306 results about "Key factors" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing method and device based on factor weight optimization, equipment and a medium. Comprising the following steps: receiving structured data and unstructured data, and extracting a basic key factor set and monitoring object information in the data to generate a data feature set; executing multi-dimensional analysis and identification value determination based on the data feature set, and generating an analysis level and an identification value parameter; and performing weight correction processing on the basic key factor set, generating an optimized factor coefficient matrix, fusing the optimized factor coefficient matrix with associated equipment source data, and outputting a processing result. According to the method, structured and unstructured data are processed in a unified manner, so that the data integration capability is enhanced; multi-dimensional elements are extracted in combination with a pre-training model, and the key factor recognition effect is improved; and the analysis level and the identification value parameter are combined to drive weight correction and fuse the optimization matrix and the equipment source data to generate a processing result, so that the processing efficiency and the result accuracy are improved.

The invention relates to a model for evaluating and predicting mild cognitive impairment risk of old people in a pension institution. The model sequentially comprises a behavior analysis module, a language recognition module, a social modeling module, a toughness calculation module, a feature fusion module, a risk reasoning module and the like. Behavior deviation characteristics and abnormal time periods are extracted by collecting behavior data of daily life, diet, social contact and the like of old people and comparing the behavior data with an institution work and rest template; in combination with nursing records, extracting language anomaly features; analyzing social frequency and structure changes in the abnormal time period, and extracting social variation features; a cognitive toughness index is calculated by integrating the health archive and the recovery ability to the health event; and performing toughness weighting on the multi-dimensional features to construct a time sequence tensor, and inputting the time sequence tensor into a recursive model to predict a cognitive impairment risk value. And if the risk value suddenly changes, the system automatically backtracks the feature trajectory of nearly 7 days, constructs and screens a prediction path with the strongest interpretation force, outputs a dominant prediction result and a key factor sequence, and realizes high-interpretability and high-reliability early recognition and intervention reference.

The invention relates to the technical field of intelligent equipment evaluation and fault prediction, and discloses a dynamic equipment quality evaluation method based on multi-agent collaborative reinforcement learning, and the method comprises the steps: obtaining original data from equipment and a sensor, and carrying out the preprocessing; analyzing the preprocessed data, and identifying key factors influencing the quality of the equipment; constructing a plurality of expert models, and predicting and evaluating an analysis result; fusing the weight of each expert model to obtain a comprehensive evaluation result; and performing risk assessment on the comprehensive assessment result, extracting key influence factors and decision rules, and generating an assessment result. Efficient fusion of multi-source data and automatic identification of key factors are realized, and the accuracy and reliability of equipment quality evaluation are remarkably improved; a model fusion result is optimized through dynamic weight distribution, so that the prediction performance is improved; the problems that in the prior art, data fusion is difficult, key factor identification depends on experience, and the interpretability of an evaluation result is insufficient are solved.

The invention relates to the technical field of data monitoring, in particular to an automatic monitoring system for plasma meltallizing coating process data, which is characterized in that a parameter association relationship analysis module performs association analysis based on real-time parameters in a plasma meltallizing coating process, identifies an association relationship among the parameters and generates a process parameter association analysis result; according to the method, through multi-dimensional correlation analysis and path screening of the technological parameters, key factors influencing the coating quality and action links of the key factors are defined, and the abnormal fluctuation state and the transfer path can be accurately predicted in combination with parameter priority dynamic adjustment and real-time data feedback, so that the stability and continuity in the technological process are ensured. In the scheme, a dynamic weight distribution strategy is adopted, so that key parameters in an abnormal state occupy a higher priority position, parameter distribution is rebalanced when the normal state is recovered, and excessive interference of a single parameter on the whole process is avoided.

The invention relates to the technical field of mechanical detection, in particular to an intelligent tool setting control method and system, and the method comprises the steps: collecting burr morphology data and tool state parameters of a slitting edge; establishing a mapping relationship between burr generation and processing parameters based on a process DOE model, wherein the process DOE model is used for extracting a key factor set influencing the slitting quality; when the real-time monitoring value of the key factor set deviates from the optimization interval of the process DOE model, generating a position and posture compensation instruction of the slitting tool rest; iteratively updating the key factor weight distribution of the process DOE model; and when the variance of the key factor weight distribution is converged to a preset threshold value, determining that the tool setting control closed-loop optimization is completed. According to the method, the defects of dynamic response hysteresis and insufficient compensation precision in traditional tool setting control are effectively overcome, and closed-loop self-optimization control of the position and posture of the slitting tool rest-process parameters-burr suppression is constructed through a three-dimensional dynamic compensation mechanism of real-time fusion of multi-source sensing data.

The invention discloses a flowmeter self-calibration method and system based on big data analysis. The method comprises the steps that multi-source data collection is conducted, and multiple kinds of data are collected and stored in a big data platform with a block chain evidence storage function; data preprocessing: adopting multiple algorithms to improve data quality; feature extraction and analysis: identifying key factors and monitoring fluid states by means of multiple technologies; calculating calibration parameters, and considering various coefficients and uncertainty analysis; performing automatic calibration, encrypting and writing parameters after digital twin simulation, and reserving a copy; and verifying the calibration effect by adopting a multi-mode verification mechanism. The system comprises a plurality of functional modules which work cooperatively. According to the invention, the data acquisition comprehensiveness is improved, the calibration period is shortened, the production efficiency is improved, the data integrity and reliability are ensured, key factors are accurately identified, the fluid state is monitored in real time, the calibration parameter adjustment is simulated, and the calibration effect is verified in a multi-mode manner, so that the reliability and accuracy of a flowmeter measurement system are improved.

The invention belongs to the field of safety and stability analysis of tailing dams, and discloses a tailing dam stability analysis method based on a GCN-AGRU fusion model and a related device, and the method comprises the steps: obtaining the monitoring data of a multi-source heterogeneous sensor of a tailing dam; preprocessing the monitoring data to obtain multi-source heterogeneous time series data; performing feature analysis on the multi-source heterogeneous time series data to obtain key factors of the stable state of the tailing dam; constructing a heterogeneous hierarchical causal graph by using the key factors; determining a key factor stable state index system according to the heterogeneous hierarchy causal diagram; a multi-source GCN-AGRU fusion space-time prediction model is utilized to obtain space information of the state of the tailing dam; performing time correlation modeling on the multi-source heterogeneous time sequence data to obtain a time sequence of the state of the tailing dam; calculating a predicted value of the multi-source heterogeneous sensor according to the spatial information and the time sequence; and determining the stability state trend of the tailing dam according to the predicted value in combination with a key factor stability state index system. The intelligent sensing of the situation of the tailing dam can be realized.

The invention belongs to the technical field of power grid big data, and particularly relates to a power grid regulation and control data asset value evaluation method and device, equipment and a medium. According to the method, key factors (data integrity, data diversity, data timeliness, data scale and data relevance) influencing the value of the power grid regulation and control data assets are determined, the weight coefficient of each factor is calculated by using an analytic hierarchy process, and then the value of the power grid regulation and control data assets is calculated. According to the scheme, a unified data value evaluation system is established, and a unified value measurement standard is provided for power grid regulation and control data assets by determining the key influence factors and the weight coefficients. Through data value evaluation, the value and distribution condition of each data asset can be clearly known, and efficient management and reasonable utilization of the data assets can be realized. Meanwhile, through data value evaluation, the potential value of the data assets can be found, and possibility is provided for value increment and achievement of the data.

The invention provides an intelligent comprehensive system and method for steel production process parameter optimization, and the system comprises a data configuration module which is used for collecting quality-related data in a steel production process; the influence factor analysis module is used for processing data related to quality by using a feature engineering technology and identifying key variables; the quality index prediction module is used for constructing a quality prediction model by using a machine learning algorithm and key variables; the process parameter optimization module is used for constructing a process parameter optimization model based on the prediction result and the quality index control target, and analyzing and recommending an optimal process parameter control range by utilizing an intelligent algorithm; and the model monitoring optimization module is used for evaluating and optimizing the operation effects of the quality prediction model and the process parameter optimization model in real time. According to the method, the whole-process intelligent management from data configuration and key factor identification to quality prediction, process parameter optimization and model dynamic monitoring is realized, a closed-loop optimization mechanism is formed, and the production efficiency and the product quality are effectively improved.

The invention relates to the technical field of data processing, and discloses an intelligent agent evaluation method and system, and the method comprises the steps: analyzing key factors and performance indexes of an intelligent agent in different combat environments, determining evaluation indexes, and constructing an evaluation index system; setting parameters and algorithms of the evaluation model, and configuring an association relationship between the evaluation model and the evaluation index system; gaming deduction data and agent decision data are collected, and evaluation calculation data are generated and stored; in response to the evaluation calculation data, configuring evaluation calculation parameters, calling the evaluation model to calculate the evaluation calculation data, and recording an evaluation calculation result; and in response to the evaluation calculation result, generating an evaluation report framework, integrating the evaluation calculation data and the corresponding evaluation calculation result into the evaluation report framework in a visual form, and outputting an evaluation report. According to the invention, standardization and systematization of the evaluation process are realized, scientificity and accuracy of evaluation are improved, and efficiency and accuracy of data processing are improved.

The invention discloses an artificial intelligence auxiliary decision-making method and system for standardized assessment, and the method comprises the steps: obtaining a decision-making scene and a decision-making target, constructing a standardized assessment model, carrying out the standardized assessment of decision variables through the standardized assessment model, and building a decision variable assessment system based on an assessment result. A neural network is adopted to construct a decision agent based on the decision variable evaluation system, a decision option set is obtained based on the decision agent, coordinated filtering is performed on the decision option set, initial decision options are obtained, the range of the initial decision options is narrowed, and real-time return and long-term return are balanced in the narrowed range to obtain an optimal decision. And optimizing the decision agent through reinforcement learning according to the adoption condition of each optimal decision. The method not only can provide comprehensive and accurate information support for the decision making process, but also can deeply analyze a complex decision making scene through an intelligent algorithm, and accurately excavates key factors and potential trends.

The invention relates to the technical field of reservoir dispatching, in particular to a method for handling events influencing cascade reservoir dispatching operation, which comprises the following steps: acquiring real-time monitoring data of a cascade reservoir to be detected; performing data preprocessing on the real-time monitoring data of the to-be-detected cascade reservoir to obtain preprocessed data; inputting the preprocessed data into a trained key factor determination model, and obtaining a key data type and corresponding data; determining whether an abnormal event occurs or not and the type of the abnormal event according to the key data type and the corresponding data; when an abnormal event occurs, inputting the type of the abnormal event into a preset intelligent diagnosis system, and obtaining a plurality of exception handling schemes; and SWOT analysis is carried out on the various exception handling schemes to obtain a final handling result. According to the method, the safety and reliability of cascade reservoir dispatching operation are improved, and the cascade reservoir operation management level is improved.

The invention discloses a system for predicting performance degradation of an automobile shock absorber, and relates to the technical field of shock absorption, and the system comprises the steps: obtaining a historical monitoring record of a preset shock absorber, matching a record under a first historical node from a plurality of sets of records, carrying out the analysis according to a preset hardness strategy to obtain first historical hardness under the node, and carrying out the prediction of the performance degradation of the shock absorber. Analyzing the corresponding relation between the first historical node and the hardness to obtain the real-time hardness, and finally analyzing the deviation between the real-time hardness and the hardness reference to obtain the real-time performance attenuation index. The technical problem that in the prior art, a means for accurately predicting the performance attenuation of the shock absorber is lacked is solved, and the technical effects of accurately predicting the performance attenuation of the shock absorber of the automobile in real time and improving the driving safety of the automobile are achieved by taking the hardness as a key factor for representing the state of the shock absorber.

The invention provides an AI-based floor mopping machine production equipment abnormity prediction system and method, and the method comprises the steps: monitoring operation parameters of floor mopping machine production equipment in real time, comparing the operation parameters with historical data, and separating periodic and non-periodic components; using a clustering algorithm to identify an unexpected fluctuation mode, and mining through association rules to obtain key factors; constructing a reference performance curve by combining performance data of components of the same type, and evaluating a potential abnormal risk level by using a fuzzy logic system based on the deviation degree of an actual performance curve; and adopting a Bayesian network to predict possible faults in a specific time period in the future, and generating a maintenance suggestion report. The maintenance efficiency and reliability of the floor mopping machine production equipment are improved.

The invention relates to a user short-term daily power consumption double decomposition method and device and a storage medium, which are applied to the technical field of power system load prediction, and comprise the following steps: extracting key feature data from historical data through a feature identification technology, and obtaining a trend component and a periodic component by using the feature data and adopting a regression periodic decomposition method, and obtaining random components by adopting empirical mode decomposition, predicting each component by adopting different prediction models, and finally, effectively fusing prediction results of each component by combining a deep learning technology and an integrated learning technology so as to improve prediction precision and robustness. According to the method provided by the invention, the key factors influencing the short-term daily electric quantity of the industrial user are accurately identified, and the subtle characteristics and rules of the electric quantity change are captured, so that a more accurate prediction model is constructed, the requirements of a power grid marketing department on high precision and timeliness of electric quantity prediction are met, and powerful support is provided for operation and decision-making of an electric power market.

The invention provides a coal bulk loading and unloading efficiency optimization method and system based on machine learning, and relates to the technical field of coal bulk loading and unloading, and the method comprises the steps: recognizing key factors and quantitative association rules between the key factors and the coal bulk loading and unloading efficiency through a large language model; the storage yard decision topological graph is input into a reinforcement learning model, a coal bulk cargo loading and unloading scheme is determined by executing the process of state space coding, action selection and reward function calculation, and a reward function is obtained by taking a predicted value of the coal bulk cargo loading and unloading efficiency in the current period as a target and being constructed according to key factors and quantitative association rules; after the coal bulk cargo loading and unloading scheme is completed, the actual value of the coal bulk cargo loading and unloading efficiency in the current period is collected, the efficiency difference value is calculated, and the coal bulk cargo loading and unloading efficiency in the next period is optimized based on the efficiency difference value. According to the method, the efficiency can be effectively improved while various necessary conditions of loading and unloading operation are accurately met.

The invention discloses an enterprise risk self-diagnosis method based on big data, and relates to the technical field of data prediction.The enterprise risk self-diagnosis method comprises the steps that enterprise internal data and competitor external data are collected, and enterprise relative competitive advantages are evaluated regularly; predicting a product sales trend, and analyzing key factors affecting sales; a raw material and product price database is established, historical price changes are recorded, and the influence of the price changes on profits is evaluated; a risk trend is analyzed and predicted by using a time sequence, different responses are triggered according to risk severity, and key risk indexes are monitored in real time. According to the method and the system, the capacity of an enterprise for coping with uncertainty and emergencies is improved, the negative influence of risks on enterprise operation is reduced, and the enterprise can keep business continuity and stability through real-time monitoring and response.

The invention relates to a user power consumption mode identification method and device considering multi-factor association and a medium. The method comprises the steps of obtaining historical load data and historical meteorological data and performing preprocessing; processing the historical load data and the historical meteorological data under different time resolutions, and constructing a load data set and a meteorological data set under different resolutions; constructing a load curve of each day based on the load data set, performing coarse clustering on the load curves by using a self-organizing mapping neural network, extracting a power utilization mode of the user, and identifying whether the user has an energy storage device or not; calculating the relevance between the meteorological factors and the load through correlation analysis, and selecting key factors; and based on the selected key factors, performing fine-grained analysis on the power consumption mode of the user by adopting a mode of combining a time convolutional network and a pyramid attention mechanism, and accurately identifying the power consumption mode. Compared with the prior art, the method provided by the invention can realize the identification of the power consumption mode of the user with coarse and fine granularity under the multi-resolution condition and multi-factor association.

The invention provides a conjoint analysis method for waste classification willingness of event activity participants, and the method comprises the steps: observing a plurality of factors affecting the waste classification willingness through latent variable hypothesis, collecting target sample data through a Likert scale, and analyzing and evaluating the reliability and validity of latent variables through verification factors; calculating a path coefficient based on SEM; taking the latent variable with the significant path coefficient and the observation variable corresponding to the latent variable as input features of an XGBoost model, training and optimizing hyper-parameters; sHAP values of the XGBoost model are calculated, key factors of multiple scenes are determined by combining the SEM path coefficients and the importance of the SHAP values, and differentiated waste classification management measures for different activity types are put forward. According to the method, two methods of XGBoost-SHAP and SEM are combined, so that a scientific and practical panoramic analysis framework can be provided for formulating a waste classification management strategy.

The invention discloses a transformer substation hidden danger point risk index model construction method, system and device and a medium, and belongs to the technical field of transformer substation risk assessment, and the method comprises the steps: collecting multi-dimensional data related to the operation of a target transformer substation; screening key factors influencing the risk of the hidden danger point, and determining the weight of each key factor; analyzing the multi-dimensional data by using a machine learning model, and identifying hidden danger points; constructing a risk assessment model and calculating risk indexes according to the weights of the key factors and the identified hidden danger points; and outputting the risk level of the hidden danger point and corresponding early warning information based on the risk index. By combining multi-dimensional data and utilizing a machine learning algorithm, the equipment operation state can be comprehensively analyzed, potential hidden dangers can be deeply mined, a large amount of historical data can be efficiently processed and analyzed, potential hidden danger points can be automatically recognized, the hidden danger point recognition accuracy is remarkably improved, early warning is ensured, equipment faults are effectively prevented, and the method is suitable for popularization and application. The operation risk of the substation is reduced.

The invention discloses a self-expanding particle anti-seepage effect evaluation method based on multivariate regression analysis, which can comprehensively consider the influence of a plurality of key factors such as permeability coefficient, flow and anti-seepage effective time on the self-expanding particle anti-seepage effect by using a multivariate regression analysis method, and is not limited to the effect of analyzing a single factor. The method has the main advantages that multiple interaction effects in a complex system can be comprehensively captured, and evaluation deviation caused by neglecting some variables is avoided. By simultaneously incorporating a plurality of factors for analysis, the multiple regression not only can reveal the independent influence of the factors on the anti-seepage effect, but also can identify the possible interaction or interdependence relationship among the factors.

The invention discloses a deep learning-based transmission and transformation project progress identification and analysis method and system, and the method comprises the steps: firstly collecting and preprocessing the multimedia data of a transmission and transformation site, constructing a project progress data set, training a deep learning model through a plane graph and the progress data set, inputting new progress data into the model, and identifying a current project state; comparing the plan progress, analyzing the deviation, and finally performing comprehensive trend prediction based on the model and the key influence factors. In the application of the design, key factors are obtained through a recursive transformer fusion network model fusing various modules and various differential data, and comprehensive trend analysis is realized in combination with differences of different voltages, so that deep-level features can be effectively extracted from various data sources, high-precision identification of the project progress is realized, and the accuracy of the project progress is improved. Compared with a traditional manual checking and recording mode, the method has the advantages that the recognition efficiency and accuracy are greatly improved, the interference of human factors is reduced, and the real-time performance and reliability of progress recognition are ensured.

The invention relates to the technical field of data analysis, in particular to a data analysis method and system based on artificial intelligence, and the method comprises the following steps: collecting and arranging variable data of a financial market, the variable data comprising asset price, market transaction volume and policy change information, and obtaining a data integration result; and based on the data integration result, constructing a causal graph by using a Bayesian network, and determining a potential relationship between variables to obtain a causal graph construction result. According to the method, the key factors influencing the financial market variables can be identified and verified, so that the accuracy of model prediction is improved. The Bayesian network helps to construct an intuitive relation graph between variables, and helps to clarify complex interaction between the variables. The introduction of Do calculation further purifies the model, and miscellaneous factors are eliminated by accurately controlling variables, so that the estimation of the causal effect is ensured not to be interfered by external noise.

The invention relates to a method, equipment and medium for predicting the compressive strength of recycled coarse aggregate concrete based on an optimized machine learning algorithm, and the method comprises the steps: collecting factors which affect the performance of the recycled coarse aggregate concrete from literature data, taking the compressive strength as an output variable, and preprocessing the data; and combining at least one machine learning model and at least one optimization algorithm into a plurality of prediction model combinations. The preprocessed data is divided into a training set and a test set, and an optimal performance model is determined through comprehensive evaluation after the model is trained. And performing reverse prediction on the key factors by using a prediction model with optimal performance to obtain the influence trend of the change of the key factors on the compressive strength of the recycled coarse aggregate concrete. Compared with the prior art, the recycled coarse aggregate concrete compressive strength prediction model based on the optimized machine learning algorithm solves the problems of insufficient precision and poor generalization ability in a traditional prediction method.

The invention relates to the technical field of financial service management, in particular to an intelligent supply chain financial service platform, and the platform comprises a supply chain data analysis module which receives and integrates market dynamics, carries out the format standardization and data cleaning of the market dynamics, and generates a data integration result; and performing dimension reduction on the data integration result by applying principal component analysis, and extracting factors influencing the health degree of the supply chain to obtain a key factor extraction result. According to the invention, standardized integration and data cleaning are carried out on market dynamics, reliability and consistency of supply chain data are enhanced, data dimension reduction is realized in cooperation with principal component analysis, key factors having the most influence on the health state of the supply chain are extracted, and accuracy and sensitivity of supply chain state evaluation are improved; the risk features of the supply chain entity are analyzed by adopting a clustering algorithm, behavior pattern analysis is performed on the basis of risk feature grouping, potential risk points are found in advance, and the risk pre-judgment capability and prevention and control accuracy are enhanced.

The invention relates to the technical field of manufacturing optimization, and discloses a manufacturing process optimization decision-making method, device and equipment based on big data analysis, and the method comprises the steps: collecting data and quality evaluation information of each link of a target product manufacturing process, forming reference data, continuously recording the reference data through a big data platform, and aligning the reference data with key characteristics; according to the method, a plurality of manufacturing reference models are obtained, key factor analysis is performed on the models, optimization factors are extracted, an objective function containing quality, efficiency and cost constraints is constructed, iterative optimization solution is performed, and an optimization result is verified through a big data platform until an optimal decision scheme is obtained. The production efficiency is improved, the cost is reduced, the stability of the product quality is ensured, and the problem that in the prior art, multi-target optimization is difficult to carry out on the multi-link manufacturing process is solved.

The invention provides a project quality tracing and early warning method based on data analysis, and relates to the technical field of data analysis, and the method comprises the steps: obtaining a project quality data set, carrying out the traversal matching of the data set according to a key factor corresponding to a project stage, carrying out the replacement and recombination of a predetermined project quality data set through key feature parameters, and obtaining a project quality data set; taking the real-time data set as input information of a project quality prediction middle station and obtaining output information of the project quality prediction middle station, if the real-time prediction quality does not meet a preset project quality threshold value, sending out a primary early warning signal, and building a completed project stage based on the primary early warning signal; and a predetermined tracing early warning mechanism is called to perform tracing analysis on the completed project stage to obtain a real-time quality abnormity category, and a fine-grained early warning signal of the predetermined project is sent out, so that the problem that the discovery and control of the project quality problem lags behind the construction process and potential quality risks cannot be timely and accurately identified is solved; and the effect of improving the timeliness, accuracy and comprehensiveness of project quality management is achieved.

The invention belongs to the technical field of Internet of Things, and discloses a fixed asset dynamic management method and system based on an RFID tag, and the method comprises the steps: obtaining the fixed asset data of an enterprise through employing a radio frequency identification technology, and providing the feature data of the fixed asset; based on the extracted feature data of the fixed assets, evaluating the feature data of the fixed assets by using a state analysis algorithm, and identifying key factors influencing the health state of the fixed assets; on the basis of a trend analysis algorithm, key factors influencing the health state of the fixed assets are analyzed, and the change trend of the health state of the fixed assets is obtained; and an asset maintenance plan and an operation strategy are optimized in combination with the change trend and the real-time operation state of the fixed assets. According to the invention, the real-time performance and accuracy of asset management are improved, the workload of manual checking is reduced, and the risk of loss and loss of assets is reduced, so that the overall management efficiency and decision-making quality are improved.

The invention discloses an engineering quantity list comprehensive unit price rationality detection method and system, and belongs to the technical field of engineering cost. The invention discloses a bill of quantity comprehensive unit price rationality detection method and system. The method comprises the steps of extracting key factor features; obtaining a comprehensive unit price corresponding to the project name; constructing a project recognition library; s4, correcting and perfecting the prices of the abnormal items; calculating the comprehensive unit price of the new engineering project; the system comprises a unit price calculation module, a database construction module, a comprehensive unit price identification module and a new engineering project determination module. The problem that the comprehensive unit price of the new engineering project cannot be determined in the prior art is solved, the comprehensive unit price of the new engineering project can be calculated, pricing dispute caused by fuzzy feature description of the new engineering project can be reduced, the abnormal quotation can be effectively identified, and by correcting the abnormal quotation deviating from the rationality interval, the pricing efficiency of the new engineering project is improved. The construction period delay or claim risk caused by pricing errors can be reduced.

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.