A comprehensive evaluation method and system for evaluating bridge capacity improvement

Abstract

The application provides a comprehensive evaluation method and system for evaluating bridge capacity improvement, and is applied to the technical field of data processing. Quantitative and qualitative indexes in basic data are pretreated to generate bridge dynamic characteristic information; weight calculation related influence factor data and evaluation abnormal state information are processed based on weight adjustment logic of the analytic hierarchy process, combined with dynamic factor correlation analysis of fuzzy comprehensive evaluation to generate abnormal state identification information in the evaluation process; the bridge dynamic characteristic information is processed to generate an evaluation grade of the bridge capacity improvement effect; evaluation results and index types, data sources in the weight calculation related influence factor data are processed in combination with a multistage evaluation model to generate key factors influencing the bridge capacity improvement effect; the evaluation results, the key factors influencing the bridge capacity improvement effect and the abnormal state identification information are processed to generate dynamic adjustment information of bridge reinforcement.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a comprehensive evaluation method and system for assessing the improvement of bridge capacity. Background Technology

[0002] With the continuous growth of traffic volume and the increase in service life, many bridges face problems such as structural performance degradation and reduced load-bearing capacity, urgently requiring reinforcement to improve their performance and safety. Various technical methods exist for evaluating the effectiveness of bridge capacity improvement and optimizing reinforcement schemes. Some existing technologies utilize conventional assessment methods to detect and evaluate bridge structures, roughly judging the bridge's condition by simply calculating indicators such as load-bearing capacity. However, these methods often only provide a single-dimensional assessment of some bridge performance aspects, failing to comprehensively and accurately consider the numerous complex factors affecting bridge capacity improvement. For example, during the assessment process, key assessment results such as the rate of increase in load-bearing capacity and the rate of reduction in deflection cannot be comprehensively analyzed using multi-source data, leading to biases in the judgment of the actual condition of the bridge.

[0003] Existing technologies rely on simplistic methods when comparing actual bridge verification results with pre-defined bridge reinforcement standard model libraries and optimization parameter libraries. Simply comparing numerical values ​​leads to inaccurate deviations between the determined reinforcement scheme's performance and the optimization target, failing to provide effective guidance for subsequent adjustments. Furthermore, existing technologies lack effective correlation analysis methods when processing reinforcement optimization deviation assessment results and anomaly feature sets. They cannot fully utilize the characteristics of fuzzy comprehensive evaluation models and optimization suggestions from actual bridge verification feedback to reasonably weight the deviations, making it difficult to generate accurate and effective effect optimization vectors and dynamic adjustment information for bridge reinforcement. This hinders the precise optimization and real-time control of bridge reinforcement schemes.

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

[0005] The purpose of this application is to provide a comprehensive evaluation method and system for assessing the improvement of bridge capacity, which at least to some extent overcomes the problems existing in the prior art. By acquiring and preprocessing basic data, the method introduces the weight matrix and membership matrix of the analytic hierarchy process (AHP) to generate dynamic characteristic information of the bridge, combines the AHP with fuzzy comprehensive evaluation to generate abnormal state identification information, calculates the evaluation level and key factors, and generates dynamic adjustment information based on fuzzy comprehensive evaluation and actual bridge verification, thereby improving the comprehensiveness, accuracy and real-time nature of the evaluation.

[0006] Other features and advantages of this 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 this application, a comprehensive evaluation method for assessing bridge capacity improvement is provided, comprising: acquiring basic data for evaluating the effect of bridge capacity improvement, including data type information, weight calculation-related influencing factor data, and evaluation anomaly information; preprocessing the quantitative and qualitative indicators in the basic data, introducing a weight matrix and an indicator membership matrix of the analytic hierarchy process (AHP) to generate bridge dynamic characteristic information, which is used to characterize the membership vector of each indicator; processing the weight calculation-related influencing factor data and evaluation anomaly information based on the weight adjustment logic of the AHP, and generating anomaly identification information in the evaluation process by combining the dynamic factor correlation analysis of fuzzy comprehensive evaluation; processing the bridge dynamic characteristic information, calculating the evaluation result vector of the first-level indicator and the target layer through a fuzzy synthesis operator, and generating the evaluation level of bridge capacity improvement effect using the maximum membership principle; processing the evaluation results, 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; and processing the evaluation results, key factors affecting the bridge capacity improvement effect, and anomaly identification information based on the fuzzy comprehensive evaluation model and a real bridge verification strategy to generate dynamic adjustment information for bridge reinforcement.

[0008] Another aspect of this application discloses a comprehensive evaluation device for assessing bridge capacity improvement, characterized by comprising: an acquisition module for acquiring basic data for evaluating the bridge capacity improvement effect, including data type information, weight calculation-related influencing factor data, and evaluation anomaly status information; a processing module for preprocessing the quantitative and qualitative indicators in the basic data, introducing a weight matrix and an indicator membership matrix from the analytic hierarchy process (AHP), generating bridge dynamic characteristic information, which is used to characterize the membership vector of each indicator; and processing the weight calculation-related influencing factor data and evaluation anomaly status information based on the weight adjustment logic of the AHP, combined with fuzzy comprehensive evaluation. The system employs dynamic factor correlation analysis to generate abnormal state identification information during the evaluation process. It processes the dynamic characteristic information of the bridge, calculates the evaluation result vectors of the first-level indicators and the target layer using fuzzy synthesis operators, and generates the evaluation level of the bridge capacity improvement effect using the maximum membership principle. It processes the evaluation results, indicator types, and data sources in the relevant influencing factor data for weight calculation using a multi-level evaluation model to generate key factors affecting the bridge capacity improvement effect. Based on the fuzzy comprehensive evaluation model and a real bridge verification strategy, it processes the evaluation results, key factors affecting the bridge capacity improvement effect, and abnormal state identification information to generate dynamic adjustment information for bridge reinforcement.

[0009] According to another aspect of this application, an electronic device is characterized by comprising: a first processor; and a memory for storing executable instructions of the first processor; wherein the first processor is configured to execute the comprehensive evaluation method for improving the bridge's capabilities as described above by executing the executable instructions.

[0010] This application provides a comprehensive evaluation method and system for assessing bridge capacity improvement. It acquires basic data, including data types, weighted influencing factors, and evaluation anomaly states. Quantitative and qualitative indicators are preprocessed, and a weight matrix and indicator membership matrix based on the analytic hierarchy process (AHP) are introduced to generate dynamic characteristic information of the bridge representing the membership vectors of each indicator. Then, combining the AHP weight adjustment logic with the dynamic factor correlation analysis of fuzzy comprehensive evaluation, anomaly state identification information is generated. The evaluation result vectors of the first and target layers are calculated using fuzzy synthesis operators, and the evaluation level is obtained according to the principle of maximum membership. The weighted influencing factor data is processed using a multi-level evaluation model to generate key factors and evaluation results, including quantitative indicators such as the bearing capacity improvement rate and qualitative indicators such as the adaptability of reinforcement technology. Finally, based on the fuzzy comprehensive evaluation model and verification with actual bridges, dynamic adjustment information, including optimization probability values, is generated to achieve precise control and improve the comprehensiveness, accuracy, and real-time nature of the evaluation.

[0011] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0012] Figure 1 A flowchart illustrating a comprehensive evaluation method for assessing bridge capacity improvement provided in an embodiment of this application is shown.

[0013] Figure 2 A schematic diagram of the structure of a comprehensive evaluation device for assessing the improvement of bridge capacity is shown in an embodiment of this application. Detailed Implementation

[0014] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0015] The following is combined Figure 1 This application describes a comprehensive evaluation method for assessing bridge capacity improvement according to exemplary embodiments thereof. It should be noted that the following application scenarios are shown only to facilitate understanding of the spirit and principles of this application, and the embodiments of this application are not limited in any way. Rather, the embodiments of this application are applicable to any suitable scenario.

[0016] In one embodiment, this application also proposes a comprehensive evaluation method and system for assessing the improvement of bridge capacity. Figure 1A schematic flowchart illustrating a comprehensive evaluation method for assessing bridge capacity improvement according to an embodiment of this application is shown. Figure 1 As shown, this method is applied to a server and includes:

[0017] S101, Obtain basic data for evaluating the bridge capacity improvement effect, including data on data types, data on relevant influencing factors for weight calculation, and information on abnormal evaluation states.

[0018] In one implementation, the data type information includes quantitative and qualitative index data. Specifically, the quantitative index data includes numerical data such as the bearing capacity improvement rate (e.g., the 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 index data includes descriptive data such as the adaptability of the reinforcement technology (e.g., "highly targeted and well-matched to the causes of the defects") and the complexity of the construction process (e.g., "low construction difficulty").

[0019] The weighted calculation of relevant influencing factors includes quantitative and qualitative influencing factors. Specifically, quantitative influencing factors include the rate of increase in bearing capacity before and after reinforcement (e.g., the rate of increase in bearing capacity of a bridge after reinforcement is 17.3%), and the rate of stress reduction (e.g., the rate of stress reduction is 13.89%). Qualitative influencing factors include the maturity of reinforcement technology (e.g., many application cases with good results, technology maturity ≥ 95%), and the impact of construction on traffic (e.g., single-lane traffic closure < 3 months).

[0020] The evaluation of abnormal status information includes abnormal indicator data and abnormal evaluation process. Specifically, abnormal indicator data includes a calculated deflection reduction rate of -17.1 for a certain bridge, which is negative and may indicate that the deflection has increased instead of decreased, thus constituting an abnormal state. Abnormal evaluation process includes situations where, during the weight adjustment process, the consistency ratio (CR) of the judgment matrix is ​​greater than 0.1, requiring readjustment of the judgment matrix.

[0021] S102 preprocesses the quantitative and qualitative indicators in the basic data, introduces the weight matrix and indicator membership matrix of the analytic hierarchy process, and generates dynamic characteristic information of the bridge.

[0022] In one implementation, based on the correlation matching degree between the weight matrix and the indicator membership matrix of the analytic hierarchy process (AHP), the effectiveness of features and the accuracy of parameters in the preprocessed quantitative and qualitative indicator data are identified, generating a set of preprocessed abnormal features. The server then automatically verifies the preprocessed quantitative / qualitative indicator data based on the correlation matching rules between the AHP weight matrix and the indicator membership matrix, identifying anomalies in feature effectiveness and parameter accuracy.

[0023] Specifically, in a bridge's "external prestressing + web thickening" scheme, the server read a calculated deflection reduction rate of -2.8. According to the membership matrix rules (deflection reduction rate < 1.0 corresponds to Level V), a membership vector (0,0,0,0,1) was generated. When matching this with the structural safety weight matrix (weight 0.35), a negative value was found, contradicting the logical requirement that the deflection should be reduced after reinforcement, and was marked as "data validity abnormal." When the server parsed the "reinforcement technology adaptability" score, the expert scored 60 points (corresponding to Level IV), but according to the standardized indicator library, "a technical scheme deviation rate ≤ 5% should belong to Level I," the score was determined to be inconsistent with reality, generating a "parameter accuracy abnormality" feature.

[0024] The preprocessed index data is compared with the preset standardized index library and weight parameter library. Verification is performed using preprocessing examples to determine the deviation between the preprocessing results and the standardized target, generating a preprocessing deviation assessment result. The server compares the preprocessed data with the preset standardized index library (e.g., a load-bearing capacity improvement rate ≥15% is Level I) and weight parameter library (e.g., structural safety weight 0.35), calculates the deviation using an algorithm, and verifies it using historical preprocessing examples. The deviation calculation formula (taking stress reduction rate as an example) is as follows:

[0025] 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 the "web steel plate + external prestressing + integrated layer" scheme to 2.9, the lower limit of standard I is 15. The deviation is calculated as (15-2.9) / 15≈0.807, and the deviation assessment result (0.807, structural safety index) is generated.

[0026] The preprocessing deviation evaluation results are correlated with the preprocessing anomaly feature set. Combining the characteristics of the analytic hierarchy process (AHP), indicator parameters, and optimization suggestions from preprocessing feedback, the preprocessing deviation evaluation results are weighted to generate an optimized vector that integrates preprocessing logic and weight calculation processes. The server correlates the deviation evaluation results with anomaly features, calls the AHP weight adjustment algorithm, and, incorporating preprocessing feedback (such as expert optimization suggestions), weights the deviation to generate a vector that integrates preprocessing logic. The weighted deviation calculation formula is as follows: If the deviation of the deflection reduction rate is 1, corresponding to a structural safety weight of 0.35, then the weighted deviation... Specifically, the server integrates the deviation of the improvement rate of crack resistance of a certain bridge by 1 (weight 0.11) and the deviation of the improvement rate of natural frequency by 0.25 (weight 0.10), and generates an effect optimization vector [0.35 (deflection), 0.11 (crack resistance), 0.025 (natural frequency), ...].

[0027] The preprocessing deviation evaluation results and effect optimization vector are normalized and comprehensively calculated. Considering the real-time requirements of indicator preprocessing, preprocessing optimization features containing preprocessing probability values, effect deviation coefficients, and optimization warning lines are generated. The server normalizes the weighted deviation and effect optimization vector, and, considering the real-time threshold of indicator preprocessing (e.g., response time ≤ 100ms), generates features containing probability values, deviation coefficients, and warning lines. The normalized result (vector [0.35, 0.11]) is as follows. The sum = 0.35 + 0.11 = 0.46, which is normalized to [0.76, 0.24]. The preprocessing probability value is calculated as follows: preprocessing probability value = 1 - normalization deviation coefficient. If the normalization deviation coefficient is 0.76, then the probability value = 1 - 0.76 = 0.24 (representing a normal probability of 24%). The server sets the warning line to 0.5. When the normalization deviation coefficient 0.76 > 0.5, an anomaly warning is triggered, and preprocessing optimized features are generated {probability value 0.24, deviation coefficient 0.76, warning line 0.5}. According to the preset bridge dynamic feature information generation rules, the preprocessing optimized features, the analytic hierarchy process (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 characterize the membership vectors of each indicator. The server integrates the preprocessing optimized features, the AHP weight matrix, and the membership matrix according to preset rules (such as JSON format protocol) to generate structured bridge dynamic feature information.

[0028] Specifically, the weighted feature vector is: "Weight Vector": [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 + thickened bottom plate" scheme as an example): "Structural safety membership": [0.79, 0.1, 0, 0.1, 0.01], "Structural durability membership": [0.654, 0.086, 0, 0.26, 0]. Dynamic feature information integration: the server will associate the optimized feature {probability value 0.24, deviation coefficient 0.76} with the weights and membership vectors to generate JSON data representing the membership degree of each indicator, which can then be used by upper-layer applications.

[0029] S103, based on the weight adjustment logic of the analytic hierarchy process, processes the relevant influencing factor data and evaluation abnormal state information for weight calculation, and combines the dynamic factor correlation analysis of fuzzy comprehensive evaluation to generate abnormal state identification information in the evaluation process.

[0030] In one implementation, the weight adjustment logic of the Analytic Hierarchy Process (AHP) is used to process the relevant impact factor data for weight calculation, generating weight-adjusted impact factor data. The server calls the AHP weight adjustment algorithm (such as the weight allocation formula when adding / reducing indicators) to re-weight the input weight calculation-related impact factor data (such as the carrying capacity improvement rate, the adaptability of reinforcement technology, etc.).

[0031] New indicator weight allocation (listed at the end): If the original indicator weight The importance ratio of the new indicator to the previous indicator Then add the weight of the new indicator. The original indicator weights were adjusted to .

[0032] The server received a load-bearing capacity improvement rate of 2.9 (original weight 0.35) for the "web plate + external prestressing + integrated layer" scheme. Due to the addition of the "natural frequency improvement rate" indicator (importance ratio 2), the weight of the load-bearing capacity improvement rate is adjusted to 0.35×(1 / (1+0.5))≈0.233.

[0033] By combining the weight adjustment logic of the Analytic Hierarchy Process (AHP) with evaluation anomaly information, abnormal state-related data is generated. The server associates the weight changes generated during the AHP weight adjustment process (such as adjustment records when consistency checks fail) with evaluation anomaly information (such as data anomalies and weight conflicts) to generate structured anomaly-related data.

[0034] A bridge's deflection reduction rate is -17.1 (data anomaly), corresponding to a structural safety weight of 0.35. The server records that this indicator weight becomes 0.233 after adjustment and marks it as an anomaly indicating a conflict between negative deflection value and weight adjustment. If the consistency ratio of the judgment matrix CR = 0.15 > 0.1 (evaluation process anomaly), the server records the difference between the adjusted weight vector and the original matrix, generating anomaly-related data CR = 0.15, with the adjusted weights = [0.233, 0.27, ...].

[0035] Dynamic factor correlation analysis of fuzzy comprehensive evaluation is used to analyze the weighted impact factor data and abnormal state correlation data to generate preliminary abnormal state identification data. The server calls the dynamic factor correlation analysis model of fuzzy comprehensive evaluation to perform fuzzy mapping between the weighted impact factor data and abnormal correlation data, and calculates the membership degree of each indicator to the abnormal state.

[0036] Trapezoidal membership function calculation of anomaly membership degree: If the lower limit of stress reduction rate standard Level I is 10, and a certain value is 9.57, the membership degree is calculated as follows: That is, the membership degree for Level II is 0.93, and for Level I it is 0.07. The server analyzes the stress reduction rate of the "web steel plate + external prestressing + integral layer" scheme as 9.57 (membership degree (0.07, 0.93, 0, 0, 0)). Combined with the adjusted weight of 0.233, preliminary anomaly identification data is generated: stress reduction rate anomaly membership degree = 0.93, weight = 0.233.

[0037] Based on the dynamic factor association rules of fuzzy comprehensive evaluation, the preliminary anomaly identification data is structured and organized to generate anomaly identification information during the evaluation process. The server, according to the dynamic factor association rules of fuzzy comprehensive evaluation (e.g., setting a membership degree ≥ 0.5 as the anomaly threshold), structurally integrates the preliminary anomaly identification data and generates interpretable anomaly identification information through weighted averaging and logical judgment. The fuzzy comprehensive evaluation synthesis operation process is as follows: Let the membership degree matrix of the preliminary anomaly identification data be... The weight vector is Then the abnormal state evaluation vector is: In this context, the level corresponding to the maximum value in the result vector B is the final anomaly level.

[0038] Preliminary anomaly identification data for a bridge using a "prestressed carbon steel plate + web steel plate" scheme: deflection reduction rate: membership degree (Corresponding to Level V anomaly) Crack resistance improvement rate: membership degree (Corresponding to Level V anomaly) Fuzzy rule: The maximum value in the membership vector corresponds to the anomaly level, and an early warning is triggered when the weighted sum is ≥0.5. The anomaly state evaluation vector is calculated as follows: The maximum value is 0.343 (corresponding to Level V), but the weighted sum of 0.343 < 0.5, which is judged as "slight anomaly". The specific structured results are as follows: ["Anomaly State Identification Information":"Bridge Number":"G2012-01","Reinforcement Scheme":"Prestressed Carbon Plate + Web Steel Plate","Anomaly Index":"Indicator Name":"Deflection Reduction Rate","Anomaly Level":"Level V","Membership Degree":1,"Weight":0.233,"Indicator Name":"Crack Resistance Improvement Rate","Anomaly Level":"Level V","Membership Degree":1,"Weight":0.11],"Comprehensive Anomaly Level":"Slight","Triggering Reason":"Deflection and crack resistance indicators both show Level V anomalies, but the weighted sum does not reach the severe threshold","Handling Suggestion":"Review the load test data, focusing on verifying the accuracy of deflection measurement".]

[0039] The server integrates weight and membership information through fuzzy synthesis operators and generates structured anomaly reports by combining preset rules (such as thresholds and level mappings). This enables automated processing from data to decision recommendations, ensuring the logicality and interpretability of anomaly identification.

[0040] S104 processes the dynamic characteristic information of the bridge, calculates the evaluation result vector of the first-level index and the target layer through fuzzy synthesis operator, and generates the evaluation level of the bridge capacity improvement effect by adopting the maximum membership principle.

[0041] In one implementation, the dynamic characteristic information of the bridge is processed, and a fuzzy synthesis operator is used to calculate the membership matrix and weight vector corresponding to the primary indicators to generate the evaluation result vector of the primary indicators. The server calls a fuzzy synthesis operator (such as a weighted average type) to perform matrix operations on the membership matrix and weight vector corresponding to the primary indicators (such as structural safety, durability, etc.) to generate the evaluation result vector of the primary indicators. Let the membership matrix of the primary indicator "structural safety" be R=[0.5450.075000.38], and the weight vector be... .

[0042] but (This is simplified to weighted summation).

[0043] When the server processes the "prestressed carbon steel plate + web steel plate" scheme, the structural safety membership vector is (0.545, 0.075, 0, 0, 0.38). After operation with the weight vector, the first-level index evaluation result vector is generated as (0.545, 0.075, 0, 0, 0.38) (consistent with the input, since single indexes do not require weighting).

[0044] Combining the evaluation result vectors of the primary indicators, the evaluation matrix and weight vector of the primary indicators corresponding to the target layer are calculated using a fuzzy synthesis operator to generate the evaluation result vector of the target layer. Based on the evaluation result vectors of the primary indicators, the server again uses a fuzzy synthesis operator to calculate the target layer weight vector (such as structural safety weight 0.35, durability weight 0.29, etc.) to generate the comprehensive evaluation vector of the target layer.

[0045] Let the first-level indicator evaluation matrix be: If the target layer weight vector W = [0.35, 0.29, 0.14, 0.12, 0.10], then... The server integrates the first-level index vector (structural safety, durability, etc.) of the "prestressed carbon steel plate + web steel plate" scheme, and after calculating the target layer weights, generates the target layer vector (0.288, 0.305, 0.183, 0.091, 0.133).

[0046] Based on 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. The server, according to cross-dimensional association rules (such as the logical relationship between structural security and durability), structurally integrates the evaluation result vectors of the target layer to form an evaluation level association network, and visualizes the dependencies between each level.

[0047] If the target layer vector is (0.619, 0.267, 0, 0.11, 0.004) (the highest membership degree for the "excellent" level), when the server builds the network, it associates the "excellent" level with indicators such as structural security ≥ 0.79 and durability ≥ 0.654 to form a hierarchical relationship diagram.

[0048] Based on the evaluation level optimization generation rules and application requirements, the evaluation level association network is verified and abnormal associations are adjusted using an optimization factor integrity detection mechanism to generate the target evaluation level optimization framework. The server automatically verifies the evaluation level association network through an optimization factor integrity detection mechanism (e.g., checking whether the dimensions of the indicator weight matrix match and whether membership calculations omit levels), identifies abnormal associations (e.g., a certain indicator weight is out of sync with its actual importance), and adjusts the association rules based on application requirements (e.g., real-time evaluation or offline analysis) to generate a structured optimization framework.

[0049] Optimize factor integrity detection, and let the target layer evaluation vector be... If it exists or If the "deflection reduction rate" membership degree of a bridge is calculated to be (0,0,0,0,1) (Level V anomaly), but the structural safety weight is not reduced synchronously, the server adjusts the weight according to the rules: , where k is an adjustment factor (e.g., 0.5). The deviation level is considered abnormal (Level V is set to 1). When the server processed the "external prestressing + web thickening" scheme, it was found that the deviation between the Level I membership degree (0.455) of the target layer vector (0.455, 0.336, 0, 0.076, 0.133) and the Level I membership degree (0.52) of the structural safety vector (0.52, 0.1, 0, 0, 0.38) was >10%, triggering optimization. Verification revealed that the membership degree of "deflection reduction rate" (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 as (0.43, 0.35, 0, 0.08, 0.14), generating an optimization framework.

[0050] According to the preset optimization factor generation rules, the bridge dynamic feature information, primary indicators, and target layer evaluation result vectors are linked and integrated with the evaluation level association network to generate an evaluation level optimization factor that includes bridge dynamic features, primary indicator evaluation results, and target layer evaluation results. The server, according to preset rules (such as JSON format or database table structure), integrates the bridge dynamic feature information (weight vector, membership matrix), primary indicators, target layer evaluation results, and evaluation level association network to generate an optimization factor containing multi-dimensional data for use by upper-layer applications.

[0051] The data integration example is as follows: "bridge_id":"G2012-01","dynamic_features":

[0052] "weight_vector":[0.35,0.29,0.14,0.12,0.10], / / First-level indicator weights;

[0053] "membership_matrix":[[0.545,0.075,0,0,0.38], / / Membership degree for structural safety;

[0054] [0.155,0.585,0.26,0,0], / / Structural durability membership degree; / / Other indicators...],

[0055] "evaluation_results":"primary_index":["index_name":"structural security",

[0056] "vector":[0.545,0.075,0,0,0.38], / / Other first-level indicators...],

[0057] "target_layer":[0.288,0.305,0.183,0.091,0.133],

[0058] "grade_network":"level_1":"condition":"primary_index.structural_safety>=0.7","related_indices":["deflection_reduction","stress_decrease"], / / Related conditions for each level... The optimization factors include the bridge's unique identifier, dynamic characteristics, evaluation results at each level, and related networks; the server supports filtering fields according to application requirements (e.g., outputting only the target layer level and key indicators); the data format conforms to bridge assessment industry standards (e.g., compatible with the "Standard for Technical Condition Assessment of Highway Bridges").

[0059] S105 processes the evaluation results, indicator types, and data sources in the relevant influencing factor data for weight calculation using a multi-level evaluation model to generate key factors that affect the improvement of bridge capacity.

[0060] In one implementation, feature extraction processing is performed on the evaluation results, indicator types, and data sources in the weighted calculation-related influencing factor data to generate evaluation result features, indicator type features, and data source features. The evaluation result features include the increase rate of bearing capacity before and after reinforcement and the reduction rate of deflection; the indicator type features include quantitative indicators and qualitative indicators; and the data source features include theoretical calculation data and experimental testing data. The server extracts structured features from the weighted calculation-related influencing factor data, including evaluation result features, indicator type features, and data source features, forming a standardized feature vector. In a bridge's "web plate steel plate + bottom plate thickening" scheme, the server extracts a 39.1% increase in bearing capacity and a 4.9% reduction rate of deflection after reinforcement, generating a feature vector [39.1, 4.9]. "Stress reduction rate" is identified as a quantitative indicator (numerical type), and "adaptability of reinforcement technology" is identified as a qualitative indicator (descriptive type), generating a feature vector [1, 0, 0, 1] (the first two digits are quantitative / qualitative identifiers, and the last two digits are other types). The load-bearing capacity data comes from experimental testing (such as load tests), while theoretical calculation data accounts for 30%, generating a feature vector [0.7, 0.3] (the proportion of experimental testing and the proportion of theoretical calculation).

[0061] Feature extraction is performed on the multi-level evaluation model to generate analytic hierarchy process (AHP) features and fuzzy comprehensive evaluation features. AHP features include a weight matrix and consistency test parameters; fuzzy comprehensive evaluation features include a membership matrix and fuzzy synthesis operator parameters. The server parses the multi-level evaluation model parameters, extracts the core features of AHP and fuzzy comprehensive evaluation, and forms a model parameter library. For AHP features, the weight matrix W=[0.35,0.27,0.17,0.11,0.10] for the structural safety of a bridge is extracted, and the consistency test parameter CR=0.045<0.1 (passes the test). For fuzzy comprehensive evaluation features, the membership matrix (e.g., the structural safety membership vector [0.79,0.1,0,0.1,0.01]) and fuzzy synthesis operator parameters (weighted average operator) are extracted.

[0062] Based on the characteristics of the assessment results, indicator types, and data sources, and combined with the characteristics of the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation, preliminary identification information of key factors is generated. This preliminary identification information characterizes the impact of structural safety and structural durability indicators on the improvement of bridge capacity. The server, through correlation analysis, combines the characteristics of the assessment results with model parameters to calculate the impact of each indicator on the improvement of bridge capacity, generating preliminary identification information. Assuming a weight of 0.35 for the bearing capacity improvement rate and a contribution value of 0.79 for membership degree to Level I, the degree of influence = weight × membership degree = 0.35 × 0.79 = 0.2765.

[0063] The server generates structured data, as follows:

[0064] "structural_safety":"load_capacity":"influence_degree":0.2765,"grade":

[0065] "Level I", "deflection_reduction": "influence_degree": 0.29 × 0.1 (assuming deflection membership degree 0.1) = 0.029,"grade": "Level II".

[0066] The initial identification information of key factors is processed to generate key factors affecting the improvement of bridge capacity. These key factors include quantitative indicators such as the bearing capacity improvement rate and stress reduction rate, as well as qualitative indicators such as the adaptability of reinforcement technology and ease of construction. This results in a key factor evaluation that includes a ranking of key factors by importance and their correlation with the improvement of bridge capacity. The server ranks the initial identification information and, based on indicator type and data source, generates a key factor evaluation that includes a ranking of importance. The quantitative key factors are the bearing capacity improvement rate (impact level 0.2765) and the stress reduction rate (0.17 × 1 = 0.17, assuming a membership degree of 1), ranked by importance as: [bearing capacity improvement rate, stress reduction rate].

[0067] The qualitative key factors are the adaptability of reinforcement technology (95 points from experts, corresponding to Level I, with an impact of 0.14×1=0.14) and the ease of construction (85 points, Level II, with an impact of 0.10×0.75=0.075), ranked as follows: [Adaptability of reinforcement technology, ease of construction].

[0068] The final evaluation results are as follows: "key_factors":["name":"bearing capacity improvement rate","type":"quantitative","importance":1,"correlation":0.2765.

[0069] S106, based on the fuzzy comprehensive evaluation model and the actual bridge verification strategy, processes the evaluation results, key factors affecting the bridge capacity improvement effect and abnormal state identification information to generate dynamic adjustment information for bridge reinforcement.

[0070] In one implementation, based on a fuzzy comprehensive evaluation model, the correlation matching degree between the actual bridge verification strategy and the bridge reinforcement logic is introduced. This allows for the identification of effect features regarding the effectiveness of features and the accuracy of parameters in the evaluation results, key factors affecting bridge capacity improvement, and anomaly identification information, generating a set of anomaly features for reinforcement optimization. The server, based on the fuzzy comprehensive evaluation model, calculates the correlation matching degree between the actual bridge verification data and the bridge reinforcement logic, identifies anomalies in the effectiveness of features and the accuracy of parameters in the evaluation results, key factors, and anomaly information, and generates a set of anomaly features.

[0071] The correlation matching degree is calculated as follows: In the actual bridge verification, the membership degree of the bearing capacity improvement rate of the "web steel plate + thickened bottom plate" scheme of a certain bridge is (0.79, 0.1, 0, 0.1, 0.01), and the matching degree with "excellent grade requires bearing capacity ≥ 0.7" in the reinforcement logic is: matching degree = 0.79 / 1 = 0.79. If the calculated value of the crack resistance improvement rate is 5.81 (membership degree (0, 0, 0, 0.9, 0.1)), which conflicts with the standard "crack resistance ≥ 20 is grade I", the server marks "crack resistance performance index abnormal" and adds it to the set.

[0072] By comparing the actual bridge verification results with the preset bridge reinforcement standard model library and optimization parameter library, and combining the verification with actual bridge tests, the deviation between the reinforcement scheme performance and the optimization target is determined, and a reinforcement optimization deviation evaluation 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 evaluation result.

[0073] Deviation calculation formula: Deviation = |Actual value - Target value| / Target value interval length. For example, if the target value for the deflection reduction rate after bridge reinforcement is ≥2.5, and the actual value is 4.9, then the target interval is... Then: Deviation (Positive deviation, excellent performance), if the actual stress reduction rate is 9.57 (target ≥ 10), and the interval length is 5, then the deviation is... The reinforcement optimization deviation assessment results are correlated with the reinforcement optimization anomaly feature set. Combining the characteristics of the fuzzy comprehensive evaluation model, input parameters, and optimization suggestions from real-bridge verification feedback, the reinforcement optimization deviation assessment results are weighted to generate an optimization vector that integrates the evaluation logic and the reinforcement process. The server correlates the deviation assessment results with anomaly features, and, combining the characteristics of the fuzzy model and real-bridge feedback, weights the deviation to generate an optimization vector that integrates the evaluation and reinforcement logic.

[0074] The weighted deviation is calculated as follows: Given a stress reduction rate deviation of 0.086, a weight of 0.17, and an anomaly correction coefficient of 1.2 (due to associated crack resistance anomalies), then: By integrating the weighted deviations of various indicators, a vector is generated, such as [0.0176 (stress), 0.05 (crack resistance), ...].

[0075] The evaluation results of the reinforcement optimization deviation and the effect optimization vector are normalized and comprehensively calculated. Considering the real-time requirements of bridge reinforcement, dynamic adjustment information for bridge reinforcement, including optimization probability values, effect deviation coefficients, and optimization warning lines, is generated. The server performs normalization and comprehensive calculations on the reinforcement optimization deviation evaluation results and the effect optimization vector. Considering the real-time requirements of bridge reinforcement (e.g., response time ≤ 100ms), dynamic adjustment information, including optimization probability values, effect deviation coefficients, and optimization warning lines, is generated.

[0076] After normalization, let the weighted deviation vector be [0.0176 (stress), 0.05 (crack resistance)], the sum is 0.0176 + 0.05 = 0.0676, and after normalization, it becomes: Crack resistance deviation coefficient The optimized probability value is calculated as follows: Optimized probability value = 1 - maximum normalized deviation coefficient = 1 - 0.74 = 0.26 (representing a normal probability of 26%). The preset warning line is 0.5. When the maximum deviation coefficient (0.74) > 0.5, an abnormal warning is triggered.

[0077] Taking the "web plate steel plate + thickened bottom plate (with added prestress)" 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.

[0078] The weighted deviation vector is The normalized deviation coefficient is [0,1] (because the sum is 0.0814, the crack resistance deviation coefficient is 0.0814 / 0.0814=1); the optimization probability value is 1-1=0 (triggering an emergency optimization warning).

[0079] The dynamic adjustment information is {"bridge_id":"G2012-01","reinforcement_scheme":"web plate + base plate thickening (additional prestressing)","optimization_info":{"deviation_coefficient":1,

[0080] "optimization_probability":0,"warning_level":"urgent",

[0081] "adjustment_suggestions":["Review the crack resistance test data and suggest increasing the prestressing tension tonnage by 15%","Prioritize addressing abnormal crack resistance indicators and recalculate the stress-strain relationship"],

[0082] "real_time_metrics":{"response_time":35ms,"validation_time":"2025-06-29 14:30:22"}}}. The server transforms real-bridge verification data into executable reinforcement and adjustment instructions through four steps: "data validation - deviation calculation - weight fusion - threshold judgment". Normalization ensures that multiple indicators are comparable; optimization of probability values ​​and warning lines enables automated early warning; the output includes technical suggestions and real-time performance indicators, supporting closed-loop decision-making.

[0083] This application obtains basic data for evaluating the effectiveness of bridge capacity improvement, including data types, weight calculation of relevant influencing factors, and information on abnormal evaluation states. Next, quantitative and qualitative indicators are preprocessed, and a weight matrix and indicator membership matrix are introduced using the analytic hierarchy process (AHP) to generate dynamic characteristic information of the bridge. This information is used to characterize the membership vector of each indicator. Then, based on the weight adjustment logic of the AHP and the dynamic factor correlation analysis of fuzzy comprehensive evaluation, abnormal state identification information is generated during the evaluation process.

[0084] The evaluation result vectors of the first-level indicators and the target layer are calculated using fuzzy synthesis operators, and the evaluation level is generated using the maximum membership principle. The relevant influencing factor data for weight calculation are processed in conjunction with a multi-level evaluation model to generate key factors affecting the bridge capacity improvement effect. These include quantitative indicators such as bearing capacity improvement rate and stress reduction rate, as well as qualitative indicators such as the adaptability of reinforcement technology and ease of construction, forming the key factor evaluation results.

[0085] Finally, based on the fuzzy comprehensive evaluation model combined with the actual bridge verification strategy, dynamic adjustment information for bridge reinforcement is generated, including optimization probability values, effect deviation coefficients, and optimization warning lines, enabling precise control of bridge reinforcement. Through multi-model fusion, actual bridge verification, and dynamic adjustment, the problems of single evaluation dimensions and inaccurate deviation calculation in existing technologies are solved, improving the comprehensiveness, accuracy, and real-time performance of bridge capacity improvement assessment, and providing a scientific basis for bridge reinforcement decisions.

[0086] In one implementation, such as Figure 2 As shown, this application also provides a comprehensive evaluation device for assessing the improvement of bridge capacity, comprising:

[0087] The acquisition module 201 is used to acquire basic data for evaluating the bridge capacity improvement effect, including data type information, data of relevant influencing factors for weight calculation, and information on abnormal evaluation states;

[0088] Processing module 202 is used to preprocess the quantitative and qualitative indicators in the basic data, introduce the weight matrix and indicator membership matrix of the analytic hierarchy process (AHP) to generate bridge dynamic characteristic information, which is used to characterize the membership vector of each indicator. Based on the weight adjustment logic of the AHP, it processes the data of influencing factors related to weight calculation and the information of abnormal evaluation states, and combines the dynamic factor correlation analysis of fuzzy comprehensive evaluation to generate abnormal state identification information in the evaluation process. It processes the bridge dynamic characteristic information, calculates the evaluation result vector of the first-level indicator and the target layer through fuzzy synthesis operator, and generates the evaluation level of the bridge capacity improvement effect using the maximum membership principle. It processes the evaluation results and indicator types and data sources in the data of influencing factors related to weight calculation 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 and the actual bridge verification strategy, it processes the evaluation results, key factors affecting the bridge capacity improvement effect and abnormal state identification information to generate dynamic adjustment information for bridge reinforcement.

[0089] The computer-readable storage medium provided in the above embodiments of this application and the comprehensive evaluation method for assessing bridge capability improvement provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0090] The various embodiments in this application are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for the comprehensive evaluation method, electronic device, electronic device, and readable storage medium for assessing bridge capacity improvement are basically similar to the embodiments of the comprehensive evaluation method for assessing bridge capacity improvement described above, and therefore are described relatively simply. Relevant parts can be referred to in the descriptions of the embodiments of the comprehensive evaluation method for assessing bridge capacity improvement described above.

Claims

1. A comprehensive evaluation method for assessing the improvement of bridge capacity, characterized in that, include: Obtain basic data for evaluating the effectiveness of bridge capacity improvement, including data on data types, data on relevant influencing factors for weight calculation, and information on abnormal evaluation states; The quantitative and qualitative indicators in the basic data are preprocessed, and the weight matrix and indicator membership matrix of the analytic hierarchy process are introduced to generate bridge dynamic feature information. The bridge dynamic feature information is used to characterize the membership vector of each indicator. The weight adjustment logic based on the analytic hierarchy process processes the relevant influencing factor data and evaluation anomaly information, and combines the dynamic factor correlation analysis of fuzzy comprehensive evaluation to generate anomaly identification information in the evaluation process. The dynamic characteristic information of the bridge is processed, and the evaluation result vector of the first-level index and the target layer is calculated by fuzzy synthesis operator. The evaluation level of the bridge capacity improvement effect is generated by adopting the maximum membership principle. The evaluation results, indicator types, and data sources in the relevant impact factor data of weight calculation are processed in combination with a multi-level evaluation model to generate key factors that affect the improvement of bridge capacity. Based on a fuzzy comprehensive evaluation model combined with a real-bridge verification strategy, this method processes the evaluation results, key factors affecting bridge capacity improvement, and abnormal state identification information to generate dynamic adjustment information for bridge reinforcement. This includes incorporating the correlation and matching degree between the real-bridge verification strategy and the bridge reinforcement logic based on the fuzzy comprehensive evaluation model, identifying the effectiveness of features and the accuracy of parameters in the evaluation results, key factors affecting bridge capacity improvement, and abnormal state identification information, and generating a set of reinforcement optimization anomaly features. The real-bridge verification results are compared with a pre-set bridge reinforcement standard model library and optimization parameter library, and verified through real-bridge experiments to determine… The deviation between the performance of the reinforcement scheme and the optimization target is calculated to generate a reinforcement optimization deviation evaluation result. This evaluation result is then correlated with the set of abnormal reinforcement optimization features. Combining the characteristics of the fuzzy comprehensive evaluation model, input parameters, and optimization suggestions from actual bridge verification feedback, the evaluation result is weighted to generate an effect optimization vector that integrates the evaluation logic and the reinforcement process. Finally, the evaluation result and the effect optimization vector are normalized and comprehensively calculated. Considering the real-time requirements of bridge reinforcement, dynamic adjustment information for bridge reinforcement, including optimization probability values, effect deviation coefficients, and optimization warning lines, is generated.

2. The method as described in claim 1, characterized in that, The quantitative and qualitative indicators in the basic data are preprocessed, and the weight matrix and indicator membership matrix of the analytic hierarchy process are introduced to generate dynamic characteristic information of the bridge, including: Based on the correlation matching degree between the weight matrix and the membership matrix of the analytic hierarchy process, the effectiveness of features and the accuracy of parameters in the preprocessed quantitative and qualitative index data are identified, and a set of preprocessed abnormal features is generated. By comparing the preprocessed indicator data with the preset standardized indicator library and weight parameter library, and verifying the results with indicator preprocessing examples, the deviation between the preprocessing results and the standardized target is determined, and the preprocessing deviation evaluation result is generated. The preprocessing deviation evaluation results are correlated with the preprocessing anomaly feature set. Combining the characteristics of the analytic hierarchy process, index parameters, and optimization suggestions in the preprocessing feedback, the preprocessing deviation evaluation results are weighted to generate an effect optimization vector that integrates the preprocessing logic and weight calculation process. The preprocessing deviation evaluation results and effect optimization vector are normalized and comprehensively calculated. Combined with the real-time requirements of indicator preprocessing, preprocessing optimization features containing preprocessing probability values, effect deviation coefficients and optimization warning lines are generated. According to the preset bridge dynamic feature information generation rules, the preprocessed optimized features, the weight matrix of the analytic hierarchy process and the index membership matrix are linked and integrated to generate bridge dynamic feature information containing weight feature vectors and membership feature vectors. The bridge dynamic feature information is used to characterize the membership vectors of each index.

3. The method as described in claim 1, characterized in that, The weight adjustment logic based on the analytic hierarchy process (AHP) processes the relevant influencing factor data and evaluation anomaly information, and combines it with the dynamic factor correlation analysis of fuzzy comprehensive evaluation to generate anomaly identification information in the evaluation process, including: Based on the weight adjustment logic of the analytic hierarchy process, the relevant impact factor data for weight calculation is processed to generate impact factor data after weight adjustment. By combining the weight adjustment logic of the analytic hierarchy process with the evaluation of abnormal state information, abnormal state correlation data is generated. By using dynamic factor correlation analysis of fuzzy comprehensive evaluation, we analyze the impact factor data after weight adjustment and the correlation data of abnormal states 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 as described in claim 2, characterized in that, The dynamic characteristic information of the bridge is processed, and the evaluation result vector of the primary index and the target layer is calculated using a fuzzy synthesis operator. The evaluation level of the bridge capacity improvement effect is generated using the maximum membership principle, including: The dynamic characteristic information of the bridge is processed, and the membership matrix and weight vector corresponding to the primary index are calculated by combining the fuzzy synthesis operator to generate the evaluation result vector of the primary index. By combining the evaluation result vector of the first-level indicators, the evaluation matrix and weight vector of the first-level indicators corresponding to the target layer are calculated by the fuzzy synthesis operator to generate the evaluation result vector of the target layer. Based on cross-dimensional association rules and the logical relationship of evaluation levels, the evaluation result vector of the target layer is structurally integrated to complete the construction of the evaluation level association network; Based on the evaluation level optimization generation rules and application requirements, the evaluation level association network is verified and abnormal associations are adjusted based on the optimization factor integrity detection mechanism to generate the target evaluation level optimization framework. According to the preset optimization factor generation rules, the bridge dynamic feature information, primary indicators and target layer evaluation result vectors are linked and integrated with the evaluation level association network to generate an evaluation level optimization factor that includes bridge dynamic features, primary indicator evaluation results and target layer evaluation results.

5. The method as described in claim 1, characterized in that, The evaluation results, indicator types, and data sources in the weighted calculation of relevant influencing factor data are processed in conjunction with a multi-level evaluation model to generate key factors affecting the improvement of bridge capacity, including: Feature extraction processing is performed on the evaluation results, indicator types, and data sources in the relevant influencing factor data for weight calculation to generate evaluation result features, indicator type features, and data source features. Among them, the evaluation result features include the rate of increase in bearing capacity before and after reinforcement and the rate of reduction in deflection; the indicator type features include quantitative indicators and qualitative indicators; and the data source features include theoretical calculation data and experimental test data. Feature extraction is performed on the multi-level evaluation model to generate analytic hierarchy process (AHP) features and fuzzy comprehensive evaluation features. The AHP features include a weight matrix and consistency test parameters; the fuzzy comprehensive evaluation features include a membership matrix and fuzzy synthesis operator parameters. Based on the characteristics of the evaluation results, the characteristics of the indicator types, and the characteristics of the data sources, combined with the characteristics of the analytic hierarchy process and the characteristics of fuzzy comprehensive evaluation, the preliminary identification information of key factors is generated. Among them, 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 bridge capacity improvement effect. The preliminary identification information of key factors is processed to generate key factors that affect the improvement of bridge capacity. These key factors include quantitative indicators such as the rate of increase in bearing capacity and the rate of reduction in stress, as well as qualitative indicators such as the adaptability of reinforcement technology and the ease of construction. This results in a key factor evaluation that includes the ranking of the importance of key factors and their correlation with the improvement of bridge capacity.

6. A comprehensive evaluation device for assessing bridge capacity improvement, characterized in that, The device includes: The acquisition module is used to acquire basic data for evaluating the bridge capacity improvement effect, including data type information, data of relevant influencing factors for weight calculation, and information on abnormal evaluation states; The processing module preprocesses the quantitative and qualitative indicators in the basic data, introducing a weight matrix and indicator membership matrix from the analytic hierarchy process (AHP) to generate dynamic characteristic information of the bridge. This dynamic characteristic information represents the membership vector of each indicator. Based on the weight adjustment logic of the AHP, it processes the data of influencing factors related to weight calculation and the information of abnormal evaluation states. Combined with the dynamic factor correlation analysis of fuzzy comprehensive evaluation, it generates information for identifying abnormal states during the evaluation process. The module further processes the dynamic characteristic information of the bridge, calculating the evaluation result vectors of the first-level indicators and the target layer using fuzzy synthesis operators, and generating the evaluation level of the bridge capacity improvement effect using the maximum membership principle. It processes the evaluation results, indicator types, and data sources in the data of influencing factors related to weight calculation, combining them with a multi-level evaluation model to generate key factors affecting the bridge capacity improvement effect. Finally, based on the fuzzy comprehensive evaluation model and a real-bridge verification strategy, it processes the evaluation results, key factors affecting the bridge capacity improvement effect, and abnormal state identification information to generate dynamic adjustment information for bridge reinforcement. This process includes: 1) Based on a fuzzy comprehensive evaluation model, introducing the correlation and matching degree between the actual bridge verification strategy and the bridge reinforcement logic; 2) Identifying the effectiveness of features and the accuracy of parameters in the evaluation results, key factors affecting bridge capacity improvement, and abnormal state identification information to generate a set of reinforcement optimization abnormal features; 3) Comparing the actual bridge verification results with a pre-set bridge reinforcement standard model library and optimization parameter library, and combining actual bridge test verification, determining the deviation between the reinforcement scheme performance and the optimization target, and generating reinforcement optimization deviation evaluation results; 4) Correlation analysis between the reinforcement optimization deviation evaluation results and the set of reinforcement optimization abnormal features, and weighting the reinforcement optimization deviation evaluation results based on the characteristics of the fuzzy comprehensive evaluation model, input parameters, and optimization suggestions from actual bridge verification feedback to generate an effect optimization vector that integrates the evaluation logic and the reinforcement process; 5) Normalizing and comprehensively calculating the reinforcement optimization deviation evaluation results and the effect optimization vector, and generating dynamic adjustment information for bridge reinforcement that includes optimization probability values, effect deviation coefficients, and optimization warning lines, taking into account the real-time requirements of bridge reinforcement.

7. An electronic device, characterized in that, include: First processor; and memory for storing executable instructions of the first processor; The first processor is configured to execute the comprehensive evaluation method for assessing bridge capacity improvement as described in any one of claims 1 to 5 by executing the executable instructions.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the second processor, it implements the comprehensive evaluation method for assessing the improvement of bridge capacity as described in any one of claims 1 to 5.

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