250 results about "Self-optimization" patented technology

The invention provides a training data synthesis method and device based on error extrapolation and inference chain analysis, a medium and a program product. The method comprises the steps of obtaining an initial sample set; performing multiple sampling reasoning on the problem of each task sample by using a small language model to generate a plurality of reasoning chains; calculating an overall error score of each reasoning chain based on a preset error evaluation rule, and determining a to-be-corrected reasoning chain; the inference chain to be corrected and the corresponding question are input into the large language model together, and a corrected answer is generated; forming a new task sample by the question and the corrected answer, and finely adjusting partial parameters of the small language model; repeatedly executing the process until the performance index change rate of the model on the task evaluation set is lower than a preset threshold value, and outputting a final task sample; and forming a training sample set by a plurality of final task samples, and performing all-parameter fine tuning on the small language model. According to the method, the training data self-optimization path is constructed by taking the model error as guidance, so that the semantic consistency and the data validity are improved.

The invention discloses a talent matching method and system based on artificial intelligence, and aims to improve human resource configuration efficiency and decision intelligence. The method comprises the following steps: collecting talent and demand data in multiple channels, especially unstructured communication data including interview records and work communication records; processing data through technologies such as multi-mode resume analysis, extracting features and fusing the features; an enterprise talent knowledge base which is used for continuous learning and dynamic maintenance based on a system operation result and multi-source feedback and integrates structured and unstructured data is constructed; based on the natural language query of the user, providing intelligent question answering and decision support by using a retrieval enhancement generation model connected with the specific enterprise talent knowledge base; an advanced deep learning algorithm is adopted, a knowledge base is combined to carry out man-post matching and generate recommendation of context perception, and self-optimization of a matching strategy is realized through mechanisms such as reinforcement learning and the like. According to the invention, accurate and dynamic talent matching and intelligent decision making can be realized.

The invention relates to the technical field of energy storage scheduling, and discloses a distributed energy storage scheduling prediction method and system driven by intelligent environment monitoring data. The method comprises the following steps: acquiring environmental parameters and processing to obtain an environmental data set; decomposing the environment data in a multi-level manner to obtain feature vectors and uncertainty indexes; inputting a hybrid deep network to obtain demand prediction distribution; constructing a robust optimization model to obtain a scheduling instruction sequence; performing decomposition execution through a layered architecture to obtain a running track; and carrying out deep reinforcement learning iterative optimization to obtain an energy storage scheduling strategy. According to the energy storage scheduling prediction method, the communication delay influence is overcome, the self-optimization capability is achieved, and efficient cooperative operation of the distributed energy storage system based on environmental data driving is achieved.

The invention relates to the technical field of artificial intelligence, and discloses a professional knowledge question and answer intelligent customer service system based on a self-optimization mechanism. The system comprises a user intention analysis module, a knowledge processing module, a self-optimization learning module and an interactive presentation module. A semantic understanding unit of the user intention analysis module generates a user intention signal; after the knowledge processing module receives the signal, a knowledge retrieval unit outputs related knowledge fragments and confidence, an answer generation unit forms candidate answers, and a quality evaluation unit determines an optimal answer according to the confidence; in the self-optimization learning module, a feedback analysis unit adjusts answer generation parameters according to user interaction data, a strategy adjustment unit optimizes a retrieval strategy in combination with an optimal answer and a historical dialogue, and a knowledge updating unit updates a knowledge base depending on an external knowledge source; and a multi-round dialogue management unit of the interactive presentation module adjusts a dialogue process, and a visual presentation unit outputs a natural language text and collects user feedback to a feedback analysis unit.

The invention relates to the technical field of new energy consumption, and discloses a new energy consumption calculation method considering a hydrogen production process, and the method comprises the steps: building a hydrogen production coupling model which comprises a new energy power generation power prediction sub-model and a hydrogen production efficiency dynamic sub-model, and obtaining a new energy power generation prediction value and a hydrogen production real-time efficiency coefficient; designing a multi-energy collaborative optimization framework, and generating a scheduling scheme based on a dynamic game theory algorithm; executing a dynamic correction strategy, and adjusting the scheme according to real-time conditions; and establishing a data feedback mechanism, and collecting data to update model parameters. According to the method, the accuracy of new energy consumption measurement and calculation is improved, multi-energy collaborative scheduling is optimized, the dynamic adaptability of the system is enhanced, model self-optimization is realized, the cost can be effectively reduced, wind curtailment and light curtailment are reduced, and remarkable economic benefits and environmental benefits are achieved.

The invention relates to the technical field of material processing control, and discloses a method for efficiently cutting a vacuum brazing soldering lug. The method comprises the following steps: firstly, receiving material parameters and target geometrical shape data of a to-be-cut soldering lug, and generating and optimizing an initial cutting track based on a preset algorithm; in combination with real-time temperature field monitoring data, thermal stress distribution is calculated through a finite element analysis method, and the cutting speed and laser power parameters are dynamically adjusted; and then the cutting precision and the equipment energy consumption are synchronously optimized through a multi-objective optimization algorithm, and final cutting parameters are output to a vacuum brazing control system. In addition, signals are collected in real time during cutting to recognize abnormity, and algorithm weights are calibrated by comparing geometric parameters after cutting. The cutting precision and efficiency can be effectively improved, equipment energy consumption is reduced, cutting abnormity is treated in time, self-optimization of the cutting process is achieved, and the method is suitable for the production field with the high requirement for vacuum brazing piece cutting.

The invention provides an intelligent question and answer method based on a preset multi-dimensional knowledge base and a large language model, and the method comprises the steps: receiving a natural language query, converting the natural language query into a query semantic vector, and recognizing a corresponding intention type and a first confidence coefficient; if the first confidence coefficient is higher than an intention threshold value and the highest similarity between the query semantic vector and a standard question semantic vector in a preset multi-dimensional knowledge base is higher than a matching threshold value, returning a standard answer; otherwise, retrieving a generated context based on the query semantic vector, fusing the user query and the generated context, inputting the fused user query and generated context into a large language model to generate a preliminary answer, checking fact consistency of the preliminary answer and a standard answer in the generated context to generate a credibility score, and obtaining a target answer when the credibility score exceeds a credibility threshold; and dynamically optimizing the system based on feedback data of the user to the target answer. The intelligent customer service system with controllability, flexibility and self-optimization capability is realized, and the semantic understanding depth and response accuracy of the system are improved.

The invention relates to the technical field of intelligent traffic monitoring, in particular to a toll station pedestrian and non-motor vehicle intrusion intelligent early warning system and method, a terminal layer comprises a sensing terminal composed of a camera, a hard disk video recorder and the like, and an early warning terminal composed of a directional sound post and the like; an edge computing unit is deployed on the edge layer, targets are detected in real time, tracks are tracked and classified, and cloud rechecking is triggered by low-confidence targets; the cloud layer utilizes a visual language large model to recheck a target, pre-mark data, generate an electronic fence and store data; the system adopts a dynamic grading early warning module, three-level early warning is triggered according to a target track, a position and staying time, and automatic degradation is carried out along with the presence of a worker; in addition, through a closed-loop optimization mechanism, a positive / false alarm feedback iteration visual small model is collected. According to the invention, cloud side-end cooperation is realized, multiple models and algorithms are fused, high-precision detection, real-time response, dynamic self-adaption and self-optimization capabilities are realized, the intrusion risk can be effectively prevented, and the safety management level of the toll station is improved.

An AI and machine learning-based system for automating employee management and work information processing within an organizational structure, the system comprising: a central processing module configured to aggregate and pre-process data from multiple sources, including attendance records, performance logs, task management systems, and communication channels, with the central processing module normalizing and filtering the data to ensure consistency and accuracy in real-time analysis; a machine learning-based analysis unit operatively connected to the central processing module, the analysis unit comprising a natural language processing (NLP) sub-module, a sentiment analysis sub-module, and a pattern recognition sub-module, and configured to extract, analyze, and interpret both structured and unstructured data for insights into employee behavior and performance assessment, and further configured to adapt and refine models based on continuous data inputs from the central processing module; a predictive task scheduling component comprising a reinforcement learning-based model that leverages employee skill profiles, historical task completion rates, and workload patterns to dynamically distribute tasks, with the predictive task scheduling component further configured to self-optimise based on real-time feedback regarding task completion efficiency, priority changes, and schedule adjustments within the organization; a performance tracking unit configured to receive inputs from the central processing module and the machine learning-based analytics unit, wherein the performance tracking unit continuously monitors employee performance, challenges, and areas for improvement and stores these insights in individualized, encrypted employee profiles that can be accessed in real time, thus supporting data-driven performance reviews and improvement plans; an input / output interface for user interaction, the interface providing managers with interactive access to review employee metrics, task assignments, and performance feedback, and enabling employees to securely view individual performance metrics, feedback, and task details, the interface further being configured with user-level access controls based on historiographical organizational roles; a secure data storage component configured to securely store employee data, task logs, and performance metrics, where the data storage component supports both local and cloud-based storage solutions, uses encryption protocols for secure data retention, and provides access control mechanisms for authorized retrieval of stored data; a communication interface module operatively connected to the central processing module and configured with multi-protocol communication capabilities, including Wi-Fi, Ethernet, and Bluetooth, wherein the communication interface module facilitates real-time data synchronization between remote and local devices and is integrated with an AI-based anomaly detection system that flags irregularities or potential security threats in data transmissions; and an adaptive learning module operatively connected to the machine learning-based analytics unit and configured to continuously retrain machine learning models based on real-time data inputs, where the adaptive learning module uses reinforcement learning and unsupervised learning techniques to refine task recommendations, adjust performance metrics, and optimize task assignment rules based on the evolving needs of the organization.

The invention relates to the technical field of cloud computing, and discloses an elastic line scheduling method based on cloud computing, comprising the following steps: S1, establishing a virtual network architecture in a cloud computing environment; comprising the steps of virtualizing network resources, defining network topology and creating network virtual machine instances (VMs) and service instances; s2, real-time flow monitoring and demand prediction; comprising a flow monitoring system, a flow prediction model and prediction result feedback. S3, intelligent elastic resource scheduling; comprising a bandwidth allocation strategy, multi-path selection, a path switching mechanism and dynamic routing adjustment. S4, ensuring fairness and priority management; comprising priority management and fair resource allocation; s5, carrying out system self-optimization and learning; comprising continuous monitoring and feedback, a self-learning mechanism and performance evaluation and adjustment. According to the invention, through the elastic scheduling method, only a larger bandwidth needs to be used in the peak period actually, so that the waste of bandwidth resources and the cost expenditure are greatly reduced.

The invention discloses a thermal power generating unit control parameter adjusting method and device based on a large model and digital twinning. The method comprises the steps that variable selection and data preprocessing oriented to multi-task learning are carried out; based on results of variable selection and data preprocessing, establishing a data-driven thermal power generating unit control parameter optimization adjustment large model; and carrying out real-time feedback and self-adaptive adjustment of a fusion digital twinborn technology based on a data-driven thermal power generating unit control parameter optimization adjustment large model. The device comprises a data preprocessing unit, a model establishing unit and a feedback adjusting unit which are connected in sequence. According to the invention, the continuous sensing, analysis and evaluation of the operation state of the unit and the intelligent sensing and self-optimization of the control parameters are realized, and a technical basis is provided for a few-person-on-duty power plant or an unattended-on-duty power plant in the future.

The invention provides an enterprise project and personnel management method and system based on a knowledge base and an AI model and a medium, and belongs to the technical field of enterprise management.The method comprises the steps that implementation experience of historical projects of an enterprise is collected and analyzed based on a knowledge graph, and a project knowledge base is constructed; enterprise employees are evaluated, classification is carried out in combination with evaluation dimensions, and labels are set; and collecting real-time operation data of the project, and recommending and adjusting personnel allocation for the project in combination with employee labels and project attributes. According to the invention, full-life-cycle digital management of the project is realized, and a traditional management mode is broken; the project management efficiency and the resource utilization rate are improved through AI-driven dynamic decision and resource allocation; a knowledge base capable of being self-optimized is constructed, and technology and talent support is provided for enterprise development.

The invention relates to the technical field of communication, and discloses a three-dimensional radio environment map construction method and system, a terminal and a storage medium, and the core is to construct a'base station unmanned aerial vehicle 'bidirectional interaction closed-loop optimization framework to realize efficient construction of a high-precision map. Self-adaptively fusing sparse radio measurement data and environmental building structure features; introducing a confidence evaluation mechanism based on adversarial learning and position weighting, and generating a pixel-by-pixel confidence map; an intelligent planning method based on a trajectory diffusion model is designed, local perception constraint and long-term information gain are cooperated with a classifier-free guide mechanism, and an optimal trajectory considering both safety and sampling efficiency is generated; and a continuously self-optimized closed-loop system is formed through newly acquired data of the unmanned aerial vehicle and periodical updating of the model. According to the invention, a high-reliability technical basis is provided for applications such as urban air communication and spectrum resource management.

The invention discloses a garment supply chain risk analysis method and system based on artificial intelligence, belongs to the technical field of garment supply chain analysis, and effectively enhances crisis pre-judgment and response capability of a supply chain through a dual mechanism of simulation deduction and dynamic optimization. A built-in game learning framework of the system can autonomously construct a multi-factor coupled complex interruption scene, for example, an extreme condition that a port is closed in a typhoon season, cross-border tax policy mutation and alternative raw material transportation route congestion occur at the same time is simulated. The deduction not only reveals the problem of excessive dependence of a single node, which is difficult to discover by traditional auditing, but also can verify the practical feasibility of a standby scheme, and helps an enterprise to establish a multi-layer defense system. The continuous self-optimization characteristic of the system enables the system to be prominent in response to novel challenges, and when the international logistics network is suddenly adjusted, a supplier recombination scheme considering both cost and time efficiency can be quickly generated, and the potential chain breakage risk is resolved in the germination stage.

Disclosed in the present invention are a lifting synchronous control system and control method for a high-rise steel structure corridor. The control system comprises a data acquisition module comprising a sensor network, an execution module, and a data-processing and decision-making module comprising a central control unit. The execution module comprises a lifting drive provided with a receiver. The sensor network comprises a coordinate sensor, an inclination angle sensor, a temperature and humidity sensor, and a wind speed sensor. The sensor network continuously acquires coordinate information, inclination angle information, and environment information and sends same to the central control unit. By means of a machine learning or deep learning algorithm, the central control unit performs self-optimization and regulates the lifting drive in real time on the basis of the continuously collected environment data and structural data. In the present invention, by combining dynamic regulation of an intelligent algorithm with the application of an Internet of Things technology, all components in the control system are modularly designed, thereby obviously mitigating the defects of conventional synchronous control systems, and significantly improving the stability of the control system and the capability of resisting the influence of external factors.

The invention discloses a defect detection system and method for a glass bottle, and belongs to the field of industrial visual defect detection and analysis. The system comprises a GBDM module, the GBDM module is a defect detection module, and the GBDM module comprises a front attention enhancement structure, a rear attention enhancement structure and a defect detection structure, a multi-branch feature extraction structure is used for generating four feature branch diagrams; and the feature fusion structure is used for fusing the four feature branch diagrams. The GBDM module is embedded in the backbone network of the YOLOv8 network model, and multi-branch structures such as wavelet transform, Gabor direction filtering and specular reflection suppression are utilized, so that the feature extraction capability of multiple types of complex defects such as cracks, smudginess and damage on the surface of the glass bottle is enhanced, the generalization capability of the model in a small sample scene is remarkably improved, and the accuracy of the model is improved. The real-time requirement of an industrial scene is met, continuous self-optimization is achieved, and the method has high robustness, high accuracy and high application value.

The invention provides an intelligent operation and maintenance method and system for a data medium table based on a multi-modal large model, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-modal data to construct a knowledge graph, expanding the knowledge graph based on node features and connection weights, matching similar scenes to recognize historical decision information, and generating and evaluating a decision scheme set. An optimal scheme is selected, a new decision scheme is generated through reinforcement learning optimization, and finally operation and maintenance operation is executed and a result is recorded. According to the method, intelligent decision-making and self-optimization of operation and maintenance of the data medium station are realized, and the operation and maintenance efficiency and accuracy are improved.

The invention relates to a thermal power plant fault early warning and diagnosis method based on a nebula system, and the method comprises the steps: collecting the multi-dimensional operation time sequence data of a thermal power plant, carrying out the feature extraction and lexical element processing of the multi-dimensional operation time sequence data through an encoder, and obtaining a unified equipment state lexical element sequence; based on the equipment state lexical element sequence, constructing a dynamic star map representing the operation state of the whole power plant; inputting the dynamic star map into a space-time fusion backbone network; the space-time fusion backbone network performs iterative processing on the dynamic star map and generates a health degree attenuation trajectory; when the slope of the health degree attenuation trajectory exceeds a preset threshold value, dynamic early warning and system diagnosis are triggered, and a natural language diagnosis report containing a causal reasoning chain is generated; new multi-dimensional operation time sequence data are collected in real time, and an incremental learning algorithm is used to update the encoder and the space-time fusion backbone network online; compared with the prior art, the system can continuously adapt to working condition changes and has high self-optimization capacity.

The invention discloses a three-dimensional temperature field online reconstruction system based on receiving and transmitting integrated sound wave sensing, and relates to the field of boiler monitoring. Comprising an information acquisition module, an emission control module, a reconstruction sensing module, a path optimization module, a sound wave receiving module, an amplitude limiting filtering module, a decoding separation module, a feature extraction module, a physical simulation module, a multi-source fusion module, a three-dimensional reconstruction module, a state monitoring module and an online calibration module. According to the method, the transmitted signal can still keep high identifiability in a high-noise and strong-interference background, the credibility of subsequent inversion and data assimilation is improved, a sensing network can be self-optimized, the observation precision and the space coverage capability are improved, a wide dynamic range and high linearity are realized, a simulation result is closer to a real working condition, and the method is suitable for large-scale popularization and application. And the reliability of temperature field inversion is obviously improved.

The invention provides a dynamic updating method and system of a knowledge graph based on reasoning enhancement. The method belongs to the cross technical field of artificial intelligence, knowledge engineering and dynamic system modeling. The method comprises the following steps: performing multi-dimensional feature extraction on original knowledge data to generate a knowledge feature vector set; constructing an initial knowledge graph structure based on the knowledge feature vector set, and defining an initial association rule between knowledge nodes to form a basic knowledge graph model; building a multi-order causal inference engine according to the basic knowledge graph model, performing deep mining on potential causal relationships among knowledge nodes, and generating a knowledge causal relationship network; and performing confidence evaluation on each causal chain in the knowledge causal relationship network to obtain the knowledge causal relationship network subjected to quality verification. Based on a multi-order causal reasoning engine, the system can mine and verify potential causal relationships among knowledge nodes in real time, and continuous updating and self-optimization of the knowledge graph are ensured.

The invention is applicable to the technical field of distributed databases, and provides a distributed database anomaly detection method based on a multivariable log, which comprises the following steps that: each node generates unstructured log data, and divides the unstructured log data into a training log and a test log; an LFA algorithm is adopted to analyze the log data; the log events are divided into log event classifications and log event groups; introducing an improved pre-training language model RoBERTa, and performing deep semantic feature extraction on the log event of each distributed database; a multi-head self-attention mechanism in a Transform architecture is adopted to carry out deep analysis; noise in the features is removed by using an encoder, and the consistency of feature representation is improved; and inputting the standardized feature vector into a random forest clustering classifier for anomaly detection. According to the method, the operation and maintenance cost is reduced, the user experience is improved, decision making is supported, the method adapts to diversified application scenes, and the continuous learning and self-optimization capabilities are achieved.

The invention discloses a vertical furnace welding parameter monitoring and adjusting method and device and computer equipment. The method comprises the steps that relevant data detected by a sensor and technological parameters of welding are obtained to obtain initial data; inputting the initial data into an LSTM prediction network for parameter prediction and determination of a confidence interval to obtain a prediction result; when the number of errors predicted by the LSTM prediction network does not exceed a set threshold value, processing the initial data and a prediction result by adopting a DDPG algorithm to obtain a decision result; and adjusting the parameters of the welding equipment according to the decision result. By implementing the method provided by the invention, accurate temperature and oxygen content prediction can be realized, energy consumption is optimized, the stability and energy efficiency of the system are improved, and meanwhile, the method has online learning and self-optimization capabilities.

The invention discloses an offshore regional communication system, and relates to the technical field of offshore aquaculture platform communication, the offshore regional communication system comprises a multi-level communication platform, a distributed adaptive networking engine and a cross-domain cross-medium communication gateway, the multi-level communication platform is integrated with a satellite communication module, a water surface communication module and an underwater communication module, a local edge calculation function is realized; the distributed self-adaptive networking engine is in communication connection with the multi-level communication platform and is used for carrying out neighbor discovery, global topology convergence, dynamic routing calculation and hub automatic switching on the multi-level communication platform; and the cross-domain cross-medium communication gateway is in communication connection with the multi-level communication platform and the distributed self-adaptive networking engine, and is used for performing signal format conversion and intelligent routing among the satellite communication domain, the water surface communication domain and the underwater communication domain for the multi-level communication platform. The communication system provided by the invention can realize cross-domain cooperation of satellite, water surface and underwater communication, and has self-sensing, self-repairing and self-optimizing capabilities.

The invention provides an AI auxiliary code generation method and system applied to a low-code platform, and relates to the technical field of low-code platforms.The AI auxiliary code generation method comprises the steps that firstly, component demand scenarized information is obtained and analyzed to obtain a scenario feature set, and a dynamic evolution rule set is generated in combination with platform real-time metadata; calling an AI code generation module, inputting the scene feature set and the rule set, and generating an initial component code; then deploying the initial code to a test environment, collecting and analyzing operation data, generating an iterative optimization instruction adjustment code and a rule set, and obtaining a target component code; and finally, embedding the target code into a platform specified function module to complete code generation. The method can adapt to diversified scene requirements, has a self-optimization capability in combination with the real-time state of the platform, and can improve the development efficiency and code quality of the low-code platform.

The invention relates to the technical field of building safety, and discloses a safety transformation monitoring system for building construction and a method thereof. The system comprises a data acquisition and standardization module, a dynamic security entity relationship map construction module, an organization capability and cognitive load modeling module, a hybrid risk identification and conduction calculation engine, a self-adaptive security transformation strategy generation engine and a closed-loop self-calibration module. According to the method, a dynamic security entity relation graph is constructed and updated in real time, the cognitive load of management personnel is quantified, and the cognitive load is used as a dynamic adjustment factor of the conduction probability of risks on the graph; when a safety transformation strategy is generated, comprehensively evaluating a risk reduction effect, resource cost and additional cognitive load cost to output an optimal adaptive strategy; and finally, performing closed-loop self-calibration on the system model by utilizing execution feedback. According to the method, dynamic prospective risk prediction, self-adaptive strategy generation and continuous self-optimization are realized.

The invention relates to the technical field of fault detection, and provides a fault identification method and a parallel processing system, through organic combination of four steps, a complete system from data acquisition, feature extraction, fault diagnosis to fault trajectory prediction is constructed, comprehensive and accurate diagnosis and detection of equipment faults are realized, and the fault detection efficiency is improved. According to the system, fault information in voiceprint signals can be fully mined, the influence of various factors on fault diagnosis is comprehensively considered, the accuracy and reliability of fault diagnosis are greatly improved, and a powerful guarantee is provided for safe operation of equipment; all the steps are mutually associated and mutually influenced, self-optimization can be continuously carried out according to the real-time operation state of the equipment and newly obtained voiceprint data, and the recognition performance of fault detection is improved.

The invention discloses a power equipment inspection method and system based on fault prediction, and relates to the technical field of power system monitoring and maintaining.The method comprises the steps that an intelligent sensor marked with current power equipment is arranged on the power equipment, and a corresponding sensor recognition device is set up on an inspection unmanned aerial vehicle; a primary inspection route is formulated by considering expert opinions, and key parameters are recognized; constructing a multi-dimensional-graph neural model to perform fault deviation prediction on the polled data, identifying a propagation path, and performing self-optimization; and formulating a secondary inspection route according to an optimized result, and performing secondary inspection again. According to the method, intelligent equipment monitoring, data analysis and dynamic adjustment strategies are combined, the fault prediction capacity of the power equipment is remarkably improved by implementing the efficient inspection method, the fault occurrence probability can be reduced, the resource configuration and use efficiency can be optimized, and finally safe and stable power supply and management are achieved.

The invention discloses a hydropower station generating capacity prediction method and system based on knowledge graph guidance, and relates to the technical field of hydropower station generating capacity prediction. Comprising the following steps: S100, in a power generation capacity prediction process, establishing a threshold trigger audit baseline, recording an operation environment state, a knowledge graph edge weight change parameter and a prediction error symbol during prediction deviation triggering each time, generating a trigger fingerprint containing a time identifier, and storing the trigger fingerprint; according to the method, through whole-process auditing, triggering fingerprint tracing, gain analysis and risk label identification, weight dynamic limitation, double-track comparison and closed-loop correction, accurate control and self-optimization of abnormal dependence of the model are realized, the accuracy and the stability of power generation capacity prediction are improved, the correction and protection capability on abnormity is enhanced, and the power generation capacity is improved. And the risk of error accumulation and dependence on curing is effectively avoided.

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. The method includes detecting whether a threshold configured for a SHR for an inter-RAT handover is satisfied when at least one of a first RRC message includes the threshold for the SHR and a mobility command includes the threshold for the SHR for the inter-RAT handover, and logging or skipping logging of the SHR for the inter-RAT handover when the threshold configured for the SHR for the inter-RAT handover is satisfied. In another embodiment, the method includes detecting a RLF during or after performing the inter-RAT handover when the mobility command includes the voice fallback indication, and logging an indicator indicating the mobility is for the voice fallback in a RLF report for the RLF during or after performing the inter-RAT handover.

The invention discloses a data processing system for territorial space planning based on big data, and relates to the technical field of big data processing, and the system comprises a data dynamic feature quantification module which is used for monitoring a data updating event in real time and quantizing generated features; the influence propagation analysis module is used for calculating an influence propagation coefficient; the dynamic processing range defining module is used for comparing the influence propagation coefficient with an adjustable decision threshold value and outputting a corresponding processing range; the data fusion processing module is used for executing data consistency verification and updating, and measuring generation accuracy, time and calculation consumption; and the strategy optimization and learning module is used for calculating a return value and reversely adjusting the adjustable decision threshold in response to the return value. According to the method, the contradiction between the efficiency and the accuracy of massive dynamic data processing is solved by intelligently evaluating the data updating influence, dynamically scheduling the processing resources and continuously performing self-optimization by applying reinforcement learning, and the response speed, the resource utilization rate and the self-adaptive capability of the system are improved.