Data analysis method and device, terminal equipment and storage medium

Abstract

The invention is applicable to the technical field of information processing, and provides a data analysis method which comprises the following steps: acquiring to-be-analyzed data from a data source in real time; performing integration processing on the to-be-analyzed data to obtain integrated data; performing standardization processing on the integrated data to obtain standardized data; performing data analysis on the standardized data to obtain an analysis result; and carrying out visualization processing on the analysis result to obtain a visual analysis result. According to the embodiment of the invention, a visual analysis result can be quickly obtained, the latest dynamic state of the market and the real-time operation condition of an enterprise can be reflected in time, and a user can quickly understand and analyze data so as to make a more intelligent decision. Besides, redundancy, noise and abnormity in the data can be eliminated by integrating, standardizing and analyzing the data, and the accuracy and reliability of data analysis are improved.

Description

Technical Field

[0001] The present application belongs to the field of information processing technology, and in particular, relates to a data analysis method, apparatus, terminal device and storage medium. Background Art

[0002] In today's ever-changing business environment, enterprises are facing unprecedented challenges and opportunities. In order to maintain competitiveness and achieve sustainable development, enterprise decision-making depends on accurate, timely and comprehensive information support. In existing technologies, information support is usually achieved by periodically collecting and analyzing historical data. Although this method can reveal the development trends and potential laws of the business to a certain extent, the collection of historical data often has a certain lag, resulting in inaccurate data analysis results, unable to timely reflect the latest market trends and the real-time operating conditions of the enterprise, which may cause the enterprise to miss important market opportunities or be unable to respond to potential risks in a timely manner. Summary of the invention

[0003] In view of this, the embodiments of the present application provide a data analysis method, apparatus, terminal device and storage medium, which can solve the problem of inaccurate analysis results due to data lag when performing data analysis in related technologies.

[0004] In a first aspect, an embodiment of the present application provides a data analysis method, comprising:

[0005] Acquire the data to be analyzed from the data source in real time;

[0006] Integrate the data to be analyzed to obtain integrated data;

[0007] Performing standardization processing on the integrated data to obtain standardized data;

[0008] Perform data analysis on the standardized data to obtain analysis results;

[0009] The analysis results are visualized to obtain visual analysis results.

[0010] In a second aspect, an embodiment of the present application provides a data analysis device, including:

[0011] The acquisition module is used to obtain the data to be analyzed from the data source in real time;

[0012] An integration module is used to integrate the data to be analyzed to obtain integrated data;

[0013] A standardization module is used to perform standardization processing on the integrated data to obtain standardized data;

[0014] An analysis module is used to perform data analysis on the standardized data to obtain analysis results;

[0015] The visualization module is used to visualize the analysis results to obtain visualized analysis results.

[0016] In a third aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned data analysis method when executing the computer program.

[0017] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned data analysis method are implemented.

[0018] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when executed on a terminal device, enables the terminal device to execute the above-mentioned data analysis method.

[0019] The beneficial effect of the embodiment of the present application compared with the prior art is: the embodiment of the present application obtains the data to be analyzed from the data source in real time, integrates and processes the data to be analyzed to obtain integrated data, then standardizes the integrated data to obtain standardized data, then performs data analysis on the standardized data to obtain analysis results, and visualizes the analysis results to obtain visualized analysis results. The embodiment of the present application can quickly obtain visualized analysis results by acquiring and analyzing data in real time, so as to timely reflect the latest market trends and the real-time operating conditions of the enterprise, so as to facilitate users to quickly understand and analyze data, so as to make more informed decisions. In addition, by integrating, standardizing and analyzing data, redundancy, noise and anomalies in the data can be eliminated, and the accuracy and reliability of data analysis can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0021] Figure 1 It is a schematic diagram of the implementation flow of the data analysis method provided in the embodiment of the present application.

[0022] Figure 2 It is a structural schematic diagram of a data analysis method device provided in an embodiment of the present application.

[0023] Figure 3 It is a schematic diagram of the structure of the terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are protected by the present application.

[0025] It should be noted that the terms "include", "comprises" and "have" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, terminal, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices. In the claims, specification and drawings of the present application, relational terms such as "first" and "second" are merely used to distinguish one entity / operation / object from another entity / operation / object, and do not necessarily require or imply any such real-time relationship or order between these entities / operations / objects.

[0026] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0027] In today's ever-changing business environment, enterprises are facing unprecedented challenges and opportunities. In order to maintain competitiveness and achieve sustainable development, enterprise decision-making depends on accurate, timely and comprehensive information support. In existing technologies, information support is usually achieved by periodically collecting and analyzing historical data. Although this method can reveal the development trends and potential laws of the business to a certain extent, the collection of historical data often has a certain lag, resulting in inaccurate data analysis results, unable to timely reflect the latest market trends and the real-time operating conditions of the enterprise, which may cause the enterprise to miss important market opportunities or be unable to respond to potential risks in a timely manner.

[0028] In view of this, the embodiments of the present application provide a data analysis method, device, terminal device and storage medium. By acquiring and analyzing data in real time, visual analysis results can be quickly obtained to timely reflect the latest market trends and the real-time operating conditions of the enterprise, so that users can quickly understand and analyze data and make more informed decisions. In addition, by integrating, standardizing and analyzing data, redundancy, noise and anomalies in the data can be eliminated, and the accuracy and reliability of data analysis can be improved.

[0029] In order to illustrate the technical solution of the present application, a specific embodiment is provided below for illustration.

[0030] Figure 1 The following is a schematic diagram of a data analysis method provided in an embodiment of the present application, which can be applied to a terminal device. The terminal device can be a mobile phone, a tablet computer, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, etc. It is understandable that the above data analysis method can be applied to an RPA robot.

[0031] Specifically, the above data analysis method may include the following steps S101 to S105.

[0032] Step S101: acquiring data to be analyzed from a data source in real time.

[0033] The data source is the source of raw data, which may include databases, log files, sensors, user input, etc. The data to be analyzed is the data obtained from the data source in real time and to be further processed.

[0034] In the implementation of the present application, the terminal device can use real-time data stream processing technology (such as Apache Kafka, Flink, etc.) to capture data from the data source in real time. Specifically, the frequency and range of data capture can be set according to business needs to ensure the real-time and accuracy of the data.

[0035] Step S102: integrating the data to be analyzed to obtain integrated data.

[0036] In the implementation of the present application, the terminal device can clean the data to remove duplicate, invalid, and abnormal data, merge data from different sources to form a unified data view, and convert the data according to the analysis requirements, such as data type conversion, data format adjustment, etc., to facilitate subsequent analysis.

[0037] Step S103, standardize the integrated data to obtain standardized data.

[0038] In the implementation mode of the present application, the terminal device may process the data in a unified format to ensure the consistency and readability of the data. The data may also be denoised to remove the influence of the noise data on the analysis results. The data may also be dimensionalized to ensure that data of different dimensions can be compared and analyzed.

[0039] Step S104, performing data analysis on the standardized data to obtain analysis results.

[0040] In the implementation of the present application, the terminal device can use a variety of methods to perform data analysis on the standardized data to discover the hidden rules of the standardized data. The specific analysis method can be a statistical analysis method (such as descriptive statistics, correlation analysis, etc.), data mining technology (such as cluster analysis, association rule mining, etc.), machine learning algorithm (such as classification, regression, prediction, etc.), etc.

[0041] Step S105: Visualize the analysis result to obtain a visualized analysis result.

[0042] In the implementation mode of the present application, the terminal device can select a suitable chart type (such as a line chart, a bar chart, a pie chart, etc.) according to the user's instructions to display the analysis results. By designing an intuitive dashboard or report, the analysis results can be presented to the user in a graphical manner. In addition, the terminal device can also provide interactive functions, allowing the user to perform operations such as filtering, zooming in, and zooming out according to needs.

[0043] The beneficial effect of the embodiment of the present application compared with the prior art is: the embodiment of the present application obtains the data to be analyzed from the data source in real time, integrates and processes the data to be analyzed to obtain integrated data, then standardizes the integrated data to obtain standardized data, then performs data analysis on the standardized data to obtain analysis results, and visualizes the analysis results to obtain visualized analysis results. The embodiment of the present application can quickly obtain visualized analysis results by acquiring and analyzing data in real time, so as to timely reflect the latest market trends and the real-time operating conditions of the enterprise, so as to facilitate users to quickly understand and analyze data, so as to make more informed decisions. In addition, by integrating, standardizing and analyzing data, redundancy, noise and anomalies in the data can be eliminated, and the accuracy and reliability of data analysis can be improved.

[0044] In some specific implementations of the present application, after the above-mentioned data analysis is performed on the standardized data to obtain the analysis results, step S401 and step S402 may also be included.

[0045] Step S401, determining whether the analysis result satisfies the automatic response condition.

[0046] In the implementation of the present application, users can pre-set specific automatic response conditions. For example, sales drop by more than 10%, inventory levels are below a safety threshold, etc. The terminal device can use complex event processing (CEP) technology to monitor and analyze analysis results, continuously monitor data changes, and detect key events or anomalies in the analysis results in real time, such as a sudden increase in transaction volume or a decrease in quality indicators, and compare the analysis results with the automatic response conditions to determine whether the analysis results meet the automatic response conditions.

[0047] Step S402: If the analysis result meets the automatic response condition, the corresponding operation is automatically performed according to the response strategy, and an analysis report is generated.

[0048] In an embodiment of the present application, when the analysis results meet the automatic response conditions, the terminal device can determine the operation to be performed according to the preset response strategy. Specifically, it may include sending an alarm, adjusting system parameters, triggering other data analysis tasks, etc. The terminal device can automatically perform these operations without human intervention. In addition, the terminal device can also organize information such as analysis results, automatic response conditions, performed operations and results, and use report generation tools or templates to convert this information into easy-to-understand documents or reports, and provide them to relevant users so that users can understand the analysis process and results.

[0049] When the analysis results meet the automatic response conditions, the implementation method of the present application can quickly trigger the corresponding operation, thereby improving the response speed to abnormal or important situations. In addition, the implementation method of the present application also generates an analysis report to provide detailed analysis process and results, providing strong data support for decision makers.

[0050] In some specific implementations, the real-time acquisition of the data to be analyzed from the data source may specifically include step S501 and step S502.

[0051] Step S501, using streaming technology to obtain initial data in real time through an application programming interface.

[0052] In an implementation manner of the present application, a data stream listener may be set up on a terminal device to obtain initial data in real time through an API, and when new data arrives, it is immediately obtained through an API call.

[0053] Step S502: perform data verification and exception processing on the initial data to obtain data to be analyzed.

[0054] In an implementation manner of the present application, after obtaining the initial data, the terminal device may perform data verification and exception processing on the acquired initial data, such as checking the integrity, format and range of the data, and processing missing values ​​or abnormal values.

[0055] The implementation methods of the present application can acquire data in real time, ensure the timeliness and accuracy of analysis, perform data verification and exception processing on the acquired data, and improve the reliability and quality of the data.

[0056] In some specific implementations of the present application, the above-mentioned integration processing of the data to be analyzed to obtain the integrated data may specifically include step S601 and step S602.

[0057] Step S601 : Standardize the format and convert the type of the data to be analyzed from different data sources to obtain converted data.

[0058] In the implementation of the present application, the terminal device can pre-set format standardization and type conversion rules. For example, for date and time type data, a unified date and time format can be formulated; for numerical type data, it can be unified as floating point numbers or integers. The terminal device can first identify the source and format of the data to be analyzed, and understand the fields, types and structures of the data in each data source. Then use the data conversion tool or write a conversion script to standardize the format and convert the type of the data to be analyzed according to the established rules to obtain the converted data.

[0059] Step S602: merge the converted data into a single view according to a preset mapping rule to obtain integrated data.

[0060] In the implementation of the present application, the terminal device may pre-set mapping rules. The mapping rules generally include the corresponding relationship between data sources, the mapping relationship between fields, and the data merging method. The terminal device may use data integration tools or write integration scripts according to the mapping rules to merge the converted data into a unified view, which may be a storage structure such as a database table, a data warehouse, or a data lake.

[0061] The implementation methods of the present application can eliminate format differences and type mismatch problems between different data sources through format standardization and type conversion, and improve the accuracy and consistency of data. In addition, the integrated data has a unified format and structure, which is convenient for subsequent data processing and analysis, helps to reduce the time and cost of data processing, and improves work efficiency. In addition, the integrated data can support more complex analysis needs, such as analysis across data sources, multi-dimensional analysis, etc., which helps to discover hidden laws and trends in the data and provide more accurate information support for business decisions.

[0062] In some specific implementations of the present application, the above-mentioned standardization processing of the integrated data to obtain the standardized data may specifically include step S701 and step S702.

[0063] Step S701, calculating the maximum value and the minimum value in the integrated data.

[0064] In the implementation of the present application, for each field or variable in the integrated data, the terminal device can traverse all its values ​​to find the maximum and minimum values ​​of the field. Specifically, this step can be implemented using built-in functions or library functions in a programming language, such as the max() and min() functions in Python.

[0065] Step S702, range normalizing the integrated data according to the minimum value and the maximum value to obtain normalized data.

[0066] In an embodiment of the present application, for each field or variable in the integrated data, the terminal device may use the following formula to perform range normalization:

[0067] Normalized value = (original value - minimum value) ÷ (maximum value - minimum value)

[0068] This formula can be used to scale the original data to between 0 and 1, where the minimum value corresponds to 0 and the maximum value corresponds to 1.

[0069] The implementation method of the present application standardizes the integrated data to unify the numerical ranges of different fields or variables to between 0 and 1, thereby eliminating the dimensional differences between them and making different features comparable in subsequent analysis. The standardized data has a unified numerical range, making data visualization simpler and more intuitive. For example, the same color coding and scale can be used to represent the numerical size of different fields or variables.

[0070] In some specific implementations of the present application, the above-mentioned data analysis is performed on the standardized data to obtain the analysis results, which may specifically include steps S801 to S803.

[0071] Step S801, performing data prediction on the standardized data to obtain the prediction result of the standardized data.

[0072] In the implementation of the present application, the terminal device can use a prediction model, such as linear regression, time series analysis, neural network, etc. The real-time processed standardized data is input into the trained prediction model to obtain the prediction result. The prediction model can be trained using historical standardized data so that it can accurately predict future data.

[0073] Step S802, performing pattern recognition and trend recognition on the prediction results to obtain the data pattern and data trend of the standardized data.

[0074] In the implementation of the present application, the terminal device can use pattern recognition algorithms (such as cluster analysis, association rule mining, etc.) to identify specific data patterns in the prediction results, and use trend analysis techniques (such as trend line fitting, moving average, etc.) to identify the overall trend in the prediction results.

[0075] Step S803, generating analysis results according to the data pattern and data trend.

[0076] In the implementation of the present application, the terminal device can integrate the results of data prediction, pattern recognition and trend recognition to generate a comprehensive understanding and in-depth analysis of the data, and present the analysis results in the form of visual charts, reports or suggestions, so that decision makers or business personnel can understand and use them.

[0077] The implementation method of the present application can make full use of the inherent laws and characteristics of the data to improve the accuracy and reliability of the prediction by predicting the standardized data. Through the pattern recognition and trend recognition steps, the potential laws and trends in the data can be discovered, providing valuable insights and references for decision makers and business personnel.

[0078] In some specific implementations of the present application, after the above-mentioned data prediction is performed on the standardized data to obtain the prediction result of the standardized data, step S901 and step S902 may also be included.

[0079] Step S901, perform anomaly detection on the prediction results to obtain anomaly information of the standardized data.

[0080] In the implementation mode of the present application, after obtaining the prediction results of the standardized data, the terminal device can apply an anomaly detection algorithm to identify anomalies in the prediction results. Specifically, the anomaly detection algorithm can be a statistical method (such as Z-score, IQR, etc.) or a machine learning method (such as isolation forest, autoencoder, etc.). According to the output of the algorithm, determine which prediction results are abnormal and collect relevant information about these anomalies.

[0081] Step S902: Generate analysis results based on the abnormal information.

[0082] In the implementation mode of the present application, the terminal device can combine the abnormal information obtained in the abnormal detection step with the original data, prediction results and business background, conduct in-depth analysis and obtain analysis results. Determine the cause of the abnormal point, such as data input error, inaccurate model prediction, change in business environment, etc. According to the analysis results, propose corresponding solutions or suggestions, such as correcting data, optimizing models, adjusting business strategies, etc. The analysis results are then presented in the form of visual charts, reports or suggestions, so that decision makers or business personnel can understand and use them.

[0083] The implementation method of the present application can timely discover and correct errors or anomalies in data through anomaly detection, thereby improving the quality and accuracy of data. In business scenarios, timely discovery and processing of abnormal data can improve user experience, such as reducing system failures and improving service quality.

[0084] Figure 2 The schematic diagram of the structure of a data analysis device provided in an embodiment of the present application is shown. The data analysis device 2 can be configured on a terminal device. Specifically, the data analysis device 2 can include:

[0085] The acquisition module 201 is used to acquire the data to be analyzed from the data source in real time;

[0086] An integration module 202 is used to integrate the data to be analyzed to obtain integrated data;

[0087] A standardization module 203 is used to perform standardization processing on the integrated data to obtain standardized data;

[0088] An analysis module 204 is used to perform data analysis on the standardized data to obtain analysis results;

[0089] The visualization module 205 is used to perform visualization processing on the analysis result to obtain a visualized analysis result.

[0090] The beneficial effect of the embodiment of the present application compared with the prior art is: the embodiment of the present application obtains the data to be analyzed from the data source in real time, integrates and processes the data to be analyzed to obtain integrated data, then standardizes the integrated data to obtain standardized data, then performs data analysis on the standardized data to obtain analysis results, and visualizes the analysis results to obtain visual analysis results. The embodiment of the present application can quickly obtain visual analysis results by acquiring and analyzing data in real time, so as to timely reflect the latest market trends and the real-time operating conditions of the enterprise, so as to facilitate users to quickly understand and analyze data, so as to make more informed decisions. In addition, by integrating, standardizing and analyzing data, redundancy, noise and anomalies in the data can be eliminated, and the accuracy and reliability of data analysis can be improved.

[0091] In some embodiments of the present application, the data analysis device 2 further includes a judgment module for: judging whether the analysis result satisfies an automatic response condition; if the analysis result satisfies the automatic response condition, automatically executing a corresponding operation according to a response strategy and generating an analysis report.

[0092] In some implementations of the present application, the acquisition module 201 is further used to: utilize stream technology to acquire initial data in real time through an application program interface; perform data verification and exception processing on the initial data to obtain the data to be analyzed.

[0093] In some embodiments of the present application, the integration module 202 may also be used to: standardize the format and convert the type of the data to be analyzed from different data sources to obtain converted data;

[0094] The converted data are merged into a single view according to a preset mapping rule to obtain the integrated data.

[0095] In some implementations of the present application, the above-mentioned standardization module 203 can also be used to: calculate the maximum value and the minimum value in the integrated data; and perform range standardization on the integrated data according to the minimum value and the maximum value to obtain the standardized data.

[0096] In some embodiments of the present application, the above-mentioned analysis module 204 can also be used to: perform data prediction on the standardized data to obtain the prediction results of the standardized data; perform pattern recognition and trend recognition on the prediction results to obtain the data pattern and data trend of the standardized data; generate the analysis results based on the data pattern and the data trend.

[0097] In some embodiments of the present application, the data analysis device 2 further includes a detection module for: performing anomaly detection on the prediction result to obtain anomaly information of the standardized data; and generating the analysis result according to the anomaly information.

[0098] like Figure 3 FIG. 3 is a schematic diagram of a terminal device provided in an embodiment of the present application. The terminal device 3 may include: a processor 301, a memory 302, and a computer program 303 stored in the memory 302 and executable on the processor 301, such as a data analysis program. When the processor 301 executes the computer program 303, the steps in the above-mentioned data analysis embodiments are implemented, such as Figure 1 Alternatively, when the processor 301 executes the computer program 303, the functions of each module / unit in the above-mentioned device embodiments are implemented, for example Figure 2 The illustrated modules include an acquisition module 201 , an integration module 202 , a standardization module 203 , an analysis module 204 , and a visualization module 205 .

[0099] The computer program may be divided into one or more modules / units, one or more modules / units are stored in the memory 302 and executed by the processor 301 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0100] The terminal device may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will appreciate that Figure 3 It is only an example of a terminal device and does not constitute a limitation of the terminal device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0101] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0102] The memory 302 may be an internal storage unit of the terminal device, such as a hard disk or memory of the terminal device. The memory 302 may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device. Further, the memory 302 may also include both an internal storage unit of the terminal device and an external storage device. The memory 302 is used to store computer programs and other programs and data required by the terminal device. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0103] It should be noted that, for the convenience and brevity of description, the structure of the above-mentioned terminal device can also refer to the specific description of the structure in the method embodiment, which will not be repeated here.

[0104] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0105] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned data analysis method can be implemented.

[0106] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the steps in the above-mentioned data analysis method can be implemented when the mobile terminal executes the computer program product.

[0107] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0108] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0109] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0110] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0111] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0112] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0113] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application is described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A data analysis method, characterized in that: include: Acquire the data to be analyzed from the data source in real time; Performing integration processing on the data to be analyzed to obtain integrated data; Performing standardization processing on the integrated data to obtain standardized data; Performing data analysis on the standardized data to obtain analysis results; The analysis result is visualized to obtain a visualized analysis result.

2. The data analysis method according to claim 1, characterized in that: After performing data analysis on the standardized data to obtain analysis results, the method further includes: Determining whether the analysis result meets the automatic response condition; If the analysis result meets the automatic response condition, the corresponding operation is automatically executed according to the response strategy, and an analysis report is generated.

3. The data analysis method according to claim 1, characterized in that: The step of acquiring the data to be analyzed from the data source in real time includes: Using streaming technology, the initial data is obtained in real time through the application program interface; The initial data is subjected to data verification and exception processing to obtain the data to be analyzed.

4. The data analysis method according to claim 1, characterized in that: The step of integrating the data to be analyzed to obtain integrated data includes: Standardizing the formats and converting the types of the data to be analyzed from different data sources to obtain converted data; The converted data are merged into a single view according to a preset mapping rule to obtain the integrated data.

5. The data analysis method according to claim 1, characterized in that: The step of performing standardization processing on the integrated data to obtain standardized data includes: Calculating the maximum and minimum values ​​in the integrated data; The integrated data is range-normalized according to the minimum value and the maximum value to obtain the normalized data.

6. The data analysis method according to claim 1, characterized in that: The step of performing data analysis on the standardized data to obtain analysis results includes: Performing data prediction on the standardized data to obtain a prediction result of the standardized data; Performing pattern recognition and trend recognition on the prediction results to obtain the data pattern and data trend of the standardized data; The analysis result is generated according to the data pattern and the data trend.

7. The data analysis method according to claim 6, characterized in that: After performing data prediction on the standardized data to obtain the prediction result of the standardized data, the method further includes: Performing anomaly detection on the prediction result to obtain anomaly information of the standardized data; The analysis result is generated according to the abnormal information.

8. A data analysis device, characterized in that: include: The acquisition module is used to obtain the data to be analyzed from the data source in real time; An integration module, used for integrating the data to be analyzed to obtain integrated data; A standardization module, used for performing standardization processing on the integrated data to obtain standardized data; An analysis module, used to perform data analysis on the standardized data to obtain analysis results; The visualization module is used to perform visualization processing on the analysis results to obtain visualized analysis results.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the data analysis method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the data analysis method according to any one of claims 1 to 7 are implemented.

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