367 results about "Potential risk" patented technology

The invention relates to an end-side malicious behavior security detection method, which comprises the following steps: acquiring multi-source data, carrying out standardization processing on the multi-source data, generating a unified quintuple sequence, and constructing a quaternary dynamic behavior graph; respectively extracting time sequence features, semantic features and context features by utilizing a quintuple sequence, calculating confidence coefficients of cross-modal association, mapping the confidence coefficients to a quaternary dynamic behavior graph, and generating an individual behavior graph, a current behavior graph and a group baseline graph; and performing projection processing on the individual behavior graph, the current behavior graph and the group baseline graph to calculate a deviation degree score and a structural anomaly contribution degree of graph-level comprehensive deviation characterization, and triggering a grading response strategy by taking the deviation degree score and the structural anomaly contribution degree as double indexes of safety detection to solve an anomaly root. According to the method, internal and external threats, especially abnormal behaviors and potential risks which are difficult to find by traditional security defense means, are detected by analyzing behavior modes of users and entities.

The invention relates to the technical field of food safety tracing and early warning, and discloses a food safety whole-course tracing and risk early warning system based on big data analysis, and the system comprises a causal map construction module which constructs a causal knowledge map of a food supply chain; the risk function modeling module is used for establishing a risk transfer function for the causal relationship edge; the risk potential energy calculation module is used for calculating the total risk potential energy value of each entity node in the graph; the risk early warning and causal tracing module is used for monitoring the risk and early warning, and reversely tracing the key causal chain; the intervention strategy generation module is used for simulating and evaluating intervention actions and generating an optimal blocking strategy; and the system interaction presentation module is used for visually presenting maps, risks, causal chains and strategies. The food safety risk is converted into a quantifiable data index by calculating the dynamic total risk potential energy value of the entity node, and the potential risk accumulation process is recognized in advance by monitoring the real-time change and development trend of the risk potential energy, so that the conversion from passive response to active prevention is realized.

The invention is suitable for the technical field of automatic driving, and provides an automatic driving vehicle blind area track prediction and planning method based on Transform, and the method comprises the steps: firstly constructing a key entrance point, calculating a shielding region, and then generating and maintaining a belief state of a ghost target in combination with a risk score; then, real and ghost targets are coded into Token in a unified mode, the Token is input into Transform for interactive modeling, and multi-modal track distribution is output; and finally, a variable anisotropic repulsive force potential field is constructed based on the predicted risk, and a safety control instruction is obtained through optimization solution and hard safety filtering. According to the method, continuous probabilistic estimation and interactive reasoning of the potential risk of the blind area are realized, a flexible defensive track can be generated according to the risk confidence and the dynamic state of the vehicle, and the traffic efficiency is guaranteed while the safety is improved.

The invention provides an opencast coal mine VR safety training dynamic difficulty regulation and control method, which comprises the steps of collecting multi-dimensional interaction behavior data of a target student in a VR training scene in real time, and encoding the multi-dimensional interaction behavior data into a behavior vector sequence; inputting the behavior vector sequence into a pre-trained prediction model, and outputting a potential risk behavior and a risk occurrence probability; when the risk occurrence probability is higher than a preset threshold value, generating a risk event continuously caused by the potential risk behavior through a risk deduction model; dynamically generating a negative guide plot based on the risk event, and implanting the negative guide plot into the VR training scene; and dynamically adjusting a subsequent generation strategy of the negative guide plot according to a response result of the target student to the negative guide plot. The method has the technical effects of deeply understanding the behavior intention of the student, early predicting the potential risk and performing dynamic intervention.

The invention discloses an engineering construction whole process supervision method and system based on multi-technology fusion, and relates to the technical field of engineering supervision, and the supervision steps are as follows: S1, collecting construction site data through a mobile terminal multi-source channel, carrying out the space-time alignment and association fusion of data preprocessing, and carrying out the panoramic visual monitoring of a construction site; abnormal events and potential risks are identified through a preset rule and a machine learning algorithm; and S2, analyzing the engineering data by using a natural language processing technology. According to the invention, on the aspect of data and risk management and control, panoramic visual monitoring is realized through multi-source data fusion, and risks are accurately identified through an intelligent algorithm; the NLP technology analyzes the engineering data, automatically generates the check list, ensures compliance, realizes conversion from passive supervision to active prevention and from artificial experience to data driving, has remarkable effects in the aspects of improving management efficiency, reducing risks, saving cost and enhancing transparency, and is suitable for various large complex engineering scenes.

The invention relates to the technical field of information, and discloses a comprehensive operation supervision system based on an intelligent supply chain. The system comprises a real-time data acquisition module, a multi-modal feature extraction module, a dynamic risk assessment module, a self-adaptive decision generation module and an execution feedback regulation and control module. By constructing a real-time data acquisition and multi-modal feature extraction module, efficient fusion and deep feature extraction of internal and external multi-source heterogeneous data of the supply chain are realized, the sensing sensitivity and accuracy of the system to market fluctuation and emergencies are remarkably improved, and the prediction deviation is reduced from the data source. The dynamic risk assessment module adopts a dual anomaly detection mechanism, various potential risks can be quickly and comprehensively identified, risk level quantification is carried out in combination with a time sequence mode, and accurate and timely risk situation assessment is provided for subsequent decision making.

A threat management facility receives data from a variety of sources such as compute instances within an enterprise network, cloud service providers supporting the enterprise network, and third-party data providers such as geolocation services. In order to facilitate prompt notification of potential risks, the threat management facility may incrementally update data for use in threat assessments as the data becomes available from these different sources, and create suitable alerts or notifications whenever the currently accumulated data provides an indication of threat meeting a predetermined threshold.

The invention relates to the technical field of smart city public safety, in particular to an urban gas pipeline leakage risk intelligent early warning and positioning method and device.Firstly, a digital twinborn construction and dynamic coupling module constructs and drives a virtual image of a gas pipe network, then, a multi-modal data acquisition and fusion module comprehensively senses the state of the pipe network, and finally, the leakage risk of the gas pipeline is monitored. The intelligent early warning and positioning decision module realizes a core intelligent algorithm; a cloud-edge-end collaborative architecture is adopted, and a terminal sensor is responsible for data acquisition; the edge computing node is deployed in a regional pressure regulating station and is used for carrying out primary data processing and local real-time alarming; the cloud center deploys a digital twin platform and a core AI algorithm model, and performs big data analysis, simulation deduction and global decision. Compared with the prior art, the method can realize early and accurate identification and prediction of gas leakage and potential risks (such as abnormal stress) of the pipeline.

The invention discloses a port unmanned aerial vehicle collaborative operation system and method based on digital twin and AI. The method comprises the following steps: collecting multi-source heterogeneous data of an unmanned aerial vehicle, a fixed sensor and a service system in a port in real time; dynamically and synchronously updating the digital twinborn model of the port based on the multi-source heterogeneous data; dividing a port physical space into three-dimensional voxels, and calculating and updating a dynamic risk potential field representing port global risk distribution in real time based on a digital twin model; based on the dynamic risk potential field, identifying a high-potential risk area, and dynamically generating an unmanned aerial vehicle task for the high-potential risk area; and scheduling the unmanned aerial vehicle to execute the unmanned aerial vehicle task, and analyzing the unmanned aerial vehicle return data to perform closed-loop verification. According to the method, a feed-forward risk prediction mechanism is constructed, a traditional passive response mode is converted into active intervention, the response delay of port abnormal events is shortened, intelligent scheduling of monitoring resources is achieved, and the safety and efficiency of port operation are improved.

The invention relates to a wharf safety intelligent monitoring method, device and equipment based on digital twinning and a medium. The method comprises the following steps: firstly, carrying out space-time alignment fusion on multi-source heterogeneous original data of a wharf site to generate a dynamic digital twinborn scene; extracting and matching entity relationships and events based on the dynamic digital twinborn scene and a pre-constructed wharf operation knowledge graph, and generating a dynamic knowledge situation sub-graph; performing graph neural network coding processing on the dynamic knowledge situation sub-graph to obtain coding features of nodes in the graph; risk and conflict analysis is carried out based on the coding features, and a current risk list and a potential conflict prediction list are generated; and generating security alarm information based on the list. By adopting the method, safety monitoring can be improved from a perception level based on simple rule matching to a cognition level fusing semantic understanding and context association reasoning, so that false alarm and missing alarm are remarkably reduced, and accurate early warning of potential risks in a complex working environment is realized.

The invention discloses a financial credit risk assessment and dynamic optimization method based on a fusion graph neural network, and relates to the technical field of financial management data processing. If the risk association degree does not exceed a preset first threshold value, marking the corresponding node as a risk node, and performing multi-round risk propagation simulation according to a difference value between the risk association degree and the preset first threshold value; the risk association degree of the risk nodes is recalculated in the simulation process, a risk association node set is obtained through summarizing and divided into a plurality of data fields, time sequence graph analysis is conducted on each data field, the risk nodes in the corresponding data fields are extracted according to the time sequence graph analysis, and differentiated risk optimization strategies are generated according to the risk nodes. Through a graph neural network and a dynamic optimization mechanism, the accuracy and real-time performance of financial credit risk assessment are remarkably improved, potential risks can be effectively identified, and targeted countermeasures can be formulated.

The invention relates to the technical field of data early warning, in particular to an intelligent power plant safety early warning method based on multi-data coupling. The method comprises the following steps: acquiring multi-source coupling data including power plant production operation data, key equipment state monitoring data, environment monitoring data and historical accident records, and calculating acquisition time intervals and synchronous delays between data sources; defining the ratio of the acquisition time interval to the topological distance between the corresponding nodes of the key equipment as a coupling propagation rate; constructing a time sequence-topology association window based on the coupling propagation rate, and judging the state type of the key equipment according to the time sequence-topology association window; a key equipment risk parameter is obtained by quantifying the state type; according to the method, key equipment risk parameter quantification and potential risk marking are realized through multi-source coupling data and time sequence-topology correlation analysis, so that dynamic grading early warning is supported, and the operation safety and early warning real-time performance of a power plant are improved.

The invention relates to a dam safety risk identification method based on a knowledge graph. The method is suitable for dam safety management. According to the technical scheme, the method comprises the steps that a dam safety risk knowledge system is drawn up based on business requirements of dam safety risk identification, and entity tags comprise dams, risk hidden dangers and dam important engineering parameters; based on the dam safety risk knowledge system, dam engineering data are collected, and a dam safety risk knowledge graph is constructed; calculating a comprehensive engineering similarity coefficient of the dam to be subjected to risk identification and each dam in the map based on the dam important engineering parameters of the dam to be subjected to risk identification and each dam in the dam safety risk knowledge map; determining a similar engineering characteristic dam set of the dam to be subjected to risk identification based on the comprehensive engineering similarity coefficient of the dam and each dam in the map; and determining a potential risk hidden danger set of the dams to be subjected to risk identification based on the number of the dams having the risk hidden dangers in the similar engineering feature dam set.

The invention discloses a quantitative early warning method for coupling risks of an urban lifeline system, and the method comprises the steps: collecting the real-time data of each system, and constructing a multi-level network which reflects the physical connection and function dependence relation between the systems; determining risk source nodes in the network, and traversing all communicated potential risk transmission paths along a dependency direction; for each path, a path cascade failure probability is calculated by fusing a node historical fault probability and a real-time state; quantitatively evaluating the risk value of each path in combination with the key level of the affected node; and finally, according to the risk value sorting, outputting the path in the front of the sorting and the key node thereof as early warning information. According to the method, global identification and quantitative evaluation of the cross-system risk conduction chain are realized, weak links of the network can be accurately positioned, and a scientific decision basis is provided for targeted risk prevention and control.

The invention discloses a new energy fan maintenance operation safety monitoring method combined with big data analysis, and relates to the technical field of big data analysis, and the method comprises the steps: collecting new energy maintenance data, carrying out the time synchronization, format standardization and space alignment processing, and forming a unified comprehensive data set; inputting the comprehensive data set into a pre-trained high-fidelity digital twinborn model, mapping risk factors in the comprehensive data set into a three-dimensional space of the digital twinborn model through a built-in risk quantification algorithm, and outputting a dynamic risk field; and associating the risk points and the intensity values in the dynamic risk field as nodes and attributes to nodes and attributes corresponding to a predefined causal knowledge graph, deducing a potential risk evolution path and occurrence probability, and generating a risk deduction conclusion report. According to the method, path exploration and stochastic simulation are carried out through the causal knowledge graph in combination with Monte Carlo tree search, and probabilistic deduction of the potential risk evolution path in the fan maintenance operation is realized.

The invention discloses a large model adaptive security detection method and system based on four-order linkage, and the method achieves the security detection of the output content of a large model through four-stage collaborative linkage: in the context consistency reasoning stage, calculating the semantic consistency score of a generated text, a multi-round dialogue history, a user prompt word and an external knowledge base; marking a potential risk; in the causal chain risk detection stage, modeling multiple rounds of dialogues into a causal graph, calculating a risk path probability, matching an attack pattern library, and marking high risks; in the dynamic game optimization stage, a game space of a detector and an attacker is constructed, an optimal detection threshold value is solved, and parameters of each stage are linked and updated; in the cycle state switching stage, switching is carried out between a low-power-consumption monitoring state and a dynamic updating state according to system performance and a risk situation, and a risk level is output by integrating results of the four stages. According to the method, the problems of low hidden attack detection rate, high false alarm, risk non-traceability and resource waste in the prior art are solved, and self-adaption, low false alarm, traceability, high efficiency and energy conservation are realized.

The invention relates to the technical field of construction management, in particular to a BIM (Building Information Modeling)-based engineering supervision management system and method.According to the BIM-based engineering supervision management system and method, spatial positions and states of construction machinery, vehicles and personnel are captured in real time through a sensor network deployed on site, and a core dynamic spatial conflict calculation module is used for calculating the safety of the construction machinery, the vehicles and the personnel according to a preset safety rule base; and millisecond-level conflict detection and prediction are carried out on the real-time dynamic safety envelope lines of the moving objects. And after potential risks are found, the system sends early warning of different levels from prompting to forced intervention to related personnel, so that a complete management closed loop from active perception and intelligent prediction to co-processing is constructed. Therefore, through pre-virtual simulation and in-process real-time intervention, the risk of major safety accidents such as collision and overturning caused by space conflicts is greatly reduced, and life safety is guaranteed. And by optimizing the construction space and logistics planning, shutdown and rework caused by conflicts are reduced, and the construction efficiency and the project management refinement level are improved.

The invention relates to the technical field of supplier full life cycle technologies, and provides a supplier full life cycle management method based on multi-dimensional evaluation, which comprises the steps of demand source searching, supplier classification and selection, supplier relationship management, contract management, purchase management, order execution, invoice reconciliation and expenditure analysis. According to the method, a whole-process closed-loop management system from demand source searching to expenditure analysis is established, so that information barriers among links are broken, and seamless circulation and sharing of data are realized; a standardized evaluation model and a classification system are adopted, traditional supplier decision-making depending on subjective experience is converted into scientific decision-making based on quantitative data, and the management efficiency and decision-making accuracy are remarkably improved. By establishing a contract performance early warning mechanism and an abnormal order grading processing flow, full-flow dynamic monitoring of contract risks and execution abnormities is realized; based on the risk assessment matrix and the change influence analysis model, various potential risks can be identified and responded in time.

The invention discloses a large model driven automatic driving limit scene test method based on vehicle and road cloud collaboration, and relates to the technical field of automatic driving, and the method comprises the steps: a vehicle end and a road side unit cooperatively collect the vehicle state and surrounding environment data, the road side unit carries out the quantitative representation of the dynamic attribute of a traffic flow, and recognizes a potential risk fragment; triggering the potential risk fragment to be uploaded to the cloud; the cloud performs structured reconstruction on the uploaded potential risk fragment data to generate a parameterized basic test scene; inputting the parameterized basic test scene into the trained large model, adjusting the coupling relationship among key scene parameters, and generating a limit test scene for exposing the performance boundary of the automatic driving decision planning module; and issuing the limit test scene to a road side unit of the target test area, and injecting scene information into the test vehicle by the road side unit. According to the method, the limitation of a traditional test method in the aspects of complex coupling scene generation efficiency and boundary exploration depth is solved.

The invention provides a lifting machine working condition evaluation method and system combining a digital model and physical simulation, and relates to the technical field of industrial equipment safety evaluation.The method comprises the steps that firstly, a working condition evolution two-way conduction mechanism of a digital twin evolution model and a physical simulation deduction system is established, and two-way data transmission is achieved; executing multiple rounds of interaction evolution cycles based on the working condition evolution bidirectional conduction mechanism to generate a full-quantity evolution data set; then analyzing the full evolution data set to obtain an operation parameter trajectory, and associating a safety threshold to mine safety risk triggering trajectory features; and finally, generating a working condition safety evaluation result according to the safety risk triggering track characteristics. The working condition safety of the hydraulic system of the lifting machine can be comprehensively and accurately evaluated, and potential risks are found in advance.

The invention relates to the technical field of edge computing, and discloses a service area hazardous chemical substance vehicle intelligent supervision method and system based on edge computing, and the method comprises the steps: obtaining the real-time position data and state information of a hazardous chemical substance vehicle in a service area; integrating the real-time position data and the vehicle state information on the edge computing node to construct a dynamic behavior portrait, and extracting motion track features in the dynamic behavior portrait; comparing the motion trail features with a planned route in the electronic waybill data, judging the consistency of the route and detecting aggregation abnormity so as to generate a potential risk signal; based on the potential risk signal, distributing a response priority and automatically triggering an unmanned aerial vehicle system, and planning a flight path by combining a geographic information system so as to control the unmanned aerial vehicle to fly to a target area and perform data acquisition; according to the method, the accuracy and real-time performance of risk identification can be improved, it is ensured that potential risks are quickly sensed and effectively responded, and a complete supervision link from risk early warning to on-site checking is constructed.

The invention discloses a three-in-one intelligent management system for rainfall monitoring and environmental protection of a subway foundation pit in a water-rich stratum, and relates to the technical field of intelligent management of foundation pit engineering. The risk granulate knowledge base module is used for performing iterative clustering on the potential risk state point cloud database through an improved coarse-grained risk granulate generation algorithm to obtain a risk granulate knowledge base; the decision module is used for judging a risk pellet to which the current high-dimensional system state vector belongs and a corresponding risk consequence, and predicting an evolution trajectory of the high-dimensional system state vector in a risk pellet space; and the control module is used for remotely regulating and controlling the rainfall and recharge equipment. According to the method, a set of intelligent immune decision normal form based on the risk granular balls is constructed by deeply fusing the granular calculation theory and foundation pit engineering risk management, and the core innovation of the method is that a set of brand-new system capable of performing multi-dimensional, dynamic and active risk pre-control is established.

The invention provides a counter service monitoring system, method, device and equipment, a storage medium and a program product, and relates to the field of artificial intelligence. The system comprises a sensing module used for acquiring multi-modal data in a counter business handling process; the multi-modal data comprises a virtual identifier, visual information and core transaction data; the knowledge base module is used for storing business compliance rules, historical error cases and anti-fraud features; the processing module is used for identifying risk control information associated with counter business based on the storage content of the knowledge base module and the multi-modal data, and generating an execution instruction when an abnormal operation or a potential risk is detected based on the risk control information; and the execution module is used for responding to the execution instruction to execute at least one of the following operations: pushing operation correction guidance to the business personnel, and pushing business verification information or risk prompt to the customer. According to the system provided by the invention, the security of counter business handling is improved.

The invention relates to the technical field of electric data processing, and particularly discloses a lining gathering and transportation pipeline stress coupling analysis system and method, the analysis system is provided with an environment evaluation module, a stress coupling evaluation module and a stress coupling module, and the problems that a traditional analysis method often depends on a simplified physical model and hypothesis, and the analysis efficiency is low are solved. The system solves the problem that the existing pipeline behavior prediction method is short of effective data integration capability and may cause inaccurate prediction of pipeline behaviors under actual working conditions, and is helpful to more accurately predict possible problems of the pipeline in actual operation, so that preventive measures can be taken in advance, and the system can predict stress responses of the pipeline under different environmental conditions and corrosion states. The stress coupling state is monitored and evaluated, the maintenance work can be more effectively planned, excessive maintenance or neglect of potential risk points is avoided, and the service life of the pipeline is prolonged.

The invention discloses a judicial cooperation management mechanism and implementation method oriented to enterprise environment conservation motivation and constraint, and the mechanism comprises a multi-source environment conservation data center which is used for collecting and storing enterprise-related environment data from ecological environment departments, courts, procuratorials, public security and other related institutions in a real-time and standardized manner, the data at least comprises enterprise basic information, environment monitoring data, administrative penalty records, judicial referee information, rectification feedback information and a law-of-conservation credit rating. The problems of different evidence formats, information lag and repeated verification in an existing mechanism are solved through a unified evidence transfer rule, block chain evidence storage and a standardized mapping table, the collaboration period is shortened by 40% or above, the judicial credit collection accuracy is improved to 98% or above, and based on a three-dimensional layered label and an intelligent portrait model, the method is simple and convenient to implement. Differentiated excitation and pre-constraint are carried out on enterprises of different industries, scales and historical records, the law-keeping power of small, medium and micro enterprises is enhanced, and meanwhile potential risks are intervened in advance.

The application relates to the technical field of facility asset information management, and discloses a cable resource management method and system based on a Revit model, which comprises the following steps: in response to a cable path conflict position marked by a mobile terminal, three-dimensional data of a target area is extracted; path compliance judgment rules are established based on voxelization and taking into account a minimum bending radius of the cable; an alternative path is generated according to the rules and is pushed to the terminal for confirmation, and then Revit model parameters are automatically updated; finally, context data of the conflict event is used to train a conflict pattern recognition model to scan and prewarn potential risks in an unconstructed area. The application converts discrete on-site construction problems into structured data for system learning, changes a management process from post-repair to risk prediction based on actual experience, and realizes reconstruction of a digital model from static physical property records to a dynamic digital history.

The present application relates to the field of information technology services, and discloses a record product risk early warning method and system based on a component library, comprising: extracting a full component list of a record product from product record data to construct a component database corresponding to the full component list by using a preset component matching rule; establishing a component quick search index and a risk feature set of the component database to match potential risk components corresponding to the full component list in the component database; constructing a multi-dimensional risk portrait corresponding to the record product by calculating the compliance correlation degree of each component in the full component list and the potential risk components; and outputting a risk early warning result of the record product according to a risk assessment conclusion corresponding to the record product and the multi-dimensional risk portrait. The present application can greatly improve the initiative foresight and prevention accuracy of record product risk early warning.

The invention discloses an industrial internet security operation and maintenance risk assessment method and system, and relates to the technical field of data management.The industrial internet security operation and maintenance risk assessment method comprises the steps that initial information collection is conducted on an industrial internet operation environment, risk links are constructed, and primary risk links are further analyzed and screened out; then obtaining dynamic operation data of each primary risk link, performing priority ranking based on the dynamic comprehensive risk score, and dynamically adjusting response triggering measures of the primary risk links at the same time to realize pertinence and timeliness of risk disposal; and finally, through cross validation of multi-level risk links, evaluation of actual effects of each adjustment measure and fine adjustment of a network security policy, potential risks can be effectively identified and quantified, a protection policy can be dynamically adjusted, the high-risk link disposal efficiency can be improved, and the security and stability of the whole system can be ensured. Therefore, the scientificity, controllability and reliability of industrial internet security operation and maintenance are obviously enhanced.

The application belongs to the technical field of garden greening construction, in particular to a garden greening engineering construction and maintenance task collaborative control system, comprising: an AI multi-factor coupling prediction and early warning module, a multi-dimensional coupling prediction model is constructed, a deep learning algorithm control logic is carried, full-amount data of construction and maintenance is associated and called through double-track coding, multi-factor weight analysis and logical operation are carried out, early warning of seedling maintenance risk is realized, and the system simultaneously has a construction scheme reverse check control function, full-amount associated data is called through double-track coding, a risk cause and disposal scheme integrated prompt is generated, the module is mainly responsible for early warning of seedling maintenance risk, integrates multi-dimensional data, analyzes by using an artificial intelligence algorithm, identifies potential risks, and provides corresponding early warning information and disposal suggestions.

The application discloses a kind of multi-virtual power plant point-to-point transaction method, electronic equipment and storage medium.First, collect and aggregate virtual power plant internal distributed resources, construct multi-virtual power plant electricity-carbon-backup point-to-point transaction model;Set conditional value at risk model confidence and risk preference coefficient, construct multi-virtual power plant electricity-carbon-backup point-to-point transaction model based on conditional value at risk;Adaptive alternating direction multiplier algorithm (ADMM) based on consensus is used to solve the model in distributed manner, and the transaction strategy of each virtual power plant is obtained.The application considers that virtual power plant aggregates photovoltaic, fuel cell, energy storage, central air conditioning, flexible load and other distributed resources to participate in electricity-carbon-backup point-to-point transaction, which widens the transaction channel of virtual power plant;Value at risk quantifies the potential risk brought by photovoltaic randomness, so that virtual power plant decision makers can weigh benefits and risks;Adaptive ADMM algorithm based on consensus effectively improves the calculation efficiency of model solving.