Multi-dimensional data analysis method, device, electronic device and medium

Abstract

The present invention relates to the field of data analysis technology, and discloses a multi-dimensional data analysis method, including: obtaining a data set to be analyzed in a business intelligence system; dividing the data set to be analyzed into an indicator data set and a dimension data set according to the data type of the data set to be analyzed; constructing an indicator dimension table according to the indicator data set and the dimension data set, the indicator dimension table including dimension data corresponding to each indicator data; deleting redundant dimension data of each indicator data in the indicator dimension table, obtaining multiple cleaned dimension data corresponding to each indicator data, and constructing an indicator model corresponding to each indicator data based on the multiple cleaned dimension data corresponding to each indicator data; cross-analyzing the indicator models to obtain data analysis results. The present invention also proposes a multi-dimensional data analysis device, equipment and storage medium. The present invention also relates to blockchain technology, and the data to be analyzed can be stored in a blockchain node. The present invention can improve the efficiency of data analysis.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a multi-dimensional data analysis method, device, electronic device and computer-readable storage medium. Background Art

[0002] In the context of big data, the scale of data is getting larger and larger, and the forms of data are becoming more and more diverse, resulting in increasing requirements for data applications. For the enterprise side, the rational use and analysis of data can bring huge value to the conduct and development direction of the enterprise's business.

[0003] Most of the existing data analysis is visual analysis. However, the existing visual data analysis is limited by graphics and is limited to one or two dimensions of data visualization analysis. Since the form of data changes rapidly, data analysis with only one or two dimensions is far from meeting the requirements of data visualization analysis. In addition, due to the limitation of dimensions, the efficiency of data analysis is difficult to improve. Therefore, how to break through the dimensional limitations of traditional visual data analysis and improve the efficiency of data analysis has become a problem that needs to be solved urgently. Summary of the invention

[0004] The present invention provides a multi-dimensional data analysis method, device, electronic device and computer-readable storage medium, the main purpose of which is to improve the efficiency of data analysis.

[0005] To achieve the above objectives, the present invention provides a multi-dimensional data analysis method, comprising:

[0006] Obtain the data set to be analyzed in the business intelligence system;

[0007] Dividing the data set to be analyzed into an indicator data set and a dimension data set according to the data type of the data set to be analyzed;

[0008] Constructing an indicator dimension table according to the indicator data set and the dimension data set, wherein the indicator dimension table includes dimension data corresponding to each indicator data;

[0009] Deleting redundant dimension data of each indicator data in the indicator dimension table, obtaining a plurality of cleaned dimension data corresponding to each indicator data, and constructing an indicator model corresponding to each indicator data based on the plurality of cleaned dimension data corresponding to each indicator data;

[0010] A cross analysis is performed between the multiple indicator models to obtain data analysis results.

[0011] Optionally, obtaining a data set to be analyzed in a business intelligence system includes:

[0012] Obtaining the name of the database storing the data set to be analyzed in the intelligent business system;

[0013] Query the server IP address and password information of the database according to the database name;

[0014] The database is connected using the server IP address and password information of the database, and the data to be analyzed is collected from the database to obtain the data set to be analyzed.

[0015] Optionally, dividing the data set to be analyzed into an indicator data set and a dimension data set according to a data type of the data set to be analyzed includes:

[0016] Acquire the data type of the data set to be analyzed, and divide the data in the data set to be analyzed into numerical type data and non-numerical type data according to the data type;

[0017] The numerical data is used as indicator data, and the non-numerical data is used as dimension data.

[0018] Optionally, constructing an indicator dimension table according to the indicator data set and the dimension data set includes:

[0019] Creating a dimension query table based on the dimension data set;

[0020] Query the dimension data corresponding to each indicator data in the indicator data set through the dimension query table;

[0021] Construct the indicator dimension table according to the dimension data corresponding to each indicator data.

[0022] Optionally, querying the dimension data corresponding to each indicator data in the indicator data set through the dimension query table includes:

[0023] Selecting one indicator data from the indicator data set in turn as target indicator data;

[0024] Obtaining text keywords corresponding to each dimension data in the dimension query table;

[0025] Calculating the similarity between the type of the target indicator data and each of the text keywords;

[0026] Determine that the corresponding dimension data when the similarity is greater than a preset threshold is the target dimension data corresponding to the target indicator data;

[0027] The target dimension data of all target indicator data are aggregated to obtain the dimension data corresponding to each indicator data in the indicator data set.

[0028] Optionally, deleting redundant dimension data of each indicator data in the indicator dimension table to obtain a plurality of cleaned dimension data corresponding to each indicator data includes:

[0029] Obtain dimension data corresponding to each indicator data in the indicator dimension table, and convert the dimension data into a standard text type to obtain a dimension text set;

[0030] Acquire multiple dimensional text sets from the dimensional text set by using a text similarity algorithm, each dimensional text set containing at least two dimensional texts;

[0031] A semantic segmentation algorithm is used to calculate the semantic range of dimensional texts in multiple dimensional text sets, retain dimensional texts with semantic ranges greater than a preset semantic range, and remove dimensional texts with semantic ranges less than the preset semantic range, to obtain multiple cleaned dimensional data.

[0032] Optionally, before using the non-numeric data as dimension data, the method further includes:

[0033] Convert the non-numeric data into text data;

[0034] Cleaning the text data to obtain cleaned text data;

[0035] Performing word segmentation processing on the cleaned digital text data, and counting the word frequency of each word after word segmentation, summarizing each word and the word frequency of each word in the form of a character string to obtain a word frequency character string set;

[0036] The words contained in the word frequency string set are converted into hash feature vectors by a hash function to obtain a vector word frequency string set;

[0037] By establishing a segment index, extracting similar hash feature vector pairs from the vector word frequency character string set;

[0038] The Hamming distance between the text data corresponding to the hash feature vector pair is calculated, and the non-numeric data corresponding to the similar word segmentation vector pair is deduplicated based on the Hamming distance.

[0039] In order to solve the above problems, the present invention further provides a multi-dimensional data analysis device, the device comprising:

[0040] A data acquisition module is used to acquire the data set to be analyzed in the business intelligence system;

[0041] A data partitioning module, used to partition the data set to be analyzed into an indicator data set and a dimension data set according to the data type of the data set to be analyzed;

[0042] A dimension table construction module, used to construct an indicator dimension table according to the indicator data set and the dimension data set, wherein the indicator dimension table includes dimension data corresponding to each indicator data;

[0043] an indicator model building module, used to delete redundant dimension data of each indicator data in the indicator dimension table, obtain a plurality of cleaned dimension data corresponding to each indicator data, and build an indicator model corresponding to each indicator data based on the plurality of cleaned dimension data corresponding to each indicator data;

[0044] The model analysis module is used to perform cross analysis between the multiple indicator models to obtain data analysis results.

[0045] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0046] at least one processor; and,

[0047] a memory communicatively connected to the at least one processor; wherein,

[0048] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the multi-dimensional data analysis method as described above.

[0049] In order to solve the above problems, the present invention also provides a computer-readable storage medium, including a data storage area and a program storage area, the data storage area stores created data, and the program storage area stores a computer program; wherein, when the computer program is executed by a processor, the multi-dimensional data analysis method as described above is implemented.

[0050] In an embodiment of the present invention, a data set to be analyzed composed of data to be analyzed is obtained from a business intelligence system, the data to be analyzed in the data set to be analyzed is divided into an indicator data set and a dimension data set, and a preliminary division of the data to be analyzed is achieved. Then, an indicator dimension table is constructed according to the indicator data set and the dimension data set, the indicator dimension table is cleaned, redundant dimension data in the dimension data corresponding to each indicator data is deleted, and an indicator model is constructed. The constructed indicator model has multiple dimensions, and cross-analysis is performed between the indicator models, so that multiple dimensions of the indicator model can be analyzed at one time, thereby realizing multi-dimensional data analysis and improving the efficiency of data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of a flow chart of a multi-dimensional data analysis method provided by an embodiment of the present invention;

[0052] Figure 2An example diagram of an indicator structure model provided by an embodiment of the present invention;

[0053] Figure 3 A schematic diagram of a module of a multi-dimensional data analysis device provided by an embodiment of the present invention;

[0054] Figure 4 A schematic diagram of the internal structure of an electronic device for implementing a multi-dimensional data analysis method provided by an embodiment of the present invention.

[0055] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0056] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0057] The embodiment of the present application provides a multi-dimensional data analysis method. The execution subject of the multi-dimensional data analysis method includes but is not limited to at least one of the electronic devices that can be configured to execute the method provided by the embodiment of the present application, such as a server, a terminal, etc. Among them, the server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms. In other words, the multi-dimensional data analysis method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0058] Reference Figure 1 FIG. 1 is a flow chart of a multi-dimensional data analysis method provided by an embodiment of the present invention. In this embodiment, the multi-dimensional data analysis method includes:

[0059] S1. Obtain the data set to be analyzed in the business intelligence system.

[0060] In the embodiment of the present invention, the business intelligence system (Business Intelligence, BI) is also called business wisdom or business intelligence, which can perform data analysis through modern data warehouse technology, online analysis and processing technology, data mining and data visualization technology to realize the commercial value of data.

[0061] In the embodiment of the present invention, the step of obtaining a data set to be analyzed in a business intelligence system includes:

[0062] Obtaining the name of the database storing the data set to be analyzed in the intelligent business system;

[0063] Query the server IP address and password information of the database according to the database name;

[0064] The database is connected using the server IP address and password information of the database, and the data to be analyzed is collected from the database to obtain the data set to be analyzed.

[0065] Specifically, the data set to be analyzed is a data set stored in the business intelligence system for data analysis, wherein the data in the data set to be analyzed may be data provided by the client, and the data in the client may be a historical data set of a target user corresponding to the client, for example, the target user's browsing history on the client, the target user's login log on the client, etc.

[0066] In the embodiment of the present invention, the database name is the name of the database in the data source storing the data set to be analyzed, and further, the database name contains the unique identification code such as the coding information and ID information of the database. The server IP address is a unified address format provided according to the IP protocol, and the password information is the login password for obtaining the data to be analyzed from the database.

[0067] In the embodiment of the present invention, the server IP address and the password information can be used to avoid erroneous acquisition when acquiring data from the data source, thereby preventing data that does not meet the requirements from being acquired.

[0068] S2. Divide the data set to be analyzed into an indicator data set and a dimension data set according to the data type of the data set to be analyzed.

[0069] In the embodiment of the present invention, the data types in the data set to be analyzed include indicator data and dimension data, wherein the indicator data can also be called measurement data, which is expressed as the quantification and digitization of things or businesses, and usually needs to be obtained through aggregation statistics such as addition and averaging, such as population, GDP, income, number of users, retention rate, etc. The dimension data is a certain feature of the thing phenomenon, such as time, gender, region, etc. Further, the indicator data relies on the dimension data to reflect its meaning.

[0070] For example, the indicator data of total population specifically includes the overall population data, and the dimensional data corresponding to the indicator data of total population includes population gender dimensional data, population age dimensional data, and population regional division dimensional data. Among them, the population gender dimensional data is male population data and female population data, the population age dimensional data is population data of different age groups, and the population regional division dimensional data is population data of different regions.

[0071] In the embodiment of the present invention, dividing the data set to be analyzed into an indicator data set and a dimension data set according to the data type of the data set to be analyzed includes:

[0072] Acquire the data type of the data set to be analyzed, and divide the data in the data set to be analyzed into numerical type data and non-numerical type data according to the data type;

[0073] The numerical data is used as indicator data, and the non-numerical data is used as dimension data.

[0074] The non-numeric data may be text data or chart data.

[0075] In another embodiment of the present invention, the value of the numerical type data may be a continuous number. For example, there is data named "age", where the minimum and maximum values ​​are 3 to 92 respectively. 0-6 years old can be classified as childhood, 7-17 years old as adolescence, 18-40 years old as youth, 41-65 years old as middle age, and over 65 years old as old age. The classified values ​​have clearer analysis value, and the data can be viewed from the perspective of different age stages for data analysis.

[0076] Furthermore, before using the non-numeric data as dimension data, the method further includes deduplicating the non-numeric data.

[0077] In the embodiment of the present invention, the deduplication of the non-numeric data includes:

[0078] Convert the non-numeric data into text data;

[0079] Cleaning the text data to obtain cleaned text data;

[0080] Performing word segmentation processing on the cleaned digital text data, and counting the word frequency of each word after word segmentation, summarizing each word and the word frequency of each word in the form of a character string to obtain a word frequency character string set;

[0081] The words contained in the word frequency string set are converted into hash feature vectors by a hash function to obtain a vector word frequency string set;

[0082] By establishing a segment index, extracting similar hash feature vector pairs from the vector word frequency character string set;

[0083] The Hamming distance between the text data corresponding to the hash feature vector pair is calculated, and the non-numeric data corresponding to the similar word segmentation vector pair is deduplicated based on the Hamming distance.

[0084] Specifically, by deduplicating the non-numeric data, it is possible to avoid adding the same data to the indicator data, thereby wasting computing resources.

[0085] In the embodiment of the present invention, cleaning the text data includes cleaning and sorting punctuation marks, spaces, Chinese and English characters, and simplified and traditional Chinese characters in the text data.

[0086] Specifically, the hash function is used to convert the words in the word frequency string into a hash feature vector, wherein the word frequency string is {word, counts}, and the vector word frequency string is {

[01] [N]: counts}.

[0087] In the embodiment of the present invention, the Hamming distance is the number of 1s after performing an XOR operation on the encoded character string obtained by text encoding. For example, the Hamming distance between the encoded character string "1011101" and the encoded character string "1001001" is 2.

[0088] S3. Construct an indicator dimension table according to the indicator data set and the dimension data set, wherein the indicator dimension table includes dimension data corresponding to each indicator data.

[0089] In the embodiment of the present invention, the indicator data set contains dimension data of each indicator, which is used to describe each indicator data in the indicator data set. For example, if the indicator data is personal data, the dimensions of the personal data include: age data, gender data and other data dimensions.

[0090] For example, the constructed indicator dimension table is shown in Table 1:

[0091] Dimension 1: Gender dimension Dimension 2: Age group dimension AUser ID male youth BUser ID female middle aged C User ID female juvenile

[0092] Table 1

[0093] In the embodiment of the present invention, the step of constructing an indicator dimension table according to the indicator data set and the dimension data set includes:

[0094] Creating a dimension query table based on the dimension data set;

[0095] Query the dimension data corresponding to each indicator data in the indicator data set through the dimension query table;

[0096] Construct the indicator dimension table according to the dimension data corresponding to each indicator data.

[0097] In the embodiment of the present invention, the dimension query table stores indicators corresponding to each dimension, and the indicator can identify the application scope of the dimension data, that is, to which indicators a certain dimension can be applied.

[0098] For example, the constructed dimension query table is shown in Table 2:

[0099]

[0100] Table 2

[0101] In the embodiment of the present invention, querying the dimension data corresponding to each indicator data in the indicator data set through the dimension query table includes:

[0102] Selecting one indicator data from the indicator data set in turn as target indicator data;

[0103] Obtaining text keywords corresponding to each dimension data in the dimension query table;

[0104] Calculating the similarity between the type of the target indicator data and each of the text keywords;

[0105] Determine that the corresponding dimension data when the similarity is greater than a preset threshold is the target dimension data corresponding to the target indicator data;

[0106] The target dimension data of all target indicator data are aggregated to obtain the dimension data corresponding to each indicator data in the indicator data set.

[0107] In the embodiment of the present invention, the text similarity algorithm for calculating the text similarity between the text keyword and each indicator data in the indicator data set may be a bag-of-words model algorithm.

[0108] In the embodiment of the present invention, the indicator dimension table is a data table established with the indicator data as the primary key and the dimension data corresponding to the indicator data as the secondary key.

[0109] In the embodiment of the present invention, the type of the target indicator data is the type of thing that the value is specifically used to describe. For example, for a personal identification number xxx, the target indicator data is xxx, and the type of the target indicator data is a personal identification number.

[0110] S4. Delete redundant dimension data of each indicator data in the indicator dimension table, obtain multiple cleaning dimension data corresponding to each indicator data, and construct an indicator model corresponding to each indicator data based on the multiple cleaning dimension data corresponding to each indicator data.

[0111] In an embodiment of the present invention, the indicator model is a model established based on various dimensional data of the indicator data. For example, if there is indicator data of [total sales] in the indicator data set, the dimensional data of the indicator may be time dimension data, city dimension data, and product type dimension data. When calculating the indicator data of the total sales of food in Beijing in 2020, the time dimension is 2020, the city dimension is Beijing, and the product type is the total food sales.

[0112] In the embodiment of the present invention, the example diagram of the indicator model is as follows: Figure 2 shown.

[0113] Figure 2 In the example, there are indicator model A and indicator model B. Each indicator model corresponds to dimension data representing the same dimension, and the values ​​of the specific dimension data may be different.

[0114] In the embodiment of the present invention, deleting redundant dimension data of each indicator data in the indicator dimension table to obtain a plurality of cleaned dimension data corresponding to each indicator data includes:

[0115] Obtain dimension data corresponding to each indicator data in the indicator dimension table, and convert the dimension data into a standard text type to obtain a dimension text set;

[0116] Acquire multiple dimensional text sets from the dimensional text set by using a text similarity algorithm, each dimensional text set containing at least two dimensional texts;

[0117] The semantic segmentation algorithm is used to calculate the semantic range of dimensional texts in multiple dimensional text sets, retain the dimensional texts with semantic ranges greater than the preset semantic range, and remove the dimensional texts with semantic ranges less than the preset semantic range, so as to obtain multiple cleaned dimensional data.

[0118] For example, when calculating the indicator data representing [XX city sales], there are dimension data a representing the city's annual food sales and dimension data b representing the city's quarterly food sales. In this case, dimension data a representing the city's quarterly food sales will be removed, and dimension data b representing the city's annual food sales will be retained.

[0119] In the embodiment of the present invention, deleting redundant dimensional data of each indicator data in the indicator dimension table can streamline the constructed indicator model and reduce the amount of data calculation for subsequent cross-analysis based on the indicator model.

[0120] Furthermore, the preset semantic range may be an average value of the semantic ranges of the dimensional texts in the dimensional text set.

[0121] S5. Perform cross analysis on the multiple indicator models to obtain data analysis results.

[0122] In the embodiment of the present invention, the cross analysis is also called a stereo analysis method, which means searching for dimensional data in one indicator model to analyze another indicator model.

[0123] Specifically, the cross-analysis between the multiple indicator models includes:

[0124] Selecting a first indicator model and a second indicator model from the plurality of indicator models;

[0125] Acquire first dimension data from the second indicator model, and analyze the first indicator model using the first dimension data;

[0126] The above operation is repeated between the multiple indicator models to achieve cross analysis between the multiple indicator models.

[0127] In an embodiment of the present invention, the analyzing the first indicator model using the first dimensional data includes: determining whether there is a correlation between the first dimensional data and the first indicator model; if so, adding the first dimensional data to the first indicator model to obtain the first indicator analysis model.

[0128] Specifically, Figure 2 As shown, the dimension data A1 of the indicator model A is obtained, and it is determined whether there is a correlation between the dimension data A1 of the indicator model A and the indicator data b of the indicator model B. If so, the indicator model B is analyzed according to the dimension data A1 of the indicator model A, that is, the dimension data A1 of the indicator model A is added to the indicator model B as the dimension of the indicator model B.

[0129] Furthermore, for example, there is indicator A city sales amount and indicator B user economic status. By cross-analyzing indicator A and indicator B, the dimension data representing user income in indicator B is added to the indicator model corresponding to indicator A to obtain the indicator analysis model. By directly analyzing the indicator analysis model, the city sales amount of indicator A can be analyzed based on the dimension user income in indicator B user economic status.

[0130] In an embodiment of the present invention, a data set to be analyzed composed of data to be analyzed is obtained from a business intelligence system, the data to be analyzed in the data set to be analyzed is divided into an indicator data set and a dimension data set, and a preliminary division of the data to be analyzed is achieved. Then, an indicator dimension table is constructed according to the indicator data set and the dimension data set, the indicator dimension table is cleaned, redundant dimension data in the dimension data corresponding to each indicator data is deleted, and an indicator model is constructed. The constructed indicator model has multiple dimensions, and cross-analysis is performed between the indicator models, so that multiple dimensions of the indicator model can be analyzed at one time, thereby realizing multi-dimensional data analysis and improving the efficiency of data analysis.

[0131] like Figure 3 The figure shows a module diagram of the multi-dimensional data analysis device of the present invention.

[0132] The multi-dimensional data analysis device 100 of the present invention can be installed in an electronic device. According to the functions to be implemented, the multi-dimensional data analysis device can include a data acquisition module 101, a data partitioning module 102, a dimension table construction module 103, an indicator model construction module 104 and a model analysis module 105. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0133] In this embodiment, the functions of each module / unit are as follows:

[0134] The data acquisition module 101 is used to acquire a data set to be analyzed in a business intelligence system;

[0135] A data partitioning module 102 is used to partition the data set to be analyzed into an indicator data set and a dimension data set according to the data type of the data set to be analyzed;

[0136] A dimension table construction module 103 is used to construct an indicator dimension table according to the indicator data set and the dimension data set, wherein the indicator dimension table includes dimension data corresponding to each indicator data;

[0137] The indicator model building module 104 is used to delete redundant dimension data of each indicator data in the indicator dimension table, obtain a plurality of cleaned dimension data corresponding to each indicator data, and build an indicator model corresponding to each indicator data based on the plurality of cleaned dimension data corresponding to each indicator data;

[0138] The model analysis module 105 is used to perform cross analysis between the multiple indicator models to obtain data analysis results.

[0139] In detail, each module described in the multi-dimensional data analysis device 100 in the embodiment of the present invention is used in the same manner as described above. Figure 1 The multi-dimensional data analysis method described in the present invention has the same technical means and can produce the same technical effects, so I will not go into details here.

[0140] like Figure 4 , which is a schematic diagram of the structure of an electronic device for implementing a multi-dimensional data analysis method according to the present invention.

[0141] The electronic device may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a multi-dimensional data analysis program.

[0142] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (for example, executing multi-dimensional data analysis programs, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device and process data.

[0143] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. The memory 11 may be an internal storage unit of an electronic device in some embodiments, such as a mobile hard disk of the electronic device. The memory 11 may also be an external storage device of an electronic device in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 11 may also include both an internal storage unit of the electronic device and an external storage device. The memory 11 may not only be used to store application software and various types of data installed in the electronic device, such as the code of a multi-dimensional data analysis program, etc., but may also be used to temporarily store data that has been output or is to be output.

[0144] The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0145] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0146] Figure 4 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 4 The structure shown does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0147] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0148] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0149] The multi-dimensional data analysis program stored in the memory 11 in the electronic device is a combination of multiple computer programs. When running in the processor 10, it can achieve:

[0150] Obtain the data set to be analyzed in the business intelligence system;

[0151] Dividing the data set to be analyzed into an indicator data set and a dimension data set according to the data type of the data set to be analyzed;

[0152] Constructing an indicator dimension table according to the indicator data set and the dimension data set, wherein the indicator dimension table includes dimension data corresponding to each indicator data;

[0153] Deleting redundant dimension data of each indicator data in the indicator dimension table, obtaining a plurality of cleaned dimension data corresponding to each indicator data, and constructing an indicator model corresponding to each indicator data based on the plurality of cleaned dimension data corresponding to each indicator data;

[0154] A cross analysis is performed between the multiple indicator models to obtain data analysis results.

[0155] Specifically, the specific implementation method of the processor 10 for the above computer program can refer to Figure 1 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0156] Furthermore, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0157] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0158] Obtain the data set to be analyzed in the business intelligence system;

[0159] Dividing the data set to be analyzed into an indicator data set and a dimension data set according to the data type of the data set to be analyzed;

[0160] Constructing an indicator dimension table according to the indicator data set and the dimension data set, wherein the indicator dimension table includes dimension data corresponding to each indicator data;

[0161] Deleting redundant dimension data of each indicator data in the indicator dimension table, obtaining a plurality of cleaned dimension data corresponding to each indicator data, and constructing an indicator model corresponding to each indicator data based on the plurality of cleaned dimension data corresponding to each indicator data;

[0162] A cross analysis is performed between the multiple indicator models to obtain data analysis results.

[0163] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0164] The modules described as separate components may or may not be physically separated, and the components shown as modules 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 modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0165] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, 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 hardware plus software functional modules.

[0166] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0167] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0168] The blockchain referred to in this invention is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0169] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0170] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The second and other words are used to indicate names, but not to indicate any particular order.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A multi-dimensional data analysis method, characterized in that: The method comprises: Obtain the data set to be analyzed in the business intelligence system; Dividing the data set to be analyzed into an indicator data set and a dimension data set according to the data type of the data set to be analyzed; Constructing an indicator dimension table according to the indicator data set and the dimension data set, wherein the indicator dimension table includes dimension data corresponding to each indicator data; Deleting redundant dimension data of each indicator data in the indicator dimension table, obtaining a plurality of cleaned dimension data corresponding to each indicator data, and constructing an indicator model corresponding to each indicator data based on the plurality of cleaned dimension data corresponding to each indicator data; Cross-analyze multiple indicator models to obtain data analysis results; The step of dividing the data set to be analyzed into an indicator data set and a dimension data set according to the data type of the data set to be analyzed includes: obtaining the data type of the data set to be analyzed, and dividing the data in the data set to be analyzed into numerical data and non-numerical data according to the data type; using the numerical data as indicator data and the non-numerical data as dimension data; The constructing the indicator dimension table according to the indicator data set and the dimension data set includes: creating a dimension query table based on the dimension data set; querying the dimension data corresponding to each indicator data in the indicator data set through the dimension query table; and constructing the indicator dimension table according to the dimension data corresponding to each indicator data; The querying of the dimension data corresponding to each indicator data in the indicator data set through the dimension query table includes: selecting an indicator data from the indicator data set as the target indicator data in turn; obtaining the text keywords corresponding to each dimension data in the dimension query table; calculating the similarity between the type of the target indicator data and each of the text keywords; determining that the corresponding dimension data when the similarity is greater than a preset threshold is the target dimension data corresponding to the target indicator data; and summarizing the target dimension data of all the target indicator data to obtain the dimension data corresponding to each indicator data in the indicator data set; The cross-analysis of the multiple indicator models includes: A1, selecting a first indicator model and a second indicator model from the multiple indicator models; A2, obtaining first dimension data from the second indicator model, and analyzing the first indicator model using the first dimension data; A3, repeating A1-A2 for the multiple indicator models to achieve cross-analysis of the multiple indicator models; The analyzing the first indicator model by using the first dimensional data includes: determining whether there is an association relationship between the first dimensional data and the first indicator model; if so, adding the first dimensional data to the first indicator model to obtain the first indicator model.

2. The multi-dimensional data analysis method according to claim 1, characterized in that: The step of obtaining a data set to be analyzed in a business intelligence system includes: Obtaining the name of the database storing the data set to be analyzed in the intelligent business system; Query the server IP address and password information of the database according to the database name; The database is connected using the server IP address and password information of the database, and the data to be analyzed is collected from the database to obtain the data set to be analyzed.

3. The multi-dimensional data analysis method according to claim 1, characterized in that: The deleting redundant dimension data of each indicator data in the indicator dimension table to obtain a plurality of cleaned dimension data corresponding to each indicator data includes: Obtain dimension data corresponding to each indicator data in the indicator dimension table, and convert the dimension data into a standard text type to obtain a dimension text set; Acquire multiple dimensional text sets from the dimensional text set by using a text similarity algorithm, each dimensional text set containing at least two dimensional texts; A semantic segmentation algorithm is used to calculate the semantic range of dimensional texts in multiple dimensional text sets, retaining dimensional texts with semantic ranges greater than a preset semantic range, and removing dimensional texts with semantic ranges less than the preset semantic range, to obtain multiple cleaned dimensional data.

4. The multi-dimensional data analysis method according to claim 1, characterized in that: Before using the non-numeric data as dimension data, the method further includes: Convert the non-numeric data into text data; Cleaning the text data to obtain cleaned text data; Performing word segmentation on the cleaned text data, and counting the word frequencies of each word after word segmentation, summarizing each word and the word frequency of each word in the form of a character string to obtain a word frequency character string set; The words contained in the word frequency string set are converted into hash feature vectors by a hash function to obtain a vector word frequency string set; By establishing a segment index, extracting similar hash feature vector pairs from the vector word frequency character string set; The Hamming distance between the text data corresponding to the similar hash feature vector pairs is calculated, and the non-numeric data corresponding to the similar hash feature vector pairs are deduplicated based on the Hamming distance.

5. A multi-dimensional data analysis device, used to implement the multi-dimensional data analysis method according to any one of claims 1 to 4, characterized in that: The device comprises: A data acquisition module is used to acquire the data set to be analyzed in the business intelligence system; A data partitioning module, used to partition the data set to be analyzed into an indicator data set and a dimension data set according to the data type of the data set to be analyzed; A dimension table construction module, used to construct an indicator dimension table according to the indicator data set and the dimension data set, wherein the indicator dimension table includes dimension data corresponding to each indicator data; an indicator model building module, used to delete redundant dimension data of each indicator data in the indicator dimension table, obtain a plurality of cleaned dimension data corresponding to each indicator data, and build an indicator model corresponding to each indicator data based on the plurality of cleaned dimension data corresponding to each indicator data; The model analysis module is used to perform cross-analysis on the multiple indicator models to obtain data analysis results.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the multi-dimensional data analysis method according to any one of claims 1 to 4.

7. A computer-readable storage medium, comprising a data storage area and a program storage area, wherein the data storage area stores created data and the program storage area stores a computer program; wherein: When the computer program is executed by a processor, the multi-dimensional data analysis method according to any one of claims 1 to 4 is implemented.

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