496 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 direct-current brushless gear motor auxiliary control, and discloses a control method and system for a direct-current brushless gear motor, and the method comprises the steps: carrying out the control of the direct-current brushless gear motor based on the real-time data during the starting and stopping of the motor, including detection data (such as temperature, vibration, noise and the like), operation data (such as current, voltage, torque and the like), and load data; and comparing and analyzing with preset historical detection data to judge whether the motor has a fault: when a large data difference is detected, evaluating the severity of the motor fault through a fault scoring mechanism. If the fault score exceeds a preset threshold value, early warning is triggered, and a corresponding control method is selected according to the fault type, so that fault processing or control strategy adjustment is carried out, and stable operation of the motor is ensured. According to the invention, the maintenance and fault diagnosis process of the motor is optimized by combining the fault score and the dynamic adjustment of the control strategy and through the real-time monitoring and analysis of the operation data and the load data, and the efficiency and accuracy of fault response 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 processing supervision, and discloses a supervision early warning system and method for tobacco leaf baking processing, the system comprises a monitoring module and an early warning part, and the early warning part comprises an acquisition module, an evaluation module, a central control module and an early warning module. According to the invention, the monitoring module is responsible for monitoring the ventilation quantity and the ventilation temperature of each monitoring area in real time and transmitting the data to the acquisition module; the acquisition module obtains a data difference value in combination with historical data, the central control module is used for screening data, data exception is identified through a Gaussian mixture model, and then an exception monitoring area is determined. The evaluation module calculates an early warning score according to the baking data of the abnormal area, and finally the early warning module judges whether early warning information is triggered or not. According to the anomaly detection method based on data difference analysis and the Gaussian mixture model, potential risks can be effectively identified, and the stability and safety of the baking process are ensured.

The invention provides a traffic incident cascade influence prediction method and device, and a storage medium, and the method comprises the steps: introducing a plurality of granularities in time and space through a space-time multi-granularity graph, carrying out the modeling of a road network, and marking the monitoring direction of a sensor and the relation between roads, so as to achieve the directional constraint and long-range association. And distinguishing and dividing the observation time window into data differences before and after event verification and before and after the occurrence of the concerned event. Data are represented hierarchically and fused, and space-time multi-granularity fusion is performed through an attention mechanism layer, so that the expression ability of the model is enhanced, and the calculation complexity is reduced. And finally, distinguishing the feature sequence before verification and the feature sequence after verification for the data, introducing an event influence degree matrix to quantify the influence degree of the traffic event on different regions, and inputting the influence degree into a traffic event cascade influence prediction module to output the node position and time period of the influenced road under the traffic event.

The invention discloses a while-drilling advanced intelligent early warning method based on multi-source disaster information entropy fusion, and the method comprises the steps: employing a multi-source sensing module to obtain multi-source data of different depths in a drilling process, then dividing depth segments, and carrying out the normalization and nondimensionalization processing of the multi-source data of each depth segment; the data processing module is used for increasing the data difference of each depth section, performing primary optimization for subsequent data processing, and then performing information entropy fusion data processing on the processed data, so that the difference of the same type of data can be further amplified, and the data fusion effect can be optimized; inputting the processed data into a KAN network, and after training is completed, establishing an early warning model; and finally, inputting the data acquired in real time into the model by using the early warning model to predict information entropy fusion data of a depth section which is not detected in front, comparing the data with a threshold value, finally judging that the risk of flood, fire or collapse exists in the depth section in front, and taking corresponding measures in time according to the risk condition. And therefore, the safety of roadway tunneling is ensured.

The invention discloses a rain condition monitoring data intelligent diagnosis method based on a spatial relationship, and the method comprises the steps: firstly screening a plurality of adjacent unmarked rainfall collectors through the management of a rainfall monitoring collector pool, and reading the data; carrying out rainfall monitoring data preprocessing and abnormal preliminary judgment, including length detection of discontinuous same rainfall data and deviation judgment of 0-value data, and labeling abnormal data; for the collector data in different time periods, analyzing the total rainfall capacity on a time sequence, and setting a threshold value for rainfall event division; a dynamic time warping algorithm is utilized to calculate the data difference degree of different collectors in different rainfall events, and a dynamic threshold value is set according to the difference degree and the spatial relation of the collectors to mark the states of the collectors; and all samples in the collector pool are marked through loop iteration of the process. The method effectively improves the accuracy and reliability of rainfall monitoring data, and is especially suitable for a rainfall monitoring and early warning system in a reservoir area.

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 power grid fault positioning, in particular to a fault detection optimization method for a honeycomb-shaped smart power grid. The method comprises the following steps: acquiring an original data sequence in a smart power grid, decomposing the original data sequence by using a time sequence decomposition algorithm to obtain an initial residual component sequence, and determining the residual component sequence according to the data distribution characteristics in the initial residual component sequence and the data difference between the initial residual component sequence and the original data sequence. Obtaining the credibility of the initial residual component sequence; if the credibility of the initial residual component sequence is not within a preset credibility range, optimizing preset parameters in a time sequence decomposition algorithm until the credibility of the new residual component sequence is within the preset credibility range, and obtaining a target residual component sequence; and finally, performing fault detection on the smart grid according to the target residual component sequence. By optimizing the preset parameters in the time sequence decomposition algorithm, the fault detection accuracy of the residual component is improved.

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 relates to the technical field of image data processing, in particular to a skeleton spacing detection method based on a high-strength alloy template structure, and the method comprises the steps: obtaining a skeleton gray image, constructing the three-dimensional point cloud data of a high-strength alloy template structure skeleton, and obtaining a point cloud skeleton region and a local skeleton region; obtaining the information credibility of the point cloud skeleton region according to the difference between the gray fluctuations of each local skeleton region corresponding to the same position in the skeleton gray image at different angles and the abundant conditions of the visual angle and the gray information; obtaining a shape error coefficient of the point cloud skeleton region according to a region matching relationship and a data difference between each point cloud skeleton region and the standard point cloud data; and in combination with the information credibility and the shape error coefficient, screening the edge feature points according to the discrete feature distribution condition of each edge feature point, and determining a skeleton spacing detection result according to a screening result. According to the invention, a more accurate skeleton spacing detection result can be obtained.

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 discloses a software upgrading method and device and a storage medium, and relates to the technical field of software upgrading. The method comprises the following steps: acquiring a new version data packet and an old version data packet of target software of a target terminal; based on the new version data packet and the old version data packet, generating a difference increment packet which is used for representing data difference information of the new version data packet and the old version data packet; evaluating the upgrading time of the difference increment packet and the upgrading time of the new version data packet to obtain an evaluation result; when it is determined that the evaluation result meets the preset condition, an upgrading strategy set is generated, and the upgrading strategy set is used for upgrading the target software from the old version to the new version. According to the technical scheme, the data transmission amount in the software upgrading process is reduced and the upgrading efficiency is improved by setting the upgrading mode of the differential increment package.

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.