160 results about "Priority list" patented technology

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 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 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 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.

The invention belongs to the technical field of computers and artificial intelligence, and discloses a priority differential cache-based MoE batch reasoning method and system, and the method comprises the steps: recognizing global hot experts with higher activation frequency in each layer, enabling the global hot experts to remain in a high-priority cache during initialization, and building a priority list of the experts in each layer; in the reasoning stage, selecting an expert set needing to be activated in the current layer; the expert priority score is dynamically updated according to the activation and reuse behaviors of the experts, and the experts are migrated and replaced among the high-level cache, the middle-level cache and the low-level cache according to the priority score and a cache threshold value; according to a current layer gating result and a historical activation track, predicting an expert set which may be activated in a next layer, and asynchronously loading the expert set to a low-priority cache during current layer calculation so as to realize time overlapping of calculation and communication. According to the method, the expert migration frequency can be reduced, the cache hit rate and the throughput rate are improved, the video memory utilization rate is remarkably optimized, and good expansibility and universality are achieved.

The invention discloses a communication power supply load measurement and calculation and air switch control method and system based on service priority, and relates to the technical field of communication power supply intelligent management. Comprising the following steps: initializing and acquiring a load service priority list and a time threshold; monitoring an AC input state, switching to a standby power supply mode when the AC input state fails, and recording a power-off moment; a hybrid algorithm of ampere-hour integration and dynamic voltage calibration is adopted to measure the residual capacity of the storage battery in real time and with high precision, and residual power supply time is dynamically predicted; when the waiting time exceeds a first threshold value and the remaining power supply time is smaller than a second safety threshold value, a turn-off instruction is generated; and dynamically generating a turn-off sequence from low to high according to the service priority list, and iteratively executing turn-off and state re-evaluation until the endurance safety requirement of the core service is met. According to the invention, the problems of low control precision, incapability of distinguishing service priorities and low resource utilization rate in the prior art are solved, and accurate, intelligent and service-aware power management is realized.

The invention relates to an intelligent agent automatic detection system and method based on a large model, and the system comprises a data collection module which collects multi-source heterogeneous data monitored in real time in an automatic detection process; the quantification module is used for carrying out confidence coefficient coding on the preprocessed multi-source heterogeneous data by utilizing a pre-trained large model to obtain a confidence coefficient score of an abnormal event; the matching module is used for carrying out multi-dimensional priority ranking on the abnormal events based on the confidence score; the strategy generation module is used for simulating a strategy selection scene in a dynamic environment by adopting a reinforcement learning algorithm according to the sorted abnormal priority list so as to obtain an optimized dynamic strategy set; and the detection processing module is used for selecting the highest matching item from the optimized dynamic strategy set and carrying out corresponding processing on the detected abnormity. According to the method, the accuracy, efficiency and adaptability of intelligent agent automatic detection are effectively improved, and a reliable anomaly detection and response mechanism is provided for complex systems such as network security and industrial monitoring.

A system and method for providing virtual desktops to client devices of users is disclosed. The system includes available cloud regions that each can provide virtual desktops and associated resources. A desktop pool resource management engine is coupled to the available cloud regions and collects constraint data from the available cloud regions as well as operational data from desktop clients and desktop agents. A cloud usage profile is created for a subset of the users from the operational data. A priority list of available cloud regions for a user in the subset is created based on the usage profile and the constraint data of the available cloud regions. The highest priority cloud region from the priority list is recommended for the user requesting a desktop. A control plane selects the highest priority cloud region according to the priority list to provide the desktop to the client device.

The application is suitable for the technical field of data storage, and provides a data disaster recovery method and device, comprising the following steps: determining a fault type in a distributed cluster system and a range of data to be recovered; acquiring state parameters of multiple normal nodes in the distributed cluster system, and service sensitivity features and data block access features of the data to be recovered; performing data preprocessing operations; respectively adopting a service sensitivity evaluation model and a node state evaluation model to determine a data recovery priority list and a node priority list; determining a fault processing strategy according to the data recovery priority list and the node priority list; and executing the fault processing strategy. Through the above method, the real-time evaluation algorithm of the heterogeneous big data node is increased on the basis of dynamically updating the data recovery priority based on the service sensitivity evaluation model, and compared with the traditional technology, the fault perception of the business system can be reduced while the data disaster recovery efficiency is improved.

The invention relates to a dynamic scheduling method and device of a household energy system, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: predicting a household load power prediction value and a photovoltaic power generation power prediction value after a preset time length based on real-time collected operation condition data of a household power distribution system; based on the household load power prediction value, the photovoltaic power generation power prediction value and the operation condition data, establishing a multi-target dynamic optimization scheduling model; solving the multi-target dynamic optimization scheduling model to obtain an optimization scheduling strategy; real-time dynamic comprehensive priority scores of the load devices are obtained, the real-time dynamic comprehensive priority scores of different load devices are sorted, and a load device priority list is obtained; and according to the optimized scheduling strategy and the load equipment priority list, generating a real-time scheduling control instruction and issuing the real-time scheduling control instruction. By adopting the method, the autonomous adjustment capability of the system can be effectively improved.

The invention discloses a learning assistance method, device and equipment based on a big language model and a storage medium, and the method comprises the steps: collecting historical interaction data of a user and the big language model, after desensitization processing and structured storage, extracting keywords and knowledge points through a natural language processing module, clustering similar problems and knowledge points, and carrying out the clustering of the similar problems and knowledge points; constructing a user knowledge graph containing knowledge point relevance and mastery score; generating a learning priority list based on the map score, and matching a preset knowledge base to generate a personalized learning plan; pushing the plan to a user side, and obtaining a learning and testing result to determine the knowledge mastery degree; and comparing the mastering degree with a preset threshold value, if the mastering degree is lower than the threshold value, regenerating the targeted plan, and if the mastering degree is not lower than the threshold value, marking the knowledge points as mastered and updating the graph. According to the scheme, knowledge blind spots in dialogues are dynamically tracked, conversion from passive question answering to active learning is achieved, and the knowledge mastering ability of a user is systematically improved.

The invention relates to the technical field of rehabilitation medical treatment, and discloses a multi-modal data analysis method for rehabilitation decision support. The method comprises the steps that current physiological index data of a patient are collected and compared with historical samples, and individual difference evaluation values are obtained; analyzing current physiological index data and training intensity related branch paths through a tree decision model, and determining a risk prediction score; action priorities are calculated in combination with the risk scores and the physiological data, and a preliminary action priority list is generated; if the calculation is overtime, optimizing the structure of the decision model to obtain a simplified priority list; fusing the change trend of the physiological state of the patient with the dynamic adjustment model to obtain an updated action sorting sequence; and finally generating and outputting a personalized rehabilitation training scheme and outputting the personalized rehabilitation training scheme to a display device. According to the method, through multi-modal data fusion, tree-shaped decision risk assessment and real-time dynamic adjustment, the problems of insufficient personalized precision of a rehabilitation scheme and system response lag are solved, and the safety and suitability of training are improved.

The invention relates to the technical field of index structure recombination, in particular to a nursing process management database index optimization method, which comprises the following steps of: performing cost calculation on multiple indexes by adopting a gradient lifting regression tree, comparing the cost calculation with a threshold value, and forming a recombination candidate priority list under the support of a numerical result; a potential recombination object has a quantifiable judgment standard, predicted consumed time is calculated in combination with the low-ebb IO capacity and compared with the time slice length, the screening process can correspond to the system resource bearing condition, a B + tree algorithm is adopted for calculating a loading factor and the continuous scanning length, and through merging, splitting and redistribution operation, the screening efficiency is improved. A node organization structure is finely adjusted, it is guaranteed that an index structure and an access mode are kept coordinated, information entropy is utilized to sort and screen a field sequence, the field arrangement with high distinction degree is preferentially used for constructing a new index, atomic switching operation is executed to replace an old index, and the field sequence has adaptability in a dynamic data environment.

Provided in the present application is a scheduling method. In the method, during the determination of a target scheduling action, action modules are traversed one by one on the basis of the current states and an action priority list corresponding to the action modules, an output is provided with a target scheduling action as the granularity, and it is not necessary to traverse all possible scheduling sequences, compare the scheduling sequences, and then output an optimal scheduling sequence, thereby greatly reducing the depth of traversal and search, and thus facilitating a reduction in time consumed for determining the target scheduling action and an improvement in the timeliness of the scheduling method, and ameliorating the problem of the degree of matching between a scheduling instruction, which is output by means of the scheduling method, and the actual state of a semiconductor process device being relatively low due to a relatively long period of time being consumed. Moreover, in the scheduling method, traversing action modules one by one to determine a target scheduling action can not only be applied to the scheduling of action modules for transport, e.g., robots, but can also be applied to the scheduling of action modules, e.g., spraying mechanisms, thereby facilitating an improvement in the applicability of the scheduling method.

The invention relates to the technical field of virtualization orchestration, in particular to an industrial control virtualization orchestration system and method, and the system comprises a task extraction module, a node behavior monitoring module, a stability evaluation module, an orchestration load adjustment module and a control task migration module. According to the method, the scheduling cycle closed-loop feature recognition is performed by collecting the control task scheduling mapping table and the execution instruction queue and combining the cycle control mark and the interrupt mark, so that an orchestratable task set can be effectively recognized, the behavior success rate is calculated according to parameters such as node response delay and task completion proportion, and node stability screening is performed; stability grade division of control nodes is achieved, a scheduling priority list is formed by setting a release threshold value, then the task migration opportunity is judged based on task residence time and IO completion conditions, migration targets are accurately selected in a behavior pattern matching mode, and the stability of task scheduling, the precision of resource allocation and the adaptability of control logic are remarkably improved.

The invention discloses a train operation and maintenance management method and system, a medium and a product. According to the method, a vehicle-mounted sensor is adopted to comprehensively collect system operation data of train power, suspension and the like, and a fault risk prediction model is input to obtain high, medium and low fault risk levels; generating a maintenance train priority list in combination with a next-day train scheduling plan, and determining a maintenance sequence; according to the priority and the maintenance resources, making a maintenance plan covering task allocation, team arrangement, a bill of materials and estimated time; and if the actual maintenance is overtime, dynamically adjusting the plan. According to the method, accurate prediction and evaluation of the train fault risk are realized, the train maintenance plan is constructed and dynamically adjusted in combination with scientific distribution of maintenance resources, the accuracy and efficiency of train operation and maintenance are effectively improved, and a powerful guarantee is provided for safe and efficient operation of the train.

In various embodiments, a live event caching application caches segments that are associated with live events. At a first point-in-time, the live event caching application stores a segment of a downloadable that is associated with a live event in a high priority list. At a second point-in-time, the live event caching application determines that the segment is at a tail of the high priority list, where the second point-in-time is subsequent to the first point-in-time. Upon determining that the segment is at the tail of the high priority list, further determining that an age of the segment is greater than a cutoff threshold. In response to determining that the age of the segment is greater than the cutoff threshold, moving the segment from the high priority list to a low priority list.

This invention discloses a method and system for constructing an AI-native operating system based on ecosystem collaboration, belonging to the field of operating system technology. The invention first acquires and decomposes target requirements to form a set of functional and non-functional requirements, constraints, and priority lists. Based on this, it generates an operating system architecture description including kernel configuration and subsystem division. Then, it generates and completes multi-dimensional checks on module code and configuration files, and compiles, links, and packages them through a toolchain to generate an operating system image. This invention employs a three-layer AI model to realize requirement reasoning, system construction, and test optimization, forming a complete automated closed loop from requirement input to runnable image output. Simultaneously, it establishes a unified ecosystem collaboration mechanism to support multi-vendor collaborative development. This invention effectively reduces errors and repetitive work caused by manual intervention, shortens the development cycle, improves system stability and adaptability, and promotes the collaborative evolution and improvement of the AI-native operating system ecosystem.

The application provides a touch event processing method, system, device and medium, wherein the method comprises the following steps: obtaining interface touch data in a target interface and an event priority list corresponding to the target interface; the interface touch data comprises a plurality of event elements; performing event priority analysis on the interface touch data according to the event priority list to obtain an event sorting queue, the event sorting queue comprising a plurality of target touch events sorted from high to low according to event priority; the target touch events comprising a plurality of event elements in the interface touch data; the number of event elements in each target touch event is less than or equal to the number of event elements in the interface touch data; and performing event response according to the event sorting queue. The method can effectively improve the processing accuracy of touch events, is conducive to reducing the difficulty of realizing complex interaction in the target interface, and can be widely applied in the technical field of digital content service.

Embodiments of the present application provide a cell switching method and related device, the cell switching method comprising: receiving a first message, the first message being used to indicate a measurement configuration, the measurement configuration comprising a measurement object and a first switching threshold; and sending a measurement report or performing cell switching based on a second switching threshold and a measurement result, wherein the second switching threshold is obtained by optimizing the first switching threshold based on channel indicator requirements of services in a service priority list by a first model, and the measurement result is obtained by performing channel quality measurement on the measurement object based on the measurement configuration. In the above technical solution, the terminal adjusts the first switching threshold based on the channel indicator requirements of the services in the service priority list, which can optimize the first switching threshold from the perspective of channel quality requirements during execution of multiple services, and ensure that the optimized switching threshold can adapt to diverse service requirements.

The invention relates to the technical field of data processing, in particular to a project application competitiveness analysis system which comprises an acquisition module, a preliminary screening module, a judgment module, a determination module, an adjustment module and an output module. According to the method, multi-dimensional data is acquired in real time, firstly, project keywords are matched with policy keywords, and then key projects are judged by utilizing technology integration density and task dependency density; then, a priority list is determined by integrating the competitive strength, the technical optimization rate and the policy matching degree, in addition, according to the dynamic optimization threshold value of the approval rate, self-adaption policy change and market dynamics, not only is the scientificity of analysis improved, but also the practicability and reliability of the system are enhanced, and finally, by outputting an analysis report, a decision basis of data support is provided for a user, and the user experience is improved. The problems of low project screening accuracy and response lag caused by excessive dependence on models and incapability of adapting to changeable markets and policy environments are effectively solved.

The application discloses a cell reselection method, an electronic device and a computer readable storage medium. The cell reselection method comprises the following steps: acquiring the reporting information of a plurality of terminals, and determining the busy level of the cell to which the plurality of terminals belong according to the reporting information of the plurality of terminals; acquiring the congestion ranking of the cell according to the busy level of all the cells corresponding to the overlapping area from high to low; determining the target terminal which is the first terminal meeting the reselection condition according to the position of the cell to which all the overlapping area terminals of the overlapping area belong in the congestion ranking; acquiring the reselection priority list of all the cells with the congestion ranking lower than that of the cell to which the target terminal belongs; and connecting the target terminal to the cell in the reselection priority list according to the cell sequence of the reselection priority list. Through the above method, the application can select a more suitable access cell for the user terminal in the overlapping area.

This invention discloses a method and system for optimizing intelligent consolidation in cross-border e-commerce. It relates to the field of container consolidation transportation technology. The method includes: Step 1: acquiring basic data on the cargo and container environment; Step 2: establishing an adaptive weight model based on the basic data on the cargo and container environment, determining the weight dimensions, and quantitatively scoring the cargo to generate a priority list; Step 3: generating an optimal stacking scheme based on the priority list, designing storage units based on the optimal stacking scheme, numbering the storage units, and binding cargo information; Step 4: calculating the container's center of gravity coordinates in real time based on the cargo information associated with the storage unit number; a preset center of gravity offset threshold is set, and when the offset exceeds the threshold, the stacking method of high-priority cargo is automatically adjusted and the storage units are optimized. This invention can significantly improve the intelligence, efficiency, and safety of cross-border e-commerce consolidation transportation.

The invention discloses a distributed cross-screen display method and system based on a digital thread, and relates to the field of distributed computing resource management.The method comprises the steps that in response to cross-screen operation of a user, a first device captures and generates an operation context information packet; the digital thread scheduler receives the operation context information packet and creates a digital thread instance corresponding to the screen-crossing task; the digital thread scheduler gathers user interaction event streams from the digital thread instance associated equipment, and models and analyzes the user interaction event streams based on a preset focus analysis rule base; when the residual signal intensity of the node with the highest priority in the attention migration priority list exceeds a preset threshold value, triggering a pre-scheduling decision process; and performing dynamic resource pre-scheduling and preheating operation on the target equipment and the target content area according to the resource state table. According to the invention, the target device can realize instant rendering and response directly based on the pre-heating ready resources, so that real non-perceptual cross-screen experience is realized on the system level.

Aspects presented herein may enable a user equipment (UE) to maintain an optimized priority list of global navigation satellite system (GNSS) signals / channels to achieve a best GNSS positioning performance with limited resources in real-time. In one aspect, a UE identifies a maximum number of GNSS signals to be used by the UE for GNSS positioning. The UE prioritizes a set of available GNSS signals for the GNSS positioning based on one or more conditions. The UE selects the maximum number of GNSS signals to be used for the GNSS positioning from the prioritized set of GNSS signals. In some implementations, the UE may detect that a number of available GNSS signals exceeds the maximum number of GNSS signals to be used by the UE for the GNSS positioning, where the prioritization of the set of GNSS signals is based on the detection.

This application provides a rapid migration method and system based on customized data packages, as well as a readable storage medium. The rapid migration method based on customized data packages includes: extracting metadata from oil and gas exploration data using a general data acquisition module, employing the system's native API and file parsing algorithms; based on the metadata and the target operating system, formulating a migration strategy and monitoring the migration process through a control module, and generating a task execution list; dynamically adjusting the execution plan of the task execution list through a scheduling module, and generating a task execution priority list; and according to the task execution priority list, encapsulating and transmitting the data to the target system through a migration module, and unpacking the data according to the mapping index file and the unpacking priority list. Through the technical solution of this application, seamless data synchronization across operating systems can be achieved through customized data packaging and index mapping strategies, ensuring that the migrated data is immediately available and improving the execution efficiency of seismic exploration projects in multi-platform collaborative scenarios.

The invention provides a video course content distributed caching method based on a CDN, and belongs to the technical field of video distributed caching, and the method comprises the steps: collecting user historical watching records and quality preference data to construct a user portrait database, calculating a content access probability based on a user portrait, and generating a pre-caching priority list; a hierarchical coding technology is adopted to decompose a high-priority video into a base layer and an enhancement layer, an optimal hierarchical strategy is determined through a game model, hierarchical contents are distributed to CDN edge nodes, and a cache version is selected according to user characteristics of each node service area; establishing a cache content dynamic updating mechanism based on deep reinforcement learning to monitor access frequency and user feedback to adjust cache combination, and establishing a cache performance evaluation feedback system to collect user delay and satisfaction data continuous optimization strategy parameters; the technical problem that the cache hit rate is low due to the fact that the cache content of the CDN edge node is not matched with the actual demand of the user is solved.

A heartbeat database stores a state of a plurality of clusters and a last time of receiving a heartbeat message from each of the clusters. A data store stores a cluster priority list associated with the application process, the cluster priority list comprises a list of the clusters in an order of priority. A process monitoring application of a cluster configured to remain in the ready state when a higher priority cluster is in the active state, update to the active state when the cluster is a highest priority cluster as indicated in the cluster priority list, remain in the active state when the cluster is a highest priority cluster as indicated in the cluster priority list, or remain in the active state when the cluster is a highest priority cluster as indicated in the cluster priority list.

The invention provides a request processing method and device, electronic equipment and a storage medium, and the method comprises the steps: determining a request priority of an access request based on a pre-configured priority list in response to a condition that a server load meets a current limiting condition, determining a current request concurrency amount of a server in response to a condition that the request priority is a high priority, and sending the current request concurrency amount to the server; and in response to the fact that the current request concurrency of the server is smaller than the maximum concurrency of the current period, processing the access request and returning a processing result. According to the method, the access request is subjected to flow limiting based on the request priority, so that core functions and services of the client can be responded in time, the maximum concurrency is dynamically updated based on the request processing condition of the server, the maximum processing capacity of the current system can be reflected more accurately, and meanwhile, the method can be suitable for various types of service nodes, and the user experience is improved. The system processing capacity waste and overload risk are reduced, the system capacity is fully utilized, and the system stability is improved.