1195results about How to "Improve timeliness" patented technology

The invention discloses an industrial software cloud base intelligent operation and maintenance and fault self-healing method based on digital twinning, and relates to the technical field of digital twinning. According to the method, the hierarchical digital twinborn model is constructed and dynamically updated, and the operation state of the cloud base is accurately mapped and visually displayed in combination with independent visual modules of a physical layer, a logic layer and an application layer; the method comprises the following steps: calibrating a reference value in real time through a dynamic reference library, quickly identifying abnormity of each layer, generating graded early warning, improving abnormity response timeliness, constructing a dynamically updated fault knowledge graph based on historical fault data, integrating a multi-dimensional correlation analysis and confidence coefficient verification mechanism, positioning a unique fault reason, greatly shortening a fault diagnosis period, and improving fault diagnosis efficiency. And a strategy library is constructed based on a fault knowledge graph and historical self-healing cases, simulation verification is carried out on candidate strategies through a digital twin model, the effectiveness of the self-healing strategies is ensured, and then the self-healing strategies are issued and executed, so that intelligent and automatic fault repair is realized, and stable operation of the cloud base is ensured.

The invention relates to the technical field of numerical control machining and automatic control, in particular to a numerical control machining self-adaptive control system and method based on multi-source data, and the method comprises the steps that FPGA hardware synchronously collects main shaft current, servo current of each feed shaft, cutting vibration and grating ruler pulse, and time bases are aligned and packaged into a machining multi-dimensional state matrix; the multi-physical field error is decoupled through a three-layer cascade machine tool dynamics model, a tool nose contour deviation vector is solved, and servo lag and cutting force deformation factors are fused to construct a dynamic constraint boundary. When the deviation amplitude exceeds the limit, generating a speed feed-forward gain compensation instruction based on an inverse control model to perform instant suppression; when it is monitored that oscillation is eliminated and returns to a steady state, full-time-domain data stream interception logic is triggered, and a process optimization instruction is generated through backtracking. According to the method, the problems of nonlinear time-varying error decoupling and real-time compensation lag under multi-physics field coupling are solved, and the dynamic precision and process stability of numerical control machining under complex working conditions are remarkably improved.

The invention discloses a fruit and vegetable storage and fresh-keeping control management system and method based on full-period detection, and relates to the technical field of fruit and vegetable storage management, and the method comprises the following steps: dividing a fruit and vegetable storage period into an initial fruit and vegetable storage period, a stable storage period and a later fruit and vegetable storage period; setting a storage environment fluctuation detection mechanism for judging whether storage environment leakage occurs or not according to the environment fluctuation degree; setting a storage environment leakage risk analysis mechanism for dynamically adjusting the time interval of data detection; setting a fruit and vegetable storage and detection period adjusting mechanism for adaptively adjusting the period duration of a stable storage period and the time interval of storage environment detection; the timeliness and accuracy of abnormal fluctuation detection are improved, so that the damage of storage environment fluctuation to the physiological state of fruits and vegetables is reduced; the duration of the stable storage period and the time interval of storage environment detection are adaptively shortened, and the problem that the rotting rate of fruits and vegetables in the later period is too high due to the fact that a fixed storage period is selected is solved.

The embodiment of the invention discloses a battery cell liquid leakage early warning method and a related device, and the method is applied to a controller of an energy storage container, and comprises the steps: obtaining multi-source monitoring data in real time; performing multi-level early warning judgment according to the multi-source monitoring data; and generating a corresponding control instruction according to a result of the multi-level early warning judgment, the control instruction being used for giving an alarm to the operation and maintenance platform and executing a corresponding protection operation. Thus, through multi-source data fusion cross validation, the defect of a single monitoring technology is effectively overcome, the accuracy and timeliness of early warning of the battery cell leakage fault of the energy storage system are improved, active safety protection is executed in time, and the safety level of the energy storage system is improved.

The invention discloses an electric energy metering error evaluation method and system for a direct current charging pile, equipment and a medium, and the method comprises the steps: constructing an electric energy disturbance response function based on transient voltage and transient current, and effectively quantifying the transient metering deviation caused by measurement lag, voltage drop, current peak and other factors in the actual operation of the direct current charging pile. Error reconstruction modeling has higher dynamic adaptive capacity and physical rationality, the limitation that in the prior art, the influence of the high-variable-load working condition on metering cannot be reflected is broken through, and the accuracy and timeliness of direct-current charging pile error monitoring are greatly improved. Besides, a standard error is introduced to carry out fitting calibration on a reconstruction error and carry out energy conservation verification, thereby realizing verifiability on a physical level and controllability in engineering implementation, enhancing credibility and authority of an evaluation result, constructing a closed-loop analysis mechanism from error evaluation to metering correction and then to conservation verification, and improving reliability and reliability of the evaluation result. And the closeness and the adaptive capability of the overall error evaluation are improved.

The invention provides a non-stationary industrial process monitoring method and system. The method comprises an offline training stage: calculating a time Laplacian matrix and a space Laplacian matrix based on a historical data matrix; constructing an objective function of the stationary subspace analysis method, and adding a time constraint term of a time Laplacian matrix and a space constraint term of a space Laplacian matrix into the objective function; solving the objective function to obtain a stable projection matrix; calculating a stationary component and a monitoring index of each sample in the data matrix X in sequence; determining a control limit by using a kernel density estimation method; an online monitoring stage: based on the real-time operation data x, calculating a stationary component of the real-time operation data x and a corresponding real-time monitoring index according to the stationary projection matrix, and if the real-time monitoring index is greater than a control limit, judging that the operation of the non-stationary process has a fault; the monitoring accuracy can be improved.

The invention discloses a digital twinning long-span bridge earthquake wind vibration and vibration reduction and isolation system and method, the system takes a high-fidelity digital model for constructing a long-span bridge as a basis, comprises full life cycle monitoring and management of the bridge under earthquake, wind vibration and vibration reduction and isolation conditions, and is based on a sky-air-ground-water integrated ubiquitous sensing module. In combination with artificial intelligence algorithms such as deep learning and a pulse neural network, real-time state evaluation of a bridge structure, material nonlinear analysis, geometric nonlinear analysis, multi-point excitation and pile-soil-structure interaction simulation, wind-induced fatigue damage prediction, earthquake vulnerability evaluation and closed-loop control scientific decision based on an artificial intelligence body are realized. According to the invention, system support and method guidance can be provided for large-span bridge anti-seismic and anti-wind design, intelligent monitoring, operation and maintenance management and vibration reduction and isolation system optimization.

The invention relates to the technical field of intelligent monitoring of power equipment, in particular to an intelligent circuit breaker state monitoring method and system. The method comprises the steps that unified time sequence modeling is carried out on multi-dimensional operation data of the circuit breaker, periodic behavior fragments with internal rhythm characteristics are extracted, state feature vectors based on historical behavior self-reference are constructed, and the state feature vectors are mapped to a continuous interpretable state space to achieve dynamic visual expression of the operation state; on the basis, the state deviation intensity and the change direction consistency are quantified, so that progressive identification and early warning from normal operation to an abnormal state are realized; and combined prediction is further carried out in combination with a state trajectory and an abnormal index, a risk quantification result and an active intervention strategy are generated, and model adaptive optimization is realized through closed-loop feedback. According to the invention, intelligent operation and maintenance closed loop from real-time monitoring and abnormal early warning to predictive maintenance is completed.

The invention discloses an electric power bid inviting and purchasing scale prediction method and system. The method comprises the following steps: firstly, acquiring historical electric power bid invitation purchase data, including unstructured text data and structured data, and establishing a cross-modal fusion model and a purchase scale prediction model; thirdly, dividing the historical data into a plurality of meta-learning task sets according to a preset electric power bid invitation purchasing scene, and performing meta-training on the model parameters by adopting a meta-learning framework to enable the model to obtain learning ability; in the real-time prediction application, the system monitors a preset condition, and when the condition is triggered, the newly acquired real-time data and the real purchase scale are used as small samples, and the purchase meta-model parameters are quickly updated online. According to the method, deep mining of multi-source heterogeneous data is realized, the adaptability and prediction precision of the model to a new bid inviting scene are enhanced, and powerful technical support is provided for accurate purchase decision making of the power industry.

The invention provides a fine configuration method and system based on a strategy mode, and the method comprises the steps: defining themes used for abstracting entrance traffic, order distribution and other events through a configuration system, and configuring a basic data source and a data processing rule for each theme, wherein the data processing rule supports a user to carry out code development implementation in a basic class of each service line according to service requirements; the system automatically stores theme data to middleware, matches a corresponding theme through a strategy mode after receiving a user request, performs secondary data processing according to a configuration rule and returns a result; the technical effects of flexible configuration mode, quasi-real-time index calculation, support of multi-source data access, high-concurrency scene adaptation and good expansibility in flow fine management are realized, and the service operation demand change can be quickly responded.

The invention discloses an exoskeleton robot abnormal working condition automatic response and risk avoiding decision-making method, and relates to the technical field of exoskeleton, and the method comprises the steps: deploying a plurality of types of sensors to synchronously collect environment data, human physiological data and exoskeleton operation data, and carrying out the preprocessing to remove interference; through fusion of abnormal harmonic detection and a finite-state machine, dangerous working conditions such as wind speed abrupt change are identified, and grades are judged; monitoring eye movement and heart rate variability data in real time, quantifying cognitive load and grading; a decision rule base is constructed, and joint locking and other adaptive risk avoiding decisions are generated in combination with working condition types, grades and cognitive loads; the controller drives the execution mechanism to execute a decision, continuously monitors data at a frequency, dynamically adjusts the decision or recovers a normal mode. According to the method, dangerous working conditions are accurately identified through multi-source data fusion, adaptive risk avoiding measures are automatically triggered, a power assisting strategy is dynamically adjusted in combination with cognitive loads, and operation flexibility and physical output are balanced.

The invention relates to the technical field of the intelligent power grid industry, in particular to a power distribution and utilization ring network fault intelligent troubleshooting method and system. According to the method, a looped network situation primitive is generated through multi-source heterogeneous data fusion. The primitives are input into synchronous compression transformation to extract transient fault initial features, and the transient fault initial features are mapped into fault propagation path features through a graph convolutional network considering topological structure constraints; then, inputting the features into a space-time correlation prediction model, integrating defect type judgment and trend pre-judgment features, and generating a fault diagnosis evidence set; and starting an investigation mechanism based on the evidence set to determine an interval to be investigated, and performing closed-loop verification by constructing a fusion judgment matrix based on multi-modal physical sensing information to realize physical space locking of a fault defect point. According to the method, deep learning diagnosis and multi-mode positioning are integrated, and the detection precision and the positioning efficiency of looped network defects and the identification capability of transient hidden faults are effectively improved.

The invention relates to the technical field of physiological signal processing, and discloses a millimeter wave radar multi-dimensional physiological feature decoupling heart failure early warning system and method, and the method comprises the steps: configuring a millimeter wave radar to transmit a dual-beam frequency modulation continuous wave signal, collecting human body reflection echoes, and generating a multi-channel original digital signal matrix; generating a chest region and acra region phase signal, an overall reflection signal amplitude sequence and an overall phase drift signal after spatial isolation enhancement through distance-Doppler combined processing; decoupling to obtain a beat-by-beat heartbeat interval sequence, a pulse wave transmission time sequence and a daily net body fluid variation sequence; obtaining a rule engine risk level and a machine learning early warning sign, and determining a final risk assessment level through fusion decision; synchronous high-precision collection and decoupling of non-contact multi-dimensional physiological parameters are achieved, and accuracy and specificity of heart failure decompensation early warning are improved through double-layer model fusion.

The invention discloses a communication base station lithium battery fault diagnosis method and device and electronic equipment, relates to the technical field of lithium battery monitoring and fault diagnosis, and is used for solving the problems that the communication base station lithium battery fault diagnosis is inaccurate, and the fault source is difficult to locate. According to the invention, multi-dimensional data acquisition, including voltage, current, temperature and electrochemical impedance spectroscopy, is carried out through the battery management unit; extracting static and dynamic inconsistency characteristics based on the collected data, and calculating a comprehensive inconsistency index; similarity matching is carried out by combining the impedance deviation spectrum with a fault feature library, and the fault type and grade are identified; comprehensively judging a fault source through fault monomer space positioning, electrical topological correlation analysis, impedance spectrum deep analysis and thermal behavior anomaly analysis; and the accuracy of fault diagnosis and root positioning is obviously improved.

The invention relates to the technical field of data acquisition, and discloses a low-power-consumption edge calculation outdoor energy storage power supply data acquisition system, which comprises a data analysis unit, a collaborative analysis unit, a probability analysis unit and an acquisition correction unit, and is characterized in that the data analysis unit, the collaborative analysis unit, the probability analysis unit and the acquisition correction unit are combined for analysis; intelligent dynamic regulation and control of outdoor energy storage power supply data acquisition are realized, the defect of data delay under low temperature and high load in a traditional fixed frequency acquisition mode is overcome, the data acquisition effect is ensured, low power consumption is ensured, meanwhile, the real-time recognition capability of hidden dangers such as over-discharge and internal resistance abnormity is improved, and the real-time recognition capability of the hidden dangers such as over-discharge and internal resistance abnormity is improved. And the cycle service life of the battery is effectively prolonged.

The invention discloses a geological disaster multi-scale environment control factor identification system based on machine learning, and relates to the technical field of geological disaster identification, and the system comprises a multi-scale environment factor collection module which obtains historical geological disaster data of a target area, extracts geological disaster multi-scale environment features, and constructs a geological disaster-environment factor correlation feature set; the quantitative evaluation module is used for establishing a geological disaster risk probability prediction model, quantitatively evaluating the marginal contribution degree of each environmental factor on different spatial scales, identifying the environmental factors dominating geological disaster development in multiple scales of a region, a drainage basin and a slope, and establishing a geological disaster risk probability curved surface of a target region and an environmental factor contribution spatial distribution diagram; and the prevention and control module introduces a human activity intensity index and an ecological system state index in a target area, establishes an environmental factor dynamic coupling risk propagation model and a real-time multi-scale risk transmission path, and generates a dynamic risk grading early warning map. According to the invention, the accuracy of geological disaster risk assessment is improved.

The invention discloses a data flow security control method and system based on dynamic trust evaluation, and belongs to the technical field of data security, and the method comprises the steps: obtaining a behavior feature sequence of a data request entity in a fixed time window; constructing a behavior map to identify a typical behavior mode; calculating a risk weight under the context by combining the network topology change, the data sensitivity and the upstream and downstream entity states; inputting the behavior map and the risk weight into a historical trust evolution model to generate a current trust value; matching an access control strategy according to the trust value and the data sensitivity; access operation is executed, and behaviors and results are fed back to the trust model for self-adaptive updating; by introducing a behavior modeling and feedback learning mechanism, dynamic regulation and control and real-time risk response of the data access permission are realized, and the security and intelligence in the cross-domain data circulation process are improved.

The invention relates to the technical field of antibacterial materials, and provides a light-enzyme-heat synergistic antibacterial nano composite material and a preparation method thereof.The preparation method comprises the steps that 1, a copper-based chalcogenide with a plasma resonance effect serves as a structural template, a transition metal oxide intermediate layer and a bismuth oxyhalide epitaxial layer are sequentially constructed on the surface of the copper-based chalcogenide with the plasma resonance effect through a step-by-step continuous solvothermal method, and the copper-based chalcogenide with the plasma resonance effect serves as the structural template; forming a core-shell heterojunction with Z-type energy band matching; (2) pre-assembling a natural enzyme and a monatomic nano-enzyme in a buffer system to form a composite catalytic unit with an electron transfer channel; and (3) by taking the core-shell heterojunction as a polymerization core, under the precise temperature control condition of 0-10 DEG C, initiating active free radical polymerization through an interface, constructing a thermal response polymer shell layer on the surface of the heterojunction, and encapsulating the composite catalytic unit in a functional microdomain of a polymer network in situ. The anti-bacterial fabric can achieve a better synergistic anti-bacterial effect.

The invention discloses a drainage basin source-sink supervision system based on space-space-ground three-dimensional perception. The system comprises a three-dimensional perception module, a water quality fluorescent fingerprint database module and a qualitative traceability analysis module. The three-dimensional perception module is used for collecting multi-source perception data of an air, sky and ground three-dimensional monitoring system in the drainage basin and monitoring water quality indexes of a monitoring section of the drainage basin; the water quality fluorescent fingerprint database module is used for storing water quality fluorescent fingerprints of pollution sources in the drainage basin; and the qualitative traceability analysis module is used for carrying out a water quality fluorescent fingerprint test, carrying out similarity comparison on the water quality fluorescent fingerprint obtained by the test and the water quality fluorescent fingerprint stored in the water quality fluorescent fingerprint database module, and determining a pollution path and a suspected pollution source. According to the system, the pollution path and the suspected pollution source can be rapidly determined under the sudden water pollution condition, and the defects that traditional manual tracing is slow in response, and the pollution source is difficult to find and lock in time are overcome.

The invention provides a security situation awareness method and system based on a three-dimensional GIS and a panoramic video, and the method comprises the steps: constructing a three-dimensional scene model of a monitoring region on a three-dimensional GIS platform, accessing a plurality of cameras and an environment sensor, and achieving the space-time alignment and coordinate unification of a video stream and multi-source sensing data; projecting a video frame to a three-dimensional scene based on internal and external parameters of a camera to generate a panoramic video picture, and performing detection and cross-camera tracking on pedestrians and vehicles by adopting a target detection model to obtain a target three-dimensional track; predicting a target position and local density change by using a time sequence prediction model, judging anomalies including congestion, retention and retrograde driving, and sending out an early warning; camera deployment and cooperative tracking are planned through a point location optimization algorithm, and monitoring blind areas are reduced; the system comprises a data acquisition module, a space-time alignment module, a three-dimensional mapping module, a target detection and tracking module, a situation analysis module, a point location planning module and a situation display module, and can improve the automation and precision of security situation awareness in a complex scene.

The invention provides a radioactive substance transportation monitoring system, and relates to the technical field of radioactive substance transportation safety monitoring. The radioactive substance transportation monitoring system comprises a transfer protection box and a monitoring unit integrated on the transfer protection box, and the monitoring unit is composed of a double-detector fusion module, a three-dimensional data linkage analysis module and an active emergency disposal module which are sequentially connected through a circuit. The double-detector fusion module comprises a GM counting tube and a scintillator detector which are arranged inside and outside the box so as to realize alpha, beta, gamma and neutron multi-nuclide coverage; the three-dimensional data linkage analysis module performs comprehensive judgment based on internal and external doses of the box, temperature and humidity and vibration data, and accurately identifies protection failure or article leakage; and the active emergency disposal module automatically starts gate closing and adsorption measures in an emergency. According to the system, the problems of monitoring blind areas, reduced protection performance, frequent false alarm, emergency passivity and the like are solved, and the transportation safety and the intelligent level are improved.

The application discloses a shooting type somatosensory game operation method, device and equipment and a computer readable storage medium. The shooting type somatosensory game operation method comprises the following steps: mapping operation keys on a touch display module of a matched somatosensory device after a preset shooting type somatosensory game is started, wherein the operation keys comprise shooting keys; obtaining somatosensory data and key operation data from the somatosensory device; adjusting the position of a shooting aiming point on a graphical user interface of the shooting type somatosensory game according to the somatosensory data; and if the operation data of the shooting keys is collected from the somatosensory device, performing a shooting operation based on the current aiming point position. The shooting type somatosensory game method has the advantages of low cost, wide applicability, strong game operation performance and the like.

The invention discloses an intelligent temperature control method and system for a high-silica glass fiber production line, and relates to the technical field of high-silica glass fiber production.The intelligent temperature control method comprises the following steps that firstly, temperature sensors arranged at all nodes in a furnace hearth of a smelting furnace collect temperature data in real time, and on the basis of a finite element analysis algorithm, the temperature data are obtained; a regional temperature field model is constructed in combination with temperature data, content data of trace impurities in the raw materials are acquired in real time by adopting online detection equipment, the viscosity change trend of the melt under the current raw materials can be analyzed, the current temperature threshold value is dynamically adjusted according to the viscosity change, and the limitation of a traditional fixed temperature threshold value is broken through; the temperature control can better adapt to real-time fluctuation of trace impurities in raw materials, and proper temperature control can be realized under different raw material conditions, so that the qualified rate of products is effectively improved, the loss rate of the raw materials is reduced, manual intervention is reduced, the timeliness and accuracy of temperature control are improved, and the production cost is reduced. And the production efficiency and the product quality are further improved.

The application discloses an intelligent scheduling control method for an automobile part automatic assembly line, and relates to the technical field of automatic control, which comprises the following steps: obtaining the task instruction response time length value and the node execution action state change value of each execution unit in a current scheduling period, and calculating the response delay value and the state fluctuation value of the corresponding node; generating a response efficiency value according to the response delay value and the state fluctuation value of each node, and generating a feedback control level label in combination with the node task attribute information; judging whether to perform compression processing based on the feedback control level label and the available idle time value of each node in the current scheduling period, and if the compression condition is met, encoding the response delay value and the state fluctuation value into a compressed feedback data frame; and completing the relay transmission of the compressed feedback data frame in a time period that meets the scheduling idle condition, so that the scheduling delay pressure control, task state accurate maintenance and instruction issuing sequence dynamic optimization of the automobile part automatic assembly line are realized.

The present application relates to the technical field of power distribution control, and particularly relates to a power intelligent dynamic distribution method for a skid steer loader based on multi-working condition identification. The method comprises the following steps: performing skid steer loader multi-modal operation characteristic analysis on power states of skid steer loader multi-source operation data to generate skid steer loader multi-modal operation characteristic data; performing skid steer loader working condition characteristic analysis on the skid steer loader multi-modal operation characteristic data to generate skid steer loader working condition characteristic data; performing power demand characteristic analysis on working condition dynamic change trends based on skid steer loader real-time operation data and the skid steer loader working condition characteristic data to generate working condition dynamic change trend power demand characteristic data; and performing skid steer loader power distribution and dynamic optimization processing based on the working condition dynamic change trend power demand characteristic data to generate skid steer loader power distribution dynamic optimization data. The present application realizes efficient and energy-saving intelligent distribution of power under multi-working conditions of a skid steer loader.

The invention discloses a node sub-health fault processing method and device, electronic equipment and a storage medium, which are applied to the technical field of distributed storage, and are used for solving the problems of poor timeliness of sub-health isolation and large influence on services in related technologies. Determining a sub-health copy corresponding to the business input and output operation; performing temporary isolation on the sub-health copy, and stopping write operation on the sub-health copy; in response to a permanent isolation instruction of the system management center, performing permanent isolation on a sub-health component corresponding to the sub-health copy; or, in response to an isolation cancelling instruction of the system management center, cancelling the temporary isolation of the sub-health copy. According to the method and the device, the corresponding sub-health copies can be temporarily isolated in time, the isolation timeliness is improved, the temporary isolation of the sub-health copies can be canceled according to requirements, and the problem of wrong isolation caused by too fast isolation is effectively avoided.

This application discloses a filter cartridge replacement method, apparatus, electronic device, and storage medium. The method includes: responding to a water pump operation command from a water purification device to obtain the current usage time of the current filter cartridge in the water purification device; determining the current service life of the current filter cartridge based on the target water quality correction information corresponding to the current filter cartridge, a preset filter cartridge attenuation rate, and the current usage time; the target water quality correction information is determined during the initial use of the current filter cartridge based on the first reference turbidity information of the water entering the water purification device within a preset time period, preset reference turbidity information, and a preset time period; and providing a replacement reminder message for the current filter cartridge if the current service life is not less than the preset service life. This method improves the accuracy of calculating the filter cartridge service life and the filter cartridge replacement reminder, enhances the timeliness of filter cartridge replacement, further reduces resource waste, and improves the user experience.

The invention discloses a residual gas treatment device of a hydrogen peroxide sterilizer, and relates to the technical field of gas treatment devices.The residual gas treatment device is technically characterized by comprising a treatment shell, a gas inlet pipe is fixedly connected to one side of the treatment shell, an exhaust pipe is fixedly connected to the upper end of the treatment shell, and a filtering assembly is arranged on one side of the gas inlet pipe; a plurality of catalyst plates distributed in a crossed mode are fixedly connected to the inner wall of the treatment shell, a preheating assembly is fixedly connected to the outer side wall of the treatment shell, the filtering assembly comprises a filtering box, a filtering net is installed on the inner wall of the frame, and a baffle is fixedly connected to one side of the filtering net; a cleaning assembly is arranged on the inner wall of the filter box and located on one side of the filter screen. Residual gas entering the treatment shell is preliminarily filtered through the filtering assembly, impurities such as solid particles in the gas can be effectively intercepted, pore channel blockage and active site loss caused by the fact that the impurities make contact with the catalyst plate are avoided, and it is ensured that the catalyst plate is always kept in an efficient catalysis state.

This invention relates to a method and system for enterprise production safety management based on process control, belonging to the field of safety management technology. The method includes: when a risk is identified based on operational data associated with safety equipment, executing: generating a scheduling plan, the scheduling plan including scheduling frequency, scheduling time period, and scheduling quantity; and controlling the scheduling system to execute the scheduling instructions corresponding to the scheduling plan. This invention classifies inspection items into safety equipment categories and obtains operational data and inspection information associated with safety equipment under each category; it identifies abnormal trends by analyzing the changing trends of inspection information, and determines abnormal trends as faults or alarm signals based on the comparison of deviation values ​​with preset thresholds. Finally, after confirming the effectiveness of the execution, it identifies them as risks, transforming subjective judgment relying on human experience into an automated risk identification process based on operational data and preset classification rules, improving the timeliness of risk identification.