3437 results about "Functional module" patented technology

The invention provides an IMU (inertial measurement unit)-assisted depth SLAM (simultaneous localization and mapping) method and system fusing language-vision multi-mode perception, and the system comprises functional modules such as initial calibration and semantic map initialization, pre-integration prediction and key frame judgment, dense point cloud reconstruction and relative pose estimation, semantic embedding extraction, semantic guidance loopback detection and semantic three-dimensional map incremental updating. IMU motion priori, depth geometric constraint and language model semantic factors are subjected to combined modeling through a graph optimization framework, and high-precision positioning and labeled map construction in a complex dynamic environment are achieved. Compared with the prior art which only depends on geometric or inertial information, the method has the advantages that the loop-back mismatching rate is reduced, the closed-loop convergence efficiency and the long-time relocation robustness are improved, and a semantic interface is provided for upper-layer tasks such as natural language navigation and target retrieval. The method can be widely applied to the fields of service robots, security inspection, intelligent driving, post-disaster search and rescue and the like.

The invention provides a compliance auditing method and system for multi-modal content. The method comprises the following steps: performing feature extraction on unstructured to-be-audited multi-modal content to obtain a structured feature vector; performing image-text semantic association on the text semantic feature vector and the image visual feature vector to obtain a fusion feature vector involving image-text semantic contradiction; constructing a domain knowledge graph based on the compliance guidance data of the domain to which the to-be-audited multi-modal content belongs; inputting the fusion feature vector into a domain knowledge graph, and performing compliance rule retrieval by adopting a sub-graph matching algorithm to determine a violation type corresponding to the fusion feature vector and a violated compliance term; and generating an interactive compliance audit report. The system comprises functional modules for realizing the steps in a one-to-one correspondence manner. According to the technical scheme, the problem that cross-modal semantic analysis of an existing multi-modal content compliance auditing method is not accurate can be solved.

The invention relates to the technical field of agricultural information management, and particularly discloses an agricultural information management system and method based on a big data platform, and the method comprises the steps: firstly deploying a multi-source data collection module at an edge calculation node, and obtaining and standardizing the soil moisture content, meteorological environment and equipment operation data in real time; secondly, constructing a local dynamic irrigation strategy model, and realizing multi-objective optimization through a reinforcement learning algorithm; establishing a federated learning framework at the cloud, dynamically distributing node weights by adopting an attention mechanism, and realizing model aggregation of privacy protection in combination with secure multi-party computing; an optimal irrigation instruction is generated through a multi-source data fusion engine, and a three-level response exception handling mechanism is established; and finally, a closed-loop feedback system containing short-term incremental learning and long-term architecture optimization is formed. The corresponding management system comprises six functional modules, namely a data acquisition module, a local modeling module, a federated learning module, a real-time decision-making module, an abnormal monitoring module and a closed-loop optimization module.

The invention relates to an intelligent agent system optimization method and device based on intelligent fault analysis and cross-generation knowledge inheritance, and belongs to the technical field of artificial intelligence. According to the method, interaction abnormal signals are captured in real time by deploying a lightweight log probe, and a tool benefit prediction model based on reinforcement learning is constructed to automatically generate an improvement proposal when the failure rate exceeds a threshold value; an agent genealogy map is established to realize automatic inheritance of a new agent on core memory and abandonment of failure knowledge, and a disastrous forgetting blocker is deployed to dynamically extract a functional module from a genealogy to deal with key capability degradation. Aiming at the problems of fault response lag, knowledge inheritance fracture, key capability degradation and the like in an intelligent agent system iteration process, the invention creatively provides a cooperation mechanism of an intelligent fault analysis layer and a cross-generation knowledge inheritance network, and the fault self-healing capability, version stability and service continuity guarantee level of the system are remarkably improved.

The invention relates to the technical field of chip development, and discloses a chip post-simulation verification acceleration method, system and device and a storage medium, and the method comprises the steps: collecting multi-dimensional data of a large-scale chip; collecting all module names of a given chip; establishing a mapping relation between a redundant module RTL and a corresponding gate-level netlist, ensuring that interfaces before and after replacement are completely consistent, and designing an automatic script identification and processing parameterization module; establishing a post-simulation verification environment, verifying the consistency of output responses before and after replacement, analyzing the time delay change of a key path, calculating the dynamic power consumption and static power consumption of each functional module before and after replacement, and generating a simulation verification result. According to the method, the hybrid simulation model is constructed, and part of the gate-level netlist irrelevant to the current simulation function is replaced with the RTL, so that the actual simulation scale of a chip is reduced, the problem of overlong simulation time after a large-scale integrated circuit is remarkably solved, and the simulation efficiency is effectively improved.

The invention relates to a prefabricated building quality defect tracing system and method applied to the field of building informatization, and the system comprises a user interaction layer, a data processing and storage layer, a data collection layer and a function layer, and the data collection layer is used for collecting key quality data of prefabricated building components in different links; the user interaction layers interact with each other through a network and the function module layer; the data processing and storage layer performs encryption, hash operation, scattered storage and index verification on the quality data transmitted by the data acquisition layer by using an intelligent contract, an IPFS network and a block chain; the function module layer provides support for each unit of the user interaction layer to trace quality defects, judge responsibility and take prevention measures, and the system can collect the quality data of the whole process of the prefabricated building, comprehensively manage the quality tracing process, strengthen multi-party collaboration and sharing, and improve the quality of the prefabricated building. And it is ensured that tracing of the quality defects of the fabricated building has comprehensiveness, transparency and non-tampering performance.

The invention discloses a scheduling system of a content distribution network based on edge computing, and relates to the technical field of computer networks. An edge resource sensing module; an NLP semantic analysis module; reinforcing a learning scheduling engine; a semantic and resource linkage module; and a federation collaboration module. According to the scheduling system of the content distribution network based on edge computing, semantic understanding is carried out on a request of a user to analyze a deep intention of the user, edge node multi-dimensional state sensing data and a semantic label vector are integrated, a scheduling action is generated based on a node state and the user intention, and the scheduling effect is improved. Therefore, each edge node is fully utilized while user requirements are met, the edge nodes have caching, computing and scheduling capabilities, the limitation of surface feature matching of a traditional CDN is broken through, technical upgrading from traditional file type recognition to intention understanding and from a fixed rule to dynamic intelligence is realized, and finally, the resource efficiency and service quality of the CDN are improved.

The invention relates to the technical field of artificial intelligence, in particular to a business contract intelligent review and risk quantification method and device, and the method comprises the steps: building and maintaining a business contract key term information base; obtaining and preprocessing a to-be-rechecked contract text; processing the text based on a bidirectional long-short term memory network and a conditional random field model, and extracting semantic representation; identifying key information through a multi-level attention mechanism; executing multi-label learning to classify and identify clause types and attributes; utilizing a dependency syntactic analysis technology to extract logical association and a responsibility chain among terms, and constructing a knowledge graph; identifying risk terms and generating risk prompts; business indexes are extracted, and risk open values are calculated; generating a rechecking report; the corresponding device comprises nine functional modules such as an information base management module, a text preprocessing module and a semantic representation extraction module, risk terms in a contract can be automatically recognized, a quantitative risk assessment result is provided, and contract auditing efficiency and accuracy are effectively improved.

The embodiment of the invention provides a multi-configuration relay-oriented aircraft electrical box parameter measurement system and method, and belongs to the field of aircraft electrical box test and diagnosis. According to the hardware level, flexible access of a multi-configuration relay electrical box is achieved based on a universal adapter interface, core parameter measurement is completed in combination with an integrated measurement and control circuit, and synchronous acquisition and processing are conducted on multiple paths of signals through a signal acquisition and processing unit; according to the software level, the pull-in / release time, the rebound time and the voltage parameters are accurately calculated through a parameter calculation module on the basis of waveform feature point recognition and dynamic excitation-response theories, and the functions of fault diagnosis, safety protection and data storage are integrated. According to the method, the crossing from on-off judgment to parameterized precise evaluation is realized, the detection efficiency, the measurement precision and the equipment reliability evaluation capability are remarkably improved, and an integrated solution is provided for external field maintenance and state prediction of the aircraft relay.

The invention relates to the technical field of artificial intelligence, and discloses a multi-agent collaborative data question-answering system and method based on a large model, and the system comprises an agent cluster module which is composed of five special agents, namely a data question-answering agent, a data extraction agent, a data analysis agent, a visualization agent and a quality examination agent, and achieves the task decomposition and collaborative execution through dynamic scheduling; the knowledge management module comprises a business knowledge base, a data element knowledge base and a user feedback base, and adopts a hierarchical knowledge fusion technology to provide domain knowledge support for the intelligent agent; and the supporting function module covers a front-end dialogue component and a verification and execution engine and is responsible for interactive interface rendering and result reliability verification. According to the method, the fine tuning requirement on the large model is remarkably reduced, the illusion of the large model is effectively intercepted through a dual verification mechanism, and the accuracy and reliability of question and answer results are improved.

The invention discloses a flowmeter self-calibration method and system based on big data analysis. The method comprises the steps that multi-source data collection is conducted, and multiple kinds of data are collected and stored in a big data platform with a block chain evidence storage function; data preprocessing: adopting multiple algorithms to improve data quality; feature extraction and analysis: identifying key factors and monitoring fluid states by means of multiple technologies; calculating calibration parameters, and considering various coefficients and uncertainty analysis; performing automatic calibration, encrypting and writing parameters after digital twin simulation, and reserving a copy; and verifying the calibration effect by adopting a multi-mode verification mechanism. The system comprises a plurality of functional modules which work cooperatively. According to the invention, the data acquisition comprehensiveness is improved, the calibration period is shortened, the production efficiency is improved, the data integrity and reliability are ensured, key factors are accurately identified, the fluid state is monitored in real time, the calibration parameter adjustment is simulated, and the calibration effect is verified in a multi-mode manner, so that the reliability and accuracy of a flowmeter measurement system are improved.

The invention relates to the technical field of intelligent work order scheduling platform design, and discloses an intelligent work order scheduling system, method and device and a medium, and the method comprises the steps: the system achieves the efficient and intelligent scheduling of a device fault work order through integrating a plurality of function modules. Firstly, the work order access module receives equipment fault information of different channels, and it is ensured that the information is comprehensive and timely. Then, the portrait construction module constructs a multi-dimensional ability portrait by using the historical service data of the maintenance personnel, and provides a data basis for intelligent matching; and then, the intelligent distribution module accurately matches and associates the most suitable maintenance personnel with the work order according to the portrait data and the work order parameters. And the order grabbing interaction module generates a to-be-grabbed work order list and an order grabbing interface, so that the scheduling flexibility is enhanced, and the scheduling efficiency is improved. The execution tracking module monitors the work order processing progress in real time, and transparent and controllable scheduling is ensured. And finally, the knowledge base question and answer module provides professional knowledge support for the maintenance personnel and helps the maintenance personnel to quickly solve the work order problem.

The invention discloses an ultrasonic image off-line acquisition and dynamic synchronous processing method and system, which realizes off-line acquisition and dynamic synchronous processing of an ultrasonic image under the condition of network interruption through six functional modules, namely a network state monitoring module, an online data synchronization module, an image acquisition processing module, an off-line mode switching module, an off-line diagnosis processing module and an increment synchronization module. The method comprises the following steps: acquiring network connection state data and judging a working mode; pulling and caching tasks and template data in an online state; obtaining multi-modal image data and performing double-writing storage; detecting network interruption and switching to an offline mode; performing AI analysis on the images acquired offline and generating a report; and detecting network recovery and executing incremental data synchronization. According to the invention, the problem of service interruption caused by dependence of a traditional PACS system on a network is solved, the network adaptability and robustness of the system are improved, the operation and maintenance cost is reduced, and the system fault recovery time is shortened.

A method or system for implementing a machine learning pipeline on a chipset comprising a plurality of hardware compute elements. The system accesses a hardware-agnostic functional description of the machine learning pipeline, wherein the description specifies a plurality of functional modules, including at least one machine learning model. Hardware specifications of the chipset are accessed to identify the available hardware compute elements. Based on the hardware specifications, the functional modules are synthesized into a plurality of interconnected executable components configured to execute on at least two different hardware compute elements. An implementation package is generated, comprising the executable components and metadata describing interconnections between them. The implementation package is then deployed to the chipset, where the executable components are executed by the identified hardware compute elements.

The invention provides an automatic government affair examination and approval system and method based on artificial intelligence, and is used for disassembling a government affair examination and approval process into a plurality of function modules which can operate independently. Intelligent modeling is carried out on key links such as material processing, rule judgment, process planning and result generation related to government affair handling through artificial intelligence and data driving technologies, and an automatic, high-efficiency and interpretable whole examination and approval process is realized through cooperation among modules; therefore, a comprehensive solution integrating artificial intelligence information processing capability, a policy knowledge rule engine and a process automation mechanism is provided, the comprehensive advantages of flexible adaptation, efficient processing, traceable result, controllable strategy and the like are achieved, and the government affair approval system can be promoted to develop to a new efficient, fair and intelligent stage.

The invention discloses a software development result traceability analysis system based on version control, and relates to the technical field of intelligent software analysis. According to the method, the non-tampering property of code submission records is ensured through a block chain technology, a credible basis is provided for traceability, the code semantic analysis module generates a data set containing semantic association in combination with a clustering algorithm and weighted calculation, and the function influence positioning module constructs a function association graph by applying a graph neural network algorithm, so that the traceability is improved. An influence path of code change on system functions is automatically identified, the time cost of complex project function influence positioning is remarkably reduced, an evolution path generation module analyzes and tracks a code evolution path in combination with a time sequence, and a core module identification module clusters key nodes and positions a core function module. And the time-tracing source data storage module generates a visual relation graph and a queried data set, so that the whole-process management from code submission to result tracing is realized.

The invention discloses an autonomous fault-tolerant system and method for dual-mode hot switching, and relates to the technical field of edge computing, and the method comprises the steps: switching a main communication link to a standby communication link when the main communication link has a fault, and generating a first hardware equipment migration instruction when any one of a plurality of hardware equipment has a fault risk, meanwhile, a second hardware equipment migration instruction is generated when any hardware equipment is in a fault state, the fault hardware equipment in the fault risk and the fault state is migrated to a target node based on the first hardware equipment migration instruction and the second hardware equipment migration instruction, and when the target node recovers execution of a service task, the target node is migrated to the target node. A service task is executed based on a communication link of the autonomous fault-tolerant system, the technical problems of long response time, delay and high cost generated by manual operation and maintenance and cloud redundant backup are solved, a dual-mode communication design idea is adopted, and all functional modules work cooperatively, so that a complete closed loop from fault monitoring to autonomous recovery is realized.

The invention discloses a multi-dimensional personality evaluation method for dynamic emotion changes, and relates to the technical field of personality evaluation, the system is composed of a plurality of functional modules, and the method comprises the following steps: step 1, recording physiological data in real time through a wearable device, capturing micro-reactions by using a computer vision technology, and determining whether the micro-reactions exist or not; the micro-reactions comprise facial micro-expressions, limb actions and voice and intonation changes; based on a preset dynamic simulation social scene, observing an emotion regulation strategy of the subject in pressure; step 2, using an LSTM model to carry out time sequence modeling on the multi-modal acquisition data, capturing dynamic evolution of emotions, and combining an attention mechanism to dynamically adjust the weight of each modal; based on six basic emotions and nine mental disorder tendencies, establishing a classification model, and capturing association between emotions and personalities; reinforcement learning is introduced, and model parameters are dynamically optimized according to feedback of a subject; and predicting the emotion trend of the subject in a period of time in the future through an autoregressive integral moving average model.

The embodiment of the invention provides a financial credit intelligent marketing system and method. Comprising a multi-source data acquisition and integration module, a data preprocessing and fusion module, a multi-modal data fusion and feature engineering module, a user portrait construction and subdivision module, an intelligent marketing strategy making module, a marketing execution and real-time monitoring module and an effect evaluation and strategy iteration module. The financial credit intelligent marketing system comprises a plurality of functional modules such as data acquisition, data processing, model training, strategy making, real-time marketing, batch marketing, execution monitoring and the like. A comprehensive and multi-dimensional user portrait is formed by integrating enterprise internal data and an external third-party authorization data source. And machine learning algorithms such as XGBoost, LightGBM and the like are utilized to carry out deep mining and analysis on user behaviors, and purchase intention and demand change of the user are predicted. Meanwhile, based on the Flink real-time data processing technology, quick response and dynamic adjustment of marketing activities are achieved.

The invention relates to safety compliance evaluation, in particular to a safety compliance evaluation system and method based on a multi-modal large model, and a functional module cluster, which are used for managing each functional module in the system, supporting cross-modal consistency detection and comparing answer consistency under different modals. The self-developed security system is responsible for execution of an assessment task, including resistance test enhancement and built-in multi-granularity data variation strategy, improves assessment robustness by simulating a real attack scene, realizes end-to-end automation from data generation, tested model test to risk assessment, supports configurable parameters and reproducible results, and has the advantages of high reliability and high reliability. The self-iteration and version management of the evaluation model are realized, and the self-iteration mechanism is deeply combined with a compliance label system, so that the risk identification capability under legal, ethical and industrial specifications can be continuously optimized; according to the technical scheme provided by the invention, the limitation that evaluation can be carried out only under a single mode and a single rule in the prior art can be effectively overcome.

The invention relates to the field of chip fault positioning, in particular to a chip fault rapid positioning method and device, equipment and a storage medium. The method comprises the following steps: acquiring temperature sensing data and time sequence signals in a chip operation process; performing multi-index deviation calculation on the temperature sensing data and the time sequence signal to generate a state deviation index; performing real-time comparison detection on the state deviation degree index based on a preset first deviation threshold value, and when the state deviation degree of the index deviation curve exceeds the preset first deviation threshold value, triggering a fault early warning mode; identifying abnormal function parameters based on the fault early warning mode; identifying the dependency relationship of each functional module of the chip; constructing a fault propagation model based on the dependency relationship; and inputting the abnormal function parameters into the fault propagation model for analysis, and outputting a fault source candidate set. According to the method, by focusing on a high-probability area, the defect searching time is remarkably shortened, and the chip fault position is efficiently and quickly positioned.

The invention discloses a low-power-consumption vehicle-mounted intelligent Internet of Things terminal device and a control method thereof. The terminal device comprises a voltage monitoring module and a mode control module. The voltage monitoring module periodically samples the input voltage of the terminal device at a preset sampling frequency to obtain a plurality of voltage sampling values; the mode control module judges the continuous state of the input voltage based on the plurality of voltage sampling values, controls the terminal device to enter a normal mode of full-power operation of all functional modules when the input voltage is continuously in a state greater than or equal to a first voltage threshold value, and controls the terminal device to enter a normal mode of full-power operation of all functional modules when the input voltage is continuously in a state less than the first voltage threshold value. Controlling the terminal device to be switched to a low-power-consumption mode in which part of functional modules are closed, and controlling the terminal device to enter a sleep mode when the input voltage is continuously in a state smaller than a second voltage threshold value; wherein the second voltage threshold value is lower than the first voltage threshold value.

The invention relates to the technical field of chip performance evaluation and test, in particular to a chip performance evaluation and fault diagnosis optimization test method, which comprises the following steps of: generating a test vector library with an optimal coverage rate on the basis of functional module distribution and circuit structure characteristics of chip design; in a chip operation environment, sequentially inputting test conditions by using the test vector library, and simultaneously collecting dynamic performance parameters in real time; based on the dynamic performance data set, establishing a multi-dimensional performance characteristic model comprising performance fluctuation characteristics, a critical state threshold value and hidden fault characteristics; based on a multi-dimensional performance characteristic model, in combination with circuit structure characteristics, a specific fault function module is positioned by reversely tracing the relevance between chip function modules. According to the method, innovative technical support is provided for chip design optimization and operation stability improvement, the fault diagnosis time is remarkably shortened, and the diagnosis precision and reliability are improved.

The invention relates to the technical field of computers, and discloses an interaction control method of intelligent glasses. The method comprises the following steps: synchronously acquiring multi-modal data such as eye movement, voice, gestures and head postures and environment and application context information; carrying out independent time sequence feature coding on each modal data; generating a modulation vector in combination with the context, and outputting probability distribution of user intentions through a cross-modal attention fusion network; and a unique execution instruction is determined through an instruction arbitration module based on rules and a state machine. The system comprises corresponding function modules. According to the method, the accuracy, robustness and naturalness of interaction are improved through multi-modal synchronous fusion and a context self-adaption mechanism.

The invention discloses a duplex-screen intelligent cabin interaction method and system, and relates to the technical field of intelligent cabin interaction, and the method comprises the steps: collecting user operation behavior data, vehicle state data and environment perception data in real time through a vehicle-mounted sensor group, a man-machine interaction interface and a vehicle bus; establishing a multi-dimensional user data model containing an interaction feature vector, a driving mode matrix and a preference configuration tree; the method comprises the following steps: analyzing user historical interaction data based on an adaptive weighting algorithm, and calculating use frequency weight coefficients of different functional modules; according to the duplex-screen intelligent cockpit interaction method and system provided by the invention, the precision of a user portrait is remarkably improved by constructing a multi-dimensional user data model, personalized interaction experience is realized, and the interface layout and interaction logic can be dynamically adjusted according to a real-time driving scene by adopting a self-adaptive weighting algorithm and a random forest classifier, so that the user experience is improved. The real-time performance and the adaptability of the system are enhanced, and the interaction experience is optimized.

The invention discloses a mechanical arm control method and system based on fuzzy self-adaptive PID control, relates to the technical field of mechanical arm control, and solves the problem that mechanical arm control is insufficient in flexibility when facing different working conditions. According to the embodiment of the invention, the universe expansion and contraction factor is adjusted through the error, then the close linkage of the quantization factor and universe expansion and contraction is realized through the self-adaptive quantization factor calculation, the fuzzy reasoning technology is combined, the intelligent mapping from the error information to the PID parameter increment is realized, the real-time updating of the PID parameter and the control output calculation are realized, and the real-time updating of the PID parameter is realized. The flexibility and adaptability of the control system are improved; and then output is controlled through incremental PID, so that a complete control link from parameter increment to actuator driving is realized, a complete closed loop is formed by a cascade control architecture, functional modules are tightly coupled, parameters are coordinated, and the whole-process intelligence from error sensing to execution control is realized.

The invention relates to the technical field of engineering cost, and discloses a data entry method for engineering cost software, which comprises the following steps of: S1, importing an engineering quantity list file in an Excel format through an interface of the engineering cost software, and automatically analyzing a data structure of the engineering quantity list file; s2, based on a preset intelligent matching algorithm, performing dynamic mapping on the Excel data field and a data field of a project cost software function module; s3, adopting a data type self-adaptive rule to automatically classify and convert numerical values, texts and compound data in the Excel; s4, based on a data association generation algorithm, automatically establishing a hierarchical relationship and a dependency relationship between the engineering quantity list items; s5, executing one-button data import, and writing the processed structured data into a project cost software database in batches; according to the method, high-precision automatic mapping of the Excel field and the software module is realized through an intelligent matching algorithm and a dynamic weight adjustment mechanism based on the feature vector similarity, the matching accuracy is greatly improved, and the manual correction frequency is greatly reduced.

The invention belongs to the technical field of industrial system integration, and provides a design method for rapid adaptive integration of a cross-domain system. Through the method, developers extract business logic rules of the industry, standardize characteristic mapping and select, develop and integrate functional modules, logic processes and scene components of the cross-domain system, so that the cross-domain system which conforms to industry characteristics and meets business circulation is formed, and meanwhile, the development efficiency of the cross-domain system is improved. The newly added industry logic component can be classified into each industry characteristic model based on the knowledge base, and a multi-dimensional characteristic vector is generated in the platform, so that cross-industry integrated component multiplexing is realized, and the cross-field applicability and flexibility of the platform are further improved. Modularized construction and configuration type development are supported, mapping and matching functions are carried out on various industrial manufacturing industry characteristics such as machining, electronic and electrical appliances and equipment sets, the requirements for subdivision production in the industrial field and rapid adaptation of business processes are met, the system integration cost is reduced, and the application development efficiency is greatly improved.

The invention is suitable for the field of computer vision and robot control, and provides a positioning method and system based on visual perception, and the method comprises the steps: receiving the definition information of a target workpiece, and positioning a space region in initial visual scene data; light and shadow interference is separated, and intrinsic visual features of the workpiece are restored; constructing a scene digital twinborn model based on the intrinsic features and physical simulation; generating a grabbing option with a priority and a dynamic confidence coefficient; identifying a key shelter removal option; and iteratively executing the removal operation until the target is grabbed. The system comprises corresponding function modules. The positioning precision and the operation safety are improved, the method is suitable for dynamic stacking scenes, target workpiece positioning and grabbing are efficiently achieved, and the method is suitable for multi-field automatic operation.

The invention discloses an agent building method based on dynamic workflow, and relates to the field of artificial intelligence and automatic process management. Comprising the steps of 1, creating an agent building system of the dynamic workflow, 2, creating a workflow node through an agent cooperation module, and enabling the workflow node to achieve collaborative decision through a standardized communication protocol, and 3, recording context information through a real-time state sensing function module by means of a shared memory, the method comprises the following steps: step 1, sending out a command for adjusting an execution sequence of workflow nodes and sub-flows of some workflow nodes according to context information, and realizing cross-workflow node data flow by adopting a standardized message format, and step 4, carrying out insertion / deletion operation on the workflow nodes through a dynamic workflow engine module when the workflow nodes run according to the command, so as to realize cross-workflow node data flow. And step 5, integrating reinforcement learning and a rule engine through a dynamic strategy controller module, formulating an abnormity self-healing strategy, presetting a threshold rule, and automatically injecting a compensation workflow node or switching a standby sub-process in combination with a time sequence prediction model.