Comprehensive evaluation method and system for evaluating bridge capability improvement

Abstract

The invention provides a comprehensive evaluation method and system for evaluating bridge capability improvement, and is applied to the technical field of data processing. The method comprises the following steps: preprocessing quantitative and qualitative indexes in basic data to generate bridge dynamic feature information; processing weight calculation related influence factor data and evaluation abnormal state information based on weight adjustment logic of an analytic hierarchy process, and generating abnormal state identification information in an evaluation process by combining dynamic factor correlation analysis of fuzzy comprehensive evaluation; processing the bridge dynamic characteristic information to generate an evaluation grade of a bridge capability improvement effect; an evaluation result, an index type and a data source in the weight calculation related influence factor data are processed in combination with a multi-stage evaluation model, and key factors influencing the bridge capability improvement effect are generated; and processing the evaluation result, the key factors influencing the bridge capability improvement effect and the abnormal state identification information, and generating dynamic adjustment information for bridge reinforcement.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a comprehensive evaluation method and system for evaluating bridge capacity improvement. Background Art

[0002] With the continuous growth of traffic volume and the increase in service life, many bridges are facing problems such as structural performance degradation and reduced bearing capacity, and are in urgent need of reinforcement to improve their performance and safety. There are already a variety of technical means to evaluate the effect of bridge capacity improvement and optimize reinforcement plans. Some existing technologies use conventional evaluation methods to detect and evaluate bridge structures, and roughly judge the condition of the bridge by simply calculating indicators such as bearing capacity. However, these methods can often only evaluate part of the performance of the bridge in a single dimension, and it is difficult to comprehensively and accurately consider the many complex factors that affect the improvement of bridge capacity. For example, during the evaluation process, it is impossible to conduct in-depth analysis of key evaluation result features such as the bearing capacity improvement rate and the deflection reduction rate based on multi-source data, resulting in deviations in the judgment of the actual condition of the bridge.

[0003] Existing technologies use relatively simple methods to compare actual bridge verification results with a pre-set library of standard bridge reinforcement models and optimization parameter libraries. Simply comparing numerical values results in inaccurate deviations between the performance of the determined reinforcement scheme and the optimization target, failing to provide effective guidance for subsequent adjustments to the reinforcement scheme. Existing technologies lack effective correlation analysis methods when processing reinforcement optimization deviation assessment results and abnormal feature sets. They cannot fully utilize the characteristics of the fuzzy comprehensive evaluation model and the optimization suggestions from actual bridge verification feedback to reasonably weight the deviations. This makes it difficult to generate accurate and effective effect optimization vectors and bridge reinforcement dynamic adjustment information, hindering the precise optimization and real-time regulation of bridge reinforcement schemes.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0005] The purpose of this application is to provide a comprehensive evaluation method and system for evaluating bridge capacity improvement, which at least overcomes the problems existing in the existing technology to a certain extent, by obtaining basic data and preprocessing it, introducing the hierarchical analysis method weight matrix and membership matrix to generate bridge dynamic feature information, combining the hierarchical analysis method with fuzzy comprehensive evaluation to generate abnormal state identification information, calculating the evaluation level and key factors, and generating dynamic adjustment information based on fuzzy comprehensive evaluation and actual bridge verification, thereby improving the comprehensiveness, accuracy and real-time performance of the evaluation.

[0006] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0007] According to one aspect of the present application, a comprehensive evaluation method for evaluating bridge capacity improvement is provided, comprising: obtaining basic data for evaluating the effect of bridge capacity improvement, including data type information, weight calculation-related influencing factor data, and evaluation abnormal state information; preprocessing the quantitative and qualitative indicators in the basic data, introducing a hierarchical analysis method weight matrix and an indicator membership matrix, and generating bridge dynamic feature information, where the bridge dynamic feature information is used to characterize the membership vectors of each indicator; processing the weight calculation-related influencing factor data and the evaluation abnormal state information based on the weight adjustment logic of the hierarchical analysis method, and generating abnormal state identification information in the evaluation process in combination with dynamic factor correlation analysis of fuzzy comprehensive evaluation; processing the bridge dynamic feature information, calculating the evaluation result vectors of the first-level indicators and the target layer through a fuzzy synthesis operator, and generating an evaluation grade of the bridge capacity improvement effect using the maximum membership principle; processing the evaluation results and indicator types and data sources in the weight calculation-related influencing factor data in combination with a multi-level evaluation model to generate key factors affecting the bridge capacity improvement effect; processing the evaluation results, key factors affecting the bridge capacity improvement effect, and abnormal state identification information based on the fuzzy comprehensive evaluation model combined with a real bridge verification strategy to generate dynamic adjustment information for bridge reinforcement.

[0008] Another aspect of the present application is a comprehensive evaluation device for evaluating bridge capacity improvement, characterized in that it includes: an acquisition module for acquiring basic data for evaluating bridge capacity improvement effects, including data type information, weight calculation-related influencing factor data, and evaluation abnormal state information; a processing module for preprocessing quantitative and qualitative indicators in the basic data, introducing a hierarchical analysis method weight matrix and an indicator membership matrix, generating bridge dynamic feature information, and the bridge dynamic feature information is used to characterize the membership vectors of each indicator; processing the weight calculation-related influencing factor data and evaluation abnormal state information based on the weight adjustment logic of the hierarchical analysis method, and combining the fuzzy comprehensive evaluation Dynamic factor correlation analysis is performed to generate abnormal state identification information during the evaluation process; the dynamic characteristic information of the bridge is processed, and the evaluation result vectors of the first-level indicators and the target layer are calculated through the fuzzy synthesis operator, and the evaluation level of the bridge capacity improvement effect is generated using the maximum membership principle; the evaluation results and indicator types and data sources in the weight calculation related influencing factor data are processed in combination with the multi-level evaluation model to generate the key factors affecting the bridge capacity improvement effect; based on the fuzzy comprehensive evaluation model combined with the actual bridge verification strategy, the evaluation results, key factors affecting the bridge capacity improvement effect and abnormal state identification information are processed to generate dynamic adjustment information for bridge reinforcement.

[0009] According to another aspect of the present application, an electronic device is characterized in that it includes: a first processor; and a memory for storing executable instructions of the first processor; wherein the first processor is configured to execute the above-mentioned comprehensive evaluation method for evaluating bridge capacity improvement by executing the executable instructions.

[0010] The present application provides a comprehensive evaluation method and system for evaluating bridge capacity improvement, which obtains basic data including data types, weight influencing factors and evaluation abnormal states. Quantitative and qualitative indicators are pre-processed, and the weight matrix of the analytic hierarchy process and the indicator membership matrix are introduced to generate bridge dynamic feature information that represents the membership vectors of each indicator. The weight adjustment logic of the analytic hierarchy process is then combined with the correlation analysis of the dynamic factors of the fuzzy comprehensive evaluation to generate abnormal state identification information. The evaluation result vector of the first and target layers is calculated by the fuzzy synthesis operator, and the evaluation grade is obtained according to the principle of maximum membership. The weight influencing factor data is processed in combination with the multi-level evaluation model to generate key factors and evaluation results including quantitative indicators such as the load-bearing capacity improvement rate and qualitative indicators such as the adaptability of the reinforcement technology. Finally, based on the fuzzy comprehensive evaluation model and actual bridge verification, dynamic adjustment information including optimized probability values is generated to achieve precise control and improve the comprehensiveness, accuracy and real-time performance of the evaluation.

[0011] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A flow chart showing a comprehensive evaluation method for evaluating bridge capacity improvement provided by an embodiment of the present application is shown; Figure 2 A schematic structural diagram of a comprehensive evaluation device for evaluating bridge capacity improvement provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0013] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0014] The following combination Figure 1 The following describes a comprehensive evaluation method for assessing bridge capacity improvement according to an exemplary embodiment of the present application. It should be noted that the following application scenarios are provided solely to facilitate understanding of the spirit and principles of the present application, and the embodiments of the present application are not limited in this respect. Rather, the embodiments of the present application are applicable to any applicable scenario.

[0015] In one embodiment, the present application also proposes a comprehensive evaluation method and system for evaluating bridge capacity improvement. Figure 1The following schematically shows a flow chart of a comprehensive evaluation method for evaluating bridge capacity improvement according to an embodiment of the present application. Figure 1 As shown, the method is applied to the server and includes: S101, obtaining basic data for bridge capacity improvement effect evaluation, including data type information, weight calculation related influencing factor data and evaluation abnormal status information.

[0016] In one embodiment, the data type information includes quantitative indicator data and qualitative indicator data. Specifically, the quantitative indicator data includes numerical data such as the load-bearing capacity improvement rate (e.g., the load-bearing capacity improvement rate of a bridge after reinforcement is 17.3%), the deflection reduction rate (e.g., a calculated value of -17.1), and the stress reduction rate (e.g., 13.89%). The qualitative indicator data includes descriptive data such as the adaptability of the reinforcement technology (e.g., "strongly targeted and well matched to the cause of the defect") and the complexity of the construction process (e.g., "easy construction").

[0017] The influencing factors involved in weight calculation include quantitative and qualitative factors. Specifically, quantitative factors include the increase in load-bearing capacity before and after reinforcement (e.g., the load-bearing capacity increase for a bridge after reinforcement was 17.3%) and the stress reduction rate (e.g., the stress reduction rate was 13.89%). Qualitative factors include the maturity of the reinforcement technology (e.g., numerous application cases with good results, with a technology maturity of ≥95%) and the impact of construction on traffic (e.g., single-lane traffic closure for less than 3 months).

[0018] Evaluation abnormality information includes both indicator data anomalies and evaluation process anomalies. Specifically, an indicator data anomaly might include a calculated deflection reduction rate of -17.1 for a particular bridge. A negative value might indicate an increase in deflection rather than a decrease, thus constituting an abnormal state. Evaluation process anomalies might include a judgment matrix consistency ratio (CR) greater than 0.1 during weight adjustment, necessitating re-adjustment of the judgment matrix.

[0019] S102, pre-processing the quantitative and qualitative indicators in the basic data, introducing the hierarchical analysis method weight matrix and indicator membership matrix, and generating bridge dynamic characteristic information.

[0020] In one embodiment, based on the correlation matching between the AHP weight matrix and the indicator membership matrix, effect feature identification is performed on the feature validity and parameter accuracy of the preprocessed quantitative and qualitative indicator data to generate a preprocessing anomaly feature set. Based on the correlation matching rules between the AHP weight matrix and the indicator membership matrix, the server automatically verifies the preprocessed quantitative / qualitative indicator data to identify anomalies in feature validity and parameter accuracy.

[0021] Specifically, for a bridge with an "external prestressing + web thickening" solution, the server read a calculated deflection reduction rate of -2.8. Based on the membership matrix rule (a deflection reduction rate <1.0 corresponds to Level V), the generated membership vector (0,0,0,0,1) was found to conflict with the logical requirement for reduced deflection after reinforcement when matched with the structural safety weight matrix (weight 0.35). This was flagged as a "data validity anomaly." When the server analyzed the "reinforcement technology adaptability" score, the expert assigned it a score of 60 (corresponding to Level IV). However, based on the standardized indicator library's "technical solution deviation rate ≤5% should be classified as Level I," the score was deemed inconsistent with reality, generating a "parameter accuracy anomaly" feature.

[0022] The preprocessed indicator data is compared with the preset standardized indicator library and weight parameter library. Combined with indicator preprocessing example verification, the deviation between the preprocessing results and the standardized target is determined, and the preprocessing deviation assessment result is generated. The server compares the preprocessed data with the preset standardized indicator library (e.g., a load-bearing capacity improvement rate ≥ 15 is Level I) and the weight parameter library (e.g., a structural safety weight of 0.35), calculates the deviation through an algorithm, and combines it with historical preprocessing examples for verification. The deviation calculation formula (taking the stress reduction rate as an example) is: , if the lower limit of standard level I is 10 and a certain value is 9.57, then: When the server processes the load-bearing capacity improvement rate of 2.9 for the "web steel plate + external prestressing + integrated layer" solution, the lower limit of standard level I is 15, and the calculated deviation = (15-2.9) / 15≈0.807, generating a deviation assessment result (0.807, structural safety index).

[0023] The preprocessing deviation evaluation results are correlated with the preprocessing abnormal feature set. The preprocessing deviation evaluation results are weighted based on the characteristics of the hierarchical analysis method, indicator parameters, and optimization suggestions in the preprocessing feedback. This generates an optimization vector that integrates the preprocessing logic and the weight calculation process. The server correlates the deviation evaluation results with the abnormal features, calls the hierarchical analysis method weight adjustment algorithm, and combines the preprocessing feedback (such as expert optimization suggestions) to weight the deviation and generate a vector that integrates the preprocessing logic. The weighted deviation calculation formula is: If the deflection reduction rate deviation is 1, the corresponding structural safety weight is 0.35, then the weighted deviation Specifically, the server integrates the deviation of the crack resistance improvement rate of a bridge (weight 0.11) and the deviation of the natural frequency improvement rate 0.25 (weight 0.10) to generate the effect optimization vector [0.35 (deflection), 0.11 (crack resistance), 0.025 (natural frequency), ...].

[0024] The preprocessing deviation evaluation results and the effect optimization vector are normalized and comprehensively calculated. Combined with the real-time requirements of indicator preprocessing, a preprocessing optimization feature containing a preprocessing probability value, an effect deviation coefficient, and an optimization warning line is generated. The server normalizes the weighted deviation and effect optimization vector, and combines the real-time threshold of indicator preprocessing (such as response time ≤ 100ms) to generate a feature containing a probability value, a deviation coefficient, and a warning line. The normalization process (vector [0.35, 0.11]) is as follows: , the sum = 0.35 + 0.11 = 0.46, which after normalization is [0.76, 0.24]. The preprocessing probability value is calculated as follows: preprocessing probability value = 1 - normalized deviation coefficient. If the normalized deviation coefficient is 0.76, the probability value = 1 - 0.76 = 0.24 (indicating a 24% normal probability). The server sets a warning line of 0.5. When the normalized deviation coefficient is 0.76 > 0.5, an abnormality warning is triggered, and the preprocessing optimization feature {probability value 0.24, deviation coefficient 0.76, warning line 0.5} is generated. According to the preset bridge dynamic feature information generation rules, the preprocessing optimization features, the AHP weight matrix, and the indicator membership matrix are associated and integrated to generate bridge dynamic feature information containing weight feature vectors and membership feature vectors. The bridge dynamic feature information is used to represent the membership vectors of each indicator. The server integrates the preprocessing optimization features, the AHP weight matrix, and the membership matrix according to preset rules (such as the JSON format protocol) to generate structured bridge dynamic feature information.

[0025] Specifically, the weight feature vector is: [0.35 (structural safety), 0.29 (structural durability), 0.14 (technical feasibility), 0.12 (economic rationality), 0.10 (construction convenience)]; the membership feature vector (taking the "web steel plate + bottom plate thickening" solution as an example) is: [0.79, 0.1, 0, 0.1, 0.01], [0.654, 0.086, 0, 0.26, 0]. For dynamic feature information integration, the server associates the optimized feature {probability value 0.24, deviation coefficient 0.76} with the weight and membership vectors, generating JSON data representing the membership of each indicator for use by upper-layer applications.

[0026] S103 , based on the weight adjustment logic of the hierarchical analysis method, the weight calculation related influencing factor data and the evaluation abnormal state information are processed, and combined with the dynamic factor correlation analysis of the fuzzy comprehensive evaluation, abnormal state identification information in the evaluation process is generated.

[0027] In one implementation, the weight calculation-related impact factor data is processed based on the weight adjustment logic of the AHP to generate weight-adjusted impact factor data. The server invokes the AHP weight adjustment algorithm (e.g., the weight allocation formula for adding / removing indicators) to re-weight the input weight calculation-related impact factor data (e.g., load-bearing capacity improvement rate, reinforcement technology adaptability, etc.).

[0028] New indicator weight distribution (sorted at the end): , if the original indicator weight , the importance ratio of the new indicator to the previous indicator , then add new indicator weights , the original indicator weight is adjusted to .

[0029] The server received the load-bearing capacity improvement rate of 2.9 (original weight 0.35) for the "web steel plate + external prestressing + integrated layer" solution. Due to the newly added "natural frequency improvement rate" indicator (importance ratio 2), the adjusted load-bearing capacity improvement rate weight became 0.35×(1 / (1+0.5))≈0.233.

[0030] The server combines the weight adjustment logic of the AHP method with the evaluation abnormality status information to generate abnormality status association data. The server associates the weight changes generated during the AHP weight adjustment process (such as the adjustment record when the consistency check fails) with the evaluation abnormality status information (such as data anomalies and weight conflicts) to generate structured abnormality association data.

[0031] The deflection reduction rate of a bridge is -17.1 (data anomaly), corresponding to a structural safety weight of 0.35. The server records this indicator weight as 0.233 after adjustment and flags a correlation anomaly: "Negative deflection value conflicts with weight adjustment." If the judgment matrix consistency ratio (CR) = 0.15 > 0.1 (evaluation process anomaly), the server records the difference between the adjusted weight vector and the original matrix, generating the abnormal correlation data CR = 0.15, and the adjusted weights = [0.233, 0.27, ...].

[0032] Using dynamic factor association analysis based on fuzzy comprehensive evaluation, we analyze the weighted impact factor data and abnormal state association data to generate preliminary abnormal state identification data. The server uses the dynamic factor association analysis model based on fuzzy comprehensive evaluation to perform fuzzy mapping between the weighted impact factor data and abnormal state association data, and calculate the degree of membership of each indicator to the abnormal state.

[0033] Abnormal membership calculation of trapezoidal membership function: If the lower limit of the stress reduction rate standard level I is 10, and a certain value is 9.57, the membership calculation is: That is, the membership for Level II is 0.93, and for Level I is 0.07. The server analyzes the stress reduction rate of the "web steel plate + external prestressing + integrated layer" solution, which is 9.57 (membership (0.07, 0.93, 0, 0, 0)). Combined with the adjusted weight of 0.233, this generates preliminary anomaly identification data: Stress reduction rate anomaly membership = 0.93, weight = 0.233.

[0034] Based on the dynamic factor association rules of fuzzy comprehensive evaluation, the preliminary abnormal state identification data is structured and organized to generate abnormal state identification information during the evaluation process. The server integrates the preliminary abnormal state identification data according to the dynamic factor association rules of fuzzy comprehensive evaluation (such as setting the membership degree ≥ 0.5 as the abnormal threshold), and generates interpretable abnormal state identification information through weighting and logical judgment. The fuzzy comprehensive evaluation synthesis operation process is as follows. Assume that the membership matrix of the preliminary abnormal state identification data is , the weight vector is , then the abnormal state evaluation vector is: , where the level corresponding to the maximum value in the result vector B is the final abnormality level.

[0035] Preliminary abnormality identification data for a bridge with a "prestressed carbon plate + web steel plate" scheme: deflection reduction rate: membership degree (corresponding to level V abnormality), ; Crack resistance improvement rate: membership degree (corresponding to level V abnormality), Fuzzy rule: The maximum value in the membership vector corresponds to the abnormal level, and the warning is triggered when the weighted sum is ≥0.5. The abnormal state evaluation vector is calculated as The maximum value is 0.343 (corresponding to Level V), but the weighted sum of 0.343 is less than 0.5, resulting in a "mild anomaly." The specific structured results are as follows: "Abnormal Status Identification Information": "Bridge Number": "G2012-01", "Reinforcement Scheme": "Prestressed Carbon Plate + Web Steel Plate", "Abnormal Indicator": "Indicator Name": "Deflection Reduction Rate", "Abnormal Level": "Level V", "Membership": 1, "Weight": 0.233, "Indicator Name": "Crack Resistance Improvement Rate", "Abnormal Level": "Level V", "Membership": 1, "Weight": 0.11", "Comprehensive Abnormal Level": "Mild", "Triggering Cause": "Both the deflection and crack resistance indicators exhibit Level V anomalies, but the weighted sum does not reach the severe threshold", and "Handling Suggestions": "Review the load test data, focusing on verifying the accuracy of the deflection measurement."

[0036] The server integrates weight and membership information through fuzzy synthesis operators, and generates structured exception reports based on preset rules (such as thresholds and level mapping), realizing automated processing from data to decision recommendations, ensuring the logic and explainability of abnormal state identification.

[0037] S104, processing the dynamic characteristic information of the bridge, calculating the evaluation result vector of the primary index and the target layer through the fuzzy synthesis operator, and generating the evaluation grade of the bridge capacity improvement effect using the maximum membership principle.

[0038] In one implementation, the dynamic characteristic information of the bridge is processed, and the membership matrix and weight vector corresponding to the first-level index are calculated in combination with the fuzzy synthesis operator to generate the evaluation result vector of the first-level index. The server calls the fuzzy synthesis operator (such as the weighted average type) to perform matrix operations on the membership matrix and weight vector corresponding to the first-level index (such as structural safety, durability, etc.) to generate the evaluation result vector of the first-level index. Assume that the membership matrix of the first-level index "structural safety" is R=[0.5450.075000.38], and the weight vector is .

[0039] but (Simplified here to weighted sum).

[0040] When the server processes the "prestressed carbon plate + web steel plate" solution, the structural safety membership vector is (0.545, 0.075, 0, 0, 0.38). After calculating with the weight vector, the first-level indicator evaluation result vector (0.545, 0.075, 0, 0, 0.38) is generated (consistent with the input, because the single indicator does not need to be weighted).

[0041] Combining the first-level indicator evaluation result vector, the server uses a fuzzy synthesis operator to calculate the first-level indicator evaluation matrix and weight vector corresponding to the target layer to generate the target layer evaluation result vector. Based on the first-level indicator evaluation result vector, the server again uses a fuzzy synthesis operator to calculate the target layer weight vector (such as a structural safety weight of 0.35 and a durability weight of 0.29) to generate a comprehensive evaluation vector for the target layer.

[0042] Assume that the first-level indicator evaluation matrix is , the target layer weight vector W=[0.35,0.29,0.14,0.12,0.10], then The server integrates the first-level indicator vector (structural safety, durability, etc.) of the "prestressed carbon plate + web steel plate" solution and calculates it with the target layer weight to generate the target layer vector (0.288, 0.305, 0.183, 0.091, 0.133).

[0043] Based on cross-dimensional association rules and the logical relationships between evaluation levels, the evaluation result vectors of the target layer are structured and integrated to complete the construction of the evaluation level association network. The server integrates the target layer evaluation result vectors according to cross-dimensional association rules (such as the logical relationship between structural safety and durability) to form an evaluation level association network, visually displaying the dependencies between each level.

[0044] If the target layer vector is (0.619, 0.267, 0, 0.11, 0.004) (the excellent grade has the highest membership), when the server builds the network, it associates the "excellent" grade with indicators such as structural safety ≥ 0.79 and durability ≥ 0.654 to form a hierarchical relationship diagram.

[0045] Based on the evaluation grade optimization generation rules and application requirements, the evaluation grade association network is verified and abnormal associations are adjusted based on the optimization factor integrity detection mechanism to generate the target evaluation grade optimization framework. The server automatically verifies the evaluation grade association network through the optimization factor integrity detection mechanism (such as checking whether the indicator weight matrix dimensions match and whether the membership calculation misses a grade), identifies abnormal associations (such as when the weight of an indicator is out of line with its actual importance), and adjusts the association rules based on application requirements (such as real-time evaluation or offline analysis) to generate a structured optimization framework.

[0046] Optimize factor integrity detection, and set the target layer evaluation vector as , if exists or , an exception check is triggered. If the membership of a bridge's "deflection reduction rate" is calculated as (0,0,0,0,1) (Level V exception), but the structural safety weight is not reduced synchronously, the server adjusts the weight according to the rules: , where k is the adjustment coefficient (such as 0.5), This is an abnormal level deviation (level V is 1). When the server processed the "external prestressing + web thickening" solution, it was found that the Level I membership (0.455) in the target layer vector (0.455, 0.336, 0, 0.076, 0.133) and the Level I membership (0.52) in the structural safety vector (0.52, 0.1, 0, 0, 0.38) deviated by more than 10%. This triggered an optimization: Verification found that the "deflection reduction rate" membership (0, 0, 0, 0, 1) did not correctly affect the structural safety weight. The structural safety weight was adjusted from 0.35 to 0.32, and the target layer vector was recalculated to (0.43, 0.35, 0, 0.08, 0.14), generating the optimization framework.

[0047] According to preset optimization factor generation rules, the bridge's dynamic characteristic information, primary indicators, and target-layer evaluation result vectors are associated and integrated with the evaluation grade association network to generate an assessment grade optimization factor that incorporates the bridge's dynamic characteristics, primary indicator evaluation results, and target-layer evaluation results. The server integrates the bridge's dynamic characteristic information (weight vector, membership matrix), primary indicators, target-layer evaluation results, and the evaluation grade association network according to preset rules (such as JSON format or database table structure) to generate an optimization factor containing multi-dimensional data for use by upper-level applications.

[0048] The data integration example is as follows, "bridge_id":"G2012-01","dynamic_features": "weight_vector":[0.35,0.29,0.14,0.12,0.10], / / First-level indicator weight; "membership_matrix":[[0.545,0.075,0,0,0.38], / / Structural security membership; [0.155,0.585,0.26,0,0], / / Structural durability membership; / / Other indicators...], "evaluation_results":"primary_index":["index_name":"Structural Security", "vector":[0.545,0.075,0,0,0.38], / / Other first-level indicators...], "target_layer":[0.288,0.305,0.183,0.091,0.133], "grade_network":"level_1":"condition":"primary_index.structural_safety>=0.7","related_indices":["deflection_reduction","stress_decrease"], / / Association conditions for each grade... Optimization factors include the bridge's unique identifier, dynamic characteristics, evaluation results at each level, and the associated network. The server supports filtering fields based on application requirements (for example, outputting only the target layer grade and key indicators). The data format adheres to industry standards for bridge assessment (for example, compatibility with the "Highway Bridge Technical Condition Assessment Standard").

[0049] S105, the evaluation results and indicator types and data sources in the weight calculation related influencing factor data are processed in combination with a multi-level evaluation model to generate key factors that affect the bridge capacity improvement effect.

[0050] In one implementation, feature extraction is performed on the evaluation results, indicator types, and data sources in the weight calculation-related influencing factor data to generate evaluation result features, indicator type features, and data source features. Evaluation result features include the load-bearing capacity increase rate and deflection reduction rate before and after reinforcement; indicator type features include quantitative and qualitative indicators; and data source features include theoretical calculation data and experimental test data. The server extracts structured features from the weight calculation-related influencing factor data, including evaluation result features, indicator type features, and data source features, to form a standardized feature vector. For a bridge with a "web steel plate + bottom plate thickening" solution, the server extracts a 39.1% increase in load-bearing capacity and a 4.9% deflection reduction rate after reinforcement, generating the feature vector [39.1, 4.9]. The server identifies "stress reduction rate" as a quantitative indicator (numerical) and "reinforcement technology adaptability" as a qualitative indicator (descriptive), generating the feature vector [1, 0, 0, 1] (the first two digits indicate quantitative / qualitative identification, and the last two digits indicate other types). The bearing capacity data comes from experimental testing (such as load tests), and the theoretical calculation data accounts for 30%, generating a characteristic vector [0.7, 0.3] (experimental testing ratio, theoretical calculation ratio).

[0051] The multi-level evaluation model is subjected to feature extraction to generate Analytic Hierarchy Process (AHP) features and fuzzy comprehensive evaluation features. The AHP features include a weight matrix and consistency check parameters, while the fuzzy comprehensive evaluation features include a membership matrix and fuzzy synthesis operator parameters. The server parses the multi-level evaluation model parameters, extracting the core features of the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation, and forming a model parameter library. The AHP features extract the structural safety weight matrix W = [0.35, 0.27, 0.17, 0.11, 0.10] for a bridge structure, and the consistency check parameter CR = 0.045 < 0.1 (passing the test). The fuzzy comprehensive evaluation features extract the membership matrix (e.g., the structural safety membership vector [0.79, 0.1, 0, 0.1, 0.01]) and the fuzzy synthesis operator parameters (weighted average operator).

[0052] Based on the characteristics of the assessment results, indicator type, and data source, combined with the characteristics of the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation, preliminary identification information for key factors was generated. This information was used to characterize the degree of influence of structural safety and durability indicators on the bridge capacity improvement. The server combined the assessment results with model parameters through correlation analysis, calculated the impact of each indicator on the bridge capacity improvement, and generated preliminary identification information. Assuming a weight of 0.35 for the load-bearing capacity improvement rate and a contribution value of 0.79 for the membership degree to Level I, the degree of influence = weight × membership = 0.35 × 0.79 = 0.2765.

[0053] The server generates structured data as follows: "structural_safety":"load_capacity":"influence_degree":0.2765,"grade": "Grade I","deflection_reduction":"influence_degree":0.29×0.1 (assuming deflection membership degree is 0.1)=0.029,"grade":"Grade II".

[0054] The preliminary identified key factors are processed to generate key factors influencing the bridge capacity improvement effect. These key factors include quantitative indicators for the load-bearing capacity improvement rate and stress reduction rate, as well as qualitative indicators for the adaptability of the reinforcement technology and ease of construction. This generates a key factor assessment result that includes a ranking of key factors by importance and their correlation with the bridge capacity improvement effect. The server sorts the preliminary identified information and, based on the indicator type and data source, generates a key factor assessment result that includes a ranking of key factors by importance. The quantitative key factors are the load-bearing capacity improvement rate (impact level 0.2765) and the stress reduction rate (0.17 × 1 = 0.17, assuming a membership level of 1). The ranking by importance is: [load-bearing capacity improvement rate, stress reduction rate].

[0055] The qualitative key factors are the adaptability of reinforcement technology (expert score is 95 points, corresponding to level I, the impact level is 0.14×1=0.14) and the convenience of construction (score is 85 points, level II, the impact level is 0.10×0.75=0.075), and the ranking is: [adaptability of reinforcement technology, convenience of construction].

[0056] The final evaluation results are as follows, "key_factors":["name":"Carrying capacity improvement rate","type":"Quantitative","importance":1,"correlation":0.2765.

[0057] S106, based on the fuzzy comprehensive evaluation model combined with the actual bridge verification strategy, the evaluation results, key factors affecting the bridge capacity improvement effect and abnormal state identification information are processed to generate dynamic adjustment information for bridge reinforcement.

[0058] In one implementation, based on a fuzzy comprehensive evaluation model, the correlation and matching degree between real-world bridge verification strategies and bridge reinforcement logic is introduced. The effectiveness feature recognition is performed on the evaluation results, key factors influencing bridge capacity improvement, and feature validity and parameter accuracy in abnormal state identification information, generating a reinforcement optimization anomaly feature set. Based on the fuzzy comprehensive evaluation model, the server calculates the correlation and matching degree between real-world bridge verification data and bridge reinforcement logic, identifies anomalies in feature validity and parameter accuracy in the evaluation results, key factors, and abnormal state information, and generates an anomaly feature set.

[0059] The correlation matching degree is calculated as follows: During actual bridge verification, the load-bearing capacity improvement rate for the "web steel plate + bottom plate thickening" solution for a particular bridge has a membership of (0.79, 0.1, 0, 0.1, 0.01). This matches the reinforcement logic's "Excellent grade requires a load-bearing capacity ≥ 0.7" as follows: Matching degree = 0.79 / 1 = 0.79. If the calculated crack resistance improvement rate is 5.81 (membership degree (0, 0, 0, 0.9, 0.1)), which conflicts with the "Crack resistance ≥ 20 for Grade I" standard, the server will flag the crack resistance indicator as abnormal and add the bridge to the collection.

[0060] The server compares the actual bridge verification results with the preset standard bridge reinforcement model library and optimization parameter library. Combined with actual bridge test verification, the deviation between the reinforcement scheme performance and the optimization target is determined, and a reinforcement optimization deviation assessment result is generated. The server compares the actual bridge verification results with the preset standard model library, calculates the deviation between the reinforcement scheme performance and the optimization target, and generates a quantitative assessment result.

[0061] Deviation calculation formula: Deviation = |actual value - target value| / target value interval length. If the target value of the deflection reduction rate of a bridge after reinforcement is ≥2.5 and the actual value is 4.9, the target interval is , then: deviation , (positive deviation, excellent performance), if the actual value of stress reduction rate is 9.57 (target ≥ 10), the interval length is 5, then the deviation The reinforcement optimization deviation evaluation results are correlated with the reinforcement optimization anomaly feature set. Combined with the characteristics of the fuzzy comprehensive evaluation model, input parameters, and optimization suggestions from actual bridge verification feedback, the reinforcement optimization deviation evaluation results are weighted to generate an optimization vector that integrates the evaluation logic and reinforcement process. The server correlates the deviation evaluation results with the anomaly features, combines the fuzzy model characteristics and actual bridge feedback, and weights the deviation to generate an optimization vector that integrates the evaluation and reinforcement logic.

[0062] The weighted deviation is calculated as follows, , the stress reduction rate deviation is 0.086, the weight is 0.17, and the abnormal correction coefficient is 1.2 (due to the abnormality of associated crack resistance), then: Integrate the weighted deviations of each indicator to generate a vector such as [0.0176 (stress), 0.05 (crack resistance), ...].

[0063] The server normalizes and comprehensively calculates the reinforcement optimization deviation assessment results and the effect optimization vector. Taking into account the real-time requirements of bridge reinforcement, dynamic adjustment information for bridge reinforcement is generated, including the optimization probability value, effect deviation coefficient, and optimization warning line. The server normalizes and comprehensively calculates the reinforcement optimization deviation assessment results and the effect optimization vector. Taking into account the real-time requirements of bridge reinforcement (such as response time ≤ 100ms), dynamic adjustment information is generated, including the optimization probability value, effect deviation coefficient, and optimization warning line.

[0064] Normalization processing, assuming the weighted deviation vector is [0.0176 (stress), 0.05 (crack resistance)], the sum is 0.0176 + 0.05 = 0.0676, after normalization: , crack resistance deviation coefficient Optimize the probability value calculation, the optimized probability value = 1-maximum normalized deviation coefficient = 1-0.74 = 0.26 (indicating a normal probability of 26%), the preset warning line is 0.5, and when the maximum deviation coefficient (0.74) is greater than 0.5, an abnormal warning is triggered.

[0065] Taking the "web steel plate + bottom plate thickening (additional prestressing)" scheme as an example: in the actual bridge verification, the crack resistance improvement rate was 5.81, the deviation was 0.74 (corresponding to Level IV), and the weight was 0.11; the stress reduction rate was 14.82, the deviation was 0 (Level I), and the weight was 0.17.

[0066] The weighted deviation vector is ; The normalized deviation coefficient is [0,1] (because the total is 0.0814, the anti-cracking deviation coefficient is 0.0814 / 0.0814=1); the optimization probability value is 1-1=0 (triggering an emergency optimization warning).

[0067] The dynamic adjustment information is {"bridge_id":"G2012-01","reinforcement_scheme":"web steel plate + bottom plate thickening (additional prestressing)","optimization_info":{"deviation_coefficient":1, "optimization_probability":0,"warning_level":"urgent", "adjustment_suggestions": ["Review the crack resistance test data and recommend increasing the prestressing tonnage by 15%", "Prioritize handling of abnormal crack resistance indicators and recalculate the stress-strain relationship"], "real_time_metrics":{"response_time":35ms,"validation_time":"2025-06-2914:30:22"}}}. The server converts real-bridge verification data into executable reinforcement adjustment instructions through a four-step process: data verification, deviation calculation, weight fusion, and threshold determination. Normalization ensures the comparability of multiple indicators; optimized probability values are combined with warning lines to achieve automated early warnings; and output results include technical recommendations and real-time performance indicators to support closed-loop decision-making.

[0068] This application obtains basic data for evaluating bridge capacity enhancement, including data types, factors influencing weight calculations, and evaluation anomalies. Quantitative and qualitative indicators are then preprocessed, and a hierarchy analysis weight matrix and indicator membership matrix are introduced to generate dynamic bridge characteristics. This information is used to represent the membership vectors of each indicator. Furthermore, based on the weight adjustment logic of the hierarchy analysis method and the dynamic factor correlation analysis of the fuzzy comprehensive evaluation, information identifying anomalies during the evaluation process is generated.

[0069] Fuzzy synthesis operators are used to calculate the evaluation result vectors for the primary indicators and target layer, and the maximum membership principle is used to generate the evaluation grade. The weighted influencing factor data is then combined with a multi-level evaluation model to generate key factors influencing the bridge capacity improvement effect. These include quantitative indicators such as the load-bearing capacity improvement rate and stress reduction rate, as well as qualitative indicators such as the adaptability of the reinforcement technology and ease of construction, and form the key factor evaluation results.

[0070] Finally, based on a fuzzy comprehensive evaluation model combined with a real-bridge verification strategy, dynamic adjustment information for bridge reinforcement is generated, including optimization probability values, effect deviation coefficients, and optimized warning lines, enabling precise control of bridge reinforcement. Through multi-model fusion, real-bridge verification, and dynamic adjustment, this approach addresses the limitations of existing technologies, such as the single evaluation dimension and inaccurate deviation calculations. This approach improves the comprehensiveness, accuracy, and real-time nature of bridge capacity improvement assessments, providing a scientific basis for bridge reinforcement decision-making.

[0071] In one embodiment, Figure 2 As shown, the present application also provides a comprehensive evaluation device for evaluating bridge capacity improvement, comprising: Acquisition module 201 is used to obtain basic data for bridge capacity improvement effect evaluation, including data type information, weight calculation related influencing factor data and evaluation abnormal status information; Processing module 202 is used to pre-process the quantitative and qualitative indicators in the basic data, introduce the hierarchical analysis method weight matrix and indicator membership matrix, and generate bridge dynamic characteristic information, which is used to characterize the membership vector of each indicator; based on the weight adjustment logic of the hierarchical analysis method, the weight calculation-related influencing factor data and the evaluation abnormal state information are processed, and combined with the dynamic factor correlation analysis of the fuzzy comprehensive evaluation, abnormal state identification information in the evaluation process is generated; the bridge dynamic characteristic information is processed, the evaluation result vectors of the first-level indicators and the target layer are calculated through the fuzzy synthesis operator, and the maximum membership principle is used to generate the evaluation level of the bridge capacity improvement effect; the evaluation results and indicator types and data sources in the weight calculation-related influencing factor data are processed in combination with the multi-level evaluation model to generate key factors affecting the bridge capacity improvement effect; based on the fuzzy comprehensive evaluation model combined with the actual bridge verification strategy, the evaluation results, key factors affecting the bridge capacity improvement effect and abnormal state identification information are processed to generate dynamic adjustment information for bridge reinforcement.

[0072] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the comprehensive evaluation method for evaluating bridge capacity improvement provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0073] Each embodiment of this application is described in a related manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from other embodiments. In particular, the embodiments of the comprehensive evaluation method, electronic device, electronic device, and readable storage medium for evaluating bridge capacity improvement are generally similar to the aforementioned embodiments of the comprehensive evaluation method for evaluating bridge capacity improvement, so the description is relatively simple. For related portions, reference can be made to the partial description of the aforementioned embodiments of the comprehensive evaluation method for evaluating bridge capacity improvement.

Claims

1. A comprehensive evaluation method for evaluating bridge capacity improvement, characterized in that: include: Obtain basic data for bridge capacity improvement effect evaluation, including data type information, weight calculation-related influencing factor data, and evaluation abnormal status information; Preprocess the quantitative and qualitative indicators in the basic data, introduce the hierarchical analysis method weight matrix and indicator membership matrix to generate bridge dynamic characteristic information, which is used to represent the membership vector of each indicator; The weight adjustment logic based on the hierarchical analysis method is used to process the data of the influencing factors related to weight calculation and the evaluation abnormal status information. Combined with the dynamic factor correlation analysis of fuzzy comprehensive evaluation, the abnormal status identification information in the evaluation process is generated. The dynamic characteristic information of the bridge is processed, and the evaluation result vector of the primary index and the target layer is calculated through the fuzzy synthesis operator. The evaluation grade of the bridge capacity improvement effect is generated using the maximum membership principle. The evaluation results, indicator types, and data sources in the weight calculation-related influencing factor data are processed in combination with a multi-level evaluation model to generate key factors that affect the improvement of bridge capacity; Based on the fuzzy comprehensive evaluation model combined with the actual bridge verification strategy, the evaluation results, key factors affecting the bridge capacity improvement effect and abnormal state identification information are processed to generate dynamic adjustment information for bridge reinforcement.

2. The method according to claim 1, wherein The quantitative and qualitative indicators in the basic data are preprocessed, and the weight matrix and indicator membership matrix of the hierarchical analysis method are introduced to generate the dynamic characteristic information of the bridge, including: Based on the correlation matching degree between the weight matrix of the hierarchical analysis method and the indicator membership matrix, the effect feature recognition is performed on the feature validity and parameter accuracy in the preprocessed quantitative and qualitative indicator data to generate a preprocessing abnormal feature set; Compare the preprocessed indicator data with the preset standardized indicator library and weight parameter library, combine the indicator preprocessing example verification, determine the deviation between the preprocessing result and the standardized target, and generate the preprocessing deviation evaluation result; The preprocessing deviation evaluation results are correlated with the preprocessing abnormal feature set. The preprocessing deviation evaluation results are weighted based on the characteristics of the hierarchical analysis method, indicator parameters, and optimization suggestions from the preprocessing feedback to generate an effect optimization vector that integrates the preprocessing logic and the weight calculation process. Normalize and comprehensively calculate the preprocessing deviation evaluation results and effect optimization vectors, and generate preprocessing optimization features including preprocessing probability values, effect deviation coefficients, and optimization warning lines in combination with the real-time requirements of indicator preprocessing; According to the preset bridge dynamic feature information generation rules, the preprocessing optimization features, the hierarchical analysis method weight matrix and the indicator membership matrix are associated and integrated to generate bridge dynamic feature information including weight feature vectors and membership feature vectors. The bridge dynamic feature information is used to represent the membership vectors of each indicator.

3. The method according to claim 1, wherein The weight adjustment logic based on the hierarchical analysis method processes the data of the influencing factors related to weight calculation and the evaluation abnormal status information. Combined with the dynamic factor correlation analysis of the fuzzy comprehensive evaluation, it generates abnormal status identification information during the evaluation process, including: Based on the weight adjustment logic of the hierarchical analysis method, the impact factor data related to weight calculation is processed to generate the impact factor data after weight adjustment; Combine the weight adjustment logic of the hierarchical analysis method with the evaluation of abnormal state information to generate abnormal state association data; Using dynamic factor correlation analysis of fuzzy comprehensive evaluation, we analyze the weight-adjusted influencing factor data and abnormal state correlation data to generate preliminary abnormal state identification data; Based on the dynamic factor association rules of fuzzy comprehensive evaluation, the preliminary abnormal state identification data is structured and organized to generate abnormal state identification information in the evaluation process.

4. The method according to claim 2, wherein The dynamic characteristic information of the bridge is processed, and the evaluation result vector of the primary index and the target layer is calculated through the fuzzy synthesis operator. The maximum membership principle is used to generate the evaluation level of the bridge capacity improvement effect, including: Process the dynamic characteristic information of the bridge, and calculate the membership matrix and weight vector corresponding to the first-level index by combining the fuzzy synthesis operator to generate the evaluation result vector of the first-level index; Combined with the evaluation result vector of the first-level indicators, the first-level indicator evaluation matrix and weight vector corresponding to the target layer are calculated through the fuzzy synthesis operator to generate the evaluation result vector of the target layer; Based on the cross-dimensional association rules and the logical relationship between evaluation levels, the evaluation result vectors of the target layer are structurally integrated to complete the construction of the evaluation level association network; According to the evaluation grade optimization generation rules and application requirements, the evaluation grade association network is verified and abnormal associations are adjusted based on the optimization factor integrity detection mechanism to generate the target evaluation grade optimization framework; According to the preset optimization factor generation rules, the bridge dynamic characteristic information, the first-level indicators and the evaluation result vector of the target layer are associated and integrated with the evaluation grade association network to generate an evaluation grade optimization factor that includes the bridge dynamic characteristics, the first-level indicator evaluation results and the target layer evaluation results.

5. The method according to claim 1, wherein The evaluation results, indicator types, and data sources in the weight calculation-related influencing factor data are processed in combination with a multi-level evaluation model to generate key factors that affect the improvement of bridge capacity, including: The evaluation results, indicator types, and data sources in the weight calculation-related influencing factor data are subjected to feature extraction processing to generate evaluation result features, indicator type features, and data source features. The evaluation result features include the load-bearing capacity improvement rate and deflection reduction rate before and after reinforcement; the indicator type features include quantitative indicators and qualitative indicators; and the data source features include theoretical calculation data and experimental detection data. Perform feature extraction on the multi-level evaluation model to generate AHP features and fuzzy comprehensive evaluation features. The AHP features include weight matrix and consistency test parameters; the fuzzy comprehensive evaluation features include membership matrix and fuzzy synthesis operator parameters. Based on the characteristics of the assessment results, indicator types, and data sources, combined with the characteristics of the hierarchical analysis method and fuzzy comprehensive evaluation, preliminary identification information of key factors is generated. The preliminary identification information of key factors is used to characterize the degree of influence of structural safety indicators and structural durability indicators on the improvement of bridge capacity. The preliminary identification information of key factors is processed to generate key factors that affect the effect of bridge capacity improvement. Among them, key factors include quantitative indicators of bearing capacity improvement rate and stress reduction rate, as well as qualitative indicators of reinforcement technology adaptability and construction convenience. The key factor evaluation results are formed, which include the ranking of key factor importance and the degree of correlation with the bridge capacity improvement effect.

6. The method according to claim 5, wherein Based on the fuzzy comprehensive evaluation model combined with the actual bridge verification strategy, the evaluation results, key factors affecting the bridge capacity improvement effect, and abnormal state identification information are processed to generate dynamic adjustment information for bridge reinforcement, including: Based on the fuzzy comprehensive evaluation model, the correlation matching degree between the actual bridge verification strategy and the bridge reinforcement logic is introduced to identify the effect characteristics of the evaluation results, the key factors affecting the bridge capacity improvement effect, and the feature validity and parameter accuracy in the abnormal state identification information, and generate a reinforcement optimization abnormal feature set. Compare the actual bridge verification results with the preset bridge reinforcement standard model library and optimization parameter library, combine the actual bridge test verification, determine the deviation between the reinforcement scheme performance and the optimization target, and generate the reinforcement optimization deviation evaluation results; The reinforcement optimization deviation evaluation results are correlated with the reinforcement optimization abnormal feature set. Combining the characteristics of the fuzzy comprehensive evaluation model, input parameters, and optimization suggestions from actual bridge verification feedback, the reinforcement optimization deviation evaluation results are weighted to generate an effect optimization vector that integrates the evaluation logic and reinforcement process. The reinforcement optimization deviation evaluation results and effect optimization vectors are normalized and comprehensively calculated. Combined with the real-time requirements of bridge reinforcement, dynamic adjustment information of bridge reinforcement including optimization probability value, effect deviation coefficient and optimization warning line is generated.

7. A comprehensive evaluation device for evaluating bridge capacity improvement, characterized in that: The device comprises: The acquisition module is used to obtain basic data for bridge capacity improvement effect evaluation, including data type information, weight calculation related influencing factor data and evaluation abnormal status information; The processing module is used to pre-process the quantitative and qualitative indicators in the basic data, introduce the hierarchical analysis method weight matrix and indicator membership matrix, and generate bridge dynamic characteristic information. The bridge dynamic characteristic information is used to characterize the membership vector of each indicator; based on the weight adjustment logic of the hierarchical analysis method, the weight calculation-related influencing factor data and evaluation abnormal state information are processed, and combined with the dynamic factor correlation analysis of the fuzzy comprehensive evaluation, abnormal state identification information in the evaluation process is generated; the bridge dynamic characteristic information is processed, and the evaluation result vectors of the first-level indicators and the target layer are calculated through the fuzzy synthesis operator, and the maximum membership principle is used to generate the evaluation level of the bridge capacity improvement effect; the evaluation results and indicator types and data sources in the weight calculation-related influencing factor data are processed in combination with the multi-level evaluation model to generate the key factors affecting the bridge capacity improvement effect; based on the fuzzy comprehensive evaluation model combined with the actual bridge verification strategy, the evaluation results, key factors affecting the bridge capacity improvement effect and abnormal state identification information are processed to generate dynamic adjustment information for bridge reinforcement.

8. An electronic device, characterized in that: include: a first processor; and a memory for storing executable instructions of the first processor; The first processor is configured to execute the comprehensive evaluation method for evaluating bridge capacity improvement according to any one of claims 1 to 6 by executing the executable instructions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the second processor, the comprehensive evaluation method for evaluating bridge capacity improvement according to any one of claims 1 to 6 is implemented.

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