156 results about "Empirical data" patented technology

The invention discloses an artificial intelligence robot path planning method and system, and the method comprises the steps: 1, carrying out the comprehensive perception of an environment geometric structure, object semantics and dynamic obstacles, eliminating scene differences, and further constructing the topological graph representation of an environment; 2, pre-training a path planning model, designing a scene context encoder, encoding a scene specific rule into a low-dimensional vector, and outputting a scene context code and updated planning model parameters to provide a planning capability adapted to a new scene for the step 3; 3, dynamically adjusting a track according to real-time sensor data by adopting a hierarchical decision-making architecture; step 2, recording success / failure path segments in the new scene, regularly updating a local planning module, regularly feeding back empirical data in the new scene to the step 2, and triggering a conservative obstacle avoidance mode when the confidence coefficient is lower than a threshold value; and 4, constructing a quantitative evaluation system, building an automatic test platform, simulating diversified scenes and dynamic interference, and automatically generating a test case.

According to the BIM-based construction process monitoring and early warning system provided by the invention, the data acquisition dimension and precision are remarkably improved through multi-source sensing fusion of millimeter-wave radar, multispectral imaging and voiceprint recognition; feature vector voxel units carrying material characteristics and process constraints are adopted, so that risk early warning has space-time relevance and process interpretability; through a composite risk calculation model containing environmental interference correction and time-varying gradient, the problem that a traditional threshold value method is poor in adaptability to complex working conditions is solved; the holographic early warning mechanism realizes upgrading from sound-light alarm to touch sense-three-dimensional projection cooperative interaction; the double-circulation self-optimization system not only guarantees real-time control, but also realizes block chain evidence storage of empirical data, and finally forms a construction monitoring closed-loop system with space-time perception, intelligent decision, accurate early warning and sustainable evolution capabilities.

The invention discloses a sewage treatment drug delivery control method based on reinforcement learning and particle swarm optimization, and the method achieves the precise control of turbidity and the optimization of drug cost through the dynamic adjustment of particle swarm parameters and the fusion of a reinforcement learning strategy. According to the technical scheme, the method comprises the following steps: combining a PSO algorithm with a DDPG algorithm to construct a drug control model; designing a reward function; the empirical data with the state coverage rate higher than a first preset threshold value are stored in a full-reservation empirical pool, the empirical data with the current policy access larger than a second preset threshold value are reserved in a first-in first-out mode through a policy empirical pool, and mixed sampling is carried out according to a preset proportion; training a drug control model; and inputting the current turbidity value, the drug delivery amount of the last time step, the water inlet flow, the parameters of the water quality detection camera and the future turbidity value into a trained drug control model, and outputting the current drug delivery amount. Through prediction-control closed-loop integration, a raw water turbidity prediction result is fed back to the optimizer, and the method is suitable for real-time adaptive control of a sewage treatment system.

The invention relates to the technical field of parameter optimization, in particular to an automatic kernel network parameter optimization method, which comprises the steps of constructing an enhanced deep Q network model, and integrating the enhanced deep Q network model with a priority playback buffer area, a meta learning module, a Bayesian optimizer and a neural architecture search module; using performance index data to train an enhanced deep Q network model, the training process including using a priority playback buffer to store and sample empirical data, using a meta-learning module to perform task adaptation, and monitoring training indexes of multiple dimensions to evaluate the convergence state of the model; selecting a kernel parameter adjustment action according to the current state through the trained enhanced deep Q network model; executing the selected kernel parameter adjustment action, and evaluating a parameter adjustment effect based on the multi-target reward function; and updating the enhanced deep Q network model according to an evaluation result, wherein the priority playback buffer area and the Bayesian optimizer are utilized in the updating process.

The invention relates to the technical field of traffic transportation environment engineering and carbon emission evaluation, in particular to a construction method and equipment of a plateau gradient zone vehicle CO2 emission factor calculation model and a medium. Through the method, a route-level CO2 emission factor distribution map is obtained, and different quantitative calculation models are formed for different altitude intervals. And a specific and quantitative design basis is provided for low-carbon highway design. It is revealed for the first time that the CO2 emission rate of the plateau gradient zone has a remarkable altitude segmentation effect through empirical data, and a method for constructing a calculation model in a partition mode is innovatively provided. According to the method, the defects that a traditional single model is poor in adaptability and misaccurate in prediction in a complex plateau environment are overcome, and differentiated quantitative models (such as a low-altitude quadratic function and a high-altitude S-type function) are established for different altitude intervals; the finally formed partition model provides a core theoretical tool and decision basis for plateau highway carbon emission accurate accounting, low-carbon route optimization and green traffic construction.

The invention discloses an intelligent agent optimization method and device, equipment, a storage medium and a product, relates to the technical field of artificial intelligence, and discloses a method for recording a task execution track of an intelligent agent in a process of executing a task based on a target large model in the intelligent agent; extracting execution empirical data from the task execution track, wherein the execution empirical data comprises problems existing in the task execution process, generation reasons of the problems and solutions; and generating a target training sample based on the execution experience data, the target training sample being used for training the target large model to optimize the agent. According to the method, the efficiency of obtaining the training sample can be remarkably improved, so that the optimization efficiency of the intelligent agent is improved, and the ability of the intelligent agent to deal with rapidly changing task requirements and complex application scenes is enhanced.

The embodiment of the invention provides an unmanned ship cooperative hunting control method, and belongs to the technical field of control, and the method specifically comprises the steps: distributing a main hunting target for each chasing unmanned ship; for each chasing unmanned ship, an original observation vector containing a variable-length interaction sequence is formed according to the distributed main hunting target of the chasing unmanned ship; a variable-length interaction sequence in the original observation vector is converted into a standardized state vector of a fixed dimension through a two-stage attention mechanism; inputting the standardized state vector into a strategy network of the unmanned ship, and outputting a continuous action control instruction; and calculating an instant reward based on empirical data obtained after executing an action control instruction, and performing iterative updating on a strategy neural network and a corresponding value neural network by adopting a centralized training and decentralized execution mode based on a multi-agent depth deterministic strategy gradient framework to obtain a control model to generate a cooperative control scheme. Through the scheme disclosed by the invention, the control precision and adaptability are improved.

The invention relates to the technical field of artificial intelligence, and provides a training method and device of a reinforcement learning model, equipment and a medium, and the method comprises the steps: obtaining experience data of an intelligent agent interacting with an environment through employing a historical strategy provided by a to-be-trained reinforcement learning model; determining an original action probability ratio of the reinforcement learning model to be trained between a historical strategy and a current strategy by using empirical data, and performing shear constraint on the original action probability ratio according to a current shear coefficient to obtain a shear action probability ratio; the current shearing coefficient is obtained by updating the difference value between the strategy divergence and the target divergence, the strategy divergence is determined through the shearing action probability ratio, the training loss is determined according to the empirical data and the shearing action probability ratio, and the current strategy of the reinforcement learning model to be trained is updated and trained by using the training loss. According to the invention, by optimizing the shear constraint mechanism, the dynamic adjustment of the current shear coefficient is realized, the training efficiency of the model is improved, and the scene adaptability of the model is improved.

The invention relates to the technical field of computer risk control, in particular to a big-data-based collaborative self-optimization method and system for a self-growth risk control system, and the method comprises the steps: obtaining the operation data of the risk control system, so as to construct a state vector; inputting the state vector into a reinforcement learning agent, and selecting one coordination action from a preset coordination action space for output; the coordination action is executed in the risk control system, and a scalar reward value used for evaluating the effect of the coordination action is obtained through calculation; and forming an empirical data tuple by using the state vector, the coordination action, the scalar reward value and a new state of the system after the action is executed, and training the reinforcement learning agent to update a decision strategy of the reinforcement learning agent. According to the risk control system, the rule engine and the model engine in the risk control system can be considered as a unified whole, and respective parameters are dynamically and cooperatively adjusted, so that the overall comprehensive efficiency of the system is maximized, and the self-adaptive capability and the iteration efficiency of the system are improved.

The invention discloses a multi-agent collaborative decision-making method based on federal reinforcement learning, and the method comprises the steps: firstly, enabling a central server to be initialized, then randomly selecting a client, enabling the client to initialize a local model into a global model, training and updating parameters, and then uploading the parameters; and the central server aggregates and updates the global model parameters until the model training is completed, and the multi-agent collaborative decision-making is completed. According to the method, the reinforcement learning model is trained in a federated learning mode, and similarity weighted staggering of two types of empirical data is combined, so that the effectiveness of the reinforcement learning model can be ensured, and possibility is provided for multiple parties to jointly train the same model; and the data sample richness and the model training efficiency are improved while the data privacy security is ensured.

The embodiment of the invention provides an enterprise information architecture (4A)-based AI Agent planning enhancement system, a control device and equipment, and the system comprises a Prompt construction module which is used for analyzing an unstructured demand to obtain a structured demand, and cooperating with other modules to generate a standardized Prompt comprising a business process, a data entity and a tool strategy; the knowledge matching module is used for matching related knowledge based on a knowledge graph employment number fusion knowledge base and sorting the related knowledge into a knowledge list containing business processes, practical experience, a data list and a tool list; and the context reasoning module is used for generating a reasoning result based on the historical data and the knowledge list and feeding back the reasoning result to the Prompt construction module so as to assist in generating the normalized Prompt. According to the system, the integration capability of the AI Agent on business logic and data resources in the task planning process is remarkably improved, so that the AI Agent can more accurately understand enterprise-level business scenes, and standardized and intelligent planning support is provided for practical application of the AI technology in enterprise digital transformation.

The invention discloses a government affair material auditing method and system based on historical case analysis, and relates to the technical field of intelligent government affair platforms, and the method comprises the steps: obtaining archived historical case data and a to-be-audited target government affair material in a government affair auditing system, carrying out the structural modeling of the historical case data, and obtaining a to-be-audited target government affair material; forming a case database containing content features, audit conclusions and business categories, and realizing computable representation of empirical data; based on the multi-dimensional feature extraction of the target material, combining the reliability weight of the business category obtained by clustering, the similarity weight between the target material and the historical case and the importance weight of each auditing dimension; a multi-layer weighted voting mechanism is adopted, a material comprehensive auditing score is generated through fusion calculation integrating three types of weights, and a machine auditing conclusion is automatically output according to a score threshold value. Therefore, the conversion from artificial experience to data-driven decision is realized, and the accuracy, consistency and intelligent level of government affair material auditing are effectively improved.

The invention provides a numerical control machine tool dual-system cooperative control method and device and a storage medium, and belongs to the field of numerical control machining. The method comprises the steps that real-time loads of a first main shaft and a second main shaft are obtained from a numerical control system; and if the load of any one of the two spindles exceeds the corresponding load upper limit, the rotating speeds of the two spindles are linearly reduced from the standard rotating speed to the corresponding transition rotating speed, and meanwhile, the feeding speeds of the two spindles are gradually reduced within the preset adjusting range. The loads of the two main shafts are monitored in real time, when any main shaft load reaches the corresponding load upper limit and machining resonance is about to be generated, the rotating speed of the main shafts is automatically lowered to eliminate the resonance of the main shafts, manual intervention of operators is not needed, full-automatic vibration elimination is achieved, and the working efficiency is improved. And the load upper limits of the two main shafts are obtained through calculation of empirical data of a numerical control machine tool dual-system database, different load upper limits are generated under different working conditions, control is flexible, and the damping effect is good.

The invention provides a dual-drive deepwater wellbore integrity failure risk evaluation method, and belongs to the technical field of deepwater drilling safety, and the dual-drive deepwater wellbore integrity failure risk evaluation method comprises the following steps: based on fault tree analysis in a bowknot model, identifying a wellbore integrity failure mode; constructing a differentiated evaluation path according to the failure mode, and quantifying a static equipment failure risk and a dynamic structure failure risk respectively; constructing a wellbore system-level failure probability model through a fault tree and Bayesian network dynamic mapping mechanism; based on event tree analysis, modeling is conducted on the safety barrier state after shaft integrity failure, accident consequences are deduced, and the occurrence probability of the accident consequences is calculated; and outputting the system-level failure probability, the key path contribution degree, the bottom event sensitivity sequence and the accident consequence probability, and solving the technical problem that the system-level failure risk is difficult to accurately predict and dynamically prevent and control because static empirical data and a dynamic mechanical mechanism are separated and cannot be coupled and updated in real time.

The invention discloses a spacecraft Hall propulsion device test working medium flow stability control system, which comprises a sensing module for detecting working condition state parameters of working medium supply equipment, and an adaptation module for exploring current working condition control parameters by combining detection data of the sensing module and similar working condition control parameter empirical data fusion, the adjusting module is used for receiving the control parameters and dynamically adjusting the adjusting weight of the adjusting device according to different phase change periods, an experience library unit and a self-adaption unit are arranged in the adaption module, the experience library unit provides experience data of similar working conditions and control parameters, and the self-adaption unit provides self-adaption data of the control parameters. And the autonomous adaptation unit fuses and explores control parameters by constructing a neural network adaptation model, so that working condition automatic identification and control parameter autonomous adaptation under the environment that the working medium type or the working medium state rapidly changes are realized; the problems of low control precision and slow response speed caused by manual presetting of control parameters of different working media in a traditional control technology are solved, and stable control of the working medium flow is realized.

The invention belongs to the field of artificial intelligence, and discloses a distributed data acquisition system based on multi-agent collaborative decision and a construction method, and the method comprises the steps: 1, collecting heterogeneous data of production equipment, an environment sensor, a man-machine interaction terminal and an enterprise information system in real time through the deployment of a distributed data acquisition network, cleaning, de-noising and standardizing the edge computing nodes to generate a structured data stream; 2, automatically extracting features and laws of structured data through a multi-dimensional engine analysis model, and completing semantic alignment and fusion of heterogeneous data; 3, multiple agents are adopted to form empirical data according to a deterministic strategy gradient model in combination with a multi-dimensional reward function optimization model; and step 4, performing incremental learning on the empirical data to optimize the anomaly detection model, realizing multi-dimensional information visualization display of first-line decision and center command, and deploying corresponding functional agents according to the multi-dimensional information. The problems that a multi-agent system is low in control precision and low in efficiency are solved.

The invention discloses an intelligent load distribution system and method for a centrifugal compressor unit. The system comprises a data sensing module, an intelligent decision-making module, an instruction execution module and a model updating module. The method comprises the steps that operation parameters of multiple parallel centrifugal compressors are synchronously collected in a high-precision mode through an industrial real-time network supporting hardware timestamps, and time-aligned system state observation data are obtained; constructing a state vector based on the data and a load prediction result, inputting a deep reinforcement learning agent fused with compressor physical characteristic constraints, and generating a load distribution instruction and a unit start-stop suggestion; after safety boundary verification is conducted on the original action, the guide vane opening degree is adjusted through a feedforward speed planning and fuzzy self-adaption PID composite servo control algorithm, and starting and stopping operation is executed; meanwhile, empirical data are collected online, and model parameters are periodically and finely adjusted. According to the invention, efficient, safe, accurate and self-evolution compressor group control is realized.

The invention discloses a railway roadbed settlement prediction method and system, and belongs to the technical field of railway engineering. According to the method, basic data such as a roadbed structure, filler parameters, geological conditions and historical monitoring data are collected, a differential prediction model is matched according to roadbed strength grades, risk grading and monitoring resource dynamic allocation are carried out after parameter optimization and multi-index precision evaluation, and iterative optimization is realized based on an empirical database. The system comprises a data acquisition module, a model matching module, a settlement simulation module, a precision evaluation module, a post-processing module and a data storage module, and all the modules cooperate to achieve full-process automation. According to the method and the system, the problem of insufficient adaptability of a traditional single model is solved through accurate model adaptation, the prediction efficiency and the data reliability are improved by means of automatic processing, the safety and the cost are balanced through risk hierarchical management and control, and the adaptability of the system to complex working conditions is continuously enhanced through an iteration mechanism.

The invention discloses an energy storage frequency modulation automatic configuration method, a storage medium and electronic equipment, and the method comprises the steps: firstly, enabling a motor group frequency modulation demand of a to-be-configured energy storage frequency modulation system to be matched with an energy storage frequency modulation configuration feature knowledge base, and obtaining an initial frequency modulation configuration scheme; an energy storage frequency modulation configuration verification model is established according to the energy storage system basic model, the thermal power generating unit AGC response model, the energy storage frequency modulation control model and the economical efficiency analysis model, and then the initial frequency modulation configuration scheme is optimized by adopting an optimization algorithm; calculating a typical daily cost return rate of each optimized frequency modulation configuration scheme in combination with an energy storage frequency modulation configuration verification model, selecting the optimized frequency modulation configuration scheme with the optimal typical daily cost return rate as a target frequency modulation configuration scheme, and determining an initial configuration scheme according to empirical data of frequency modulation configuration; and then the schemes are optimized by adopting an algorithm, and the scheme with the optimal cost yield is selected, so that the energy storage frequency modulation configuration scheme with better economical efficiency can be determined at a relatively high speed.

A cantilever type tension sensor with a probe structure relates to the technical field of force measuring structures and comprises a knife handle dynamometer, a knife head is detachably fixed at the head end of the knife handle dynamometer, a sensor signal leading-out wire is detachably fixed at the tail end of the knife handle dynamometer, and an elastomer beam is arranged on the knife handle dynamometer. And a metal resistance strain gauge connected with a sensor signal outgoing line is arranged on the elastomer beam. According to the utility model, the problem that the force value in the process cannot be detected and controlled in real time because the force value is controlled by the current of the motor or experience or displacement in the prior art is solved.

The invention discloses an agent training method and device, electronic equipment and a storage medium, and relates to the technical field of computers, in particular to the artificial intelligence fields of deep learning, large models, agents and the like. According to the specific implementation scheme, for each sub-task of a sample task, the action priority of a first candidate action in multiple groups of experience data corresponding to the sub-task in an experience pool of an intelligent agent is determined; wherein the action priority is used for representing the value of the first candidate action; screening target empirical data corresponding to the subtasks from the multiple groups of empirical data according to the action priorities; and training the intelligent agent according to the target empirical data.

Disclosed herein is a method for high aspect ratio (HAR) structure formation in semiconductor manufacturing using atomic layer etching (ALE). The method involves dynamically adjusting process parameters such as surface modification step duration and sputtering step bias level in relation to the ALE cycle count. This adjustment can be guided by a pre-established model or empirical data from prior tests, enhancing precision and efficiency in HAR structure etching.

The invention discloses a 3D printing method for realizing high color accuracy and real-time effect preview based on a pre-calculated material stacking scheme database, and belongs to the technical field of 3D printing. The core of the method is that a material stacking scheme database of the corresponding relation between printing result colors and material stacking schemes and printing effects is constructed in advance: after multiple basic semitransparent printing materials are arranged according to a specific rule, stacking printing is carried out through different sequences and combinations, and a physical color sample array is formed; after highlight interference is eliminated through a polaroid, color data of all samples are accurately collected, and a mapping relation is established between the color data and corresponding material stacking schemes and printing effects and stored in a database. When target color content is printed, the system queries the database through an efficient color matching algorithm, the optimal printing formula is rapidly obtained, meanwhile, the printing effect is accurately previewed through the real-time rendering technology, and the query efficiency of the algorithm is improved through the ordered arrangement structure of the database. And finally, the printer prints layer by layer according to the obtained stacking scheme, and a pattern effect with accurate colors is presented on the bottom surface of a finished product. According to the method, repeated calculation of a complex color model is avoided, and color printing which is high in color accuracy, low in cost, high in efficiency and compatible with various consumables is achieved through the experience database constructed at a time.

The invention discloses an alternating-current and direct-current hybrid power transmission network rack optimization evaluation method and system, which are used for solving the technical problems that a traditional alternating-current and direct-current hybrid power transmission network rack evaluation method depends on static parameters or empirical data, cannot dynamically respond to cross-regional load fluctuation and the like, is difficult to cope with risks, and causes low quality of a rack scheme. The method comprises the following steps: generating comprehensive evaluation indexes of a plurality of grid schemes according to acquired operation data of an AC / DC hybrid power transmission network by adopting power system simulation software; constructing a fuzzy evaluation matrix of each grid scheme according to the comprehensive evaluation index of each grid scheme by adopting an improved descending semi-trapezoidal membership function and a Gaussian membership function; calculating a target weight corresponding to the comprehensive evaluation index of each grid scheme; and according to the target weight corresponding to the comprehensive evaluation index of each grid scheme and the fuzzy evaluation matrix, calculating the comprehensive score of each grid scheme, and selecting the grid scheme with the maximum comprehensive score as the target grid scheme of the AC / DC hybrid power transmission network.

The invention discloses an electric vehicle charging scheduling optimization method based on the Stackelberg game, and the method comprises the steps: constructing a system model of a light storage charging station, and defining an energy management strategy; modeling the interaction relationship between the optical storage charging station and the plurality of electric vehicles as a Stackelberg Markov game; constructing an agent network for the leader agent and each follower agent; in each time step, the leader agent formulates a charging price and an energy storage scheduling strategy in advance and broadcasts the charging price and the energy storage scheduling strategy, and each follower agent decides respective charging power in parallel according to the charging price and the energy storage scheduling strategy; after all the intelligent agents execute actions, empirical data are stored in a local empirical playback pool; each agent samples empirical data to update the agent network; and obtaining the optimal charging station dynamic charging price and energy storage scheduling strategy and the optimal charging strategy of the electric vehicle until the strategies of all the intelligent agents converge, thereby realizing the maximization of the operation income of the optical storage charging station. According to the invention, the operation income of the optical storage charging station can be maximized.

The invention discloses an intelligent agent optimization method and system based on task similarity clustering, and belongs to the technical field of intelligent power grids, and the method comprises the steps: constructing a simulation model of a power system according to a topological structure of the power system and power equipment data; constructing a Markov decision process model of the power system in combination with the simulation model and a power task scheduling target of an intelligent agent; based on the Markov decision process model, collecting historical operation data of the simulation model as an empirical data set, pre-training an intelligent agent through the empirical data set, outputting similarity among power tasks and grouping the power tasks to obtain a plurality of similar task groups; and performing grouping optimization on the pre-trained agents through the similar task groups to obtain a multi-task execution model. Therefore, by implementing the method and the device, the problem of poor model applicability caused by the fact that the constructed intelligent agent can only execute the electric power task in the specific electric power environment in the prior art can be solved.

Systems and methods for detecting and predicting faults in an industrial process automation system use trend data to forecast alerts and allow action to be taken before a problem occurs. The systems and methods provide fault / failure predictions that improve over time as more empirical data is collected for a related set of system components. The systems and methods may identify relationships among the components of a process automation system; identify and collect changes to system configuration; identify and collect data to inform reliability and predictive models; develop a domain-specific predictive model for one or more components that allows for component-based failure or degradation prediction; develop a system-predictive model that leverages reliability and criticality relationships, component-based predictions and operating parameters to predict the health of a part of or the entire process automation system; deliver a prioritized alert system; and identify root-cause failures of a component.

The invention relates to a target-driven digital model orthogonal analysis and dual-adaptation fusion method and system, and the method comprises the steps: obtaining an overall demand, and combining the overall demand with empirical data and a knowledge graph to generate a model demand; obtaining a model composition file and a model description file which is determined based on a model demand and is used for dividing model categories, generating a meta-model description file according to the model composition file and the model description file, and generating a meta-model based on the meta-model description file; analyzing input and output data logic of the meta-model, scheduling parameters according to meta-model interface specifications, and redefining an interface by adopting a nonlinear iteration and aggregation optimization method; correlation adaptation and proxy packaging are carried out on a result of transmission logic numerical value fitting, and numerical value verification is completed in combination with a precision constraint condition; and adapting the incidence relation of the meta-agent model, generating a business-level numeralization agent model, scheduling parameters to perform precision verification on the business-level numeralization agent model, and generating a unified and operable system model.

The invention provides a resource adjustment method, a resource adjustment device and computing equipment. The resource comprises a reasoning instance group and a training instance group, the reasoning instance group is used for processing the first input data through the AI model to obtain response data, the training instance group is used for updating model parameters of the AI model according to empirical data, and the empirical data is obtained according to the response data and stored in the buffer. The method comprises the steps that first information and second information of a buffer are obtained, the first information is used for representing the load of a reasoning instance group, and the second information is used for representing the load of a training instance group; and adjusting configuration information of a target instance group according to the first information and the second information, wherein the target instance group comprises a reasoning instance group and / or a training instance group. According to the scheme, the load between the instance groups is sensed through the buffer, and the configuration information of the instance groups can be adjusted in time under the condition that the load between the instance groups is unbalanced, so that the load between the instance groups is balanced.

The invention discloses a method, a device and a system for planning an SEEG electrode implantation path. The method comprises the following steps: processing medical image data of the brain of an epileptic to obtain a target brain anatomical image; matching and searching a target experience module in an experience database according to the clinical description data of the epileptic; the experience database is established according to prior experience, a plurality of experience modules are arranged in the experience database, and each experience module comprises a plurality of combinations of a first target range and a second target range of a fixed electrode; and determining a target path of each electrode based on the target brain anatomy image and the target experience module. According to the method, the experience database containing a plurality of experience modules is established, the target experience module is screened in combination with the clinical description data of the patient, the target path of each electrode is further screened in combination with the target brain anatomy image of the patient, the proper path of the SEEG electrode is accurately and efficiently planned for the individual patient, and the accuracy of the SEEG electrode is improved. The method provided by the invention can reduce the learning and use threshold of doctors, and has high popularization value.