Fair competition impact assessment optimization method based on counterfactual reasoning

Abstract

The application discloses a fair competition influence evaluation optimization method based on counterfactual reasoning, relates to the technical field of data analysis, and comprises the following steps: collecting running state data of a target object in a running process for pretreatment, forming an evaluation data set, analyzing a running state change relationship, and determining a state change path of a target influence factor; based on the state change path and the evaluation data set, the target influence factor is intervened, the associated state is adjusted based on a state change path constraint mechanism, and counterfactual state data is generated; and the result states of the actual running state data and the counterfactual state data are subjected to difference analysis to obtain a counterfactual influence difference result. The application realizes accurate identification of an influence factor action path and fair competition influence evaluation optimization based on counterfactual reasoning, and improves the accuracy and reliability of fair competition influence evaluation under a complex dynamic running scene.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to an optimization method for fair competition impact assessment based on counterfactual reasoning. Background Technology

[0002] With the development of data acquisition, intelligent analysis, and decision optimization technologies, fair competition impact assessment is gradually moving towards a data-driven approach. Existing technologies collect operational status data of target objects, construct evaluation indicators, and combine statistical analysis, correlation analysis, and predictive analysis methods to identify the relationship between changes in operational status and influencing factors, thereby achieving competitive status assessment. Some technologies utilize predictive models to analyze operational trends and adjust relevant parameters based on the analysis results, providing a basis for operational optimization.

[0003] Existing fair competition impact assessment technologies mainly rely on existing data analysis to analyze the correlation between influencing factors and outcome states. In scenarios with multiple factors in dynamic action, it is difficult to accurately identify the impact path and true relationship after changes in target influencing factors. Existing predictive analysis methods have limited ability to identify hidden influencing factors, and the optimization process lacks a verification mechanism based on counterfactual state changes, making it difficult to comprehensively assess the role of parameter adjustments in the fair competition impact outcome. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an optimization method for fair competition impact assessment based on counterfactual reasoning to address the problems of inaccurate identification of the action paths of influencing factors and insufficient verification of optimization results in the prior art.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] This invention provides an optimization method for fair competition impact assessment based on counterfactual reasoning, which includes collecting operational status data of the target object during its operation, preprocessing it to form an assessment data set, analyzing the relationship between operational status changes, and determining the status change path of the target influencing factors.

[0008] Based on the state change path and the evaluation data set, intervention is carried out on the target influencing factors, and the associated states are adjusted based on the state change path constraint mechanism to generate counterfactual state data.

[0009] A difference analysis was conducted between the actual operational status data and the counterfactual status data to obtain the results of the difference in counterfactual impact.

[0010] Based on the counterfactual impact difference results and the evaluation data set, the state change prediction results and target influencing factor prediction results are obtained through a dual predictive analysis mechanism. Hidden influencing factors are identified based on the residual information between the prediction results and the actual results, and the real influencing factor set is obtained.

[0011] Based on the real set of influencing factors and the results of counterfactual impact differences, the fair competition impact assessment results are generated, and the parameter configuration status and operation adjustment strategy are optimized to obtain optimized operation status data.

[0012] The optimized operational status data is compared and verified with the fair competition impact assessment results. The impact changes corresponding to the optimized operational status data are analyzed to obtain the final optimized fair competition impact assessment results.

[0013] As a preferred embodiment of the fair competition impact assessment optimization method based on counterfactual reasoning described in this invention, the assessment data set includes:

[0014] Collect runtime status data of the target object during its operation, and perform missing value detection, outlier identification, and format unification processing to obtain standardized runtime status data;

[0015] Standardized operational status data are processed by extracting and combining status features to form an evaluation data set.

[0016] As a preferred embodiment of the fair competition impact assessment optimization method based on counterfactual reasoning described in this invention, the state change path of the target influencing factor includes:

[0017] Obtain the operational status parameter data and corresponding status change information from the evaluation dataset to obtain the status change correlation data;

[0018] By performing correlation analysis of influencing factors and identification of state change trends on the data related to state changes, the correlation data of target influencing factors is obtained.

[0019] Based on the correlation data of the target influencing factors, the analysis of the interaction relationship of the influencing factors and the determination of the state change path are carried out to determine the state change path of the target influencing factors.

[0020] As a preferred embodiment of the fair competition impact assessment optimization method based on counterfactual reasoning described in this invention, the counterfactual state data includes:

[0021] Based on the state change path and the evaluation data set, the operational state parameter data in the evaluation data set are correlated and matched according to the state change direction corresponding to the target influencing factor to obtain the operational state parameter data before and after the change of the target influencing factor.

[0022] Based on the operational status parameter data before and after the change of the target influencing factors, calculate the correlation between the magnitude of the status change, the direction of the status change, and the response time of the status change, extract the associated status parameters corresponding to the target influencing factors, and determine the intervention scope of the target influencing factors;

[0023] Based on the intervention range of the target influencing factors, a gradient of the target influencing factors is constructed. According to the gradient of the target influencing factors, the target influencing factors are intervened in stages. The boundary of the state change during the adjustment process of the target influencing factors is obtained based on the state change path constraint mechanism.

[0024] Specifically, a state change path constraint mechanism is constructed based on the relationship between the change order, change direction and change magnitude of the associated state parameters in the state change path. By limiting the cooperative change relationship of the associated state parameters, the adjustment process of the target influencing factors is constrained. Based on the state change path constraint mechanism, the associated state is dynamically corrected to obtain the adjusted associated state data.

[0025] Based on the adjusted associated state data and the state change path constraint mechanism, the path of the associated state parameter change process in the adjusted associated state data is reconstructed. Based on the response relationship between the target influencing factors and the associated state parameters in the state change path, the state evolution result of the target influencing factors after adjustment is predicted, and counterfactual state data is generated.

[0026] As a preferred embodiment of the fair competition impact assessment optimization method based on counterfactual reasoning described in this invention, the counterfactual impact difference results include:

[0027] Based on the actual operating status data and counterfactual status data, extract the corresponding status parameter change information, and perform correlation matching on the same status parameters in the actual operating status data and counterfactual status data to obtain status difference correlation data.

[0028] Based on state difference correlation data, identify the changing trends, directions and ranges of state parameters in actual operating state data and counterfactual state data, and obtain state change difference data;

[0029] By comparing and analyzing the results of actual operating state data and counterfactual state data using state change difference data, the influence relationship of changes in target influencing factors on the result state is determined, and the counterfactual influence difference results are obtained.

[0030] As a preferred embodiment of the fair competition impact assessment optimization method based on counterfactual reasoning described in this invention, the set of real influencing factors includes:

[0031] Based on the counterfactual impact difference results and the assessment dataset, the state change information in the counterfactual impact difference results is correlated and matched with the state parameter change process in the assessment dataset to extract the state change information.

[0032] Based on the correlation and matching between the target influencing factor change information in the counterfactual impact difference results and the state response relationship in the assessment dataset, the target influencing factor change information is extracted to obtain state change information and target influencing factor change information.

[0033] Based on state change information and target influencing factor change information, state change prediction relationship and target influencing factor prediction relationship are constructed according to the correlation between changes in state parameters and the response relationship between target influencing factors and state parameters.

[0034] Based on the state change prediction relationship and the target influencing factor prediction relationship, the target influencing factor change process and state change process in the evaluation dataset are predicted and analyzed. The target influencing factor change information is input into the target influencing factor prediction relationship to obtain the target influencing factor prediction result, and the state change prediction result is obtained based on the target influencing factor prediction result.

[0035] Based on the state change prediction results, target influencing factor prediction results, and actual results, the mismatched change relationships between the prediction results and actual results are identified, and the influencing relationships not explained by the state change prediction results and target influencing factor prediction results are extracted to obtain hidden influencing factors.

[0036] The correlation between the state change process corresponding to the hidden influencing factors and the change relationship in the counterfactual impact difference results is verified. Hidden influencing factors that conform to the state change path and the relationship between the target influencing factors are screened to obtain the set of real influencing factors.

[0037] As a preferred embodiment of the fair competition impact assessment optimization method based on counterfactual reasoning described in this invention, the fair competition impact assessment results include:

[0038] Based on the set of real influencing factors and the results of counterfactual impact differences, the change information of target influencing factors in the set of real influencing factors is correlated with the state change information in the results of counterfactual impact differences to obtain the correlation of changes in influencing factors.

[0039] Based on the correlation of changes in influencing factors, we analyze the impact process of the state changes of the actual set of influencing factors and determine the influence relationship between the actual set of influencing factors and the outcome state.

[0040] Based on the set of real influencing factors, counterfactual impact differences, and impact relationships, a fair competition impact assessment result is generated.

[0041] As a preferred embodiment of the fair competition impact assessment optimization method based on counterfactual reasoning described in this invention, the optimized operational status data includes:

[0042] By using the results of the fair competition impact assessment and the set of real influencing factors, we extract the information on changes in operational status and the adjustment needs of target influencing factors corresponding to the impact results, and determine the direction of parameter optimization.

[0043] The parameter configuration status in the operation adjustment strategy is adjusted based on the parameter optimization direction, and the optimized parameter configuration status is matched with the operation status change information to obtain the optimized parameter configuration status.

[0044] Based on the optimized parameter configuration status and operation adjustment strategy, the operation process of the target object is adjusted, and the adjusted operation status data is obtained.

[0045] Based on the adjusted operational status data, the consistency of the status is verified, and the operational status data that meets the optimized parameter configuration status and operational adjustment strategy is selected to obtain the optimized operational status data.

[0046] As a preferred embodiment of the fair competition impact assessment optimization method based on counterfactual reasoning described in this invention, the comparative verification includes:

[0047] Based on the optimized operational status data and the fair competition impact assessment results, we extract the status change information from the optimized operational status data and the impact relationship information from the fair competition impact assessment results to obtain validation correlation data.

[0048] A consistency correlation analysis was conducted between the state change information in the optimized operational status data and the impact relationship information in the fair competition impact assessment results to obtain the state change verification results.

[0049] Based on the state change verification results, determine the matching relationship between the optimized operating state data and the fair competition impact assessment results, and complete the comparative verification of the optimized operating state data and the fair competition impact assessment results.

[0050] As a preferred embodiment of the fair competition impact assessment optimization method based on counterfactual reasoning described in this invention, the final optimized fair competition impact assessment result includes:

[0051] By extracting the state change information and impact change information corresponding to the optimized operating state data, we can obtain the optimized state impact change data.

[0052] Based on the correlation analysis between the data on changes in the optimized operating status and the results of the fair competition impact assessment, the impact changes corresponding to the optimized operating status data are identified, and the impact change analysis results are obtained.

[0053] The impact assessment results of fair competition are updated and adjusted based on the impact change analysis results, resulting in the final optimized impact assessment results of fair competition.

[0054] The beneficial effects of this invention are as follows: By collecting operational status data of the target object during its operation and forming an evaluation data set, the state change path of the target influencing factors is determined based on the operational status change relationship. An intervention mechanism is used to constrain the target influencing factors and generate counterfactual state data, thus simulating the state evolution process after the changes in the target influencing factors. The difference analysis between the actual operational status data and the counterfactual state data yields the counterfactual impact difference results, and a dual predictive analysis mechanism is used to identify hidden influencing factors, obtaining a set of true influencing factors. Based on the set of true influencing factors, the fair competition impact assessment results and operational adjustment strategies are optimized, generating optimized operational status data. The fair competition impact assessment results are updated through comparative verification. This achieves accurate identification of the influencing factor action path and optimization of the fair competition impact assessment based on counterfactual reasoning, improving the accuracy and reliability of fair competition impact assessment in complex dynamic operational scenarios. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a flowchart of an optimization method for fair competition impact assessment based on counterfactual reasoning. Detailed Implementation

[0057] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0058] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0059] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0060] Reference Figure 1 This is one embodiment of the present invention, which provides an optimization method for fair competition impact assessment based on counterfactual reasoning, including the following steps:

[0061] S1. Collect operational status data of the target object during operation, preprocess it to form an evaluation data set, analyze the relationship of operational status changes, and determine the status change path of the target influencing factors.

[0062] S1.1 Collect the running status data of the target object during its operation, and perform missing value detection, outlier identification and format unification processing to obtain standardized running status data.

[0063] Furthermore, missing value detection is performed on the operational status data. By identifying the locations of missing data in the operational status data and supplementing the missing status information in conjunction with the status change trends of adjacent operational stages, the operational status data is kept continuous. By analyzing the change patterns between operational status data, it is determined whether there are abnormal states that deviate from the normal change range. For example, if there are data points in the operational status data that are inconsistent with the change trends of the preceding and following operational states, the corresponding operational status data will be marked as an anomaly. After completing the missing value detection and anomaly identification, the operational status data is processed to unify the format, converting operational status data from different sources and with different expression forms into a unified data expression form, enabling different types of operational status data to be correlated and analyzed to obtain standardized operational status data.

[0064] S1.2 Extract and combine features from standardized operating status data to form an evaluation data set.

[0065] Furthermore, based on standardized operational status data, state features that reflect the operational change patterns of the target object are extracted, including state change trend features, state response features, and state correlation features. By analyzing the changes in standardized operational status data at different operational stages, information characterizing the operational status change process is extracted. The state features are then processed through feature combination, and correlated state features are matched. Operational status parameter change features are combined with corresponding state response features, expanding single state parameter information into comprehensive state information that reflects operational status change relationships. Through feature combination processing, the ability of standardized operational status data to describe the operational status change process of the target object is improved. The processed state feature information is then aggregated and organized to form an evaluation data set.

[0066] S1.3 Obtain the operating status parameter data and corresponding status change information from the evaluation data set to obtain the status change correlation data.

[0067] Furthermore, based on the operational status parameter data in the evaluation dataset, status information is filtered. According to the changes in status parameters at different operational stages, operational status parameter data reflecting the changing process of the target object's operational status is extracted. Corresponding status change information is obtained based on the changes in operational status parameter data during continuous operation. For example, by identifying the time nodes, direction, and trend of changes in operational status parameter data, the corresponding status change information when the operational status parameter data changes is determined. The operational status parameter data and corresponding status change information are then correlated and matched, ensuring that each operational status parameter data corresponds to a specific status change process, forming status change correlation data that reflects the relationships between status parameters. Through the correlation between operational status parameter data and status change information, subsequent influencing factor analysis can be based on the status change process, rather than judging only a single status parameter, thus obtaining status change correlation data.

[0068] S1.4 Perform influencing factor correlation analysis and state change trend identification processing on the state change correlation data to obtain the target influencing factor correlation data.

[0069] Furthermore, based on the operational status parameter data and status change information in the status change correlation data, correlation analysis is performed on the factors affecting operational status changes. By matching the common change characteristics in the different operational status parameter data changes, influencing factors that are related to the status change process are identified. When a certain influencing factor changes, multiple operational status parameter data change synchronously, and the corresponding influencing factor is identified as a related influencing factor. Trend identification is performed on the status change information in the status change correlation data. By analyzing the continuous change direction of the operational status parameter data in the change process, it is determined whether the status change process is a continuous change, a staged change, or a fluctuating change. Based on the results of the influencing factor correlation analysis and the results of the status change trend identification, the correspondence between influencing factors and operational status parameter data is organized to obtain the target influencing factor correlation data.

[0070] S1.5. Based on the correlation data of the target influencing factors, perform analysis of the relationship between the influencing factors and determine the state change path to identify the state change path of the target influencing factors.

[0071] Furthermore, based on the correlation data of target influencing factors, the interaction between target influencing factors and operational status parameter data is analyzed. According to the response sequence, direction of change, and process of different operational status parameter data during the change of target influencing factors, the transmission relationship of the effect of target influencing factors on operational status changes is identified. According to the order of action between target influencing factors and related status parameters, the path of status change process is organized. For example, if a change in target influencing factors first causes a change in a certain operational status parameter data, affecting other related operational status parameter data, then the corresponding change process is determined as the status change path. By analyzing the interaction relationships in the correlation data of target influencing factors, the status change path of target influencing factors is determined.

[0072] S2. Based on the state change path and the evaluation data set, intervene in the target influencing factors, adjust the associated states based on the state change path constraint mechanism, and generate counterfactual state data.

[0073] S2.1 Based on the state change path and the evaluation data set, the operating state parameter data in the evaluation data set are correlated and matched according to the state change direction corresponding to the target influencing factor to obtain the operating state parameter data before and after the change of the target influencing factor.

[0074] Furthermore, based on the state change path, information on the direction of change of the target influencing factor is obtained. The associated state parameters involved in the change of the target influencing factor and the order of change of these associated state parameters are determined. Operational state parameter data matching the direction of change of the target influencing factor are screened from the evaluation dataset. By performing time and state correlation matching on the operational state parameter data of different operational stages, the operational state parameter data before and after the change of the target influencing factor are determined. When the corresponding operational state parameter data shows a synchronous increasing trend after the change of the target influencing factor, operational state parameter data with the same direction of change are matched as associated state parameter data. When the corresponding operational state parameter data shows a delayed change after the change of the target influencing factor, the operational state parameter data of the corresponding time stage is matched according to the response relationship in the state change path. The matched operational state parameter data before and after the change of the target influencing factor are correlated with the change process of the target influencing factor, so that the state change process before and after the change of the target influencing factor can form a continuous correspondence, thus obtaining the operational state parameter data before and after the change of the target influencing factor.

[0075] S2.2 Calculate the correlation between the amplitude of state change, the direction of state change, and the response time of state change based on the operational state parameter data before and after the change of the target influencing factors, extract the associated state parameters corresponding to the target influencing factors, and determine the intervention scope of the target influencing factors.

[0076] Furthermore, based on the operational status parameter data before and after the change of the target influencing factors, the differences in the state of the same operational status parameter data before and after the change of the target influencing factors are analyzed. The degree of change of the operational status parameter data before and after the change of the target influencing factors is expressed by the amplitude of the state change. Specifically, the amplitude of the state change is obtained by the relationship between the operational status parameter data after the change of the target influencing factors and the operational status parameter data before the change of the target influencing factors. The impact of abnormal fluctuations when the operational status parameter data approaches a stable state on the judgment of state change is reduced by adjusting parameters. The direction of state change is determined according to the trend of the operational status parameter data before and after the change of the target influencing factors. When the operational status parameter data after the change of the target influencing factors is higher than the operational status parameter data before the change of the target influencing factors, it is determined that the operational status parameter data is increasing. When the operational status parameter data after the change of the target influencing factors is consistent with the operational status parameter data before the change of the target influencing factors, it is determined that the operational status parameter data is stable. When the operational status parameter data after the change of the target influencing factors is lower than the operational status parameter data before the change of the target influencing factors, it is determined that the operational status parameter data is decreasing.

[0077] Specifically, based on the time points when the target influencing factors change and the time points when the operational status parameter data completes the state change response, the state change response time is obtained. This is used to identify the sequential relationship of the responses of different operational status parameter data after the target influencing factors change. Some operational status parameter data respond immediately after the target influencing factors change, resulting in a shorter state change response time; others change after state transmission, resulting in a longer state change response time. Based on the correlation between the amplitude, direction, and response time of state changes, the change characteristics of different operational status parameter data are comprehensively matched. The amplitude of the change determines the degree of state change, the direction determines the trend, and the response time determines the state response process. This identifies the associated state parameters corresponding to the change process of the target influencing factors. Based on the change range and response characteristics of the associated state parameters, the intervention range of the target influencing factors is determined, resulting in the associated state parameters and the intervention range of the target influencing factors.

[0078] The expression for the magnitude of the state change is:

[0079] ;

[0080] in, For the first The magnitude of state change of each running status parameter data. For the operational status parameter data after changes in the target influencing factors, The data represents the operational status parameters before the changes in the influencing factors of the target. The adjustment parameter is used to prevent abnormal fluctuations when the parameter changes close to zero.

[0081] The expression for the direction of state change is:

[0082] ;

[0083] in, For the first The state change direction of the operating status parameter data is 1, which indicates that the operating status parameter data is increasing; 0 indicates that the operating status parameter data remains stable; and −1 indicates that the operating status parameter data is decreasing.

[0084] The expression for the response time to a state change is:

[0085] ;

[0086] in, For the first Response time of status changes for each running status parameter data The time point at which the factors influencing the target change. For the first The time point at which the running status parameter data completes the status change response.

[0087] The expression relating the magnitude of state change, the direction of state change, and the response time of state change is as follows:

[0088] ;

[0089] in, For the first The correlation between the status changes corresponding to each running status parameter data. This represents the correlation mapping relationship between the changing trajectories.

[0090] S2.3 Construct the gradient of target influencing factors based on the scope of intervention for target influencing factors, conduct phased state intervention for target influencing factors according to the gradient of target influencing factors, and obtain the state change boundary in the process of adjusting target influencing factors based on the state change path constraint mechanism.

[0091] Furthermore, based on the intervention range of the target influencing factors, the adjustable state change range of the target influencing factors is determined. According to the response relationship of the corresponding associated state parameters during the change of the target influencing factors, the intervention process is divided into multiple continuous change stages, forming a target influencing factor change gradient. The target influencing factor change gradient describes the degree of change of the target influencing factors at different stages during the change from the current state to the target state, enabling the adjustment process to proceed gradually according to the state change process. When there is a gradual response process for the corresponding associated state parameters after a change in the target influencing factors, the target influencing factor change gradient is determined based on the state response process, ensuring that the adjustment process corresponds to the change process of the associated state parameters. When there is a rapid change process for the associated state parameters after a change in the target influencing factors, the target influencing factor change gradient is adjusted according to the direction of the state change, ensuring that the adjustment process adapts to the state change trend.

[0092] Specifically, based on the gradient of changes in target influencing factors, phased state intervention is performed on the target influencing factors. In each stage of change, the changes in corresponding associated state parameters are acquired. Combined with the order, direction, and magnitude of these changes in the associated state parameters along the state change path, state constraints are applied to the adjustment process of the target influencing factors. When associated state parameters change according to the state change path during the adjustment process, the current adjustment state of the target influencing factors is maintained. When associated state parameters deviate from the state change path, the change stage of the target influencing factors is adjusted according to the state change path constraint mechanism, restoring the associated state parameters to a range consistent with the state change path. The state change boundary during the adjustment process is obtained through the state change path constraint mechanism, determining the range within which the intervention process can produce effective state changes.

[0093] Specifically, a state change path constraint mechanism is constructed based on the relationship between the change order, change direction and change magnitude of the associated state parameters in the state change path. By limiting the coordinated change relationship of the associated state parameters, the adjustment process of the target influencing factors is constrained. Based on the state change path constraint mechanism, the associated state is dynamically corrected to obtain the adjusted associated state data.

[0094] Furthermore, the sequence of changes of each associated state parameter in the state change path is obtained. Based on the order in which different associated state parameters respond after a change in the target influencing factor, the state transmission relationship between associated state parameters is determined. Based on the directional relationship of the associated state parameters, it is determined whether the changing trends of different associated state parameters remain consistent during the adjustment process of the target influencing factor. For example, when one associated state parameter increases, another associated state parameter should maintain a corresponding changing trend. Therefore, the corresponding directional relationship is used as a constraint condition for the state change path. Based on the magnitude relationship of the changes between associated state parameters, the matching relationship of the degree of change between different associated state parameters is determined, ensuring that the change process of the associated state parameters corresponds to the state change path. A state change path constraint mechanism is formed based on the correlation between the sequence, direction, and magnitude of changes, enabling the state change path constraint mechanism to describe the coordinated change relationship between associated state parameters during the adjustment process of the target influencing factor.

[0095] Specifically, based on the state change path constraint mechanism, the associated states during the adjustment process of target influencing factors are dynamically corrected, and the adjusted associated state data is obtained. The changes in associated state parameters in the adjusted associated state data are then matched with the state change path constraint mechanism. When the order of changes in associated state parameters is consistent with the order of changes in the state change path, and the direction and magnitude of changes in associated state parameters satisfy the state change path constraint relationship, the current associated state is maintained. When the order of changes in associated state parameters deviates, the direction of changes is inconsistent, or the magnitude of changes exceeds the range of the association relationship, the associated state parameters are adjusted according to the state change path constraint mechanism to make the associated state parameters satisfy the collaborative change relationship in the state change path again. When an associated state parameter changes prematurely after the target influencing factor is adjusted, but the corresponding associated state parameter in the associated state path should respond in a subsequent stage, the associated state change process is adjusted according to the state change path constraint mechanism to restore the order of changes in associated state parameters to the corresponding relationship in the state change path, thus obtaining the adjusted associated state data.

[0096] S2.4 Based on the adjusted associated state data and the state change path constraint mechanism, the path of the associated state parameter change process in the adjusted associated state data is reconstructed. Based on the response relationship between the target influencing factors and the associated state parameters in the state change path, the state evolution result of the target influencing factors after adjustment is predicted, and counterfactual state data is generated.

[0097] Furthermore, based on the adjusted associated state data, the change process information of associated state parameters during the adjustment of target influencing factors is obtained. The change process of associated state parameters is matched with the change sequence, change direction and change magnitude relationship in the state change path constraint mechanism to determine the state change nodes and state change transmission relationships corresponding to the adjusted associated state data. According to the response relationship between the target influencing factors and associated state parameters in the state change path, the path of associated state parameter change process is reconstructed so that the change process of associated state parameters in the adjusted associated state data is reorganized according to the state change path after the target influencing factors take effect. When a certain associated state parameter changes after the target influencing factors are adjusted, causing other associated state parameters to respond, the transmission process between associated state parameters is determined by the response sequence in the state change path, so that the change process of associated state parameters can reflect the state evolution process after the target influencing factors take effect step by step.

[0098] Specifically, based on the change process of associated state parameters after path reconstruction, and according to the response relationship between the target influencing factors and associated state parameters in the state change path, the state evolution result after the adjustment of the target influencing factors is predicted. When the change of the target influencing factors causes the associated state parameters to produce continuous responses, the subsequent state change trend is determined according to the response relationship between the associated state parameters; when the change of the target influencing factors causes some associated state parameters to produce asynchronous responses, the change boundary between the associated state parameters is determined according to the state change path constraint mechanism, and the state evolution result satisfying the state change path constraint relationship is predicted. By combining the associated state parameter change results corresponding to the change process of the target influencing factors, a state change result different from the actual operating state data is formed, generating counterfactual state data.

[0099] S3. Perform a difference analysis on the results of the actual operating status data and the counterfactual status data to obtain the results of the difference in counterfactual impact.

[0100] S3.1 Extract the corresponding state parameter change information based on the actual operating state data and the counterfactual state data, and perform correlation matching on the same state parameters in the actual operating state data and the counterfactual state data to obtain state difference correlation data.

[0101] Furthermore, corresponding state parameter change information is extracted from actual operating state data and counterfactual state data. By identifying state parameters with the same state identifier, the same change type, and the same response stage in actual operating state data and counterfactual state data, a parameter correspondence relationship is established between actual operating state data and counterfactual state data. Based on the parameter correspondence relationship, the state parameter change process in actual operating state data is matched with the state parameter change process in counterfactual state data to obtain the change difference information of the same state parameter under different state conditions. When a certain state parameter in actual operating state data changes without adjustment of the target influencing factor, the corresponding state parameter change process is matched with the change process of the same state parameter in counterfactual state data under adjustment of the target influencing factor. By comparing the two state change processes, state difference correlation data is formed, which can reflect the state change difference before and after the change of the target influencing factor.

[0102] S3.2. Based on state difference correlation data, identify the changing trends, directions and ranges of state parameters in actual operating state data and counterfactual state data, and obtain state change difference data.

[0103] Furthermore, based on the changes in actual operating state data and counterfactual state data in the state difference correlation data, the continuous change process of the corresponding state parameters is identified. By analyzing the changing trends of state parameters under different state conditions, it is determined whether the state parameters are in a state of continuous change, stable change, or fluctuating change. The change direction of the same state parameters in the actual operating state data and counterfactual state data is used to make a judgment. When the corresponding state parameters in the actual operating state data and counterfactual state data all show the same change direction, it is determined that the change trend of the state parameters is consistent. When the corresponding state parameters in the actual operating state data and counterfactual state data show opposite change directions, it is determined that the change in the target influencing factor has caused a change in the direction of state change. Based on the difference in the range of change between the actual operating state data and counterfactual state data during the state parameter change process, the boundary of state change before and after the adjustment of the target influencing factor is identified, and state change difference data containing the change trend, change direction, and change range is obtained.

[0104] S3.3. By comparing and analyzing the results of the actual operating state data and the counterfactual state data through the state change difference data, the influence relationship of the change of the target influencing factors on the result state is determined, and the counterfactual influence difference results are obtained.

[0105] Furthermore, based on the trends, directions, and ranges of state parameter changes in the state change difference data, a correlation analysis is performed between the actual operating state data and the counterfactual state data. By identifying the changes in the result state before and after the change of the target influencing factor, the state response relationship caused by the change of the target influencing factor is determined. When multiple related state parameters in the counterfactual state data change accordingly with the change of the target influencing factor, the relationship of the related state parameter changes is determined as the correlation relationship in which the target influencing factor affects the result state. When some state parameter changes do not respond to the change of the target influencing factor, the unresponsive state parameters are distinguished as non-related state changes. By analyzing the difference relationship between the result states of the actual operating state data and the counterfactual state data, the influence relationship of the change of the target influencing factor on the result state is determined, and the counterfactual impact difference results are obtained.

[0106] S4. Based on the counterfactual impact difference results and the evaluation data set, the state change prediction results and target influencing factor prediction results are obtained through a dual prediction analysis mechanism. Hidden influencing factors are identified based on the residual information between the prediction results and the actual results, and the real influencing factor set is obtained.

[0107] S4.1 Based on the counterfactual impact difference results and the evaluation data set, the state change information in the counterfactual impact difference results is correlated and matched with the state parameter change process in the evaluation data set to extract the state change information.

[0108] Furthermore, based on the counterfactual impact difference results, information on the state changes resulting from the differential responses after changes in the target influencing factors is obtained. The corresponding state parameter change processes are extracted from the assessment dataset. The state change information in the counterfactual impact difference results is correlated and matched with the state parameter change processes in the assessment dataset through state parameter identification, change trends, and response relationships. Corresponding associated state parameters are determined based on the state change information in the counterfactual impact difference results. Then, the changes in the associated state parameters in the assessment dataset are located at different operational stages, ensuring that the state change information in the counterfactual impact difference results corresponds to the actual state change processes in the assessment dataset. Matching is performed based on the direction, sequence, and response relationships of the state parameter changes. When the state parameter change directions are consistent and a corresponding response relationship exists, the corresponding state parameter change process is identified as the associated state change process. When the state parameter change directions are consistent but the response times differ, the state parameter change processes at the corresponding change stages are matched based on the response relationships in the state change paths. By correlating the state change information in the counterfactual impact difference results with the state parameter change processes in the assessment dataset, the obtained state change information reflects the actual state change processes corresponding to changes in the target influencing factors, thus extracting the state change information.

[0109] S4.2. Based on the information on changes in target influencing factors in the counterfactual impact difference results, the correlation and matching are performed with the state response relationship in the evaluation data set to extract the information on changes in target influencing factors, thus obtaining the information on changes in state and the information on changes in target influencing factors.

[0110] Furthermore, state-response relationships related to the target influencing factors are extracted from the assessment dataset. By analyzing the response processes of associated state parameters before and after changes in the target influencing factors, the correspondence between changes in the target influencing factors and changes in state parameters is determined. Based on the direction of change, stage of change, and state-response relationships in the information on changes in the target influencing factors, the state-response relationships in the assessment dataset are matched. When the direction of change in the target influencing factors corresponds to the direction of response in the state parameters, and the response process of the state parameters is temporally correlated with the change process of the target influencing factors, the corresponding state-response relationship is determined as the target influencing factor-related response relationship. When there are multiple state parameter response processes after a change in the target influencing factors, the state-response relationship corresponding to the change in the target influencing factors is determined based on the correlation in the state change path, thus avoiding the impact of irrelevant state parameter changes on the extraction of information on changes in the target influencing factors.

[0111] Specifically, when a change in a target influencing factor leads to a change in a certain state parameter, which in turn causes changes in other state parameters, the associated response chain in the process of the target influencing factor change is determined through the state-response relationship. When a change in a target influencing factor only causes changes in some state parameters, the corresponding state parameter change process is filtered according to the state-response relationship, and change information directly related to the change in the target influencing factor is extracted. Through the above association and matching process, the state change information in the counterfactual impact difference results is correlated with the target influencing factor change information to obtain state change information and target influencing factor change information.

[0112] S4.3 Based on state change information and target influencing factor change information, construct state change prediction relationship and target influencing factor prediction relationship according to the change correlation between state parameters and the response relationship between target influencing factors and state parameters.

[0113] Furthermore, based on state change information, the correlation between different state parameters in the evaluation dataset is extracted. By analyzing the order, direction, and trend of state parameter changes during the process of target influencing factors, the correlation process between state parameters is determined. Based on the correlation between state parameters, the change processes of different state parameters are linked and organized to form a state change prediction relationship to describe the state parameter change transmission process. When one state parameter changes and another state parameter responds, the correlation between the state parameters is determined based on the order and trend of the changes between the two state parameters. When multiple state parameters change simultaneously, the corresponding correlation is determined based on the direction and process of the changes between the state parameters, so that the state change prediction relationship can reflect the dynamic change process between state parameters.

[0114] Specifically, based on the analysis of changes in target influencing factors, the response relationship between target influencing factors and state parameters is determined. By matching the change process of target influencing factors with the response process of state parameters, the correspondence between the impact of changes in target influencing factors on state parameters is determined. If a change in a target influencing factor immediately causes a change in a certain state parameter, then the corresponding change process is determined as the direct response relationship between the target influencing factor and the state parameter. If a change in a target influencing factor causes changes in other state parameters after being transmitted through associated state parameters, then the indirect response relationship between the target influencing factor and the state parameter is determined according to the state change path. Based on the response relationship between target influencing factors and state parameters, a correspondence between the change process of target influencing factors and the change result of state parameters is established, forming a prediction relationship for target influencing factors. State change prediction relationships and target influencing factor prediction relationships are constructed respectively through the correlation relationships between changes in state parameters and the response relationships between target influencing factors and state parameters.

[0115] S4.4 Based on the state change prediction relationship and the target influencing factor prediction relationship, predict and analyze the target influencing factor change process and state change process in the evaluation data set. Input the target influencing factor change information into the target influencing factor prediction relationship to obtain the target influencing factor prediction result, and obtain the state change prediction result based on the target influencing factor prediction result.

[0116] Furthermore, by leveraging the correspondence between the change process of the target influencing factors and the response process of the state parameters in the target influencing factor prediction relationship, the impact results after the change of the target influencing factors are predicted. When there are multiple associated state parameter responses during the change process of the target influencing factors, the response relationships corresponding to different associated state parameters are determined based on the target influencing factor prediction relationship, and the target influencing factor prediction results are obtained. When there are indirect influence relationships during the change process of the target influencing factors, the state influence process after the change of the target influencing factors is determined based on the response transmission relationship between the target influencing factors and associated state parameters, and the target influencing factor prediction results are obtained.

[0117] Specifically, the method analyzes the propagation relationships between associated state parameters through state change prediction relationships. Based on the order, direction, and trend of change among state parameters, it predicts the corresponding state change process after a change in the target influencing factor. When the prediction result of the target influencing factor indicates a change in a certain state parameter, the method identifies the changes in other state parameters related to that state parameter through the state change prediction relationship, and obtains the corresponding state change prediction results based on the propagation process between the state parameters. When there is a continuous response relationship between state parameters, the method predicts the subsequent changes in associated state parameters based on the state propagation process in the state change prediction relationship. Through the joint prediction of the target influencing factor prediction relationship and the state change prediction relationship, a correspondence is established between the change process of the target influencing factor and the state change process, thus obtaining the state change prediction results.

[0118] S4.5 Based on the state change prediction results, target influencing factor prediction results, and actual results, identify the mismatched change relationships between the prediction results and actual results, extract the influencing relationships that are not explained by the state change prediction results and target influencing factor prediction results, and obtain hidden influencing factors.

[0119] Furthermore, the state change information under the same operational stage is matched accordingly. The predicted state change process in the state change prediction results, the predicted influence process in the target influencing factor prediction results, and the actual change process in the actual results are correlated and compared to identify discrepancies between the prediction and actual results. By analyzing the state parameters, change direction, and response process corresponding to these discrepancies, it is determined whether the discrepancies can be explained by the state change prediction results and the target influencing factor prediction results. When the state change process in the actual results is consistent with the state change prediction results, the state change process is determined to be explainable by existing influence relationships. When the state change process in the actual results deviates from the state change prediction results, and the target influencing factor prediction results cannot explain the source of the change, the corresponding change relationship is determined to be an unmatched change relationship.

[0120] Specifically, based on unmatched change relationships, the system extracts influence relationships not explained by the predicted state changes and target influencing factors. By analyzing the state parameter change process corresponding to the unmatched change relationships, the system searches the evaluation dataset for influencing factors associated with these relationships. When a state parameter changes in the actual result, but the state change prediction did not predict the corresponding change, and the target influencing factor prediction does not include the corresponding source of influence, the corresponding state parameter change process is extracted as a potential influence relationship. When multiple unmatched change relationships exist simultaneously, correlation analysis is performed based on the change associations and response relationships between different unmatched change relationships to filter the influence relationships that explain the actual state change process. By identifying and analyzing unmatched change relationships, the system extracts influence relationships not explained by the predicted state changes and target influencing factors, thus obtaining hidden influencing factors.

[0121] S4.6. Verify the correlation between the state change process corresponding to the hidden influencing factors and the change relationship in the counterfactual impact difference results, screen out the hidden influencing factors that conform to the state change path and the relationship between the target influencing factors, and obtain the set of real influencing factors.

[0122] Furthermore, the state change process corresponding to the hidden influencing factor is correlated and matched with the change relationship in the counterfactual impact difference result. By comparing the state change characteristics generated after the change of the hidden influencing factor with the state change characteristics in the counterfactual impact difference result, it is determined whether the hidden influencing factor can explain the change relationship in the counterfactual impact difference result. The associated state parameters in the state change process corresponding to the hidden influencing factor are identified, and then the change direction, change sequence, and change response process of the associated state parameters are matched with the state change relationship in the counterfactual impact difference result. When the change direction of the state change process corresponding to the hidden influencing factor is consistent with that in the counterfactual impact difference result, and the response relationship between the associated state parameters conforms to the state change path, it is determined that there is an association between the hidden influencing factor and the counterfactual impact difference result. When the change relationship between the state change process corresponding to the hidden influencing factor is inconsistent with that in the counterfactual impact difference result, the hidden influencing factor is excluded.

[0123] Specifically, hidden influencing factors identified through association verification are matched with state change paths. The analysis examines whether changes in the associated state parameters, following the state change path, result in corresponding changes in the associated state parameters. Simultaneously, considering the interaction with the target influencing factors, it is determined whether the hidden influencing factors can jointly explain the resulting state change process. If the order of changes in associated state parameters caused by a change in a hidden influencing factor matches the order of changes in the state change path, and the state changes generated by the hidden influencing factor supplement the changes that the target influencing factor cannot explain, then the hidden influencing factor is identified as a valid influencing factor. If a hidden influencing factor only has a change relationship with some state parameters but cannot match the response process in the state change path, then the hidden influencing factor is not included in the set of true influencing factors. Through association verification and screening of the hidden influencing factors, the set of true influencing factors is obtained.

[0124] S5. Based on the set of real influencing factors and the results of counterfactual impact differences, generate fair competition impact assessment results, optimize parameter configuration status and operation adjustment strategies, and obtain optimized operation status data.

[0125] S5.1 Based on the set of real influencing factors and the results of counterfactual impact differences, correlate the change information of target influencing factors in the set of real influencing factors with the status change information in the results of counterfactual impact differences to obtain the correlation of changes in influencing factors.

[0126] Furthermore, based on the set of real influencing factors, change information corresponding to each target influencing factor is extracted, including the change process, direction of change, and corresponding state response information of the target influencing factors. At the same time, state change information generated after the change of the target influencing factors is obtained from the counterfactual impact difference results. The change information of the target influencing factors and the state change information are correlated and matched. By analyzing the correspondence between the change process of the target influencing factors and the change process of the associated state parameters, the state change relationship caused by the change of different target influencing factors is determined. When a target influencing factor changes and the corresponding state parameter responds according to the change direction in the counterfactual impact difference results, the change information of the target influencing factor is correlated with the corresponding state change information. When multiple target influencing factors work together to produce state changes, the state change relationship corresponding to different target influencing factors is distinguished according to the response relationship in the state change path, and the correlation relationship of influencing factor changes is obtained.

[0127] S5.2 Based on the correlation of changes in influencing factors, analyze the impact process of the state changes of the real influencing factor set, and determine the influence relationship between the real influencing factor set and the result state.

[0128] Furthermore, by identifying the state response process after changes in target influencing factors, the process by which target influencing factors affect the outcome state is determined. Based on the direction of change, response process, and state change result in the correlation of changes in influencing factors, the relationship between different target influencing factors and the outcome state is judged. When a change in a target influencing factor directly leads to a change in the outcome state, the corresponding change relationship is determined as a direct influence relationship. When a target influencing factor further affects the outcome state through changes in associated state parameters, the transmission influence relationship between the target influencing factor and the outcome state is determined based on the state change path. By analyzing the state change influence process corresponding to the set of real influencing factors, the influence relationship between the set of real influencing factors and the outcome state is determined.

[0129] S5.3. Based on the set of real influencing factors, counterfactual impact differences, and impact relationships, generate fair competition impact assessment results.

[0130] Furthermore, the information on changes in target influencing factors in the set of real influencing factors, the information on changes in state in the counterfactual impact difference results, and the influence relationship between the set of real influencing factors and the result state are linked and integrated. By analyzing the impact process of changes in different target influencing factors on the result state, a basis for fair competition impact assessment is formed. Based on the influence relationship corresponding to the set of real influencing factors, the process of changes in target influencing factors and the process of changes in result state are comprehensively evaluated to determine the impact of different influencing factors on the fair competition state during the operation of the target object, and generate fair competition impact assessment results.

[0131] S5.4. Based on the results of the fair competition impact assessment and the set of real influencing factors, extract the information on changes in the operating status corresponding to the impact results and the adjustment requirements of the target influencing factors, and determine the direction of parameter optimization.

[0132] Furthermore, based on the impact of changes in target influencing factors on the resulting state, the potential trend of operational state changes after adjusting the target influencing factors is analyzed. This determines the operational parameters that need adjustment and their direction. When a change in a target influencing factor causes related state parameters to deviate from the target state, the adjustment requirement for the target influencing factor is determined based on the corresponding impact relationships in the actual influencing factor set. Combined with operational state change information, it is judged whether the parameter configuration needs to be adjusted further, reduced, or maintained, thereby determining the parameter optimization direction. Through the correlation analysis between the fair competition impact assessment results and the actual influencing factor set, the parameter optimization direction can correspond to the actual impact relationships.

[0133] S5.5 Adjust the parameter configuration status in the operation adjustment strategy based on the parameter optimization direction, and match the optimized parameter configuration status with the operation status change information to obtain the optimized parameter configuration status.

[0134] Furthermore, the adjusted parameter configuration status is matched with the operational status change information to analyze whether the adjusted parameter configuration status can meet the status change requirements after the change of the target influencing factors. When the adjusted parameter configuration status can move the operational status change information in the direction of the target status change, the corresponding parameter configuration status is determined as the optimized parameter configuration status; when the adjusted parameter configuration status cannot meet the operational status change requirements, the parameter configuration status is readjusted according to the parameter optimization direction to maintain the correspondence between the parameter configuration status and the operational status change information, thus obtaining the optimized parameter configuration status.

[0135] S5.6. Based on the optimized parameter configuration status and operation adjustment strategy, adjust the status of the target object's operation process and obtain the adjusted operation status data.

[0136] Furthermore, during the operation of the target object, the operational status information after the status adjustment is collected. By analyzing the process of changes in the adjusted operational status, the results of the status changes of the target object under the influence of the optimized parameter configuration are obtained. When the operational status of the target object changes accordingly according to the optimized parameter configuration, the operational status information during the corresponding adjustment process is retained. When the change in the operational status of the target object does not meet the status requirements corresponding to the parameter optimization direction, the operational status is further adjusted according to the operational adjustment strategy, and the adjusted operational status data is obtained.

[0137] S5.7. Based on the adjusted operating status data, perform status consistency verification, filter the operating status data that meets the optimized parameter configuration status and operating adjustment strategy, and obtain the optimized operating status data.

[0138] Furthermore, the state change information is correlated and matched with the optimized parameter configuration state and the operational adjustment strategy to determine whether the adjusted operational state data meets the state change requirements corresponding to the parameter optimization direction. When the state change trend and state response process in the adjusted operational state data are consistent with the correspondence between the optimized parameter configuration state and the operational adjustment strategy, the corresponding adjusted operational state data is determined to be operational state data that meets the optimization conditions. When there are state changes in the adjusted operational state data that do not match the optimized parameter configuration state, the corresponding operational state data is filtered out. Through state consistency verification, operational state data that meets the optimized parameter configuration state and operational adjustment strategy is filtered to obtain the optimized operational state data.

[0139] S6. Compare and verify the optimized operational status data with the fair competition impact assessment results, analyze the impact changes corresponding to the optimized operational status data, and obtain the final optimized fair competition impact assessment results.

[0140] S6.1 Based on the optimized operational status data and the fair competition impact assessment results, extract the status change information from the optimized operational status data and the impact relationship information from the fair competition impact assessment results to obtain validation correlation data.

[0141] Furthermore, based on the optimized operational status data, the state change process of the target object after parameter optimization and operational adjustment is obtained. By extracting the direction of operational status change, the state response process, and the information on changes in associated state parameters, state change information corresponding to the optimized operational status data is formed. The influence relationship information corresponding to the set of real influencing factors is obtained from the fair competition impact assessment results, including the correlation between changes in target influencing factors and the result state, as well as the influence transmission relationship. The state change information in the optimized operational status data is correlated and matched with the influence relationship information in the fair competition impact assessment results. By identifying the correspondence between the state change information and the influence relationship information, the state change process in the optimized operational status data that can reflect the changes in the impact of fair competition is determined, and verification correlation data is obtained.

[0142] S6.2. Conduct a consistency correlation analysis between the state change information in the optimized operating status data and the impact relationship information in the fair competition impact assessment results to obtain the state change verification results.

[0143] Furthermore, a correlation analysis is performed between the state change information in the optimized operational status data and the impact relationship information in the fair competition impact assessment results. By determining whether the state change process in the optimized operational status data conforms to the impact change pattern in the fair competition impact assessment results, the consistency relationship between the two is established. The direction and process of change of associated state parameters in the optimized operational status data are identified and then matched with the impact relationship corresponding to the target influencing factors in the fair competition impact assessment results. When the direction of state change in the optimized operational status data is consistent with the direction of change corresponding to the impact relationship, and the state response process matches the impact transmission relationship, the optimized operational status data is determined to be consistent with the fair competition impact assessment results. When there is a deviation in the direction of state change or the state response process cannot correspond to the impact relationship, a mismatch is determined between the optimized operational status data and the fair competition impact assessment results. Through consistency correlation analysis, the state change verification results are obtained.

[0144] S6.3. Based on the state change verification results, determine the matching relationship between the optimized operating state data and the fair competition impact assessment results, and complete the comparison and verification of the optimized operating state data and the fair competition impact assessment results.

[0145] Furthermore, a comprehensive assessment is conducted on the relationship between the state change processes in the optimized operational status data and the impact of the fair competition impact assessment results. If the state change verification results indicate that the state change processes in the optimized operational status data correspond to the impact relationships in the fair competition impact assessment results, then the optimized operational status data is determined to meet the optimization requirements corresponding to the fair competition impact assessment results. If the state change verification results indicate that some state change processes in the optimized operational status data do not correspond to the impact relationships in the fair competition impact assessment results, the mismatched state change processes are identified, and the impact deviation during the optimization adjustment process is determined. The matching relationship between the optimized operational status data and the fair competition impact assessment results is determined through the state change verification results, thus completing the comparative verification of the optimized operational status data and the fair competition impact assessment results.

[0146] S6.4 Extract the state change information and impact change information corresponding to the optimized operating state data from the optimized operating state data, and obtain the optimized state impact change data.

[0147] Furthermore, based on the response of the adjusted target influencing factors in the optimized operational status data, the impact of the optimization adjustment process on the resulting state is identified. By analyzing the differences between the optimized operational status data and the state before adjustment, the impact change process of the adjusted target influencing factors is determined. When the associated state parameters in the optimized operational status data change according to the parameter optimization direction, the corresponding state change process is identified as the impact change information. When some associated state parameters in the optimized operational status data do not produce the expected changes, the corresponding impact change is identified based on the state change process. Through the correlation extraction of state change information and impact change information, the impact change data of the optimized state is obtained.

[0148] S6.5. Based on the correlation analysis between the data on changes in the optimized state and the results of the fair competition impact assessment, identify the impact changes corresponding to the optimized operating state data and obtain the impact change analysis results.

[0149] Furthermore, by comparing the state change information in the optimized state impact change data with the impact relationship in the fair competition impact assessment results, it is determined whether the impact changes corresponding to the optimized operational state data conform to the expected impact direction. When the state change process in the optimized state impact change data is consistent with the impact relationship in the fair competition impact assessment results, it is determined that the optimized operational state data generates the corresponding optimized impact; when some state change processes in the optimized state impact change data deviate from the impact relationship in the fair competition impact assessment results, the impact change process corresponding to the deviation is identified based on the impact relationship information. Through correlation analysis between the optimized state impact change data and the fair competition impact assessment results, the impact changes corresponding to the optimized operational state data are identified, and the impact change analysis results are obtained.

[0150] S6.6. Based on the results of the impact change analysis, update and adjust the fair competition impact assessment results to obtain the final optimized fair competition impact assessment results.

[0151] Furthermore, based on the impact change analysis results, the actual impact change process corresponding to the optimized operational status data is obtained. The impact relationship information in the impact change analysis results and the fair competition impact assessment results are adjusted accordingly. When the impact change analysis results indicate that the optimized operational status data reduces the adverse impact of the target influencing factor, the corresponding impact relationship in the fair competition impact assessment results is updated so that the fair competition impact assessment results can reflect the optimized impact state. When the impact change analysis results indicate that the optimized operational status data does not achieve the expected impact change, the corresponding impact relationship is retained and the state change description in the impact assessment results is adjusted. By updating and adjusting the fair competition impact assessment results through the impact change analysis results, the final optimized fair competition impact assessment results are obtained.

[0152] In summary, this invention collects operational status data of the target object during its operation and forms an evaluation data set. Based on the operational status change relationship, it determines the state change path of the target influencing factors. Through a state change path constraint mechanism, it intervenes in the target influencing factors and generates counterfactual state data, thereby simulating the state evolution process after the target influencing factors change. By analyzing the difference between the actual operational status data and the counterfactual state data, it obtains the counterfactual impact difference results and identifies hidden influencing factors by combining a dual prediction analysis mechanism, thus obtaining a set of real influencing factors. Based on the set of real influencing factors, it optimizes the fair competition impact assessment results and operational adjustment strategies, generates optimized operational status data, and updates the fair competition impact assessment results through comparison and verification. This achieves accurate identification of the influencing factor action path and optimization of fair competition impact assessment based on counterfactual reasoning, improving the accuracy and reliability of fair competition impact assessment in complex dynamic operational scenarios.

[0153] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An optimization method for fair competition impact assessment based on counterfactual reasoning, characterized by: include, The operational status data of the target object during operation is collected, preprocessed, and used to form an evaluation data set. The relationship between operational status changes is analyzed to determine the status change path of the target's influencing factors. Based on the state change path and the evaluation data set, intervention is carried out on the target influencing factors, and the associated states are adjusted based on the state change path constraint mechanism to generate counterfactual state data. A difference analysis was conducted between the actual operating status data and the counterfactual status data to obtain the results of the difference in counterfactual impact. Based on the counterfactual impact difference results and the evaluation data set, the state change prediction results and target influencing factor prediction results are obtained through a dual predictive analysis mechanism. Hidden influencing factors are identified based on the residual information between the prediction results and the actual results, and the real influencing factor set is obtained. Based on the set of real influencing factors and the results of counterfactual impact differences, the fair competition impact assessment results are generated, and the parameter configuration status and operation adjustment strategy are optimized to obtain optimized operation status data. The optimized operational status data is compared and verified with the fair competition impact assessment results. The impact changes corresponding to the optimized operational status data are analyzed to obtain the final optimized fair competition impact assessment results.

2. The method for optimizing the impact assessment of fair competition based on counterfactual reasoning as described in claim 1, characterized in that: The evaluation dataset includes, Collect runtime status data of the target object during its operation, and perform missing value detection, outlier identification, and format unification processing to obtain standardized runtime status data; Standardized operational status data are processed by extracting and combining status features to form an evaluation data set.

3. The method for optimizing the impact assessment of fair competition based on counterfactual reasoning as described in claim 2, characterized in that: The state change paths of the target influencing factors include, Obtain the operational status parameter data and corresponding status change information from the evaluation dataset to obtain the status change correlation data; By performing correlation analysis of influencing factors and identification of state change trends on the data related to state changes, the correlation data of target influencing factors is obtained. Based on the correlation data of the target influencing factors, the analysis of the interaction relationship of the influencing factors and the determination of the state change path are carried out to determine the state change path of the target influencing factors.

4. The method for optimizing the impact assessment of fair competition based on counterfactual reasoning as described in claim 3, characterized in that: The counterfactual state data includes Based on the state change path and the evaluation data set, the operational state parameter data in the evaluation data set are correlated and matched according to the state change direction corresponding to the target influencing factor to obtain the operational state parameter data before and after the change of the target influencing factor. Based on the operational status parameter data before and after the change of the target influencing factors, calculate the correlation between the magnitude of the status change, the direction of the status change, and the response time of the status change, extract the associated status parameters corresponding to the target influencing factors, and determine the intervention scope of the target influencing factors; Based on the intervention range of the target influencing factors, a gradient of the target influencing factors is constructed. According to the gradient of the target influencing factors, the target influencing factors are intervened in stages. The boundary of the state change during the adjustment process of the target influencing factors is obtained based on the state change path constraint mechanism. Specifically, a state change path constraint mechanism is constructed based on the relationship between the change order, change direction and change magnitude of the associated state parameters in the state change path. By limiting the cooperative change relationship of the associated state parameters, the adjustment process of the target influencing factors is constrained. Based on the state change path constraint mechanism, the associated state is dynamically corrected to obtain the adjusted associated state data. Based on the adjusted associated state data and the state change path constraint mechanism, the path of the associated state parameter change process in the adjusted associated state data is reconstructed. Based on the response relationship between the target influencing factors and the associated state parameters in the state change path, the state evolution result of the target influencing factors after adjustment is predicted, and counterfactual state data is generated.

5. The method for optimizing the impact assessment of fair competition based on counterfactual reasoning as described in claim 4, characterized in that: The counterfactual effect difference results include Based on the actual operating status data and counterfactual status data, extract the corresponding status parameter change information, and perform correlation matching on the same status parameters in the actual operating status data and counterfactual status data to obtain status difference correlation data. Based on state difference correlation data, identify the changing trends, directions and ranges of state parameters in actual operating state data and counterfactual state data, and obtain state change difference data; By comparing and analyzing the results of actual operating state data and counterfactual state data using state change difference data, the influence relationship of changes in target influencing factors on the result state is determined, and the counterfactual influence difference results are obtained.

6. The method for optimizing the impact assessment of fair competition based on counterfactual reasoning as described in claim 5, characterized in that: The set of real influencing factors includes, Based on the counterfactual impact difference results and the assessment dataset, the state change information in the counterfactual impact difference results is correlated and matched with the state parameter change process in the assessment dataset to extract the state change information. By associating and matching the target influencing factor change information in the counterfactual impact difference results with the state response relationship in the assessment dataset, the target influencing factor change information is extracted to obtain state change information and target influencing factor change information. Based on state change information and target influencing factor change information, state change prediction relationship and target influencing factor prediction relationship are constructed according to the correlation between changes in state parameters and the response relationship between target influencing factors and state parameters. Based on the state change prediction relationship and the target influencing factor prediction relationship, predictive analysis is performed on the target influencing factor change process and state change process in the evaluation dataset. The target influencing factor change information is input into the target influencing factor prediction relationship to obtain the target influencing factor prediction result, and the state change prediction result is obtained based on the target influencing factor prediction result. Based on the state change prediction results, target influencing factor prediction results, and actual results, the mismatched change relationships between the prediction results and actual results are identified, and the influencing relationships not explained by the state change prediction results and target influencing factor prediction results are extracted to obtain hidden influencing factors. The correlation between the state change process corresponding to the hidden influencing factors and the change relationship in the counterfactual impact difference results is verified. Hidden influencing factors that conform to the state change path and the relationship between the target influencing factors are screened to obtain the set of real influencing factors.

7. The method for optimizing the impact assessment of fair competition based on counterfactual reasoning as described in claim 6, characterized in that: The results of the fair competition impact assessment include: Based on the set of real influencing factors and the results of counterfactual impact differences, the change information of target influencing factors in the set of real influencing factors is correlated with the state change information in the results of counterfactual impact differences to obtain the correlation of changes in influencing factors. Based on the correlation of changes in influencing factors, we analyze the impact process of changes in the state of the actual set of influencing factors and determine the influence relationship between the actual set of influencing factors and the outcome state. Based on the set of real influencing factors, counterfactual impact differences, and impact relationships, a fair competition impact assessment result is generated.

8. The method for optimizing the impact assessment of fair competition based on counterfactual reasoning as described in claim 7, characterized in that: The optimized operating status data includes, By using the results of the fair competition impact assessment and the set of real influencing factors, we extract the information on changes in operational status and the adjustment needs of target influencing factors corresponding to the impact results, and determine the direction of parameter optimization. The parameter configuration status in the operation adjustment strategy is adjusted based on the parameter optimization direction, and the optimized parameter configuration status is matched with the operation status change information to obtain the optimized parameter configuration status. Based on the optimized parameter configuration status and operation adjustment strategy, the operation process of the target object is adjusted, and the adjusted operation status data is obtained. Based on the adjusted operational status data, the consistency of the status is verified, and the operational status data that meets the optimized parameter configuration status and operational adjustment strategy is selected to obtain the optimized operational status data.

9. The method for optimizing the impact assessment of fair competition based on counterfactual reasoning as described in claim 8, characterized in that: The comparative verification includes, Based on the optimized operational status data and the fair competition impact assessment results, we extract the status change information from the optimized operational status data and the impact relationship information from the fair competition impact assessment results to obtain validation correlation data. A consistency correlation analysis was conducted between the state change information in the optimized operational status data and the impact relationship information in the fair competition impact assessment results to obtain the state change verification results. Based on the state change verification results, determine the matching relationship between the optimized operating state data and the fair competition impact assessment results, and complete the comparative verification of the optimized operating state data and the fair competition impact assessment results.

10. The method for optimizing the impact assessment of fair competition based on counterfactual reasoning as described in claim 9, characterized in that: The final optimized fair competition impact assessment results include, By extracting the state change information and impact change information corresponding to the optimized operating state data, we can obtain the optimized state impact change data. Based on the correlation analysis between the data on changes in the optimized operating status and the results of the fair competition impact assessment, the impact changes corresponding to the optimized operating status data are identified, and the impact change analysis results are obtained. The impact assessment results of fair competition are updated and adjusted based on the impact change analysis results, resulting in the final optimized impact assessment results of fair competition.

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