4492results about "Telemetry/telecontrol selection arrangements" patented technology

The invention belongs to the technical field of water conservancy and hydropower engineering, and discloses a water conservancy and hydropower engineering construction safety supervision system based on multi-source data fusion. The system comprises a multi-source sensing acquisition module, a heterogeneous data fusion processing module, a risk identification and early warning module, a safety behavior evaluation and feedback module, and a command scheduling and visualization module. According to the invention, by fusing multi-dimensional data such as image monitoring, environment sensing, personnel positioning, equipment state and the like, a space-air-ground three-dimensional sensing network is constructed, and in a high slope area, the distributed optical fiber strain sensors are linked with thermal imaging data of the unmanned aerial vehicle, so that millimeter-level deformation and temperature field abnormity can be captured in real time; a video stream is analyzed in real time by means of a YOLOv8 algorithm, illegal operation behaviors of personnel can be accurately identified, a cross-modal fusion model of a Transform architecture is combined, the system can dynamically capture potential correlation among data, and millisecond-level response to risks such as side slope landslide, equipment faults and personnel dangerous operation is achieved.

The invention provides a Beidou electric power data intelligent acquisition and dynamic communication scheduling method and system. The method comprises the steps that a master station constructs a database and trains a data priority classification model, a channel quality prediction model and an intelligent compression model; a substation collects power data through an edge calculation unit, constructs a skyline candidate set to perform data screening, and performs intelligent classification marking by using a data priority classification model; performing reliability evaluation on the data stream by using a Gaussian mixture model, selecting a compression strategy according to a reliability score, a data type and a priority, and packaging into a data frame; determining a transmission strategy in combination with a channel quality prediction result and a context-aware intelligent switching protocol, and sending a data frame; the master station receives the data frame, performs integrity verification, decompresses and reconstructs the data frame, and feeds back a communication state for model updating; and monitoring the operation state, performing early warning based on the anomaly detection model, and triggering a self-healing strategy. According to the invention, the Beidou communication resource utilization rate and the system reliability are improved.

The invention discloses a communication and optimization method and system of an energy storage system, and relates to the technical field of communication. Real-time state parameters of the battery pack are obtained through wireless communication, the state parameters are predicted through time sequence analysis and a data compensation mechanism, the problems of numerous battery packs, communication time delay and packet loss are solved, the real-time performance and accuracy of data are ensured, and the real-time performance of the battery pack is improved by combining a digital twinborn model. The system can optimize a control strategy based on battery aging evaluation and simulation analysis, realizes dynamic adjustment and refined energy management, adopts a differential privacy technology to encrypt and transmit data, ensures privacy protection of the control strategy, and ensures the safety of the control strategy through real-time monitoring and self-adaptive adjustment mechanism and minimization of the difference between a digital twin model and an actual system. The stability, efficiency and safety of the energy storage system are further improved, and long-term reliable operation of the system is guaranteed.

The invention relates to the technical field of information processing, in particular to a hard mountain side slope digital twinborn monitoring method and system based on a cloud platform. The method comprises the following steps: obtaining real-time monitoring data of a slope in a hard mountain area, and carrying out data preprocessing to obtain standardized slope dynamic monitoring data; performing data compression and feature extraction on the standardized slope dynamic monitoring data, and transmitting a feature extraction result to a cloud platform to obtain cloud slope key information data; the method comprises the following steps: constructing a digital twinborn model by utilizing cloud side slope key information data based on a cloud platform to obtain a preliminary side slope digital twinborn model; through construction and optimization of the digital twinborn model, stability evaluation and risk early warning, the precision and real-time performance of slope monitoring in the hard mountainous area are remarkably improved, and a scientific basis and an executable long-term scheme are provided for slope treatment.

The invention relates to the technical field of Internet of Things, in particular to a water meter data acquisition method based on an open source gap, which comprises the following steps of: based on an open source gap distributed architecture, acquiring water meter sensing and communication data, analyzing flow fluctuation, marking low-efficiency nodes, optimizing communication and energy consumption matching, screening abnormal links, and identifying over-limit nodes. And generating an exception and switching control table. According to the method, intelligent division of a data fluctuation interval is realized by analyzing a flow change trend in unit time, on the basis, a data acquisition task and an energy consumption state are matched in combination with an operation period and electric quantity distribution, meanwhile, signal strength and delay in a transmission path are comprehensively evaluated, and the flow output change is continuously tracked, so that the flow output change is accurately determined. And abnormal operation nodes are identified and switching control signals are triggered, so that the stability and accuracy of data acquisition in a complex environment are improved, the coordination and execution efficiency of the whole process are enhanced, the information loss risk caused by communication faults is reduced, and the intelligent response level is improved.

Provided herein is a non-invasive, continuous biometric sensor system for determining a characteristic of a state of a user based on a value of a biomolecule. The system includes a biomolecule sensing device and attachment structure for coupling the sensor to the body. The biomolecule sensing device is configured for generating digital signal read data pertaining an effect of a biomolecule on the tissues of the body such as by employing Near Infrared Spectroscopy (NIS) techniques. Accordingly, the sensing device includes electronic sensing components having a photoemitter for directing electromagnetic radiation into the tissue of a body, the tissue suspected of being affected by the presence of the biomolecule, and a photoreceiver for receiving electromagnetic radiation reflected back from the tissue. The reflected electromagnetic radiation may then be used by the system to determine a presence and / or concentration of a biomolecule of interest and / or its effect on the user.

The invention provides a field data verification device based on a money-free clamp buckle type single-phase electric energy meter, and the device comprises a host which is used for starting a measurement process through the contact of a probe with a signal contact of the electric energy meter, receiving current data transmitted by a wireless clamp-shaped current transformer, and carrying out the filtering, calibration and error analysis of the measurement data; and the wireless split core type current transformer is used for being clamped on a current loop of the measured electric energy meter, starting to measure current data and sending the current data to the host in a wireless mode. According to the invention, a wireless communication technology, a field data processing algorithm and a verification function are integrated, and field real-time data acquisition, processing, analysis and verification can be realized, so that the efficiency and accuracy of verification work are improved.

The invention discloses a meter reading system and a meter reading method based on dual-mode communication, and relates to the technical field of meter reading systems and methods, the meter reading system comprises a dual-mode communication module, intelligent data demand switching, strong NB-IoT penetration and far coverage; the BLE is short-distance, high-speed, low-power-consumption and fast in switching, the intelligent electric meter module and the high-precision metering chip measure electric energy accurately, historical data is stored in a large-capacity mode, the shell protection is high, the intelligent electric meter module adapts to severe environments, the data processing and storage module and the high-performance processor compress and process data, the SSD stores in a large-capacity mode and backups redundancy, and the user interaction module and the large-screen LCD clearly display power utilization information. And a button or a touch screen is convenient to operate, and a remote management and monitoring module is provided. Innovative technologies such as dual-mode communication are fused, high-precision metering, intelligent switching communication and convenient interaction are achieved, power line carrier standby and fault early warning diagnosis are achieved, meter reading efficiency, precision and reliability are improved, and development of an intelligent power grid is promoted.

The invention provides a multi-data fusion fire-fighting early warning method and platform, and relates to the technical field of fire fighting, and the method comprises the steps: collecting a multi-dimensional environment data set of a target region; performing noise filtering on the multi-dimensional environment data set to obtain a multi-dimensional environment de-noised data set; comprehensively analyzing the plurality of data sources based on the multi-dimensional environment de-noised data set to generate fire-fighting comprehensive data, and fusing the multi-dimensional environment de-noised data set to obtain a multi-dimensional environment fused data set; building a fire risk model according to the fire-fighting comprehensive data, synchronizing the multi-dimensional environment fusion data set to the fire risk model for evaluation according to a plurality of data sources, and obtaining a fire risk level of the target area; and generating a fire-fighting early warning instruction based on the fire risk level, and sending the instruction to the remote control terminal. According to the method, the technical problem that a traditional fire early warning method is single in monitoring means and is easily interfered by environmental factors to cause false alarm or missing alarm is solved, and the accuracy and response speed of fire early warning are remarkably improved.

The invention provides an intelligent picking robot and a monitoring system. Field environment monitoring data are generated through an environment monitoring module; identifying one or more target picking objects based on a pre-trained picking object identification model through a picking object identification module according to the field environment monitoring data and the acquired image data of each to-be-picked object; generating a three-dimensional map of a to-be-picked area and an optimal picking path through a picking navigation module; a picking execution control module is used for controlling the walking structure to arrive at each target picking position in sequence, and controlling the mechanical arm structure to execute a picking task; therefore, the intelligent picking robot can adapt to picking tasks of fruits and vegetables of different types and different maturity degrees, accurate recognition, positioning and picking of a target picking object can be achieved in a complex agricultural environment, and the fruit and vegetable picking efficiency and quality are improved.

The invention discloses a circuit breaker on-line monitoring edge terminal and a high and low speed synchronous acquisition method. The edge terminal comprises a high-speed acquisition card, a low-speed acquisition card, a time synchronization module and an edge computing host. And the high-speed acquisition card and the low-speed acquisition card are respectively connected with the circuit breaker monitoring sensor and respectively acquire signals at different sampling frequencies. And the time synchronization module realizes time synchronization of the high-speed and low-speed acquisition cards through a trigger signal generation unit and a timestamp alignment unit. The edge computing host comprises a data processing unit, a data storage unit and a data uploading unit and is responsible for filtering, feature extraction, fault diagnosis, historical data storage and data uploading of collected signals. The invention further provides a high and low speed acquisition time synchronization method, high and low frequency signals can be synchronized in real time in a circuit breaker operation event, the accuracy and synchronism of data acquisition are ensured, and therefore the reliability and comprehensiveness of monitoring are improved.

The invention relates to the technical field of municipal pipe network monitoring and maintenance, in particular to a municipal pipe network intelligent monitoring and comprehensive maintenance system, which is characterized in that a water supply pipe network node is provided with a data monitoring and acquisition module, and the data monitoring and acquisition module is connected with a data transmission module; the data processing module transmits data to an intelligent early warning and analysis module after the data is processed by the data processing module, and the intelligent early warning and analysis module is connected with a comprehensive maintenance management module. The data monitoring and collecting module is installed at the water supply pipe network node to comprehensively and accurately monitor and collect data in real time, the hybrid communication network and the edge computing gateway are adopted, the data are efficiently and stably transmitted and preprocessed, reliability and safety are improved, the block chain storage and artificial intelligence analysis platform is utilized, pipe network state diagnosis, fault early warning and trend prediction are assisted, and the system has a good application prospect. The intelligent early warning, analysis and comprehensive maintenance management module timely finds, locates and responds to pipe network faults, ensures safe and stable operation, improves efficiency and quality, and reduces cost.

The invention relates to the technical field of electric power intelligent judgment, in particular to an AI-based intelligent electric energy meter fault diagnosis system, which comprises an operation data acquisition module, a jump correlation judgment module, an operation deviation judgment module, a current waveform diagnosis module and an abnormity alarm module. According to the invention, through synchronous acquisition of the current data and the operation data of the refrigeration storage, whether the current jump is related to the gating action is judged, and the refrigeration performance abnormity of the refrigeration storage and the fault type of the intelligent electric meter are effectively identified by further combining multiple cross validation of baseline energy consumption and temperature change; an ideal current waveform is constructed through an AI algorithm and is compared with an actually measured waveform, so that intelligent judgment and classification of fault types are realized; meanwhile, the diagnosis result is linked with the operation and maintenance system, the response efficiency is improved, and the method is particularly suitable for scenes with high requirements on the stability of the electric meter, such as cold chain storage, and has good practical value and popularization prospect.

The invention relates to the technical field of environment monitoring, and discloses a radio-based environment monitoring method and system, and the method comprises the steps: deploying an ocean buoy integrated with a plurality of parameter sensors, and detecting the water temperature, salinity, dissolved oxygen, pH value and illumination intensity in real time; transmitting the data detected in real time to a shore-based receiving station by using a radio communication module; performing drift correction and noise filtering on data transmission detected in real time; performing data fusion analysis according to the mutual relation among different data, and generating a marine environment assessment report; and abnormal early warning information is generated according to the marine environment assessment report and is fed back to a monitoring center in real time through radio. According to the invention, through integration of various parameter sensors, comprehensive monitoring of multiple parameters of the marine environment is realized, and the accuracy and reliability of monitoring are improved.

The invention relates to the field of building structure monitoring, and discloses a large-span space steel structure roof enclosure system wind uplift resistance monitoring method and system.The method comprises the steps that a sensing layer is established based on a sensor to monitor roof sensing data, and a transmission layer is established through cooperative work of LoRa and an optical fiber composite cable to transmit the sensing data; an analysis layer is established through a dynamic wind-structure coupling model, a multi-source data fusion algorithm and early warning grading, and dynamic evaluation and risk early warning are carried out; according to the method, historical data is subjected to data preprocessing and feature extraction, Bayesian parameters are updated and deep learning compensation is constructed, sensor errors are adaptively compensated through drift modeling and multi-sensor cross validation compensation, and an early warning threshold value is dynamically adjusted based on the number of samples exceeding a threshold value.

Disclosed are a data acquisition optimization method and system for an NB-Internet of Things (IoT) gas device, a device, and a medium. The method comprises: acquiring a service registration packet of a target NB-IoT gas device, and parsing the service registration packet to obtain first service parsing data; performing decryption and enhancement processing on the first service parsing data to obtain first target data, and returning a registration success notification on the basis of the first target data and a corresponding device linkage rule; acquiring an actual service packet of the target NB-IoT gas device, and parsing the actual service packet to obtain second service parsing data; and performing decryption and enhancement processing on the second service parsing data to obtain second target data, returning a receiving success notification on the basis of the second target data and a corresponding device linkage rule, and forwarding the second target data to an IoT SaaS application platform. The present invention improves the acquisition success rate and the acquisition efficiency of data acquisition of NB-IoT gas devices, and can be applied to the technical field of IoT.

A while-drilling multi-element signal sensing and early warning method for coal rock dynamic disasters comprises the steps that in the early stage of coal seam recovery, bedding gas extraction drilling is utilized, while-drilling signals generated in the drilling period of each drill rod are collected in real time through a multi-parameter sensing system integrated with a drilling machine, and while-drilling data are obtained based on the while-drilling signals; constructing a mechanical parameter inversion model, and calculating the compressive strength of the coal rock mass; a gas parameter inversion model is constructed, and gas pressure and coal rock mass permeability are obtained through decoupling; according to the method, coal rock mass compressive strength, gas pressure and coal rock mass permeability are used as core indexes, a spatial interpolation method is adopted to realize continuous representation of disaster risk distribution of a whole area, and a comprehensive risk index is established accordingly; and according to the value range of the comprehensive risk index, further quantifying the risk level of the coal and rock mass dynamic disaster based on the comprehensive risk index. According to the method, real-time sensing, accurate inversion and active prevention and control of coal rock dynamic disasters can be realized, and the problems of low detection accuracy, poor real-time performance, insufficient overall performance and the like in the traditional technology can be effectively solved.

The invention discloses an intelligent motion capture system and method of a multi-mode sensor, and the method comprises the steps: creating and installing links among an optical module, an inertia module, a touch module, a synchronization core module and a to-be-detected object; sending a synchronous starting pulse signal based on the synchronous core module, setting the synchronous starting pulse signal as a clock reference starting point, and starting the optical module, the inertia module and the touch module based on the clock reference starting point; performing data acquisition on a to-be-measured object based on the optical module, the inertia module and the touch module to obtain an object acquisition information data set; and creating an acquisition original timestamp based on the synchronous start pulse signal, creating a sensor network delay model to dynamically adjust the acquisition original timestamp to obtain a calibration timestamp, and analyzing and correcting the time offset of the object data acquisition information. The method and the device have the effect of improving the accuracy of intelligent motion capture of the multi-mode sensor.

To provide a method of monitoring physiological status of a patient on the basis of patient data obtained from a plurality of sensor sources such as a bio-vibrational sensor and a radio frequency sensor and diagnosing cardiac failure symptoms early.SOLUTION: A patient monitoring device includes: an ECG sensor coupled to a patient; a sensor coupled to the patient and configured as bio-vibrational signals; and a radio frequency monitoring device configured to produce information responsive to electromagnetic energy reflected from the patient's thoracic cavity. A processor processes the ECG signals, the bio-vibrational signals, and the radio frequency information to generate a plurality of physiological parameters of the patient. The processor also performs at least one of a predictive analysis and a trend analysis of the plurality of physiological parameters to determine a current clinical condition of the patient. The trend analysis includes determining a substantial relationship between changes in the plurality of physiological parameters.SELECTED DRAWING: Figure 11

The embodiment of the invention discloses a real-time state monitoring method and device for high-frequency time sequence data and a medium, and relates to the technical field of the Internet of Things, and the method comprises the steps: obtaining a state monitoring task of target production equipment, carrying out the initialization configuration, building a multi-topic subscription channel corresponding to the state monitoring task, and carrying out the multi-topic subscription through the multi-topic subscription channel, acquiring multi-channel high-frequency sensing data of a field side; multi-channel high-frequency sensing data is stored to a time sequence storage node preset on the edge side by utilizing communication among multiple processes, and the time sequence storage node comprises a super table structure storage library constructed based on TD engine; and performing multi-thread analysis on the multi-channel high-frequency sensing data in the time sequence storage node according to an AI model node preset on the edge side, and determining real-time state data of the target production equipment. Through collaborative design of a multi-topic subscription channel, edge side time sequence storage optimization and multi-thread AI analysis, the high-frequency data real-time processing capability and the analysis efficiency are remarkably improved.

The invention relates to the technical field of inspection robots, and particularly discloses an underground pipe gallery environment detection system and method based on an inspection robot, and the system comprises a robot inspection system, a data transmission system, a monitoring system, an alarm system, and an artificial auxiliary inspection system. The robot inspection system comprises a pipeline, a charging storage bin, a controller, a carrying mechanism, a ground moving mechanism and an external detection mechanism. The carrying mechanism is arranged outside the charging storage bin; the ground moving mechanism is arranged outside the carrying mechanism; and the external detection mechanism is detachably placed outside the pipeline. Mobile inspection can be quickly and automatically carried out in the pipe gallery, inspection personnel are prevented from directly entering a dangerous environment, data are remotely analyzed in real time, early warning is carried out in time when fault hidden dangers are found, detection operation can be carried out along an external area of a pipeline and a ground area, and comprehensive state information of the pipe gallery is obtained at a time; and the time cost of multiple times of detection is reduced.

The invention discloses a power utilization acquisition intelligent anti-interference method in a heterogeneous network environment, and the method comprises the following steps: S1, carrying out the information collection of a power utilization acquisition node through a heterogeneous network, and obtaining the power utilization acquisition information; s2, constructing a feature data set, and training a BP neural network until convergence; s3, acquiring electric power acquisition information in real time, preprocessing the data, and sending the data into the training network to obtain a decision result; s4, the engine judges whether the decision parameters meet the current requirements or not according to the feedback transmission performance, and real-time dynamic adjustment is conducted according to the judgment result to resist interference, the invention further discloses a system for achieving the method, and the system belongs to the technical field of electric power data collection. The neural network can receive the processed signal in real time and quickly generate a decision result in a complex environment, dynamic optimization adjustment of system parameters is achieved to effectively suppress interference, and therefore it is ensured that the system can still operate stably in the complex interference environment and efficient data transmission is achieved.

The invention relates to the technical field of radio positioning monitoring, in particular to a multi-warehouse cooperative state monitoring method and system for intelligent warehouse logistics docking. Comprising the following steps: acquiring real-time state data of a plurality of warehouses, including warehouse cargo position data, equipment state data, logistics vehicle position data and personnel distribution data; analyzing the data by adopting a multi-warehouse collaborative state evaluation model, and identifying potential logistics bottleneck points and risk points; and according to an identification result, generating a multi-warehouse collaborative optimization scheme including logistics path planning, a resource allocation strategy and an emergency processing suggestion. Wherein the evaluation model adopts a multi-level architecture, and comprehensive and accurate risk identification is realized through combination of static environment monitoring and dynamic environment monitoring. The system forms a complete optimization-implementation-evaluation closed loop, so that the multi-warehouse cooperative operation efficiency is improved, the logistics cost is reduced, the ability of the system to cope with demand fluctuation and emergencies is enhanced, and a reliable guarantee is provided for upgrading and optimization of the intelligent warehouse logistics system.

The invention discloses a wireless temperature measurement system based on the Internet of Things, and the system comprises the steps: S1, at least one temperature measurement node which comprises a temperature sensor, a radio frequency identification module, a local electromagnetic interference detection module, and a self-adaptive frequency hopping control module; s2, a wireless data transmission module which is electrically connected with the temperature measurement nodes and is used for transmitting temperature data and interference detection data; s3, the central processing unit receives the temperature data and the interference detection data through the wireless data transmission module, and stores and processes the temperature data and the interference detection data; and S4, the power supply module provides working electric energy for the temperature measuring nodes, the wireless data transmission module and the central processing unit. The system has the advantages of being stable in data transmission, high in anti-interference capability, wide in coverage range and high in temperature monitoring precision.

The invention relates to the technical field of electric power inspection, and provides a multi-unmanned aerial vehicle cooperative electric power inspection method, system and device, a medium and equipment, and the method comprises the steps: obtaining environment information of a to-be-inspected area, inspection task information and equipment state information corresponding to a plurality of unmanned aerial vehicles; collecting multi-source sensor data of a to-be-inspected area based on multiple sensors on each unmanned aerial vehicle, and constructing a three-dimensional real-time environment model based on the multi-source sensor data and environment information of the to-be-inspected area; generating a task allocation scheme based on the three-dimensional real-time environment model, the inspection task information and the equipment state of each unmanned aerial vehicle; based on the scheme, a three-dimensional real-time environment model and a path planning algorithm, determining a flight path of each unmanned aerial vehicle; and controlling each unmanned aerial vehicle to inspect according to the flight path to obtain inspection information. According to the embodiment of the invention, efficient cooperative inspection of multiple unmanned aerial vehicles in a complex environment is realized, the accuracy and comprehensiveness of inspection are improved, and the efficiency and quality of electric power inspection work are effectively improved.

A conveyance system includes a transport vehicle configured to transport an article to or from a passive device and including a communication unit configured to communicate wirelessly with the passive device. The transport vehicle includes: a counter configured to generate a counter value corresponding to an elapsed time from a timing when reception has become impossible due to a communication failure during wireless communication with the passive device; and an output unit configured to output the counter value when a time-out error has occurred in the transport vehicle, and the counter resets the counter value whenever reception is possible.

The invention provides a data acquisition method, system and device based on HPLC and HRF dual-mode communication and a medium, and relates to the technical field of data acquisition. The data acquisition method based on HPLC and HRF dual-mode communication comprises the following steps: acquiring a pre-constructed data acquisition network topology, a networking state of the data acquisition network topology and an initial routing table; determining a target data transmission path according to the networking state, the data acquisition network topology and a preset shortest path calculation mode; generating a target routing table according to the target data transmission path and the initial routing table; and acquiring key load data according to the target routing table, allocating weights to the plurality of ammeters, determining a data acquisition sequence according to the key load data, the weights and a preset sorting mode, and acquiring power utilization data of the plurality of ammeters according to the data acquisition sequence. According to the method, the shortest path is calculated and the acquisition sequence is set, so that the accuracy and integrity of the data are ensured, and the precision of the acquired power consumption data is improved.

The invention provides a dual-mode communication meter reading anomaly analysis method and a related device. The method comprises the following steps: acquiring first power data of a target channel region and a target communication state of a dual-mode channel; performing data preprocessing and feature extraction on the first power data to obtain second power data; performing model training according to the second power data to obtain an anomaly detection model; analyzing the meter reading information of the target channel region to obtain a first anomaly analysis result; if the power line channel and / or the wireless channel are / is in the blocked state, determining that the target communication state is a second anomaly analysis result; determining a first target anomaly analysis result according to the first anomaly analysis result and the second anomaly analysis result; and if the power line channel and the wireless channel are both in the unblocked state, carrying out channel transmission test and channel switching test to obtain a third anomaly analysis result, and determining a second target anomaly analysis result according to the first anomaly analysis result and the third anomaly analysis result.

The invention discloses a portable spontaneous transcutaneous acupoint electrical stimulation device. The portable spontaneous transcutaneous acupoint electrical stimulation device comprises a wireless communication and control module; the wireless module is in wireless connection with an external control end to realize remote control and parameter setting; the electrical stimulation generation and output module is responsible for generating electrical stimulation information according to the received stimulation parameters and outputting the electrical stimulation information to human body acupuncture points through a plaster sheet or an ear clip; the physiological monitoring interface module is responsible for providing a connecting interface with other physiological monitoring devices; the stimulation parameter adjusting and feedback module is responsible for automatically adjusting stimulation parameters according to user settings and physiological monitoring data; the safety protection module is responsible for ensuring the safety of the equipment in the operation process; according to the invention, wireless connection is established between the wireless communication and control module and the external control end, so that remote control and parameter setting are realized, and a user can flexibly control the device through mobile equipment such as a mobile phone anytime and anywhere without being limited by a wire.

The invention is suitable for the technical field of coal seam seismic exploration, and particularly relates to a coal seam seismic exploration acquisition method and system, and the method comprises the steps: delimiting an exploration region where a coal seam earthquake needs to be carried out, dividing the exploration region into a mined region and an unmined region, obtaining a daily mining task in the mined region, and positioning a blasting point location; the unmined area is divided into a plurality of levels, the deployment position of an optical fiber sensor is positioned, the linear distance between the deployment position and a blasting point position is calculated, mapping between the linear distance and the deployment density is established, look-up tables are constructed, each level corresponds to one look-up table, and each look-up table corresponds to a blasting point position; the lookup table is composed of a linear distance item and a deployment density item. According to the method, the coal seam earthquake can be quickly acquired by generating the snapshot, so that the speed and the precision of coal seam seismic exploration can be greatly improved, and the safety management and the resource utilization of an exploration area can be greatly optimized.