570 results about "Continuous optimization" patented technology

The invention discloses a territorial space planning method based on big data, and belongs to the technical field of territorial space planning. Comprising the following steps: step 1, constructing a territorial space knowledge graph; step 2, constructing a multi-dimensional territorial space planning solution space, identifying key constraints and determining an optimization path; 3, searching and generating an optimal planning scheme in a solution space; 4, performing quantification and grading processing on hard constraints and soft constraints in territorial space planning to realize multi-objective comprehensive optimization; step 5, performing multi-dimensional confidence evaluation and validity verification on the planning scheme generated by optimization; and step 6, realizing continuous optimization and adaptive evolution of territorial space planning.

The invention discloses a process automation execution method based on a large language model, and belongs to the technical field of artificial intelligence and process automation. User intention is analyzed through multi-modal input, and a structured task definition is constructed; the semantic reasoning layer is used for performing task layering, complexity evaluation and sorting optimization; the task execution layer completes subtask scheduling and execution; and the feedback and optimization layer performs performance evaluation and model updating based on execution data to realize closed loop and continuous optimization of the process, so that the technical problems of dynamically analyzing unstructured instructions, automatically optimizing a complex task dependency relationship and adapting to business changes in real time by a process automation tool are solved; according to the method, end-to-end conversion from an unstructured instruction to a structured task is realized, a subtask execution path is dynamically optimized, cross-platform tool calling is supported, the existing system integration cost of an enterprise is reduced, visual display task decomposition logic and prediction and execution time consumption comparison are provided, and the system credibility is enhanced.

The invention relates to the technical field of mining industry, and discloses an intelligent prediction method for gold ore beneficiation process parameters based on cloud and edge fusion, which realizes space-time correlation modeling of beneficiation process parameters and accurately depicts dynamic interaction influence among equipment. The cloud edge collaborative architecture considers global optimization and real-time response requirements, and the prediction stability under complex working conditions is effectively improved. The introduction of physical constraints enhances the applicability of the model in an actual production environment, a bidirectional feedback mechanism ensures the adaptive ability of the system in a dynamic change environment, and through the joint reasoning of a knowledge graph and a neural network, the consistency of a prediction result and a process principle is enhanced, and the risk of misjudgment under an abnormal working condition is reduced; the man-machine cooperation mechanism significantly improves the labeling efficiency of high-value samples, shortens the model iteration period, and ensures the continuous optimization capability of the prediction system in the actual production environment.

The invention, which relates to the technical field of payment security, discloses a payment-scene-oriented interaction intention identification and error correction system comprising an input analysis module, an intention simulation module, a dynamic decision module, a biological verification module, an audit evidence storage module, and a cross-scene knowledge migration module. According to the method, multi-modal data such as voice, texts, images and touch tracks are integrated, structured feature vectors are generated through a cross-modal attention network, the problem of incomplete single-modal coverage is solved, cross-modal data consistency verification is achieved based on a unified semantic tag system, and the reliability of input sources is graded by combining equipment fingerprints and geographic positions, so that the reliability of the input sources is improved. A high-risk transaction protection capability is enhanced, a generative adversarial network is utilized to construct a virtual attack sample library, attacks such as tampering with characters similar in shape and AI faking voiceprints are simulated, unknown threats are actively defended through cosine similarity matching, a user historical behavior statistical model is integrated, and known risks such as high-frequency small-amount transfer are passively intercepted. And a closed-loop incremental learning continuous optimization model is supported.

The invention provides a question and answer system construction method and system based on a large language model, and the method comprises the steps: obtaining multi-modal data, constructing a question and answer knowledge base and a knowledge graph, and carrying out the dynamic updating of the question and answer knowledge base; obtaining a query text, and respectively carrying out vectorization processing on the query text and the multi-modal data to generate a corresponding query semantic vector and a multi-modal vector; an entity in the query text is extracted by using the recognition model, a triple associated with the entity is extracted from the knowledge graph, the query text and the triple are spliced and vectorized, and a query semantic enhancement vector is generated; according to the method, through a dynamic knowledge base incremental updating mechanism, a context-aware hybrid retrieval strategy, a cross-modal semantic enhancement technology and a user feedback-driven continuous optimization method, real-time processing requirements of various modal data such as texts, images and voices can be effectively met, and accurate semantic understanding and answer generation of complex queries are achieved.

The invention discloses a low-code development automatic generation method based on a large language model, which comprises the following steps: collecting natural language description information input by a user, and preprocessing; inputting the standardized demand corpus set into a large language model, and executing semantic understanding and context modeling; matching the low-code component library based on the structured semantic representation to generate component assembly description information; generating and verifying an engineering skeleton according to the assembly description information, and outputting an executable low-code application initial version; running the executable low-code application initial version, and monitoring and analyzing execution difference to generate an increment adjustment instruction; and inputting the increment adjustment instruction into the large language model, performing reconstruction and adaptive optimization, and outputting an executable low-code application final version. According to the method, large language model semantic understanding and adaptive optimization technologies are fused, automatic generation and continuous optimization of low-code applications are realized, and the method has the advantages of intelligence, high precision and engineering reliability.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a product decision optimization method, device, equipment and medium based on a knowledge graph. And generating a user feature portrait in combination with the user related information, taking the user feature portrait and the information in the knowledge graph as an input state, taking the product information set as an action space, training and generating a decision model based on a preset incentive mechanism, outputting a target item by using the decision model, and updating the knowledge graph and the decision model based on user feedback information. The comprehensiveness of an input state is improved through a fusion modeling mode containing user information, product information and environment information, a dynamic updating mechanism is constructed in combination with a knowledge graph and user feedback, a decision model is driven to be continuously optimized, and then the pertinence of a recommendation result and the self-adaptive capacity of a system are improved.

The invention discloses a cable life dynamic evaluation system based on multi-physics field coupling, and particularly relates to the field of industrial automation and control systems, which comprises a multi-physics field sensing module, a coupling analysis engine module, a dynamic life evaluation module, a digital twin interaction module and an environmental interference suppression module, through a distributed optical fiber temperature sensor, a capacitive electric field sensor and a magnetostrictive stress sensor, temperature, electric field, magnetic field and mechanical stress data of a cable are collected in real time, multi-physical field characteristics and a cable defect database are matched in real time by using a cross-scale dynamic association algorithm, a damage state is evaluated, and the cable defect detection accuracy is improved. A time sequence neural network architecture is adopted to predict the remaining life, model self-correction is achieved through digital twin comparison, interference is suppressed in combination with an environment-physical field coupling compensation matrix, sensing and evaluation of the health state of the cable, life prediction and continuous optimization of the model are achieved, and the efficiency of cable life evaluation is improved.

The invention relates to the technical field of science and technology operation management and control, and discloses a science and technology operation intelligent management and control method and system based on big data. According to the method, firstly, heterogeneous data sources in the scientific and technological operation process are collected, and a standardized operation data set is generated through multi-modal fusion processing; performing dynamic feature classification on the key operation indexes, extracting time sequence features and spatial correlation features of the key operation indexes, and constructing a multi-level operation state graph based on feature importance weights; then matching a service rule base with the atlas, identifying abnormal nodes and resource conflict paths, and generating an optimization instruction set containing node repair priorities and conflict resolution strategies; an executable management and control operation sequence is generated; and finally, collecting a feedback data stream, and updating the service rule base and the feature importance weight through an incremental learning mechanism to form a closed-loop optimization link. According to the method, heterogeneous data can be effectively integrated, the operation problem can be accurately identified, the optimization strategy can be quickly generated, and intelligent management and control and continuous optimization of scientific and technological operation can be realized.

The invention provides a shield tunnel dynamic settlement compensation construction method based on an adaptive optimization algorithm, and the method comprises the steps: collecting the ground surface settlement, soil stress and underground water level data in real time through an Internet of Things sensor, achieving the data preprocessing and feature extraction in combination with an edge calculation node, constructing a three-dimensional geologic model, integrating the historical engineering data through transfer learning, and achieving the dynamic settlement compensation of a shield tunnel. The method comprises the following steps: identifying a high-risk area by using a clustering algorithm, designing a hybrid adaptive optimization framework with fusion of random forest and incremental learning, dynamically adjusting shield tunneling speed and soil bin pressure construction parameters, introducing an adaptive step length mechanism to cope with geological complexity change, and identifying a settlement abnormal mode through Fourier transform. Precise compensation is achieved in combination with a layered grouting strategy, the pressure of a soil bin is dynamically adjusted based on a hydraulic system, a closed-loop feedback mechanism is established, the predicted deviation rate is compared with an actual monitoring value, model parameters and the compensation strategy are continuously optimized, the settlement control precision is improved, and the construction risk is reduced.

The invention relates to a self-adaptive multi-algorithm scheduling method and system based on cloud edge collaboration, and the method comprises the steps: constructing a cloud algorithm knowledge base and a scheduling strategy model, which are used for storing a plurality of algorithms, and providing a unified scheduling rule and optimization criterion; designing an edge node real-time sensing and reporting module, dynamically monitoring the computing power state, task characteristics and operation environment of the node, and transmitting related information to the cloud in real time; a cloud intelligent scheduling decision module is constructed, a knowledge base and a scheduling strategy are combined, a received edge state is comprehensively analyzed, and an optimal algorithm selection and execution position decision is generated; an algorithm dynamic scheduling and heterogeneous execution module is deployed on the edge side, and a target algorithm is flexibly loaded and executed on a local cache or heterogeneous computing resources according to an instruction issued by the cloud; and the cloud edge collaborative closed-loop iteration and adaptive optimization module realizes adaptive iteration and continuous optimization of algorithm scheduling, so that high robustness and high efficiency of task execution in a complex and changeable scene are ensured.

The invention relates to the technical field of smart factory decision-making, and discloses a digital twin-driven smart factory AI intelligent decision-making system, which comprises a data acquisition and preprocessing module used for acquiring and preprocessing real-time and historical data of a production line; the twinborn modeling identification module is used for constructing a twinborn body and carrying out parameter identification and uncertainty quantification; the alignment evaluation credible module is used for comparing twin prediction data with real data and generating availability marks and trust scores; the strategy simulation optimization module is used for generating candidate strategies and risk evidences based on the constraints and the key performance indicators; the grayscale online publishing module is used for screening and grayscale publishing a strategy according to the trust score and the performance result; the operation evaluation auditing module is used for collecting operation data and generating an auditing packet; and the drifting detection updating module is used for detecting distribution drifting and recalibrating and updating the twinborn body and the strategy library. According to the invention, the reliability, robustness and continuous optimization of the production decision of the smart factory are realized.

The invention belongs to the technical field of intelligent manufacturing and automatic design optimization, and discloses an automatic template generation method and system based on UG software, and the method comprises the steps: capturing a sketch operation event flow, carrying out the deep binding of geometric parameters and tolerance rules, generating a technology enhancement parameter set, and constructing a dual-channel instruction set; synchronously generating a two-dimensional engineering drawing projection and a three-dimensional expansion drawing preview; detecting conflicts in real time, and calling an exception correction plan library for dynamic correction; generating a zero-conflict BREP boundary model and a correction track log, and establishing a log-plan library mapping relation; generating an enhanced BERP model through a template drawing optimization mechanism; further generating a processing path instruction set, and integrating the processing path instruction set into a process compliance template drawing package; generating a cross-platform manufacturing package; constructing a quality index set, and generating an abnormal event association graph; and calculating a rule parameter adjustment amount, dynamically updating the process rule base, generating a global strategy packet, and reversely injecting sketch parameter constraints to form a continuous optimization cycle.

The invention discloses a tank equipment sealing micro-leakage intelligent early warning and positioning method, which comprises the following steps of: deploying acoustic sensors, vibration sensors, gas concentration sensors, temperature and humidity sensors and the like aiming at different positions of a tank to realize multi-mode original signal acquisition; various signals are standardized and corrected through denoising, normalization and structural difference mapping, and the influence of the environment and the material structure on the signals is compensated; extracting multi-dimensional feature parameters, and carrying out space-time weighted fusion by adopting an attention mechanism to generate feature vectors marked by confidence; according to the method, a self-learning anomaly recognition algorithm is combined, a judgment model is continuously optimized through transfer learning and online sample accumulation, micro-leakage anomaly judgment is achieved, accurate positioning of a tank leakage source point is executed based on a physical propagation model, and the sensitivity and the self-adaptive capacity of micro-leakage detection are improved; and leakage identification and positioning requirements in a complex environment and a dynamic working condition can be effectively met.

The invention relates to an automatic driving risk quantification method based on a conflict risk field. Comprising the steps of 1, constructing a basic risk field; step 2, under a basic risk field framework, constructing a conflict risk field by taking an ADV as a center; step 3, regarding the conflict risk field as a repulsive force acting on the ADV, and using the repulsive force to quantify the dynamic influence of the environmental elements and various TP states on the driving safety of the ADV; the method comprises the following steps: firstly, constructing a basic risk field on the basis of traffic vehicle distribution and motion characteristics, further forming a vehicle forward, lateral, backward and traffic regulation constraint multi-dimensional conflict risk field on the basis, and converting an abstract traffic conflict relationship into a computable repulsive force model; dynamic quantitative characterization of driving risks in a state dimension, a space dimension and a time dimension is realized, and continuous optimization and evolution of an end-to-end automatic driving algorithm are supported by means of a repulsive force model constructed based on a conflict risk field.

The invention relates to a steel structure engineering welding quality defect analysis method based on voiceprint monitoring, and the method comprises the steps: carrying out the multi-channel voiceprint synchronous collection, time-frequency feature fusion, wavelet packet analysis and Mel-frequency cepstral coefficient extraction for a plurality of defect features fused in voiceprint data in a welding process; a hierarchical semantic concept space and a dynamic causal relationship generation model are established in combination with a welding physical knowledge base, a causal knowledge graph is constructed, causal association between semantic concepts is deduced through a gating circulation unit and a graph neural network, anti-fact disturbance and path aggregation analysis is carried out on a causal graph structure, and a result is obtained. And finally, defect category probability output and causal traceability graph visual display are realized. According to the scheme, the accuracy, traceability and result interpretability of welding defect recognition are effectively improved, and data support is provided for intelligent diagnosis and continuous model optimization in the welding process.

The invention relates to the technical field of distributed energy aggregation cooperative regulation and control, and discloses a virtual power plant hierarchical cooperative intelligent scheduling method and system based on multi-objective optimization, and the method comprises the steps: obtaining the operation parameters and prediction data of each subsystem of a virtual power plant, generating an energy storage system hour-level charge state target trajectory, and carrying out the prediction of the target trajectory; establishing an energy storage system charge state constraint budget pool; generating a budget allocation table; monitoring a short-term budget allowance state, when the short-term budget allowance state is lower than a safety threshold value, borrowing a part of budget from a long-term budget for redistribution, and dynamically adjusting a power amplitude limit value of deviation correction according to charge state constraint tensity; and when the accumulated deviation exceeds the autonomous correction capability or the constraint tensity reaches a critical value, generating an interlayer deviation report and triggering global re-optimization, and adjusting the budget distribution proportion of the next period according to the actual budget consumption condition at the end of the hour-level period. According to the invention, the continuous optimization and self-learning capability of the hierarchical scheduling strategy are realized.

The invention relates to the technical field of intelligent education, and discloses a teaching programming system based on large language model assistance and a teaching recommendation method. According to the system, a multi-dimensional ability graph and a knowledge point topology network are constructed, and a large language model is utilized to carry out multiple rounds of intention analysis and semantic reasoning to generate a personalized learning path sequence. The system continuously tracks a learning track, realizes continuous optimization of a teaching strategy by dynamically calibrating strategy parameters, and actively configures special reinforcement resources based on track prediction. According to the system, a breakthrough from static recommendation to dynamic adaptation is realized, the accuracy of learning path planning and the timeliness of teaching intervention are improved through deep semantic understanding and a closed-loop optimization mechanism, so that the programming teaching system can really understand learning requirements and adapt to changes of learning states in real time, and the teaching efficiency is improved. And the knowledge mastering firmness and the learning efficiency are improved.

The invention discloses a building construction intelligent safety monitoring method based on the Internet of Things, and the method comprises the following steps: obtaining construction environment data, structure state data and operation behavior image data, and carrying out the preprocessing; environment state modeling, local structure strain and behavior recognition and operation scene segmentation are carried out through the edge intelligent processing unit; feature fusion is carried out, and a fusion situation vector is constructed; performing high-frequency anomaly identification and emergency preliminary screening, and generating an edge preliminary early warning result and a high-risk data fragment; constructing a safety evolution trajectory crossing a time window, and fusing historical data to generate a risk semantic map; cloud semantic reasoning operation is executed, and a final risk level judgment result and a corresponding trigger source identifier are generated; and automatically triggering a safety response instruction according to a risk level judgment result. According to the method, the Internet of Things and intelligent semantic analysis are fused, multi-source safety monitoring and self-adaptive response are realized, and the method has the advantages of high real-time performance, global perception and continuous optimization.

The invention provides an agent illusion correction method and device. The method comprises the following steps: firstly, analyzing and globally planning a problem input by a user, generating an execution plan comprising at least one subtask, performing reasoning circulation and retrieval enhancement based on the execution plan, generating an enhanced context evidence set, then performing screening and context optimization processing on the enhanced context evidence set, generating an optimized context, and finally, performing context optimization processing on the optimized context. And then, generating an initial answer based on the optimized context and the question, carrying out factuality and logic consistency closed-loop verification on the initial answer to detect whether illusion exists or not, if the illusion is detected, correcting the initial answer, generating a corrected answer, and recording the illusion event and the correction process to the reflection knowledge base. According to the method, self-correction and continuous optimization are realized through a closed-loop mechanism of problem decomposition, global planning, reasoning circulation, retrieval enhancement and optimization and correction reflection, so that the reliability and interpretability of agent answering are improved.

The invention discloses a network attack active defense strategy optimization method based on deep reinforcement learning, and the method comprises the following steps: collecting multi-source data of a network environment, and carrying out the feature clipping and white list feature reservation; performing normalization and coding processing to generate a security situation vector; constructing a multi-index reward function, and generating an instant reward value and an event-level reward value; executing a double-closed-loop mechanism through an improved PPO model, and respectively outputting an instant strategy instruction and a long-term strategy parameter; performing multi-source evidence commissioning on the instant strategy instruction and the security situation vector, and judging a key evidence loss condition to obtain an execution token; inputting a risk budget pool to carry out resource quota checking, and executing anti-jitter and cooling control; and optimizing parameters of the multi-index reward function through a causal account book. According to the method, rapid response and continuous optimization of various attack behaviors can be realized, the defense effect and the resource utilization rate are considered, the false report and missing report rate is reduced, and the self-adaptability and stability of a network defense system are improved.

The invention discloses an active defense method and system based on a large model, and relates to the technical field of security protection, and the method comprises the steps: intercepting a malicious request of an external attacker, cleaning sensitive information and adversarial samples in the malicious request, and outputting standardized data; injecting the standardized data as training data into a training confrontation sample to optimize a protection model, and ensuring the leakage traceability of the protection model by embedding a digital watermark; and trapping an attacker by deploying a honey spot interface and triggering a countering strategy, generating a dynamic defense rule by using the protection model, and updating the training confrontation sample in real time for continuous optimization of the protection model. Active attack sensing and advanced attack blocking are achieved through malicious request interception cleaning and honey spot trapping countering, dynamic defense rule generation and protection model continuous optimization are combined to adapt to attack iteration, a digital watermark tracing mechanism is matched, an'interception-protection-optimization 'closed-loop full link is constructed, and the security defense capability of the protection model is improved.

The invention discloses a middle and primary school test question intelligent generation method based on an education big model. The method comprises the steps of generating a structured education data set; constructing an education large model specially used for intelligent generation of middle and primary school test questions; inputting target knowledge points, question types, difficulty and grade parameters, and generating a candidate test question set; setting a test question fitness function, comprehensively evaluating the knowledge coverage rate, difficulty distribution and language expression indexes of the candidate test questions, screening and optimizing the candidate test question set, and generating an optimized candidate test question set; detecting semantic accuracy, logic preciseness and teaching conformity of the test questions by using an automatic quality evaluation mechanism, and outputting test question evaluation results; and continuous optimization and self-adaptive updating of test question generation are realized. According to the method, the optimal multi-question type test question set can be automatically output on the premise of ensuring comprehensive coverage of knowledge points, reasonable difficulty distribution and excellent language quality, and the quality, balance and diversity of automatically generated test questions are greatly improved.

The invention relates to the field of multi-source data management, in particular to an intelligent security collaborative management system based on multi-source perception and a large language model. Comprising a multi-source data acquisition module which is accessed to various intelligent monitoring devices and is output and converted into a unified standard tuple format through a mapping function; the information fusion module is used for screening a candidate alarm set according to the space-time tolerance, and generating composite alarm information by adopting confidence ranking and semantic embedding weighted averaging; the RAG knowledge base module is used for generating a composite event description through a large language model and vectorizing the composite event description as a retrieval index; the intelligent retrieval module is used for acquiring related historical events by adopting a mixed retrieval strategy and constructing structured cue words; the LLM decision module is used for outputting root cause analysis and classification disposal suggestions based on the composite alarm and the priori knowledge; the double-path response module is used for distributing decision suggestions to management personnel and an agent system to realize collaborative execution; and the feedback optimization module is used for collecting disposal data and adjusting the weight of the knowledge base and the decision template to realize continuous optimization.

The invention relates to the technical field of automatic control, and discloses a thermal power generating unit cooperative control method based on digital twinning, which comprises the following steps: firstly, off-line executing benchmark construction, and obtaining a benchmark digital twinning model and a benchmark cooperative control parameter set which represent the ideal state of a unit; a twinborn deviation vector is obtained by comparing real-time output of a physical unit with theoretical output of a model, a deviation characteristic vector of quantitative characteristic deviation is obtained through processing of a deconstruction algorithm, then historical deviation characteristics are analyzed by adopting a time sequence prediction model, a prediction output vector representing a future evolution trend is calculated, and then a prediction result is obtained. And calculating a prospective control parameter correction vector based on the prediction vector, and combining the prospective control parameter correction vector with a reference parameter set to generate a dynamic cooperative control parameter set. According to the method, the slow time-varying drift of the dynamic characteristics of the unit can be actively compensated, the continuous optimization of a control strategy is realized, and the stability and the operation economy of a control system are improved.

The invention relates to a database table field description intelligent generation and dynamic maintenance method and device based on RAG and a large model. The method comprises the following steps: constructing a domain-based dynamic knowledge base system through multi-source corpus collection, domain adaptation vectorization model processing and a dynamic knowledge base construction and updating process; receiving a description generation request of a table or a field, recalling related knowledge from the knowledge base through an RAG technology, assembling cue words, and then calling a large model for reasoning to generate a description result; the generated description result is confirmed through manual auditing, the knowledge base is updated according to confirmation feedback, and continuous optimization of the knowledge base is achieved. According to the method provided by the invention, the problems of low accuracy, high maintenance cost, poor interpretability and the like in a traditional method are effectively solved by constructing a field-enhanced RAG architecture and combining a three-layer knowledge system and a self-adaptive prompt framework which can be dynamically adjusted according to a business scene, and a new technical implementation path is provided for table and field description in intelligent data governance.

The invention provides a plane layout optimization method and device based on CAD drawing automatic identification, relates to the field of plane layout optimization, and solves the technical problems that only non-overlapping constraints among equipment are considered, actual space obstacles cannot be automatically avoided, and a generated layout scheme cannot be implemented. The method comprises the following steps: extracting barrier data in a layer; and through a parameter configuration mechanism, generating an equipment parameter information structural body containing the mapping relationship between the equipment and the process. And establishing a continuous optimization model containing an IPOPT solver, and obtaining an initial layout solution containing the relative orientation of each device. And based on the relative orientation of each device in the initial layout solution, introducing an obstacle rejection constraint and a device non-overlapping constraint to construct a discrete optimization model, obtaining the center position coordinates of each device through a Gurobi solver, and integrating a dynamic adjustment mechanism and an interlayer relationship optimization mechanism. And generating a new layout map for visual output. The method and the device are used in a plane layout optimization process.

The invention relates to an APP intelligent marketing service method based on intelligent routing and multi-agent cooperation. The method comprises the following steps: receiving input information, wherein the input information comprises user active inquiry information or trigger event information generated based on user behavior monitoring; semantic analysis and intention recognition are carried out on the input information, a task planning directed acyclic graph is generated based on a recognition result, and the task planning directed acyclic graph comprises a plurality of subtask nodes and dependency relationships among the nodes; based on the task planning directed acyclic graph, the state of each functional agent and the historical performance index, executing dynamic routing so as to dispatch the plurality of sub-tasks to the corresponding functional agents; and in the execution process of the plurality of subtasks, performing dependency scheduling and state consistency management on an external tool call chain which is initiated by the functional agent and comprises a plurality of steps, and updating the shared memory associated with the user based on an execution result. By adopting the method, self-adaptive planning, robust execution and continuous optimization of marketing tasks can be realized.

The invention relates to the technical field of intelligent fertilization, and discloses an agricultural precise fertilization method which comprises the following steps: step 1, synchronously collecting multi-modal real-time data of single wheat plants, and automatically identifying the current growth stage of the wheat plants; 2, searching historical fertility data of the position of the wheat; 3, fusing the real-time data, the historical fertility data and the growth stage label to generate an enhanced fusion feature vector; step 4, selecting a corresponding causal diagram model according to the current growth stage, and estimating an average processing effect of different fertilization schemes based on the enhanced fusion feature vector; and 5, inputting the enhanced fusion feature vector, the average processing effect estimated value and the growth stage label into the prediction model and the adjustment model. According to the system and the method, the fertilization accuracy and the crop response effect are improved, continuous optimization of the system is realized through a closed-loop intelligent decision framework, and a landing and evolvable efficient fertilization solution is provided for intelligent agriculture.

The invention relates to the technical field of exchange decision making, and discloses a multi-objective optimized capacitor bank intelligent exchange decision making system, which comprises a data acquisition and twin synchronization module, a strategy generation module, a risk preview module, an optimization decision making module and a calibration updating module, and is characterized in that real-time state sensing is realized through the data acquisition and twin synchronization module; the strategy generation module formulates a low-oscillation risk exchange strategy, the risk rehearsal module verifies the safety of the strategy, the optimization decision module screens an optimal scheme, and the calibration update module ensures the accuracy of the digital twin and the continuous optimization of the system. By dynamically adjusting the simulation step size and a real-time oscillation detection mechanism, high-frequency numerical oscillation introduced by the power electronic equipment is effectively suppressed, the accuracy of transient process simulation is remarkably improved by combining the regularized admittance matrix and multi-time scale coupling simulation, the recognition capability of a safety verification system on operation risks is ensured, and the safety verification system has a wide application prospect. And mistaken release of a high-risk strategy is avoided.