185 results about "Neural network analysis" patented technology

The invention relates to a fault diagnosis method for a server. Efficient fault positioning and processing are achieved by constructing a multi-dimensional intelligent diagnosis system. Constructing a server virtual model at each node and establishing a connection relationship, and collecting various data and mapping the data to the virtual model; performing weighted fusion on the data through correlation analysis, and extracting fault features including time and space correlation by using a bidirectional long-short-term memory network; fault probability distribution is obtained by means of a dynamic fault knowledge graph and graph neural network reasoning, and a fault evaluation result is visualized in combination with a virtual model; and finally analyzing the behavior deviation through a neural network and generating a processing strategy. Real-time processing of multi-source data, intelligent extraction of fault features and dynamic deduction of fault propagation are achieved, the accuracy, the real-time performance and the automation level of server fault diagnosis are effectively improved, and predictive maintenance and intelligent decision making of a complex server system are achieved.

The invention provides a substation self-adaptive inspection method based on equipment health degree dynamic evaluation. The substation self-adaptive inspection method based on equipment health degree dynamic evaluation comprises the steps of S1, collecting electrical parameters, mechanical vibration parameters and environmental parameters of substation equipment in real time through a multi-source sensor network, and S2, performing data cleaning and time synchronization on the electrical parameters, the mechanical vibration parameters and the environmental parameters, and performing subset dynamic normalization processing. According to the substation self-adaptive inspection method based on equipment health degree dynamic assessment, electrical, mechanical and environmental parameters of the equipment are acquired in real time through the multi-source sensor network, and the real-time performance and accuracy of equipment health degree assessment are remarkably improved by combining dynamic normalization processing and real-time anomaly detection. And the LSTM neural network is used for analyzing the change trend of the equipment health index, so that the residual life of a key component can be predicted, and differentiated inspection and resource optimization allocation can be realized.

Disclosed herein is an artificial neural network-based medical image analysis apparatus for analyzing a medical image based on a medical artificial neural network. The artificial neural network-based medical image analysis apparatus includes a computing system, and the computing system includes at least one processor. The at least one processor is configured to acquire or receive a first analysis result obtained through the inference of a first artificial neural network from a first medical image, to input the first analysis result to a second artificial neural network, to acquire a first evaluation result obtained through the inference of the second artificial neural network from the first analysis result, and to provide the first evaluation result to a user as an evaluation result for the first medical image and the first analysis result.

The invention discloses a charging pile intelligent heat dissipation method and device based on heat conduction line optimization and a medium, belongs to the technical field of charging pile heat dissipation, and aims to solve the technical problem of how to avoid the defects of high thermal resistance, response lag and large energy consumption in a traditional charging pile heat dissipation scheme and improve the heat dissipation efficiency and operation reliability of a charging pile. According to the technical scheme, a heat conduction structure is optimized; deploying a thin film thermocouple and constructing a three-dimensional temperature field model through infrared thermal imaging; establishing a thermal resistance-flow-power transfer function model by adopting a fuzzy PID (Proportion Integration Differentiation) and model predictive control hybrid algorithm; historical charging data are analyzed based on an LSTM neural network, a power peak value is predicted 300 ms ahead of time, and heat dissipation equipment is pre-started.

The invention discloses an edge calculation optimization and compression algorithm based on distributed optical fiber sensing data, and the algorithm achieves the full-section continuous monitoring of an embankment through the layering burying of a distributed optical fiber network along the axis of the embankment, and eliminates the blind area of a conventional point sensor. Strain data and temperature data are analyzed by using Brillouin scattering and Raman scattering, time sequence characteristics are analyzed through an LSTM neural network, and piping and crack hidden dangers are identified. External environment parameters such as water level and rainfall are fused to construct a multiple regression model, and the seepage risk level is evaluated. Through combination of BIM three-dimensional modeling and a seepage simulation algorithm, hidden dangers are accurately positioned, and spatial distribution of the hidden dangers is visualized. Based on edge calculation optimization, a data compression technology is adopted to reduce transmission requirements, a self-repairing material injection and high-pressure plugging scheme is triggered through a grading early warning system, closed-loop management from monitoring to repairing is formed, the real-time performance and accuracy of hidden danger recognition are improved, and efficient and reliable monitoring support is provided for dike engineering.

The invention provides a thermal imager intelligent temperature measurement method based on a neural network, and relates to the technical field of thermal imaging temperature measurement, and the method comprises the steps: firstly collecting an infrared image and a visible light image of a target object, and environment temperature and humidity data; then, the infrared image is preprocessed; then calculating an initial temperature value through a temperature compensation formula, correcting energy attenuation by utilizing humidity data, and processing an abnormal heating signal in combination with the visible light image and the humidity data; and finally, related data are analyzed by means of a neural network, and a corrected temperature value is output. According to the method, a temperature compensation formula combining heat exchange and atmospheric radiation difference is constructed, correction factors are adjusted according to humidity and atmospheric pressure, a visible light image is used for identifying the dirt accumulation level, infrared image features are analyzed in combination with a neural network, and multi-source data fusion and environmental factor dynamic compensation are achieved. The interference of environmental factors on the temperature measurement of the thermal imager is effectively overcome, and the temperature measurement precision and the equipment defect detection accuracy in a complex environment are improved.

The invention discloses a digital station building equipment state intelligent monitoring system and method. The system comprises a data acquisition module, a multi-dimensional data processing module, an LSTM neural network analysis module and a state evaluation module. The data acquisition module acquires multi-modal time series data through a multi-type sensor group; the multi-dimensional data processing module performs space-time alignment and feature fusion on the data to construct a composite feature vector; the LSTM neural network analysis module adopts an attention gating mechanism to fuse dual-scale features, and outputs equipment state probability distribution and a residual life prediction value; and the state evaluation module updates the posterior probability of the health state of the equipment based on the dynamic Bayesian network so as to realize graded early warning and maintenance decision. According to the invention, the accuracy and predictability of equipment state monitoring are improved, the requirements of the digital station building for accurate monitoring and predictive maintenance of the equipment state are met, and the intelligent level of operation and maintenance management is improved.

The invention discloses a cervical lesion intercellular relation modeling and analysis system based on a graph neural network, and the system comprises a medical image collection module which is used for collecting a digital image of a cervical tissue pathological section or a cervical TCT slide; the cell detection and segmentation module is used for extracting spatial position information and morphological characteristics of cells; the cell feature extraction module is used for extracting and fusing the spatial position, morphology, texture and biological marker features of the cells; the cell relation graph construction module is used for constructing a heterogeneous cell relation graph with cells as nodes and inter-cell relations as edges; the graph neural network analysis module is used for carrying out feature learning and modeling on the heterogeneous cell relation graph; the intelligent auxiliary diagnosis module is used for generating auxiliary diagnosis suggestions; and the data management and automatic control module is used for realizing automatic control and case data management of the whole process of the data. The intelligent and automatic level of cervical lesion cell analysis can be comprehensively improved, and the accuracy and efficiency of diagnosis are improved.

The invention provides a device and method for predicting the risk of Pierre Robin syndrome by using audio, and the device comprises an audio collection module, a signal preprocessing module, a feature extraction module, a deep neural network analysis module and a result output module, and forms a complete processing link from signal collection to risk prediction. A continuous mapping function from a high-dimensional acoustic feature space to a low-dimensional risk assessment space is constructed mathematically, the problem of association between nonlinear time-frequency features and pathological states is solved, full-process automation from audio acquisition to risk prediction is realized, the accuracy and accessibility of PRS early diagnosis are improved, and the PRS early diagnosis efficiency is improved. Precious time is won for clinical intervention, the occurrence rate of PRS-related complications is expected to be remarkably reduced, the life quality of sick children is improved, and meanwhile, a new way is opened up for non-invasive acoustic screening of other diseases.

The invention relates to the field of network security, and particularly discloses a network security defense strategy optimization method based on machine learning, and the method comprises the steps: aggregating isolated security events into attack activity clusters through causal association analysis, constructing a dynamically evolved global attack graph, and improving a defense perspective from a discrete event to a full combat view. In order to realize foresight, a graph neural network is utilized to analyze a graph to identify attack tactics and predict the next intention. A hierarchical reinforcement learning framework is innovatively introduced in the decision-making stage; an upper-layer strategic agent formulates a macroscopic defense target based on a global situation; and the lower-layer tactical agent focuses on the related attack sub-graph under the strategic guidance, and selects and executes the specific tactical action which can reach the target most. The strategy and tactical separated decision-making mode ensures that each defense action serves a long-distance target, so that strategic passivity caused by only taking care of previous threats is avoided.

The invention provides an intelligent pod control method and system for an unmanned aerial vehicle. According to the method, multiple frames of thermal radiation images are obtained through an infrared imaging module of an unmanned aerial vehicle pod, a dynamic temperature field atlas is generated through heat source decoupling, and spatial relevance between heat sources is analyzed through an atlas neural network. Damping torque matched with wind disturbance is generated based on the temperature gradient vector, filtering parameters of the magnetofluid holder are optimized, and meanwhile the interference intensity between energy transmission paths is calibrated. A threat propagation path is generated by combining thermal diffusion characteristics, and the pod deflection angle is dynamically adjusted, so that the center of an optical view field is synchronized with the threat centroid, and accurate tracking and anti-interference control are realized. The method is used for intelligent pod control of the unmanned aerial vehicle. Through intelligent heat source analysis and adaptive adjustment, accurate and stable tracking of a threat target by the unmanned aerial vehicle pod is realized.

The invention relates to the technical field of robot control, in particular to a cross-modal perceptual driving body robot compliance control system which comprises the steps that a sensor is adopted to synchronously collect environment information, multi-source data bias is eliminated through a space-time alignment algorithm, a radial basis function neural network is adopted to analyze multi-modal fusion features, and a multi-modal model is obtained; human operation intention probability distribution is extracted to decompose a task into a path planning layer and a motion control layer, a collision-free trajectory is generated through an RRT algorithm, a high-fidelity physical engine is adopted to construct a virtual interaction scene, and robot learning results are shared through federal learning. According to the method, the problems of inaccurate perception, incoordination between intention recognition and interaction control, difficulty in control strategy verification, slow new task adaptation and difficulty in multi-robot learning result sharing caused by multi-source data deviation and large multi-modal semantic difference of the body robot in a complex environment are solved.

The invention discloses a boiler operation state intelligent monitoring method and system, and the method comprises the steps: collecting multi-source heterogeneous data in a boiler operation process, carrying out the cleaning and normalization processing of the multi-source heterogeneous data, extracting key features through wavelet transform, and obtaining standard multi-source heterogeneous data; modeling time series data in the standard multi-source heterogeneous data on the basis of an LSTM (Long Short-Term Memory) network, capturing a dynamic change trend of boiler operation to obtain time series characteristics, analyzing a hearth flame image and an infrared thermal image by using a CNN (Convolutional Neural Network), and extracting combustion state characteristics and thermal distribution characteristics; and based on the comprehensive state vector, a confidence interval of each feature dimension is calculated through a GMM Gaussian mixture model, an operation state deviation degree is analyzed and evaluated in combination with an entropy value, and when the deviation degree exceeds a preset threshold value, graded early warning is triggered. And the accuracy of boiler operation state intelligent monitoring is improved.

The invention relates to the technical field of the Internet of Things, and discloses a biological material component analysis method and system based on the combination of the Internet of Things and a neural network, and the method comprises the steps: obtaining material sample data of a to-be-detected biological material and environment state data of an environment where the to-be-detected biological material is located, and extracting an intrinsic attribute index of the to-be-detected biological material from the material sample data, calculating the basic substance content of the biological material to be detected; calculating the characteristic absorption rate of the to-be-detected biological material so as to analyze the characteristic absorption intensity of the to-be-detected biological material; constructing a spectral line graph corresponding to the to-be-detected biological material, and re-extracting fingerprint spectral information of the to-be-detected biological material from the spectral line graph; and inputting the fingerprint spectrum information into a biological detection unit of a preset Internet of Things, outputting a corresponding induction signal sequence by using the biological detection unit so as to identify molecular configuration information of the to-be-detected biological material, and analyzing the material composition of the to-be-detected biological material by using a trained neural network. The accuracy of biological material component analysis can be improved.

The invention discloses a GIS equipment fault identification method based on a double-domain neural network, and relates to the technical field of power equipment fault diagnosis, and the method comprises the steps: extracting time domain critical features and frequency domain critical features of a health state and a fault state of GIS equipment, and forming a fault evolution feature library; calculating a time domain characteristic gradient and a frequency domain characteristic gradient by using a double-domain neural network, and measuring a difference value; and fusing the difference values through nonlinear mapping to generate a comprehensive feature vector, and identifying the GIS equipment fault type according to the matching degree of the comprehensive feature vector and a preset fault type feature. According to the method, the fault evolution feature library is created to record the fault critical features, the gradient difference between the real-time operation data and the critical features is analyzed by using the double-domain neural network, and the difference features of different domains are fused through nonlinear mapping for comprehensive matching, so that the accurate recognition of the GIS equipment fault is realized; the technical problem that a traditional single-domain method cannot effectively recognize early weak faults is solved.

The invention discloses a supply chain full-process intelligent monitoring and dynamic scheduling optimization method based on the Internet of Things and digital twinning, and the method comprises the following steps: constructing a lightweight twinning body for a supply chain physical entity, binding the twinning body with a sensor corresponding to the entity, and storing the basic data of the entity; carrying out local data processing on each twinborn body through an edge computing node, and constructing a distributed training network by utilizing federated learning; the twinborn body monitors sensor data through a self-diagnosis module, and when data drift is detected, calibration parameters are generated and sensor output is corrected; according to the method, local anomaly detection and self-calibration of sensor data are realized through lightweight twin and edge computing node deployment: a data feature space is constructed by using a variational auto-encoder to identify drift, and spatial correlation of adjacent nodes is analyzed in combination with a graph neural network to realize data mutual verification, so that centralized monitoring dependence is broken through; the problem of data distortion caused by faults of a single sensor is solved.

The invention discloses an engineering construction digital intelligent supervision management and control system, and relates to the technical field of engineering construction supervision. The system comprises an information acquisition module which is used for acquiring equipment state, image and environment data; the data fusion layer module is used for carrying out cleaning, alignment and Kalman filtering fusion on the multi-source data to generate a multi-modal construction data flow; a progress calculation and prediction unit, a deviation judgment unit and a digital modeling unit are arranged in the analysis monitoring module, and the analysis monitoring module is used for updating a progress state based on an XGBoost model, analyzing a node dependency relationship by using a graph neural network, calculating a deviation propagation risk coefficient and generating a hierarchical deviation signal in combination with three-level judgment conditions; and the management and control layer module executes hierarchical response according to the level of the deviation signal, and feeds back execution effect data to the front-end module to realize model optimization and closed-loop iteration. According to the invention, intelligent perception, risk prediction and adaptive management and control of the construction process are realized, and the real-time performance, accuracy and autonomy of project supervision are improved.

The invention discloses an MPCVD semiconductor film uniformity real-time in-situ monitoring method and system, and the method comprises the steps: obtaining the spectral information and characterization data of a semiconductor film under different MPCVD growth technologies, and constructing a database; analyzing and training according to the database and the dual-channel residual architecture neural network to obtain a semiconductor film growth uniformity identification model; and acquiring real-time spectral information of the semiconductor film, and combining the semiconductor film growth uniformity identification model to obtain the growth condition of the semiconductor film. According to the method, rapid high-flux non-contact real-time analysis of the thickness, the doping concentration and the morphology of the thin film in the MPCVD growth process is realized by combining real-time spectral information and characterization data of the MPCVD and a machine learning algorithm. The method is also suitable for various material systems and growth conditions, does not need excessive human intervention and adjustment, and has high universality and applicability.

The invention relates to the technical field of personnel state monitoring, in particular to a non-contact intelligent monitoring method for a fatigue state of an operator, which comprises the following steps of: a, acquiring a face heat distribution image and a dynamic behavior video stream through a non-contact sensor array; b, extracting facial thermal features based on a multispectral fusion algorithm; c, analyzing behavior characteristics through a space-time attention neural network; d, fusing physiological and behavior characteristics, and outputting a fatigue level; and e, triggering a multi-level linkage early warning mechanism according to the fatigue level. A common direct contact type sensor brings discomfort to operators and affects normal work, manual observation has subjectivity, misjudgment is prone to occurring, and real-time and comprehensive monitoring cannot be achieved. Compared with the prior art, the non-contact physiological feature sensor and the non-contact behavior feature sensor can be used for detecting a worker together, direct contact with the worker is not needed, interference to the working state of the worker is reduced, long-time continuous monitoring can be achieved, and misjudgment is avoided.

The invention belongs to the technical field of power distribution station operation state monitoring, and discloses a station building degradation track prediction method fusing physical prior and spatio-temporal knowledge embedding. Comprising the steps of collecting multi-source heterogeneous data; constructing a physical constraint equation of key physical quantities in the station building equipment; constructing a space-time knowledge graph, adopting a graph attention network as a graph embedding model, embedding attention weights of nodes to neighbor nodes, and obtaining a node embedding vector set; performing feature extraction on the multi-source heterogeneous data to obtain mixed features; a degradation track prediction result is obtained based on physical information neural network analysis, health degree evaluation is carried out on the degradation track prediction result, a health degree score is obtained, and corresponding early warning is carried out according to the health degree score; according to the method, high-precision prediction of the degradation track of the station building equipment in a fault-free data scene is realized, and meanwhile, the physical rationality and operation and maintenance guidance of a prediction result are guaranteed.

The invention provides an overwater anchor rod detection method, and belongs to the technical field of overwater anchor rod detection.The overwater anchor rod detection method comprises the steps that a ship type excavator carries detection equipment to construct a platform, firstly, an anchor rod is positioned and fixed, and then original point cloud data is obtained through underwater ultrasonic scanning; meanwhile, real-time shaking parameters caused by water surface fluctuation are measured through an attitude sensor, and a three-dimensional shaking influence matrix is constructed; and performing compensation calculation on the original point cloud based on the shaking data, eliminating water surface fluctuation errors, evaluating the stress state of the anchor rod by applying an anchor rod mechanical analysis equation set, and identifying an abnormal region. The integrity of the internal structure of the anchor rod is analyzed through hammering sound wave detection, sound wave data and a point cloud matrix are fused to generate a comprehensive evaluation model, the health state rating and risk area identification of the anchor rod are analyzed and output through a deep neural network, and the technical problem that the detection precision of the water anchor rod is affected by water surface fluctuation, and consequently the data reliability is low is solved.

The present application relates to the technical field of clinical nursing, in particular to a clinical nursing system for interventional surgery, in the present application, through the combination of preoperative medical history, intraoperative real-time physiological data and operation type, depth neural network analysis can accurately identify and predict potential complications, and the nursing plan can be adjusted according to the individual differences of patients, the nursing plan can be adjusted according to the individual differences of patients, the depth neural network can process complex physiological data and identify key physiological data, the long short-term memory network can dynamically adjust the scheme in the nursing process, the change trend of intraoperative data is deeply mined, the physiological index fluctuation is tracked, the abnormality is identified in time and the potential risk is predicted, the nursing decision can be updated synchronously with the physiological state of the patient, the working efficiency of the nursing staff is optimized, and the postoperative recovery effect and comfort of the patient are improved.

The invention discloses a fan mechanism modeling and optimizing method and system based on a neural network, and the method comprises the steps: building a model of a wind energy utilization coefficient and a thrust coefficient of a fan, and carrying out the steady-state simulation, so as to obtain a two-dimensional relation table model of the wind energy utilization coefficient and the thrust coefficient, establishing a fan aerodynamic model based on the two-dimensional relation table model; respectively establishing a fan transmission chain system model and a fan blade and tower coupling model based on the nonlinear pneumatic torque Tr and the nonlinear air thrust Ft in the fan aerodynamic model to obtain a complete fan mechanism model; and collecting actual operation data of the fan, analyzing the deviation between the fan mechanism model and the actual operation data of the fan based on the neural network, and correcting the deviation through the neural network to obtain an optimized fan mechanism model. According to the invention, the precision of fan modeling can be improved.

The invention relates to the technical field of e-commerce recommendation systems, and discloses a commodity recommendation system and method based on deep learning. The method comprises the steps of obtaining an interaction behavior sequence of a target user, performing framing and slicing to generate a behavior time period data frame set, and extracting multi-dimensional interaction features; entities and relationships are extracted from the commodity knowledge graph, the features are associated and matched with the entities, and a dynamic preference sub-graph is constructed; analyzing the sub-graph by using a multi-modal fusion neural network, and outputting an implicit intention vector; executing multi-hop reasoning in the knowledge graph based on the vector, and retrieving to generate a candidate commodity pool; and calculating potential association strength of the commodities and the users through cross-domain transfer learning, and performing sorting to generate a final recommendation list. According to the method, the short-term preference dynamic state of the user can be described in a fine-grained manner, and the implicit intention of the user is deeply mined through dynamic knowledge association and fusion analysis, so that the accuracy and individuation degree of a recommendation result are effectively improved.

The invention relates to the technical field of enterprise climate risk prevention and control, and provides an enterprise climate risk prevention and control system applying big data and artificial intelligence, and the system comprises a multi-source climate data collection module which integrates satellite, ground station and enterprise Internet of Things data through federal learning; the enterprise asset digital twin module integrates BIM / GIS to construct a dynamic asset model; the AI risk prediction engine analyzes climate-asset relevance by adopting a space-time convolutional neural network, and quantifies a physical / transformation risk in combination with physical simulation and a generative adversarial network; the dynamic risk assessment module is used for generating a risk thermodynamic diagram and calculating a supply chain interruption probability; and the adaptive response decision module outputs graded early warning and is linked with the insurance platform to trigger automatic claim settlement. The system verifies the credibility of data through a block chain, realizes a local disaster recovery decision when a network is interrupted by using an edge computing node, forms a'monitoring-prediction-evaluation-response-optimization 'closed-loop management and control mechanism, and solves the problems of data island, response delay and unknown risk prediction of a traditional scheme.

The invention relates to the technical field of numerically-controlled machine tool monitoring, in particular to a numerically-controlled machine tool monitoring method and system, terminal equipment and a storage medium, and the numerically-controlled machine tool monitoring method comprises the following steps: based on key parts of a numerically-controlled machine tool, monitoring tiny wear or deformation by adopting a time domain reflection method and a frequency domain reflection method; and the collected ultrasonic signals are analyzed in combination with a convolutional neural network, phase changes and abnormal features in the signals are identified, data fusion processing is carried out, and wear identification indexes are generated. According to the method, the advanced monitoring and analysis technology is adopted, comprehensive optimization of the performance of the numerical control machine tool is achieved, the time domain reflection method and the frequency domain reflection method are combined with convolutional neural network analysis, the detection precision of tiny abrasion or deformation is improved, dynamic optimization is achieved on clamping force adjustment through fuzzy logic control, the method adapts to different machining conditions, and the machining precision of the numerical control machine tool is improved. Workpiece damage is reduced, the machining quality is improved, and the machining precision is improved through deep diagnosis and dynamic compensation strategies of multi-axis synchronous errors.

The invention provides a power distribution automatic monitoring and early warning method and system based on voltage measurement, and belongs to the field of power distribution monitoring. Performing wavelet decomposition on a voltage signal of each node in the time-space matrix of the topological structure of the power distribution network to obtain a frequency band component of each layer and calculating an energy entropy of the frequency band component; regarding each layer of frequency band component as an independent individual, and integrating the lowermost layer of energy entropy from bottom to top to form an individual feature; analyzing correlation among individuals based on mutual information, screening individual pairs as independent states of nodes, and integrating all states to obtain node features; on the basis of node characteristics, the correlation between any two nodes is calculated by using an improved dynamic time warping distance to serve as a voltage fluctuation correlation coefficient; and constructing a dynamic fault propagation graph by taking the voltage fluctuation correlation coefficient as an edge, analyzing node features by using a pre-trained multi-head attention neural network to obtain a node risk coefficient, and calculating a total risk coefficient of a connected path to perform early warning, thereby realizing automatic monitoring and early warning of the power distribution network.

The invention provides an intelligent monitoring and regulation and control method and system based on a distributed power supply access unit, and relates to a distributed power supply intelligent regulation and control technology. The method comprises the following steps: collecting and processing power grid load and power output data to obtain a load fluctuation trend and an output characteristic deviation; establishing a prediction model by using a support vector machine, and identifying dynamic mismatching points; when the mismatching points exceed the limit, parameter variation is analyzed through a neural network, and an adjustment coefficient is calculated; generating a regulation and control instruction by adopting a particle swarm optimization algorithm, and calibrating the output characteristics of the power supply; key synchronization points are extracted for matching judgment, and stable access configuration is obtained; performing risk assessment through a feedback mechanism, and updating the regulation and control model according to the risk assessment; and finally realizing balanced distribution of the power grid pressure. The system correspondingly comprises a data acquisition module, a dynamic analysis module, an instruction generation module, a configuration and evaluation module, an optimization balancing module and the like. According to the invention, accurate monitoring and adaptive regulation and control of distributed power supply access are realized, and the stability and efficiency of power grid operation are improved.

The invention belongs to the field of engineering geology, particularly relates to an engineering geology soil layer name judgment system, method and equipment carried on a static sounding instrument, and aims to solve the problem that real-time dynamic soil layer name presumption cannot be carried out in an existing geotechnical engineering investigation technology. The system comprises a spectrum acquisition module which comprises a spontaneous light source and a multispectral sensor and is integrated between static sounding probe rods; the data processing module is used for analyzing the spectral data through a neural network and processing end resistance / side resistance / friction resistance ratio data of static sounding through a regression algorithm; and the data output module compares the two types of judgment results: outputting a single soil layer name when the two types of judgment results are consistent, and outputting double results and converting the double results into standard results for display when the two types of judgment results are inconsistent. According to the method, the soil layer name is presumed by adopting a real-time dynamic method, the detected sample does not need to be remodeled, the recognition accuracy and efficiency are improved, the model is fed back and optimized according to a new test set, the prediction precision is kept, and the detection speed is improved.