63 results about "Performance risk" patented technology

A work order management system automates the process of scheduling maintenance tasks and generating corresponding work orders via analysis of monitored data generated by the industrial assets. The work order management system can monitor control, status, or operational data from industrial devices on the plant floor, and initiate creation of work orders based on a determination that the monitored industrial data indicates a current or predicted performance risk requiring investigation or maintenance. The system can leverage generative artificial intelligence (AI) or other types of AI in connection with determining when and how to schedule a maintenance task intended to mitigate asset risk. The system can also factor contextual information when determining whether to create and schedule a work order, such as the cost of operator or maintenance time, scheduled plant downtimes, environmental factors (e.g., humidity), time of year, supplier issues, and other considerations.

The invention relates to the technical field of new energy systems, and particularly discloses a new energy engineering cost progress two-dimensional dynamic prediction and risk prevention and control system, which comprises a data processing module, a dynamic prediction module, a risk prevention and control module and a visualization module. Cost and progress linkage prediction is realized based on the coupling relationship between the cost subject tree and the progress node network, the error is reduced by more than 40% compared with the traditional single-dimensional prediction, and the cost hyper-branched and progress lagging risks of the future N cycles can be identified in advance; a multi-level early warning mechanism automatically triggers risk prevention and control suggestions including super-budget attribution analysis, cost compression and progress compression strategies, the response speed is increased, and human decision delay is reduced; supplier full-process management quantifies the performance capability, quality and delivery cycle through a weighted scoring model, an elimination mechanism is automatically triggered based on an accumulated performance problem or a scoring threshold, and the performance risk is reduced.

The present invention relates to the field of access management technology, and discloses a management system for the safety management and on-the-job performance of outsiders and a management method thereof, comprising the following steps: by integrating the Internet of Things, artificial intelligence and big data analysis technology, real-time monitoring, identity authentication, on-the-job status tracking and performance behavior analysis of outsiders entering the work area are realized. The identity information, functional information and visited area information of the visiting outsiders are obtained, and the activity trajectory of the outsiders during their on-the-job period is monitored in real time to ensure that they perform their duties within the specified area, and the performance risk of outsiders in the specified area is estimated by constructing a risk estimation model, and an early warning is automatically triggered if there is a high risk. The present invention can solve the problems of low efficiency, incomplete supervision, inaccurate data and the like in the prior art in the on-the-job performance management of outsiders, and provide data support for optimizing the management process.

The invention relates to the technical field of intelligent logistics, in particular to an AI-based supply chain risk early warning method and system, and the method comprises the following steps: extracting a staggering degree and a jump vector based on a performance time sequence, calculating an abnormal risk through a Transform model in combination with a density mutation node, and generating a performance risk early warning result. According to the method, the plan of the node in the logistics order is compared with the completion time, the performance time sequence is constructed, and change behaviors such as rhythm reversal and execution interval fluctuation are identified, so that rhythm deviation caused by external disturbance in the performance process can be effectively described; through comparison between the performance completion sequence and the node numbers, the displacement degree of performance sequence disturbance is extracted, and the nonlinear sequential relation between the nodes is further captured; the two types of disturbance vectors are jointly coded into a deep expression structure with a time sequence structure, and cross-dimension and cross-channel feature alignment and correlation mapping are achieved.

The invention relates to the technical field of data processing and risk monitoring, and discloses a performance risk intelligent monitoring and collaborative response method and system, and the method comprises the steps: an edge sensing node calculates the compression characteristics of performance flow data through employing a mean aggregation algorithm according to a current sliding window parameter, and transmits the compression characteristics to a cloud end; the cloud decision-making platform executes data standardization management, calculates performance risk probability by using a model reasoning algorithm, and calculates feature uncertainty indexes by using a game theory interpretation method and a variance statistical method; a feedback control strategy module generates a self-adaptive sampling control instruction according to the index, drives an edge node to dynamically update a sliding window parameter, and constructs an acquisition feedback closed loop; and the collaborative response module calculates a response priority by utilizing multi-dimensional weighted logic, calculates desensitized data by utilizing a differential privacy algorithm and then executes response. According to the invention, adaptive adjustment of data acquisition granularity and safe cooperative processing of risk events are realized.

A work order management system automates the process of scheduling maintenance tasks and generating corresponding work orders via analysis of monitored data generated by the industrial assets. The work order management system can monitor status and operational data from industrial devices on the plant floor, as well as mobile industrial robots that traverse the plant floor, and initiate creation of work orders based on a determination that the monitored industrial data indicates a current or predicted performance risk requiring investigation or maintenance. The system can leverage generative artificial intelligence (AI) or other types of AI in connection with determining when and how to schedule a maintenance task intended to mitigate asset risk.

The invention provides a transaction risk early warning system and method for electric power marketization reform. Relates to the technical field of electric power systems and electric power marketization reform, and comprises a risk data acquisition module used for acquiring multi-source data in the whole electric power marketization transaction process in real time; the risk data fusion module is used for performing time sequence synchronization, format standardization and heterogeneous data fusion on the multi-source data to generate a unified risk feature data set; and the risk assessment module is used for calculating market multi-dimensional risk indexes in real time through the risk feature data set and a Gaussian mixture model. According to the trading risk early warning system and method for electric power marketization reform, the false alarm rate and the missing report rate are remarkably reduced, and full-chain and all-weather monitoring and intelligent warning of price risks, performance risks, credit risks and market manipulation risks are achieved.

The invention relates to the technical field of plate processing production management and industrial data processing, in particular to a plate production order management system based on edge sealing drilling, which comprises an order data acquisition step: constructing a holographic data input layer, and acquiring a process data set and real-time performance process data; a performance benchmark construction step: establishing a dual reference system, generating an ideal performance time benchmark, and generating theoretical performance delay evaluation data in combination with a process complexity parameter; a performance difference analysis step: separating the noise and the signal, and respectively calculating an actual performance deviation and a theoretical performance deviation only containing a process complexity factor; an order state judgment step: analyzing the distribution mode similarity of the deviation, if the distribution mode similarity is higher than a threshold value, judging that compliance process delay exists and the delivery time is adjusted, and if the distribution mode similarity is lower than the threshold value, judging that abnormal performance risk exists and generating a management instruction; according to the method, confusion of processing endogenous complexity and exogenous abnormity is successfully stripped, and fine attribution oriented management is realized.

The invention relates to the field of data fusion, in particular to a supplier performance risk evaluation system based on multi-source data fusion, and the system comprises a static data feature extraction module which enables supplier condition data to pass through a static feature extraction model based on an MLP architecture so as to generate supplier general situation features; the dynamic data feature extraction module enables the supplier production data to pass through a dynamic feature extraction model based on an LSTM architecture so as to generate supplier production condition features; the basic risk identification module generates a risk prior index through an analytic hierarchy process; and the fusion evaluation module is used for generating a supplier multi-dimensional performance risk evaluation value through a risk fusion mapping model based on a gating mechanism, an attention mechanism and a full-connection network architecture. According to the method, the importance of long-term static supplier features, dynamic supplier features and analytic hierarchy process weights with prior experience is dynamically deployed through the fusion model, and risk symptoms are accurately captured.

A work order management system automates the process of scheduling maintenance tasks and generating corresponding work orders via analysis of monitored data generated by the industrial assets. The work order management system can monitor control, status, or operational data from industrial devices on the plant floor, and initiate creation of work orders based on a determination that the monitored industrial data indicates a current or predicted performance risk requiring investigation or maintenance. The system can leverage generative artificial intelligence (AI) or other types of AI in connection with determining when and how to schedule a maintenance task intended to mitigate asset risk. The system can also factor contextual information when determining whether to create and schedule a work order, such as the cost of operator or maintenance time, scheduled plant downtimes, environmental factors (e.g., humidity), time of year, supplier issues, and other considerations.

The invention discloses a defect basin-type insulator mechanical property risk degree evaluation method and system based on infrared imaging, and belongs to the field of power equipment partial discharge detection.The method comprises the steps that infrared radiation signals of the surface of a test product in the temperature change process are collected, and time sequence temperature field distribution data are generated; calculating a temperature change rate based on the temperature field distribution data, positioning an internal heat conduction abnormal area and identifying bubble defects; carrying out mechanical property simulation on the basin-type insulator with the bubble defect, applying the same fixed mechanical load in the simulation process, and analyzing the stress conditions of the defect part and the whole body of the basin-type insulator; and evaluating the harmfulness to the mechanical property of the basin-type insulator. According to the method, the risk degree of the defective basin-type insulator can be judged, power transmission interruption or fluctuation caused by blind replacement or wrong judgment is avoided, various inconveniences and losses caused by power failure accidents are further reduced, maximum utilization of resources is achieved, and the power industry is promoted to develop towards the green and sustainable direction.

The embodiment of the application discloses a robot control method, computer equipment and a computer readable storage medium. Wherein, the method comprises the following steps: in response to the operation instruction input by the user, the acquired robot data and the operation instruction are processed into feature data; determine the task type corresponding to the operation instruction; determine the task alignment degree corresponding to the feature data according to the task type; calculate the confidence of the operation instruction according to the task alignment degree; execute the operation instruction according to the confidence. Therefore, when the application can receive the operation instruction of the user, the robot can understand the control intention of the user, the safety and performance risk caused by human error operation is reduced, the dependence on high-level operators is also reduced, and the universality of takeover is improved. The confidence mechanism realizes the "consensus control" of man-machine, and improves the success rate of complex tasks.

The invention discloses a dynamic intention quantification and 'BUG' early warning system for enterprise performance management. The system analyzes texts such as work logs and plans of employees, extracts work gravity center and target intention information of the employees, and compares the work gravity center and the target intention information with a preset target intention baseline, so that intention deviation is dynamically quantified. Once the actual attention point of the employee is found to have significant drifting relative to the target, abnormal early warning similar to BUG is triggered, and a manager is prompted to pay attention to possible target deviation and performance risks. The system uses semantic analysis to position the specific aspect of deviation occurrence, explains semantic reasons (such as task gravity center transfer, strategy simplification and the like) of performance deviation, and provides deviation correction suggestions. According to the mechanism, the idea about efficient abstract decision in the BUG theory in the field of artificial consciousness is used for reference, and the high-level intention deviation is regarded as a bug needing to be corrected in time.

The invention relates to the technical field of engineering management, in particular to an engineering scene-oriented salary performance risk identification and control method and system. According to the technical scheme, the method comprises the steps of obtaining a risk tag type, a risk tag standard value and risk tag data; and judging whether the risk tag data corresponding to the risk tag type is empty or not. And constructing salary performance weights in one-to-one correspondence with the risk tag types. And calculating the coincidence coefficient of the contractor. And calculating the project capability goodness of fit, the project risk adaptation value and the risk avoidance degree of each contractor. And carrying out sorting recommendation on the contractor according to the risk avoidance degree from large to small. The higher the project capability goodness of fit and the higher the project risk adaptation value are, the higher the contractor is matched with the project, so that manual matching of related workers is avoided, and the management burden is reduced. Calculation is carried out in two aspects of project capability and project risk adaptation, calculation is more comprehensive, and the obtained recommended contractor better meets the requirement.

The invention relates to the technical field of artificial intelligence, can be applied to the field of science and technology finance / digital medical treatment, and discloses a front-end code performance recognition method and device, electronic equipment and a storage medium. The method comprises the steps that front-end code grammar, a front-end code framework and front-end historical codes serve as data sources, and front-end performance change feature data are extracted; performing performance change rule analysis on the front-end performance change characteristic data, and creating a corresponding front-end performance detection rule according to analysis result data; converting the front-end performance detection rule into front-end code performance detection logic executable by the front-end code analysis tool; packaging the front-end code performance detection logic as a front-end code performance detection plug-in, and accessing the front-end code performance detection plug-in to a front-end item to be detected; and running the front-end code performance detection plug-in, and performing performance analysis on the to-be-detected front-end item to obtain a front-end code performance detection result. According to the method, accurate identification and early warning of front-end performance risks are realized in a development stage.

The application discloses a supply chain performance path optimization and collaborative decision-making method based on an AI algorithm, and particularly relates to the field of supply chain performance path optimization, and comprises the following steps: firstly, order goods information and warehouse resource data are acquired, and it is judged whether a single warehouse can independently perform the performance by matching; if yes, a comprehensive evaluation function containing total transportation time, time redundancy, path complexity and resource utilization rate is constructed, and an optimal path from the warehouse to a delivery place is generated; if multiple warehouses need to be cooperated, a centralized point warehouse is dynamically selected, the goods transfer cost and historical performance risk value of each warehouse are comprehensively calculated, a comprehensive early warning score is generated, and an optimal centralized point is selected, finally, the transfer path from the supply warehouse to the centralized point and the performance path from the centralized point to the terminal are planned, and a full-link collaborative scheme is formed; the application breaks through the traditional single-warehouse decision-making mode, realizes global scheduling of resources through a double-path optimization mechanism, significantly reduces the transportation delay risk, and improves the operation efficiency of the supply chain.

The invention provides a contract term intelligent comparison and performance risk monitoring method and device and electronic equipment, and belongs to the technical field of software engineering.In the method, contract version comparison and difference analysis can be automatically completed, and the examination efficiency is improved; risk points in the contract terms can be identified through preset compliance rules, and the risk identification rate is high; the system can be connected with an external business system to realize performance risk monitoring and effectively prevent passive default; and data analysis can be carried out on the historical contract document, so that the contract template and / or the contract compliance rule are / is optimized, and effective utilization of data is realized.

The invention discloses a ship electric propulsion efficiency evaluation method and system based on multi-modal data fusion, and the method comprises the steps: obtaining a blade cavitation corrosion image of a propeller, and constructing a multi-modal propulsion state data set through space-time alignment in combination with ship state parameters and electric propulsion parameters; extracting structural dynamic characteristics and electric propulsion state characteristics of the propeller, and fusing to generate a real-time propulsion state characteristic vector; based on the real-time propulsion state feature vector and the multi-modal propulsion state data set, obtaining a real-time propulsion efficiency estimated value and a corresponding propulsion efficiency risk level through a pre-constructed efficiency evaluation model; performing risk association analysis in combination with an efficiency risk mode library, determining an efficiency risk mode and associated data, and further obtaining a data adjustment amount through efficiency trend association analysis; and finally, generating a ship evaluation result according to the data adjustment amount and the efficiency risk mode so as to improve the comprehensiveness of ship evaluation.

A work order management system automates the process of scheduling maintenance tasks and generating corresponding work orders via analysis of monitored data generated by the industrial assets. The work order management system can monitor status and operational data from industrial devices on the plant floor and initiate creation of work orders based on a determination that the monitored industrial data indicates a current or predicted performance risk requiring investigation or maintenance. The system can leverage generative artificial intelligence (AI) or other types of AI in connection with determining when and how to schedule a maintenance task intended to mitigate asset risk. The system dynamically updates the work orders in response to changing contextual conditions within the plant to ensure that work order priority, scheduling, and resource allocations satisfy a defined maintenance optimization criterion.

This invention belongs to the field of power system optimization and scheduling technology for multi-microgrid systems, and particularly relates to a method and system for collaborative optimization scheduling of active distribution networks and multi-microgrids. It includes establishing a multi-entity shared energy storage operation framework; considering the bilateral uncertainties of active distribution networks and multi-microgrids, introducing adjustment costs and default penalties to quantify performance risks, and designing multi-timescale operation strategies for shared energy storage operators; establishing a multi-entity collaborative scheduling model based on Nash game theory, and decomposing the collaborative scheduling problem into two sub-problems: maximizing social benefits and allocating cooperative revenue; and introducing an ADMM distributed solution algorithm based on consensus variables and event-triggered communication to solve the two sub-problems, thereby achieving collaborative optimization scheduling among multiple entities. This invention improves the utilization efficiency of cross-layer energy storage resources and the level of renewable energy consumption, reduces system operating costs, and enhances the performance reliability of leasing plans under uncertain scenarios, while protecting the data privacy of multiple entities.

The invention discloses a cloud cost optimization suggestion generation method and device based on a resource map. The method comprises the following steps: collecting cost data, a resource relation graph and resource performance monitoring data; according to the cost data, a preset rule and the resource performance monitoring data, identifying a target resource to be optimized, executing an optimization operation on the target resource, and calculating a cost saving expectation; topological positioning is carried out in the resource relation graph, and business service nodes depending on the to-be-optimized target resources are determined; evaluating the performance influence of the optimization operation on the business service node, and calculating the performance risk degree; and according to the cost saving expectation and the performance risk degree, generating an optimization suggestion with a risk assessment level. The technical effects of dynamically simulating the influence on the performance of the business when the optimization suggestion is generated and outputting an executable scheme with risk assessment are realized.

The invention discloses a bid invitation risk early warning method and system based on big data, relates to the technical field of bid invitation risk early warning, and solves the technical problems that in bid invitation risk early warning, the risk that a bidding enterprise fulfills a project is difficult to adaptively analyze, and weight factors with autonomous changes are difficult to set for different projects. According to the method, the anti-risk factors are obtained by analyzing the basic data; analyzing the target data to obtain a scheme factor; analyzing the historical bid-winning data to obtain capability factors; integrating the anti-risk factor, the scheme factor and the ability factor into a bid invitation risk factor representing the performance risk of the bidding enterprise; setting an early warning threshold 2 based on the bid invitation risk factors of the plurality of bidding enterprises, and obtaining a preset early warning threshold 1; performing risk early warning on the bidding enterprise based on the early warning threshold value I and the early warning threshold value II; according to the method, the complex enterprise qualification can be converted into a measurable risk index, and preposed management of the bid invitation risk is realized.

The present application relates to the technical field of property transaction, in particular to an intelligent contract automatic execution and supervision system for the whole process of property transaction, comprising: an intelligent contract generation unit; a contract automatic execution unit, which maps the intellectual property right transfer and transaction settlement process agreed in the standardized intelligent contract into a multi-stage execution phase sequence of corresponding performance nodes, and each execution phase is subject to the necessary condition of performance risk assessment; a contract supervision and verification unit; and an intellectual property right ownership verification unit.The present application builds a whole-process intelligent contract automatic execution and supervision system, uses a time sequence attention mechanism and an improved lightweight residual time sequence convolution network to complete dynamic risk assessment, combines hierarchical execution and reversible rollback to realize abnormal disposal, forms an execution, supervision and ownership verification closed-loop architecture, can monitor and dispose transaction abnormalities in real time, ensures stable transaction execution, realizes whole-link ownership tracing and compliance supervision.

The application discloses a solid state disk testing method, device, equipment and storage medium. The method comprises the following steps: periodically acquiring error correction data of the solid state disk during temperature cycle testing and dynamic voltage regulation testing environment of the solid state disk; and determining a performance risk state of the solid state disk based on the error correction data. Through real-time monitoring of the error correction data, early identification of potential faults of the solid state disk is realized, and a lag mode of traditional testing relying on post-failure block detection is changed. The application dynamically determines the performance risk based on the error correction data, and improves real-time performance and early warning capability of the testing. Meanwhile, the method can be combined with working conditions such as temperature and voltage, but the core determination step only needs to compare the error correction data with preset conditions to realize risk state determination, effectively solves the problem of lag in failure determination in the prior art, and significantly improves the accuracy and diagnosis depth of the solid state disk reliability testing.

The invention discloses an all-working-condition risk quantification method applied to a new energy civil aircraft power system, which comprehensively considers the influence of discrete factors and continuous factors, quantifies the performance risk of the power system under all working conditions into a probability density distribution function, and comprises the following steps: defining a discrete variable and continuous variable distribution set; establishing a power system performance change degree probability density function PDFcon under the influence of continuous variables; establishing a power system performance probability density function PDFsys under the common influence of the continuous variable and the discrete variable; and step 4, quantifying the system risk, namely quantifying the system risk under the common influence of the discrete variable and the continuous variable into a probability density distribution function for evaluating the risk of the system at the moment. According to the method, quantitative analysis of all-working-condition risks can be realized, and reliable support is provided for airworthiness verification, safety evaluation and engineering application of a new energy power system.

The invention relates to an intelligent photovoltaic project operation and maintenance monitoring and optimization method, which belongs to the technical field of photovoltaic intelligence, and comprises the following steps: collecting the physical state and operation environment data of a component, and establishing a performance attenuation index to quantify the decline degree of the component; based on this, constructing a performance risk index and a financial risk index, and evaluating power generation loss and default risk; in combination with preset maintenance cost and time, an option value index representing the economic potential of the component is established through the power contract terms; introducing a risk weight vector, and establishing a risk mismatch index to quantify a risk association deviation; generating an operation and maintenance task priority weight based on the option value and the risk mismatching index; after operation and maintenance are executed, a control efficiency index is set by comparing actual and predicted option values before and after intervention, and the risk weight is dynamically adjusted to realize strategy closed-loop optimization; according to the method, theoretical power is calculated by using real-time environment data as a reference, the health condition of the component is accurately reflected, and collaborative management of technical parameters and commercial risks is realized.

The method for performance optimization of a digital twin system and related equipment provided in the application comprises: when it is determined that the digital twin system is in a performance risk state based on a key performance indicator, determining a processing resource bottleneck type according to a resource load performance indicator; constructing a corresponding cause-effect traceability chain according to a service load performance indicator corresponding to the processing resource bottleneck type and a corresponding target resource load performance indicator; calculating a resource load performance indicator density of each space unit of the digital twin system according to the cause-effect traceability chain, determining a bottleneck space range, and evaluating a load contribution degree of each service object to the processing resource bottleneck in the bottleneck space range to locate a bottleneck service object; and generating a performance optimization strategy of the digital twin system based on a scene type of the digital twin system, in combination with the processing resource bottleneck type, the bottleneck space range and the bottleneck service object. Thus, the accuracy of performance problem positioning of the digital twin system and the pertinence of the optimization strategy are improved.

This application discloses a fuel rod PCI performance risk assessment method, device, storage medium, and electronic device, relating to the technical field of nuclear reactor fuel rod analysis. The method comprises: obtaining the current power and current burnup of the fuel rod; based on the current power and current burnup, calculating the target power limit corresponding to the current power and current burnup using the fuel rod limit power surface equation, wherein the fuel rod limit power surface equation is determined based on the initial power, initial burnup, and power limit of the fuel rod at each operating moment; calculating the power margin of the fuel rod based on the target power limit and the current power; and assessing the PCI performance risk of the fuel rod based on the power margin. This application can effectively assess the PCI performance risk of the fuel rod during operation.

The invention discloses a non-invasive MySQL user login auditing method, relates to the technical field of database auditing, and solves the technical problem of security risk and performance risk caused by log record triggering by intercepting login operation in the prior art. The method comprises the following steps: capturing accounts account information of MySQL in real time, wherein the accounts account information comprises a user name, a client I P, a current connection number and a historical total connection number; calculating a newly added connection, an interrupted connection and a continuous connection according to the account number connection number acquired continuously twice; repeating the previous steps to form a connection log of the user in a certain time period so as to realize user access auditing; according to the method, the MySQL account information is regularly collected, so that the login user information can be quickly obtained, which is obviously different from the log recording triggered by intercepting the login operation in the prior art, and the method is a non-invasive log recording method, so that the safety and performance problems are solved.

The invention relates to the technical field of voltage monitor operation and maintenance evaluation, in particular to a voltage monitor multi-parameter fusion operation and maintenance evaluation system and method.In the system, a performance evaluation early warning feedback module generates performance evaluation results of all voltage monitors based on obtained operation performance risk evaluation values of all the voltage monitors; the method comprises the steps of obtaining performance risk early warning information of a corresponding voltage monitor, performing self-calibration on the corresponding voltage monitor, determining a performance fault source based on fluctuation deviation of an operation performance risk assessment value of the corresponding voltage monitor before and after self-calibration, and performing secondary updating on the performance risk early warning information of the corresponding voltage monitor. And the information is fed back to the remote monitoring terminal through a wireless network to assist a supervisor in generating an operation and maintenance management scheme. According to the invention, the limitation of traditional single parameter evaluation is broken through, and intelligent operation and maintenance management of the voltage monitor is realized by comprehensively utilizing various parameters generated by the voltage monitor and the operating environment thereof through an advanced data fusion technology.