139results about How to "Realization risk" patented technology

The invention discloses a multi-sensor fusion unmanned equipment agricultural monitoring method and system, and belongs to the technical field of agricultural monitoring, and the method specifically comprises the steps: obtaining a farmland image and multispectral data through an image shooting device and a multispectral sensor carried by an unmanned aerial vehicle, and recognizing the features of diseases and pests through a deep learning model, generating a distribution diagram and statistical data, and performing early warning and risk assessment in combination with the multispectral data; formulating a pesticide management strategy according to an evaluation result, and pushing the pesticide management strategy to an unmanned equipment control system in real time; meanwhile, according to actual and operation requirements of the farmland, generating an optimal operation path by using a path planning algorithm, setting operation parameters, and allocating tasks by using a task allocation algorithm; in the operation process, environment changes are monitored in real time, an edge computing device is used for finding out abnormity in time and adjusting operation instructions, and efficient and accurate monitoring and intelligent operation management of farmland diseases and insect pests are achieved.

The invention provides a method and system for realizing electronic channel risk control processing based on a code insertion technology, and relates to the technical field of electronic channel risk control, and the method comprises the steps: encrypting user information and channel identification information through a dual Hash algorithm to generate a dynamic session identification code, and forming a target coding sequence through employing a self-adaptive segmentation algorithm and a business feature flag bit. And constructing a dynamic risk control rule model by using a graph neural network, extracting user multi-dimensional features, carrying out weighted fusion to generate a user risk score, comparing the user risk score with an adaptive risk threshold, analyzing abnormal behaviors based on a probability graph model, and generating an intelligent risk early warning report. And calling a reinforcement learning risk control strategy engine to determine an optimal disposal strategy, and issuing a multi-factor interaction verification instruction. And after verification, selectively activating a blocking mechanism according to a verification result, synchronizing interaction data and a result to a distributed risk control data center, and continuously optimizing the risk control model based on federal learning.

The embodiment of the invention provides a multi-factor security authentication method and device based on AI adaptive recognition and a block chain, and the method comprises the steps: collecting user biological features, behavior data and equipment information, and extracting static and dynamic features through employing a convolutional neural network and a long-short term memory network; a feature fusion network is innovatively constructed, an attention mechanism is introduced to carry out dynamic weight distribution and fusion on different features, and feature correction is carried out based on a user behavior baseline model. And meanwhile, a block chain network and an intelligent contract are deployed, so that credible storage and risk level evaluation of authentication data are realized. According to the method, the limitation of traditional single authentication is broken through, the effective balance between safety and user experience is realized, and an advanced technical scheme is provided for the field of identity authentication.

The invention relates to the technical field of target detection, and discloses a personnel state recognition method and system based on target recognition detection. The method comprises the steps of performing parameterization processing on collected original image data to obtain intelligent BIM data; performing multidisciplinary data exchange processing on the intelligent BIM data to obtain target detection data; performing intelligent posture analysis processing on human body information in the target detection data to obtain optimized posture data; behavior recognition processing is carried out on the optimized attitude data to obtain behavior analysis data, and the behavior analysis data comprises violation behavior types, climbing state judgment and risk level evaluation; performing fuzzy logic evaluation processing on the behavior analysis data to obtain safety compliance data; and performing intelligent early warning processing on the safety compliance data and the real-time monitoring data to obtain full-life-cycle management data. According to the invention, the efficiency of personnel state recognition based on target recognition detection is improved.

The invention discloses a network threat deduction system and method based on Transform and a graph attention network model. The system mainly comprises four modules including a threat intelligence extraction module, a network threat entity construction module, a network threat deduction module and a deduction result application module. The method comprises the following steps: preprocessing and mining network security threat intelligence data by utilizing a graph attention network (GAT) model, constructing a complete network threat intelligence knowledge graph based on a data association relationship, and determining a vulnerability utilization weight in the graph; modeling is carried out on a reasoned path sequence based on a Transform model, a next reasoning sequence is predicted in a generative mode, and rapid and efficient network threat intelligence reasoning is achieved. According to the method, the complexity of attack deduction calculation is reduced, the deduction efficiency is improved, and security risk investigation and early warning can be carried out for a large-scale network environment.

The invention relates to the technical field of power supply chains, and discloses a power supply chain risk early warning method driven by a knowledge graph and a related device. The method comprises the following steps: acquiring multi-source data of a power supply chain; the multi-source data comprises structured data and unstructured data; inputting the multi-source data into a risk early warning model, and outputting a risk probability; wherein the risk early warning model takes a multi-modal graph neural network as a backbone network and combines a graph attention mechanism and comparative learning to realize fusion and risk prediction early warning of multi-source data. According to the invention, the advanced knowledge graph technology and the deep learning algorithm are utilized to realize intelligent risk early warning of the power supply chain; according to the power supply chain risk early warning method driven by the efficient knowledge graph, the risk management level of the power supply chain can be effectively improved, and a powerful guarantee is provided for stable operation of the power industry.

The embodiment of the invention discloses a boiler anti-wear and anti-explosion online early warning system based on a sensor and big data, and relates to the technical field of boiler safety monitoring, and the system comprises a sensor module which collects operation parameters in a boiler in real time; the data acquisition module is used for summarizing various data acquired by the sensor module; the data processing module is used for carrying out processing, analysis and trend prediction on the collected real-time data through a big data analysis technology; the early warning module is used for evaluating the anti-abrasion and anti-explosion risk of the boiler according to the analysis result output by the data processing module and generating an alarm when a risk threshold value is exceeded; and the display and interaction module is used for displaying the boiler operation state, the risk assessment result and the early warning information to an operator. According to the invention, intelligent monitoring and risk early warning of boiler equipment can be realized, and through accurate data analysis and prediction, the operation safety of the boiler can be improved, the occurrence of safety accidents can be reduced, and the maintenance strategy of the boiler can be optimized.

The invention relates to the technical field of financial leasing systems, in particular to a financial leasing system and method based on a cloud platform, and the system comprises a leasing personnel input unit which is used for obtaining leasing demand information; the rental equipment data acquisition unit is used for acquiring equipment state information; the lease service management unit is used for processing lease matching and risk control; the demand unit is used for analyzing the lease demand; the leasing personnel registration unit is used for managing leasing personnel information; the rental platform resource library is used for storing system data; wherein the lease service management unit realizes lease matching and risk management through a dynamic multi-dimensional lease matching and risk control algorithm, and a state time sequence matching model greatly improves the accuracy of lease matching by combining historical data and real-time market demands; the multi-dimensional lease scoring system further considers a plurality of factors such as lessee credit and equipment conditions, and provides a comprehensive evaluation basis for subsequent decision making.

The invention relates to the technical field of unmanned aerial vehicle cooperative control, and discloses a geological surveying and mapping method and system based on an unmanned aerial vehicle and a storage medium. The method comprises the following steps: processing real-time wind field data and a compensation control instruction set to obtain a wind field compensation model, and optimizing the wind field compensation model to obtain adaptive route data; analyzing and processing the battery state data, the adaptive route data and the task completion degree data of the unmanned aerial vehicle through an energy consumption prediction model to obtain task optimization parameters, and dynamically distributing and processing the task optimization parameters to obtain a task execution instruction set; and processing the state information and the task execution instruction set of the multiple unmanned aerial vehicles to obtain cooperative control data, and performing real-time calculation processing on the cooperative control data to obtain a multi-vehicle cooperative control instruction set. According to the invention, by establishing a distributed cooperative control mechanism, intelligent task allocation and anti-collision control during multi-machine cooperative operation are realized, and the efficiency and reliability of geological surveying and mapping are remarkably improved.

The invention provides a resource management method and system for a server operating system, and the method comprises the steps: carrying out the double-layer graph modeling of process and resource data in the server operating system, and obtaining a process competition graph layer and a resource dependence graph layer; utilizing a preset graph neural network to perform feature extraction on the node information of the process competition graph layer and the node information of the resource dependence graph layer to obtain a node feature vector containing a competition relation and a distribution state; according to the node feature vectors, deadlock probability calculation is carried out through a multi-layer perceptron, and a process priority adjustment scheme is obtained; according to the node feature vector and a process priority adjustment scheme, performing resource allocation strategy learning to obtain a first resource allocation scheme; according to the first resource allocation scheme, performing multi-level queue allocation on the process to obtain a second resource allocation scheme; and performing resource management on the server according to the second resource allocation scheme.

The invention discloses an electric power material supply management and control system and a method thereof. The electric power material supply management and control system comprises a master control center, a sub-control center, a material warehouse, a mobile control terminal and a data processing platform. The electric power material supply management and control method comprises the following steps: S1, material demand prediction; s2, intelligent scheduling; s3, performing real-time monitoring; s4, risk assessment and early warning; and S5, emergency response is carried out. The electric power material supply management and control system and the method thereof can improve the material supply efficiency, enhance the decision support capability, make the decision more accurate and efficient, help to optimize resource configuration, improve the overall operation efficiency, improve the transparency and visualization of a supply chain, enhance risk management, guarantee the safe and stable operation of a power grid, improve the emergency response speed, and improve the power supply efficiency. The resource allocation is optimized, the inventory cost is reduced, the fund utilization efficiency is improved, the optimal allocation of resources is realized, and the electric power material requirements of each region can be better met.

The embodiment of the invention provides an e-commerce operation behavior risk monitoring and early warning method and device, and is applied to the technical field of e-commerce risk early warning. The method comprises the following steps: acquiring risk assessment data of a to-be-monitored e-commerce operator; selecting a corresponding monitoring model according to the type of the risk assessment data; performing risk evaluation on the corresponding risk evaluation data by using the corresponding monitoring model; and generating risk monitoring early warning information according to the risk evaluation result. In this way, through establishing a bogus transaction, license information, user evaluation, commodity quality, protocol rules and self-operation business distinguishing multi-dimensional e-commerce operation behavior risk monitoring model and an early warning algorithm, each e-commerce platform is connected to obtain data required by the model, and analysis and early warning are performed by using the algorithm, so that automatic risk monitoring and early warning of e-commerce operation behaviors are realized.

The invention relates to the technical field of remote execution control, in particular to a blasting operation control system and method based on the Internet of Things, and the system comprises a vibration simulation analysis module, a risk level calculation module, an operation process planning module and a real-time abnormity response module. According to the method, shock wave propagation under various blasting conditions is simulated and analyzed, the safety distance is accurately predicted, blasting safety is improved, risk assessment and adjustment of project planning are achieved by integrating the geological structure, the climate condition and the building position, the sustainability and social responsibility of a project are enhanced, and the method is suitable for popularization and application. The operation condition of a target project is monitored in real time in combination with an Internet of Things sensor, blasting parameters are adjusted in real time or operation is stopped, accurate and dynamic operation control is provided, the personnel safety risk is effectively reduced, the operation efficiency is improved, the safety and economic benefits of blasting operation are enhanced, and the adverse effect on the environment is reduced.

The invention relates to the technical field of logistics management and control of cloud computing, and particularly discloses a remote logistics management method and system based on cloud big data management and control, and the method comprises the steps: obtaining multi-source data in a logistics transportation process in real time through a data collection module, including the information of road conditions, weather, warehouse states and the like; the real-time data and historical data are compared and analyzed through a data comparison and analysis module, and the optimal logistics transportation path is determined; monitoring the real-time data based on the selected path through a path risk assessment module, assessing the risk existing in the current logistics transportation, and dividing the risk into a high risk or a low risk according to the assessment result; different countermeasures are taken according to the risk levels, that is, the current transportation process continues to be executed for low-risk paths, and for high-risk paths, risk factors are analyzed, and alternative schemes are implemented; according to the invention, functions of real-time data acquisition, intelligent path planning and dynamic risk assessment are integrated, and efficient management of logistics transportation is realized.

The invention relates to the technical field of intelligent monitoring, regulation and control of welding process parameters, and particularly discloses an intelligent regulation and control method and system for resistance welding process parameters of a junction box. Abnormal pressure applying links in the welding process are recognized, and the low-risk pressure applying process is extracted for welding; after welding is completed, the strength and size indexes of welding spots are evaluated through welding spot quality detection, the welding spot quality deviation coefficient is calculated, and whether the welding quality reaches the standard or not is judged; and for the welding spots with unqualified welding quality, the system calculates the adjustment direction and range of the pressure parameters through an optimization algorithm, test welding is conducted according to the optimized parameters, and the improvement effect of the pressure applying process is verified in real time till the welding spot quality meets the preset standard.

The invention provides a chemical industry park risk management and control system based on identification analysis, and belongs to the technical field of industrial control, and the system comprises a sensor monitoring module which monitors the park environment and park equipment based on sensors, and reports the data to an Internet of Things gateway corresponding to each sensor; the identification coding module is used for carrying out identification coding on park equipment which needs to be incorporated into an identification system in the park; the Internet of Things gateway module is used for querying the identification codes of the sensors, processing the sensor data and then forwarding the processed sensor data to the park platform; and the terminal processing module is used for analyzing and processing the received data, generating a corresponding risk analysis report and pushing report data to the terminal management platform. Uniform identity identification and analysis services are provided for all elements of park safety risk management and control, and the intelligence and safety of park management are improved.

The invention relates to the technical field of visual health management, in particular to a ciliary muscle adjustment training system and method based on eye movement tracking, and the method comprises the steps: collecting eye movement data of a fixation point track, an adjustment amplitude change rate and pupil response time in real time, and combining personalized parameters such as the age of a user, diopter and a training target; inputting a ciliary muscle training dynamic planning algorithm module, dynamically generating a stage scheme containing a training action type, a single-group duration, a stimulation interval and a focal length adjustment strategy, and adaptively adjusting the strategy according to real-time adjustment stability and a historical ability curve; through multi-dimensional matching analysis of a pre-stored standard eye movement template, a training specification score is calculated from the track goodness of fit, speed uniformity, amplitude standard-reaching rate and binocular coordination, visual fatigue parameters are obtained based on continuous training duration, pupil fluctuation and retina reflex change, a scoring result is dynamically corrected, and a visual fatigue evaluation result is obtained. Personalized, self-adaptive and quantitative safety control of the training process is realized.

The invention discloses an electric power operation safety risk identification method and system based on fine granularity detection, relates to the field of electric power operation safety management, and solves the problems that the area proportion of substances on the surface of equipment is difficult to obtain through image analysis software, and the parallax value of a target pixel point is difficult to obtain through an SAD algorithm. The method solves the technical problems that a model is not constructed by taking a convolutional neural network as a basic framework to carry out region judgment and double early warning is not carried out, and comprises the following steps: obtaining a corrosion area proportion and a wear area proportion through image analysis software to obtain an equipment comprehensive damage proportion; a high-resolution binocular camera is used for collecting video data of an electric power operation site; obtaining a parallax value of a target pixel point and a corresponding parallax map, and obtaining a depth value and a three-dimensional coordinate of a corresponding point after obtaining the parallax value through an SAD algorithm; and a model is constructed by taking the convolutional neural network as a basic framework to judge the vehicle type, and whether an early warning signal is triggered or not is judged according to two judgment conditions.

The invention discloses a congenital agomphosis gene detection panel and application thereof, and belongs to the technical field of gene detection. The congenital odontia gene detection panel comprises probes for detecting or hybridizing 104 congenital odontia related genes shown in a capture table 1, a sample DNA and the panel are subjected to solution / chip hybridization, sequencing is carried out after DNA enrichment of a target area is completed, harmful sites or genes related to congenital odontia are screened / predicted according to a determination result, and the congenital odontia gene detection panel is used for detecting or hybridizing the 104 congenital odontia related genes. The expansibility, speed and resolution required for evaluating the target gene can be met, multiple genes in multiple samples can be evaluated at the same time, time is saved, and cost is reduced. Besides, the panel sequencing is high in coverage rate, strong in pertinence, high in detection sensitivity, higher in effectiveness, easier to manage and capable of more accurately, comprehensively and effectively detecting the related genes of the congenital odontoprisis and mutation conditions of the related genes, and accurate diagnosis and risk assessment of the congenital odontoprisis are achieved.

The invention discloses a regional target and environment dynamic sensing method for construction site video monitoring, and the method comprises the steps: carrying out the initial deployment of distributed cameras for a construction site, and carrying out the deployment optimization; collecting video data based on Kafka Broker cluster multithreading, collecting environmental parameters, and performing fusion based on Mahalanobis distance to obtain fused environmental parameters; performing multi-target detection on the collected video data, and analyzing behavior characteristics of the collected video data to obtain video target data; environment parameter feature representation is extracted from the fused environment parameters, multi-modal dynamic fusion is carried out on the environment parameter feature representation and video target data based on learnable parameters, and a comprehensive situation is analyzed through a situation awareness model to obtain real-time comprehensive situation data; and performing real-time risk assessment on personnel behaviors and environmental conditions in the construction site by adopting Bayesian network analysis based on the real-time comprehensive situation data, identifying risk events, and automatically generating alarm information. According to the invention, the intelligent level of construction site video monitoring is obviously improved.

The invention discloses a fund flow monitoring method and system based on cloud computing, and belongs to the technical field of internet consumption finance. The method comprises the following steps: acquiring network state data, fund flow data and cloud computing platform performance data; a network state evaluation value is obtained through comprehensive analysis according to the network state data, a cloud computing platform performance evaluation value is obtained through comprehensive analysis according to the cloud computing platform performance data, a fund abnormity evaluation value of each account is obtained through comprehensive analysis according to the fund flow data, and adjustment feedback is carried out according to the fund abnormity evaluation value of each account. Performing comprehensive analysis according to the network state evaluation value, the cloud computing platform performance evaluation value and the fund abnormity evaluation value of each account to obtain a cloud computing platform state evaluation value; and performing evaluation feedback according to the state evaluation value of the cloud computing platform. According to the invention, the independence of fund flow monitoring is enhanced, the guarantee of fund safety is improved, and the accuracy of fund flow monitoring is improved.

The invention provides a converter station personnel and vehicle positioning early warning method based on multi-platform data interaction, and relates to the technical field of positioning early warning, carrier phase information of a target positioning label is obtained through a Beidou satellite, the carrier phase information is analyzed through an RTK positioning model to obtain a three-dimensional space coordinate and a corresponding timestamp, and when a label association object is a vehicle part, the vehicle part is subjected to positioning early warning. Vehicle operation state matching is carried out according to the coordinates and the timestamp to obtain a vehicle operation state model, operation risk assessment is carried out according to the model to obtain a danger index, and when the danger index is larger than or equal to a danger index threshold value, a first alarm signal is generated according to the coordinates, the timestamp and the model, and visual display alarm is carried out on a display terminal. The technical problems that the positioning system is low in positioning precision and reliability, insufficient in real-time data processing and risk assessment early warning and limited in flexibility and expansibility in a complex scene are solved, and the effects of improving the positioning precision and the flexible processing capacity in a complex environment and achieving dynamic risk assessment and intelligent early warning are achieved.

The invention provides a combustible metal dust spontaneous combustion early warning system used in an industrial dust removal facility, relates to the technical field of industrial safety monitoring, and aims to comprehensively monitor multiple parameters such as temperature, carbon monoxide concentration, hydrogen concentration, smoke concentration and the like and perform intelligent analysis by using a deep learning technology. A deep neural network model based on spatio-temporal feature fusion is adopted in the deep learning module, dynamic motion information is extracted by introducing an optical flow method, intelligent analysis and risk prediction of field images are achieved, the system can capture tiny changes in the initial stage of spontaneous combustion in time, the spontaneous combustion risk is accurately pre-judged through multi-parameter correlation analysis, and the risk prediction efficiency is improved. And alarm signals of different levels are automatically triggered according to the risk levels, so that the accuracy and timeliness of early warning are remarkably improved, and the occurrence rate of spontaneous combustion accidents of combustible metal dust is effectively reduced.

The invention belongs to the technical field of e-commerce logistics management, and particularly relates to an e-commerce product storage full-process monitoring management method, which comprises the steps of synchronously acquiring multi-source heterogeneous monitoring data in real time through a sensing equipment network; performing space-time alignment preprocessing on the original data, and converting heterogeneous data into a uniform data format; semantic fusion is carried out on the standardized data, and a job behavior knowledge graph with a complete spatio-temporal context is constructed; inputting the atlas into a pre-training hybrid model, and detecting an inventory abnormal event; and generating a strategy instruction in combination with the real-time order and resource data, carrying out joint analysis on an operator, and outputting a compliance score and a specific violation operation type. Through adoption of the technical scheme, complete reconstruction of the context of the operation behavior can be realized, the anomaly detection accuracy is remarkably improved, the abnormal event response delay is shortened, and the inventory loss rate caused by human errors is reduced.

The invention discloses a risk early warning model construction method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a target image; detecting the target image, and determining a target object and a moving speed of the target object; identifying the posture of the target object to obtain a target posture; identifying the safety measure wearing condition of the target object to obtain a wearing result; and according to the moving speed, the target attitude and the wearing result of the target object, constructing an anti-high-fall safety risk early warning model. According to the technical scheme, automatic tracking and monitoring and safety risk pre-judgment of safety protection measure implementation conditions of the high-altitude operation personnel are realized.

The invention relates to the technical field of artificial intelligence, and discloses a contract multi-return prediction method and device, equipment and a medium, and the method comprises the steps: obtaining a contract A party type, a contract amount of a to-be-predicted contract, an appointed return number, each appointed return amount and each appointed return time; acquiring historical refunding data of the historical contract; training a contract return classification prediction model according to the historical return data; and inputting the contract A party type, the contract amount, the agreed money return number, each agreed money return amount and each agreed money return time corresponding to the to-be-predicted contract into the contract money return classification prediction model to obtain the money return risk category and the money return default probability of the contract A party customer, thereby realizing money return risk management and default prediction of different customer types. Fine management risk prediction can be conveniently carried out on the return money, corresponding coping strategies can be conveniently adopted for different return money risks, and the occurrence probability of bad debts and dull debts is reduced.

The application provides a positive plate preparation method based on electrolyte moisture control and a battery. The core rule that "moisture of solid-state electrolyte wet powder is a key factor affecting the moisture of the positive plate" is first determined, and the long-standing error cognition in the industry that "controlling the total moisture of the slurry is enough" is corrected, providing a scientific theoretical basis for moisture control. By detecting three indexes before the slurry is used, the moisture of the positive plate can be accurately predicted whether it will exceed the standard, and the risk of scrapping the whole batch of positive plates is fundamentally avoided. By strictly controlling the moisture of the positive plate, the side reactions in the battery are effectively reduced, the capacity retention rate, rate performance and cycle life of the battery are improved, and the safety hazards such as swelling and thermal runaway caused by excessive moisture are reduced.

The invention discloses a wire tape production equipment remote monitoring and fault early warning system based on the Internet of Things, and relates to the technical field of equipment fault early warning, and the system comprises the steps: obtaining the operation state data of an industrial equipment part, the operation state data comprises vibration amplitude data and temperature change data, processing the running state data by adopting a time sequence analysis method so as to identify a performance degradation starting point and generate a performance degradation starting point identifier; acquiring sensor position information and determining a signal monitoring area through the performance degradation starting point identifier, and generating a coverage map identifier; according to the boundary of the signal monitoring area, extracting temperature change data and vibration amplitude data from the operation state data, and constructing a performance evolution law model by adopting clustering analysis and interpolation fitting; according to the invention, by constructing a spatial coverage modeling and performance evolution analysis mechanism based on multi-source state data, performance degradation identification, coverage area perception and fault risk early warning in the remote monitoring process of industrial equipment are realized.

The invention relates to the technical field of power grid operation, and provides a power grid operation safety distance accurate sensing device and method in a non-fixed scene, and the device comprises a hardware device and terminal software which is in communication connection with the hardware device. The hardware device is integrated and deployed on a movable support or a holder, and comprises a laser radar sensor which is used for rapidly scanning a power grid operation scene and obtaining three-dimensional point cloud data including three-dimensional coordinates, laser intensity and echo information; the high-definition visible light camera is approximately parallel to the optical axis of the laser radar sensor and is used for collecting real-time video images of the operation scene; an AI visual analysis module and a point cloud processing unit are arranged in the embedded processor; and the terminal software is deployed on the edge computing equipment or the background server and is used for receiving and processing the data from the hardware device. According to the invention, the safe distance can be accurately sensed.

The invention provides a battery system state evaluation and early warning method based on multi-source information fusion, and the method comprises the steps: synchronously collecting the multi-source state data of a battery system, which at least comprises a real-time synchronization parameter group for cooperatively evaluating the mechanical integrity and environment interaction risk, the real-time synchronization parameter group comprises vibration acceleration data, organic volatile matter concentration data, humidity data and corrosion rate data; processing the data of the real-time synchronization parameter group, extracting combined feature parameters having an association relationship with each other to form a feature vector, the combined feature parameters at least comprising: a structure risk feature, a sealing risk feature, and a connection risk feature; and inputting the feature vectors into a preset multi-parameter fusion analysis model to analyze a linkage mode among the combined feature parameters, and synchronously generating and outputting a safety risk report based on an analysis result of the linkage mode, the safety risk report at least comprising a mechanical integrity risk index and an environment interaction risk index.