422 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 relates to the technical field of construction progress management, in particular to a building construction progress intelligent tracking and pushing method and system based on BIM, and the method comprises the following steps: S1, constructing a 3D-BIM model, building a mapping relation, and forming a progress plan model; s2, collecting dynamic construction progress data of a construction site, and generating a construction real spot cloud model; s3, based on the generated construction real spot cloud model, analyzing the construction progress monitoring data difference between the Ti time period and the Ti + 1 time period, and evaluating the actual construction speed; s4, comparing the construction progress deviation between the actual construction speed and the planned construction speed, and judging whether progress delay exists or not; and S5, when progress delay exists, identifying and classifying delay reasons, generating a subentry progress deviation report, and pushing a deviation correction scheme. According to the invention, dynamic and real-time visual progress tracking and control are realized, the real-time requirement of project design file progress dynamic tracking is met, and the fine management level of construction progress tracking is improved.

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 discloses a big data analysis system and method for leaking stoppage and repayment of transport vehicles. The big data analysis system comprises a traffic behavior monitoring module which collects vehicle tracks and load records in a road network traffic data flow, analyzes a traffic time distribution rule, calculates a path deviation index, compares the difference between actual load and declaration data, and generates an abnormal traffic index; and the fee evasion risk prediction module is used for positioning passing nodes of high-risk vehicles based on the abnormal passing indexes, analyzing a matching relationship between node characteristics and historical fee evasion events, evaluating a road network risk level and generating a fee evasion probability prediction result. According to the big data analysis system and method for leaking stoppage and repayment of the transport vehicle, a quantum key distribution and lightweight encryption fusion mechanism is constructed, full-link dynamic encryption of data transmission is realized, man-in-the-middle attack and data tampering are effectively resisted, the security of core indexes in links of acquisition, transmission and analysis is guaranteed, and a trusted data basis is provided for intelligent inspection.

The invention provides a firmware updating method, a firmware updating device, equipment and a storage medium, which can be applied to the technical field of servers. The firmware updating method comprises the following steps: obtaining respective version information of a plurality of firmware partitions in first version firmware installed by a target component, wherein the plurality of firmware partitions are obtained by dividing the firmware into a plurality of logic regions according to a plurality of functions of the firmware; comparing the respective version information of a plurality of firmware partitions in the current second version firmware with the respective version information of a plurality of firmware partitions in the first version firmware, and determining a to-be-updated partition in the plurality of firmware partitions of the first version firmware; determining a data difference between the to-be-updated partition and a corresponding partition in the second version firmware to obtain an updated data set; and sending the update data set to the target component, so that the target component updates the first version firmware according to the update data set.

The invention discloses a temperature and humidity sensor data association analysis method based on deep learning, and relates to the technical field of data association analysis. Comprising the following steps: calculating a temperature and humidity gradient difference value of any two adjacent nodes based on a continuous temperature and humidity data sequence acquired by a sensor, and performing partition division in combination with a geographic distance; constructing a correlation coefficient matrix based on a historical time window for all sensor nodes in the same partition, and dividing the nodes into a plurality of correlation groups with high correlation; dividing a master node and a slave node for each association group; and generating a redundancy substitution matrix based on the correlation coefficient matrix, and when the main node is in an abnormal state, screening a standby node as a new main node in combination with the real-time data difference degree, and maintaining the stability of an inference link. The method effectively reduces the node energy consumption and communication redundancy while guaranteeing the prediction accuracy and reliability, is suitable for a large-scale deployment scene, and is especially suitable for a temperature and humidity sensing network analysis task in an edge environment.

The invention provides a tumor radiotherapy adverse reaction prediction system based on federal learning, and relates to the field of artificial intelligence medical treatment, and the system comprises the steps: carrying out the data volume comparison, data reliability and data difference analysis of a plurality of training data sets, and carrying out the evaluation to obtain a plurality of data contribution degrees; setting a first model parameter aggregation strategy according to the plurality of data contribution degrees, aggregating the plurality of model parameters, constructing a first adverse reaction prediction model, and respectively putting the first adverse reaction prediction model to a plurality of training points; and performing iteration cycle training until a preset iteration cycle number is met, and outputting an adverse reaction prediction model for prediction. The method aims to solve the technical problem that the accuracy and generalization ability of model prediction are affected due to the fact that an existing prediction system lacks accurate measurement of actual contributions of participants in the model parameter aggregation process. By introducing a multi-dimensional data contribution degree evaluation mechanism and dynamically setting a model parameter aggregation strategy, the accuracy and generalization ability of the adverse reaction prediction model can be significantly enhanced.

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 discloses an intelligent property service platform based on software and hardware integration, and belongs to the technical field of property services, and the platform specifically comprises the steps: collecting ground and wall space data in real time based on a multi-view image collection device disposed in a public area, and comparing the ground and wall space data with a pre-stored standard traffic area three-dimensional model; identifying an article exceeding a safety boundary through image feature analysis, and generating a risk level label in combination with the size and shape of the article and the position of surrounding facilities; according to the article risk level, matching a skill authentication level of the cleaning personnel and a current work order load state, and generating a dispatch scheme including a disposal priority; a structured electronic work order is sent to a cleaning personnel mobile terminal, wherein the structured electronic work order comprises article positioning coordinates, disposal operation guidance and path navigation information; and receiving a disposal completion feedback through the mobile terminal, automatically comparing image data differences before and after disposal, updating the three-dimensional model and generating a disposal compliance report.

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 discloses an intelligent fault diagnosis and rapid recovery system for glue preparation equipment, and relates to the technical field of industrial automation and intelligent manufacturing, and the system comprises an edge sensing layer, a real-time diagnosis control layer, and a cloud collaboration layer. The corrosion-resistant multi-mode sensor group collects glue solution viscosity, temperature, pressure and flow parameters of glue preparation equipment and equipment vibration signals in real time, and transmits the glue solution viscosity, temperature, pressure and flow parameters and the equipment vibration signals to the edge AI calculation unit through the anti-interference communication module. A mixed diagnosis mechanism of a physical driving model and a data driving model is combined, complex glue solution rheological behaviors and equipment mechanical abnormity are effectively recognized, a residual attention mechanism dynamically fuses a physical mechanism and actually measured data difference, the misjudgment rate is remarkably reduced, the timeliness and accuracy of fault alarm are ensured, and production interruption caused by missing detection or misjudgment is avoided.

The invention relates to the technical field of teaching, and particularly discloses an electrocardiographic examination operation method and system for simulation teaching, and the method comprises the steps: obtaining a data change record in a data synchronization process, the data change record comprising data definition language DDL sample set information, an operation timestamp and operation content; acquiring multiple pieces of statement sample information according to the DDL sample set information, and acquiring semantic similarity of each piece of statement sample information; and obtaining an operation identifier corresponding to each piece of statement sample information according to the operation content. According to the method and the device, the problem of insufficient operation analysis in the prior art is comprehensively and deeply solved by acquiring the operation identifier, calculating the operation similarity value, the operation data difference index and the like. Operation key feature information can be accurately extracted, the actual significance and association of data operation are clarified in combination with context identification, and misjudgment caused by inaccurate information extraction and lack of context consideration is avoided.

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 provides a water quality intelligent monitoring method and system, and the method comprises the steps: obtaining the historical water quality data of a water transmission and distribution pipe network, and obtaining parameters in a water quality dynamic system model through a least square method based on the historical data; a parameter estimation result is applied in a laboratory simulation environment, if the difference between model output and measured data does not exceed a preset threshold value, parameters are deployed in an actual water supply system, and the model is dynamically calibrated according to real-time operation data of a water transmission and distribution pipe network; and acquiring real-time water quality data through an Internet of Things sensor, inputting the real-time water quality data into the water quality dynamic system model to predict future water quality indexes, and adjusting water treatment process parameters in advance according to a prediction result. Through the method and the corresponding system, a water quality supervisor can be helped to make an adjustment decision in advance before the water quality is deteriorated, and more intelligent monitoring is realized.

The invention relates to the technical field of motor manufacturing, and discloses a motor stator and rotor iron core sheet stamping precision detection system. The detection system comprises an image acquisition module which is used for acquiring stamping area image data including a lamination contour, a punching position and a surface appearance; and the deformation detection module is used for carrying out dynamic compensation strategy processing on the image data, inputting a three-dimensional reconstruction processing layer to analyze errors and generating deformation characteristic data. A preprocessing unit of the three-dimensional reconstruction processing layer calibrates blocks and balances gray levels, a feature analysis unit carries out joint modeling based on historical data, a geometric matching layer aligns data, a difference quantization layer models morphological differences, and a compensation decision-making layer carries out fusion to generate deformation feature data. The detection method corresponds to the method. The punching precision of the iron chip can be accurately and efficiently detected, detection is optimized through historical data, the method is suitable for the complex iron chip, good universality and expansibility are achieved, and the motor manufacturing quality and efficiency are 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 provides an incinerator temperature field optimization method and system based on CFD numerical simulation, and the method comprises the steps: firstly recognizing a dynamic interference event in the operation of an incinerator, extracting the feature information of the event, constructing a hearth space association graph, learning the association relation between the interference feature and the temperature field abnormality through a graph neural network, and outputting a temperature abnormality association link; inputting the data into a CFD simulation system to generate and execute a target simulation task; acquiring dynamic response data of a temperature field; analyzing the data to determine a simulation correction direction; generating a parameter adjustment instruction to update simulation parameters; and finally, extracting a final simulation parameter as an optimization scheme, so that the temperature field of the incinerator can be accurately optimized.

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.

Industrial systems, and equipment are constrained by limited real-time sensor measurements, and monitoring of key performance indicators is difficult which may lead to sub optimal operations of systems. Embodiments of the present disclosure provide a method and system for developing pretrained models for industrial digital twins. A data associated with entity is preprocessed to obtain preprocessed data. The data associated with the entity is mapped with known parameter or unknown parameter of the entity. A n data-driven model is developed based on the identified parameters to select top m models. Top k unknown parameter is selected based on highest value of a parameter attribution score for selected top m model. Estimated value is determined for the top k unknown parameter to develop and iteratively train a physics informed data driven model. A physics-based error and a data-based error must be less than pre-defined physical discrepancy threshold and data discrepancy threshold.

The invention relates to the technical field of data processing, in particular to a concentrator intelligent fusion terminal based on multi-source fusion data regulation and control, the terminal comprises a processor and a memory, and the processor executes a computer program of the memory to realize the following steps: for any equipment acquired by a concentrator, acquiring a data fusion moment, for any data fusion moment, obtaining a multi-dimensional electric energy data sequence; recording the multi-dimensional electric energy data at any data fusion moment as multi-dimensional target electric energy data, and obtaining the electric energy use degree of the electric energy data of each dimension according to the data difference in the multi-dimensional electric energy data sequence; acquiring a fusion weight of the electric energy data of each dimension according to data fluctuation characteristics in the multi-dimensional electric energy data sequence; according to the electric energy use degree and the fusion weight of the electric energy data of each dimension in the multi-dimensional target electric energy data, the fusion data of any data fusion moment is obtained, and the operation overhead is reduced while more accurate multi-source fusion data is obtained.

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.