280results about How to "Precise management" patented technology

The invention provides a crop accurate management method and system based on a crop growth model. The method comprises the following steps: collecting environment data of a crop growth period by integrating a remote sensing technology, a field intelligent sensor and an unmanned aerial vehicle, and carrying out space-time alignment to form a comprehensive environment data set; and dynamically adjusting parameters of the crop growth model by using a Bayesian rule, evaluating the influence of environmental uncertainty on crops in combination with Monte Carlo simulation, and providing yield prediction with a confidence interval. Based on the prediction, a particle swarm optimization algorithm is applied to formulate management schemes such as irrigation, fertilization, pest control and harvesting opportunities, and meanwhile, a multi-objective optimization strategy is adopted to balance economic benefits and environmental protection. After the scheme is implemented, the crop growth response is monitored in real time so as to reinforce learning and automatically adjust management measures, and precise agricultural management is realized. According to the technical scheme provided by the embodiment of the invention, the crop management precision and efficiency are improved.

The invention relates to the technical field of electric line fault detection, and discloses a power transmission line fault early warning system and method based on big data driving, and the system comprises a state prediction module, a risk assessment module, an early warning decision module and a management execution module. The state prediction module constructs a prediction model by using historical operation data, environmental conditions and line parameters, and outputs future state indexes; the risk assessment module comprehensively considers multi-source information such as real-time operation data and historical maintenance records, and assesses a current fault risk index; the early warning decision-making module models the management process into a Markov decision-making process, and carries out early warning decision-making based on the output of the two modules; and the management execution module is responsible for managing and executing the decision result in real time. By comprehensively utilizing the big data technology, the accuracy and the real-time performance of power transmission line fault early warning are improved, the defects of a traditional fault management mode are overcome, and a powerful guarantee is provided for safe and stable operation of a power system.

The invention discloses a large-scale sample storage management method and system based on a multi-dimensional label system. The method comprises the following steps: acquiring large-scale sample data input, and establishing a multi-dimensional label system for each piece of sample data based on a sample source, sample quality and sample feature dimensions; performing automatic auxiliary identification and classification and quality evaluation on the sample data, generating a multi-dimensional label for the sample according to a multi-dimensional label system, selecting a proper storage layer according to the multi-dimensional label of the sample, and storing the sample in a specified storage layer after the storage space is optimized; and returning a retrieval result according to a retrieval condition input by a user, and distributing the retrieved sample to a required task or analysis scene according to a requirement. According to the method, by establishing the multi-dimensional metadata labels, retrieval, positioning and management of the samples can be completed quickly and efficiently. Meanwhile, automatic auxiliary classification and recognition of the samples are achieved, and the data management efficiency is improved.

The invention relates to a supply relation management method based on artificial intelligence. The method comprises the following steps: performing data analysis on public text data and communication records of suppliers by adopting an artificial intelligence technology based on deep learning, respectively extracting semantic feature representations of the public text data and the communication records of the suppliers, and performing semantic feature adaptive fusion processing on the public text data and the communication records of the suppliers; and the potential risk level of the supplier is judged based on cross validation analysis of public text data and communication records. In this way, an enterprise can be helped to identify and manage potential risks in a supply chain more accurately, and therefore the stability and safety of production and operation of the enterprise are improved.

The invention provides a sewage treatment plant drainage water quality evaluation method and system, and the method comprises the steps: collecting and processing water quality parameters of a plurality of point locations of a sewage treatment plant, recognizing abnormal data points through a deep learning anomaly detection model, and carrying out the dynamic correction or elimination of the abnormal data points through employing a particle swarm optimization-based adaptive filtering algorithm, thereby achieving the evaluation of the drainage water quality of the sewage treatment plant. Obtaining normal water quality data; constructing a water quality prediction model by using an integrated learning method, and predicting a water quality change trend; generating a target water quality improvement measure combination by using a first water quality improvement strategy recommendation mechanism based on a multi-objective optimization algorithm; and establishing a closed-loop feedback mechanism, evaluating the application effect of the target water quality improvement measure combination by using a reinforcement learning algorithm, adjusting key parameters of the water quality prediction model and the multi-target optimization algorithm, optimizing the first water quality improvement strategy recommendation mechanism, and generating an optimal target water quality improvement measure combination. The system realizes comprehensive, accurate and real-time monitoring and management of the drainage water quality of the sewage treatment plant, and improves the water quality improvement effect.

The invention provides a drug target affinity prediction algorithm based on protein three-mode and multi-feature fusion, and the algorithm makes full use of sequence information and two-dimensional and three-dimensional structural features of drugs and proteins, and realizes deep integration of data. The technical innovation point lies in that a multi-modal data fusion mechanism and an advanced graph neural network technology are introduced, and the sensitivity and recognition capability of the model to key biomarkers are enhanced through a dynamic attention mechanism. According to the technical advantage, the method has higher accuracy and stability when processing complex biological data, and compared with the prior art, the generalization ability and the interpretability are remarkably improved. In the aspect of technical maturity, the algorithm has passed multiple rounds of verification and shows good prediction performance and adaptability. The method is simple in operation and wide in market application prospect, can assist in new drug discovery and accelerate the drug design process, can also promote the development of the precision medical field, and particularly has important value in targeted therapy and disease prevention strategy formulation.

The invention particularly relates to a data analysis method and system. The method comprises the steps of extracting original data corresponding to a target service from a target data source according to a pre-configured data extraction strategy of the target service, loading the original data to an operation data storage layer of a data warehouse, and storing the original data; operating a data storage layer to output the original data to a detail data layer; the detailed data layer performs data preprocessing on the original data to obtain basic data and outputs the basic data to the summarized data layer; the summarizing data layer performs aggregation processing on the basic data according to a current data processing task corresponding to the target business, obtains structured data corresponding to the target business, and outputs the structured data to the application data service layer; and the application data service layer performs data analysis on the structured data according to the service configuration information corresponding to the current data processing task, and outputs a corresponding data analysis result. According to the scheme, efficient and accurate data management and analysis can be realized.

The invention discloses a cargo loading information management method and system based on an intelligent shelf, and relates to the technical field of Internet of Things information management, and the method comprises the steps: collecting cargo warehousing information; uWB anchor points are deployed on the intelligent goods shelf, sensor signal features are collected, a support vector machine classification algorithm is used to identify NLoS conditions, and error correction is carried out; determining the distance between the cargo and the anchor point by using a three-dimensional positioning formula to obtain the three-dimensional coordinate of the cargo, and monitoring the safety state of the cargo in real time; historical demand data is collected, the cargo demand at the future moment is simulated and predicted by using Monte Carlo, and a replenishment plan is generated in combination with the Bayesian law to replenish the warehouse cargo inventory; and after the goods are delivered, a business report is generated. According to the invention, accurate HE efficient management of cargo information is realized, the safety, transparency and intelligent degree of the information management system are improved, optimization of warehouse management is realized, and the efficiency and safety of cargo management and the comprehensive efficiency of warehouse management are improved.

The invention provides an intelligent water-saving control method and system for an agricultural irrigation device, and the method comprises the following steps: generating a farmland state prediction index based on soil comprehensive information, weather information and crop information, and generating a farmland irrigation strategy; calculating an irrigation emergency degree based on the farmland state prediction index; if the irrigation emergency degree reaches the highest emergency degree, the filtering irrigation module is controlled to execute alternate irrigation operation; if the irrigation emergency degree does not reach the highest emergency degree, calculating to obtain the backwashing emergency degree of the filtering irrigation module; if the backwash emergency degree is greater than or equal to the irrigation emergency degree, generating a backwash strategy, and sequentially executing the backwash strategy and the farmland irrigation strategy; if the backwash emergency degree is smaller than the irrigation emergency degree, the filtering irrigation module is controlled to execute alternate irrigation operation on the target farmland. According to the invention, intelligent irrigation and backwashing control can be realized, and a better water-saving effect is achieved.

The invention discloses a data integrity protection method in an archive record tracing process based on a block chain, and the method comprises the steps: generating a hash value for each archive record as a tracing starting point, carrying out the traversal from the tracing starting point, generating an integrity check code when passing through a node of the block chain every time, comparing the current time with an archive secrecy period, and carrying out the data integrity protection. The method comprises the steps of obtaining an access control result, creating an encryption index for an archive record allowed to be accessed by a user in the access control result, selectively exposing data and obtaining identification data through the encryption index based on a traceability purpose, decomposing a traceability request into a plurality of sub-requests, and distributing the sub-requests to different block chain nodes to perform traceability processing based on the identification data. And aggregating different sub-query results to obtain a final traceability result. According to the method, a highly safe, reliable and transparent data integrity protection mechanism is provided for archive recording by utilizing the core characteristics of the block chain, the data integrity is enhanced, and meanwhile, the method has relatively good interpretability.

The invention discloses an RISC-V-based thread bundle scheduling method and device and a medium, belongs to the technical field of general graphics processors, and aims to solve the technical problem that the execution efficiency of instructions is affected by the fact that an existing thread bundle scheduling module is affected by a synchronous fence mechanism and a thread bundle scheduling strategy, and the thread bundle scheduling efficiency is easily reduced. The method comprises the following steps: performing instruction configuration on a current thread bundle state to obtain a real-time thread bundle state instruction; performing state control on the thread bundle queue to obtain a state change control strategy; performing instruction fetching sequence control on the thread beam queue, and determining a thread beam instruction fetching control strategy; performing address fetch control on the thread bundle queue, and determining an address type control strategy; and performing thread beam scheduling processing based on a thread beam scheduling system architecture on the thread beam queue through the real-time thread beam state instruction and the thread beam scheduler after the strategy configuration is completed to obtain a thread beam scheduling result.

The invention relates to the technical field of data storage, and discloses a lossless compression algorithm-based data storage method, which comprises the following steps of: receiving to-be-stored data through a data interface, automatically identifying a data format by using a data analysis tool, and performing feature analysis to obtain the size, the type and the complexity of the data; through deep analysis of data features, the method can flexibly select a proper Huffman coding processing mode, and optimizes a compression process according to different data types and complexity. In addition, a context modeling optimization mechanism is introduced, the coding mode is dynamically adjusted, and the compression ratio and the storage efficiency are further improved; efficient storage and reliability verification of data are ensured through a block storage strategy in combination with metadata management; the problem that an existing compression algorithm is low in efficiency is solved, the integrity and reliability of data storage are improved, and the method is suitable for storage requirements of large-scale data and diversified data.

The invention discloses an unmanned aerial vehicle double-unmanned aerial vehicle cooperative intelligent pesticide spraying method for fruit trees, and relates to the technical field of plant protection unmanned aerial vehicles, and the method comprises the steps: deploying a laser radar and a high-precision GPS on an unmanned aerial vehicle, collecting orchard three-dimensional point cloud data, and constructing an orchard three-dimensional point cloud data set; performing data preprocessing on data in the constructed orchard three-dimensional point cloud data set, cleaning the data and performing position correction; an orchard three-dimensional grid model is constructed, orchard areas are divided based on the height of fruit trees, and tasks are allocated to the unmanned aerial vehicle; planning a pesticide spraying path of the unmanned aerial vehicle to execute pesticide spraying operation, and monitoring pesticide sedimentation and spraying effects in real time; the comprehensive score YL of the orchard is obtained according to pesticide spraying data collected in real time, the comprehensive score YL and a preset pesticide spraying evaluation threshold value YZ are compared and analyzed, the current pesticide spraying condition of the orchard is evaluated, and supplementary operation is conducted; pesticide waste caused by height difference of fruit trees is effectively reduced, the coverage rate and uniformity of pesticide spraying are improved, and the working efficiency of the unmanned aerial vehicle is improved.

The invention discloses a barn pest monitoring and early warning method, system and device based on big data and a medium, particularly relates to the technical field of pest monitoring, and is used for solving the problem that the distribution trend of newly appearing pests in a barn cannot be accurately monitored in the prior art. The method comprises the following steps: dividing a granary into a plurality of monitoring areas, arranging an independent spectrum sensor network, carrying out real-time monitoring based on a spectrum analysis technology, carrying out contrastive analysis with a granary pest library, rapidly identifying new pests, and after the new pests are monitored, generating a distribution trend chart in real time through a spectrum imaging technology, according to the method, a trend chart is obtained, whether early warning needs to be sent out is judged according to the trend chart, and the data quality can be mastered more comprehensively by evaluating the spectral distortion hidden danger and randomness of spectral data in a high-risk monitoring area, so that the accuracy evaluation of the distribution trend chart of newly-emerged pests is improved, and the reliability and accuracy of a granary pest monitoring and early warning system are remarkably improved.

The invention discloses an intelligent monitoring system for construction environment pollution, particularly relates to the field of tunnel construction environment pollution monitoring, is used for solving the problems of pollution diffusion and risk assessment, and accurately calculates an air flow vector field through an intelligent air pollution monitoring system in combination with a fluid dynamics principle and a Navier-Stokes equation. Air flow and pollutant diffusion paths in a tunnel are disclosed. And constructing a pressure gradient field, quantifying the pressure difference and air exchange capacity between regions, and providing driving force analysis of pollutant diffusion. Calculating a pollutant transmission coefficient through nonlinear coupling calculation in combination with an air flow correlation degree, a pressure gradient field and an air exchange capability coefficient, and quantifying the pollutant transmission capability of adjacent regions; and then real-time evaluation and grading are carried out on pollution risks, high-risk areas are identified in time, and early warning information is pushed through the intelligent terminal. Therefore, the ventilation strategy is optimized, the energy consumption is reduced, and the health of constructors and the construction safety are guaranteed.

The invention relates to the technical field of tunnel disease detection, in particular to a tunnel pavement disease prediction system based on a digital twinborn technology and a construction method thereof, and a tunnel digital module is used for constructing a digital twinborn model; the pavement disease updating module is used for constructing a digital twinborn database and updating twinborn pavement information of a digital twinborn model; the disease prediction module constructs a tunnel disease development model based on the pavement disease information in the digital twinborn model and the digital twinborn database, and is used for predicting the formation and development degree of tunnel pavement diseases; and the pavement condition visualization module displays the real-time state of the tunnel pavement and the information of the digital twinborn model based on the pavement disease updating module and the disease prediction module. A virtual model corresponding to an entity tunnel is created, and pavement disease formation and development are monitored, analyzed and predicted under the condition that tunnel operation is not affected; the intelligent and automatic level of tunnel management is optimized, and the safety and reliability of tunnel operation are improved.

A water leakage monitoring method for a digital twinning system of a coal mine shaft comprises the steps that an RFID antenna and a Twinin-Tag sensor are installed on the wall of the shaft, a monitoring terminal is arranged on the ground, and an RFID reader is arranged at an outlet of the shaft; a shaft monitoring signal is collected in real time through a Twindow-Tag sensor, the shaft monitoring signal is read through an RFID reader, and then shaft monitoring data are obtained by analyzing and processing the shaft monitoring signal; collecting RSSI reading and phase data by using a short time window, and performing state identification by using a decision tree; carrying out coarse segmentation and fine segmentation on the leakage signal, and determining the location of a water leakage point in combination with a change point detection algorithm; and the leakage volume and the leakage rate are estimated. According to the method, the water leakage point can be accurately positioned, the volume and flow velocity information of the leaked water in the shaft can be obtained, so that a reliable basis is provided for subsequent maintenance decision making, and the method is suitable for long-term monitoring operation of the coal mine shaft.

The invention provides a service area customer group portraying method and system based on deep clustering. The method comprises the steps that S1, customer group information of a service area is acquired and preprocessed; s2, performing feature processing on the customer group information to obtain feature variables; s3, further extracting feature variables by using the trained model to obtain deep features; s4, performing clustering analysis on the customer group information of the service area, and outputting a clustering result; s5, calculating a contour coefficient to evaluate the clustering quality; optimizing and adjusting the model according to an evaluation result; and S6, according to a clustering result, dividing the customer group information of the service area into multiple types to construct customer group portraits. According to the method, the deep clustering algorithm is introduced into the customer group portrait of the high-speed service area, the limitation of a traditional method is effectively solved, different types of customer groups are identified, and the accurate customer group portrait is constructed.

The invention discloses a wireless monitoring system for real-time deformation of a negative ring segment during shield tunnel starting tunneling, which comprises a standard block, an adjacent block and a top sealing block which are mounted on the negative ring segment, and a data acquisition system for monitoring corner deformation in X, Y and Z directions generated by the negative ring segment; the data post-processing system comprises a local server and is used for cleaning and pre-processing the monitoring data and calculating deformation data by applying a corresponding algorithm in the local server; the visual monitoring system is used for carrying out three-dimensional visual dynamic display on the negative ring duct piece according to the deformation data and the accumulated deformation data; when the threshold value is exceeded, alarm processing is carried out. The system has the beneficial effects that real-time automatic acquisition, remote wireless data transmission and remote real-time monitoring and early warning of negative ring duct piece deformation during shield tunnel starting tunneling can be achieved, and the requirement for shield attitude control during shield tunnel starting tunneling construction is met.

The invention relates to the technical field of intelligent chemical engineering, and discloses an intelligent chemical engineering construction safety and quality detection method based on big data, and the method comprises the following steps: 1, installing a monitor and a sensor; step 2, intelligent analysis; step 3, predictive maintenance; step 4, risk assessment; step 5, quality control; step 6, safety management; 7, accident prevention; 8, remote real-time management is carried out; step 9, decision support; step 10, intelligent reporting; and step 11, displaying an interface. According to the intelligent chemical engineering site construction safety and quality detection method and system based on big data, through real-time monitoring and risk assessment, the intelligent chemical engineering site system can improve the safety management level of a construction site and reduce accidents, and the system can predict potential safety risks and take measures to avoid the potential safety risks by utilizing predictive maintenance and accident prevention technologies; and by using big data and an intelligent optimization algorithm, the resource allocation and the construction process are optimized, and the construction efficiency is improved.

The invention discloses an unmanned aerial vehicle management method and system, a nonvolatile storage medium and electronic equipment. The method comprises the steps that a base station sends a first public key stored in the base station to an unmanned aerial vehicle management object, and a second private key is stored in the unmanned aerial vehicle management object; a first certificate and a second certificate sent by the unmanned aerial vehicle management object are received, the first certificate is obtained by performing digital signature on the first public key by using a second private key, and the second certificate is obtained by performing digital signature on the unmanned aerial vehicle identification information by using a third private key; receiving the encrypted flight data generated by the unmanned aerial vehicle; and verifying the flight data generated by the unmanned aerial vehicle after encryption processing by using the second certificate, and sending a verification result and the first certificate to the unmanned aerial vehicle management object. According to the invention, the technical problem that the unmanned aerial vehicle flies irregularly due to the fact that the base station cannot accurately identify and manage the unmanned aerial vehicle is solved.

The embodiment of the invention discloses a real-time warehouse management method and equipment based on digital twinning, relates to the technical field of Internet of Things, and is used for solving the problems of low efficiency and high cost of existing warehouse management. The method comprises the following steps: constructing a digital twin storage model according to building data and Internet of Things data corresponding to a current storage scene; clustering and grouping the current goods according to the arrival scanning information of the current goods and the historical storage information to obtain key storage information of the current goods in each group; querying the digital twin storage model according to the key storage information to obtain storage area information corresponding to each group of current goods; according to the warehousing starting point position corresponding to each group of current goods, the position information of each warehousing area and the key storage information, a logistics warehousing path of each group of current goods is planned, an initial logistics warehousing path is obtained, scheduling optimization is performed on the initial logistics warehousing path based on a data twin warehousing model, and the logistics warehousing path of each group of current goods is obtained. And warehousing and storing the current goods of each group.

The invention relates to the technical field of network security analysis, in particular to a big data network signal security early warning system. The system comprises a network signal big data stream transmission module, a network signal ratio difference detection module, a network signal division result safety early warning module and a network signal abnormity safety response module. Data acquisition equipment can be deployed at an entrance and an exit of each network equipment, and network signal data real-time acquisition and big data stream type transmission conversion can be carried out, so that a network signal big data stream can be obtained; carrying out time sequence flow size and access frequency statistical analysis on the network signal big data flow, and carrying out network signal ratio difference detection division and abnormal safety early warning processing at the same time to generate a network signal abnormal safety early warning signal; and uploading the network signal abnormity safety early warning signal to a cloud platform to carry out abnormity safety response processing so as to generate a network signal abnormity safety response protection strategy. According to the invention, network signal abnormity can be efficiently monitored in real time and early warning can be provided.

The invention provides a shared memory-based data interaction method for an embedded virtualization platform, which comprises the following steps of: dividing a shared memory area, including a management information area and a shared data area, initializing the management information area, including setting a magic number, configuring a mutual exclusion access flag and recording use information of the shared data area; acquiring a physical initial address of the shared memory, verifying the availability of the management information area, and ensuring that the management information area is correctly initialized; a proper data transmission mode is selected according to the data size, small data volume can be directly transmitted through an inter-core communication register, and large data volume is transmitted through a shared memory; the Szymanski algorithm is used to realize mutual exclusion access control, and the consistency of data is ensured; after data interaction is finished, if shared memory resources which do not need to be used any more need to be released, use information in the management information area is updated; and other related operation areas or virtual machines are notified according to needs, and state changes of data interaction are notified to the operation areas or the virtual machines.

The invention discloses a wireless router access management method and system and a wireless router, relates to the technical field of wireless routers, and remarkably improves network security and resource utilization efficiency through multiple authentication and a dynamic management mechanism. According to the system, first behavior characteristic data and network state data of equipment are collected, intelligent analysis is carried out in combination with a CNN model, and the security of the access equipment is judged in real time. For equipment meeting the security standard, the system automatically allocates bandwidth and provides continuous network service, and for abnormal equipment, the system blocks access of the abnormal equipment in time through multiple rounds of authentication and load evaluation, so that the security and stability of a network environment are ensured. In the aspect of bandwidth management, the system intelligently sorts and dynamically allocates bandwidth resources according to the second access safety index # imgabs0 # and bandwidth requirements of the access equipment, and low efficiency and resource waste of a traditional static allocation mode are avoided.

The invention discloses a whole-tray warehouse-out system and method, and belongs to the technical field of cigarette manufacturing, and the system comprises a stacking robot, a cigarette carton temporary storage channel, a cigarette carton backflow channel, a latent AGV, an elevator, a tray on-line device and a whole-tray warehouse-out line; wherein the latent AGV is used for moving to a designated goods location of an ex-warehouse cache region to carry finished cigarette trays and carrying the finished cigarette trays to the tray online device, moving to a designated goods location of a target floor to carry the finished cigarette trays, descending to an ex-warehouse floor through the elevator and then carrying the finished cigarette trays to a connection platform on the same floor; then the tray is carried from the connection platform to the tray on-line device; and the whole-tray delivery line provides a conveying channel for the finished cigarette trays, so that the finished cigarette trays can be conveyed to a delivery area for whole-tray loading and delivery. According to the invention, accurate tracking and management of goods are facilitated, and the accuracy and efficiency of delivery of the whole pallet are improved.

The invention discloses a system and a method for resisting a pseudo link layer protocol attack in an electric power communication network, and relates to the technical field of communication. The system comprises: a switching unit, which is used for receiving all data frames transmitted from an Ethernet port; the data identification module analyzes the protocol type and the destination address of the data frame, and forwards the data frame to the service processing unit or directly discards the data frame; the buffer area module sets an independent buffer area for each link layer protocol, and dynamically adjusts the size of the buffer area based on the protocol characteristic weight and the equipment memory resource through the configuration unit; the buffer area management module monitors the state of a buffer area in real time and triggers data discarding operation when the buffer area is fully loaded; and the data extraction module extracts the data frames from the buffer area according to a set time interval and analyzes and forwards the data frames in combination with a priority rule. According to the method, the size and the time interval of the buffer area can be dynamically adjusted, the resource utilization efficiency is improved, the pseudo link layer protocol attack is effectively resisted, and the security and the real-time performance of an electric power communication network are guaranteed.

The invention discloses an equipment health management system and method based on artificial intelligence and computer equipment, and the system comprises an equipment data collection module which is used for obtaining equipment environment data transmitted by a sensor disposed in an influence area corresponding to each piece of electrical equipment, and obtaining equipment state data corresponding to each piece of electrical equipment; the data preprocessing module is used for carrying out standardized preprocessing on the equipment environment data and the equipment state data; the fault diagnosis module is used for performing fault diagnosis on the electrical equipment based on the preprocessed equipment environment data and equipment state data so as to identify faulty electrical equipment and generate fault information of the faulty electrical equipment; the research and judgment processing module is used for constructing a fault analysis prompt word according to the fault information, inputting the fault analysis prompt word into a research and judgment large model, and generating fault processing information of the fault electrical equipment through the research and judgment large model; and the fault handling module is used for carrying out fault handling on the fault electrical equipment based on the fault handling information.

The invention relates to the technical field of three-dimensional visualization park management, and particularly discloses a three-dimensional visualization-based smart park management platform, which comprises a self-adaptive sensor calibration module for dynamically calculating a sensor data accuracy coefficient to ensure the precision of data acquisition; the multi-dimensional data fusion and three-dimensional modeling module is used for calculating an adaptation coefficient to evaluate the response capability of the system to environmental change by integrating multi-source data; the intelligent image data registration module optimizes a matching precision coefficient and improves the space docking precision of the image data and the three-dimensional model; the real-time error monitoring and feedback optimization module uses a machine learning algorithm to correct an error between the sensor and the three-dimensional model, dynamically calculates an error correction coefficient, and ensures data consistency; and the comprehensive performance evaluation module calculates a sensor data accuracy coefficient, an adaptation coefficient, a matching precision coefficient and an error correction coefficient through normalization to obtain a comprehensive performance coefficient, and optimizes a system operation strategy in real time.

The invention discloses an industrial Internet of Things edge gateway system and a data processing method thereof, and the system comprises a data conversion module which is used for converting source JSON data in a specific format into target JSON data in a template format through a predefined template; the data acquisition module uses a configurable data acquisition pipeline as a northbound application node, binds the northbound application node with a southbound equipment node, receives data transmitted by equipment, and dynamically adjusts a data acquisition strategy through configuration management; the northbound AI reasoning module is used for being bound with a southbound equipment node and performing edge side model reasoning according to data transmitted by equipment to obtain an anomaly detection result; and the internal API of the edge gateway system is used for writing in the internal data point of the PLC or sending an information notification to a specified cloud end. By adopting the technical scheme of the invention, the data processing capability is improved, and the intelligent level of the system is enhanced.