[0060] (2) The solution configures the first authentication strategy for the native data approval link, including file source verification and approval verification; Subsequently, according to the updated approval specifications, the second authentication strategy is configured for the twin data approval link. This double-layer authentication mechanism ensures the comprehensiveness and security of the approval process. At the same time, the updated approval specifications are extracted through the data interface to realize the flexible update of the authentication strategy; The double-layer authentication mechanism effectively improves the security and accuracy of the approval data, preventing illegal or non-compliant data from entering the approval process; The flexible authentication strategy update mechanism ensures that the approval specifications can be adjusted in a timely manner as the business requirements change, improving the adaptability and flexibility of the approval.

[0061] (3) The solution analyzes the correlation degree between each approval node and the target twin abnormal approval node in the twin data approval link by constructing triple data and calculating the sum of correlation degrees; generates an abnormal approval node investigation table according to the sorting of correlation degree values, providing a scientific investigation order for the approval personnel; the correlation degree analysis technology can comprehensively evaluate the influence range of abnormal nodes, providing a scientific basis for formulating an efficient abnormal investigation strategy. By giving priority to processing nodes with higher correlation degrees, the approval personnel can locate and solve problems more quickly, improving the efficiency and accuracy of the investigation work.

[0062] The above invention content is only an overview of the technical solution of the present invention. In order to be able to more clearly understand the technical means of the present invention, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the following specifically illustrates the specific implementation manners of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, purposes and advantages of the present invention will become more obvious. The drawings are only used for the purpose of showing the preferred embodiments, and are not considered as limiting the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components.

[0064] Figure 1 It is a flowchart of the engineering data bilateral authentication method based on interface fusion technology of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] To make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific implementation manners described here are only the best embodiments of the present invention, which are only used to explain the present invention and do not limit the protection scope of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0066] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations (or steps) can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operations are completed, but there can also be additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0067] Embodiment 1:

[0068] AsFigure 1 As shown in Figure 1 , a technical solution is an engineering data bilateral authentication method based on interface fusion technology, including the following steps:

[0069] S1. Perform interface management on the audit platform to obtain standard data tuples, and configure native data approval links and twin data approval links according to the business types of the standard data tuples.

[0070] Specifically, S1 includes the following steps:

[0071] S11. The audit platform extracts the source features and content features of the interface data, uses the source features as the Key values of the standard data tuples, and the content features as the Value values of the standard data tuples;

[0072] S12. Determine the business type according to the Key values of the standard data tuples, match the corresponding approval nodes and approval paths for the business type; construct native data approval links and twin data approval links for the corresponding interface data according to the approval nodes, approval paths, and Value values.

[0073] In this embodiment, it can be understood that in the power engineering approval process, the audit platform extracts interface data from various systems related to power engineering projects. These data may come from multiple data sources such as project management systems, financial systems, and material management systems. Source characteristics (Key values) may include project ID, data type identifier (such as contract information, material purchase order, etc.), name of the data source system, etc.; content characteristics (Value values) contain specific business data, such as contract amount, material purchase details, invoice information, etc.; for example, the source characteristics (Key values) of an interface data item may be "Project ID: P12345, Data type: Contract information, Source system: Project management system", and the content characteristics (Value values) are the specific contract text and its related attributes (such as contract number, signing date, amount, etc.). The audit platform determines the business type to which the data item belongs based on the extracted source characteristics (Key values). In the field of power engineering, business types may include multiple stages such as project establishment, contract signing, material procurement, payment of construction progress funds, and final accounts settlement; the approval personnel and approval rules corresponding to each stage are different; assuming that the data item we are currently processing is of the "material procurement" business type, the audit platform will match the corresponding approval nodes and approval paths for this business type. Approval nodes may include "Purchase application approval", "Purchase contract approval", "Warehouse receipt and inspection approval", etc., and the approval path is a combination of these nodes arranged in the order of business logic. Based on the approval nodes, approval paths, and content characteristics (Value values), the audit platform can construct the corresponding native data approval link B1 and twin data approval link D1. Native data approval link B1; Example B1 = [b1, b2, b3] = ["Purchase application approval node", "Purchase contract approval node", "Warehouse receipt and inspection approval node"], where each node (b1, b2, b3) corresponds to an approval link in the material procurement business, and the audit platform will approve the native data in this order; Twin data approval link D1; Example D1 = [d1, d2, d3] = ["Twin purchase application approval node", "Twin purchase contract approval node", "Twin warehouse receipt and inspection approval node"], corresponding to the native data approval link, the twin data approval link (D1) contains the same approval nodes, but these nodes process twin data (i.e., data corresponding to the native data but possibly processed or simulated). The purpose of doing this is to enhance the reliability and accuracy of the approval by comparing the approval results of the native data and the twin data. It should be noted that in actual applications, each approval node (whether native or twin) will be associated with specific approval rules, inspection items, and possible data sources, which will be customized according to specific business scenarios and requirements.

[0074] S2. Configure a first authentication policy for the native data approval link according to the first data interface; configure a second authentication policy for the twin data approval link according to the second data interface and the first authentication policy.

[0075] Specifically, S2 includes the following steps:

[0076] S201. Configure a first data interface for the data to be approved, and extract the Value-1 value of the data to be approved; the Value-1 value includes at least the file source address and the initial approval specification.

[0077] S202. Match the initial approval specification to the corresponding approval node in the native data approval link in sequence to obtain the first authentication policy.

[0078] Further, configuring the second authentication policy for the twin data approval link according to the second data interface and the first authentication policy includes the following steps:

[0079] S211. Configure a second data interface for the approval specification data, and extract the Value-2 value of the approval specification data; the Value-2 value includes at least the file source address and the updated approval specification.

[0080] S212. Replace the initial approval specification of each approval node in the first authentication policy with the updated approval specification in sequence to obtain the second authentication policy belonging to the twin data approval link.

[0081] Specifically, the first authentication policy and the second authentication policy include at least file source verification and approval verification.

[0082] Specifically, the file source verification includes at least file compliance verification and file integrity verification.

[0083] Specifically, the approval verification includes at least approval qualification verification, approval data verification, and approval logic verification.

[0084] In this embodiment, first, the audit platform configures a first data interface for the data to be approved; the first data interface is responsible for data interaction with each system related to the power engineering project (such as the project management system, the financial system, etc.) and extracts the data to be approved. Through the first data interface, the audit platform extracts the Value-1 value of the data to be approved. In power engineering approval, the Value-1 value may include the file source address (such as the URL of a certain project management system) and the initial approval specification (such as the initial review requirements for the procurement contract of a power engineering project); the audit platform sequentially matches the extracted initial approval specifications to the corresponding approval nodes in the native data approval link B1. For example, for the "procurement contract approval node", the audit platform will use the initial review requirements of the procurement contract as the authentication strategy for this node; the first strategy includes file compliance verification (checking whether the contract complies with relevant laws, regulations and company internal regulations), file integrity verification (ensuring that the contract content is complete), approval qualification verification (verifying whether the reviewers have the corresponding qualifications), approval data verification (checking whether data such as contract amount and supplier information is accurate) and approval logic verification (ensuring that the logical relationship between contract terms is reasonable and error-free).

[0085] Furthermore, the audit platform configures a second data interface for the approval specification data; the second data interface is used to obtain the latest approval specification update information, which may come from company policy updates, changes in laws and regulations, etc. Through the second data interface, the audit platform extracts the Value-2 value of the approval specification data. The Value-2 value includes the file source address (the same as or related to the file source address in the Value-1 value) and the updated approval specification (i.e., the latest review requirements and standards); the audit platform uses the extracted updated approval specifications to sequentially replace the initial approval specifications of each approval node in the first authentication strategy. In this way, each node in the twin data approval link (D1) will apply the latest review requirements. For example, if the latest approval specifications have more stringent requirements for some terms of the procurement contract, these new requirements will be applied to the "procurement contract twin approval node" as the second authentication strategy for this node.

[0086] It is understandable that due to the irregular update of approval specification data and the problem of a relatively large time span that may exist in the data approval process, there may be a situation where the approval process is not completed but the approval specifications of some nodes have been updated. Therefore, in order to improve the comprehensiveness and accuracy of approval, after performing approval verification on the initial data approval link, if an abnormal node is found, the twin data approval link is updated and configured through the second data interface, and verified to determine the final target twin abnormal approval node, which can overcome the above problems. In the approval of power engineering, the file source verification ensures that all approved files come from trusted sources. The audit platform will check whether the source address of the file matches the systems or databases recognized within the company to prevent illegal or tampered files from entering the approval process. The audit platform will check whether the content of the approved file complies with the latest laws, regulations and internal company regulations. For example, a procurement contract must comply with the relevant provisions of the "Contract Law" and meet the internal requirements of the company for the procurement process. The audit platform will verify the integrity of the file to ensure that the file has not been tampered with or damaged during transmission or storage. This is crucial for ensuring the accuracy of the approval result. During the review process, the audit platform will verify whether the qualifications of the reviewers meet the requirements. For example, the personnel responsible for reviewing procurement contracts must have the corresponding financial or legal background knowledge and have undergone internal training and certification within the company, etc. At the same time, the audit platform will verify the key data in the approved file to ensure the accuracy and consistency of the data. In the approval of power engineering, this may include key data such as contract amount, quantity of materials, supplier information, etc. Finally, the audit platform will check whether the logical relationship within the approved file is reasonable. For example, in a procurement contract, the terms should be consistent and coherent, and there should be no self-contradictory situations. Through the above steps, the audit platform configures a comprehensive and flexible authentication strategy for the native data approval link and the twin data approval link in the field of power engineering, ensuring the compliance, accuracy and security of the approval process.

[0087] S3. Determine the approval result of the native data approval link according to the first authentication strategy. When the approval result meets the triggering conditions of the threshold trigger mechanism, obtain the abnormal approval node of the native data approval link, and determine the target twin abnormal approval node in the twin data approval link according to the abnormal approval node and the second authentication strategy.

[0088] Specifically, to determine the approval result of the native data approval link according to the first authentication strategy, when the approval result meets the triggering conditions of the threshold trigger mechanism, obtain the abnormal approval node of the native data approval link, including the following steps:

[0089] S301. Determine the approval result type according to the verification items in the first authentication policy. The approval result type includes the file source verification result and the approval verification result. Set the first threshold trigger mechanism belonging to the file source verification result and the second threshold trigger mechanism belonging to the approval verification result.

[0090] S302. When the file source verification result meets the trigger condition of the first threshold trigger mechanism, the audit platform performs interface management to regenerate the standard data tuple and execute S1.

[0091] S303. When the approval verification result meets the trigger condition of the second threshold trigger mechanism, obtain the abnormal approval nodes in the native data approval link.

[0092] Further, set the first threshold trigger mechanism belonging to the file source verification result and the second threshold trigger mechanism belonging to the approval verification result, including the following steps:

[0093] Determine the file source verification items according to the file source address and the approval file list extracted from the interface data. Set the file compliance verification range and the file integrity verification range corresponding to the file source verification items. Take not meeting the file compliance verification range or / and the file integrity verification range as the trigger condition of the first threshold trigger mechanism.

[0094] Determine the approval verification items according to the approval qualifications, approval data, and approval logic extracted from the interface data. Set the file approval qualification verification range, the approval data verification range, and the approval logic verification order corresponding to the approval verification items. Take not meeting the file approval qualification verification range or / and the approval data verification range or / and the approval logic verification order as the trigger condition of the second threshold trigger mechanism.

[0095] Specifically, the step of determining the twin abnormal approval nodes in the twin data approval link according to the abnormal approval nodes and the second authentication policy includes the following steps:

[0096] S311. Determine the primary twin abnormal approval nodes according to the node positions of the abnormal approval nodes in the twin data approval link.

[0097] S312. Re-verify the primary twin abnormal approval nodes in combination with the approval verification items in the second authentication policy to determine the secondary twin abnormal approval nodes. Take the twin abnormal approval nodes as the target twin abnormal approval nodes.

[0098] In this embodiment, the audit platform classifies the approval results into two categories: the verification results of the file source and the approval verification results according to the verification items in the first authentication policy; based on the file source address and the approval file list, the audit platform sets the verification scope of file compliance and the verification scope of file integrity; when the verification results of the file source do not meet these scopes, the first threshold mechanism is triggered. Based on the approval qualifications, approval data, and approval logic, the audit platform sets the corresponding verification scope and verification order. When the approval verification results do not meet these conditions, the second threshold mechanism is triggered; if the verification results of the file source meet the triggering conditions of the first threshold triggering mechanism, it indicates that there are serious problems with the file source or the file itself. At this time, the audit platform will perform interface management, regenerate the standard data tuple (similar to the process in S1), and execute the approval process from the beginning (i.e., execute S1). This step aims to ensure the reliability of the source of the approval data and prevent further processing of incorrect or invalid data.

[0099] Furthermore, when the approval verification results meet the triggering conditions of the second threshold triggering mechanism, the audit platform will identify and obtain the abnormal approval nodes in the native data approval link. These nodes are usually associated with specific approval problems, such as data errors, logical inconsistencies, or qualification discrepancies, etc.; according to the positions of the abnormal approval nodes in the native data approval link, the audit platform can find the corresponding nodes in the twin data approval link, that is, the first twin abnormal approval nodes; however, only mapping by position may not be sufficient to confirm the abnormalities in the twin data; therefore, the audit platform will combine the approval verification items in the second authentication policy (configured in S212) to re-verify the first twin abnormal approval nodes. This step ensures that the abnormal nodes in the twin data approval link are accurately judged based on the latest approval specifications; after re-verification, if it is confirmed that the first twin abnormal approval nodes do indeed have abnormalities, they will be regarded as the target twin abnormal approval nodes.

[0100] It can be understood that: the audit platform first conducts a comprehensive verification of the file source and approval verification of the native data according to the first authentication policy; when the verification results trigger the threshold triggering mechanism, the audit platform identifies the potential problem areas, that is, the abnormal approval nodes; in order to confirm the universality and severity of the abnormalities, the audit platform re-verifies the abnormal nodes through the twin data approval link; finally, the audit platform determines the target twin abnormal approval nodes, providing a clear direction for subsequent approval investigation and processing; through the above process, the audit platform can quickly and accurately locate problems in complex power engineering approval scenarios, improving the approval efficiency and accuracy.

[0101] S4. Determine the correlation degree values of each approval node in the twin data approval link according to the target twin abnormal approval nodes; determine the abnormal approval node investigation strategy according to the correlation degree values.

[0102] Specifically, S4 includes the following steps:

[0103] S41. Obtain the approval verification items corresponding to the target twin anomaly approval nodes; the approval verification items include the file approval qualification verification scope, the approval data verification scope, and the approval logic verification sequence;

[0104] S42. Construct triple data according to the numerical characteristics in the approval verification items; calculate the sum of the correlation degrees between each target twin anomaly approval node and the primary twin anomaly approval node in the twin data approval link according to the triple data;

[0105] S43. Sort the target twin anomaly approval nodes from largest to smallest according to the sum of the correlation degrees to obtain an anomaly approval node investigation table, and execute the anomaly approval node investigation strategy according to the anomaly approval node investigation table in combination with the corresponding approval verification items.

[0106] Specifically, constructing triple data according to the numerical characteristics in the approval verification items; calculating the sum of the correlation degrees between each target twin anomaly approval node and the primary twin anomaly approval node in the twin data approval link according to the triple data; includes the following steps:

[0107] S421. Extract the numerical characteristics in the approval verification items to obtain the verification item values, where the numerical characteristics include numerical features and non-numerical features; the verification item values corresponding to the numerical features include the file approval qualification verification scope and the approval data verification scope; the verification item value corresponding to the non-numerical feature is the approval logic verification sequence; specifically, when the approval logic verification sequence corresponding to the approval node is correct, it is "1", and when it is wrong, it is "0";

[0108] S422. Construct a triple data set M corresponding to the target twin anomaly approval nodes and a triple data set N corresponding to the primary twin anomaly approval nodes according to the approval nodes, approval verification items, and verification item values respectively;

[0109] S423. Calculate the sum of the correlation degrees between each element in the verification item triple data set M and all elements in the triple data set N according to the correlation degree calculation formula.

[0110] In this embodiment, first, the audit platform obtains the approval verification items corresponding to the target twin abnormal approval nodes. These verification items include, but are not limited to, the scope of approval qualification verification for documents, the scope of approval data verification, and the order of approval logic verification. These verification items are the basis for subsequent correlation analysis and troubleshooting strategy formulation. The audit platform extracts the numerical characteristics in the approval verification items and converts them into verification item values. Numerical features (such as the scope of approval qualification verification for documents and the scope of approval data verification) are directly represented in numerical form, while non-numerical features (such as the order of approval logic verification) are converted into numerical form through specific rules (such as "1" for correct and "0" for incorrect); According to the approval nodes, approval verification items, and verification item values, the audit platform constructs the triple data set M corresponding to the target twin abnormal approval nodes and the triple data set N corresponding to the primary twin abnormal approval nodes respectively. These triple data sets represent the key information and verification results of the approval nodes in a structured manner; The audit platform uses correlation calculation formulas (such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc.) to calculate the correlation degree between each element in set A (representing a target twin abnormal approval node) and all elements in set B (representing all primary twin abnormal approval nodes) respectively. Then, the correlation degree values between each target twin abnormal approval node and all primary twin abnormal approval nodes are summed to obtain the sum of correlation degrees; The audit platform sorts the target twin abnormal approval nodes in descending order according to the sum of correlation degrees to generate a troubleshooting list of abnormal approval nodes. This troubleshooting list provides an order of priority for approval personnel, helping them to first focus on the nodes that are most relevant to the abnormality; Combining the troubleshooting list of abnormal approval nodes and the corresponding approval verification items, the audit platform formulates and executes specific troubleshooting strategies. Approval personnel will check each abnormal approval node one by one according to the order in the troubleshooting list and conduct detailed review and analysis according to the requirements of the verification items. During this process, approval personnel may further collect evidence, communicate with relevant departments, or conduct on-site investigations to ensure the accurate identification and handling of problems.

[0111] It can be understood that through the implementation of step S4, the troubleshooting work of abnormal approval nodes in the power engineering approval scenario has been significantly optimized: Correlation analysis helps approval personnel quickly locate the nodes that are most relevant to the abnormality, reduces ineffective troubleshooting work, and improves the overall troubleshooting efficiency; Based on the structured triple data and scientific correlation calculation methods, the troubleshooting strategies are more objective and accurate, reducing the risk of human misjudgment; The generated troubleshooting list of abnormal approval nodes provides a visual overview of approval problems for management, helping them quickly understand the problem distribution and severity, so as to formulate more targeted decision-making measures.

[0112] In step S4, if the Pearson correlation coefficient method is used to calculate the correlation degree between each approval node in the twin data approval link, we can use the standard mathematical formula of the Pearson correlation coefficient. Based on the calculated correlation degree value, we can further generate a list for investigating abnormal approval nodes.

[0113] In this embodiment, if the Pearson correlation coefficient method is selected to calculate the correlation degree, the calculation process is as follows:

[0114] The mathematical formula for the Pearson correlation coefficient r is:

[0115] ,

[0116] where and are the values of two different approval nodes (or triple data elements) on the same verification item; and are the average values of two different approval nodes on the same verification item. is the number of abnormal approval nodes participating in the correlation degree calculation; since the approval verification items may include numerical features and non-numerical features (such as logical verification order), it is necessary to perform appropriate numerical processing on the non-numerical features (such as mapping "correct" in the logical verification order to "1" and "wrong" to "0"). For each target twin abnormal approval node (element in set A) and a single twin abnormal approval node (element in set B), their correlation degree on all relevant verification items can be calculated according to the Pearson correlation coefficient method. Generally, first determine the correlation degree between two nodes on a specific verification item, and sum or weighted average these correlation degree values to obtain a comprehensive correlation degree value. For each target twin abnormal approval node, sum (or weighted average as needed) the correlation degree values between it and all single twin abnormal approval nodes to obtain the total correlation degree of this node. Sort the target twin abnormal approval nodes from largest to smallest according to the total correlation degree value. Use the sorted list of target twin abnormal approval nodes as the list for investigating abnormal approval nodes. The content of the list for investigating abnormal approval nodes can include information such as node ID, total correlation degree value, relevant verification items, and their correlation degree details; Table 1 is the list for investigating abnormal approval nodes.

[0117] Table 1. List for Investigating Abnormal Approval Nodes

[0118]

[0119] In Table 1, each target twin exception approval node has a unique node ID and a corresponding total correlation value. In addition, the verification items related to this node and their specific correlation values are also shown for further analysis and processing by the approval personnel. Note that the correlation details here (such as the correlation values of qualification verification, data verification, and logic verification) are exemplary, and different verification items and correlation calculation methods may be included in actual applications.

[0120] Embodiment 2:

[0121] In a second aspect, a technical solution provided in an embodiment of the present invention is an engineering data bilateral authentication system, including:

[0122] An approval link construction module: manages the interfaces of the audit platform to obtain standard data tuples, and configures the native data approval link and the twin data approval link according to the business types of the standard data tuples;

[0123] An authentication module: configures a first authentication policy for the native data approval link according to the first data interface; configures a second authentication policy for the twin data approval link according to the second data interface and the first authentication policy;

[0124] An exception determination module: determines the approval result of the native data approval link according to the first authentication policy. When the approval result meets the trigger condition of the threshold trigger mechanism, obtains the exception approval node of the native data approval link, and determines the target twin exception approval node in the twin data approval link according to the exception approval node and the second authentication policy;

[0125] An execution module: determines the correlation values of each approval node in the twin data approval link according to the target twin exception approval node; determines the exception approval node troubleshooting strategy according to the correlation values.

[0126] This embodiment has the following technical effects: By managing the interfaces of the audit platform, standard data tuples are obtained, and the native data approval link and the twin data approval link are flexibly configured according to the business type, which not only ensures the standardized processing of data but also provides adaptability for different business scenarios. Further, the first authentication policy and the second authentication policy are respectively configured for the native data approval link and the twin data approval link, forming a two-layer authentication mechanism, effectively improving the security and accuracy of the approval data investigation. Further, when the approval result of the native data approval link triggers the threshold mechanism, it can automatically identify abnormal approval nodes and locate the corresponding target twin abnormal approval nodes in the twin data approval link, realizing the rapid response and accurate positioning of abnormal situations. By calculating the correlation values of each approval node in the twin data approval link, the influence range of abnormal approval nodes can be analyzed more comprehensively, and then a more effective abnormal approval node investigation strategy can be formulated. The solution realizes standardized processing, two-layer security verification, intelligent abnormal processing, and abnormal security investigation strategy in engineering approval data through interface fusion and bilateral authentication technologies, significantly improving the efficiency, security, and intelligent level of approval data processing.

[0127] Embodiment 3:

[0128] A technical solution provided in an embodiment of the present invention is an electronic device, including a memory and a processor. A computer program is stored in the memory. When the processor calls the computer program in the memory, the steps of the engineering data bilateral authentication method based on the interface fusion technology are implemented.

[0129] Embodiment 4:

[0130] A technical solution provided in an embodiment of the present invention is a storage medium. A computer-executable instruction is stored in the storage medium. When the computer-executable instruction is loaded and executed by a processor, the steps of the engineering data bilateral authentication method based on the interface fusion technology are implemented.

[0131] Through the description of the above embodiments, those skilled in the art can understand that for the convenience and simplicity of description, only the above division of each functional module is used as an example. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the specific device is divided into different functional modules to complete all or part of the functions described above.

[0132] In the embodiments provided in the present application, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the embodiments of the structures described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another structure, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces. The indirect couplings or communication connections of structures or units can be in electrical, mechanical or other forms.

[0133] The units described as separate components may or may not be physically separated. The components displayed as units may be one physical unit or multiple physical units, that is, they can be located in one place, or they can be distributed to multiple different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0134] In addition, each functional unit in the embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0135] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to enable a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read only memory (ROM), random access memory (RAM), magnetic disks or optical discs and other various media that can store program codes.

[0136] The above-mentioned specific implementation manners are the preferred implementation manners of the engineering data bilateral authentication method and system based on the interface fusion technology of the present invention, and do not limit the specific scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation manner. All equivalent changes made according to the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A bilateral authentication method for engineering data based on interface fusion technology, characterized in that: The steps include: S1. Perform interface management on the audit platform to obtain standard data tuples, and configure native data approval links and twin data approval links according to the business type of the standard data tuples; S2. Configure a first authentication strategy for the native data approval link according to the first data interface; Configure a second authentication strategy for the twin data approval link according to the second data interface and the first authentication strategy; S3. Determine the approval result of the native data approval link according to the first authentication strategy. When the approval result meets the trigger condition of the threshold trigger mechanism, obtain the abnormal approval node of the native data approval link, and determine the target twin abnormal approval node in the twin data approval link according to the abnormal approval node and the second authentication strategy; S4. Determine the correlation value of each approval node in the twin data approval link according to the target twin abnormal approval node; determine the abnormal approval node troubleshooting strategy according to the correlation value; The S3 comprises the following steps: S301, determining the file source verification result and the approval verification result according to the verification items in the first authentication strategy; S302: When the file source verification result meets the triggering condition of the first threshold triggering mechanism, the audit platform performs interface management to regenerate the standard data tuple and executes S1; S303: When the approval verification result meets the triggering condition of the second threshold triggering mechanism, the abnormal approval node of the native data approval link is obtained; Among them, failure to meet the document compliance verification scope and / or the document integrity verification scope is used as a trigger condition for the first threshold trigger mechanism; failure to meet the document approval qualification verification scope and / or the approval data verification scope and / or the approval logic verification sequence is used as a trigger condition for the second threshold trigger mechanism; The S303 comprises the following steps: S311. Determine a twin abnormal approval node according to the node position of the abnormal approval node in the twin data approval link; S312, re-verify the first twin abnormality approval node in combination with the approval verification item in the second authentication strategy to determine the second twin abnormality approval node; and use the twin abnormality approval node as the target twin abnormality approval node; The configuring a first authentication strategy for the native data approval link according to the first data interface comprises the following steps: S201, configuring a first data interface for the data to be approved, and extracting the Value-1 value of the data to be approved; the Value-1 value at least includes a file source address and an initial approval specification; S202, sequentially matching the initial approval specification to the corresponding approval node in the native data approval link to obtain a first authentication strategy; The method of configuring the second authentication strategy for the twin data approval link according to the second data interface and the first authentication strategy includes the following steps: S211, configuring a second data interface for the approval specification data, extracting the Value-2 value of the approval specification data; the Value-2 value at least includes the file source address and the update approval specification; S212. The initial approval specification of each approval node in the first authentication strategy is replaced in sequence by updating the approval specification to obtain a second authentication strategy belonging to the twin data approval link.

2. According to claim 1, the bilateral authentication method for engineering data based on interface fusion technology is characterized in that: The interface management of the audit platform is performed to obtain a standard data tuple, and the native data approval link and the twin data approval link are configured according to the business type of the standard data tuple; the steps include: S11. The audit platform extracts the source characteristics and content characteristics of the interface data, and uses the source characteristics as the Key value of the standard data tuple and the content characteristics as the Value value of the standard data tuple; S12. Determine the business type based on the Key value of the standard data tuple, and match the corresponding approval node and approval path for the business type; construct the native data approval link and twin data approval link of the corresponding interface data based on the approval node, approval path and Value value.

3. The method for bilateral authentication of engineering data based on interface fusion technology according to claim 1 is characterized in that: The first authentication strategy and the second authentication strategy at least include document source verification and approval verification; The document source verification at least includes document compliance verification and document integrity verification; The approval verification at least includes approval qualification verification, approval data verification and approval logic verification.

4. The method for bilateral authentication of engineering data based on interface fusion technology according to claim 1 is characterized in that: Determine the file source verification items based on the file source address and approval document list extracted from the interface data; set the file compliance verification scope and file integrity verification scope corresponding to the file source verification items; determine the approval verification items based on the approval qualifications, approval data and approval logic extracted from the interface data; set the file approval qualification verification scope, approval data verification scope and approval logic verification sequence corresponding to the approval verification items.

5. The method for bilateral authentication of engineering data based on interface fusion technology according to claim 1 is characterized in that: The method of determining the correlation value of each approval node in the twin data approval link according to the target twin abnormal approval node; and determining the abnormal approval node troubleshooting strategy according to the correlation value comprises the following steps: S41. Obtain the approval verification items corresponding to the target twin abnormal approval node; the approval verification items include the document approval qualification verification scope, the approval data verification scope and the approval logic verification sequence; S42. Construct triplet data according to the numerical characteristics in the approval and verification items; calculate the sum of the correlations between each target twin abnormal approval node and the primary twin abnormal approval node in the twin data approval link according to the triplet data; S43. Sort the target twin abnormal approval nodes from large to small according to the sum of the correlation degrees to obtain an abnormal approval node screening table, and execute the abnormal approval node screening strategy according to the abnormal approval node screening table in combination with the corresponding approval verification items.

6. The method for bilateral authentication of engineering data based on interface fusion technology according to claim 5 is characterized in that: The method of constructing triplet data according to the numerical characteristics in the approval and verification items; and calculating the sum of the correlations between each target twin abnormal approval node and the primary twin abnormal approval node in the twin data approval link according to the triplet data; comprises the following steps: S421. Extract the numerical characteristics in the approval verification items to obtain the verification item values, wherein the numerical characteristics include numerical characteristics and non-numerical characteristics; the verification item values ​​corresponding to the numerical characteristics include the document approval qualification verification scope and the approval data verification scope; the verification item values ​​corresponding to the non-numerical characteristics include the approval logic verification sequence; specifically, when the approval logic verification sequence corresponding to the approval node is correct, it is "1", and when it is wrong, it is "0"; S422. Construct a triple data set M corresponding to the target twin abnormal approval node and a triple data set N corresponding to the first twin abnormal approval node according to the approval node, the approval verification item, and the verification item value; S423. Calculate the sum of the correlation between each element in the verification item triple data set M and all elements in the triple data set N according to the correlation calculation formula.

7. A bilateral authentication system for engineering data, applicable to the bilateral authentication method for engineering data based on interface fusion technology as claimed in any one of claims 1 to 6, characterized in that: Included are: Audit link construction module: manage the interface of the audit platform to obtain standard data tuples, and configure native data approval links and twin data approval links according to the business type of the standard data tuples; Authentication module: configuring a first authentication strategy for the native data approval link according to the first data interface; Configure a second authentication strategy for the twin data approval link according to the second data interface and the first authentication strategy; Abnormal determination module: Determine the approval result of the native data approval link according to the first authentication strategy. When the approval result meets the trigger condition of the threshold trigger mechanism, obtain the abnormal approval node of the native data approval link, and determine the target twin abnormal approval node in the twin data approval link according to the abnormal approval node and the second authentication strategy; Execution module: Determine the correlation value of each approval node in the twin data approval link based on the target twin abnormal approval node; determine the abnormal approval node troubleshooting strategy based on the correlation value.

8. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, it implements the steps of the bilateral authentication method for engineering data based on interface fusion technology as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores computer executable instructions, which, when loaded and executed by the processor, implement the steps of the bilateral authentication method for engineering data based on interface fusion technology as described in any one of claims 1 to 6.

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