298 results about "Data differencing" patented technology

The invention relates to the technical field of simulation model optimization, and discloses an aero-engine model Bayesian optimization method for quantizing uncertainty, and the method comprises the steps: building a probability mapping relation from a component index to an output response through constructing a Bayesian neural network agent model based on a probability weight coefficient; and by taking the difference between the output response and the corresponding complete machine test data as a multi-objective loss function and taking the minimization of the multi-objective loss function as an optimization objective, optimizing the component indexes by adopting a Bayesian optimization method based on a Gaussian process to obtain an optimal component index combination. Not only is a nonlinear relationship between high-dimensional parameters and simulation-test deviation accurately modeled through a neural network, but also efficient search of a parameter space is realized through a Gaussian process. The technical problems that when a traditional optimization method is used for processing the high-dimensional, strong-nonlinearity and multi-parameter coupling complex optimization problem of the aero-engine, the calculation efficiency is low, local optimum is prone to occurring, and result uncertainty cannot be quantified are solved.

The invention provides a cross-park enterprise data collaborative analysis method based on federated learning, and relates to the technical field of distributed machine learning and data security, and the method comprises the steps that a central server distributes an initial global model and configuration parameters to each park node; the nodes execute local data feature alignment to generate standardized feature vectors; calculating dynamic collaborative factors of local data and global distribution; adjusting a training strategy based on the collaborative factors and updating model parameters; collecting model updating through an encrypted channel, and screening effective updating by adopting a dynamic aggregation offset threshold value; performing weighted aggregation to generate a new global model; and terminating the process when the cross-park convergence condition is met or the maximum round is reached. According to the method, heterogeneous data differences are eliminated through a dynamic feature alignment mechanism, dual-channel collaborative evaluation and adaptive security protection are combined, multi-park collaborative modeling efficiency and robustness are remarkably improved on the premise of guaranteeing data sovereignty, and the problems of feature space splitting, weak attack protection and node contribution imbalance are solved.

The invention belongs to the field of fault monitoring, and particularly relates to a fault self-healing method and system under an intelligent operation and maintenance global P2P architecture, and the method comprises the steps: constructing a global operation and maintenance monitoring double-layer node network which comprises a basic monitoring subnet and a collaborative management and control subnet; the basic monitoring subnet collects platform operation data in real time according to a preset inspection strategy, determines an abnormal root node and an association node through causal association analysis, generates an early warning signal and uploads the early warning signal to the collaborative management and control subnet; after the collaborative management and control subnet receives the early warning, a fault self-recovery strategy is started, resources are dynamically allocated, abnormal node data batch migration and recovery are executed, and a self-recovery result is fed back to the basic monitoring subnet to trigger loop check until abnormity is eliminated; according to the method, real-time monitoring and intelligent analysis of operation and maintenance data are realized through cooperative work of the double-layer node network, and disaster tolerance and load adaptive adjustment capability of the system are effectively improved in combination with an automatic fault self-healing mechanism and data difference migration.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an operation guidance and optimization method, device, equipment and medium, and the method comprises the steps: obtaining multi-source heterogeneous data to construct a dynamic knowledge graph, and generating an initial guidance scheme containing target operation, and in combination with the visual interaction model, presenting a target operation in real time, collecting real-time dynamic data, generating operation correction information based on data differences, updating display contents of the visual interaction model, and dynamically optimizing a guidance scheme according to an effect analysis value. According to the method, the dynamic knowledge graph is constructed by fusing multi-source heterogeneous data, real-time presentation and data acquisition are performed in combination with a visual interaction model, operation correction information is generated based on data differences, and a guidance scheme is dynamically optimized, so that personalized, dynamically updated and real-time feedback guidance scheme generation and optimization in multi-field application are realized; and the operation accuracy and adaptability are improved.

The invention relates to the technical field of fault monitoring, in particular to a concentrator intelligent fusion terminal based on multi-sensor monitoring and maintenance. Obtaining a suspected fault moment according to discrete features of data distribution in the state data sequence of any dimension; obtaining a target moment according to the data difference characteristics of the suspected fault moment and the adjacent moment; state characterization points in a multi-dimensional space are constructed according to the data of all dimensions at the target moment, clustering is carried out, and the fault characterization credibility of the state cluster is obtained according to the distribution characteristics of the state characterization points in the state cluster and the data discrete characteristics between the state characterization points. According to the method, the abnormal degree of the latest moment is obtained according to the distance feature between the state characterization point corresponding to the latest moment and the nearest state cluster, the range feature of the nearest state cluster and the fault characterization credibility; the operation state of the concentrator is monitored according to the abnormal degree, and the monitoring accuracy of the concentrator terminal is improved.

The invention discloses a new energy equipment health management intelligent question-answering system based on a large model and a knowledge graph. The system integrates eight modules including a data acquisition and preprocessing module, a multi-source heterogeneous data deep fusion module, a knowledge graph construction module and the like. The data acquisition and preprocessing module is used for collecting and processing multi-source data and providing support for subsequent modules; a multi-source heterogeneous data deep fusion module eliminates data differences; the knowledge graph construction module stores domain knowledge; the large model training module endows the system with semantic understanding and reasoning capabilities. Through cooperation of the modules, the system can realize efficient integration and analysis of multi-source heterogeneous data, accurately diagnose and predict equipment faults, and quickly and accurately perform knowledge retrieval and question and answer. Meanwhile, based on fault prediction and health assessment results, visual interaction and scientific decision support are provided, and full-life-cycle management of the equipment is facilitated. The system improves the health management level of new energy equipment, reduces the operation and maintenance cost, and promotes the intelligent development of the new energy industry.

The invention relates to the technical field of data processing, in particular to a fan tower fastening bolt loosening early warning method, which comprises the following steps of: acquiring multi-dimensional monitoring data of any fastening bolt in a fan tower at each moment to obtain a multi-dimensional monitoring data sequence containing the current moment; obtaining the suspected looseness degree of the bolt according to the fluctuation characteristics of the multi-dimensional monitoring data sequence, and obtaining the bolt looseness confidence coefficient according to the data difference between the multi-dimensional monitoring data at the current moment and the multi-dimensional monitoring data at the previous moment and the change trend characteristics of the monitoring data of each dimension in the multi-dimensional monitoring data sequence; the aging degree of any fastening bolt is obtained, and the bolt loosening degree is obtained according to the bolt suspected loosening degree, the bolt loosening confidence coefficient and the aging degree of any fastening bolt; according to the bolt looseness degree, the preset early warning threshold value is adjusted, the self-adaptive early warning threshold value of any fastening bolt at the current moment is obtained, looseness early warning is carried out on any fastening bolt, and the early warning accuracy is improved.

The invention relates to the technical field of distributed data processing systems, and discloses a data processing system supporting full localization deployment and a compliance guarantee method thereof.The data processing system supporting full localization deployment comprises an audit event collection module used for collecting and storing processing event hash; the event organization module is used for realizing logic time sequence management of events through topological sorting and causal relationship marking; the hash chain construction module is used for constructing a local-to-global double-layer hash chain by utilizing the constructed directed acyclic graph and event hash; the increment synchronization module is used for executing increment synchronization based on Hash tree difference identification; the conflict solving module is used for detecting and solving event conflicts in the distributed environment by applying a version vector technology aiming at data differences found in the incremental synchronization process; according to the method, the lightweight linear hash chain is used for replacing a traditional binary tree structure, high-efficiency and low-cost audit chain maintenance is realized, and the data integrity is guaranteed in an unstable network environment.

The invention relates to the technical field of data monitoring, in particular to an intelligent storage supervision system for biological samples. Obtaining a data anomaly degree according to data discrete features at the current moment in the monitoring data sequence; obtaining the data credibility according to the data difference characteristics of the current moment and the adjacent moment, the recent data distribution characteristics and the data difference characteristics of the adjacent sensors; according to the recent data change characteristics, the difference change characteristics of the recent data and the preset door opening time period data and the time interval characteristics of the current moment and the door opening moment, the external isolation degree is obtained; according to the data credibility and the external isolation degree, obtaining an abnormal feature confidence degree; and obtaining an enhancement coefficient according to the change trend characteristics of the monitoring data sequence. According to the invention, the abnormal score of the current moment is obtained according to the data anomaly degree, the abnormal feature confidence and the enhancement coefficient, and environmental anomaly early warning is carried out, so that the accuracy of environmental anomaly early warning is improved.

The invention relates to the technical field of data processing, in particular to an automobile charging pile safety monitoring system based on multi-modal sensor fusion, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, when the processor executes the computer program, the following steps are realized: according to data differences of various monitoring indexes of any to-be-detected automobile charging pile and the automobile charging piles with normal working conditions, the electrical abnormality degree of any to-be-detected automobile charging pile is obtained; fusing and analyzing various monitoring indexes, temperature and vibration frequency of any to-be-detected automobile charging pile to obtain an abnormal confidence coefficient; according to the abnormal confidence coefficient and the electrical abnormal degree, the comprehensive abnormal degree is obtained, safety monitoring is carried out on any one to-be-detected automobile charging pile according to the similarity between the comprehensive abnormal degree of each of other to-be-detected automobile charging piles and the comprehensive abnormal degree of any one to-be-detected automobile charging pile, and the safety monitoring accuracy of the automobile charging piles is improved.

The invention relates to the technical field of intelligent data correction, and discloses an intelligent accounting data correction method and system based on data difference identification, and the method comprises the steps: S, synchronously executing data standardization and data portrait construction based on accounting data and a preset business rule, business rule injection type feature extraction is carried out on the standardized accounting data to obtain a difference composite feature vector, multi-level confidence classification is carried out based on the vector, difference type labels are generated, a cost-risk-efficiency optimization correction decision is carried out, an optimal correction strategy instruction and a traceability record are obtained, and the traceability record is obtained. Performing directional correction process management and control on the data, then searching similar cases, performing case auxiliary verification on the corrected accounting data, generating a correction verification result, and finally outputting a correction traceability report; according to the invention, the accuracy of intelligent accounting data correction based on data difference identification can be improved.

The invention relates to the technical field of health management of dry-type air-core reactors, in particular to a dry-type air-core reactor turn-to-turn fault identification method based on acquired data optimization. The method comprises the steps of firstly obtaining a discharge feature vector of each event; further obtaining associated defect source cost dispersion according to the same-dimensional data difference of the discharge feature vectors between adjacent monitoring periods; further obtaining the structure entropy of the discharge system according to the distribution of the discharge feature vectors in each monitoring period; further constructing an insulation state diagnosis space according to the change of the structure entropy of the discharge system in the adjacent monitoring periods and the cost dispersion of the associated defect source; and finally, according to the distribution of diagnosis points in the insulation state diagnosis space, carrying out multi-stage state evaluation, capturing early-stage degradation information, realizing accurate prediction of the insulation degradation severity and the dominant mode, quantifying the abnormal evolution process, and providing a reliable basis for health management.

The invention relates to the technical field of on-line monitoring, in particular to a water level on-line detection method and system for water conservancy project waterproofing, and the method comprises the following steps: analyzing water level changes before and after an event, screening abnormal measurement points, recognizing an abnormal region in combination with an excitation signal, and adjusting transmission priority configuration according to data change characteristics. And judging a trend boundary to construct trend structure information, and analyzing a multi-cycle water level record to output a cycle gradient change result. According to the method, through combination of event attributes and the liquid level change relation, abnormal signal identification is optimized, data difference comparison before and after disturbance is utilized, the abnormal area judgment capacity is enhanced, jump trend capture is achieved in combination with the period slope change trend, and a dynamically-adjusted data uploading priority rule is constructed, so that the measuring point channel response has the real-time regulation and control capacity; the accuracy of trend continuation judgment is improved, the water level change direction and the periodic gradient offset state are subjected to association expression, and the dynamic tracking and grading response capability of water level monitoring is enhanced.

The invention belongs to the technical field of data security management, and discloses a smart city data processing method and platform based on artificial intelligence, and a storage medium. Comprising the following steps: monitoring real-time data between network entities in real time to obtain a real-time data stream; constructing a dynamic data blood relationship map by analyzing an instantaneous behavior mode and a content derivation relationship of the real-time data stream; constructing a node symbiont by analyzing the data difference degree between different nodes in the dynamic data blood relationship map; calculating an interaction risk of the node symbiont and an external entity, and constructing a minimum access control rule according to the interaction risk; and constructing a life cycle management model, dynamically managing an access control strategy, and managing and controlling real-time data. According to the method, the optimization effect from passive response to active perception is realized on the data stream security management level, the collaborative effect of the authority structure and system resource allocation is improved, and the intelligence and efficiency of the overall data security management are improved.

The invention discloses a transformer substation-oriented multi-source heterogeneous data checking method and system. The method comprises the following steps of obtaining various types of data sources, executing a preliminary data cleaning process and outputting standardized data; aiming at database systems of different manufacturers, designing a customized migration tool for each database; extracting specific attribute information contained by the special domain analysis module by constructing the special domain analysis module; sequentially verifying the source database and the target database by adopting a progressive consistency verification strategy, and recording data difference information detected in each stage; a change rationality verification module is configured to analyze an SCD file of the transformer substation, and change rationality judgment is performed on difference items from a source database to a target database; performing unified modeling and graphical display on the obtained difference information through a difference data visualization module; according to the method, the data consistency and correctness in the upgrading and transformation process of the transformer substation monitoring system database are comprehensively verified, so that the stability and the operation reliability of the system are improved.

The invention relates to the technical field of multi-source data fusion, in particular to a multi-source communication data fusion method and system and a storage medium, and the method comprises the steps: obtaining various data of communication equipment in real time; acquiring a monitoring deviation index of any two types of data in the current period according to data difference characteristics between any two types of data, and clustering all types of data; according to the data deviation degree between each type of data and all other types of data in the cluster in which the data is located in the current period, the deviation degree of each type of data relative to historical data, and the synchronization degree of the mutation time of each type of data, obtaining a cheating factor of each type of data in the current period; therefore, the initial credibility of various types of data in the current period is optimized, and then all types of data in the current period are fused. According to the method, the data credibility of each type of data is analyzed, the initial credibility of each type of data is optimized, and the accuracy of multi-source communication data fusion is improved.

The invention relates to an intelligent mold calibration optimization method based on big data analysis. The method comprises the following steps: firstly, collecting current state data of a mold, including shape, size, wear degree and production environment parameters, and simultaneously obtaining ideal state data of a product; and constructing a mold-product correlation model by utilizing a physical simulation algorithm, and simulating a product forming process under different production parameters. Key control parameters are determined based on simulation results, the parameters are optimized through a machine self-learning algorithm, and an intelligent calibration optimization strategy is generated. Actual product data is collected in real time and compared with ideal state data, a product data difference value is generated, and an optimization strategy and key control parameters are dynamically adjusted according to the product data difference value. And finally, the production parameters of the mold are automatically adjusted according to the optimization strategy, intelligent calibration optimization is achieved, and the product quality and the production efficiency are improved.

The invention relates to the technical field of data processing, in particular to a pressure vessel data monitoring method based on a high-precision sensor. Obtaining a data period according to data change characteristics of the monitoring data sequence; obtaining the data deviation degree of the current moment according to the data difference characteristics of the current moment and the same moment in the historical data cycle; obtaining a suspected abnormal moment according to the data deviation degree; obtaining comprehensive stability according to the data difference characteristics of the suspected abnormal moment at the same moment in the historical data cycle and the fluctuation difference characteristics of the suspected abnormal moment at the same moment in the historical data cycle; and according to the data deviation degree of the suspected abnormal moment, the comprehensive stability and the quantity characteristics of the suspected abnormal moments of other sensors at the same moment, obtaining the fault abnormity degree. According to the invention, the pressure vessel at the current moment is monitored according to the fault abnormity degree, and the accuracy of pressure vessel data monitoring is improved.

The invention provides a dynamic credit risk assessment and early warning system for a new citizen group, and relates to the technical field of risk early warning. The behavior sequence construction module is used for preprocessing the data and generating user behavior sequence data; the difference feature generation module is used for calculating a difference metric value between adjacent time slices according to the user behavior sequence data; the credit state calculation module is used for calculating credit variation data in combination with the historical credit evaluation record and generating initial credit state data; the dynamic correction module is used for determining a deviation correction parameter according to a matching result of the initial credit state data and an actual repayment record, and generating dynamic credit evaluation data; the risk early warning generation module is used for generating a risk early warning signal when the change rate or the change direction of the dynamic credit assessment data reaches a preset risk triggering condition; according to the invention, the autonomy and accuracy of dynamic credit risk assessment and early warning are improved.

The invention provides a spatial intelligent visual physical process inference method based on an implicit physical large model, and belongs to the field of spatial intelligent and artificial intelligence modeling calculation. The problems of low prediction precision and lack of physical consistency for complex physical scenes in the prior art are solved. The method comprises the following specific steps: acquiring multi-modal data of an environment, and representing the multi-modal data in a unified coordinate system; preprocessing the multi-modal data, designing a geometric coding model, a dynamic prediction model and an energy conservation constraint, introducing a space-time attention mechanism, predicting the motion state of an object according to the multi-modal data, and obtaining prediction data of the motion state of the object; acquiring real observation data of object motion, comparing the difference between the prediction data and the real observation data, and performing correction and weight updating on the prediction data of the model through a self-adaptive residual term; according to the method, visual information and implicit physical law modeling are fused, and the self-supervised physical consistency constraint is utilized, so that automatic learning and prediction of a potential physical process are realized.

The invention relates to the technical field of anomaly detection, in particular to an industrial Internet of Things equipment behavior pattern anomaly detection method. Clustering the data points according to the time domain features and the frequency domain features of the data points in the local time period, and obtaining an outlier degree and a target data point according to the number features of the data points in the data cluster and the distance features between the data points and the maximum data cluster; obtaining a first confidence coefficient according to the data difference of the target data point in other data points in a preset neighborhood range in the clustering space, the number ratio of other data clusters to which the other data points belong, and the discrete feature of the target data point; obtaining a second confidence coefficient according to the data difference between the target data point and the same historical moment, the trend difference between the target data point and the same local time period corresponding to the same historical moment, and the data fluctuation in the local time period of the target data point; and obtaining the abnormal degree of the target data point according to the comprehensive confidence coefficient and the outlier degree, and detecting the behavior state of the equipment, thereby improving the accuracy of anomaly detection.

The invention relates to the technical field of underground pump fault early warning, in particular to a side cloud collaborative diagnosis early warning system for a tower type pumping unit cloud platform. The system comprises an edge end module and a cloud platform module, wherein the edge end module comprises a data acquisition unit used for acquiring operation data of the tower type pumping unit and a local diagnosis unit used for determining a fault moment; the cloud platform unit comprises a remote diagnosis and decision-making unit used for obtaining a fault response value according to the correlation degree of the same type of operation data and the importance degree of the difference of each type of operation data in an analysis period before and after the fault moment; determining a response characteristic sequence based on the fault response value of the same type of operation data at the fault moment; and training a neural network by using the neural network to obtain an oil pumping unit fault diagnosis model, and further performing diagnosis and early warning on the tower type oil pumping unit. According to the invention, the accuracy of diagnosis and early warning of the fault of the oil pumping unit is effectively improved.

The invention relates to the technical field of data processing, in particular to an industrial electromagnetic environment multi-dimensional health degree assessment model, which comprises a processor and a memory, and the processor executes a computer program of the memory to implement the following steps: acquiring a multi-dimensional electromagnetic environment data sequence of any equipment in an industrial electromagnetic environment in a current time period, if the abnormal fluctuation time period exists in the current time period, obtaining the abnormal performance degree of each abnormal fluctuation time period according to the data difference of each abnormal fluctuation time period; according to the data fluctuation characteristics of the multi-dimensional electromagnetic environment data sequence and the change trend of the multi-dimensional electromagnetic environment data of each reference device, the interference degree of any device in each abnormal fluctuation time period is obtained; according to the abnormal performance degree of each abnormal fluctuation time period and the interfered degree of any device, the affected degree of any device is obtained, the health degree of the industrial electromagnetic environment is obtained by using the affected degree of each device, and the accuracy of a health degree evaluation result is improved.

This invention relates to the field of water supply network leak identification, and discloses a method, system, medium, and equipment for improving leak identification performance. The method includes: dividing unlabeled monitoring data into homogeneous and heterogeneous data pairs after data processing, and inputting both types of data pairs into a pre-trained model for feature extraction and pre-training; constructing a leak detection and identification model based on the features extracted from the pre-trained model, transferring the ability to distinguish data differences learned from unlabeled data to the leak detection and identification model; fine-tuning the leak detection and identification model using labeled data, and continuously optimizing the parameters of the leak detection and identification model through backpropagation to improve leak identification performance. This invention effectively solves the problems of high cost and difficulty in labeling data.

The invention relates to the technical field of data processing, in particular to a process detection method for a manufacturing process of a chicory preparation, which comprises the following steps: acquiring a concentrated solution concentration data sequence and a corresponding chicoric acid yield data sequence in the manufacturing process of the chicory preparation under each ethanol concentration; dividing each concentrated solution concentration data sequence into at least two stage sequences according to data change characteristics in each concentrated solution concentration data sequence; each concentrated solution concentration data sequence is divided into at least two subsequences, and according to the data difference of the subsequences contained in each stage sequence in each concentrated solution concentration data sequence and the fluctuation characteristics of the concentrated solution concentration data in each stage sequence, the concentrated solution concentration data in each stage sequence is obtained; according to the method, the stage influence weight of each stage sequence in each concentrated solution concentration data sequence is obtained according to the concentration data sequence of each concentrated solution and the similarity between each concentrated solution concentration data sequence and the corresponding chicoric acid yield data sequence, a regression analysis model is constructed, and the accuracy of a regression analysis result is improved.

The invention provides a multi-source heterogeneous data fusion and scheduling method based on an industrial internet data center, and the method comprises the steps: carrying out the full-quality data sampling of an industrial internet of things, obtaining a plurality of pieces of object data, carrying out the key quality index detection, and obtaining an object index difference set; performing corresponding analysis on the data source entities to obtain a data source-scene type matrix, performing traversal to perform fusion quality evaluation, and positioning heterogeneous data source entities; performing scheduling hierarchical classification and multi-level to-be-scheduled entities on heterogeneous data source entities in a standard process of obtaining a data source line; multi-level progressive scheduling is performed on heterogeneous data source entities, and data source index scheduling of data source lines obtained by the industrial Internet of Things is performed, so that efficient data fusion of multi-source heterogeneous data is realized, the problem of large data difference in an industrial Internet environment is solved, potential data values are fully mined, tasks can be reasonably arranged, and the data fusion efficiency is improved. The data processing timeliness and the resource utilization rate are improved, and the industrial production cost is reduced.

The invention provides a bearing fault detection method and device. The method comprises the following steps: acquiring a to-be-detected bearing signal; inputting the to-be-detected bearing signal into a pre-trained fault detection model to obtain a fault detection result; wherein the fault detection model is obtained by training a pre-constructed training data set based on a neural network, and the training data set is generated by inputting an actual measurement signal sample and a simulation signal sample into a pre-trained frequency domain bidirectional cyclic generative adversarial network. According to the invention, the pre-trained frequency domain bidirectional cyclic generative adversarial network is used to generate the fault sample with the fault features and the actually measured noise features, so that the training data set is obtained, and the training data set is used to train the fault detection model. The problems of fault sample scarcity, simulation-actual measurement data difference and insufficient feature fidelity in the prior art are solved, and the precision and generalization ability of bearing fault detection are improved.

The invention relates to a distributed data consistency characterization and credibility verification method based on a block chain, and the method comprises the steps: collecting multi-source power grid operation data in real time among a dispatching master station, a transformer substation and edge equipment, carrying out the unified structural processing of the multi-source power grid operation data, and extracting a hash abstract to guarantee the normalization of a data source; hash abstracts generated by all the nodes are uploaded to a consistency comparison engine, a multi-dimensional consistency calculation model is established based on key parameters including timestamps, voltages and currents, and data differences and conflicts are identified; state coding is carried out on a comparison result, a power grid data consistency state identifier is generated, and the state identifier is written into a block chain in combination with a device ID and a time label; and the system constructs an index mapping mechanism based on the on-chain state identifier, so that the master station and the dispatching center can perform cross-system consistency verification. According to the method, automatic consistency judgment and credible evidence storage of the distributed power grid data can be realized, and the accuracy of cross-system data sharing, the real-time performance of verification and the safety of system operation are improved.

The invention relates to the technical field of data processing, in particular to an efficient monitoring and intelligent irrigation management platform for radar rainfall data. Obtaining a first abnormal point location and a non-initial abnormal point location according to a data volume feature in an intensity data cluster of the echo intensity and a data difference feature of the intensity data cluster; obtaining a point location connected domain according to the intensity data cluster of the non-initial abnormal point location; obtaining a spatial noise feature value according to the position distribution feature between the point location connected domains and the data difference feature in the point location connected domains; obtaining a time noise characteristic value according to the historical echo intensity of the non-initial abnormal point location; obtaining a second abnormal point location according to the spatial noise characteristic value and the time noise characteristic value; correcting the corresponding echo intensity according to the position features of the first abnormal point location and the second abnormal point location; and the rainfall is determined according to the corrected echo intensity, and the irrigation rule is adjusted, so that the accuracy of rainfall judgment and irrigation regulation is improved.

The invention discloses a digital twinning-based three-dimensional visual intelligent factory equipment management method and system, and the method comprises the steps: obtaining scanning data obtained by scanning a factory under a plurality of poses through a scanning device, the scanning device comprises an image collection module and a point cloud collection module, and the scanning data comprises image data and point cloud data; determining data difference information between the image data and the point cloud data based on the scanning data; generating voxel information of each pose based on the data difference information and the scanning data; generating a first three-dimensional model based on the voxel information of the plurality of poses; determining a three-dimensional factory model based on the first three-dimensional model; the real-time equipment data of the factory is mapped to the three-dimensional factory model to generate the factory digital twinborn body and output the factory digital twinborn body to the display terminal, so that a user can perform equipment management on the factory through the factory digital twinborn body displayed on the display terminal, the factory equipment data can be displayed more intuitively, and the user experience is improved. Therefore, the user can perform more accurate equipment management on the factory.