115 results about "Evolution strategy" patented technology

The invention relates to a joint estimation method for SOC and SOH of a power battery, in particular to a joint estimation method for SOC and SOH of a lithium ion power battery based on FOASEKF-EKF, comprising fractional order equivalent circuit models of two parallel fractional order CPE branches, and providing a hybrid genetic algorithm HGA fusing a differential evolution strategy and an adaptive variation mechanism. Accurate estimation of SOC and terminal voltage under a fast time scale is realized by introducing a sliding-mode observer and an FOASEKF, periodic online correction is performed on model parameters and battery capacity based on an EKF under a slow time scale, and high-precision and high-robustness battery SOC and SOH joint estimation is realized. The method is suitable for complex industrial environments such as electric automobiles and rail transit, does not need to set a large number of hyper-parameters, does not excessively depend on the quality and quantity of data, has good interpretability, adaptability and engineering practicability, can still achieve high-precision cooperative estimation of SOC and SOH especially under the working conditions of frequent start and stop and unsteady operation, and has good application prospects. And misjudgment and drift estimation risks are obviously reduced.

The invention relates to a clustering and entropy-guided reentrant hybrid flow shop scheduling method. The method comprises the following steps: step 1, establishing a problem model; step 2, setting algorithm operation parameters; 3, adopting an initialization strategy to generate an exploration population and a development population; 4, judging whether a first-stage termination condition is met or not, if not, executing a first-stage evolutionary strategy and an updating strategy on the exploration population and the development population, and otherwise, executing the step 5; 5, constructing an elite population; 6, judging whether a second-stage termination condition is met or not, and if not, executing a second-stage evolutionary strategy on the elite population; otherwise, outputting a Pareto solution set; and 7, updating the elite population. According to the method, dynamic balance of global exploration and local development is realized, and the solution distribution can be improved while the solution set convergence is ensured, so that the completion time and the total energy consumption are reduced, the production cost is reduced, and the workshop scheduling efficiency is improved.

The invention discloses a commercial building energy monitoring and intelligent control method and device and a storage medium, and belongs to the technical field of building intelligent control, and the method comprises the steps: collecting data, building a nonlinear mapping relation, and generating an energy consumption demand prediction tensor; injecting an adversarial disturbance sample, and evaluating the robustness of the prediction model; in combination with the energy consumption baseline, performing cross confirmation and correction on the prediction data exceeding the threshold value; performing attribution analysis on the corrected energy consumption sequence to generate an energy consumption attribution map; adjusting the solution of a multi-objective optimization function according to the atlas, and generating an optimal cooperative control strategy; and the comprehensive efficiency is used as a reinforcement learning reward, strategy parameters are iteratively updated, and a control knowledge base is formed. According to the method, a closed-loop control framework integrating robust demand prediction, dynamic attribution analysis, collaborative optimization decision and a self-evolution strategy is adopted, intelligent regulation and control of building energy consumption can be realized, and the long-term adaptive optimization capability is improved.

The invention discloses a low-altitude economic unmanned aerial vehicle data processing method and system based on a large model, and relates to the technical field of low-altitude economic data processing, and the method comprises the steps: extracting low-altitude multi-target decision features of a low-altitude semantic decision map, carrying out the fuzzy reasoning of the low-altitude multi-target decision features, and generating an anti-interference control instruction set; based on the anti-interference control instruction set, fault logs and task execution data during operation of the unmanned aerial vehicle are collected, and an evolution strategy library is generated through association rule mining; and performing causal analysis according to the evolution strategy library, generating a cluster coordination rule upgrade package, performing dynamic verification on the cluster coordination rule upgrade package, and outputting a self-healing strategy. According to the method, the reliability and the safety of task execution of the unmanned aerial vehicle are improved by constructing the semantic association large model and generating the anti-interference control instruction set.

The invention discloses a linear guide rail optimization design method and device, a medium and a program product, and the method comprises the steps: (1) constructing a simulation model and a MaOP design model which can optimize the rigidity, modal and weight at the same time based on the linear guide rail structure and static load analysis; (2) generating an elite population based on a Latin hypercube and diversity criterion, obtaining target values of the elite population, establishing a database, and constructing a Gaussian process function model; (3) designing mixed mutation operation based on OCC to generate a filial generation guide rail set; (4) designing a DEP-driven DPM evolutionary strategy to generate a candidate guide rail set, and selecting an optimal candidate guide rail; and (5) obtaining each target value of the optimal candidate guide rail, updating the database and the Gaussian process function model, returning to the step (3) until all optimization targets meet requirements, and outputting an optimal parameter value. According to the method, the MaOP process aiming at the rigidity, the modal and the weight of the linear guide rail can be effectively balanced, and higher precision and better comprehensive performance are achieved.

The invention discloses an SIP voice outbound routing method and system based on artificial intelligence, and relates to the technical field of mobile data communication services. The SIP voice outbound routing method and system based on artificial intelligence comprises the steps that S1, gateway operation state data and call feedback data in the SIP voice outbound process are collected and preprocessed, and a standardized outbound state data set is constructed; s2, carrying out gateway comprehensive evaluation on the conversation capability and the stability of the gateway, and dynamically adjusting the priority order of the gateway in a routing path; s3, performing path performance analysis on the real-time performance of the routing path, and correcting the scheduling participation frequency of the corresponding channel in real time; and S4, performing scheduling evaluation on the scheduling state, and updating a scheduling control strategy in real time. The problems that the SIP outbound service lacks an end-to-end intelligent routing closed-loop optimization mechanism, an evolution strategy based on data feedback is not constructed, and the overall routing efficiency and the intelligent evolution capability are seriously limited are solved.

The invention belongs to the technical field of communication networks, and discloses a block chain topology-consensus co-evolution method and system based on reinforcement learning, and the method comprises the steps: collecting the state information of each node in a block chain network, carrying out the standardization processing and multi-dimensional feature extraction of the state information of each node, and obtaining a block chain topology-consensus co-evolution model; mapping into node state feature vectors available for reinforcement learning, and forming node state codes; a reinforcement learning strategy space is constructed according to the node state codes, a topology evolution strategy is dynamically generated by using a strategy iteration method, and topology adaptive evolution of the block chain network is realized; a structural dynamic constraint mechanism is introduced in the reinforcement learning process, and endogenous driving of topological evolution is carried out; calculating the dynamic difference between the potential energy change between the block chain network nodes and the global structure entropy, establishing an evolution constraint function, and dynamically updating the topological evolution strategy parameters; and the elasticity and the expandability of the network structure are effectively improved.

The invention relates to the technical field of computer science and industrial engineering, in particular to a tool workshop scheduling method, which comprises the following steps of: acquiring production characteristics of a current tool workshop, generating an optimal batch scheduling strategy according to the production characteristics of the current tool workshop based on a preset evolutionary strategy, and automatically designing an algorithm by utilizing a preset large model, and calculating the minimum completion time and the maximum completion time of the optimal batch scheduling strategy, and completing tool workshop scheduling according to the minimum completion time and the maximum completion time based on the optimal batch scheduling strategy. Therefore, the problems of high workshop scheduling complexity and the like caused by strong coupling of batch division and working procedure sorting, strict constraint between working procedures and high resource networking degree in workshop scheduling are solved, an automatic algorithm design framework based on a large model is introduced, the expert algorithm design time is shortened, and the workshop scheduling efficiency is improved. Therefore, the efficiency and feasibility of aircraft tooling production scheduling are improved.

The invention belongs to the technical field of machine learning models, particularly relates to a personnel performance evaluation method based on IWOA-SVM, and solves the problems that a traditional support vector machine (SVM) is low in precision, difficult in parameter selection and the like in performance intelligent evaluation. The method comprises the steps that Tent chaotic mapping and a pseudo-opposition learning strategy are utilized to increase the diversity and quality of an initial population, and the whale algorithm (WOA) is prevented from falling into local optimum; the global optimization capability of the WOA is improved by adopting a differential evolution mechanism; a penalty factor and kernel function parameters of the SVM are optimized through an improved whale algorithm (IWOA), and performance evaluation can be effectively carried out while optimal parameters are obtained. According to the method, the whale algorithm can be improved by using Tent chaotic mapping, pseudo-opposition learning and a differential evolution strategy, SVM parameters are searched in a global range, and better model performance is obtained.

The invention discloses an active power distribution network power flow optimization method based on a large language model, and belongs to the field of power system optimization. The method comprises the following steps: firstly, realizing optimal power flow problem modeling according to topology and equipment parameter data of an active power distribution network to be subjected to power flow optimization; designing structured cue words through an optimal power flow problem, and driving a large language model zero sample to generate a diversified heuristic algorithm initial population; and then a thinking chain evolutionary strategy is adopted, calculation errors serve as judgment criteria, another large language model is guided to simulate selection, intersection and mutation operation to achieve heuristic algorithm iterative optimization, output needed by all distributed energy sources of the active power distribution network is obtained based on the heuristic algorithm with the minimum calculation error, and power flow optimization of the active power distribution network is completed. According to the method, the core problems of low design efficiency and high dependence on expert experience of a heuristic algorithm in the complex optimal power flow problem of the active power distribution network are solved, and automatic generation of a high-quality algorithm can be realized for a user-defined problem scene.

The invention discloses an intelligent food storage tank internal environment adaptive control system and control method, and belongs to the technical field of artificial intelligence and Internet of Things control. The system specifically comprises a multi-mode sensing module, a feature extraction and preprocessing module, a reinforcement learning module, an instruction adaptive control module and a digital simulation module. The multi-modal sensing module deploys a sensor and an environment adjusting device in an array mode; the feature extraction and preprocessing module carries out filtering, standardization and feature extraction on the data; the reinforcement learning module generates an optimization control strategy by using an algorithm; the instruction self-adaptive control module converts the strategy into an instruction and accurately adjusts parameters of the environment adjusting device; the digital simulation module constructs a digital model for simulation learning and evolution of a strategy agent. Compared with a traditional monitoring system, the method has the technical advantage of generating an optimization strategy, solves the problem of insufficient adaptive control capability caused by a fixed strategy of the traditional monitoring system, and provides a more efficient monitoring service.

The invention discloses a development management system and method for an API interface, and relates to the technical field of software engineering. The system comprises: an intention understanding and design engine, which analyzes a multi-modal service demand into a structured API development task; the agents collaboratively develop a network, and the multiple AI agents are dispatched to generate API specifications, codes, test cases and deployment configurations in parallel; the API ecological dynamic map construction module is used for automatically constructing and updating a dynamic map of dependency and data flow direction between APIs based on a generated product; a continuous learning and evolution engine analyzes the atlas and operation data to identify optimization points and generate or execute evolution policies. All the modules form a closed loop, and output of the evolution engine is fed back to the intention understanding engine to optimize subsequent design. The corresponding method comprises the steps of demand analysis, intelligent collaboration, atlas construction and closed-loop optimization. According to the method, automatic development, intelligent collaboration and continuous architecture optimization of the API are realized, and the development efficiency and the system maintainability are improved.

The invention discloses an image super-resolution reconstruction method based on a double-domain chimeric attention mechanism, and belongs to the technical field of image super-resolution reconstruction. The method comprises the steps that an image is collected and preprocessed; constructing an image processing model; the image processing model is trained by adopting the preprocessed image, a self-adaptive attention distribution strategy is adopted in training, local attention weight is dynamically adjusted, an edge perception multi-constraint loss function is adopted, overall reconstruction and edge quality are dynamically coordinated, and detail definition and contour naturalness of a reconstructed image are improved; and carrying out image super-resolution reconstruction by adopting the trained image processing model. According to the method, the ViT module based on the double-domain chimeric attention mechanism is embedded into the bottleneck layer of the U-Net network, the edge perception multi-constraint loss function based on the gradient alignment course evolution strategy is adopted, and the obtained model has good image super-resolution reconstruction performance.

The invention discloses a linear guide rail lightweight optimization design method and device, a medium and a program product, and the method comprises the steps: (1) constructing a lightweight optimization mathematical model on the basis of a linear guide rail structure and load characteristics under the condition of satisfying frictional resistance and first-order modal constraints; (2) quantizing a parameter effect based on a PCE model and constructing an effect space to generate a population; (3) designing an evolutionary strategy guided by a bidirectional information individual; (4) constructing a double-layer Stacking integration model based on DBSCAN clustering, and screening an optimal filial generation guide rail; and (5) if the current optimal filial generation guide rail meets the optimization requirement, outputting an optimal guide rail parameter value, otherwise, returning to the step (3) until the optimization requirement is met. According to the method, the weight of the guide rail can be minimized for the effect space of high-effect guide rail parameters, transition optimization for redundancy and low-effect parameters is avoided, and better lightweight performance is achieved.

The invention discloses an insulation defect feature detection method and system based on a switch cabinet, and relates to the field of insulation defect feature detection, and the method comprises the steps: installing a sensor array in the switch cabinet, collecting an original data stream of a multi-modal space-time physical field, and generating a reconstructed four-dimensional space-time field tensor through a multi-modal space-time cooperative sensing network; based on the four-dimensional space-time field tensor, generating an enhanced sample set conforming to a physical rule through physical regularization; generating an optimized detection parameter combination through gradient-evolution collaboration based on the enhanced sample set conforming to the physical law; digital twin enhanced closed-loop adaptive optimization is adopted, and the optimized detection parameter combination is converted into verified stable parameter configuration; according to the verified stable parameter configuration, real-time optimization parameters are obtained through embedded real-time acceleration optimization and solidification; the detection parameters are effectively adjusted and optimized by adopting a multi-target covariance evolutionary strategy to optimize a parameter combination process based on a space-time gradient characteristic spectrum.

The invention relates to the technical field of artificial intelligence question answering systems, and discloses an interactive question answering system based on multi-model parallel reasoning. The system comprises an interactive interface module, a query cognition construction module, a hierarchical index module, a multi-model parallel reasoning module, an interactive answer synthesis module and a tool calling adaptation module. The system constructs query cognition mapping by deeply analyzing a time sequence query stream containing texts and media of a user, and drives dynamic evolution of hierarchical indexes according to the query cognition mapping. The multi-model parallel reasoning is based on evolution strategy coordination processing, and knowledge slices with state vectors are generated. And finally, synthesizing a natural language answer attached with the interaction intention unit, and adapting the natural language answer to an external tool calling instruction. According to the system, the cognition and retrieval precision under complex query is improved through intention-driven dynamic indexing, and automatic closed loop from information question answering to business operation is realized through executable answers.

The invention discloses a saline-alkali soil water-salt-fertilizer real-time monitoring system and method based on the Internet of Things, and relates to the technical field of saline-alkali soil monitoring. Multi-parameter sensor nodes are arranged in a preset area to collect soil salinity, moisture, conductivity, pH value and ground temperature information; acquiring a remote sensing image, terrain elevation and land utilization type data, and performing preprocessing and spatial registration; based on the spatial position relation, the hydrological connectivity and the irrigation and drainage structure, establishing a spatial graph structure required by graph neural network input; deploying a model on the constructed graph, and predicting the future salinity trend or saline-alkali risk grade of each monitoring unit; utilizing adjacent node features and an edge weight propagation mechanism to realize prediction extrapolation of a data sparse region; based on a prediction result and an agronomic rule, generating a multi-objective optimization regulation and control scheme through an evolutionary strategy algorithm; and displaying the prediction map, the risk map and the regulation and control suggestions, and triggering early warning and control linkage when the threshold value exceeds the limit.

The invention discloses a social media harmful information identification method and device based on large-small model collaborative optimization, and the method comprises the steps: firstly, carrying out the feature analysis of social media information through an LLM, and automatically matching an optimal professional small model; secondly, an automatic deployment engine is utilized, model deployment codes are generated through LLM, and localized deployment is completed; then, constructing a dynamic code generation unit, automatically generating a code and a fine tuning code for calling a professional small model to identify social media harmful information according to the model ID and deployment parameters, and performing grammar verification, performance evaluation and safety detection on the generated code by adopting LLM; further, a social media information pipeline is designed, and functions of data slice input, recognition result classification, challenging sample screening and fine tuning data set construction are included; and finally, implementing a model persistent evolution strategy, and completing model version upgrading by iteratively executing a recognition-screening-fine tuning process. Through a collaborative decision-making mechanism of the LLM and the professional small model, end-to-end automation of a harmful information identification process is realized, the manual intervention cost is remarkably reduced and the identification efficiency is improved on the premise of ensuring the identification quality, and the method is particularly suitable for a large-scale harmful information identification scene of multi-field heterogeneous data.

According to the optimization method for data acquisition of the unmanned aerial vehicle in the wireless power supply Internet of Things, a nonlinear energy collection model is adopted, an optimization target is modeled as a mixed integer nonlinear programming problem, and the optimization problem is decomposed into an equipment service sequence generation module (a main module) and an unmanned aerial vehicle trajectory optimization module (a sub module). In the main module, the service priority of equipment is dynamically adjusted on the basis of equipment data volume and historical service times, meanwhile, in view of mutual conflict of optimization targets, a multi-objective evolutionary strategy is adopted to generate a Pareto frontier solution set, and an optimal equipment access sequence of each round is screened from the Pareto frontier solution set, and in the sub-module, for the continuous action parameter adjustment problem of the unmanned aerial vehicle, the optimal equipment access sequence of the unmanned aerial vehicle is obtained. The input dimension of the neural network is reduced by constructing a lightweight state space, and a multi-target reward function including data collection amount and energy consumption is designed, so that collaborative optimization and long-term balance of equipment service fairness, the data collection amount and the energy consumption of the unmanned aerial vehicle are realized.

The application relates to the technical field of battery life prediction, and discloses a lithium ion battery capacity prediction method, which comprises the following steps: obtaining lithium ion battery data, performing normalization processing and variational mode decomposition preprocessing on a battery capacity sequence to obtain a data set serving as model input; performing multi-scale decomposition on the battery capacity sequence obtained by S01 by using a self-adaptive mode selection mechanism based on an MAPE criterion, to obtain intrinsic mode functions (IMFs) of the best decomposition mode number; the lithium battery data is preprocessed by using variational mode decomposition (VMD), the battery capacity sequence is multi-scale decomposed by using a self-adaptive mode selection mechanism based on an MAPE criterion, and intrinsic mode functions (IMFs) of the best decomposition mode number are obtained; a self-adaptive step Gaussian random walk strategy, an auxiliary correction strategy and a differential evolution strategy are introduced to improve a white whale optimization algorithm (WOA), so that the robustness of the algorithm and the precision of the final solution are improved.

The embodiment of the invention discloses an attack event processing method and device, electronic equipment, a storage medium and a program, and the method comprises the steps: obtaining event triggering operation associated data of a target triggering event in a target system in real time; performing broad-spectrum detection on the event triggering operation associated data through an attack event broad-spectrum mapping model to generate broad-spectrum attack detection associated data; performing intention reasoning on the broad-spectrum attack detection associated data through an attack event dynamic evolution engine to obtain a to-be-verified attack intention hypothesis of the target trigger event; and determining an attack event dynamic evolution strategy matched with the to-be-verified attack intention hypothesis, and performing attack detection on current real-time operation associated data of the target trigger event through the attack event dynamic evolution strategy. According to the technical scheme of the embodiment of the invention, the monitoring efficiency and detection precision of the attack event can be improved, and the response efficiency of the attack event is improved.

The application discloses a kind of building energy-saving scheme intelligent generation method based on two-stage agent-assisted evolution, belong to building energy-saving field, according to resident building model decision variable and range, generate uniform feasible solution with rejection sampling method, and the real target value obtained by EnergyPlus simulation is stored in sample set DB;Uniformly construct reference vector in target space and divide space, DB sample is divided into corresponding vector file according to the nearest distance, and the initial RBF global agent model is constructed.The first stage selects the crowding density minimum non-dominated solution from each vector file as the initial population, evaluates the individual using the global agent model, fills the solution according to the rules, updates the local agent model, and enters the second stage when the hyper volume increment value is greater than the threshold value, otherwise directly enters S5, and the second stage dynamically updates the reference vector according to the solution quality.The application uses the above method, by combining agent model and phased evolution strategy, significantly improves the efficiency and accuracy of building energy-saving optimization.

This application provides a recommendation method, apparatus, and computer-readable storage medium based on multi-objective optimization. The method includes: acquiring user rating information, including ratings from multiple users for multiple items; clustering users into different clusters using K-means clustering based on the user rating information; predicting the predicted ratings of users for items in each cluster based on a probability propagation algorithm with improved resource allocation; generating an initial population in each cluster based on the predicted ratings of users for items; and solving a multi-objective optimization problem based on an accuracy objective function and a diversity objective function using an evolutionary strategy guided by accuracy-preference users, thereby obtaining the target recommendation result for each cluster. By using the above method to guide the evolutionary direction of the algorithm through user preferences, the target recommendation result can be made more biased towards the accuracy objective function while taking into account diversity.

The invention relates to a planetary gear box fault diagnosis algorithm automatic generation system and method based on a large language model, and the system comprises a large language model generation module which is used for receiving cue words, selecting a parent planetary gear box fault diagnosis algorithm and architecture component knowledge, and combining the characteristics of a planetary gear box fault diagnosis task, calling a large language model to generate a new planetary gearbox fault diagnosis algorithm code; the dynamic code execution and verification module is used for code execution and verification; the architecture evaluator module is used for performing training and performance evaluation; the intelligent evolutionary strategy module is used for analyzing the training history to obtain an analysis result; the program database module is used for storing and managing populations of planetary gearbox fault diagnosis algorithms and constructing cue words; and the evolutionary control engine is used for coordinating and controlling the working process of each module and controlling the number of iterative evolutionary times to realize evolutionary circulation. Compared with the prior art, the method has the advantages of automatic generation, iterative evolution and the like.

The application relates to the technical field of data encryption and discloses an AI driving-based adaptive optimization method of a post-quantum cryptographic algorithm. The method collects multi-source cryptographic parameter data, generates a standardized cryptographic parameter set through adaptive noise injection and parameter normalization preprocessing; a dynamic optimization network model based on multi-head attention and reinforcement learning is constructed, an adversarial evolution strategy is adopted for training, a quantum attack scene is simulated to screen an anti-quantum parameter combination; a differential parameter space mapping algorithm is used to convert into an optimal encryption protocol configuration and correct conflicts; through iterative optimization of a meta-learning framework, local parameter sensitivity and global anti-attack capability are fused, and an adaptive post-quantum cryptographic algorithm is output. The application effectively improves the performance and security of the post-quantum cryptographic algorithm, can optimize the algorithm according to different scene requirements, and provides a reliable technical scheme for coping with quantum computing threats.

The application belongs to the technical field of mobile visual inspection planning, and specifically discloses a mobile visual inspection station-point joint clustering and sequence planning method. The method is a genetic algorithm based on an alternating evolution strategy, and comprises steps of data encoding / decoding, evolution strategy updating, individual selection, individual crossover, individual mutation, population updating and the like. The length of a chromosome is equal to the number of points participating in sequencing. The integer part of a gene represents a point sequence number, and the decimal part represents a station sequence number to which the point belongs. Point sequences belonging to the same station follow the order of appearance in the chromosome; station sequences follow the order of the first appearance position. The application can integrally realize station-point clustering and sequence planning, and improve visual inspection efficiency; the proposed evolution strategy also significantly reduces the running time of the planning program. The application is suitable for large and complex component mobile visual inspection and other similar scenarios.

The invention provides a sub-array optimal distribution method and system based on a quantum antelope optimizer, and belongs to the field of array signal processing. The invention aims to solve the problem of inaccurate estimation of distributed array performance attenuation and direction of arrival due to reduction of the number of snapshots and change of interference noise under the conditions of small sampling snapshots and impact noise. According to the method, the array structure is optimized under the impact noise condition, received signals are processed by constructing an infinite norm weighted fraction low-order covariance matrix, and the method is more suitable for the environment where the number of snapshots is reduced and the impact noise is severe; a quantum optimization mechanism is introduced, and the performance and search efficiency of an original algorithm in the distributed array research direction are improved based on an evolution strategy of a quantum rotation angle and a quantum rotation door; according to the method, the direction finding accuracy of the distributed array system can be improved, and the situation that the array performance influence factors in the actual direction finding application are not considered sufficiently and the performance improvement is not large due to the fact that the directional diagram of the array serves as the optimization target can be effectively avoided.

The invention discloses a decentralized cooperative control method for distributed power quality equipment. Comprising the following steps: collecting a harmonic phase sequence of each device, analyzing asymmetric transmission delay between the devices, and generating a delay anisotropic tensor representing delay directivity characteristics; based on the harmonic phase sequence and the delay anisotropy tensor, constructing an adaptive multi-modal distribution model of phase deviation, and outputting a multi-modal phase distribution function and a modal separation index; a multi-modal phase distribution function is utilized to predict and coordinate distributed compensation conflicts, and conflict-free optimization compensation vectors are generated; and integrating and optimizing the compensation vector and the modal separation index, and generating an adaptive compensation control instruction through a feedback-driven self-evolution strategy. By constructing the adaptive multi-mode distribution model, coordinating and compensating conflicts and adopting a feedback-driven self-evolution strategy, decentralized cooperative control of the distributed power quality equipment can be realized, and the stability of system operation and the power quality are improved.

The invention provides a photovoltaic array wind load prediction method and system based on depth prediction network optimization, and belongs to the technical field of civil engineering structure wind resistance and artificial intelligence application crossing. According to the method, firstly, a deep prediction network is constructed to replace traditional cross validation, and self-adaption and efficient tuning of XGBoost model hyper-parameters are achieved; secondly, a multi-target composite loss function is obtained based on a self-adaptive hyper-parameter optimization model of a deep prediction network, and the prediction precision of the model is remarkably improved through a gradient guidance and evolutionary strategy hybrid search algorithm; finally, in combination with SHAP analysis, it can be revealed that the wind direction angle and the dip angle are key factors influencing the wind load, a direct theoretical basis is provided for wind resistance optimization design of the photovoltaic array, and meanwhile, a photovoltaic array wind load prediction model has the advantages of being high in optimization efficiency, high in generalization ability and the like.

The invention relates to a product subversive innovation opportunity generation method based on scene innovation, and the method comprises the steps: firstly determining the maturity of a product through a Gompertz curve, and judging the opportunity of a subversive innovation window; secondly, performing mainstream product scene construction, performing formalized expression on a scene based on an extension principle, and forming a modification rule and an evolution strategy of scene elements in combination with characteristics of subversive innovation so as to perform scene evolution to mine a subversive opportunity scene; secondly, positioning a functional difference region in the opportunity scene based on the sub-scene and the compatibility function, and deeply digging user requirements in the subversive opportunity scene by using a fishbone diagram method to find a product subversive innovation opportunity; and finally, forming a judgment criterion of the product subversive innovation opportunity according to the characteristics of various types of subversive products so as to judge the type of the innovation opportunity. According to the method, enterprises can be helped to systematically identify high-value subversive innovation opportunities, and the limitations that a traditional method is single in dimension and insensitive to market changes are overcome by integrating multi-dimensional scene elements.