198 results about "Adaptive management" patented technology

The invention discloses an industrial equipment real-time monitoring system based on edge computing, and the system comprises the following steps: collecting, processing and analyzing the operation data of industrial equipment through distributed edge computing nodes, constructing a node cooperation mechanism, and completing the processing of the data; the dynamic data scheduling module is used for dynamically optimizing a data transmission path and task allocation according to node performance; the intelligent model dynamic migration module is used for adjusting lightweight model deployment in combination with node resource conditions; the multi-modal fault sensing module is used for extracting and analyzing multi-modal features by using a cross-node collaborative algorithm; the edge node self-adaptive management module is used for adjusting node operation parameters in real time and repairing an abnormal state; and the equipment relevance reasoning module is used for constructing an equipment relevance model through a graph calculation method and generating a real-time relevance graph. According to the method, efficient monitoring and anomaly detection of industrial equipment are realized through resource optimization and intelligent analysis of collaborative edge computing nodes.

A system is provided for managing Internet of Things (IoT) networks. The system includes a learning module configured to employ machine learning models with hyperparameters optimized through a hyperparameter optimization process; wherein the process includes evaluating a set of hyperparameters against a performance metric to select optimal hyperparameters that enhance the adaptability and efficiency of dynamic membership functions within an adaptive fuzzy logic engine (AFLE).

The invention discloses an ecological environment health intelligent assessment and management system based on PSR and entropy weight dynamic self-adaption, and belongs to the technical field of ecological environment monitoring and intelligent management. The system comprises a data acquisition module, a preprocessing module, a classification and dynamic empowerment module, an intelligent prediction module, a feedback optimization module and a data security module. The system uses a sensor and edge calculation to acquire and process data, and the EQR is calculated through preprocessing. The core lies in that based on PSR model classification indexes, index weights are determined by applying an entropy weight method in combination with a dynamic adjustment mechanism, and pressure, state and response factors are generated. The intelligent prediction module adopts a deep learning model, outputs a health comprehensive index (Jzzs) based on factors, and performs adaptive optimization. And the feedback optimization module compares the Jzzs with a dynamic threshold value, and generates a graded, quantized and optimized treatment scheme in combination with machine learning to realize closed-loop management. And the data security module ensures that the data cannot be tampered and can be traced by using a block chain. According to the invention, dynamic accurate evaluation and intelligent adaptive management of ecological environment health are realized.

The invention discloses a self-adaptive management method and system for intelligent terminal equipment of a distributed power distribution network. According to the method, a self-organizing peer-to-peer communication network covering the whole network is constructed, and neighbor discovery and information sharing among intelligent terminal devices are achieved; the terminal independently completes control instruction calculation based on the local electrical parameters and a preset multi-objective function and optimizes an area control effect through a cooperation mechanism; introducing a self-adaptive adjustment strategy of a control algorithm; when abnormal operation is detected, the terminal can autonomously complete fault positioning and isolation, and power supply recovery is realized based on standby path judgment or micro-grid switching; the central management unit periodically carries out acceptance capacity evaluation, and terminal model parameters are aggregated in a federated learning mode; the terminal has an edge autonomous capability, and can maintain local control and data retention when communication is interrupted. The system has the characteristics of quick response, flexible structure, intelligent cooperation, high adaptability and the like, and is suitable for intelligent operation and management and control scenes of various types of power distribution systems.

The invention relates to the technical field of evaluation and management, in particular to a classroom learning dynamic evaluation and management system, which comprises a multi-source heterogeneous data acquisition module, an acoustic sensor array, an optical motion capture unit, an intelligent writing track acquisition device and a brain wave monitoring terminal, the data preprocessing and feature fusion module is used for eliminating environment interference signals and generating preprocessed data; the dynamic learning state evaluation module is used for extracting three-dimensional features and outputting a dynamic evaluation matrix; the group learning portrait generation module is used for generating a multi-dimensional learning portrait in combination with a preset knowledge graph topological structure; the self-adaptive management strategy generation module is used for dynamically generating a management strategy set; and the intelligent feedback execution module is used for driving the self-adaptive illumination system, the wearable vibration reminding device and the AR enhancement projection equipment to execute physical environment regulation and control. According to the invention, personalized strategy dynamic generation is realized, and adaptive regulation and control of the classroom environment and the teaching rhythm are realized in combination with the intelligent Internet of Things equipment, so that an intelligent collaborative feedback closed loop is formed.

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 relates to the technical field of smart community data management, in particular to a smart community metadata interaction method and system based on an edge computing framework, and the method comprises the steps: carrying out the dynamic storage of multi-dimensional data through a hierarchical rolling cache; generating a uniform resource vector through sequential progressive mapping; community semantic tags are added for the uniform resource vectors through extensible tag slots, connection is established according to association rules, and a resource semantic graph oriented to community services is constructed; receiving a resident task request, constructing a multi-factor weighted scoring engine based on the resource semantic graph, and generating a task data packet with a priority label; a two-channel hybrid scheduling network is constructed, a first channel outputs resource availability and node association degree, and a second channel outputs time sequence characteristics and load prediction parameters; and fusing dual-channel output to obtain task-node correlation and generate an optimal matching strategy. According to the method, task collaboration and resource adaptive management are realized through semantic resource modeling and hybrid scheduling.

The invention discloses an intelligent management system applied to a convention and exhibition center, relates to the technical field of convention and exhibition intelligent management, and realizes unified and high-precision data input through protocol conversion and multi-mode denoising. Second-level alarm and topology reconstruction are supported in cooperation with anomaly detection and self-adaptive management on the edge side; the comprehensive interpretation precision of the passenger flow and the environment situation is improved by using a multi-modal fusion algorithm; then mapping the perception information into a virtual venue situation by means of a digital twinning and self-learning mechanism, and realizing continuous optimization of sensor calibration and algorithm weight; finally, multi-Agent complex network dynamics is introduced, interaction evolution of a large-scale crowd in an emergency scene is simulated, closed-loop management of active prediction and emergency layout is provided for intelligent exhibition, and leap-type improvement in multiple levels of monitoring precision, safety disposal, resource scheduling and the like is expected to be achieved so as to cope with exhibition layout adjustment.

The invention relates to the technical field of sensors, and discloses a detection and management method and system for purification equipment. The method comprises the following steps: carrying out three-phase coupling dynamic modeling on a gas phase flow state, a particle phase movement track and a sensor phase interface characteristic in the purification equipment to obtain a gas mass transfer characteristic parameter; performing response characteristic analysis on a real-time detection signal of a sensor array in the purification equipment to obtain a sensor calibration compensation parameter; based on the sensor calibration compensation parameters and the gas mass transfer characteristic parameters, deposition kinetics analysis of pollutants on the surface of the sensor is carried out, and equipment operation state evaluation data are obtained; and performing spatial layout optimization on the sensor array according to the equipment operation state evaluation data and the gas flow consistency constraint condition to obtain an optimal layout scheme and generate a staged adaptive management control instruction. According to the invention, self-adaptive adjustment and intelligent switching of the sensor management strategy are realized, and the detection performance and reliability of the purification equipment sensor system are significantly improved.

The invention provides a multi-mode satellite terminal power supply adaptive management method. The method comprises the following steps: S1, acquiring an operation state and resource sensing data of a satellite terminal power supply; obtaining business demand data; s2, determining a plurality of working modes corresponding to the satellite terminal power supply based on the operation state and the resource sensing data; s3, based on the plurality of working modes, determining total power consumption corresponding to the plurality of working modes; s4, determining a satellite terminal power management optimization strategy based on the total power consumption, the operation state, the resource sensing data and the service demand data; s5, adjusting a satellite terminal power supply control instruction in a corresponding working mode by adopting a satellite terminal power supply management optimization strategy; judging whether the system operation parameters of the satellite terminal power supply meet preset change conditions or not; and if yes, repeating the steps S1-S5. Signal abrupt change is responded in real time through a closed-loop feedback mechanism, the sensing and decision-making process is cyclically updated, and self-adaptive balance of energy efficiency, communication quality and battery life in a complex scene is achieved.

The invention relates to a self-adaptive control method for a rain and snow mode of rail transit, electronic equipment and a medium, and the method comprises the steps: comprehensively studying and judging a rain condition based on multi-sensor data and numerical weather forecast data, and calculating a weather grade in the rain and snow mode, the multi-sensor data including video detection data, laser radar data and rainfall data; whether a rain and snow mode is set or not is judged according to the weather level, if yes, the current track slip degree is evaluated according to the multi-sensor data, and the GEBR value is adaptively updated based on the slip degree; the CC calculates the running safety braking envelope of the train by using the updated GEBR value, and feeds back a slip detection result and a dynamic parameter adjustment result in real time; and the ATS dynamically adjusts the operation plan of the train according to the slip detection result reported by the CC, and optimizes the operation scheduling of the whole train. Compared with the prior art, the method has the advantages that self-adaptive management of the rain and snow mode is achieved based on the dynamically-adjusted GEBR, and the running safety and reliability of the train in the rain and snow weather are effectively improved.

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 relates to the technical field of cognitive behavior therapy, in particular to a scheme intelligent pushing and task self-adaptive management method based on cognitive behavior therapy, which comprises the following steps: S1, acquiring physiological signals, behavior logs and environment interaction data of a user through a multi-modal data acquisition module; s2, constructing a dynamic psychological assessment model based on a cognitive behavior theory, and fusing multi-source data by adopting a Bayesian network to generate a user cognitive state map; s3, generating a personalized intervention scheme through a reinforcement learning algorithm according to the cognitive state map and a preset CBT intervention rule base; according to the method, through multi-modal data acquisition, dynamic psychological assessment, reinforcement learning scheme generation, task decomposition optimization, difficulty adaptive adjustment and digital twinborn simulation, full-process closed-loop management from data acquisition to intervention optimization is constructed, and efficient, safe and personalized cognitive behavior treatment scheme intelligent pushing and task adaptive management are realized.

The invention relates to the field of design editor platforms, in particular to a dynamic real-time rendering design editor platform which comprises a dynamic grid body module, an outline and scene management module, a real-time cooperation module and a universal class library. The dynamic grid body module realizes efficient processing of a dynamic grid through input preprocessing, dynamic simplification, detail enhancement, topology repair and LOD self-adaptive management; the outline and scene management module realizes organization and scheduling of a complex scene by means of hierarchical management and cross-platform resource management; the real-time cooperation module utilizes an operation log algorithm to solve editing conflicts and ensure multi-terminal consistency; the general class library comprises a math library and a geometric library, and the scene requirements of graphic rendering, physical simulation and the like are met. The rendering efficiency and the collaboration experience are improved, and the method has a wide application prospect.

The invention discloses an intelligent analysis-based security vulnerability intelligent research and judgment and co-processing system and method, and relates to the technical field of network security, and the system comprises a multi-source data collection module which obtains security data in real time by adopting a multi-level intelligent hierarchical collection architecture; the intelligent analysis module adopts a three-level progressive analysis architecture to analyze and collect data; the intelligent agent scheduling module designs a unified control framework, realizes standardized management and control of security equipment through hierarchical abstraction, receives analysis results, integrates a self-adaptive scheduling algorithm based on multi-objective optimization and an alarm vulnerability intelligent circulation strategy relying on an optimization mechanism of the self-adaptive scheduling algorithm, and executes a security mechanism by means of an adapter factory mode, a strategy arrangement engine and a four-level execution guarantee mechanism. Equipment self-adaptive management, strategy intelligent scheduling and vulnerability grading processing are supported, and a processing result is fed back; and the report generation and evidence collection module displays the disposal result and automatically generates an electronic evidence collection package meeting the standard. According to the invention, intelligent research and judgment of vulnerabilities and automatic generation of disposal suggestions can be realized.

The invention relates to the technical field of intelligent storage operation and maintenance management, and discloses a jasmine tea intelligent storage dynamic operation and maintenance management system, which comprises an aroma modeling and acquisition module, which is used for acquiring batch information and key volatile matter concentration and determining an aroma fidelity rate; the environment quota distribution module is used for determining an environment quota based on the microclimate parameters and distributing a storage location; the risk assessment and early warning module is used for calculating an aroma risk index and generating an early warning mark; the prediction operation and maintenance management module is used for carrying out equipment trend evaluation and generating an operation and maintenance work order; the scheduling decision module is used for generating a turnover priority queue according to the aroma residual margin and the risk index; the job resource scheduling module is used for executing joint scheduling to generate a job scheduling table; and the rebalance auditing module is used for updating the environment quota and the priority queue based on the execution receipt so as to realize dynamic closed-loop management. According to the invention, dynamic monitoring, intelligent decision making and closed-loop adaptive management of the storage environment, aroma quality and operation and maintenance scheduling of the jasmine tea are realized.

The invention provides a cloud-edge collaborative enterprise knowledge graph automatic construction method and system, electronic equipment and a storage medium, and relates to the technical field of computer and knowledge graph construction. Link data and inter-service communication monitoring data are called by collecting interface endpoints, and data are fused and associated by edge nodes; a dependency graph containing the service calling relation is generated; analyzing the time delay contribution degree and the error propagation probability, positioning a performance bottleneck link, measuring and calculating a service state in combination with a historical mode, adjusting flow distribution and isolating substandard instances; meanwhile, a multi-entity enterprise knowledge graph is constructed on the cloud based on dependency graph topology, performance attributes of service instances and interface endpoints and dependency relationship attributes among services are associated, and graph updating is automatically triggered when the attributes change. And finally, adaptive management strategies and configuration suggestions are generated through graph analysis, and automatic construction and application support of the enterprise knowledge graph can be realized.

The invention discloses a factory production process digital management method and system based on the Internet of Things, and belongs to the technical field of factory production management, and the method specifically comprises the steps: collecting related data of production equipment in a production line, carrying out the semantic fusion processing of the related data, generating a data stream with an incidence relation, and storing the data stream in a database; establishing a multi-relational graph structure based on the data flow, constructing a multi-dimensional digital model of the production process, receiving production intention information input by a manager, generating a production scheduling scheme according to the multi-dimensional digital model, selecting a scheduling scheme which is most matched with the production intention information, and sending the scheduling scheme to the manager; monitoring production data in the process of executing the scheduling scheme, determining influence factors according to a causal relationship when abnormality or deviation is detected, and updating the multi-dimensional digital model; according to the method, the production scheme can be dynamically adjusted under complex constraint conditions, so that a factory has self-adaptive management capability, and the intelligent level of production and the resource utilization efficiency are improved.

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 relates to the technical field of intelligent agriculture, provides an unmanned farm AI autonomous operation and life cycle management method and system, and aims to solve the problems that traditional agricultural production depends on artificial experience, management is extensive and resource utilization efficiency is low. An integrated sensor network is deployed, multi-source environment and image data in the crop growth process are periodically collected, the data are preprocessed and standardized, and the processed data are input into an AI model fused with deep learning for intelligent analysis so as to identify the crop growth stage and predict the demand of the crop growth stage. The system further has a closed-loop feedback mechanism, crop response data are recorded, an online learning technology is utilized to continuously optimize a decision model, autonomy and adaptive management and knowledge iteration of the whole life cycle are realized, and the system has a wide application prospect. And the intelligent level and the resource utilization efficiency of agricultural production are remarkably improved.

The invention discloses a chronic disease risk prediction and dynamic management system based on multi-modal data fusion, and relates to the technical field of chronic disease management, the chronic disease risk prediction and dynamic management system comprises a data acquisition layer, a data fusion processing layer, a risk prediction management layer and an application service layer, the data fusion processing layer performs preprocessing, feature extraction and fusion analysis, the risk prediction management layer constructs a risk prediction model according to result analysis, and the application service layer provides a risk prediction result, a personalized management scheme and an interactive interface service for a user. Through the arrangement of the data acquisition layer, the data fusion processing layer and the risk prediction management layer, 'symptom-physiology-image 'full-dimensional data is covered, more comprehensive health state evaluation is supported, dynamic intelligent prediction can be carried out, diet, exercise and medication suggestions are automatically adjusted according to patient execution feedback and the latest prediction result, and the health state evaluation efficiency is improved. And a self-adaptive management cycle is formed.

The invention belongs to the technical field of digital exhibition hall intelligent management, and particularly relates to a remote visual digital exhibition hall intelligent management platform and system. According to the invention, by dynamically monitoring and adjusting the resource allocation of each exhibition area, the system can reasonably utilize the server and network resources, avoid the resource waste, improve the overall performance, and combine the browsing weight and access traffic of the exhibition area, the system preferentially renders and loads the high-quality three-dimensional data of the hot exhibition area, thereby ensuring the smooth experience of visitors in a high-traffic period, and improving the experience of the visitors. Meanwhile, a simplified loading strategy is adopted in the unpopular exhibition area, unnecessary system burden is reduced, the rendering level and the loading mechanism of the exhibition area can be intelligently adjusted according to real-time data, self-adaptive management is achieved, manual intervention is reduced, the management efficiency is improved, and the method is based on server load information and access flow data.

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 discloses an intelligent self-adaptive solar power supply system and an energy management method, and relates to the technical field of solar intelligent power supply. A multispectral dynamic capture module improves the spectrum utilization rate and the photoelectric conversion efficiency; the global environment sensing module accurately predicts shadow and weather; the multi-mode MPPT cooperative control module reduces the power loss under the shadow; the heterogeneous energy storage cooperative scheduling module optimizes energy storage distribution; the intelligent load interaction module realizes accurate load management; the edge intelligent decision center realizes high-efficiency energy decision and safety guarantee, and all the modules work cooperatively, so that intelligent adaptive management of the solar power supply system is realized. Through multi-module innovation and cooperation, the performance of a solar power supply system is remarkably improved, the power generation efficiency is improved, environmental changes are accurately coped, power loss is reduced, energy storage and load management are optimized, efficient energy decision is achieved, safe operation of the system is guaranteed, the operation and maintenance cost is reduced, and intelligent development of solar power supply is promoted.

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.

Systems and methods for managing processors of a heterogenous computing platforms of IHS (Information Handling Systems) are described. In an illustrative, non-limiting embodiment, a heterogeneous computing platform includes devices and a memory storing firmware instructions. Based on execution of these firmware instructions by a respective device, a corresponding firmware service is provided such that one of the devices operates as an orchestrator. The orchestrator receives reports of changes in a context of operation of the IHS by a user and. Based on the context, the orchestrator determines power allocations for the two or more discrete processors, where the power allocations are mapped to the reported user context. The orchestrator configures the power that is available for use by each of the two or more discrete processors of the heterogeneous computing platform, and thus adjusts the performance of the processors in response to the change in user context.

The invention belongs to the technical field of data transmission, and provides an internet-of-things data transmission protocol adaptation method and system based on big data, and the method comprises the steps: obtaining data transmission protocol adaptation features according to data collection features, data essential feature coefficients and protocol parameters of internet-of-things equipment; and according to the data transmission protocol adaptation characteristics and the corresponding transmission protocol adaptation method, constructing an Internet of Things data transmission adaptation model, using the model to carry out adaptive transmission protocol adaptation on subsequent collected data and output a real-time transmission protocol adaptation method, and according to the real-time transmission protocol adaptation method, controlling a protocol converter device. And thus, the data transmission of the internet of things is subjected to adaptive management. The conversion efficiency of the data adaptation transmission protocol is enhanced through multi-dimensional feature processing of the data of the internet of things and the improved activation function big data model, the transmission speed and quality of the data of the internet of things are improved, and the manual adaptation loss of the data transmission protocol is reduced.

The invention discloses a forest seedling raising period management optimization method based on an adaptive algorithm. The method comprises the following steps: S1, collecting multi-source environment data and seedling growth state data in a forest seedling raising process; s2, the original index data set is processed, a standardized index matrix is obtained, and a stage index evaluation input set is constructed; s3, constructing a multi-target periodic scheduling model based on the stage index evaluation input set; s4, introducing an entropy weight adaptive grey wolf optimization algorithm to solve the multi-target scheduling function set, and outputting a periodic scheduling optimal solution; s5, generating a seedling culture management execution strategy based on the period scheduling optimal solution; s6, forming a feedback monitoring data set by the environment feedback data collected in real time and the seedling feedback data, and generating an updated seedling management execution strategy; and S7, synchronizing the updated seedling culture management execution strategy to the seedling culture management platform. According to the invention, intelligent scheduling and adaptive management optimization of the whole forest seedling raising process 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 discloses a labor capability allocation method and device based on a dynamic weight, a medium and a product, and the method comprises the steps: obtaining the complexity and personnel skill data of each task in response to a project labor capability allocation request; based on the dynamic coupling model, calculating the real-time matching degree of the personnel and the tasks in combination with the dynamic weight factors corresponding to the tasks; then, according to a matching result and multi-dimensional constraints such as project cost and time, a multi-objective optimization algorithm is adopted to generate an optimal labor force and energy distribution scheme and expected progress data; after the scheme is executed, the actual progress of the project is monitored in real time and compared with expected data; if the deviation exceeds a set threshold value, a feedback mechanism is triggered, the personnel skill data and the dynamic weight factor are updated, the matching and optimizing process is restarted, and closed-loop self-adaptive management of the whole period of the project is achieved until the project is completed, and the technical scheme of the embodiment of the invention remarkably improves the accuracy of resource allocation and the self-adaptive capacity of project management.