174 results about "Collaborative network" patented technology

The invention discloses a software multi-agent collaboration method and system based on a large language model, and the method comprises the steps: receiving natural language task description submitted by a user at the same time, carrying out the semantic understanding and intention recognition through a pre-trained large language model center, and generating a structured task element set; based on the structured task element set, the large language model center generates a task dependency graph through multiple rounds of reasoning, and the task dependency graph comprises a plurality of atomic subtasks, logic relations among the tasks and data flow constraints; according to a topological structure and resource demand characteristics of a task dependency graph, a double-layer graph attention network is adopted to dynamically match a professional agent with specific domain capability, and a distributed collaborative network is formed. Through the dynamic graph network scheduling and cross-domain semantic alignment mechanism, the problems that the multi-agent dynamic collaborative adaptation capability is insufficient and cross-domain semantic fusion is difficult are solved.

The invention discloses a data security risk assessment method based on a big data model, and relates to the technical field of data security, and the method comprises the steps: collecting and preprocessing multi-source data, collecting security-related data from network equipment, a server and an application system, carrying out the preprocessing, carrying out the adaptive feature extraction, and carrying out the data security risk assessment. The feature importance is evaluated by calculating the mutual information amount of features and risk tags, a standardized feature vector set is constructed, multi-model collaborative analysis is performed, feature vectors are input into a cascade collaborative network composed of an anomaly detection model, a threat recognition model, a correlation analysis model and a prediction model, and a risk risk is obtained. Through cross-model feature transmission and a bidirectional information feedback mechanism, deep collaborative analysis and multi-model deep fusion decision making are carried out, a weight is calculated according to historical accuracy of each model, a comprehensive risk score is calculated by adopting dynamic gating deep fusion, and a dynamic threshold value is calculated based on a sliding time window. And the risk is divided into three levels of high risk, medium risk and low risk.

The invention discloses an explainable appearance feature learning and quantitative representation method for aerodynamic parameter prediction, and belongs to the technical field of aerodynamics and artificial intelligence crossing. The method comprises the following steps: constructing a collaborative network architecture comprising an aerodynamic prediction module, an airfoil concept learning module and a quantitative distillation agent module; while high-precision and high-efficiency aerodynamic parameter prediction is realized, a prediction result can be decomposed into the sum of quantitative contributions of different airfoil profile concepts, so that a direct explainable basis is provided for the prediction result, and expert users are assisted in understanding and verifying the prediction process of the model.

A collaborative transformation matrix learning system extends adaptive compression and encryption architectures through federated, privacy-preserving optimization. Each node analyzes local data distributions to generate anonymized distribution profiles using differential-privacy mechanisms, securely exchanging profiles and validated transformation matrices across a collaborative network. A trust and validation engine verifies mathematical properties and evaluates claimed performance metrics. Validated matrices are integrated into local optimization when trust and performance thresholds are satisfied. The system employs secure multi-party computation, homomorphic encryption, and conflict-resolution logic to ensure integrity of shared insights while preventing exposure of sensitive information. By combining collective learning with local adaptation, the invention accelerates convergence to optimal matrix configurations, mitigates cold-start inefficiencies, and improves compression-encryption efficiency and cryptographic strength across distributed deployments.

The invention relates to the technical field of medical image information processing, and discloses a medical image information management system based on wisdom medical treatment, which comprises a scene perception preprocessing module, a cross-modal attention fusion module, a weak supervision annotation module, a comparative analysis module, a collaborative report generation module, a knowledge graph reasoning decision module and a consultation collaborative platform module. The image quality is improved through scene adaptive preprocessing; a hierarchical cross-modal attention mechanism is adopted to realize multi-modal feature alignment and fusion; lesion labeling is carried out based on a cascade weak supervised learning strategy; obtaining a focus evolution trend through time sequence correlation analysis; generating a standardized diagnosis report by using a visual language collaborative network; providing treatment scheme recommendation based on individualized knowledge graph reasoning; and a multidisciplinary consultation cooperation platform is constructed. According to the method, the scene adaptability of image quality evaluation is improved, the multi-modal semantic alignment capability is enhanced, the dependence of focus labeling on a fine sample is reduced, and dynamic disease monitoring and individualized diagnosis and treatment decision support are realized.

The invention discloses an infrared thermal image enhanced real-time rectal blood vessel blood flow detection method. The method comprises the following steps that S1, a thermal image of an operation area is collected through an infrared thermal imager to serve as a to-be-processed medical purpose image; s2, adopting a double-branch collaborative network architecture, adopting an improved U-Net structure for a deep denoising priori network DDPNet, introducing a detail sensing unit DAU, and separating noise and organizational structure features through residual learning; and S3, obtaining a supervision signal in the image, and processing each supervision model to obtain a processed image. The method can effectively improve the accuracy and comprehensiveness of information obtained after image processing in the operation and improve the effectiveness of information acquisition in the operation process.

This disclosure provides an AI filmmaking workflow including AI-assisted storyboarding, AI animation, and post-production processes for creating films. The workflow provides techniques for reconstructing 3D digital environments and characters, and for virtual camera control. The workflow also provides techniques for capturing 2D live-action performances and extracting visual cues. The AI animation process generates synthetic images and video using prompts that are based on virtual camera control in case of 3D digitization and / or visual cues in case of 2D camera capturing. Further, the workflow provides techniques for compositing with AI assistance, to generate a composited video based on the AI-animated video and inputs resulting from 3D digitization and / or 2D video processing. Advanced post-processing techniques are also provided for generating a complete film based on the composited video. This framework is designed to facilitate creative collaborative networks by using a hybrid digitization approach to enhance consistency, directability, and scalability in AI filmmaking.

The invention relates to the technical field of sensor networks and Internet of Things, in particular to a data center air quality intelligent early warning method and system based on a sensor network. The method comprises the following steps: constructing a self-organizing cooperative network by deploying multiple types of sensor nodes in a key area of a data center, and collecting and correcting multi-dimensional air data in real time; the data reliability is improved through inter-node dynamic weight fusion and local anomaly recognition; establishing an air parameter and machine room structure correlation model by utilizing space mapping and time sequence analysis, and identifying a micro-scale diffusion trend; a self-adaptive dynamic threshold mechanism is constructed, and threshold rolling optimization is realized in combination with historical statistics and real-time feedback; and generating a graded alarm strategy based on multi-stage early warning judgment and triggering conditions, and continuously self-optimizing early warning precision and response efficiency through closed-loop feedback. According to the invention, all-around, intelligent and high-reliability early warning and regulation and control of the air quality of the data center are realized.

The invention, which belongs to the technical field of tunnel engineering, discloses a tunnel surrounding rock pressure arch calculation system comprising a multi-source data fusion acquisition module, a surrounding rock grade intelligent identification module, a self-adaptive pressure arch parameter calculation module and a dynamic load prediction and optimization module. The multi-source data fusion acquisition module acquires geological parameters, drilling monitoring data and case data and generates fusion feature vectors; the surrounding rock grade intelligent identification module outputs a surrounding rock classification result based on a deep residual network and an attention mechanism; the self-adaptive pressure arch parameter calculation module calculates pressure arch parameters by adopting an improved Prscherski theory and a stress release time-varying function; the dynamic load prediction and optimization module predicts the load through the space-time collaborative network and feeds the deviation back to the calculation module to form closed-loop optimization, precise dynamic calculation of the surrounding rock pressure arch parameters is achieved, and the calculation precision is improved by 20% or above compared with a traditional method.

The invention belongs to the technical field of nuclear power station maintenance management, and particularly relates to a nuclear power maintenance decision-making system and method based on multi-Agent cooperation. The system comprises an input layer, a multi-Agent cooperation layer, a knowledge support layer, a decision processing layer, an output and interaction layer and a feedback learning layer. The input layer receives initial work order information including equipment basic information and fault description; the multi-Agent collaboration layer allocates sub-tasks to predefined six types of professional Agents according to work order information and completes information interaction; the knowledge support layer comprises a nuclear power professional knowledge base, a historical maintenance database and a rule and regulation library; the decision processing layer performs conflict detection, negotiation and decision fusion on Agent output; the output and interaction layer provides a decision result display and man-machine interaction interface; and the feedback learning layer realizes comprehensive improvement of nuclear power maintenance decision-making efficiency and safety. The method has the beneficial effects that a multi-professional Agent collaborative network technical means is adopted, and the technical effects of maintenance decision cross-professional information instant sharing and efficient collaboration are realized.

The invention relates to the technical field of mine safety, and particularly discloses a gas monitoring and early warning system for roadway tunneling construction, which comprises a data acquisition module, a data processing module, a prediction module, an early warning analysis module, a sensor sensitivity compensation judgment module and a self-adaptive calibration module. Harmful gas concentration data and environment, geology and tunneling operation data are collected by constructing a three-level collaborative network of fixed nodes, an inspection robot and a tunneling machine micro sensor; carrying out data cleaning and fusing multi-source gas concentration data to obtain real-time gas concentration; predicting the gas concentration at the next moment through an LSTM network training model based on the real-time gas concentration; performing gradient early warning according to the predicted value and a safety threshold value, and linking ventilation, equipment and a risk avoiding system; sensitivity attenuation of the gas sensor is further judged and calibrated, full-domain real-time sensing, trend prediction and early warning and intelligent calibration of the sensor of the driving face gas are achieved, and monitoring timeliness, early warning scientificity and data reliability are improved.

The invention discloses a corn seedling and weed detection method and device based on a space-spectrum collaborative network. The method comprises the following steps: obtaining corn seedling and weed images in a natural farmland environment to construct a data set, and marking the data set; performing data enhancement on the annotation data set, and dividing the annotation data set into a training set, a verification set and a test set; a corn seedling and weed detection model is constructed based on a YOLOv11 architecture, and the model comprises a feature distribution unit, a spatial structure feature extraction branch, a spectrum context modeling branch, a space-spectrum feature fusion unit, a neck network unit and a detection head unit. Initializing network parameters of the detection model, designing a BCG-WIoU bounding box regression loss function, and training the detection model by using a training set; and reasoning the input image by using the trained detection model, and outputting the category and position information of the corn seedlings and weeds.

The invention relates to a network security domain knowledge graph construction method. The method comprises the steps of (1) performing named entity extraction for a network security domain based on multi-model cooperative verification, and training a lightweight model; (2) segmenting a long text based on an entity perception multi-dimensional scoring dynamic sliding window; (3) performing named entity and relation extraction and lightweight entity relation identification model construction based on multi-model collaborative network security; and (4) based on the extracted and disambiguated entities and relationships, designing a knowledge graph mode to construct a network security knowledge graph. The invention also relates to a corresponding system, device, processor and computer readable storage medium. By adopting the network security domain knowledge graph construction method, system and device, the processor and the computer readable storage medium, the computing power demand of a large model during element extraction is effectively reduced, and the accuracy and recognition types of network security entities and relationships thereof when the large model processes a long text are improved.

The invention provides an intelligent engine collaboration method and system oriented to multi-modal query, and relates to the technical field of data collaboration process.The method comprises the steps that query dimension analysis is conducted according to a received multi-modal query request, a query analysis result is generated, tasks are dynamically distributed to matched engines based on the analysis result, and the tasks are distributed to the matched engines. All the engines execute processing operation in parallel or in a serialization mode, multi-source data association fusion is conducted on heterogeneous engine results, and fusion results are packaged into visual data to be fed back to a user; the system comprises a query dimension analysis module, an engine task distribution module, an engine execution module, a multi-source data association fusion module and a result visualization packaging module. According to the method, the multi-modal query fingerprint database and the graph structure collaborative network are constructed, and the pre-training graph convolutional network is combined to optimize the control parameters, so that the query performance is remarkably improved, the query delay is reduced, the accuracy is improved, and the method is suitable for complex scenes such as smart cities and business intelligence.

The embodiment of the invention provides an unmanned aerial vehicle defense positioning method and system for a complex and wide area, and relates to the technical field of unmanned aerial vehicle defense positioning, and the method comprises the steps: firstly constructing a multi-source detection node dynamic cooperation network which comprises different detection type nodes, a real-time interaction link, an environment adaptation parameter set and a detection parameter dynamic adjustment rule; detection parameters are updated in real time according to regional environment data, then inter-level signal cross detection is executed based on the multi-source detection node dynamic collaborative network, related data are acquired, a positioning guide parameter set is generated according to the detection data, detection nodes are scheduled to carry out cross-level collaborative positioning confirmation, and an unmanned aerial vehicle cross-level collaborative positioning result is obtained. And finally, generating an unmanned aerial vehicle defense positioning instruction adaptive to the regional environment according to an unmanned aerial vehicle cross-level cooperative positioning result, and feeding back a positioning result to a network to update a detection parameter adjustment rule, thereby effectively coping with a complex and wide region unmanned aerial vehicle threat, and realizing accurate defense positioning.

The invention relates to a resource orchestration method and device for a collaborative network, equipment, a storage medium and a program product. Comprising the following steps: constructing an optimization model taking maximization of total satisfaction, minimization of service duration and minimization of energy consumption of an unmanned aerial vehicle as targets; the total satisfaction is the sum of the satisfaction of each target area, the service duration is the sum of the sensing duration, the transmission duration, the calculation time and the flight time corresponding to each target area, and the energy consumption of the unmanned aerial vehicle is the sum of the sensing energy consumption, the transmission energy consumption, the flight energy consumption and the hovering energy consumption corresponding to each target area; solving the optimization model under the constraint of a preset constraint condition to obtain a computing resource allocation strategy and a sensing communication strategy; comprise computing time, sensing duration, service sequences, flight heights and transmission power of various computing resources. By adopting the method, multi-target collaborative optimization is realized, namely, the service duration and the energy consumption of the unmanned aerial vehicle are minimized while the total satisfaction degree of the system is maximized.

The invention discloses a collaborative network sensing method and device, and relates to the technical field of data communication, and the method comprises the steps: collecting original data by each function branch of network sensing, carrying out the preprocessing of the original data to generate intermediate data, carrying out the data conversion and safety processing of the intermediate data, and sharing the intermediate data to other function branches; each functional branch generates a sensing result according to the received intermediate data and the own intermediate data, and the sensing results which can be utilized by other functional branches are shared; meanwhile, operation suggestions are generated and executed according to sensing results, and operation execution results are fed back to the system and shared to other functional branches generating influences. According to the invention, the barrier of each functional branch is broken, so that each functional branch can be kept relatively independent and can cooperate with each other. The technical problems that the consideration factors are not comprehensive and the sensed operation is not accurate enough due to the fact that a single network function branch is depended at present are solved.

The invention provides a multi-agent-based power market risk monitoring method and system, relates to the field of power market risk control, and integrates information perception, inference analysis and execution feedback functions by constructing a three-layer progressive multi-agent collaborative network composed of an information monitoring layer, a collaborative analysis layer and a supervision execution layer. A hybrid communication protocol based on semantic similarity is adopted, structural data transmission and a natural language collaborative discussion mechanism are combined, deep collaboration between agents is comprehensively supported from quantitative analysis to qualitative reasoning, and in the process of generating a power market decision scheme, the decision scheme of the power market is generated based on an antagonism debate mechanism. The efficiency optimization agent and the risk control agent are debate for multiple rounds, the weight is dynamically adjusted through the reinforcement learning algorithm to generate a balance decision scheme, the limitation of a traditional single-target optimization method is broken through, multiple targets of supervision efficiency and risk control can be completely considered, and self-adaptive decision support is provided for intelligent supervision and risk prevention and control of the power market.

The invention relates to the technical field of intelligent management of power generation enterprises, in particular to an on-line cooperative heat supply control system and method of a cooperative network system.The cooperative network system is composed of a plurality of heat supply units and comprises a collecting part, a data processing part and a data processing part, heat supply data and environment temperature change data of each unit and power grid load change data of the collaborative network system are collected; and the control part is used for identifying the actual state of each unit based on the heat supply data, the environment temperature change data and the power grid load change data, forming an optimal combination scheme under various heat supply modes and peak regulation modes, generating an online cooperative heat supply control instruction and controlling each unit to work. Therefore, the problems that in the related technology, a peak regulation means only focuses on deep peak regulation, the unit tip peak capacity is weakened, an online cooperation scheme is lacked after multi-mode transformation, then the deep peak regulation and the high rated load output capacity need to be considered in flexible transformation of the heat supply unit, and it is difficult for a power generation enterprise to achieve economic benefit maximization through cooperation are solved.

The present disclosure provides a modeling method, a related device, a medium, and a product. The method comprises: receiving a first message sent by a second network device, wherein the first message comprises first task information and / or second task information, the first task information indicates a modeling task of a first network device, the second task information indicates a modeling task of a terminal, and the first network device, the second network device, and the terminal access the same collaborative network; and sending a second message to the second network device, wherein the second message comprises a distributed model integrated with a terminal model.

The invention discloses a data processing method and system based on a multi-party cooperative network. The method comprises the following steps: deploying a digital steward for an operation main body and participants; generating a main body identifier for each participant, and signing and issuing an identity certificate; each participant stores a subject identifier, an identity certificate and a private key of the participant in a digital steward; the participant of the data request issues an invitation link to the participant of the data owner; the data owner receives the invitation link through the digital steward and verifies the identity of the participant of the data request, and the data request verifies the identity of the participant of the data owner; the identity verification comprises main body identification and identity certificate verification; when the identities of the data request and the participant of the data owner pass verification, the data owner and the participant of the data request establish an encrypted connection channel based on the exclusive secret key determined by negotiation; and transmitting the data of the participant of the data request and the participant of the data owner through the encrypted connection channel.

The invention discloses a big data processing method and system for annual display of a customer insurance policy scheme, and relates to the technical field of data processing, and the processing method comprises the specific steps: S1, cross-mechanism data authorization and collaborative network construction; s2, building a federal learning health risk portrait; s3, differential privacy sensitive data processing; s4, zero-knowledge proof data verification; s5, generating a multi-source data fusion year book; according to the method, the cross-mechanism privacy computing cooperation network is constructed, federal learning dynamic aggregation is combined, efficient integration and safe modeling of multi-source data are achieved, the risk portrait accuracy is improved, prediction errors are stably controlled within 1%, and sensitive data desensitization is reliable; meanwhile, 'risk analysis-guarantee matching-data traceability 'year book logic is innovated, health portraits and insurance policy data are deeply fused, information transparency and decision support are enhanced through multi-terminal adaptation differentiation display, data source labeling and conclusion verification, and the problem of traditional insurance policy display fragmentation is solved.

The application relates to the field of construction scheduling control, in particular to a water conservancy engineering construction mechanical group collaborative operation intelligent scheduling system, which comprises the following steps: a geological mechanical coupling twin platform constructs a bidirectional coupling closed loop of a geological field and construction mechanical behavior according to real-time working condition data of a region to be constructed, obtains a dynamic geological attribute field quantity, and fits a geological disturbance sensitive coefficient of the region to be constructed; a cloud edge end collaborative network collects real-time working condition data through an intelligent agent node; a cloud computing center solves a global construction task scheduling scheme by adopting a multi-objective optimization algorithm; an edge computing node adopts a multi-agent deep reinforcement learning algorithm to perform real-time rolling optimization on the collaborative operation of construction machines in a local jurisdiction region according to a screened optimal local construction task distribution scheme, generates a local collaborative scheduling instruction, and schedules each construction machine. The scheduling system can realize real-time sensing of geological changes, reduce the probability of engineering accidents, and dynamically optimize the collaborative operation of the mechanical group.

The invention discloses a crane electricity approaching early warning system and a using method thereof, and belongs to the technical field of electricity approaching detection. The system mainly comprises a sensing unit and a cloud end collaborative network, the sensing unit is connected with a control unit, the control unit is connected with a grading alarm unit, and the grading alarm unit is in communication connection with the cloud end collaborative network. The distance between the suspension arm and the electric wire can be judged in real time through multi-source sensing fusion, AI visual recognition and a dynamic digital twinborn model, and the core beneficial effect of the invention is that three-level guarantee from early warning to limitation and then active intervention is constructed, and cooperation is realized through sound and light, a force feedback handle and the cloud.

The invention relates to the related technical field of patient service management, in particular to an intelligent interaction method and system for improving hospital patient service and information collaboration, and the method comprises the steps: constructing a three-dimensional feature vector of a hospital service state, deploying a demand guidance response strategy, setting a double-delay gradient under a service collaboration framework, and combining a doctor-seeing constraint condition to obtain a patient service state; and loading the optimized interaction response parameter to a terminal cluster, and configuring a communication interaction protocol and a collaborative semantic mapping rule corresponding to the information collaborative network. The technical problems that an existing service response mode is fixed, it is difficult to accurately mine the coupling relation between patient core demands and medical care resource supply, and a resource scheduling decision lags behind service dynamic change are solved, three-dimensional feature vectors containing patients, medical care and environments are constructed, the coupling relation between patient core demand features and medical care resource supply features is extracted, and the resource scheduling decision is determined to be more accurate. And dynamically deploying the demand guidance strategy and collecting the interaction parameters, thereby improving the response efficiency of the service in the hospital and the information cooperation reliability.

The invention provides a cooperative fault ride-through and frequency support method for a following-networking converter, and belongs to the technical field of converter cluster cooperative control. The method comprises the steps of performing modeling and association arrangement on operation state information of a power grid and operation information of a follow-up grid construction converter; based on the operation state information of the power grid after modeling and association arrangement and the operation information of the follow-up network construction converters, a social cooperation network is constructed, a cooperation control instruction is generated according to the social cooperation network, and the cooperation control instruction is used for indicating cooperation control requirements of the follow-up network construction converters; issuing the cooperative control instruction to the corresponding follow-up network construction converter; and each networking converter executes a control action according to the cooperative control instruction to realize cooperative fault ride-through control and frequency support control. According to the method, the decentralized social cooperative network is constructed, so that the network-following converter cluster can be quickly self-organized to form cooperative control when the power grid fails.

The present application relates to the field of machine vision and intelligent detection technology, and provides a multi-detection head vehicle body sealant detection system and method based on attention guidance, which comprises a multi-detection head organization module, a regional attention distribution module, a cross-validation module and a dynamic response strategy module. The multi-detection head is organized into a collaborative network by constructing a detection head topological relationship diagram, the detection area is intelligently divided based on the attention mechanism to eliminate overlapping redundancy, multi-view cross-validation is performed using adjacent detection heads, and the decision weight is dynamically adjusted according to the real-time state. The calculation efficiency of the present application is improved by 35%, the false positive rate is reduced by 47%, and the false negative rate is reduced by 29%.

The invention belongs to the field of network social group analysis, and provides a multi-expert collaborative network social group description method based on modal dynamic fusion. The method comprises the following steps: firstly, inputting multi-modal original data of a network social group, and respectively capturing multi-modal private feature representations through a modal representation adaptive extraction module; then, deep fusion of cross-modal information is realized by utilizing a bidirectional state space model through a modal complementary information fusion module, and unified group multi-modal fusion representation is generated; thirdly, a KAN multi-expert network architecture is introduced through a multi-expert collaborative prediction module, different experts are adapted to diversified group characteristic modes such as mainstream, small crowds and temporary groups, and dynamic weights are calculated through a gating network to achieve accurate fusion of expert output; and finally, in combination with long-tail boundary perception loss function optimization model training, outputting a multi-dimensional feature description result of the group. According to the method, more comprehensive and accurate group feature representation can be obtained, and the depicting performance of the network social group in a complex scene is improved.

The invention belongs to the technical field of image recognition, and discloses a remote sensing small sample target detection method based on a generative meta-learning strategy, and the method comprises the steps: inputting an image into a dynamic relation double-flow collaborative network through constructing a K-way N-shot meta-learning task, and carrying out the feature extraction and alignment enhancement through an improved non-causal Mama unit and a gating fusion mechanism; representing a support set category as probability distribution by using a generative meta-learner, and calculating a Wasserstein distance between the support set distribution after enhancement and the query feature distribution; inputting the query features into an orthogonal frequency domain decomposition detection head for frequency domain analysis and reconstruction; and outputting a target category and a bounding box based on the reconstructed features, adopting non-maximum suppression post-processing, and calculating an IoU optimization parameter during training. According to the method, the classification accuracy and generalization performance in a small sample scene are effectively improved, the calculation complexity overfitting risk is remarkably reduced, the detection efficiency is high, and the method is easy to deploy in hardware equipment.

Embodiments of the present application disclose a kind of group adaptive integrated control method, device, electronic equipment and storage medium, involve embodied intelligent technical field, wherein, the method includes: according to target scene characteristic, utilize physical modeling and domain randomization technology to generate diversified environment task set, and pre-processing is carried out to ensure data quality, train reinforcement learning coach based on these tasks, combine decoupling and backtracking policy distillation technology to synthesize training sequence, enhance the generalization and adaptive ability of model, adopt long-time memory model structure design base model, integrate multi-agent observation module to capture time sequence and spatial correlation, dynamic noise injection and asynchronous optimization are carried out in training process, the trained field base model is deployed to target scene, form collaborative network, and set fault-tolerant mechanism to deal with agent offline and other sudden situations, continuously collect actual data to optimize model parameters.The present application effectively solves the problems of high customization, weak migration and high maintenance cost in the prior art.