926 results about "System optimization" patented technology

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 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 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 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 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 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 electric power marketing business abnormity real-time detection method and system based on stream-oriented computation, and belongs to the technical field of electric power system optimizing.The method comprises the steps that data snapshots are extracted from an electric power marketing business system, difference comparison is conducted on the data snapshots and historical snapshots of an intermediate library, and standardized increment events are generated and stored; capturing an incremental event in real time through a data change capturing tool and pushing the incremental event to a message queue; a streaming computation engine consumes the event stream, sequentially performs data cleaning, association with a static dimension table and sliding window statistical feature calculation, and constructs a feature vector; and performing parallel analysis and weighted fusion on the feature vectors based on a business rule base and an online machine learning model to generate a comprehensive risk score, and outputting an abnormal event when the score exceeds a threshold value. According to the method, the problems of exception identification lagging and complex work order process in a traditional batch processing mode are solved, the crossing of the business risk from hour-level detection to minute-level real-time perception is realized, and the timeliness and accuracy of power marketing risk management and control are improved.

An equipment system optimization method driven by deep reinforcement learning comprises the following steps: sequentially constructing an equipment system architecture model and a combat scene simulation model based on a DoDAF framework, then carrying out parameter space sampling in the equipment system architecture model by using a uniform design method, and according to capability items and equipment elements defined by the equipment system architecture model, carrying out parameter space sampling on the equipment system architecture model; constructing a combat effectiveness evaluation model and a system cost evaluation model for evaluating the system; constructing a parameter-modulated deep reinforcement learning (PM-DRL) model to explicitly embed system parameters into an agent state space, and performing data collection and evaluation through the trained PM-DRL model to obtain lt; system parameter-evaluation result gt; a data set; and finally, constructing an optimization model by taking the agent model as a target function, and determining an optimal solution, namely system parameter configuration, by combining the solved Pareto frontier with the preference of a decision maker. According to the method, under the same combat effectiveness requirement, system parameter configuration with lower construction cost can be obtained through optimization.

The invention belongs to the technical field of power system optimization scheduling, and discloses an intelligent port logistics energy scheduling method based on multi-modal digital twinning, and the method specifically comprises the following steps: obtaining collected multi-modal perception data of a port operation scene; processing the multi-modal perception data through an evolutionary reinforcement learning-based target detection algorithm, and obtaining optimized target detection information in combination with a spatial semantic attention mechanism; performing multi-modal fusion on the target detection information and the multi-modal perception data to generate a port global situation semantic vector; constructing a multi-granularity digital twin model based on the semantic vector, and performing simulation prediction; generating a cooperative scheduling strategy of the logistics and energy system according to the simulation result; according to the method, the problems of insufficient multi-modal perception fusion, lack of logistics energy collaborative optimization and insufficient simulation modeling precision in the prior art are effectively solved by combining a target detection algorithm based on evolutionary reinforcement learning with a spatial semantic attention mechanism.

The invention discloses a machine room energy efficiency optimization simulation system and method based on digital twinning, and relates to the technical field of machine room energy efficiency optimization. The system comprises a model construction and load analysis module, an equipment combination design module, an energy efficiency simulation calculation module and an optimal scheme adaptation and output module. According to the method, related parameters of a machine room are collected to construct a digital twinborn simulation model, different levels of load intervals are divided, a differentiated combination scheme composed of a high-voltage fixed-frequency large cold source unit and a low-voltage variable-frequency small cold source unit is designed for each interval, an energy efficiency optimal scheme is matched through simulation, a correlation database is established, and a result is output. Dynamic adaptation of the cold source equipment and the load interval is achieved, low-load energy efficiency attenuation is avoided, the full-working-condition energy efficiency of a machine room is remarkably improved, and reliable support is provided for optimal configuration of a cold source system.

The invention discloses a J-A model parameter identification method, system, equipment and medium based on RBF and an improved brownish bear algorithm, and belongs to the technical field of power system optimization, and the method comprises the steps: building a Jiles-Atherton hysteresis reverse model of a current transformer, determining a to-be-identified parameter vector, and building a model with a root-mean-square error between actually measured magnetic field intensity and simulated magnetic field intensity as a target function, training a radial basis function neural network model, expanding data through linear interpolation processing, obtaining a predicted magnetic induction intensity value, inputting an objective function and radial basis function prediction data into an improved brownish bear optimization algorithm, and iteratively optimizing model parameters through hierarchical population position updating and fitness evaluation until convergence conditions are met. And outputting an optimal parameter identification result. According to the method, high-precision and high-efficiency identification of hysteresis model parameters is realized, the generalization capability and robustness of the system are improved, and reliable technical support is provided for hysteresis characteristic analysis of a complex physical system.

The invention belongs to the technical field of multi-energy micro-grid system optimization scheduling, and particularly relates to a multi-energy micro-grid distribution robust low-carbon economic scheduling method based on deep learning, electronic equipment and a medium. According to the method, a mathematical model of a multi-energy micro-grid system is established according to coupling characteristics of various energy sources among power systems. In order to improve the economical efficiency and the low-carbon property of the system, a load demand response mechanism and a carbon transaction mechanism are adopted, and an electric heating load demand response model and a reward and punishment type stepped carbon transaction model are constructed. In order to solve the wind and light uncertainty of the integrated energy system and improve the robustness of the system, a scene set of uncertain variables is generated by using a conditional generative adversarial network in deep learning, and the generated scenes are clustered by using a K-means clustering method to obtain typical scenes. In order to obtain more real probability distribution, a fluctuation range of a typical scene is constrained by using a comprehensive norm, and a probability distribution fuzzy set of uncertain variables is obtained. And based on the constructed fuzzy set, the demand response model and the reward and punishment type stepped carbon transaction model, a two-stage distribution robust low-carbon economic optimization model of the multi-energy microgrid is established, in the first stage, an energy storage equipment start-stop plan of the system is determined, and in the second stage, an initial plan is adjusted and supplemented after uncertainties are revealed. And finally, carrying out iterative solution on the established model by utilizing a column and constraint generation method to obtain an optimal scheduling scheme, thereby ensuring the low-carbon property, the economical efficiency and the robustness of the system.

The invention discloses a traditional Chinese medicine grinding control system, which relates to the technical field of traditional Chinese medicine grinding control and comprises a medicinal material characteristic sensing module, an intelligent calculation analysis module, a dynamic execution adjustment module, a multi-mode optimization control module and a system coordination updating module. The medicinal material characteristic sensing module collects physical and texture characteristic data of medicinal materials; the intelligent calculation and analysis module fuses the medicinal material processing knowledge base and historical grinding process data to generate initial grinding control parameters; the dynamic execution adjustment module adjusts the operation parameters of the grinding mechanism in combination with the real-time grinding state data; the multi-modal optimization control module monitors the deviation between actual quality parameters and target quality parameters of powder, and a self-adaptive decision algorithm is used for optimizing regulation and control parameters of a grinding area; the system coordination updating module collects whole-process data and performance index evaluation and generates a system optimization instruction, and the instruction corresponding module adjusts the rotating speed and pressure parameters of a grinding area of the vertical grinding machine. According to the system, intelligent and precise control over medicinal material grinding is achieved, and the stability and consistency of the grinding quality are improved.

The present application relates to the field of power system optimization. Disclosed are a multi-objective particle swarm optimization method and system based on multi-strategy improvement. The method comprises: using a multi-strategy improved multi-objective particle swarm optimization algorithm to solve a multi-objective optimization model of a power supply of a generator state monitoring apparatus; and combining three improved strategies, i.e., adaptive adjustment of an inertia weight, coexistence of a decomposition algorithm and Pareto dominance, and introduction of a mutation factor. The present application overcomes the defects of conventional multi-objective particle swarm algorithms, and achieves a better distribution of a Pareto front, thereby obtaining the best Pareto optimal solution set. The present application solves the problems of conventional multi-objective particle swarm algorithms in solving a multi-objective optimization problem, such as premature convergence to a local non-dominated solution, and sub-optimal distribution of a Pareto front caused by an improper external archive update strategy, thereby improving the operational efficiency and reliability of a power supply system of an apparatus.

The invention belongs to the technical field of heat supply systems, and discloses a regional heat supply system optimization control method for multi-heat-source networking operation. The method comprises the steps that S1, physical entity data, hydraulic working condition data and temperature data in a regional heat supply pipe network are collected in real time, an adjacent matrix is constructed according to the actual connection relation of the physical entity data, and a digital network architecture is formed; s2, dividing the digital network architecture into a plurality of sub-architectures based on an improved GN algorithm, and establishing a state space model for each sub-architecture; and S3, constructing a rapid simulation model based on the state space model, and continuously fusing each physical entity data and a preset deviation range through a physical information neural network. According to the method, the thermal state change caused by hydraulic adjustment and the reaction of the thermal change on the hydraulic resistance characteristic can be captured in real time, meanwhile, all physical entity data and hydraulic working conditions are processed in a unified mode, and deviation caused by decoupling simplification in a traditional method is reduced.

The invention belongs to the related technical field of integrated circuit design, and discloses an integrated circuit design process collaborative optimization method and system, and the optimization method comprises the steps: S1, randomly generating a plurality of groups of process parameters; s2, TCAD simulation is carried out, and an electrical characteristic curve of the device is obtained; s3, extracting electrical characteristics and inputting the electrical characteristics into the intelligent parameter extraction model to obtain compact model parameters; s4, inputting the compact model parameters into the SPICE model, and generating an integrated circuit layout by using an automatic layout generation system; s5, taking the optimized circuit performance index as an optimization target, and adopting a Bayesian optimization method to generate an optimal process parameter of a next iteration round; and S6, judging whether an iteration termination condition is met or not, if not, skipping to S2, and if yes, outputting the current process parameters. According to the method, the process / design barrier can be broken, it is ensured that the process development and the circuit design process can be collaboratively carried out, the whole optimization process is completely automatic, and the high-precision and high-efficiency collaborative optimization process is achieved.

The invention relates to the technical field of data processing, in particular to an intelligent financial contract approval management system and method, and the system comprises an obtaining part, an analysis part, a rule part, an evolution part, a learning part, and a decision part. The acquisition component captures an electronic contract document and analyzes the electronic contract document to generate a structured text sequence; the analysis component extracts entity features, condition features and behavior features through legal grammar dependency analysis to form a coupling vector; the rule component constructs a rule topology network and calculates a matching weight; the evolutionary component recombines the nodes to generate a new rule path when the matching weight is insufficient; the learning component executes hierarchical adversarial training on the new rule path to verify compliance; and the decision component decides an approval result based on the compliance confidence coefficient, and feeds back a manual recheck result to drive system optimization. According to the method, dynamic evolution and closed-loop feedback of the rule network are realized, complex logic branches of a contract are accurately identified, and the misjudgment defect of a predefined rule engine is eliminated.

The invention relates to the field of animal husbandry industry environment regulation and control system optimization, in particular to a lightweight digital twinborn breeding environment monitoring method and system, and the method comprises the following steps: S1, carrying out the construction of a computational fluid dynamics (CFD) model for a breeding farm environment; s2, environment data are collected; s3, comparing model prediction with real-time data, and monitoring a breeding environment according to a comparison result; and S4, data presentation. According to the invention, through cooperative work of the flow field modeling unit, the environment data acquisition unit, the model prediction and data calibration unit and the visualization unit, light-weight, full-range and high-precision breeding environment monitoring and regulation are realized.