333 results about "Priority list" patented technology

The invention relates to the technical field of engineering progress management, in particular to an engineering construction digital project management method and system, and the method comprises the steps: database building, real-time data collection, and model building: generating a visual construction progress model; progress deviation judgment: calculating progress deviation and performing judgment; deviation analysis: calculating a resource gap of an affected process, and generating a resource allocation priority list; resource adjustment: pushing an adjustment instruction to the construction terminal according to the priority list; simulation: simulating the adjusted construction progress, and if the deviation is not eliminated, executing a redistribution step; redistribution: executing the deviation analysis step again; and optimization: optimizing subsequent project progress plan generation logic. The system comprises a database building module, a real-time data acquisition module, a model building module, a progress deviation judgment module, a deviation analysis module, a simulation module, a redistribution module and an optimization module. The method and the device have the effect of facilitating fine management of the engineering project.

The invention discloses a building operation and maintenance management method and system based on big data, and relates to the technical field of building intellectualization, and the method comprises the steps: collecting multi-source heterogeneous data, executing data standardization processing, and generating a standard data stream; constructing a virtual building model based on a digital twinning technology, mapping the standard data stream to a corresponding device node of the virtual building model, and generating a device feature matrix; inputting the equipment characteristic matrix into a deep learning network, training to generate an energy consumption prediction model, and outputting an energy consumption prediction sequence; a multilayer perceptron health scoring model is constructed based on the energy consumption prediction sequence, and a fault early warning priority list is generated in combination with an equipment weight coefficient; and automatically generating a maintenance work order according to the fault early warning priority list. Through the maintenance effect evaluation and secondary maintenance mechanism, a complete closed-loop management process is formed, the maintenance quality is ensured, the maintenance strategy is continuously optimized, and the overall operation and maintenance management level is improved.

The invention discloses a network key node identification and optimization method, system and device, and a storage medium, and relates to the field of key node identification and dynamic optimization of an electric power Internet of Things communication network, and the method comprises the steps: collecting the original data of the electric power Internet of Things, and obtaining a clean data cube in space-time alignment; constructing a dynamic space-time map, and further generating a dynamic weight matrix; evaluating the key nodes based on the dynamic weight matrix to obtain a node priority list; establishing a multi-target optimization model based on the node priority list in combination with a real-time network state; solving the multi-objective optimization model to obtain an optimal solution set, performing strategy matching and verification to obtain a verified optimization instruction set, and identifying and optimizing the network key nodes through the verified optimization instruction set; according to the method, through multi-dimensional quality portrait construction and space-time atlas analysis, network key nodes are scientifically identified and dynamically optimized, so that the stability, the safety and the performance of the network are improved.

The invention discloses a multi-robot task scheduling method, system and device and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the role definition of a robot, collecting environment information in real time, and constructing a dynamic environment model; according to the dynamic environment model, identifying current tasks to be executed, evaluating urgency, complexity and resource requirements of each task, and forming a priority list; disassembling a high-priority task in the priority list into sub-tasks, and assigning a role and a task allocation scheme of the robot; a biologically inspired self-organizing algorithm is adopted, the robot exchanges information according to the state of the robot and task requirements, and a task allocation scheme is dynamically adjusted; the robot exchanges information according to the own state and the task demand, and dynamically adjusts the task allocation scheme, so that the flexibility and adaptability of task allocation are realized, the change of environment and state can be responded in time, and task delay caused by insufficient resources is avoided.

The invention relates to the technical field of network communication, provides a dual-stack network adaptive scheduling method and system based on a DNS analysis strategy, and is used for establishing an effective protocol stack feature association mechanism so as to realize cross-protocol-layer intelligent scheduling. The method comprises the steps that a DNS analysis request sent by a client side is received, and the DNS analysis request carries a target domain name and a request protocol type; analyzing the request protocol type to determine a network protocol stack type supported by the client; generating an adaptive scheduling strategy corresponding to the target domain name according to the network protocol stack type and a preset address type priority list; selecting a target IP address type from a pre-configured IP address pool based on the self-adaptive scheduling strategy; and returning an analysis result corresponding to the target IP address type to the client so as to guide the client to establish network connection through the target IP address type.

The invention relates to the technical field of information, and discloses an intelligent scheduling method and system for a vehicle-mounted mobile charging system, and the method comprises the steps: obtaining the emergency degree values, geographic positions and charging demand grade values of all charging demands in a current region, and building a demand priority list; acquiring real-time electric quantity states, position information and electric quantity consumption rates of all charging vehicles, and establishing a vehicle availability list; according to the two tables, combined with the target position distance, the road condition and the traffic condition, calculating the estimated time and the residual electric quantity of each charging vehicle arriving at the target position; when the estimated time is smaller than a preset time threshold value and the residual electric quantity meets the requirement, the vehicle is marked as an available vehicle; when the available vehicles are insufficient, judging whether cross-regional scheduling is carried out or not; and optimizing the global scheduling instruction by adopting a scheduling algorithm and dynamically scheduling the charging vehicles. The method can achieve the reasonable scheduling of the charging vehicles, and improves the overall efficiency.

The invention provides a large model reasoning optimization method and system for operation and maintenance technical services, and the method comprises the steps: firstly obtaining a set comprising a plurality of to-be-processed operation and maintenance service requests, each to-be-processed operation and maintenance service request carrying operation and maintenance demand description information and an equipment operation scene identifier; performing priority dynamic calibration on operation and maintenance reasoning tasks based on the information, generating a priority list, deploying computing resources and operation and maintenance knowledge resources of large model reasoning according to the priority list and reasoning link requirements, forming a collaborative scheduling scheme, configuring a reasoning process according to the collaborative scheduling scheme, and executing large model reasoning; and finally, converting the result into an operation and maintenance response instruction in combination with the equipment operation scene identifier, and sending the operation and maintenance response instruction to the execution terminal, so that accurate processing of the operation and maintenance task, reasonable allocation of resources and efficient response of the operation and maintenance service are realized.

The embodiment of the invention provides a scheduling method, which comprises the following steps of: in a process of determining a target scheduling action, traversing by taking an action module as a unit based on a current state and an action priority list corresponding to the action module, and outputting by taking the target scheduling action as granularity, the optimal scheduling sequence does not need to be output after all possible scheduling sequences are traversed and compared, the traversing and searching depth is greatly reduced, the time consumption of the determining process of the target scheduling action is reduced, the timeliness of the scheduling method is improved, and the problem that the scheduling efficiency is low due to long time consumption is solved. The matching degree between the scheduling instruction output by the scheduling method and the actual state of the semiconductor process equipment is poor; besides, in the scheduling method, traversal is carried out by taking the action module as a unit to determine the target scheduling action, so that the scheduling method can be suitable for scheduling of the action module such as a manipulator for transmission and also can be suitable for scheduling of the action module such as a spraying mechanism, and the applicability of the scheduling method is improved.

The invention discloses a task scheduling method for an intelligent fire-fighting management service platform, and relates to the technical field of fire-fighting informationization, and the method comprises the steps: collecting environment data and position coordinates, constructing a support vector machine (SVM) model, carrying out the task grade division of a monitoring region, and forming a task priority list; extracting a historical inspection record, calculating a deviation value between task completion duration and standard duration in the historical inspection record as an efficiency coefficient, and performing grade grouping on firefighters according to the efficiency coefficient; the position coordinates are converted into task distance parameters, the task matching degree is calculated in combination with the efficiency coefficient, a two-way selection queue is established, and firefighters are matched for the monitoring area according to a task priority list; and recording a task execution track of the firefighter through a positioning module, recording execution data of the task to a historical inspection record, and updating an efficiency coefficient to form a task scheduling mechanism. Through multi-dimensional data analysis and a dynamic evaluation mechanism, the task scheduling efficiency of the fire-fighting management platform is improved.

Systems and methods for determining video streams for presentation in a user interface (UI) of a video conference are provided. In response to a request of a first user of a video conference platform to participate in video conference, a user priority list associated with the first user is accessed. The user priority list identifies a plurality of users of the video conference platform. Participants of the video conference are identified. The participants include the first user associated with a first client device of client devices of the participants of the video conference. A UI presenting video streams from client devices of at least a subset of the participants is provided for display on the first client device. presentation of each of the video streams in the UI depends on whether a respective participant of the at least the subset of the participants is included in the user priority list.

The invention provides a database fault processing method and system based on reinforcement learning, and relates to the technical field of database management, and the method comprises the steps: collecting a performance index, an execution plan and error log data at a fault moment, constructing a causal decision tree to recognize a fault root cause, and generating a propagation link; constructing a reinforcement learning action sequence based on the priority list; dividing a master control action set and an adjustment action set according to the influence degree of the action on the node state; and the execution strategy is dynamically adjusted according to the performance improvement ratio. According to the method, the fault root cause can be accurately positioned, the processing strategy is adaptively optimized, and the fault recovery efficiency and the system reliability are improved.

The invention discloses a multi-view-angle-based power recovery method and system for a power failure, and particularly relates to the technical field of power recovery of a power grid. The method comprises the following steps: acquiring multi-source data of a power system, acquiring multi-view time sequence data, performing feature extraction and classification by utilizing a power grid operation model based on time sequence analysis and combining a block type distributed load time sequence portrait method, and generating a node-level load dynamic evaluation result; based on an evaluation result, adopting a parallel computing strategy based on iterative optimization to identify a local overload risk, and generating a local priority list; based on the priority list, a node group power recovery sequence is set in a distributed scheduling mode, and a power supply line is recovered segment by segment through a parallel judgment circuit breaker control algorithm; in the power restoration process, based on a real-time monitoring result of a node load state, dynamically correcting a parallel computing strategy, and isolating and shunting load overrun and implicit sudden increase nodes; and the accuracy of the power recovery strategy and the safety of power grid operation are improved.

The invention discloses an Internet of Things edge computing task unloading method based on a comprehensive gateway, which belongs to the technical field of computer data processing, and comprises the following steps: acquiring tasks, equipment and network data of Internet of Things equipment, and fusing to generate a unified information view; determining task priorities based on the view by applying a dynamic priority evaluation mechanism, and generating a dynamic priority list; applying the time sequence analysis model to generate short-term prediction data; in combination with the dynamic priority list and the short-term prediction data, multi-objective optimization is performed through a collaborative decision model, and a task unloading strategy is generated; generating feedback data according to the task unloading strategy; and generating an adjustment signal by using the feedback data, and updating the dynamic priority evaluation mechanism and the collaborative decision model. According to the method, the unified information view is generated by fusing the multi-source data, collaborative decision making is carried out in combination with dynamic priority evaluation and short-term prediction data, and the model is continuously optimized by utilizing feedback data, so that self-adaptive, forward-looking and efficient task unloading can be realized.

The invention discloses a network security protection management system and method based on big data, and relates to the technical field of network security management, and the method comprises the steps: collecting industrial protocol layer data, physical sensor data and process parameters, extracting network behavior features, physical features and process correlation features, and constructing an industrial security feature vector; according to the security weight coefficient and the industrial security feature vector of the current process stage, calculating a threat score of the equipment, and dynamically adjusting a threat level through a three-level verification mechanism; constructing an equipment dependency graph, performing risk diffusion calculation based on the adjacent matrix and the attenuation coefficient, and generating a risk diffusion priority list to adjust an isolation strategy; an improved multi-target genetic algorithm is adopted to solve a Pareto optimal solution set, an equipment recovery sequence and a process parameter adjustment scheme are determined, and a recovery instruction is issued to realize safe recovery, so that the dynamic perception and accurate defense capability for industrial network threats is improved, and the safety and stability of industrial production are guaranteed.

The invention discloses an intelligent charging scheduling method and system, and the method comprises the steps: collecting a real-time multi-source data set, building an LSTM charging demand prediction model based on an LSTM neural network, inputting the real-time multi-source data set into the LSTM charging demand prediction model for prediction, and defining a dynamic priority weight function according to the charging demand and the power grid schedulable margin. Generating a priority list; establishing a multi-target hierarchical optimization algorithm by utilizing MIP mixed integer programming and an NSGA-II genetic algorithm, and solving a charging scheduling scheme in a hierarchical manner by taking a priority list as a constraint based on the multi-target hierarchical optimization algorithm; active peak load shifting is performed by using a grading response strategy, the regional peak load is reduced, the capacity expansion investment of the transformer is delayed, and the service life of equipment is prolonged. And in combination with the V2G reverse power supply capability, electric vehicle energy storage resources can be called in an emergency state of a power grid, so that the local power supply reliability is improved, and the carbon emission of a traditional peak regulation power plant is reduced.

The invention relates to a dynamic page loading optimization method and device based on user behaviors and a network environment. Comprising the following steps: collecting user behavior data in real time to obtain a user behavior analysis result: dynamically generating a loading priority list according to the user behavior analysis result; wherein the loading priority list comprises key resources, high-priority resources, secondary resources and delay resources; determining a resource loading strategy based on the loading priority list; wherein the resource loading strategy comprises priority loading of key resources, necessary loading of high-priority resources, and delayed loading or batch loading of secondary resources and delayed resources; detecting the network state of the user to evaluate the performance of the current network; and dynamically adjusting a resource loading strategy based on the performance of the current network so as to load resources required by the page according to the dynamically adjusted resource loading strategy. According to the invention, the resource loading strategy can be dynamically adjusted according to the network state, and the user experience during resource loading is improved.

The invention discloses an intelligent logistics scheduling method based on Beidou positioning, and the method comprises the steps: obtaining the real-time position information of a vehicle, and constructing a traffic state matrix in combination with road congestion data; judging a weather influence area based on the weather change trend, and obtaining a weather risk area in combination with the traffic state matrix; obtaining inventory state data, and generating a resource demand priority list in combination with the weather risk area; adopting a multi-objective optimization algorithm, taking the resource demand priority list, the traffic state matrix and the weather risk area as input, obtaining an optimal path set, and generating a preliminary scheduling scheme; if a dynamic task insertion request is received, the task insertion feasibility is judged, the resource demand priority list is adjusted, the multi-objective optimization algorithm is operated again, and a corrected scheduling scheme is generated. Through multi-dimensional data fusion and intelligent algorithm optimization, the real-time performance, flexibility and reliability of logistics scheduling are improved, and an effective solution is provided for resource allocation in a complex environment.

The invention relates to the technical field of deep sea test task management, in particular to a deep sea test task scheduling and management method based on multi-platform collaboration, comprising the following steps: S1, generating an environmental adaptability task priority list; s2, generating a multi-platform interface configuration scheme based on the priority list in S1; s3, constructing a space-time conflict probability model, and outputting an obstacle avoidance path and a resource allocation strategy; s4, generating a data link topological graph; and S5, calculating a deep sea test efficiency index, and optimizing a subsequent test task priority list. According to the method, intelligent scheduling and efficient execution of deep sea test tasks are realized through task priority dynamic optimization, space-time conflict prediction, data link adaptive regulation and control and efficiency index feedback optimization, and the stability of multi-platform collaborative operation, the communication reliability and the resource utilization rate are improved.

The invention discloses a task resource dynamic adaptive scheduling method, an automobile electronic physical system and an automobile, and relates to the field of artificial intelligence. The task resource dynamic self-adaptive scheduling method comprises the following steps: in each round of scheduling process, calculating an uplink sequence value and scheduling relaxation time of each task in real time; performing dynamic sorting based on the uplink sequence values and the scheduling relaxation time of the tasks, and outputting a priority list of the tasks; selecting a preset number of tasks from the priority list of the tasks and adding the tasks into a to-be-executed task list; and allocating processors to the tasks in the to-be-executed task list, and executing the corresponding tasks by the allocated processors. According to the task resource dynamic self-adaptive scheduling method, the real-time requirement of task resource scheduling can be met under the condition that task release is uncertain, and the overall efficiency of task scheduling is improved.

The invention discloses a medical network collaborative penetration test system and method based on artificial intelligence, and belongs to the technical field of medical information security, and the method comprises the steps: integrating a hospital intranet, medical equipment, cloud data center data and historical network delay data to construct a medical network data center; analyzing association between network vulnerabilities and delay data through a graph neural network, constructing an attack path analysis model containing delay weights, and quantifying risk scores to generate a test target priority list; defining a protocol vulnerability and a delay threshold, designing a test scene and generating an attack vector, matching an attack tool to perform a simulation test, calculating a vulnerability triggering success rate and evaluating a risk; generating a penetration test case according to the test target priority list and the risk level, executing multiple rounds of attack tests and generating a report; and monitoring the change of the vulnerability triggering success rate by dynamically adjusting the delay parameter, setting an early warning threshold value, and monitoring and sending a risk early warning notification in real time.

The invention relates to a link self-adaption method for a multimode communication system, which comprises the following steps of: defining a priority list of a waveform, and defining a signal quality evaluation standard of each waveform; according to the waveform priority list, the real-time signal quality of each waveform is monitored in sequence, if it is monitored that the signal quality of any waveform exceeds a preset good signal quality threshold value, the waveform is determined to be the optimal waveform, and otherwise, the current signal quality of the waveform is recorded; if all the waveforms cannot meet the standard of good signal quality, selecting the waveform with the minimum difference as the optimal waveform; the priority list and threshold criteria are dynamically adjusted according to system, user feedback to improve the accuracy and efficiency of future decisions. The problems of insufficient judgment basis and single condition in the multimode waveform switching technology are solved, the resources can be effectively managed and allocated, the waveform with better signal quality is preferentially processed, the response speed and efficiency of the whole system are improved, and the optimal performance is maintained.

The invention discloses a mass resource access edge method suitable for a gridding power grid, and relates to the technical field of power grids, and the method comprises the steps: dividing the gridding power grid into a plurality of hierarchical nested sub-grids, and configuring an edge calculation node for each sub-grid; acquiring an original resource data set in the sub-grids through edge computing nodes; analyzing the original resource data set by using a multi-factor weighted scoring algorithm to generate a second resource access priority list; obtaining a resource access scheme of each sub-grid according to the second resource access priority list and the real-time load state of the sub-grid; and constructing a distributed collaborative decision-making mechanism, and coordinating resource access schemes of adjacent levels of sub-grids to complete massive resource access. According to the method, the power grid is gridded and divided into the multi-level nested sub-grids, so that the resource access pressure of each region is effectively dispersed, and efficient access and management of resources are realized through optimized deployment of the edge computing nodes.

The invention relates to the technical field of aviation safety data analysis and pilot training optimization, provides an aviation safety data and training data integrated analysis method and system, electronic equipment and a storage medium, and solves the problems of poor pertinence of pilot training and poor foresight of safety guarantee. The method comprises the following steps: automatically extracting threats, errors and unexpected aircraft states in aviation unsafe events from multi-source heterogeneous data, and judging a management state; based on the aviation safety knowledge graph, positive and negative competency and behavior indexes corresponding to the managed and unmanaged states are identified through graph calculation; the statistical frequency is analyzed, and a priority list of the security data side is generated; carrying out weighted fusion to obtain a target training list by combining a training data statistical analysis result of the same period; and in combination with the threat priority, generating a training scene configuration scheme containing threat elements and capability items, and forming a course content, evaluation standard and resource optimization configuration scheme. According to the invention, the pertinence of pilot training is improved.

The invention discloses an enterprise project database intelligent management method and system, and relates to the technical field of data management and resource optimization. The method comprises the following steps: verifying the integrity of business unit project data in real time through an intelligent cleaning engine, and structurally storing the business unit project data to a central project database; integrating the progress, the cost, the risk field, the commercial value and the strategic weight to dynamically calculate a comprehensive value index; based on the value index and the real-time resource inventory, a resource allocation scheme is generated through a hierarchical adaptation strategy, and a priority list is output; tracking progress deviation during execution, and triggering data isolation and index optimization when risk offset exceeds a threshold value; risk root causes are analyzed, value weights are dynamically adjusted, a rule base is managed in a back-feeding mode, and system self-optimization is achieved. According to the method, the problems of resource allocation rigidness, risk response lag and lack of dynamic value guidance in traditional project management are solved, the resource utilization rate is improved, and the project delay risk is reduced.

The invention discloses a weather-based HPC resource scheduling method, which comprises the following steps of: storing meteorological data blocks into a high-speed cache region on the basis of meteorological data and access frequency to form a high-frequency data cache set; the meteorological tasks in the high-frequency data cache set are analyzed and processed, and a task priority list is generated; performing resource allocation on the task priority list to generate a resource scheduling scheme; on the basis of a resource scheduling scheme, according to a preset load balancing threshold value, dynamically adjusting tasks to be distributed to computing nodes with light loads, and generating a dynamic load balancing mechanism; performing three-dimensional grid division on the meteorological data blocks in the high-frequency data cache set to form a meteorological spatial-temporal distribution coordinate system; and transmitting resource information of each meteorological block in the task priority list when the meteorological task is executed and a corresponding coordinate value of each meteorological block in the meteorological space-time distribution coordinate system to a neural network model for prediction, and performing secondary adjustment of resource configuration according to a prediction result to complete meteorological task resource scheduling.

The invention discloses a privacy leakage traceability method based on a data consanguinity map, and relates to the technical field of privacy leakage traceability, and the method comprises the steps: obtaining any node in a block chain network, and enabling the node to correspond to any enterprise; performing blood relationship analysis on the plurality of privacy data of any enterprise to generate a data blood relationship map; obtaining leaked privacy data, recording the data as a tracing starting point, and performing tracing analysis to obtain a tracing record; generating a visual traceability chart; performing prediction analysis on a first tracing path in the plurality of tracing paths to obtain a first leakage probability; and generating a troubleshooting priority list, and performing leakage troubleshooting and traceability on the private data. The technical problems that in the prior art, it is difficult to quickly locate the source for privacy data leakage, the checking process is lack of accuracy, and data security management is lack of effective means for accurately evaluating the leakage risk are solved, and the technical effects of achieving efficient tracing and accurate checking of privacy data leakage and improving the data security management capacity are achieved.

The invention relates to the technical field of dialogue management, and discloses an algorithm AI customer service dialogue management method and system based on big data, and the method comprises the steps: obtaining user input content and a verbal skill atlas; performing keyword retrieval according to the user input content and the verbal skill atlas to obtain verbal skill tags and activate corresponding verbal skill content; constructing a verbal skill directed graph according to the verbal skill label and the verbal skill map, and performing greedy optimization to obtain a verbal skill priority list; outputting all the activated verbal skill contents according to the verbal skill priority list, and ending the dialogue after the verbal skill is output; performing emotional state evaluation according to the user input content, and when it is detected that the emotion of the user is negative, outputting a pacifying verbal skill and ending a dialogue; and performing convergence degree calculation according to the user input content, and when the topic convergence degree is greater than a preset convergence degree threshold value, ending the dialogue. The method has the following effect that the customer service dialogue efficiency can be optimized.

The invention relates to an AI Agent-based operation maintenance scheduling method and system. The method comprises the steps of collecting multi-modal operation state data of a plurality of industrial devices in real time through a multi-modal sensing network; key feature extraction is carried out on the multi-mode operation state data; performing fault risk assessment by using an equipment health assessment model in combination with the key features and historical working condition data; generating an optimal operation and maintenance scheme of the potential fault industrial equipment according to the evaluation result and the real-time operation state data; finally, an equipment operation and maintenance task priority list and an automatic operation and maintenance scheduling scheme are generated on the basis, the scientificity and systematicness of industrial equipment operation and maintenance scheduling can be improved, the problems that data of a traditional operation and maintenance mode is not comprehensive, and an operation and maintenance scheme is lack of planning and intelligentization are effectively solved, and the operation and maintenance efficiency and quality of industrial equipment are improved.

A communication equipment receives a plurality of network sessions and includes a memory and a processor. The memory stores a plurality of software modules. The processor is connected to the memory and configured to implement the following steps. A network session inferring step is performed to execute an inference software module, and the inference software module processes at least one network packet of each of the network sessions and a packet characteristic module according to a machine learning algorithm to infer a priority level. An adaptive priority list establishing step is performed to execute a classification software module, and the classification software module establishes an adaptive priority list according to the priority levels of the network sessions. The communication equipment transmits the network packets of each of the network sessions to a network according to the adaptive priority list so as to set QoS of the network sessions.

The invention provides an NFC chip intelligent data loading method and system, and the method comprises the steps: effectively extracting and modeling historical use data, so as to predict a future use scene; generating a priority list of the loaded data according to the scene prediction result; the loading strategy is adjusted according to the real-time signal intensity, and the loading reliability of the high-priority data is ensured; the loaded data is quickly analyzed, and it is ensured that the data can be used by a user in time; and optimizing the scene prediction model and the loading strategy based on feedback data in the loading process to form closed-loop optimization. According to the method, intelligent scene prediction, signal intensity perception and hierarchical loading strategies are introduced, so that the data loading efficiency and reliability are comprehensively improved.