102 results about "Mission critical" patented technology

The invention discloses an intelligent inspection method for fusion of Internet of Things and virtual reality based on an AI large model, particularly relates to the technical field of industrial intelligent inspection, and is used for solving the problem of end-to-end response lag caused by multi-modal data fusion delay and resource competition under an existing layered architecture. The method comprises the following steps: synchronously acquiring heterogeneous data of target equipment through a multi-source sensor and visual equipment, and generating time-space synchronous data through cross-modal feature extraction and time-space alignment; computing resources are dynamically allocated to an AI model or a rendering pipeline in combination with cross-modal correlation analysis and environmental interference assessment, and key tasks are preferentially guaranteed to be executed; performing deep correlation reasoning on the multi-modal data by using an AI large model, and generating equipment state features and an abnormal region mask; and finally, superposing the abnormal features and the three-dimensional scene through a virtual-real fusion rendering technology to form a visual interaction interface. Efficient fusion and real-time interaction of multi-modal data are realized, and the accuracy of anomaly detection and the decision response efficiency of an operator are remarkably improved.

The invention discloses a high-efficiency computer software system resource scheduling method, system and device and a medium, and the method comprises the steps: deploying a hardware counter in an operating system, and monitoring the CPU utilization rate, memory occupation, disk I / O, network bandwidth, the use condition of data aggregation system resources and the resource demand of each process or thread in real time; based on monitoring data, analyzing a current resource supply and demand state by adopting an advanced algorithm, and predicting a future resource demand trend; according to an analysis result, a resource allocation strategy is dynamically adjusted, it is ensured that a high-priority task obtains needed resources in time, and meanwhile fairness of a low-priority task is considered; data feedback after scheduling execution is collected, a scheduling algorithm is continuously optimized, and the scheduling efficiency is improved; the system, the equipment and the medium are used for realizing the high-efficiency computer software system resource scheduling method. The problems that an existing computer system is low in resource utilization rate, poor in key task stability and high in operation and maintenance cost are solved.

The invention discloses an intelligent resource scheduling method and system based on task characteristics and system state perception, and relates to an operating system kernel optimization technology. According to the method, resource demand types, importance labels and time sensitivity of tasks are captured in real time through an eBPF technology, system bottlenecks are recognized by combining / proc and sysfs file system data, a dynamic scheduling strategy is generated by adopting an improved Q-Learning model, and resource allocation is executed through a schemed * function and a cgroup v2 interface. The problems that a traditional scheduler is poor in self-adaptive capacity and resources are mismatched are solved, the actually-measured resource utilization rate is increased to 85%-92%, and the key task delay standard reaching rate gt is achieved; the method is suitable for edge computing, cloud servers and other high-concurrency scenes.

The invention relates to the technical field of vehicle-mounted intelligent computing, in particular to a hybrid key task scheduling method, system and equipment based on a virtualization technology. The method comprises the following steps: firstly, carrying out hybrid criticality grading on vehicle-mounted intelligent cabin tasks, and establishing a priority table; then, creating a dynamic resource partition based on a QNX Hypervisor platform, monitoring a system load state in real time through a hardware performance counter, and predicting a computing power demand of a large model; dynamically calculating an allocation scheme in combination with the task criticality level and the system energy efficiency state; then determining an optimal calculation unit selection strategy and a model precision grade by adopting an energy consumption-time delay joint optimization model; and finally, executing encrypted storage and integrity verification of large model parameters in a trusted environment, and completing end-side deployment and security verification. According to the method, the dynamic resource scheduling of the mixed key task is realized, the resource utilization rate is improved, the real-time requirement of the safety key task is ensured, and the dynamic balance between the performance and the energy efficiency is realized.

The invention provides a shipborne intelligent network security protection architecture and method, and relates to the technical field of network security, and the architecture comprises a security domain division module which is used for dividing a shipborne network into three physically isolated security domains, including a key task domain, an operation management domain and a crew life domain; the longitudinal protection strategy module is used for generating a longitudinal strategy comprising a protection instruction, a communication control instruction and a risk quantification instruction based on the security domain structure; and the risk calculation module is used for detecting intra-domain equipment access behaviors by trapping addresses in the key task domain, collecting intra-domain network traffic and service unit logs, and outputting a service asset quantized value, a vulnerability severity quantized value and a threat behavior deviation degree according to a risk quantization instruction. According to the invention, accurate identification, graded response and efficient disposal of shipborne network threats are realized, and the safety of a ship key system is guaranteed.

The invention provides a lightweight intelligent control method for a low-altitude operation scene, and the method comprises the steps: focusing a low-altitude key target through an attention mechanism based on a lightweight deep learning model; data enhancement is carried out through a dynamic data enhancement module so as to expand the diversity of training samples; calling a space-time calibration model to process the multi-source data, and controlling a time synchronization error and a space positioning deviation within a preset range; based on a dynamic obstacle trajectory prediction module, historical motion data and environmental constraints are fused, and a lightweight LSTM network is adopted to predict a trajectory of a dynamic target in a future time period; and calling a task priority-computing power load dynamic matching mechanism to realize task allocation, and adjusting computing power occupation of non-critical tasks according to a designed energy consumption perception scheduling strategy. According to the method, the autonomous perception, decision making and resource management capabilities of the low-altitude operation unmanned aerial vehicle in a complex environment are comprehensively improved, so that the low-altitude operation unmanned aerial vehicle better meets the requirements for safety, efficiency and continuity in actual operation.

Examples relate to an adaptive AI coworker system for enhancing organizational operations. The system generates personalized AI coworkers based on role requirements, employing adaptive learning to understand unique organizational practices. It utilizes multi-agent coordination for complex task execution, automatically generating, prioritizing, and allocating tasks based on organizational context. The system integrates data from various sources, implementing data governance measures. Customized large language model instances are created for specific roles and organizations, incorporating organization-specific data and operational practices. The system provides explainable AI features for operational decision-making, ensuring transparency in critical tasks. Security measures and access controls are implemented to maintain data integrity and compliance with relevant regulations and standards.

The embodiment of the invention provides a task management method and related equipment. The method comprises the steps of recording execution starting time and execution ending time when a task is executed in a statistical window each time; collecting a frequency point when a processor of the electronic equipment executes a task; according to the execution starting time, the execution ending time and the frequency point, calculating the normalized execution duration of the task; according to the normalized execution duration of the task, determining a load in a window of the task; according to the in-window load of the task and the statistical window, determining the in-window load proportion of the task; and if the load proportion in the window of the task is greater than or equal to a preset threshold value, determining that the task is a high-load task, and managing and controlling the task according to a preset strategy. According to the embodiment of the invention, the high-load task can be accurately identified and controlled, the high-load task is prevented from influencing the execution of the key task, and the power consumption of the system is effectively reduced.

The invention discloses an aircraft multi-target performance constraint collaborative optimization method based on multi-dimensional virtual and real parameter mapping, and the method comprises the steps: constructing a real parameter space and a virtual parameter space, and uniformly organizing the task key elements, such as target arrival, obstacle risk, formation maintenance error and minimum safety margin, into multi-dimensional real parameter input; speed scheduling quantity is uniformly organized into virtual parameters to be output, so that consistent parameter expression and a unified decision interface of cross-scene and cross-risk levels are realized, and engineering applicability and reusability in a task constraint scene are improved; interaction response data are obtained in a multi-scene set, a track sample set is established, and PPO reinforcement learning is adopted to train a mapping network from actual parameters to speed virtual parameters, so that the system does not need to explicitly construct complex manual rules or segmented threshold control logic; therefore, self-adaptive tradeoff can be formed among multiple targets such as arrival efficiency, formation keeping and speed stability, so that the manual parameter debugging cost is reduced, and the strategy generalization capability is improved.

The invention discloses a flexible manufacturing resource collaborative allocation optimization method and system oriented to variable delivery periods, and relates to the technical field of scheduling control, and the method comprises the steps: installing a process completion confirmation label in each work-in-process link, and directly obtaining an equipment load, a work-in-process state and the completion time; in combination with order arrival prediction and equipment health conditions, a disturbance intensity evaluation algorithm is set, the current scheduling disturbance quantity is calculated, and the disturbance intensity of each sub-problem is quantified; based on the disturbance intensity of each sub-problem and the longest response time of the system, setting an adaptive sub-problem time window algorithm, and dynamically adjusting the time window range of the to-be-rearranged task; and carrying out hierarchical division on the key tasks and the secondary tasks in the sub-problem range, carrying out parallel calculation and then carrying out merging scheduling to form an updated collaborative scheduling scheme. According to the invention, during order change, equipment failure or material shortage, the affected subset tasks can be quickly identified, the scheduling range is limited, global recalculation is avoided, and quick response is realized.

The invention discloses an abnormal node positioning method and device, equipment, a storage medium and a computer program product, and relates to the technical field of big data platform operation and maintaining.The method comprises the steps that target data is packaged, and packaged data is obtained; determining a critical path based on the encapsulation data; and performing node analysis on the key path, and positioning abnormal nodes of the cross-domain calculation task according to an analysis result. According to the method, after the operation and maintenance data contained in the cross-domain computing task in the big data cloud platform is packaged in a unified mode, the key path of the cross-domain computing task is determined based on the packaged data, and the key path is the path with the longest time for completing the sub-task in the cross-domain computing task; in other words, the critical path contains the most critical task for determining the circulation period of the cross-domain calculation task, so that the abnormal node which has the greatest influence on the circulation time of the whole cross-domain calculation task can be positioned more clearly by analyzing each node in the critical path.

A system and method using remote sensing instrument with hyper spectrum quantitatively measure metal dust elements in lubricating oil, which includes (not limited): Al, Cd, Cr, Cu, Fe, Pb, Mg, Mn, Mo, Ni, Ag, Sn, Ti, V, Zn, B (Boron, for Coolant), Ca (Calcium for water contaminant), and particle size, cone penetration, dropping point, steel mesh oil separation, moisture, PQ concentration, in few seconds. The instrument integrates near-field communication (NFC), Internet of Thing (IoT), Cloud computing, spectral matching and other data processing, and application software forming a system to easily operated and build a model enable self-learning to improve precision through collection accumulation. With the system, the instrument as FIG. 1 can provide comprehensive on-site analysis enable preventive maintenance of mission critical engine and rotating equipment. The characteristics of the system are easy to operate, get result quickly, and self-learning to improve precision.

This invention belongs to the field of flight control computer hardware architecture technology, specifically relating to a heterogeneous flight control core circuit and system. This invention integrates a microcontroller unit and an artificial intelligence coprocessor unit on the same printed circuit board, achieving data and command interaction through isolated high-speed interconnect channels. The microcontroller unit constitutes the safety control and real-time decision-making domain, used to execute safety-critical and hard real-time tasks, and to implement operational management and fault isolation for the artificial intelligence coprocessor unit; the artificial intelligence coprocessor unit constitutes the intelligent sensing and computing domain, used to execute computationally intensive and non-real-time critical tasks. This architecture achieves decoupling and collaboration between the safety domain and the computing domain at the hardware level, ensuring both high functional safety and determinism in flight control, while fully releasing the high-performance computing power required for intelligent sensing, thus meeting the dual requirements of advanced aircraft for highly reliable flight control and intelligent function expansion.

The invention belongs to the field of artificial intelligence platform development, operation and maintenance, and particularly relates to an artificial intelligence platform task management method and system. The method specifically comprises the steps of creating a Pod corresponding to a training task of an artificial intelligence platform through K8s; the created Pod comprises a resource demand of a training task; the K8s is utilized to allocate nodes meeting resource requirements to the Pod according to the resource requirements of the training tasks so as to execute the training tasks corresponding to the Pod; reducing the upper limit value of the resource demand of the Pod corresponding to the non-key training task in a set resource consumption peak period; and increasing the upper limit value of the resource demand of the Pod corresponding to the non-key training task in the set resource consumption valley period. In this way, it can be ensured that the key tasks are executed preferentially in the time period of resource shortage, completion of the non-key training tasks is accelerated in the time period of resource sufficiency, and therefore the completion progress of the training tasks of the artificial intelligence platform is optimized on the whole.

A method of managing synchronization source (SSRC) conflicts in a critical task service (MCX) by a server device is provided. The method includes receiving a request message from one or more sender devices for requesting transmission of one or more media streams to one or more receiver devices during a session, determining a unique SSRC identifier for each of the one or more media streams, and transmitting the unique SSRC identifier associated with each of the one or more media streams to the one or more sender devices and the one or more receiver devices.

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. Embodiments of the present invention relate to a method and system for managing an ad hoc group call in a mission-critical (MC) communication system. The method for managing an ad hoc group call in an MC communication system comprises a primary MC system (10) and a secondary MC system (30). A first MC service server (25) in the primary MC system (10) receives an ad hoc group call initiation request from a first MC service client (15) including MC service identifiers of target users. The first MC service server (25) invites a second MC service client (20) by forwarding the request using a service ID assigned by the secondary MC system (30). Upon detecting that a third user has migrated to the secondary MC system (30), the first MC service server (25) transmits a redirection notification to the first MC service client (15) including both the old and new MC service IDs and the migration reason. The first MC service server (25) sends an ad hoc group call response indicating success or failure with details, and the ad hoc group call is established across the included MC systems (10, 30, 50).

The invention discloses a multi-terminal parallel authentication authorization and equipment perception method for a key task communication system, which belongs to the technical field of key task communication, and specifically comprises the following steps: S1, configuration preparation: a server pre-configures a terminal initial configuration file for a terminal; the initial configuration file of the terminal comprises lt; m < cpt >-UE-idgt; element, lt; m < cpt >-UE-idgt; the elements comprise the next-level lt; the method comprises the following steps: carrying out Instance-ID-URNgt; an element; lt; the method comprises the following steps: carrying out Instance-ID-URNgt; the elements are instantiated to be unique identifiers by the configuration terminal; s2, configuration issuing and effectiveness: the server issues the initial configuration file of the terminal to the terminal, and the initial configuration file of the terminal is restarted to take effect through the terminal; s3, authentication and information reporting: the terminal initiates an authentication and service authorization request, wherein the request carries the result; the method comprises the following steps: carrying out Instance-ID-URNgt; an element; s4, service authorization and configuration issuing: the server receives the request, analyzes the terminal instantiation unique identifier, records analysis information, and determines whether to issue matched user configuration file data to the terminal; according to the invention, the terminal registration authentication process is deeply coupled with the terminal type and capability evaluation, and intelligent decision-making can be carried out based on terminal difference.

Embodiments herein provide a user equipment (UE) and a method for performing communication in a mission critical services (MCX) off-network. The method includes: sending an announcement message for a slot reservation for a complete scheduling assignment (SA) cycle; determining whether a second UE in the MCX Off-network is trying to reserve same slot and having a higher priority than priority of the first UE. Further, the method includes performing one of: backing off and sending a new announcement message for the slot reservation in response to determining that the second UE is trying to reserve a same slot and has the higher priority than the priority of the first UE, and reserving the same slot in response to determining that the second UE is trying to reserve the same slot and the second UE has a lower priority than the priority of the first UE.

In one aspect, a user equipment (UE) acting as a source remote UE for performing layer 2 (L2) UE-to-UE (U2U) relay communication, is configured to establish a public or mission critical 5 (PC5) link with a target remote UE through a relay UE. The PC5 link is assigned a pair of local IDs identifying the source remote UE and the target remote UE. The UE is further configured to transmit to the relay UE, a link modification request to modify the PC5 link and receive, from the relay UE, a configuration information to release the pair of local IDs or add a new pair of local IDs. In a further aspect, the UE is also be configured to transmit, to the relay UE, QoS information indicating the QoS requirements and receive, from the relay UE, split QoS information indicating after split QoS requirements per target remote UE

At a mission critical (MC) gateway (GW) user equipment (UE), in case of a connection authorisation request including an identity: in an instance when a status of the identity is pre-authorised, enabling access for a MC client associated with the identity to a first set of services of a MC core network (NW); and in response to a notification from the MC core NW that the MC client associated with the identity is authenticated, further enabling access for the MC client to a second set of MC services of the MC core NW. At an MC core network (NW), in case of a connection authorization request including an identity: setting a status of the identity as pre-authorised; and in an instance when an MC client associated with the identity is successfully authenticated at the MC core NW, setting the status of the identity as authenticated and sending a notification to an MC GW UE that the MC client associated with the identity is authenticated.

The embodiment of the invention provides an electronic information method and related equipment, and belongs to the technical field of electric power internet of things operating system management. The method comprises the following steps: acquiring system monitoring information; starting of a software high-precision timer and a hardware high-precision timer is controlled through a dynamic resource evaluation model according to system monitoring information; calculating clock cycle data corresponding to the sleep duration according to the system monitoring information, and if it is determined that the clock cycle data is smaller than a preset cycle threshold value, selecting to operate a shallow sleep mode; if it is determined that the clock cycle data is larger than or equal to the preset cycle threshold value, a deep sleep mode is selected to run; the shallow sleep mode is to keep a core clock running, and the deep sleep mode is to close a non-essential clock domain. According to the embodiment of the invention, the timing precision of the key task can be ensured, the overall energy consumption of the electric power internet-of-things operation system can be subjected to refined and adaptive management, and the resource utilization efficiency and the cruising ability can be improved.

Various embodiments of the present disclosure provide a method for an MC client device to inform the MC service server that the MC client device is connected to an MC gateway UE and thus is capable of having QoS managed by the MC service server via the MC gateway UE. The MC client device can send a message to the MC service server that includes an indication, such as a flag, an IP address of the MC gateway UE, or an MC gateway UE identifier, that indicates to the MC service server that the MC client device is connected to the MC gateway UE. The MC service server verifies with the MC gateway UE whether the MC client device is connected to the MC gateway UE, and then sends a request to the network for network resources (e.g., for QoS management) for the MC gateway UE to use for the MC client device.

The invention discloses a key task seamless switching method based on a Mesh network, and belongs to the technical field of key task communication, a server and a client of a key task establish a group call session, when a current bearer network fails, both the server and the client are switched to the Mesh network from the current bearer network, and the server and the client are switched to the Mesh network from the current bearer network when the current bearer network fails. The method specifically comprises the steps of S1, session pre-storage, S2, fault triggering, S3, session recovery request, S4, server session verification, S5, client session verification and S6, server decision making, smooth switching and service continuity are realized on the basis of various heterogeneous network bearers for 3GPP standard key task communication, the integrity of service context information in a switched network is ensured during network switching, and the service continuity is ensured. The method comprises the following steps of: receiving data of a network to be switched, synchronizing the data to the network to which the network is to be switched, reconstructing a service session before a network switching critical point, applying for corresponding resources, and after service context information is completely established, completing network switching, releasing service context information of a standby switching network, and switching a target network to complete service recovery.

This application discloses a task processing method, apparatus, device, storage medium, and program product, relating to the field of industrial internet technology. The method includes: updating the global ready state of task sets with dependencies on each terminal at scheduling time slot boundaries; calculating the scheduling priority of tasks in the global ready state and sorting them by scheduling priority to obtain a ready task list; wherein the scheduling priority of a task defines the criticality of the ready task; traversing the ready task list to generate a set of actionable actions for each ready task in the list; selecting candidate tasks based on the set of actionable actions; and selecting an execution position from the set of actionable actions of the candidate tasks and initiating execution. This solution not only ensures that critical tasks are executed first, reducing the risk of critical tasks being blocked by non-critical tasks, but also eliminates rollback and reselection due to infeasible decisions, significantly improving the stability and reliability of task scheduling in industrial scenarios and reducing end-to-end task processing latency.

The invention relates to the technical field of model deployment, in particular to a cloud aggregation AI model automatic arrangement system which comprises a task scheduling module, a compatibility analysis module, a framework matching module, a deployment optimization module and a scheduling intervention module. According to the method, the real-time load condition of the platform, the calculation requirements of the tasks and the pre-estimated execution time are comprehensively evaluated before task arrangement, and the execution sequence with the priority is dynamically constructed, so that priority processing of key tasks is ensured, and the overall utilization efficiency of calculation resources is improved; resource congestion and scheduling delay caused by simple queuing execution of tasks are avoided, success rate, error rate and performance data in historical execution records of the tasks are deeply analyzed in terms of deployment framework selection, compatibility judgment is formed by combining real-time performance feedback at the initial stage of deployment, and the task scheduling efficiency is improved. Furthermore, the structural features of the AI model are matched with the underlying capability of the frame, and the transformation from coarse-grained resource linkage to fine capability adaptation is realized.

The application discloses a kind of mission critical monitoring method, device and equipment and storage medium, the method comprises: obtaining task dependency relationship and baseline configuration information and the time information of each task in task dependency relationship;Time information includes scheduling time, running time length and actual start time and actual end time;Task dependency relationship is divided into several connected components;For each connected component, the earliest start time of each task is calculated according to scheduling time and actual start time;The earliest end time of each task is calculated according to the earliest start time and running time length and actual end time;According to each connected component and the earliest end time and baseline configuration information, the predicted completion time of each baseline and critical path are determined;According to predicted completion time and critical path and baseline configuration information, it is judged whether to carry out timeout early warning to each baseline.The method of the application can reduce the computing power of monitoring critical tasks, reduce invalid or repeated alarm.

According to an aspect, there is provided a computer- implemented method comprising the following Initially, number of terminal devices per cell for one or more cells is monitored. In response to detecting that the number of terminal devices within a cell is larger than a pre-defined threshold for a pre-defined amount of time, the following is performed. One or more neighboring cells of the cell are detected. One or more first terminal devices operating in any non-mission-critical operating mode in the cell or its neighboring cells are determined. A first set of values for radio transmission power control parameters for the one or more first terminal devices is determined. The first set of values is associated with lower terminal device transmit power compared to current default values. Configuration of the one or more first terminal devices to use the first set of values is caused.

With the development of technologies such as artificial intelligence, edge computing, and the Internet of Things, modern computing systems often need to run multiple computationally intensive tasks simultaneously. These tasks have different requirements for computing resources. Allocating dedicated resources to each task independently would lead to a surge in hardware costs and low resource utilization. Furthermore, in real-time systems such as robotics, autonomous driving, and industrial control, competition for computing resources among different tasks (such as perception, decision-making, and control) is also prominent. Existing real-time operating systems mostly use fixed-priority scheduling, which cannot dynamically adjust the computing resource configuration of tasks, resulting in delays or resource waste in critical task responses. This application provides a resource reuse method, system, and computer-readable storage medium, aiming to achieve efficient multi-task concurrent processing by dynamically scheduling computing units and actively releasing idle resources.

This invention provides a wireless communication device and method that offers low latency and high reliability in communication-based train control (CBTC). [Solution] The wireless communication device includes a transceiver set 120 and a control node 110 having a processor electrically connected to the transceiver set. The transceiver set communicates with a first base station via a first channel. The processor controls the transceiver set to transmit and receive mission-critical data with the first base station in a first communication mode, collect data other than mission-critical data from the first base station, and control the transceiver set to transmit delay-tolerant data, including the collected data, with the first base station in a second communication mode, and decides whether or not to switch the transceiver set from the first communication mode to the second communication mode based on the mission-critical data.