A qualification empowerment and double ring guiding algorithm for online transaction matching

Abstract

This invention relates to a qualification weighting and dual-loop guidance algorithm for online transaction matching, belonging to the field of online transaction matching technology. The method includes: constructing a qualification weight constraint evaluation system; analyzing the domain correlation between the multi-source qualification indicator data to obtain qualification matching weighting adjustment parameters; establishing a cyclic matching model, including the qualification scores of the organizers in a candidate guidance pool, ranking the qualification weight priorities, and outputting a matching push preference scheme; selecting a matching suggestion push mode and simulating the qualification resource push process; using a push critical state discrimination mechanism to determine the cluster boundary of the selected matching suggestion push mode and adjust the matching demand adaptation range; periodically updating the qualification matching weighting adjustment parameters incrementally according to changes in market demand, verifying the matching guidance efficiency in a simulation environment, and generating a matching integration weighting compliance report.

Description

Technical Field

[0001] This invention belongs to the field of online transaction matching technology, specifically relating to a qualification empowerment and dual-loop guidance algorithm for online transaction matching. Background Technology

[0002] With the rapid development of internet technology, online transactions have become one of the main forms of commodity circulation. As the core technology of online trading platforms, transaction matching algorithms directly determine the efficiency, accuracy, and security of transactions. Existing online transaction matching algorithms often employ single-dimensional or limited-dimensional matching logic, such as matching based solely on transaction price and quantity, without fully considering the comprehensive qualifications of the trading entities. This leads to the participation of some entities with poor qualifications and high credit risk, increasing the risks of transaction default and cargo damage. Furthermore, existing matching mechanisms are mostly one-way, passively matching demand and supply without actively guiding transactions based on market supply dynamics and the trading preferences of the trading entities. This results in low matching success rates and unreasonable resource allocation, especially in scenarios such as bulk commodities and fresh agricultural products where the qualifications of trading entities and the timeliness of transactions are crucial.

[0003] While some existing matching algorithms incorporate weighted evaluation, the weighting indicators are often singular and fail to cover key qualification indicators such as margin amount and length of service. Furthermore, the determination of weighting coefficients is often subjective and lacks scientific rigor. Additionally, the absence of a two-way, cyclical matching mechanism hinders precise matching of supply and demand and market-driven proactive matching, thus failing to meet the demands of online trading platforms for efficient, secure, and accurate matching. Therefore, there is an urgent need for an online trading algorithm that can comprehensively evaluate the qualifications of trading entities and achieve two-way, cyclical matching to address the shortcomings of existing technologies. Summary of the Invention

[0004] To address the aforementioned problems in existing technologies, this invention provides a qualification-based weighting and dual-loop guidance algorithm for online transaction matching. The objective of this invention can be achieved through the following technical solutions: S1: Obtain multi-source qualification indicator data, construct a qualification weight constraint evaluation system, analyze the domain correlation between the multi-source qualification indicator data, obtain a set of qualification matching parameters constrained by different themes, and combine the matching balance domain partitioning function to coordinately adjust the qualification sensitive weights of the dimensions covering the qualification subjects to obtain qualification matching weight adjustment parameters. S2: Using the change in qualification constraints before matching as the input variable, establish a cyclic matching model, include the qualification score of the organizer in the candidate guidance pool, sort the qualification weight priority, and perform double-layer cyclic guidance adjustment on the matching path of the candidate guidance subject based on the double-loop guidance driving function, and output the matching push tendency scheme. S3: Analyze the weighted matching window in the matching stage, select the matching suggestion push mode, and simulate the qualification resource push process. Through the push critical state discrimination mechanism, determine the cluster boundary of the selected matching suggestion push mode and adjust the matching demand adaptation range. S4: Construct a matching feedback optimization space, periodically update the qualification matching weighting adjustment parameters according to changes in market demand, and verify the matching guidance efficiency in a simulation environment. When the matching guidance efficiency meets the preset matching push decision, the current qualification matching weighting adjustment parameters and matching push tendency scheme are structured and encapsulated to generate a matching integration weighting compliance report.

[0005] Specifically, the method for constructing the qualification weight constraint evaluation system is as follows: Use multi-source qualification indicator data as the feature input set in the qualification weight constraint evaluation system; The multi-source qualification indicator data includes domain topic proportion information and matching conversion characteristics; The qualification weights are dynamically adjusted according to the qualification weight balancing rules to constrain the domain association between different topics. Based on the state distribution characteristics in the matching feedback optimization space, a qualification weight constraint evaluation system that matches the qualification matching state is constructed to screen the weight combination regions that meet the matching conditions.

[0006] Specifically, the method for parsing the domain relevance is as follows: Calculate the domain migration gradient based on the continuity of qualification associations under adjacent topic nodes; The matching constraint determination is performed on the domain migration gradient. When a nonlinear abrupt change is detected in the constraint relationship between the matching feature of the target node and its neighboring nodes, a feasible region backtracking search is performed to obtain the qualification matching parameter set. The feasible domain backtracking search uses the domain migration gradient change rate as a constraint on the backtracking step size, and performs a reverse traversal of historical nodes along the qualification association path; The set of qualification matching parameters includes: qualification weight allocation parameters, matching constraint threshold parameters, and matching balance domain boundary parameters.

[0007] Specifically, the coordination process for the aforementioned dimensional qualification-sensitive weights is as follows: The dimension-based qualification sensitivity weights serve as dynamic matching constraint factors, and are initially allocated to each dimension-based sensitivity weight. Weight modulation is performed based on the matching path guidance value output by the dual-loop guiding driving function, and the state changes in the feedback optimization space of sensitive weight deviations in each dimension are matched in real time to maintain the driving potential field of qualification structure rearrangement in the matching evolution process.

[0008] Specifically, the method for constructing the cyclic matching model is as follows: Using the change in qualification constraints before matching as the input variable, the matching offset of different candidate entities before and after the adjustment of qualification constraints is mapped, and the matching degree between candidate entities is quantified based on the multi-source qualification indicators and demand characteristics of both parties in the matching process. During the evolution of the matching state, the current matching result is used as the input for the next round of matching calculation. The matching path and weight distribution are dynamically updated to construct a cyclic matching model.

[0009] Specifically, the execution process of the candidate bootstrapping pool is as follows: Based on the energy level differences between each theme node, the potential energy distribution between nodes is quantitatively modeled to obtain the potential field driving the quality structure rearrangement. The qualification structure rearrangement driving potential field is used to guide and constrain the migration trend and rearrangement path of qualification elements among multiple theme nodes, and to adjust the flow direction and aggregation degree of qualification elements. The participation rights of different qualification topic nodes are non-linearly transferred and allocated. Combined with the qualification topic relaxation time, the matching status is used as the compression guidance distribution value to sort the qualification weight priority.

[0010] Specifically, the dual-layer cyclical guidance and regulation includes: a direct demand-supply matching cycle and a market-oriented suggested matching cycle; The direct matching loop between demand and supply: Based on the qualification matching constraints and demand characteristics of the matching parties, the matching degree between the demand side and the supply side is calculated in real time, and when the preset matching threshold condition is met, a fast matching path is triggered, and the candidate entities are directly output from the candidate guidance pool to the matching execution unit. The market-oriented matching cycle is as follows: based on the overall market supply and demand distribution and historical matching behavior data, the potential matching value of candidate entities is assessed, and market trend weights and strategy guidance factors are introduced to dynamically adjust the matching priority of candidate entities. The adjusted matching priority of candidate topics is fed back to the candidate guidance pool, and the matching of demand and supply is carried out in an alternating cycle until the optimal matching result is gradually converged, and the matching push preference scheme is output.

[0011] Specifically, the selection process for the matching suggestion push mode is as follows: Based on time series and qualification level, the qualification indicator response data is reconstructed in layers to quantify the matching change rate and cumulative effect of each push node in the continuous matching cycle, and generate a matching weight stability index. The matching weight stability index is used to jointly constrain the timing response of adjacent qualification layer nodes, and select the matching suggestion push mode according to the push priority score of each candidate subject, which serves as the input for matching guidance adjustment, and executes personalized matching push for candidate subjects.

[0012] Specifically, the execution process of the push critical state discrimination mechanism is as follows: A critical state threshold is constructed based on historical matching behavior data and the characteristics of high homogeneous areas, and the threshold is dynamically and adaptively updated by combining real-time matching deviation and market guidance signals. Nonlinear entropy increase mutation detection is performed on the updated threshold to identify potential critical nodes; When a critical state is detected, feasible domain backtracking and boundary correction based on qualification association network are performed to dynamically adjust the matching demand adaptation range of candidate guiding entities. The matching demand adaptation interval serves as the state reference benchmark domain in the cyclic matching model. By limiting the matching transformation matching degree of the qualification weight constraint evaluation system, the qualification matching control process is transformed from single-topic compliance control to dynamic interval constraint control of multi-source coupled state feasible domain.

[0013] Specifically, the matching feedback optimization space includes: a matching state representation layer and a guidance transition mapping layer; The matching state representation layer is used to uniformly model the multidimensional state information in the matching process, map discrete matching behavior data into a continuous state representation space, and obtain the state vectorization expression basis of the qualification state in the high-dimensional mechanism feature space according to the qualification topic migration distribution law. The multidimensional status information includes at least qualification matching degree, weight distribution status, matching path structure, and push response result; The guidance conversion mapping layer is used to establish the mapping relationship between the matching state and the guidance strategy, match the conversion mapping relationship between response behaviors, and divide the corresponding guidance behavior response area in the matching feedback optimization space according to the matching guidance stability margin.

[0014] Specifically, the verification process for the matching guidance efficiency is as follows: High-frequency sampling is performed on the multidimensional state information of each candidate entity in the matching feedback optimization space to extract the matching feature vector; The matching feature vector is used to characterize the inclusion status of candidate entities in terms of qualification matching degree, matching path convergence, and push response effect. Based on the aforementioned feature vector, in a simulated matching environment, multi-cycle iterative simulation is performed according to the current qualification matching weighting adjustment parameters and push preference scheme. By combining the critical state discrimination mechanism to identify matching deviations and abnormal behaviors, and calculating the incremental efficiency improvement value based on simulation feedback, the matching weighting adjustment parameters and push tendency scheme are adjusted according to the matching weighting basis, and the matching guidance efficiency is verified.

[0015] Specifically, the method for generating the matching integration empowerment compliance report is as follows: Based on the matching feedback optimization space, the homogeneity of qualification matching is calibrated, and matching features of high homogeneity regions are extracted. The matching features include: matching path convergence features, push efficiency features, and abnormal matching deviation features. Develop adjustment strategies for qualification weighting components and push frequency based on preset matching failure benchmarks; The adjustment strategy is executed, and the matching behavior log data during execution is combined to encapsulate the current qualification matching weighting adjustment parameters and matching push preference scheme in a structured manner, generating a matching integration weighting compliance report.

[0016] The beneficial effects of this invention are as follows: This invention provides a qualification weighting and dual-loop guidance algorithm for online transaction matching. By constructing a qualification weight constraint evaluation system, it realizes unified modeling and dynamic weighting of multi-source qualification indicator data, effectively improving the accuracy and comprehensiveness of qualification assessment. By introducing domain correlation analysis and dimension qualification sensitive weight coordination mechanism, it can characterize the coupling relationship between different qualification themes, making the matching process more in line with the needs of actual business scenarios.

[0017] By constructing a cyclical matching model and a two-layer cyclical guidance and adjustment mechanism, the direct matching of demand and supply is combined with market-oriented suggested matching, which realizes dynamic optimization and adaptive adjustment of the matching path, improving the success rate and response efficiency of matching. At the same time, by introducing a candidate guidance pool and a qualification structure rearrangement driving potential field, the rationality and stability of resource allocation in the matching process are enhanced.

[0018] By setting up a critical state judgment mechanism for push notifications and a matching suggestion push mode selection process, the matching strategy can be adjusted in real time in complex dynamic environments to avoid abnormal matching and resource waste. By constructing a matching feedback optimization space and conducting multi-cycle simulation verification, continuous optimization and closed-loop control of matching guidance efficiency are achieved.

[0019] In summary, this invention enables an efficient, stable, and adaptive transaction matching process under multidimensional constraints and dynamic market environments, significantly improving matching accuracy, system robustness, and resource utilization efficiency. Attached Figure Description

[0020] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0021] Figure 1 This is a schematic diagram of the framework of a qualification-based and dual-loop guidance algorithm for online transaction matching according to the present invention.

[0022] Figure 2This is a schematic diagram illustrating the working principle of the qualification weight constraint evaluation system in the qualification weighting and dual-loop guidance algorithm for online transaction matching of the present invention.

[0023] Figure 3 This is a schematic diagram illustrating the execution of the double-layer cyclic guidance adjustment in the qualification empowerment and double-loop guidance algorithm for online transaction matching of the present invention. Detailed Implementation

[0024] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0025] Please see Figure 1 A qualification-based and dual-loop guidance algorithm for online transaction matching: S1: Obtain multi-source qualification indicator data, construct a qualification weight constraint evaluation system, analyze the domain correlation between the multi-source qualification indicator data, obtain a set of qualification matching parameters constrained by different themes, and combine the matching balance domain partitioning function to coordinately adjust the qualification sensitive weights of the dimensions covering the qualification subjects to obtain qualification matching weight adjustment parameters. S2: Using the change in qualification constraints before matching as the input variable, establish a cyclic matching model, include the qualification score of the organizer in the candidate guidance pool, sort the qualification weight priority, and perform double-layer cyclic guidance adjustment on the matching path of the candidate guidance subject based on the double-loop guidance driving function, and output the matching push tendency scheme. S3: Analyze the weighted matching window in the matching stage, select the matching suggestion push mode, and simulate the qualification resource push process. Through the push critical state discrimination mechanism, determine the cluster boundary of the selected matching suggestion push mode and adjust the matching demand adaptation range. S4: Construct a matching feedback optimization space, periodically update the qualification matching weighting adjustment parameters according to changes in market demand, and verify the matching guidance efficiency in a simulation environment. When the matching guidance efficiency meets the preset matching push decision, the current qualification matching weighting adjustment parameters and matching push tendency scheme are structured and encapsulated to generate a matching integration weighting compliance report.

[0026] In this embodiment, the method for constructing the qualification weight constraint evaluation system is as follows: Use multi-source qualification indicator data as the feature input set in the qualification weight constraint evaluation system; The multi-source qualification indicator data includes domain topic proportion information and matching conversion characteristics; The qualification weights are dynamically adjusted according to the qualification weight balancing rules to constrain the domain association between different topics. Based on the state distribution characteristics in the matching feedback optimization space, a qualification weight constraint evaluation system that matches the qualification matching state is constructed to screen the weight combination regions that meet the matching conditions.

[0027] In this embodiment, the method for parsing the domain relevance is as follows: Calculate the domain migration gradient based on the continuity of qualification associations under adjacent topic nodes; The matching constraint determination is performed on the domain migration gradient. When a nonlinear abrupt change is detected in the constraint relationship between the matching feature of the target node and its neighboring nodes, a feasible region backtracking search is performed to obtain the qualification matching parameter set. The feasible domain backtracking search uses the domain migration gradient change rate as a constraint on the backtracking step size, and performs a reverse traversal of historical nodes along the qualification association path; The set of qualification matching parameters includes: qualification weight allocation parameters, matching constraint threshold parameters, and matching balance domain boundary parameters.

[0028] In this embodiment, taking a cold chain supply trading platform as an example, the platform faces the problem of difficulty in matching multiple qualification indicators (enterprise certification level, credit score, delivery capability, financial health, etc.) and easy deviation of the matching path.

[0029] Specific technical solution: Obtain multi-source qualification indicator data, construct a qualification weight constraint evaluation system, and obtain qualification matching weight adjustment parameters; Synchronize multi-source qualification indicators (e.g., certification level, credit score, delivery completion rate) from enterprise credit platforms, tax systems, and logistics tracking systems via API.

[0030] like Figure 2 The qualification weight constraint evaluation system is constructed as follows: the indicators are used as the feature input set (domain topic ratio + matching conversion features), the topic correlation is dynamically adjusted according to the qualification weight balance rule, and feasible weight combination regions are selected.

[0031] Domain Relevance Analysis: Calculating the Transfer Gradient When a nonlinear mutation occurs, a feasible region backtracking is performed to obtain the parameter set {weight allocation, constraint threshold, equilibrium domain boundary}.

[0032] Coordinated adjustment of dimensional qualification-sensitive weights: Initially allocate sensitive weights, and combine them with real-time modulation of dual-loop guiding values ​​to maintain the potential field of structural rearrangement.

[0033] Generate adjustment parameters: {authentication weight, credit weight, equilibrium domain}.

[0034] Establish a cyclic matching model, perform candidate guidance pool sorting and double-layer cyclic guidance, and output a matching and push preference scheme. Using the changes in pre-matching constraints as input, a cyclic matching model is established: quantifying the offset and matching degree, and dynamically updating the path.

[0035] Candidate guidance pool execution: Quantify the differences in node energy levels to construct a potential field, compress the guidance distribution, and prioritize the weights (authentication > credit > delivery).

[0036] Dual-layer cyclic guidance and regulation: Demand and supply are directly matched in a loop: when the matching degree is greater than the preset matching threshold, the result is output directly.

[0037] Market-oriented matching cycle: Introduce trend weights (historical behavior, market index) and dynamically adjust priorities.

[0038] Alternating iteration convergence, output scheme: {Push preference: authentication priority + market suggestion, priority sequence [company A, company B]}.

[0039] The weighted matching window is parsed, the push mode is selected, simulation and cluster boundary determination are performed, and the adaptation range is adjusted. The time series data is reconstructed hierarchically to generate stability indicators (rate of change + cumulative effect). The push mode is selected: high stability → direct push, low stability → suggested push.

[0040] Push critical state judgment: dynamically update the threshold, perform entropy increase mutation detection, perform backtracking to correct the adaptation interval, and use it as the reference benchmark domain for the cyclic model.

[0041] The simulation process is used to determine the cluster boundary and then adjust the interval.

[0042] Build a matching feedback optimization space, incrementally update parameters, verify efficiency, and generate a matching integration empowerment compliance report; Matching feedback optimization space: The state representation layer vectorizes multidimensional information (matching degree, path, response), and the guiding transformation mapping layer divides the response region.

[0043] Verification of matching guidance efficiency: High-frequency sampling feature vectors, multi-cycle iterative simulation, calculation of incremental improvement value, and parameter adjustment.

[0044] Report generation: Calibrate homogeneity, extract convergence / efficiency / deviation features, formulate adjustment strategies (weight + push frequency), and generate a structured report (JSON format: {adjustment parameter update values, push plan, compliance audit records}).

[0045] In this embodiment, the coordination process of the dimension qualification-sensitive weights is as follows: The dimension-based qualification sensitivity weights serve as dynamic matching constraint factors, and are initially allocated to each dimension-based sensitivity weight. Weight modulation is performed based on the matching path guidance value output by the dual-loop guiding driving function, and the state changes in the feedback optimization space of sensitive weight deviations in each dimension are matched in real time to maintain the driving potential field of qualification structure rearrangement in the matching evolution process.

[0046] In this embodiment, the method for constructing the cyclic matching model is as follows: Using the change in qualification constraints before matching as the input variable, the matching offset of different candidate entities before and after the adjustment of qualification constraints is mapped, and the matching degree between candidate entities is quantified based on the multi-source qualification indicators and demand characteristics of both parties in the matching process. During the evolution of the matching state, the current matching result is used as the input for the next round of matching calculation. The matching path and weight distribution are dynamically updated to construct a cyclic matching model.

[0047] In this embodiment, the execution process of the candidate bootstrapping pool is as follows: Based on the energy level differences between each theme node, the potential energy distribution between nodes is quantitatively modeled to obtain the potential field driving the quality structure rearrangement. The qualification structure rearrangement driving potential field is used to guide and constrain the migration trend and rearrangement path of qualification elements among multiple theme nodes, and to adjust the flow direction and aggregation degree of qualification elements. The participation rights of different qualification topic nodes are non-linearly transferred and allocated. Combined with the qualification topic relaxation time, the matching status is used as the compression guidance distribution value to sort the qualification weight priority.

[0048] In this embodiment, as Figure 3 The dual-layer cyclical guidance and regulation described herein includes: a direct demand-supply matching cycle and a market-oriented recommendation matching cycle; The direct matching loop between demand and supply: Based on the qualification matching constraints and demand characteristics of the matching parties, the matching degree between the demand side and the supply side is calculated in real time, and when the preset matching threshold condition is met, a fast matching path is triggered, and the candidate entities are directly output from the candidate guidance pool to the matching execution unit. The market-oriented matching cycle is as follows: based on the overall market supply and demand distribution and historical matching behavior data, the potential matching value of candidate entities is assessed, and market trend weights and strategy guidance factors are introduced to dynamically adjust the matching priority of candidate entities. The adjusted matching priority of candidate topics is fed back to the candidate guidance pool, and the matching of demand and supply is carried out in an alternating cycle until the optimal matching result is gradually converged, and the matching push preference scheme is output.

[0049] In this embodiment, the selection process for the matching suggestion push mode is as follows: Based on time series and qualification level, the qualification indicator response data is reconstructed in layers to quantify the matching change rate and cumulative effect of each push node in the continuous matching cycle, and generate a matching weight stability index. The matching weight stability index is used to jointly constrain the timing response of adjacent qualification layer nodes, and select the matching suggestion push mode according to the push priority score of each candidate subject, which serves as the input for matching guidance adjustment, and executes personalized matching push for candidate subjects.

[0050] In this embodiment, the calculation process of the incremental efficiency improvement value calculated by simulation feedback and the matching degree between the demand side and the supply side is as follows: Let the set of demand-side subjects be denoted as D = {d} i The set of supply-side entities is denoted as S = {s} j The multi-source qualification index vectors are represented as Q. di =(q i1 q in ) and Q sj =(q j1 q jn The corresponding qualification weight vector is W = (w1, w2, w3) n The specific calculation formula is as follows: , Among them, M ij For the demand side d i With the supply side s j The degree of matching, w k Let Φ(.) be the weight of the k-th qualification dimension, and let Φ(.) be the matching function used to characterize qualification similarity, defined as: , Where λ is the matching attenuation coefficient, and a path-guided correction term is further introduced to obtain the final matching degree: , Among them, G ij This represents the path gain value output by the dual-loop bootstrap function. ij For the matching bias term, α and β are weight adjustment coefficients.

[0051] In a simulated matchmaking environment, let the overall efficiency function for the t-th round of matchmaking be: , Where R （t） To achieve better matching success rate, H （t） To improve the hit rate, T （t） For the average response delay, C （t）As a path convergence index, 1~ 4 represents the weighting coefficient.

[0052] In this embodiment, the execution process of the push critical state discrimination mechanism is as follows: A critical state threshold is constructed based on historical matching behavior data and the characteristics of high homogeneous areas, and the threshold is dynamically and adaptively updated by combining real-time matching deviation and market guidance signals. Nonlinear entropy increase mutation detection is performed on the updated threshold to identify potential critical nodes; When a critical state is detected, feasible domain backtracking and boundary correction based on qualification association network are performed to dynamically adjust the matching demand adaptation range of candidate guiding entities. The matching demand adaptation interval serves as the state reference benchmark domain in the cyclic matching model. By limiting the matching transformation matching degree of the qualification weight constraint evaluation system, the qualification matching control process is transformed from single-topic compliance control to dynamic interval constraint control of multi-source coupled state feasible domain.

[0053] In this embodiment, the matching feedback optimization space includes a matching state representation layer and a guidance conversion mapping layer; The matching state representation layer is used to uniformly model the multidimensional state information in the matching process, map discrete matching behavior data into a continuous state representation space, and obtain the state vectorization expression basis of the qualification state in the high-dimensional mechanism feature space according to the qualification topic migration distribution law. The multidimensional status information includes at least qualification matching degree, weight distribution status, matching path structure, and push response result; The guidance conversion mapping layer is used to establish the mapping relationship between the matching state and the guidance strategy, match the conversion mapping relationship between response behaviors, and divide the corresponding guidance behavior response area in the matching feedback optimization space according to the matching guidance stability margin.

[0054] In this embodiment, the verification process for the matching guidance efficiency is as follows: High-frequency sampling is performed on the multidimensional state information of each candidate entity in the matching feedback optimization space to extract the matching feature vector; The matching feature vector is used to characterize the inclusion status of candidate entities in terms of qualification matching degree, matching path convergence, and push response effect. Based on the aforementioned feature vector, in a simulated matching environment, multi-cycle iterative simulation is performed according to the current qualification matching weighting adjustment parameters and push preference scheme. By combining the critical state discrimination mechanism to identify matching deviations and abnormal behaviors, and calculating the incremental efficiency improvement value based on simulation feedback, the matching weighting adjustment parameters and push tendency scheme are adjusted according to the matching weighting basis, and the matching guidance efficiency is verified.

[0055] In this embodiment, the method for generating the matching integration empowerment compliance report is as follows: Based on the matching feedback optimization space, the homogeneity of qualification matching is calibrated, and matching features of high homogeneity regions are extracted. The matching features include: matching path convergence features, push efficiency features, and abnormal matching deviation features. Develop adjustment strategies for qualification weighting components and push frequency based on preset matching failure benchmarks; The adjustment strategy is executed, and the matching behavior log data during execution is combined to encapsulate the current qualification matching weighting adjustment parameters and matching push preference scheme in a structured manner, generating a matching integration weighting compliance report.

[0056] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A qualification-based weighting and dual-loop guidance algorithm for online transaction matching, characterized in that, include: S1: Obtain multi-source qualification indicator data, construct a qualification weight constraint evaluation system, analyze the domain correlation between the multi-source qualification indicator data, obtain a set of qualification matching parameters constrained by different themes, and combine the matching balance domain partitioning function to coordinately adjust the qualification sensitive weights of the dimensions covering the qualification subjects to obtain qualification matching weight adjustment parameters. S2: Using the change in qualification constraints before matching as the input variable, establish a cyclic matching model, include the qualification score of the organizer in the candidate guidance pool, sort the qualification weight priority, and perform double-layer cyclic guidance adjustment on the matching path of the candidate guidance subject based on the double-loop guidance driving function, and output the matching push tendency scheme. S3: Analyze the weighted matching window in the matching stage, select the matching suggestion push mode, and simulate the qualification resource push process. Through the push critical state discrimination mechanism, determine the cluster boundary of the selected matching suggestion push mode and adjust the matching demand adaptation range. S4: Construct a matching feedback optimization space, periodically update the qualification matching weighting adjustment parameters according to changes in market demand, and verify the matching guidance efficiency in a simulation environment. When the matching guidance efficiency meets the preset matching push decision, the current qualification matching weighting adjustment parameters and matching push tendency scheme are structured and encapsulated to generate a matching integration weighting compliance report.

2. The algorithm according to claim 1, characterized in that, The method for constructing the qualification weight constraint evaluation system is as follows: Use multi-source qualification indicator data as the feature input set in the qualification weight constraint evaluation system; The multi-source qualification indicator data includes domain topic proportion information and matching conversion characteristics; The qualification weights are dynamically adjusted according to the qualification weight balancing rules to constrain the domain association between different topics. Based on the state distribution characteristics in the matching feedback optimization space, a qualification weight constraint evaluation system that matches the qualification matching state is constructed to screen the weight combination regions that meet the matching conditions.

3. The algorithm according to claim 1, characterized in that, The method for analyzing the domain relevance is as follows: Calculate the domain migration gradient based on the continuity of qualification associations under adjacent topic nodes; The matching constraint determination is performed on the domain migration gradient. When a nonlinear abrupt change is detected in the constraint relationship between the matching feature of the target node and its neighboring nodes, a feasible region backtracking search is performed to obtain the qualification matching parameter set. The feasible domain backtracking search uses the domain migration gradient change rate as a constraint on the backtracking step size, and performs a reverse traversal of historical nodes along the qualification association path; The set of qualification matching parameters includes: qualification weight allocation parameters, matching constraint threshold parameters, and matching balance domain boundary parameters.

4. The algorithm according to claim 1, characterized in that, The coordination process for the qualification-sensitive weights of the aforementioned dimensions is as follows: The dimension-based qualification sensitivity weights serve as dynamic matching constraint factors, and are initially allocated to each dimension-based sensitivity weight. Weight modulation is performed based on the matching path guidance value output by the dual-loop guiding driving function, and the state changes in the feedback optimization space of sensitive weight deviations in each dimension are matched in real time to maintain the driving potential field of qualification structure rearrangement in the matching evolution process.

5. The algorithm according to claim 1, characterized in that, The method for constructing the cyclic matching model is as follows: Using the change in qualification constraints before matching as the input variable, the matching offset of different candidate entities before and after the adjustment of qualification constraints is mapped, and the matching degree between candidate entities is quantified based on the multi-source qualification indicators and demand characteristics of both parties in the matching process. During the evolution of the matching state, the current matching result is used as the input for the next round of matching calculation. The matching path and weight distribution are dynamically updated to construct a cyclic matching model.

6. The algorithm according to claim 1, characterized in that, The execution process of the candidate bootstrapping pool is as follows: Based on the energy level differences between each theme node, the potential energy distribution between nodes is quantitatively modeled to obtain the potential field driving the quality structure rearrangement. The qualification structure rearrangement driving potential field is used to guide and constrain the migration trend and rearrangement path of qualification elements among multiple theme nodes, and to adjust the flow direction and aggregation degree of qualification elements. The participation rights of different qualification topic nodes are non-linearly transferred and allocated. Combined with the qualification topic relaxation time, the matching status is used as the compression guidance distribution value to sort the qualification weight priority.

7. The algorithm according to claim 1, characterized in that, The dual-layer cyclical guidance and adjustment includes: a direct demand-supply matching cycle and a market-oriented recommendation matching cycle; The direct matching loop between demand and supply: Based on the qualification matching constraints and demand characteristics of the matching parties, the matching degree between the demand side and the supply side is calculated in real time, and when the preset matching threshold condition is met, a fast matching path is triggered, and the candidate entities are directly output from the candidate guidance pool to the matching execution unit. The market-oriented matching cycle is as follows: based on the overall market supply and demand distribution and historical matching behavior data, the potential matching value of candidate entities is assessed, and market trend weights and strategy guidance factors are introduced to dynamically adjust the matching priority of candidate entities. The adjusted matching priority of candidate topics is fed back to the candidate guidance pool, and the matching of demand and supply is carried out in an alternating cycle until the optimal matching result is gradually converged, and the matching push preference scheme is output.

8. The algorithm according to claim 1, characterized in that, The process for selecting the matching suggestion push mode is as follows: Based on time series and qualification level, the qualification indicator response data is reconstructed in layers to quantify the matching change rate and cumulative effect of each push node in the continuous matching cycle, and generate a matching weight stability index. The matching weight stability index is used to jointly constrain the timing response of adjacent qualification layer nodes, and select the matching suggestion push mode according to the push priority score of each candidate subject, which serves as the input for matching guidance adjustment, and executes personalized matching push for candidate subjects.

9. The algorithm according to claim 1, characterized in that, The execution process of the push critical state discrimination mechanism is as follows: A critical state threshold is constructed based on historical matching behavior data and the characteristics of high homogeneous areas, and the threshold is dynamically and adaptively updated by combining real-time matching deviation and market guidance signals. Nonlinear entropy increase mutation detection is performed on the updated threshold to identify potential critical nodes; When a critical state is detected, feasible domain backtracking and boundary correction based on qualification association network are performed to dynamically adjust the matching demand adaptation range of candidate guiding entities. The matching demand adaptation interval serves as the state reference benchmark domain in the cyclic matching model. By limiting the matching transformation matching degree of the qualification weight constraint evaluation system, the qualification matching control process is transformed from single-topic compliance control to dynamic interval constraint control of multi-source coupled state feasible domain.

10. The algorithm according to claim 1, characterized in that, The matching feedback optimization space includes: a matching state representation layer and a guidance conversion mapping layer; The matching state representation layer is used to uniformly model the multidimensional state information in the matching process, map discrete matching behavior data into a continuous state representation space, and obtain the state vectorization expression basis of the qualification state in the high-dimensional mechanism feature space according to the qualification topic migration distribution law. The multidimensional status information includes at least qualification matching degree, weight distribution status, matching path structure, and push response result; The guidance conversion mapping layer is used to establish the mapping relationship between the matching state and the guidance strategy, match the conversion mapping relationship between response behaviors, and divide the corresponding guidance behavior response area in the matching feedback optimization space according to the matching guidance stability margin.

11. The algorithm according to claim 1, characterized in that, The verification process for the matching guidance efficiency is as follows: High-frequency sampling is performed on the multidimensional state information of each candidate entity in the matching feedback optimization space to extract the matching feature vector; The matching feature vector is used to characterize the inclusion status of candidate entities in terms of qualification matching degree, matching path convergence, and push response effect. Based on the aforementioned feature vector, in a simulated matching environment, multi-cycle iterative simulation is performed according to the current qualification matching weighting adjustment parameters and push preference scheme; By combining the critical state discrimination mechanism to identify matching deviations and abnormal behaviors, and calculating the incremental efficiency improvement value based on simulation feedback, the matching weighting adjustment parameters and push tendency scheme are adjusted according to the matching weighting basis, and the matching guidance efficiency is verified.

12. The algorithm according to claim 1, characterized in that, The method for generating the matching integration empowerment compliance report is as follows: Based on the matching feedback optimization space, the homogeneity of qualification matching is calibrated, and matching features of high homogeneity regions are extracted. The matching features include: matching path convergence features, push efficiency features, and abnormal matching deviation features. Develop adjustment strategies for qualification weighting components and push frequency based on preset matching failure benchmarks; The adjustment strategy is executed, and the matching behavior log data during execution is combined to encapsulate the current qualification matching weighting adjustment parameters and matching push preference scheme in a structured manner, generating a matching integration weighting compliance report.

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