5429 results about "Statistical analysis" patented technology

The invention discloses an intelligent robot spraying method based on a spraying system. Firstly, three-dimensional contour data of a workpiece are obtained through three-dimensional scanning equipment, a model is constructed, and key information is marked. And then according to the model, the workpiece material, the spraying requirement and the environment condition, a spraying path is planned through an intelligent algorithm, coating parameters are set, a robot executes spraying in sequence, and real-time monitoring is conducted through a high-precision position and a visual sensor in the process. After spraying is completed, the quality is detected through detection equipment, and when the requirements are not met, the computer system adjusts parameters according to deviation and directs the robot to conduct supplementary spraying or re-spraying. Whole-course data are recorded, and technological parameters and a path algorithm are optimized through statistical analysis. According to the method, high-precision, intelligent and efficient spraying is achieved, the method adapts to various workpiece and process requirements, the spraying quality and the production efficiency are improved, good adaptability and expandability are achieved, and intelligent development of industrial spraying is powerfully promoted.

The invention discloses a hybrid energy storage system optimization scheduling method based on AI intelligent regulation and control, and relates to the technical field of hybrid energy storage, and the method comprises the following steps: collecting real-time data, and carrying out the cross verification of data consistency through a multi-source data fusion technology; and identifying data abnormity caused by sensor faults, communication delay and environmental interference by combining adaptive threshold detection with a statistical analysis method, and eliminating abnormal data. According to the method, energy storage scheduling is optimized through data cleaning, time sequence prediction and reinforcement learning, and the intelligent management and dynamic adaptive capacity is improved. Multi-source data fusion and anomaly detection are adopted to ensure data accuracy, energy consumption is predicted by means of LSTM and Transform, and an energy storage strategy is optimized in advance. By combining reinforcement learning and system dynamic adjustment charging and discharging, the photovoltaic consumption rate is improved, the electricity purchasing cost is reduced, the SOC is intelligently controlled to be 40%-80%, and the service life of the battery is prolonged. Meanwhile, the system stability is improved through anomaly detection and correction, and the maintenance cost is reduced.

The invention discloses a method and system for dynamically calling a Java statistical analysis interface based on LLM and MCP protocols, belongs to the technical field of data processing and analysis technology, artificial intelligence technology and application program interface (API) integration, and aims to solve the technical problem of how to improve the response speed and flexibility of statistical analysis requirements. In order to reduce the development and maintenance cost of a statistical analysis function, the adopted technical scheme is as follows: a Java annotation mechanism is utilized to mark and describe an existing Java statistical analysis system, a Java method of which the annotation is defined is obtained, and the Java method of which the annotation is defined can be automatically identified by an MCP server and registered as an MCP tool; the MCP server follows an MCP protocol and communicates with the LLM serving as an MCP client, and the LLM dynamically discovers available MCP tools through the MCP protocol; after natural language query of a user is understood through LLM, one or more appropriate MCP tools are intelligently selected, calling parameters are generated, and an MCP server is requested to execute through an MCP protocol.

The invention discloses a marketing decision analysis system and method based on artificial intelligence, and aims to improve the intelligent level of marketing decision, optimize resource allocation and improve the rate of return on investment. The system comprises a data analysis module, a prediction evaluation module, an AI decision engine and a knowledge management module. The data analysis module obtains marketing-related data from a plurality of data sources and generates a market insight result. And the prediction evaluation module predicts the potential effect of the marketing scheme by adopting a statistical analysis method based on the market insight result and the historical marketing data, and obtains a marketing prediction result. And the AI decision engine receives the market insight result and the marketing prediction result, and determines an optimal marketing decision scheme based on multi-round dialogue management, knowledge graph construction and decision reasoning technologies. According to the marketing decision analysis system and method, accurate, efficient and reusable marketing decisions are realized in a data driving mode, and the scientificity and the performability of enterprise marketing strategies are improved.

The invention discloses a CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, and relates to the technical field of carbon sink monitoring and optimization. According to the CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, space-time estimation is carried out on CCER forestry carbon sink project data through the deep learning model, and the dynamic change of the carbon sink amount is accurately predicted. Spatial features are extracted through a convolutional neural network, time sequence data are analyzed in combination with a long-short-term memory network, and a carbon sink prediction model is constructed. And through statistical analysis and a spatial interpolation algorithm, carbon sink quantity change is monitored in real time, an abnormal trend is identified, and early warning is provided. Based on a mixed integer linear programming algorithm, forest land resource allocation and vegetation types are optimized, a carbon sink optimization scheme is generated, and carbon sink benefit maximization is promoted. And through an intelligent carbon sink management scheme, sustainable ecological development and green and economic popularization are assisted.

The invention discloses a low-altitude unmanned aerial vehicle flight statistical system, and relates to the technical field of air traffic management. A flight statistical system of a low-altitude unmanned aerial vehicle comprises a multi-source sensing fusion module, a twin airspace modeling module, a behavior portrait modeling module, a non-cooperative target recognition module, a flight statistical analysis module and a self-adaptive scheduling module. According to the invention, a three-dimensional twin airspace model of a monitoring area is constructed based on high-precision map data, a building three-dimensional model and flight information, and space mapping and collision risk analysis of a flight path are carried out, so that real restoration of the flight path and environment collision prediction are realized; the behavior feature vector is generated and the random forest algorithm is adopted to carry out classification modeling, and the behavior portrait of the unmanned aerial vehicle is constructed, so that accurate identification and classification of the behavior pattern of the unmanned aerial vehicle are realized.

An apparatus and method for real-time assessment and mapping of quality of life indicators includes: selecting a set of quality of life dimensions, receiving objective quality of life indicators based on the quality of life dimensions, receiving subjective quality of life indicators based on the quality of life dimensions, performing a statistical analysis on the objective quality of life indicators and the subjective quality of life indicators, storing the objective quality of life indicators, the subjective quality of life indicators, and the results of the statistical analysis in a database, and outputting the objective quality of life indicators, the subjective quality of life indicators, and the results of the statistical analysis.

The invention relates to the field of information management, and discloses a constructional engineering information management system, which comprises a data acquisition module, an intelligent analysis module, a risk prediction module, a resource optimization module and a feedback learning module, the data acquisition module is used for acquiring multi-source data related to constructional engineering; the intelligent analysis module carries out fusion, preprocessing and feature extraction on the collected data; the risk prediction module predicts possible risks in the future by using a statistical analysis model; the resource optimization module dynamically adjusts and optimizes manpower, equipment, materials and process arrangement of the construction site according to the risk prediction result; and the feedback learning module is used for collecting a system execution result, risk prediction accuracy and user feedback, and recording an optimized execution result. The method has the advantage of realizing efficient engineering management and decision support.

The invention provides an innovative deep concealed ore deposit detection method, which comprehensively utilizes geology, geophysics, geochemistry and remote sensing technologies, and realizes effective integration of multi-source geological data by developing an advanced data fusion algorithm. According to the method, data collected by geological survey, wide-area electromagnetic method exploration, hydrocarbon-mercury superposition halo analysis and aviation hyperspectral remote sensing are subjected to standardized preprocessing, so that the consistency and accuracy of the data are ensured; and then, deep analysis is carried out on the fused data by utilizing machine learning and statistical analysis technologies so as to identify and predict mineralization modes and abnormal regions. Finally, a three-dimensional geologic model is constructed, the form and distribution of an ore deposit are visually displayed, and a scientific basis is provided for geological exploration. The method is characterized in that a multi-source data fusion technology and mineralization mode identification automation and precision are realized, so that the efficiency and the accuracy of deep hidden ore deposit detection are remarkably improved.

The invention discloses a user data intelligent protection method and system based on differential privacy, and belongs to the technical field of data protection, and the method specifically comprises the steps: collecting and preprocessing user health data; setting a dynamic privacy budget according to the data sensitivity, the analysis purpose and the privacy demand, and adding random noise to generate disturbance data by adopting a multi-strategy fused data disturbance strategy; statistical analysis is carried out, and an improved differential privacy algorithm is used to assess the privacy leakage risk, the protection degree and the data availability; based on an evaluation result, a privacy budget and a disturbance strategy are iteratively adjusted through a feedback loop mechanism; the health data analysis method provided by the invention not only can protect user privacy, but also can ensure data availability.

The invention discloses an abnormal behavior detection method based on multi-modal fusion and interpretability analysis, and the method comprises the steps: carrying out the data formatting, and converting unstructured behavior data into structured employee behavior data; thirdly, constructing a daily employee behavior sequence and extracting daily employee behavior characteristics; constructing a daily interaction graph and extracting an embedded representation of each employee node; statistical analysis is introduced to obtain daily employee statistical characteristics; splicing the three features to obtain a daily multi-modal feature fusion vector; and finally, identifying an abnormal vector set through an LOF algorithm, and carrying out importance analysis by adopting an SHAP value analysis method to output an abnormal reason. According to the method, multi-angle and multi-level feature representation is constructed, so that behavior diversity is captured more comprehensively, and perception and representation of complex user behaviors are improved; and meanwhile, fusion detection is carried out based on behavior feature importance analysis, so that abnormal employees can be locked, abnormal behavior reasons can be traced, and the interpretability of an abnormal detection result is enhanced.

A pressure sensor calibration method based on environmental interference compensation relates to the technical field of pressure sensor calibration, and comprises the following steps: acquiring an output value and environmental data of a target pressure sensor, marking acquisition time, and setting an acquisition period; performing statistical analysis on the environmental data of the target sensor in each historical acquisition period to obtain key values of each type of environmental parameters; a feature database is constructed, a simulation experiment is carried out based on the environment feature sets in the feature database, and pressure deviation values under different environment feature set conditions are obtained; and constructing a deviation output model, inputting the environmental data of the current acquisition period into the feature database for feature matching, and performing compensation adjustment on the target pressure sensor based on the deviation output model according to a feature matching result, or performing flexible compensation adjustment operation on the target pressure sensor. Therefore, the measurement precision, the stability and the adaptability of the pressure sensor are obviously improved.

The invention discloses a cancer image denoising method and system based on a multi-scale neural network, and the method comprises the steps: carrying out the adaptive segmentation of a cancer image, carrying out the noise estimation and statistical analysis of each segmented region, and automatically adjusting the denoising parameters; performing multi-scale feature extraction by adopting wavelet transform and pyramid decomposition, and analyzing and processing the image after parameter adjustment; performing quality evaluation and distortion detection on the image after multi-scale processing, automatically identifying and positioning artifacts and distortion, and correcting and compensating interference by adopting a point spread function inversion and Wiener filtering method to obtain a corrected image; and after histogram equalization and contrast-limited adaptive histogram equalization are applied to the corrected image, denoising processing is carried out by using a convolutional neural network in combination with sparse representation and a dictionary learning algorithm, and a denoising result is obtained. According to the method, noise in different types of cancer images can be effectively removed, meanwhile, key diagnosis information is reserved, and the image quality and definition are improved.

The invention discloses a cloud data anomaly detection and safety response system based on artificial intelligence, relates to the technical field of data processing, and solves the problems that firstly, an incremental compression algorithm is difficult to store and preprocess multi-source heterogeneous data; secondly, it is difficult to fuse statistical analysis and a deep learning model, locate outliers and analyze abnormal semantics in unstructured data, and then it is difficult to effectively predict a potential attack path; then, on the premise that data security and traceability are guaranteed, correlation analysis of cross-node anomalies is difficult to achieve so as to identify distributed attacks; and finally, an attack and defense confrontation model is difficult to construct for safety response, and the safety response effect is difficult to evaluate. According to the method, cloud environment data are processed through an adaptive probe cluster and the like, multiple models are fused to generate anomaly detection features and predict attack paths, and response and evaluation are carried out through reinforcement learning and digital twinning by means of cooperative detection such as federated learning and the like.

The invention relates to the technical field of image processing, and discloses a precast concrete T-beam bubble detection method and system. The method comprises the following steps: carrying out multiband reflection data acquisition and calibration on the surface of a T-beam, and obtaining optimized data through light intensity compensation and band screening; performing spatial registration and noise reduction processing on the data, and fusing the data into an image; edge detection and image segmentation are carried out, and pore morphological features are extracted; calculating depth data and performing spatial reconstruction to obtain pore distribution data; performing statistical analysis on the distribution data to obtain a defect evaluation result; and finally generating a detection report. According to the method, accurate positioning and quantitative evaluation of bubble defects are realized through multi-source data fusion and deep analysis, a complete early warning and evaluation mechanism is established, and the reliability and practicability of a detection result are improved.

The invention provides an intelligent statistical analysis method and system for the implementation progress of a production project, and the method comprises the steps: carrying out the initial classification of all task lists in the production project, and constructing a directed acyclic task graph according to the initially classified task lists to represent the dependency relationship between tasks; performing data acquisition on the execution state of the task according to the initial monitoring granularity of each task, and judging whether the actual progress of each task deviates from the planned progress or not according to the acquired data; behavior data of each project role is collected, then the behavior deviation of each role is calculated based on regularization, the cooperation efficiency among the roles is calculated according to the behavior deviation of each role, and then task allocation and progress management are dynamically adjusted. Re-optimizing the task priority and resource allocation according to a dynamic adjustment result; and feeding back to a project manager after the optimization task is executed, and providing intelligent decision support. The system has higher intelligence and real-time reaction capability.

The invention relates to a rail transit energy efficiency balance dynamic monitoring method and system, relates to the field of rail transit energy conservation and monitoring, and solves the problems that in an energy consumption monitoring link, simple statistical analysis and a rule-based system are difficult to adapt to a complex and changeable operating environment due to fixed threshold values and rules, and misjudgment and missed judgment are easy to occur. The method comprises the following steps: according to a comparison result of actual energy consumption of equipment and a personalized energy efficiency standard threshold value, determining whether the corresponding equipment has energy consumption abnormity or not; if yes, the personalized threshold value and historical energy consumption data of the equipment are combined, the built comprehensive energy loss analysis model based on the fault tree and the Bayesian network is utilized, the fault mode of the equipment is started, the operation history, the maintenance record and the environmental factors of the equipment are combined, and Bayesian network reasoning and dynamic fault tree analysis are carried out; and the source reason of the energy loss is positioned and sent to the terminal held by the person in charge. The method has the following effect that the energy efficiency management level of rail transit is effectively improved.

The present application provides a digital twin-based power distribution network power supply reliability calculation method and system. The method comprises: obtaining basic data and operation data of a power distribution line; constructing a digital twin power grid on the basis of the digital twin technology as well as the basic data and the operation data; obtaining scores in respect of power distribution network reliability indexes of the power distribution line on the basis of the digital twin power grid, wherein the power distribution network reliability indexes comprise an element reliability index, a load point reliability index, a system reliability index, a series system reliability index, and a parallel system reliability index; and on the basis of a weighted fusion result of the scores of the power distribution network reliability indexes, determining the power distribution network power supply reliability to be calculated. The present application solves the problem that current methods for analyzing the power distribution network power supply reliability relate to treating entire power distribution networks as research objects and implementing manual sorting, and thus are time-consuming and labor-intensive, are difficult to ensure the accuracy of calculation results, and fail to conduct statistical analysis for weak links in the network distribution networks.

The invention provides an intelligent agricultural environment monitoring system based on the Internet of Things, and relates to the technical field of data processing. The feature extraction and analysis module is used for performing segmented statistical analysis on the soil humidity original data according to the soil humidity original data in combination with historical soil humidity data, historical environment temperature data and preset near-period soil humidity feedback data, and automatically extracting a humidity change rate and an abnormal trend index data set under multiple time windows; the self-learning judgment module is used for dynamically adjusting the current judgment threshold value based on a self-learning algorithm, judging the soil humidity original data in combination with the humidity change rate and the abnormal trend index, and generating a judgment signal for judging whether local irrigation response is triggered or not; the response control module is used for automatically generating an irrigation equipment starting instruction; the irrigation execution module is used for executing irrigation operation according to the irrigation equipment starting instruction; according to the invention, the accuracy of agricultural environment monitoring is improved.

The invention relates to the technical field of yarn detection, and particularly discloses a yarn quality detection system, method and equipment. Shooting parameter information is adjusted based on yarn quality standards and external configuration information, and a high-speed camera corresponding to the shooting parameter information is used for collecting original image data of yarn in the production process in real time; processing the original image data to obtain yarn feature information, wherein the yarn feature information comprises yarn material, yarn color, yarn thickness, fiber uniformity, hairiness and knot faults; classifying defects on the surface of the yarn based on the yarn feature information to obtain yarn defect categories; and a defect abnormal area on the enhanced image is identified based on the yarn defect category, statistical analysis is performed on the defect abnormal area to generate a quality detection report, and the quality detection report is transmitted to the control module and the display alarm module.

A data processing system implements receiving, via a user interface, a first request for first content to be generated by a generative model, the first request including a first prompt describing the first content associated with output value(s) of a statistical test; executing a query on expert knowledge source(s) to obtain statistical test parameters; constructing, based on a portion of the parameters using a prompt construction unit, prompt(s) to query a user for values associated with the parameters; receiving, via the user interface, response(s) to the prompt(s); retrieving historical data associated with the statistical test; providing the parameter value(s) and the historical data to a calculation tool to generate output value(s); constructing a second prompt as an input to the generative model, by appending at least the prompt(s) and the response(s) with an instruction string comprising instructions to generate an interpretation of the output value(s) as the first content.

The present invention relates to the technical field of oil and gas exploration and development. Provided are a digital core-based shale pore type recognition method and a quantitative characterization method. The digital core-based shale pore type recognition method comprises: on the basis of a gray image of a shale sample to be recognized, recognizing an organic matter and a transition zone to obtain an organic matter recognition result; and on the basis of the organic matter recognition result and the gray image, obtaining an organic pore image and an inorganic pore image as a shale pore type recognition result. The present invention can be used to quantitatively recognize organic pores, inorganic pores and micro-cracks of shale, quantitatively characterize shale pore structures, and analyze structural parameters of the organic pores, the inorganic pores and the micro-cracks, so as to perform statistical analysis of distribution of different types of reservoir spaces of shale, thereby being of great significance for shale reservoir quality evaluation and effective shale oil and gas development method optimization.

The invention discloses a chemical industry park safety risk evaluation management system comprising a data acquisition and monitoring module used for acquiring real-time data and business data of a chemical industry park; the risk assessment module is used for performing risk assessment through a safety production risk assessment model to obtain a risk assessment result; the risk early warning and accident prediction module is used for carrying out risk early warning and accident prediction according to the risk assessment result; the emergency response and event management module is used for carrying out emergency response and safety event management according to risk early warning and accident prediction; the safety monitoring and reporting module is used for performing safety monitoring according to the emergency response and safety event management and generating a monitoring report; the risk management and control and safety improvement module is used for evaluating the monitoring report and generating a safety improvement suggestion; the decision support and report analysis module is used for providing a decision support tool and generating a statistical analysis report; and the comprehensive management platform module is used for realizing control and data communication among the modules.

The invention relates to the technical field of brand promotion, in particular to a brand promotion method and system based on user positioning, and the method comprises the following steps: collecting user browsing history, purchase records and social media interaction data, carrying out the statistical analysis of the data, recognizing the field in which a user is interested, and constructing and generating a user interest recognition result. In the invention, through detailed analysis and real-time updating of user data, a dynamic user portrait can be constructed, promotion content can be accurately customized, advertisement content is ensured to be highly related to current interest of a user, a user behavior mode is revealed through clustering analysis, key features are extracted, an advertisement system is allowed to adaptively adjust content and putting time, and user experience is improved. According to the method, the personalized degree and effect of advertisements are remarkably improved, dynamic advertisement configuration is automatically optimized according to real-time changes of user behaviors, promotion activities can accurately aim at potential key user groups, the conversion rate and the user interaction rate are increased, and finally more effective market penetration is achieved.

The invention relates to the technical field of data analysis, and discloses a precise targeted early warning method and device for meteorological disasters of kiwifruit canker and a medium. The method comprises the following steps: performing standardization processing on historical occurrence time, morbidity degree and distribution range data of kiwifruit canker as well as meteorological observation data and cultivation management data of corresponding time periods to obtain a historical disease database; performing multivariate statistical analysis processing on the three-dimensional meteorological monitoring network data and the historical disease database to obtain a disease risk assessment index; performing feature extraction processing on the disease risk assessment indexes through a deep learning algorithm to obtain disease early warning parameters; performing gridding partition processing on the disease early warning parameters to obtain partition early warning data; and performing dynamic optimization processing on the accuracy, the advance and the coverage rate of the partition early warning data to obtain accurate targeted early warning information of the meteorological disaster of the kiwifruit canker. According to the application, the accuracy and timeliness of the existing kiwi fruit canker meteorological disaster early warning method are improved.

The invention discloses a computer network security monitoring system and method. The system comprises a quantum encryption traffic acquisition module, a biological recognition feature extraction module, a block chain detection traceability analysis module and a visual management module. The quantum encryption flow collection module generates and distributes a secure quantum key through a quantum key distribution unit, encrypts a collected data packet through an encryption transmission unit, and generates a flow data segment through a flow collection subunit and a data cache statistical analysis subunit. The biological recognition feature extraction module verifies the identity of a user through a biological recognition unit, and extracts a multi-dimensional feature vector by using a feature extraction subunit. And the block chain detection traceability analysis module performs detection and traceability analysis on the abnormal traffic through the anomaly detection subunit and the traceability analysis subunit, and stores a result in the block chain storage unit. The visual management module provides visual network state display and user identity verification functions through a visual display subunit and an identity verification and access control subunit. According to the invention, quantum encryption, biological recognition and block chain technologies are combined, so that the accuracy, security and traceability of network security monitoring are improved.

The invention provides a lower limb and gravity center dynamic balance posture early warning method based on a body measurement all-in-one machine, and the method comprises the steps: obtaining a pelvis forward inclination angle, an arch height and a gravity center position under a standard standing posture, taking the pelvis forward inclination angle, the arch height and the gravity center position as individualized baseline data, and carrying out the statistical analysis of the baseline data, and generating a dynamic balance reference value reflecting the normal balance capability of an individual; on the basis of the dynamic balance reference value, in combination with a gravity center forward trend and a lower limb muscle compensation mode, a balance offset index in standing posture change is calculated through a time sequence analysis algorithm, and a dynamic balance risk assessment result is obtained; and if a dynamic balance risk assessment result exceeds a danger threshold, clustering analysis is carried out on associated parameters of the pelvic forward inclination angle and the arch height, grouping processing is carried out on the associated parameters, and potential reasons causing balance capability degradation are identified.

The invention relates to the technical field of data matching, in particular to an information matching management system and method for financial marketing platform data, and the system comprises a data acquisition module, a data analysis module, a dynamic adaptation module, a marketing strategy module, an anomaly detection module and a strategy implementation module. According to the method, behaviors and demands of different customer groups can be deeply understood through comprehensive analysis of the market data set and clustering analysis, and such statistical analysis can provide a scientific basis for making a marketing strategy, so that a financial institution can identify and utilize market trends and modes on the basis of data driving; according to the invention, the market change is predicted and quickly responded, and the improvement of the risk assessment result and the anomaly detection capability not only optimizes the resource allocation, but also enhances the control of potential risks, improves the overall efficiency and effect of marketing activities, enhances the accuracy of data-driven decisions, improves the customer service experience and brand loyalty, and promotes the sales growth.

The invention relates to the field of image processing, in particular to an on-line monitoring method for shaft forging polishing, which comprises the following steps: acquiring a grayscale image of a shaft forging polishing image, performing edge detection on the grayscale image, extracting an edge connected domain, and calculating edge features of the edge connected domain; clustering the edge features to obtain a plurality of clustering clusters, calculating the abnormal degree of the clustering clusters, and calculating the abnormal performance of each edge connected domain in the clustering clusters according to the abnormal degree; and according to the abnormal performance and the edge features, the suspected defect degree of the edge connected domain of the clustering cluster is calculated, and the polishing degree of the shaft forging is judged based on the suspected defect degree. According to the method, through statistical analysis of the edge features and abnormal performance in clustering clusters, the recognition accuracy of the surface defects is improved.

The invention relates to the technical field of data processing, and discloses a coal mine area gas control and safety guarantee method and system under complex geological conditions. The method comprises the steps that geophysical prospecting, drilling and logging exploration are conducted on a coal mine area, and geological parameters are obtained; performing statistical analysis on the data, and constructing a three-dimensional gas model; identifying a high-gas area based on the model, and arranging a directional drilling network; differential extraction and anti-reflection measures are implemented according to the drilling network; fusing extraction and sensor data, and predicting a gas change trend by using a neural network; and performing intelligent diagnosis based on the early warning matrix to form a disposal scheme. Under the complex geological condition, the gas distribution rule is accurately mastered, scientific arrangement of directional drilling and optimization design of differential extraction parameters are achieved, meanwhile, a gas early warning decision support system based on artificial intelligence is established, the accuracy of gas treatment and the timeliness of early warning are improved, and therefore safe production of a coal mine is guaranteed.