Visual data information acquisition system based on big data and medium thereof

Abstract

The invention discloses a visual data information acquisition system based on big data and a medium thereof, and particularly relates to the field of electronic commerce, and the visual data information acquisition system comprises a data encryption acquisition module, a data cleaning and integration module, a data analysis module, a real-time data monitoring module, a visual template customization module and a dynamic data interaction module. According to the visual data information acquisition system based on big data and the medium thereof, target platform data is encrypted through a data encryption acquisition module, so that hidden dangers of data acquisition are reduced; the real-time data monitoring module is used for carrying out real-time data processing operation and setting a data change rule and an early warning mechanism so as to obtain and update data in time; third key information is received through the dynamic data interaction module and displayed on a visual interface according to a preset mode, autonomous exploration and prediction analysis of the dynamic data by the user are provided, and the interactivity of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of e-commerce, and more specifically, to a visual data information collection system based on big data and a medium thereof. Background Art

[0002] With the rapid development of information technology and Internet technology, in the field of e-commerce technology, data has become the core asset of enterprise and social development. The collection, processing and analysis of big data can help enterprises gain insight into market trends, optimize business processes, and improve user experience, and ultimately maximize commercial value.

[0003] The data information collection system greatly improves the comprehensibility and memorability of data by converting complex data sets into intuitive charts and images; in e-commerce, it comprehensively collects user behavior data and clearly presents the distribution of market demand through visualization, thereby improving marketing effectiveness and user experience.

[0004] However, it still has some shortcomings in actual use. For example, there are hidden dangers in the security of data collection. In the process of large-scale data collection, it is easy to face the risk of data leakage, threatening personal privacy and corporate trade secrets; in scenarios with high real-time requirements, it is impossible to obtain and update data in a timely manner, resulting in delayed decision-making and missing the best time to respond; the visualization interface has poor interactivity, and it is difficult to conduct independent exploration and predictive analysis of dynamic data, which limits the role of groups other than professional data analysts. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a visual data information acquisition system based on big data and a medium thereof, and solves the problems raised in the above-mentioned background technology through the following scheme.

[0006] To achieve the above object, the present invention provides the following technical solutions: The visual data information collection system based on big data is characterized by comprising a system operation database, a system central processing module and a user information terminal, and also comprising: Data encryption collection module: used to respond to the incoming data of the visual data information collection system, obtain the first encrypted information through encryption processing, and transmit it to the data cleaning and integration module and the real-time data monitoring module; Data cleaning and integration module: used for cleaning and integrating the first encrypted information transmitted by the data encryption collection module, the cleaning and integration operation is used for obtaining the first key feature corresponding to the first encrypted information, and transmitting it to the data analysis module; Data analysis module: used to obtain the intelligent analysis model, obtain the first key information according to the first key feature transmitted by the intelligent analysis model through the data cleaning and integration module, and transmit it to the real-time data monitoring module and the visualization template customization module; Real-time data monitoring module: used to perform real-time data processing operations on the first encrypted information in the data encryption collection module and the first key information in the data analysis module through the real-time data monitoring channel. The real-time data processing operation is used to obtain the second key information corresponding to the data change of the first encrypted information and the first key information, and transmit it to the visualization template customization module; Visualization template customization module: used to obtain the correlation feature information of the first key information in the data analysis module and the second key information in the real-time data monitoring module, formulate a visualization template according to the correlation feature information, so as to construct the third key information through the visualization template, and transmit it to the dynamic data interaction module; Dynamic data interaction module: used to obtain dynamic interaction data information corresponding to the second key information through the third key information in the visualization template customization module according to the intelligent analysis model, so as to display the dynamic interaction data information corresponding to the second key information on the visualization interface in a preset manner; The system operation database includes all data texts of the visual data information acquisition system, and collects the information texts output by each module in real time. The system central processing module is used for the information text instructions output by each module in the central control system, and the user information terminal is an information output device for receiving the visual data information acquisition system.

[0007] Preferably, the data encryption collection module includes an application platform identification unit, a data information collection unit, an encryption processing unit, and a first encryption information output unit; The application platform identification unit is used to identify the target platform of the application visualization data information collection system, and determine the service type, operating system environment, and data transmission protocol of the target platform; The data information collection unit generates a data collection request corresponding to the target platform based on the service type of the target platform determined by the application platform identification unit, and collects and obtains data information of the target platform; The encryption processing unit is used to encrypt the target platform data information, generate first encrypted information, and add a response mark to the first encrypted information; The first encrypted information output unit establishes an encrypted transmission channel for transmission based on the response tag of the first encrypted information.

[0008] Preferably, the data encryption acquisition module obtains the first encrypted information, specifically including: The application platform identification unit detects the characteristics of the target platform through network detection technology, determines the protocol header information of the target platform by scanning specific ports, and extracts the characteristic fingerprint information of the target platform operating system to determine the operating system environment; A target platform feature library is established to store and classify common feature information of different types of platforms. During the identification process, the visual data information acquisition system compares and matches the detection results of the target platform with the target platform feature library.

[0009] Preferably, the data encryption acquisition module obtains the first encrypted information, specifically including: The data collection request is based on a pre-set collection rule corresponding to the target platform. The pre-set collection rule includes the type of collected data, the data generation time, and the data format; In the preset system operation database, a data collection request corresponding to the service type of the target platform is obtained. The preset system operation database is used to store the corresponding relationship between the service type of the target platform and the data collection request.

[0010] Preferably, the data encryption acquisition module obtains the first encrypted information, specifically including: The encryption processing unit encrypts the target platform data information collected by the data information collection unit according to the data sensitivity and system security policy of the target platform; Add response tags to distinguish the real-time requirements of target platform data information; Generate first encrypted information, the first encrypted information including user identity information, user account information, transaction order, transaction logistics, browsing behavior, and search behavior.

[0011] Preferably, the data analysis module obtains the first key information, specifically including: According to the first key feature, a classification operation is performed on the data transmitted by the data cleaning and integration module to obtain an analysis topic corresponding to the first key feature, where the analysis topic is a target business field corresponding to the visualized data information; In the preset analysis database, the intelligent analysis model corresponding to the analysis subject is obtained. The preset analysis database is used to store the corresponding relationship between the analysis subject and the intelligent analysis model.

[0012] Preferably, in the real-time data monitoring module, the second key information includes the corresponding correlation strength value, change amplitude, change trend, fluctuation cycle, trend deviation reason, and impact range assessment report of the target platform.

[0013] Preferably, in the dynamic data interaction module, the dynamic interaction data information includes specific values ​​and change trends of association rules, association detail information, real-time data fluctuation values, dynamic adjustment range, and interaction reports.

[0014] The present invention further proposes a computer-readable medium having a computer program stored thereon, wherein the program executes the visual data information acquisition system based on big data as described in any one of the above items.

[0015] Technical effects and advantages of the present invention: The present invention encrypts the target platform data through the data encryption acquisition module to reduce the hidden dangers of data acquisition. The present invention performs real-time data processing operations and sets data change rules and early warning mechanisms through a real-time data monitoring module to obtain and update data in a timely manner; The present invention receives the third key information through the dynamic data interaction module, and displays it on the visual interface in a preset manner, providing users with autonomous exploration and predictive analysis of dynamic data, thereby increasing the interactivity of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a system flow chart of the present invention.

[0017] Figure 2 It is a system step diagram of the present invention.

[0018] Figure 3 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations on the present application. As used in the specification of the present application, the singular expressions "a", "a kind of said above" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0021] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0022] As attached Figure 1 The big data-based visual data information collection system shown includes a system operation database, a system central processing module and a user information terminal, and also includes a data encryption collection module, a data cleaning and integration module, a data analysis module, a real-time data monitoring module, a visual template customization module, and a dynamic data interaction module.

[0023] The system operation database includes all data texts of the visual data information acquisition system, and collects the information texts output by each module in real time. The system central processing module is used for the information text instructions output by each module in the central control system, and the user information terminal is an information output device for receiving the visual data information acquisition system.

[0024] The data encryption acquisition module is used to respond to the inflow data of the visual data information acquisition system, obtain the first encrypted information through encryption processing, and transmit it to the data cleaning and integration module and the real-time data monitoring module.

[0025] In a possible implementation, the data encryption collection module includes an application platform identification unit, a data information collection unit, an encryption processing unit, and a first encryption information output unit.

[0026] It should be noted that the application platform identification unit is used to identify the target platform of the application visualization data information collection system, determine the service type, operating system environment, and data transmission protocol of the target platform, where the service type of the target platform includes but is not limited to e-commerce transaction platform, user behavior analysis platform, third-party payment platform, etc.

[0027] In this embodiment, the target platform is an e-commerce platform built on the Linux system for illustration, and the target platform supports the HTTPS protocol for data transmission.

[0028] Specifically, the visual data information collection system detects the characteristics of the target platform through network detection technology, determines the protocol header information of the target platform by scanning specific ports, and extracts the characteristic fingerprint information of the target platform operating system to determine the operating system environment; establishes a target platform feature library to store and classify common characteristic information of different types of platforms. During the identification process, the visual data information collection system compares and matches the detection results of the target platform with the target platform feature library; at the same time, when the target platform feature library does not match the characteristic information of the target platform, the visual data information collection system automatically updates the target platform feature library.

[0029] The data information collection unit generates a data collection request corresponding to the target platform based on the service type of the target platform determined by the application platform identification unit. The data collection request is based on a pre-set collection rule corresponding to the target platform. The pre-set collection rule includes but is not limited to the type of collected data, data generation time, data format, etc., and collects and obtains the target platform data information; obtains the data collection request corresponding to the service type of the target platform in the preset system operation database. The preset system operation database is used to save the correspondence between the service type of the target platform and the data collection request.

[0030] Specifically, the pre-set collection rules dynamically adjust the type, time range, and format of the collected data according to the overall operating status of the target platform and data analysis requirements, and associate the dynamically adjusted collection rules with the service type of the target platform and store them in the preset system operation database; multi-threading and asynchronous collection technology is used.

[0031] In this embodiment, when the visual data information collection system detects that the target platform is in a peak trading period, the collection frequency and data type of promotional data are increased, and the frequency of data collection requests is reduced, wherein the promotional data includes but is not limited to discount information, sales volume of products participating in the promotion, activity time, etc.; In this embodiment, for the target platform, multiple collection threads are started at the same time, which are responsible for the collection tasks of different types of data and different data areas respectively; when a user places an order on the target platform, the data collection operation is automatically triggered based on the collection rules corresponding to the preset system running database.

[0032] The encryption processing unit is used to encrypt the target platform data information, generate first encrypted information, and add a response mark to the first encrypted information.

[0033] In a possible implementation, obtaining the first encrypted information includes: encrypting the target platform data information collected by the data information collection unit according to the data sensitivity and system security policy of the target platform, the encryption processing is to dynamically select a corresponding encryption algorithm in a preset system database, the encryption algorithm includes but is not limited to AES, RSA, ECC, Blowfish, etc.; adding a response tag, the response tag is to distinguish the real-time requirements of the target platform data information; generating the first encrypted information, the first encrypted information includes user identity information, user account information, transaction orders, transaction logistics, browsing behavior, and search behavior; Specifically, the target platform data information is subjected to the AES algorithm to generate the first encrypted information, and the specific steps are as follows: A1: Group the data length of the target platform data information. If the data length is not an integer multiple of the group length, fill it. In this embodiment, it is assumed that the packet length of the target platform data information is 128 bits. When the data length is 200 bits, 56 bits are supplemented according to the PKCS#7 rule, and the value of each byte of the supplemented 56 bits of data is 56; A2: performing a byte replacement operation on each packet data, wherein the byte replacement operation is based on a predefined matrix, mapping each packet data byte to another byte input matrix; In this embodiment, assuming that the input data byte is b, when b=0x2A, the replacement byte is 0xC5; A3: Perform row shift operation on the matrix, and circularly shift the rows of the data grouping matrix to the left; In this embodiment, the data element of the data grouping matrix is ​​denoted as a ij , where i represents the row index of the data grouping matrix, and j represents the column index of the data grouping matrix. The data grouping matrix is ​​specifically expressed as: ; Perform row shift operation on the data grouping matrix, and the shifted data element is recorded as a' ij , the row shift operation is specifically expressed as: a' ij =a (i+si)%4，j , Where si represents the displacement of the i-th row, s1=0, s2=1, s3=2, s4=3; that is, the first row remains unchanged, the second row is shifted left by 1 byte, the third row is shifted left by 2 bytes, and the fourth row is shifted left by 3 bytes; The corresponding data grouping matrix after the row shift operation is specifically expressed as: ; A4: Perform column confusion operation on the matrix; In this embodiment, the column vector is assumed to be [a0, a1, a2, a3] T =[0x1A, 0x2B, 0x3C, 0x4D] T , the column vector after column confusion is recorded as [b0, b1, b2, b3] T , specifically expressed as: , Then [b0, b1, b2, b3] T =[0x06, 0xB9, 0x9C, 0xE0]; A5: Repeat steps A2-A4 and perform encryption according to a preset number of rounds to generate first encrypted information; Specifically, when the data group is 128 bits, the preset number of rounds is 10 rounds; when the data group is 192 bits, the preset number of rounds is 12 rounds; when the data group is 256 bits, the preset number of rounds is 14 rounds.

[0034] It should be noted that the user identity information in the first encrypted information includes the user's name, telephone number, and email address; the user account information includes the user's username, login password, account balance, and points information on the target platform; the transaction order includes the order number, order generation time, order status, and product details and price; the transaction logistics includes the logistics order number, logistics company name, shipping address, receiving address, and logistics status; the browsing behavior includes the URL, browsing time, browsing order, and page dwell time of the product page browsed by the user on the target platform; the search behavior includes the user's search keywords, search time, and search result clicks on the target platform.

[0035] The first encrypted information output unit establishes an encrypted transmission channel for transmission based on the response tag of the first encrypted information.

[0036] Specifically, the first encrypted information uses multiplexing technology to transmit multiple data flows through an encrypted transmission channel; In this embodiment, the response marks are respectively recorded as A and B, A indicates that the first encrypted information needs to be displayed in real time in the visual data information collection system, and B indicates that the first encrypted information does not need to be displayed in real time in the visual data information collection system; when the response mark is A, the first encrypted information is output to the data cleaning and integration module; when the response mark is B, the first encrypted information is immediately output to the real-time data monitoring module when it is generated.

[0037] The data cleaning and integration module is used to perform cleaning and integration operations on the first encrypted information transmitted by the data encryption collection module. The cleaning and integration operation is used to obtain the first key feature corresponding to the first encrypted information and transmit it to the data analysis module.

[0038] Specifically, after obtaining the first encrypted information transmitted by the data encryption acquisition module, the visual data information acquisition system obtains multiple keywords corresponding to the first encrypted information, i.e., the first key feature, through the cleaning and integration operation. The cleaning and integration operation is as follows: Decryption operation: Based on the response tag of the first encrypted information, the corresponding decryption key is obtained. In this embodiment, the encryption algorithm is AES, and the corresponding AES decryption key is used for decryption. Processing null values: Scan each data field in the decrypted first encrypted information to detect null values. In this embodiment, when the order amount is empty, it is marked as erroneous data and traced back to the source of the data for correction. Detecting and processing outliers: Detecting outliers using statistical analysis rules. In this embodiment, for the commodity price data of the target platform, the normal range is determined by calculating the mean and standard deviation of the commodity price. If the price is significantly higher than the average price range of similar commodities, it is marked as an outlier. Data association and merging: The first encrypted information of different data sources is associated based on the internal logical relationship of the data. In this embodiment, the user identity information of the e-commerce platform is associated with the transaction order through the user account information. Extracting features: Extracting features based on user needs to obtain the first key feature. In this embodiment, the characteristics of the user behavior are extracted by a principal component analysis algorithm.

[0039] The data analysis module is used to obtain the intelligent analysis model, obtain the first key information according to the first key feature transmitted by the intelligent analysis model through the data cleaning and integration module, and transmit it to the real-time data monitoring module and the visualization template customization module.

[0040] Specifically, the intelligent analysis model is a pre-constructed learning model. By inputting the first key feature into the intelligent analysis model, the intelligent analysis model obtains the first key information according to the first key feature and sends the first key information to the visualization template customization module.

[0041] Specifically, the first key information includes cross-category association rules, user behavior association rules, data fluctuation time, data change trend, and data change cycle.

[0042] In this embodiment, if a user who purchases a specific product on the target platform also purchases a matching product, this information is listed as a list and stored as association rule information; the sales data of the target platform fluctuates over time; the target platform corresponds to a key trend, and sales volume shows an upward trend.

[0043] In one possible implementation, obtaining the first key information includes: classifying the data transmitted by the data cleaning and integration module according to the first key feature to obtain an analysis topic corresponding to the first key feature, the analysis topic being a target business field corresponding to the visualized data information, the target business field including but not limited to e-commerce, finance, industrial manufacturing, etc.; obtaining an intelligent analysis model corresponding to the analysis topic in a preset analysis database, the preset analysis database being used to store the correspondence between the analysis topic and the intelligent analysis model.

[0044] In this embodiment, a neural network model is used to predict user purchase behavior; if commodity classification management is performed, a decision tree model is used.

[0045] It should be noted that after receiving the first key feature, the intelligent analysis model analyzes and recognizes the pattern of the first key information according to the algorithm structure within the intelligent analysis model and the parameter system determined by pre-training; when the neural network model is used to predict user purchasing behavior, the intelligent analysis model increases the weight of the product categories that the user frequently browses and browses within a fixed time period through the collaborative operation of multiple layers of neurons, based on the user's historical purchase frequency, the category and duration of recent browsing of products, the past consumption amount, and the first key feature corresponding to the geographical location, and calculates the user's purchase probability value for each product in combination with the historical purchase frequency and consumption amount; when the decision tree model is used for product classification management, the intelligent analysis model performs classification judgment along the branch structure of the decision tree according to the first key feature corresponding to the product; in this embodiment, the intelligent analysis model determines the product category and classification basis information, i.e., the first key information, based on the product function, main material and price range.

[0046] The real-time data monitoring module is used to perform real-time data processing operations on the first encrypted information in the data encryption and acquisition module and the first key information in the data analysis module through the real-time data monitoring channel. The real-time data processing operation is used to obtain the second key information corresponding to the data changes of the first encrypted information and the first key information, and transmit it to the visualization template customization module.

[0047] It should be noted that the second key information includes the corresponding data correlation relationship of the target platform, the correlation strength value, the change range, the change trend, the fluctuation range, the fluctuation cycle, the trend turning point, the reason for the trend deviation, and the impact range assessment report.

[0048] Specifically, the first encrypted information is received from the data encryption collection module, and a decryption operation is performed using an encryption algorithm specified by the data encryption collection module; a data change rule is set, and when an abnormality occurs in the first encrypted information, an early warning mechanism is triggered, the early warning mechanism includes a detailed description of the data change, an early warning level, and an impact assessment report; and second key information is generated, the second key information being a key feature based on a comprehensive analysis of the data change of the first encrypted information and business needs; Specifically, the first key information received from the data analysis module is compared and analyzed with the decrypted first encrypted information and the first key information. For numerical data, matching is performed according to data fields, and the numerical change, numerical change rate, and trend smoothness are calculated using the current data value and historical data. For text data, stop words, punctuation marks, and extra spaces are removed, and natural language processing technology is used to calculate text similarity.

[0049] The visualization template customization module is used to obtain the associated characteristic information of the first key information in the data analysis module and the second key information in the real-time data monitoring module, formulate a visualization template based on the associated characteristic information, construct the third key information through the visualization template, and transmit it to the dynamic data interaction module.

[0050] In a possible implementation, constructing the third key information includes: constructing the third key information through a visualization template based on the associated characteristic information of the first key information in the data analysis module and the second key information in the real-time data monitoring module, the visualization template being the visualization style of the third key information through the user display interface; and storing the correspondence between the visualization template and the third key information in a preset system operation database.

[0051] Specifically, the visualization templates include but are not limited to bar chart templates, line chart templates, pie chart templates, map templates, scatter plot templates, etc.; based on the bar chart template, the third key information includes the class represented by each column, the numerical value corresponding to the height of the column, and the comparative relationship between the columns; based on the line chart template, the third key information includes the horizontal and vertical coordinate values ​​of the data, the slope, and the intersection points and distance relationships between different lines; based on the pie chart template, the third key information includes the category represented by each sector area, the area ratio of the sector, and the relative size relationship between the parts; based on the map template, the third key information includes the geographical area division, the data value corresponding to each area, the data difference value between areas, and the spatial distribution; based on the scatter plot template, the third key information includes the coordinate value of each scatter point, the distribution pattern of the scatter points, the distance relationship between the scatter points, and the clustering situation.

[0052] The dynamic data interaction module is used to obtain the dynamic interaction data information corresponding to the second key information according to the intelligent analysis model and the third key information in the visualization template customization module, so as to display the dynamic interaction data information corresponding to the second key information on the visualization interface in a preset manner.

[0053] Specifically, the dynamic data interaction module uses an encrypted communication protocol to receive the third key information, extract visual chart data, and according to the intelligent analysis model, use the third key information to obtain the dynamic interaction data information corresponding to the second key information, predict the user's behavior and data trend, and extract key turning points and change rates as dynamic interaction data information. The dynamic interaction data information includes the specific values ​​and change trends of association rules, association detail information, real-time data fluctuation values, dynamic adjustment ranges, and interaction reports.

[0054] As attached Figure 2 The visual data information collection method based on big data shown includes: S1: encrypting collected data, S2: cleaning and integrating data, S3: analyzing data information, S4: real-time monitoring data, S5: formulating visual templates, and S6: dynamically interacting with data information.

[0055] S1: Encrypting collected data: responding to the incoming data of the visual data information collection system, performing encryption processing to obtain first encrypted information; S2: Cleaning and integrating data: performing a cleaning and integrating operation on the first encrypted information, where the cleaning and integrating operation is used to obtain a first key feature corresponding to the first encrypted information; S3: Analyze data information: obtain an intelligent analysis model, and obtain first key information through the first key feature according to the intelligent analysis model; S4: real-time monitoring data: performing real-time data processing operation on the first encrypted information and the first key information through a real-time data monitoring channel, the real-time data processing operation is used to obtain second key information corresponding to data changes of the first encrypted information and the first key information; S5: Formulate a visualization template: obtain correlation feature information of the first key information and the second key information, formulate a visualization template according to the correlation feature information, and construct the third key information through the visualization template; S6: Dynamic interaction data information: According to the intelligent analysis model, the dynamic interaction data information corresponding to the second key information is obtained through the third key information, so as to display the dynamic interaction data information corresponding to the second key information on the visualization interface in a preset manner.

[0056] This embodiment provides a computer readable medium on which a computer program is stored. The program executes the following Figure 1 The visual data information collection system based on big data is shown in .

[0057] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A visual data information collection system based on big data, including a system operation database, a system central processing module and a user information terminal, characterized in that: Also includes: Data encryption collection module: used for responding to the inflow data of the visual data information collection system, obtaining the first encrypted information through encryption processing, and transmitting it to the data cleaning and integration module and the real-time data monitoring module; Data cleaning and integration module: used for cleaning and integrating the first encrypted information transmitted by the data encryption collection module, the cleaning and integration operation is used for obtaining the first key feature corresponding to the first encrypted information, and transmitting it to the data analysis module; Data analysis module: used to obtain the intelligent analysis model, perform classification operations according to the first key feature transmitted by the data cleaning and integration module, determine the analysis topic, and obtain the intelligent analysis model corresponding to the analysis topic from the preset analysis database; obtain the first key information according to the obtained intelligent analysis model and the first key feature, and transmit it to the real-time data monitoring module and the visualization template customization module; wherein the analysis topic is the target business field corresponding to the visualization data information, the preset analysis database stores the corresponding relationship between the analysis topic and the intelligent analysis model, and the intelligent analysis model analyzes and performs pattern recognition on the first key feature according to its internal algorithm structure and pre-trained parameter system to obtain the first key information; Real-time data monitoring module: used to perform real-time data processing operations on the first encrypted information in the data encryption collection module and the first key information in the data analysis module through the real-time data monitoring channel. The specific steps are as follows: SS1. Decrypt the first encrypted information; SS2. Set data change rules to trigger an early warning mechanism when the first encrypted information is abnormal; SS3. Compare and analyze the decrypted first encrypted information with the first key information. For numerical data, calculate the numerical change, numerical change rate and trend smoothness. For text data, calculate the text similarity after removing stop words, punctuation marks and extra spaces. SS4, based on the data change of the first encrypted information and the business requirements, obtain the second key information corresponding to the data change of the first encrypted information and the first key information, and transmit it to the visualization template customization module; Visualization template customization module: used to obtain the correlation feature information of the first key information in the data analysis module and the second key information in the real-time data monitoring module, formulate a visualization template according to the correlation feature information, so as to construct the third key information through the visualization template, and transmit it to the dynamic data interaction module; Dynamic data interaction module: used to obtain dynamic interaction data information corresponding to the second key information through the third key information in the visualization template customization module according to the intelligent analysis model, so as to display the dynamic interaction data information corresponding to the second key information on the visualization interface in a preset manner; The system operation database includes all data texts of the visual data information acquisition system, and collects the information texts output by each module in real time. The system central processing module is used for the information text instructions output by each module in the central control system, and the user information terminal is an information output device for receiving the visual data information acquisition system.

2. The visual data information collection system based on big data according to claim 1 is characterized in that: The data encryption collection module includes an application platform identification unit, a data information collection unit, an encryption processing unit, and a first encryption information output unit; The application platform identification unit is used to identify the target platform of the application visualization data information collection system, and determine the service type, operating system environment, and data transmission protocol of the target platform; The data information collection unit generates a data collection request corresponding to the target platform based on the service type of the target platform determined by the application platform identification unit, and collects and obtains data information of the target platform; The encryption processing unit is used to encrypt the target platform data information, generate first encrypted information, and add a response mark to the first encrypted information; The first encrypted information output unit establishes an encrypted transmission channel for transmission based on the response tag of the first encrypted information.

3. The visual data information collection system based on big data according to claim 2 is characterized in that: The data encryption acquisition module obtains the first encryption information, specifically including: The application platform identification unit detects the characteristics of the target platform through network detection technology, determines the protocol header information of the target platform by scanning specific ports, and extracts the characteristic fingerprint information of the target platform operating system to determine the operating system environment; A target platform feature library is established to store and classify common feature information of different types of platforms. During the identification process, the visual data information acquisition system compares and matches the detection results of the target platform with the target platform feature library.

4. The visual data information collection system based on big data according to claim 2 is characterized in that: The data encryption acquisition module obtains the first encryption information, specifically including: The data collection request is based on the pre-set collection rules corresponding to the target platform. The pre-set collection rules include the type of collected data, data generation time, and data format. The collection rules will be dynamically adjusted according to the overall operation status of the target platform and data analysis requirements. The adjusted collection rules are associated with the service type of the target platform and stored in the preset system operation database; In the preset system operation database, a data collection request corresponding to the service type of the target platform is obtained. The preset system operation database is used to store the corresponding relationship between the service type of the target platform and the data collection request.

5. The visual data information collection system based on big data according to claim 2 is characterized in that: The data encryption acquisition module obtains the first encryption information, specifically including: The encryption processing unit encrypts the target platform data information collected by the data information collection unit according to the data sensitivity and system security policy of the target platform; Add response tags to distinguish the real-time requirements of target platform data information; Generate first encrypted information, the first encrypted information including user identity information, user account information, transaction order, transaction logistics, browsing behavior, and search behavior.

6. The visual data information collection system based on big data according to claim 1 is characterized in that: The data analysis module obtains the first key information, specifically including: According to the first key feature, a classification operation is performed on the data transmitted by the data cleaning and integration module to obtain an analysis topic corresponding to the first key feature, where the analysis topic is a target business field corresponding to the visualized data information; In the preset analysis database, the intelligent analysis model corresponding to the analysis subject is obtained. The preset analysis database is used to store the corresponding relationship between the analysis subject and the intelligent analysis model.

7. The visual data information collection system based on big data according to claim 1 is characterized in that: The real-time data monitoring module, the second key information includes the corresponding correlation strength value, change range, change trend, fluctuation cycle, trend deviation reason, and impact range assessment report of the target platform.

8. The visual data information collection system based on big data according to claim 1 is characterized in that: The dynamic data interaction module, the dynamic interaction data information includes the specific values ​​and change trends of the association rules, the association detail information, the real-time data fluctuation values, the dynamic adjustment range, and the interaction report.

9. A computer readable medium having a computer program stored thereon, characterized in that: The computer program, when executed by a processor, is a visual data information acquisition system based on big data according to any one of claims 1 to 8.

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