185 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 provides a project cost management method based on artificial intelligence, and relates to the technical field of project cost, and the method comprises the following steps: 1, data mining and analysis: extracting valuable information from a large amount of historical project cost data through building a data mining model, and analyzing the mined data through a data analysis tool; step 2, automatic measurement and pricing; step 3, constructing a cost prediction model; step 4, risk assessment and early warning; step 5, project progress and cost collaborative management; step 6, intelligent cost control; and 7, intelligent supplier management. By constructing the data mining model and using the neural network algorithm to cooperate with the association rule mining algorithm, deep mining can be effectively performed on the engineering cost data, and meanwhile, the data analysis tool is used to analyze the data, so that the workload of data collection is reduced, and the efficiency of engineering cost management is improved.

The invention discloses an environmental pollutant toxicity detection and evaluation method, which comprises the following steps: deploying a multi-parameter sensor array in a target monitoring environment, and obtaining initial monitoring data; performing deep mining and pollutant toxicity feature extraction on the initial monitoring data to generate a pollutant feature data set; forming a customized evaluation model framework according to the target environment monitoring requirement, the preset monitoring parameters and the configuration of the evaluation model; inputting the generated pollutant characteristic data set into a preset customized evaluation model, and performing toxicity evaluation on the environmental pollutants to obtain a preliminary evaluation result; and automatically adjusting parameters of the customized evaluation model based on the obtained preliminary evaluation result by adopting an adaptive learning algorithm to obtain an optimized evaluation model, carrying out toxicity evaluation again to obtain an optimized evaluation result, and the like. The method adapts to monitoring requirements under different environmental conditions, and comprehensively evaluates the toxicity risk of pollutants.

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 discloses a wind generating set fault monitoring system based on the Internet of Things, relates to the technical field of fault monitoring, and solves the technical problems that in the aspect of fault reason analysis, deep mining and correlation analysis are lacked, and historical data cannot be effectively utilized to improve the monitoring accuracy and reliability. Mechanical and electrical data are comprehensively collected through the operation parameter acquisition unit, a rich information source is provided for accurate analysis, the operation state recognition unit performs preliminary judgment by combining real-time parameters and preset normal parameters, normal and abnormal states can be quickly distinguished, the normal state analysis unit establishes a risk evaluation standard by using historical data, and the risk evaluation standard is analyzed. According to the fault early warning system, normal operation parameters are continuously monitored and subjected to risk prediction, potential risks can be found in advance, preventive maintenance measures can be taken, a state abnormity analysis unit deeply analyzes fault reasons and related parameters, risk prediction is carried out in combination with historical data, and the accuracy and timeliness of fault early warning 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 a canal channel dredging construction big data basic business management system, and relates to the field of dredging construction data management. A canal channel dredging construction big data basic business management system comprises a data processing module, a data analysis module and a data management module. The method solves the problems that in the actual use process in the prior art, only business data in construction of the trailing suction dredger can be managed, and accurate decision support cannot be provided for channel dredging work, and the real-time processing of the data in the channel dredging process can be realized by collecting and processing various data in real time and applying a big data analysis method, so that the real-time processing of the data in the channel dredging process can be realized. And deep mining and intelligent analysis are carried out on the collected data, and accurate decision support is provided for channel dredging work. Besides, the system has the functions of remote monitoring, fault early warning and the like, comprehensive and efficient management of the canal channel dredging construction big data basic business is realized, the construction efficiency and quality are improved, and the construction cost and risk are reduced.

The invention discloses an industrial park soil pollution monitoring system which comprises a monitoring terminal, the output end of the monitoring terminal is connected with the input end of a data acquisition and processing center, and the invention relates to the technical field of environment monitoring. According to the industrial park soil pollution monitoring system, through arrangement of the plurality of monitoring terminals, comprehensive monitoring of the soil pollution condition in an industrial park is realized, a high-precision sensor and a real-time data processing technology are adopted, the real-time performance and accuracy of monitoring data are ensured, a big data analysis technology is utilized, and the real-time monitoring of the soil pollution condition in the industrial park is realized. The system performs deep mining and analysis on monitoring data, generates a valuable visual result, has early warning and emergency response functions, can quickly inform management personnel and start emergency measures when pollution occurs, is friendly in system interface and simple and convenient to operate, reduces the operation difficulty and cost of the management personnel, supports real-time data transmission and analysis, and improves the safety and reliability of the system. And timely and effective data support is provided for environmental governance.

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.

The invention provides an AI-based flexible DC support capacitor production scheme determination method and system, and relates to the technical field of artificial intelligence. The method comprises the following steps: firstly, carrying out deep mining on a capacitor performance semantic vector corresponding to target capacitor performance data, and outputting a capacitor performance depth vector; secondly, performing deep mining on a production scheme semantic vector corresponding to the target capacitor performance data, and outputting a production scheme depth vector; then, performing merging mining according to the capacitor performance depth vector and the production scheme depth vector, and outputting a production scheme mining vector; and finally, according to the production scheme mining vector, generating a target capacitor production scheme corresponding to the target capacitor performance data. Based on the above content, the problem that the reliability of capacitor production scheme determination is relatively low in the prior art can be 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 discloses an underground water intelligent system based on a three-dimensional geological model in the technical field of underground water. The underground water intelligent system comprises the following modules: a three-dimensional geological modeling module; an underground water monitoring module; the data analysis and visualization module is used for carrying out deep mining and analysis on the monitoring data by utilizing big data analysis and machine learning technologies, and identifying a rule and a trend of dynamic change of underground water; the geological change prediction module is used for carrying out numerical simulation on geological change, underground water flow and pollutant migration through the three-dimensional geological model, and predicting future water resource conditions and environmental influences to generate geological prediction data; an early warning and decision support module; and a water resource configuration and protection module. The system is scientific and reasonable, the geological change prediction module is based on a three-dimensional geological model, an advanced numerical simulation technology is applied, underground water flowing, pollutant migration and geological change processes are simulated, and the problem that geological change prediction of the underground water surface is not accurate is solved.

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 relates to the technical field of prospecting, in particular to an intelligent prospecting method based on multi-source information fusion and physical-data dual-drive, and the method comprises the steps: obtaining a target multi-source information database based on the granularity and reliability of multi-source information data in the target multi-source information database; performing complementary fusion on the multi-source information data according to the matched fusion level to obtain a multi-source information complementary fusion result; extracting mineral resource distribution information of the target area according to a multi-source information complementary fusion result, and generating an earth crust mineral resource refined distribution diagram of the target area by using the mineral resource distribution information; and based on the target multi-source information database and the earth crust mineral resource refined distribution map, generating a mineral resource information inference result of the target area by using a target identification system. According to the method, the target identification system can be utilized to perform deep mining on the earth crust mineral resource refined distribution map drawn based on the multi-source information, the mineral resource information inference result is obtained, the earth crust mineral resource exploration efficiency can be effectively improved, the resource exploration cost can be reduced, and strategic mineral resources can be accurately locked.

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 provides a deep mining data management method for the full life cycle of underground engineering, and relates to the technical field of data processing, and the method comprises the steps: surveying a target underground engineering, and if the target geological information meets the preset geological requirements of the target engineering design information, determining that the target engineering design information does not meet the preset geological requirements; dynamically monitoring the construction process of the target underground engineering based on the target engineering design information, detecting first construction monitoring data of first construction monitoring equipment in the construction monitoring information, generating target construction information, and constructing a target digital model by the target construction information and target geological information; and rendering the monitored target maintenance information to the target digital model for visual management, thereby solving the technical problem of low construction safety and quality caused by insufficient management and control of the construction technology and technical measures in the underground construction process in the prior art. The construction technology and technical measures in the underground construction process are reasonably and accurately managed and controlled, and the construction safety and quality 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.