47 results about "Uncertain data" patented technology

The invention discloses a hypergraph feature representation method, system and device based on fuzzy logic and a medium, and the method comprises the steps: obtaining original data, and constructing a hypergraph structure containing vertexes and hyperedges according to the original data; the hypergraph structure is initialized, and an initialized hypergraph structure is obtained; and inputting the initialized hypergraph structure into a preset hypergraph convolutional fuzzy network model for feature representation processing to obtain a hypergraph structure after feature representation processing. According to the hypergraph convolutional fuzzy network model, fuzzy representation and the hypergraph technology are deeply fused, the membership core concept of fuzzy logic is introduced, the hard coding association limitation of an existing hypergraph representation learning method that no black is white is broken through, the technical problems that a traditional hypergraph model cannot capture association gradients and fuzzy semantics are lost are effectively solved, and the learning efficiency is improved. Therefore, the flexibility and accuracy of the model are effectively improved, and stable prediction is provided in an uncertain data environment.

The embodiment of the invention provides a request processing method and device, a storage medium and a program product. In the request processing method, when a user has a service request, a main intelligent processing module can obtain request context information, and can automatically plan a target processing scheme based on the request context information. The main intelligent processing module can schedule the sub-intelligent processing modules corresponding to the processing links in the target processing scheme to process the service request, so as to dynamically distribute the processing task of the service request to the appropriate sub-intelligent processing modules, and reduce the dependence on manual customer service. A part of the sub-intelligent processing modules can process uncertain and fuzzy information by using the target language model, and can make a reasonable decision according to incomplete or uncertain data. And the main intelligent processing module and the sub intelligent processing module cooperate with each other, so that service requests of users in different scenes can be efficiently coped with. And furthermore, the automatic processing proportion of user complaints can be improved, and the labor cost brought by manual customer service is reduced.

A multi-field data analysis method based on a Bayesian information enhanced neural network comprises a BITRNN model, and a final optimization objective of the model combines expected cumulative rewards of reinforcement learning, an evidence lower bound (ELBO) of Bayesian reasoning and an exploration mechanism of information entropy. Through combination of Bayesian reasoning, information entropy and reinforcement learning, prediction and decision optimization in multi-field complex data are realized. The model provided by the invention can dynamically adapt to environmental changes and maintain efficient prediction performance in high-dimensional uncertain data. Through Bayesian reasoning, the model can effectively process parameter uncertainty; the introduction of the information entropy increases the exploratory performance of the strategy, and avoids falling into a local optimal solution. According to the method, theoretical advantages of Bayesian statistics, an information theory and reinforcement learning are fused, high-dimensional and high-uncertainty data can be effectively processed, the prediction precision and generalization ability of the model are improved, the method is suitable for various fields such as polymer material performance optimization, agricultural planting strategy making and financial investment decision making, and the method has wide application prospects. Wide application prospects and practical values are realized.

The invention relates to the technical field of data processing and distribution, and discloses an internet of things data distribution engine algorithm. The method comprises the following steps: firstly, cleaning and standardizing collected original power data; performing feature extraction and semantic analysis, and generating a dynamic priority score representing the real-time emergency degree of the data and a static importance factor representing the inherent importance of the equipment by calculating a plurality of dynamic feature indexes of the equipment and combining the business metadata of the equipment; a hierarchical scheduling strategy is adopted, secondary judgment based on the change trend is started for uncertain data, and accurate routing of the data to channels with different priorities is achieved; finally, the system dynamically adjusts a distribution threshold value according to actual service utility feedback of data distribution, and closed-loop optimization is formed; according to the method, the problems of key information submerging, response delay and extensive resource allocation in massive Internet of Things data are effectively solved, and low-delay transmission of high-value data and self-adaptive optimization of system resources are realized.

The invention discloses an intelligent form processing method and system based on rule tree optimization and large model screening, and relates to the technical field of artificial intelligence and data automation. The problems that the processing efficiency is low due to static solidification of a rule system, redundancy of execution paths and dispersion of homogeneous rules in a traditional form processing method, and the screening accuracy is low and the labor cost is high due to the lack of effective judgment capability for complex, fuzzy or implicit condition data of semantic understanding are solved. By constructing and optimizing the rule tree structure and combining with the semantic comprehension ability of the large language model, automatic grading processing and intelligent screening of the form are realized. Firstly, deterministic rule matching and filtering are completed by a rule tree, then semantic judgment is carried out in a logic context by utilizing a large model for uncertainty data, and finally, a screening result is fused and output. According to the method, the efficiency, the accuracy and the automation level of form processing in a complex business scene are remarkably improved.

The present invention discloses a deep learning-based precipitation inversion method and system for satellite data. The method comprises: using data acquired from satellite data in a preset area within a specified time as the data to be analyzed; determining a first precipitation area using the determined data, performing uncertainty estimation on the uncertain data to obtain a second precipitation area, performing rainfall scale prediction on the data to be analyzed within the first and second precipitation areas to obtain a precipitation scale probability; extracting precipitation scale features from the data to be analyzed to obtain precipitation scale characteristics, and performing feature fusion on the precipitation scale probability and the precipitation scale characteristics to obtain fused data; constructing a deep learning precipitation inversion model based on the attenuation rate using the fused data, outputting the data to be inverted to the deep learning precipitation inversion model, and outputting an inversion result. This method not only improves the accuracy of satellite data precipitation inversion but can also be directly applied to satellite data precipitation inversion systems.

The invention discloses a fatigue recognition method for a target person based on machine learning, and the method comprises the steps: taking data obtained from a worker database in a preset industrial scene as to-be-analyzed data; performing difference comparison on the to-be-analyzed data and the reference database, taking the to-be-analyzed data with difference as first data, and otherwise, taking the to-be-analyzed data as uncertain data; performing consistency comparison on the uncertain data by adopting the undetermined database, taking data without differences as problem data, and adding the problem data into the first data; performing fatigue feature extraction on the first data to obtain key features, performing personalized time sequence analysis on the key features to obtain second data, and performing multi-modal dynamic fusion on the second data by adopting an attention mechanism to obtain fused data; and performing fatigue judgment according to the fusion data based on a dynamic threshold value to obtain identification data, and constructing a target person fatigue identification model according to the identification data.

The present invention provides a method and system for detecting prefix hijacking for uncertain sources. The method comprises: obtaining data from the routing information base, resource public key infrastructure, and Internet routing registry in the Border Gateway Protocol; constructing prefix cloud droplets from the time, space, and data source dimensions based on cloud model theory, and deriving deterministic data and uncertain data based on the discreteness of the cloud model; performing spatiotemporal stability analysis on the deterministic data, dynamically scoring each prefix cloud droplet, and establishing a dynamic deterministic information base; for the uncertain data, performing online route detection using a custom dictionary tree; utilizing prefix coverage and matching rules in combination with a dual verification mechanism to determine whether the route is legitimate, thereby completing anomaly detection and online updating. The present invention proposes a prefix hijacking detection method based on cloud model theory and a Trie tree to overcome the characteristics of the Border Gateway Protocol, such as dynamic changes, complex and large-scale networks, and uncertain data.

The application provides a dynamic scheduling method for uncertain data-intensive workflow in a cloud environment, solves the scheduling problem caused by the lack of transmission data size information, and helps to simultaneously reduce the cross-data center data transmission amount of the workflow and the execution cost of the workflow. First, the workflow structure is abstracted to obtain a DAG graph; then, static task pre-allocation is performed under the condition of partial data size information missing, an executable task forest graph of each data center is obtained, each task node in the forest graph is sorted according to the saved transmission data size, the task node with the largest saved data transmission size is allocated in the corresponding data center, and the predecessor node and the successor node of the node in the data center are also allocated in the data center, until all tasks are pre-allocated; then, dynamic adjustment of task allocation is performed based on the static task pre-allocation result and the actual transmission data size generated after the execution of each task in the workflow, and finally, an allocation scheme is obtained.

The present application relates to the technical field of intelligent adjustment of air conditioner, in particular to a kind of air conditioner resource flexible regulation and control method of simplifying easy operation implementation, including establishing adjustment model, suggestion calculation model, establishing distribution model and closed loop regulation, beneficial effect is: by introducing the numerical concept of power transfer proportion, so only need to carry out preliminary data collection, power transfer proportion can be calculated based on the preliminary collection of data, so in subsequent adjustment process, the environmental data needed to be collected, only need to be calculated based on power proportion to realize the intelligent air conditioner adjustment based on power grid, and by calculating the power proportion data of different time periods, hedge a large number of uncertain data and factors, greatly reduce the difficulty of data collection and analysis, improve the simplicity of regulation.

The invention provides a probabilistic nearest neighbor query method on high-dimensional continuous uncertain data, and relates to a method for quickly retrieving the nearest neighbor of the uncertain data by combining a locality sensitive hashing technology and probability calculation. The method comprises the following steps: firstly, constructing locality sensitive Hash for mapping, and mapping the uncertainty of a data object into a plurality of Hash tables through sampling; in a query stage, candidate adjacent objects are extracted from a given query point by using an index, and the probability that each candidate object becomes the nearest neighbor of the query point is calculated through Monte Carlo simulation. And finally outputting the neighbor object with the maximum probability and the probability value thereof. According to the method, the efficiency of high-dimensional uncertain data nearest neighbor query is greatly improved while the query accuracy is guaranteed, and the method can be widely applied to the fields of big data analysis, uncertain information retrieval and the like.

The application discloses a flotation condition recognition method and system based on a weighted K nearest neighbor algorithm, and the method comprises the following steps: setting fuzzy multi-neighbor particles for fuzzy and uncertain data and different distributions of various features in a bubble image data set; introducing the concept of entropy to calculate the information amount provided by the fuzzy multi-neighbor particles induced by a feature subset; fully considering the multiple relationships among the bubble image features under the fuzzy multi-granularity information theory framework; constructing a target evaluation function according to the multiple relationships among the features, calculating the function value of each feature, and sorting the features; weighting the K nearest neighbor algorithm by using the calculated feature function value; and selecting the feature subset with the highest classification accuracy as the optimal feature subset for the flotation condition recognition. The application fully considers the multiple relationships among the bubble image features, can fully utilize the useful information provided by the features, and has high classification efficiency and classification accuracy.

The application provides a kind of fusion trust loss and decision reclassification method for graph convolution heart disease detection.Belongs to medical information intelligent processing technology field, solved in heart disease detection, the problem of uncertain data affecting classification accuracy.Its technical scheme is: first, collect heart disease information data sample set, then, the above data is pretreated, and an adjacency matrix is constructed;Then, the processed heart disease information data is input into the graph convolution neural network GCN for model training;Finally, the model enters the test stage, gets the preliminary classification result and enters the decision layer, filters out the uncertain node information according to the threshold value, and maps it using the Gaussian kernel, so that it is high-dimensional separable, and achieves the effect of reclassification.

The invention relates to the technical field of operation and maintenance anomaly detection, and discloses an intelligent operation and maintenance anomaly detection method and system based on fuzzy logic, and the method comprises the steps: carrying out the modeling of operation and maintenance data through the fuzzy logic, so as to process the uncertainty and fuzziness of the data; analyzing the operation and maintenance data by using a fuzzy logic model to identify an abnormal mode; and outputting an anomaly detection result. According to the method, the fuzzy logic is deeply integrated into the whole anomaly detection process, an intelligent operation and maintenance anomaly detection mechanism with accuracy, adaptability and interpretability is constructed, the limitation of a traditional method in processing uncertain data is effectively overcome, and the intelligent level and fault response efficiency of an operation and maintenance system are remarkably improved.

The invention provides an uncertain information source-oriented prefix hijacking detection method and system. The method comprises the following steps of: acquiring a routing information base, a resource public key infrastructure and Internet routing registration center data in a border gateway protocol; prefix cloud droplets are constructed from time, space and data source dimensions based on a cloud model theory, and deterministic data and uncertain data are obtained according to the dispersion degree of a cloud model; performing space-time stability analysis on the deterministic data, dynamically scoring each prefix cloud droplet, and establishing a dynamic deterministic information base; for uncertain data, a self-defined dictionary tree is used for carrying out online detection on a route; and judging whether the routing is legal or not by utilizing prefix coverage and matching rules and combining a dual verification mechanism, so as to complete anomaly detection and online updating. The invention provides a prefix hijacking detection method based on a cloud model theory and a Trie tree so as to overcome the characteristics of dynamic change of a border gateway protocol, complex network, large scale, data uncertainty and the like.

The invention discloses a distributed uncertainty multidisciplinary optimization design method and system for a carrier rocket, and aims to process uncertain parameters, convert high-dimensional uncertain data into low-dimensional independent variables, reduce the calculation amount and improve the optimization design efficiency. When the data in the quantization parameter matrix is reconstructed, the optimization problem is solved based on the quantized parameters to obtain the probability density function, compared with a traditional uncertainty quantization means, the method can more accurately describe the probability distribution of the uncertain parameters, a reliable data basis is provided for subsequent optimization design, and the probability density function is obtained. The later calculation result is more accurate and does not need to depend on design experience. The constructed subject uncertainty and subject analysis agent model replaces high-cost high-fidelity subject analysis, and by constructing an offline agent model and combining with high-performance parallel calculation, frequent calling of a high-fidelity model in a UMDO solving process is avoided, and the calculation efficiency is remarkably improved.

The present invention discloses a data classification method and system based on a deep multi-path attention adaptive graph convolutional network, which relates to the field of medical image processing technology. rs-fMRI data is acquired and pre-processed to obtain a BOLD sequence, a functional connectivity feature vector is constructed and input into a DMAGCN model, and finally the optimal model is obtained through five-fold cross-validation for classification. The present invention ensures data quality through data preprocessing, laying the foundation for accurate analysis. The functional connectivity feature vector effectively represents the data. After input into the model, the Transformer backbone network and the MLP branch network can extract multi-source domain features. In combination with the graph network, non-imaging data is utilized, so that the model can learn rich features and enhance generalization and adaptability. The present invention improves classification accuracy, especially when processing uncertain data classification such as autism spectrum disorder, promotes research on related diseases, and provides an efficient and reliable method for medical data classification.

A pumped storage power station intra-day optimization scheduling method considering multi-source input comprises the steps that multi-source uncertainty data such as wind power, load and electricity price are collected and preprocessed, historical data are dynamically captured through a sliding window, time development trends and key parameters such as a mean value and covariance are calculated, a future uncertain source interval is predicted based on historical fluctuation, and optimal scheduling of a pumped storage power station is achieved. The method comprises the following steps of: establishing a water pumping and power generation power scheduling scheme, deducing a prediction interval of power generation and water pumping power, optimizing the accuracy of the interval through parameter adjustment, finally establishing an optimization model by taking economic cost minimization and power grid net power stability maximization as targets, generating the water pumping and power generation power scheduling scheme, and improving the robustness and scheduling efficiency of a system to deal with uncertainty. The method not only can adapt to complex scenes with frequent climate anomalies and effectively expand the application range of the optimization scheduling method, but also can adjust time development trend data and key parameters of historical uncertain sources and effectively cope with strong fluctuation characteristics of wind power.

To provide a method and system for secure provision of undetermined data from a determined or undetermined source in a consensus-based blockchain.SOLUTION: A first transaction is received at a node in a blockchain network, the first transaction including a first script that includes at least a first value, at least a portion of which includes data that is unconstrained by a second script, and a second value. A second transaction is obtained at the node. The second transaction having been validated and including the second script that, as a result of being executed, causes the node to obtain the first value and the second value as a result of execution of the first script, and validate, based at least in part on the first value and the second value, that the data is associated with a specific source. The first transaction is validated by executing the first script and the second script by the node.SELECTED DRAWING: Figure 15

The purpose of the present invention is to improve the reliability of an image processing system by taking into account the distribution of positive solution data corresponding to the output of a learning model and determining the output of the learning model while containing a deviation. For example, the present invention is provided with: a model application unit for generating an estimated high-quality image by applying a machine learning model to a low-quality image; an evaluation value calculation unit that calculates an evaluation value for the estimated high-quality image; and a determination unit that determines the estimated high-quality image on the basis of the evaluation value of the estimated high-quality image, the determination unit determining the estimated high-quality image using a deviation range of the evaluation value of a high-quality image obtained by capturing the low-quality image with high quality, the deviation range being acquired in advance and corresponding to the evaluation value of the estimated high-quality image. It is determined whether the speculated high quality image is uncertain data.

The invention discloses a satellite data rainfall inversion method and system based on deep learning, and the method comprises the steps: taking data obtained from satellite data in a preset region within a specified time as to-be-analyzed data; determining a first rainfall area through the determined data, performing uncertainty estimation on the uncertain data to obtain a second rainfall area, and performing rainfall scale prediction on the to-be-analyzed data in the first rainfall area and the second rainfall area to obtain a rainfall scale probability; performing precipitation scale feature extraction on the to-be-analyzed data to obtain precipitation scale features, and performing feature fusion on the precipitation scale probability and the precipitation scale features to obtain fusion data; and constructing a deep learning rainfall inversion model by using the fused data based on the attenuation rate, outputting the to-be-inverted data to the deep learning rainfall inversion model, and outputting an inversion result. The method not only can improve the precision of satellite data rainfall inversion, but also can be directly applied to a satellite data rainfall inversion system.

The invention relates to a method for determining the recruitment process of pilots using artificial intelligence-based fuzzy multi-criteria decision-making models, which creates an artificial intelligence-based decision-making model by considering numerous criteria such as socio-cognitive abilities, performance competencies, communication skills, visual perspective perception, and spatial working range in the selection process of airline pilots; processes and evaluates uncertain, vague, and subjective data with fuzzy logic techniques (fuzzy TOPSIS, fuzzy VIKOR, and fuzzy PROMETHEE) and enables the ranking of candidates; transforms non-numerical linguistic terms into fuzzy mathematical models; ensures the integration of qualitative and quantitative data and the transformation of uncertain data into reliable results in pilot selections; facilitates the successful ranking of candidate pilots and the selection of competent personnel for safe operations by companies; improves the recruitment process of pilots and utilizes an artificial intelligence-based decision-making model application in their selection

The invention provides a data estimator determination method and device based on fuzzy self-adaption, and relates to the technical field of data processing, and the method comprises the steps: obtaining an uncertain data set comprising a plurality of data observation values; calculating a mutual distance value between every two data observation values, and superposing the mutual distance values to obtain a mutual distance sum value; in combination with the mutual distance value and the mutual distance sum value, introducing an index coefficient greater than 1 to calculate the fuzzy membership degree of each data observation value relative to the uncertain data set estimator representing the trend of the data observation value; and determining an uncertain data set estimator, namely a data estimator, through a weighted median algorithm and an HL estimator algorithm according to the fuzzy membership degree. According to the method, the influence of abnormal values and data pollution on the estimator can be effectively reduced, the robustness of the estimator is improved, the real trend of the data can be accurately reflected, and particularly, the robustness and the accuracy are relatively high when the method faces uncertain or noise data.

The present application relates to the technical field of data center demand response, solves the technical problem of uncertain data center load regulation capacity, and particularly relates to a multi-agent game method considering uncertain data center demand response regulation capacity, comprising: constructing a data center user load optimization model; solving a microgrid operator optimization model to obtain a microgrid operator optimization strategy, and solving a non-data center user load optimization model to obtain a non-data center user load optimization strategy; inputting the non-data center user optimization strategy and the microgrid optimization strategy into the data center user load model for solving. The present application supports the participation of data centers in demand response through demand side resources of microgrid parks and non-data center user load regulation, solves the problem of uncertain data center load regulation capacity, thereby improving the flexibility of the power system, ensuring the safe and stable operation of the power system, and promoting renewable energy power consumption.

This invention discloses a duplicate data fusion detection method based on pre-trained active learning. The method includes generating candidate pairs in a labeled dataset and serializing these candidate pairs to obtain a serialized dataset; inputting the serialized dataset into a NER model to identify known types and using regular expressions to identify specific types; preprocessing the serialized dataset; inputting the preprocessed serialized dataset into a pre-trained BERT model, selecting the most uncertain data in the unlabeled dataset for labeling, and then inputting the labeled data into the labeled dataset; using the R-Drop strategy to augment the labeled dataset from step 4; and iterating through the augmented labeled dataset to obtain the final labeled dataset. This invention combines active learning algorithms, selecting the most valuable data for manual annotation, reducing the cost of manual annotation, and rapidly improving model quality with a small amount of data.

The present application discloses a method, device, equipment, medium and product for classifying part quality grades based on uncertain data pattern classification, which relates to the field of artificial intelligence. The method first obtains part dimension data with multiple-dimensional features at different parts and different processing stages of the part monitored by sensors; then performs data preprocessing on the part dimension data to obtain the preprocessed part dimension data; forms a feature selection genetic algorithm based on the fitness function of the granularity and elastic net improved genetic algorithm; uses the feature selection genetic algorithm to screen multiple-dimensional features of the preprocessed part dimension data to obtain an optimal feature subset; extracts feature data corresponding to the optimal feature subset from the preprocessed part dimension data to construct a feature data set; and based on the feature data set, uses the evidence K-nearest neighbor model to classify the part quality grades, which can significantly improve the efficiency and accuracy of part quality grade classification in an uncertain data environment.

The invention discloses a network intrusion detection method based on multi-granularity kernel fuzzy rough entropy, which belongs to the technical field of network data analysis, and comprises the following steps: calculating a fuzzy relation matrix for normalized network data by adopting a Gaussian kernel function to obtain a fuzzy information particle set; according to the kernel fuzzy information particles, calculating a knowledge rough entropy under global attributes of the global sample; calculating the weight of each attribute according to the knowledge rough entropy; constructing an attribute subset sequence according to the attribute weight; constructing a multi-granularity kernel fuzzy information particle set, and according to the multi-granularity kernel fuzzy information particles, calculating rough entropies of rough sets after each sample is removed under different kernel fuzzy relations one by one; calculating the importance of the samples according to the rough entropy of the rough set, and calculating the rough entropy outlier factors of the samples one by one in combination with the relative proportion of information particles; and finally, judging whether the outlier degrees of the samples are greater than a threshold value one by one, if so, outputting network data abnormal samples, otherwise, regarding the network data abnormal samples as normal data until all the samples are judged. According to the method, the problems of extraction and fusion of multi-granularity features, mining and extraction of a relation between fuzzy uncertain data and simplification of complicated marking work of existing network intrusion detection are solved.

The invention relates to the technical field of auxiliary medical evaluation, and particularly discloses an early Parkinson's disease interpretable three-branch intelligent evaluation method under multi-task remote data, and the method comprises the steps: fusing the multi-task remote data, introducing a fuzzy division strategy capable of processing uncertain data and an interpretable three-branch decision-making mechanism based on a decision-making tree, and carrying out the intelligent evaluation of the early Parkinson's disease. And combined modeling of the multi-dimensional symptom features is realized. In the modeling process, a decision tree division point is taken as a center, learnable fine adjustment parameters and offset parameters are respectively introduced, the division point is continuously finely adjusted, the fuzzy region boundary of the division point is optimized, and adaptive fuzzification processing of the decision tree division point is realized, so that a fuzzy decision tree is constructed, and on the basis of the fuzzy decision tree, a three-way decision mechanism is introduced, so that the fuzzy decision tree is constructed. Uncertain processing and sensitive identification of early symptoms can be effectively realized, so that evaluation accuracy and clinical availability are improved, and early screening and intervention of diseases are supported. Therefore, the method has relatively high flexibility, self-adaptability and capability of processing and explaining sample uncertainty.

The invention discloses an industrial equipment fault diagnosis method and equipment based on machine learning and a medium, and the method comprises the steps: collecting operation data of target equipment, and carrying out the preprocessing of the operation data, so as to obtain monitoring data; a pre-trained fault diagnosis model is determined, the fault diagnosis model is an improved graph neural network model, and an input layer of the improved graph neural network model adopts a fuzzy reasoning mechanism; and inputting the monitoring data into the fault diagnosis model to determine the equipment state and the fault type of the target equipment. By using the graph neural network, the topological structure relation between samples can be fully mined and utilized, the method is particularly suitable for processing complex data from a multi-source sensor, interdependence between nodes can be captured, and therefore the diagnosis accuracy is improved. A fuzzy reasoning mechanism is adopted in an input layer of the graph neural network and used for processing uncertainty in data, adjustment can be carried out according to fuzziness of the data, and the capacity of processing uncertain data is improved.

The invention discloses a real-time acquisition and storage method for key operating parameters of frequency converters, which belongs to the technical field of frequency converters, and comprises the following steps: aiming at the characteristics of concurrent access of multiple frequency converters, various data types, out-of-order arrival and continuous growth, performing acquisition, time alignment and packaging according to the data types on the operating parameters at an edge side; and a logic storage address is constructed on the center side based on the time interval, the equipment identifier and the data type, and mapping and ordered writing of the logic address and a physical storage position are realized through a storage positioning mechanism, so that the problem that the operation data of the plurality of frequency converters cannot be uploaded under the conditions of high concurrency and disordered uploading is solved. The technical problems of complete acquisition, stable storage and rapid positioning query are solved, the integrity of the original operation data is ensured, the network transmission pressure is effectively reduced, storage disorder caused by an uncertain data arrival sequence is avoided, and the storage efficiency and query efficiency of the operation data of the large-scale frequency converter are improved.