157 results about "Deep mining" patented technology

The invention relates to the technical field of thermal power generation, in particular to a thermal power plant fuel intelligent supervision system and method based on big data analysis and early warning, and the system comprises a multi-modal data sensing module, a hierarchical enhanced decision module, a real-time early warning and evaluation unit, and a digital twinborn decision center. Wherein the multi-modal data sensing module is used for constructing a fuel digital twinborn body; the hierarchical enhanced decision module is used for constructing a double-ring intelligent decision system and performing hierarchical optimization and full life cycle management; the real-time early warning and evaluation unit is used for acquiring data, performing deep mining, risk identification and dynamic adjustment of an early warning threshold in combination with a reinforcement learning algorithm, and grading the risks; the digital twinborn decision center performs virtual deduction by means of a digital twinborn model, generates a target strategy through a multi-target optimization algorithm, ensures instruction traceability, and dynamically adjusts the strategy according to real-time data. Therefore, the problems of limited data processing capability, low model adaptability and the like in the prior art are solved.

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 provides a ground pressure control method based on a deep oversize underground mine frame type structure, and belongs to the field of underground mine mining. Horizontal isolation jambs are reserved on a middle section top plate of a deep mining area, and deep area jambs extending to a deep lowest service middle section are arranged; the whole deep mining area adopts a descending mining sequence, and the middle section between the two horizontal isolation pillars adopts an ascending mining sequence; the stopes between the adjacent deep area pillars form a frame unit, the stopes at the two ends are preferentially mined in the frame unit, and the stopes are filled in time to form a horizontal pressure relief stope, so that stoping stability of the stopes is facilitated; and meanwhile, before stoping of each stope, single-row downward fan-shaped pressure relief holes are drilled in surrounding rocks of upper and lower plates of the stope for advanced presplitting loosening blasting, and high-level stress load conduction of the deep stope is blocked. According to the method, high stress conduction of a deep mining area can be blocked, and the effects of relieving ground pressure appearing in the deep mining process and maintaining mine stability and stoping safety are achieved.

The invention relates to the technical field of power systems, and discloses a digital power grid area operation optimization method, system, device, medium and product, and the method comprises the steps: obtaining the operation data of a plurality of preset monitoring nodes in a power grid area, and carrying out the deep mining of the data; and performing clustering analysis on the operation characteristic data, dividing the power grid region into a plurality of sub-regions, and inputting the operation data corresponding to each sub-region into a digital twinborn model of the power grid region for real-time simulation. Based on real-time simulation data, a plurality of operation scenes are generated, electric energy loss minimization, load imbalance minimization, new energy consumption maximization, electric power cost minimization and power grid reliability maximization of a power grid area are taken as multi-optimization targets, an optimal operation scene is determined through the targets, and then an optimal operation strategy of the power grid area is formulated. Operation optimization can be rapidly carried out, and the stability of power grid operation is effectively improved.

A data processing method includes: obtaining a service surface activity feature of an object in a service, and inputting the service surface activity feature to a deep mining and analysis model, the deep mining and analysis model being configured to deeply mine one or more factor semantic representation features for a surface activity feature based on a configuration affecting factor system of the service; performing deep mining and analysis processing on the service surface activity feature in the deep mining and analysis model based on the configuration affecting factor system, to obtain a factor semantic representation feature of the service surface activity feature for each configuration affecting factor; and outputting a service policy of the object for the service and policy interpretation information for the service policy based on the factor semantic representation feature of the service surface activity feature for each configuration affecting factor.

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 belongs to a prediction method, and provides a short-term power load prediction method and a related device aiming at the technical problems that deep features in time series data are difficult to fully mine and deep mining of load data timestamp features is lacked when short-term power load prediction is carried out by adopting a deep learning-based method at present. The prediction method of the short-term prediction model comprises the following steps: decomposing trend term and periodic term components from a historical power load sequence; step-by-step multi-scale decoupling is carried out on the trend term component and the period term component, and first trend decoupling results of different scales and first period decoupling results of different scales are obtained; and layer-by-layer up-sampling is carried out on the first trend decoupling results of different scales, layer-by-layer down-sampling is carried out on the first period decoupling results of different scales, then coupling processing is carried out on the second trend decoupling result and the second period decoupling result, and a prediction result is generated. And a new thought is provided for accurate prediction of the power load.

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 mining collapse prediction and early warning method and system. The method comprises the following steps: collecting multi-dimensional data; performing deep mining on the acquired multi-dimensional data by using a big data analysis technology, and extracting key features related to mine collapse risks; performing fusion processing on the multi-dimensional data, removing redundant information among different data sources, estimating and filling missing data, and organically combining the multi-dimensional data through a Bayesian network algorithm; in the multi-data fusion process, a data quality evaluation mechanism is adopted to carry out quality inspection on fused data, and according to a data quality evaluation result, a multi-data fusion algorithm and parameters are dynamically adjusted to form real-time mine state description; and based on the real-time mine state description, performing collapse risk prediction by using the collapse prediction model. Based on data acquisition, data analysis, multi-data fusion and risk prediction, accurate prediction and safety early warning of the collapse risk in the mining process can be realized.

The invention provides a sulfur trioxide monitoring and emission control optimization adjustment method, and belongs to the technical field of optimization adjustment, and the method comprises the steps: carrying out the real-time synchronous monitoring of sulfur trioxide in supercritical boiler flue gas through different types of sensors, carrying out the data preprocessing and data fusion processing of a synchronous monitoring result, and carrying out the real-time monitoring of the sulfur trioxide in the supercritical boiler flue gas; and correcting the processed data by combining a correction model established with the parameters of the supercritical boiler, carrying out deep mining on historical monitoring data, historical operation parameters of the supercritical boiler and historical operation states of the flue gas treatment equipment, establishing a sulfur trioxide control model, and carrying out real-time monitoring on the sulfur trioxide control model. And a cooperative control instruction of the flue gas treatment equipment and desulfurization adjustment parameters in a desulfurization system are determined according to an output result, optimization control adjustment is performed, a model is established, an effective control result is output through analysis of a current monitoring result, and optimization and regulation of the equipment are intelligently realized.

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 invention relates to the field of deep mining and analysis of ship AIS (Automatic Identification System) data, and provides a method and a system for real-time anomaly monitoring of global shipping data. Shipping indexes are generated according to data such as cargo types, loads, ship departure places and destinations of shipping ships, abnormal operation data are automatically screened out through abnormal rule setting, and decision analysis support is provided for users in time; meanwhile, the abnormal frequency of a single shipping index in a plurality of continuous periods or a specified range can be analyzed, and the problem that the shipping index can only analyze and display a single index is solved; according to the method, all the abnormal indexes are concentrated on the map to be displayed, the display content comprises the fluctuation range, the period length and the abnormal concentration area of the abnormal data indexes, and a user is helped to combine a plurality of abnormal data indexes, analyze abnormal reasons and assist in decision making reference.

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 discloses a wind power evolution analysis device for complex terrains, which comprises a wind power monitoring shell, a displacement power shell and a connecting steel cable, and has the beneficial effects that the device adopts a point-to-point displacement structure, the structure is erected at two ends of a to-be-measured and analyzed area, and meanwhile, the structure is connected with the connecting steel cable; the device is further provided with a wind power measuring and calculating structure, by means of the unique design, the device can freely move in a target interval, and the wind speed in the area can be flexibly and accurately monitored and deeply analyzed. In the monitoring process, the device can collect a large amount of wind speed related data, comprehensive carding and deep mining are carried out on the data through a big data integration technology, and on the basis, full and systematic analysis can be carried out on wind power evolution in an interval.

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.