1127 results about "Bottleneck" patented technology

The invention discloses a production process state monitoring scheduling optimization method based on real-time data acquisition, relates to the technical field of manufacturing process scheduling, and is used for solving the problem of insufficient real-time performance and stability of process scheduling. According to the method, a process monitoring mechanism based on real-time acquisition and closed-loop scheduling is constructed, a time sequence structured data frame is formed under a unified time reference, operation stability and quality offset characteristics are extracted, a data credible label and a current process state factor vector are generated, an optimized scheduling model is input, and task conflicts and resource bottlenecks are identified according to the data credible label and the current process state factor vector. According to the method, path compression and sequence adjustment are implemented in combination with scheduling priority mapping and a resource path diagram, scheduling deviation vectors are constructed through task response time delay and process blocking in operation, rules and parameters are triggered to be updated online and locally rearranged, and solidification is performed after verification in a prediction window, so that equipment idling and switching fragmentization in the production process are reduced, and the production efficiency is improved. And the real-time performance of the production process, the resource utilization rate and the system stability are improved.

The invention relates to the technical field of intelligent scheduling, in particular to an enterprise production real-time monitoring and intelligent scheduling system based on artificial intelligence, which comprises a multi-source heterogeneous data fusion unit, a priority resource coupling decision unit, a bottleneck prediction and tracing unit and a scheduling instruction generation unit, the multi-source heterogeneous data fusion unit collects multi-dimensional data such as equipment vibration, temperature, order delivery time and the like in real time and constructs a joint feature vector, and the priority resource coupling decision unit dynamically adjusts task priority and resource allocation through a dual-channel depth Q network to cope with order insertion tasks and equipment health degree fluctuation. The bottleneck prediction and tracing unit predicts production bottlenecks and traces root causes by using a process dependency graph, a multi-modal fusion model and a causal discovery algorithm, and supports preventive maintenance and dynamic scheduling, and the scheduling instruction generation unit synthesizes a preorder result to generate an adaptive scheduling instruction. And enterprise production equipment utilization rate and production efficiency are improved.

The invention discloses an artificial intelligence-based industrial production line dynamic scheduling method and system, and relates to the technical field of intelligent manufacturing and industrial intelligent scheduling, and the method comprises the steps: collecting real-time state data, carrying out the preprocessing, taking the maximization of productivity, the shortest delivery time and the lowest energy consumption as optimization objectives, constructing a multi-objective reinforcement learning model, and carrying out the optimization of the multi-objective reinforcement learning model; scheduling priority data is generated, and operation distribution of each process node is adjusted in combination with a current equipment load threshold value and production bottleneck node information; when an abnormal condition is detected, triggering a rescheduling mechanism according to scheduling priority data, and updating an operation sequence and a resource allocation result; and synchronously feeding back the updated job allocation result and execution effect to the multi-target reinforcement learning model, and carrying out iterative optimization on the multi-target reinforcement learning model through a priority experience playback mechanism to realize continuous optimization of a scheduling strategy. According to the method, through a mode of combining multi-target reinforcement learning and dynamic scheduling, the learning efficiency and the optimization effect are improved.

The invention relates to the technical field of artificial intelligence, in particular to a cloud edge collaborative production manufacturing management system based on artificial intelligence, which comprises a task scheduling optimization module, a resource scheduling and load balancing module, a production efficiency evaluation module, a dynamic load adjustment module and a fault tracing analysis module. According to the invention, by accurately analyzing the task dependency relationship, optimizing the task scheduling strategy and improving the response sequence and delay control of task execution, the processing bottleneck problem caused by data transmission delay is avoided, the task execution period is flexibly adjusted, the high-frequency tasks in the production process are effectively managed, and the load unevenness caused by fluctuation is reduced; the method can accurately identify and regulate the production bottleneck, improve the adaptive capability of the production process, accurately identify the potential fault source, shorten the fault diagnosis time, reduce the complexity, more rapidly position the problem, improve the stability, efficiency and fault tolerance of the production system, and integrally enhance the self-optimization capability of the production system.

The invention discloses an AI-driven industrial automatic production line intelligent scheduling method and system, and relates to the technical field of production line intelligent scheduling, and the method comprises the steps: carrying out the multi-source real-time collection based on an industrial automatic production line, obtaining a multi-source heterogeneous data set, carrying out the deep analysis and prediction, and recognizing a production dynamic prediction data set; positioning and identifying production equipment, determining production bottleneck position information, and drawing a demand change trend chart; carrying out production operation analysis, generating production bottleneck early warning information, carrying out control scheduling, and formulating a production scheduling strategy; and scheduling monitoring is carried out on the industrial automatic production line, continuous optimization response is carried out, and a real-time production scheduling optimization strategy is obtained. The technical problems of low scheduling efficiency and insufficient adaptability caused by lack of dynamic prediction capability of the industrial automatic production line in the prior art are solved, and the technical effects of realizing dynamic prediction of the industrial automatic production line and improving the scheduling efficiency and adaptability of the production line are achieved.

The invention discloses an intelligent file service system generation method based on a low-code development mode. The method comprises the following steps: constructing a file service core data model, defining file metadata fields, a storage structure and an association relationship, and forming a standardized data architecture. According to the method, the core data model, the visual page component library and the drag-and-drop workflow template are constructed, linkage binding of data-component-process is achieved in combination with a low-code engine, and the efficiency bottleneck of a traditional development mode is broken through; through a three-way real-time synchronization mechanism, real-time hot repair and dynamic branch judgment, page layout and flow rule adjustment can take effect immediately through visual configuration, and the problem of dynamic demand response lag is solved; through the intelligent resource scheduling logic compiled through environment adaptation, a differentiation scheme is generated for different deployment environments, the configuration difference influence under a private cloud and server mixed deployment scene is remarkably reduced, the deployment failure rate is greatly reduced, and the system online stability is improved.

The invention discloses an enterprise production management method based on digital twinning and workflow simulation, and particularly relates to the technical field of industrial internet and intelligent manufacturing, and the method comprises the steps: constructing a physical production system digital twinning body and business process workflow model, and carrying out the dynamic association through a model fusion engine to form an integrated digital twinning model; real-time event driving is used for deducing and simulating a future production process, and bottleneck and conflict prediction is output; based on the prediction result, utilizing a multi-objective optimization engine to generate a plurality of alternative scheduling schemes; performing parallel simulation quantitative evaluation on the KPI of each scheme, selecting an optimal scheme, analyzing the optimal scheme into a control instruction, and issuing and executing the control instruction; and model self-correction and closed-loop optimization are realized through real-time monitoring and feedback. According to the method, the problem of service and physical state disjunction caused by digital twinning and workflow independence is solved, the whole-process closed-loop management from prediction to execution is realized, and the production self-adaption and intelligent level is improved.

The invention relates to the technical field of semiconductor manufacturing, in particular to an intelligent manufacturing process optimization method and system of an integrated industrial large model. Dynamic optimal combination solution of technological parameters such as photoetching dose, etching rate and film thickness is realized; an equipment operation instruction and an abnormal intervention strategy are generated in real time, and the accuracy and timeliness of operation are improved; and cross-process collaborative scheduling and bottleneck prediction are carried out, the whole production process is optimized, and finally the yield, efficiency and flexibility of semiconductor manufacturing are improved.

The invention relates to the technical field of project management, in particular to a large-model batch reasoning and data flow optimization system oriented to an MOE architecture. According to the method, a collaborative architecture of the request access module, the environment sensing module, the expert routing engine, the resource scheduling module and the dynamic optimization control module is set, the text length and the subject type are extracted by using the request access module, a basis is provided for accurate routing, and the GPU video memory, the I / O bandwidth and the request queue depth are acquired in real time through the environment sensing module, so that the real-time routing is realized. The system load is comprehensively monitored, meanwhile, an expert sub-network is activated through an expert routing engine according to request features, invalid calculation is avoided, weight loading and resource allocation are managed through a resource scheduling module, the I / O bottleneck is reduced, and finally an optimization strategy is intelligently triggered through a dynamic optimization control module based on routing conflict factors. The problems of large reasoning delay fluctuation and unbalanced resource utilization rate mentioned in the background technology are solved, and stable low-delay response and resource collaborative optimization in a high-concurrency scene is realized.

The invention discloses a government affair department knowledge base access authority control method based on a block chain, and relates to the technical field of block chains and information security, distributed nodes jointly participate in decision making, the performance bottleneck of a single control point and the single point fault risk are effectively eliminated, and the reliability and stability of a system are remarkably improved; when a plurality of departments need to collaboratively update a strategy, the intelligent contract based on the threshold multi-signature mechanism can automatically execute voting aggregation and a strategy effective process without depending on manual operation of a central administrator, so that the efficiency and the automation level of cross-department collaboration are greatly improved, and the instantaneity and the consistency of strategy change are ensured; the inherent non-tampering and traceable characteristics of the block chain provide powerful auditing guarantee for the whole authority management process; and each policy update request, voting process, final decision and policy version switching are completely recorded on the chain to form a non-repudiated audit clue.

The invention relates to a low-code visualization optimization method and system for power credential development, and the method comprises the following steps: S1, obtaining data in a credential environment, recognizing the interface difference and performance bottleneck points between components through a compatibility test matrix, and building a standardized adaptive interface specification; s2, obtaining a standardized business object model based on the standardized adaptive interface specification; based on the business object model, defining attributes, relationships and constraint rules of each business entity, and forming a data dictionary and an interface specification special for the electric power field; s3, on the basis of the data dictionary and the interface specification special for the power field, constructing a DSL (field specific language) of power visualization; s4, a dragging type interactive design is adopted, and the visual operation is converted into a DSL configuration file; and S5, automatically converting the DSL configuration into a React or Vue component code through a code generation engine, and carrying out compatibility optimization on the kernel of the creative browser. According to the invention, the power credential development efficiency is effectively improved.

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 computers, can be applied to the field of science and technology finance / digital medical treatment, and discloses a method and device for dynamically adjusting threads of a thread pool, electronic equipment and a storage medium. The method comprises the steps that initialization setting is conducted on thread pool parameters through a management center of a target service system; when a thread pool parameter adjustment request is received, generating a first thread pool parameter according to the thread pool parameter adjustment request; receiving a configuration change event through a configuration change monitor, and analyzing the configuration change event to obtain a first thread pool parameter; positioning a thread pool configuration field of the distributed service instance according to a mapping relation between a program entity factory and a parameter cache; injecting the first thread pool parameter into a thread pool component instance of the distributed service instance; and calling the setting function of the distributed service instance, and resetting the corresponding configuration parameters in the thread pool component of the distributed service instance. The method improves the adaptability of the system to load change, and breaks through the system performance bottleneck.

The invention discloses a block chain fragment security expansion method of a trusted state synchronization chain, which realizes state consistency verification between fragments by constructing a Layer1 state synchronization chain, and adopts an unbiased random leader rotation mechanism based on a preorder block signature and an efficient two-stage submission protocol. Three rounds of communication of traditional BFT consensus are compressed into two rounds of parallel processing under a state fragmentation architecture, and a dynamic fragmentation recombination strategy and a state difference synchronization technology are combined, so that the core problems of throughput bottleneck, cross-fragmentation transaction atomicity, attack risk prediction by a leader, reduction of recombination efficiency and the like in a block chain fragmentation system are systematically solved; the parallel transaction processing is realized, the consensus time delay is reduced by 30%, the cross-fragment transaction verification efficiency is improved by 60%, the system throughput is linearly expanded along with the fragment number, and the system security is enhanced through a pledge economic model and a malicious node detection mechanism.

The invention discloses a method and a device for optimizing dynamic task scheduling resources of a distributed system. The method comprises the following steps of: monitoring a task dependency relationship and hardware resource state data in real time; a dependency release amount R representing the downstream task activation capability is generated based on the dependency relationship; generating an index A representing the resource utilization efficiency based on the resource state; a scheduling strategy is dynamically selected according to the load state: when the load is low, the task with the maximum R is preferentially allocated to improve the CPU parallelism degree, when the load is stable, allocation is carried out according to R and A weighted values to balance resource utilization, and when the load is high, the task with the maximum A is preferentially allocated to reduce the memory pressure; and finally driving task execution and updating a resource state. By dynamically sensing the task topological relation and the resource state, adaptive scheduling under load fluctuation is realized, the collaborative utilization efficiency of CPU and memory resources is effectively improved, and the problem of performance bottleneck of a traditional scheme under complex dependence and high-concurrency scenes is solved.

The invention belongs to the technical field of computer aided design, and provides a spinning equipment design assisting method and system based on model collaboration. The method comprises the steps that a collaborative design system is constructed, a large language model receives design requirements of a user, performs task decomposition and calls corresponding professional agent models to execute design subtasks, and the professional agent models feed simulation calculation results back to a closed-loop feedback engine; the closed-loop feedback engine analyzes feedback results of the professional agent models and generates attention prompt signals pointing to potential design conflicts or optimization bottlenecks on the basis of interdisciplinary parameter coupling relations; and the large language model performs multidisciplinary coupling analysis by using the attention prompt signal, dynamically adjusts task planning and design parameters by focusing on key parameters and key disciplines pointed by the attention prompt signal, and drives related professional agent models to perform directional iterative optimization. According to the method, the blind iteration problem can be effectively avoided, and the convergence speed of the design and the scheme quality are improved in a high-quality mode.

The invention discloses a small and medium-sized enterprise information management method and system based on a multi-layer architecture, and belongs to the technical field of information management, and the method comprises the steps: constructing a decoupling architecture of a user interface layer, a business logic layer and a data storage layer; analyzing the evaluation standard through a dynamic rule engine of a business logic layer, and generating a configurable rule unit comprising a logic expression and a dynamic weight parameter; an offline priority strategy is adopted at the mobile terminal, and a vector clock identifier containing a client ID and a logic clock sequence is added to a data record; executing dynamic weighted evaluation calculation to generate a total evaluation score; and performing industry reference threshold verification on the numeric field. The technical bottleneck of tight coupling of multiple layers of modules in a traditional single framework is broken through by constructing a decoupling framework of the user interface layer, the service logic layer and the data storage layer and an asynchronous message bus communication mechanism, so that cross-layer codes do not need to be synchronously modified when the system adds or adjusts the evaluation dimension, and the system reliability is improved. And the framework flexibility and the expansion efficiency are obviously improved.

The invention relates to a multi-physics field coupling simulation method and equipment for industrial digital twinning. The method comprises the following steps: automatically dividing a plurality of heterogeneous physical sub-domains based on actual physical field data of industrial equipment, and defining a solving region and a boundary condition for each sub-domain; and then, a variable-driven region division mechanism is utilized to construct a parallel computing map of multiple physical domains, and efficient synchronization of inter-region coupling state variables and real-time interaction of boundary data are realized. In the parallel simulation process, calculation tasks and resources are dynamically and optimally distributed through a self-adaptive scheduling algorithm, bottleneck areas are effectively recognized and eliminated, and the overall simulation efficiency and stability are improved. According to the invention, co-evolution of a plurality of physical processes such as structure, heat, flow, magnetism, electricity and the like can be realized, and unified, flexible and extensible simulation support is provided for digital twinning, state monitoring, performance prediction, control optimization and other applications of a complex industrial system.

The invention relates to the technical field of high-concurrency data processing, and discloses a storage and calculation integrated data scheduling system and method for a high-concurrency scene, and the system comprises a storage and calculation fusion architecture module, a distributed cache module, a dynamic fragmentation module, a parallel calculation module, a preloading module and a resource scheduling center. The method corresponds to the system. According to the method, by deeply fusing data storage and calculation and utilizing a distributed caching technology and a data preloading mechanism, I / O bottleneck is effectively reduced, dynamic data fragmentation and parallel calculation are supported, and the method is particularly suitable for large-scale data analysis scenes.

The invention provides a DCS-based multi-RTO cooperative control system and method, and overcomes the bottleneck of traditional waste gas treatment. The system comprises double waste gas pipelines, a DCS central controller and other modules, modes can be switched according to whether the waste gas concentration reaches a preset threshold value or not, high-concentration waste gas is subjected to two-stage oxidation during series connection, and loads are distributed according to the flow weight during parallel connection, so that the energy consumption deviation is smaller than or equal to 5%; the fan cooperation module synchronously adjusts the frequency of a main fan through a PID algorithm by taking the pressure of a header pipe as a reference to meet the dynamic balance relationship of the pressure; and the fault detection module isolates a fault unit in a scanning period, redistributes a load and maintains the total processing amount, and cuts in at a fan frequency raising rate after repair. The method comprises the steps of concentration monitoring, mode switching triggering, pressure PID control, fault processing and the like. The adaptability of the system to concentration fluctuation is improved, the waste gas treatment efficiency and stability are improved, energy consumption is reduced, production continuity and environmental protection compliance are guaranteed, and the system is suitable for industrial waste gas treatment.

The invention provides a dangerous large project special construction scheme AI auxiliary compilation system knowledge system construction and retrieval method, which comprises the steps of S1, multi-dimensional knowledge base construction: constructing a multi-dimensional knowledge base for storing dangerous large project compilation rules, specification standards, enterprise standards and project knowledge; s2, establishing a multi-level knowledge graph, establishing the dangerous and large project construction multi-level knowledge graph, and enabling the multi-dimensional knowledge base to form a knowledge system; s3, intelligently analyzing the multi-type documents; s4, knowledge management system construction, wherein the construction steps comprise knowledge classification; importing knowledge; automatic label prediction; and S5, constructing a multi-level knowledge representation and retrieval model. According to the method, on the basis of fully researching the current situation of scheme compilation, the knowledge characteristics in the building field and the bottleneck of the current large language model, the targeted knowledge system construction and efficient retrieval method is realized, and the technical problems of knowledge system construction and efficient retrieval and utilization of the AI auxiliary compilation system for the special construction scheme of the dangerous and large project are solved.

The invention discloses a body intelligent controller architecture based on an end side intelligent model and an operation method, and belongs to the technical field of industrial body intelligence. According to the architecture, a CPU, a GPU and an FPGA are integrated by adopting a heterogeneous multi-core processor, and high-speed interconnection is realized through a star-type + NoC hybrid topology; a multi-level task scheduling mechanism is constructed based on a real-time operating system, and it is ensured that control tasks are executed preferentially. End-side visual model lightweight (YOLOv5s model parameters are reduced to 3.5 M) and FPGA hardware acceleration (control instruction time delay is 10ns) are innovatively realized on an algorithm framework layer, and a transmission bottleneck is eliminated through a zero-copy data path. The system supports model hot switching less than or equal to 10ms and FPGA bit stream online updating less than or equal to 50ms, and has MD5 check and an automatic rollback mechanism. Compared with a traditional separation type scheme, the processing time delay is reduced by 25 times, the hardware cost is reduced, the size is reduced, and the real-time performance, reliability and deployment flexibility of the industrial robot and other intelligent agents with the body are remarkably improved.

The invention relates to a coal mine equipment cooperative control system based on digital twinning, and belongs to the field of coal mine intellectualization. According to the system, the equipment state, environmental parameters and fault data are acquired in real time through the data acquisition layer; the data processing layer realizes multi-source data fusion and fault prediction by using a deep fusion neural network and a space-time attention network; the digital twinborn layer constructs a three-dimensional dynamic model based on unified geodetic coordinate system coupling equipment and geodetic survey information; and the control layer adopts a distributed stream computing framework to execute collaborative scheduling, active disturbance rejection speed control and fault prediction compensation. The problems that in the prior art, spatial position expression is missing, collaborative delay is high, data are not synchronous, and complex working condition adaptability is poor are solved. The method has the beneficial effects that total-factor high-precision space coupling is realized, the collaborative delay bottleneck is overcome, the adaptability to complex working conditions is enhanced, a closed-loop intelligent optimization system is constructed, and full-chain transparent management and control of the coal mine are promoted.

The invention discloses a computing power collaborative management method and system based on a cross-architecture state sensing engine, and relates to the technical field of GPU computing power pooling scheduling. The method comprises the following steps: establishing a communication link set between a plurality of GPU instances and a plurality of CPU controllers; collecting and analyzing indexes such as data throughput rate, queue length and communication time delay of each link in real time, and dynamically identifying a system bottleneck link; aiming at the detected bottleneck link, the fine granularity of the bottleneck link is split into a plurality of sub-links, and each sub-link is allocated with an independent cache queue and a flow control strategy, so that fine management and isolation of link resources are realized; furthermore, the scheduling priority is calculated according to the real-time state index of each sub-link, and the optimal sub-link is preferentially selected for task scheduling during task allocation. Therefore, the resource utilization rate and the task scheduling efficiency in the multi-tenant heterogeneous computing power pool environment are effectively improved, and the conditions of local link congestion and false idle / false busy of the computing power pool are remarkably relieved.

The invention discloses an online order full-process automatic processing system and method. The system comprises an order state analysis module, a cross-platform collaborative adaptation module, a resource scheduling planning module, an environment load evaluation module and a task scheduling optimization module. According to the invention, by analyzing the trend of the key time sequence change of the order life cycle, the circulation bottleneck can be identified and the time section exceeding the threshold value can be generated, so that the task sequence is matched with the system load, the scheduling coordination and the response precision are enhanced, the execution capability is dynamically compared to form the optimal distribution instruction, and the cooperation efficiency and the transmission continuity are improved. Based on a utilization rate change identification trend, resource distribution is balanced, scheduling flexibility and controllability are enhanced, resource and energy consumption cooperative adjustment is realized through environment load evaluation, operation stability and environment adaptability are improved, high efficiency and continuous optimization of an order flow are realized, and processing capability and operation toughness of a system under high concurrency are enhanced.

The invention relates to the technical field of mobile terminal data processing, in particular to a CPU-GPU dual-core collaborative execution method and system for a mobile terminal deep learning reasoning task. The method comprises the following steps: in an offline stage, performing structural analysis on a target deep learning model, and predicting execution time and data transmission overhead of each operator in combination with hardware characteristics; in the online stage, the two key modules of the task scheduler and the heterogeneous execution engine ensure efficient CPU and GPU cooperative execution through the innovative dynamic scheduling and resource management technology. The reasoning tasks are dynamically distributed to the CPU and the GPU, flexible cooperative scheduling is achieved, the calculation advantages of all processors are fully played, the overall utilization rate is remarkably improved, and the idle or bottleneck of a single calculation resource is avoided. For resource-limited scenes such as a mobile terminal, the system optimizes the memory use efficiency, improves the task execution concurrency and stability and realizes efficient reasoning performance through a double-buffer structure and a data multiplexing and concurrent execution strategy.

The invention provides a dynamic factory production line balancing method and system based on adaptive control, and the method comprises the steps: obtaining a real-time operation state set of a production line, carrying out the work station correlation feature analysis processing of the real-time operation state set of the production line, generating a work station correlation feature matrix, carrying out the production line bottleneck recognition based on the work station correlation feature matrix, and carrying out the dynamic factory production line balancing. Generating a bottleneck station feature set, wherein the bottleneck station feature set comprises bottleneck station identifiers and corresponding operation parameter deviation features and material overstock features; and generating a production line dynamic adjustment strategy set according to the bottleneck station feature set, wherein the production line dynamic adjustment strategy set comprises a parameter adjustment strategy and a material distribution strategy for the bottleneck station. According to the invention, the timeliness and effectiveness of production line balance control can be improved.

The invention discloses a storage and calculation separation key value storage delay optimization method based on prediction driving, and aims to solve the problems of performance bottleneck and high tail delay caused by passive management and high network delay in a key value storage system in a storage and calculation separation scene. The method comprises the following steps: time load prediction: deploying a time sequence prediction model at a client, and predicting a future read-write request based on a historical access sequence; active cache prefetching: according to the predicted read request, actively preloading hotspot data from a server side to a client side for caching so as to improve the cache hit rate and hide network delay; active write-in optimization: according to the predicted write request, executing maintenance at a server side through predictive pre-insertion and active node splitting, and moving high index structure adjustment overhead out of a key request path to eliminate a write delay peak; and structure sensing batch synchronization: pre-fragmenting a local write buffer by using a server index top layer model of a client cache, and combining multiple independent remote insertion operations into one efficient batch update to reduce data synchronization overhead. Compared with an existing passive management system, the characteristics of model prediction and active cooperation are fully utilized, and the average delay and the tail delay of the system are reduced.

The invention discloses a multi-mechanical-arm flexible production line scheduling method and system based on a visual language model, and belongs to the technical field of production line scheduling. The method comprises the steps of obtaining a task target image and a task description text, and converting the task target image and the task description text into a task state joint embedding vector through a visual language model; the task urgency degree, the semantic matching degree between the task state joint embedding vector and the mechanical arm state embedding vector and the future state of the mechanical arm are introduced into a space-time weighted cost function, task allocation is conducted through the space-time weighted cost function, and a mechanical arm task is determined and executed; and the task completion state of the mechanical arm is determined, and the task of the mechanical arm is dynamically adjusted with the minimization of the space-time weighting cost and the feedback cost as the target. The multi-mechanical-arm cooperative work efficiency, the task scheduling precision and the production takt time stability can be improved in the dynamic production environment, and the problem that an existing multi-mechanical-arm flexible production line faces double bottlenecks of efficiency and reliability is solved.

The invention discloses a source load risk management and control method for cooperative operation of multiple types of adjusting power supplies and new energy, and the method comprises the steps: depicting a prediction error tail dependency relationship through employing an extreme value theory and a Copula function, constructing a distribution robust optimization model in combination with a Wasserstein distance, and obtaining the standby capacity sharing, triggering threshold and risk index of the multiple types of adjusting power supplies; performing conformal prediction based on the prediction residual error to generate a dynamic confidence interval, and mapping the dynamic confidence interval into a pipeline radius of model prediction control; executing rolling MPC containing information bottleneck regularization under the constraint of the reserve capacity and the pipeline, and generating a robust scheduling strategy; and constructing a polyhedral feasible region model of the flexible load group according to the scheduling strategy and the risk index, and performing scaling to obtain a group control action. And the new energy consumption level and the system operation safety are obviously improved.