AI-based intelligent data analysis method, system and storage medium

Abstract

The present invention discloses an AI-based intelligent data analysis method, system, and storage medium, belonging to the field of data analysis technology. The method includes: obtaining monitoring equipment information of the data source, obtaining data labels corresponding to the data source based on the equipment type; determining whether the analysis command contains the equipment label, if so, obtaining the monitoring data corresponding to the equipment label in the analysis command; otherwise, obtaining the analysis purpose of the analysis command, extracting standard equipment corresponding to the analysis purpose, and obtaining the data to be analyzed; testing and screening outliers in the data to be analyzed to generate standard analysis data, building a model resource library based on AI technology, and extracting the best analysis model from the model resource library based on data features and analysis commands; inputting the standard analysis data into the best analysis model for training and learning, outputting the analysis results, and completing the intelligent decision corresponding to the analysis command. The present invention can improve the accuracy of data analysis and the diversity of data analysis types.

Description

Technical Field

[0001] The present invention belongs to the field of data analysis technology, and specifically relates to an AI-based intelligent data analysis method, system and storage medium. Background Art

[0002] Artificial Intelligence (AI) can provide advanced algorithms and models to process, analyze, and interpret large amounts of data. As a result, the application of AI in data analysis has become more extensive and in-depth, providing more powerful data-driven solutions for all walks of life.

[0003] Similar prior art includes a Chinese patent application with publication number CN117785920A, which discloses an intelligent data analysis and processing method, apparatus, device and medium. The method includes calling a client API interface through a shared platform; the client API interface obtains input information submitted by the user, and in combination with a template engine, integrates the input information and the corresponding prompt word template and inputs it to the AI ​​engine; the AI ​​engine generates an SQL statement based on the input information and the corresponding prompt word template, and uses the data processing engine to execute the SQL statement to perform data query operations and generate data query results. The advantages of this invention are: the entire data analysis and processing process is automatically completed by the client API interface, template engine, AI engine and data processing engine, which can effectively lower the user threshold and improve data analysis efficiency, so that users without professional skills can also perform efficient data analysis and processing, and can improve the accuracy of data analysis. There is also a Chinese patent application with publication number CN117216419A, which discloses a data analysis method based on AI technology. The method includes the following steps: acquiring user data; performing dynamic behavior analysis on the user data using artificial intelligence technology to generate user dynamic behavior data; performing behavioral pattern analysis on the user dynamic behavior data to generate user behavior pattern data; performing time series analysis on the user dynamic behavior data based on the user behavior pattern data to generate user behavior time series data; performing emotion fluctuation identification on the user dynamic behavior data based on the user behavior time series data to obtain user emotion fluctuation data; using computer vision technology to fit a user profile to the user dynamic behavior data based on the user emotion fluctuation data to construct a user dynamic profile; and performing eye trajectory analysis on the user dynamic profile to generate user personality preference data. This invention achieves efficient and accurate user data analysis.

[0004] However, the above-mentioned existing technologies only analyze a single type of data, which limits the diversity of artificial intelligence technology. In actual situations, it is necessary to collect necessary analysis data based on analysis commands and perform combined analysis on different types of analysis data. By generating the corresponding optimal analysis model through AI technology, the accuracy of data analysis and the accuracy of intelligent decision-making can be improved. It can also reduce data analysis errors by extracting necessary analysis data. Summary of the Invention

[0005] To solve the above problems, the present invention provides an AI-based intelligent data analysis method, system and storage medium to solve the problems in the prior art.

[0006] In order to achieve the above-mentioned object of the invention, the present invention proposes an AI-based intelligent data analysis method, comprising:

[0007] Acquire monitoring device information of the data source, the monitoring device information including device tag, device type, and device location, and acquire a data tag corresponding to the data source based on the device type;

[0008] Setting an acquisition and transmission mode, receiving and identifying an analysis command, determining whether the analysis command includes the device tag, and if so, obtaining monitoring data corresponding to the device tag in the analysis command based on the acquisition and transmission mode, and setting the data as the data to be analyzed; otherwise, obtaining an analysis purpose of the analysis command, comparing the analysis purpose with the monitoring device information, extracting a standard device corresponding to the analysis purpose from all monitoring devices, obtaining monitoring data corresponding to the standard device based on the acquisition and transmission mode, and setting the data as the data to be analyzed;

[0009] checking the data to be analyzed for outliers based on the data labels, screening the data to be analyzed based on the outliers to generate standard analysis data, building a model resource library based on AI technology, extracting data features of the standard analysis data, and extracting the best analysis model from the model resource library based on the data features and the analysis command;

[0010] The standard analysis data is input into the optimal analysis model for training and learning, and an analysis result is output based on the analysis command, and an intelligent decision corresponding to the analysis command is completed based on the analysis result.

[0011] Furthermore, the acquisition transmission mode is set based on the following steps:

[0012] Setting the device corresponding to the data source as a monitoring device, building a preset number of intermediate devices for the monitoring device, storing the monitoring data corresponding to the monitoring device in any intermediate device, combining the monitoring device and all the corresponding intermediate devices to construct a status analysis table, the status analysis table records and updates the storage occupancy corresponding to all the intermediate devices, and the status analysis table switches the storage state of any of the intermediate devices based on the storage occupancy, thereby completing the collection and storage process of the monitoring device;

[0013] After identifying the analysis command, a data request is sent to the intermediate device based on the device tag, and the monitoring data stored in all the intermediate devices are extracted in sequence based on the data request to complete the transmission process of the monitoring data. The collection and storage process and the transmission process are combined and set to the collection and transmission mode.

[0014] Furthermore, generating the data to be analyzed includes the following steps:

[0015] The device tag or the standard device in the analysis command is set as the target device, and the data request is sent to the target device, wherein the data request includes the data tag and time information, and all the intermediate devices are proofread based on the data request. If the intermediate device meets the data request, the corresponding stored monitoring data is output, and the monitoring data corresponding to all the intermediate devices are combined based on the time information and set as the data to be analyzed.

[0016] Furthermore, it is determined whether the analysis command includes the device tag based on the following steps:

[0017] Analyze and interpret the analysis command based on a semantic analysis algorithm, generate an extended command, set an interpretation tag, the interpretation tag includes a data analysis object and a data analysis purpose, and extract the analysis object command corresponding to the data analysis object and the analysis purpose command corresponding to the data analysis purpose from the extended command based on the interpretation tag;

[0018] Based on the character similarity, it is determined whether the character corresponding to the device tag exists in the analysis object command. If so, it is determined that the analysis command contains the device tag. Otherwise, the extended command is set as the analysis purpose corresponding to the analysis command.

[0019] Furthermore, extracting standard equipment corresponding to the analysis purpose from all monitoring equipment includes the following steps:

[0020] Match the characters corresponding to the analysis purpose with the characters corresponding to any monitoring device information respectively, calculate the device position of any monitoring device and the word vector similarity between the device type and the analysis purpose based on the semantic analysis algorithm, and set them as first values. If both first values ​​are greater than the first threshold, any monitoring device is set as the standard device.

[0021] Furthermore, extracting the best analysis model from the model resource library based on the data features and the analysis command includes the following steps:

[0022] Setting a model tag, the AI ​​technology generates multiple data analysis models based on the model tag, and all the data analysis models are arranged and combined based on the model tag to generate the model resource library, wherein the model tag includes the model name, model function, model defect and model analysis process;

[0023] Checking outliers between the data to be analyzed corresponding to the same data label based on a data preprocessing process, deleting the outliers, and setting the remaining data as the standard analysis data;

[0024] Setting a feature analysis algorithm corresponding to the data tag based on the data preprocessing algorithm, and extracting the data features of the standard analysis data based on the feature analysis algorithm;

[0025] In the model analysis process, a model that meets the data features and the data labels is extracted and set as an alternative model. Based on the semantic analysis algorithm, the word vector similarity between the analysis command and the model function of any alternative model is obtained and set as a second value. The model with the largest second value is set as the optimal analysis model.

[0026] Furthermore, the intelligent decision-making corresponding to the analysis command based on the analysis result includes the following steps:

[0027] The optimal analysis model is trained based on the standard analysis data to generate an estimation model, the estimation model outputting an estimation sequence result based on the data features, and a monitoring mode of the standard analysis data is obtained based on the estimation sequence result and set as the analysis result;

[0028] A monitoring decision table is constructed, and corresponding decision information is obtained from the monitoring decision table based on the analysis result and the estimation sequence result, and is set as the intelligent decision.

[0029] The present invention also provides an AI-based intelligent data analysis system, which is used to implement the above-mentioned AI-based intelligent data analysis method. The system mainly includes:

[0030] The device processing module obtains the monitoring device information of the data source, wherein the monitoring device information includes the device tag, device type and device location, and obtains the data tag corresponding to the data source based on the device type;

[0031] a data acquisition module, setting an acquisition and transmission mode, receiving and identifying an analysis command, determining whether the analysis command contains the device tag; if so, acquiring the monitoring data corresponding to the device tag in the analysis command based on the acquisition and transmission mode, and setting the data as the data to be analyzed; otherwise, acquiring the analysis purpose of the analysis command, comparing the analysis purpose with the monitoring device information, extracting a standard device corresponding to the analysis purpose from all monitoring devices, acquiring the monitoring data corresponding to the standard device based on the acquisition and transmission mode, and setting the data as the data to be analyzed;

[0032] a model generation module, which checks the data to be analyzed for outliers based on the data labels, filters the data to be analyzed based on the outliers to generate standard analysis data, builds a model resource library based on AI technology, extracts data features of the standard analysis data, and extracts the best analysis model from the model resource library based on the data features and the analysis command;

[0033] The data analysis module inputs the standard analysis data into the optimal analysis model for training and learning, outputs analysis results based on the analysis command, and completes the intelligent decision corresponding to the analysis command based on the analysis results.

[0034] The present invention also provides a computer storage medium, which stores program instructions, wherein when the program instructions are executed, the device where the computer storage medium is located is controlled to execute the above-mentioned AI-based intelligent data analysis method.

[0035] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0036] The present invention first determines the acquisition path of the data source by marking the monitoring equipment information corresponding to the data source, then limits the required equipment by identifying the analysis command, and obtains the required data to be analyzed by collecting the transmission mode, which can reduce the problem of data storage capacity overload. At the same time, it can also accurately obtain the data to be analyzed contained in the analysis command, and improve the accuracy of obtaining the data to be analyzed. Finally, a model resource library is constructed through AI technology, and the optimal analysis model is extracted by data features and data labels, which can improve the accuracy of data analysis and the diversity of data analysis types.

[0037] The present invention also realizes intelligent decision-making based on analysis results in data analysis by constructing a monitoring decision table, thereby improving the intelligence of data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a flowchart of the steps of an AI-based intelligent data analysis method of the present invention;

[0039] Figure 2 This is a structural diagram of an AI-based intelligent data analysis system of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0041] It is understood that the terms "first," "second," etc., used herein may be used to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script without departing from the scope of this application.

[0042] like Figure 1 As shown, an AI-based intelligent data analysis method includes:

[0043] S1: Obtain the monitoring device information of the data source. The monitoring device information includes the device tag, device type and device location. Based on the device type, obtain the data tag corresponding to the data source.

[0044] Specifically, in this embodiment, the data source refers to the device destination for receiving data, such as various types of sensor devices, mobile devices for logging, etc. Monitoring device information refers to detailed information about the device corresponding to the data source, where the device tag refers to the device ID, the device type refers to the device type name, and the device location refers to the device installation information. For example, a temperature sensor M1 installed at the north gate of the campus has the device tag M1, the device type is temperature sensor, and the device location is the north gate of the campus. The data tag refers to the data type information collected using the device corresponding to the device type, including but not limited to the data sampling rate and data type. Data types include but are not limited to images, text, numbers, and signals.

[0045] S2: Set the acquisition and transmission mode, receive and identify the analysis command, and determine whether the analysis command contains a device tag. If so, obtain the monitoring data corresponding to the device tag in the analysis command based on the acquisition and transmission mode, and set it as the data to be analyzed. Otherwise, obtain the analysis purpose of the analysis command, compare the analysis purpose with the monitoring device information, extract the standard equipment corresponding to the analysis purpose from all monitoring devices, obtain the monitoring data corresponding to the standard equipment based on the acquisition and transmission mode, and set it as the data to be analyzed.

[0046] Specifically, in this embodiment, the acquisition and transmission mode refers to the acquisition, storage, and transmission methods used by various devices corresponding to the data source when collecting data. The analysis command refers to an instruction used to describe data analysis requirements, including but not limited to character commands, voice commands, and database commands. The analysis command can be received and recognized by the processing platform. If the analysis command includes a device tag, it indicates that the analysis command has already specified the data to be analyzed. The collected data of the device corresponding to the device tag, i.e., the monitoring data, can be directly retrieved using the acquisition and transmission mode. For example, if the analysis command L1 is: "Analyze the collected data of temperature sensors M1 and M2," the monitoring data corresponding to the device tags M1 and M2 will be retrieved based on the acquisition and transmission mode. If the analysis command does not include a device tag, semantic analysis of the analysis command is required to extract the analysis objective. The analysis objective refers to the analysis requirement. Standard equipment can be extracted by comparing the monitoring equipment information. Standard equipment refers to the list of equipment involved in the analysis command. For example, if the analysis command L2 is: "Monitor the temperature distribution of the campus," the semantic analysis algorithm will expand "monitoring," "campus," and "temperature" to form the analysis objective: "Monitor and analyze the temperature of the campus." Standard devices can be extracted based on the device type and location information in the monitoring device information. For example, all "temperature" type devices belonging to the "park" location can be set as standard devices. The data to be analyzed is the data set collected by the monitoring data of each device through the collection and transmission mode.

[0047] S3: Check the outliers of the data to be analyzed based on data labels, filter the data to be analyzed based on the outliers to generate standard analysis data, build a model resource library based on AI technology, extract the data features of the standard analysis data, and extract the best analysis model in the model resource library based on the data features and analysis commands.

[0048] Specifically, outliers refer to data points in the data being analyzed that significantly deviate from other observations. After filtering out outliers, standard analysis data can be generated. Standard analysis data is the complete data used for data analysis using analysis commands and can include data sources corresponding to multiple data labels. AI technology refers to artificial intelligence technology, including machine learning and deep learning, which can automatically create, optimize, and select multiple models to solve complex data analysis problems. A model repository refers to a repository of various models used for data analysis. Data features refer to the attributes or variables used to describe or characterize data points in standard analysis data. They are the basic elements that constitute standard analysis data and are used to capture and describe the essential characteristics of data from different perspectives. In data analysis, machine learning, and statistical modeling, data features are the foundation for understanding and analyzing data. Therefore, data features can be combined with analysis commands to extract the optimal analysis model from the model repository. The optimal analysis model is the model used to perform the data analysis corresponding to the analysis command.

[0049] S4: Input the standard analysis data into the optimal analysis model for training and learning, and output the analysis results based on the analysis command, and complete the intelligent decision corresponding to the analysis command based on the analysis results.

[0050] Specifically, in this embodiment, the optimal analysis model is generated by combining neural network models corresponding to various AI technologies. Therefore, standard analysis data is fed into the optimal analysis model for training. This training process involves adjusting model parameters, selecting optimal model hyperparameters, and outputting analysis results corresponding to the analysis command. Intelligent decision-making refers to a reference solution for addressing the data analysis requirements of analysis commands, enabling automated decision-making and improving efficiency, accuracy, and reliability.

[0051] As a preferred technical solution of the present invention, the acquisition transmission mode is set based on the following steps:

[0052] The device corresponding to the data source is set as the monitoring device, a preset number of intermediate devices are built for the monitoring device, the monitoring data corresponding to the monitoring device is stored in any intermediate device, the monitoring device and all corresponding intermediate devices are combined to construct a status analysis table, the status analysis table records and updates the storage occupancy corresponding to all intermediate devices, the status analysis table switches the storage status of any intermediate device based on the storage occupancy, and completes the collection and storage process of the monitoring device.

[0053] After identifying and analyzing the command, a data request is sent to the intermediate device based on the device tag. Based on the data request, the monitoring data stored in all intermediate devices are extracted in sequence to complete the transmission process of the monitoring data. The collection and storage process and the transmission process are combined and set to the collection and transmission mode.

[0054] Specifically, the monitoring device corresponds one-to-one to the monitoring device information. The intermediate device refers to the edge storage device of the monitoring device, which is used to receive and store the monitoring data of the monitoring device, wherein the preset number is an integer greater than or equal to 2. The capacity memory when the data is stored in the centralized storage can be reduced through the intermediate device. The status analysis table is a usage status record table for counting all monitoring devices and corresponding intermediate devices. The storage occupancy rate refers to the capacity ratio of the intermediate device. The larger the storage occupancy rate, the larger the capacity ratio of the intermediate device, and the lower the corresponding storage efficiency. The status analysis table can switch the storage status of different intermediate devices corresponding to the same monitoring device based on the comparison between the storage occupancy rate and the preset threshold. For example, the same monitoring device has two intermediate devices P1 and P2. If the storage occupancy rate of the intermediate device P1 is greater than the preset threshold, the intermediate device P1 is turned off, and the storage status of the intermediate device P2 is started, so that the monitoring data of the monitoring device is stored in the intermediate device P2. In this way, the collection and storage process of the monitoring device can be completed.

[0055] After recognizing and analyzing the command, a data request is sent to the corresponding monitoring device. After the monitoring device receives the data request, the corresponding intermediate device can transmit and extract the stored monitoring data, thus completing the monitoring data transmission process. Combining the collection and storage process with the transmission process can be set to the collection and transmission mode. The collection and transmission mode can improve the stability of the data collection and transmission process and reduce data storage losses.

[0056] As a preferred technical solution of the present invention, generating the data to be analyzed includes the following steps:

[0057] The device tag or standard device in the analysis command is set as the target device, and a data request is sent to the target device, wherein the data request includes the data tag and time information. All intermediate devices are checked based on the data request. If the intermediate device meets the data request, the corresponding stored monitoring data is output. The monitoring data corresponding to all intermediate devices are combined based on the time information and set as the data to be analyzed.

[0058] Specifically, in this embodiment, the target device refers to the device selected and obtained by the analysis command from the devices corresponding to all data sources, including the device tag corresponding to the analysis command or the device corresponding to the standard device. After identifying the analysis command, a data request is sent to the target device, wherein the time information refers to the time range corresponding to the data analysis required in the data request. All intermediate devices corresponding to the target device are proofread by the data request, wherein the proofreading refers to whether the data type and time information are consistent with the data request. The monitoring data corresponding to all intermediate devices that meet the data request are obtained, and after being combined in sequence according to the time information, the data to be analyzed can be generated.

[0059] As a preferred technical solution of the present invention, the following steps are used to determine whether the analysis command contains a device tag:

[0060] The analysis command is analyzed and interpreted based on the semantic analysis algorithm, an extended command is generated, and an interpretation label is set. The interpretation label includes the data analysis object and the data analysis purpose. Based on the interpretation label, the analysis object command corresponding to the data analysis object and the analysis purpose command corresponding to the data analysis purpose are extracted from the extended command respectively.

[0061] Based on the character similarity, it is determined whether the character corresponding to the device label exists in the analysis target command. If so, the analysis command is determined to contain the device label. Otherwise, the extended command is set as the analysis purpose corresponding to the analysis command.

[0062] Specifically, in this embodiment, the semantic analysis algorithm refers to a series of computational methods and techniques used to understand and interpret the meaning, intent, and emotion in natural language, thereby achieving more complete natural language processing (NLP) tasks. The algorithm has functions including but not limited to semantic role labeling, text summarization, semantic expansion, and natural language reasoning. The extended command refers to the command generated by using the semantic analysis algorithm to perform natural language reasoning on the analysis command. For example, if the text characters of the analysis command are: "Temperature monitoring of the park", the text characters corresponding to the extended command are: "Predict and analyze the temperature of Park A and Park B respectively." The interpretation label refers to the label corresponding to the text information that provides the basis for data analysis. Among them, the data analysis object refers to the text information corresponding to the target object, and the data analysis purpose refers to the text information corresponding to the specific goal or problem that the data analysis hopes to achieve. The analysis object command refers to the command information in the extended command that belongs to the data analysis object, and the analysis purpose command refers to the command information in the extended command that belongs to the data analysis purpose. For example, in the extended command "perform prediction analysis on the temperature of Park A and Park B respectively", the analysis object commands are "temperature of Park A" and "temperature of Park B" respectively, and the analysis purpose command is "prediction analysis".

[0063] Character similarity refers to an indicator that measures the similarity between two character strings (text sequences) in character composition, order, or structure. In this embodiment, the character similarity Q is calculated using the first formula: Where q2 is the number of characters corresponding to any device tag, and q1 is the number of identical characters between the analysis target command and the device tag. If the character similarity is 1, then the analysis target command contains characters identical to the device tag. Therefore, it is determined that the analysis target command contains a device tag, and all device tags are compared for character similarity. Otherwise, it is determined that the device tag does not exist. If it does exist, the analysis target command specifies the monitoring device for data analysis. The corresponding monitoring device can be directly selected based on the existing device tag. If it does not exist, the extended command can be directly set to the characters corresponding to the analysis target.

[0064] As a preferred technical solution of the present invention, extracting standard equipment corresponding to the analysis purpose from all monitoring equipment includes the following steps:

[0065] Match the characters corresponding to the analysis purpose with the characters corresponding to any monitoring device information respectively, calculate the device position of any monitoring device based on the semantic analysis algorithm, and calculate the word vector similarity between the device type and the analysis purpose, and set them as the first value. If both first values ​​are greater than the first threshold, any monitoring device is set as a standard device.

[0066] Specifically, in this embodiment, if there is no character directly corresponding to the device label in the analysis command, it is necessary to extract the standard equipment through the analysis purpose corresponding to the analysis command. The character corresponding to the analysis purpose is matched with the character contained in any monitoring device information. The word vector between the two can be extracted through the Word2Vec model in the semantic analysis algorithm, and the corresponding word vector similarity is calculated by cosine similarity, that is, a first value is generated. If both first values ​​are greater than the first threshold, it means that the device location and device type match the analysis purpose. For example, the analysis purpose is: "Predict and analyze the temperature of Park A and Park B respectively." Through word vector similarity, it can be extracted that the "temperature of Park A" includes devices T1 and T2, and the "temperature of Park B" includes device T3. Then, devices T1 and T2 are set as the first group of standard equipment, and device T3 is set as the second group of standard equipment. The monitoring data corresponding to the two groups of standard equipment are obtained respectively, and predictive analysis is performed.

[0067] As a preferred technical solution of the present invention, extracting the best analysis model from the model resource library based on data features and analysis commands includes the following steps:

[0068] Set model tags. AI technology generates multiple data analysis models based on model tags. All data analysis models are arranged and combined based on model tags to generate a model resource library. Model tags include model name, model function, model defects, and model analysis process.

[0069] Based on the data preprocessing process, outliers between the data to be analyzed corresponding to the same data labels are detected, and the outliers are deleted, and the remaining data are set as standard analysis data.

[0070] Based on the data preprocessing algorithm, the feature analysis algorithm corresponding to the data label is set, and the data features of the standard analysis data are extracted based on the feature analysis algorithm.

[0071] In the model analysis process, a model that meets the data characteristics and data labels is extracted and set as an alternative model. Based on the semantic analysis algorithm, the word vector similarity between the analysis command and the model function of any alternative model is obtained and set as the second value. The model with the largest second value is set as the best analysis model.

[0072] Specifically, in this embodiment, the model label refers to the label information used to describe each model, wherein the model name refers to the model type information generated using AI technology, the model function refers to the main technical function implemented by the model during data analysis, the model defect refers to the shortcomings of the model, and the model analysis process refers to the step information when the model performs data analysis. A variety of data analysis models can be generated through AI technology. These models are widely used in various industries and fields to solve complex data analysis problems, including but not limited to prediction models, classification models, clustering models, anomaly detection models, image processing models, deep learning models, and multimodal learning models. Based on the model labels, the information contained in various data analysis models is summarized and arranged to generate a model resource library.

[0073] The data preprocessing process refers to a series of operations performed on data before data analysis and processing, which can make the data more suitable for statistical analysis, machine learning or other forms of data mining, including but not limited to data cleaning, data integration, data dimensionality reduction and feature engineering. In this step, data preprocessing refers to the data cleaning algorithm, which uses the data cleaning algorithm to remove outliers from the data to be analyzed corresponding to any identical data label, and set the remaining data as standard analysis data. Among them, outliers refer to data that does not conform to data specifications, such as erroneous data and missing values.

[0074] A feature analysis algorithm is an algorithm used for feature extraction. Different feature analysis algorithms are selected for different data labels to ensure that the generated data features are applicable. For example, if the data label includes image type, the feature analysis algorithm can be set to the SIFT scale-invariant feature transform algorithm.

[0075] The model that meets the data feature refers to the name of the model that can be used for data analysis of the data feature. It is extracted through the model analysis process in the model tag. If the model analysis process includes analyzing and processing the data feature and the data label, the data analysis model corresponding to the model analysis process is extracted as the alternative model. Since most models can analyze and process the same data feature, and at the same time, a set of standard analysis data may have multiple data features, the number of alternative models may be large. Therefore, they can be filtered through the information corresponding to the model function in the model tag. Similarly, the word vector similarity between the analysis purpose in the analysis command and the model function is obtained through the semantic analysis algorithm. The model that best meets the analysis command can be extracted from the alternative models, that is, the best analysis model. The best analysis model can complete the data analysis corresponding to the analysis command through the data features corresponding to the standard analysis data.

[0076] As a preferred technical solution of the present invention, completing intelligent decision-making corresponding to the analysis command based on the analysis results includes the following steps:

[0077] The best analysis model is trained based on standard analysis data to generate an estimation model. The estimation model outputs an estimation sequence result based on the data characteristics. The monitoring mode of the standard analysis data is obtained based on the estimation sequence result and set as the analysis result.

[0078] Construct a monitoring decision table, obtain corresponding decision information in the monitoring decision table based on the analysis results and estimation sequence results, and set it as an intelligent decision.

[0079] Specifically, in this embodiment, all standard analysis data is input into the optimal analysis model. The training process adjusts and sets the various hyperparameters of the optimal analysis model to generate an estimation model. The estimation model is a model whose parameters have been adjusted based on the optimal analysis model. The estimation model intelligently outputs an estimation sequence based on the data features of the standard analysis data. The estimation sequence results are the output of the learning algorithm within the estimation model analyzing the data features of the standard analysis data. Monitoring mode uses the specific algorithms within the estimation model to identify potential relationships and patterns within the estimation sequence results.

[0080] Estimation sequence results can be used in a variety of scenarios, such as system fault diagnosis, network security monitoring, predictive maintenance of industrial systems, and intelligent monitoring of industrial parks. Therefore, by constructing a monitoring decision table, corresponding decision information can be quickly located based on the estimation sequence results, generating intelligent decisions. The monitoring decision table is a schedule set based on different data sources, and the decision information is the corresponding solution in the monitoring decision table. For example, the analysis results can determine the type of data source to be monitored. If the estimation sequence results exceed the maximum threshold corresponding to that data source in the monitoring decision table, the corresponding decision information can be set to send an early warning message and set as an intelligent decision. This step can improve the accuracy, intelligence, and applicability of data analysis.

[0081] like Figure 2 As shown, the present invention also provides an AI-based intelligent data analysis system, which is used to implement the above-mentioned AI-based intelligent data analysis method. The system mainly includes:

[0082] The device processing module obtains the monitoring device information of the data source. The monitoring device information includes the device label, device type and device location, and obtains the data label corresponding to the data source based on the device type.

[0083] The data acquisition module sets the collection and transmission mode, receives and identifies the analysis command, and determines whether the analysis command contains a device tag. If so, the monitoring data corresponding to the device tag in the analysis command is obtained based on the collection and transmission mode, and set as the data to be analyzed. Otherwise, the analysis purpose of the analysis command is obtained, the analysis purpose is compared with the monitoring device information, and the standard equipment corresponding to the analysis purpose is extracted from all monitoring devices. The monitoring data corresponding to the standard equipment is obtained based on the collection and transmission mode, and set as the data to be analyzed.

[0084] The model generation module verifies outliers in the data to be analyzed based on data labels, filters the data to be analyzed based on outliers to generate standard analysis data, builds a model resource library based on AI technology, extracts data features of standard analysis data, and extracts the best analysis model from the model resource library based on data features and analysis commands.

[0085] The data analysis module inputs standard analysis data into the optimal analysis model for training and learning, outputs analysis results based on analysis commands, and completes intelligent decision-making corresponding to the analysis commands based on the analysis results.

[0086] The present invention also provides a computer storage medium, which stores program instructions. When the program instructions are executed, the device where the computer storage medium is located is controlled to execute the above-mentioned AI-based intelligent data analysis method.

[0087] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0088] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0089] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0090] The above embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0091] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An AI-based intelligent data analysis method, characterized in that: The method comprises the following steps: Acquire monitoring device information of the data source, the monitoring device information including device tag, device type, and device location, and acquire a data tag corresponding to the data source based on the device type; Setting an acquisition and transmission mode, receiving and identifying an analysis command, determining whether the analysis command includes the device tag, and if so, obtaining monitoring data corresponding to the device tag in the analysis command based on the acquisition and transmission mode, and setting the data as the data to be analyzed; otherwise, obtaining an analysis purpose of the analysis command, comparing the analysis purpose with the monitoring device information, extracting a standard device corresponding to the analysis purpose from all monitoring devices, obtaining monitoring data corresponding to the standard device based on the acquisition and transmission mode, and setting the data as the data to be analyzed; The acquisition transmission mode is set based on the following steps: Setting the device corresponding to the data source as a monitoring device, building a preset number of intermediate devices for the monitoring device, storing the monitoring data corresponding to the monitoring device in any intermediate device, combining the monitoring device and all the corresponding intermediate devices to construct a status analysis table, the status analysis table records and updates the storage occupancy corresponding to all the intermediate devices, and the status analysis table switches the storage state of any of the intermediate devices based on the storage occupancy, thereby completing the collection and storage process of the monitoring device; After identifying the analysis command, a data request is sent to the intermediate device based on the device tag, and the monitoring data stored in all the intermediate devices are sequentially extracted based on the data request to complete the transmission process of the monitoring data, and the collection and storage process and the transmission process are combined to set the collection and transmission mode; checking the data to be analyzed for outliers based on the data labels, screening the data to be analyzed based on the outliers to generate standard analysis data, building a model resource library based on AI technology, extracting data features of the standard analysis data, and extracting the best analysis model from the model resource library based on the data features and the analysis command; The extraction of the best analysis model includes the following steps: Setting model tags, the AI ​​technology generates multiple data analysis models based on the model tags, and all the data analysis models are arranged and combined based on the model tags to generate the model resource library, wherein the model tags include model name, model function, model defects and model analysis process; Checking outliers between the data to be analyzed corresponding to the same data label based on a data preprocessing process, deleting the outliers, and setting the remaining data as the standard analysis data; Setting a feature analysis algorithm corresponding to the data tag based on the data preprocessing algorithm, and extracting the data features of the standard analysis data based on the feature analysis algorithm; Extracting a model that meets the data features and the data labels in the model analysis process and setting it as an alternative model; obtaining a word vector similarity between the analysis command and the model function of any alternative model based on a semantic analysis algorithm; setting the word vector similarity as a second value; and setting the model with the largest second value as the optimal analysis model; The standard analysis data is input into the optimal analysis model for training and learning, and an analysis result is output based on the analysis command, and an intelligent decision corresponding to the analysis command is completed based on the analysis result.

2. The method according to claim 1, characterized in that Generating the data to be analyzed includes the following steps: The device tag or the standard device in the analysis command is set as the target device, and the data request is sent to the target device, wherein the data request includes the data tag and time information, and all the intermediate devices are proofread based on the data request. If the intermediate device meets the data request, the corresponding stored monitoring data is output, and the monitoring data corresponding to all the intermediate devices are combined based on the time information and set as the data to be analyzed.

3. The method according to claim 1, characterized in that Determine whether the analysis command contains the device tag based on the following steps: Analyze and interpret the analysis command based on a semantic analysis algorithm, generate an extended command, set an interpretation tag, the interpretation tag includes a data analysis object and a data analysis purpose, and extract the analysis object command corresponding to the data analysis object and the analysis purpose command corresponding to the data analysis purpose from the extended command based on the interpretation tag; Based on the character similarity, it is determined whether the character corresponding to the device tag exists in the analysis object command. If so, it is determined that the analysis command contains the device tag. Otherwise, the extended command is set as the analysis purpose corresponding to the analysis command.

4. The method according to claim 3, characterized in that The step of extracting standard equipment corresponding to the analysis purpose from all monitoring equipment comprises the following steps: Match the characters corresponding to the analysis purpose with the characters corresponding to any monitoring device information respectively, calculate the device position of any monitoring device and the word vector similarity between the device type and the analysis purpose based on the semantic analysis algorithm, and set them as first values. If both first values ​​are greater than the first threshold, set any monitoring device as the standard device.

5. The method according to claim 1, wherein The intelligent decision-making corresponding to the analysis command based on the analysis result includes the following steps: The optimal analysis model is trained based on the standard analysis data to generate an estimation model, the estimation model outputting an estimation sequence result based on the data features, and a monitoring mode of the standard analysis data is obtained based on the estimation sequence result and set as the analysis result; A monitoring decision table is constructed, and corresponding decision information is obtained from the monitoring decision table based on the analysis result and the estimation sequence result, and is set as the intelligent decision.

6. An AI-based intelligent data analysis system for implementing the method according to any one of claims 1 to 5, characterized in that: The system includes the following modules: The device processing module obtains the monitoring device information of the data source, wherein the monitoring device information includes the device tag, device type and device location, and obtains the data tag corresponding to the data source based on the device type; a data acquisition module, setting an acquisition and transmission mode, receiving and identifying an analysis command, determining whether the analysis command contains the device tag; if so, acquiring the monitoring data corresponding to the device tag in the analysis command based on the acquisition and transmission mode, and setting the data as the data to be analyzed; otherwise, acquiring the analysis purpose of the analysis command, comparing the analysis purpose with the monitoring device information, extracting a standard device corresponding to the analysis purpose from all monitoring devices, acquiring the monitoring data corresponding to the standard device based on the acquisition and transmission mode, and setting the data as the data to be analyzed; a model generation module, which checks the data to be analyzed for outliers based on the data labels, filters the data to be analyzed based on the outliers to generate standard analysis data, builds a model resource library based on AI technology, extracts data features of the standard analysis data, and extracts the best analysis model from the model resource library based on the data features and the analysis command; The data analysis module inputs the standard analysis data into the optimal analysis model for training and learning, outputs analysis results based on the analysis command, and completes the intelligent decision corresponding to the analysis command based on the analysis results.

7. A computer storage medium, characterized in that The computer storage medium stores program instructions, wherein when the program instructions are executed, the device where the computer storage medium is located is controlled to execute the method according to any one of claims 1 to 5.

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