4038 results about "Execution unit" patented technology

A system for multi-stage planning of construction processes and resource allocation, consisting of: a central planning engine configured to receive input data, including architectural design models, structural constraints, procurement schedules, and historical performance indicators; a task decomposition processor that is operationally connected to the central planning engine and configured to generate a hierarchical construction task graph by decomposing macro-level construction milestones into mid-level and micro-level subtasks, with each subtask having time estimates, location identifiers, resource requirements, and mutual dependencies; a hybrid planning processing unit configured to resolve time and resource constraints across the entire task diagram; a resource coordination controller that is operationally connected to the central planning engine, wherein the resource coordination controller includes a real-time database of work units, machines and material stocks, each resource being tagged with attributes such as availability, usage history, operating status and spatial location; a multitude of distributed execution units distributed across the construction zones, each distributed execution unit comprising an embedded controller, sensor interfaces, task status processing logic, and communication circuitry, each distributed execution unit being configured to receive planning instructions from the central planning machine, execute localized control logic for task confirmation and resource activation, and transmit task execution data back to the central planning machine; an adaptive conflict resolution processing unit that is operationally connected to the central planning engine and configured to detect conflicts in task execution or resource conflicts, simulate alternative task-resource allocation scenarios using a real-time multi-agent model, and autonomously update the task graph with revised task sequences and resource allocations; and A dashboard for the construction process, configured to visualize task progress, deviations from the planned schedule, and resource efficiency metrics, with the dashboard also being able to receive manual override inputs or approve automated conflict resolution proposals generated by the adaptive conflict resolution module.

The invention relates to the technical field of production line control, and discloses an automatic control method and system for a plastic processing production line. The method comprises the steps of collecting technological parameters of a plastic processing production line and transmitting the technological parameters to a central control system to generate a database; multi-dimensional parameter correlation analysis is executed, a parameter and quality mapping relation is established through a CNN-LSTM hybrid network, and an optimization model is formed; calculating an optimal control parameter, generating a control strategy and issuing the control strategy to an execution unit; and monitoring a response result, updating the model in real time, and forming closed-loop adaptive control. According to the method, the mapping relation between the process parameters and the product quality is accurately established through the deep learning model, the optimal control parameters are automatically calculated, and closed-loop adaptive control based on production feedback is realized.

The invention relates to the technical field of electronic manufacturing equipment, in particular to an adaptive thermal compensation system and method for a high-precision mounting head of a chip mounter, and the system comprises a temperature-deformation sensing unit, a thermal-mechanical coupling analysis unit, a dynamic compensation control unit, and a closed-loop execution unit. The temperature-deformation sensing unit collects temperature and deformation data of multiple parts of the mounting head in real time, the thermal-mechanical coupling analysis unit reconstructs a three-dimensional temperature field based on a finite element algorithm, the thermal expansion distribution quantity is dynamically calculated, the problem of rough model of traditional single-point temperature measurement is solved, and the measurement precision is improved. The dynamic compensation control unit predicts the thermal drift amount in the future 5 ms through online parameter identification and a long-short-term memory network model, a compensation strategy is adaptively adjusted in combination with the motion working condition, the closed-loop execution unit decomposes the compensation amount into displacement and torsion correction instructions, accurate offset of thermal deformation is achieved, and a whole-process thermal compensation closed loop is constructed. The precision stability of the mounting head in a complex thermal environment is improved, and the production efficiency is improved.

The invention relates to a heat dissipation control method and device for a high-order computing power automatic driving FPC sensing module, and the method comprises the following steps: carrying out the temperature data collection of a target FPC sensing module through a preset distributed thermosensitive array, and obtaining the data of a three-dimensional temperature field; performing heat flow path calculation on the target FPC sensing module based on the three-dimensional temperature field data to obtain a heat flow density distribution diagram; performing topological optimization calculation on the target FPC sensing module based on the heat flux density distribution diagram to obtain a heat dissipation structure layout scheme; performing dynamic optimization solution on the heat dissipation structure layout scheme through a branch and bound algorithm to obtain a heat dissipation control parameter set; and inputting the heat dissipation control parameter set into a preset execution unit, and performing real-time heat dissipation control on the target FPC sensing module to obtain an ideal heat dissipation state, thereby solving the problems that a traditional method often cannot accurately and effectively cool local hot spots and lacks flexibility to adapt to dynamic thermal changes under different working loads.

A microprocessor includes execution units that execute macro-operations (MOPs), a decode unit that decodes architectural instructions into MOPs, an instruction fetch unit (IFU) having an instruction cache that caches architectural instructions and a macro-operation cache (MOC) that caches MOPs into which the architectural instructions are decoded. A prediction unit (PRU) predicts a series of fetch blocks (FBs) in a program instruction stream to be fetched by the IFU from the MOC if hit or from the instruction cache otherwise. A branch target buffer (BTB) caches information about previously fetched and decoded FBs. A counter of each BTB entry is incremented when the entry predicts the associated FB is present again. For each FB in the series, the PRU indicates whether the counter has exceeded a threshold for use deciding whether to allocate the MOPs into the MOC in response to an instance of decoding the instructions into the MOPs.

The invention relates to the technical field of numerical control machine tools, in particular to a five-axis machining center real-time thermal error compensation system based on digital twinning and a medium. Firstly, a data acquisition unit is used for acquiring physical actual measurement data in real time; then, the digital twin processing unit constructs a model set used for representing the thermal dynamic behavior of the five-axis machining center, and the model set is composed of a plurality of virtual thermal model members; thirdly, fusing physical measured data into the model set through a data assimilation algorithm to dynamically correct the state of each model member and predict the thermal error of the machine tool; the prospective compensation decision-making unit receives the thermal error prediction result output by the digital twinning processing unit and generates a prospective compensation instruction; and finally, the compensation execution unit issues the prospective compensation instruction to a numerical control system of the five-axis machining center for real-time adjustment. The control precision of real-time thermal error compensation of the five-axis machining center can be improved.

The invention discloses a low-power-consumption single-emission out-of-order execution RISC-V processor, which comprises seven stages of pipeline architecture, namely an instruction fetch unit, an instruction buffer unit, a decoding unit, an emission unit for single instruction emission, a physical register file, an execution unit for parallel out-of-order calculation and a retirement unit in sequence, and the instruction fetch unit is used as the front end of the RISC-V processor; a five-level pipeline architecture formed by the decoding unit, the transmitting unit, the physical register file, the execution unit and the retirement unit is used as the rear end of the RISC-V processor; through the technical scheme, the invention provides a micro-architecture which is more balanced in performance and power consumption, so that the applicability of a processor core in a scene with high computing requirements and sensitive power consumption is improved.

The embodiment of the invention provides an instruction processing method and a processor, and the method comprises the steps: obtaining a to-be-executed instruction, and determining a conflict type of the to-be-executed instruction; distributing a physical register for the to-be-executed instruction based on the conflict type; determining a dependency conflict condition and a resource conflict condition of the to-be-executed instruction, and determining an execution sequence of the to-be-executed instruction based on the dependency conflict condition and the resource conflict condition; determining a target execution unit based on the execution sequence, and sending the instruction to be executed to the target execution unit; and controlling the target execution unit to execute the to-be-executed instruction, and writing the to-be-executed instruction into the physical register when the to-be-executed instruction is executed, so that efficient out-of-order execution is realized through refined conflict detection, resource allocation and instruction scheduling, and the performance of the RISC-V processor can be remarkably improved. And the adaptability to complex applications is enhanced.

The invention relates to the technical field of digital twinning, in particular to a multi-machine running-in test distributed control system and method based on digital twinning. The method comprises the steps that a sensing execution unit collects equipment operation state data based on a multi-source sensor module and carries out preliminary data processing and detection; the digital twinning unit receives multi-dimensional sensing data of the sensing execution unit based on a dynamic twinning modeling module, establishes an equipment state model, and performs simulation verification and parameter optimization on a control strategy through a Q-learning strategy to obtain optimized control parameters; the intelligent control unit constructs an optimal decision-making model of multi-machine cooperative control based on a cooperative decision-making module, adjusts parameters of an execution mechanism in real time through a PID control algorithm and feed-forward compensation, and generates a self-adaptive control instruction; and the fusion analysis unit constructs a multi-dimensional equipment operation knowledge base based on the knowledge graph module, and generates global optimization parameters through a multi-target genetic algorithm.

The invention relates to the technical field of data compression and transmission, in particular to a dynamic data compression and transmission system and method based on big data. The method comprises the following steps: a data acquisition and processing unit acquires original data from various data sources and processes the original data; the data compression execution unit compresses original data through an LZ4 compression algorithm, and introduces a layered matching DEW-LZ4 technology based on dynamic entropy weight into the LZ4 compression algorithm to optimize the compression process; the data transmission unit performs network transmission on the compressed data through a transmission protocol HTTPS, and dynamically adjusts transmission parameters by adopting a machine learning Q-Learning model according to a network state; and a receiving end decompression unit obtains the transmitted compressed data from the network, decompresses the compressed data by using an LZ4 decompression algorithm, and recovers the compressed data to an original data format. According to the design of the invention, by introducing a layered matching DEW-LZ4 technology based on dynamic entropy weight, the system can intelligently select an optimal compression strategy according to different characteristics (Shannon entropy values) of data.

The invention discloses a distributed natural gas pipeline network safety monitoring system and method based on edge computing, and belongs to the field of natural gas pipeline network safety monitoring, and the method comprises the steps: deploying monitoring units at a plurality of target nodes of a natural gas pipeline network, collecting the operation data of a local node by each monitoring unit, and carrying out the multi-dimensional anomaly detection through a first deep learning model; a local security risk event is identified. And the monitoring unit uploads the node operation data and the security risk event to the cloud monitoring center. And the cloud end summarizes and analyzes the data of the plurality of monitoring units to obtain global operation data of the pipe network, and identifies global safety risks through a second deep learning model. And based on the global security risk identification result, the cloud generates a corresponding control instruction and issues the control instruction to the target monitoring unit. And an execution unit connected with the target monitoring unit executes a safety action according to the control instruction. According to the method, accurate, real-time and distributed safety monitoring of the natural gas pipeline network can be realized, and the operation safety and the risk prevention and control capability of the pipeline network are improved.

The invention relates to the technical field of industrial automation control, and discloses a change gear assembly processing technological parameter intelligent optimization system, which comprises a multi-dimensional data synchronous acquisition unit, an angular domain static map construction unit, a phase lead constraint control unit and an angle trigger execution unit, according to the method, a static angle-load topological graph with a main shaft angle as an index is established, a phase lead angle is calculated by combining inherent physical response lag time of a servo system, and a parameter adjustment instruction sequence is directly injected into a servo current loop at a preset angle window before physical load impact occurs; according to the feed-forward control mechanism based on angular domain reconstruction and phase lead constraint, the problem of physical lag of traditional time domain feedback control in response to high-frequency intermittent cutting is avoided, and zero time difference of periodic load impact and even pre-judgment torque compensation are achieved.

The invention relates to the technical field of electrical digital data processing, and provides a task scheduling optimization method and system based on collaborative analysis. Initializing task information of the target task, and disassembling the target task into a task set composed of a plurality of sub-tasks; performing priority ranking on the sub-tasks according to the emergency degrees, the important parameters and the resource requirements of the sub-tasks; obtaining current execution resources through a resource manager to generate an execution unit set, and generating a collaborative strategy in combination with the priority ranking of the subtasks; and according to the priority ranking and the collaborative strategy, calculating a target value of each task scheduling mode in the intelligent scheduling algorithm to select the scheduling mode, and finally arranging the sub-tasks to the corresponding execution units. Through fine task disassembly, priority ranking, real-time resource monitoring and an intelligent scheduling algorithm, the accuracy and efficiency of task execution are improved, and the flexibility and expandability of the system are enhanced.

The invention relates to the technical field of artificial intelligence, in particular to a real-time anti-fraud monitoring system and method based on behavior reasoning and sentiment analysis, and the system comprises a multi-modal data collection unit, an edge preprocessing unit, a feature fusion and behavior reasoning unit, a large language model context reasoning unit, a risk assessment and decision unit, and an intervention execution unit. A log recording and federal incremental learning unit; the method has the beneficial effects that the traditional isolated single-mode detection is evolved into an emotion and behavior dual-channel collaborative multi-mode recognition system through millisecond-level coaxial alignment of voice, video and user operation logs; the robustness of dialect, noise and expression shielding is greatly improved through the multi-modal fusion model, so that the cross-scene recognition accuracy is improved by nearly three percent compared with that of a traditional single-voice scheme, and high-sensitivity capture of hidden and emotion control type fraud is truly achieved.

The invention relates to the technical field of distributed simulation based on containerized deployment and elastic expansion and contraction, and discloses a distributed simulation method and system based on containerized deployment and elastic expansion and contraction. According to the distributed simulation method and system based on containerized deployment and elastic expansion, real-time resource monitoring and historical load trend data of a simulation task are collected, and unified load evaluation is carried out in combination with a simulation calculation complexity parameter and an I / O density parameter; the refined modeling and resource demand pre-judgment of the simulation task are realized, and the accuracy of task scheduling and allocation is improved; modularized deployment of simulation tasks is achieved through subtask segmentation based on minimum executable units and a standard containerization packaging mechanism in cooperation with a container arrangement platform, and then concurrent execution and elastic scheduling in a multi-node environment are supported.

The invention discloses a high-precision attitude sensor dynamic calibration system and a method thereof, and belongs to the technical field of attitude sensors. The invention discloses a dynamic calibration system and method for a high-precision attitude sensor. The dynamic calibration system comprises a data acquisition unit, a dynamic calibration unit and a calibration execution unit. The problem that the calibration effect is limited in the prior art is solved, the state of the sensor can be known more comprehensively by collecting the output data of the high-precision attitude sensor, the environmental data and the multi-source data of the accelerometer, the gyroscope and the magnetometer in real time, so that more accurate error analysis and calibration are carried out, and the calibration accuracy is improved. Modeling and error analysis are carried out by utilizing a machine learning algorithm, parameters are automatically adjusted through a model adaptive updating mechanism, error analysis can be carried out on data collected in real time so as to provide reference for subsequent dynamic calibration, and dynamic calibration operation of the high-precision attitude sensor is accurately executed according to an analysis result of the model. And high precision and high stability of attitude measurement are ensured.

The invention provides an instruction scheduling method and apparatus, an electronic device and a medium. The method comprises the steps of obtaining a target instruction through a first instruction scheduling unit; identifying a target unit for executing the target instruction, the target unit being one of an execution unit or a first functional unit; under the condition that the target unit is the first function unit, a target tensor block corresponding to the target instruction and a target working group corresponding to the target tensor block are determined, and the target working group is used for indicating the memory address range of the target tensor block; and scheduling the target instruction to the target working group through the first instruction scheduling unit, scheduling the target working group to the target unit, reading the target tensor block based on the memory address range by the target unit, and executing the target instruction on the target tensor block. Based on the instruction scheduling method disclosed by the invention, the adaptation degree between a hardware layer and block-based GPU programming languages such as triton and the like can be improved.

The invention relates to the technical field of electromagnetic relay control, in particular to an electromagnetic relay control system, which comprises a relay execution unit, a contact state monitoring unit and an intelligent viscosity elimination control unit, and is characterized in that the contact state monitoring unit acquires quantum tunneling effect impedance spectroscopy and acoustic emission energy entropy change signals in real time; the intelligent viscosity-eliminating control unit analyzes signals through a fusion welding risk multi-dimensional evaluator, triggers a three-stage control sequence, firstly starts a magnetostriction high-frequency oscillator to inject an inverted eddy current array, activates a hysteresis loop restorer to apply a gradient demagnetization potential field if the fusion welding risk multi-dimensional evaluator is not recovered, and finally, recovers the fusion welding risk multi-dimensional evaluator; when an acoustic emission signal is abnormal, a chaotic encryption message is generated through the fault feature topology generator and is linked with the magnetic solitary wave breaking device, ultrasonic frequency mechanical energy can be captured and converted into regenerative current, an adhesion evolution path is reconstructed after diagnosis, the problems of contact adhesion risk identification lagging and lack of multi-physics field cooperative control in the prior art are solved, and the reliability of the contact adhesion risk identification is improved. And the adhesion early warning success rate and the removal efficiency are improved.

The invention relates to the technical field of integrated circuits, in particular to a processing system of a single-precision configurable matrix operation unit based on RISC-V instruction expansion, which comprises an RISC-V processor used for executing a software program and identifying a user-defined expansion instruction, and the RISC-V processor is connected with the single-precision configurable matrix operation unit through a user-defined coprocessor interface; the single-precision configurable matrix operation unit comprises an instruction response and decoding module and an instruction retirement and write-back module, and the instruction response and decoding module is used for receiving an instruction function code, source operation data and a destination register number and decoding the instruction function code; after decoding is completed, different execution units are selected according to a decoding result to send corresponding control signals; and after the execution unit completes the corresponding task, the instruction retirement and write-back module returns an execution result and a completion signal to the RISC-V processor, so that execution of a self-defined extension instruction is completed. According to the system, the matrix operation speed can be accelerated through bottom hardware under limited hardware resource consumption.

The invention provides a semiconductor manufacturing machine control method and system, and relates to the technical field of semiconductors, and the method comprises the steps: obtaining the operation parameters of an equipment cavity, and building a multi-objective optimization model containing the manufacturing cost, the manufacturing quality and the manufacturing efficiency. And training the model by adopting a deep reinforcement learning algorithm to generate an optimized prediction result. And performing deviation analysis on the prediction result and the target parameter, and inputting a deviation feature vector into a compensation amount calculation model constructed based on a long short-term memory network to generate a compensation control instruction. And inputting the compensation instruction into the multi-objective optimization model, calculating an optimal parameter combination by adopting a multi-objective optimization algorithm of dynamic weight distribution, and generating a parameter adjustment instruction. And finally, the adjustment instruction is sent to the execution unit, and self-adaptive feedback regulation and control in the semiconductor manufacturing process are realized, so that the manufacturing cost is reduced, and the manufacturing quality and the manufacturing efficiency are improved.

The invention provides a sewage AI intelligent management and control system based on a neural network algorithm, which comprises multi-source data acquisition units, an algorithm processing unit and an execution feedback unit, and is characterized in that the multi-source data acquisition units are deployed at a water inlet and a water outlet of a sewage treatment assembly line and in a biological reaction tank; comprising a fast parameter detection array composed of a pH sensor, a conductivity sensor, a dissolved oxygen sensor, a turbidity sensor, a temperature sensor and an ORP sensor. The process state monitoring group consists of an aeration equipment rotating speed sensor, a reflux pump flowmeter and a sludge concentration meter; the fast parameter detection array and the process state detection group are connected with the edge computing node through an industrial bus, perform sliding window mean filtering preprocessing on original data, generate regulation and control instructions of aeration rate, reflux ratio and sludge discharge frequency through model calculation, and send the regulation and control instructions to the execution unit; the complete refined AI intelligent control sewage treatment system based on the neural network algorithm is realized.

The invention discloses a low-carbon building monitoring system and method based on digital twinning, and relates to the technical field of digital twinning, the system comprises a data acquisition unit, a computing power cooperation unit, a regulation and control execution unit and a user interaction unit, the data acquisition unit carries out regional targeted timing monitoring, builds a unified semantic standard and a dynamic knowledge graph, and sends the unified semantic standard and dynamic knowledge graph to the user interaction unit; the comprehensiveness and efficiency of data association analysis are improved; a three-level computing power collaboration system is constructed through the computing power collaboration unit, building monitoring information is integrated, complex simulation of a digital twin module is carried out, an energy transmission path and equipment fault risks are accurately tracked, prediction optimization and long-term regulation and control management are carried out, and the construction efficiency is improved. Therefore, a full-life-cycle integrated management framework in a macroscopic geographical environment, a mesoscopic energy link and microscopic equipment of a building cluster is built, cross-domain collaborative analysis is realized, the real-time requirement is met, multi-target optimization design for balancing energy cost, energy efficiency and comfort is realized, and carbon neutralization targets in the building industry are assisted to fall into the ground.

The invention relates to the technical field of industrial control, in particular to a crude foil engine multistage tension cooperative control method and system based on PID, and the method comprises the steps: firstly, deploying intelligent edge computing nodes at each stage of tension control execution unit of a crude foil engine, and carrying out the real-time parallel processing of a high-frequency operation data flow from a local process monitoring unit; secondly, a state evolution prediction algorithm is introduced, a tension dynamic fluctuation trend and an equipment part evolution trend are accurately predicted in combination with multi-modal material characteristic information and a time domain attention LSTM algorithm, and a feed-forward adjustment instruction is sent through a hierarchical intelligent control strategy; a deep reinforcement learning framework is applied to construct a multi-stage tension control system, and multi-stage tension collaborative optimization control is achieved through interaction with the operation condition of the crude foil engine; and finally, according to the output of the deep reinforcement learning framework, a production scheduling strategy is dynamically adjusted, resource configuration is optimized and maintained, and the stability, efficiency and product quality of crude foil production are comprehensively improved.

The invention relates to the technical field of new energy, in particular to a fan blade composite deicing system and method.The fan blade composite deicing system comprises a sensing layer, a decision-making layer, an execution layer and a feedback layer, and the decision-making layer comprises a graph neural network ice type recognition module, a CFD-icing coupling simulation module, a health management module and an energy consumption scheduling algorithm unit; the execution layer comprises a control unit and an execution unit, the control unit comprises a dynamic partition heating controller and a self-adaptive vibration controller, and the execution unit comprises an electric heating system and a vibration deicing system; compared with the prior art that a single deicing mode is usually adopted, and the adaptability to different ice type physical characteristics is poor, the scheme adopts a differential execution strategy, and partition electric heating control is carried out on different chord length areas of the blade based on the ice type recognition result; and high-frequency piezoelectric standing wave vibration is adopted for glaze ice, low-frequency eccentric wheel vibration is adopted for glaze ice, and the beneficial effects that the deicing efficiency is remarkably improved, invalid energy consumption is reduced, and damage to blades is reduced are achieved.

The invention discloses an intelligent precise farmland irrigation direction control system based on the Internet of Things, and relates to the technical field related to intelligent irrigation. Comprising a data acquisition module, a data processing and analyzing module, a data labeling and classifying unit, a three-dimensional coordinate model building module, a nozzle angle adjusting device, a nozzle water quantity adjusting unit and a control instruction execution unit. According to the invention, the distance between the nearest nozzle and the water-requiring position is calculated, so that irrigation water can reach the water-requiring area more efficiently, the distance is accurately calculated to ensure that water can be quickly and accurately conveyed to the required area, water loss and energy waste caused by long-distance water conveying are avoided, the water spraying direction is adjusted in combination with the wind direction and the wind speed, and the water spraying efficiency is improved. The device can effectively cope with the influence of natural wind on irrigation, and by monitoring wind direction and wind speed information in real time and combining the wind direction and wind speed information with water spraying parameters of the spray head, the system accurately calculates horizontal and vertical angle adjustment values of the spray head, so that sprayed water flow can accurately reach a water needing position.

The invention discloses a hardware detection process exception handling method, a hardware detection process exception handling device and electronic equipment, and relates to the technical field of computers, a hardware detection process is split into a plurality of independent execution units according to various detection functions supported by the hardware detection process, and state monitoring is carried out on each execution unit in the running process, so that the hardware detection process is more accurate. When a certain execution unit is abnormal, the abnormal execution unit is processed in a targeted manner, so that the abnormality of the process is processed fundamentally, the hardware detection process can be recovered to a normal operation state in time, and the situation that hardware faults in a system are diffused due to the fact that the hardware faults cannot be detected in time is avoided; and a foundation is laid for improving the hardware safety of the to-be-detected computing equipment.

The invention discloses an electrical work ticket intelligent generation method and device, a medium and equipment in the technical field of power systems, and aims to solve the problems of serious module decoupling, dependence on manual connection and insufficient intelligent generation capability in the prior art. The method comprises the following steps: generating a required digital knowledge base and an electrical work task description generation work ticket based on a pre-constructed work ticket by utilizing a multi-model collaborative generation architecture; the multi-model collaborative generation architecture comprises a rule driving module used for calling a corresponding template based on task element matching; the task execution unit is used for executing a specific subtask according to a pre-deployed lightweight model or rule engine; and the large language model is used for performing semantic understanding and reasoning according to the electrical work task description, calling the rule driving module and the task execution unit according to a semantic understanding and reasoning result, and generating a work ticket in combination with a digital knowledge base required by work ticket generation. The method has both the accuracy of structured knowledge recognition and the flexibility of natural language generation.

The invention relates to the technical field of intelligent war game deduction, in particular to an intelligent war game deduction simulation system based on a digital twin fusion large model. Comprising a digital twin modeling unit which adopts a dynamic precision adaptive modeling mechanism; a large model decision unit; a deduction execution unit; and a dynamic interaction unit. A dynamic precision self-adaptive modeling mechanism is adopted by the digital twin modeling unit, the model precision can be dynamically switched on demand, the detail simulation demand and the computing resource consumption are balanced, and meanwhile, the sub-second-level dynamic synchronization of a virtual model and a war game deduction scene is realized through a time sequence calibration algorithm; and space confrontation, time sequence decision and rule triggering features are fused through the multi-modal feature analysis module, and the Wargame rule verification module is combined to embed entity performance boundaries and scene rule constraints, so that compliance confrontation decision parameters conforming to Wargame deduction logic can be generated, and the problem of insufficient model precision adaptation and decision constraints is solved.

To reliably complete settlement using a balance of settlement means.SOLUTION: A determination unit (102) of a settlement system (1) determines the amount of balance to be used in settlement where a balance of settlement means is used. An execution unit (103) makes the settlement on the basis of the use amount. When the balance is used for another settlement during a period from when the use amount is determined until the settlement is made, and the balance is insufficient at the time of making the settlement, the determination unit (102) determines the use amount again. The execution unit (103) makes the settlement on the basis of the determined the use amount again.SELECTED DRAWING: Figure 4

The invention discloses a security control method, device and equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: receiving an operation instruction sent by an execution unit, and the operation instruction is generated by an artificial intelligence technology carried in the execution unit; real-time hardware state information of the execution unit is obtained, whether the operation instruction is a safe operation instruction or not is determined according to the real-time hardware state information of the execution unit, and therefore when it is determined that the operation instruction is the safe operation instruction, the operation instruction is sent to the execution unit to be executed, and when it is determined that the operation instruction is a non-safe operation instruction, the operation instruction is sent to the execution unit to be executed. And the operation instruction is forbidden to be sent to the execution unit for execution, and early warning information is sent to the main control unit, so that the safety of the system is improved.