1055 results about "System optimization" patented technology

The invention relates to an intelligent question-answering system optimization method and device based on a knowledge graph, and the method comprises the steps: obtaining original knowledge data of a target knowledge domain, and constructing a knowledge graph structure model; extracting term information of entity nodes in the knowledge graph structure model, and constructing an entity term set; receiving a natural language question input by a user, executing a semantic understanding operation based on the standardized expression set to obtain a structured question semantic representation, and matching the question semantic representation with the case training set to obtain context semantic features; constructing a cue word template, and executing a query instruction generation operation to obtain a target query statement of the graph database; submitting the target query statement to a graph database to execute data retrieval operation, and obtaining query result data corresponding to the question semantic representation; and performing personalized rendering processing on the query result data based on the user portrait information to generate final question and answer return content. The method has the effect of improving the query accuracy.

The invention relates to an intelligent agent system optimization method and device based on intelligent fault analysis and cross-generation knowledge inheritance, and belongs to the technical field of artificial intelligence. According to the method, interaction abnormal signals are captured in real time by deploying a lightweight log probe, and a tool benefit prediction model based on reinforcement learning is constructed to automatically generate an improvement proposal when the failure rate exceeds a threshold value; an agent genealogy map is established to realize automatic inheritance of a new agent on core memory and abandonment of failure knowledge, and a disastrous forgetting blocker is deployed to dynamically extract a functional module from a genealogy to deal with key capability degradation. Aiming at the problems of fault response lag, knowledge inheritance fracture, key capability degradation and the like in an intelligent agent system iteration process, the invention creatively provides a cooperation mechanism of an intelligent fault analysis layer and a cross-generation knowledge inheritance network, and the fault self-healing capability, version stability and service continuity guarantee level of the system are remarkably improved.

The invention discloses a dynamic sensitive information filtering system and method based on context semantic understanding, and relates to the technical field of information security and natural language processing. Comprising the steps of 1, creating a dynamic sensitive information filtering system, 2, carrying out cleaning, structuring and standardization processing on an input text through a text preprocessing module, 3, capturing deep semantic features of preprocessed text data through a semantic feature extraction module by utilizing a deep learning model, constructing a context-associated semantic representation space, and carrying out dynamic sensitive information filtering on the context-associated semantic representation space. 4, performing multi-level sensitive information detection based on the semantic features through a sensitive information identification module, and identifying the type, the position and the risk level of the sensitive content; 5, on-line iteration of knowledge base and model ability is carried out through a dynamic updating module to cope with dynamic changes of sensitive information types, and 6, safety disposal is carried out on detected sensitive information through a result output module, a filtering result is output, auditing tracing ability is provided, and the auditing tracing ability is provided. And 7, forming a system optimization closed loop through a feedback mechanism module according to user feedback and manual auditing, wherein the system optimization closed loop is used for continuously improving the detection accuracy and adaptability.

The invention relates to the technical field of power system optimization, and discloses a regional building group source network load storage demand response optimization method. Comprising the following steps of multi-source heterogeneous data fusion collection and intelligent preprocessing, power utilization behavior spatial-temporal characteristic deep mining, multi-dimensional response potential dynamic evaluation modeling, multi-target layered optimization decision generation, personalized excitation strategy self-adaptive generation and closed-loop cooperative regulation execution and feedback. According to the method, user strategy updating is simulated through a replication dynamic equation of an evolutionary game, efficient search of excitation parameters is realized by combining a Bayesian optimization Gaussian process and an expectation improvement function, a user group strategy evolution rule can be dynamically captured, parameters such as electricity price discount and subsidy gradient are accurately optimized in a limited sampling range, and the method is suitable for large-scale popularization and application. A'behavior modeling-data optimization 'closed loop is formed, users are stimulated to participate in demand response, optimal configuration of power resources is realized, and the flexibility and economy of the system are improved.

The invention relates to the technical field of section design, and discloses a shipyard steel bent frame column and beam section design simulation method and system. The method comprises the following steps: classifying and storing the historical data of the shipyard steel bent, establishing an experience library and extracting initial section parameters; inputting a parametric modeling system to generate a geometric model and a load condition; obtaining an internal force envelope value based on finite element analysis; obtaining a stress ratio and a displacement ratio according to standard combination and checking calculation; using an NSGA-II algorithm to optimize section parameters; and modeling and marking based on an optimization result, and generating a structure optimization scheme and a design drawing. The problems that in the prior art, preliminary section type selection of a steel bent frame structure lacks system optimization, a large amount of manual adjustment is needed in the design process, the overall design efficiency is low, and the material utilization rate is low are effectively solved, and automatic, refined and multi-target comprehensive optimization of steel bent frame column and beam section design is achieved.

The invention discloses a reservoir group joint scheduling optimization method based on multi-agent deep reinforcement learning, and relates to the technical field of hydroelectric energy system optimization scheduling and control, and the method comprises the steps: dividing X reservoirs in the same drainage basin into J agent subsystems, each intelligent agent only senses the local water level-inflow state and outputs the target water level / discharge amount in the next time period; in the training stage, a centralized evaluation-distributed execution (CTDE) framework is adopted, a value function is constructed by combining a central Critic network with global state-action information, and iterative updating is performed on each Actor policy network by utilizing a multi-agent depth deterministic policy gradient (MADDPG); and the reward function integrates power generation benefits, ecological discharge and final water level penalty to realize global collaborative optimization. After the offline training convergence, the autonomous and complementary scheduling instruction of each reservoir can be obtained only by executing millisecond-level forward reasoning based on real-time monitoring data in the online deployment stage.

The invention discloses an intelligent energy storage system multi-target hierarchical scheduling method and system oriented to source network load storage cooperation, and belongs to the technical field of energy storage system optimization control. The method comprises three levels of day-ahead layer multi-objective game optimization, intra-day layer rolling correction optimization and real-time layer adaptive droop control. The day-ahead layer establishes three objective functions of economy, environmental protection and smoothness, and solves and outputs a day-ahead charging and discharging power plan by using a Nash negotiation algorithm. And the intra-day layer obtains ultra-short-term prediction data of the source load, performs rolling correction on the day-ahead plan by adopting a model prediction control method, and outputs a corrected real-time power instruction. The real-time layer collects power grid frequency deviation and a battery health state value, calculates an adaptive droop coefficient according to the health state value, and superposes and outputs primary frequency modulation response power and a real-time power instruction. According to the invention, source network load storage collaborative optimization is realized through multi-time scale hierarchical scheduling, and the service life of an energy storage system is prolonged through adaptive droop control based on health state perception.

The invention provides an industrial safety risk monitoring and early warning system and method for an autonomous planning intelligent agent, relates to the technical field of industrial safety production, and solves various limitation problems still existing in an existing safety risk monitoring scheme. In the system, an environment sensing layer is used for collecting multi-source sensing data in an industrial production environment in real time; the edge calculation layer is used for carrying out primary processing on the multi-source sensing data and executing data compression and key information extraction so as to generate key analysis data; the cloud analysis layer is used for performing deep intelligent analysis on the key analysis data, executing security risk assessment and risk diffusion prediction, and dynamically generating and adjusting an early warning decision through an autonomous planning agent; and the action execution layer is used for executing a corresponding safety action instruction according to the early warning decision and feeding back an execution result to the cloud analysis layer to realize system optimization. According to the invention, the risk early warning accuracy and the emergency disposal timeliness can be improved finally.

The invention discloses an integrated energy system low-carbon scheduling method considering energy-carbon coupling, and the method is based on a carbon emission flow theory, is combined with the strong fitting capability of a neural network, proposes a carbon flow constraint learning method, converts a complex mapping relation between power flow and carbon flow into mixed integer linear constraint, and achieves the low-carbon scheduling of an integrated energy system. And effective embedding of the carbon flow constraint in the optimization model is realized. Meanwhile, in order to reduce the structural complexity of the neural network, a sparse training strategy is introduced, the model parameter scale is effectively compressed, a ReLU activation function is linearized through an improved large-M method, and a cut plane constraint is introduced to gradually tighten a feasible region, so that the solving efficiency of an optimization model is remarkably improved. And finally, embedding the carbon flow constraint model into the optimal scheduling problem of the integrated energy system, exciting the carbon emission reduction consciousness of the load side, and promoting the load side to perform low-carbon energy consumption adjustment by guiding the demand response behavior of the load side based on the carbon signal of the load side, thereby realizing low-carbon scheduling under energy-carbon coordination and reducing the overall carbon emission level of the system.

The invention provides a multi-temperature-zone temperature control system optimization method based on a gradient algorithm and segmented control, and the method comprises the steps: building a temperature control prediction model of each temperature zone through the gradient algorithm based on the thermal response characteristics of multiple temperature zones, determining a heat transfer path based on the predicted temperature data determined by the temperature control prediction model, and carrying out the optimization of the multi-temperature-zone temperature control system based on the thermal response characteristics of the multiple temperature zones. A temperature control process is divided into a plurality of stages, a corresponding stage temperature control strategy is established based on temperature control characteristics of each stage, optimal system precision is ensured through optimization and segmented control strategies based on a gradient algorithm, the stage temperature control strategies are optimized based on temperature difference between real-time temperature data and predicted temperature data, and the optimal system precision is ensured. The system can feed back and adjust the control parameters in real time, collect the actual temperature data at the end of each stage of the multiple temperature zones, optimize the heat transfer path based on the actual temperature data, and improve the energy efficiency of the whole temperature control system by optimizing the heat transfer path.

The invention discloses a ship power system optimization control method and system based on a simulated annealing algorithm, and relates to the technical field of ship power control, and the method comprises the steps: obtaining operation parameters and historical energy consumption data in real time, and constructing a multi-objective optimization function of dynamic weight distribution; generating an initial temperature parameter and a solution set in combination with the navigation state and the environment data; a neighborhood search strategy disturbance solution set is improved, a new solution is evaluated by using a dynamic acceptance probability function, temperature parameters are adaptively adjusted for iterative optimization, and an optimal control parameter combination is output; and an adjustment instruction set is generated after multi-dimensional efficiency verification, and a propulsion device, a generator set and an energy storage module are cooperatively controlled, so that global energy consumption optimization is realized. According to the method, through data driving and intelligent algorithm fusion, energy efficiency and environmental adaptability are improved, and stable operation under complex working conditions is guaranteed. According to the ship power system optimization control method and system based on the simulated annealing algorithm, the energy efficiency level and the environmental adaptability of the ship power system are improved.

The invention discloses a layered optimization regulation and control method and system for electricity-hydrogen coupling in a multi-energy complementary system, and relates to the technical field of multi-energy complementary system optimization regulation and control, and the method comprises the following steps: obtaining the operation data of the multi-energy complementary system, and carrying out the aggregation of the operation data based on the correlation of an optimization target, and forming a layered optimization data set; constructing a corresponding state space model based on the hierarchical optimization data set, defining an action space of each level, and designing a multi-target reward function; inputting the state space model, the action space and the multi-target reward function into a reinforcement learning agent for training to obtain a strategy model; based on the strategy model and the multi-target reward function, collaborative optimization is carried out on the strategies of all levels to generate a global optimization strategy; inputting the global optimization strategy and the state space model into a prediction optimization module, and performing rolling optimization to generate an optimization scheduling scheme; and according to the optimized scheduling scheme, generating a scheduling instruction after security constraint inspection, issuing the scheduling instruction to a system for execution, and updating the strategy model based on operation feedback.

The invention relates to the technical field of artificial intelligence and judicial informatization, and particularly discloses a multi-format document intelligent retrieval and semantic association system driven by a large model. Comprising a multi-modal document intelligent analysis module, an intention-driven semantic retrieval module, a knowledge graph enhanced association recommendation module, a retrieval result visualization and interaction module, a reinforcement learning-driven system optimization module and a multi-format document data storage module. According to the method, the multi-format judicial document is intelligently analyzed through a large model technology; the query intention of the user is accurately understood by means of a semantic retrieval technology Semantic association among the documents is deeply mined through the knowledge graph technology; a retrieval result is visually displayed through a visualization and interaction interface; and the retrieval strategy and model performance are continuously optimized according to user feedback by relying on a reinforcement learning algorithm, so that the retrieval efficiency and semantic association capability of the multi-format document in the judicial field are effectively improved, and the development of judicial informatization is promoted.

The invention discloses an electric power AI security detection model optimization method and system fusing attribution quantification and adversarial correction. The optimization method comprises the following steps: step 1, carrying out structured semantic representation on heterogeneous security alarms of an electric power network; 2, performing model decision logic analysis based on hybrid attribution quantization; step 3, automatically diagnosing decision prejudice based on domain knowledge masks; step 4, constructing an adversarial sample generated based on an anti-fact text; and 5, performing closed-loop fine adjustment and optimization on the attribution regularization model. According to the method, the interpretable ability of large model decision analysis, the root cause positioning ability of misinformation and the autonomous repair optimization ability are improved, the transparency and credibility of model decision are improved, the model misinformation caused by environmental influence is reduced, and the efficiency of model autonomous correction is improved.

The invention discloses a digital twin-driven water and fertilizer real-time dynamic balance transfer system, which comprises a sensing and data acquisition module for monitoring moisture, salinity, pH value, illumination and rainfall meteorological data of soil in real time through various sensors deployed in a farmland; and the digital twinborn modeling and simulation module is used for constructing a digital twinborn model of the farmland based on the real-time data collected by the perception and data acquisition module. According to the invention, by arranging the perception and data acquisition module, the digital twin modeling and simulation module, the intelligent decision-making and regulation module, the execution and feedback module, the historical data tracing and system optimization module, the expansion module, the networking module and the edge-cloud cooperative computing architecture, the farmland environment and the crop growth condition can be monitored in real time; the digital twinborn modeling and simulation module constructs a digital twinborn model of a farmland based on real-time data, simulates a soil environment and crop growth, and optimizes a water and fertilizer proportioning strategy.

The invention discloses a distributed collaborative decision-making system based on multi-modal data driving and an implementation method thereof, and relates to the technical field of group intelligence and distributed decision-making, and the system comprises a user end interaction module which provides a multi-modal interaction and decision-making scheme visual interface; the distributed node management module comprises a main node and an edge node, and the main node manages node registration, state monitoring and task distribution; the information fusion and preprocessing module is used for processing multi-source heterogeneous data; the decision analysis module is used for carrying out clustering analysis on the opinions and generating candidate schemes in combination with domain knowledge; the domain knowledge graph module is used for constructing a domain entity relationship network; the consensus mechanism and credit evaluation module determines multiple rounds of interaction rules, calculates a user credit value and influences an opinion weight; and the decision result output and feedback module is used for collecting user feedback for system optimization. According to the method, the stability, the response speed and the load balancing capacity are improved, the multi-source information processing and opinion aggregation quality is optimized, and efficient and reliable support is provided for distributed collaborative decision making.

The invention relates to the technical field of power grid control, in particular to a control method of a network following / constructing new energy LCC-HVDC grid-connected system, electronic equipment and a storage medium. System optimization control is realized through six key steps: firstly, obtaining basic parameters such as a network construction new energy capacity ratio and a system short-circuit ratio; secondly, calculating reactive vacancy of the system; then determining a system working mode; establishing a new energy mathematical model; a cooperative control strategy is obtained; and finally formulating a high-low penetration control strategy. Therefore, a complete control closed loop is established, and a systematic solution is formed from parameter acquisition to strategy execution. And the advantages of the following network type new energy and the constructed network type new energy are integrated, so that the high efficiency of the following network type new energy is reserved, and the power grid supporting capability of the constructed network type new energy is also realized.

The invention belongs to the technical field of novel power system photovoltaic and energy storage system optimization configuration, and discloses an optical storage system optimization method based on carbon emission and line loss rate collaborative modeling, which comprises the following steps: constructing a double-layer planning structure of an upper layer planning model and a lower layer operation model, a carbon emission calculation model and an improved line loss rate calculation model are introduced into the lower layer, joint optimization of configuration and operation is realized through parameter coupling, and the charge and discharge efficiency loss power consumption of the energy storage device is introduced as an independent parameter in line loss rate calculation, so that the line loss calculation precision is improved; and solving by adopting an improved particle swarm optimization algorithm, and objectively sorting candidate schemes in combination with an information entropy method and a TOPSIS comprehensive evaluation method. A simulation result based on an IEEE33 node power distribution system shows that the model can effectively reduce carbon emission and line loss rate, improves node voltage level, and has good convergence and engineering applicability.

The invention belongs to the technical field of power system optimization scheduling, and discloses a mathematical model dual-drive-based port intelligent scheduling method, which comprises the following steps of inputting real-time operation data of a port logistics system and an energy system; on the basis of a deep reinforcement learning model, operation data is adopted for training, a state-action-reward mapping relation is established, and a data-driven preliminary scheduling strategy is generated; based on the operation data, constructing a model-driven traffic distribution-user balance optimization model, and obtaining an energy pricing strategy capable of minimizing the operation cost; constructing a multi-agent collaborative decision framework to coordinate a preliminary scheduling strategy and a traffic distribution-user equilibrium optimization model, and realizing iterative optimization of dual-drive strategy collaboration; dynamically selecting an optimal sub-heuristic strategy by adopting a dual deep Q network structure based on an iterative optimization result; the problems of insufficient strategy generalization ability and poor dynamic environment adaptability in the prior art are solved.

The invention discloses a building energy system multi-type demand response operation strategy optimization method and system, and relates to the technical field of building energy management and demand response optimization control, and the method comprises the steps: collecting building user side multi-source information, building a building cooling load prediction structure, and bringing the structure into an optimization scheduling model input system. Constructing a building energy system optimization scheduling equipment model; collecting power grid side information, and dynamically loading an optimal scheduling model for a time-of-use electricity price scene and a peak clipping scene based on judgment of power grid demand response type information of the next day; analyzing an optimization problem of a unified objective function in a double-type response scene, introducing a power reservation coefficient based on a time-of-use electricity price scene, and controlling flexible resource retention; a mixed integer linear programming algorithm is adopted to solve the multi-scene optimization scheduling model, and a corresponding strategy, scheme and power configuration plan are generated; according to the method, flexible dynamic regulation and control and unified scheduling in a multi-response scene are realized, and the strategy adaptability and collaboration are improved.

The invention provides a traffic control decision-making method, device and equipment based on data analysis, and aims to solve the technical problems of high subjectivity, insufficient data value mining, disjunction of strategy and practical application and lack of continuous learning optimization capability in the traditional traffic control decision-making. Through standardized fusion processing of multi-source traffic data and reverse optimization configuration of a traffic feature library, in combination with a progressive effect evaluation and reverse deduction verification mechanism, a multi-time scale effect tracking and strategy evolution trajectory acquisition system is innovatively established, and a decision cycle mechanism with autonomous learning and dynamic adjustment capabilities is constructed. The association relationship between the traffic state evolution rule and the control strategy is systematically analyzed, and finally an intelligent traffic control decision framework based on operation state data driving is formed; scientific and reliable decision support and technical basis are provided for application scenes such as urban traffic fine management, intelligent traffic system optimization control, traffic jam treatment and traffic safety guarantee.

The invention discloses an enterprise intelligent consultation management method and system based on big data analysis, and the system comprises a data collection module, a data preprocessing module, a consultation question input and analysis module, a big data intelligent analysis engine module, an intelligent consultation suggestion generation module, a visualization module, and a feedback learning and system optimization module. The data preprocessing module is used for data preprocessing, the consultation question input and analysis module is used for question analysis, the big data intelligent analysis engine module is used for information extraction, the intelligent consultation suggestion generation module is used for generating consultation suggestions, the visualization module is used for displaying consultation results, and the feedback learning and system optimization module is used for system optimization. The invention provides an improved text concept semantic fusion algorithm for text understanding of consultation problems, provides an improved bit order focusing-bidirectional collaborative entity information extraction algorithm for information extraction, and provides a better scheme for an enterprise intelligent consultation management method and system based on big data analysis.

The invention relates to a complex manufacturing system optimization decision support method, which comprises the following steps of: collecting multiple types of data through distributed sensing nodes, fusing the data into a unified data model through semantic mapping, and constructing a dynamic data graph; key features are mined by using an improved deep learning model, and a multivariable prediction model is constructed to realize advanced prediction of equipment faults and the like; constructing a multi-objective optimization model, and generating and dynamically sorting a Pareto optimal scheme by adopting an adaptive algorithm; and issuing the scheme to an execution system, tracking execution and quantifying an evaluation result, and feeding back the evaluation result to a platform to optimize model parameters and feature weights to form closed-loop iteration. The invention aims to solve the problems of low data integration level, insufficient prediction precision, poor decision scheme adaptability and difficulty in continuous optimization in a complex manufacturing system.

The invention relates to the technical field of big language model dialogue system optimization, in particular to a big language model dynamic dialogue history compression method and system.The method comprises the steps that the maximum length of a context window matched with a target big language model, the maximum number of newly-generated lexical elements and the size of a safety buffer area are set, and then dialogue history is loaded; initial compression and verification are carried out through a keyword and TF-IDF mixed scoring system, multiple times of dynamic compression are carried out according to gradients if the conditions are not met, and finally, parameters are adjusted to adapt to the residual space when a model is called to generate response. The system comprises a dialogue history loading module, a parameter configuration module, a dynamic compression engine module and a large language model integration module. Through a multi-stage compression verification mechanism and a progressive multi-stage dynamic compression strategy, super-long texts such as engineering technology documents can be processed, service interruption is reduced, the compression efficiency is improved on the premise that key semantics are reserved, and multi-language dynamic compression is supported. The problems of system token overrun and service instability in the prior art are solved.

The invention relates to the technical field of glass hot working control, and discloses a rapid prototyping control system and a rapid prototyping control method for tempered glass production. Comprising a thermal coupling module, a temperature control decision module, a flow field solving module, a photoelastic stress analysis module, a quantum annealing optimization module and a time domain synchronous control module. According to the system, a three-dimensional thermal-stress field is constructed on the basis of physical properties and thermal boundaries of glass, heating power is predicted through reinforcement learning, flow field simulation and stress image analysis are combined, control parameter self-adaptive adjustment is achieved through quantum annealing optimization, beats of all subsystems are coordinated through a synchronization module, and an efficient closed-loop control structure is formed. By introducing the quantum annealing optimization module, parameter adjustment in the control system is optimized, the technical effect of improving the precision of complex control decisions is achieved, and the optimization speed and the decision quality of the system are improved.

The invention discloses a buried pipe heat pump system optimization method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining soil temperature data of a current buried pipe heat pump system in an operation cycle; dynamically correcting the heat exchange capacity of the current buried pipe heat pump system according to the soil temperature data to obtain actual heat exchange capacity data; the buried pipe distribution of the current buried pipe heat pump system is optimized and adjusted based on the actual heat exchange capacity data until a target buried pipe layout scheme capable of meeting the real-time heat exchange requirement is obtained; real environment parameter support is provided for dynamically correcting the heat exchange capacity by obtaining soil temperature data of an operation cycle; actual heat exchange capacity data are obtained through correction, and the problem that traditional fixed value evaluation is inaccurate is solved; and then the distribution of the buried pipes is optimized on the basis of the actual heat exchange capacity, so that the layout is accurately matched with the real-time heat exchange requirement, and finally the heat exchange efficiency of the system and the dynamic adaptability to the real-time requirement are improved.

The invention relates to the technical field of intelligent traffic, and discloses a dynamic traffic guidance method based on traffic flow prediction under the influence of navigation information, and the method specifically comprises the steps: constructing a random dynamic traffic network model, and carrying out the quantitative description of OD demands and the time-varying characteristics of road traffic flow; establishing a path travel time perception model under the influence of navigation information, and calculating a path selection probability by adopting a Logit model; constructing a hybrid traffic distribution model based on dynamic system optimization and dynamic user balance, and performing iterative solution by adopting a continuous averaging method with a residual flow updating mechanism; forming a reinforcement learning environment by constructing a state space function, an action space function and a reward function; and training the model by using a DDQN algorithm, and optimizing a path selection strategy. According to the method, the problems of low induction precision and poor adaptability caused by neglecting node delay and lacking information fusion and utilization in the existing method are effectively solved, and the dynamic traffic induction effect is remarkably improved.

The invention discloses an AI-based server power consumption dynamic optimization and cooperative heat dissipation control system, which belongs to the technical field of computer system optimization, and comprises a multi-source data acquisition module for acquiring server hardware, machine room environment and heat dissipation equipment data in real time through a distributed sensor and a software probe; the AI intelligent analysis and decision module constructs a hybrid intelligent framework, extracts multi-dimensional features to predict power consumption and temperature trends, and generates a comprehensive optimization decision; the power consumption dynamic adjustment module adjusts hardware parameters and cooperates with process scheduling; the cooperative heat dissipation control module dynamically adjusts heat dissipation equipment; and the running state monitoring and feedback module monitors data, compares the data with a threshold value, and performs early warning and feedback in case of abnormality. Through multi-module collaboration and AI enabling, intelligent dynamic collaborative management of power consumption and heat dissipation is realized, data acquisition is accurate, power consumption is balanced, operation cost and energy consumption are reduced, and requirements of data centers of different scales are met.

The invention discloses an automatic fault repairing method for an operating system based on artificial intelligence, and relates to the technical field of automatic fault repairing, and the method comprises the steps: carrying out the feature analysis of system operation data through a pre-trained fault feature extraction model, generating a fault feature vector, inputting the fault feature vector into a fault classifier, and obtaining a fault feature vector; a current fault type is identified through a multi-classification algorithm, fault cause primary tracing is performed according to the fault type to obtain a fault generation factor, secondary tracing is performed on the fault generation factor to obtain a fault influence factor, positioning is performed based on the fault generation factor, and a corresponding repair strategy is matched from a knowledge base. The method comprises the following steps: acquiring historical system operation data with relevance on the basis of a fault influence factor, acquiring updated real-time system operation data after executing a repair operation to calculate a system optimization coefficient, judging a forward trend of a repair strategy according to a preset optimization threshold value, and updating the forward trend into a knowledge base to realize rapid and efficient automatic repair.

The invention relates to an intelligent diagnosis system for echelon utilization and recovery of a power battery, and aims to evaluate the health state of the battery with high precision and assist the echelon utilization and recovery of the battery. The system integrates five modules: the battery health state monitoring module acquires parameters such as voltage, current and temperature in real time through a high-precision sensor; the battery health state evaluation module accurately evaluates the battery state by applying algorithms such as ICA, DVA and LSTM neural networks in combination with a transfer learning technology; the echelon utilization recovery decision-making module outputs an optimal recovery scheme based on a three-level decision-making model; the intelligent disassembling and safety protection module adopts an advanced process and a monitoring system to guarantee safe and efficient disassembling; and the data storage and cloud prediction feedback module realizes system dynamic optimization by using big data analysis and machine learning. The evaluation accuracy of the system exceeds 95%, the single diagnosis time is shorter than 5 minutes, multiple types of batteries are supported, the self-learning iteration period is shorter than 24 hours, the detection cost can be remarkably reduced, the resource utilization rate is increased, carbon emission is reduced, and key technical support is provided for sustainable development of the new energy automobile industry.