1255 results about "Bottleneck" patented technology

The invention provides an intelligent factory production scheduling method and system based on digital twinning, and the method comprises the steps: firstly obtaining a real-time monitoring data set, including equipment operation states, material flow tracks and environment sensing data, of a physical workshop; generating a three-dimensional digital twinborn model containing an equipment dynamic operation topology, a material real-time distribution thermodynamic diagram and an environment state simulation layer, then performing operation state correlation analysis on the three-dimensional digital twinborn model, and generating a feature set of equipment operation efficiency, material flow bottleneck, environment interference and the like; generating a dynamic scheduling parameter set containing equipment load balancing, material priority distribution and environment adaptation compensation parameters, and finally calling a preset scheduling strategy optimization algorithm to perform multi-objective optimization processing on the dynamic scheduling parameter set to generate an equipment scheduling, material distribution and environment regulation and control instruction set. And synchronizing to a physical workshop to execute real-time scheduling operation, thereby realizing intelligent and efficient production scheduling.

The invention belongs to the technical field of multi-target global dynamic production scheduling, and discloses a manufacturing resource dynamic production scheduling data comprehensive processing method, which comprises the following steps: acquiring multi-source heterogeneous data, constructing a field mapping table, constructing an equipment fingerprint database in combination with historical equipment performance data, dynamically adjusting confidence coefficient weight, and further judging an equipment state. Based on an equipment state judgment result, integrating operator report data and multi-source heterogeneous data through a preset rule base trigger event, and generating an abnormal event flow with a context tag; based on the abnormal event type and the equipment state judgment result, a recommendation scheme is constructed through a production scheduling knowledge graph, and a production scheduling instruction set is generated; according to the production scheduling instruction set, generating a local optimization scheme and a global optimization strategy, and forming a final execution scheme; calculating a resource utilization rate and a bottleneck process index through the 3D virtual workshop model, and generating a difference analysis report; according to the method, global resource scheduling and closed-loop iteration are realized, and the production efficiency and the system robustness are improved.

The invention relates to the technical field of project management, in particular to an intelligent management method and system for a civil engineering project, and the method comprises the following steps: obtaining personnel distribution, equipment state and material inventory data, extracting a key task node and an execution state, recognizing a progress lagging task, screening a key path node, analyzing a time difference, and marking a progress trend. And identifying an equipment influence section, judging resource allocation imbalance, and generating a progress monitoring index. According to the invention, through multi-source data acquisition of personnel distribution, equipment state and material inventory, difference identification between a task state and a plan node is enhanced, progress lag early warning efficiency is improved, progress fluctuation dynamic classification and trend identification are realized, construction rhythm optimization is assisted, and through coincidence analysis of equipment operation and a task fluctuation area, the progress lag early warning efficiency is improved. The construction efficiency bottleneck is identified, the scheduling accuracy is improved, fine progress monitoring is realized through investment and period difference comparison, and the progress control capability and the collaboration of on-site resource allocation are comprehensively enhanced.

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 automatic deployment of cloud computing platforms, in particular to a self-adaptive deployment method and system oriented to a credential heterogeneous environment. Aiming at three technical bottlenecks of low multi-CPU architecture adaptation efficiency, frequent software dependence conflicts and complex security baseline configuration in a localization process, a heterogeneous computing resource intelligent scheduling engine and a dynamic security policy generation mechanism are innovatively provided. The method comprises the following steps: constructing a heterogeneous resource portrait through a hardware feature automatic identification technology, and realizing component installation sequence optimization based on a DAG dependency relationship analysis algorithm and Kahn topological sorting; and creating an adaptive network security policy, and sensing the firewall state of the target system in real time through a probe. Compared with a traditional deployment mode, the method supports cross-architecture compatibility, solves the problem of dependency conflicts, improves deployment efficiency, and guarantees consistency and safety of the system.

The invention provides a network equipment monitoring time sequence data processing method based on artificial intelligence, which comprises the following steps: acquiring network topology structure data and historical load data, constructing a topological graph model containing node importance indexes and connection strength parameters, and determining a key equipment candidate set; according to real-time load data and the topological graph model, calculating a dynamic importance score of each device in the current network structure, and generating a key device list; based on an equipment dependency relationship of the topological graph model, extracting a time sequence feature vector of collaborative operation between equipment from the time sequence data set; when the fluctuation amplitude of the time sequence feature vector exceeds a preset threshold value, a group cooperation abnormal state is judged, and a performance bottleneck early warning signal is generated; analyzing the high-frequency time sequence data of the key equipment, and identifying potential fault equipment; and updating the topological graph model according to the fault probability, adjusting node weight parameters, and regenerating a key equipment candidate set.

The embodiment of the invention provides a distributed training method and system, electronic equipment and a storage medium. The distributed training method is applied to a distributed training system, in a current round of iterative training, training of at least one stage of a target model is cooperatively executed by at least one corresponding device and at least one corresponding host, and the distributed training method comprises the following steps: for each stage of the target model, the device executes a forward calculation task, obtaining a forward calculation result; the equipment executes a reverse calculation task based on the forward calculation result to obtain gradient data; and in response to the completion of the reverse calculation task, the equipment triggers to execute the optimization update task and obtains the updated parameters for the use of the next round of iterative training. According to the distributed training method, waiting time can be reduced, computing resources of equipment can be saved, efficient overlapping of communication and computing can be realized, assembly line efficiency and parallelism can be effectively improved, and thus the communication bottleneck problem in large-scale distributed deep learning training can be relieved.

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 enterprise digital management method based on data mining. The method comprises the following steps: acquiring information flow data and resource allocation data; analyzing according to the information flow data and the resource allocation data to obtain an initial network diagram; performing clustering analysis according to the initial network diagram, and performing path optimization to obtain a dynamic network diagram; performing feature extraction on the dynamic network diagram to obtain prediction analysis data; according to the dynamic network diagram and the prediction analysis data, network nodes are identified, and key nodes and high-risk nodes are obtained; in combination with the key nodes and the high-risk nodes, identifying a key propagation path to obtain the key propagation path; and based on the key nodes, the high-risk nodes and the key propagation paths, predicting key positions where resource bottlenecks and conflicts occur, and generating resource optimization suggestions. According to the method, the dynamic change of the implicit dependency relationship of the enterprise can be identified, high-risk nodes and key propagation paths are positioned, and resource configuration is optimized.

The invention discloses a high-availability testing method and system for task scheduling of a computing power host management platform, belongs to the technical field of high-availability testing of computers, and aims to solve the technical problems that in the prior art, high-concurrency scene testing coverage rate is insufficient, cross-domain scheduling, failover and self-healing scene verification are imperfect, and testing efficiency is high. According to the technical scheme, the method structurally comprises the steps of hybrid load testing, wherein the elastic capacity expansion and contraction capacity and the performance stability of a system are verified by simulating a long-time stable load and burst flow scene; cross-regional intelligent scheduling: tasks are dynamically scheduled based on cost, time delay and regional strategies, and optimal resource allocation and service continuity are ensured; resource fault migration recovery: simulating node-level or region-level faults, and verifying the automatic detection, migration and recovery capabilities of the system; monitoring and data collection: collecting affairs, resources and stability indexes in real time, and providing multi-dimensional data support for verification; and analysis and optimization: identifying a bottleneck based on test data to generate optimization suggestions, and continuously improving the high availability of the 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 provides a defective part reprocessing decision-making method based on multi-target balance, which comprises the following steps: acquiring real-time defect rate data and part distribution information from production, analyzing a defect rate change trend and a part quantity fluctuation condition, and setting a standard defect threshold according to an industrial standard and historical data; if the defect rate exceeds a standard defect threshold value, identifying and fitting part performance parameters to obtain an average performance curve, calculating slope change and abrupt change amplitude of the curve, and determining time and influence range of a variation point; acquiring a factory processing load, combining the priority distribution sequence with the target part grouping scheme and the factory processing load to obtain a reprocessing flow configuration scheme, and identifying a balance point of resource consumption and a reprocessing coverage rate through simulation; and acquiring a real-time ratio of the reprocessing coverage rate and the resource consumption corresponding to the balance point, comparing the real-time ratio with a historical optimal ratio, and if the real-time ratio is not smaller than the historical optimal value, determining that a performance bottleneck risk exists, and analyzing a risk cause.

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 hydraulic power plant automatic debugging system with a self-adaptive communication function. The system comprises a processing unit, a storage unit, a communication module, an input / output interface, a task classification module, a model architecture configuration module and other functional modules. The use method comprises the following steps: analyzing task complexity and high-dimensional features of input data through a task classification module, and grading by adopting algorithms such as principal component analysis and a support vector machine; the model architecture configuration module is used for matching a model architecture according to the complexity level and determining an initial calculation path; the dynamic path adjustment module optimizes a calculation path in combination with real-time data, and the resource distribution module redistributes resources when the calculation power exceeds the limit; the model compression module generates a compression model adaptive to the edge device through pruning and quantization algorithms; the parameter fine tuning module is used for jointly optimizing model parameters for a multi-task scene; according to the method, the adaptability and efficiency bottleneck of an existing debugging tool in a complex scene is effectively solved, and the intelligent debugging level of an automatic system of a hydraulic power plant is improved.

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 flexible intelligent manufacturing device which comprises a rack, a working module and a detachable fixing mechanism, and the detachable fixing mechanism is used for fixing the working module to the rack in a detachable connection mode. According to the method, the module replacement efficiency and the equipment reuse rate are remarkably improved. Meanwhile, a suction assembly detachably arranged in the feeding module can flexibly replace a matched suction assembly according to the material form (such as size, material and surface characteristics), the technical limitation that a traditional modular device is single in grabbing mode is broken through, and the multi-module position layout is dynamically supported by combining a standardized interface; the flexible production bottleneck caused by a fixed grabbing structure and insufficient module cooperation capability is effectively solved, and finally, an integrated solution with high compatibility and high response speed is provided for a multi-variety, small-batch and rapid-process-switching manufacturing scene while the equipment modification cost is reduced.

The invention discloses a personalized federal incremental learning method based on instance-level prompt generation, which realizes finer-grained feature capture of data by deploying a dynamic adjustable prompt generation module for a basic network of each client. The core of the module is to mine hidden attributes in data, and the attributes not only can significantly improve the generalization ability of the model for new classes, but also can realize effective migration of base class knowledge to the new classes. The hidden attribute features learned from the base class can be naturally generalized into unseen classes, so that the adaptability of the model to unknown distribution is enhanced while historical knowledge is kept. According to the method, the bottleneck of the static prompt pool in the aspects of expandability and adaptability is broken through, and a more flexible solution is provided for personalized federal continuous learning.

The invention discloses a computing system and a control method, device, equipment, medium and product thereof, and relates to the technical field of computers.A host device, a first switch and an acceleration computing device are divided, the first switch comprises a plurality of interconnected switching controllers, and uplink ports of the switching controllers are connected with the host device; the downlink port is connected with the acceleration computing device, the binding relation of the physical link layer between the mainboard and the acceleration card is removed, the port configuration and the resource allocation path of the exchange controller can be flexibly adjusted, and the acceleration computing resources can be allocated according to needs. The first board card provided with the first slot and the cable adapter plate are arranged in the acceleration computing device, so that the acceleration computing device with high-density deployment is realized. Therefore, the method solves the problem that the expansion capability of acceleration computing resources of a multi-card system reaches a bottleneck, and lays a foundation for expanding large-scale computing power by a single host and constructing a multi-host shared large-scale computing resource pool.

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.