353 results about "Middle tier" patented technology

The invention belongs to the field of intelligent edge systems and privacy enhancement computing, and particularly relates to a privacy protection-oriented robot large model cloud edge end collaborative reasoning and federated learning system, which comprises a cloud server layer used for deploying a large-scale pre-training model and executing complex reasoning and global federated learning coordination; the edge calculation layer is used for deploying an intermediate layer model and executing local data aggregation, privacy protection processing and intermediate feature calculation; the terminal equipment layer is used for deploying a lightweight model and executing data acquisition, primary processing and lightweight reasoning; the federated learning framework is used for optimizing the model; the privacy protection module is used for integrating data localization, differential privacy, homomorphic encryption, secure multi-party computing and a block chain verification mechanism; the adaptive allocation module is used for dynamically adjusting computing resources. According to the method, the problems of privacy leakage risk, computing resource limitation, network delay, insufficient data isolation and the like of the traditional AI service in a robot scene are solved, and efficient privacy protection and data security isolation are realized.

The invention relates to the technical field of intelligent fusion terminal communication, and discloses an intelligent fusion terminal multi-protocol communication method and system based on edge computing. According to the method, a protocol adaptive engine is deployed at an edge node, an original data stream of a communication link is collected and analyzed in real time, and a current protocol type is dynamically identified in a fuzzy matching mode. And based on an identification result, the system dynamically loads a corresponding protocol analysis module, generates an adaptive instruction set, and realizes standardized data frame encapsulation through a protocol conversion intermediate layer. And meanwhile, the system monitors the link state, triggers incremental updating of the protocol feature library, and realizes seamless protocol switching. According to the invention, the communication compatibility and reliability are improved, and the requirements of high-reliability scenes such as the industrial Internet of Things are met.

The invention discloses a knowledge cross validation question-answering method and system for reducing hallusion of a large language model, and belongs to the technical field of artificial intelligence, and the method is implemented by the following steps: generating results through multiple times of sampling: when a user puts forward a question, controlling the large model to perform multiple times of sampling, and generating a specified number of results; calculating hidden state related indexes: extracting the hidden state of the last token of the middle layer of the large model corresponding to the result, and calculating covariance matrixes and answer discrete feature values of the hidden states; mLP model prediction: inputting the discrete feature value of the answer and the length of the answer into a multilayer perceptron MLP, and outputting a hallucination-free probability; querying and summarizing a knowledge graph; and calculating a final illusion-free score and outputting a result. According to the method, the answer quality and credibility of a large language model can be remarkably improved, and the method is particularly suitable for application scenes with extremely high requirements on the accuracy of single-mode text generation contents.

The invention provides a communication segmentation learning system and method for adaptive channel compression, and a medium, and relates to the technical field of segmentation learning and communication compression. The system comprises a plurality of clients, a server side and a channel compression device arranged between the clients and the server side, the channel compression device comprises a channel sensitivity modeling module, a rate distortion adaptive compression module and a cross-client fair coordination module. In a channel sensitivity modeling module, channel importance is dynamically evaluated through intermediate layer activation value fusion; differentiated quantization and compression strategies are designed on the basis of sensitivity scores in a rate-distortion self-adaptive compression module, so that key information is reserved while communication overhead is reduced; and meanwhile, the cross-client fair coordination module realizes balanced distribution of communication resources among multiple clients through a fairness regularization and dual optimization mechanism, so that the influence of excessive compression on global convergence is avoided. According to the method, the communication efficiency and the training stability of segmentation learning in a complex heterogeneous environment are remarkably improved.

The invention relates to the technical field of artificial intelligence model optimization, and discloses a multi-granularity dynamic pruning method and system for a generative AI model. The system comprises a model state acquisition module which acquires output of a middle layer in real time through a probe, and constructs a feature information set of neuron activation distribution, weight matrix norm and connection topology; the sparseness evaluation module outputs a sparseness risk value based on the feature information set, the initial sparseness parameter and the real-time computing resource state; a pruning planning collaborative analysis screening module generates a multi-granularity pruning scheme when the risk value exceeds a threshold value, calculates a collaborative interference value of precision recovery operation and screens an optimal scheme; and the pruning strategy execution feedback module updates model parameters, generates logs and feeds the logs back to the management terminal. The system realizes adaptive structure optimization and resource scheduling of the generative model in the reasoning process.

The invention discloses a large visual language model vulnerability detection method and device based on security sensitive layer activation guidance and a storage medium, and belongs to the technical field of artificial intelligence security, the method comprises the following steps: S1, constructing a security sensitive layer activation induction sample data set; s2, performing quantitative analysis on activation differences of the model intermediate layer under attack and normal input; s3, on the basis of the recognized security sensitive layer combination and an output layer of the large visual language model, generating an in-process confrontation image with an attack attribute through optimization of a loss function; and S4, recording an optimal security sensitive layer combination and a harmful response path guided by the optimal security sensitive layer combination, and constructing a structured representation of the potential security vulnerabilities of the model. According to the method, the security fragile links of the model are accurately positioned, the attack guiding force is improved in combination with multiple losses, the generated confrontation disturbance is controllable and imperceptible, the good cross-instruction migration capability is achieved, and the method adapts to various security assessment and red team test tasks.

The invention discloses a BeeGFS-based metadata query efficiency improvement method and system. The BeeGFS-based metadata query efficiency improvement method comprises the following steps: constructing a three-dimensional index network consisting of a PMEMKV hot data layer, a RocksDB common data layer and a ZFS cold data layer; dividing all metadata into three independent sets of hot data, common data and cold data according to the heat values, and respectively storing the three independent sets to corresponding layers; after a query request is received, a top layer, a middle layer and a bottom layer are queried in sequence, and corresponding metadata is returned if the query request is hit; monitoring query indexes in real time, wherein the query indexes are used for updating the heat value of the metadata and adjusting the storage configuration of each level; performing dynamic promotion or degradation on the metadata between the three-dimensional metadata index networks according to the latest popularity value of the metadata and a preset popularity threshold value, and performing data synchronization and query path switching in the promotion or degradation process; the method can reduce query delay, improve throughput and balance storage resources.

The invention provides an FPGA (Field Programmable Gate Array) multi-modal control method based on dynamic partial reconfiguration, which comprises the following steps that: a main body adopts a layered hardware architecture to realize multi-modal control, firstly, a system module of a whole system is divided, so that FPGA logic resources are divided into a static region and a reconfigurable region; wherein the static area is responsible for global control and a communication interface part, the reconfigurable area realizes a multi-mode function by loading different configurations, and the middle layer is used for deploying the dynamic part to reconfigure a controller and is responsible for time sequence coordination and bit stream management. Compared with the prior art, the method has the advantages that the mapping relation between the modal feature library and the hardware module is established, the priority preemptive reconfiguration strategy and the modal switching cost model are adopted, the real-time matching of the sensor data flow and the hardware architecture is achieved, the dynamic data path reconstruction technology supports multi-modal parallel processing, and the real-time matching of the sensor data flow and the hardware architecture is achieved. Meanwhile, time sequence constraint, resource occupancy rate and power consumption budget are considered.

The invention discloses a multi-core platform-oriented collaborative scheduling method and system and terminal equipment thereof, and belongs to the technical field of collaborative scheduling of operating systems. The method comprises the steps of obtaining a task initial process based on a multi-core heterogeneous chip platform; a task dependency graph is obtained, loop dependency detection and parameter consistency detection are executed based on the task dependency graph, and an intermediate layer task description file is obtained; acquiring real-time operation data and a master control heartbeat survival table based on the intermediate layer task description file; obtaining a target master control node based on the real-time operation data and a preset election method when it is judged that the preset failure condition is met; and carrying out a breakpoint snapshot action based on the target master control node, and carrying out degradation processing based on the original master control node. According to the collaborative scheduling method and system for the multi-core platform and the terminal equipment thereof, the technical problem that an effective master control hot switching and task migration mechanism is lacked in the prior art can be solved, and the intelligent level and reliability of heterogeneous collaboration of multiple operating systems are remarkably improved.

The invention belongs to the technical field of data processing, and particularly discloses a multi-source heterogeneous data acquisition access method and system based on intelligent mapping and a dynamic interface, and the method comprises the steps: registering an input data source class, generating a data source identifier corresponding to the data type, and storing the data source identifier; respectively executing meta-model analysis operation to generate a standardized meta-model associated with the data source identifier and a container instance template; generating a mapping rule set through a twin neural network architecture with multi-modal feature fusion and a preset mapping rule; a middle layer API interface is constructed through interface logic, and real-time conversion and access between input and output data are achieved; and realizing containerized parallel access based on the container instance template and the containerized interface service instance. According to the method, data source change is dynamically adapted, the real-time performance and accuracy of the mapping rule are ensured, and meanwhile, multi-source heterogeneous data can be accessed efficiently in real time in combination with containerized parallel processing and a data source change monitoring mechanism.

The invention discloses an intelligent design research and development system based on domestic AI and an implementation method thereof, the system adopts a modular loose coupling architecture, and comprises a front end layer, a middle layer, a core layer, a base layer and a data layer; the front-end layer is a Web front-end interface, and the middle layer is an API gateway layer; the core layer comprises a knowledge graph module, a simulation engine module, a decision center module and a digital twin manager module; the basic layer comprises a system management module, the data layer comprises a multi-layer data storage and communication component, and the method comprises the steps of initializing a system manager, constructing a knowledge graph, executing multi-physics field simulation and evaluating simulation confidence; generating a decision strategy based on reinforcement learning and knowledge graph reasoning; according to the method, digital twin bodies are constructed, virtual-real linkage and real-time data interaction are achieved, domestic AI is comprehensively adopted and comprises a MindSpore framework and an ancient big model, the technology is autonomous and controllable, and the method is suitable for the whole process of power equipment design, inspection and detection and supervision and construction.

The invention relates to the technical field of voice processing, in particular to a real-time intention recognition method and system based on streaming incremental reasoning, and the method comprises the following steps: receiving the voice input of a user through a voice collection module, slicing the voice into a plurality of audio frames, and carrying out the recognition of the intention of the user through an incremental large language model module by adopting an Early-Exit reasoning mechanism; side outlets are arranged at multiple levels of the model, incremental reasoning is performed on token streams based on a QLoRA4-bit quantization technology, prediction results of multiple tokens are smoothed by using a stream ASR decoding module and an accumulative fusion module, and a stable final label is generated; the method has the beneficial effects that by combining streaming ASR decoding with incremental large language model reasoning, intention recognition and risk assessment can be immediately performed on each speech token after the speech token is generated. Through an Early-Exit reasoning mechanism, under the condition of high confidence, the system can output a fraud intention in advance in a middle layer in the reasoning process and stop subsequent calculation, and unnecessary calculation overhead is reduced.

The invention provides a model task classification method and device, a model selection method and electronic equipment, and the method comprises the steps: obtaining a to-be-processed data set which comprises task data of a plurality of tasks; processing each piece of task data based on the multi-task processing model to obtain a task processing result corresponding to each piece of task data and an output result of each piece of task data in a middle layer of the multi-task processing model; determining a path vector of each piece of task data based on the output result of each piece of task data; based on the path vectors of the task data of the tasks, classifying the tasks to obtain a plurality of task classification groups; and determining a task category label corresponding to each task classification group according to a task processing result corresponding to the task data of the tasks in each task classification group. According to the embodiment of the invention, the task execution accuracy of the multi-task processing model can be improved on the basis of defining the executable task category of the multi-task processing model.

The invention discloses a personalized federated graph learning method oriented to equipment resource isomerism, and aims to solve the defects in graph data isomerism, equipment resource adaptation and privacy protection in the prior art. According to the method, collaborative optimization is realized through a closed-loop process of local pre-training, embedding aggregation, personalized training, soft label collaboration and classifier distillation. Each client pre-trains a model based on a local graph data set and generates interlayer embedding, and uploads the model to a server after sampling and privacy enhancement; the server performs aggregation to form a public embedded data set and distributes the public embedded data set to the client to support distillation training and soft label generation; the soft labels are filtered and subjected to weighted aggregation to form global soft labels, and the client completes classifier knowledge distillation by combining the public embedded data set and the global soft labels, and iteratively optimizes the performance of the model. According to the method, the generalization ability and the resource utilization efficiency of the model are remarkably improved while the data privacy is guaranteed, and the method is suitable for distributed graph data training tasks of multiple scenes such as social networks.

The invention provides a knowledge distillation-based model compression method and system, and the method comprises the steps: inheriting a soft label of a maturely trained teacher model, defining a distillation loss function of a student model through the soft label, enabling the student model to be compressed, generating a new sample extension training set through random sample extraction and mixing, and achieving the compression of the student model. The problem of insufficient data is relieved, meanwhile, the robustness of the model to noise and distribution offset is improved, through dynamic weight adjustment, a student model is made to quickly fit teacher model knowledge, autonomous optimization is conducted in the later training period, and over-fitting of soft labels is avoided. On a test set in which the proportion of unlabeled data is 30%, on the basis that the accuracy of using a real label is 78.3%, the accuracy is improved by 11.2% by using a pseudo target, deployment is performed by using a maturely trained student model, middle layer feature alignment is omitted, the occupation of a GPU video memory is reduced from 3.2 GB to 1.8 GB, and the deployment requirement of edge equipment is met.

An architecture is disclosed for submission and recordation of application programmer interfaces (APIs) using non-fungible tokens (NFTs) inserted into a blockchain. The API-NFT pairings are validated by nodes of the network. The system automatically searches for and generate NFTs for APIs and / or intermediate layers on a computer network based on metadata associated with the API and a hash of the API / intermediate layer. The API-NFT pair binding ensures that developers / consumers are ingesting APIs that are secure and do not include malicious rules. Furthermore, performance monitoring of the API is performed using artificial intelligence (AI) and machine learning (ML), including long short term memory (LSTM) neural networks.

The invention discloses a protocol conversion method, which comprises the steps of receiving an original data frame from a charging power supply, extracting a protocol feature vector of the original data frame through a pre-trained machine learning model, and matching a first protocol type of the original data frame from a protocol feature library based on the protocol feature vector; calling an analysis rule base corresponding to the first protocol type to deconstruct an original data frame into intermediate layer data irrelevant to the protocol; and calling a field conversion rule from a protocol mapping table associated with the second protocol type, dynamically recombining the standardized field label in the intermediate layer data into a target data frame structure, and injecting a protocol control character originating from a target protocol specification to generate a target data frame conforming to the second protocol type. The method has the capability of adaptively learning a new protocol, reduces the manual intervention and maintenance cost, and improves the compatibility, communication reliability and overall operation efficiency of a battery replacement system for vehicles of different brands.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a cross-model knowledge editing and updating method, device, equipment and medium, and the method comprises the steps: receiving a knowledge updating instruction, and generating a knowledge editing vector; writing the knowledge editing vector into an intermediate layer parameter of the target model set to obtain a parameter-updated target model set; generating an input sequence with a regulation mark when the input request and the knowledge update content meet semantic matching conditions, and inputting the input sequence into the target model set to obtain a model output set; performing output alignment and fusion through a consistency fusion module to generate a fusion output result; and when the output conflict is not eliminated, performing a re-reasoning operation based on the knowledge editing vector, and outputting a final result. According to the method, knowledge sharing and dynamic triggering among multiple models are realized through a cross-model mapping and consistency fusion mechanism, and the efficiency, timeliness and credibility of knowledge updating are improved.

The invention provides a micro-service unified access control and governance system in a hybrid cloud environment, and the system comprises a unified entrance layer which takes an Istio gateway as a unique access point of external flow, and receives all business requests from the outside; according to the authentication and authorization intermediate layer, the Istio Gateway forwards the service request to the Oathkeeper, and Kratos authentication and SpiceDB authentication are carried out. The rule routing layer is used for performing routing decision on the service request passing the Kratos authentication and the SpiceDB authentication based on a predefined dynamic rule set, and generating a routing instruction; and the heterogeneous service layer comprises a VM environment and a K8S environment, and forwards the request to a corresponding target service instance according to the routing instruction. According to the method, the problems of flow entrance dispersion, authentication splitting and non-uniform routing rules in a mixed cloud environment are solved, and uniform, safe and efficient access control and management of heterogeneous micro-services are realized.

The invention discloses a large model-oriented split privacy protection training method, which comprises the following steps that: a pre-trained large model is divided into a client side model bottom layer and a top layer and a cloud server side model middle layer along a network depth direction, a client only processes local private data, and a terminal cloud performs cooperative training through middle activation. The method is oriented to privacy protection of a large model, a privacy strategy adaptive to characteristics of the large model, application of a cross-model architecture, realization of accurate privacy-utility balance, noise injection of token importance perception, lightweight noise post-calibration, provision of privacy guarantee of theoretical constraints, a token-level differential privacy mechanism and parameterized adjustable privacy constraints. According to the method, deployment feasibility and system efficiency are optimized, extra calculation and storage overhead is low, an efficient parameter fine tuning technology is compatible, the communication traffic and calculation requirements are greatly reduced while the privacy protection effect is guaranteed, and efficient and practical privacy fine tuning of a large-scale model under a split learning architecture is supported.

The invention provides a mixed speech recognition method and system based on an AI large model architecture. The method comprises the following steps: constructing a mixed system comprising a traditional ASR system and an ASR system of an AI large model; based on a large amount of unlabeled audio data, pre-training the large model through self-supervised learning, mining audio signal features, and performing supervised fine tuning on the AI large model; using cross entropy loss to train text output in the base layer; an acoustic modeling branch is introduced into the middle layer or the auxiliary head, and a CTC loss function is adopted to train acoustic output; combining a CTC loss function with cross entropy loss training to realize acoustic and text information fusion; in a model training or reasoning stage, an audio partitioning mode and a KV cache technology are adopted to realize efficient streaming identification; and the recognition result can be obtained while speaking, and after the calling is finished, the large model quickly completes the subsequent autoregression text reasoning based on all cache information, and outputs a high-precision recognition text. According to the invention, compatibility of high precision and streaming experience is realized.

The invention discloses a firmware upgrading system and method, electronic equipment, a storage medium and a program product, and relates to the technical field of server hardware management.The system comprises a firmware issuing end used for conducting block encryption processing on target upgrading firmware so as to generate a hash tree, conducting signature on a root hash value of the hash tree based on a root layer private key, generating first signature data and sending the first signature data to a server; storing a root layer private key; the firmware supervision end is used for generating second signature data by using the intermediate layer private key and storing the intermediate layer private key; the firmware use end is used for generating third signature data by using the leaf layer private key and storing the leaf layer private key; the firmware publishing end writes a data packet formed by the first signature data, the second signature data, the third signature data and the root hash value into the block chain; and the firmware using end verifies the data packet, and if the verification is passed, firmware upgrading is carried out. The technical problem of relatively low security of a firmware upgrading mode in related technologies is solved, and the technical effect of improving the firmware upgrading security is achieved.

A deep learning model Hash identification method based on multi-feature fusion comprises the following steps: firstly, extracting structural features (layer type sorting de-duplication), parameter statistical features (quantile normalization and histogram statistics) and functional behavior features (middle layer output quantiles) of a deep learning model; then, model structure hash, parameter hash and function hash are calculated respectively; thirdly, generating a comprehensive hash value by fusing triple hash; meanwhile, a TLSH (Trend Micro Location-Sensitive Hashing) algorithm is adopted, so that the tolerance of the TLSH algorithm to the tiny fluctuation of the parameters is improved; and finally, based on Hamming distance, calculating model similarity, and realizing efficient black box model traceability and infringement detection. The method does not need to modify a model structure or parameters, does not depend on external data, supports high robustness to attacks such as pruning and fine tuning, and is suitable for various network architectures such as CNN and Transform. The method has the advantages of lightweight calculation, high concealment and cross-platform adaptation capability, and can be widely applied to intellectual property protection and infringement tracking scenes of a deep learning model.

The invention discloses a TEE endogenous lightweight proving method oriented to AI model behavior verifiability, and belongs to the technical field of big data and artificial intelligence, the method comprises the following steps: deploying a behavior monitoring probe in a TEE, capturing an activation value of a model middle layer in real time, calculating a feature contribution degree, then compressing a behavior record into a behavior abstract through a Merkle tree, and obtaining the behavior abstract. Finally, an authentication report fusing traditional TEE authentication and behavior abstracts is generated, a verifier can verify the compliance of model behaviors through the report, and fine-grained auditing of specific reasoning behaviors is supported. According to the method, the verifiability of the behavior during the operation of the AI model is realized while the privacy of the model and the data is ensured, the problem of performing efficient and verifiable monitoring on the behavior during the operation of the AI model on the premise of protecting the privacy of the model and the data is solved, and the method has the characteristics of light weight, high efficiency and low overhead.

The invention relates to a method for realizing multi-modal large-model fine-grained privacy grading protection in an edge-cloud collaborative inference system, belongs to the technical field of large-model data security and privacy protection, and aims to solve the problems that privacy grading is rough, inference intermediate features are easy to leak and precision is damaged by protection measures in the prior art. According to the method, multi-modal data such as images and texts are received by edge computing nodes, deep semantic analysis and fine-grained segmentation are performed by using a local pre-trained multi-modal large model, and privacy scores are calculated to divide high and low privacy levels; uploading low-privacy data to a cloud computing center, and locally processing high-privacy data; and selectively shielding a middle layer feature map F generated by the edge based on the correlation degree and the contribution degree, then completing local reasoning, and uploading a result to be fused with the cloud. According to the method, precise fine-grained protection is achieved, the sensitive information leakage risk is remarkably reduced, and meanwhile the model reasoning precision and the system real-time performance are kept to the maximum degree.

In accordance with an embodiment, described herein is a system and method for providing query acceleration with a computing environment such as, for example, a business intelligence environment, database, data warehouse, or other type of environment that supports data analytics. A middle layer is provided as a long-term table data storage format; and one more acceleration formats, or acceleration tables, can be periodically regenerated from the middle layer, wherein a determination can be made as to whether an accelerated table exists for a dataset table, and if so, then the accelerated table is used to process the query.

Techniques and architecture are described for a method that includes an in network compute (INC) manager receiving from switches of a fat tree configured network, arithmetic logic unit (ALU) capacity of the switches. Based at least in part on the ALU capacity of the switches and bandwidth, the INC manager determines one or more switches within each tier that are capable of supporting the processing units and based at least in part on the determining, the INC manager selects a first switch as a root, wherein the first switch is included within a tier of switches having intermediate tiers of switches located between the tier and the plurality of processing units within the fat tree configured network. The INC manager creates one or more paths of switches within each of the intermediate tiers from the root to the plurality of processing units to provide a constrained disjoint spanning tree of switches.

The invention provides a vehicle control method which is applied to a middle layer assembly of a vehicle control system, and the middle layer assembly is located between an interface layer and a processing method layer. The method comprises the steps of receiving a request sent by a vehicle from an interface layer; the request at least comprises a first key; the first key is used for pointing to a first control object of the vehicle, and the request is used for representing execution of first processing on the first control object; reading a target function module corresponding to the first key from a plurality of function modules included in the target function library; the plurality of function modules are function modules of a plurality of control objects of the vehicle; and calling a target processing method in the processing method layer based on the target function module to realize first processing on the first control object. According to the scheme, decoupling of the interface layer, the middle layer and the processing method layer is achieved, the method can be used for control over various vehicles, the application scene is wide, the research and development cost is reduced, and expansibility is good.

The invention discloses an enterprise data evaluation and analysis system and method based on big data, and belongs to the technical field of data processing, and the method specifically comprises the steps: building a scene ontology grammar rule base, disassembling ERP, CRM and supply chain management fields into grammar atoms, marking entity relationship semantics according to a business scene, and summarizing and sorting results into a reusable grammar atom template; deploying a grammar perceptron at a data acquisition end, capturing field names, data formats and association logics, and generating grammar fingerprints; according to the grammar fingerprint and the grammar atomic template, generating field naming mapping, format mapping and association mapping, and forming an alignment mapping table as input of structure normalization; according to the scene label and the alignment mapping table, a structure normalization assembly line is constructed, field renaming, format conversion and association relationship assertion verification are executed, and a unified structure interlayer table is generated; the method comprises the following steps: sensing table structure and field change of a source system, updating grammar fingerprints, and aligning a mapping table and a structure normalization pipeline.

The present application relates to the field of intelligent manufacturing technology, and in particular to a distributed flexible job shop scheduling method and system based on dual-deep reinforcement learning and multi-layer intelligent agents. By introducing a multi-level intelligent agent scheduling framework, the top-level, middle-level and bottom-level intelligent agents work together and make hierarchical decisions to decompose global complex problems into multiple local problems; in addition, different scheduling tasks of the distributed flexible job shop are decentralizedly executed by intelligent agents at each level, without the need for a central coordinator to manage each decision. When the environment changes locally, each level can adapt quickly and make the best decision based on its specific context, while still serving the overall goal of the entire system. It aims to solve the problem of how to improve the scheduling accuracy of distributed flexible job shops.