43results about How to "Accurate Data Base" patented technology

The invention relates to the technical field of coal mining and rock mechanics numerical simulation, and discloses a stress field reconstruction method of a plane strain numerical simulation model for coal mining, and the method comprises the following steps: firstly, obtaining an actually measured vertical stress increment and an actually measured bearing pressure peak value position of a stope face; then, an initial plane strain numerical simulation model is constructed, the space error between the peak position of the simulated bearing pressure and the actual measurement position meets a preset threshold value by adjusting the excavation step pitch, and therefore the numerical simulation calculation state is locked to eliminate space-time dislocation; a simulation strain increment is extracted in a locked state, a theoretical vertical stress difference equation is constructed based on an elastic mechanics plane strain constitutive relation, and a parameter inversion equation set is established in combination with multi-measurement-point data; and solving the corrected elastic modulus and Poisson's ratio by using a least square optimization algorithm. According to the method, through physical state locking and incremental theory inversion, system errors are effectively eliminated, and the reconstruction precision and mechanical preciseness of the mining-induced stress field are improved.

The invention discloses an intensive culture precise feeding control method based on fuzzy-MPC control, and relates to the technical field of aquaculture intelligent control, and the method comprises the steps: employing a sensing system, collecting multi-source sensing data including fish activity images, healthy growth states and water quality environment data in real time, and forming a comprehensive state data set; and based on the acquired multi-source sensing data, constructing an MTL-LSTM-SAT multi-task learning model. According to the method, the multi-task learning model fusing the LSTM and the soft attention mechanism is constructed, the long-term dependency relationship and dynamic association among the water quality, the feeding and the fish state can be captured, the attention mechanism enables the model to be automatically focused on a key time step, the perception ability of the water quality fluctuation and the feeding influence stage is enhanced, and the accuracy of the water quality fluctuation and feeding influence stage is improved. Meanwhile, double-task collaborative prediction of the growth speed and the disease proportion is achieved through the shared feature layer, a prospective state sequence of the future 7 days is output, and a quantitative and reliable prediction basis is provided for feeding decision making.

The application discloses a kind of satellite radiation product-based remote sensing image geometric calibration method, electronic equipment and medium.The method can include: determining reference image, inputting image to be calibrated;For the same name point matching of image to be calibrated and reference image, obtain the same name point coordinates;The third polynomial distortion model about CCD probe element number is constructed;Based on the same name point coordinates, curve fitting is carried out on the distortion model, and the distortion coefficient is obtained;The resampling of image to be calibrated is carried out by distortion coefficient, and the correction of internal distortion of image is realized.The application can quickly and accurately carry out geometric calibration processing to satellite image, greatly improve the efficiency and quality of image processing.

A broadband voltage transformer error compensation method is proposed. First, a broadband test platform is constructed using a signal generator, power amplifier, standard voltage transformer, and high-bandwidth data acquisition system. The ratio and phase errors of the transformer at multiple frequency points are acquired through primary-side excitation and secondary-side synchronous acquisition. Based on the slope, first derivative, and second derivative characteristics of the error curve, the entire frequency band is adaptively divided into segments. Within each segment, multiple candidate models are fitted using a polynomial or exponential-linear combination model. The optimal model is selected based on the coefficient of determination, mean absolute error, and root mean square error. Ratio and phase error compensators are constructed based on the fitting functions of each segment, and error compensation is achieved using a feedforward approach. This method can significantly reduce amplitude and phase errors over a wide frequency range, improving the dynamic measurement accuracy of the voltage transformer.

This invention discloses an IoT-based intelligent control and management system for coal mines, relating to the field of intelligent control technology in coal mines. It includes a sensing fusion module that performs time-scale alignment, format normalization, and fusion of multi-source heterogeneous sensor data from underground sources to generate a panoramic dynamic data image. A status assessment module evaluates the system's status level based on this image. A control decision module matches corresponding multi-objective collaborative control plans and decomposes them into executable parameters for each executing device. An execution feedback module issues commands and receives feedback data. An adaptive optimization module corrects the control plan parameters online based on feedback deviations, achieving closed-loop optimization. This system solves the problems of data silos and control rigidity underground, improving the overall safety and operational efficiency of coal mine production systems through a unified data base and closed-loop adaptive optimization.

The application discloses a register configuration combination verification information generation method, a verification method and a device, and relates to the technical field of chip verification. First, the bit width and the value range of a domain segment are obtained by analyzing a register description file, and then the value range is divided according to the bit width, and verification points are automatically obtained, so that the generation efficiency and consistency of the verification points are improved. Secondly, the value range of a random variable corresponding to the domain segment is determined according to one or more verification points, and the diversity of the test configuration is increased. Thirdly, each verification point is mapped into a corresponding coverage bin of a coverage point, so that each coverage bin corresponds to a corresponding verification point, and whether the corresponding verification point is covered can be determined according to the hit condition of the coverage bin in the subsequent process. Finally, the test constraint condition and the coverage rate model are both generated according to the same set of verification points, so that the generation efficiency, completeness and accuracy of the verification information are improved. When the register configuration combination verification is performed, the efficiency, completeness and accuracy of the register configuration combination verification are ensured.

The invention provides an unmanned aerial vehicle hyperspectral data radiation correction method and device, equipment and a storage medium. The method comprises the following steps: acquiring a hyperspectral image to be corrected; extracting a water body part in the hyperspectral image; a radiation correction result of a to-be-corrected air strip in the water body part and a radiation correction result of a to-be-corrected air strip in the water body part are determined with the space behavior radiation correction unit of the hyperspectral image, and the to-be-corrected air strip and the to-be-corrected air strip form all air strips of the water body part; acquiring atmospheric parameters corresponding to each air strip; performing radiation correction on the air strips based on the radiation correction result of each air strip and the atmospheric parameters to obtain a reflectivity correction result of each air strip; the first fitting radiation brightness correction value and the first radiation brightness correction value are obtained through quadratic regression analysis, the calculation efficiency is high, the complex radiation transmission process is simplified with the space behavior radiation correction unit of the hyperspectral image, and the accuracy of radiation correction of the water body part in the air strip and between the air strips is improved.

This invention relates to the field of tunnel construction technology, and more particularly to a system and method for dynamically optimizing tunnel construction parameters based on geological information from point cloud data. At multiple monitoring time points, point cloud data is collected from the tunnel segment to be constructed, obtaining multiple point cloud datasets. The tunnel segment is divided into multiple construction areas, and multiple regional point cloud datasets are obtained from these datasets. Point cloud change analysis is performed on each regional dataset to obtain multiple point cloud change rates. Based on these regional point cloud datasets, reflection intensity analysis is conducted to obtain multiple reflection intensity coefficients. Combined with the multiple point cloud change rates, geological analysis is performed to obtain the distribution of concealed geological information. Based on this distribution, tunnel construction parameters are dynamically optimized to obtain optimized tunnel construction parameters for tunnel construction. This achieves the technical effect of improving the efficiency and safety of tunnel construction while reducing construction risks and costs.

This invention discloses a real-time visualization system and method for multiple port areas, relating to the field of data visualization. The system includes a real-time port area data acquisition and access module, a port area scheduling data visualization module, a loading and unloading machinery and equipment operation status visualization module, an AIS data access visualization module for vessels not yet arriving at the port, a multi-indicator layer visualization control module, and a multi-menu data visualization information center. It aims to connect with video surveillance equipment, weather forecast data, vessel scheduling data, and equipment control systems deployed in various port areas through standardized interfaces. AIS data enables real-time acquisition, unified aggregation, and preprocessing of multi-source data in the port area. It flexibly overlays multiple business-themed layers, such as the distribution of port facilities and pipelines, to provide a real-time and accurate data foundation for upper-level visualization applications, achieving one-stop port operation situation awareness.

The utility model discloses a robot sensor platform with two degrees of freedom and a damping function. The robot sensor platform comprises a damping base, a spherical hinge, a spring, an upper stepping motor, a damping connecting plate, a sensor support, a sensor clamping jaw and a lower stepping motor. The damping device is characterized in that the damping base and the damping connecting plate are connected through a spherical hinge and a spring, and the spherical hinge is a telescopic spherical hinge with damping and is matched with the spring to achieve double damping; the upper stepping motor is fixedly installed on the damping connecting plate, and an output shaft of the upper stepping motor is connected with the sensor support. One side of the sensor support is fixed to a lower stepping motor through a groove, and an output shaft of the lower stepping motor is connected with a sensor clamping jaw. According to the platform, the operation range of the sensor is increased through the two stepping motors, and the precision is ensured while the degree of freedom of the sensor is increased through buffering and damping of the damping assembly.

The invention relates to a task operand determination method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring a target frequency of a target processor for processing a to-be-analyzed task; determining the computing power of the target processor under the target frequency according to the computing power sample of the processor cluster corresponding to the target processor under the target frequency; according to the processing time and the computing power required by the processor cluster to process the to-be-analyzed task under the target frequency, the computation amount of the to-be-analyzed task is determined, so that the accurate computation amount of the to-be-analyzed task can be obtained, and the magnitude of the processor load corresponding to the to-be-analyzed task is represented through the computation amount. And an accurate data basis is conveniently provided for performance optimization, power consumption balance and the like.

This invention discloses an online visual inspection and laser dressing closed-loop control system for grinding wheel allowance, comprising a multi-axis precision linkage platform, a laser ablation dressing unit, a dual-modal composite visual sensing component, a real-time data processing unit, and a closed-loop feedback control unit. The dual-modal composite visual sensing component consists of a structured light scanning module and an infrared thermal imaging module connected by a coaxial optical path, and uses the same trigger signal to achieve spatial alignment and temporal synchronous acquisition, obtaining registered geometric point cloud and temperature field data of the grinding wheel surface. The real-time data processing unit incorporates a real-time dynamic decoupling model for thermally induced deformation, calculates the thermal expansion displacement field based on the temperature field, and separates it from the comprehensive radial deviation of the geometric point cloud to obtain the true geometric allowance. The closed-loop feedback control unit generates adjustment commands based on the deviation between the true allowance and the target contour, achieving high-precision closed-loop dressing. This invention solves the technical challenge of detecting thermal expansion interference accuracy during laser dressing, improving dressing accuracy and consistency.

The application relates to the technical field of collaborative allocation of warehouse resources, and discloses a warehouse resource adaptive collaborative allocation system based on multi-time scale deep reinforcement learning, which comprises a warehouse data acquisition unit, which acquires equipment operation data and environmental parameters and standardizes the same; a warehouse resource state feature extraction unit, which extracts resource state features to form abnormal features; a reinforcement learning state space construction unit, which analyzes the features to form state space construction parameters; a resource allocation strategy generation unit, which constructs a model to generate a strategy and divides influence factors; and a strategy adaptive optimization unit, which updates and optimizes the strategy by using an algorithm. In addition, the system is provided with a multi-source information fusion unit and a task queue dynamic adjustment module. The system realizes adaptive collaborative allocation of warehouse resources, improves warehouse operation efficiency, adapts to dynamic changes of a warehouse system, and meets the demand of modern warehouse intelligent operation.

The invention discloses a multi-source meteorological data fusion processing method for new energy output prediction, and the method comprises the steps: S1, determining a new energy type and a core meteorological impact factor, and determining the data time granularity according to a prediction time scale; s2, collecting localized observation, reanalysis and numerical forecasting data, removing abnormal values and unifying the abnormal values into a structured format; s3, unifying data resolution through bilinear interpolation, and realizing meteorological data and station space matching in combination with station boundary cutting and space verification; s4, unifying all data as Beijing time, aligning timestamps, filling missing values according to priorities, and controlling a single factor missing rate to be less than or equal to 5%; s5, quality control is carried out from the dimensions of data integrity rate, spatial correlation and the like, and if the data does not reach the standard, an interpolation method is adjusted or a data source is supplemented; s6, outputting CSV or netCDF format fusion data according to requirements, and performing classified storage according to stations and time so as to facilitate subsequent association; the method has the advantages of being good in input data basis, good in output data space-time consistency and convenient to use.

The invention relates to the technical field of building curtain wall construction, and discloses a multi-direction adjustable aluminum-based metal curtain wall installation method which comprises the following steps: determining the plate division size and the reinforcing rib layout based on wind pressure load and finite element analysis, designing a multi-dimensional adjustable hanging system, generating design parameter data, and setting the design parameter data as the design parameter data; on the basis of the design parameter data, aluminum-based metal plates and reinforcing ribs are machined through numerical control equipment, and machined curtain wall plate data are generated; design parameter data, processed curtain wall plate data and on-site measurement reference data are systematically integrated, so that a uniform and accurate data reference is provided for keel installation and plate hanging, and the definition and consistency of a whole-process construction basis from surveying and setting-out to final fastening are ensured; the initial in-position accuracy of integral installation of the curtain wall is improved, and accumulative errors caused by data disjunction are reduced.

This application provides a method for finely characterizing the fractured area of ​​the floor strata in a mining face, relating to the field of coal seam floor characterization technology. The method includes: collecting the original microseismic event signals and environmental correlation parameters corresponding to each microseismic event in the fractured area of ​​the mining face floor strata; performing feature extraction to obtain a microseismic event feature matrix corresponding to each microseismic event; inputting a microseismic event classification model to obtain the rupture correlation confidence score of the microseismic event; if the source type of any microseismic event is rupture type, and the corresponding rupture correlation confidence score is greater than a preset rupture correlation confidence score threshold, then the fractured area of ​​the mining face floor strata is characterized based on this microseismic event. This application achieves fine characterization of the fractured area of ​​the mining face floor strata, providing a reliable basis for engineering safety control.

This application provides a robot control method and electronic device. The method includes: a three-layer state machine based on a task layer, a sub-task layer, and an action layer; upon receiving a task pause signal, generating and storing context information based on the signal reception time; upon receiving a recovery signal, acquiring the current environment, time, and resource information, and determining whether the task can continue execution based on the context information; if execution is possible, acquiring the robot's current pose information, performing position correction based on the context information to obtain the continuation position, and finally controlling the robot to continue task execution from the continuation position based on the context information. This application achieves refined control and precise breakpoint continuation of robot tasks, effectively solving the problems of inconsistent states and position drift during task pause and recovery, avoiding invalid task execution, improving the flexibility, reliability, and accuracy of robot task execution, and optimizing the human-computer interaction experience.

The application provides a refractory compression resistance detection device and method, and relates to the technical field of refractory detection. The detection device comprises a detection box body and an internal positioning assembly, a first heating assembly, a pressurizing assembly and a data acquisition assembly. The positioning assembly supports both ends of the sample to achieve suspended positioning through two positioning blocks. The heating assembly comprises a first heating device that can be lifted and is in contact with the surface of the sample to heat. The pressurizing rod of the pressurizing assembly vertically penetrates the heating device to press the center of the sample. The data acquisition assembly monitors the pressure, temperature and deformation in real time. The device realizes synchronous thermal loading through the cooperative work of the heating and pressurizing assemblies, avoids the thermal history interference problem caused by the separation of heating and pressurizing in the traditional method, can truly simulate the service state of the refractory in the high-temperature industrial environment, and provides accurate data support for the high-temperature compression resistance evaluation.

The invention provides a digital twinning-based power equipment comprehensive monitoring and predictive maintenance system and method. The system comprises a physical layer, a data acquisition and transmission layer, a digital twinning model layer, a data analysis and intelligent early warning layer and a decision support and visualization layer. The method comprises the steps of data acquisition, model driving, simulation, calculation evaluation, early warning and feedback optimization. According to the method, equipment faults are predicted in advance by means of a digital twinning technology and a machine learning algorithm. The system can accurately analyze the health state of the equipment and predict the remaining service life of key parts by continuously monitoring equipment operation parameters such as vibration, temperature and pressure and combining historical data and an equipment operation mechanism. Once the health index of the equipment is lower than the preset threshold value or the remaining service life is close to the safety margin, the system immediately and automatically gives an early warning, and maintenance personnel can arrange maintenance work in advance, so that unplanned shutdown caused by sudden failure of the equipment is avoided, and the production continuity is ensured.

The application relates to a photovoltaic simulation prediction method and system based on a cellular automaton, which comprises the following steps: cleaning, spatial registration, interpolation, supervised classification and rasterization processing of multi-source data, construction of a photovoltaic distribution data and environmental factor database; construction of a photovoltaic expansion and photovoltaic loss optimal classification model to obtain the contribution degree result and marginal effect characteristics of each influence factor; calculation of a photovoltaic expansion and photovoltaic loss comprehensive suitability score, generation of a photovoltaic expansion suitability map and a photovoltaic loss suitability map through spatial mapping; after the operation rules and parameter constraint set of the cellular automaton model are determined, the candidate pixel set is screened, the conversion quantity is controlled, and each cellular state is iteratively updated step by step, so that the prediction simulation of the photovoltaic facility space expansion and loss process is realized. According to the technical scheme, the photovoltaic space bidirectional dynamic accurate simulation can be completed through the cellular automaton, and interactive parameter adjustment is supported to adapt to diversified planning scenes.

The invention relates to the technical field of vehicle fault diagnosis, and discloses a wheel bolt looseness detection method and device, a vehicle and a medium, and the method comprises the steps: collecting an inertial measurement unit signal, a wheel speed signal and a steering wheel hand torque signal of the vehicle; determining target detection data based on the inertial measurement unit signal, the wheel speed signal and the steering wheel hand torque signal; detecting whether a wheel bolt of the vehicle is loosened by using the target detection data to obtain a detection result; and controlling the vehicle to execute corresponding actions based on the detection result. According to the method, the existing sensor of the vehicle is directly used for collecting the signals, then the signals are fused with the multi-dimensional vehicle running state signals, feature extraction and combination processing are combined to form target detection data, the target detection data are used for wheel bolt looseness detection, full-process automation from signal collection, data processing, state detection to vehicle control action execution is achieved, and the detection efficiency is improved. And the detection accuracy and the driving safety are improved, so that the vehicle fault diagnosis requirements of low cost, high real-time performance and easy deployment are met.

The present disclosure provides a kernel upgrade method, device, equipment, storage medium and program product, and relates to the technical field of digital data processing. The method comprises the following steps: installing a deployment file of a target version kernel to a preset position, wherein the deployment file comprises a start configuration item; obtaining parameters of the start configuration item; starting the target version kernel in parallel under a running state of a current version kernel based on the parameters of the start configuration item; and switching running resources and running tasks occupied by the current version kernel to the target version kernel. The present disclosure can start the target version kernel in parallel while the current version kernel is normally running, thereby solving the problem of business interruption caused by operating system upgrade.

The invention relates to the technical field of intelligent power grids and industrial automation control, in particular to a DC power supply coordination control system based on industrial big data. The system comprises a multi-dimensional heterogeneous data sensing module, a feature mapping module, a multi-target collaborative decision-making module and a self-adaptive execution module. The system constructs a load characteristic and electric energy quality correlation model based on the operation state and the power consumption demand, outputs dynamic electrical characteristics and sensitivity characteristics, and solves a power distribution instruction by taking power grid quality, load balance and efficiency as cooperative targets; according to the method, a perception decision-making framework crossing an electrical domain and a physical domain is constructed, hidden physical layer states such as impedance drift are mined through deep learning, the problem that a load physical state is ignored and production quality is disjointed in traditional control is solved, and the industrial yield and energy efficiency are improved while the quality of a power grid is guaranteed.

The invention discloses a material price data analysis method based on artificial intelligence, and the method comprises the steps: S1, obtaining an original file from a diversified data source, analyzing text semantics, achieving the template-free adaptive analysis of a key field, and obtaining an initial data set; s2, for the initial data set, performing semantic normalization and logic association verification on material and specification expressions, and determining a unified material identifier set; s3, according to the unified material identifier set, extracting price values and associating market dynamic information to obtain a preliminary price data sequence; and S4, performing three-level intelligent verification of format, logic and market rationality on the preliminary price data sequence in sequence, and performing automatic correction on extraction deviation to obtain a corrected price sequence.

The invention relates to the technical field of intelligent operation and maintenance of power equipment, and discloses an intelligent distribution transformer operation and maintenance method and system, and the method comprises the steps: fusing the four-dimensional parameters of electricity, state, nameplate and environment; a dynamic attention mechanism anchored by nameplate parameters is adopted to realize adaptive fusion of key features; abnormal detection is based on a transfer learning model, and cross-device knowledge transfer is promoted; residual life prediction is completed through a dynamic method of reference life and personalized aging coefficient correction; a multi-dimensional health assessment system is constructed, quantitative health scores are generated, and a multi-stage intelligent early warning mechanism is established according to the quantitative health scores and the residual life; meanwhile, an intelligent man-machine interaction system based on a BERT model is integrated, and natural language and voice questions and answers are supported; according to the invention, omnibearing intelligent monitoring, refined state evaluation, scientific life prediction and auxiliary decision making of the distribution transformer can be realized.

The application relates to a road network adjustment method and device. The method comprises the following steps: acquiring road network data of a target area; constructing a road network graph corresponding to the road network data according to the road network data; obtaining node embedding of all nodes and edge embedding of all edges of the road network graph according to the road network graph; obtaining an action probability distribution according to the node embedding of all nodes and the edge embedding of all edges; selecting a corresponding action according to the action probability distribution, wherein the action is an atomic operation on the road network graph; and adjusting the road network graph according to the selected action. The application abstracts a city road network into a graph structure for modeling, realizes deep understanding and reservation of spatial topological relations of the road network, understands and analyzes the road network from the perspective of overall correlation, solves the bottleneck problem that a traditional model cannot effectively process non-Euclidean space data such as the road network, and provides accurate and structured data basis for subsequent intelligent decision-making.

This invention provides a method for segmenting rock mass structural surfaces in a three-dimensional mesh model of the tunnel face, relating to the fields of three-dimensional laser point cloud processing and tunnel geotechnical engineering. The method involves collecting multimodal sensing data of the tunnel face during excavation, generating a colored three-dimensional mesh model based on this data, and performing tensor quantization on the colored three-dimensional mesh model data. Each vertex of the three-dimensional mesh model is encoded as a normal vector tensor, and the saliency of the vertices is determined based on the tensors to extract the vertex data of the structural surfaces. The structural surface vertices are then clustered, and structural surfaces are extracted from the obtained clustered vertices. Based on the orientation of the structural surfaces, they are grouped, and rock mass parameters are calculated. This invention solves the technical problems in existing technologies where structural surface rock mass segmentation easily leads to over-segmentation or under-segmentation, and where manual geological sketching is inefficient and inaccurate.

This invention provides a method, system, equipment, and storage medium for optimizing greenhouse crop habitats, belonging to the field of smart agriculture technology. It continuously acquires multi-element environmental data within the greenhouse, meteorological data outside the greenhouse, current soil moisture data in the crop root zone, and equipment status data. Based on this data, it predicts multi-element environmental data within the greenhouse and soil moisture content in different soil layers for future periods. Based on the multi-element environmental data within the greenhouse and soil moisture content in different soil layers in the crop root zone for future periods, it predicts the photosynthetic physiological habitat status of crops within the greenhouse. Using the predicted habitat status as the optimization target, it makes optimization decisions on current irrigation and multi-element environmental regulation, generates control schemes, and then generates regulation signals to control the operation of greenhouse terminal equipment. This invention significantly improves the accuracy, real-time performance, coordination, and intelligence of greenhouse environmental regulation and irrigation management, thereby increasing crop yield, quality, and resource utilization efficiency.

The invention discloses an ash deposition online monitoring and accurate ash blowing optimization system suitable for a large thermal power boiler, and mainly relates to the technical field of boiler cleaning and intelligent control. Comprising a data acquisition unit for acquiring operation parameters of each heating surface in real time; the ash deposition state evaluation unit is used for evaluating the ash deposition degree on line based on a heat transfer analysis and data fusion algorithm; the soot blowing strategy optimization unit is used for generating a precise soot blowing strategy by adopting a multi-objective optimization algorithm; and the soot blowing control unit drives the soot blower to execute and feed back the effect. The system has the beneficial effects that real-time and quantitative online monitoring of the soot state of the heating surface of the boiler is achieved, meanwhile, accurate control over the soot blowing time, sequence and strength is achieved, accumulated soot is effectively cleaned, meanwhile, steam consumption and heating surface abrasion are remarkably reduced, and the overall operation efficiency and economical efficiency of the boiler are improved.

The invention discloses a human resource data visual management method and system based on a semantic analysis technology, and relates to the technical field of human resource management.The method comprises the steps that digital production data and text production data of employees are collected and analyzed, and employee evaluation indexes are determined; performing normalization processing on the digital production data and the text production data, calculating expected output, and determining an employee ability evaluation level; constructing a neural network evaluation model, and training the neural network evaluation model by using the digital production data and the text production data until the model converges; and evaluating the employee ability by using the converged neural network evaluation model, and outputting an employee evaluation level. According to the method, the digital production data and the text production data of the employees are collected at the same time, preprocessing and semantic analysis are carried out, dominant and implicit information in the human resource data can be more comprehensively mined, and the problem that key information is difficult to rapidly and accurately extract in a traditional data processing mode is solved.