283 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.

A collaborative autonomous vehicle sensor fusion system enables multiple vehicles to share multimodal sensor data for enhanced perception capabilities beyond individual vehicle limitations. Each autonomous vehicle captures multimodal sensor data, identifies safety-critical objects, applies priority-based compression based on safety criticality, and shares compressed data via vehicle-to-vehicle communication. An enhanced multi-vehicle AI deblocking network receives the compressed sensor data and enhances perception data for each vehicle using sensor data from multiple vehicles in the collaborative network. The system prioritizes reconstruction quality for safety-critical objects over non-safety-critical objects and enables detection of safety-critical objects occluded from individual vehicles through collaborative sensor fusion. The network fuses multimodal sensor data by identifying cross-modal correlations between different sensor types and uses these correlations to reconstruct sensor information that is degraded or occluded in individual vehicles, providing improved situational awareness for autonomous vehicle operation.

The invention provides a personalized commodity recommendation method and system based on user behavior data analysis, and the method comprises the steps: firstly constructing a user behavior sequence and an interest stability model, and then carrying out the correlation modeling of the user behavior sequence; the behavior transfer association degree of adjacent interaction behavior units and the commodity attribute dynamic association degree capable of being adjusted along with user interest stability are calculated in combination with an interest stability model, then a dynamic commodity collaborative network is constructed based on the commodity attribute dynamic association degree, and node importance parameters are updated according to user real-time interaction behaviors; according to the method, path optimization mining is carried out in a dynamic commodity collaborative network, a user potential behavior path set is generated, finally, the potential behavior path set is analyzed, and a commodity personalized recommendation sequence is generated in combination with path attribute association feature distribution and user current interaction behaviors, so that the recommendation accuracy and the personalized degree are greatly improved, and the user experience is improved. And the shopping experience of the user is effectively improved.

The invention discloses an automatic operation and maintenance method based on an agent technology and a collaborative network, an operation and maintenance agent responds to an alarm event and initializes an operation and maintenance task, a data agent is triggered to collect and preprocess multi-source heterogeneous operation and maintenance data, the operation and maintenance agent initiates a deep root cause analysis request according to the preprocessed data, and the deep root cause analysis request is sent to the collaborative network. Driving the code agent to generate and execute an analysis code, positioning a root cause and generating a repair scheme by the operation and maintenance agent based on an execution result of the code agent, verifying a repair effect after execution, and presenting an agent cooperation path and an evidence chain in an operation and maintenance process in real time by the visual agent through a preset visual protocol. A structured report is generated by the reporting agent. Dynamic code generation and safe execution are driven through a multi-agent collaborative architecture, end-to-end automatic root cause positioning and closed loop repairing are achieved, meanwhile, an evidence chain and a collaborative process are presented in real time by means of protocol visualization, and the operation and maintenance intelligent level and fault diagnosis transparency are improved.

The invention belongs to the technical field of cloud computing and edge computing collaboration, and particularly discloses an intelligent scheduling and optimizing method for a cloud-edge collaboration network based on reinforcement learning. By constructing the state sensing matrix and generating the action decision vector, the problem that a traditional scheduling method is insufficient in correlation analysis of multi-dimensional operation state data in a complex network environment is solved, and the comprehensive sensing capability of the operation state of the network node is improved; a dynamic mapping mechanism among the running state, the resource limitation and the task allocation strategy is established, the task allocation and resource scheduling strategy is automatically and differentially adjusted according to the real-time state of the node, and the optimal matching between the task demand and the resource supply and the dynamic balance between the performance and the efficiency are realized; through performance index monitoring and closed-loop feedback optimization, the scheduling effect is mastered in real time, continuous iterative optimization is performed on the reinforcement learning model according to objective data, and resource waste and scheduling delay are reduced.

The invention discloses a fresh commodity after-ripening regulation and control planning method oriented to maturity grading, and relates to the technical field of food science and biologication.The method comprises the steps that physiological parameters of fresh commodities are collected in real time based on a multi-modal sensor, and the physiological parameters comprise the epidermis pigment content, the ethylene release rate, the fruit hardness and the respiration intensity; the collected physiological parameters are input into a multi-objective optimization model based on deep learning, the model fuses a metabolic kinetic equation and an LSTM time sequence prediction algorithm, and a regulation and control parameter combination adaptive to the current mature stage is output; according to the regulation and control parameter combination, the temperature, the relative humidity and the O2 / CO2 concentration gradient of the controlled atmosphere storage equipment are synchronously regulated through a distributed control system, and a plant hormone antagonist is injected through an ultrasonic atomization device; and constructing a supply chain collaborative network based on a block chain technology, binding a grading result with logistics path planning, and adjusting a temperature control strategy and a shelf life countdown parameter of the cold chain transport vehicle in real time through an edge computing node.

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 relates to the technical field of artificial intelligence, and provides a marketing data generation method and device based on portrait data, equipment and a medium, marketing association data can be collected and purified based on a three-level data gateway, feature fusion is carried out by using a star-shaped collaborative network constructed based on a dynamic weight mechanism and a federated learning mechanism, and the marketing data generation efficiency is improved. The problems of data dimension limitation and data island are solved; scene recognition is performed based on a marketing data graph constructed by a secondary scene classification tree including a gift scene, and the problems of low utilization efficiency of unstructured data and insufficient crowd portrait granularity are solved; the marketing strategy is generated by using the target engine matched with the scene, so that the problems of scene engine deficiency and gift scene adaptation imbalance are solved; and generating the target marketing data according to the target marketing strategy and the marketing data graph. The problems of low operation efficiency and insufficient content accuracy are solved.

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 discloses a cross-organization project collaborative management method and system, a medium and a product, and the method comprises the steps: carrying out the project creation permission verification of a target user in response to a project creation request of the target user; if the target user passes the project creation permission verification, constructing a cross-tenant virtual organization corresponding to the target project in the project creation request; the cross-tenant virtual organization is determined as a project node in a preset project tree, creation event data of the project node is generated, and the project node comprises a plurality of project child nodes; obtaining a task dependency relationship among the plurality of first project child nodes, and constructing a cross-organization collaborative network based on the creation event data and the task dependency relationship; and in response to a state change event of a source project child node in the cross-organization cooperative network, generating a state synchronization instruction, and sending the state synchronization instruction to one or more second project child nodes associated with the source project child node based on the task dependency relationship. According to the invention, the cross-organization project collaborative management efficiency can be improved.

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.

A collaborative Network-on-Chip (NoC) development environment (CNDE) is accessed. The CNDE is based on cloud-native software. The CNDE enables graphical design of a NoC topology within a database. A first NoC sub-topology within the NoC topology is created within the CNDE. The creating includes coupling a first network initiator, a first router, and a first network target. A second NoC sub-topology within the NoC topology is generated within the CNDE. The generating includes coupling a second network initiator, a second router, and a second network target. An interfacing block is inserted within the CNDE, enabling two-way communication between the first NoC sub-topology and the second NoC sub-topology. The NoC topology is validated. The validating ensures that the first network initiator is coupled to the first and second network targets, and that the second network initiator is coupled to the first and second network targets.

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.

The invention relates to an automatic modulation identification method, device and equipment based on a lightweight neural network, and belongs to the technical field of wireless communication. The method comprises the following steps: constructing a multi-stream lightweight global-local collaborative network, firstly adopting a multi-stream input architecture in the network, and performing fusion processing by taking parallel joint I / Q streams, independent I streams and independent Q streams as inputs, thereby effectively reducing feature preprocessing redundancy; secondly, a reverse residual module is introduced, parameter quantity is greatly reduced, and feature extraction efficiency is improved; and finally, developing a lightweight global-local collaboration module, and carrying out modulation type identification and classification by utilizing a classification module after local feature extraction and global relation modeling are deeply fused. According to the method, higher modulation type recognition accuracy can be achieved with lower calculation complexity, and higher robustness is shown in a low signal-to-noise ratio environment.

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 discloses an employee performance evaluation method and system based on multi-dimensional data, and belongs to the technical field of enterprise talent intelligent management, and the method comprises the steps of multi-dimensional employee data preparation, employee behavior modeling, employee performance fluctuation prediction, employee performance attribution analysis and employee performance evaluation. According to the method, employee behavior modeling is carried out by adopting a graph construction method combining multi-dimensional cooperation characteristics and behavior characteristics, and overall modeling and dynamic sensing of real positions, interaction strength and multi-dimensional behavior states of individual employees in an organization cooperation network are realized; employee performance fluctuation prediction is carried out by using a graph convolution bidirectional long-short term network model optimized by a joint loss function, and employee individual performance and dynamic evolution of mutual influence in an organization cooperation network are comprehensively modeled; the employee performance attribution analysis method based on anti-fact simulation is adopted to perform attribution analysis, and the influence of the key cooperation relation on employee performance change is quantitatively evaluated by simulating the hypothesis situation.

The invention provides an operation and maintenance task allocation method and system combined with Agent agent assistance. The method comprises the following steps: firstly, establishing an Agent agent collaboration network; obtaining a to-be-allocated operation and maintenance task demand set containing an execution target, a resource demand and a constraint condition; then, performing interactive evaluation on the task demand set through each Agent agent in the Agent agent collaborative network, and determining an suitability evaluation result of the task and the agent; the coordination Agent agent generates a preliminary operation and maintenance task allocation plan according to the evaluation result; and finally, carrying out feasibility verification on the preliminary plan by the verification Agent agent, obtaining a final operation and maintenance task allocation scheme after confirming that all constraint conditions are met, and outputting the final operation and maintenance task allocation scheme to an operation and maintenance management system. According to the invention, efficient and reasonable distribution of smart home operation and maintenance tasks can be realized, and operation and maintenance efficiency and system stability are improved.

The invention relates to the field of data processing, and provides a multi-dimensional feature driven B2B2C collaborative recommendation method and system. The method comprises the steps of performing multi-dimensional collection on B-end merchant features, C-end user features and commodity features through a heterogeneous data source interface to obtain standardized multi-dimensional features; performing dynamic weight learning on the standardized multi-dimensional feature data set through a multi-head self-attention mechanism to obtain a fusion feature vector; performing three-layer cooperative matrix construction on the fusion feature vector based on tensor decomposition to obtain a multi-dimensional factor matrix; performing causal relationship modeling on the multi-dimensional factor matrix through a causal graph structure-based collaborative filtering algorithm to obtain a deep collaborative network model; and performing real-time recommendation of to-be-recommended items through the deep collaborative network model to obtain a personalized B2B2C recommendation list. According to the method, the complex mode in the business scene can be captured, and the accuracy of the personalized recommendation result is improved.

The invention discloses a multi-modal feature integrated risk assessment method and system for a stroke dangerous group, and relates to the technical field of telemedicine collaboration, a telemedicine collaboration network platform is built, and multi-modal data of a stroke high-risk group is collected and processed; analyzing the relation between risk factors and a cerebral apoplexy pathological mechanism by applying bioinformatics and medical knowledge, and defining a key action path; and based on an analysis result of the key action path, integrating multi-modal data by taking a pathological mechanism as an axis, and constructing a dynamic risk knowledge network based on a knowledge graph. According to the method, multi-modal data are integrated, a comprehensive patient individual feature matrix is constructed, a dynamic risk knowledge network is combined, and the association between patient individual features and a cerebral apoplexy pathological mechanism and the dynamic change of risk factors are accurately analyzed, so that an accurate risk score is calculated, the cerebral apoplexy risk of a cerebral apoplexy risk crowd is analyzed, and the cerebral apoplexy risk of the cerebral apoplexy risk crowd is analyzed. A powerful basis is provided for prevention of the cerebral apoplexy, and the occurrence rate of the cerebral apoplexy is reduced.

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 artificial intelligence and data processing, and discloses a game experience sentiment classification method based on Transform and stacked ensemble learning, which comprises the following steps: constructing a user game behavior map, and obtaining a game situation and user behavior information by adopting map neural network coding; based on a game context awareness neural collaborative network, fusing the information with the original text content, and outputting initial emotion probability distribution; outputting a time sequence correction result and predicting uncertainty by combining a plurality of probability distributions and time sequence characteristics through a time sequence evolution perception integration mechanism; triggering a personalized processing flow according to a comparison result of the predicted uncertainty and a preset threshold value; and finally, generating a final sentiment classification result, and feeding back and updating the user game behavior map to form a closed loop. According to the method, game context and time sequence dynamics are combined, the consistency of sentiment classification results is improved, the adaptability of the method in processing complex samples is enhanced, and self-adaptive optimization of the model is achieved.

The invention discloses a mine road intelligent maintenance system for multi-source sensing of a mining dump truck. The mine road intelligent maintenance system comprises a mine car sensor array, an edge computing unit and an equipment collaboration network, the mine car sensor array, the edge computing unit and the equipment collaborative network are in signal connection in sequence; the mine car sensor array is used for collecting whole car and road data and comprises a load sensor, a six-axis IMU, a laser radar, a binocular vision sensor and a vibration sensor. According to the invention, real-time monitoring and dynamic maintenance of the mine road state are realized, and a manual inspection blind area is effectively eliminated. The accuracy of road degradation identification is improved through multi-dimensional data fusion, and resource waste caused by misjudgment is avoided. And an equipment cooperation mechanism solves the conflict problem of maintenance operation and transportation tasks, and the road use efficiency is remarkably improved. The edge computing architecture reduces the data transmission load, and ensures the stable operation of the system under a complex working condition.

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 the field of supply chain optimization, in particular to a supply chain logistics efficiency collaborative optimization method and system based on artificial intelligence. The method comprises the following steps: obtaining a real-time supply chain logistics data stream, carrying out node coupling density analysis and self-adaptive layered mapping, and constructing a multi-layer logistics cooperation network; performing path node load response backstepping on the multi-layer logistics cooperative network, and performing nonlinear distortion evaluation to obtain a nonlinear distortion degree of a response curve; and carrying out sensitivity quantitative evaluation according to the nonlinear distortion degree of the response curve, carrying out topological association reconstruction among nodes, and constructing a supply chain logistics topological optimization network. And performing future load demand rehearsal according to the real-time supply chain logistics data flow, performing call operation path evolution analysis, and generating call operation path evolution data. According to the invention, the configuration of supply chain logistics resources is optimized, and the overall operation efficiency and response capability of supply chain logistics are improved.

The invention belongs to the technical field of building reclaimed water treatment systems, and particularly relates to a building reclaimed water treatment system based on edge self-control. The building reclaimed water treatment system comprises a sensing layer, an edge self-control layer and a cloud platform layer, the cloud platform layer is connected with the edge self-control layer through asynchronous communication, and the edge self-control layer controls the sensing layer in real time; the sensing layer comprises a water quality sensor unit, an equipment detection unit, a flow detection unit and an environment sensing unit; the edge self-control layer comprises an edge computing gateway, an edge intelligent engine, a local control unit and an edge collaborative network; the cloud platform layer comprises a model training unit, a data analysis unit, a comprehensive management unit and a strategy optimization unit. The invention provides the building reclaimed water treatment system with high reliability, high intelligence and high real-time performance, an advanced water treatment process and an edge intelligent control technology are deeply fused, the dependence on centralized control and network connection is reduced, refined and intelligent management of reclaimed water treatment is realized, the water resource utilization efficiency and the system operation benefit are maximized, and the water resource utilization efficiency and the system operation benefit are improved. Meanwhile, energy consumption and maintenance cost are reduced, and system safety and user experience are improved.

The invention provides an uncertainty perception pulmonary nodule segmentation method based on a multi-branch collaborative network, and the method introduces an expert conflict perception mechanism, and enables a model to learn consensus information and divergence features between scorers at the same time through constructing a probability graph soft label and a conflict graph based on multi-scorer labeling. Therefore, the uncertainty existing in clinical practice can be reflected more comprehensively; a multi-branch collaborative segmentation network structure is provided, an encoder-decoder trunk is combined with three functional branches of consensus, conflict and boundary, and adaptive weighted integration is realized through a conflict regulation and control fusion module. The method not only improves the overall precision of the segmentation result, but also can generate probability distribution prediction which better meets the actual diagnosis demand under the condition that the opinions of multiple scorers are inconsistent, and provides a more reliable basis for clinical auxiliary decision making.