107 results about "Continuous evaluation" patented technology

The invention discloses a crop nitrogen fertilizer management system and method based on multi-agent reinforcement learning, and belongs to the technical field of intelligent agriculture. Comprising the following steps: collecting and preprocessing multi-source data of a target area, calibrating a crop growth-nitrogen cycle model based on the multi-source data, and constructing a dynamic simulation environment; constructing a nitrogen fertilizer application strategy model and a multi-target award function, and performing agent reinforcement learning training by adopting a centralized training-decentralized execution architecture; introducing a large language model, and updating a strategy network and / or a value network of each agent in a training process according to a reward adjustment signal and a decision constraint; when the index fluctuation ratio in the continuous evaluation period is smaller than a preset threshold value, it is judged that the nitrogen fertilizer application strategy model is converged, and an optimal nitrogen fertilizer application strategy model is obtained; and generating a nitrogen fertilizer application scheme based on the optimal nitrogen fertilizer application strategy model in combination with the real-time state data, and generating a natural language interpretation and risk assessment report based on a large language model.

Certain aspects of the disclosure pertain to machine learning evaluation and selection in a streaming environment. A machine learning model can generate inferences based on real time streaming data. A plurality of machine learning models can be available for a particular domain or task. Performance of the plurality of machine learning models can be continuously evaluated. Based on evaluation results, at least one of the plurality of machine learning models can be selected to provide output. For example, the streaming data can be routed to a selected machine learning model. Further, a poor-performing model, as determined based on evaluation results, can be fine-tuned based on real time data to improve performance.

The invention provides a resource configuration and optimal scheduling method for supervision resources, and the method comprises the steps: building a cable laying three-dimensional visual model, enabling the attributes of supervision personnel to be matched with a supervision region, generating an initial configuration scheme, and carrying out the correlation analysis of a historical electrical engineering quality problem and the attributes of the supervision personnel, thereby achieving the optimal scheduling of the supervision resources. And dynamic adjustment of supervision personnel configuration is realized. And the construction process is monitored in real time by using the Internet of Things technology, and early warning and positioning are carried out on abnormal conditions. Quality of key processes is judged through image analysis, and professional supervisors are matched for disposal according to severity of problems. And skill training is carried out in combination with a virtual reality technology, and a personnel scheduling strategy is optimized. And through big data analysis, continuous evaluation and dynamic adjustment of supervision personnel configuration are carried out to form closed-loop optimization. According to the method, the supervisor and the supervision area are highly matched, the supervisor can quickly and efficiently complete the supervision work, the construction efficiency of the cable laying project is effectively improved, and the quality of the cable laying project is improved.

The invention relates to the field of electric power measurement, and provides a voltage transformer error evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting the voltage phasor information of a measurement node in a preset time window, and obtaining the reference technical parameters of a voltage transformer corresponding to the measurement node; feature extraction based on a sliding window is carried out, sequence diagram structural features are obtained in combination with an adjacent matrix, and the adjacent matrix is constructed based on network topology and line electrical parameters of a power system; error estimation is carried out through a trained error estimation model based on the time sequence diagram structure features, an error estimation value is obtained, and the error estimation model is obtained by training a graph neural network model based on historical voltage phasor information and corresponding error labels; and performing error state evaluation based on the error estimation value. According to the method, the device, the electronic equipment and the storage medium provided by the invention, online continuous evaluation of the voltage transformer error is realized, and the limitations of long period and high cost of a traditional offline verification method are broken through.

Methods and systems for guiding interventional surgery are provided. The method includes receiving a plurality of intracardiac echocardiography (ICE) images of a target object acquired during a preset period, analyzing of the plurality of ICE images to obtain image analysis results, and generating an interventional surgical plan based on the image analysis results to guide the interventional surgery. This method conducts image analysis on ICE image before puncture to plan the interventional surgery, thus identifying the optimal puncture angle, depth, and timing for the interventional surgery. The method can help conduct real-time detection and tracking of the puncture process, identifying and mitigating potential risks during the surgical process to improve the success rate of the surgery. Furthermore, continuous assessment and management of possible complications can be carried out through postoperative evaluation to comprehensively reduce the risk of the interventional surgery and enhance its effectiveness and safety.

The invention discloses a rehabilitation nursing intelligent monitoring method and system based on image acquisition, and belongs to the technical field of rehabilitation nursing intelligent monitoring, and the method comprises the steps: collecting limb image data of a patient and a synchronous pain response value to generate a monitoring model; processing the current image data based on a monitoring model to evaluate and output a pain risk value; analyzing the fluctuation trend of a pain response curve formed based on the pain risk value to generate and output a nursing scheme; collecting subsequent image data in the process of executing the nursing scheme, monitoring a limb recovery state and outputting recovery feedback information to adjust the nursing scheme; according to the method, continuous assessment of pain risks is achieved by establishing quantitative association of visual features and pain responses, the nursing scheme is generated based on dynamic fluctuation trend analysis, a closed-loop management process is formed by recovering feedback information, and the efficiency and safety of rehabilitation nursing are improved.

The invention relates to the technical field of attitude evaluation, and discloses an attitude evaluation method and system based on motion perception. A posture evaluation method based on motion perception comprises the following steps: S1, collecting basic static parameters of a user's body, setting optical mark points on main joint points of the user's body, and arranging the user to execute a target motion in a predetermined dynamic capture space; s2, capturing a three-dimensional space coordinate sequence of the optical mark point in the process that the user executes the target action by using a plurality of optical cameras arranged in the dynamic capture space; and S3, constructing a personalized three-dimensional skeleton model according to the human body basic static parameters. According to the method, the deviation integral value of the actual motion track and the standard motion track in the whole motion period is calculated, so that the process continuous evaluation of the motion quality is realized.

The invention relates to the technical field of charging pile management, in particular to a charging pile safety monitoring method and system based on diversified analysis, and the system comprises a charging pile safety monitoring center, a potential state division unit, a safety monitoring analysis unit, a multi-dimensional evaluation unit, a persistence evaluation unit and a state early warning unit. According to the method, analysis is preliminarily carried out from the state angle of the charging gun head of the charging pile so as to carry out early warning management on the charging gun head which is not completely installed, so that the safety of the charging pile is improved, analysis is carried out from the fire early warning angle of the charging pile, and through the whole process design of image recognition-dynamic verification-multi-source cross verification, the safety of the charging pile is improved. Real-time and reliable technical guarantee is provided for safe operation of the charging pile, multi-dimensional health comprehensive evaluation analysis is carried out in an information progressive mode, comprehensive, objective and quantitative evaluation of the safe state of the charging pile is achieved, and powerful support is provided for safe operation and intelligent management of the charging pile.

The invention discloses an intelligent control method for equipment for chemical production, and relates to the technical field of intelligent control, and the method comprises the following steps: collecting process variable data, an automatic control instruction, an operator intervention behavior and equipment health state information; based on the collected process variable data, constructing a causal association relationship model between the operator intervention behavior and the equipment health state; identifying abnormal points by combining sliding time window prediction and a dynamic tolerance threshold value, and removing polluted data; constructing a multivariable causal diagram based on process variable deviation, and distinguishing control error paths caused by equipment degradation and intervention; deploying a multi-version control strategy model in the control system, and evaluating and screening an optimal strategy; an operator is guided to record the intervention purpose and mode on a human-computer interface; continuously evaluating the performance of the control strategy, and triggering a model retraining and replacing mechanism; the control logic is always close to the real state.

The invention provides a game big data real-time processing method and system, and belongs to the technical field of computer software, and the method comprises the steps: obtaining operation comparison data of new and old version functions according to a version switching preparation state, and verifying the output consistency of a data cleaning conversion link to obtain a verification passing signal; a data flow is gradually switched to a new version function by adopting a progressive flow migration mode according to the unloading process of the old version function through the verification pass signal, and the flow migration completion state is determined; through operation data monitored in real time, the stability of a new version function is continuously evaluated, dynamic adjustment details are stored by adopting a log recording module, and a complete adjustment record is obtained; and according to the complete adjustment record, constructing a prediction model to analyze a data flow interruption risk, and determining an optimized adjustment strategy.

The invention discloses a subjective evaluation method for the sound quality of in-vehicle noise under all working conditions, and the method comprises the steps: constructing an in-vehicle noise sample database under all working conditions, and the database comprises a steady-state noise sample and an unsteady-state noise sample; evaluating the steady-state noise sample to obtain a first evaluation result, and mapping the first evaluation result to a continuous evaluation interval to generate a first evaluation result curve; evaluating the unsteady noise sample to obtain a time-varying second evaluation result which takes the first evaluation result as an initial reference to generate a second evaluation result curve; and performing dynamic correlation analysis on the first evaluation result curve and the second evaluation result curve, and establishing a corresponding relation between the subjective evaluation results of the steady-state noise and the non-steady-state noise by calculating an optimal matching path of the two curves. According to the method, the integrity and systematic coverage of an in-vehicle noise sound quality evaluation system are realized, and a unified data basis is provided for sound quality analysis under all working conditions.

This invention discloses a robot visual servo control method based on depth point clouds, belonging to the field of intelligent control technology. The method includes: statistically analyzing the local point density, depth continuity, occlusion ratio, and missing trend of a target point cloud, and attaching these parameters to the corresponding point cloud region to form a target point cloud package; constructing a set of point cloud confidence servo error output terms based on a viewpoint maintenance index, and performing dynamic weighting in conjunction with local point cloud region quality markers to generate point cloud confidence servo error data and active viewpoint adjustment commands; performing robot visual servo control based on the currently executed control package, and outputting the target alignment state and end effector positioning state when the point cloud confidence servo error data meets preset joint discrimination conditions. This invention, by generating viewpoint maintenance indices, continuously evaluates the coverage changes, boundary changes, and center position changes of the target object region in the current field of view, achieving a stable representation of continuous observation maintenance.

The invention relates to the technical field of intelligent energy control, in particular to a central air conditioner optimizing energy-saving regulation and control method based on an artificial intelligence algorithm, which comprises the following steps of: acquiring operation parameters of central air conditioner parts and indoor and outdoor environment data, obtaining a standard data set through abnormal value cleaning and normalization processing, and storing the standard data set into a database; the method comprises the following steps: extracting characteristic variables influencing the energy efficiency of an air conditioner, constructing an energy-saving regulation and control model through a neural network algorithm, capturing a nonlinear relationship between operation parameters and environmental conditions, taking equipment safety as a constraint condition, applying the parameters to the energy-saving regulation and control model in real time, establishing a feedback mechanism to continuously monitor the operation state, and optimizing the air conditioner operation control model. And a continuously improved air conditioner energy-saving regulation and control scheme is obtained. The method solves the problems that traditional central air conditioner regulation and control cannot accurately capture the non-linear relation between operation parameters and the environment, a real-time feedback dynamic adjustment mechanism is lacked, and the energy-saving effect cannot be continuously evaluated and optimized, so that energy saving is insufficient, and operation is unstable.

The present application relates to oil and gas geology evaluation technical field, specifically to a kind of construction method of resistivity model for evaluating the adsorbed oil content of shale oil reservoir.The method includes: based on effective medium symmetric conduction theory, establish the shale adsorbed oil resistivity model consisting of rock skeleton, adsorbed oil, free oil, free water and clay bound water five components;According to well logging data, determine the mineral skeleton content, argillaceous content and porosity in the model;Using the whole rock XRD of core sample, porosity determination and rock pyrolysis experimental data, the model geometric parameters are determined by using Powell optimization algorithm;Finally, the adsorbed oil component content is solved according to dichotomy iteration algorithm and converted into adsorbed oil mass.The present application overcomes the problem that traditional step pyrolysis experiment is limited by core sample and is difficult to realize continuous evaluation of whole well section, realizes continuous and quantitative evaluation of the adsorbed oil content of shale oil reservoir, and provides technical support for the evaluation of adsorbed oil content of shale oil reservoir.

The application discloses an airport clearance protection zone low, slow and small aircraft risk assessment method and system, belongs to the technical field of risk assessment of an airport clearance protection zone, and comprises the following steps: acquiring and analyzing airport clearance protection zone data, aircraft data, low, slow and small aircraft data and external environment data; establishing a static comprehensive risk assessment coordinate system; comparing the low, slow and small aircraft data with the static comprehensive risk assessment coordinate system to obtain a static comprehensive risk; on the basis of the static comprehensive risk result, a plurality of dynamic risk correction results are obtained, and a final low, slow and small aircraft risk assessment result is further obtained; and according to the low, slow and small aircraft data and the airport clearance protection zone data, a risk upgrade and downgrade trend result is obtained. The application realizes the functions of continuously assessing the risk of low, slow and small aircraft and warning the risk upgrade and downgrade, and has important significance for risk assessment and management of an airport clearance protection zone.

The invention provides a high-density storage article real-time positioning system and method based on 2.4 G wireless communication, and relates to the technical field of wireless communication.The system comprises a sensing layer, the sensing layer transmits collected wireless environment information to an active decision-making layer, the active decision-making layer analyzes the received information, generates a coping strategy and then transmits the coping strategy to an execution layer, and the execution layer sends the coping strategy to a server; according to the method, a'sensing-decision-execution-optimization 'closed loop is constructed, conversion from passive response to active prevention is realized aiming at 2.4 G frequency band interference, the system can prospectively detect and identify an interference source, an avoidance strategy is generated through intelligent decision before the interference takes effect, precision is synchronized, and communication parameters are seamlessly switched in the whole network; and meanwhile, the system has a self-learning capability, and the accuracy and robustness of interference resistance are continuously improved by continuously evaluating the effect and performing feedback optimization, so that the continuity and stability of a positioning signal and the overall efficiency of the system are ensured in a complex electromagnetic environment.

The application provides an experimental intelligent system self-evolution and knowledge sedimentation method and system based on experimental process data, and relates to the technical field of artificial intelligence. The method comprises the following steps: obtaining experimental process data generated in an experimental execution process; extracting experimental knowledge such as a successful path, a failure path and a key parameter combination based on the experimental process data; evaluating the experimental knowledge and generating corresponding labels, weights and confidence; storing the evaluated experimental knowledge as a structured knowledge unit containing a parameter set, an applicable condition and version information; updating a decision model or an experimental decision strategy based on the structured knowledge unit, and performing knowledge migration and reuse in a new experimental scenario. The application realizes the structured sedimentation, version management, continuous evaluation and update and cross-scene migration and reuse of experimental knowledge, so that the experimental intelligent system has the ability of continuous self-evolution based on experimental process data.

The invention discloses a robot pursuit game method based on a retrieval enhancement driving large language model. Firstly, a system model is established for a multi-agent pursuit game, and four core components of retrieval enhancement generation, a reward designer, a reward analyzer and evolution iteration are defined. Then, a large-scale language model is introduced to serve as a core engine of reward design and analysis, and a vectorization knowledge base containing domain expert knowledge is constructed; then, a mechanism for autonomously generating an initial reward function is provided; and finally, proposing a reinforcement learning framework which iteratively optimizes the weight, the structure and the component of the reward function by continuously evaluating the interaction performance of the intelligent agent in the environment and generating a feedback instruction until the reward function is converged to the optimal form. According to the method, a complex collaborative strategy can be autonomously found and designed, the learning efficiency and the final performance of the agents in various pursuit scenes are remarkably improved, and finally efficient and robust multi-agent collaborative pursuit is achieved.

The invention relates to the technical field of marine environment forecasting, in particular to a marine environment accurate forecasting method and system based on multi-scene dynamic matching, and the method comprises the steps: firstly constructing a dynamically updated scene feature library through unsupervised learning, and training a hierarchical management sub-model for each scene to balance the precision and efficiency; during forecasting, through a real-time scene identification and fuzzy matching mechanism, a corresponding model is called, and real-time data is used for dynamic correction and rapid learning; and finally, outputting a forecasting result with confidence, forming closed-loop optimization through continuous evaluation, and realizing precision and adaptive evolution of marine environment forecasting. According to the invention, the accuracy of marine environment accurate forecasting based on multi-scene dynamic matching is improved.

The present invention relates to a method and system for automatically sanctioning users in an online community based on risk scores derived from user-generated content and behavior data. The system includes a machine learning risk score engine that processes segmented sessions of user activity, assigning risk scores according to predefined violation categories.These risk scores are aggregated and compared to configurable thresholds specific to each violation category. If the aggregated risk score exceeds the threshold, the system automatically triggers a sanction, such as a warning, muting, temporary suspension, or permanent ban. The system continuously monitors user activity, recalculating risk scores with new data, ensuring ongoing evaluation and appropriate sanctioning. The invention offers flexibility through customizable thresholds and violation categories, enhancing the effectiveness and precision of automated moderation in online communities.

An object of a system according to an embodiment is to continuously evaluate a health condition of an elderly person or a person to be cared for and provide an optimal care plan.SOLUTION: A system includes a data collection part, an analysis part, a care plan generation part, and a provision part. The AI collector continuously collects date of the elderly person or the person to be cared for by utilizing the generated date. The analysis unit analyzes the data collected by the data collection unit and evaluates a health condition of the elderly person or the person to be cared for. The care plan generator automatically generates an optimum care plan based on the health condition evaluated by the analyzer. The provider provides the care plan generated by the care plan generator to the caregiver or the concerned party.SELECTED DRAWING: Figure 1

The invention belongs to the technical field of psychological activities, and particularly relates to a psychology-based team role intelligent matching system, which comprises the following steps: S1, carrying out multi-dimensional role capability modeling; s2, real-time team state sensing; s3, psychological and behavior prediction analysis; s4, dynamic role demand identification; s5, performing multi-target intelligent matching optimization; s6, adaptive role adjustment and recommendation; s7, generating and executing an intervention strategy; and S8, continuous evaluation and model evolution. According to the scheme, a dynamic ability portrait is generated for each member by constructing a multi-dimensional role ability model and integrating real-time behavior data, accurate description of personal ability is realized, potential problems can be recognized in advance by utilizing machine learning and natural language processing technologies, role ability vectors are automatically generated based on project requirements, and the user experience is improved. And intelligent matching of personnel and roles is realized through a multi-objective optimization model, so that the team cooperation efficiency and the task execution quality are effectively improved.

The present invention discloses a system for managing treatment of behavioral health conditions including substance use disorder (SUD). The system comprises a computing device in communication with a database and user devices via a network. The computing device provides a questionnaire comprising a plurality of categories. Each category comprises a set of questions to assess progress of the client. The computing device enables a clinician to input a score for each question based on analyzing response of client to each question. The computing device is configured to apply predefined scoring guidelines for the input score and generate the score data including the category score data for each category and the recovery score data for the questionnaire. The computing device enables the clinician to continuously evaluate and track the progress of the client, and provide a treatment plan based on current score data and progress data of the client.

The present application belongs to the field of hydrological cycle monitoring, and discloses a kind of wetland recovery near-surface water cycle evaluation method based on dew amount monitoring, which comprises the following steps: simultaneously arranging three types of sample plots in the target study area, constructing a continuous evaluation system based on the degradation state as the benchmark and the natural state as the target; dew amount in situ monitoring is carried out for each sample plot, and after eliminating invalid data, the annual dew amount is counted; based on the monitoring data for many years, the dew amount threshold value of degraded wetland and the dew amount benchmark value of natural wetland are determined; the dew recovery index of the wetland to be evaluated is calculated; according to the numerical interval of dew recovery index, combined with annual dew amount, the recovery grade is divided; according to the recovery grade and dew recovery index, the recovery degree of near-surface water cycle is determined. The present application takes dew amount as the core evaluation index, accurately reflects the near-surface water cycle dynamics in real time, overcomes the lag of traditional indicators, can be monitored in a large area for a long time and has low cost, and has good practical value and application prospect.

The invention relates to the technical field of tunnel engineering intelligent perception and surrounding rock grading, and discloses a mountain tunnel surrounding rock grade automatic identification method, system and device based on image-point cloud fusion, and a storage medium. The automatic mountain tunnel surrounding rock grade identification method based on image-point cloud fusion comprises the following steps of multi-source surrounding rock perception data acquisition, surrounding rock image apparent semantic feature extraction, surrounding rock point cloud geometric state feature extraction, surrounding rock evaluation unit construction and image-point cloud feature fusion, surrounding rock engineering state comprehensive evaluation and grade identification. And surrounding rock grade zoning and result output. According to the invention, through image-point cloud fusion, a continuous evaluation unit is constructed along the axial direction of the tunnel, and the surrounding rock grade is automatically identified and output; according to the method, the objectivity and consistency of surrounding rock grade identification are improved, continuous sensing and grading of surrounding rock states are realized, the real-time performance of acquiring surrounding rock grade information is improved, deep fusion and engineering output of multi-source information are realized, and the method has good engineering applicability.

The invention relates to a vehicle health degree assessment method based on multi-feature data fusion, and belongs to the technical field of data mining and intelligent vehicle assessment. The method comprises the steps that operation characteristic data of multiple vehicles in the operation process are acquired, and each vehicle collects at least two types of operation characteristics; the single feature health degree corresponding to each operation feature is locally calculated at each vehicle client and uploaded to the server side, and the server side performs semantic consistency constraint processing on the single feature health degrees from the plurality of vehicle clients; fusing the processed health degrees of the single features, and then analyzing the health state of the vehicle; and in the continuous evaluation process of the vehicle health state, the server side updates the single-feature health degree of each operation feature by calculating the similarity between the single-feature health degree and the health degree semantic prototype. The stability, consistency and reliability of the multi-feature health assessment result are improved, and the method is suitable for multi-vehicle and large-scale application scenes.

The invention relates to the technical field of motion time sequence control, in particular to a multi-agent motion time sequence control method and system. Comprising a path sensing module, a spatial-temporal feature extraction module, a dynamic road right distribution module, an anomaly detection and recovery module, a distributed cooperative communication module, a real-time path updating module, a performance evaluation and optimization module and a central coordination module. The right of way is distributed according to the passing sequence to ensure that the intelligent agent orderly passes through the intersection. Meanwhile, motion abnormity can be monitored, a recovery mechanism can be triggered, local information sharing and negotiation are achieved, paths are dynamically adjusted according to road right changes, system performance is continuously evaluated and optimized, and all modules are planned as a whole to process global constraints. The problem that an existing multi-agent motion time sequence control system cannot efficiently cope with the motion speed difference of agents and dynamic obstacles while precise time synchronization is guaranteed is solved.

A software based application for assessing, processing, and remediating cyber-risk in real time may comprise, without limitation, a profiling component, an analytic component, an evaluation component, and a monitoring component which may, in conjunction therewith, operate to allow an organization to adaptively adjust an organization's network security to continuously improve and mature same. Such components may operate to: (1) determine an organization's operational baseline; (2) identify risks and hazards inherent therein; (3) generate, and verify the efficacy of, remedial controls to such risks and hazards; (4) document and audit such determinations; and (5) continually monitor the organization's network security. In such a manner, the network security architecture of an organization may be remediated according to threat scenario-based control efficacy and residual risk determinations according to the agnostic, risk-focused, and system-based approach disclosed herein.

The invention discloses a system, a method and a workflow for real-time monitoring of modern city infrastructures. A computer-implemented method; a computer software product; and a machine learning model based on the seismic data. The infrastructure comprises bridges, highway tunnels, airports, buildings and wind power towers. Monitoring is not limited to recorded seismic data. The disclosure also emphasizes the importance of monitoring various key infrastructures using seismic sensors, including security sites and manufacturing facilities, to enhance security and security capabilities, detect infrastructure health, and prevent unauthorized intrusion. In addition, the seismic sensor can also be used for indoor human activity monitoring. Bridge monitoring relates to continuous evaluation to ensure structural integrity and prolong service life; highway tunnel monitoring focuses on optimizing traffic conditions and monitoring structure images. The airport monitoring is beneficial to improving the runway safety, ensuring the optimal performance of the aviation infrastructure, reducing the potential risk to the greatest extent, and improving the overall safety and efficiency of aviation travel. By deploying the seismic sensor to monitor the infrastructure of the building, the potential risk of the building structure can be detected and positioned. Monitoring of a wind power generation tower is crucial to optimization of green energy production and guarantee of structural health of a fan. The security and manufacturing place monitoring aims to guarantee the security and security of facilities and prevent unauthorized invasion. By monitoring and analyzing seismic data of indoor human activities, different types of activities can be identified, and events such as walking and falling down can be detected. By using seismic sensors and processing seismic data in real time, these monitoring systems jointly improve the security, security, toughness and reliability of key infrastructures in different fields.

The invention relates to the technical field of intelligent monitoring, in particular to a behavior monitoring and early warning system for tourists in a zoo, which comprises a data acquisition layer, an edge calculation layer, a central processing layer and an application service layer. Through the multi-source data fusion and deep learning technology, the purposes of park dead-corner-free monitoring, multi-dimensional perception, accurate abnormal behavior recognition and accurate behavior context understanding can be achieved, and then the coverage and accuracy of safety management are greatly improved; a multi-level early warning mechanism from prompt to emergency can be provided, early warning information can be accurately distributed, differentiated intervention suggestions can be provided according to specific conditions, the intervention effect can be continuously evaluated, and accurate and effective safety management can be realized; the method can predict security requirements, dynamically adjust resource configuration, optimize patrol paths and recommend proper emergency plans, and can enable limited security resources to play the maximum utility, thereby improving the safety management efficiency.