304 results about "Smart factory" patented technology

The invention relates to the technical field of factory energy consumption management, in particular to a smart factory dynamic optimization management system based on digital twinning and big data analysis. Comprising a data acquisition and fusion module, a digital twinning construction module, a data analysis module, a dynamic optimization decision module and an anomaly diagnosis module. Constructing a digital twinborn model of a factory physical entity according to the collected data; constructing an energy consumption prediction model based on deep learning frameworks such as LTSM; when the energy consumption deviation exceeds the limit, abnormal root causes are positioned; and generating an energy consumption scheduling scheme based on a multi-objective optimization algorithm, and issuing an instruction to realize dynamic energy consumption adjustment. Through deep fusion of digital twinning and big data technologies, comprehensive and accurate simulation, multi-target collaborative optimization, rapid abnormality diagnosis and dynamic control of factory energy consumption are realized, the energy utilization efficiency is effectively improved, the cost is reduced, the intelligent level is improved, and the method has remarkable economic benefits and environmental benefits.

The invention belongs to the technical field of lean manufacturing monitoring, and particularly relates to an anomaly detection and processing method for a production process of an intelligent factory, and the method comprises the steps: constructing a dual monitoring node chain and a layered anomaly association evaluation node chain based on a preset production process flow chain, and collecting the operation state and process parameter data of equipment in real time; the hierarchical anomaly association assessment node chain analyzes the equipment health degree through the operation state assessment sub-chain, mines a cross-process anomaly propagation relationship through the whole-process technology anomaly assessment sub-chain, generates a whole-process association anomaly assessment space, fuses real-time data and historical anomaly strategy index information based on a distributed algorithm, and finally performs the whole-process association anomaly assessment on the basis of the real-time data and the historical anomaly strategy index information. Dynamically constructing a difference exception processing strategy library, and realizing processing strategy synchronous feedback of positioning exception; according to the invention, through a dual monitoring architecture and hierarchical association evaluation analysis, the problems of fuzzy abnormal propagation path and response lag are solved, and the real-time performance and processing accuracy of abnormal detection are improved.

The invention discloses a smart factory management system and method based on MES, and relates to the technical field of smart manufacturing, and the method comprises the steps: calculating a health degree score based on equipment historical maintenance records, and generating a time-space correlation multi-dimensional analysis data set containing an equipment topological structure and health degree parameters in combination with a digital twin network; performing spatial-temporal feature coupling and topological weight dynamic adjustment on the spatial-temporal correlation multi-dimensional analysis data set through a dynamic topological analysis algorithm, and generating a high-risk equipment list and an anomaly control instruction set; based on the high-risk equipment list, constructing a standardized multi-dimensional abnormal feature vector, and generating an aging weighted danger level signal through an entropy weight method; according to the method, space-time alignment of equipment operation state data and physical topology data is realized through a multi-rate Kalman filtering algorithm and an iterative nearest point algorithm, a high-fidelity digital twin network is constructed in combination with a dynamic graph convolutional network, and accurate anomaly detection and health degree evaluation are supported.

The invention relates to the technical field of equipment cooperative control, and discloses a multi-equipment cooperative control method and system under an autonomous cooperation algorithm, and the method comprises the steps: collecting the physical attribute data, real-time operation data, production task data and a smart factory scene graph of operation equipment under a multi-equipment cooperative task in a smart factory application; based on the physical attribute data and the real-time operation data, identifying a space conflict mode and a time conflict mode of the operation equipment to obtain collaborative conflict information; according to the production task data and the collaboration conflict information, carrying out collaboration conflict decoupling on the operation equipment to obtain a feasible collaboration mode; performing obstacle avoidance reinforcement learning on the operation equipment by using the smart factory scene graph to construct an obstacle avoidance global path of the operation equipment in the smart factory; and based on the feasible coordination mode and the obstacle avoidance global path, constructing a target coordination control scheme of the operation equipment so as to perform coordination control on the operation equipment. According to the invention, the reliability of multi-device cooperative control under the autonomous cooperation algorithm can be improved.

The invention relates to the technical field of smart factory management platforms, and discloses a data real-time monitoring method based on a smart factory management platform system, and the method comprises the steps: carrying out the standard format conversion of supplier Excel data, carrier GPS track data flow and warehouse RFID goods location data in a smart factory ERP system, and obtaining standardized business data; calculating comprehensive scores of suppliers based on the standardized business data and generating a dynamic sorting list; receiving purchase order information and creating a logistics scheduling plan, and pre-allocating an optimal goods allocation for the warehousing commodities to obtain a warehousing pre-allocation scheme; monitoring the execution progress of the storage pre-distribution scheme in real time, and synchronously updating service state information; the auditing state of the purchase order information is monitored based on the service state information, and an intelligent collaborative decision-making scheme is generated, real-time synchronous updating of the service states of the supplier management module, the logistics scheduling module and the warehouse management module is ensured, and the supply chain collaborative management efficiency is improved.

The invention discloses an intelligent factory data intelligent analysis and management system, and relates to the technical field of data analysis. Multi-source data are uniformly accessed through an industrial gateway, and a knowledge graph is constructed after cleaning and standardization; an edge node deploys a lightweight AI model to realize real-time analysis such as equipment anomaly detection; intelligent distribution of cloud side tasks is realized based on a decision model; optimizing production scheduling and quality control by using an algorithm; a zero-trust architecture is adopted to guarantee safety, and efficient energy management is realized in combination with reinforcement learning; all the modules work cooperatively, and the intelligent level of a factory is improved. The operation efficiency and quality of the intelligent factory are effectively improved. Efficient data fusion processing is realized, and equipment anomaly detection is more accurate; the order delivery period is shortened, and the product reject ratio is reduced; network security protection is enhanced, and energy waste is reduced; the decision response speed is accelerated, the decision accuracy is improved, cost reduction and efficiency improvement of enterprises are comprehensively assisted, and the competitiveness is enhanced.

The invention discloses an AI-based intelligent factory dynamic production scheduling optimization method and system, and belongs to the technical field of factory dynamic production scheduling, and the method comprises the following steps: obtaining and integrating the working states of various types of equipment in a factory, the stock position states of different materials, and the manual data; the production order state, the process route requirement, the plan and the completion time of each process and the delivery date requirement of the customer are determined; generating an initial production scheduling plan through an AI optimization algorithm in combination with supply chain material data, factory storage space, equipment switching cost, production rules, each order process dependency relationship and a production target; according to the method, the initial scheduling plan is generated by acquiring and integrating multiple types of production data, the dynamic events are monitored, multiple schemes are generated through evaluation, the optimal scheme is selected through economic model evaluation, and production refinement, dynamic response and benefit optimization are achieved.

The invention relates to the technical field of data analysis, provides an intelligent factory automatic monitoring method and system based on knowledge base enhancement, and realizes more accurate anomaly analysis and more effective process adjustment of an intelligent factory. The method comprises the steps of performing knowledge enhancement fusion processing on an obtained real-time monitoring data set of an intelligent factory through a pre-constructed process knowledge base and a pre-constructed monitoring rule base, and generating a process knowledge graph; performing abnormal mode recognition processing on the process knowledge graph based on a semantic matching strategy, extracting feature description of an abnormal event and a semantic association path with a historical monitoring text, and generating an abnormal mode analysis result containing abnormal root cause inference; according to the abnormal mode analysis result and the dynamic incidence relation in the process knowledge graph, an automatic monitoring report containing root cause priority ranking and optimization operation guidance is generated, and the automatic monitoring report is fed back to the intelligent factory control terminal to trigger process adjustment operation.

The invention relates to the technical field of smart factory decision-making, and discloses a digital twin-driven smart factory AI intelligent decision-making system, which comprises a data acquisition and preprocessing module used for acquiring and preprocessing real-time and historical data of a production line; the twinborn modeling identification module is used for constructing a twinborn body and carrying out parameter identification and uncertainty quantification; the alignment evaluation credible module is used for comparing twin prediction data with real data and generating availability marks and trust scores; the strategy simulation optimization module is used for generating candidate strategies and risk evidences based on the constraints and the key performance indicators; the grayscale online publishing module is used for screening and grayscale publishing a strategy according to the trust score and the performance result; the operation evaluation auditing module is used for collecting operation data and generating an auditing packet; and the drifting detection updating module is used for detecting distribution drifting and recalibrating and updating the twinborn body and the strategy library. According to the invention, the reliability, robustness and continuous optimization of the production decision of the smart factory are realized.

The embodiment of the invention provides an intelligent factory semantic decision generation method and system based on a knowledge graph, and the method comprises the steps: obtaining an operation data set of an intelligent factory, carrying out the semantic feature extraction of the operation data set, and generating the target semantic feature of an equipment operation parameter and the context correlation feature of a semantic description text; and based on a pre-constructed knowledge graph structure, performing dynamic semantic matching processing on the target semantic features and the context association features, generating a semantic decision instruction set corresponding to the equipment operation parameters, generating an equipment control strategy set according to instruction priorities and instruction execution conditions in the semantic decision instruction set, and sending the equipment control strategy set to a server. And feeding back the equipment control strategy set to a control system of the intelligent factory to trigger operation optimization operation, and updating a semantic node association relationship in the knowledge graph structure. According to the method, the problem of fragmentation of equipment operation state representation is effectively solved, and the interpretability of feature extraction is improved by utilizing a collaborative verification mechanism of numerical parameters and text description.

The invention provides an intelligent factory production optimization method and system based on AI scheduling, and belongs to the technical field of intelligent factory production management.The method comprises the steps that firstly, process disassembling is conducted on batch production orders, an order task atlas is obtained, a dynamic resource pool is constructed according to the real-time resource state of a factory, the order task atlas is input into a pre-trained scheduling AI model, and a scheduling AI model is established; a dynamic resource pool is combined to generate multiple groups of initial scheduling paths, conflict detection and conflict point identification are performed on the initial scheduling paths, an adaptive adjustment module is called to correct parameters based on conflict point types and influence ranges, a final production optimization scheme is generated and imported into a factory execution system, and intelligentization and high efficiency of production scheduling are realized. And the production efficiency and benefits are improved.

The invention provides an intelligent factory manufacturing optimization method and system based on energy consumption prediction, and the method comprises the steps: carrying out the time sequence structural processing of historical energy consumption record data continuously collected in a factory manufacturing process, and generating an energy consumption sequence unit with a continuous time stamp through time window division and data correlation verification; constructing a manufacturing process-energy consumption association knowledge graph based on the energy consumption sequence unit, and establishing a structured knowledge representation containing node attributes and edge relationship weights by identifying a causal association relationship between process execution nodes and energy consumption fluctuation features; calling a pre-trained graph neural network model to carry out time sequence evolution prediction processing on the graph, and generating an energy consumption prediction sequence of a future preset manufacturing cycle; and according to the energy consumption prediction sequence, performing dynamic scheduling optimization on the execution sequence of the current manufacturing process and the resource allocation scheme, and generating an energy consumption optimization oriented manufacturing execution instruction. The energy consumption management level and the resource utilization efficiency in the manufacturing process of the intelligent factory are improved.

The invention relates to the technical field of intelligent manufacturing and industrial Internet of Things, in particular to an intelligent factory equipment safety management system and method, and the system comprises an identity authentication module, a dynamic safety interaction module, an intelligent monitoring response module and a self-adaptive optimization module. An identity authentication module; through fusion of multi-dimensional information authentication, block chain decentralized storage, intelligent algorithm behavior analysis, encrypted communication and fine-grained authority control, multi-source data threat detection and reinforcement learning driven strategy optimization, a whole-process security system is constructed. The method comprises the steps of identity authentication, security interaction, monitoring response and adaptive optimization. The problems that in a traditional industrial scene, equipment identity authentication is fragile, data transmission is not safe, threat response lags and strategies are staticized are solved, high-safety authentication, dynamic encryption communication, real-time threat response and strategy self-optimization are achieved, and the equipment safety protection capacity and the system intelligence level are improved.

The invention particularly relates to an intelligent factory integrated operation management method based on multi-system integration, and relates to the technical field of intelligent factory operation management. Performing intelligent planning and robustness decision making; performing production execution and monitoring; and carrying out equipment full-life-cycle management. According to the method, the limitation of a traditional static plan is thoroughly broken through through a collaborative mechanism of multi-dimensional risk index linkage, dynamic time buffering and a plan chain; on one hand, the DTB performs real-time calculation based on a supply chain risk index, an equipment health index and a process stability index, approaches reference buffer to avoid resource waste when the risk is low, automatically expands to absorb disturbance when the risk is high, and ensures dynamic balance of plan efficiency and disturbance resistance; and on the other hand, the pre-generated plan chain is rapidly triggered when the disturbance exceeds the limit, global rearrangement is not needed, and continuous production can be guaranteed only through local adjustment.

The invention relates to the technical field of computers, and provides an intelligent interaction guiding method and system based on user intention recognition. According to the embodiment of the invention, deep and comprehensive user interaction analysis is realized; the user demand dynamic state can be accurately captured by acquiring the interaction data set in real time; and context association features and interaction behavior features generated by multi-modal feature extraction provide a rich and meticulous information basis for intention analysis. On the basis, a preset intention recognition rule base is used for carrying out joint intention analysis processing, an intention label set and user portrait features are generated, accurate insight of the user intention is achieved, and then an interaction path guiding strategy is generated and pushed to a target user terminal in real time to update an interaction interface. Therefore, the method and the system can closely meet user requirements, provide personalized operation guidance, remarkably improve man-machine interaction efficiency and user operation accuracy, and enhance the intelligent service level of an intelligent factory.

The invention provides an intelligent management system and method based on multi-agent cooperation. The intelligent management system comprises a data acquisition module used for acquiring production field data in real time; the data processing module is used for data cleaning; the large model module is used for performing semantic analysis on a task input by a user based on a large model; the agent module is used for performing task scheduling based on a TSQlearning scheduling algorithm; and the RAG knowledge base module is used for constructing an RAG knowledge base and providing knowledge retrieval based on hierarchical indexing and a mixed retrieval mechanism. According to the method, user requirements are analyzed through the large model module, the task comprehensive scheduling capability is improved through the distributed decision of the agent module and the TSQlearning algorithm, the resource utilization rate is improved, the information retrieval accuracy is improved from 70% to 92% through the hierarchical indexing and mixed retrieval mechanism of the RAG knowledge base, and the management efficiency and the intelligent level of the smart factory are improved.

The invention relates to the technical field of AGV robot scheduling and control, and discloses an AGV robot scheduling method and system of an intelligent factory and a storage medium. The method comprises the steps of collecting bearing sensing data to identify a product height value according to a product carrying instruction and a transportation route, and if the value is higher than a reference value, generating an auxiliary scheduling instruction; next, nearby non-loaded AGVs are searched to serve as temporary robots, scheduling values are obtained in combination with task load and distance sorting of the temporary robots, and the optimal robot is selected to serve as a slave robot to be paired with the original AGV robot to jointly bear products; and finally, the master and slave robots respectively respond to the matched scheduling instructions. According to the method, through master-slave cooperation and accurate scheduling, the AGV transportation stability and efficiency are improved.

The invention relates to the technical field of smart factory supply chain traceability, and discloses a smart factory supply chain traceability method and system based on a block chain, and the method comprises the steps: recording the geographic position and logistics trajectory data of a supply chain node through the block chain, constructing a network topological graph, carrying out the merging optimization of the node, calculating a shortest path, and recognizing a key link; and the supply chain traceability path is determined. According to the method, firstly, the data credibility is ensured by using a block chain technology, then network optimization is performed based on node attributes and a service dependency relationship, and finally, a key traceability path is determined through an improved path algorithm and a node importance score. According to the method, the supply chain network structure can be effectively simplified, important nodes and abnormal links can be identified, reliable technical support is provided for supply chain management and risk control, and the transparency and traceability of the supply chain are improved.

The embodiment of the invention relates to the technical field of data monitoring, in particular to an intelligent factory storage monitoring method and system based on the Internet of Things, and the method comprises the steps: carrying out the data fusion processing of original storage data collected by Internet of Things equipment, and obtaining a monitoring data set containing the environment perception data and the cargo state data with the aligned timestamps; performing storage feature mining on the monitoring data set to obtain environment correlation features and cargo state evolution features of a storage space; then, a trained storage anomaly detection model is called to carry out joint anomaly detection on the two features, and a storage anomaly detection result containing an anomaly type identifier is generated; and finally, based on the abnormal type identifier and the abnormal evolution trend information in the detection result, generating a risk early warning label containing time-space positioning information, and pushing the risk early warning label to a warehouse management terminal, thereby realizing timely and accurate early warning of warehouse abnormity.

The invention discloses a multi-dimensional data monitoring and collaborative management and control method and system for a plant intelligent factory, and belongs to the technical field of collaborative control, and the method specifically comprises the steps: collecting multi-dimensional data of the plant intelligent factory in real time, the multi-dimensional data comprising plant physiological parameters, environmental factor parameters, nutrient solution parameters and equipment and operation state parameters, and based on the multi-dimensional data, constructing a dynamic multi-level causal association model among equipment, plant physiology, environment and operation, identifying a potential cooperative influence path in an abnormal state, and automatically performing cross-level management and control when an abnormality is detected or a growth process is predicted to deviate from a trend. The cross-level management and control instructions are distributed to an equipment level, a cultivation unit level and a workshop level according to levels, and multi-link linkage regulation and control are carried out on the intelligent plant factory; according to the method, by establishing a causal association and path cooperation mechanism, regulation and control are more accurate, flexible and adaptive, and the stability, resource utilization rate and intelligent level of plant production are effectively improved.

The invention relates to the technical field of digital twinning, in particular to a smart factory data management system based on digital twinning. The device comprises an abnormal lagging unit, a window changing unit and a window updating unit. According to the method, a window module is defined to obtain a change speed sensitive coefficient through historical data, and then a data change speed value and a digital twin model update frequency are combined with the change speed sensitive coefficient to calculate an update frequency weight coefficient; the size of the sliding window of the digital twin model is defined by using the data change speed value, the update frequency of the digital twin model, the change speed sensitivity coefficient and the update frequency weight coefficient, and the size of the sliding window is adjusted, so that newest data can be quickly included, model parameters can be updated in time, update lag caused by interference of old data is avoided, and the update efficiency is improved. According to the method, the data utilization efficiency is improved, and when the data change speed is high or low, fine changes can be captured in time while the sliding window is properly enlarged, so that model parameters are updated more accurately.

The invention provides a data stream real-time scheduling method based on an intelligent agent, and the method comprises the steps: building an intelligent scheduling framework driven by reinforcement learning according to a factory production environment, and the framework comprises an environment, the intelligent agent and a reward function; converting the data stream features and the system state into feature vectors so as to construct a reinforcement learning state space of multi-dimensional features; the intelligent agent perceives the reinforcement learning state space, calls the prediction model to predict a data stream feature change trend and a system state change trend in a future preset time period, and generates a resource pre-allocation scheme based on a prediction result; and the intelligent agent generates at least one scheduling action based on a multi-target scheduling optimization algorithm according to the current production target, the resource pre-allocation scheme and the reinforcement learning state space to form a scheduling strategy. According to the method, resource waste and data processing delay are effectively avoided, the resource utilization rate is remarkably improved, and stable and efficient production of an intelligent factory production environment is guaranteed.

The invention provides a digital smart factory equipment operation management system based on deep learning, and the system comprises a data collection module, a data preprocessing module, a feature engineering module, a model training and fusion module, a dynamic optimization module, a fault judgment and early warning module and a data storage module which are in communication connection in sequence. According to the method, by providing a multi-algorithm fusion deep learning model architecture and integrating the advantages of time sequence prediction and a nonlinear classification model, the problem that complex data association cannot be mined by a single algorithm is solved, a precise data processing scheme adaptive to industrial data characteristics is designed, and the data quality and the characteristic discrimination degree are improved; and by constructing a model dynamic optimization mechanism, the problem of poor model generalization ability is solved, early-stage accurate early warning of faults is realized, the early warning advance time is prolonged, the non-planned shutdown probability is reduced, the real-time performance and robustness of the system are improved, and the method is adaptive to high-noise industrial scenes.

The utility model belongs to the technical field of furnace slag treatment, particularly relates to an AI identification automatic dust removal robot for a furnace slag treatment smart factory, and aims to solve the problems that furnace slag in a furnace is difficult to completely move out in the process of moving the furnace slag out of the furnace at present, and the furnace slag in the furnace is accumulated during long-time use, so that the furnace slag in the furnace cannot be completely removed. In order to solve the problems that in the prior art, ventilation in a stove is not smooth enough, and normal combustion of coal briquettes is affected, according to the scheme, the stove comprises an electric crawler-type base, a mounting box is welded to the top of the electric crawler-type base, a door plate is hinged to an opening in one side of the mounting box, and a vacuum pump is fixedly mounted on one side of the top of the electric crawler-type base through bolts; according to the furnace slag collecting device, the suction pipe can be transferred into the furnace, furnace slag stored in the furnace can be sucked out and collected into the net cage in a centralized mode, and therefore the furnace slag contained in the furnace can be conveniently and rapidly collected in the centralized mode in the actual use process, and the furnace can work stably and continuously.

The invention relates to a smart factory production process supervision method based on data elements, and belongs to the technical field of production data processing and analysis. According to the method, the value conditions and fluctuation conditions of various operation data under each type of equipment in a historical fault-free operation period are analyzed, and the influence of each type of operation data on the state characterization of the corresponding equipment is accurately determined; and integrating the influence of all kinds of operation data on the state representation of the corresponding equipment to obtain a fusion state coefficient capable of representing the overall state of the corresponding equipment, and constructing a threshold range corresponding to the equipment type according to the fusion state coefficient of each kind of equipment. And more accurate fault judgment of corresponding types of equipment is completed in a subsequent operation cycle, and the production supervision efficiency of water treatment products is improved.

The invention discloses a semiconductor wafer manufacturing factory logistics jam prediction method and system, and relates to the technical field of semiconductor manufacturing process optimization, and the method comprises the steps: collecting and preprocessing historical logistics data of a semiconductor manufacturing intelligent factory, carrying out the spatial-temporal feature fusion of the preprocessed data, generating high-dimensional representation data, and carrying out the prediction of the logistics jam of the semiconductor wafer manufacturing factory. Performing coding feature extraction processing on the high-dimensional representation data, extracting spatio-temporal features through a time sequence dynamic sensing operation and a spatial relationship self-adaptive operation, performing spatio-temporal feature fusion processing, performing cross-sequence feature integration on a processing result and complex spatio-temporal features, and performing decoding prediction processing on the integrated features, and through a causal constraint attention mechanism and cross-sequence correlation modeling, predicting a logistics state matrix sequence, and based on the logistics state matrix sequence, carrying out congestion state determination. According to the method, high-precision prediction and quick response of logistics blockage of the semiconductor factory are realized, and the problems of spatial-temporal characteristic splitting, response lag and high false alarm rate of a traditional method are solved.

The invention discloses a smart factory management method, system and device based on the industrial Internet of Things and a medium, and relates to the technical field of smart factories, and the method comprises the steps: obtaining the state information of each piece of production equipment in a workshop; according to the state information, risk equipment information is obtained, and the risk equipment information comprises a fault type and a fault probability corresponding to the fault type; obtaining maintenance personnel information, wherein the maintenance personnel information comprises personnel position information and maintenance personnel skill scores; according to the fault type, the fault probability and the maintainer information, the maintainer priority is obtained, and the maintainer priority is used for representing the adaptation degree of the maintainer and the risk equipment; and according to the priority of the maintainer, sending the maintenance work order of the risk equipment to the maintainer meeting a preset condition. According to the method, the industrial Internet of Things technology is utilized, real-time data collection and analysis are combined, the problem that maintenance personnel are not matched with maintenance tasks is solved, the method adapts to complex maintenance tasks for precise maintenance, and the maintenance efficiency is improved.

The invention discloses a multi-module collaborative integration method based on an intelligent factory management and control system, particularly relates to the technical field of intelligent manufacturing, and is used for solving the problem of cross-module control inconsistency caused by the fact that an interface receipt is not equivalent to an actual state to take effect in the prior art. The method comprises the following steps: receiving a control intention, generating a cooperative control transaction risk index, packaging the cooperative control transaction risk index into a control transaction recording unit, and allocating a unique identifier and an idempotent key; after cross-module consistency check is executed, a control instruction is issued, a preparation receipt and an acceptance receipt are collected, and a to-be-validated verification state is entered; in the allowable effective time window, constructing an execution effective evidence chain based on the equipment and service state evidence, calculating a control effective confidence coefficient index and judging an effective result; and performing compensation rollback and consistency recovery on the effective abnormal transaction, and updating the execution reliability portrait. According to the invention, the observable evidence is used for replacing the receipt as a closed-loop basis, so that the verifiability, rollback performance and traceability of cross-module linkage control are improved.

The invention provides a business processing method of a smart factory platform based on a Beidou grid code, and the method comprises the steps: carrying out the multi-stage grid subdivision of a factory physical space through the Beidou grid code, and generating a unique grid code; the grid coding adopts an integer format to represent position information; equipment sensor data, personnel positioning data and business logic data are bound with the grid codes, and a space-time joint index table is constructed; performing real-time deviation correction on the positioning data through a grid neighborhood topological relation and personnel moving speed constraint to generate a high-precision positioning track; and mapping the service logic to the grid codes through a rule engine, and triggering automatic operation according to grid attributes. Traditional floating point latitude and longitude coordinates are replaced by integer coding, data calculation efficiency is optimized through bit operation, cross-level business association and fast path planning are supported, and business response efficiency is improved.

The invention discloses an intelligent factory data monitoring method and system based on distributed collaborative sampling, belongs to the field of intelligent factory data monitoring, and aims to solve the problems of real-time monitoring, quality control, equipment maintenance and production optimization in the production process. The system is composed of a data acquisition module, a production control module, a local processing module, a data center module and a user interaction module. And efficient data processing and storage are realized through the distributed collaborative sampling network, and the expandability and flexibility of the system are optimized. The production management and control module is responsible for distribution and sampling detection of production tasks, ensures product quality and realizes production continuity when equipment is abnormal. The local processing module analyzes equipment data, establishes a prediction model, and timely finds and early warns deviation in equipment and production. The data center module analyzes early warning production data in real time, transmits an early warning signal through a central node, and drives a production line to operate. And the user interaction module provides dynamic information updating, so that a user can obtain and feed back data in real time.