103 results about "Adaptive management" patented technology

The invention relates to the field of power data management, and discloses a power big data adaptive management method and system fused with spatial-temporal feature mapping, and the method comprises the steps: obtaining a real-time operation data flow from a multi-source power terminal, and constructing an original power data set with a time sequence label and a device identifier; the method comprises the following steps: dividing an original power data set into parallel processing units based on a storage-while-computing architecture, performing dynamic index updating by adopting an event-driven index mapping rule, and constructing a multi-dimensional data index cache system with real-time responsiveness; identifying a key abnormal trajectory through a time-varying feature nesting mechanism, and performing hierarchical measurement and entropy disturbance analysis on a data fluctuation degree in the key abnormal trajectory by using a streaming feature aggregation network; identifying potential security risk nodes in combination with the structure matching degree between the historical abnormal event evolution graph and the key abnormal trajectory; and generating a multi-level response instruction chain based on the risk assessment result. The method has the advantage of improving the operation safety of the power grid.

The invention discloses an air conditioner energy consumption adaptive management system and method based on dynamic feature selection, and relates to the technical field of air conditioner energy consumption management, and the method comprises the steps: building a system energy consumption digital twin model as a theoretical optimal energy consumption baseline; operating parameters are collected in real time, and key parameter subsets are screened through working condition recognition and feature importance dynamic evaluation; inputting the key parameters into the model to obtain theoretical energy consumption, and comparing the theoretical energy consumption with a measured value to generate an energy consumption deviation rate; smooth processing is carried out on the deviation ratio sequence, recognition is carried out in combination with a dynamic threshold value and various anomaly detection algorithms, and grading early warning is triggered; the system comprises four core modules, namely a digital twinborn model construction module, a key parameter dynamic screening module, an energy consumption deviation calculation module and a grading early warning triggering module. The method can adapt to different working conditions, accurately capture energy consumption abnormities, and effectively improve the intelligent level and accuracy of energy efficiency management of the air conditioning system.

The invention discloses an engineering construction digital intelligent supervision management and control system, and relates to the technical field of engineering construction supervision. The system comprises an information acquisition module which is used for acquiring equipment state, image and environment data; the data fusion layer module is used for carrying out cleaning, alignment and Kalman filtering fusion on the multi-source data to generate a multi-modal construction data flow; a progress calculation and prediction unit, a deviation judgment unit and a digital modeling unit are arranged in the analysis monitoring module, and the analysis monitoring module is used for updating a progress state based on an XGBoost model, analyzing a node dependency relationship by using a graph neural network, calculating a deviation propagation risk coefficient and generating a hierarchical deviation signal in combination with three-level judgment conditions; and the management and control layer module executes hierarchical response according to the level of the deviation signal, and feeds back execution effect data to the front-end module to realize model optimization and closed-loop iteration. According to the invention, intelligent perception, risk prediction and adaptive management and control of the construction process are realized, and the real-time performance, accuracy and autonomy of project supervision are improved.

The invention discloses a dynamic adaptive management system for road traffic safety planning driven by artificial intelligence. According to the system, multi-source data are acquired through a data acquisition and preprocessing layer and are cleaned and standardized; the knowledge engineering layer constructs a policy knowledge graph and a rule engine; the large model service layer realizes natural language generation, semantic understanding and multi-modal reasoning; the intelligent generation layer uses a GAN model to generate multi-modal planning content and dynamically adapts the multi-modal planning content; the implementation process management layer monitors planning implementation, middle-stage evaluation and last-stage summarization in real time; and the interaction and output layer provides visual editing and multi-format output. The system integrates the technologies of deep learning, generative adversarial network and the like, realizes intelligent planning generation and dynamic supervision, has the capabilities of multi-modal data processing, dynamic self-adaption and full-process management, can effectively improve the scientificity, high efficiency and adaptability of road traffic safety planning, and provides powerful support for road traffic safety management.

The invention relates to a resource quota dynamic management and control method for multi-tenant sub-accounts in a cloud communication platform, and belongs to the technical field of cloud computing and software. The method comprises the steps of obtaining sub-account data and a tenant grading system and a standardization protocol preset by a platform, performing verification and format regulation, and generating a standardization data set; secondly, based on a grading system, binding a permission boundary, extracting resource and service features for verification, constructing an associated weight distribution mechanism, and forming a grading quota evaluation benchmark; various thresholds and rules are preset, a reference quota is generated in combination with real-time monitoring data and a service load prediction result, and a differentiated adjustment scheme is formed by constructing an elastic adjustment mechanism and dynamically adapting to a resource quota. And finally, quota allocation is executed, indexes such as use saturation and response time delay are monitored through a two-dimensional verification system, a full-link management and control log is generated, and a resource dynamic management and control closed loop is formed. Refined and adaptive management and efficient utilization of resource quotas are realized.

The invention discloses a multi-protocol self-adaptive management docking station core board control method and system, and belongs to the technical field of docking station core board monitoring control, and the method comprises the steps: analyzing the stability risk analysis necessity of a docking station core board, analyzing the stability risk score of the docking station core board, and determining the stability risk of the docking station core board; and performing control optimization adjustment on the docking station core board based on the stability risk score of the docking station core board, performing protocol layer clock recovery circuit sampling phase drift analysis, analyzing a protocol layer clock recovery circuit sampling phase drift characterization factor, and executing control adjustment of sampling phase drift. According to the method, the stability and reliability of system operation can be improved, the link retraining frequency is reduced, sampling phase adjustment is optimized, sampling point dislocation, inter-frame phase lock out-of-step and systematic communication faults are prevented under the condition of multi-protocol concurrent working, and long-term stable operation and multi-protocol compatibility of the docking station core board are guaranteed.

The invention relates to the technical field of large model service management, and discloses a system for carrying out self-adaptive current limiting on a token number and a QPS called by a large model. A dynamic monitoring module of the system is responsible for collecting a request flow data set comprising a token consumption sequence, a request frequency sequence and a response delay sequence; a traffic feature extraction module performs multi-dimensional feature analysis on the data set to generate a traffic feature matrix containing token consumption rate, request frequency fluctuation coefficient and delay sensitivity index; the adaptive current-limiting decision module dynamically matches a load balancing strategy based on the matrix, and generates a current-limiting control parameter set containing a token quota threshold and a QPS upper limit threshold; the real-time regulation and control module dynamically adjusts the request queue according to the request queue to generate a new request scheduling sequence; and the feedback optimization module monitors the execution effect and iteratively updates the load balancing strategy. According to the invention, through real-time perception and dynamic adjustment, refined and adaptive management of large model service resources is realized.

The invention relates to an electromechanical system energy efficiency scheduling method based on edge-cloud collaboration, and relates to the technical field of computer processing, and the method comprises the following steps: S01, collecting motor operation parameters, environment data and energy consumption data in real time through edge devices deployed at all nodes of an electromechanical system to obtain original data at the same time bit, processing the original data by using an edge computing platform to obtain first processed data; s02, deploying a lightweight energy efficiency analysis model on an edge computing platform, calculating a current energy efficiency value of the motor in real time based on the first processed data, and if the energy efficiency is detected to be lower than a preset threshold value, triggering a local optimization strategy and recording optimization effect data; and S03, storing the optimization effect data uploaded by the edge device into a cloud database. According to the method, efficient, accurate and adaptive management of the energy efficiency of the electromechanical system is realized by constructing an intelligent scheduling architecture with edge real-time response and cloud global optimization collaboration.

This invention discloses an adaptive zoning management method for water environment pollutants based on differences in ecosystem service functions, belonging to the field of environmental risk assessment and pollutant management technology. The method includes the following steps: S1, ecosystem service function zone division; S2, establishment of a candidate pollutant inventory and concentration measurement; S3, construction of a differentiated multi-criteria evaluation system; S4, comprehensive subjective and objective weighting, determining the weights of each evaluation indicator for each ecosystem service function zone using a comprehensive subjective and objective weighting method; S5, pollutant priority ranking and inventory generation; S6, implementation of differentiated management measures. This invention, by constructing a zoning assessment framework and integrating multi-criteria decision analysis and a mixed subjective and objective weighting model, achieves accurate identification and scientific ranking of the environmental and health risks of pollutants in different functional zones. By generating a differentiated priority management inventory for each zone, it achieves the technical effect of transforming from unified supervision to precise adaptive management.

A system is provided for detecting and remediating computing system breaches using computing network traffic monitoring. In particular, the system may identify one or more technology elements within a network as well as relationships between computing systems associated with said elements to determine a network topology. Based on the network topology, the system may use historical network traffic data associated with the technology elements in the network to generate predicted entry points and lateral pathways of a security breach that may take place within particular computing systems. Then, based on the technology elements affected as well as entry points and path traversals of the breach, the system may generate and / or implement one or more remediation steps to address existing and / or future breaches. In this way, the system may provide an intelligent method of augmenting the security of a computing network.

The invention relates to the technical field of image transmission, in particular to an energy consumption self-adaptive management method and system of a wireless image transmission module. Comprising the steps of obtaining operation data of at least one node, and analyzing a network load to adjust interaction frequency configuration; load balancing parameters are determined according to interaction frequency configuration, transmission task priorities are dynamically adjusted, node energy consumption data are collected, and resource waste points are checked to update an energy consumption distribution scheme; predicting the traffic of a transmission path according to an energy consumption distribution scheme, optimizing path allocation and reallocating resources when the traffic is greater than a set value, and updating node state shared data; and finally, the network operation state is calibrated according to the node state shared data, and energy consumption optimization configuration is obtained, so that energy consumption is reduced, and network efficiency is improved. According to the invention, the problem that the energy consumption management of the wireless image transmission network is difficult to finely regulate and control along with the load change is solved, and the self-adaptive energy conservation and stable transmission of the distributed image transmission module are realized.

The invention relates to a self-adaptive management method and system for a database plan cache, and the method comprises the steps: firstly judging whether the plan cache is hit or not after a database receives an SQL statement, and triggering a hard analysis generation plan and caching the plan when the plan cache is not hit; if the cache is hit, further judging whether a corresponding multi-execution plan exists or not; and when only a single execution plan exists, analyzing the relationship between the number distribution of execution tuples and the number of execution times by adopting an execution efficiency algorithm to dynamically judge whether the data is inclined. And when a plurality of execution plans exist, entering a multi-plan cache management stage, selecting the execution plans according to a matching result of the current binding variable predicate selection rate and the cache plan historical selection rate, and generating and caching a new plan when the matching fails. According to the method, the data skew is accurately identified by executing the efficiency algorithm, intelligent matching and multiplexing of plans are realized by means of a multi-plan cache management mechanism, and the overall performance of the database in a data skew scene is improved.

This invention discloses an ecological management zoning method and system based on static and dynamic supply and demand matching, belonging to the field of ecological management technology. The invention constructs a node feature matrix by calculating a comprehensive static supply and demand matching index and a comprehensive dynamic change trend index, then generates a dynamic spatiotemporal adjacency matrix to construct a spatiotemporal physical connectivity graph. This graph is then input into a spatiotemporal graph neural network for processing, outputting predicted static supply and demand matching indices and predicted dynamic change trend indices. Based on the zero-value boundaries of these indices, the study area is divided into initial four-level basic management zones. A Markov decision process is then constructed, and a Pareto optimal solution set is obtained through reinforcement learning algorithms to fine-tune the spatial boundaries of the initial four-level basic management zones. The resulting refined ecological management zoning map and management priority sequence are output, making the zoning results more targeted for management. This solves the problem that existing ecological management zoning results lag behind actual changes and are difficult to effectively support adaptive management.

The invention discloses a self-adaptive management and control system for constructing a novel electric power system standard system, and relates to the technical field of novel electric power, and the system comprises an electric power system monitoring module, an electric power system management and control module, an electric power system self-adaptive management and control module, an early warning terminal and a database. Further analyzing to obtain the corresponding operation state of the novel power system, analyzing the result, analyzing the corresponding management and control direction of the novel power system, and when the direction is incorrect, executing the corresponding secondary management and control adjustment of the novel power system in time, so as to realize the comprehensive understanding of the novel power system under the corresponding operation. Therefore, an effective reference is provided for construction of a novel power system standard system, more accurate system construction is realized, more accurate state judgment of the operation application of the novel power system can be executed, and the safety or instability problem of the novel power application can be found and maintained in time.

Disclosed methods for adaptive management in a multi-cluster environment perform operations including maintaining an available resource pool comprising one or more resources available for use in at least one cluster selected from a plurality of clusters corresponding to a multi-cluster environment and detecting a request for multi-cluster management. The request indicates a multi-cluster management task. The operations include analyzing the request to identify needed resources, if any, required for the multi-cluster management task, determining whether the available resource pool includes sufficient suitable resources for the needed resources, if any, and responsive to determining the available resource pool includes sufficient suitable resources, performing the multi-cluster management task. Performing the multi-cluster management task may include allocating a selected resource from the available pool to a targeted cluster and removing the selected resource from the pool of available resource.

The invention discloses a medicine warehouse storage management method, which comprises the steps of collecting multi-dimensional parameters such as temperature, humidity, illumination and air components of a storage environment through environment monitoring equipment, and constructing a time-space synchronous fusion database; meanwhile, digital feature tags are set based on physical and chemical attributes, importance levels and component stability of the drugs, and a drug classification feature list is formed; the method comprises the following steps: performing correlation analysis on environment data and drug characteristics, establishing a drug-environment relationship list, and dynamically setting an environment threshold value and a storage time threshold value of each drug according to the drug-environment relationship list; environment parameters are monitored in real time according to a threshold value, a storage strategy is automatically adjusted when a monitoring value exceeds a limit, and medicine stock optimization based on epidemic case prediction is supported; according to the method, the medicine quality problem caused by static setting of a traditional storage environment is solved, refined and self-adaptive management is achieved, and the medicine storage safety and the medicine effect stability are remarkably improved.

The application discloses a tourism area phosphorus metabolism dynamic simulation method based on tourist behavior big data, relates to the field of data processing technology in the resource and management class, and first gathers multiple source data to construct an integrated knowledge base, then delimits a phosphorus metabolism boundary, sets an independent tourist consumption link and three types of statistical time periods, and builds a basic mechanism framework; then, a coupling algorithm is run to calculate phosphorus load and generate tourist emission details; then, a geographic information platform is combined to realize pollution space mapping and water quality early warning; finally, a benefit optimization algorithm is used to organize core indicators, filter the optimal management and control scheme, and package the whole-process data assets; the application can accurately distinguish tourist source and local source phosphorus pollution, break the traditional accounting mixed limitations, realize time and area fine simulation, lock pollution hotspots through a space heat map and water quality early warning, generate a scientific and adaptive management and control scheme by combining multiple index optimization, give consideration to ecological management and travel development, and form whole-process reusable data assets.

The invention belongs to the technical field of switch management, and particularly relates to a switch port self-adaptive management method and system based on real-time flow awareness, and the method comprises the steps: constructing a unified telemetering access layer, and achieving the unified access of real-time telemetering data of switch ports of different brands; designing an intelligent self-adaptive telemetering sampling strategy, and performing millisecond-level real-time sensing and acquisition on performance indexes of the switch; performing port state intelligent decision, and generating a control strategy including port starting, dormancy, speed limiting or service migration; converting the decision into an equipment instruction through a southbound interface protocol, and immediately performing effect verification through a real-time telemetering channel after the instruction is executed; and a centralized management and control platform based on the micro-service architecture is constructed, and various functions and panoramic visual display are realized. According to the invention, the real-time performance, the refinement level and the resource utilization efficiency of switch port management can be improved.

The invention relates to a network equipment self-adaptive management method based on a reference template and a dynamic capability value, and the method is applied to the technical field of network management, and comprises the steps: obtaining the dynamic capability value after equipment is on line, matching a corresponding template in a reference function template library, carrying out the dynamic operation of the template according to the difference between the template and the dynamic capability value, and obtaining the dynamic capability value. According to the method, a global capability view is obtained to serve as a basis for generating a front-end equipment management interface, and the method covers the certainty of'pre-definition 'of a reference function template and the flexibility of'dynamic transmission'. The reference function template ensures the stability of the system architecture and the predictability of front-end rendering; and the dynamic capability value endows the system with the flexibility of coping with equipment diversity and function iteration. Through the method, real-time, dynamic and automatic adaptation of the network management front end to multiple types of equipment is realized, so that developers are liberated from endless adaptation work, the development and maintenance costs are remarkably reduced, and the agility of network management services is greatly improved.

The invention discloses a water resource dynamic toughness evaluation method based on absorption and recovery dual-drive, which comprises the following steps: collecting multi-source data of a target drainage basin, and constructing a water resource evaluation basic database; selecting a multi-dimensional drought index, and extracting a drought impact strength factor after dimensionality reduction by a principal component analysis method; calculating the marginal expected loss of each index under the extreme drought condition based on the factor and each drought index; simulating a dynamic response path of a comprehensive factor of the water resource system in each period in the future through an impulse response function by relying on a time-varying parameter vector autoregression model; toughness parameters such as absorption intensity and recovery time are calculated according to the response path, a dual-drive toughness measurement function is constructed, the dynamic toughness value of the target drainage basin is measured and calculated, and evaluation is completed. According to the method, the response characteristics and the recovery capability of the water resource system under drought impact can be systematically evaluated, and a theoretical basis is provided for regional water resource risk prevention and control and adaptive management.

This invention relates to the field of hovering management technology and discloses a UAV simulation adaptive management system based on a multimodal neural network, comprising: a data fusion unit, a hovering analysis unit, a pose planning unit, and a pose management unit. This solution generates an accurate sea surface deformation map by fusing flight, power, and wave modal data, and understands in real time the dynamic changes in sea surface morphology caused by rotor downdraft and rescue vessel wake. The generated dynamic hovering tolerance domain can effectively offset wave-induced displacement, generate multi-step optimal pose trajectories and adaptive management commands, reduce simulation distortion, improve the stability and targeting of UAV hovering, ensure the effective implementation of management strategies, and enhance the reliability of maritime rescue.

The invention provides a partial product decoupling-based dynamic precision approximate multiplication implementation method and a multiplier, and the multiplier comprises a significance evaluation unit which splits a first operand and a second operand into high digits and low digits, and correspondingly generates a first amplitude and a second amplitude; different precision instructions are generated based on the amplitudes and the threshold values; the dynamic precision unit performs zero setting processing on the second low digit based on the precision instruction to form an approximate number, and calculates a partial product of the first high digit and the approximate number; the accurate calculation unit accurately calculates products of the other three parts; and the final addition unit sums the products of the four parts according to the weight. The calculation unit can be used for constructing or optimizing a multiplication calculation array in a neural network hardware accelerator, is particularly suitable for scenes with dense fixed-point number operation such as convolution, and performs efficient approximate operation on the input fixed-point number through a data driving mechanism by utilizing the common sparsity and amplitude difference characteristics of neural network data, so that the calculation efficiency is improved. And self-adaptive management of power consumption is realized.

The ACMMS enables a flexible, secure, confidentiality and adaptive management system across multiple LLMs, incorporating data classification, abstraction adaptive routing and feedback for continuous learning. The system utilizes abstraction, feedback mechanisms, and varied routing configurations to ensure secure, confidential and efficient LLM usage. ACMMS supports multiple embodiments, providing a versatile framework adaptable to different security and operational requirements.

The invention discloses a reinforcement learning-based cross-sea-air transmission parameter intelligent decision-making method and related equipment, and relates to the technical field of cross-sea-air communication, in the invention, a cross-sea-air channel model is constructed, and the cross-sea-air channel model is used as a dynamic environment of reinforcement learning, so that the cross-sea-air transmission parameter intelligent decision-making method is realized. Reinforcement learning is adopted to replace expert rules to intelligently decide cross-sea-air transmission parameters according to environmental changes, and the communication performance of the system is improved; environment rewards, action mapping and cross-sea-air channel states are integrated into experience to be stored in an experience playback pool, intelligent agents are trained through the experience in the experience playback pool, cross-sea-air optimal transmission parameter strategies are output through the trained intelligent agents, multi-parameter joint decisions of a modulation mode, a coding mode and a transmitting sound source level are achieved, and the multi-parameter joint decisions of the multi-parameter joint decisions of the multi-parameter joint decisions of the multi-parameter joint decisions of the multi-parameter joint decisions of the multi-parameter joint decisions are obtained. And the adaptive management and decision-making capability of the cross-sea-air communication system is improved.

The invention provides an intelligent regulation and control method and system for a plateau afforestation habitat, and belongs to the technical field of ecological intelligent regulation and control. The method comprises the following steps: S1, acquiring multi-source data to construct a habitat database, and quantifying habitat demand parameters of target species; s2, calculating a comprehensive ecological function value and a species suitability index, and identifying a habitat demand conflict region; s3, designing a regulation and control scheme and pre-evaluating benefits; s4, outputting an optimal scheme through a multi-objective optimization algorithm by taking multi-species overall benefit maximization and balance degree optimization as objectives; s5, after the scheme is implemented, feedback data are obtained through a monitoring network, actual and expected benefits are compared, and when the deviation exceeds a threshold value, model parameters are corrected in a linkage mode, and closed-loop optimization is formed. According to the intelligent regulation and control method and system for the plateau afforestation habitat, habitat quality dynamic accurate diagnosis and simulation optimization decision making are achieved, a real-time feedback self-adaptive management mechanism is constructed, and the diversity of plateau animals and birds is improved.

The application belongs to the field of network security management, relates to data analysis technology, and is used for solving the problem that the prior art cannot linearly track from the perspective of an operation user and overall risk assessment of short-term behavior of the same network user, and particularly relates to a network adaptive management system based on dynamic network security, which comprises a behavior monitoring module, a security analysis module and a risk analysis module connected in sequence in communication, and the behavior monitoring module, the security analysis module and the risk analysis module are all connected in communication with a database; the application can finely and weightedly quantitatively evaluate the security of each combination element; the evaluation method fully considers the differentiated influence of different security attributes such as permission, privacy and network fluctuation on the security of network behavior, so that the finally calculated security coefficient of the combination element is more representative and accurate.

The invention relates to the technical field of data analysis, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a dynamic bandwidth adjustment method, device and equipment based on bucket-level resources and a medium. Obtaining high-priority resource buckets and common resource buckets, collecting system resource states of the resource buckets, performing health degree analysis on the high-priority resource buckets and the common resource buckets, and generating health degree scores of the resource buckets; and performing weighted averaging on the health degree score of the high-priority resource bucket and the health degree score of the common resource bucket according to a preset weight to obtain a global health degree, and performing different dynamic bandwidth allocation and dynamic speed-limiting adjustment on the high-priority resource bucket and the common resource bucket according to the global health degree to obtain a target bandwidth adjustment result. According to the invention, the differentiation and adaptive management of the bandwidth can be realized according to the service importance and the load change.

This application discloses a high-speed data acquisition and processing method and system for PLC based on FPGA. First, it constructs a detailed real-time heat map and load vector by real-time sensing of multi-point temperature, data input rate, and task queue status on the FPGA chip. Based on this real-time information, it accurately predicts future power consumption and potential hotspot areas to optimize the modeling of internal thermal coupling effects and overcome the shortcomings of traditional models that underestimate peak power consumption. Finally, based on the prediction results, it intelligently generates task execution plans and refined clock gating masks, achieving proactive and adaptive management of FPGA resources. This approach significantly improves the operational stability and reliability of FPGA in high-speed data acquisition and processing scenarios, thus laying a solid foundation for precise industrial control and intelligent decision-making.

The invention provides a construction process carbon emission optimization method and system, and relates to the technical field of carbon emission optimizing.The construction process carbon emission optimization method comprises the following steps that energy consumption data in the construction process are collected and preprocessed, and preprocessed energy consumption data are obtained; based on the preprocessed energy consumption data, calculating carbon emission in the construction process by adopting an emission factor method; constructing a carbon emission optimization model, setting a dynamic threshold value for the carbon emission, and when the carbon emission reaches the dynamic threshold value, solving the carbon emission optimization model by adopting a multi-objective optimization algorithm to obtain an optimized equipment load rate; the equipment is adjusted according to the optimized equipment load rate, and carbon emission optimization in the construction process is achieved. Accurate self-adaptive control over construction carbon emission is achieved, the balance between the construction period and the cost is considered, and the feasibility of the optimization scheme and the project landing performance are improved.

The invention relates to the technical field of machine learning, and particularly discloses a metal fitting production parameter optimization method and system based on machine learning, and the method and system achieve the intelligent and adaptive management of a production process through the fusion of the machine learning technology and a real-time monitoring mechanism, thereby remarkably improving the production efficiency and product quality on the whole. According to the method, the characteristic change of a new batch of raw materials can be quickly responded, through systematic parameter optimization and feedback adjustment, the trial and error cost and resource waste in production are effectively reduced, the stability and consistency of the machining process are ensured, meanwhile, the adaptability of a metal accessory production parameter optimization system based on machine learning to complex conditions is enhanced, and the production efficiency is improved. And reliable data driving support is provided for metal fitting manufacturing.