1157results about How to "Avoid security issues" patented technology

The invention relates to the technical field of lithium battery charging and discharging management, and discloses a lithium battery charging and discharging detection and management method, device and equipment and a storage medium, and the method comprises the steps: carrying out the data collection of the charging and discharging process of a lithium battery pack, applying consistent Gaussian noise, and obtaining an electrode-level expansion data set and a battery-level expansion data set; performing feature mapping and feature mixing by minimizing the maximum average difference loss function to obtain a mapping feature vector; estimating the state of charge and the state of health of the battery through a fusion model of a recurrent neural network and a physical information hybrid neural network to obtain a state of charge estimated value and a state of health estimated value; and constructing a battery pack electric heating model of the lithium battery pack, and generating a corresponding charging and discharging control instruction sequence. Self-adaptive adjustment can be carried out according to the real-time state of the battery, potential safety hazards such as over-charging and over-discharging and out-of-control temperature are effectively avoided, and the use safety of the battery pack is remarkably improved.

The invention discloses a multi-modal knowledge question and answer retrieval method and system for a specific professional field, and belongs to the technical field of data processing. According to the method, the cosine similarity between the feature vectors is calculated in each mode through large model preprocessing to form the similarity matrix, and direct vector calculation of the problem and the knowledge base text in the professional field is realized by adopting a vectorization processing mode which is the same as that of the knowledge base text. And according to an input problem vector, through vectorization index retrieval in the professional domain knowledge base, selecting a plurality of text vectors closest to the problem vector from the professional domain knowledge base to obtain a plurality of pieces of knowledge related to the problem and the source of each piece of knowledge. According to the method, the knowledge question and answer reply accuracy of the target field is effectively improved, the knowledge question and answer reply integrity of the target field is guaranteed, basis support is realized, local deployment is supported, the information security problem is solved, and an intelligent application scene of knowledge question and answer retrieval is realized.

The invention relates to the technical field of fire early warning, in particular to an electrical fire early warning method, device and equipment based on leakage current temperature detection. The method comprises the following steps: acquiring information data of electrical equipment; performing electrical equipment power line position positioning on the electrical equipment information data to generate electrical equipment power line position information data; performing leakage current sensor data acquisition on the electrical equipment power line position information data to obtain standard electrical equipment leakage current monitoring data; and performing maximum fluctuation curve segment confirmation on the leakage current monitoring data of the standard electrical equipment to obtain a maximum fluctuation curve of the leakage current of the electrical equipment. According to the invention, by introducing leakage current fluctuation mode identification, accurate power line positioning, intelligent anomaly detection and heat map analysis, the accuracy, response speed and positioning capability of the electrical fire early warning system are improved.

The invention relates to the technical field of lithium batteries, and discloses a lithium battery life decline quantitative prediction and health state intelligent evaluation method, which comprises the following steps: S1, multi-source data acquisition: arranging strain gauge sensors at battery electrode plates and a shell to acquire stress-strain data, integrating a gas sensor in a battery module to acquire gas data, and calculating the data of the stress-strain data; battery data and environment data are acquired by a high-precision synchronous acquisition system through nanosecond clock signals and are processed and stored, and the data quality is monitored at the same time; s2, data preprocessing: generating an extended data set through data transformation and combination and GAN-based data, and processing abnormal data by using a method based on statistics and machine learning; according to the invention, through multi-source data fusion acquisition, a stress-strain sensor and a plurality of gas sensors are added to monitor the internal mechanical change and gas component concentration of the battery, so that the problem that the accuracy and practicability of the model are greatly reduced due to a traditional method based on an electrochemical model is improved.

The invention discloses an intelligent wireless charging power distribution method and system, and aims to improve the efficiency and safety of simultaneous charging of multiple devices, and the system design comprises a plurality of key modules: a device detection and recognition module automatically recognizes the charging devices and collects the power demands of the charging devices; the power demand analysis module calculates the power demand of each device according to the device electric quantity and the charging mode, and the power distribution control module dynamically adjusts the power output according to the priority, the distance and other factors; the real-time monitoring and feedback module ensures the stability and safety of the charging process, and the data recording and learning optimization module optimizes the charging strategy by analyzing historical data. Through cooperative work of the modules, the system can adjust the charging power in real time, optimize the charging efficiency, improve the user experience, and ensure the safety of the charging process. The system shows good adaptability in a multi-device environment, and important reference and application prospects are provided for development of a wireless charging technology.

The invention provides an ultrafast laser heat dissipation state real-time monitoring system based on cloud computing, and relates to the technical field of laser heat dissipation, and the system comprises a laser monitoring module which is used for transmitting ultrafast laser and is internally provided with a temperature sensor so as to monitor the working temperature of a laser in real time; the data acquisition module is used for being connected with the laser monitoring module, acquiring temperature data and key parameters, including laser power and working current, of the temperature sensor in real time, and uploading the temperature data and the key parameters to the cloud server; and the cloud analysis module is used for receiving and analyzing the temperature data and the key parameter data, identifying an abnormal temperature mode, predicting a potential overheating risk and calculating heat dissipation efficiency to obtain an analysis result. According to the invention, comprehensive, real-time and intelligent management of the heat dissipation state of the laser is realized, the operation stability and safety of the laser are effectively improved, the overheating risk is reduced, the heat dissipation efficiency is optimized, and remote monitoring and data analysis services are provided for users.

The invention relates to the technical field of charging power management of charging piles, and particularly discloses a charging power distribution method and device for a multi-gun charging pile, and the method comprises the steps: calculating the theoretical power of each charging gun through collecting the battery information, including the current electric quantity, the battery capacity, the rated charging power and the charging demand, of each vehicle in real time; the safety and the stability are comprehensively evaluated according to the dynamic change of the battery temperature and the load rate of the charging gun; according to the method, the charging power can be dynamically adjusted, the safety and the high efficiency of the charging process are ensured, and the problems of battery damage and unstable charging caused by improper power distribution are avoided; the invention further provides a real-time monitoring interface and an alarm mechanism, abnormal conditions can be found and processed in time, and it is ensured that the charging process is carried out smoothly.

The invention belongs to the field of time sequence anomaly detection, and discloses a time sequence anomaly detection method and system based on frequency enhanced convolution Transform, and the method comprises the steps: carrying out the sequence-level preprocessing of an input sequence, including normalization and block generation; encoding the input sequence by adopting a multi-head self-attention mechanism; extracting interrelationships in the sequence by a full convolution module having a plurality of stacked 1D convolution layers of different kernel sizes; s2 and S3 are repeated, and multi-layer coding is carried out on the sequence; converting the encoded embedding into a frequency signal by using a Fourier transform method; the KL divergence and the absolute error are respectively used for calculating the consistency loss of the time domain and the frequency domain, calculating the point-by-point anomaly score, and identifying whether the point is abnormal or not. Wide experiments on five common data sets show that the method is superior to an existing method in the aspect of abnormal recognition.

The invention relates to the technical field of cables, and discloses an intelligent cable grounding box temperature monitoring method comprising the following steps: obtaining material information of a cable grounding box; acquiring temperature and humidity data of the surrounding environment of the cable grounding box; according to the material performance of the three types of cable grounding boxes, temperature monitoring precision requirements required by different types of grounding boxes are determined; according to the environment type, in combination with a preset temperature monitoring scheme database, respectively matching temperature monitoring schemes meeting three types of cable grounding box temperature monitoring precision requirements; acquiring historical temperature monitoring data of different types of cable grounding boxes; establishing a temperature prediction model of the cable grounding box in combination with environment temperature and humidity and a grounding box material; and according to the optimized temperature monitoring scheme, continuously perfecting the temperature monitoring scheme and the monitoring threshold value through data accumulation and feedback. According to the invention, accurate monitoring and early warning can be realized for different types of grounding boxes in various environments, and the safe operation level of the cable grounding box is effectively improved.

The invention discloses a charging pile intelligent control method and system based on face recognition, and relates to the technical field of information security, and the method comprises the steps: collecting a user face image through a camera when a user gets close to a charging pile, and extracting a user face feature vector through a lightweight algorithm; matching the user face feature vector with a user face feature vector pre-stored in a local database, and verifying user identity information; based on the user identity information passing verification, the charging authority bound with the user identity information is distributed, meanwhile, the relation between the user and the vehicle is verified, and a charging scheme is dynamically generated in combination with user historical charging preference information and charging initial data; charging is started according to the charging scheme, and charging parameters are dynamically adjusted by monitoring real-time charging data in the charging process in real time; the face recognition technology, personalized charging scheme generation and real-time charging parameter dynamic adjustment are introduced, and the intelligent level and user experience of the charging pile system are remarkably improved.

The invention provides a tourism management market demand prediction method and system based on deep learning. The method comprises the steps of obtaining tourism related information corresponding to a preset tourism management platform; the tourism management platform is used for managing a plurality of preset scenic spots; performing sentiment analysis on the tourism related information based on a natural language processing technology to obtain positive sentiment information, negative sentiment information and tourist interested information; obtaining travel information corresponding to the travel related information; the travel information comprises multiple pieces of travel preference information and a travel group type corresponding to each piece of travel preference information; acquiring scenic spot access information of each preset scenic spot corresponding to the tourism related information; generating market demand information in a preset time period according to the positive emotion information, the negative emotion information, the tourist interested information, the travel preference information, the travel group type and the scenic spot access information; the market demand information at least comprises tourist number information, popular time information and tourist preference information of the preset scenic spot.

The invention relates to the technical field of photovoltaic modules, in particular to a photovoltaic module control method and device. The method comprises the following steps: obtaining an installation position according to design data of the photovoltaic module, carrying out illumination intensity change measurement, and further evaluating the luminous flux change of the photovoltaic module; on the basis of the luminous flux change data, calculating the generating capacity fluctuation of the photovoltaic module, further performing power quality attenuation evaluation, and extracting power grid transmission voltage fluctuation data; according to the power grid voltage fluctuation and power quality attenuation data, the battery overcharge condition and the battery life attenuation are evaluated, the battery capacity attenuation is further calculated, and the photovoltaic module system efficiency attenuation is evaluated; by analyzing system efficiency attenuation data, photovoltaic module degradation risk early warning is carried out, and risk early warning information is fed back to the terminal in real time; according to the invention, through early warning of power generation of the photovoltaic module, the deterioration prediction of the photovoltaic module is more accurate.

The invention relates to the technical field of Internet of Things gateways, in particular to an Internet of Things gateway and an operation method thereof. The method comprises the following steps: acquiring power system data, and constructing a power grid node topological structure diagram, so as to form a power system Internet of Things gateway; based on an Internet of Things gateway, power grid transmission demand prediction is carried out, an abnormal load state is analyzed, abnormal fluctuation of voltage and frequency is detected, and the stability of a power system is ensured; according to the frequency abnormity and voltage fluctuation data, the aging trend of power grid equipment is estimated, power resource loss estimation is carried out, abnormity early warning is carried out on power resource loss and equipment damage prediction data by utilizing an Internet of Things gateway, safe operation of a power grid is ensured, and early warning information is fed back to a terminal in time. According to the method, the intelligent level of the power system is effectively improved, and efficient resource configuration and fault management can be realized. According to the method, the Internet of Things gateway early warning is optimized, so that the Internet of Things gateway prediction is more efficient and accurate.

The invention relates to the technical field of numerical simulation, in particular to a grid model generation method for the fluid displacement process of well cementation engineering. The method comprises the following steps: acquiring geological data of a well cementation project area, and constructing an initial geological model; well cementation engineering design data are obtained, well cementation engineering topological structure modeling is carried out, and a well cementation engineering topological structure model is obtained; according to the initial geologic model and the well cementation engineering topological structure model, fluid dynamic simulation is carried out, and a fluid characteristic matrix is obtained; performing fluid displacement process simulation according to the fluid characteristic matrix to obtain fluid displacement process data; performing fluid displacement process optimization on the well cementation engineering design data based on the fluid displacement process data to obtain optimized fluid displacement process data, and performing resource allocation redistribution on the well cementation engineering design data according to the optimized fluid displacement process data to obtain optimized well cementation engineering design data. According to the invention, the accuracy of fluid displacement simulation can be improved.

The invention discloses an anti-interference linear motor module motion control method, and relates to the technical field of linear motor module control. The method comprises the following steps: monitoring and analyzing multiple parameters of displacement error # imgabs0 #, acceleration fluctuation value # imgabs1 #, motor load capacity # imgabs2 #, motor working current I, motor real-time voltage value U, dragging force # imgabs3 # and guide rail friction force # imgabs4 # in the operation process of a motor module in real time, and interference of external environmental factors (such as airflow velocity # imgabs5 #, airflow pressure # imgabs6 #, deposition thickness # imgabs7 # and electrostatic effect coefficient # imgabs8 #); according to the method, the motion state of the motor module can be comprehensively evaluated, so that the deviation of the motion track is effectively reduced, and the motion stability of the motor module is remarkably improved. An interference model is established by collecting external environment interference parameters (airflow interference, deposition thickness, environment vibration and the like) and combining a neural network technology, a motion control strategy of a motor module can be dynamically adjusted, and different types of interference factors are compensated in real time.

The invention discloses a new energy station network security assessment method and system, and relates to the technical field of new energy stations, and the new energy station network security assessment method comprises the following steps: obtaining network operation feature data; identifying the change trend of the network stability; identifying key security risk factors influencing the network stability; and adjusting the network protection strategy of the new energy station based on the evaluation result. According to the method, the network behavior recognition algorithm is used for carrying out stability analysis on the network operation characteristic data, the stability change trend is recognized, potential unstable behaviors or hidden dangers in the new energy station network can be found in time, the network state change can be pre-judged in advance, sufficient time and basis are provided for risk control, and the risk control efficiency is improved. According to the method, the key security risk causing network instability is accurately identified, generalization or fuzzy risk assessment is avoided, a basis is provided for formulating an accurate network protection strategy, and the risk prevention and control cost is reduced.

The invention relates to the field of vehicle chassis control systems, in particular to an online monitoring device and method for end face runout of a brake disc. Comprising the steps that firstly, the preset wheel speed of a wheel is set, the real-time wheel speed of the wheel is collected, then the real-time wheel speed is compared with the preset wheel speed, and if the real-time wheel speed is larger than the preset wheel speed, the second step is executed; secondly, the real-time clamping force of the friction plate on the brake disc and the instruction clamping force sent by a driver are collected, and the collected real-time clamping force is compared with the instruction clamping force; 3, collecting the times that the real-time clamping force is not equal to the instruction clamping force in unit time, and obtaining the error frequency; 4, the real-time wheel speed is compared with the error frequency, and whether the real-time wheel speed and the error frequency meet the multiple relation or not is judged; and 5, calculating the difference value between the real-time clamping force and the instruction clamping force, setting the difference value as a clamping force error, meanwhile, setting an error threshold value, comparing the clamping force error with the error threshold value, and if the clamping force error is greater than the error threshold value, triggering an alarm.

The invention provides a bridge concrete strength detection method based on a pigeon inspired algorithm and an inversion theory, which comprises the following steps: establishing a two-dimensional concrete slab model of a railway overpass, and carrying out discretization modeling by adopting a finite element method; on the premise that initial parameters and boundary conditions are known, numerical analysis is conducted on the model, and the temperature field distribution situation of the concrete structure is obtained; carrying out optimization inversion on the temperature field data through a pigeon inspired algorithm, and constructing an objective function in combination with the existing concrete temperature field data, so as to invert the temperature distribution in the concrete; and evaluating the strength of the concrete based on the internal temperature data of the concrete, the cement hydration heat release characteristic and the transmissivity curve. The method has the beneficial effects that nondestructive testing of the internal temperature of the concrete is realized, so that the slump is accurately judged, the strength is accurately evaluated, and the problems that in the prior art, only the strength can be roughly judged, and damage to different degrees of a concrete structure cannot be effectively recognized are solved.

The invention discloses a lane changing obstacle avoidance track generation method and related equipment, and the method comprises the steps: calculating a geometric distance between a matching reference point and each point in an ordered set of discrete points of a target matching track through a matching point obtaining module, so as to obtain the information of the matching reference point of a vehicle on a global track; a track availability judgment module based on the distance from the obstacle judges whether the local track meets the vehicle passing requirement or not according to the judgment track discrete point ordered set, the judgment obstacle information set, the vehicle width and the redundancy so as to obtain a track availability result; a quintic polynomial local obstacle avoidance track is generated and fused with a global track through matching reference point information obtained by calling a matching point by a lane changing obstacle avoidance track generation main module and a track availability result of a track availability judgment module for judging the distance between the quintic polynomial local obstacle avoidance track and an obstacle, and an updated current global track discrete point ordered set is generated; and controlling the transverse and longitudinal directions of the vehicle based on the updated current global track discrete point ordered set.

The invention relates to the technical field of network security analysis, in particular to a big data network signal security early warning system. The system comprises a network signal big data stream transmission module, a network signal ratio difference detection module, a network signal division result safety early warning module and a network signal abnormity safety response module. Data acquisition equipment can be deployed at an entrance and an exit of each network equipment, and network signal data real-time acquisition and big data stream type transmission conversion can be carried out, so that a network signal big data stream can be obtained; carrying out time sequence flow size and access frequency statistical analysis on the network signal big data flow, and carrying out network signal ratio difference detection division and abnormal safety early warning processing at the same time to generate a network signal abnormal safety early warning signal; and uploading the network signal abnormity safety early warning signal to a cloud platform to carry out abnormity safety response processing so as to generate a network signal abnormity safety response protection strategy. According to the invention, network signal abnormity can be efficiently monitored in real time and early warning can be provided.

The invention discloses an off-line and on-line combined unmanned aerial vehicle simulation flight path planning method, which belongs to the field of unmanned aerial vehicle flight path planning, and specifically comprises the following steps: obtaining a series of insertion waypoint longitudes and latitudes through a waypoint interpolation algorithm according to a selected route before a flight task is executed in an off-line mode; then altitude information stored in an elevation database is obtained in an elevation query mode, the target height of each waypoint of the unmanned aerial vehicle is calculated, and then route planning in the whole task process is completed; in the online mode, in the flight mission execution process, a real-time to-ground laser ranging system is adopted to obtain real-time to-ground height information of the unmanned aerial vehicle, after a measurement error is calibrated by combining flight attitude information of the unmanned aerial vehicle, the measurement error is compared with the actual flight height of the unmanned aerial vehicle, and the to-ground height of the unmanned aerial vehicle is calibrated on the basis of a pre-planned route; and the ground-imitating flight precision of the unmanned aerial vehicle is improved. According to the invention, high-precision ground-imitating equal-altitude flight route planning of the long-endurance unmanned aerial vehicle in a complex environment is realized.

The invention belongs to the technical field of fused salt energy storage, and particularly relates to a start-stop peak regulation coal-fired power generation system and method coupled with fused salt energy storage. According to the system and method for achieving safe and efficient start-stop peak regulation of the unit through coupled fused salt energy storage, when the unit needs to be shut down, a boiler operates at the lowest stable combustion load, a back pressure turbine is arranged to absorb boiler main steam to provide necessary auxiliary power for the unit, and fused salt and a water heat storage system are added to store boiler hot resteam; the original steam turbine of the unit stops, and the unit stops and does not stop the boiler; heat stored by the fused salt and water heat storage system is used for generating steam in unit starting and load increasing stages to increase the load increasing rate of a unit turbine. According to the method, the operation safety problem, the economical problem and the service life loss problem of unit start-stop peak regulation are solved.

The invention discloses a double-arm collaborative operation method suitable for a transformer substation tubular bus carrying electrified robot, and relates to the technical field of power system automation. Comprising the following steps: comparing an electromagnetic change threshold value with a dynamic threshold value to obtain an electromagnetic environment change parameter and an early warning signal; adjusting the control parameters of the robot and the sensitivity of a sensor through the early warning signal; determining a strong electromagnetic field area through the electromagnetic environment change parameters, and planning an inspection path of the robot; and according to the inspection path, carrying out tubular bus-bar drainage wire lapping / dismounting operation at the target position. According to the method, the electromagnetic environment change in the transformer substation is monitored in real time, the electromagnetic change threshold value is determined and compared with the dynamic threshold value, and the early warning signals of different levels are generated, so that an operator can be reminded in time or robot parameters can be automatically adjusted, and equipment faults or safety accidents caused by a strong electromagnetic field are avoided.

The invention discloses a large-current switch built-in electric energy meter terminal temperature measurement and temperature control method, and relates to the technical field of electric energy meter terminal temperature measurement and temperature control. The method comprises the following steps: carrying out real-time temperature acquisition on an electric energy meter terminal, and adaptively adjusting communication parameters; the temperature is adjusted and controlled based on a PID algorithm, and the temperature adjusting stability is analyzed; and the temperature is automatically judged, an ending instruction is sent, and quitting is conducted within the safe temperature range. According to the terminal temperature measurement and temperature control method for the large-current switch built-in electric energy meter, the accuracy and the real-time performance of temperature monitoring are improved by adopting an incremental PID control algorithm and a dynamically calibrated thermal resistance sensor, communication errors in the temperature measurement and temperature control process are reduced by optimizing Bluetooth communication, and the terminal temperature measurement and temperature control efficiency is improved. The whole reliability of the system is improved, the safety and convenience of operation are improved through an automatic test process and a safe exit mechanism, and better effects are achieved in the aspects of accuracy, real-time performance and safety.

The invention discloses a steel bar strength prediction system based on digital twinning, and the system comprises a high-precision data collection module which employs an optical fiber sensor and an MEMS sensor, and monitors the stress, strain and temperature parameters of a steel bar in real time; according to the digital twinborn model, the high-precision digital twinborn model is established by adopting a multi-source data fusion technology and combining physical characteristics and environmental factors of reinforcing steel bars, and the digital twinborn model can adapt to reinforcing steel bar structures of different types and scales and support expansion to adapt to new technologies and data sources. According to the method, efficient data processing is adopted, real-time data streams can be better processed and analyzed, the accuracy and the real-time performance of data are ensured, the accuracy of the digital twin model and the adaptability to different steel bar main structures are enhanced by introducing machine learning and deep learning technologies, and the accuracy and the real-time performance of the digital twin model are improved. The safety risk of the steel bar main body structure can be evaluated in real time, and related personnel can be notified in time through the early warning system, so that potential safety problems can be prevented.

The invention relates to the technical field of organic electroluminescence adjustment, and provides a power equipment abnormity early warning system for a mobile terminal, which integrates a real-time monitoring module, an early warning identification module and a data detection module. By monitoring operation data of the power equipment in real time, combining with a random forest model to carry out abnormity early warning, sending early warning information to a mobile terminal in real time and receiving user feedback to optimize an early warning strategy, a data detection module carries out grouping processing on the power equipment based on a time period, so that the data processing efficiency is improved; the system achieves the comprehensive monitoring of the operation state of the power equipment, remarkably improves the early warning accuracy, response speed and user interaction experience, effectively guarantees the safe and stable operation of a power system, promotes the scientific maintenance and management of the power equipment, is suitable for various types of power equipment, and improves the safety and reliability of the power equipment. The method has a wide application prospect and an important practical value, and provides powerful support for intelligent and safe operation of an electric power system.

The invention relates to the technical field of automatic loading and unloading of logistics forklifts, in particular to a novel automatic loading and unloading system of an unmanned forklift in a van environment, and the system comprises a positioning map binding module which is used for binding platform areas where a plurality of trucks are parked on a positioning map; and the task issuing module is configured to receive a task issued by a driver through a pad system when a truck arrives and transmit the task to a warehouse management system (WMS), and the WMS issues a cargo taking and placing task of a corresponding map point position according to a platform number. And the visual servo system adopting the solid-state laser radar provides high-precision and high-stability navigation capability for the AGV.

The invention relates to the technical field of operation monitoring, in particular to an operation parameter monitoring and early warning method and system for medical beauty equipment. The method comprises the following steps: acquiring medical beauty equipment data; performing equipment action range analysis on the medical beauty equipment data to generate medical beauty equipment action range data; carrying out equipment monitoring parameter integration through a multi-source sensor based on the action range data of the medical beauty equipment, and generating multi-modal monitoring data of the standard medical beauty equipment; and performing long-term performance degradation trend monitoring on the multi-mode monitoring data of the standard medical beauty equipment to generate long-term performance degradation trend data of the key parts of the equipment. Through multi-modal data integration, long-term performance trend analysis, biomechanical modeling and intelligent early warning decision optimization, the reliability and the intelligent level of monitoring and early warning of the operating parameters of the medical beauty equipment are improved.

The invention discloses a fault location method based on primary and secondary fusion complete pole-mounted circuit breakers, and relates to the field of power distribution network fault judgment, and the method comprises the following steps: selecting one pole-mounted circuit breaker between pole-mounted circuit breakers of a power transmission line as a master device, and taking the other pole-mounted circuit breakers as slave devices; the main equipment is configured to be a pole-mounted circuit breaker with the best 4G / 5G signal or a pole-mounted circuit breaker closest to the main station; optical fibers are connected among the pole-mounted circuit breakers and ad hoc networks are formed; the pole-mounted circuit breakers are subjected to time synchronization through optical fibers. When the device is applied, devices on the whole line are cascaded through optical fiber communication, ultrahigh time precision is provided, the fault distance measurement accuracy is improved, the network safety problem is completely eradicated in an ad hoc network mode, the device is not affected by networks and regions, and active fault detection and fault distance measurement are achieved by actively sending pulse signals.