391results about How to "High forward-looking" patented technology

The invention discloses a self-adaptive power grid state court intelligent fusion terminal and a control method thereof, relates to the technical field of intelligent power grids, and solves the defects that an existing fusion terminal is insufficient in self-adaptive capability and limited in data processing and analysis capability. Learning power grid state change in real time through a deep power grid dynamic control algorithm and a prediction analysis engine combining LSTM and random forest, and autonomously adjusting a control strategy; the edge calculation and distributed processing module is utilized, so that the data processing flow is optimized, and the data processing speed and efficiency are greatly improved; the application of time sequence analysis and a machine learning model further excavates and understands a complex data mode; the resource configuration efficiency is improved through an intelligent scheduling algorithm, and a cross-layer coordination mechanism is established through a distributed coordination algorithm, so that rapid issuing and feedback collection of a control instruction are ensured; according to the invention, the intelligent degree of the existing fusion terminal is greatly improved.

The invention relates to the technical field of pipeline data processing and health state evaluation, and discloses a pipeline interior detection method and system based on multi-sensor fusion. The method comprises the following steps: performing health index calculation on pipeline state evaluation data to obtain a pipeline health state index, and performing risk prediction on the pipeline health state index to obtain a risk level; performing pipeline defect feature recognition to obtain defect feature data, and performing multi-category classification processing on the defect feature data to obtain defect type data; performing accurate position analysis on the defect type data to obtain defect space distribution data, performing structural integrity evaluation to obtain a defect severity quantitative data set, performing multi-criterion decision analysis on the defect severity quantitative data set and risk levels to obtain defect repair priority ranking data, and performing defect repair priority ranking on the defect repair priority ranking data; and performing intelligent repair scheme generation on the defect repair priority ranking data to obtain repair suggestion data. According to the invention, the state of the pipeline can be evaluated more comprehensively and accurately.

The invention discloses an intelligent supply chain scheduling optimization method and system based on an autonomous controllable large model, and the method comprises the steps: employing a demand prediction agent to predict a future market demand condition according to product sales data; according to supplier supply product data, adopting a supplier management agent to select an optimal supplier as a target supplier; according to the predicted future market demand condition and inventory data, an inventory optimization agent is adopted to generate an optimal inventory replenishment plan; according to the inventory level after the optimal inventory replenishment plan and the logistics transportation data, adopting a logistics scheduling agent to calculate and obtain an optimal transportation path; feedback data of all the agents are collected, performance indexes of all the agents are calculated through the information feedback agents according to the feedback data, and agent parameters are adjusted according to the performance indexes of all the agents; and the strategy output of each agent is integrated into the scheduling strategy of the supply chain. The scheduling strategy can be dynamically scheduled and updated in real time.

The invention discloses a poultry breeding monitoring management system based on big data, and relates to the technical field of poultry breeding supervision, the poultry breeding monitoring management system monitors the environment in a poultry house in real time, immediately triggers a behavior monitoring module when abnormality is detected, generates a specific environmental abnormality score, and combines visual monitoring and infrared thermal imaging to realize the monitoring and management of poultry breeding. According to the method, individual behavior characteristics of each poultry are effectively extracted, an individual behavior data stream is generated, individual potential health risks are identified, a health archive is adopted to accumulate long-term behaviors and environmental data of each poultry, and a time sequence analysis model is utilized to identify individual behavior modes and health change trends. Possible health risks are predicted based on historical data and models, management perspectiveness and disease prevention and control efficiency are effectively improved, meanwhile, abnormal features with the most influence are automatically recognized through comprehensive risk scoring and contribution degree analysis and are pushed to management personnel, the health archive of each poultry is updated in real time, and the management efficiency is improved. And the overall operation efficiency of the farm is improved.

The invention discloses a knowledge graph-based dynamic vocational ability and post matching education evaluation method. The method comprises the steps of S1, constructing a vocational ability knowledge graph; s2, constructing a post demand knowledge graph; s3, designing and training a double-layer generative adversarial network model; s4, matching the vocational ability enhancement scheme and the post demand change generated by the double-layer generative adversarial network model with the existing vocational ability knowledge graph and post demand knowledge graph; s5, updating the vocational ability knowledge graph and the post demand knowledge graph in real time based on the dynamic change of the market demand and the development condition of the individual vocational ability, generating a new matching relationship by using a double-layer generative adversarial network model, and continuously optimizing a matching result; and S6, according to the optimized matching result, generating an education evaluation report and an occupational planning suggestion. According to the invention, the dynamic adaptability and the matching accuracy of vocational ability and post matching are remarkably improved, and accurate docking between job seekers and post requirements is realized.

The invention provides a modular power supply system configuration method and system for a micro-module machine room, and relates to the technical field of electric power, and the method comprises the steps: collecting power utilization state data and load distribution data of functional equipment in the micro-module machine room, building a power supply network topology model, dividing a power supply system into a first-stage load, a second-stage load and a third-stage load, an independent power supply loop and a data acquisition unit are respectively configured in the first-stage load, the second-stage load and the third-stage load; a power supply network topology model is updated according to the power utilization data acquired by the data acquisition unit, a reinforcement learning algorithm is adopted to generate an adaptive power distribution strategy, and a modular energy storage unit is arranged to provide a standby power supply for a primary load and a secondary load; and constructing a machine learning fault prediction model based on the power consumption data, and dynamically adjusting a power supply path and a power distribution scheme of a power supply loop.

The invention discloses a multi-type energy complementary island micro-grid coordination control method and system, and relates to the technical field of energy coordination control, and the method comprises the steps: collecting an island micro-grid structure and a predetermined prediction time zone, and carrying out the historical data analysis; performing energy coordination control on the island microgrid based on the energy coordination control scheme; and performing simulation evaluation through a simulation tool. According to the island micro-grid coordination control method for multi-type energy complementation, the island micro-grid structure and the preset prediction time zone are collected for historical data analysis, the accuracy and consistency of data analysis are improved, different power utilization levels are set according to the actual power utilization condition and management requirements of the island, data are arranged according to the time sequence, and the power utilization rate of the island is improved. The power supply reliability and efficiency are improved, power generation prediction is carried out based on the prediction model, the perspectiveness and accuracy of energy management are improved, and better effects are achieved in the aspects of the power supply reliability, the power generation prediction precision and the power generation prediction efficiency.

The invention discloses a big data analysis-based early warning method for abnormal emission of pollution facilities. The method comprises the following steps of S1, collecting data and constructing a data set; s2, storing and preprocessing a data set; s3, constructing a space-time propagation network of the external events by using the graph neural network model; s4, modeling and predicting the time sequence features by using a mixed depth generation model; s5, analyzing the causal relationship between the external event and the facility emission behavior through a causal inference model; s6, adopting a Bayesian optimization algorithm based on uncertainty estimation of a strategy to dynamically adjust an emission early warning threshold value; s7, generating a new abnormal emission mode label by using a self-supervised learning model and a few-sample learning technology; and S8, adopting an interpretable artificial intelligence technology to generate an abnormal source tracing report, and providing coping suggestions and action schemes. According to the method, the early warning of the abnormal emission of the pollution facilities is realized by utilizing a cross-time-space event inference and self-supervised learning method.

The invention relates to the technical field of intelligent dehumidification, in particular to a dehumidification control method and system based on humidity detection and an intelligent dehumidifier, and the method comprises the following steps: collecting outdoor humidity data and indoor humidity data through a humidity sensor, executing time synchronization and marking, and carrying out data normalization processing to obtain initial environment humidity detection data; according to the invention, through humidity detection, advanced data processing and trend prediction, the precision and foresight of dehumidification control are improved, subtle humidity change is identified by using a difference technology, real-time fluctuation of environment humidity is accurately reflected, the humidity change in a short period is predicted by using a linear regression model, the accuracy of a prediction result is improved, prediction errors are reduced, and the method is suitable for large-scale popularization and application. By means of the calculation result of the humidity setting difference of the different areas, accurate adjustment, continuous monitoring and dynamic adjustment of dehumidification parameters of the dehumidification equipment in the different areas are achieved, it is ensured that the equipment is kept in the optimal operation state all the time, the energy efficiency is remarkably improved, and the comfort and safety of the living and working environment are improved.

The invention provides a network security risk assessment system and method, and relates to the technical field of network security. The network security risk assessment system comprises a data acquisition layer, the data acquisition layer is connected with an analysis processing layer, the analysis processing layer is connected with an assessment decision-making layer, the assessment decision-making layer is connected with a display interaction layer, and the data acquisition layer acquires business scene data, network topology data, user behavior logs and social network conditions. According to the system, through highly integrated module design, 'information isolated island 'between traditional security tools is broken, comprehensive security situation awareness is realized, novel and unknown threats are dealt with by utilizing situational analysis and dynamic adjustment capability, false alarms and missing alarms are remarkably reduced, and the system is based on machine learning and behavior analysis. Intelligent priority ordering and automatic response can be realized, and the speed and accuracy of risk identification and response are improved.

The invention relates to the technical field of data processing, and discloses an operation mode switching control method and device for an offshore wind turbine generator. The method comprises the following steps: carrying out model classification on historical operation data of a unit to obtain operation mode classification data and a characteristic parameter library; performing reinforcement learning analysis on the current state evaluation data and the short-term prediction data of the unit to obtain a target operation mode; performing fuzzy control processing on the target operation mode to obtain a mode switching strategy and a parameter adjustment sequence; performing multi-objective optimization and cooperative control analysis on the mode switching strategy and the parameter adjustment sequence to obtain accurate control signals of each actuator in the target offshore wind turbine generator; and generating an operation mode switching control strategy according to the accurate control signal of each actuator, and performing mode switching control on the target offshore wind turbine generator through the mode switching control strategy. According to the invention, the efficiency and accuracy of the operation mode switching control of the offshore wind turbine generator are improved.

The invention discloses a low-voltage power distribution area topology identification method and system based on clustering analysis and a graph theory, and relates to the technical field of low-voltage power distribution area topology classification. The system can accurately identify the topological structure of each partition through the combination of a clustering analysis model and a graph theory algorithm, and automatically calculate a topological characteristic coefficient Tp; therefore, the complex topological structure of the power distribution area can be classified and analyzed more carefully and comprehensively. According to a traditional method, accurate classification is often difficult when a complex branch structure is faced, and a topology recognition module ensures the accuracy of a classification result through a calculation mode of a graph theory. Through the feedback management module, when the power performance risk coefficient Lpjx of the partition exceeds a safety threshold R, a fault early warning signal can be sent out in time, abnormal nodes are automatically identified, faults are isolated, meanwhile, load transfer is executed, the power demand is rapidly transferred to the normally operating partition, and the power failure range and time are reduced.

The invention relates to the technical field of network security, in particular to a network security evaluation information processing method and system, and the method comprises the following steps: collecting configuration information and operation logs of a target computer system based on network security evaluation information, sorting data according to time information, and executing formatting and standardization processing, and generating an operation data processing result. According to the method, time sorting and formatting processing are carried out on data, data quality is optimized, security events are classified according to time information and severity, event frequency analysis is combined, threat identification accuracy is improved, damage risk levels of multiple security threat types are analyzed, and weights of security assessment indexes are adjusted, so that threat identification accuracy is improved. The response sensitivity and prediction capability of the evaluation model are enhanced, the risk of misjudgment is reduced, the adaptability and foresight of safety measures are improved, and it is ensured that the safety level evaluation accurately reflects the actual safety condition.

The invention discloses a conflict detection processing method and system based on a BIM model, a medium and a product, and relates to the technical field of constructional engineering, and the method comprises the steps: obtaining a construction project design scheme; based on a preset model framework, determining a BIM model according to the construction project design scheme; predicting potential conflict points in the BIM model based on a preset conflict mode library; matching the potential conflict points with the building structure, the electromechanical pipeline and the equipment layout by using a space mapping algorithm, and determining possible conflict areas; on the basis of a natural language processing technology, operable solution suggestions are generated according to the possible conflict areas; after the operable solution suggestions are sent to the user, the solution suggestions determined by the user are received; and adjusting the BIM model based on the solution suggestion. By implementing the method, the accuracy and intelligence of conflict detection can be improved.

The invention provides a self-adaptive network security early warning system and method based on deep learning, and aims to continuously update a threat recognition model through an advanced autonomous learning mechanism to adapt to a rapidly changing network environment. The system establishes a dynamic behavior baseline by integrating multi-source data including network traffic, system logs, user behaviors and the like, and realizes real-time monitoring of network behaviors and accurate identification of abnormal behaviors; once potential threats are detected, the system can automatically trigger preset response measures, such as suspicious traffic isolation and access request blocking, so that actions can be taken quickly before damage caused by the threats; according to the design of the system, the dependence on manual intervention is reduced, the real-time performance and effectiveness of early warning are improved, and the recognition accuracy of unknown attacks is remarkably improved; through the automatic and intelligent early warning mechanism, more reliable and intelligent protection is provided for network space security, and high efficiency and accuracy of network security management are ensured.

The invention provides a wireless communication and computing resource collaborative optimization method and system. The method provided by the invention comprises the following steps: establishing an initialized global model matched with a business demand for each service provider, and matching service resources of each service provider according to the business demand; establishing a service agent corresponding to each service provider; each service agent generates an optimal strategy based on the corresponding service resources and the state information of other service providers; sending the initialized global model to a distribution client according to an optimal strategy; each distribution client trains the received to-be-trained model based on the distributed service resources, and sends the trained model to the corresponding service provider; and aggregating the received trained models by each service agent to obtain a global model of the corresponding service. The method and the system provided by the invention are used for realizing collaborative optimization of wireless communication and computing resources and improving the overall performance and the resource utilization efficiency of federated learning.

The invention provides a micro robot motion control method and system, and the method comprises the steps: carrying out the synchronization and calibration processing of environment state information, and generating a comprehensive perception image; analyzing the comprehensive perception image to obtain a classification result, and predicting the environment state change influencing the motion track of the micro robot in a period of time in the future based on the classification result; generating a candidate path by using a fast random tree, and optimizing the candidate path in combination with an AI search algorithm to obtain a target path; constructing a three-dimensional dynamic probability graph, continuously updating the three-dimensional dynamic probability graph by using a reinforcement learning algorithm, and generating an instant decision map; and estimating the position of the micro-robot by using a Kalman filter to obtain the position state of the micro-robot, adjusting the speed and steering angle of a driving motor of the micro-robot, and generating trajectory tracking of the micro-robot. According to the technical scheme provided by the invention, the flexibility and intelligence of the micro robot are improved.

The invention relates to the field of voltage regulation and control, in particular to a real-time voltage regulation method for a new energy automobile. Initializing a power distribution network voltage measurement module, and collecting voltage data of each node of the power distribution network; setting a voltage deviation threshold value, and judging whether the voltage is in a normal range or not; collected voltage is compared with a set voltage deviation threshold value, a result obtained after preliminary voltage regulation of an on-load voltage regulation transformer is compared with the set voltage deviation threshold value, and a result obtained after cooperative voltage regulation of an electric vehicle charging pile is compared with the set voltage deviation threshold value. According to a comparison result, tap adjustment of an on-load voltage regulation transformer, cooperative voltage regulation of an electric vehicle charging pile and voltage regulation participated by a distributed photovoltaic system are carried out in sequence; and formulating an optimization strategy based on historical data and results of three times of voltage regulation. By means of cooperative voltage regulation, optimal control, intelligent management and the like, the voltage stability is effectively improved, and the adaptability and flexibility of the system are enhanced.

The invention relates to the technical field of cross-border e-commerce storage, in particular to a deep learning cross-border e-commerce storage management system, which comprises a data acquisition processing unit, a space-time diagram neural network analysis unit, an alliance management scheduling decision unit and an equipment execution feedback unit, a data acquisition and processing unit acquires and preprocesses storage data, a space-time diagram neural network analysis unit constructs a space-time diagram and predicts task requirements and equipment states, an alliance management scheduling decision-making unit constructs a dynamic alliance and generates a scheduling instruction, and an equipment execution feedback unit receives the instruction and autonomously adjusts a strategy. The problems of unreasonable equipment scheduling, poor collaboration and resource waste in traditional cross-border e-commerce warehouse management are solved, intelligent scheduling and collaborative operation of the warehouse equipment are realized, and the warehouse management efficiency and the resource utilization rate are improved.

The invention relates to the technical field of signal control, and discloses an intelligent signal I / O control system and method for industrial automation. The method comprises the following steps: carrying out acquisition and feature analysis on various signals in the industrial automation system to obtain a signal feature model; designing a two-stage I / O control scheme to obtain a first control strategy; constructing a state-trend matrix, and introducing an evaluation function to quantify a control effect to perform dynamic iteration updating to obtain a target control parameter; performing PLC autonomous fault diagnosis to obtain a fault diagnosis result; performing network cooperative control analysis on the plurality of PLCs based on a fault diagnosis result to obtain a hierarchical central coordination mechanism; and establishing a multi-dimensional performance index system of a hierarchical central coordination mechanism, and performing evaluation and adaptive optimization on the first control strategy to obtain a second control strategy, thereby realizing intelligent perception, accurate control and fault diagnosis of a complex industrial environment, thereby improving the performance and stability of the whole automation system.

The invention discloses a dam safety monitoring agent system based on a multi-dimensional large model and digital twinning, and belongs to the technical field of safety monitoring of water conservancy and hydropower engineering. According to the method, a cognitive decision kernel is used as a core innovation module, multi-source data is collected by relying on a sky-ground hydraulic engineering integrated sensor network, cross-modal data fusion and anomaly recognition are achieved through a multi-dimensional large model cluster, and multi-physics coupling simulation is completed in combination with a digital twinborn body; the cognitive decision kernel generates an optimal combination scheme through case retrieval and measure unit adaptation degree calculation, a control instruction is output after digital twin rehearsal verification, and finally closed-loop control of dam safety real-time early warning, autonomous decision making and efficient disposal is achieved through an autonomous response mechanism. The problems of data island, passive response, expert dependence and the like of a traditional monitoring system are solved, the dam safety management intelligent level and the emergency response capability are remarkably improved, and the method has wide engineering application value.

The invention discloses an automatic input vital sign detection method and system, and relates to the technical field of medical information processing, and the method comprises the steps: collecting vital sign data of a target individual, and carrying out the synchronous processing; a vital sign data set is formed through cleaning and multi-channel data fusion; uploading the encrypted data to a medical information system to finish classified storage and updating of the data; performing feature extraction and analysis to generate a health risk prediction result; health management suggestions are generated, and data display is completed through a dynamic feedback mechanism. According to the method, the privacy and the integrity of the vital sign data are guaranteed through a dynamic updating and verification mechanism in a mode of combining symmetric encryption and asymmetric encryption, and the risks of data leakage, data tampering and version management disorder in a traditional system are overcome. By introducing an intelligent analysis method combining a classification model and a regression model, the limitation of traditional single-dimensional static assessment is solved, and a comprehensive health risk report is generated by integrating logic association among multi-modal feature data.

The invention discloses a water plant whole-process intelligent optimization scheduling method based on adaptive feedback control. The method comprises the following steps: S1, dividing into a low-demand stage, a medium-demand stage and a high-demand stage; s2, in a low-demand stage, a medium-demand stage and a high-demand stage, correspondingly obtaining real-time monitoring data sets of the water plant; s3, constructing a multi-stage evolutionary game model based on the water plant real-time monitoring data set; s4, generating a local optimal scheduling scheme of the current stage; s5, outputting a local optimal scheduling scheme according to the evolutionary game model by utilizing a self-adaptive feedback control mechanism; s6, performing global optimal scheduling of the system in each stage; s7, optimizing a scheduling scheme in advance; and S8, displaying the optimized scheduling effect through the integrated intelligent scheduling platform, monitoring the operation state of the whole water plant, and generating a scheduling report for reference of management personnel. According to the invention, the predictability and perspectiveness of the scheduling system are effectively improved through the prediction model, and it is ensured that the water plant is always in an efficient and safe operation state.

The invention discloses a coal mining vegetation disturbance analysis method based on long-time-sequence multi-source data, and relates to the technical field of mining area ecological monitoring and evaluation.The coal mining vegetation disturbance analysis method comprises the steps that on the basis of preprocessed mining area long-time-sequence spectral image data, a vegetation classification map is obtained through principal component analysis in combination with a classification algorithm; based on the vegetation classification map, obtaining a vegetation health index, evaluating the vegetation health condition and the vegetation change trend of the mining area, and obtaining vegetation health state time sequence data; based on the vegetation health state time sequence data, obtaining high-quality vegetation comprehensive data; constructing a vegetation state prediction model based on an LSTM model; and predicting a future vegetation state through a vegetation state prediction model based on the high-quality vegetation comprehensive data. According to the method, through an efficient multi-source data fusion mechanism and an advanced LSTM time sequence prediction technology, the accuracy and comprehensiveness of mining area vegetation state evaluation are remarkably improved.

The invention discloses an electric power project risk prediction method and system based on data mining, and relates to the technical field of electric power project risk prediction.The method comprises the steps that electric power project data are collected, the electric power project data are preprocessed, key features influencing project risks are extracted, and feature vectors are established; constructing an electric power project risk prediction model, and performing risk prediction analysis on the electric power project based on the feature vector; and taking a part of areas with risks as nodes to form a topological graph, identifying key paths and bottlenecks, and formulating corresponding resource allocation strategies. According to the method, the electric power project risk prediction model is constructed based on the feature vectors, risk prediction analysis is carried out on the electric power project in combination with the time change trend, the accuracy and foresight of risk prediction are improved, the topological graph is formed by taking part of areas with risks as nodes, key paths and bottlenecks are identified, and the risk prediction accuracy is improved. And a corresponding resource allocation strategy is formulated, so that the security and the stability of the project are improved.

The invention discloses an energy carbon data analysis method and system, electronic equipment and a computer storage medium, and belongs to the technical field of large models. The method comprises the following steps: receiving an energy carbon data analysis request of a user side; analyzing the energy-carbon data analysis request through a pre-constructed large language model to obtain an overall task corresponding to the energy-carbon data analysis request; decomposing the overall task into a plurality of sub-tasks, wherein each sub-task processes data analysis in a specific field; calling a corresponding sub-model for each sub-task, and obtaining an intermediate result output by the sub-model; and fusing the intermediate results to obtain an energy-carbon data analysis result corresponding to the energy-carbon data analysis request. Through the method, the technical problems that a traditional decision analysis method is single in data source and limited in dimension are solved.

The invention discloses an arid region lake basin water environment bearing capacity evaluation and prediction system, and relates to the technical field of lake drought early warning. A training data collection module is arranged to collect historical meteorological data, historical water level label data, historical meteorological characteristic data and historical drought duration label data; a training sample construction module is set to construct a plurality of lake water level training samples and a plurality of drought duration training samples, a model training module is set to train a water level sequence prediction model and a drought duration prediction model, and a bearing capacity prediction module is set to output future drought duration based on future meteorological characteristic data and the drought duration prediction model. Outputting a future lake water level sequence based on the future meteorological data, the future drought duration, the water level sequence prediction model and the real-time lake water level, and performing early warning on the lake bearing capacity based on the future lake water level sequence; and the prediction accuracy of lake water level change and drought occurrence is improved.

The invention relates to the technical field of public health safety, in particular to an infectious disease transmission path prediction method and device and a medium, and the method comprises the following steps: extracting an initial tetrad of an event from infectious disease outbreak data, mapping the initial tetrad into a numerical value, and obtaining a numerical value tetrad; dividing the numerical tetrad according to time to obtain a time sequence knowledge graph; performing initialization embedding representation on entities and relationships in the time sequence knowledge graph; constructing a relational graph attention network, and learning structure information in the time sequence knowledge graph to obtain entity embedding and relation embedding; calling a gating circulation unit, and extracting time sequence information of the time sequence knowledge graph; and combining the structure information with the time sequence information by adopting a decoder, generating a prediction result, and realizing infectious disease transmission path prediction based on the prediction result. According to the method, the transmission path of the infectious diseases is predicted by adopting the time sequence knowledge graph, and compared with a traditional public health prevention method, the method has higher dynamic nature and multi-dimensional correlation analysis capability.

The invention relates to the technical field of breach image processing, in particular to a breach rapid situation awareness method and system based on multi-source data. The method comprises the following steps: acquiring dam monitoring station data; performing hydrological sensor network setting on the dam monitoring station data to obtain dam hydrological sensor network data; performing ultrasonic water level change trend analysis on the dam hydrological sensor network data to generate an ultrasonic water level change trend chart; performing breach node sensing on the dam hydrological sensor network data according to the ultrasonic water level change trend chart to generate breach node data and breach prediction node data; and according to the breach node data, macroscopic disaster risk analysis is carried out on the breach area, and a breach area macroscopic disaster risk schematic diagram is generated. According to the invention, through real-time data integration, comprehensive risk assessment, unmanned aerial vehicle monitoring, dynamic prediction model and multi-party cooperative response, the accuracy and response speed of breach rapid situation awareness are improved.

The invention relates to the field of underground environmental pollution prevention and control of refuse landfills, in particular to a comprehensive evaluation method for long-term antifouling performance of a clay separating wall of a refuse landfill, which comprises the following steps: S1, preparing clay separating wall materials with multiple proportions; s2, designing a test according to the long-term anti-fouling performance of the clay separation wall, and obtaining physical and chemical parameters of a clay separation wall material through the test; and S3, setting screening conditions according to the physicochemical parameters, and evaluating and screening the clay separation wall materials with multiple proportions by utilizing the screening conditions to obtain the clay separation wall material with long-term effective antifouling performance. According to the comprehensive evaluation method provided by the invention, the permeability of the clay separating wall, the chemical compatibility with the pollutants and the blocking time of the separating wall to the pollutants are comprehensively considered, so that the long-term antifouling property of the clay separating wall of the refuse landfill is more comprehensively judged, and the screened material can meet the long-term antifouling requirement; and the evaluation accuracy and reliability are greatly improved.