130 results about "Deep mining" patented technology

The invention discloses an alarm information research and judgment noise reduction method. According to the method, multiple pieces of alarm information with internal relation are combined into a structured alarm cluster through deep mining and by utilizing context association information between alarm events, so that redundant information is greatly compressed, and the alarm signal-to-noise ratio is improved. And multi-dimensional and comprehensive evaluation is carried out on the alarm cluster by fusing three mechanisms of information entropy analysis, customization rule evaluation and semantic evaluation of a large model, so that high threat alarms can be identified more accurately. Wherein the information entropy analysis helps to identify potential unknown threats by quantifying the uncertainty of the alarm, and the large model semantic evaluation can deeply analyze the alarm content, so that the accuracy and efficiency of alarm analysis are improved.

The invention provides a coal mine fluidized mining dynamic disaster prevention and control method and system, and relates to the technical field of disaster prevention and control. The method comprises the following steps: establishing a risk map based on mining area multi-field information, setting a seismic source intensity upper limit, a path attenuation target and an energy absorption displacement target, implementing in-situ transformation under the constraint of a control index to form a distributed micro-fracture network, and calculating a residual dynamic load spectrum to weaken a seismic source; according to the residual dynamic load spectrum, constructing an artificial barrier and an energy absorption band with wave impedance abrupt change so as to realize propagation energy reduction; a flexible energy absorption protection system is configured according to the residual dynamic load spectrum and the propagation attenuation coefficient, micro-seismic, stress and displacement data are monitored in real time, and parameter self-adaptive correction is achieved through linkage treatment triggered by a threshold value. According to the method, a source-diameter-field integrated active prevention and control system is constructed, dynamic regulation and control and closed-loop management in the fluidized mining process are achieved, and the intrinsic safety and the intelligent level of deep mining are remarkably improved.

The invention discloses an environmental protection management system based on big data service integrated management, and the system comprises a data collection layer which is used for collecting multi-source environmental data; the data management layer is used for storing, cleaning and standardizing the collected data and encrypting the data transmission and storage process; the data analysis layer is used for carrying out deep mining and intelligent analysis on the processed data; the application service layer is used for visually displaying the analysis result and realizing query, management and sharing of environment information through a user interface; and the block chain traceability layer is used for recording a source, a timestamp and a processing process of the data, and realizing tampering prevention and traceability of the data through a block chain technology. By integrating various advanced technologies, efficient acquisition, processing, analysis and management of environment data are realized, and comprehensive and scientific technical support is provided for environment management.

The invention discloses a new energy intelligent operation and maintenance system based on AI and digital twinning, and relates to the technical field of photovoltaic power generation and energy storage, and the system comprises a sensing and control layer which is disposed at the site of an optical storage power station. Power station equipment and environment data are collected in real time through the sensing and control layer, the data collection and edge calculation layer completes preliminary analysis and quick response locally, the timeliness of fault processing is effectively improved, the cloud platform conducts deep mining and model training on mass data through an AI analysis engine, the system performance is continuously optimized, and the fault processing efficiency is improved. And the digital twinning and decision visualization module realizes holographic mirroring and dynamic deduction of the operation state of the power station and assists management personnel in accurate decision making and predictive maintenance, and the system finally forms a continuously evolved closed-loop intelligent operation and maintenance system, so that the operation and maintenance efficiency of the power station is improved. And the energy utilization efficiency, the equipment reliability and the operation and maintenance automation level are obviously improved.

The invention is suitable for the field of geological disasters, and discloses a photovoltaic station geological disaster key influence factor analysis and induction mining method, system and device and a medium, and the method comprises the steps: obtaining influence factors of geological disaster development, and carrying out the preprocessing; spatial distribution and types of geological disaster points are obtained through historical data and field investigation, and danger levels of the geological disaster points are obtained through an expert scoring method; based on the spatial distribution, the type and the danger level, adopting an MDLP algorithm to construct a geological disaster database; based on the geological disaster database, performing evaluation factor correlation analysis through RC table chi-square test to obtain an optimized geological disaster database; based on the optimized geological disaster database, confidence and support are introduced, and an Apriori algorithm is adopted to obtain a strong association rule; through combination of a strong association rule and geodynamics, a core causal chain is extracted, deep mining of a geological disaster induction mechanism is realized, and a direct and reliable decision basis is effectively provided for accurate disaster prevention and control and the like of a photovoltaic field station.

The invention relates to the technical field of mine safety monitoring and early warning, in particular to a fusion reforming group multi-parameter fault activation induced roadway instability precursor identification method. The method comprises the following specific steps: collecting a microseismic event signal and roadway convergence deformation, calculating a correlation length, a b value, a spatial fractal dimension and quantitative indexes of roadway activation response to fault, and calculating a critical early warning index according to a constructed multi-parameter fused critical early warning index calculation formula, according to the method, a fault activation induced roadway instability precursor is identified according to a critical early warning index, a physical model capable of realizing linkage early warning of fault activation factors and roadway instability fruits is constructed through deep fusion of multi-parameter indexes of a reformed group and roadway response information, the problems of isolation, singleness and lagging of early warning of a traditional method are solved, and the early warning efficiency is improved. Early recognition, early warning and early disposal of roadway instability disasters induced by fault activation in deep mining are achieved, and unprecedented technical guarantee is provided for mine safety production.

The invention discloses an underground mine deep mining rockburst monitoring and early warning system, which relates to the technical field of mining, and comprises a sensing module, the output end of the sensing module is communicated with the input end of a wireless network module, the output end of the wireless network module is communicated with the input end of a data acquisition module, and the output end of the data acquisition module is communicated with the input end of a data processing module. The output end of the data acquisition module is communicated with the input end of the Ethernet module, and the output end of the Ethernet module is respectively communicated with the input ends of the terminal display module, the communication module and the early warning decision module. According to the invention, through the sensing module, the data acquisition module and the early warning decision module, real-time acquisition of rock mass multi-dimensional parameters, adaptive coverage monitoring and interference signal filtering are realized, rock burst precursor features are accurately identified, the problems of many monitoring blind areas, large data interference and low identification precision of a traditional fixed sensor network are effectively solved, and the method is suitable for popularization and application. And the real-time performance and the accuracy of deep mining rock burst early warning are obviously improved.

The invention discloses a metal mine mountain pressure monitoring and early warning method and system for deep mining, and the method comprises the steps: dynamically adjusting the layout of monitoring equipment according to the initial state data of ground pressure distribution and mining progress data, and collecting the stress change information of different mining stages in real time, thereby obtaining the dynamic change trend data of ground pressure distribution; integrating the ground pressure distribution dynamic change trend data and the mining progress data by adopting a data fusion technology, and constructing correlation model data between the ground pressure change and the mining progress; according to the correlation model data, through comprehensive evaluation of the vertical stress, the horizontal stress and the change rate, obtaining ground pressure abnormal distribution data of each region; and according to the ground pressure abnormal distribution data, monitoring equipment layout is adjusted, updated monitoring point position data is obtained, and key monitoring strategy data of the high-risk area is obtained by optimizing equipment configuration and monitoring frequency. According to the invention, accurate monitoring and risk early warning of the ground pressure state of the mining area can be realized, and powerful support is provided for mine safety production.

The invention provides an information deep mining method and system based on a multi-agent cooperation framework, and the method comprises the steps: firstly receiving a to-be-mined information demand, carrying out the task decomposition and cooperative scheduling through a plan expert agent, generating a cooperative task scheduling scheme, and then carrying out the multi-source information cooperative search through a search expert agent according to the scheme, the method comprises the following steps: acquiring multi-source original information, reading an expert agent to carry out deep analysis and association extraction on the multi-source original information, generating structured association information, analyzing the expert agent to carry out multi-level deep reasoning on the structured association information to obtain a deep mining intermediate result, and finally, integrating multi-dimensional verification and fusion on the intermediate result by the expert agent to obtain a deep mining result. And generating a final information deep mining result meeting the requirement, thereby realizing automation, intelligence and high efficiency of information mining, and mining the potential value of the information.

The invention relates to the technical field of data mining, in particular to a power plant income index deep mining method based on historical operation data, which comprises the following steps: based on input power plant operation data, selecting core income dimension time and key income measurement generating capacity to carry out initial structure design, and setting a hierarchical division standard of the income dimension time, and aggregating the key income measurement generating capacity according to a set time hierarchy, and establishing a hierarchical aggregation power plant income data structure. According to the method, the time dimension and the power generation capacity measurement in the input power plant operation data are subjected to structured processing, a hierarchical aggregation structure with hierarchical division capability is constructed, and higher query efficiency and clear expression of time sequence aggregation logic are provided in a data organization mode. By embedding the data integrity state and accuracy into the aggregation unit, a composite data structure with business measurement and quality marking capabilities at the same time is constructed.

The invention relates to the technical field of tunnel state diagnosis and early warning, and discloses a tunnel health state intelligent diagnosis and early warning method based on multi-modal fusion, which comprises the following steps of: 1, arranging various types of sensors on a tunnel structure, multi-modal data information such as vibration response, reinforced concrete corrosion condition, circumferential strain, structural temperature, longitudinal pipe joint opening displacement and water leakage, traffic load and the like is collected in real time. According to the tunnel health state intelligent diagnosis and early warning method based on multi-modal fusion, through deep fusion of sensing data of multiple modals, the advantages of different sensors are fully played, information islands among data of all modals are broken through, advanced data fusion algorithms and models are utilized, deep mining and analysis are carried out on multi-source heterogeneous data, and the tunnel health state intelligent diagnosis and early warning method based on multi-modal fusion is realized. The health information of the tunnel structure can be obtained more comprehensively and accurately, fine evaluation of the health state of the tunnel is achieved, and the reliability and accuracy of the evaluation result are improved.

The invention discloses a reserved air shaft formed by a nano impermeable membrane and multiple layers of variable-diameter sleeves and a construction method thereof.The variable-diameter sleeves comprise the middle-bottom-layer sleeve, the middle sleeve and the top-layer sleeve, and all the sleeves are connected end to end to form a reserved air shaft framework; the nanometer anti-seepage film is located at the top end of the variable-diameter sleeve, the variable-diameter sleeve can be wrapped by the nanometer anti-seepage film, and the nanometer anti-seepage film is used for controlling leakage of the filling slurry. The device is simple in structure and easy to operate on site, the risk that the air shaft is blocked by filling slurry in the filling mining process can be effectively reduced, and the production efficiency is improved. Specifically, by means of the method, the labor amount of traditional air shaft reserving can be reduced, the efficiency and safety of the air shaft reserving process are improved, and the economic benefits of mine production are increased; the application range is wide, and the method is suitable for air shaft reserving behaviors of various mines, especially in the fields of deep mining and the like; in addition, the method can be adjusted according to specific conditions of each mine, and has very high theoretical and practical values.

The utility model relates to a metal surface mine large dip angle belt conveyor transportation system which comprises a coarse crushing station system, a first transfer station, a second transfer station and a third transfer station. The first transfer station is connected with the second transfer station through a belt conveyor, and the second transfer station is connected with the third transfer station through a large-dip-angle belt conveyor; the inclination angle of the large-inclination-angle belt conveyor is larger than 15 degrees and smaller than 35 degrees. Compared with the prior art, the large-dip-angle belt conveyor has the advantages that by applying the large-dip-angle belt conveyor technology, the capital construction investment of a mine can be greatly reduced; the capital construction time is shortened, the metal surface mine deep mining transportation distance is shortened, and the mine transportation cost is saved; and meanwhile, the routing length is shortened through the routing arrangement of the large-dip-angle belt conveyor, side slopes under unfavorable geological conditions are effectively avoided, and the safety and reliability of the system are improved.

The invention discloses a new energy charging pile charging process early warning system based on artificial intelligence. Comprising a dynamic charging parameter real-time acquisition and multi-dimensional analysis unit, a feature deep mining and space-time correlation mapping unit, a charging state evaluation and abnormal trend pre-judgment unit, a multi-source abnormal information fusion and grading judgment unit, an intelligent early warning strategy generation and dynamic adjustment unit and an early warning information real-time push and execution feedback unit. The dynamic charging parameter real-time acquisition and multi-dimensional analysis unit acquires various types of parameters, after the parameters are processed by the feature deep mining and time-space correlation mapping unit, the charging state evaluation and abnormal trend pre-judgment unit evaluates the state and pre-judges the trend, and the multi-source abnormal information fusion and grading judgment unit performs fusion grading. The intelligent early warning strategy generation and dynamic adjustment unit generates an adjustment strategy, and the early warning information real-time push and execution feedback unit pushes information and receives feedback. The system adapts to various scenes, and the charging safety and stability are improved.

The invention relates to the technical field of data processing, and provides a reservoir bank landslide soil body shear strength parameter inversion method, device and equipment and a storage medium, the effective value range of soil body shear strength parameters is determined by obtaining a target safety coefficient, a training sample set is generated in the effective value range through numerical simulation, and according to the training sample set, the reservoir bank landslide soil body shear strength parameter inversion method and device are obtained. And constructing a safety coefficient prediction model through a machine learning method, searching in an effective value range according to the safety coefficient prediction model and the target safety coefficient, and determining a soil shear strength parameter inversion result matched with the target safety coefficient. According to the method, accurate acquisition of the shear strength parameters of the reservoir bank landslide soil body is achieved, meanwhile, more data values are released through existing information in the deep excavation exploration stage, automation and high efficiency of the inversion process are achieved, dependence on manual experience trial calculation is thoroughly eliminated, and the method is suitable for large-scale popularization and application. And finally, a scientific basis is provided for engineering design optimization and disaster prevention and control decision making of reservoir bank landslide.

The invention discloses a deep mining and storage space rock mass damage evolution and long-term stability prediction system and method, and the system is characterized in that a long-term monitoring module comprises an embedded fiber grating strain sensor array, a resistivity CT imaging unit, a chamber convergence laser range finder and a rock sound wave testing unit, and is used for collecting multi-source monitoring data; the data preprocessing module is used for preprocessing the multi-source monitoring data; the aging analysis module is used for predicting the remaining service life based on the multi-source monitoring data; and the operation and maintenance decision module is used for generating a maintenance strategy and risk prompt based on the residual service life prediction result. The method comprises the following steps: arranging the long-acting monitoring module on site; collecting multi-source monitoring data; de-noising the multi-source monitoring data, and extracting a long-term trend term and a short-term fluctuation term; predicting the remaining service life; and performing stability grading and operation and maintenance decision according to the remaining service time. The system and the method can efficiently and accurately realize quantitative evaluation of the long-term stability of the rock mass.

The invention discloses a method for judging the grouting effect of a coal mine tunnel bottom plate, and belongs to the technical field of coal mine tunnel safety control. The technical problem that in the prior art, the grouting effect of a coal mine roadway bottom plate is not accurate is solved. The method comprises the following steps: determining an evaluation index according to a main control factor of a coal mine tunnel floor grouting effect; standardizing the plurality of groups of evaluation index data by using MATLAB (Matrix Laboratory); a CRITIC method is used for giving weights to the evaluation indexes, a CRITIC-CEO-KMeans grouting effect prediction model is constructed, an evaluation database of the coal mine tunnel floor aquifer grouting effect is constructed, evaluation index data of a to-be-evaluated sample is input into the evaluation database, a prediction result is output, and grouting effect grades are divided. According to the method, the prediction accuracy of the coal mine tunnel floor grouting effect can be improved, and technical guidance is provided for improving the mine deep mining safety.

The application provides a super-large three-dimensional deep metal mine intelligent ventilation simulation system, and relates to the technical field of deep metal mine ventilation. The system comprises a deep metal mine complex ventilation roadway and stope structure simulation module for simulating underground roadway, stope and heading face of a deep metal mine; a roadway constant temperature control module for simulating preset rock temperature of a roadway and regulating temperature of a simulated roadway test section; an adjustable air source input control module for providing an air source meeting preset temperature, humidity and flow rate for the underground roadway and stope of a mine; a pollutant stable release module for simulating preset concentration release of dust, gun smoke and multi-component pollutant gas in deep mining operation; a particle image velocimetry module for realizing fluid dynamics measurement of multi-phase and multi-component air flow of the simulated roadway test section and stope; and an intelligent ventilation control module for realizing monitoring, calculation and automatic control of multi-element air flow information in the simulated roadway and stope and ventilation facilities.

The present application provides a hierarchical information deep mining and matching method applied to a procurement service system, which relates to the field of procurement management technology. The method includes: executing data interaction with the procurement service system to establish a multi-source data set; calling a surface information recognition layer to perform data preprocessing and recognition to establish an initial matching candidate library; inputting the preprocessed multi-source data set and the initial matching candidate library into a multi-layer matching channel, configuring the multi-layer matching channel with historical data, performing procurement task matching reconstruction, and generating matching reconstruction results; and performing procurement management based on the matching reconstruction results. This application solves the technical problem in the prior art that it is difficult to accurately identify the best match between procurement tasks and procurement products, resulting in inaccurate procurement decisions, thereby affecting procurement efficiency. Through hierarchical information deep mining and matching, accurate matching of procurement services is performed, thereby improving procurement efficiency.

The present application relates to a kind of triaxial coupling test active deformation testing device and testing method, the device includes rack, two cylindrical test cylinders are symmetrically arranged on the rack, confining pressure power mechanism and hydraulic pump station, the two ends of test cylinder are respectively provided with flange, two test cylinders are detachably connected by flange, two test cylinders form closed complete test cylinder after butt joint, displacement pressure mechanism is provided at one end of test cylinder.The testing device provided by the present application meets the current deep mining conditions: different confining pressure conditions, different moisture content conditions, different media, active deformation test device of interactive filling.By active deformation test, the fiber-soil coupling deformation mechanics characterization under the condition of multiple media replacement interaction is researched.

The invention relates to high-strength mine round-link chain steel and a manufacturing method thereof, and belongs to the technical field of round-link chain steel preparation. In order to solve the problems of insufficient alloy strengthening effect and high cost of the existing steel for the mining round-link chain, the invention provides a manufacturing method of the high-strength steel for the mining round-link chain, smelting, refining and continuous casting are performed according to target chemical components to obtain a casting blank, and quenching heat treatment and tempering heat treatment are performed after the casting blank is rolled to the size of a finished product to obtain the high-strength steel for the mining round-link chain. The high-strength steel for the mining round-link chain is obtained. Through V-N microalloying component design and an optimized heat treatment process, precipitation strengthening and secondary hardening of V (C, N) compounds are promoted through V-N microalloying at relatively high quenching and tempering temperatures, collaborative optimization of strength and toughness is realized, and the produced steel for the mining round-link chain completely meets extreme load requirements of deep mining. The material cost of the steel for the mining round-link chain is remarkably reduced, and a feasible solution is provided for large-scale industrial application of the mining round-link chain.

The invention provides a deep high-stress thick and large ore body pressure relief mining method, and relates to a deep mining technology, a pressure relief roadway is arranged outside a rock stratum moving angle of an ore body hanging wall, the pressure relief roadway and a vertical pressure relief vertical groove jointly cut a rock body structure, the integrity of primary surrounding rock is manually weakened, and a continuous and effective pressure relief zone is formed. In the stoping process, the undercutting roadway with the horizontal pressure relief cutting groove is arranged in advance, and the stress concentration area is further dispersed; meanwhile, the horizontal pressure relief grooves are filled with large-aperture filling bodies, a structure that the grooves and the filling bodies are coupled is formed, surrounding rock deformation is effectively limited, the overall stability of a roadway and a stope is improved, and the sudden ground pressure and rockburst risks in the deep mining process are remarkably reduced.

The application discloses a ground pressure management system suitable for deep mining of underground mines, and relates to the technical field of mine safety monitoring and intelligent control, comprising a perception and collection layer, a transmission and communication layer, a data processing and analysis layer, a risk assessment and early warning layer and a linkage control layer; by arranging multiple types of monitoring units at key parts of the stope, roadway and support, multi-source data such as surrounding rock stress, deformation, dynamic response, support state and environmental disturbance are obtained, and after unified processing by an edge gateway, the data are transmitted to the analysis layer for fusion modeling and abnormality identification. Based on multiple indexes, a comprehensive risk evaluation system is constructed, and dynamic weights are used to realize risk grading and trend prediction, and generate early warning information. The system further links pressure relief, support, mining adjustment and personnel and equipment control measures, and realizes parameter self-adaptive correction through a feedback mechanism, forming a closed-loop management. The system improves the ground pressure monitoring precision, early warning accuracy and disposal efficiency of deep mines.

The present application relates to the technical fields of deep rock in-situ simulation preparation, and discloses a deep in-situ rock true triaxial preparation device containing internal stress, which comprises a mold, the mold comprises a rectangular frame structure formed by connecting rigid plate A, rigid plate B, elastic plate A and elastic plate B in sequence, and further comprises a first axial compression device, the first axial compression device comprises a top plate, a first hydraulic cylinder and a first compression plate, the top plate is detachably connected to one end of the rectangular frame structure, the other end of the rectangular frame structure is detachably connected to a bottom plate, the first compression plate is slidably matched with the interior of the rectangular frame structure, the first hydraulic cylinder can drive the first compression plate to slide, the lower end and the upper end of the side of the rigid plate A close to the elastic plate B are respectively provided with a first grouting hole and a first back grouting hole, and the lower end and the upper end of the side of the rigid plate B close to the elastic plate A are respectively provided with a second grouting hole and a second back grouting hole; the device can prepare deep in-situ rock containing internal stress under different three-directional stress loading environments, and is beneficial to the in-depth study of deep mining theory.

The invention provides a job-hunting dialogue intention deep mining method and system applied to college student employment tutoring, and the method comprises the steps: firstly obtaining a dialogue data set of a target user in a job-hunting tutoring scene, the dialogue data set comprises a plurality of dialogue sequences arranged according to a time sequence, and then carrying out the feature extraction of the dialogue data set, the method comprises the steps of generating semantic association features and interaction behavior features, then performing intention recognition on the semantic association features and the interaction behavior features based on a preset intention recognition network, generating an intention classification label set to indicate the distribution priority of potential job hunting demands of a user, and finally constructing an intention optimization strategy according to the intention classification label set. And a dialogue service module of the job-seeking tutoring system adjusts a response content generation rule, so that the service quality of the tutoring system is improved.

The invention provides a goods-carrying person recommendation method and device, electronic equipment and a medium, and relates to the technical field of big data processing, and the method comprises the steps: obtaining the commodity information of a to-be-promoted commodity and the user portrait data of at least one goods-carrying person; performing feature extraction on the commodity information of the to-be-promoted commodity, and obtaining commodity feature data of each preset dimension of the to-be-promoted commodity; the preset dimension comprises at least one of a commodity content attribute dimension, a commodity commercial attribute dimension and a commodity target audience dimension; according to the user feature data corresponding to the commodity feature data of each preset dimension in the user portrait data of the cargo-carrying person, determining the matching degree of the cargo-carrying person and the to-be-popularized commodity in each preset dimension; and determining at least one target cargo-carrying person from the at least one cargo-carrying person according to the matching degree of the cargo-carrying person and the to-be-popularized commodity on each preset dimension. According to the method, multi-dimensional deep mining is realized, and the objectivity and accuracy of matching of the commodity and the commodity-carrying person are improved.

The invention discloses a coal seam floor water guide crack identification method based on micro-seismic Krauklis waves, and belongs to the technical field of coal seam floor water disaster detection.The method comprises the steps that firstly, micro-seismic original signals are collected and segmented, and continuous wavelet transformation and time-frequency domain conversion are sequentially carried out on each signal segment to obtain a time-frequency spectrogram; according to the time-frequency spectrogram, separating and extracting the time-frequency characteristics of the Krauklis waves; for each frequency, determining an energy extreme point corresponding to the frequency and Krauklis wave energy packet arrival time; a frequency dispersion curve is obtained by calculating the phase velocity of each frequency, and the slopes of different positions of the frequency dispersion curve are obtained through calculation; establishing a quantitative mapping model between the crack opening and the frequency dispersion curve slope on the basis of the propagation theory of Krauklis waves in the slab crack; and the slope is substituted into the quantitative mapping model for inversion, so that the average opening degree of the water guiding crack in the current detection area is accurately obtained, and data support is provided for accurate monitoring and early warning of water inrush of a deep mining coal seam floor.

The invention provides an intelligent rail smoothness adjusting method and system based on dynamic detection and big data analysis, and belongs to the technical field of railway engineering maintenance. The method comprises the steps that track geometric data, vehicle dynamics response data and environment data are collected in real time through a multi-dimensional sensor array installed on an operating train; uploading the data to a cloud big data platform for fusion and deep mining, constructing a track state degradation prediction model by using a machine learning algorithm, and accurately predicting the development trend of track irregularity; and based on the prediction result, automatically generating a targeted track fine adjustment scheme, and issuing the scheme to an intelligent maintenance robot deployed on the line or guiding a manual maintenance team. The system comprises a data acquisition layer, a cloud analysis layer and an intelligent execution layer. According to the method, the change of the track state from'regular maintenance 'to'predictive maintenance' is realized, the maintenance efficiency, the precision and the line smoothness are remarkably improved, and the maintenance cost of the whole life cycle is reduced.

The invention discloses a power grid customer appeal mining method and system, and the method integrates regional meteorology, customer service data and geographic information, constructs a'micrometeorology-regional customer-microtopography 'coupled space-time dynamic model, and achieves the precise early warning of regional power supply service risks in extreme weather. The method comprises the following steps: constructing an electric power emergency dynamic knowledge graph by utilizing a natural language processing technology, performing semantic analysis and structured storage on unstructured laws and regulations, plans and cases, and establishing a dynamic updating mechanism; according to the method, an emergency response agent based on a generative large model is constructed, context perception and deep mining are carried out on customer complaints and appeals through a retrieval enhancement generation technology, and accurate, professional and personalized reply content is automatically generated. According to the method, the problems that the existing power customer service technology knowledge is lagged in updating, is disjointed with real-time business data, and is weak in complex context understanding capability are effectively solved, and the intelligent level and the operation efficiency of the power grid customer service are remarkably improved.

The invention relates to the technical field of coal mine simulation tests, in particular to a gas and underground water simulation test device and method.The gas and underground water simulation test device under coal mine fault disturbance comprises a vibration box and an installation box arranged on one side of the vibration box, and a servo driving assembly is arranged in the installation box; the servo driving assembly is connected with a vibration arm capable of knocking the vibration box periodically. The fault simulation unit has a fault closed state and a fault extension state, and when the driving piece drives the fault simulation unit to be converted from the fault closed state to the fault extension state through the pushing plate, liquid in the water tank is sprayed outwards through the spraying shaft to simulate underground water seepage. Meanwhile, the airflow generating plate sprays air outwards to simulate gas seepage, and the technical problem that an existing testing device cannot synchronously simulate the fault slippage and catastrophe process induced by dynamic disturbance and multi-physical field dynamic coupling in coal mine deep mining is solved.