529 results about "Evaluation algorithm" patented technology

The invention relates to the technical field of computing power resources, and discloses a flexible allocation monitoring system for computing power resources, which defines a clear resource use boundary for different types of tasks through a container-level QoS strategy of a resource isolation unit, and adjusts resource allocation in real time in combination with a dynamic partition management module, thereby avoiding resource waste in a traditional fixed allocation mode, and improving the resource allocation efficiency. The dynamic preemption unit accurately screens low-priority tasks for resource recovery through a multi-factor decision engine and a preemption cost evaluation algorithm, in 50 concurrent task scenes, the resource preemption response time is shortened to 20-35 ms and is improved by 50%-70% compared with 70-100 ms of Docker / K8s, the blocking duration of high-priority tasks is reduced to 15-25 ms from 80-120 ms, and the core service interruption risk is greatly reduced.

The invention discloses a field personnel positioning method cooperating with multi-view images and spatial vectors of an unmanned aerial vehicle, and the method comprises the steps: detecting a personnel target in real time through a pre-trained lightweight model, obtaining a surrounding radius in combination with a self-adaptive surrounding radius algorithm, and achieving the collection of a multi-view image sequence; carrying out quality quantitative analysis on the multi-view image sequence by adopting a multi-source fixed weight quality evaluation algorithm; a DBOF coordinate system is established, and coordinate conversion is completed; extracting high-frequency matching points based on a cross-image bidirectional matching mechanism, and performing composite distortion correction on the high-frequency matching points; carrying out weighted least square method solution on the multi-view observation equation set to generate a three-dimensional space coordinate of the target point in a DBOF coordinate system; and introducing a robust iterative optimization strategy to eliminate abnormal values, and outputting geographic coordinates. On the basis of low-cost monocular vision, the real-time performance and robustness of personnel positioning in a complex field environment are improved through a dynamic surround sampling and geometric resolving mechanism, and spatial data support is provided for field rescue.

The invention discloses automatic complete equipment for intelligent predictive maintenance of a primary and secondary fusion circuit breaker. The automatic complete equipment comprises a multi-sensor fusion unit, an edge calculation and analysis module; a parameter interaction module; a predictive maintenance decision unit; the primary and secondary converged communication architecture is used for managing control information and state information on the basis of an IEC61850 (International Electrotechnical Commission 61850) standard; wherein a noise covariance matrix and a feature weight coefficient of the adaptive Kalman filtering health assessment algorithm are dynamically adjusted according to a data quality index and prediction error feedback, and input features of the residual life prediction algorithm based on the LSTM comprise a health index, a change rate and component-level health state information from the health assessment algorithm. Accurate evaluation of the health state of the circuit breaker and accurate prediction of the residual life are achieved, the optimal maintenance strategy is generated, the operation reliability of the circuit breaker is improved, and the maintenance cost is reduced.

The invention relates to the technical field of panel display brightness adjustment, and discloses a panel display module brightness adjustment method based on multi-sensor fusion. The method comprises the following steps: collecting multi-source sensor data of a panel display module; processing multi-source sensor data by using a dynamic feature fusion algorithm, distributing dynamic weights according to the real-time confidence of each sensor data source through a weighted fusion mechanism, and generating a brightness adjustment feature set; constructing a dynamic brightness adjustment index table, representing display partition brightness parameters by nodes, representing visual relevance among display partitions by edges, and updating node attributes and edge relevance strength in real time based on an environment change triggering mechanism; according to the brightness adjustment feature set, executing hierarchical association extension calculation in a dynamic brightness adjustment index table, and dynamically determining a brightness optimization action range; and in the optimization action range, synchronously analyzing the color deviation tolerance, the power consumption constraint condition and the visual comfort index through a multi-dimensional evaluation algorithm, and generating a brightness adjustment decision parameter.

The invention discloses a bidding and incentive combined strategy optimization method, system and device for a source-load double-side peak regulation auxiliary service market, and a storage medium, and belongs to the technical field of electricity market transaction. The method comprises the following steps: constructing a load aggregator and thermal power generating unit-oriented double-market joint optimization model according to parameter data of a peak regulation auxiliary service market and an incentive demand response market; the double-market joint optimization model is formalized into a Markov decision process; constructing a multi-task multi-agent reinforcement learning training framework; and solving the multi-task multi-agent reinforcement learning training framework through an asynchronous training multi-task multi-agent flexible action-evaluation algorithm to obtain a load aggregator bidding and incentive optimal joint strategy and an optimal bidding amount and quotation strategy of the thermal power generating unit. According to the method, game behaviors among multiple market participants and joint decision behaviors of demand-side market subjects in double markets are considered at the same time, and the method is widely applicable to multi-task agent collaborative optimization in the electricity market.

The invention discloses an analysis and evaluation algorithm for the temperature regulation effect of a variable air volume air conditioner terminal device, and relates to the technical field of air conditioner regulation. Generating a temperature evaluation parameter and an air valve evaluation parameter based on the operation data; based on the temperature evaluation parameters and the air valve evaluation parameters, establishing a joint judgment model; according to the method, the energy waste working condition of counteracting the regional cold and hot demand can be found in time, the passive situation that treatment is carried out after the temperature is seriously deviated is avoided through early recognition and root diagnosis of the abnormal track, and the method is suitable for large-scale popularization and application. And the indoor comfort is improved.

The invention discloses an AI diffusion model pattern generation platform and method, the generation platform comprises a visual interaction module, an image generation module and a pattern mapping module, and the image generation module comprises a training sub-module and a reasoning sub-module; the generation method comprises the following steps: preprocessing a text input by a user and an optional structure image, and outputting standardized data; a semantic embedding vector is generated through text coding; carrying out LoRA fine tuning to obtain a style weight module; a ControlNet structure condition is injected; performing diffusion sampling to generate candidate patterns; performing CLIP scoring to screen an optimal pattern; and outputting the optimal pattern and previewing the carrier map. According to the platform and the method provided by the invention, the LoRA low-rank fine tuning, the ControlNet structure condition control and the CLIP similarity evaluation algorithm are fused, so that the effects of customizing the special style pattern with a small number of samples at low cost and accurately controlling the structure and style of the pattern are realized.

The invention provides a printing color consistency automatic calibration and management system and method, and the method comprises the steps: collecting a current environment temperature and humidity ink matching parameter density curve data dot increase compensation parameter in real time through a state detection device, and forming a current state data set; performing similarity analysis on the reference data set and the current state data set by adopting a matching degree evaluation algorithm, and judging a relationship between a matching degree numerical value and a preset threshold value; adjusting and correcting the calibration reference value and the color control parameter in the reference data set according to the offset compensation set to obtain a compensated and adjusted parameter set; on the basis of the compensated and adjusted parameter set, generating update code data by combining a current time identifier and an operator identifier, and forming new two-dimensional code information; and outputting the new two-dimensional code information to a subsequent work order carrier by adopting a code generation module, and establishing a parameter transmission tracing chain.

The invention belongs to the technical field of Internet of Things security, and particularly relates to a multi-level Internet of Things equipment security management and access control method, which comprises the following steps of: performing bidirectional verification on a digital certificate and a hardware fingerprint through an edge node when equipment is accessed; calculating credibility in real time and dynamically dividing security levels by using a hybrid evaluation algorithm based on equipment operation context data; a fine-grained access strategy is generated in combination with equipment attributes and environmental risks, and cross-level collaboration is realized through a lightweight protocol; abnormal behaviors are detected in real time in the access process, and high-risk operation is blocked within milliseconds; and strategy conflict resolution and adaptive optimization are realized through formalized verification and deep reinforcement learning. According to the technical scheme, the identity authentication reliability, the authority dynamic adaptability, the strategy consistency and the threat response speed of the Internet of Things equipment are remarkably improved, and a systematic security guarantee is provided for large-scale Internet of Things applications with high security requirements.

The invention discloses a substation primary and secondary equipment communication prefabricated optical cable online monitoring method and system, and belongs to the technical field of communication prefabricated optical cable monitoring. The system comprises an optical cable parameter acquisition module, a communication quality evaluation module, a physical parameter analysis module, a first-aid repair path planning module and a fault database. The method comprises the following steps: optical cable parameter acquisition: acquiring data in real time, and converting high-dimensional data into low-dimensional data through a principal component analysis algorithm; communication quality evaluation: identifying communication characteristics in the low-dimensional signal parameter matrix based on a fuzzy comprehensive evaluation algorithm, and outputting a communication quality score; the physical parameter analysis module extracts physical deep features of the optical cable by using a deep belief network DBN, and inputs an improved Bayesian classifier to output a state classification result; according to the first-aid repair path planning, OTDR detection parameters are dynamically adjusted, and an optimal first-aid repair path is planned in combination with the position of the inspection robot; according to the invention, optical cable communication quality evaluation, accurate physical state classification and efficient fault repair planning are realized.

The invention relates to the technical field of Internet of Things gateways, and discloses an Internet of Things gateway device and system based on edge computing, and the device comprises a hardware heterogeneous computing module and a multi-interface protocol adaptation module. The edge calculation local processing module is used for performing preprocessing, feature extraction and local decision on the acquired terminal data; the network redundancy and self-healing module is used for realizing accurate network disconnection detection, seamless switching of a main network and a standby network and equipment fault self-recovery through a multi-dimensional network health degree evaluation algorithm; the local autonomous storage module is used for realizing hierarchical storage of high-frequency data and network disconnection emergency data and seamless synchronization after network recovery through network disconnection data hierarchical caching and an incremental synchronization algorithm; and the cloud collaborative management module is used for realizing remote configuration, OTA upgrading and state monitoring. The method is high in integration, low in delay and fast in response, adapts to real-time scenes, saves bandwidth cost, improves cloud efficiency, is high in reliability, and realizes network disconnection and continuous operation.

The invention relates to a rail transit engineering digital twin management and control method based on unmanned aerial vehicle honeycomb inspection, and solves the problem that a twin model is disjointed from the real-time state of an engineering field, and the method comprises the steps: firstly obtaining multi-source data containing BIM, GIS and Internet of Things data and an unmanned aerial vehicle initial image; carrying out BIM lightweight processing, carrying out registration with a GIS (Geographic Information System) and establishing an engineering entity-environment-event dynamic association network to form a digital twin reference model; based on the real-time state data of the model and external factors, a multi-dimensional evaluation algorithm is used to generate an inspection task priority; an adaptation rule is established in combination with an unmanned aerial vehicle honeycomb performance threshold, and unmanned aerial vehicle obstacle avoidance inspection is scheduled; the inspection data is preprocessed and then fused to the twin model, and the twin model is driven to dynamically iterate so as to realize real-time synchronization with the site. The method has the following effects: real-time and precise rail transit engineering management and control are realized through digital twin virtual-real mapping and unmanned aerial vehicle honeycomb intelligent inspection closed loop, and the core management and control efficiency is improved.

The invention relates to the technical field of quality regulation and control, in particular to a self-adaptive pile forming quality regulation and control system and method for soft foundation treatment pile construction. The method comprises the steps that a construction parameter acquisition unit acquires key construction parameters in real time through a multi-type sensor network; the data preprocessing and feature extraction unit is used for preprocessing the collected key construction parameters and extracting key feature parameters reflecting the construction stability and the pile forming uniformity; and the pile-forming quality judgment and deviation recognition unit is used for comparing the key characteristic parameters with a preset pile-forming quality threshold model in real time, judging the quality state of the pile-forming process and outputting a deviation category and a deviation strength index. According to the method, the limitation that judgment only depends on a single parameter threshold in the prior art is broken through, and a multi-mode coupling resonance intensity evaluation algorithm is provided for solving the problem that nozzle abrasion is high in concealment and great in influence.

The invention discloses a low-altitude unmanned aerial vehicle communication network dynamic modeling and multi-dimensional evaluation method and a related device, and relates to the technical field of low-altitude communication networks, and the method comprises the steps: constructing a time sequence network model of a low-altitude unmanned aerial vehicle communication network according to the obtained real-time state data of an unmanned aerial vehicle and a preset link connection condition; constructing a communication link dynamic model based on the time sequence network model and the acquired environment data; processing the sequential network model and the communication link dynamic model based on a preset communication evaluation index to obtain a multi-dimensional evaluation index data set; processing the multi-dimensional evaluation index data set by using a preset dynamic evaluation algorithm to obtain an optimized instruction set; the optimization instruction set is used for optimizing the network performance of the low-altitude unmanned aerial vehicle communication network. The application can improve the utilization rate of network resources and ensure the reliability of the cooperative task of the unmanned aerial vehicle.

The invention discloses an intelligent error correction and style optimization system and method for business English writing, and relates to the technical field of natural language processing and artificial intelligence, and the system comprises the following components: a user interaction module, a data storage module, a language ability evaluation module and a self-adaptive error correction feedback and style optimization module. Through a multi-dimensional evaluation algorithm based on dynamic weight adjustment, historical writing data of a user is deeply analyzed, an evaluation system including four dimensions of grammar errors, vocabulary application, sentence structures and business profession is constructed, the system comprehensively evaluates the language ability of the user, and the user experience is improved. And the evaluation weight is dynamically adjusted according to the capability improvement rates of the users in different dimensions, so that the language capability levels of the users are more accurately divided, and the system can provide personalized error correction feedback and style optimization suggestions for the users with different language levels based on the evaluation result.

The invention discloses a taxi Internet of Vehicles data secure transmission method, and relates to the technical field of Internet of Vehicles data transmission, and the method comprises the following components: a digital twin model construction step, a virtual environment data pre-transmission step, a transmission path security verification step, and a real data transmission and comparison detection step. According to the method, the digital twin model of the taxi is constructed, data pre-transmission verification is carried out in the virtual environment, problems possibly occurring in the data transmission process can be found and corrected in advance, meanwhile, a network node risk assessment algorithm based on fuzzy comprehensive evaluation is adopted to carry out safety verification on a data transmission path, and the safety of the data transmission path is improved. The security of a data transmission path is ensured, the security and the reliability of taxi Internet of Vehicles data transmission are remarkably improved through the measures, and the data are effectively prevented from being tampered or leaked in the transmission process.

The invention discloses a simulation test method and system for an unmanned aerial vehicle control algorithm, relates to the field of intelligent system simulation test, and can solve the defects of an unmanned aerial vehicle simulation system in high real scene rendering and physical engine precision at the present stage. The method solves the problems that the real characteristics of a visual sensor and a flight platform cannot be accurately simulated, the performance of an algorithm in a complex real scene is difficult to effectively evaluate, and the adaptability of the algorithm to a hardware environment is difficult to verify, and comprises the following steps: constructing a high-trueness virtual environment according to scene requirements and rigid body dynamics simulation requirements of an unmanned aerial vehicle simulation test; constructing a simulation model according to the physical motion characteristics of the unmanned aerial vehicle hardware and the performance of the sensing equipment; importing the simulation model into a high-reality virtual environment, and performing parameter setting on the simulation model; and testing and verifying a control algorithm of the unmanned aerial vehicle based on the high-trueness virtual environment and the simulation model completed by parameter setting. The method is used for performing simulation test and verification on the control algorithm of the unmanned aerial vehicle.

The invention relates to a low-voltage transformer area meter box burning loss fault detection method and system, and the method comprises the following steps: obtaining the time sequence data of a low-voltage meter box, detecting an abnormal behavior through a trained PidanGAE-U model, recognizing the state of the low-voltage transformer area meter box, and generating a preliminary risk assessment result; obtaining multi-dimensional historical and real-time data, carrying out classification division and preliminary evaluation on the identified faults, and constructing a risk factor evaluation system; and based on a risk factor evaluation system, carrying out risk factor correction on a preliminary risk evaluation result, introducing a comprehensive weight evaluation algorithm to carry out quantitative calculation on the influence degree of each corrected factor, and finally generating a risk level of the burning loss fault. Potential faults can be identified and positioned in advance, and the stability of a power system and the power utilization safety of a user are ensured.

The invention discloses a low-altitude economic monitoring and collaborative countering method and device. The method comprises the following steps: S1, acquiring original monitoring data of a flying target; s2, fusing the monitoring data to generate a target threat level; s3, executing response operation; according to the invention, through a multi-modal sensor depth cooperation and Bayesian decision-making multi-modal data fusion model, a trajectory entropy evaluation algorithm is introduced, the detection efficiency of unknown targets outside a preset route is improved, the recognition precision of low-altitude targets is improved, and the supervision requirement of low-altitude economic high-density flight is met; based on a dynamic threat assessment and countering strategy decision tree linkage mechanism, response delay is reduced, collaborative scheduling among equipment is realized, the countering success rate is improved, and accidental damage to cooperative unmanned aerial vehicles is avoided; a federated learning driven model optimization framework is adopted, so that the system identification precision is increased along with deployment time; and good real-time processing capability is still kept on edge equipment with limited computing power.

The invention provides a multi-modal medical image intelligent analysis system and method based on a large language model, and the system comprises a key frame extraction module which carries out the key frame extraction through a multi-frame quality evaluation algorithm; the coronary artery blood vessel extraction module is used for realizing accurate extraction of a coronary artery tree structure through multi-scale blood vessel enhancement; the pathological recognition module adopts an improved DeepLabV3 + algorithm to construct a cardiac blood vessel blockage region segmentation model; the multi-modal diagnosis module adopts LLaVA-Med multi-modal diagnosis, integrates vision and language understanding ability, and combines DeepSeek to generate a standardized diagnosis report; the model optimization module is used for implementing model parameter iterative updating based on a near-end strategy optimization algorithm; and the visual interaction module is used for visually displaying the lesion position and the spatial relationship thereof, so that a doctor can conveniently carry out preoperative path planning and intraoperative navigation judgment. On the basis of a large language model and a multi-mode technology, full-process support from image uploading to diagnosis decision making is provided.

The invention relates to the technical field of wind power, and discloses a wind generating set fault diagnosis method and device and computer equipment, and the wind generating set fault diagnosis method comprises the steps: constructing a fault feature library, a fault recognition algorithm, a fault evaluation algorithm and a fault early warning algorithm of wind generating sets of different models and specifications; operation parameters of the wind generating set in the real-time operation process are obtained, wherein the operation parameters comprise operation condition data and vibration signal data; a fault recognition algorithm is utilized, the operation parameters and the fault feature library are compared, the fault types of the wind generating set are determined, and the fault types comprise a fan blade breakage fault and a fan transmission system fault; determining the fault severity of the wind generating set based on the operation parameters by using a fault evaluation algorithm; and performing fault early warning based on the fault type and the fault severity by using a fault early warning algorithm. The problem that fault monitoring of the wind generating set is difficult can be solved.

The invention relates to the technical field of bid procurement, in particular to a bid procurement management platform based on artificial intelligence and a processing method. Comprising a multi-source heterogeneous data acquisition module which is configured to be in real-time butt joint with an enterprise ERP system, a supplier database and a third-party credit investigation platform through an API interface; a dynamic weight distribution unit and a time sequence feature extraction unit are arranged in the feature engineering processing module, and the feature engineering processing module is used for carrying out dimension reduction on the historical transaction data of the suppliers; the deep learning matching engine comprises a double-tower neural network architecture based on an attention mechanism and is used for establishing dynamic association between a purchase demand vector and a supplier capability vector; the intelligent decision matrix generation module is used for integrating a fuzzy comprehensive evaluation algorithm and a multi-objective optimization algorithm and outputting a three-dimensional decision space for comprehensive evaluation of suppliers; according to the scheme, the problem that cross-system data formats are incompatible in a traditional technical scheme can be solved.

The invention discloses an energy storage system control method and system based on time-of-use electricity price and multi-modal optimization, and the method comprises the steps: simulating supply and demand fluctuation in a complex scene through a pre-constructed energy storage system control strategy model based on deep reinforcement learning, dynamically optimizing a charging and discharging plan of energy storage equipment, and generating an energy storage resource distribution efficiency optimization scheme; generating a real-time electricity price adjustment scheme by adopting a game theory price adjustment algorithm based on the energy storage resource distribution efficiency optimization scheme, the real-time demand of power grid supply and demand balance, the user electricity consumption behavior change and the power grid operation state dynamic state; generating a user behavior response data set through a pre-constructed user behavior response model based on the real-time electricity price adjustment scheme in combination with user economic benefit protection requirements; and calculating a fluctuation ratio of a power grid load change trend by combining the user behavior response data set with real-time monitoring data through a power grid supply and demand balance dynamic evaluation algorithm, and determining a final energy storage scheduling scheme. The purposes of real-time monitoring and collaborative optimization of energy storage system control are achieved.

The invention discloses a cross-regional municipal road maintenance resource scheduling method and system. The scheduling method comprises the following steps: collecting road disease data, maintenance historical records and real-time traffic flow data of each region; constructing a judgment matrix by applying an analytic hierarchy process, calculating a road factor weight through a root method, calculating a maintenance emergency degree score of each region according to the quantized basic data and the weight, and dividing region maintenance grades; according to the maintenance level of each area, maintenance groups with different priorities are divided; counting the existing maintenance resources of each region, and predicting the resource demand quantity of each region by combining a maintenance group division result and taking historical maintenance resource consumption data as a benchmark; for a resource shortage region, selecting a scheduling path by adopting a greedy algorithm, and formulating a preliminary cross-region resource scheduling scheme; and constructing a multi-target cost evaluation algorithm, and iteratively optimizing the scheme in combination with a particle swarm optimization algorithm. According to the invention, efficient, accurate and low-cost scheduling of cross-regional municipal road maintenance resources is realized.

The invention relates to methods, devices, and software for distinguishing hemoglobin (Hb) from myoglobin (Mb) in urine samples using peroxidase-reactive test strips. A time-resolved video of the color change on the test pad is recorded, and hue values are extracted and analyzed over time. Diagnostic differentiation between Hb and Mb is achieved by evaluating the hue-time curve, in particular via endpoint hue values and the ratio of integrated hue values in distinct time intervals. The invention further comprises a standardized optical setup for recording, a software-based evaluation algorithm, and a refillable cartridge for in-situ urine analysis devices containing appropriate test fields. The invention enables combined testing with additional urine parameters and is suitable for medical and non-clinical applications such as sport and home diagnostics.

The invention provides an information query method and system based on the Internet of Things, and belongs to the field of information query of the Internet of Things, and the method comprises the following steps: S1, receiving multi-modal query input, and converting the multi-modal query input into structured query representation; s2, performing credibility evaluation on the Internet of Things data by adopting an improved data credibility evaluation algorithm to obtain a credibility score of each data item; s3, performing complexity calculation on the structured query representation based on a nonlinear evaluation model to obtain a query complexity score; s4, determining a query execution strategy according to the query complexity score and the Internet of Things resource state; s5, executing information query according to the query execution strategy to obtain an initial query result; and S6, performing fusion processing on the query result based on the credibility score to obtain a final information query result. According to the method, self-adaptive processing and quality optimization of information query in the Internet of Things environment are realized, and the optimal execution strategy can be dynamically selected according to the query complexity and the resource state.

The invention provides a public transport supply and demand matching method and a related device, and belongs to the technical field of traffic engineering and urban planning. According to the invention, the matching degree of a supply network and a demand network is calculated through a supply-demand matching evaluation algorithm, and a public transport supply-demand matching index of a spatial analysis unit is obtained; and obtaining a public traffic supply and demand matching result based on the public traffic supply and demand matching index. According to the invention, the problem of low accuracy of public transport supply and demand matching is solved.

The invention relates to the technical field of LED display screens, in particular to an LED display screen brightness regulation and control method and device, and the method comprises the steps: obtaining light intensity data which comprises environment light intensity data collected by a distributed sensing assembly and brightness intensity data of display screen partitions; processing the light intensity data of the light intensity data to obtain a brightness uniformity index, and outputting a dynamic adjustment instruction in combination with ambient light intensity data; the dynamic adjustment instruction is converted into a partition driving parameter; and adjusting the brightness level of the partition of the display screen according to the partition driving parameter. Partitioned data acquisition and independent drive design are combined with a brightness uniformity evaluation algorithm and a dynamic adjustment strategy to realize accurate regulation and control of brightness in a complex scene. Meanwhile, a closed-loop feedback correction mechanism is introduced, so that the system stability and response speed are improved. The display effect can be remarkably improved, the user experience is enhanced, and the method is suitable for multi-scene application requirements.

The invention relates to the field of block chain fragmentation, in particular to a fragmented block chain node multi-dimensional evaluation and intelligent dynamic reconstruction method, which comprises the following steps: constructing a double-layer fragmented block chain architecture; designing a multi-dimensional assessment to compute node performance and reputation; adaptively executing a fragmentation decision based on a system state; and executing a node distribution optimization algorithm based on the performance and reputation scores of the nodes, adjusting the distribution of the nodes in the system, and balancing the performance and safety. Through the double-layer fragmentation architecture design, the dynamic parameter optimization model and the multi-dimensional node evaluation mechanism, reconfiguration of the fragmentation block chain under the large-scale heterogeneous network is realized. A designed multi-dimensional evaluation algorithm calculates node performance and reputation, fragment parameters are determined through a deep reinforcement learning algorithm, reasonable allocation of nodes in a system is achieved by optimizing a node distribution strategy, and finally long-term dynamic balance of throughput and safety in the reconfiguration process is achieved. And the stability and the high efficiency of the fragmented block chain system in a large-scale application scene are obviously enhanced.