259 results about "Predictability" patented technology

The invention discloses a multi-agent task arrangement method and system, and relates to the technical field of artificial intelligence. In the method, firstly, a task demand is obtained, key information and intention of the task demand are extracted, and task semantics are obtained; secondly, based on the task semantics and the business standard process template, obtaining a business standard process template matched with the task demand; then, according to the matched business standard process template and task semantics, a task context is constructed, and a domain specific language DSL is generated through reasoning according to the task context; thirdly, the DSL is analyzed, a task dependency graph is constructed according to the DSL analysis result, and the task dependency graph is converted into BPMN process model data; and finally, sub-task scheduling and execution of the sub-agents are carried out according to the BPMN process model data. According to the method provided by the invention, the dependence on professional developers or process engineers can be reduced, the response and planning time of complex tasks can be shortened, the result predictability can be improved, and the reasoning cost can be obviously reduced.

The invention discloses a task scheduling method based on predictable resource state atlas modeling, a platform is oriented to a heterogeneous computing environment, a unified resource state atlas is constructed by collecting multi-dimensional resource state parameters of computing nodes, and performance characteristics and communication topological relations among the nodes are comprehensively described. On the basis, a bidirectional time sequence model and an attention mechanism are fused, and the load trend of each node in a future short time is predicted. The platform constructs a multi-factor scheduling scoring function based on a task feature vector and resource state prediction map, integrates parameters such as resource matching degree, prediction load, communication delay and energy consumption cost, dynamically evaluates the adaptability of tasks and resources, and realizes adaptive scheduling and optimal resource allocation of the tasks. Compared with the prior art, the method has the advantages of being high in resource state predictability, high in task allocation intelligence degree, outstanding in platform evolution capability and the like, and is suitable for intelligent task scheduling application in a large-scale heterogeneous resource environment.

The invention relates to the technical field of mineral processing visual detection, and discloses a visual analysis system for detecting the grade of a phosphorite flotation froth layer. The system comprises an image acquisition and decomposition module, a parallel feature extraction module, a dynamic feature fusion module, a foam evolution analysis module and a grade decision output module. The system performs multi-scale decomposition on a foam image, extracts physical and semantic features in parallel, constructs a dynamic fusion network based on bidirectional mapping to perform iterative interaction, and generates a multi-modal feature descriptor. Therefore, self-organizing growth of a foam evolution graph is driven, an evolution track of a key foam primitive is positioned and tracked, a foam grade state vector is formed, and finally, a regulation and control decision is output in combination with external control parameters. According to the system, deep fusion of physical and semantic features and deep analysis of the foam dynamic evolution process are achieved, the accuracy and predictability of foam grade state sensing are improved, and an effective means is provided for accurate control over the flotation process.

Systems and methods iteratively train, using training data, a natural language processing (NLP) model to interpret conversational input during a conversation between an agent and a user by predicting key statements to be used in the prediction of a user intent, the training comparing outputs to a target variable during each iteration and adjusting parameters of the NLP model during each iteration to improve predictability of the user intent from the conversational input. Real-time conversational data is transmitted to the NLP model and the trained NLP algorithm derives key statements predicted to indicate intents and predicts one or more user intents based on the data from the conversation. One or more pre-filled forms predicted to effectuate the one or more user intents is generated, the pre-filled forms including generated text derived from information from the data of the conversation, and the form is transmitted to an agent device.

The invention discloses a cold-chain logistics temperature control supervision method and system based on GPS linkage, and relates to the related technical field of cold-chain logistics, and the method comprises the steps: installing a vehicle-mounted terminal on a cold-chain logistics transport vehicle, and collecting the vehicle position information and carriage temperature data in real time; encrypting and uploading to a monitoring processing center, and calling cold-chain logistics temperature control analysis dual channels of the target cold-chain goods; obtaining a temperature comparison result with a preset safe temperature threshold value, and matching and determining a target temperature control analysis channel; and performing temperature control analysis, determining a temperature control adjustment strategy parameter, and performing cold-chain logistics temperature control supervision. The technical problems that in the prior art, temperature control supervision lags behind, deep fusion analysis with the vehicle position and the transportation state is not carried out, specific reasons of temperature abnormity cannot be distinguished, and the regulation and control strategy is lack of accuracy and predictability are solved, and real-time, accurate and intelligent cold chain transportation temperature control abnormity recognition and supervision are achieved. And the reliability, the safety and the efficiency of cold-chain logistics are improved.

The invention relates to the technical field of geotechnical material local modeling, in particular to a geotechnical material data driving local modeling method and device based on a knowledge migration strategy, and the method comprises the steps: building a first mapping data pair based on the stress-strain physical quantity of a test curve sampling point, and building a high-fidelity data set of a geotechnical material; constructing a second mapping data pair based on the stress-strain state of the simulation curve sampling point and the intermediate physical quantity label, and establishing a low-fidelity data set of the geotechnical material; pre-training the target data-driven constitutive model by using the low-fidelity data set to generate an initial geotechnical material data-driven constitutive model; and driving the model to be fine-tuned by using the high-fidelity data set for the initial geotechnical material data, and generating final geotechnical material data to drive the model. Therefore, the problem of insufficient generalization ability and prediction precision of a data-driven model due to limited training data and unpredictability of a stress path in the prior art is solved.

The invention discloses a network topic hot spot extraction method based on bullet screen semantic recognition, and aims to solve the problems of bullet screen data semantic sparsity, semantic offset, noise interference and the like. The method is characterized by comprising the following steps: carrying out semantic coding on a bullet screen by utilizing a Transform structure; a dynamic semantic evolution perception model is constructed, semantic offset is measured through KL divergence, and topological correlation analysis is carried out through GCN; constructing a space-time density field in combination with a video time axis to realize space-time coupling feature fusion; automatically extracting a hot spot cluster by adopting an improved density peak clustering algorithm; and predicting a hotspot evolution trend by using an LSTM model. By means of the technical scheme, topic hotspots can be accurately captured, semantic evolution logic can be recognized, and the purity and predictability of hotspot extraction are improved.

The invention provides an intelligent road management and maintenance system and method based on multi-model fusion, and relates to the technical field of road maintenance and intelligent traffic. The method comprises the following steps: acquiring road condition data and positioning data through a multi-source data acquisition module; the disease recognition module is combined with a neural network model to analyze the multi-modal data, so that the disease detection and recognition precision is improved; the big data processing early warning module mines a disease rule through a big data model, and realizes disease early warning and predictive maintenance; the digital twin modeling module is used for constructing a road digital twin model and realizing real-time monitoring and simulation analysis; and the decision scheduling module realizes automatic maintenance scheduling based on the output of the modules. Through multi-model deep fusion, the problems of high manual dependence, low precision, lack of predictability and scheduling lag in traditional road management and maintenance are solved, the road maintenance efficiency, quality and intelligent level are remarkably improved, and the maintenance cost is reduced.

The invention relates to the technical field of tunnel engineering informatization and intelligent construction safety monitoring, in particular to a tunnel construction-oriented blasting data acquisition management platform system, which comprises a data standardization acquisition module for obtaining a standardized management input data set; the compliance conflict sensing module is used for generating a space-time mismatching degree index; the dynamic trust decision module is used for generating a security adjustment threshold window allowing the execution parameter to deviate from the reference value; the execution supervision module compares and verifies the to-be-executed construction parameters with the safety adjustment threshold window, judges compliance attributes of the to-be-executed construction parameters, and generates a corresponding release instruction or a blocking instruction; the process tracing module is used for recording the authorization basis and the environment background of each parameter adjustment to form a non-tampering audit log; according to the method, the problem that the geological uncertainty cannot be accurately represented by the traditional means is effectively solved, and the predictability and the accuracy of construction risk identification are remarkably enhanced.

The invention provides an AI-based distribution network unmanned aerial vehicle inspection defect identification and work order self-generation method and system. According to the method, inspection images, historical images, environment context and actual working condition constraint data are obtained, and an equipment state calculation rule base is constructed to describe a rule of defect characteristics evolving along with time and environment; carrying out state analysis and deduction in combination with current data to generate a defect evolution prediction set; risk assessment and work order decision making are carried out based on the prediction result and resource configuration, work order recommendation is formed, work order feasibility verification and optimization are carried out in combination with a time window and geographical constraints, and an execution work order set is output. According to the method, the problems that the existing inspection data analysis dimension is single, the defect cognition is lack of predictability, and the operation and maintenance decision and resource allocation depend on artificial experience and are disjointed with actual execution conditions are solved.

The invention relates to the technical field of thunder and lightning monitoring and protection, in particular to a thunderbolt positioning and energy analysis method and system based on a lightning rod monitoring network. The method comprises the following steps: collecting and verifying a lightning current waveform and a microsecond timestamp in real time, and measuring grounding resistance regularly; the terminal carries out filtering analog-to-digital conversion on the signal and extracts characteristic parameters, after packaging, the priority is set according to the current amplitude, and the priority is uploaded to the cloud platform; the cloud platform is associated with multi-terminal synchronous data, when the number of effective terminals is not less than three, coordinates of a thunderbolt point are calculated, integral operation is performed on a lightning current waveform in combination with actually measured grounding resistance to quantify single-time lightning stroke energy, and historical data are analyzed by using a long-short-term memory network model to realize equipment health diagnosis and life prediction. And displaying the thunderbolt point distribution thermodynamic diagram and the state evaluation report through a visual interface. According to the invention, the whole process management from data acquisition to intelligent decision making is realized, and the accuracy and predictability of lightning monitoring and protection are improved.

The invention discloses an intelligent state monitoring method and system based on petrochemical equipment, and relates to the technical field of intelligent state monitoring, and the method comprises the steps: constructing a petrochemical equipment digital twin based on the parameters and physical characteristics of target petrochemical equipment, and collecting multi-source time sequence data; inputting the multi-source time sequence data into the digital twin of the petrochemical equipment, and performing real-time simulation to obtain theoretical operation state data; and based on the multi-source time sequence data and the theoretical operation state data, obtaining deviation data between the measured value and the theoretical value, and updating the parameters of the digital twin of the petrochemical equipment by using the deviation data to obtain equivalent real operation state data. Through deep fusion of a mechanism model, data driving and an intelligent algorithm, accurate prediction of equipment life, effective distinguishing of fault types and automatic generation of a diagnosis report are realized, and the industrial problems of high false alarm rate, poor interpretability and lack of predictability in the prior art are fundamentally solved.

The invention relates to the technical field of intelligent exhibition hall control, and discloses an intelligent exhibition hall control system based on the Internet of Things. The system synchronizes the operation state of a physical exhibition hall through a digital twin model, and performs operation situation slicing and feature extraction on a virtual mirror image to obtain a situation feature vector. And constructing an exhibition hall operation situation evolution graph based on historical data, mapping the real-time feature vectors to graph nodes, and performing multi-hop state deduction along the edges, thereby generating a future situation prediction path and identifying potential abnormal nodes. And finally, simulation regulation and control are performed in the digital twinborn model according to a deduction result, and a preventive regulation and control instruction is generated. According to the scheme, a closed loop from real-time state sensing to future evolution prediction and active regulation and control is realized, and the predictability and management and control perspectiveness of the operation risk of the complex exhibition hall are improved.

The invention discloses a remote rail fault intelligent detection system based on big data driving, and relates to the technical field of rail traffic intelligent monitoring and state maintenance. The problems that a traditional detection method is high in manual dependence, slow in response and lack of prediction and self-repairing capacity are solved. According to the method, multi-source data are acquired through a fixed sensing network and a mobile inspection vehicle, a multi-scale digital twin is constructed and updated, and a probabilistic fault evolution graph is generated based on a random phase field model; collaborative judgment is carried out based on distributed edge computing nodes, and a maintenance scheme is generated by adopting a hybrid geometric distance retrieval immune memory bank; and finally, collaborative repair is executed through mixed reality and a micro-robot cluster, and continuous optimization of the system is realized based on federated learning and case-based reasoning. According to the system, integrated closed loop of track state sensing, fault prediction, intelligent decision making and self-repairing is achieved, and the detection foreseeability, the maintenance accuracy and the system self-adaptive capacity are improved.

The invention relates to the technical field of geological disaster early warning, in particular to a risk grading method for tunnel seismic wave data and geological information fusion, which comprises the following steps: S1, collecting tunnel multi-temporal seismic wave amplitude, frequency response and propagation delay information, identifying an abnormal response event, extracting a spatial propagation path of the abnormal response event in combination with a tunnel structure, generating a seismic wave evoked response event chain set; s2, geological information such as a drilling profile, a geological structure line and lithologic distribution is fused, and a structure chain type conduction diagram is output; and S3, counting the activeness, the derivation direction and the time sequence trend of the structural unit in the conduction diagram, calculating a trend activation factor, and generating a risk level map with predictability and evolution ability. According to the method, the earthquake response event chain and the structure conduction network are constructed, the multi-source geological information is fused, and the trend activation factor is introduced, so that the dynamic, accurate and predictable grading evaluation of the tunnel structure risk is realized.

The invention discloses an intelligent question-answering system of a water affair service system, which adopts a four-layer architecture of a sensing layer, a control layer, a computing layer and an application layer, realizes closed-loop intelligent management from data sensing to equipment execution, and ensures data quality from the source by the sensing layer through a self-checking water affair data acquisition device; and the calculation layer processes multi-source data by using a time sequence mechanism double-drive fusion algorithm, analyzes user questions by using an intelligent question and answer engine, and generates answers containing business decisions and equipment operation instructions in combination with a water affair knowledge base. The system has the prominent advantages that after a question and answer equipment linkage control unit of a deep linkage control layer receives an instruction, the real-time mechanical performance of field equipment is verified in advance, it is ensured that the instruction can be executed and then issued, the execution progress is monitored in the whole process, synchronous feedback is conducted, the data reliability and decision scientificity are fundamentally improved, and the system is suitable for popularization and application. Question and answer output is established on credible data, and decision predictability and accuracy are improved in emergency scheduling and other scenes.

The invention discloses a tire electromagnetic induction heating system control method based on multi-physics field coupling simulation. The method comprises the following steps: carrying out multi-physics field coupling simulation modeling; performing system optimization analysis; deploying a real-time control system; and performing performance verification and iterative optimization. According to the method, control over the complex physical process is achieved through multi-physics field coupling simulation, a closed-loop coupling system of physical entity-digital twinborn body-intelligent optimization-real-time control is constructed, data two-way circulation and state real-time synchronization are achieved, and the accuracy and timeliness of system regulation and control are improved; the global optimization of the system performance is realized by adopting a multi-objective optimization method, and the problem of local optimization is avoided; and through deep integration of a digital twinning technology and real-time control, predictability and controllability of the production process are realized, and the tire quality is remarkably improved.

The invention relates to the technical field of internal combustion engine testing and industrial automation control, in particular to a data acquisition and cooperative control method and system of a piston oscillation cooling test system. The system comprises a multi-source heterogeneous data acquisition and synchronization module, a cooperative control logic engine, an intelligent algorithm execution module, a safety interlocking management module and a man-machine interaction and data management module. According to the method, through a predictive pre-cooling strategy, the industrial problem of temperature overshoot of the inertial system is fundamentally solved, temperature control is achieved, and the control precision reaches + / -0.5 DEG C; besides, by constructing a parameter self-tuning PID cluster based on multi-source information, predictive suppression of coupling disturbance in the system is achieved, the hysteresis problem of head pain and foot pain of a multivariable control system is solved, interactive interference in the dynamic adjustment process is reduced by 60% or above, and the overall stability of the system is remarkably improved.

The invention relates to the field of reinforcement learning, in particular to a reinforcement learning-based large language model antagonism fine-tuning enhancement system. The state prediction module is used for acquiring system state data and robot action data; based on the robot action data, generating a prediction state vector through a neural network prediction model, and comparing the prediction state vector with the system state data to obtain a residual vector; the action decoding module inputs the residual vector into a feedforward neural network, codes the residual vector to generate a natural language state report, and performs autoregression decoding through a large language model to obtain a loss value; the strategy optimization module converts a penalty function value into a reinforcement learning reward signal through a nonlinear function, and the PPO algorithm calculates the total loss according to the reinforcement learning reward signal; and calculating, optimizing and updating the gradient of the low-rank adaptive parameter in the large language model based on the total loss. According to the invention, through the feedforward neural network and the PPO algorithm, the tiny dynamic deviation is captured, and the predictability and safety of the system are improved.

The invention discloses a cable core wire eccentricity automatic detection compensation method and system based on image recognition, and belongs to the technical field of quality control in cable manufacturing, and the method comprises the steps: collecting an image of a cable in operation in real time in a cross section direction, extracting a boundary contour of a core wire and a sheath, and calculating a core wire eccentricity vector; inputting the eccentricity vector into a neural network identification model, predicting an eccentricity trend at the next moment, and comparing the eccentricity trend with a preset threshold value; if the eccentricity exceeds the limit, calculating compensation displacement for adjusting the position of a mold core or a guide wheel based on a current and predicted eccentricity vector difference in combination with equipment structure fluctuation characteristics and displacement response characteristics, and further updating an extrusion path and continuing closed-loop control; the method has the advantages of being high in predictability, high in response precision and suitable for complex working conditions, high-precision real-time regulation and control of core wire eccentricity can be achieved, and the consistency and the yield of fine cable products are improved.

The invention discloses a mine safety decision deduction method based on digital twinning, and relates to the technical field of mineral engineering, and the method comprises the steps: building a three-dimensional geometric mine structure model based on a multi-source fusion data set, carrying out the attribute injection and discretization processing of the three-dimensional geometric mine structure model, and generating a real-time mine digital twinning body; based on the multi-source fusion data set, mine element nodes and relation edges are defined, and a security-process-resource knowledge graph is constructed; obtaining a mine state field from the safety-process-resource knowledge graph and the real-time mine digital twinborn body, and constructing a mine safety decision state space and an action space; and identifying a decision disturbance event in the multi-source monitoring data according to the trigger threshold, and constructing a mine safety decision reward function in combination with the mine safety decision state space and the action space. The semantic reasoning capability of the decision making process is improved, and the effects of improving the decision making accuracy, real-time performance and intelligent predictability are achieved.

The invention discloses a hydrofracture network construction method and system based on geomechanical characteristic driving, and belongs to the technical field of oil and gas reservoir well site fracturing, and the method comprises the steps: building a reservoir geologic characteristic model based on multi-source geological data; calculating a crack controllability index (CFI) based on the model so as to quantitatively evaluate the predictability and controllability of the crack; defining a target fracture network structure with geometric morphology and flow conductivity as quantification targets based on CFI and development requirements; constructing a geomechanics-fluid-particle three-field coupling model, and performing crack and proppant co-evolution numerical simulation; and performing inversion optimization on a simulation result by taking the target network as a constraint to obtain an optimal injection parameter sequence. According to the method, the fundamental transformation of fracturing design from experience driving to geological characteristic quantitative driving, from passive prediction to active design and from parameter trial and error to intelligent optimization is realized, and the controllability, the transformation volume and the propping agent laying effect of a fracture network are remarkably improved.

The invention discloses a freezing design method for an inclined shaft freezing crossing strong permeable pebble bed, and particularly relates to the technical field of freezing method construction. Acquiring rock and soil parameters and regional historical environment temperature of a pebble bed of a crossing section of the target inclined shaft; establishing a three-dimensional inclined shaft geometric model, and determining the arrangement position, inclination angle and spacing of the freezing pipe along the axis of the inclined shaft; constructing a frozen region space model coupled with the pebble bed; the method comprises the following steps: constructing a freezing-heat transfer-seepage multi-field coupling model according to historical environment temperature and freezing pipe arrangement information, performing numerical simulation on a frozen body forming process, and obtaining an evolution function of a freezing boundary expansion range and a freezing thickness at different times; according to the method, multi-physical field coupling modeling and freezing body evolution accurate control of the freezing process can be achieved, and the method has the advantages of being high in predictability, uniform in freezing effect and the like and is particularly suitable for inclined shaft freezing design and construction under complex geological conditions.

The application discloses a kind of power energy storage online maintenance equalization management method and system, belong to power energy storage technical field.The method includes: multi-source heterogeneous data acquisition and pre-processing;Battery health state evaluation;Equalization demand evaluation based on deep reinforcement learning;Self-adapting equalization strategy generation;Equalization strategy simulation verification;External alternating current power supply circulating equalization execution and dynamic monitoring;Maintenance decision generation based on time series prediction network.The system includes multi-source heterogeneous data acquisition module, battery health state evaluation module, equalization demand intelligent evaluation module, self-adapting equalization strategy generation module, digital twin simulation verification module, external equalization execution control module, maintenance decision intelligent generation module and cloud edge collaborative communication module.The application uses external alternating current power grid power supply, realizes energy external circulation, does not consume battery pack itself energy;And the precise equalization and predictability maintenance of this energy storage system, significantly improve equalization efficiency, reduce operation and maintenance cost.

The application relates to the technical field of energy storage power station intelligent operation and maintenance and artificial intelligence application, in particular to an energy storage power station operation and maintenance management system based on distributed photovoltaic power generation. The system comprises a data acquisition and preprocessing module, which is used for collecting the operation data and external environment data of the energy storage power station in real time and calculating the state of charge of each energy storage unit; an energy storage unit health state prediction module, which is used for generating the health state prediction value of each energy storage unit; an optimal operation and maintenance decision module, which is used for generating optimal power instructions with the maximization of the whole life cycle benefit as the target; and an operation and maintenance instruction execution and feedback module, which is used for executing the optimal power instructions generated by the optimal operation and maintenance decision module and feeding back the executed operation data to the data acquisition and preprocessing module to form a closed loop correction. The system accurately captures the degradation process of the battery from the micro mechanism to the macro feature, provides a high-precision health state evaluation basis for subsequent operation and maintenance decisions, and significantly enhances the predictability and scientificity of the whole system.

The invention discloses a bypass multi-core particle interconnection system and a data transmission method. The system comprises a main calculation core particle, a first accelerator core particle and a second accelerator core particle which are connected in sequence. According to the method, a one-way loop structure is combined with a packet header routing mechanism to ensure that loop waiting does not exist in the transmission process of data between core particles, so that the hidden danger of deadlock is eliminated fundamentally; meanwhile, for a plurality of data packets which need to be subjected to the same substitution processing process, the sequences of the data packets before and after processing are kept consistent, so that the correctness and predictability of the system are ensured.

The invention discloses a multi-agent combat state characterization and collaborative decision-making method based on structural information, and the method comprises the steps: S10, collecting data, and carrying out the preprocessing of the data; s20, performing state action representation learning on the data: based on the preprocessed interaction data, learning state action embedding dynamically related to a combat environment by adopting a structural mutual information guided variational auto-encoder framework; s30, performing single agent exploration: constructing a hierarchical structure of a state action space based on value estimation, and calculating a structure entropy as an internal reward; and S40, carrying out multi-agent cooperation: constructing an interaction graph based on dynamic prediction of a battlefield task situation, and fusing two types of internal rewards of prediction and novelty to form a cooperative combat decision. According to the method, a unified framework is constructed through a structural information theory, and three core problems of'novelty-predictability 'balance, single / multi-agent scene adaptation and low sample efficiency in multi-agent combat state representation are solved.

The invention belongs to the field of power system monitoring and fault diagnosis, and particularly relates to a cable branch box health early warning method based on fusion of insulation resistance and leakage current. The method comprises the following steps: acquiring insulation resistance and leakage current signals which are synchronously aligned by using a sensing monitoring unit, and extracting deterministic characteristics through nonlinear preprocessing; performing phase-space reconstruction on the leakage current signal, and calculating the maximum Lyapunov exponent, correlation dimension and other dynamic characteristic indexes so as to quantify the chaotic evolution degree responded by the insulating medium; and constructing an evaluation model based on fusion of insulation resistance and dynamic indexes, calculating a comprehensive health index, comparing the comprehensive health index with a threshold value, and determining an early warning level. According to the method, the qualitative change omen at the initial stage of insulation degradation can be sensitively captured by fusing the macroscopic electrical parameters and the microdynamics evolution characteristics, the advancement and the anti-interference capability of early warning are improved, reliable support is provided for refined operation and maintenance of the cable branch box, and the power supply reliability of a power system is guaranteed.

The invention relates to the technical field of corrosion state monitoring, and discloses a multi-dimensional data grounding grid corrosion state monitoring method, system, equipment and medium, and the method comprises the steps: forming an electrochemical three-electrode measurement system by using a current constraint sensor and a tested grounding grid metal; building a system circuit of the grounding grid corrosion monitoring device to monitor the corrosion of the grounding grid to obtain a real-time monitoring result; evaluating the corrosion state by combining the initial health baseline data of the grounding grid at the initial stage of commissioning, and predicting the corrosion development trend of the grounding grid by using a pre-constructed first prediction model; and carrying out integrated display and early warning management on the real-time monitoring result and the prediction result through the transformer substation grounding grid corrosion online monitoring and management platform so as to realize visualization of the corrosion state of the grounding grid. According to the method, the scientificity, predictability and operation and maintenance management efficiency of grounding grid state evaluation are remarkably improved, and reliable technical support is provided for guaranteeing long-term safe and stable operation of a power system.

The invention discloses an intelligent management system and method for expressway comprehensive services, and belongs to the technical field of intelligent management. A first construction module obtains multi-source heterogeneous data and preprocesses the multi-source heterogeneous data, a feature engineering module carries out feature engineering processing on the processed multi-source heterogeneous data to obtain industry features, and a second construction module carries out feature engineering processing on the industry features; the second construction module inputs the industry characteristics into the trained service model to obtain a service result, the large model module obtains a vertical industry large model based on a large language model, and the engine construction module constructs five service decision engines based on the multi-source heterogeneous data, the industry characteristics, the service result and the vertical industry large model. According to the method, multi-source heterogeneous data can be deeply fused, a unified industry data base can be constructed, post-analysis is changed into pre-prediction and in-process intervention, the perspectiveness and predictability of management are improved, the whole process from analysis to execution can be automatically completed by constructing a decision closed loop, and fine and automatic intelligent management is achieved.