51results about How to "Promote intelligent development" patented technology

The invention discloses an automatic fine ore sorting method and system based on XRT (X ray tomography) and hyperspectrum, which are used for realizing comprehensive fusion of ore surface and internal information by combining X-ray tomography and hyperspectral imaging technologies so as to improve the recognition precision and the sorting efficiency. XRT is used for extracting the internal structure and density characteristics of the ore, hyperspectral imaging is used for identifying the mineral composition characteristics of the ore surface, and information complementation is achieved. The system adopts a multi-modal convolutional neural network to perform independent feature extraction on XRT and hyperspectral data, and the extracted features are fused through a deep residual network. And the fused features are processed by a cross-modal attention mechanism to ensure that key features of different modals are fully integrated and optimized, and finally high-precision ore classification and sorting are realized. According to the method, the limitation of a single data source on feature extraction is overcome, the ore recognition and sorting precision is greatly improved, and the refined and intelligent development of ore sorting is promoted.

The invention provides a power transmission and transformation project ecological influence space-time boundary determination method and system based on multi-source remote sensing information deep fusion interpretation, and belongs to the technical field of power transmission and transformation project management. The method comprises the steps of data acquisition, data preprocessing and multi-source data integration, multi-scale space-time analysis, ecological influence index construction, remote sensing ecological index model construction, ecological influence space-time boundary determination, dynamic monitoring and early warning system platform establishment and long-term monitoring and evaluation. By integrating satellite remote sensing, unmanned aerial vehicle aerial photography and ground monitoring data and combining an image fusion algorithm, a principal component analysis method and a random forest algorithm technology, the method is beneficial to promoting accurate evaluation of the influence of the power transmission and transformation project on the ecological environment, visualizes the space-time boundary of the ecological influence, breaks through the limitation of a traditional evaluation method on time and space coverage, and improves the evaluation efficiency of the power transmission and transformation project. And a scientific method system for analyzing and evaluating the ecological influence of the power transmission and transformation project is further improved.

The invention discloses an artificial intelligence perioperative period anesthesia depth detection system which comprises a data acquisition module which is provided with a multi-mode sensor and is used for acquiring physiological data of a patient in a perioperative period, and an artificial intelligence model module which is used for constructing an anesthesia depth prediction model based on a deep learning algorithm. The result output and display module is used for outputting the prediction result of the anesthesia depth in real time and visually displaying the prediction result on a display screen in the form of digits and charts, and the communication and interface module is provided with an interface for data communication with a hospital information system and an electronic medical record system. By means of multi-mode collection and innovative design, the anesthesia depth detection accuracy is improved, precise monitoring and intelligent auxiliary decision making are achieved, the method can be used for various scenes, the safety of patients in the perioperative period is effectively guaranteed, and the medical efficiency is improved.

The invention discloses a cross-domain data fusion method and system for distribution network middle stations based on a knowledge graph, and particularly relates to the technical field of data processing and fusion based on the knowledge graph. Firstly, intelligent gateway equipment is adopted to collect operation, fault, maintenance and other data of all systems and all lines related to distribution network operation, the data are transmitted to a data processing and storage module through a 5G communication protocol port transmission technology, and data cleaning, data integration, data conversion and other operations are carried out; classifying and storing the data, and then constructing a mapping knowledge domain in the distribution network; according to the method, a 5G communication protocol port transmission technology and a composite model are specifically adopted to perform knowledge extraction and knowledge fusion of graph construction, so that the technical problems of data islands and other data processing in a distribution network are solved, data sharing, service sharing and resource integration are effectively realized, and the reliability of the distribution network is improved. The problem that data between distributed systems in the current distribution network field is difficult to cooperate is solved, semantic integration and sharing of data between multiple systems are achieved, and the operation and maintenance efficiency and the intelligent level of a power distribution network are improved.

The invention discloses a thermal load prediction method, system and equipment based on multivariate input and a storage medium, relates to the technical field of heat supply control, and is used for solving the technical problem that multi-step prediction of thermal loads is realized by adopting multivariate time sequence data in the prior art. According to the technical scheme, the method mainly comprises the steps of obtaining multivariate time sequence data of the heat supply system; establishing a data set based on the multivariate time series data, and performing normalization processing on the data set to obtain a normalized data set; the normalized data set comprises a training set; a thermal load multi-step prediction model is constructed; the thermal load multi-step prediction model comprises a sequence-to-sequence model, an encoder, a decoder and a time sequence attention mechanism; the input data of the thermal load multi-step prediction model comprises a training set; and obtaining thermal load multi-step prediction data by using the thermal load multi-step prediction model. According to the method, the multi-step prediction of the heat supply system heat load data is realized by using the heat load multi-step prediction model.

The invention discloses a maintenance personnel operation behavior monitoring method based on artificial intelligence, and relates to the technical field of artificial intelligence. In order to solve the problems that the current locomotive depot personnel behavior monitoring is mainly performed by manual monitoring and cannot give an alarm at the first time when an accident occurs, so that hidden dangers are caused to safety production, and video monitoring personnel often face relatively high working pressure. Through the face recognition and human body posture estimation technology, the system can accurately recognize the identity and the position of the maintenance personnel and monitor the behaviors of the maintenance personnel in real time, the workload of the monitoring personnel is relieved, the behaviors of the maintenance personnel are automatically recognized through the OpenPose algorithm, manual monitoring is not needed, the monitoring efficiency and the automation degree are improved, powerful support is provided for maintenance safety management, and the system is suitable for popularization and application. The system can effectively improve the monitoring efficiency, reduce the safety risk, improve the safety awareness, and promote the scientific and intelligent development of the maintenance safety management, thereby effectively preventing and controlling the safety accidents, and improving the maintenance safety management level.

The invention discloses an adjusting system for controlling the rotating speed of an automobile engine, and the system is characterized in that the system comprises a data collection module which collects the conventional operation parameters of the automobile engine in real time, the conventional operation parameters comprise the rotating speed, the torque, the air inflow, the oil temperature and the oil pressure, and also collects the acceleration, the gradient information, the external environment temperature and humidity of a vehicle and the driving behavior data of a driver; such as accelerator pedal depth change rate and brake frequency; the data analysis module is connected with the data acquisition module and is used for deeply mining a relationship and a potential rule in data by applying a long short-term memory (LSTM) network algorithm in deep learning and a fusion analysis method based on a physical model, identifying a real-time running state and a future change trend of the engine and intelligently diagnosing potential fault hidden dangers; the fuel injection amount and the ignition time can reach the optimal state through accurate rotation speed adjustment, fuel consumption is greatly reduced, fuel economy is improved, and expenditure is saved for users.

The invention belongs to the technical field of dredging engineering, and discloses a method for calculating a standardized drag head excavation model of a drag suction dredger, which comprises the following steps: determining a standardized drag head corresponding to the drag suction dredger based on water area position information of a current water area, and determining corresponding rake tooth information based on the standardized drag head; the rake tooth information and the water area position information calculate the mechanical ground breaking amount in unit time and a corresponding rake tooth ground breaking parameter table based on a mechanical ground breaking amount calculation model; on the basis of high-pressure water outlet characteristic information corresponding to the trailing suction dredger, the hydraulic ground breaking amount in unit time and a corresponding high-pressure water outlet characteristic table are calculated on the basis of a hydraulic ground breaking amount calculation model according to the high-pressure water outlet characteristic information; the hydrological environment information is divided into dredging influence grades according to the dredging requirements of the to-be-dragged suction area through a fuzzy function; and a multi-factor coupling excavation yield calculation model is established, and the excavation yield of the drag suction dredger under the action of the standardized drag head is obtained by considering the environmental constraint of the dredging influence level on the mechanical ground breaking amount and the hydraulic ground breaking amount in unit time.

The invention discloses a high-standard bearing steel quality prediction model based on machine learning. The high-standard bearing steel quality prediction model comprises the following steps: 1) data acquisition: collecting on-site key process and component data having potential influence on the qualification rate and defects of high-standard bearing steel; 2) data preprocessing: processing the collected various process and component characteristic variables; 3) data division: dividing the data into a training set and a test set according to a proportion; 4) constructing a model: training a deep neural network model constructed by using Python through the training set; 5) performing model evaluation: performing evaluation comparison on the trained model through a test set, and taking the trained integrated learning model as a prediction model; and 6) quality prediction: importing the data of the steel to be tested into the trained model to obtain a prediction result. According to the method, a perfect enterprise digital process and a component acquisition system are constructed, the high-standard bearing steel quality is accurately predicted by utilizing a machine learning technology, and the accuracy rates of water immersion flaw detection qualification prediction and crack defect prediction are both greater than or equal to 80%.

The invention discloses a visual identification detection system based on rice seedlings, relates to the field of visual identification detection of rice seedlings, and aims to overcome the defects of a rice transplanter operation detection method, assist a driver in mastering rice seedling leakage information, reduce manual monitoring on a rice transplanter and the like by a rice seedling transplanting and seedling leakage detection product. An MCU control board is arranged on the surface of the computing mainboard, a power switch and a function switch are embedded into the left end of the computing mainboard, the MCU control board is connected with an RDK X3 through a UART asynchronous transceiving transmitter, the MCU control board is connected with a GPIO small lamp board, an RTK board is arranged on the surface of the computing mainboard, and the RDK X3 is connected with a 4G module through a USB. The seedling leakage monitoring system of the rice transplanter can improve the agricultural production efficiency, reduce the agricultural production cost, improve the agricultural product quality and promote the agricultural intelligent development, and is of great significance to agricultural modernization.

The invention provides an end-cloud collaborative agricultural condition image acquisition system based on 5G communication and machine learning, and relates to the technical field of end-cloud collaboration of 5G communication and machine learning. A data acquisition end is used for acquiring multispectral image data of a farmland to be observed, and compression preprocessing is carried out, so that the data transmission burden is reduced; the compressed image is sent to a data processing module, and the data processing module carries out denoising and feature extraction on the image, constructs a feature data vector and transmits the feature data vector to a cloud server; the cloud server analyzes feature data vectors by using a random forest algorithm model, judges crop types and classifies the crop types, and finally returns an analysis result to the data display module, and the data display module generates an instrument panel by using a Flask framework and visually displays agricultural condition data including key indexes such as crop types and coverage conditions. Data transmission is carried out through 5G communication to realize a terminal-cloud collaborative architecture, and efficient and real-time data transmission and processing are realized.

The invention belongs to the technical field of power distribution network data quality management, and particularly relates to a power distribution network multivariate time series data-oriented quality evaluation method, which comprises the following steps of: selecting power distribution network multivariate time series data quality evaluation dimensions based on power distribution network data characteristics and distribution characteristics; comprising integrity, normalization, consistency, timeliness, uniqueness and accuracy; designing an evaluation method of the power distribution network multivariate time series data quality evaluation dimension; designing a weight distribution scheme of each dimension, and determining the weight of each dimension; and according to the quality score of each dimension and the weight of each dimension, calculating the total score of the quality of the to-be-measured data, and evaluating the data quality grade. According to the method, multi-level, multi-azimuth and multi-angle analysis is carried out on the power distribution network data, so that one-sidedness and limitation in a traditional evaluation method are avoided, and the data quality can be reflected more truly by the result, thereby optimizing the enterprise decision flow, promoting the intelligent development of the power distribution network, and realizing more efficient and sustainable power data management.

The invention relates to the technical field of power plant equipment management, and particularly discloses an intelligent power plant equipment management system, and the system specifically comprises an equipment data collection and judgment module which collects the real-time voltage of equipment in a power plant, judges whether the voltage of the equipment is abnormal or not, and generates a voltage abnormal signal; the abnormal data calculation and processing module is used for acquiring a corresponding time unit based on the generated voltage abnormal signal, performing data processing and calculating an abnormal time characterization value; and the abnormity judgment module is used for comparing the abnormal time characterization value with an abnormal time characterization threshold value and judging the severity of the voltage abnormity in the abnormal time unit. Therefore, by monitoring the voltage of the equipment and performing abnormity judgment, abnormity of the equipment can be known and found in time, and further analysis is performed based on the abnormal time, so that the severity of a fault of the equipment can be assessed, and which level of countermeasures can be taken can be determined in an assisted manner; and the maintenance strategy can be optimized.

The invention provides a low-code system for data analysis and data cleaning based on Internet of Vehicles data. The low-code system comprises a multi-protocol adapter, a user-defined analysis rule writing interface, a dragging type rule configuration interface, a data processing module, a forwarding mechanism unit, a monitoring and log tracking unit and a message labeling and fingerprint generation unit. Wherein the multi-protocol adapter is used for supporting log data acquisition of various network protocols; the user-defined analysis rule writing interface allows a user to write specific analysis logic according to a data format and a service requirement; meanwhile, for compatibility of various data codes and formats, rule templates and examples are provided to guide a user to compile analysis rules, and the user is helped to quickly position and correct problems in the analysis rules by integrating corresponding error detection and debugging tools; and a user carries out graphical operation combination input, output and analysis on rule nodes through the drag-and-drop rule configuration interface.

The invention relates to a method for predicting the remaining service life of a transformer substation battery based on electrochemical impedance spectroscopy characteristics. The method comprises the following steps: step 1, collecting battery EIS data; step 2, preprocessing and normalizing the collected data; step 3, performing reconstruction denoising on the input data by using DAE, and extracting features; 4, inputting the features extracted by the DAE into a Transform model, capturing the long-time dependency of an input sequence through a multi-head self-attention mechanism, and training the model; 5, evaluating errors of the energy storage battery RUL prediction model, and when the errors of the energy storage battery RUL prediction model are smaller than a preset value, ending prediction; and otherwise, returning to the step 3 for optimization, and repeating the steps 4-5 until the prediction model passes evaluation. The method has high robustness, is suitable for different environment conditions and operation scenes, and significantly improves the prediction accuracy and reliability.

This invention relates to a navigation method for underwater unmanned vehicles (UUVs), comprising the following steps: acquiring data from multiple sensors and performing spatiotemporal synchronization, feature extraction, and data fusion preprocessing; defining the underwater operating area as a directed graph, with edge weights determined based on terrain complexity, obstacle risk, and water flow influence; based on the directed graph, employing an improved A* algorithm for global path planning and a fast exploratory random tree algorithm for local path planning and dynamic obstacle avoidance; assessing the environmental state considering aquatic environmental parameters, water flow characteristics, and topographic changes; and adjusting the navigation strategy using a deep reinforcement learning algorithm, with the navigation strategy adjustment objective function as the optimization goal, based on the environmental assessment results and the UUV's own state. Compared with existing technologies, this invention offers advantages such as improved navigation accuracy, reliability, and adaptability of underwater UUVs in complex underwater environments.

The application discloses a lithium battery walking electric pallet truck and relates to the technical field of electric pallet trucks, which comprises an electric pallet truck body, wherein the electric pallet truck body comprises a frame, a power supply, an electrical system, a hydraulic system and a driving system, the electrical system comprises a main controller, an accelerator and an instrument panel, and the power supply adopts a lithium battery power supply; a wedge-shaped shell is arranged on the front side of the main controller, an electronic lock is arranged in the wedge-shaped shell, and the electronic lock is electrically connected with the main controller. The lithium battery is adopted as the power supply, the quick charging performance of the lithium battery is utilized, the defects of the lead-acid power supply electric pallet truck are improved, the influence of long-time charging on use is avoided, the electronic lock is used to lock the equipment, the problem that the equipment cannot be used due to the loss of keys is completely solved, and the intelligent development of the overall equipment is promoted.

The invention discloses an infusion bottle liquid level monitoring method and system based on cloud and edge computing, which overcome the problems of complicated installation, easy interference, high cost and the like of the traditional system through computer vision and intelligent analysis technologies, provide a more convenient and reliable liquid level monitoring means, and improve infusion management efficiency and medical service quality. Meanwhile, the technical innovation also provides reference for intelligent monitoring and management in other fields, and promotes technical progress and intelligent development of the medical industry; moreover, the accuracy and reliability of liquid level monitoring during infusion are improved, the medical safety level can be improved, the risk of misoperation is reduced, and the credibility and speciality of medical services are enhanced.

The invention relates to the field of computer-aided diagnosis, discloses a system for judging and recognizing the intestinal tract cleanliness level of an enteroscope patient, and aims to solve the problems that existing intestinal tract cleanliness evaluation is high in subjectivity, low in evaluation efficiency and lack of quantitative standards. The method comprises the following steps: acquiring and preprocessing enteroscopy image sequence data, and extracting multi-scale features; and the intestinal tract cleanliness is identified through a deep learning model, and a quantitative scoring report is generated. The system comprises an image data acquisition module, a preprocessing module, a multi-scale feature extraction module, a cleanliness identification and classification module and a report generation module. According to the technical scheme, evaluation objectivity, accuracy and efficiency can be improved, automation, standardization and high-precision quantification are achieved, and dependence on doctor experience is reduced.

The application discloses an intelligent pipeline type self-propelled pesticide spraying vehicle, which is characterized by comprising a vehicle frame, a driving wheel set, a pipeline wheel set and a supporting wheel set, a pesticide spraying rod, a pesticide box, a control cabinet, a lithium battery, an air pump, a water pump, a driving motor, a speed reducer and an encoder which are installed on the vehicle frame, a T-shaped pesticide spraying pipe being bound on the pesticide spraying rod, a display system being installed on the control cabinet, and the speed reducer being connected with the driving wheel set.

The invention relates to a human body joint motion range measurement system based on artificial intelligence vision, and relates to the technical field of AI vision joint measurement, and the system comprises an image collection and contour generation module which is used for obtaining a time sequence image sequence, recognizing a human body contour, and generating a time sequence human body contour map sequence; the multi-scale time sequence feature fusion module is used for extracting multi-scale contour features and generating fusion time sequence contour features; the joint point identification and extraction module is used for identifying blind areas and visible joint points and extracting node coordinates; the constraint optimization function construction module is used for constructing an initial joint point set and establishing a kinematics constraint optimization function; and the coordinate optimization and angle calculation module is used for solving the kinematics constraint optimization function, carrying out collaborative optimization on all node coordinates, and outputting and calculating the movement angle of the target joint. According to the method, the problems of joint position judgment deviation or fuzzy estimation caused by the fact that traditional single-camera measurement is easily affected by shielding and key point recognition is inaccurate are solved.

The invention provides a human traffic analysis and prediction method and system based on big data of artificial intelligence, and belongs to the field of big data analysis. The method comprises the steps of data collection, data feature extraction, model construction, model training and model verification. Firstly, a large amount of people flow data, such as position information, moving paths, time information and the like of people, are collected through a camera, a mobile phone signal and the like; then, feature extraction is carried out on the data, for example, a regional traffic matrix is constructed, time sequence attribute division is carried out, and splicing with external attributes is carried out; thirdly, constructing a human traffic analysis and prediction model according to the features, and training the model to optimize the performance of the model; and finally, testing the performance of the model by using the verification set or the test set. According to the method, big data and artificial intelligence technologies are combined, the visitor flow is analyzed and predicted dialectically, and the method can be applied to places such as shopping malls, parks and stations where the visitor flow is large and resources need to be allocated dynamically.

The application discloses a sintering machine tail state monitoring method based on machine vision. The method comprises the following steps: S1, preprocessing of the input sintering machine tail image; S2, color gamut conversion of the original image to extract the combustion area in the image; S3, image processing after extracting the combustion area and extraction of the machine tail area; S4, feature value calculation of the extracted machine tail area; and S5, sintering state judgment and monitoring according to the feature value. The application constructs a sintering machine tail state monitoring system through machine vision and image processing related technologies, realizes real-time monitoring of the sintering machine, gives a prompt and a warning to the abnormal part, further realizes intelligent production of the sintering process section, improves the production efficiency of the steel enterprise, reduces the labor cost, and further promotes the intelligent manufacturing transformation and upgrading and intelligent development of the steel metallurgy field in China.

The application discloses a downhole information interpretation method and system based on drill bit vibration data, and the method comprises the following steps: integrating an intelligent sensing module in the water eye of an existing drill bit, collecting downhole vibration data and environmental parameters in the drilling process at a high sampling frequency; synchronously aligning the downhole vibration data and the ground drilling parameters; calibrating and compensating the synchronized vibration data by using the environmental parameters and extracting features to obtain a vibration feature parameter vector reflecting the interaction between the drill bit and the stratum; and identifying the stratum lithology or the drilling working condition based on the vector and in combination with the environmental parameters. The application solves the problems of information distortion and low resolution caused by the fact that the sensor is far away from the drill bit and the data bandwidth limitation of the traditional measurement-while-drilling system, provides an unprecedented high-precision data basis for post-drilling fine analysis, geological model calibration and future drilling decision optimization, and the good compatibility with the existing drill bit does not require redesign and manufacture of the drill bit, thereby significantly reducing the application cost.

The invention relates to the technical field of workpiece machining, in particular to an automatic detection and machining integrated cutter and a working method thereof. The device comprises a probe, a probe rod, a detection unit, an air cylinder, a guide pipe, an air cylinder pushing rod, a blade and a cutter body, the blade is arranged on one side of the top of the cutter body, and the blade is used for machining a machined part; the guide pipe is arranged on the side surface of the cutter body and is far away from one side of the blade; the air cylinder is mounted at the bottom of the guide pipe; the detection unit is connected with the air cylinder through an air cylinder pushing rod, and the detection unit is arranged at the upper part of the air cylinder; the probe rod is arranged at the top of the detection unit, and a probe is arranged at the top of the probe rod. A contact type probe sensor is additionally arranged on a common cutter, after cutter machining is completed, the sensor is moved to the position where a workpiece is just machined, moving detection and measurement data collection are conducted, and data analysis and cutter parameter automatic correction are achieved through a numerical control system.

The invention provides an innovative train-ground joint control system and method based on real-time data aiming at the requirements of high efficiency and high safety in the current railway transportation field. According to the system, comprehensive intelligent management and optimization of the railway transportation process are realized by fusing advanced big data analysis technology, artificial intelligence algorithm, Internet of Things technology and cloud computing platform. The system can efficiently process and analyze mass transportation data, and uses an artificial intelligence algorithm to automatically learn and adjust a scheduling strategy, thereby realizing more accurate route planning and risk early warning. Meanwhile, information exchange among locomotives, vehicles and rail facilities is more real-time and accurate due to application of the Internet of Things technology, powerful data processing capacity and flexible resource allocation are provided for the system due to introduction of the cloud computing platform, and efficient operation and easy expansion of the system are ensured. The implementation of the invention is expected to obviously improve the railway transportation automation level, reduce the operation cost and enhance the reliability and stability of the system.

The invention discloses a forklift safety distance warning method and system based on Bluetooth distance measurement, and relates to the technical field of forklift safety monitoring, and the method comprises the steps: detecting the distance between a forklift and an operator based on an RSSI signal intensity algorithm; through multiple reminding modes, when an operator enters an early warning range, an acousto-optic alarm is given out, and related information is displayed on a display screen; alarm events and related information are recorded, and the operation process is optimized. According to the method, the distance between the forklift and an operator is detected through an RSSI (received signal strength indication) algorithm, real-time distance monitoring and automatic response are ensured, a multiple reminding mechanism is combined with two modes of sound-light alarm and display screen reminding, effective transmission of warning information is ensured, accident potential is reduced, and through deep analysis of data, the safety of the forklift is improved. The system can identify areas with frequent operation and concentrated risks, and promotes the overall intelligent development of warehouse management.

The present application belongs to the technical field of dredging engineering, and discloses a method and system for calculating the multi-factor coupled excavation yield of a drag suction dredger. Specifically, the present application determines the corresponding tooth information of the drag suction dredger based on the position information of the current water area; substitutes the tooth information and the position information of the water area into a mechanical soil breaking amount calculation model to calculate the mechanical soil breaking amount per unit time and generate a corresponding tooth soil breaking parameter table; and substitutes the high-pressure water discharge characteristic information of the drag suction dredger into a hydraulic soil breaking amount calculation model to calculate the hydraulic soil breaking amount per unit time and generate a corresponding high-pressure water discharge characteristic table. The hydrological environment information is processed by a fuzzy function, and the dredging influence level is divided according to the dredging demand of the drag suction area. Under uncertain external environment, the multi-factor coupled excavation yield calculation model is established through parameter identification learning, which takes into account the environmental constraints of the dredging influence level on the mechanical soil breaking amount and the hydraulic soil breaking amount per unit time, and finally calculates the excavation yield of the drag suction dredger.

The invention discloses a shielded pump bearing wear detection circuit and method. The detection circuit comprises a data acquisition module, a data processing module, a display module, a data transmission module for collecting shielded pump bearing wear information, and a host computer for predicting and analyzing the bearing wear amount according to the shielded pump bearing wear information and issuing a detection report. The data processing module is respectively connected to the data acquisition module, the display module and the data transmission module, and the host computer is connected to the data transmission module. The advantages of the invention are: a high degree of digital display, strong display mode and expansibility, real-time data reception and real-time display, real-time visual monitoring, prediction of bearing wear, and modular design. The circuit is in a weak current environment and does not need to be powered by three-phase electricity, thereby avoiding safety hazards. The entire detection circuit is conducive to the digital and intelligent development of shielded pumps.

The invention belongs to the field of engineering detection (monitoring), and discloses a remote control device and method for automatic falling of a sensor, and the device comprises a mounting support, a heating device, a cloth belt ring, a bolt and a sensor. The mounting support is provided with a base and a clamp, and the inner side of the clamp is rough and provided with an anti-skid device used for stably clamping the sensor. And the cloth belt is annularly sleeved at the bottom of the clamp and is made of a tensile and high-temperature easy-to-break material, such as nylon. The bolt is arranged in the clamp round hole in a penetrating mode, the heating device is arranged on the installation support, the heating portion of the heating device is adjacent to the cloth belt ring, and remote operation can be achieved through a wireless control switch and a remote controller. When the heating device is started, the cloth belt ring is heated and fractured, so that the sensor automatically falls off under the action of the bolt. In addition, a cable is arranged on the sensor, so that connection and data transmission are facilitated. And the round hole in the base is used for fixing the whole device on a specific structure. According to the invention, remote automatic falling-off control of the sensor is realized, and the convenience and safety of operation are improved.