640results about How to "Reduce false positive rate" patented technology

The invention discloses an intelligent nursing management system and method for a critical patient, and relates to the technical field of medical health monitoring, and the method comprises the steps: setting a multi-physiological signal collection channel, synchronously collecting multi-dimensional physiological signals of the patient, carrying out the preprocessing of the collected signals, and forming a standardized multi-modal physiological data flow; the preprocessed multi-modal physiological data are input into a deep learning model based on LSTM and a multi-head attention mechanism, intelligent fusion analysis of different physiological signals is achieved in combination with a dynamic weight adaptive algorithm, and a patient individualized physiological system coupling model is established; and based on an analysis result of the deep learning model, calculating an illness state risk assessment value of the patient in real time, and outputting a specific early warning level according to a set multi-level early warning threshold. According to the invention, the multi-stage and multi-dimensional early warning threshold system is constructed, so that the condition of the patient can be monitored and early warned in all directions. The monitoring quality of critically ill patients can be remarkably improved, and the missed judgment rate and the misjudgment rate are reduced.

The invention provides a method and system for realizing electronic channel risk control processing based on a code insertion technology, and relates to the technical field of electronic channel risk control, and the method comprises the steps: encrypting user information and channel identification information through a dual Hash algorithm to generate a dynamic session identification code, and forming a target coding sequence through employing a self-adaptive segmentation algorithm and a business feature flag bit. And constructing a dynamic risk control rule model by using a graph neural network, extracting user multi-dimensional features, carrying out weighted fusion to generate a user risk score, comparing the user risk score with an adaptive risk threshold, analyzing abnormal behaviors based on a probability graph model, and generating an intelligent risk early warning report. And calling a reinforcement learning risk control strategy engine to determine an optimal disposal strategy, and issuing a multi-factor interaction verification instruction. And after verification, selectively activating a blocking mechanism according to a verification result, synchronizing interaction data and a result to a distributed risk control data center, and continuously optimizing the risk control model based on federal learning.

The invention provides a space competition situation threat level evaluation method and system based on credibility weighting, and relates to the technical field of information, and the method comprises the steps: constructing a threat feature vector based on historical orbit data and an operation state, and obtaining an initial target threat probability vector through a deep learning model; then, a credibility weighting coefficient is calculated based on the multi-source situation awareness data, and an initial threat probability vector is corrected in combination with a feature weighting coefficient; and finally, classifying target threat levels by adopting an integrated learning method, determining a target priority in combination with the target orbit parameters and an ant colony algorithm, and outputting a threat target sequence and a situation threat assessment report. By fusing the multi-source situation awareness data and the credibility weighting mechanism, the threat level of the space target can be evaluated more accurately, and the reliability and efficiency of space situation awareness are effectively improved.

The invention proposes a GIS equipment partial discharge mode recognition method based on deep learning, and relates to the technical field of GIS partial discharge recognition, and the method comprises the steps: collecting a partial discharge signal of GIS equipment through an ultrahigh frequency sensor, and carrying out the signal conditioning; performing digital sampling and processing on the conditioned partial discharge signal to generate a phase-resolved pulse sequence (PRPS) map, and performing intelligent noise reduction processing on the PRPS map, the PRPS map including three-dimensional information of phase, amplitude and frequency; inputting the PRPS atlas into a preset neural network model, and extracting atlas features; and identifying and classifying the partial discharge type based on the extracted map features, and outputting an identification result. According to the method, automatic acquisition, intelligent processing and accurate identification of the partial discharge signals are realized, and compared with a traditional manual identification method, the identification efficiency and accuracy are greatly improved, and reliable technical support is provided for state monitoring and fault diagnosis of GIS equipment.

The invention relates to the technical field of traffic safety management, in particular to a vehicle abnormal driving recognition and early warning method and system. The method comprises the following steps: firstly, acquiring vehicle position, speed, direction and driver operation data in real time, then correcting time sequence data by adopting a smooth denoising method of adaptive segmented clustering, generating an information confirmation code through standardization, and then carrying out weighted fusion on multi-source data to obtain a multi-source data fusion model; the strategy decision-making module is used for extracting vehicle speed change rate, lane deviation degree and driver behavior characteristic key indexes, identifying and grading abnormal driving behaviors by setting a threshold value, calculating a comprehensive abnormal score, and generating a grading intervention strategy for slight, medium and serious anomalies according to a comparison result of the abnormal score and the set threshold value. And finally, combining automatic intervention in the vehicle with global coordinated intervention of a road manager. According to the invention, the intervention strategy is generated based on multi-source data acquisition, processing, analysis and decision, so that the vehicle driving safety and the road management efficiency are improved.

The invention discloses a human body tumble identification method based on millimeter wave radar perception. The method comprises the following steps: acquiring echo data of various human body actions by using a millimeter wave radar; processing the echo data to respectively obtain point cloud data of human body actions; a DBSCAN clustering method is used for clustering point cloud data, point clouds are divided into different clusters based on a central point, then the obtained point cloud data are input into a deep neural network, space structure and dependency relationship information of the point cloud data is captured, the category of human body actions is recognized according to the space structure and dependency relationship information, and a human body tumble recognition result is output. According to the invention, a deep neural network model fusing a PointNet network and a Transform module is designed, and the model can enhance the description of the spatial relationship of point clouds, thereby improving the judgment of human body tumble recognition, and reducing the false alarm and false alarm rate. According to the invention, human body postures and actions are sensed through millimeter wave radar signals, human body tumble recognition is realized, and the system has the advantages of good anti-interference performance, high recognition accuracy and the like.

The invention relates to the technical field of genomics, in particular to a gene detection data cleaning method and system based on artificial intelligence. The method comprises the following steps: acquiring gene detection data, and performing preliminary filtering on the gene detection data based on removal of low-quality reads to obtain preliminary filtering data; performing linker sequence identification and removal on the preliminarily filtered data, and performing sequencing error correction to generate gene sequence data; performing GC content analysis and repetitive sequence identification on the gene sequence data to obtain sequence feature data; performing dynamic quality threshold adjustment according to the sequence feature data, and performing secondary filtering on the gene sequence data to generate high-quality sequence data; and performing sequence pattern recognition on the high-quality sequence data to obtain sequence pattern feature data. According to the method, deep analysis is carried out by utilizing sequence characteristics such as GC content and nucleotide distribution, and the recognition capability on sequence complexity and variation modes is improved.

The invention relates to the technical field of equipment risk management and control, and particularly discloses an equipment risk management and control system based on model prediction control, which comprises a data acquisition processing module, a data analysis module, a model construction prediction module and an analysis regulation and control module, obtaining an equipment health condition score through standardization processing and weighted calculation; the data analysis module is used for distinguishing linear and nonlinear characteristic signals according to the score change trend; the model construction and prediction module selects corresponding algorithms to establish a prediction model according to different types of signals, so as to predict the health state of the equipment in the future; the analysis regulation and control module formulates a reasonable equipment maintenance plan based on the prediction result, and dynamically adjusts the production arrangement, the equipment state is continuously monitored and intelligently analyzed, faults can be effectively prevented, the probability of occurrence of non-planned shutdown events is reduced, and finally the purpose of improving production safety and economic benefits is achieved.

The invention discloses a multi-source modal data fusion-oriented charging pile fault diagnosis method and system. The method comprises the following steps of: acquiring message stream data, audio data and infrared sensing data associated with a charging equipment fault through a sensor system; and based on the characteristics of each modal data, fault characterization and identification are carried out by adopting a random forest, a convolutional neural network and a YOLO-v8 target detection model. According to the marked fault data, a mapping relation among multi-source data is established, and a Dempster-Shafer multi-source fusion theory is utilized to aggregate a multi-modal fault judgment result, so that the equipment fault is accurately diagnosed. According to the method, the accuracy of fault diagnosis and diversified presentation of fault types are effectively improved, the repair rate is reduced, and the method has a good engineering application prospect.

The invention relates to the technical field of financial data processing, in particular to a data processing method and system based on financial information bills. The data processing method comprises the following steps: acquiring multi-modal financial information bill data; and performing bill modal decomposition on the multi-modal financial information bill data to generate financial information bill modal decomposition data. Bond information price income prediction needs a large amount of financial data, such as market interest rate, bond expiration date, face interest rate and the like, the data are similar to the data form of bill modal decomposition, and a more accurate model can be obtained through standardization and feature extraction of multi-modal data. Therefore, valuable input is provided for bond price and income prediction. According to the invention, through multi-modal data fusion, counterfeited feature comparison modeling, abnormal bill clustering analysis and dynamic risk assessment, the security and accuracy of financial bill identification are improved.

The invention relates to a PICO extraction method and device based on a large language model and a storage medium, and is applied to the technical field of medical information, and the method comprises the steps: obtaining and preprocessing titles and abstracts of a large number of medical literatures, then combining preset cues related to PICO with texts to construct an instruction data set, and storing the instruction data set in a database; a text in an instruction data set is subjected to word segmentation processing to be converted into an input sequence, a large language model framework is constructed, a false positive suppression loss function is set, the false positive rate of PICO element extraction by the model is remarkably reduced, the large language model framework is trained through the input sequence, and a trained PICO element extraction large language model is obtained. Preprocessing medical text data to be retrieved, inputting the preprocessed medical text data into the trained model, and outputting a PICO extraction text by the model; and finally, each PICO classification result is output through the PICO classifier. The data are data related to the crowd / problem, intervention, comparison and result in the to-be-retrieved data.

The invention discloses an abnormal defect detection method and system applied to PCB intelligent welding, and relates to the technical field of PCB welding detection, and the method comprises the following steps: carrying out the image collection of a whole PCB through a high-definition camera, and recognizing and positioning all welding spots on the PCB through an image segmentation technology; and each welding spot is detected according to the initially set expected resolution, and after the welding spots with obvious welding problems are recognized, the recognized welding spots are screened out. According to the method, the welding spots are accurately positioned through the image segmentation technology, the detection efficiency is rapidly screened and improved by using the initial resolution, and obvious non-pseudo-soldering welding spots and potential pseudo-soldering welding spots are efficiently classified in combination with a deep learning model. Aiming at potential cold solder joints, the resolution is dynamically adjusted, high-resolution fine detection is performed, the cold solder problem is accurately identified, the detection accuracy and precision are remarkably improved, the omission ratio and the misjudgment ratio are reduced, and reliable technical support is provided for PCB intelligent welding quality control.

The invention discloses an intelligent self-adaptive real-time task scheduling method and system based on multi-dimensional optimization. The method comprises the following steps: S1, inputting a task; s2, a DAG task is decomposed into independent sequential sub-tasks; s3, dynamically calculating the priorities of the tasks by adopting a multi-factor dynamic priority scheduling module, and updating the priorities of the tasks; s4, dynamically adjusting an acceleration factor by adopting a self-adaptive acceleration factor management module; s5, vertex correlation analysis is carried out, and a variance of processor load imbalance is calculated by adopting a load balanced distribution algorithm and is minimized; s6, in combination with static analysis and dynamic inspection, a hybrid schedulability judgment module is adopted to judge the tasks, and judgment results are fused; and S7, fusing decisions by adopting a decision fusion algorithm, obtaining a scheduling strategy through a scheduling strategy coordinator, executing the task, and monitoring the task in real time. Real-time monitoring and real-time optimization scheduling are realized, and the utilization rate of resources is improved.

The invention discloses a drawing projection talent selection test system and method based on combination of hierarchical quantum evolution and particle swarm optimization. The system comprises a sensing data acquisition module, an intelligent situation identification preprocessing module, a multi-dimensional feature collaborative extraction module, a distributed intelligent quantitative analysis module, a self-adaptive intelligent prediction module and a multi-source data dynamic evaluation module. All the modules work cooperatively, and perceptual acquisition, intelligent preprocessing and depth feature extraction of multi-dimensional drawing projection data are achieved, and efficient quantitative analysis, intelligent prediction and dynamic evaluation are achieved. Through the combination of the hierarchical quantum evolutionary algorithm and the improved particle swarm algorithm, the drawing projection data can be subjected to multi-level and refined analysis, an accurate and automatic talent screening tool is provided, the accuracy and the intelligent level of evaluation are remarkably improved, and the method has a wide application prospect.

The invention relates to the technical field of meteorological cloud detection, and discloses a satellite-borne hyperspectral cloud layer identification method based on a dual-band fusion technology, and the method comprises the steps: firstly obtaining a hyperspectral image, carrying out the preprocessing of the hyperspectral image, and obtaining a spectrum calibration image; respectively calculating the visible light wave band transmittance and the near-infrared wave band transmittance of each pixel in the spectrum calibration image; identifying a semitransparent weather phenomenon area and a non-semitransparent weather phenomenon area according to the visible light wave band transmittance and the near-infrared wave band transmittance of each pixel; and finally, constructing a real cloud layer spectral feature model, performing real cloud layer identification on the non-translucent weather phenomenon area by using the model, and fusing the identified real cloud layer area and the translucent weather phenomenon area to generate a final cloud detection result graph. According to the method, spectral information of visible light and near-infrared wavebands is fused, a real cloud layer spectral feature model is constructed for cloud detection, and the cloud detection precision under complex atmospheric conditions is improved.

The invention discloses a chromogenic culture medium for detecting gram-negative bacteria in bovine mastitis and a test plate thereof, the culture medium comprises a nutritional ingredient mixture, a composite inhibitor, a composite chromogenic enzyme substrate and cold water gel, the compound chromogenic enzyme substrate is red tetrazole, 5-bromo-6-chloro-3-indole-beta-D-galactoside, and 5-bromo-4-chloro-3-indole-beta-D-glucopyranoside, and the compound chromogenic enzyme substrate is a compound chromogenic enzyme substrate, and the compound chromogenic enzyme substrate is 5-bromo-6-chloro-3-indole-beta-D-glucopyranoside. The chromogenic culture medium and the test board prepared from the chromogenic culture medium can effectively inhibit the growth of non-gram negative bacteria, have the characteristics of strong specificity, high sensitivity, short detection period, easy observation and low detection cost, and have huge development potential and wide application prospects in microbiological detection.

The invention belongs to the technical field of mechanical engineering and structural health monitoring, and particularly relates to an ultrasonic bolt pre-tightening state judgment method based on empirical wavelet transform and random forest. The invention provides an ultrasonic bolt pre-tightening state judgment method based on empirical wavelet transform and random forest. The method comprises the following steps: step 1, acquiring ultrasonic echo signals of a bolt in different stress states, and marking the signals according to the stress states during acquisition; environment temperature and bolt medium temperature are extracted to serve as input of a follow-up model; step 2, carrying out adaptive filtering processing on the collected original ultrasonic echo signal to optimize the signal quality, and removing noise and irrelevant frequency components; and 3, taking the data obtained in the step 2 as the input of empirical wavelet transform (EWT).

The invention provides a multi-path street lamp data acquisition and analysis method based on a centralized controller, relates to the field of intelligent measurement, and realizes intelligent monitoring and fault detection of running states of street lamps through group control, centralized data acquisition and dynamic analysis of the street lamps. According to the method, grouping is carried out based on a distribution network connection relation of street lamps, switching operation of each street lamp group is controlled through a centralized controller, and operation data such as current, voltage and load are collected. And for the street lamp which operates abnormally, through multiple times of data acquisition and trend analysis, an accurate fault detection result is generated. The invention further provides a method for dynamically adjusting the grouping strategy and the delay time, and the accuracy and the detection efficiency of data acquisition are improved. According to the invention, comprehensive monitoring of the operation state of the street lamp in a complex environment can be realized under the condition that the hardware cost is limited, and the method is especially suitable for management and maintenance of an urban intelligent lighting system.

The invention relates to the technical field of power equipment detection, in particular to a method for detecting turn-to-turn short circuit of an iron core reactor, which is used for solving the problems that the turn-to-turn short circuit risk of the iron core reactor cannot be comprehensively evaluated, the state change of equipment cannot be captured in real time and the sensitivity and accuracy of turn-to-turn short circuit risk monitoring are reduced in the prior art. According to the method, the inter-turn short circuit risk of the iron core reactor is comprehensively evaluated by integrating the operating current, the direct-current resistance, the inductance value and the temperature parameters of key parts, and the state change of equipment is captured in real time by dynamically calculating the change rate of each parameter, so that the monitoring sensitivity and accuracy are remarkably improved; the logistic regression model trained based on historical data can accurately predict the turn-to-turn short circuit probability, automatic risk monitoring is achieved, manual intervention is reduced, and the misjudgment rate is reduced.

The invention discloses a document proofreading processing method based on a large model. The document proofreading processing method comprises the following steps: step 1, preprocessing a document and identifying the document as a text; step 2, segmenting the text into independent text blocks; step 3, performing vectorization processing on the text blocks; step 4, performing retouching on the text block by using the random degree large model to obtain a new text block; step 5, performing vectorization processing on the new text block; 6, comparing the vector similarity of the text block and the new text block, if the similarity is high, entering the next step, and if the similarity is low, repeating the steps 4-6; 7, comparing the text of the text block with the text of the new text block, and marking difference characters in pairs; and 8, proofreading the text blocks and the new text blocks according to the document, and integrating and outputting the text blocks and the new text blocks. According to the method, more accurate and comprehensive document proofreading service can be provided, dynamic change of languages and diversified document requirements can be met, and the specialty and credibility of documents are remarkably improved.

The embodiment of the invention relates to the technical field of power systems, and provides an island detection method and device, and the method comprises the steps: carrying out the periodic bilateral reactive power disturbance of an energy storage converter in a distributed power generation grid-connected system; under the condition that the frequency change of the power grid common coupling point in the periodic bilateral reactive power disturbance process meets a first preset condition, performing unilateral reactive power disturbance on the energy storage converter, and determining the frequency variation of the power grid common coupling point after the unilateral reactive power disturbance to obtain a unilateral disturbance frequency difference; and when the unilateral disturbance frequency difference is greater than a first preset frequency variation threshold, determining that the distributed power generation grid-connected system is in an island state. Under the condition that the output power of the energy storage converter is highly matched with the load power, the island phenomenon is effectively detected.

The invention discloses an internet of things-oriented federated learning anti-poisoning attack method. The method comprises the following steps of S1, initializing an internet of things federated learning system; s2, performing mask processing on the privacy data of the Internet of Things participants; s3, the cluster server collects mask data provided by all the Internet of Things participants and aggregates the mask data; and S4, identifying malicious poisoning participants of the Internet of Things through robust clustering. According to the method, an advanced machine learning algorithm and a privacy protection technology are introduced, a more effective anti-poisoning protection measure is provided for the Internet of Things system, the security and robustness of the Internet of Things system are remarkably improved through successful application of the method, and an important guarantee is provided for coping with potential hostile attacks and data tampering.

The invention relates to the technical field of intelligent household appliances, and discloses a control method for preventing dry burning of a stove, and the control method comprises the following steps: detecting the current stove fire power, and determining the current water cooking condition corresponding to the current stove fire power; collecting cookware temperature information under the current cooker fire power; and under the condition that the pot temperature information meets the current water cooking condition, executing corresponding dry burning prevention operation according to the water cooking state and the pot temperature information. The corresponding water cooking condition is determined by combining the current cooker fire power, the cooking state is judged, and therefore the dry burning prevention operation corresponding to the cookware temperature information and the cooking state is executed. The accuracy of judging the cooking state under the current stove fire is improved, then the accuracy of anti-dry-burning control is improved, and the misjudgment rate of anti-dry-burning is effectively reduced. The invention further discloses a control device for preventing dry burning of the kitchen range, the kitchen range and a computer storage medium.

The invention discloses a distance-speed deception jamming identification method based on space-time-frequency feature fusion. The method comprises the following steps: radar signal spatial domain feature statistics are selected; radar signal frequency domain feature statistics are selected; radar signal time-space domain feature statistics are constructed; constructing time-frequency domain characteristic statistics of the radar signals; and radar signal space-time frequency fusion feature judgment. According to the method, the condition that a radar net is subjected to distance-speed composite deception jamming is taken as a research background, and after possible target tracks are formed for echo trace points by utilizing the characteristic differences of a real target and a false target in a space domain, a frequency domain and a time domain, the space domain characteristics and the frequency domain characteristics of the echo trace points at a single moment are extracted; and over-threshold detection is carried out on the statistics corresponding to the spatial domain features and the frequency domain features at each moment, and the detection result is represented by '0' or '1'. And finally, the detection results are accumulated at multiple moments, so that the judgment on whether the radar is subjected to distance-speed composite deception jamming or not is completed.

The invention discloses a human body induction intelligent control system based on an infrared and radar combination technology, and relates to the technical field of human body induction, and the system comprises a control end, an induction end and a power-on end. Wherein after initialization setting, the control end is used for judging a corresponding mechanism to be executed, receiving a signal sent by the sensing end and controlling a power supply on-off condition of the electrifying end according to a result of the corresponding mechanism; according to the technical key points, in three aspects of micro-motion judgment, motion judgment and infrared source judgment, a mode of combining a main sensor and an auxiliary sensor is adopted, so that the accuracy and reliability of the system are improved, lamplight and a power supply are automatically controlled to be turned on and turned off according to the activity condition of personnel, and the energy consumption is accurately reduced; lighting lamps and other electric equipment in the system are automatically managed in a unified manner, so that the safety and the energy-saving property are improved, the energy cost is reduced, and the operation cost is reduced; the system can be seamlessly integrated with an existing smart home or building management system.

The invention discloses a network information intelligent analysis method and system based on a large model, and the method comprises the steps: firstly carrying out the preprocessing of network information, constructing a prompt word template through a multi-step thinking method, and processing the network information; calling the large model to obtain a network information analysis result presented by JSON format data; performing format judgment on the analysis result by using a feedback optimization method, and if the format requirement is not met, repeatedly calling the large model, and updating the network information analysis result; and performing JSON format matching on the analysis result of which the output format of the large model still does not meet the requirement to obtain an analysis result meeting the JSON template. According to the method, emotion analysis can be more accurately carried out on the network information, and the efficiency and accuracy of analysis work are remarkably improved.

The invention provides a sightseeing vehicle chassis fault early warning system combined with a sensing technology, and relates to the technical field of fault early warning, and the system comprises a real-time monitoring module which is used for obtaining multi-dimensional operation state data; the database retrieval module is used for retrieving to obtain a chassis fault sample data set; the clustering analysis module is used for performing clustering analysis to obtain a multi-dimensional chassis fault sample data set; the model construction module is used for constructing a chassis fault positioning model; the multi-dimensional feature extraction module is used for performing multi-dimensional feature extraction and outputting a vibration fault positioning result, a stress fault positioning result, a heat fault positioning result and an atypical fault positioning result through a chassis fault positioning model; and the fault early warning signal generation module is used for generating a fault early warning signal. According to the invention, the technical problem that the precision of fault identification is insufficient because the fault diagnosis of the chassis of the sightseeing vehicle mostly depends on a single sensor for monitoring and the multi-dimensional operation state of the chassis is difficult to comprehensively perceive in the prior art is solved.

The invention relates to the technical field of intelligent household appliances, and discloses a control method for preventing dry burning of a cooker, and the control method comprises the steps: obtaining the current boiling frequency, and obtaining the temperature information of the cooker under the condition that the current boiling frequency is smaller than or equal to a boiling frequency threshold value; the current stove fire power is obtained, and the current water-free cooking condition corresponding to the current stove fire power is determined; under the condition that the cookware temperature information meets the current water-free cooking condition, if the cookware temperature information meets the safe cooking judgment condition corresponding to the water-free cooking state, continuously obtaining the current boiling frequency; wherein the safe cooking judgment condition corresponding to the water-free cooking state is determined according to the fire power of the current cooker. When the water-free cooking state is determined and the safe cooking judgment condition is met, the accuracy of dry burning prevention control is improved by continuously identifying the water cooking state. The invention further discloses a control device for preventing dry burning of the kitchen range, the kitchen range and a computer storage medium.

The present invention discloses a battery module safety status assessment method and device based on a convolutional neural network. The method comprises: extracting the output voltage, output current and surface temperature at the center position of battery cells under different decay states and different working states to form a three-parameter collaborative assessment database, and dividing the samples in the database into a training set and a test set; inputting the training set into the convolutional neural network; inputting the test set into the convolutional neural network, and if the output accuracy does not meet the requirements, returning to the above steps until the output result accuracy meets the requirements; inputting the battery module data collected in real time into the final convolutional neural network model to obtain the safety status information and health status information of the battery cell; the advantages of the present invention are: simultaneously assessing the safety status and health status of the battery cell, identifying the battery cell that is about to or is experiencing thermal runaway, and thus timely discovering the overall risk of the energy storage battery module.

The invention relates to the technical field of gas detection, in particular to a gas detection method and system for a dangerous workplace. The method comprises the following steps: acquiring gas concentration and position information of each detection point in real time; dividing the whole detection area into a plurality of grids, and calculating the concentration density of each grid by adopting a kernel density analysis method based on the gas concentration and position information of each detection point; based on the concentration densities of all grids, constructing a gas concentration plane distribution diagram of the whole detection area; based on the gas concentration plane distribution diagram, manual inspection is carried out on the gas concentration overrun position, and the reliability and accuracy of gas detection in the working environment where gas diffusion is complex or uneven are guaranteed.