197 results about "Environmental risk assessment" patented technology

The invention relates to the technical field of network operation and maintenance supervision, in particular to a network operation and maintenance environment risk assessment method and system based on a mimicry security technology, and the method comprises the steps: carrying out the collection and preprocessing of network operation and maintenance environment data, and generating standardized security data; abnormal feature data and disturbance response data are extracted through real mimicry analysis and active challenge mimicry analysis, and associated threat feature data are generated through fusion; based on the associated threat feature data, constructing a heterogeneous twin model, performing state modeling, threat modeling and resource modeling, and generating risk assessment data; and generating an optimal response strategy through a multi-objective optimization algorithm, including flow control, node isolation and resource scheduling, and dynamically updating a risk state through a feedback regulation mechanism. Through dynamic threat identification and real-time optimization response, the risk assessment accuracy and the security defense level of the network operation and maintenance environment are improved, and the method has the advantages of being efficient, intelligent and dynamic.

The invention discloses a surrounding environment risk assessment method based on a cover excavation subway foundation pit, and relates to the technical field of subway construction. The evaluation method comprises the following steps of multi-source data acquisition and preprocessing: collecting geological survey data, environment monitoring data, construction monitoring data, existing building data, pipeline data and electric power facility data of a foundation pit area. The deep analysis of the complex interaction between the foundation pit construction and the surrounding environment is realized, the innovative method overcomes the limitations of single data and one-sided analysis in the traditional risk assessment, the comprehensiveness and accuracy of the risk assessment are remarkably improved, and the risk assessment efficiency is improved through comprehensive analysis. The potential influence of foundation pit construction on the surrounding environment can be accurately monitored in real time, the reasonable early warning threshold value is set, and once the monitoring data exceed the preset threshold value, the early warning signal is triggered immediately, so that the safety of the surrounding environment is effectively guaranteed, and the risk in the construction process is reduced.

The invention provides a credit assessment method and device based on artificial intelligence and a big data technology, and aims to improve the accuracy of credit risk assessment through the analysis of multi-dimensional behavior data. The method comprises the following steps: firstly, collecting multi-dimensional behavior data of a target customer, and extracting multi-dimensional behavior characteristics of the customer based on the data, including a behavior stability index, an abnormal behavior frequency, behavior trend data and multi-dimensional behavior correlation data; and generating a credit risk assessment result through a pre-trained neural network model in combination with the credit report data of the customer, the environmental risk assessment data and the like. And finally, through analysis of the credit risk assessment result, credit approval suggestions and personalized loan schemes for clients are generated. According to the invention, multi-dimensional data and macroeconomic factors are integrated in credit risk assessment, comprehensive assessment of customer credit is realized, and the accuracy of credit decision is improved.

The invention provides a CO2 sequestration simulation method based on transfer learning and a physical information neural network, which aims at a geological carbon dioxide sequestration process by combining the transfer learning and the physical information neural network, and comprises the following steps of: simulating and generating data by using a Matlab oil reservoir simulation tool box, and constructing a neural network basic model; the physical law of carbon dioxide migration in the porous medium is trained and learned through coarse resolution, then fine tuning is performed on the generated actual geological model data by adopting a migration learning strategy to adapt to different geological structures and injection conditions, and a neural network loss function is embedded in combination with a Darwest equation and a mass conservation equation to obtain a carbon dioxide migration model. Compared with a traditional numerical simulation method, the method has the advantages that the calculation efficiency is remarkably improved while the prediction precision is kept, and the method is suitable for carbon dioxide sequestration optimization and environmental risk assessment under complex geological conditions and has good industrial popularization value and environmental benefits.

The invention relates to the technical field of artificial intelligence, in particular to a driver fatigue state monitoring system and method based on multi-modal biological feature fusion, and the system comprises a sensing module, a data processing and analysis module, an environment risk assessment module, a personalized model module, a self-adaptive decision fusion module, and a vehicle interaction and vehicle control module. Compared with the prior art in which fatigue judgment is performed by adopting a fixed threshold value, the fatigue judgment cannot adapt to a complicated and changeable driving environment and may cause excessive alarm or missing alarm under a monotonous road condition in a dangerous road section, an environment perception and dynamic threshold value adjustment algorithm is introduced, so that the system can intelligently evaluate the environmental risk and monotonicity, and the fatigue judgment accuracy is improved. The alarm sensitivity is automatically adjusted, and the early warning accuracy and the scene adaptability are both improved.

The invention discloses a distributed intelligent control method for unmanned aerial vehicle cluster cooperative path planning, and relates to the technical field of unmanned aerial vehicle cluster cooperative control, and the method comprises the steps: collecting a dynamic feature set of an unmanned aerial vehicle in real time, and carrying out the time synchronization, noise filtering and formatting integration, and forming a local state data set; performing neighborhood collaborative analysis and temporary link planning on the local state data set to obtain relay chain layout data; performing environmental risk assessment and path feasibility analysis on the relay chain layout data to generate a candidate path set; adjusting the candidate path set through local obstacle avoidance and dynamic constraint to generate an executable path sequence, and generating a collaborative execution path through neighborhood communication and collaborative adjustment; performing task execution result integration and state updating on the collaborative execution path to generate group state data; and performing path feedback and loop optimization on the group state data to obtain a group collaboration result. And the task completion rate, the cooperation efficiency and the environment adaptability are improved.

The invention provides a transformer environmental protection risk assessment method and system based on a dynamic threshold value. The method comprises the steps of obtaining operation state parameters and environmental parameters of an abnormal transformer in a construction site; obtaining a current composite risk degree of the abnormal transformer based on the operation state parameters, the environmental parameters and a pre-constructed composite environmental protection risk calculation model; obtaining a dynamic early warning threshold based on the environmental parameters and a pre-constructed early warning threshold dynamic adjustment model; performing risk early warning based on the current composite risk degree and the dynamic early warning threshold value; wherein the early warning threshold dynamic adjustment model is constructed by using the obtained historical composite risk degree and historical environmental parameters of the abnormal transformer and the obtained time attenuation coefficient. And a dynamic threshold adjustment mechanism is adopted, so that the system can automatically optimize early warning triggering conditions according to the change of the construction environment, the flexibility and adaptability of risk assessment are improved, and the situation of false alarm or missing alarm does not exist.

The invention relates to the technical field of glacier research, and discloses a drainage basin glacier material balance analysis method and system based on big data, and the method comprises the steps: collecting remote sensing images, ground observation and meteorological data of a glacier drainage basin, carrying out the standardization processing and fusion of multi-source data, constructing a random forest model, analyzing the relevance between glacier changes and climate factors, and carrying out the analysis of the relevance. And determining a key driving factor. The dynamic response of glacier substances is further predicted by using a long-short-term memory network, high-sensitivity climate factors are determined through sensitivity analysis, and the prediction model is optimized. A dynamic response database is also established for long-term monitoring, and an automatic early warning mechanism is set to evaluate the abnormal change risk. According to the method, accurate analysis and prediction of glacier change and climate factor interaction are realized, and an effective tool is provided for glacier change research and environmental risk assessment.

The invention discloses a semantic analysis-based user abnormal intention recognition method and system, and belongs to the technical field of computer data processing, and the method comprises the steps of analyzing logic contradiction word combinations in a user session text to generate abnormal semantic marks, and collecting a user operation behavior sequence and environmental parameters to construct a spatio-temporal behavior trajectory map. And matching the abnormal semantic mark with a preset extensible abnormal mode library, if the generated primary abnormal confidence exceeds a threshold value, further comparing the behavior trajectory map to generate a behavior abnormal index, associating the abnormal semantic mark with the primary abnormal confidence to form a comprehensive abnormal feature vector, and calculating an environmental risk coefficient in combination with environmental parameters. And finally, judging by a triple decision arbiter, and if the abnormity is suspicious abnormity, activating a secondary verification process and updating an abnormal mode library. According to the method, semantic logic contradiction analysis, behavior trajectory map analysis and environmental risk assessment are fused, deep and camouflage abnormal intentions can be accurately recognized, and the method has adaptive learning ability.

The invention discloses a drinking water source environment risk assessment method and system, and relates to the field of risk assessment, and the method comprises the steps: S1, obtaining a comprehensive feature value of a to-be-assessed drinking water source; s2, generating a pollutant migration capability value of the drinking water source land to be evaluated; s3, obtaining an environment risk assessment range of the drinking water source land to be assessed; s4, generating a potential pollution risk value of the drinking water source land to be evaluated; according to the method, the basic and meteorological information is integrated, the water source features are represented, the remote sensing monitoring period is optimized, and the monitoring efficiency is improved. The method scientifically assesses risks and reasonably allocates monitoring resources. Meanwhile, by analyzing pollution source activity data and pollutant migration ability, potential risks are effectively obtained, risk management is enhanced, and targeted measures are formulated.

The invention discloses an escalator operation environment monitoring and risk assessment method, and relates to the related field of escalator safety monitoring, and the method comprises the following steps: cooperatively carrying out escalator monitoring data acquisition; real-time receiving and transmission are carried out based on the Internet of Things; performing data association mining on the time sequence environment parameters and the time sequence operation parameters, and outputting environment risk association data; loading to an environment risk assessment model for operation risk assessment, and constructing an integrated assessment system from the aspects of structural stability, durability, safety protection and the like; and escalator operation and maintenance decision making is carried out according to the risk assessment result, multi-dimensional risk prevention and control and response are achieved, performance assessment, risk grade division and early warning are included, and preventive maintenance, environmental adaptability transformation and emergency response mechanisms are established. The technical problems that existing escalator risk assessment is incomplete in monitoring, not real-time, inaccurate in risk identification and insufficient in assessment result reliability are solved, and the technical effect of improving the risk identification accuracy and the assessment result reliability is achieved.

The invention discloses an ecological environment big data processing method based on mining area pollutant migration behavior modeling, and relates to the technical field of big data processing. Comprising the steps that protection points in a target range and geodetic coordinate values of a mining area are obtained, the protection points comprise a mass residential area, an ecological protection area and a water source protection area, and the geodetic coordinate values are used for representing latitude and longitude coordinates and the altitude; and according to the first group of geodetic coordinate values and the geodetic coordinate values of the mining area, the maximum diffusion range interval of the mining area pollutants is determined, and the first group of geodetic coordinate values specifically refer to the geodetic coordinate values taken by any protection point type. According to the method, the three-dimensional geodetic coordinates of the protection point and the mining area are obtained, the pollutant diffusion range is scientifically defined, it is ensured that the space coverage of pollution monitoring is comprehensive and effective, potential threats of mining area pollution to surrounding people residential areas, ecological protection areas and water source protection areas can be accurately recognized, and the risk of mining area pollution to the surrounding people residential areas, ecological protection areas and water source protection areas is reduced. And the accuracy and pertinence of environmental risk assessment are improved.

The invention discloses an environmental risk step-by-step assessment method in a multi-medium and multi-scale complex scene, and belongs to the technical field of environmental risk assessment. The method comprises the following steps: obtaining the heavy metal concentration of the waste residue through a leaching experiment, and determining the toxicity grade by using a single pollution index method and a potential ecological risk index method; dividing volume grades based on quantiles, dividing local diffusion risk grades by using an inverse distance weighted interpolation method and the like, dividing remote receptor sensitivity grades according to a surrounding land and water source construction model, and generating priority control grades by integrating the grades; and finally, fusing multi-source data to construct a spatio-temporal conjoint analysis model verification result. Therefore, by integrating pollution source toxicity, volume, diffusion risk and receptor sensitivity to carry out risk step-by-step assessment, a multi-source data fusion and spatio-temporal conjoint analysis method is proposed for the first time, and the problems of single assessment dimension, insufficient data fusion, lack of dynamic verification and the like of an existing assessment method in a multi-medium and multi-scale scene are effectively solved.

The invention discloses an environmental event dynamic risk assessment method and system based on a Bayesian network, belongs to the technical field of artificial intelligence, and aims to solve the technical problems that in existing environmental risk assessment, dynamic data adaptability is poor, multi-source heterogeneous data fusion is difficult, and real-time performance is insufficient. Comprising the following steps: acquiring environment data through a sensor cluster deployed in an environment, and uploading the environment data to an edge computing node; performing data preprocessing on the environmental data through the edge computing node; extracting time series data fragments from the standardized data stream based on a predefined sliding time window; constructing a risk prediction model based on the dynamic Bayesian network; and taking the extracted time series data fragments as input, performing risk level analysis through a risk prediction model in combination with particle filter reasoning, predicting and outputting a risk level, a risk level probability value and a risk conduction path as prediction results, and constructing a visual risk conduction map based on the prediction results.

The invention discloses a distribution box room environment parameter integrated measurement method, and particularly relates to the field of multi-parameter measurement, and the method comprises the steps: firstly constructing a machine room three-dimensional model and a sensor correlation degree matrix, then building a heat balance, humidity diffusion and airflow motion model, and calibrating parameters; deploying a measurement system and calibrating through dual synchronization and cross check; establishing a sensor confidence evaluation system and an adaptive threshold based on 72-hour reference data; fusing calibration data by adopting a three-level correlation calibration mechanism; four types of measurement modes and conversion logics are designed, and measurement resources are dynamically allocated through environmental risk assessment; multi-dimensional state parameters are extracted, a comprehensive evaluation value is calculated through normalization, dynamic weighting and combinatorial algorithms, and five-level early warning response is achieved; according to the method, multiple models and an intelligent algorithm are integrated, the parameter measurement precision and the environment risk identification efficiency are improved, the response delay is reduced, the fault diagnosis accuracy is improved, and reliable technical support is provided for safe operation and maintenance of the distribution box room.

The invention relates to the technical field of computer simulation, and discloses a coal mine underground reservoir environment risk assessment method and system, and the method comprises the steps: constructing a digital twinborn body of a coal mine underground reservoir; establishing a risk assessment index system; collecting monitoring data corresponding to the index system in real time; calculating risk coupling factors among different dimension indexes; according to the risk coupling factor and the real-time numerical value of each index, dynamically adjusting the weight of the index system, and generating a dynamic weight set; calculating a current comprehensive risk level; and extracting physical field parameter values in the real-time monitoring data, inputting the physical field parameter values into a multi-physical field coupling simulation engine for simulation, and outputting a prediction report. According to the invention, dynamic assessment and advanced early warning of the environmental risk of the coal mine underground reservoir can be realized, the risk identification precision and the emergency response capability are improved, and the method is suitable for safety management and control of the whole life cycle of the underground reservoir under complex geological conditions.

The invention provides a transformer construction period environmental protection risk assessment method and system, and the method comprises the steps: inputting transformer image data of a construction site in a construction period into an image recognition model, and recognizing the current abnormal coordinate information of a transformer; obtaining current abnormal state data based on the current abnormal coordinate information; inputting the current abnormal state data, the obtained current operation state parameters of the transformer and the obtained current environment parameters into a pre-constructed dynamic evaluation model for evaluation to obtain the current risk degree of the transformer; based on the current risk degree, outputting an environmental protection risk assessment result and an equipment safety monitoring result of the transformer; wherein the dynamic evaluation model is obtained by training an adaptive neural network by adopting historical abnormal state data, historical operation state parameters, historical environment parameters and historical risk degrees of the transformer. The current environmental protection risk degree of the transformer can be output in real time, and the dynamic monitoring requirement of the transformer in the construction period is met.

The invention relates to the technical field of environmental risk assessment, and particularly discloses a heavy metal industry-related enterprise environmental risk assessment method and device and a storage medium, and the method comprises the steps: constructing an index system of heavy metal industry-related enterprise environmental risk assessment according to related enterprise pollution characteristics and risk characteristics; an analytic hierarchy process is adopted to construct an environmental risk assessment model for the heavy metal-related industries, and a T.L.Saty1-9 scaling method is combined with a Delphi method to determine the weight value of each evaluation index in the index system, so that a scoring standard corresponding to the environmental risk assessment model is obtained; collecting and classifying characteristic data required by the enterprise, and obtaining a weighted average value of each evaluation index of the enterprise according to the environmental risk evaluation model and the scoring standard to obtain an enterprise environmental risk score; and establishing a risk grading gradient, and performing comparison based on the environmental risk score to obtain an enterprise environmental risk grade.

The invention discloses a rare and endangered organism toxicity prediction method based on a machine learning algorithm and a quantitative structure-activity relationship, which constructs a toxicity prediction model through the machine learning algorithm, can effectively assess the toxicity influence of environmental pollutants on rare and endangered organisms, and provides technical support for rare and endangered organism protection and ecological environment risk assessment. Comprising the following steps: 1, collecting data related to rare and endangered organisms from a public database, and establishing a rare and endangered organism toxicity prediction database based on machine learning; 2, generating molecular descriptors for the chemical substances; step 3, data preprocessing; 4, development of acute and chronic toxicity prediction models of rare and endangered organisms based on multiple machine learning is carried out, and performance evaluation is carried out; 5, performing internal and external verification on the rare and endangered biotoxicity prediction model; step 6, analyzing the importance of the features by using the valuable, rare and endangered biotoxicity prediction model, and finding out the most influential features; and 7, predicting the toxicity value of the pollutants in combination with the optimal machine learning model.

The invention provides a park environment risk priority management and control dynamic sorting system based on a Bayesian network. The system comprises a data acquisition layer, a data analysis layer and an output layer, the data acquisition layer is the basis of park environment risk assessment and is used for acquiring real-time and historical data from different sources; the data analysis layer constructs a dynamic index weight matrix by using a Bayesian network and is used for calculating a dynamic risk score RSI of an enterprise; the output layer is used for calculating the comprehensive risk score of the enterprise through the weight generated by the Bayesian network, dynamic weight adjustment and multi-source data fusion are utilized, an original static major / larger risk enterprise list is upgraded to a real-time dynamic sorting list, accurate resource allocation is achieved, and the risk assessment efficiency is improved. Through dynamic index fusion and Bayesian network weight adjustment, traditional static classification is upgraded to a real-time sorting priority management and control list, and an intelligent solution is provided for park environment risk management.

The invention relates to the technical field of environmental risk assessment, and particularly discloses a heavy metal area environmental risk assessment method and device and a storage medium, and the method comprises the steps: selecting a research object area, and employing the combination of an administrative region boundary and a grid partitioning method to divide assessment units; establishing an evaluation index system, selecting regional environment risk evaluation indexes in the evaluation index system, and obtaining index weights of the evaluation index system by using an analytic hierarchy process; obtaining an environmental risk value of the evaluation unit in combination with the evaluation index system and the index weight; grading the quantified environmental risk value of each evaluation unit; and performing spatial rendering on the graded evaluation units to obtain a regional environment risk grading and partitioning map. The problem that in the prior art, it is difficult to comprehensively and deeply reflect the environmental risk situation of a heavy metal-related area by simply analyzing sudden elements is solved.

The invention relates to the technical field of environment monitoring, in particular to an environment parameter real-time early warning method and system for a digital chicken house, and the method comprises the following steps: collecting ammonia gas concentration, temperature and humidity and illumination intensity data in a chicken house, uniformly integrating and generating a monitoring record, extracting a safety interval, counting the exposure time of ammonia gas, and judging a multi-factor deviation state; and carrying out conjoint analysis on environment distribution change, and outputting a risk state and early warning information. According to the method, multi-source environment data are integrated through a unified time reference, the time sequence consistency among parameters is improved, the identification capability of low-concentration long-term exposure is enhanced by screening an ammonia gas safety interval and counting the exposure duration, and a multi-factor time sequence offset feature is constructed by collecting the deviation state of temperature and humidity and illumination intensity; the conjoint analysis of exposure and deviation distribution is combined, the continuity and accuracy of environmental risk judgment are improved, high-risk time periods and monitoring data thereof are extracted, and the positioning and response efficiency of abnormal states is enhanced.

The invention discloses an insurance intelligent risk control system, and relates to the technical field of risk control, and the system comprises a data collection module which is used for collecting corresponding vehicle operation data and external environment data in the driving process of a target vehicle; the data analysis module is used for analyzing the vehicle operation data corresponding to the target vehicle in the driving process, generating a driving behavior evaluation coefficient corresponding to the target vehicle based on an analysis result, and processing the external environment data corresponding to the target vehicle to obtain a driving behavior evaluation result; determining an environment risk assessment coefficient corresponding to the target vehicle according to the processing result; the insurance adjustment module is used for generating a comprehensive risk assessment coefficient according to the driving behavior assessment coefficient and the environmental risk assessment coefficient, and determining a rate adjustment strategy in combination with a pricing model preset by an insurance company; and the strategy execution module is used for responding to the rate adjustment strategy and dynamically adjusting the rate corresponding to the insurance of the target vehicle. The method and the device have the effect of improving the risk assessment accuracy.

The invention discloses a solid fuel environmental protection project online intelligent early warning system based on big data, relates to the technical field of data processing, and solves the problem that it is difficult to establish a solid fuel environmental protection project database and store monitoring data; a temperature pollutant diffusion correlation model is difficult to establish, and the pollutant discharge rate and the pollutant diffusion influence range are analyzed; the influence degree of the solid fuel on environment sensitive areas, soil and underground water is difficult to evaluate; and an environmental protection risk assessment index is difficult to comprehensively assess and three-level early warning is difficult to establish according to the above technical problems. According to the invention, the monitoring and early warning of the solid fuel environmental protection project are realized by comprehensively evaluating the environmental protection risk evaluation indexes of the solid fuel to atmosphere, soil, underground water and environmental sensitive areas in each link.

The invention belongs to the technical field of environmental risk assessment, and particularly discloses a BIM-based fabricated steel structure assembly scheme comparison and selection method and system, and the method comprises the steps: building a three-dimensional building model based on BIM software, and extracting component information and GIS data; establishing national and local standard libraries, matching applicable standards through building positioning information, and generating parameterized assembly rules; generating a plurality of assembly type combination schemes according to rules and component logic, and interactively binding with the BIM model; calculating an assembly rate and a standard score of each scheme, and screening candidate schemes meeting structural safety and a cost threshold value; and constructing an incremental cost library, completing cost difference analysis of the candidate schemes, and outputting an optimal assembly type steel structure combination scheme. And data driving and automatic comparison and selection in the whole process are supported, the method can be applied to prefabricated building scheme design and optimization links, and the design efficiency and the scheme economy are improved.

The invention discloses an active power distribution network fault risk assessment system and method, and relates to the technical field of power distribution network operation risk assessment, and the system comprises a natural environment data collection module, an environment risk assessment model construction module, and an environment risk assessment module. On the basis of historical data analysis and trend prediction functions, by analyzing historical meteorological data, geological disaster record data and historical fault records of the power distribution network, the current fault risk of the power distribution network can be accurately evaluated, and by predicting the change of environmental factors in a period of time in the future, the power distribution network fault risk can be accurately evaluated. And the prediction data are substituted into the risk calculation model to assess the possible fault risk of the power distribution network in the future according to the change trends of the rainfall, the wind speed, the temperature, the humidity and the water level. Therefore, the timeliness and accuracy of risk assessment are ensured, and more time is provided for the operation of the power grid to take preventive measures.

The invention discloses a CO2 non-point source leakage area diffusion simulation method, and aims to solve the problems that a CO2 oil displacement storage project has the characteristics of multiple well sites, large range and long operation time, and non-point source soil leakage and multi-point source shaft leakage coexist in each well site. According to the method, for large-range diffusion of leakage of multiple well sites, real-time point source leakage source parameters are provided for CFD simulation through flux monitoring and leakage rate calculation of each well site, and therefore real-time distribution of the mass fraction of atmospheric CO2 in a diffusion area, support safety and environmental risk assessment are obtained.

The invention belongs to the technical field of data security, and discloses a cloud data security management method and device, and the method comprises the steps: carrying out the environment risk assessment of the current cloud health file access, and then adaptively adjusting the security parameter of a dynamic verification code through an environment risk assessment value. And according to the adjusted security parameter, generating a dynamic verification code for granting the cloud health record access permission to the user of the session, thereby effectively ensuring the security of the cloud personal health record data, simplifying the user operation process, reducing the hardware equipment cost, and improving the public service efficiency.

The embodiment of the invention provides a tree-based risk assessment method and device, electronic equipment and a storage medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: acquiring tree growth data and growth environment data of a target tree; detecting tree attribute information based on the tree structure sub-data and the tree form sub-data; evaluating the tree structure subdata and the growth environment data based on a health risk evaluation model to obtain health risk evaluation data; evaluating the growth environment data and the tree attribute information based on an environment risk evaluation model to obtain environment risk evaluation data; evaluating the growth environment data, the tree structure sub-data and the tree attribute information based on a natural disaster evaluation model to obtain natural risk evaluation data; and performing risk type assessment based on the tree attribute information, the health risk assessment data, the environmental risk assessment data and the natural risk assessment data. According to the embodiment of the invention, the risk assessment efficiency and accuracy of trees can be improved.

The invention provides a safety control method for a field management system of a high-horsepower tractor, relates to the technical field of electric control of tractors, realizes real-time monitoring and control through double CAN (controller area network) buses, ensures operation safety and efficiency, adopts a multifunctional handle unit, operates through an activation button and a safety button, monitors the state of the button in real time, and prevents mistaken touch. The action sequence is recorded and called, and the operation path and speed of the tractor are optimized through real-time feedback of sensor data in combination with a PID controller and a fuzzy logic control algorithm. When the system is started, the vehicle control unit carries out hardware self-inspection and environmental risk assessment, and safe operation is ensured. And on the basis of a fault prediction and multi-level early warning mechanism of machine learning, downtime is reduced, and maintenance efficiency is improved. According to the invention, the response speed and reliability of the tractor field edge management system and the operation safety are obviously improved, the accident risk caused by misoperation is reduced, and the damage to people and machines during abnormity is avoided.