643 results about "Information sharing" patented technology

The invention discloses a distributed optical storage micro-grid control system based on a large model and an energy management method, and the system collects various data through a data collection module, captures a time sequence long-term dependence relation based on a self-attention mechanism through a large model prediction system, and predicts the photovoltaic power generation amount, the load demand and the energy storage charging and discharging demand. The network-forming inverter integration module dynamically adjusts the output power, the energy storage strategy and the interaction power of the power generation system according to a prediction result, the distributed control strategy module adopts a distributed consensus algorithm to realize information sharing and collaborative decision making, and the energy management module makes a multi-time scale plan and introduces an economic optimization model. The energy management method comprises the steps of data collection, real-time monitoring, prediction modeling, plan making, distributed control, economic optimization, system monitoring, fault processing and the like. The method can improve the new energy utilization rate, the electric energy quality and the system stability, adapts to the change of environmental factors, and maximizes the economic and environmental benefits of the micro-grid.

Various systems, computer-readable media, and computer-implemented methods of providing improved data privacy, anonymity and security by enabling subjects to which data pertains to remain “dynamically anonymous,” i.e., anonymous for as long as is desired—and to the extent that is desired—are disclosed herein. Embodiments include systems that create, access, use, store and / or erase data with increased privacy, anonymity, and security—thereby facilitating the availability of more qualified and accurate information. When personal data is authorized by data subjects to be shared with third parties, embodiments described herein may facilitate the sharing of information in a dynamically-controlled manner that also enables the delivery of temporally-, geographically-, and / or purpose-limited information to the receiving party. In one example, the disclosed techniques may be used to functionally separate geospatial information, such that it remains “dynamically anonymous,” i.e., anonymous for as long as is desired—and to the extent or degree that is desired.

The invention proposes a flood disaster dynamic prediction method based on multi-source remote sensing data and a knowledge graph, and relates to the field of data prediction, and the method comprises the specific steps: firstly, building a space-time disaster dynamic model through a physical drive text generation module, and describing the evolution process of a flood disaster; generating personalized flood disaster description in combination with the static characteristics and the dynamic remote sensing data; then, a multi-scale residual diffusion enhancement module improves sensitivity to dynamic change of disasters through multi-scale trend extraction and residual calculation, noise is removed, and useful information is reserved; then, the multi-modal fusion module enhances interdependence and information sharing among different modals by using adaptive modal mapping, a cross attention mechanism and a weighted fusion strategy; and finally, training through a regression model, dynamically adjusting the feature weight by using an adaptive feature weighting mechanism and an incremental learning mechanism, and finally obtaining a flood disaster prediction value through the prediction set.

The invention provides a fan gearbox dynamic integration fault detection method and system based on feature fusion, and relates to the technical field of data processing, and the method comprises the steps: obtaining each preliminary feature set; performing feature-to-feature and feature-fault nonlinear relation quantization on each preliminary feature set to obtain each preliminary screening feature set; analyzing the contribution degree of each feature in each preliminary screening feature set, executing feature fine screening, and generating each fine screening feature set; and calling a multi-agent integrated fault detection system, executing multi-layer agent information sharing and collaborative decision from bottom to top based on each fine screening feature set, and generating fan gearbox fault detection information. According to the method and the device, the technical problem of low fault detection accuracy caused by lack of deep feature mining and dependence on a single-machine diagnosis system in the prior art is solved, and the technical effect of improving the fault detection accuracy is achieved by constructing the multi-agent integrated fault detection system, so that the false alarm rate and the missing report rate are remarkably reduced.

The invention discloses a multi-dimensional natural resource intelligent monitoring method and system based on big data analysis, and relates to the technical field of resource monitoring, and the method comprises the steps: calculating the information sharing degree between different data streams, constructing an abnormal feature credibility distribution map, and carrying out the calculation of the abnormal feature credibility distribution map; and inputting the abnormal feature credibility distribution graph and the multi-source auxiliary information into an integrated classifier, carrying out joint analysis and comprehensive research and judgment on multi-dimensional information to output a target list, constructing an event response relation graph reflecting a monitoring task priority and an execution logic relation according to the attribute features of each element in the target list, and carrying out event response analysis on the monitoring task priority and the execution logic relation. Wherein each node represents a to-be-monitored target, an edge represents a dependency relationship between tasks, and an optimal monitoring execution path is planned for various intelligent monitoring carriers by using an improved A algorithm integrated with a multi-factor cost function. Through natural resource monitoring of multi-modal data interference correction, intelligent classification and path optimization and dynamic response, the monitoring quality and the execution efficiency are effectively improved.

The invention discloses a mobile communication network multi-dimensional competition insight method based on DeepSeek large model and multi-agent cooperation, and relates to the technical field of mobile communication network and artificial intelligence crossing, and the method comprises the steps: collecting and preprocessing multi-modal data; carrying out fusion on the preprocessed multi-modal data; constructing a competing network insight agent comprising a special agent and a universal agent; a communication and cooperation framework is constructed, and information sharing and cooperation work of the special intelligent agent and the universal intelligent agent are achieved; carrying out data model design on the special intelligent agent from different dimensions; performing special training on the DeepSeek large model to obtain an enhanced Qwen-32B large model; user requirements are input based on the question and answer interface, the intention recognition module recognizes user intentions, the Qwen-32B large model is called to generate answers, or the Qwen-32B large model and the competition network insight intelligent agent are called to complete professional analysis, thinking and reasoning, and the questions and answers are output and displayed to users for viewing through the question and answer interface. According to the invention, efficient and accurate competition decision support can be provided for mobile communication operators.

The invention provides a multi-agent collaborative visual completion method and system based on semantic communication, and the method comprises the steps: a first agent collects an occluded overall original image through a visual sensor, generates an occluded region mask through a lightweight semantic segmentation network, extracts mask semantic information, and transmits the mask semantic information to a second agent; the second intelligent agent matches the global semantic feature of the second intelligent agent with the received mask semantic information, locates a sheltered area, then cuts the sheltered area to obtain a semantic feature vector of the area, and returns the semantic feature vector to the first intelligent agent; and after the first intelligent agent receives the image, performing high-fidelity reconstruction on the occlusion area by using a conditional diffusion model to obtain a complemented image with consistent semantics, and performing weighted fusion and local color matching and splicing on the complemented image and the original image to obtain a global perception result. According to the scheme, perceptual information sharing and occlusion area reconstruction with low bit rate, high robustness and high real-time performance can be realized under limited communication resources.

The invention relates to the technical field related to medical systems, in particular to a medical teaching and research integrated system based on a common bottom layer, and aims to break through information islands among medical treatment, teaching and scientific research and realize closed-loop linkage and dynamic knowledge conversion of a whole process. The system comprises a diagnosis and treatment decision subsystem, a scientific research queue management subsystem and a teaching subsystem, integrates data through a three-in-one data engine, and realizes information sharing and cooperative work. The diagnosis and treatment decision subsystem provides diagnosis and treatment support for severe diseases based on an expert thinking chain. And the scientific research queue management subsystem is responsible for screening cases, sorting data, implementing quality control and statistical analysis, and adjusting an expert thinking chain according to scientific research results. And the teaching subsystem carries out teaching by using a standardized case presentation mode. Through a feedback closed loop of clinical discovery, scientific research verification, knowledge updating and teaching feedback, rapid propagation and application of medical knowledge are promoted, and the medical service quality and the medical education level are improved.

The invention relates to the technical field of adaptive filtering, discloses a variable-step robust adaptive filter and a filter network, and aims to solve the technical problem that a traditional adaptive filter cannot effectively consider convergence speed, steady-state precision and impulse noise interference resistance. A non-linear error factor is introduced, large errors caused by impulse noise are effectively suppressed through a generalized function form of the non-linear error factor, and the robustness of the filtering process is remarkably improved; meanwhile, a variable step length mechanism is provided, the theoretically optimal candidate step length is calculated on line by minimizing the mean square deviation of the next moment, and a self-adaptive target variable step length is generated through truncation and time smoothing processing, so that dynamic balance is realized between rapid convergence and low-steady-state maladjustment. According to the invention, the filter is expanded to the distributed network, each node follows a diffusion strategy of first updating and then combining, and global information sharing and performance collaborative optimization are realized by using a combined coefficient.

Apparatus and methods are provided for COT sharing in sidelink unlicensed communication systems. In novel aspect, an COT initiating UE shares one or more SL resources within a COT to one or more responding UEs. In one embodiment, the responding UE is a target receiver of the COT initiating UE. In one embodiment the responding UE uses the shared resources for transmission that the COT initiating UE is one of the target receivers. In one embodiment, COT sharing information comprises COT time and frequency location information, shared COT starting offset information, remaining COT duration information, one or more resource block (RB) set(s) in the COT, channel access type, channel access priority type (CAPC) level, and ID information. In one embodiment, the COT sharing information is carried in a 1st-stage SCI, a 2nd-stage SCI, a combination of a 1st-stage SCI and 2nd-stage SCI, or a MAC-CE. In one embodiment, the responding UE performs an channel access before using the shared COT resource. The channel access type is (pre-) configured based on a transmission gap or indicated by the COT initiating UE.

The invention relates to the technical field of information, and discloses a multi-machine collaborative operation control method and system based on a common inductance calculation control technology, and the method comprises the steps: obtaining task granularity distribution and a resource demand matrix, and carrying out the classification of the task granularity distribution and the resource demand matrix through a classification algorithm, and obtaining an initial task segmentation feature set; based on the optimal segmentation point set, a preliminary task allocation scheme is generated in combination with node calculation heterogeneity, and a task segmentation feature set is dynamically updated through a real-time sensing technology; a real-time task state set is generated through a group information sharing mechanism, a task allocation scheme is optimized through a dynamic programming algorithm, and it is ensured that tasks are reasonably allocated according to priorities and node resources; and the cooperation state matrix is updated through a real-time communication protocol, so that the accuracy and timeliness of a multi-node cooperation execution result are ensured. According to the invention, task allocation can be dynamically adjusted in a complex environment, resource utilization is optimized, the cooperation efficiency of a multi-robot system is improved, and the method can be widely applied to the fields of industrial production, warehouse logistics, disaster rescue and the like.

The invention provides a high-robustness data real-time synchronization method and system, a storage medium and electronic equipment, relates to the technical field of data synchronization, and can significantly reduce the data synchronization delay between different systems by introducing mixed timestamps and fragment increment verification and calculation and adopting a reliable transmission mechanism and idempotent processing protocols based on message queues, thereby improving the data synchronization performance. Almost real-time information sharing is realized; meanwhile, the data consistency of the cross-heterogeneous information system can be effectively improved, and the accuracy and integrity of the data among multi-source systems are ensured; in addition, the reliability and robustness of data synchronization in unstable environments such as limited network bandwidth, easy jitter or interruption and the like can be greatly enhanced.

The invention discloses a tumor and target region synchronous segmentation method and system based on visual language guidance. The method comprises the following steps: constructing a multi-modal data set fusing a medical image and a clinical text; a unified encoder is adopted to extract shared visual features, a double-branch decoder is adopted to predict a tumor region (GTV) and a clinical target region (CTV) respectively, and information sharing and task differentiation modeling are achieved; introducing a medical pre-training large language model, and respectively extracting language features related to GTV and CTV; a visual language collaborative attention module is designed, dynamic fusion and alignment of visual languages are carried out, task related semantics are guided and enhanced, and redundant information is inhibited; and outputting a precise synchronous segmentation result of the GTV and the CTV after fusing the multi-modal features. Compared with an existing scheme, the problems that the boundary of a tumor target region in a medical image is fuzzy and multi-modal information is insufficient in utilization are effectively solved, the segmentation precision and robustness are remarkably improved, and reliable support is provided for clinical radiotherapy planning.

The invention discloses an intelligent stationery supply chain optimization system based on big data analysis, belongs to the field of big data analysis, and aims to solve the problem of how to construct a high-precision demand prediction model through real-time and accurate data acquisition and processing to quickly identify and deal with abnormal conditions. The system comprises a data acquisition module, a storage management module, an intelligent prediction module, a mining analysis module and a supply chain cooperation module. The Internet of Things automatically collects supply chain product data, uploads the data to the cloud platform, stores the data in a database after cleaning, and classifies and adaptively compresses the data according to access evaluation indexes. A demand prediction model is constructed based on historical data, periodical and triggered updating is carried out, and the order demand of a product is predicted. According to the method, historical data with frequent access is screened for abnormal mode identification and reason classification, dynamic adjustment and collaboration of supply chains are realized through an information sharing platform, inventory purchase is optimized, and decision support is provided for abnormal modes.

The invention discloses a traffic information sharing method and device for vehicle and road cloud integration, and a medium. The method comprises the following steps: constructing a vehicle social network graph; analyzing a natural language subscription request of a user, and generating a structured decision element; a data and knowledge dual-driven collaborative decision framework is adopted to carry out multi-attribute decision, and optimal working group configuration is generated; the sensing demand parameters are encrypted and issued to all nodes in the optimal working group, sensing fusion of multi-node space-time association is achieved, and multi-source sensing data are obtained; performing standardized packaging and credibility evaluation on the acquired multi-source sensing data, and filtering untrusted data to obtain trusted data; cleaning the credible data, and performing hierarchical desensitization on sensitive information in the credible data to obtain traffic information; and pushing the traffic information to the subscribing user, and constructing an intelligent closed-loop optimization system based on user feedback to optimize the traffic information sharing service. According to the invention, the dynamic cooperative sensing capability in traffic information service can be improved.

A user equipment (UE) may transmit a configured-grant uplink control information (CG-UCI) to a base station during a channel occupancy time (COT) initiated by the UE in a shared spectrum, the CG-UCI comprising COT sharing information, the COT sharing information indicating, at least, an index value corresponding to a combination of: an indication of an offset to a beginning of a downlink transmission opportunity during the COT; an indication of a duration of the downlink transmission opportunity during the COT; and an indication of a channel access priority class (CAPC) value used by the UE to initiate the COT. The base station may transmit a downlink transmission to the UE within the downlink transmission opportunity and in accordance with the COT sharing information in the transmitted CG-UCI. Apparatuses are also disclosed.

The invention relates to an SCR system self-adaptive flow guide regulation and control method based on real-time flow field feedback, and belongs to the technical field of flue gas denitration. The method comprises the following steps: acquiring inlet flue gas flow velocity distribution and outlet partition NOx concentration distribution; establishing a flow field and concentration field uniformity online evaluation rule, and constructing an initial knowledge base based on adaptive regulation and control logic; dynamically adjusting the angle of the adjustable guide plate according to the uniformity evaluation result, synchronously recording the state change of the flow field, and iteratively updating the regulation and control knowledge base; and an exclusive data interaction link of diversion regulation and control and partition ammonia spraying control is established, and flow field pre-judgment information sharing and ammonia spraying parameter presetting are achieved. The real-time collaborative optimization of the flow field and the concentration field in the SCR system is realized, the denitration efficiency and the ammonia escape control precision are improved, and the self-adaption and intelligent regulation and control capabilities of the system are enhanced.

The invention discloses a formation riding danger information sharing method and system for intelligent riding helmets, and relates to the technical field of information sharing, and the method comprises the steps: carrying out the deep fusion of the adjacent beacons of all intelligent helmets, the state information of a vehicle, and the multi-source heterogeneous data of an external sensor, vehicle-road cooperation and the like; and constructing and dynamically maintaining a topological graph reflecting the space-time relationship among the formation members in real time. On the basis, intelligent source end propagation strategy decision is carried out on perceived semantic danger information, and topology-based directional relay and propagation are carried out. And finally, the decided propagable information is combined with the state of the rider, so that a highly personalized early warning instruction is generated. In this way, undifferentiated global information broadcast can be effectively converted into accurate risk announcement based on individual context, and the efficiency of dangerous information sharing in formation riding and the cooperative safety capability of the whole system are remarkably improved.

The invention discloses a digitized collaborative management and control platform and method for a highway roadbed slope, and belongs to the field of highway engineering. The method comprises the steps that a three-dimensional geological information model of the highway roadbed slope is constructed; sensing and fusing multi-source heterogeneous data; intelligent analysis and early warning are realized; co-processing and closed-loop management; optimization and self-learning of a machine learning algorithm model; and visualizing the slope of the whole highway. According to the invention, through the AI algorithm, the conversion from manual interpretation to intelligent prediction is realized, the early warning time is advanced, and passive response is changed into active prevention and control. According to the invention, information sharing and business collaboration are realized through process driving, and the emergency disposal efficiency is greatly improved. Visual and scientific decision support is provided for slope state evaluation and treatment scheme making based on the digital twinborn model and the multi-source fusion data, and dependence on human experience is reduced. According to the invention, a complete slope electronic health record is established, a data basis is provided for long-term performance evaluation and maintenance planning of the slope, and full-life-cycle management of assets is realized.

The invention discloses a permanent magnet synchronous motor parameter optimization method based on an improved moth flame particle swarm algorithm. In order to solve the problems that a traditional PID controller is difficult in parameter setting, poor in adaptability, insufficient in control precision and the like under complex working conditions, in the strategy, an MFPSO algorithm fuses a particle swarm optimization (PSO) algorithm and a moth flame optimization (MFO) algorithm. An adaptive inertia weight and Gaussian mutation strategy is introduced, so that particles can focus on local fine search, and the speed of convergence to an optimal solution is accelerated. The optimization capacity of the algorithm in different stages is enhanced by adopting a nonlinear acceleration coefficient, and local optimum is effectively avoided. A grouping evolution mechanism is designed to promote intra-group information sharing and inter-group competition, the population diversity is enriched, and the ability of the algorithm to jump out of a local optimal solution is further improved. The fuzzy PID controller based on the MFPSO algorithm optimization parameters can better meet the requirements of high-performance control of permanent magnet synchronous motors in different fields, and has application prospects and economic values.

The invention provides a construction site laboratory digital management method and system based on information sharing, and the method comprises the steps: firstly collecting the information sharing demands of a quality supervision mechanism, a project owner, a grade detection mechanism, a construction site laboratory and other subjects on the business processing, supervision and guidance, cooperative management and other aspects of the construction site laboratory; secondly, building a multi-subject adaptation module by utilizing an NET research and development framework, selecting and combining functional modules according to acquired requirements to complete building, then setting a transfer node and associating subject operation authority based on a sub-pressing sharing requirement type so as to dynamically configure an information sharing approval process, and then utilizing a front-end tool with a table editing function to complete the information sharing approval process. Sharing information items are integrated according to business scenes, transfer nodes are associated, a scenarized sharing information template is generated, finally, directional information sharing among multiple subjects is executed based on the scenarized sharing information template, feedback information is collected to optimize adaptation module function combination and information sharing approval process configuration, and construction site laboratory digital efficient management is achieved.

The invention discloses an edge computing node collaborative task unloading method for guaranteeing a low-delay service, and relates to the field of edge computing, each edge computing node generates a collaborative view comprising the edge computing node and a neighbor edge computing node through a local LSTM prediction model and federated learning, and the collaborative view comprises a predicted resource state and a predicted network state; according to the invention, the local LSTM prediction model and federated learning are combined to generate the collaborative view, so that accurate prediction and global information sharing of edge node resources and network states are realized, and the accuracy of decision making is improved; the tasks are analyzed into a dependency graph with key path marks, so that priority scheduling of the key tasks is ensured, and the overall task time delay is reduced; based on a weighted voting consensus mechanism of node credibility and resource adequacy, the efficiency and reliability of decision consensus among nodes are improved; the task unloading efficiency and the service quality of the low-delay service are effectively improved, and the stability and the reliability of the edge computing system are enhanced.

The invention discloses a supply chain material inventory control method and system, and the method comprises the steps: carrying out the demand distortion analysis of the demand signal data of each node in a target supply chain, and obtaining the demand signal distortion amplitude of each node; if the demand signal distortion amplitude exceeds a preset distortion amplitude threshold, performing distortion degree classification prediction on the demand signal distortion amplitude to obtain a real-time demand prediction result of each node; performing sensitivity analysis on the real-time demand prediction result, and generating a safety inventory parameter of each node; generating a collaborative inventory distribution scheme of the target supply chain according to the safety inventory parameters and the information sharing evaluation indexes; and performing cooperative control on the material inventory of each node in the target supply chain according to the cooperative inventory distribution scheme. The flexibility and cooperation efficiency of the supply chain to adapt to the market change can be improved through the cooperation inventory distribution scheme, so that the risk resistance capability is enhanced, and the capability of the supply chain to cope with the market demand fluctuation complex scene is improved.

The invention provides a dynamic optimization and visual decision-making system for port and navigation multimodal transport knowledge graph collaboration, and solves the problems of high transport mode conversion cost, difficult information sharing and the like. Aiming at the defects that a traditional static data warehouse (such as Hadoop) is insufficient in real-time performance, a general data fusion framework (such as Apache NiFi) is poor in field adaptability and the like, the system innovatively constructs a multi-source heterogeneous database, integrates port production, economic association, infrastructure and freight data, and realizes accurate path planning and personalized logistics scheme recommendation through an intelligent algorithm. The system adopts a React + ECharts + ShadcnUI technology stack to realize knowledge graph visualization, practice shows that the operation efficiency can be remarkably improved, the transportation cost is reduced, and technical support is provided for sustainable development of ports.

The invention discloses a standardized unified code generation method and system for building material names and specifications, and relates to the technical field of building engineering.The system obtains multi-dimensional unstructured original information of building materials through a data acquisition module, and a data feature extraction module is used for recognizing fields such as categories and specifications to generate standardized metadata; the standardized mapping rule module maps the metadata into standardized names, specifications and unique codes capable of reversely analyzing parameters based on a three-level matching rule base, and outputs a matching result; and the code generation and output module automatically generates standardized names, specifications and unique codes according to the matching result and a fixed format, and outputs the standardized names, specifications and unique codes to a database or an external system to realize information sharing. According to the invention, by establishing a unified standard rule, diversified material names and specifications are automatically converted into standardized expressions and unique codes, the standardization and efficiency of building material information management are improved, and a series of industry pain points caused by information discretization are solved.

The invention discloses an emergency management method and system based on multimodal transport, and relates to the technical field of emergency management, and the method comprises the following steps: S1, obtaining the dynamic geographic information of a disaster area in real time through a satellite remote sensing technology; according to the method, a dynamic cross-modal cooperative scheduling mechanism is constructed by fusing satellite remote sensing data, real-time mastering of geographic information of a disaster area is achieved, the problem that scheduling lacks real-time geographic information support is solved, connection nodes and transfer processes of different transportation modes are automatically adjusted through a model according to real-time road conditions, and the scheduling efficiency is improved. The conditions of transportation mode adjustment lagging and too long transfer waiting time are avoided, the timeliness of emergency material transportation is improved, information sharing and collaborative cooperation among the transportation modes are promoted by establishing cross-modal collaborative scheduling, an efficient transportation chain is formed, the emergency demand of emergency management for material transportation is met, and the emergency management efficiency is improved. Dynamic adaptation and efficient scheduling of multimodal transport of emergency materials are realized, and timeliness and effectiveness of emergency response are guaranteed.

The invention relates to the technical field of power distribution networks, and discloses a deep learning-based protection setting value setting method for a power distribution network containing a distributed power supply, and the method comprises the following steps: obtaining real-time operation data of the power distribution network, and constructing a dynamic graph representation of the power distribution network according to the real-time operation data; processing the dynamic graph representation by using a graph attention network model, and extracting deep features representing the topological structure and node dynamic information of the power distribution network; according to the method, intelligent perception and rapid diagnosis of faults in a distributed power supply environment, dynamic optimization of protection constant values and serialization decision of subsequent control measures are unified in one framework, information sharing and action cooperation are achieved, and fault diagnosis and constant value calculation are processed in parallel in combination with multi-task learning. The adaptive capacity and decision accuracy of complex operation scenes such as high permeability and variable topological structures of the distributed power supply are improved, and an effective guarantee is provided for safe and stable operation of the distributed power supply after the distributed power supply is connected to the power distribution network.

The invention discloses a drug inspection information sharing method and system based on a block chain, and relates to the technical field of drug inspection data management, and the method comprises the steps: sending an authentication request to a block chain network when a drug inspection device is started, verifying the state of the device according to a device digital identity certificate after an intelligent contract receives the authentication request, and sending the verification result to a server; generating a temporary operation token, encrypting the temporary operation token by using the equipment public key and returning the temporary operation token to the medicine inspection equipment; the medicine inspection equipment performs medicine inspection through the temporary operation token, synchronously acquires inspection data, a GPS position, an inspection timestamp and an equipment fingerprint, calculates a composite hash value, and encrypts the inspection data; and submitting the composite hash value to a block chain network, performing space-time compliance verification through an intelligent contract, obtaining a drug batch DID, and generating an on-chain block associated with the drug batch DID. According to the invention, the identity credibility of the medicine inspection equipment is ensured, and the authenticity, integrity and time-space consistency of inspection data are realized.

The invention discloses an agent multi-role cooperation method for an electric power two-ticket system, which is characterized in that each sub-agent is connected through message middleware, and efficient and stable communication and cooperation among the sub-agents are realized; through a multi-role agent arrangement mechanism, the problems of low efficiency and insufficient accuracy of a traditional electric power two-ticket system in links of operation ticket generation, auditing, abnormal ticket detection and the like are effectively solved. The semantic consensus memory module design solves the problem that information sharing and understanding are inconsistent in the multi-agent cooperation process. In a traditional multi-agent collaboration system, all agents are lack of an effective information interaction and sharing mechanism, so that collaboration efficiency is low, and misunderstanding and conflicts are prone to occurring.

The invention relates to the technical field of Internet of Vehicles, in particular to a control method and system of vehicle environment sensing equipment, a server and a vehicle. The method comprises the following steps: acquiring position information and environment sensing information uploaded by a plurality of vehicles in a preset area; if the weather information of the preset area is matched with weather represented by the environment sensing information, distribution information of the preset area is generated according to the position information and the environment sensing information, and the distribution information is used for adjusting the sensitivity of environment sensing equipment of the vehicle in the preset area. Therefore, by verifying the matching of the weather information, unnecessary task processing can be effectively reduced; through fusion processing of the position information and the environment sensing information uploaded by the plurality of vehicles, information sharing and collaborative sensing among the vehicles can be realized, the reliability of the generated distribution information is ensured, and the vehicles can accurately adjust the sensitivity of the environment sensing equipment of the vehicles.