172 results about "Track analysis" patented technology

The invention provides a real-time deviation analysis method and system for grid-connected operation data of a new energy station, and relates to the technical field of data deviation analysis, and the method comprises the steps: collecting the real-time grid-connected operation data of the station, and carrying out the adaptive decomposition based on a data fluctuation trend, and obtaining a plurality of groups of original feature sequences; screening features through a recursive support degree model and information entropy, and generating a reconstructed feature matrix by using a manifold embedded network; the double-branch verification network analyzes the features from the time sequence and the frequency domain, and generates time sequence feature vectors and frequency domain feature vectors; the topological continuous coherence analyzer extracts the topological relation between the features; the dynamic coupler constructs a multi-dimensional feature space, a deviation source is tracked, analyzed and positioned through a feature propagation link, and a propagation path is established; constructing a fractional order differential operator based on a propagation path, depicting a long-range dependency relationship among parameters, generating a dependency intensity matrix, performing causality test, constructing a dynamic causality graph, quantifying contribution degrees of propagation nodes, analyzing a deviation evolution mechanism, and outputting a deviation analysis result.

The invention relates to the technical field of network security, in particular to a network security risk assessment method and device based on artificial intelligence and a medium, and the method comprises the following steps: S1, collecting network flow data, user behavior data and device state data, and generating a standardized data set; s2, extracting features related to network threats, and constructing a risk data set with labels; s3, dividing the risk data set with the label into a training set and a verification set, and training the risk assessment model by using a support vector machine algorithm; s4, outputting a risk score and a threat level, and identifying a high-risk event; and S5, performing backtracking analysis on the high-risk events identified in the step S4, and forming a risk assessment report. According to the invention, through multi-source data processing, intelligent feature extraction and support vector machine model training technologies, accurate identification and real-time evaluation of network threats and deep traceability analysis of high-risk events are realized, and the initiative of network security protection is improved.

The invention discloses a data tracking analysis management platform based on a digital life insurance enterprise, and the platform comprises a management platform, and the operation process of the management platform specifically comprises the following steps: obtaining multi-source customer data; performing structured processing on the multi-source customer data to generate a customer portrait data set containing a customer label set; generating an initial risk prediction set based on the customer tag set; monitoring a customer behavior event and external environment data in real time, and generating a dynamic adjustment signal according to a preset trigger rule; risk re-evaluation is carried out, and an updated risk prediction set and a differentiated insurance strategy are generated; and generating a visual analysis report according to the customer portrait data set. The method has the following advantages and effects that a closed-loop management platform is constructed through multi-source data dynamic fusion and label weight real-time iteration, dynamic risk assessment is finally achieved, and the risk response speed and customer service accuracy of life insurance enterprises are remarkably improved.

The invention relates to the technical field of directional drilling track control, and discloses a coal mining underground directional drilling track control method and system, and the method comprises the steps: carrying out the fusion track analysis of a drilling machine based on sensor data, drilling parameters and pre-obtained geological hardness parameters, and obtaining a reference track of the drilling machine, a sensor, an azimuth angle sensor and a bit pressure sensor are used for synchronously collecting drill bit attitude data of a drilling machine to generate a real-time track of the drilling machine, then track offset calculation is carried out to obtain inclination angle deviation and azimuth angle deviation, and dynamic gain adjustment analysis is carried out on the drilling machine to obtain adjustment parameters of the drilling machine. And finally, the drill bit posture of the drilling machine is adjusted based on the adjusting parameters. According to the method, the directional control accuracy of the underground drilling track can be improved.

The invention relates to the technical field of track analysis, in particular to a track application reasoning method and system based on a track pre-training large model, and the method comprises the steps: obtaining a high-precision open-source track data set, cleaning the data set, and constructing a self-supervised training data set; constructing a track pre-training large model for extracting high-dimensional semantic features of the track; based on the self-supervised training data set, constructing a learning loss function to pre-train the track pre-training large model; aiming at a specific track downstream application, constructing a small sample labeling training data set; and cross-domain application training and reasoning are realized based on a small sample labeling training data set. According to the method, the semantic features of the track are extracted by using the track pre-training large model, and cross-domain application training and reasoning are carried out based on cross-domain data, so that the requirement on the number of application labeling samples in special fields can be remarkably reduced.

The embodiment of the invention discloses a terminal application behavior data analysis method and computer equipment, and the method comprises the steps: obtaining an interaction behavior data set of an RPA terminal application in a preset time window, the interaction behavior data set comprising a plurality of interaction behavior sequences, each interactive behavior sequence comprises at least one user interface operation event and a corresponding system response event; performing behavior track analysis processing on the interactive behavior data set, and extracting an interface circulation feature and an operation intention feature of each interactive behavior sequence; generating an interface optimization strategy according to the relevance between the interface circulation feature and the operation intention feature, wherein the interface optimization strategy comprises an interface layout adjustment rule and an interface response logic optimization rule; and feeding back the interface optimization strategy to a background management system of the RPA terminal application. Therefore, the problem that an existing RPA application interaction interface is insufficient in design and optimization can be effectively solved, and the usability of the interface and the response capability of the system are improved.

The invention belongs to the technical field of wind power plant management, and relates to a method and system for real-time positioning and trajectory analysis of wind power plant workers. The method comprises the following steps of: preprocessing multi-source position data by utilizing an edge computing node; fusing the preprocessed multi-source position data to generate a high-precision positioning result; performing trajectory analysis by using a deep learning algorithm to obtain a trajectory analysis result; performing intelligent task allocation by combining wind power plant equipment state data and environment data; on the basis of the physiological data and the environmental data of the staff, health condition monitoring and safety risk assessment of the staff are carried out; generating an equipment maintenance strategy by using a predictive maintenance algorithm in combination with the trajectory analysis result and the wind power plant equipment state data; and a worker real-time trajectory analysis result, an intelligent task allocation result, a monitoring and evaluation result and a standby maintenance strategy are visually presented. The positioning precision is improved, track analysis is accurate and reasonable, real-time safety monitoring of workers can be achieved, and the intelligent degree is high.

The invention provides an unmanned aerial vehicle flight attitude self-adaptive imbalance control method, device and equipment, and aims to accurately obtain an ideal control track and recognize a beneficial imbalance mode to generate a controllable imbalance strategy library by constructing a reverse track analysis and controllable imbalance strategy generation mechanism. A dynamic balance reference establishment and track reconstruction technology is adopted to construct an unbalance-recovery control track, and dynamic response characteristics are accurately obtained; an unbalance recovery space-time manifold construction and topology analysis technology is introduced, a core unbalance control feature set is extracted through space-time folding transformation, and unbalance control key parameters are determined; establishing an unbalance-recovery mapping rule and self-adaptive parameter fusion mechanism, balancing the control effect and the safety requirement, and generating a disturbance control parameter and a self-adaptive mapping parameter; a real-time control output regulation mechanism is constructed to generate a flight control signal in combination with reverse unbalance combined control and a multi-axis cooperative control mechanism; finally, a self-adaptive unbalance control system is formed, and precise unbalance control over the attitude of the unmanned aerial vehicle is achieved.

The invention discloses a cross-modal large model construction method and system based on track spatio-temporal characteristics, and belongs to the crossing field of artificial intelligence and dynamic spatio-temporal data processing, and the method comprises the steps: carrying out the sliding sampling and spatial distribution difference judgment through the semantic dynamic segmentation of multi-scale track spatio-temporal data, and generating spatio-temporal data blocks with consistent semantics; designing a space-time encoder of a hybrid architecture, extracting track time sequence association and spatial features, and unifying dimensions; constructing a text space-time fusion mechanism, and dynamically adapting cross-modal features by means of an anchor interface and gating fusion; a staged instruction fine tuning strategy is adopted, semantic alignment of space-time and text features is optimized firstly, then model top-layer parameters are trained cooperatively, complex scene adaptation is enhanced in combination with instruction difficulty progression and hard sample mining, and space-time constraint regular terms are introduced to guarantee output rationality. According to the method, high-precision cross-modal reasoning capability is provided for scenes such as track analysis and track prediction.

The invention provides an online calibration method for a household water meter, which comprises the following steps of: taking a closestool as a standard device for calibrating the household meter, collecting the maximum water consumption of the closestool as standard data for online calibration, and calibrating actually measured data of the maximum water consumption of the closestool by using the standard data. Wherein the process of identifying the maximum water consumption of the closestool comprises the following steps: firstly identifying the working mode of the water tank, then further identifying the water consumption mode of the closestool from the working mode of the water tank, and further extracting the maximum water consumption of the closestool. According to the method, the closestool used by each family is adopted as a calibration standard device of the household water meter, the characteristics of the closestool water consumption mode are recognized through long-term tracking analysis of the user water consumption mode, accurate extraction of the closestool water consumption is achieved on the basis, and a new development direction is provided for water meter calibration.

The invention discloses a one-key performance test method, medium and equipment based on JMeter and template configuration, and the method comprises the steps: obtaining a parameterized performance test template from a template configuration center in response to a test instruction, injecting runtime parameters through a template engine, and generating an executable JMeter test plan; parsing the plan to identify a load pressure demand, thereby dynamically scheduling resources from a distributed load injector cluster and distributing a test plan; uniformly triggering test execution on the cluster, and synchronously acquiring a performance data stream from the JMeter and a resource data stream of the tested system; and finally, timestamp alignment and fusion are carried out on the two types of data streams, intelligent diagnosis is carried out on a formed monitoring data set based on a diagnosis rule base or an intelligent analysis model, and a visual test report containing bottleneck positioning, resource association and link tracking analysis results is generated. According to the invention, full-process automation and intelligentization of performance testing are realized, and the testing efficiency is remarkably improved.

The invention relates to the technical field of production process control, in particular to an intelligent control system and method for a shower valve production process, and the system comprises an environment monitoring module which collects the operation state of a machine, the batch quality of raw materials, and the temperature and humidity data of a surrounding environment, carries out the real-time monitoring of the operation state of the machine, and carries out the tracking analysis of the batch of the raw materials. According to the method, by continuously monitoring the machine state and the environment condition, key variables in the production process are controlled, dynamic data analysis is conducted, production parameters are adjusted in time, optimal matching of raw material use and the production rhythm is ensured, waste is effectively reduced, the operation efficiency of a production line is improved, and the risk assessment model is utilized; potential problems in the production process can be predicted and relieved, production safety is improved, energy consumption is optimized by applying an evolutionary algorithm, balance between environmental protection and cost effectiveness is achieved, production configuration is continuously optimized through a real-time feedback mechanism, it is ensured that production activities meet the latest operation standard, and overall production efficiency is improved.

The invention provides a security risk analysis method and system based on an intelligent camera. The method comprises the following steps: acquiring a real-time video stream from a monitoring area through an intelligent camera, and performing target detection on each frame of image in the real-time video stream to obtain a target list; the target list comprises a first detection result of the target object detected in each frame of image; the cloud server receives a target list sent by each intelligent camera; associating the first detection result of the target object according to the spatial position of each intelligent camera and the corresponding target list to obtain a second detection result; according to the second detection result of each target object, carrying out tracking analysis on the motion track of the target object to obtain behavior information and track information of the target object; when it is judged that the behavior information and the track information of the target object meet early warning conditions, an alarm message is sent to terminal equipment; and the terminal equipment displays the alarm information, so that abnormal behaviors and dangerous events can be identified timely and accurately.

The invention relates to the technical field of road surface monitoring, in particular to a sensor-based road surface quality detection system, which comprises a disturbance extraction module, a trajectory analysis module, a consistency identification module, a sudden change sorting module and a crack confirmation module. According to the invention, through the time sequence correlation analysis of the vertical structure vibration acceleration, the GPS direction vector and the transverse angular velocity signal in the vehicle driving process, the disturbance behavior and the trajectory deviation trend change in the vehicle and road surface contact process can be identified, and after the structure disturbance and the trajectory variation are correlated and compared, the stability of the vehicle is improved. Further screening real disturbance response from change consistency, constructing a space transfer model of crack mutation in combination with a joint state structure of disturbance intensity and offset amplitude, identifying a joint abnormal interval of structural damage and trajectory deviation in time and space, and determining a joint abnormal interval of structural damage and trajectory deviation in time and space through a multi-dimensional index distribution density and time continuity discrimination standard. And accurate judgment and spatial calibration of the crack area are realized.

The invention relates to a method, device and equipment for detecting shopping settlement fraud risk behaviors and a medium, and the method comprises the steps: obtaining a plurality of continuous video frames, and detecting a human body bounding box and a posture key point of a character object in the video frames, thereby obtaining a posture detection result; determining an upper body bounding box according to the human body bounding box, and tracking the upper body bounding box in a plurality of continuous video frames to obtain a motion track; according to the posture detection result and the motion track, determining whether the character object triggers a shopping behavior through track analysis and posture analysis, and if the character object triggers the shopping behavior, outputting and storing a detection record for a shopping behavior triggering event of the character object to obtain a shopping behavior detection result; and under the condition that the shopping behavior detection result represents that a shopping behavior trigger event exists, determining a wrist area image according to the wrist key point, performing commodity identification on the wrist area image through open set target detection, outputting and storing a commodity identification event record, and obtaining a shopping settlement detection result.

The invention discloses a DC power transmission fault diagnosis method and system based on multi-modal data analysis, and relates to the field of DC power transmission line fault diagnosis, and the method comprises the steps: starting temporary line protection measures when a DC power transmission line has abnormal fluctuation, and importing parameters into a data processing center for feature extraction; obtaining fault types corresponding to the effective features; and performing statistics on each associated feature of each fault type to obtain a fault diagnosis result. According to the invention, the multi-modal parameters of the DC transmission line are monitored in real time, and electromagnetic interference is eliminated, so that the accuracy of fault diagnosis is improved, the reliability of data quality is ensured, and the precision of fault diagnosis is improved. And monitoring and protection strategies are adjusted, so that the optimal operation guarantee is achieved. And through dynamic monitoring and tracking analysis of potential faults, the stability and reliability of the system are improved.

The invention discloses an e-commerce platform advertisement effect tracking analysis method based on a big data technology, and the method comprises the following steps: S1, collecting user data, and constructing a user behavior graph; s2, introducing an advertisement intervention path, comparing propagation states with or without advertisement intervention, calculating a disturbance consistency value and labeling an edge weight, and forming a structure chart; s3, inputting the structure diagram into a diagram causal reasoning network, and outputting advertisement individual effect estimation; s4, constructing a behavior disturbance sampler, generating a plurality of anti-fact behavior path training samples, and improving the causal fitting capability of the model; s5, executing path-level attribution on the actual user behavior graph by using the trained model, and outputting a causal weight matrix; and S6, generating an optimized advertisement putting strategy according to an attribution result. The advertisement causal attribution precision is improved, the putting strategy is optimized, and the conversion efficiency and the budget utilization rate are remarkably improved.

The invention relates to the technical field of battery pack capacity attenuation analysis, and discloses a battery pack capacity attenuation characteristic decoupling analysis method, which comprises the following steps: planning an analysis process based on battery pack design parameters and working conditions, determining electrochemical impedance spectroscopy content and cyclic aging test content, and determining different discharge rate types; generating a capacity attenuation analysis process set, determining an execution sequence according to an optimal matching principle, and generating a spectrogram acquisition setting list and a test scheme list of each magnification; and finally, associating to generate an overall analysis scheme. And the analysis content is updated through real-time operation data, the process execution sequence is adjusted, and a database is established for data management and long-term tracking analysis. According to the method, decoupling analysis of the capacity attenuation characteristics of the battery pack is realized, the analysis accuracy and reliability are improved, and technical support is provided for optimization of the battery pack.

The invention discloses a cold-chain pork quality dynamic evaluation method and system based on multi-source data fusion, and relates to the technical field of data processing and intelligent evaluation, and the method comprises the following steps: collecting multi-source signal information of a whole cold-chain transportation process, sorting according to a unified time sequence, continuously arranging all signals on a time axis, and carrying out data processing on all the signals; a complete dynamic record is formed; and extracting each time interval and signal amplitude change track from the formed dynamic record, analyzing a signal section with repeated wave crests in a short time, and generating a preliminary data manuscript for reflection interference judgment. According to the invention, through time sequence arrangement and dynamic association of multi-source signals, a wave crest track analysis mechanism is constructed, real-time identification and correction of reflection interference are realized, and continuity and accuracy of a gas concentration curve are ensured; and by generating a reflection source position list and adjusting numerical value distribution of repeated fragments, false fluctuation is eliminated, and the stability and reliability of cold-chain pork quality evaluation are improved.

The embodiment of the invention discloses a digital management method and system for a main and distribution network project. The method comprises the following steps: performing project information structured modeling on a full life cycle of a target main and distribution network project to obtain a project stage node index model; chain type tracking analysis is carried out on the engineering stage nodes, an information interaction rule and a data transmission path are established, and a data flow topological graph is obtained; extracting front-end node state layer information and rear-end engineering monitoring layer information based on a front-end and rear-end separation strategy; and constructing a multi-dimensional influence factor association network by using the information to perform risk evolution path deduction and risk event simulation to obtain a simulation result and generate a cross-node cooperative response strategy. According to the embodiment of the invention, through the digital support of the node chain type tracking and the front and back end separation architecture, the data island of each stage of the main and distribution network project is eliminated, the cooperation efficiency of the project management is improved, and the controllability of the whole life cycle process is enhanced.

The invention discloses a livestock farm inspection robot autonomous inspection system and method based on reinforcement learning, and relates to the technical field of livestock health management. Comprising the following steps: constructing a plurality of farm environment maps by an inspection robot, dividing a virtual inspectable area, obtaining environment data, receiving real-time position information of a livestock positioning collar, obtaining historical position data of livestock, establishing a trajectory analysis model, and obtaining livestock trajectory analysis data to generate a trajectory prediction model; a livestock activity area in a specific time period is predicted, a to-be-inspected area is determined based on the livestock activity area and the real-time position information, and the inspection robot determines and constructs a learning reinforcement model to optimize an inspection path according to the number, distance and time of livestock in the to-be-inspected area and detects the physical condition of the livestock. And the livestock health condition is determined based on the livestock physical condition and the livestock track analysis data, and the information is uploaded to the cloud server. According to the invention, efficient, fine and intelligent farm management can be realized.

Systems and techniques for generating track geometry data (TGD) for railroad track analysis. In embodiments, rail profile data for each of the rails of a track may be obtained at regular intervals as a vehicle carrying the system traverses the track. The rail profile data may be validated, and reference points on the profiles of each of the rails corresponding to specific geometric features of the rails may be identified. The rail profile data may be filtered and processed to generate track geometry data. The track geometry data may include data for a plurality of data channels. The track geometry data may be transmitted to a backend system receives for further processing to determine rail defects, and to generate notifications and alerts based on the determined defects. The system utilizes machine learning algorithms and models for defect detection and validation.

The invention relates to the technical field of ship safety, in particular to a ship navigation monitoring management system and method based on edge computing, and the system comprises seven cooperation modules: a data collection module synchronously obtains ship multi-source data, and a processing storage module achieves the data compression, marking and storage. The encryption security module completes full-process AES-256 encryption through a hardware encryption chip, the dual-power supply module guarantees stable power supply, the satellite positioning module obtains accurate positioning through Beidou / GPS dual modes, the self-inspection maintenance module monitors faults in real time, and the AI edge calculation unit achieves channel object recognition, track analysis and violation warning through a deep neural network model. According to the method, closed-loop processes of data acquisition, encryption, processing, intelligent analysis and fault handling are completed through module cooperation; the invention aims to solve the problems of high monitoring delay, insufficient data security, unreliable power supply and the like of the existing system.

A distributed link tracking, analysis method and server, global scheduler, in the process of processing user request, when producing detail event, the meta event for detail event search is produced, the meta event contains tracking identification and event production time information, and the data quantity is less than the data quantity of corresponding detail event; The server saves the detail event and the meta event produced separately in local. The global scheduler in the distributed cluster sends a link query request to the server in the distributed cluster, receives the event information related to the tracking identification returned by the server after local search; and based on the received event information, the user request is analyzed by link. The application adopts local storage, on-demand analysis, removes the central warehouse and large database, solves the problems of high cost of data storage, analysis and network bandwidth, and scalability.

The invention provides a sea surface target stable handover and track tracking method and system based on a navigation radar. The method comprises the following steps: the navigation radar performs multi-target stable tracking by using sea surface target echoes; analyzing and screening, and appointing important targets which need to be focused; photoelectric tracking target parameters are calculated by utilizing a sea surface dynamic constraint equation or different-station deployment longitude and latitude conversion according to radar and photoelectric positions; and a photoelectric tracking target is guided, and the track of the target is dynamically and finely adjusted to realize stable tracking of a stable target image. Due to the fact that coordinates of targets measured by the radar are different, each datum cannot be converted to three-dimensional target tracking, and position conversion of the targets cannot be achieved. According to the method, under the condition that a sea surface target lacks measurement dimensions, mutual conversion between measurement coordinates can still be achieved by means of a constraint equation and longitude and latitude conversion, and rapid guide tracking of the target is stably achieved.

The invention relates to an intelligent safety management and control technology, in particular to an intelligent safety management and control system for a hydropower station, which solves the defects of the traditional method in the aspects of real-time performance, accuracy and intelligence by integrating advanced data acquisition, personnel identification and track recording and analysis technologies. According to the system, positioning information is collected through portable equipment, personnel images are collected through face recognition and video monitoring, tracks of internal personnel and tracks of external personnel are distinguished, and abnormal behaviors are recognized through historical track analysis and a set time threshold value. The monitoring of the behaviors of the external personnel is enhanced, the management capability of the abnormal behaviors of the internal personnel is also improved, the safety management and control efficiency and the response speed are effectively improved, and an efficient and intelligent solution is provided for the safety management of the hydropower station.

The invention discloses a mechanical arm anti-collision analysis system based on an explosive ordnance disposal robot, and relates to the technical field of public safety, the mechanical arm anti-collision analysis system comprises a multi-source heterogeneous data acquisition module, a space-time alignment characteristic distillation module, an attribute-track coupling prediction module and the like, and precise anti-collision is achieved by fusing obstacle attributes and tracks and gradient protection boundaries. The system has the advantages that attribute data such as obstacle material components and mechanical arm state data are synchronously obtained through the multi-source heterogeneous data acquisition module, features are extracted through fusion of the space-time alignment and feature distillation module, and a standardized data set containing rigid, explosive and quality tags is generated; and the attribute tag is incorporated into a state space by the obstacle attribute-track coupling prediction model, and the obstacle attribute and the motion track are associated to carry out collaborative analysis, so that the defect of separation of dynamic obstacle attribute and track analysis in the prior art is effectively overcome, and an accurate attribute-track association basis is provided for anti-collision analysis.

The invention discloses a personalized virtual-real interaction cognitive game system based on AI adaptive brain cognitive enhancement, comprising: a cognitive game design module for designing a plurality of cognitive tasks corresponding to attention, memory, execution function and perception function based on a dynamic difficulty adjustment algorithm according to user characteristics; the dynamic difficulty adjustment algorithm is used for dynamically adjusting the difficulty of a specific level task according to the behavior state and the psychological state of the user; the cognitive task guiding module interacts with the user through a multi-mode guiding mechanism to guide the user to complete a cognitive task; the data tracking analysis module is used for recording the performance and growth trajectory of the user in the completion process of each cognitive task; the instant feedback module provides feedback based on an instant feedback generation algorithm; the cognitive competence evaluation report module is used for generating a corresponding cognitive competence evaluation report according to the performance and data tracking analysis algorithm; and the data storage module is used for storing behavior data, emotion records and cognitive competence evaluation reports of the user.

The invention relates to the field of electric vehicle mileage prediction, in particular to a method and system for predicting the vehicle driving mileage based on real-time data and electronic equipment. The system comprises a battery charging and discharging period identification module, a data extraction and cleaning module, a track segment identification and distance calculation module, an energy consumption behavior modeling and mileage coefficient calculation module, a sample screening and aggregation module and a remaining mileage prediction output module. According to the method, the battery working state, the GPS track and the historical charging and discharging behavior are used as basic data input, multi-layer data processing, track analysis, energy consumption modeling and prediction output are used as intermediate processing paths, and high-precision prediction of the mileage of the electric vehicle under different batteries, different environments and different working conditions can be achieved.

The invention provides a server cluster load balancing state tracking analysis method, and relates to the technical field of servers, and the method comprises the steps: collecting a load migration triggering moment, the number of control messages generated by single migration, and the number of active connections to be migrated of each server from each server in a cluster, meanwhile, the bandwidth bearing upper limit of a network control channel is obtained, migration triggering events are arranged according to the time sequence, and a list of the number of triggered migration servers is obtained; the migration scheduling strategy of each server in the load balancing system is updated by adopting the target migration coordination delay interval, a migration rhythm coordination confirmation signal is obtained by monitoring the network congestion change, and whether the cluster load balancing state is recovered to a safe range is verified by combining a high-risk time period list, so that the stability and efficiency of load migration are improved, and the load balancing efficiency is improved. And a reliable flow coordination guarantee is provided for a large-scale cluster system.