64 results about "Online decision making" patented technology

The invention discloses an air conditioning-storage-charging micro-grid and power distribution network collaborative optimization control method, which comprises the steps of constructing a multi-target optimization model with low carbon, economical efficiency and low load as targets, and realizing dynamic prediction and rolling optimization of a system state based on model prediction control; introducing an online decision-making tool, and dynamically adjusting an energy exchange strategy of the micro-grid and the power distribution network according to the air quality and the operation state; and the control effect is verified through system simulation. Compared with an existing static optimization method, the method has the advantages that dynamic cooperative mutual aid control under multiple targets is achieved, higher real-time performance, higher adaptive capacity and higher operation efficiency are achieved, and the stability and the overall performance of a building energy system in a complex environment are remarkably improved.

A routing and scheduling method based on multi-cycle CSQF mechanism and GDRL is provided. The routing and scheduling method includes the following steps: S1, initializing a cycle index detection mechanism, a queue mapping and a queue mapping constraint of a multi-cycle CSQF; S2, constructing a DFRLLS model; S3, optimizing the DFRLLS model based on GDRL; S4, off-line training a learning strategy of a GDRL model; S5, making a decision on-line based on a trained GDRL model. The routing and scheduling method based on multi-cycle CSQF mechanism and GDRL is adopted, and GCN network is used to extract topology information between networks. Compared with the method of only using reinforcement learning, the routing and scheduling method can achieve more flow scheduling, and its performance is also stable under complex network topology, multi-cycle CSQF can reduce the start-to-end delay of the flow compared to the CSQF.

The invention provides a large model and reinforcement learning combined hierarchical command control method, which is based on agents of a hierarchical structure, and is characterized in that the first layer of agents are RAG-based large model intelligent formation command agents; the second-layer intelligent agent is composed of an air strike marshalling intelligent agent and a reconnaissance and strike mixed marshalling The second-layer intelligent agent judges which subtask is executed according to the instruction sent by the first-layer intelligent agent; comprising the following steps that S1, an RAG-based large-model intelligent formation command system comprises an offline knowledge preparation stage and an online decision execution stage; s2, setting a state space, an action space and a reward and punishment function; and S3, respectively training the air strike marshalling agent and the reconnaissance and strike mixed marshalling agent, and constructing an intelligent game model based on a multi-agent reinforcement learning algorithm. According to the invention, exclusive knowledge support is provided for the large model through the RAG technology, then the structured decision instruction is generated through the large model, training is carried out based on multi-agent reinforcement learning to obtain the agent of a specific task, and formation-level command control is realized.

The invention relates to the technical field of artificial intelligence, provides an edge computing task unloading method based on DRL and dynamic scheduling collaboration, and solves the problem that a task unloading decision is not matched with execution efficiency in an MEC environment. Task unloading is modeled as a Markov decision process, a state space containing task attributes, a transmission rate and computing resources is defined, a discrete action space covers local or edge server execution, and a reward function is designed based on time delay and overtime penalty. And carrying out offline training by adopting an LSTM-D3QN algorithm integrated with a long short-term memory network, extracting time sequence features and optimizing a state-action value function. A time-aware dynamic priority scheduling mechanism is introduced, an emergency area, a balance area and a waiting area are divided according to task remaining deadline, and remaining time sorting, dynamic weight priority and FIFO variant strategies are adopted respectively. In the online decision-making stage, the real-time environment state is input into the model to generate an unloading action, and task execution is scheduled according to the dynamic priority.

The invention discloses a heat supply unit deep peak regulation and heat supply optimization operation method based on multi-mode collaboration, and particularly relates to the technical field of thermal power generation and heat supply, and the method comprises the steps of S1, system modeling and parameter identification, S2, thermoelectric load prediction, S3, multi-objective optimization solution, S4, operation decision and feedback, and S5, data agent mapping acceleration. According to the method, the thermoelectric load is accurately predicted through mechanism and data dual-drive modeling, and based on the target of maximizing the economic benefits of the whole plant, the optimal cooperation scheme of multiple operation modes such as heat supply, power supply and deep peak regulation is dynamically solved by utilizing the multi-target optimization algorithm on the premise of ensuring the safety of the unit, so that the power supply efficiency is improved. Finally, production is guided through an online decision-making and closed-loop feedback system, and safe, economical and flexible operation of the unit under the working condition of deep peak regulation is achieved.

The invention discloses a multi-role large model assisted hydrogen-containing building energy system operation optimization method, and belongs to the technical field of building energy system optimization control, and the method comprises the steps: firstly, building a hydrogen-containing building multi-energy system operation cost minimization problem in an off-grid operation mode; secondly, re-modeling the problem into a security Markov decision process, and defining a system state space, an action space and a composite reward function; then, solving a safety Markov decision process of modeling based on a multi-role large language model assisted near-end strategy optimization algorithm, and obtaining an intelligent agent operation strategy related to the hydrogen-containing building multi-energy system; finally, the intelligent agent makes an online decision based on the obtained optimization strategy, the decision acts on the actual hydrogen-containing building multi-energy system, the system operation cost can be effectively reduced, and the energy supply reliability is improved.

The invention relates to the field of laser welding quality monitoring, in particular to a copper-aluminum heterogeneous material laser welding mechanical property prediction model. Comprising the steps that a multi-source data acquisition module acquires laser power, scanning speed and the like in real time through a sensor array; the physical mechanism modeling module is based on a multi-physics field coupling finite element algorithm, utilizes a self-adaptive heat source unit to dynamically compensate copper-aluminum heat conductivity difference, and simulates temperature field and residual stress evolution; the data driving prediction module adopts a dual-channel deep neural network, a first channel inputs a process parameter vector, a second channel inputs a molten pool geometric feature and a thermal cycle vector, and prediction values of tensile strength, elongation and hardness are jointly output through a feature fusion layer; and the online decision feedback module compares the predicted strength with a safety threshold value, generates a process parameter correction instruction through Bayesian optimization, and drives an execution mechanism to perform closed-loop control. A physical mechanism and dynamic feature capture are fused, the problem of high cost of a traditional trial and error method is solved, and prediction precision and process stability are synchronously improved.

The invention provides a vehicle dynamic stability control method based on reinforcement learning and variable pole assignment, and relates to the field of vehicle dynamics control. According to the method, the stability state and the energy evolution trend of the vehicle are recognized in real time by constructing a multi-dimensional criterion of a side slip angle phase plane and an energy phase plane; a reinforcement learning agent is utilized to make an expected closed-loop pole position of a system according to a current state online decision, aggressive poles are configured under a stable working condition to improve the flexibility, and conservative poles are configured under an unstable working condition to guarantee the stability. Besides, a safety monitoring mechanism containing pole physical domain constraints is further designed, the dynamic characteristics of the vehicle are remodeled through feedback control on the premise that the steering intention of a driver is not changed, and the problem of real-time balance of flexibility and safety in intelligent chassis control is effectively solved.

The invention relates to a terahertz communication safety beamforming method and a communication system, and the method comprises the steps: observing a system state through an intelligent agent to construct a state vector, and the system state comprises channel state information of a legal user and a potential eavesdropper; outputting a beamforming vector according to the state vector through a strategy network in the intelligent agent; the power constraint layer normalizes the beam forming vector to obtain a final beam forming vector, and performs signal transmission; and calculating a reward value according to a result after signal transmission, and updating network parameters of the deep reinforcement learning agent based on the reward value. After training is completed, only one time of neural network forward propagation is needed for online decision making, the delay of single-time beam updating is lower than 1.5 ms, the complexity is far lower than that of a convex optimization method such as SDR, and the method is suitable for a terahertz high-speed real-time communication scene.

The application discloses a kind of wisdom family online energy management methods of fusing prediction and learning idea, including the following design steps: first, under the premise of maintaining indoor thermal comfort, the minimum problem of wisdom family energy management system operating cost is established;Second, the above-mentioned problem is re-modeled as Markov decision process, and an agent is constructed to learn and the implicit world model related to the operation of family energy management system;Then, the environment model is constructed and interacted with the agent;Then, according to the constructed environment model, the agent training framework based on model predictive control and deep reinforcement learning is established;After training, the agent makes online decision based on the obtained implicit world model, and the decision is applied to the actual wisdom family system.Compared with the prior art, the present method combines the dual advantages of model predictive control and deep reinforcement learning, which can simultaneously reduce the operating cost of wisdom family and improve indoor thermal comfort.

The invention discloses an industrial load aggregation response scheduling strategy under day-ahead-day invitation bid winning, and the strategy comprises the steps: 1), carrying out the day-ahead-day multi-stage aggregation response scheduling decision after an aggregator receives bid winning information after the bid winning information is cleared by a power grid operation mechanism; 2) an aggregator takes day-ahead bid winning capacity assessment and industrial load adjustable potential constraints into consideration, and makes a day-ahead aggregation response scheduling decision with the maximum day-ahead bid winning income as an optimization target; and 3) constructing an intra-day aggregation response scheduling deep reinforcement learning model considering intra-day bid winning response and day-ahead response error compensation, and online deciding an intra-day adjustable industrial load response power plan by the trained deep reinforcement learning model based on a near-end strategy optimization algorithm. According to the strategy, day-ahead and intra-day aggregation response scheduling is coordinated, the problems caused by uncertainty of clearing of the multi-time scale demand response market and uncertainty of day-ahead response errors can be solved, and online intra-day decision-making has excellent rapidity, accuracy and generalization ability.

The application discloses a kind of 6G network multidimensional resource AI model dynamic deployment optimization methods based on category theory, belongs to intelligent collaborative optimization technical field;Method is: the cross-layer consistency dependency of end-to-end AI reasoning service is formalized by functor form;Establish the joint optimization model with long-term average end-to-end delay minimization as target, while being constrained by multidimensional resource and service quality;Convert long-term random optimization problem into time-slot online decision problem;Get AI model dynamic deployment and task scheduling result.The application realizes cross-layer consistency description to task scheduling and model deployment through category theory unified modeling and functor composite mechanism, reduces the inconsistency and redundant constraint caused by hierarchical modeling, improves the structured degree and explainability of joint decision;Under the constraint of multidimensional resources such as calculation, memory, storage and bandwidth, dynamic adaptive optimization is realized, node resource over-limit and load imbalance are effectively avoided, and congestion and queuing delay are reduced.

The invention belongs to the technical field of electrical system operation optimization and control, and discloses an online cooperative operation real-time control method for an electric vehicle quick charging station equipped with battery energy storage. The optimization model is subjected to space-time decoupling by establishing an online collaborative operation optimization model. In the off-line training stage, each quick charging station and battery energy storage train a value function in a dynamic planning algorithm based on historical data; in the online scheduling stage, each quick charging station and battery energy storage decide an optimal operation action based on current observation information, and information such as future charging load does not need to be predicted. The offline training process and the online decision-making process are both based on an analytic expression, a complex calculation process is not needed, the offline training process and the online decision-making process are both executed in a distributed mode, and the data privacy of each quick charging station and the expandability of an algorithm are guaranteed. A dynamic programming algorithm and a consistency algorithm are combined, and the online cooperative operation problem of a plurality of rapid charging stations with highly random charging loads is effectively solved.

The invention discloses a workpiece cooling method and system based on medium space-time field twinning and generative control, and belongs to the technical field of intelligent manufacturing and metallurgical hot working. The method comprises the steps of collecting physical field data of a cooling medium in real time, performing data fusion and space-time deduction through a fluid dynamic model, and constructing four-dimensional space-time field digital twinning of the cooling medium; geometric data and material attribute data of the workpiece are obtained, four-dimensional space-time field digital twinning is used as a boundary condition, the multi-physics field coupling model is driven to carry out calculation, and internal state evolution information of the workpiece in the cooling process is predicted; by taking preset workpiece target microstructure distribution and / or target residual stress distribution as an optimization target, taking internal state evolution information and four-dimensional space-time field digital twinning as state input, performing online decision making through a deep reinforcement learning model, generating an optimal time sequence control strategy and analyzing the optimal time sequence control strategy into an equipment control instruction, and controlling the equipment according to the optimal time sequence control strategy. And a multi-zone independent execution unit in the active cooling equipment is driven.

Embodiments of the present application provide an information processing method and device, and a computing device. The method comprises: detecting a system access request initiated by a target user for a target processing system; in response to the system access request, determining decision results respectively generated by a plurality of online decision models for the target processing system; determining target recommendation information based on the decision results respectively corresponding to the plurality of online decision models; and outputting the target recommendation information for the target user. Embodiments of the present application improve recommendation accuracy and effectiveness.

The invention discloses a multi-process machining process high-temperature intelligent prediction method based on sparse sensing extension, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: constructing a three-dimensional simulation model of a key part of a machine tool, carrying out the finite element thermal analysis, screening nodes in an initial layout network based on a graph node centrality measurement algorithm, and laying sensors, forming a sparse sensing network; acquiring a sparse temperature sensing data set based on the network, and setting a global temperature prediction threshold and an auxiliary anomaly judgment threshold in combination with material characteristics, finite element results and historical data; machine tool numerical control system parameters are collected and analyzed in real time, a process knowledge graph is constructed, and a current process type is judged through a model; inputting the sparse data set and the process type into a space-time extension model to obtain temperature data of all positions; and the process type and the overall data set are input into a high-temperature intelligent prediction model, and the future high-temperature abnormal state is judged in combination with a threshold value, so that the high calculation cost of a mechanism driving method is avoided, and the real-time monitoring and online decision-making requirements are met.

The invention relates to the technical field of power systems, in particular to a power transaction online aid decision-making method considering N-1 security constraints and load flow calculation, which comprises the following steps: S1, data acquisition and preprocessing: acquiring a power grid real-time operation state, transaction data, power grid model parameters and equipment operation limit value data; s2, based on the acquired data, establishing an optimization model with the goal of maximizing the social total welfare or minimizing the total electricity purchase cost, the optimization model comprising a ground state operation constraint and an N-1 security constraint; s3, efficiently solving the model based on constraint screening and parallel computing; s4, generating security boundary visualization and decision suggestions; according to the method, the generated transaction scheme is ensured to meet the security requirement of the power grid, the operation reliability of the power grid is improved, the technical bottleneck that an optimization model containing N-1 constraints is complex in calculation and time-consuming in solution is solved, and the real-time requirement of online decision making is met.

This invention provides a vehicle dynamic stability control method based on reinforcement learning and variable pole configuration, relating to the field of vehicle dynamics control. This method constructs multi-dimensional criteria for the centroid sideslip angle phase plane and energy phase plane to identify the vehicle's stability state and energy evolution trend in real time. It utilizes a reinforcement learning agent to make online decisions about the expected closed-loop pole positions of the system based on the current state, enabling the configuration of aggressive poles to improve flexibility under stable conditions and conservative poles to ensure stability under unstable conditions. Furthermore, this invention designs a safety monitoring mechanism that includes pole physical domain constraints, enabling the reshaping of the vehicle's dynamic characteristics through feedback control without altering the driver's steering intentions, effectively solving the challenge of real-time trade-off between flexibility and safety in intelligent chassis control.

The present invention discloses a workshop scheduling method based on a dynamic equipment interaction model, which includes the following steps: setting up a workshop scheduling system; inputting data of production orders; performing priority ranking processing by MES; establishing a workshop scheduling model; and coordinating production by multiple workshop production line systems. The present invention also discloses a workshop scheduling system, which includes an ERP system, an MES system, a scheduling system, and multiple workshop production line systems. The scheduling system includes a database cluster module, a state machine module, a database storage module, a data dictionary module, an abnormality alarm module, and an upstream and downstream collaboration module. By establishing a workshop scheduling model, the present invention can autonomously perceive and analyze the status of multiple workshop production line systems, and by setting up a status module for adaptive online decision-making to dynamically regulate the mapping relationship between production plans and scheduling rules, it can prevent inaccurate production task allocation or material shortages due to changes in production status, thereby ensuring production efficiency.

The application relates to the field of intelligent control of heat exchangers, and discloses a heat exchanger heat distribution control method based on multi-target optimization, which collects operation data under multiple working conditions and carries out labeling; nonlinear decoupling mapping of heat exchanger multi-working condition thermodynamic parameters and low-dimensional working condition feature extraction; flow distribution multi-target optimization based on double pheromone cooperation and thermodynamic constraint projection; online decision and valve opening degree instruction dynamic smoothing mapping based on variable weight target heart closeness; heat distribution closed-loop control based on variable weight decision and valve dynamic smoothing mapping. The application has the beneficial effect that the sensitivity of entropy production rate to each operation parameter is used as physical guiding information to reweight the original operation state vector in the sense of thermodynamics, so that the subsequent low-dimensional feature extraction and optimization process preferentially pays attention to key variables that truly affect irreversible loss, instead of simply relying on numerical compression.

This invention discloses an adaptive control method for bearing grinding processes based on reinforcement learning, comprising: S1, real-time acquisition of multi-source state data; S2, construction of a high-dimensional state vector; S3, construction of a historical grinding dataset; S4, construction of an improved IQL model incorporating an uncertainty perception mechanism, offline training using the historical dataset, outputting the Q-value distribution, and learning a safety policy at the safety policy layer; S5, online deployment of the model, generation of candidate commands, and evaluation of uncertainty; S6, judgment by the safety constraint layer: issuing candidate commands when uncertainty is low, and invoking the safety policy to generate safe commands when uncertainty is high; S7, execution of commands and storage of new data in the dataset, periodically optimizing the model. This invention improves the safety and reliability of online decision-making through uncertainty quantification and a safety policy safety net, achieving adaptive and precise control of complex grinding conditions.

The application discloses an open source dialogue model-oriented automatic jailbreaking prompt word generation and attack method and system. The application first screens a prompt word set from an original prompt word set; secondly, based on an attack question, the prompt word with the optimal attack efficiency is screened out from the original open source prompt word set, multi-path parallel testing is carried out by using a proxy model, and the attack success rate and the prompt word length of different prompt words in the prompt word set are evaluated in real time through a dynamic evaluation mechanism based on a greedy selection strategy, the final attack prompt word is output to a target model, and after being spliced with the attack question, the attack prompt word is returned and an analysis report is output; finally, the prompt word in the analysis report is adjusted by using an automatic mutation and expert modification strategy, and the prompt word is reconstructed and iterated according to the feedback result of each round of attack, so that a new prompt word is generated. The application combines offline evolution and online decision-making to automatically generate a high-success-rate jailbreaking prompt word, controls the length and overhead, and optimizes mutation by using semantic rewriting and logic skeleton extraction.

The invention belongs to the technical field of intelligent traffic, and particularly relates to a vehicle-road cloud integrated communication system and method based on environment backscattering. Comprising the following steps: step 1, constructing a vehicle-road-cloud integrated simulation environment; 2, defining reinforcement learning decision elements; step 3, cloud strategy training and model issuing; 4, vehicle online decision making and communication simulation are carried out; 5, strategy closed-loop updating and performance evaluation are carried out; according to the invention, an AmBC module is integrated at a vehicle end, and a lightweight deep Q network agent is deployed at a cloud platform, so that a closed-loop optimization mechanism of cloud training-road distribution-vehicle execution is realized; according to the invention, vehicles are allowed to carry out interference-free communication by reflecting RSU broadcast signals when cellular users occupy frequency spectrums; meanwhile, the DQN strategy dynamically selects three modes of'idle / active V2X / AmBC ', and the long-term average throughput is maximized.

The invention discloses a linkage response system and method for sudden water quality impact of an industrial sewage treatment plant, and the method comprises the following steps: carrying out the training of historical operation data based on the operation data of the sewage treatment plant in a historical time period, employing a reinforcement learning algorithm, taking the effluent reaching the standard as an optimization target, and carrying out the training of the historical operation data, generating a new optimal weight coefficient set; updating the current optimal weight coefficient set used in the online decision making process by using the new optimal weight coefficient set obtained by training; calculating a comprehensive pollution index CPI based on real-time inflow water quality parameters and flow data by using the updated optimal weight coefficient set; and comparing the CPI value with a preset early warning threshold value, and if the CPI value reaches the early warning threshold value, triggering a corresponding grading response operation. The system comprehensively evaluates the impact degree by calculating the CPI in real time based on the method, and automatically triggers the grading response, so that the response time can be effectively shortened, and the impact risk can be greatly reduced.

The invention provides a data center refrigeration equipment energy-saving optimization method and system based on a layering rule. The method comprises the steps that a multi-source heterogeneous data set is acquired; constructing a deep learning model through Transform, a time sequence convolutional network and a bidirectional recurrent neural network, and training the deep learning model; processing the multi-source heterogeneous data set through a deep learning model to obtain a prediction result; performing fuzzy control on the refrigeration equipment of the data center according to the prediction result and a preset layering rule; and updating a historical event library and expert rules according to a fuzzy control result to form a dynamic optimization closed loop. According to the method, through high-quality data management, intelligent model construction and online decision making, on the premise that constraint conditions such as temperature and humidity and reliability of the machine room are met, energy consumption minimization and operation and maintenance optimization are achieved, and the data center is assisted to achieve the goals of green, low-carbon and sustainable development.

The present application belongs to the technical field of intelligent algorithm, heterogeneous satellite planning and dynamic target monitoring, and particularly relates to a heterogeneous constellation intelligent task decision method for space dynamic target observation, comprising: step 1: establishing a multi-constraint multi-objective optimization model for dynamic target observation, and designing a Markov dynamic decision model, i.e. determining the elements of the state set of the task decision model; step 2: designing a heterogeneous star cluster intelligent decision algorithm architecture based on experience learning-Mask mechanism-iterative improvement, i.e. realizing offline training of the heterogeneous star cluster task decision algorithm; and step 3: online decision, i.e. using the trained network to perform real-time relay observation on dynamic targets. The present application proposes a general relay observation task scheduling framework which comprehensively considers double coverage, system switching times and observation quality, realizes relay observation on dynamic targets by selecting the optimal multiple stars in the heterogeneous star cluster to maintain target positioning, and improves the space early warning capability.

This invention discloses a self-healing decision optimization method for power information systems based on reinforcement learning feedback. The method includes: acquiring operational data to construct a dynamic graph structured state representation; formalizing the self-healing objective into a temporal logic reduction based on signal temporal logic; predicting the quantitative robustness of the state trajectory to the reduction, and generating a dense reward signal in the form of a potential function difference to reshape the feedback reward; finally, training the agent based on the reshaped reward, the graph network foundation, and the course learning mechanism, outputting an optimal decision sequence including topology reconstruction and resource scheduling. This invention eliminates the reward sparsity problem of traditional models, guides the agent to find the optimal solution under strict physical temporal constraints, effectively avoids the risk of cascading failures caused by trial and error, and significantly improves the convergence speed and online decision reliability of self-healing in high-dimensional complex power grids.

The present invention discloses an online determination method for the global system state based on chassis modularization. Based on the dynamic optimization of the target tolerance value and the time step, a dynamic constraint function is established by considering the original response data set at the current time T, the time step variable and the change rate of the original response data set, and the maximum effective step length that both meets the redundant error constraint and maximizes the fusion speed is found to realize the data fusion of multiple module sensors. The statistical characteristics of the fused information data set are extracted by calculating the standard deviation thereof, and valid and invalid data are divided and inversion calculations are performed to obtain valid original response data and invalid original response data. On this basis, the information inefficiency of the response data of each module can be obtained, thereby completing the dynamic determination and online decision-making of the chassis sub-module state and the global performance of the system.

The invention discloses an online decision-making method, program, medium and system for transient stability of a power system, and relates to the technical field of automation and artificial intelligence crossing of the power system, and the method comprises the following steps: constructing a prediction model, and enabling the prediction model to master a nonlinear mapping relation between the dynamic response of the system and the sensitivity of each control measure through training; the method comprises the following steps: after transient instability of a system occurs, acquiring DVRSI time sequence values of all branches of the system; inputting the DVRSI time sequence value and a branch DVRSI tail end prediction value when control is not applied into a prediction model, and predicting and outputting a control sensitivity sequence of all alternative generator shedding points and load shedding points in real time; taking the control sensitivity as a known parameter, and constructing and solving an emergency control optimization model which takes the control cost and action penalty into consideration and minimizes the comprehensive cost as an optimization target on line; and outputting an optimal emergency control strategy combination, and issuing a control instruction for execution. And the optimal control strategy combination for recovering the stability of the system can be solved online.

The invention relates to the technical field of industrial SCADA (supervisory control and data acquisition) systems, in particular to a real-time online decision execution SCADA system based on artificial intelligence, which comprises an expert system module used for receiving real-time data images of an SCADA system and performing logic processing through an inference engine; the real-time data image construction module is used for organizing the real-time data acquired by the SCADA system into a system operation image and injecting the system operation image into the expert system module; the execution module is used for converting a processing result of the expert system module into a control instruction, and sending the control instruction to the field equipment for execution through the SCADA system; according to the invention, the expert system is integrated and provided for the real-time efficient inference engine of the SCADA system, and then the expert system knowledge editing system based on WEB is constructed to provide various rule editing interfaces in a WEB mode, so that various control logic landing problems in system implementation are solved.