70results about How to "Achieve perception" patented technology

The invention discloses a forest fire early recognition and early warning system and method based on multi-source sensing and artificial intelligence, and belongs to the technical field of forest fire intelligent monitoring. The system comprises a gridding multi-source sensing deployment module, a multi-modal time sequence data collaborative acquisition module, a cross-modal feature coupling and state diagnosis module, a multi-source heterogeneous feature adaptive fusion and fire behavior identification module and an intelligent grading early warning and dynamic resource collaborative scheduling module. The method comprises the steps of heterogeneous grid division and sensing deployment, multi-modal time series data collaborative acquisition, cross-modal feature coupling analysis, multi-channel attention fusion recognition, and graded early warning and dynamic scheduling. Through multi-source data collaborative sensing, cross-modal feature coupling analysis, adaptive fusion recognition and intelligent scheduling response, accurate and rapid intelligent early warning of the early stage of the forest fire is realized, and the intelligent level and emergency response efficiency of forest fire prevention and control are significantly improved.

The application discloses a kind of joint dynamic sliding window blind detection methods for non-cooperative unmanned aircraft downlink signal, comprising: receiving array observation data, and utilizing sliding window to divide array observation data into local array data block;Each local array data block is executed and includes: establishing the original hypothesis condition and alternative hypothesis condition of this local array data block;First likelihood function under the original hypothesis condition is constructed, and first noise power under the original hypothesis condition is estimated according to first likelihood function;Second likelihood function under the alternative hypothesis condition is constructed, and second noise power and graph signal under the alternative hypothesis condition are estimated according to second likelihood function;According to the first and second likelihood function, the expression of likelihood ratio detection statistic calculation formula is constructed, and first, second noise power and graph signal are substituted into the expression of likelihood ratio detection statistic calculation formula to calculate the likelihood ratio detection statistic, when the likelihood ratio detection statistic is greater than detection threshold, judge graph signal in corresponding sliding window.

The present application relates to the field of substation wiring, especially to a wiring system and method for skid-mounted electric control integrated substation, the method comprising: constructing time data sequences of cabinet temperature, cabinet temperature and humidity outside the substation, and comprehensive hidden features of the last time step, and passing them through a multivariate attention mechanism and a parallel LSTM encoding model to generate encoding features; passing the time data sequences through a water vapor mass conservation equation to generate physical prediction features; and passing the encoding features and the physical prediction features through a fusion mapping layer, a time attention mechanism and a control model to generate dehumidifier working power and heater working power. The present application realizes intelligent control of the dehumidifier and the heater in the anti-condensation wiring of the skid-mounted electric control integrated substation, and ensures appropriate heat dissipation and dehumidification effects.

The invention discloses an optimal scheduling method for a virtual power plant, and relates to the technical field of power regulation, and the method comprises the steps: obtaining multi-modal data of a detection region, calculating a regional load fluctuation index and a resource mobility index, and generating a dynamic feature data set; performing real-time clustering division on the power grid nodes through a clustering algorithm based on the dynamic feature data set to generate a dynamic sub-region set; if the sub-region feature fluctuation exceeds a boundary stability threshold value, triggering boundary redivision; according to the dynamic sub-region set, comparing a building power consumption predicted value with the available load of the charging pile in real time to calculate a demand difference, and generating an elastic scheduling instruction through multi-objective optimization; uncertain factors are monitored in real time, a risk index is calculated, and the priority and the resource allocation strategy of the elastic scheduling instruction are dynamically adjusted based on the risk index; and generating a risk avoidance scheduling scheme through a robust optimization algorithm.

The application provides a static pressure balance type underwater self-driven flexible touch pressure sensing finger, which comprises a touch pressure sensing unit, a hydraulic cylinder, a pressure transmission unit, a piezoelectric transducing unit, a transducing unit support, a platinum electrode and a base, the touch pressure sensing unit and the pressure transmission unit are connected with the hydraulic cylinder through bolts, and the oil cavity between the piston end faces is filled with hydraulic oil; the piezoelectric transducing unit is inserted into the stepped through hole in the cylinder wall of the transducing unit support; the pressure transmission unit and the base are connected with the transducing unit support through a flange and a threaded pair; the platinum electrode is fixed in the lead hole of the piezoelectric transducing unit, and the lead of the platinum electrode is led out from the lead hole of the base and connected to a pressure detection circuit. Based on the area effect of Pascal's principle and the solid-liquid interface double electric layer principle, the application can convert the tiny pressure detected by the finger tip into a larger elastic deformation of the flexible hose, so as to generate a high-amplitude ionic current signal and transmit it to the pressure detection circuit, and realize the sensing, amplification and measurement of the pressure signal.

The application provides a wireless charging system, method, external charging device and storage medium, including an implantable medical device and an external charging device for wireless energy transmission with the implantable medical device, the wireless charging system further includes a voice prompt module and a control module, the control module is configured to: control the external charging device to work in a scanning mode to obtain a charging efficiency local distribution map for a region where the implantable medical device is located; based on the charging efficiency local distribution map, determine at least one optimal charging target position, and generate user navigation instructions corresponding to the optimal charging target position; control the voice prompt module to broadcast the navigation instructions to guide the user to move the external charging device to the optimal charging target position, the scheme solves the problem that the user cannot determine the best charging position, thereby causing difficult alignment, low charging efficiency, and improves the alignment accuracy, energy transmission efficiency, operation safety and user operation convenience of wireless charging.

The hydropower station perimeter intrusion detection system comprises a radar monitoring module, a video monitoring module, a data processing module, a communication module, a control module, a power supply module, an early warning module, a data storage module and a compatible integration module. The radar monitoring module adopts laser radar equipment, and targets such as intruded foreign personnel in the surrounding environment are obtained by transmitting radar waves to the surrounding environment of the hydropower station and receiving and reflecting the radar waves. The accuracy and reliability of the hydropower station perimeter intrusion detection system are improved by adopting the Leiyu fusion technology and combining various technical means, so that the perimeter safety of the hydropower station is guaranteed. Meanwhile, the system has the advantages of real-time performance and reliability, the system can be compatibly integrated to a hydropower station electronic precaution system platform, new technical means and support are provided for hydropower station safety management, and the system has great significance in improving the safety level and accuracy of a hydropower station perimeter intrusion detection system.

The invention discloses a reinforcement learning intelligent traffic signal control method and system based on state-driven retrieval enhancement. The method comprises the steps of collecting real-time traffic flow state information of a traffic environment and constructing a global traffic state vector, retrieving a knowledge base to obtain related knowledge fragments and performing weighted aggregation to obtain a comprehensive knowledge representation vector, and constructing a fusion decision model fusing retrieval enhancement and a Markov decision mechanism, training convergence of the strategy network is completed after the enhanced state vector is generated, traffic signal control is achieved based on the converged strategy network, the generalization adaptation capacity, the anti-interference capacity and the operation stability of the signal control strategy to various traffic scenes are improved, random fluctuation of traffic flow and sudden traffic conditions can be effectively coped with, and the traffic signal control method is suitable for being popularized and applied. Through knowledge retrieval and prior knowledge fusion, traceability and interpretability of a control decision are realized, the efficiency of traffic signal control is effectively improved, the occurrence rate of intersection traffic delay and congestion is reduced, and reliable support is provided for intelligent collaborative management and control of a road network.

The invention belongs to the technical field of wind power equipment operation and maintenance, and discloses a wind power equipment fault early warning method based on edge calculation. The method comprises the following steps: acquiring vibration, temperature and current parameter operation data through a wind power equipment sensor, generating an original data sequence, and preprocessing to obtain a preliminary feature set; the time sequence gradient of each parameter is analyzed and calculated through a time sequence, and a parameter change rate sequence is obtained through optimization of a gradient descent algorithm. When the sequence exceeds a threshold value, a stable change trend is obtained through sliding window smoothing, and anomaly classification and risk grading are completed in combination with a historical working condition database and a support vector machine. And starting deep analysis according to an edge node resource state, constructing a fine prediction model to generate an early warning signal containing a fault probability and occurrence time, matching a risk level to activate a local response, and outputting an alarm. According to the invention, through edge localization processing and multi-algorithm cooperation, accurate pre-judgment of faults is realized, and the operation and maintenance response efficiency and early warning accuracy of wind power equipment are improved.

The embodiment of the invention provides an iridium satellite monitoring and early warning system and method, and belongs to the wireless communication signal monitoring technology. The system comprises a signal acquisition subsystem used for acquiring wireless signals in an iridium communication frequency band; the signal processing subsystem is connected with the signal acquisition subsystem and is used for performing iridium communication characteristic-oriented processing on the wireless signal so as to obtain analyzable effective signal data; the information analysis subsystem is connected with the signal processing subsystem and is used for analyzing the effective signal data and generating communication information for representing iridium communication activity; and the monitoring and early warning subsystem is connected with the information analysis subsystem and is used for executing monitoring analysis based on the communication information and outputting a corresponding monitoring result or early warning information. According to the scheme of the invention, acquisition, processing, analysis, monitoring and early warning of iridium communication signals are integrated in a unified system architecture, and continuous monitoring, structured analysis and timely early warning output of iridium communication activities are realized.

The invention belongs to the technical field of mobile communication, and particularly relates to a mobile communication terminal network switching method and system, and the method comprises the steps: collecting multi-dimensional network parameters and terminal state parameters; judging the working mode of the terminal and dynamically distributing parameter weights; pre-judging a source network state through a machine learning model, and screening a target network; establishing a pre-connection channel, and executing differentiated seamless switching; generating a feedback result, inhibiting ping-pong switching and optimizing the model; and multi-terminal collaborative switching is supported. The system comprises functional modules on a terminal side and an optional cloud auxiliary module. Core logic of multi-dimensional decision making, active pre-judgment and seamless switching language feedback optimization is adopted, switching precision, real-time performance and low power consumption are achieved, switching delay is smaller than or equal to 50 ms, the ping-pong switching occurrence rate is reduced by more than 80%, hardware does not need to be newly added, and the method is suitable for various mobile communication terminals and application scenes.

The invention relates to the technical field of creative mobile terminals, in particular to a trusted startup method and system for a creative mobile terminal, and the method comprises the steps: carrying out hierarchical verification and state monitoring on a hardware trust root module, security startup firmware and an operating system kernel, extracting multi-dimensional security features, and constructing a system security feature vector; secondly, establishing a trust chain component dependency graph, and generating a trusted startup sequence with the minimum security risk through constraint optimization; in combination with the trusted startup sequence and the feature vector, intelligently identifying startup threats and dividing risk levels to form a layered security protection strategy; and dynamically adjusting a protection strategy and allocating a resource weight, loading and dividing the trusted component into a high-security verification area and a basic verification area for block verification, and outputting a trusted startup execution result. According to the method, full-link trusted measurement, intelligent risk management and control and resource optimization scheduling in the starting process are realized, and the safety and starting efficiency of the credential terminal are remarkably improved.

The invention provides a conference room water cup water replenishing strategy optimization method and system based on reinforcement learning, and relates to the technical field of intelligent services, and the method comprises the steps: obtaining the liquid remaining amount feature and the spatial position feature of a container in a target space, and constructing a state representation vector to represent the incidence relation between the two features; predicting the liquid consumption trend of each container in the target time window through the time sequence analysis model to obtain a demand prediction result; inputting the state representation vector and the demand prediction result into a reinforcement learning strategy network, and based on the multi-container liquid residual amount distribution state and the prediction consumption trend, generating a decision sequence containing a supplement priority and a supplement opportunity by optimizing a long-term accumulation reward function; and dynamically adjusting the supplementary execution sequence of each container according to the supplementary priority in combination with the available supplementary resource constraint, and generating and executing a supplementary execution scheme. According to the invention, intelligent optimization of the water replenishing strategy is realized through reinforcement learning, and the resource utilization efficiency and the service quality are improved.

The application relates to a Transformer-based power multi-modal full-element sample fusion labeling method and system, which comprises the following steps: S1: obtaining multi-modal original data of a production operation site, and obtaining a preprocessed multi-modal data set through preprocessing; S2: performing multi-modal feature coding and Transformer representation modeling according to the preprocessed multi-modal data, and obtaining a fusion feature representation through cross-modal feature fusion and information completion; S3: acquiring a multi-modal semantic alignment label set through semantic consistency detection and a semantic mapping mechanism according to the fusion feature representation and a corresponding candidate label set; S4: constructing a label system structure tree according to the multi-modal semantic alignment label set; and S5: constructing an automatic labeling module to generate preliminary labeling based on the fusion feature representation and the label system structure tree, and directly inferring labels based on similar samples and category probabilities. The application realizes efficient automatic label generation and sample dynamic classification.

The embodiment of the present application provides a kind of light perception structure group, spectral imager and its use method.The light perception structure group includes hot light detector, including at least one feature unit structure, the feature unit structure includes multiple metal nano structures;And mobile device, including at least one moving component, the moving component is set to one-to-one group corresponding with the feature unit structure;The moving component is set to drive the feature unit structure to move, to change the relative position of the feature unit structure and the light source to be detected.The light perception structure group provided in the embodiment of the present application is set by moving component, and the feature unit structure is moved by moving component, to change the relative position of the feature unit structure and the light source to be detected, reduces the structural complexity of spectral imager, and realizes the perception of spectrum in ultra-wideband range.

The application provides a communication method and device and a computer readable storage medium. The communication method comprises the following steps: receiving configuration information of a first uplink signal; and receiving the first uplink signal, wherein the first uplink signal is obtained according to the configuration information of the first uplink signal, and the first uplink signal is used for sensing. The application provides a scheme for realizing sensing between terminal devices in the case where sidelink communication is not supported, and ensures the smooth development of sensing services.

This invention discloses a three-beam coaxial adaptive laser melting and forming method and apparatus, relating to the fields of metal additive manufacturing and laser welding technology. The method includes: activating a protective gas system; starting a high-precision real-time control system, acquiring multi-source signals from the molten pool region in real time through a monitoring module; transmitting the multi-source signals acquired by the monitoring module to a central processing unit to establish a mapping relationship model between monitoring features and process quality; based on the output of the mapping relationship model, real-time feedback control of laser power, wire feeding speed, and powder feeding rate; and repeating the adaptive control steps to achieve full-process adaptive closed-loop control of the forming path. This invention employs the aforementioned three-beam coaxial adaptive laser melting and forming method and apparatus, which has the advantages of good synergy and multiple control dimensions, enabling online real-time closed-loop control of the manufacturing process and solving the problems of uneven gap repair and forming defects and composition control in the preparation of functionally graded materials.

This disclosure provides a communication method and a communication apparatus. The communication method includes: determining a first message frame, wherein the first message frame includes parameters for setting WLAN sensing functions; and sending the first message frame. The technical solutions provided by the example embodiments of this disclosure can better realize WLAN sensing.

The present application relates to the technical field of battery management, in particular to a lithium battery pack intelligent management system based on multi-modal sensing. It comprises: a multi-modal sensing and fusion unit that collects battery pack operating environment and internal state data in real time, and fuses the operating environment and internal state data through weighted average to generate fused multi-modal data; an intelligent management master unit that predicts battery health state calculation based on cumulative gas production life mapping model, and predicts thermal runaway risk through a long short-term memory network model to generate control instructions, state information and alarm signals; a power execution unit that performs overcharge and overdischarge protection operations on the battery pack based on the control instructions. The present application collects multi-dimensional data such as voltage, current, temperature and gas concentration through a multi-modal sensing module, and extracts key features such as voltage consistency, temperature distribution and current fluctuation by combining statistical analysis and time-frequency transformation methods, thereby achieving comprehensive perception of the battery pack operating state.

This invention discloses a method and system for decoupling shear force and predicting and controlling the evolution of the microenvironment in the sacrococcygeal region, belonging to the field of medical diagnostic monitoring technology. It includes collecting multidimensional raw signals of pressure, shear force, and microenvironment in the sacrococcygeal region; obtaining real-time decoupling sensor data through cross-decoupling calculations; performing bidirectional driven inference analysis by combining data feature mapping and prior rule matching to obtain tissue damage risk prediction instructions and extract the intervention range and intensity, generating dynamic intervention instructions; controlling an active offsetting dynamic intervention actuator to perform physical intervention actions, collecting the reflected sensor change signals after execution, and generating tissue response data; optimizing the bidirectional driven inference analysis and updating the inference parameters. This invention employs a combination of multidimensional sensing decoupling, bidirectional driven prediction, and closed-loop active offsetting intervention, enabling accurate prediction and targeted active control of sacrococcygeal tissue damage risk, achieving personalized and adaptive risk management effects.

This application belongs to the field of multi-source information fusion for object recognition, localization, and navigation technology design. Specifically, it relates to an environmental mapping method based on multi-source sensor information fusion, comprising: Step 1, receiving image information, inertial information, and point cloud information; Step 2, inputting the image information and inertial navigation information into a visual odometry system, performing ORB feature extraction and feature tracking on the image information, and combining it with the inertial navigation information for pose estimation; Step 3, inputting the inertial navigation information and point cloud information into a laser odometry system, performing distortion correction, feature extraction, and pose estimation on the point cloud information based on the inertial navigation information, and then performing BA (Body Alignment) joint with the pose estimation output by the visual odometry system; using the BA joint result as the initial pose, performing point cloud registration optimization on the current point cloud and the accumulated point cloud map; using the registered and optimized pose information, overlaying the current point cloud and the previously accumulated point cloud to construct a point cloud map.

This application provides a vehicle control method, system, vehicle, and computer-readable storage medium, relating to the field of vehicle control technology. The vehicle control method includes: acquiring first state information of a target vehicle, driver behavior information, road environment information, and second state information of a target object, wherein the target object poses a collision risk to the target vehicle; determining relative motion parameters based on the first state information, road environment information, and second state information, wherein the relative motion parameters are used to describe the motion state of the target object relative to the target vehicle from multiple dimensions; determining a collision warning level based on the relative motion parameters, the first state information, and the driver behavior information; determining a vehicle control strategy based on the collision warning level, and performing vehicle control according to the vehicle control strategy. This application solves the technical problem in related technologies where collision warnings rely solely on single data points, resulting in low warning accuracy and poor protective effects.

The invention relates to the field of central heating, and discloses an energy-saving optimization method and system for a central heating system, sensors are arranged on a pipe network and a user side to collect data such as temperature, flow, pressure and thermal load in real time, a multivariable time sequence model is constructed after standardization processing, future heating demands and pipe network heat loss are predicted, and the energy-saving optimization method and system for the central heating system are provided. A control strategy including valve opening, pump frequency and heat exchange station outlet temperature is generated in combination with a multi-objective optimization algorithm, the system operates in the optimal energy-saving state through closed-loop control and online self-adaptive adjustment, meanwhile, the user comfort degree is considered, and a self-learning closed-loop mechanism of collection-prediction-optimization-feedback is achieved. The energy efficiency and the comfort degree of the central heating system are improved, the operation cost is reduced, and the system is suitable for a modern intelligent heating network.

The application relates to the technical field of intelligent water and fertilizer allocation, and discloses a mineral fertilizer intelligent allocation control method for water and fertilizer integration. The method comprises the following steps: collecting real-time data of soil humidity, pH value and nutrient concentration through a wireless sensor network, and synchronously acquiring precipitation, air temperature and light intensity data of a remote meteorological server; combining environmental data and a crop growth calendar to construct a soil nutrient consumption simulation model; after being corrected according to real-time monitoring data, generating a farmland nutrient deficiency distribution map by using a geographic statistics method; inputting the map into a fertilizer recommendation algorithm to calculate the application amount per unit area, and simultaneously analyzing the trends of the two types of data to identify abnormal fertilization; when the application amount exceeds the safe range, triggering multi-level alarms and adjusting the amount of fertilizer to be put according to the abnormal results. The method is suitable for the dynamic changes of soil and environment, and realizes intelligent and accurate fertilizer allocation.

The application relates to the field of shield model testing and monitoring, and particularly discloses a slurry balance shield model testing device, which comprises a soil bin, a counterforce frame fixed to one side of the soil bin, a moving cabin capable of reciprocating sliding between the soil bin and the counterforce frame, and a multi-source sensor system. The moving cabin comprises a slurry cabin and an air cushion cabin which are independent of each other. A cutter head is arranged at the front end of the slurry cabin. A centrifugal pump is connected in series to a slurry inlet pipeline and a slurry discharge pipeline of the slurry cabin. The multi-source sensor system comprises a torque sensor connected to the cutter head, a pressure sensor connected to the slurry cabin, a pump body acceleration sensor connected to the centrifugal pump, and a condenser microphone arranged in the soil bin and facing a tunneling face. The device can simulate the coupling process of shield tunneling and slurry circulation under laboratory conditions in a high simulation mode, and can realize the synchronous and real-time collection of key multi-source physical parameters of cabin pressure, cutter head load, pumping vibration and tunneling face acoustic signals, thereby providing technical support for in-depth research on the evolution mechanism of complex working conditions such as slurry balance shield lagging.

The invention relates to the technical field of semitrailers, in particular to a new energy driven particle powder material transportation semitrailer which comprises a data acquisition system, a data analysis system, a control system and a correction system. The data acquisition system acquires a parameter set and road condition information; the data analysis system judges a dominant abnormal state based on the pressure gradient and the electrification rate, and judges a recessive abnormal state based on the response amplitude; the control system adjusts torque distribution of a driving shaft or suspension damping based on the cooperation coefficient during dominant abnormity, and adjusts the suspension height and / or in-tank auxiliary vibration frequency based on the response damping ratio during implicit abnormity; and the correction system calculates the comprehensive change rate after the primary adjustment, and dynamically adjusts the preset comprehensive change rate in combination with the road condition information to judge whether the secondary adjustment is performed or not. According to the method, explicit and implicit abnormity accurate identification and secondary adjustment optimization in the particle powder material transportation process are achieved, and the transportation safety and the material fluidity are effectively improved.

The application provides a direct-current receiving end power grid transient voltage stability emergency control method and system, a medium, first constructs a GCN-DDPG fusion model, on the basis, introduces a double evaluation network mechanism and an evaluation network and action network asynchronous update strategy to improve the algorithm effect. Based on the GCN-DDPG fusion model, a transient voltage stability emergency control model is constructed, after the model training is completed in the dispatching center, it is issued to the safety control master station, the transient voltage stability emergency control model based on the GCN-DDPG fusion model after the training is completed will be according to the actual operation mode and fault information of the power grid, the emergency control strategy, namely the direct-current current control scheme is quantitatively calculated online, and is sent to the direct-current control and protection system for execution. The application realizes the rapid given of the emergency control strategy, improves the efficiency and reduces the strategy conservatism, and has important significance for the online generation and application of the emergency control strategy.

The invention discloses a multi-dimensional physical simulation method and system based on large construction equipment, and relates to the technical field of digital assembly type construction. The method comprises the following steps: acquiring dynamic parameters of large-scale construction equipment and physical attribute parameters of prefabricated parts, constructing a multi-dimensional physical simulation model based on MATLAB, and constructing a high-fidelity virtual construction environment containing a construction site model, an equipment model and a component model through IsaacSim; a simulation model is used for calculating a hoisting track and load data, and the hoisting track and the load data are transmitted to IsaacSim through ROS standardization to complete virtual simulation; real data are collected through a multi-mode sensor and transmitted to a digital twin platform through a cloud server, the platform fuses simulation and real data, and a deviation calculation and dynamic calibration algorithm is adopted to carry out comparative analysis to generate a safety assessment result and early warning information. The system correspondingly comprises a multi-dimensional physical modeling unit, a virtual simulation unit, a sensor unit, a cloud server unit, a communication transmission unit and a digital twin platform unit, precise simulation, real-time monitoring and intelligent early warning of hoisting operation are achieved, and support is provided for construction safety and fine management.

This invention belongs to the field of information security and relates to a security monitoring device based on a VPX architecture. It includes: a main processing module for processing and computing business data; a bridge module electrically connected to the main processing module for expanding auxiliary peripherals; a management and control module electrically connected to both the main processing module and the bridge module for system status monitoring and management; a network communication module electrically connected to the bridge module for providing four gigabit Ethernet interfaces to enable data interaction between systems; and a first level conversion module electrically connected to both the management and control module and the main processing module. This invention achieves end-to-end autonomous control from computation and control to security, enabling the perception and management of device health status, significantly improving fault diagnosis efficiency and system maintainability. It can operate stably in harsh environments such as national defense and aerospace, significantly improving system reliability and lifespan.

The invention particularly relates to a multi-mode intelligent protection battery changing cabinet. The multi-mode intelligent protection battery replacement cabinet comprises a cabinet body structure, a battery storage unit, a multi-mode monitoring module, an intelligent control module, an execution protection module, a communication module and a cloud management platform, and all the modules are connected in a circuit or wireless communication mode and work cooperatively to achieve safe operation and intelligent management and control of the battery replacement cabinet. According to the multi-mode intelligent protection battery changing cabinet, comprehensive perception of various safety risks in the running process of the battery changing cabinet is achieved, corresponding protection measures are triggered by conducting real-time analysis and intelligent decision making on multi-mode data, remote monitoring and operation and maintenance management of equipment are achieved through the Internet of Things, and the safety of the equipment is improved. Therefore, the problems of insufficient safety protection and low intelligent degree of the existing battery replacement cabinet are solved.