Method and system for integrated import of collection data and automatic matching with quota

Abstract

The application discloses a method and system for integrated import and automatic matching of collection data and quota, and relates to the technical field of engineering cost data processing, comprising the following steps: obtaining and respectively performing feature processing on collection data and quota data to form collection feature information and quota feature information; constructing a matching relationship between the collection feature information and the quota feature information, and generating a corresponding matching relationship object; establishing dependency description information for the matching relationship object; when the collection data or the quota data changes, judging whether the change affects the existing matching relationship based on the dependency description information; when it is judged that the existing matching relationship is affected, only performing re-matching processing on the affected matching relationship object. The application introduces a matching dependency description mechanism to realize accurate control of the matching relationship between the collection data and the quota, only triggers necessary re-matching processing when the data changes, thereby improving the matching accuracy and reducing the system calculation overhead.

Description

Technical Field

[0001] This invention relates to the field of engineering cost data processing technology, and more specifically, to a method and system for integrated import of revenue and cost data and automatic quota matching. Background Technology

[0002] In existing technologies, the matching relationship between revenue and cost data and quota data is usually established once during the import phase and then permanently saved after matching is completed. When the content of the revenue and cost data changes, the quota library version is updated, or the matching rules are adjusted, the existing system cannot automatically detect and update the established matching relationship, resulting in inconsistencies between historical matching results and the current data status, which in turn leads to problems such as incorrect quota selection and distorted quantity calculation.

[0003] Due to the lack of a modeling mechanism for the evolution of matching relationships, existing technologies struggle to achieve dynamic maintenance and ensure the continued effectiveness of matching results.

[0004] The above-disclosed technical solutions have at least the following technical problems: In the existing technology, the matching relationship between the revenue data and the quota lacks a dynamic evolution mechanism. When the data on either side changes, it is impossible to automatically detect and adjust the existing matching results, resulting in the failure or error of historical matching results. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an integrated import method and system for revenue and capital data and automatic quota matching. By constructing a matching relationship model between revenue and capital data and quota data, and introducing a matching dependency description mechanism, the method achieves accurate identification and selective rematching of affected matching relationships in data change scenarios, thereby avoiding full recalculation and improving the accuracy, stability, and processing efficiency of revenue and capital data matching with quotas.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The integrated import and automatic quota matching method for revenue and capital collection data includes the following steps: acquiring revenue and capital collection data and quota data, and performing feature processing on the revenue and capital collection data and quota data respectively to form revenue and capital collection feature information and quota feature information; constructing a matching relationship between revenue and capital collection feature information and quota feature information, and generating corresponding matching relationship objects; establishing dependency description information for the matching relationship objects to characterize the matching conditions; when revenue and capital collection data or quota data changes, determining whether the change affects the existing matching relationship based on the dependency description information; when it is determined that the existing matching relationship is affected, performing re-matching processing only on the affected matching relationship objects.

[0008] In a preferred embodiment, the step of characterizing the revenue and quota data to form revenue and quota feature information includes: structurally splitting the revenue data into several basic feature items, and classifying them into determinant features and constraint features based on the degree of influence of each basic feature item on the applicability of project pricing; structurally parsing the quota data, splitting the quota data into several quota entries that can independently describe project pricing rules according to their internal structure, and extracting the corresponding applicable condition features and consumption rule features from each quota entry; and standardizing the revenue and quota feature items according to a unified feature coding rule to form structured feature information containing feature identifiers, value ranges, and semantic types.

[0009] In a preferred embodiment, the step of constructing a matching relationship between revenue collection feature information and quota feature information, and generating a corresponding matching relationship object, includes: constructing multiple candidate matching relationships based on the semantic association between revenue collection features and quota features; extracting a set of feature conditions on which the establishment of each candidate matching relationship depends, wherein the set of feature conditions describes the feature combination state that needs to be satisfied for the establishment of the matching relationship; and structurally encapsulating the candidate matching relationships and their corresponding set of feature conditions to generate a matching relationship object, wherein the matching relationship object includes at least: a corresponding revenue collection feature identifier; a corresponding quota feature identifier; and a set of feature conditions representing the conditions for the establishment of the matching relationship.

[0010] In a preferred embodiment, the step of constructing multiple candidate matching relationships based on the semantic association between the payment collection feature and the quota feature includes: semantically aligning the payment collection feature information and the quota feature information, and semantically mapping the payment collection feature and the quota feature to form a set of associated features; calculating the semantic association degree between the payment collection feature and the quota feature based on the set of associated features, and filtering the quota feature according to the semantic association degree to determine candidate quota features that have semantic matching possibility with the current payment collection feature; and constructing multiple sets of candidate matching relationships by taking a single payment collection feature and its corresponding candidate quota feature combination as a unit.

[0011] In a preferred embodiment, the step of structurally encapsulating the candidate matching relationships and their corresponding feature condition sets to generate a matching relationship object includes: for each candidate matching relationship, extracting the revenue feature items and quota feature items that constitute the candidate matching relationship, and recording their feature identifiers, feature values, and feature categories respectively to form a feature condition set; based on the influence of each feature item on the matching result during the matching determination process, dividing the feature items in the feature condition set into decisive feature items and constraint feature items; and using the decisive feature items and constraint feature items as core elements to structurally encapsulate the candidate matching relationships to generate a matching relationship object.

[0012] In a preferred embodiment, establishing dependency description information for matching relationship objects to characterize the conditions for matching includes: analyzing the role of each feature in the matching formation process based on the decisive and constraining features recorded in the matching relationship objects, and determining whether it has a decisive or conditional impact on the matching result; for features with a decisive impact, generating a strong dependency condition description to determine whether the matching relationship still holds; for features with a conditional impact, generating a weak dependency condition description to determine whether the matching relationship needs to be adjusted; and structuring the strong and weak dependency conditions in the form of feature-action mode-impact result to form dependency description information corresponding one-to-one with the matching relationship objects.

[0013] In a preferred embodiment, the step of analyzing the role of each feature in the matching process based on the decisive and constraining features recorded in the matching relationship object includes: extracting a set of features involved in the matching process from the matching relationship object; determining the mode of influence of each feature on the matching result based on the degree of influence of the matching relationship on its establishment; marking features that have a direct determining effect on the establishment of the matching relationship as decisive features, and marking features that only restrict the matching range or applicable conditions as constraining features; and writing the decisive features, constraining features, and their corresponding relationships into the matching relationship object to form dependency description information for subsequent matching validity judgment.

[0014] In a preferred embodiment, when the revenue data or quota data changes, determining whether the change affects the existing matching relationship based on the dependency description information includes: parsing the changed content and identifying the target feature item that has changed; retrieving matching relationships in the matching relationship object that have a dependency relationship with the target feature item; and determining whether the change triggers the failure of the matching relationship based on the feature type of the target feature item in the corresponding matching relationship, wherein: when the target feature item is a decisive feature item, the matching relationship is directly determined to be invalid; when the target feature item is a constraint feature item, the matching relationship is determined to be invalid only when its change exceeds the constraint range.

[0015] In a preferred embodiment, when it is determined that an existing matching relationship is affected, the rematching process is performed only on the affected matching relationship objects, including: for the matching relationship objects marked as needing rematching, reading their corresponding dependency description information, extracting the target feature items that caused the matching failure, and using them as rematching trigger features; based on the trigger features, limiting the matching range in the collection feature set and the quota feature set, selecting only the feature subset that has a dependency relationship with the trigger features to form a local rematching dataset; reconstructing the matching relationship based on the local rematching dataset, and replacing the original matching relationship with the newly generated matching relationship or marking it as having no valid match.

[0016] Furthermore, the integrated import and automatic quota matching system for revenue and payment data includes the following modules: Feature extraction module: used to acquire revenue and payment data and quota data, and perform feature processing on the revenue and payment data and quota data respectively to form revenue and payment feature information and quota feature information; Matching relationship construction module: used to construct the matching relationship between revenue and payment feature information and quota feature information, and generate corresponding matching relationship objects; Matching dependency modeling module: used to establish dependency description information for matching relationship objects to characterize the conditions for matching; Matching impact judgment module: used to determine whether changes in revenue or payment data or quota data affect existing matching relationships based on dependency description information; Local rematching execution module: used to perform rematching processing only on the affected matching relationship objects when it is determined that existing matching relationships are affected.

[0017] The technical effects and advantages of the integrated import and automatic quota matching method and system for data collection and payment of this invention are as follows:

[0018] This invention unifies the feature processing of revenue and quota data, transforming data with significant structural differences and semantic inconsistencies into comparable and computable feature information. Based on this, matching relationship objects and their dependency descriptions are constructed, transforming the matching process between revenue and quota data from traditional static rule-based matching to a dynamic matching process with condition awareness. When revenue or quota data changes, it can accurately determine whether the change substantially affects the existing matching results based on established dependencies, and perform local rematching only on the affected matching relationships, avoiding redundant calculations for all data. This significantly reduces computational overhead and manual verification costs while ensuring matching accuracy. Through these technical means, this invention achieves highly consistent matching and dynamic maintenance between revenue and quota data, improving the stability, traceability, and automation level of quota matching, making it particularly suitable for application scenarios with frequent data changes in engineering pricing processes. Attached Figure Description

[0019] Figure 1This is a flowchart illustrating the integrated import and automatic quota matching method for data collection of the present invention.

[0020] Figure 2 This is a schematic diagram of the integrated import and automatic quota matching system for data collection and payment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1, Figure 1 The present invention provides a method for integrated import of revenue and expenditure data and automatic quota matching, comprising the following steps:

[0023] S1, acquire the revenue data and quota data, and perform feature processing on the revenue data and quota data respectively to form revenue feature information and quota feature information;

[0024] In this embodiment, the step of performing feature processing on the collection data and quota data to form collection feature information and quota feature information includes:

[0025] The revenue and expenditure data are structured and broken down into several basic feature items according to material attributes, specifications, and engineering usage conditions. Based on the potential impact of each basic feature item on the engineering pricing result, these basic feature items are categorized into at least two types: determinative features for determining pricing applicability and constraint features for limiting the scope of application. The material attributes characterize the material category, model, and functional attributes; the specifications characterize the material's size, capacity, or technical parameters; and the engineering usage conditions characterize the material's installation method, usage location, and applicable environment in the project.

[0026] The quota data is structurally analyzed, breaking it down into several quota items that can independently describe engineering pricing rules. For each quota item, corresponding applicable condition features and consumption rule features are extracted, and the applicable condition features are mapped into structured feature units that can be compared with revenue and expenditure features. The applicable condition features are used to characterize the engineering type, construction conditions, and scope of application of the quota item; the consumption rule features are used to characterize the consumption rules of labor, materials, or machinery corresponding to the quota item.

[0027] The revenue and quota features are standardized and expressed according to a unified feature coding rule to form structured feature information that includes feature identifiers, value ranges and semantic types. This makes data from different sources comparable and combinable at the feature level, providing basic data support for subsequent feature-based processing.

[0028] S2, construct the matching relationship between the revenue characteristic information and the quota characteristic information, and generate the corresponding matching relationship object;

[0029] A matching relationship object is a structured data unit used to describe the matching status and conditions between a set of income data and a quota item. Its essence is not a simple matching result identifier, but a logical carrier that explicitly expresses "why it can match and under what conditions it still holds true".

[0030] In this embodiment, the step of constructing a matching relationship between revenue feature information and quota feature information, and generating a corresponding matching relationship object, includes:

[0031] Based on the structured expression of revenue and cost feature information and quota feature information, feature dimension alignment processing is performed on the two to make revenue and cost features and quota features comparable in terms of attribute type, value method and engineering semantics.

[0032] Based on the feature alignment, multiple candidate matching relationships are constructed based on the semantic association between the revenue feature and the quota feature. Each candidate matching relationship corresponds to a set of possible combinations of revenue feature and quota feature.

[0033] For each candidate matching relationship, extract the set of feature conditions on which its establishment depends. The set of feature conditions is used to describe under what feature combination states the matching relationship is established.

[0034] The candidate matching relationships and their corresponding feature condition sets are structured and encapsulated to generate a matching relationship object, wherein the matching relationship object includes at least: the corresponding revenue feature identifier; the corresponding quota feature identifier; and the feature condition set on which the matching depends.

[0035] Based on the semantic association between payment characteristics and quota characteristics, multiple candidate matching relationships are constructed, including:

[0036] Semantic hierarchical alignment is performed on the revenue and cost feature information and the quota feature information. The material name feature, specification parameter feature and engineering use condition feature in the revenue and cost feature are semantically mapped with the applicable condition feature and consumption rule feature in the quota feature to form a semantic association feature that can be used to measure the degree of association between the two.

[0037] Based on the semantic association features, the semantic association degree between each revenue feature and each quota feature is calculated, and quota features that meet the preset association threshold are selected according to the association degree as candidate quota features that have a potential correspondence with the revenue feature.

[0038] Multiple candidate matching relationships are constructed by taking a single revenue feature and its corresponding candidate quota feature as a unit.

[0039] The calculation of the semantic relevance includes:

[0040] Based on the structural differences between revenue collection characteristics and quota characteristics, semantic relevance is decomposed into multiple quantifiable sub-relevances, and then comprehensively calculated according to preset weights, specifically including:

[0041] The correlation between revenue collection features and quota features at the name level is calculated based on semantic similarity of names.

[0042] Based on the consistency or inclusion relationship of specification parameters, calculate the degree of matching between the two at the specification level;

[0043] Based on the logical consistency between the engineering usage conditions and the quota application conditions, the degree of conformity between the two at the engineering application level is calculated.

[0044] The above sub-associations are weighted according to their influence on the matching results, and then weighted and fused to obtain the comprehensive semantic association between the revenue feature and the quota feature.

[0045] The step of structurally encapsulating the candidate matching relationships and their corresponding feature condition sets to generate a matching relationship object includes:

[0046] For each candidate matching relationship, extract the revenue feature items and quota feature items on which the matching relationship is based, and record the corresponding feature identifier, feature value and feature type respectively to form a set of feature conditions to describe the candidate matching relationship;

[0047] Based on the role of each feature in the matching determination, the feature items in the feature condition set are divided into decisive feature items and constraint feature items. The decisive feature items are used to indicate the conditions that must be met for the matching relationship to be established, and the constraint feature items are used to indicate the conditions that restrict or modify the matching results.

[0048] For each candidate matching relationship, a matching relationship object is generated, which includes: the associated revenue feature identifier; the associated quota feature identifier; the decisive feature item and its value range; and the constraint feature item and its function.

[0049] S3, establish dependency description information for the matching relationship object to characterize the conditions for matching, the dependency description information includes the feature terms and their constraint relationships on which the matching relationship depends;

[0050] In this embodiment, establishing dependency description information for the matching relationship object to characterize the conditions for matching includes:

[0051] Based on the decisive and constraining features recorded in the matching relationship object, we analyze the role of each feature in the matching process and determine whether it has a decisive or conditional influence on the matching result.

[0052] For features that have a decisive impact, extract their value range, logical relationship and constraint form when participating in the matching judgment, and generate a strong dependency condition description for determining whether the matching relationship still holds.

[0053] For feature terms with conditional influence, extract their restriction methods and scope of influence on matching results, and generate weak dependency condition descriptions to determine whether the matching relationship needs to be adjusted.

[0054] The strong and weak dependency conditions are structured and organized in the form of feature item-action mode-impact result to form dependency description information that corresponds one-to-one with the matching relationship object.

[0055] The dependency description information is used to characterize: under what feature states the matching relationship must be valid; under what feature changes the matching relationship may fail; and under what circumstances the matching calculation does not need to be re-executed.

[0056] The analysis of the roles of each feature in the matching process, based on the deterministic and constraining features recorded in the matching relationship object, includes:

[0057] Based on the matching relationship object, extract all feature items involved in the formation of the matching relationship, construct the corresponding feature item set, and obtain the original value and matching status of each feature item in the matching relationship;

[0058] Without introducing actual data changes, feature change scenarios are constructed for each feature item in the feature set to simulate the impact on the matching results when the feature changes or becomes invalid.

[0059] Based on the aforementioned feature change scenarios, a comparative analysis is performed on the establishment status of the matching relationship to determine the degree of influence of changes in each feature item on the stability of the matching result.

[0060] Based on the degree of influence, feature items that have a decisive impact on the establishment of a match are marked as decisive feature items, and feature items that only have a binding effect on the matching range or applicable conditions are marked as binding feature items.

[0061] The decisive features, constraint features, and their influence relationships are written into the matching relationship object to form dependency description information for subsequent matching validity judgment.

[0062] Among them, features that meet at least one of the following conditions are marked as decisive features: removing or changing the feature will no longer establish a matching relationship; the change of the feature will cause the matching object to change; the feature is a necessary prerequisite for triggering the matching rule.

[0063] Features that meet the following conditions are marked as constraint features: their changes do not cause the matching relationship to fail; but they affect the matching confidence, ranking, or priority; they are used to limit the scope of matching or correct the matching results.

[0064] S4, when the data on revenue or quota changes, determine whether the change affects the existing matching relationship based on the dependency description information;

[0065] In this embodiment, the step of determining whether changes in revenue data or quota data affect existing matching relationships based on dependency description information includes:

[0066] When changes are detected in the revenue data or quota data, the changes are first analyzed to extract the target feature items that have changed and their change types. The change types include at least numerical changes, value range changes, or attribute status changes.

[0067] Based on the target feature, retrieve matching relationship objects that have a dependency relationship with the established matching relationship objects to form a set of matching relationships to be evaluated;

[0068] Read the dependency description information corresponding to each matching relationship object in the set of matching relationships to be evaluated, and identify the feature type of the target feature in the matching relationship, including deterministic feature or constraint feature;

[0069] When the target feature is a decisive feature, it is determined that the conditions for the establishment of the matching relationship have been substantially changed, and the matching relationship is marked as invalid or needs to be rematched.

[0070] When the change feature is a constraint feature, it is further determined whether the change exceeds the constraint range recorded in the matching relationship. Only when it exceeds the constraint range is the matching relationship marked as needing to be rematched.

[0071] For matching relationships that do not trigger the above-mentioned failure conditions, their original matching state remains unchanged, thereby enabling selective judgment of the validity of the matching relationship.

[0072] S5: When it is determined that an existing matching relationship is affected, only the affected matching relationship objects are re-matched.

[0073] When it is determined that an existing matching relationship is affected, the re-matching process is performed only on the affected matching relationship objects, including:

[0074] After determining that a certain matching relationship object is affected, the target feature item that caused the matching relationship to fail or become unstable is extracted based on the dependency description information recorded in the matching relationship object, and used as the trigger feature for this rematch.

[0075] Based on the triggering feature, the search range for rematching is limited in the collection feature set and the quota feature set, and only a subset of features that are dependent on the triggering feature are selected to form a local rematching dataset;

[0076] Based on the local rematch dataset, the matching relationship construction process is re-executed to generate new candidate matching relationships, and the matching consistency and fit degree between them and the original matching relationships are calculated.

[0077] When the newly generated matching relationship meets the preset matching conditions, the original matching relationship object is replaced by the new matching relationship;

[0078] When the matching condition is not met, the original matching relationship is marked as invalid, and the corresponding reason for the invalidation is recorded.

[0079] The updated matching relationship object and its corresponding dependency description information are written back to the matching relationship set for subsequent change judgment and matching maintenance.

[0080] The matching consistency is used to characterize the degree of consistency between the newly generated matching relationship and the original matching relationship at the level of decisive features, and its calculation includes:

[0081] Based on the decisive feature terms recorded in the original matching relationship, check one by one whether the values ​​of the corresponding feature terms in the new matching relationship satisfy the original value constraints;

[0082] If any decisive feature in the new matching relationship does not meet the value conditions corresponding to the original matching relationship, the consistency of the matching relationship is determined to be invalid.

[0083] When all deterministic features satisfy the value constraints of the original matching relationship, the matching relationship is determined to be valid at the consistency level.

[0084] The degree of fit is used to characterize the rationality and quality of the new matching relationship relative to the original matching relationship, and its calculation includes:

[0085] Based on the constraint features recorded in the matching relationship, calculate the degree of deviation of the new matching relationship in each constraint dimension;

[0086] The deviations of each constraint feature are weighted and summed according to preset weights to obtain the matching fit index.

[0087] The degree of deviation is used to characterize the magnitude of the deviation between the new matching relationship and the original matching relationship in terms of engineering conditions, specification range, or usage restrictions.

[0088] The matching conditions include:

[0089] The newly generated matching relationship is considered valid in the matching consistency judgment.

[0090] Furthermore, its matching degree is not lower than the preset matching threshold, or better than the matching degree corresponding to the original matching relationship;

[0091] It simultaneously satisfies the engineering applicable conditions and business rule constraints corresponding to the matching relationship.

[0092] Example 2, Figure 2 The present invention provides an integrated data import and automatic quota matching system for revenue collection, comprising the following modules:

[0093] Feature extraction module: used to acquire revenue data and quota data, and to perform feature processing on the revenue data and quota data respectively to form revenue feature information and quota feature information;

[0094] Matching Relationship Construction Module: Used to construct the matching relationship between revenue feature information and quota feature information, and generate the corresponding matching relationship object;

[0095] Matching Dependency Modeling Module: Used to establish dependency description information for matching relation objects to characterize the conditions for matching to be true;

[0096] Matching Impact Determination Module: Used to determine whether changes in revenue data or quota data affect existing matching relationships based on dependency description information;

[0097] Local rematch execution module: When it is determined that an existing matching relationship is affected, rematch processing is performed only on the affected matching relationship objects.

[0098] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0099] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0100] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0101] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0102] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0103] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for integrated import of revenue and expenditure data and automatic quota matching, characterized in that, Includes the following steps: Acquire collection data and quota data, and perform feature processing on the collection data and quota data respectively to form collection feature information and quota feature information: Construct a matching relationship between revenue characteristic information and quota characteristic information, and generate the corresponding matching relationship object, including: Based on the semantic relationship between revenue collection features and quota features, multiple candidate matching relationships are constructed; Extract the set of feature conditions on which each candidate matching relationship depends for the establishment of the matching relationship. The set of feature conditions is used to describe the combination of features required for the matching relationship to be established. The candidate matching relationships and their corresponding feature condition sets are structured and encapsulated to generate a matching relationship object. The matching relationship object includes at least: the corresponding revenue feature identifier; the corresponding quota feature identifier; and the feature condition set representing the conditions for the matching relationship to be established. The step of structurally encapsulating the candidate matching relationships and their corresponding feature condition sets to generate a matching relationship object includes: For each candidate matching relationship, extract the revenue feature items and quota feature items that constitute the candidate matching relationship, and record their feature identifiers, feature values ​​and feature categories respectively to form a feature condition set; Based on how each feature item affects the matching result during the matching determination process, the feature items in the feature condition set are divided into decisive feature items and constraint feature items; Using decisive and constraining features as core elements, candidate matching relationships are structurally encapsulated to generate matching relationship objects; To establish dependency description information for matching relationship objects to characterize the conditions for matching, the following steps are taken: based on the decisive and constraining features recorded in the matching relationship objects, analyze the role of each feature in the matching process to determine whether it has a decisive or conditional impact on the matching result; for features with a decisive impact, generate strong dependency condition descriptions to determine whether the matching relationship still holds; for features with a conditional impact, generate weak dependency condition descriptions to determine whether the matching relationship needs to be adjusted; and organize the strong and weak dependency conditions in a structured manner according to the form of feature-action mode-impact result to form dependency description information that corresponds one-to-one with the matching relationship objects. When the data on revenue collection or quota changes, the system determines whether the change affects the existing matching relationship based on the dependency description information. When it is determined that an existing matching relationship is affected, only the affected matching relationship objects are re-matched.

2. The method for integrated import of revenue and expenditure data and automatic quota matching according to claim 1, characterized in that, The process of performing feature processing on the revenue collection data and quota data to form revenue collection feature information and quota feature information includes: The revenue data is structured and broken down into several basic feature items. Based on the degree of influence of each basic feature item on the applicability of project pricing, it is further divided into determinant features and constraint features. The quota data is structurally analyzed, and the quota data is divided into several quota items that can independently describe the engineering pricing rules according to its internal structure. The corresponding applicable condition features and consumption rule features are extracted from each quota item. The revenue and quota features are standardized and expressed according to a unified feature coding rule to form structured feature information that includes feature identifiers, value ranges, and semantic types.

3. The method for integrated import of revenue and expenditure data and automatic quota matching according to claim 2, characterized in that, Based on the semantic association between payment characteristics and quota characteristics, multiple candidate matching relationships are constructed, including: Semantic hierarchical alignment is performed between the revenue and quota feature information, and semantic mapping is performed between the revenue and quota features to form a set of related features; Based on the set of associated features, the semantic correlation degree between the collection feature and the quota feature is calculated, and the quota feature is screened according to the semantic correlation degree to determine the candidate quota feature that has the possibility of semantic matching with the current collection feature. Multiple candidate matching relationship sets are constructed, using a single revenue feature and its corresponding candidate quota feature combination as the unit.

4. The method for integrated import of revenue and expenditure data and automatic quota matching according to claim 3, characterized in that, The analysis of the roles of each feature in the matching process, based on the deterministic and constraining features recorded in the matching relationship object, includes: Extract the set of feature terms involved in the matching process from the matching relationship object; Based on the degree of influence of each feature on whether the matching relationship is valid, determine how it affects the matching result; Feature terms that directly determine the establishment of a matching relationship are marked as decisive feature terms, and feature terms that only restrict the matching range or applicable conditions are marked as restrictive feature terms. The decisive feature items, the constraint feature items, and their corresponding interaction relationships are written into the matching relationship object to form dependency description information for subsequent matching validity judgment.

5. The method for integrated import of revenue and expenditure data and automatic quota matching according to claim 4, characterized in that, When the data on revenue or quota changes, the method of determining whether the change affects the existing matching relationship based on the dependency description information includes: Analyze the changes and identify the target feature items that have changed; Retrieve matching relationships that have a dependency relationship with the target feature from the matching relationship object; Based on the feature type of the target feature in the corresponding matching relationship, it is determined whether the change triggers the failure of the matching relationship, wherein: When the target feature is a decisive feature, the matching relationship is directly determined to be invalid. When the target feature is a constraint feature, the matching relationship is deemed invalid only when its change exceeds the constraint range.

6. The method for integrated import of revenue and expenditure data and automatic quota matching according to claim 5, characterized in that, When it is determined that an existing matching relationship is affected, the re-matching process is performed only on the affected matching relationship objects, including: For matching relationship objects marked as needing to be rematched, read their corresponding dependency description information, extract the target feature items that caused the matching failure, and use them as rematch trigger features; Based on the triggering feature, the matching range is limited in the set of collection feature and the set of quota feature, and only the feature subset that has a dependency relationship with the triggering feature is selected to form a local rematch dataset. The matching relationships are reconstructed based on the local rematch set, and the original matching relationships are replaced with the newly generated matching relationships or marked as having no valid matches.

7. A system using the integrated import and automatic quota matching method for revenue and expenditure data as described in any one of claims 1-6, characterized in that, Includes the following modules: Feature extraction module: used to acquire revenue data and quota data, and to perform feature processing on the revenue data and quota data respectively to form revenue feature information and quota feature information; Matching Relationship Construction Module: Used to construct the matching relationship between revenue feature information and quota feature information, and generate the corresponding matching relationship object; Matching Dependency Modeling Module: Used to establish dependency description information for matching relation objects to characterize the conditions for matching to be true; Matching Impact Determination Module: Used to determine whether changes in revenue data or quota data affect existing matching relationships based on dependency description information; Local rematch execution module: When it is determined that an existing matching relationship is affected, rematch processing is performed only on the affected matching relationship objects.

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