426 results about "Collection analysis" patented technology

The invention provides a technical condition prediction method for a tunnel civil engineering structure, and relates to the technical field of traffic control monitoring systems.A biological acoustic composite sensing system comprising a distributed optical fiber acoustic sensing system and microorganism sample collection and analysis is constructed, and acoustic depth mode characteristics are mined by applying a deep learning model; the microbial dynamic biomarker is analyzed and identified by using bioinformatics; multi-modal information, a domain knowledge graph and a causal inference algorithm are fused, and a dynamic causal network model for revealing an internal driving relation in the degradation process is constructed; further, training and applying an adaptive neural network prediction model based on causal driving and fusing physical and biochemical mechanism constraints, and carrying out probabilistic prediction on future technical conditions of the tunnel; and executing anti-fact reasoning by using the causal network and the prediction model, and generating and optimizing an intervention strategy of cooperation of physical measures and biochemical measures. According to the method, the accuracy and advance of tunnel structure state prediction can be remarkably improved.

The invention provides an intelligent station multi-source heterogeneous data acquisition and remote collaborative analysis method and system, and relates to the technical field of heterogeneous data, and the method comprises the steps: collecting multi-source heterogeneous data, carrying out the standardization processing, constructing a station real-time adaptive three-dimensional model through a self-calibration laser scanning technology, and carrying out the semantic mapping of the model and a BIM model; constructing a self-evolution feature library based on a digital twinborn model, and carrying out space-time correlation analysis by adopting a graph convolutional neural network; a remote cooperation platform is established to realize real-time cooperation of multiple experts, and a diagnosis process is recorded through a block chain technology. According to the invention, efficient acquisition, analysis and remote collaborative diagnosis of the data of the smart station are realized, and the accuracy and efficiency of fault diagnosis are improved.

The invention relates to the technical field of user behavior analysis, and discloses a user behavior intelligent analysis and management system based on a big data technology. The system comprises a user behavior data acquisition module for acquiring behavior data in a multi-dimensional scene; the behavior feature intelligent recognition module is used for extracting features by using a deep neural network and a time sequence analysis algorithm and generating a map; the behavior pattern dynamic analysis module is used for analyzing pattern changes through dynamic clustering and a hidden Markov model; the abnormal behavior autonomous detection module is used for detecting anomalies based on the multi-dimensional anomaly score and an adaptive threshold value; and the behavior management intelligent optimization module is used for optimizing a management strategy by utilizing a reinforcement learning algorithm. In addition, a behavior data archiving module is further arranged to guarantee safe storage of data. The system can comprehensively collect and analyze user behavior data, accurately detect abnormity, intelligently optimize a management strategy, improve user experience, system safety and operation efficiency, and have wide application value in multiple fields.

The invention relates to the technical field of information management, in particular to an information scheduling scenarized service agent system. The system comprises an information scheduling acquisition and analysis module, a scene classification and service matching module, a service scheme instruction generation module and a service intelligent execution module, user behavior data, environment perception data and business system data corresponding to information scheduling can be obtained, and an original information set is generated; the original information set is preprocessed, an information scheduling scene classifier is constructed, the service matching degree is calculated in combination with the real-time service resource state, and a candidate service scheme set is generated; performing optimization sorting on the candidate service scheme set to generate an optimal service scheme, and disassembling the optimal service scheme into an executable service instruction sequence; receiving a service instruction sequence and generating a service evaluation report; and if the display does not reach the preset service quality threshold value, triggering a dynamic adjustment mechanism to realize self-iteration upgrading of the information scheduling scenarized service. According to the invention, intelligent scheduling and service of information in a complex scene can be realized.

The invention provides a water supply data real-time collection and analysis method, device and equipment and a medium, and the method comprises the steps: obtaining multi-source monitoring data of a water supply network, and carrying out the space-time alignment and spatial interpolation of the multi-source monitoring data, and obtaining a water supply data set; according to the topological structure of the water supply network, the water supply data set is mapped into a dynamic topological graph, and the dynamic topological graph comprises node attributes, edge attributes, real-time pressure and flow data; according to the dynamic topological graph, calculating stability parameters of each node based on a fluid mechanics principle; the dynamic topological graph and the stability parameters are input into a pre-trained machine learning model, a system balance degree quantitative index is generated, and the system balance degree quantitative index is used for representing the operation stability of the water supply pipe network. By adopting the method, the overall balance state of the water supply system can be accurately quantified to realize early risk warning.

The invention discloses a power grid data acquisition and analysis system based on big data, particularly relates to the technical field of power network monitoring, and is used for solving the problem of key feature loss caused by cross-hierarchy data semantic association missing of an existing hierarchical aggregation mechanism. Performing standardized filling and time label layered alignment processing on the original data through the equipment acquisition module; the feature extraction module screens an abnormal waveform fragment and a steady-state parameter offset based on the equipment type and the real-time fluctuation amplitude; the semantic association module generates a space-time association weight in combination with the regional physical topological relation and the reactive circulation path sensitivity; the feature clustering module reconstructs a region-level feature aggregation packet through kernel density estimation and spatial weighted fusion; the cross-layer analysis module dynamically corrects the aggregation weight based on the current flow direction and the transient energy distribution; and the decision generation module generates an equipment positioning instruction and load scheduling strategy combination through alarm template matching, and finally realizes accurate positioning of fault equipment and generation of a scheduling strategy.

The invention relates to the technical field of power monitoring, in particular to a moisture-proof environment-friendly ring main unit online monitoring system with continuous fault indication and a ring main unit, and the system comprises an electric parameter dynamic analysis module, an environment collaborative verification module, a closed-loop control execution module and a behavior path optimization module. According to the method, the current fluctuation and the power difference value are matched through dynamic time warping, the temperature rise rate and the condensation index are calculated through linear regression, multi-dimensional verification is formed through fuzzy logic judgment, operation parameters are adjusted in real time through PID control, strategy weight is optimized through a genetic algorithm, feature extraction is reversely corrected, and the fault recognition precision and the response speed are improved; closed-loop feedback is established to continuously optimize the system state, environment and electrical parameter coupling analysis is enhanced, the misjudgment probability of a single threshold value is reduced, parameters are dynamically corrected, the adjustment real-time performance and accuracy are improved, the adaptive capacity under the complex working condition is enhanced, a complete closed loop of collection, analysis and feedback is established, and the insulation degradation judgment reliability is improved.

The invention relates to the technical field of charging device detection, and discloses a charging device intelligent detection system and method based on dynamic load simulation. The system comprises a load configuration module, a charging parameter acquisition module, a load fluctuation calculation module, an abnormity identification module, a central processing unit and the like. The load configuration module configures dynamic load parameters according to the type of the charging device and generates a load simulation rule; the charging parameter acquisition module acquires an operation parameter set according to a rule; the load fluctuation calculation module generates a load fluctuation curve; the anomaly identification module calculates an anomaly fluctuation index; and the central processing unit fuses and processes the detection dimension parameters to generate state evaluation parameters. In addition, the system is further provided with a fault tracing module and a storage module. The system can simulate actual load change, collect and analyze parameters in multiple dimensions, realize fault intelligent diagnosis and traceability, can be widely applied to detection of various charging devices, improves the detection accuracy, reliability and efficiency, and reduces the detection cost.

The invention discloses an information acquisition and analysis method, device and equipment, a medium and a product, and relates to the technical field of cloud computing. According to the scheme, through the gateway with a multi-protocol identification function and the dynamic flow processing platform, the language model calling request transmitted by multiple data sources is monitored, unified management of LLM calling data is realized, and multiple protocols are adapted; when a language model calling request is received, forwarding the language model calling request to a language model service cluster, generating corresponding question and answer data, and obtaining user behavior data generated by a target data source according to the question and answer data, so as to carry out full-link multi-dimensional analysis based on the question and answer data and the corresponding user behavior data; the analysis process not only pays attention to the call request and response, but also considers the corresponding user behavior, so that the generated call analysis result is more accurate, and the credibility of LLM application effect analysis is improved.

The invention relates to the technical field of production management in the injection molding industry, and discloses a method and system for automatically generating a production scheduling plan based on production in the injection molding industry, and the method comprises the steps: obtaining a production order, an equipment state and material inventory data, generating original production parameter data, and deploying a distributed production scheduling network; collecting and analyzing multi-stage equipment load data and equipment state data, and generating corresponding influence factors; order priority data are collected, and production task areas are divided; and optimizing and calculating a production scheduling plan based on the task area data, integrating the multi-factor data to generate comprehensive production scheduling data, and further generating and outputting a production scheduling instruction. The system comprises a production scheduling network deployment module, an equipment load analysis module, an equipment state analysis module, a production task area analysis module and a production scheduling instruction generation module. Through multi-data integration and scientific modeling, the intelligent precision of injection molding production scheduling is realized, the equipment management efficiency, the order delivery capability and the overall production efficiency are improved, and the system is suitable for automatic production scheduling management in the injection molding industry.

The invention relates to the field of industrial automation, in particular to an intelligent dispatching industrial robot material sorting system. The system integrates various sensors such as a visual sensor and a laser radar sensor, and high-precision material identification is realized by applying multi-source information fusion and deep learning; a sorting path is dynamically planned through reinforcement learning, operation of multiple robots is coordinated according to a distributed collaborative optimization algorithm, and the motion stability is improved through self-adaptive control; and by matching with an intelligent monitoring management platform, data acquisition, analysis and early warning diagnosis are realized. Through practical application, the system effectively improves the material recognition accuracy and sorting efficiency, enhances the multi-robot cooperative ability and motion stability, and meets the efficient and accurate sorting requirements of modern industry.

The invention relates to the technical field of chip scheduling and optimization, in particular to a neural network-based main control chip task scheduling and dynamic performance optimization method, which comprises the steps of data acquisition and feature engineering, neural network model design, simulation environment training, model compression and deployment preparation, real-time state monitoring, dynamic decision reasoning, scheduling strategy execution and performance optimization. The data acquisition and feature engineering comprises the following steps: S1, hardware index acquisition; analyzing task attributes (calculation-intensive / IO-intensive), a dependency relationship (DAG), deadline (Deadline) and a resource demand (CPU / GPU occupancy rate); collecting data during chip operation through a performance counter (IPC, cache hit rate and branch prediction error rate), a temperature sensor and a power consumption monitoring unit (PMU); the neural network scheduler can achieve the energy efficiency ratio which is 20%-40% higher than that of a traditional method (such as a CFS scheduler), meanwhile, the neural network scheduler adapts to sudden load changes, and the practicability and the application range of a main control chip are wider.

The invention relates to the technical field of artificial intelligence and biomedicine, and provides a multi-agent diagnosis planning device and method based on a consultation thinking process. The multi-agent diagnosis planning device comprises a diagnosis planning agent, a pathological section analysis agent, an in-hospital information aggregation agent, an information search agent, a knowledge base search agent, an evaluation agent, an arbitration agent and a risk assessment agent. Each agent realizes data interaction through a dynamic priority message bus, and the evaluation agent and the arbitration agent form a progressive verification closed loop: after the evaluation agent outputs a question evidence chain, the arbitration agent triggers diagnosis correction only when question items are greater than 3 items, and otherwise, final diagnosis is output based on a preset rule. According to the invention, the thinking process of multidisciplinary expert collaboration in clinical consultation is simulated by constructing a multi-agent collaborative diagnosis framework, and the whole process intelligence from medical data acquisition and analysis to diagnosis decision is realized.

The invention discloses an electric power data acquisition and analysis method and system based on an electric power complex working environment, and relates to the technical field of intelligent electric power systems, and the method comprises the steps: collecting data through a first network, and carrying out the primary processing through a first algorithm; performing anomaly detection through a first method, and performing deep analysis through cloud service; and a cloud analysis result is fed back to an edge side through a control signal for collaborative optimization. According to the method, the possibility of misjudgment and missed judgment is reduced, the robustness of anomaly detection is enhanced, continuous monitoring of the operation state of the power system is ensured, a scientific basis is provided for timely taking remedial measures, closed-loop optimization of cloud-side collaboration is realized, and the method is suitable for large-scale popularization and application. The advantages of the cloud computing capability and the real-time response capability of the edge equipment are fully exerted, a global view angle is provided for optimization regulation and control of the power system, and the operation efficiency and reliability are improved.

The invention belongs to the technical field of atmospheric environment monitoring, and particularly relates to an atmospheric pollution channel transmission data analysis system which comprises an acquisition and analysis end and a visual display end which are in communication connection. The acquisition and analysis end comprises a multi-source heterogeneous data acquisition and integration unit, a data preprocessing unit, a pollution channel transmission identification unit, a transmission process analysis unit, a pollution source tracking unit and a dynamic prediction unit; according to the invention, a full-chain analysis framework from multi-source data acquisition and processing, pollution channel dynamic identification to source analysis and prediction and early warning is constructed, the identification precision and timeliness of a pollution transmission path are significantly improved, and a key supervision park in a monitoring area is determined through pollution management and control decision analysis. The key supervision parks are continuously monitored, and the atmospheric pollution emission management performance of the corresponding key supervision parks is judged, so that the targeted industrial park management is realized, and the pollution of each industrial park to the atmospheric environment is reduced.

The invention relates to a heat supply credible intelligent control method and system based on full trust creativity, and the method comprises the steps: collecting heat supply parameters in real time through Internet of Things equipment, and carrying out the preprocessing of the heat supply parameters through an edge calculation node, and obtaining the preprocessing data; performing thermal load prediction on the pre-processed data through a bidirectional LSTM neural network model based on a full trust creation platform, and outputting a thermal load prediction curve in a future time period; generating a dynamic scheduling instruction based on the thermal load prediction curve in combination with a real-time equipment state and an energy consumption optimization strategy; and the dynamic scheduling instruction is issued to the edge computing node, the operation parameters of the heat source, the pipe network and the user side are automatically adjusted, and when it is detected that the equipment is abnormal, standby equipment is automatically switched or a heat supply path is adjusted, and unmanned regulation and control of the heat supply system are achieved. According to the application, edge computing, the Internet of Things, trusted transmission and industrial control safety capabilities are integrated, so that full-flow coverage of heat supply scene data acquisition and analysis is realized, and the unattended scheduling capability of heat supply operation is improved.

The invention discloses a laboratory instrument data automatic acquisition and analysis method and system, and relates to the technical field of laboratory instrument acquisition and analysis, and the method comprises the steps: obtaining sample identification information, automatically sorting and distributing the sample identification information to different analyzers, uploading detection data to a central database in real time, and associating a sample number, a detection timestamp and an instrument number; the method comprises the following steps: constructing a multi-dimensional feature space based on historical detection data, performing sub-cluster division by adopting a density clustering algorithm, establishing a cross-index logic constraint relationship in combination with an expert rule base, mapping real-time detection data to the multi-dimensional feature space, judging a data exception type according to sub-cluster affiliation and the logic constraint relationship, and when judging that the data is exceptional, judging that the data is abnormal. And triggering a secondary detection process, automatically dispatching a mechanical arm to send the sample back to a specified instrument to execute differentiated reinspection operation, and generating a comprehensive report containing detection process tracing. According to the invention, the automation level of detection is improved, the labor cost is reduced, the detection quality and efficiency are improved, and the laboratory detection work efficiency is improved.

The invention discloses a vacuum combined preloading intelligent regulation and control method and system for coastal soft foundation treatment, and the method comprises the steps: cleaning a construction area, laying a drainage sand cushion layer and a plastic drainage plate, constructing a vacuum system and a monitoring network, carrying out preloading according to a layering grading and tide adaptation principle, and starting a vacuum control unit. Data is collected and analyzed in real time to trigger a regulation and control instruction, a vacuum degree compensation value and supplementary loading operation are adjusted according to the instruction, secondary strengthening is conducted on an area with the strength not reaching the design, and finally unloading and leveling are conducted according to the standard; the system comprises a data acquisition module, an edge calculation module, an intelligent control module, a stacking execution module and a vacuum regulation and control module, and the modules are connected through wired or wireless communication links to realize data interaction and instruction transmission; according to the method, the problems that the vacuum degree is unstable and regulation and control are lagged in the coastal tidal environment in a traditional method are solved, and the soft foundation treatment efficiency and safety are improved.

The invention provides a fracture deformation-seepage cross-scale visualization experiment device and a test method, and relates to the technical field of hydraulic rock mechanics and measurement. Comprising a macroscopic observation assembly, a microscopic observation assembly, a microscopic seepage model, a temperature-stress-seepage loading system, a monochromatic coaxial light source and a data acquisition and analysis system, the microscopic seepage model is used for placing a fractured rock mass; the temperature-stress-seepage loading system is used for injecting fluid with fluorescent microspheres into the microcosmic seepage model, applying load, regulating and controlling temperature and collecting data; the data acquisition and analysis system is used for processing data and images. By arranging the macroscopic observation assembly and the microcosmic observation assembly, double-scale synchronous observation is achieved, the whole process of fracture internal fluid interface evolution and rock mass deformation can be obtained in real time under the multi-physics field coupling conditions of temperature, stress, seepage and the like, visual correlation of deformation, flow and stress is achieved, and the measurement accuracy is improved. And an experimental basis is provided for establishing a fracture multi-field coupling mechanism model.

The invention relates to the technical field of welding fault analysis, in particular to a rail welding process information intelligent collection and analysis method based on the Internet of Things technology, and the method comprises the steps: obtaining the data of each welding parameter in the rail welding process through an Internet of Things intelligent sensor group; according to the discrete degree of all modal component data in each welding parameter before each collection moment, the instantaneous interference complexity of each welding parameter at each collection moment is obtained in combination with the average level of the similarity degree between the modal components of the welding parameters, and then the credibility weight of each welding parameter at each collection moment is obtained; the method comprises the following steps: analyzing the interference removing singularity of each acquisition moment in the track welding process to obtain the interference removing singularity of each acquisition moment in the track welding process, analyzing the correlation between the interference removing singularity and each welding parameter in the track welding process to obtain the correlation fault significance of each acquisition moment in the track welding process, and judging the fault condition of the track welding equipment based on the correlation fault significance. The accuracy of intelligent analysis in the track welding process can be improved.

The invention discloses a dual-carbon data acquisition and analysis system and method based on multi-source information fusion, relates to the technical field of dual-carbon data acquisition and analysis, and solves the technical problem that in the prior art, energy consumption monitoring and analysis cannot be performed on petrochemical enterprises in a dual-carbon data acquisition and analysis stage, so that the accuracy of data acquisition cannot be ensured. Specifically, an energy consumption analysis unit carries out operation energy consumption monitoring analysis on a petrochemical enterprise, collects statistical deviation information and data floating information, and deduces an energy consumption monitoring analysis result according to information comparison; the production process detection unit is used for detecting the production process of the petrochemical enterprise, and deducing whether the production process meets the actual requirements or not through data acquisition and analysis; and the carbon emission data analysis unit performs carbon emission data analysis on the petrochemical enterprise, and deduces whether the carbon emission of the petrochemical enterprise meets the actual demand through carbon emission data analysis.

The invention discloses an automatic ransomware collection and analysis method and system based on a large language model in the technical field of network security, and the method comprises the steps: constructing a target data source, collecting a first sample of a ransomware, and capturing the first sample which is actually triggered in an enterprise-level network environment and context behavior data of the first sample from the first sample; an automatic crawler system is established by adopting an open source web crawler framework to establish real-time connection with a malicious code platform, the popularity of a first sample on the malicious code platform is analyzed, and an optimization task is set. According to the method, the natural language processing capacity and the automation technology of a large language model are combined, efficient capture, accurate analysis and quick response of the samples are achieved, an anti-virtualization detection mechanism of the blackmail virus is effectively coped with by implementing an anti-virtualization detection confrontation strategy, and an intelligent protection chain is constructed for network security.

The present invention relates to the technical field of data processing, and is a sewage treatment intelligent dosing adjustment system based on fuzzy logic, comprising: a data acquisition module, which collects water quality data of sewage and optimizes the data acquisition process, including chemical oxygen demand, ammonia nitrogen content, total phosphorus content and sewage flow; a data analysis module, which is used to receive real-time data and perform small batch gradient descent analysis on the data to obtain analysis results; an optimization module, which is used to optimize the parameters and rules of the fuzzy logic controller through differential evolution according to the analysis results to obtain optimized parameters and rules; a fuzzy logic control module, which performs fuzzy logic calculation of dosing amount through simulated annealing according to the received water quality parameters and optimized rules, and converts it into a dosing control signal. The present invention realizes comprehensive intelligent management from data acquisition to dosing adjustment by integrating advanced data acquisition, analysis, optimization and control technologies.

The invention relates to the technical field of medical instruments, in particular to a negative pressure treatment system for diabetic feet, which comprises an intelligent negative pressure control module, a multi-cavity flexible dressing, a multi-parameter sensing unit, a data processing terminal, a percolate collecting unit, a monitoring feedback module and a portable power supply, wherein the intelligent negative pressure control module is used for generating, regulating and controlling negative pressure; the multi-parameter sensing unit is used for collecting wound microenvironment physiological parameters; the data processing terminal analyzes the received physiological parameters and wound image data based on an AI algorithm, and sends an instruction to the intelligent negative pressure control module to dynamically adjust the negative pressure intensity and the work cycle; the percolate collecting unit is used for collecting percolate; and an image acquisition module in the monitoring feedback module acquires a wound image. Therefore, the problems that in the prior art, the negative pressure intensity and the work period cannot be dynamically adjusted, effective collection and analysis means for wound microenvironment physiological parameters and image data are lacked, and percolate collection lacks an intelligent monitoring and early warning mechanism are solved.

The invention discloses a collection, analysis and utilization linkage method for the full life cycle of vulnerability intelligence, which comprises the following steps: respectively acquiring original vulnerability intelligence of a plurality of heterogeneous data sources, and carrying out standardization processing on the original vulnerability intelligence to obtain vulnerability description information; constructing an asset knowledge graph, wherein the asset knowledge graph is used for representing IT asset objects in the enterprise and dependency and deployment relationships among the IT asset objects; performing graph traversal query on the vulnerability description information based on an asset knowledge graph, and identifying an affected asset list affected by vulnerabilities; and according to a preset response strategy, generating a safety response instruction from the affected asset list and sending the safety response instruction to the safety protection system. According to the method, the asset knowledge graph is constructed and the multi-source vulnerability information is subjected to standardization and mapping correlation analysis, so that the accurate and rapid identification of the affected assets of the power system and the automatic response strategy generation are realized, and the efficiency and the accuracy of the vulnerability management of the key information infrastructure of the power grid are effectively improved.

Aspects discussed herein relate to systems, apparatuses, and methods to determine when a user remains engaged or stops paying attention to a content item. In response, a snapshot of the user's activity and environment may be obtained to ascertain the reason the user stopped paying attention. If the user stopped paying attention as a result of being uninterested in the content item, the system may replace the content item, such as an advertisement, with a different content item to regain the user's attention. Further, demographic information and the user's interests may be updated for use in scheduling future content items.

The invention belongs to the technical field of cable fault positioning, and discloses a cable branch box fault early warning and positioning system based on online partial discharge monitoring. Comprising a topology building module which is used for building a topology model in a cable branch box and defining a signal propagation path rule; pre-calculating signal expectation characteristics from each topological node to all monitoring sensors to form a structured expectation library; the acquisition and analysis module is used for acquiring multi-source transient signals and performing feature extraction to obtain an information fusion matrix; the decoupling positioning module is used for performing analysis based on the information fusion matrix and the structured expectation library to obtain a fault positioning point; the evaluation and early warning module is used for extracting all transient events in a preset time period corresponding to the fault positioning point, forming a time sequence feature sequence, extracting and analyzing an evolution index of the time sequence feature sequence, obtaining a risk transition probability, dividing risk levels and carrying out corresponding early warning; according to the invention, non-confusion accurate positioning of the partial discharge source of a specific joint or a cable section in the branch box is realized.

The invention discloses a geothermal resource dynamic investigation method, system and equipment and a storage medium, and relates to the field of geothermal resource dynamic investigation, comprising the steps of multi-source data acquisition, preprocessing, analysis modeling and dynamic monitoring updating, acquisition of multiple data such as temperature and pressure, adaptive weighted filtering and the like; a distribution probability graph is obtained through deep learning modeling and is dynamically monitored, the system comprises an acquisition unit, a preprocessing unit and the like for cooperative work, and equipment comprises a processor and a memory for executing related steps and storing a memory program for realizing the method steps. According to the method, multiple technical means are integrated, data are comprehensively and accurately collected and analyzed, geothermal resource distribution and changes are accurately predicted, auxiliary decisions are visually displayed, geothermal resource exploration efficiency and development rationality are effectively improved, and sustainable utilization of the geothermal resources is promoted.

The invention relates to the technical field of mining engineering, in particular to an extra-thick coal seam fully mechanized caving face bearing pressure distribution simulation analysis method which comprises the steps of S1, bearing stability analysis; s2, crack analysis; s3, analyzing the influence of the topping operation; s4, evaluating feasibility of roof cutting roadway protection; according to the method, the stress data of the coal pillar are collected and analyzed to obtain the bearing stability of the coal pillar, the crack propagation speed is analyzed through image acquisition, so that the crack severity degree of the top plate is obtained, and the effect evaluation coefficient of the coal seam roof cutting operation is obtained according to the pressure parameter analysis of the coal seam working face after the roof cutting operation. The roadway deformation amount and the surrounding rock stability are obtained through video recording analysis, and then the feasibility of roof cutting roadway protection is obtained through analysis, so that the coal seam fully mechanized caving face bearing pressure influence evaluation index is comprehensively analyzed, targeted measures can be better taken to guarantee operation safety, the mining technology and process can be optimized, and the mining efficiency is improved.

An emotional disorder recognition control model acquisition method and system based on near-infrared brain imaging map characteristics, a controller and a control method, and the method comprises the following steps: step 100, fNIRS-based multi-dimensional nerve marker acquisition and analysis, step 200, an integrated handheld diagnostic device, step 300, a dual-mode high-speed intelligent analysis system, step 300, a near-infrared brain imaging map characteristic analysis system, and step 300, a near-infrared brain imaging map characteristic analysis system. And step 400, a closed-loop AI training and database iteration system realizes a link of collection-analysis-diagnosis-display by fusing technologies of signal collection, data preprocessing and normalization, characteristic parameter extraction, intelligent report display and the like, so that the transformation of mood disorder screening from subjectivity to objectivity and from experience driving to data driving is promoted, and the mood disorder screening efficiency is improved. Therefore, the embedded analysis system for the rehabilitation medical scene is provided.