A data warehouse management system based on artificial intelligence

Abstract

The present invention discloses a data warehouse management system based on artificial intelligence, which belongs to the field of data warehouse management, and includes an identity authentication module for verifying the identity of a user; a demand input and analysis module for inputting visualization requirements and analyzing the user's personality preference during the input process; a data collection and processing module for collecting big data that needs to be visualized according to the input visualization requirements, and cleaning and processing the data to remove duplicate, erroneous or inconsistent data; a solution screening module for screening out nine visualization solutions for the user to make a final selection according to the user's personality preference; and a visualization implementation module for realizing visualization results of the collected big data according to the final selected visualization solution. The present application can quickly screen out visualization solutions that are more in line with the user's personality preference for selection, and does not require the user to find a satisfactory visualization solution by themselves, saving time and effort.

Description

Technical Field

[0001] The present invention relates to the field of data visualization management, and in particular to a data warehouse management system based on artificial intelligence. Background Art

[0002] A data warehouse is a system that centrally stores large amounts of data. These data are mainly used to support the decision-making process within an organization. They usually contain data from multiple internal and external sources. The data warehouse stores a large amount of integrated historical data that can meet the needs of various systems and is more in line with the user's personalized preferences.

[0003] In order to make the data more intuitive, data visualization management is often used. Data visualization management is to present big data in a graphical way instead of the traditional digital way. This management method allows people to understand data more directly and intuitively, making it easier to discover patterns, trends and correlations in the data, supporting decision making and problem solving.

[0004] The important value of big data visualization is reflected in many aspects. First, it can help people quickly understand the importance of individual data and the meaning behind the data. Second, through interactive data visualization, people can drill down to explore details, making it easier to find data patterns and data anomalies. This helps to achieve intelligent decision-making and bring faster insights to enterprises.

[0005] Big data visualization has a wide range of application scenarios, including but not limited to the following aspects:

[0006] Production and supply chain management: Enterprises can monitor key indicators on the production line and the logistics of the supply chain in real time, analyze production efficiency and supply chain efficiency, and optimize production planning and supply chain management.

[0007] Network and IT system monitoring: Enterprises can monitor the performance, security status, fault conditions, etc. of networks and IT systems in real time, including network traffic, server load, user access, etc., to detect problems in a timely manner and take measures.

[0008] User behavior analysis: By visualizing user behavior data, companies can gain a deeper understanding of user preferences, interests, and needs, thereby improving product design, launching more targeted marketing campaigns, and enhancing user experience and loyalty.

[0009] In addition, big data visualization can also be combined with artificial intelligence to help companies optimize business processes, improve efficiency and profitability by centrally displaying valuable data information, while stimulating innovative thinking and discovering new business opportunities and market trends.

[0010] The existing data warehouse management method based on big data visualization has certain defects when used. After the user inputs his own needs, it is impossible to quickly filter out the visualization scheme that is more in line with the user's personality preference for selection. The user needs to find a satisfactory visualization scheme by himself, which is time-consuming and laborious. Therefore, those skilled in the art provide a data warehouse management system based on artificial intelligence to solve the problems raised in the above background technology. Summary of the invention

[0011] The purpose of the present invention is to provide a data warehouse management system based on artificial intelligence, which can quickly screen out visualization solutions that are more in line with the user's personality preferences for selection, without the need for users to find satisfactory visualization solutions on their own, saving time and effort, so as to solve the problems raised in the above background technology.

[0012] To achieve the above object, the present invention provides the following technical solutions:

[0013] A data warehouse management system based on artificial intelligence, comprising:

[0014] Authentication module, used to verify the user's identity;

[0015] Demand input and analysis module, used to input visualization requirements and analyze the user's personality preferences during the input process;

[0016] The data collection and processing module is used to collect the big data that needs to be visualized according to the input visualization requirements, and clean and process the data to remove duplicate, erroneous or inconsistent data;

[0017] The scheme screening module is used to screen out nine visualization schemes for the user to make the final selection based on the user's personality preference;

[0018] The visualization realization module is used to realize the visualization results of the collected big data according to the final selected visualization scheme;

[0019] The scoring module is used to rate the satisfaction of the visualization results.

[0020] As a further solution of the present invention, the specific analysis process of the user's personality preference is as follows:

[0021] The typing speed of the user when inputting the visualization requirement is collected and marked as A, and then A is compared and calculated with the first preset value to obtain a first weight value a=(A / first preset value)*100, where A and the first preset value are both greater than zero;

[0022] The typing error rate of the user when inputting the visualization requirement is collected and marked as B, and then B is compared and calculated with the second preset value to obtain a second weight value b=(B / second preset value)*100, where both B and the second preset value are greater than zero;

[0023] The total number of words when the user inputs the visualization requirement is collected, marked as C, and then C is compared and calculated with the third preset value to obtain a third weight value c=(C / third preset value)*100, where both C and the third preset value are greater than zero;

[0024] Collect the words used by the user when inputting the visualization requirements, identify and analyze them, and calculate the fourth weight value d;

[0025] The punctuation marks when the user inputs the visualization requirements are collected, and the punctuation marks are identified and analyzed to calculate the fifth weight value e;

[0026] Generating a pentagonal pattern P in real time according to the first weight value a, the second weight value b, the third weight value c, the fourth weight value d, and the fifth weight value e;

[0027] P is output in real time as the analysis result of the user's personality tendency.

[0028] As a further solution of the present invention: the specific calculation process of the fourth weight value d is:

[0029] Identify the number of professional terms and Internet buzzwords from the collected words and mark them as D1 and D2 respectively;

[0030] Compare D1 with D2. If D1>D2, it means that the user tends to use professional terms. If D1<D2, it means that the user tends to use Internet buzzwords. If D1=D2, calculate the fourth weight value d=0.

[0031] If the user tends to use professional terms, a fourth weight value d=(D1 / fourth preset value)*100 is calculated, where D1 and the fourth preset value are both greater than zero;

[0032] If the user tends to use professional terms, the fourth weight value d=(-D2 / fourth preset value)*100 is calculated, where D2>0.

[0033] As a further solution of the present invention: the specific calculation process of the fifth weight value e is:

[0034] Identify the types and number of punctuation marks from the collected punctuation marks and mark them as E1;

[0035] Identify the total number of punctuation marks from the collected punctuation marks and mark it as E2;

[0036] The fifth weight value e=(E1*E2 / fifth preset value)*100 is calculated, wherein E1, E2 and the fifth preset value are all greater than zero.

[0037] As a further solution of the present invention: the specific screening process of the visualization solution is:

[0038] The preset visualization schemes in the database are marked as Qi, i=1···n, where n is a positive integer;

[0039] The pentagonal pattern corresponding to Qi is marked as Pi, i = 1···n;

[0040] Match the overlapping areas of P real-time with each Pi, and select the top 100 visualization schemes with the largest overlapping areas with P real-time from Pi, and mark them as pj, j = 1···100;

[0041] Then mark the chart type of pj as k1, the color scheme as k2, and the layout as k3;

[0042] The largest number of the same group pj belonging to the same chart type k1 is divided into the first sequence;

[0043] The largest number of groups pj belonging to the same color scheme k2 are divided into the second sequence;

[0044] The pj of the same group with the largest number belonging to the same layout k3 is divided into the third sequence;

[0045] Randomly select three visualization schemes from the first sequence. If there are less than three visualization schemes in the first sequence, randomly select the insufficient visualization schemes from outside the first sequence, the second sequence, and the third sequence;

[0046] Randomly select three visualization schemes from the second sequence. If there are less than three visualization schemes in the second sequence, randomly select the insufficient visualization schemes from outside the first sequence, the second sequence, and the third sequence;

[0047] Randomly select three visualization solutions from the third sequence. If there are less than three visualization solutions in the third sequence, randomly select the insufficient visualization solutions from outside the first sequence, the second sequence, and the third sequence.

[0048] The nine selected visualization schemes are scaled down in equal proportions, shuffled and displayed in the form of a nine-square grid for users to make their final selection.

[0049] As a further solution of the present invention: if there are two or more groups of pj in the first sequence, then when screening visualization solutions from the first sequence, all pj in the first sequence are shuffled and randomly screened; if there are two or more groups of pj in the second sequence, then when screening visualization solutions from the second sequence, all pj in the second sequence are shuffled and randomly screened; if there are two or more groups of pj in the third sequence, then when screening visualization solutions from the third sequence, all pj in the third sequence are shuffled and randomly screened.

[0050] As a further solution of the present invention: the customer's satisfaction score is marked as V. If V is greater than or equal to a sixth preset value, it means that the user is satisfied with the visualization result, and the visualization scheme and the pentagonal pattern corresponding to the visualization result are stored in the database; if V is less than the sixth preset value, it means that the user is not satisfied with the visualization result, and the user optimizes the visualization result by himself, and then the visualization scheme and the pentagonal pattern corresponding to the optimized visualization result are stored in the database.

[0051] As a further solution of the present invention: the method of verifying the user identity includes face recognition, fingerprint recognition and password verification.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] This application can quickly screen out visualization solutions that are more in line with the user's personality preferences for selection, without the need for the user to find a satisfactory visualization solution on their own, saving time and effort. Among them, after screening out one hundred visualization solutions that are more in line with the user's personality preferences, in order to save the user's selection time, the visualization solutions are divided according to the chart type, color scheme, and layout, and the nine most representative visualization solutions are screened out, and these nine visualization solutions are shuffled and presented to the user in a nine-square grid format, so that the user can compare and select the visualization solution that he or she is most satisfied with. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow chart of a data warehouse management system based on artificial intelligence;

[0055] Figure 2 A schematic diagram of a pentagonal pattern in a data warehouse management system based on artificial intelligence;

[0056] Figure 3 This is a structural diagram of a big data visualization management system based on artificial intelligence. DETAILED DESCRIPTION

[0057] 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.

[0058] As mentioned in the background technology of this application, the inventors have discovered through research that when using existing big data visualization management methods, after users input their own needs, they are unable to quickly screen out visualization solutions that are more in line with the user's personality preferences for selection. Users are required to find satisfactory visualization solutions on their own, which is time-consuming and labor-intensive and has certain defects.

[0059] In order to solve the above-mentioned defects, the present application discloses an artificial intelligence-based data warehouse management system, which can quickly screen out visualization solutions that are more in line with the user's personality preferences for selection, eliminating the need for users to find satisfactory visualization solutions on their own, saving time and effort.

[0060] The following will describe in detail how the solution of the present application solves the above technical problems in conjunction with the accompanying drawings.

[0061] See also Figure 1 In an embodiment of the present invention, an artificial intelligence-based data warehouse management system includes: an identity authentication module for verifying a user's identity; a demand input and analysis module for inputting visualization requirements and analyzing the user's personality preferences during the input process; a data collection and processing module for collecting big data that needs to be visualized according to the input visualization requirements, and cleaning and processing the data to remove duplicate, erroneous or inconsistent data; a solution screening module for screening out nine visualization solutions for the user to make a final selection based on the user's personality preferences; a visualization implementation module for realizing visualization results of the collected big data according to the final selected visualization solution; and a scoring module for scoring the visualization results in terms of satisfaction.

[0062] In this embodiment, the specific analysis process of the user's personality preference is as follows: the typing speed of the user when inputting the visualization requirement is collected, marked as A, and then A is compared and calculated with the first preset value to obtain a first weight value a=(A / first preset value)*100, wherein A and the first preset value are both greater than zero; the typing error rate of the user when inputting the visualization requirement is collected, marked as B, and then B is compared and calculated with the second preset value to obtain a second weight value b=(B / second preset value)*100, wherein B and the second preset value are both greater than zero; the typing error rate of the user when inputting the visualization requirement is collected, marked as B, and then B is compared and calculated with the second preset value to obtain a second weight value b=(B / second preset value)*100, wherein B and the second preset value are both greater than zero; The total number of words when the user inputs the visualization requirement is collected, marked as C, and then C is compared with the third preset value to obtain a third weight value c=(C / third preset value)*100, wherein C and the third preset value are both greater than zero; the words used by the user when inputting the visualization requirement are collected, and the recognition and analysis are performed to calculate the fourth weight value d; the punctuation marks when the user inputs the visualization requirement are collected, and the recognition and analysis are performed to calculate the fifth weight value e; the pentagonal pattern P is generated according to the first weight value a, the second weight value b, the third weight value c, the fourth weight value d, and the fifth weight value e in real time, as shown in FIG. Figure 2 As shown; P is output as the analysis result of the user's personality bias in real time. The user's personality preference can be analyzed from the user's typing input habits. This analysis is usually based on multiple aspects such as the user's typing speed, error rate, word choice, punctuation use, and the total number of input words. Typing speed: Users with fast typing speed may be impatient and pursue efficiency; while users with slow typing speed may be calm and pay attention to details. Error rate: Users with high error rate may be careless or not pay much attention to the input content; users with low error rate may be more careful and have higher requirements for input content. Word choice: The vocabulary and expressions used by users can reflect their education level, interests and hobbies, and thinking style. For example, users who use more professional terms may have higher professional literacy; users who use more Internet buzzwords may be young and open. Punctuation use: The use of punctuation habits can reflect the user's emotional expression and logical thinking. For example, users who frequently use the same symbol may be lazy and do not pursue rigor; while users who are good at using separators such as commas and semicolons may have strong logical thinking. Total number of words entered: The more words a user enters during the typing process, the richer the content the customer wants to obtain; the fewer words entered, the simpler the content the customer wants to obtain.

[0063] In this embodiment, the specific calculation process of the fourth weight value d is as follows: identify the number of professional terms and the number of Internet buzzwords from the collected words, and mark them as D1 and D2 respectively; compare D1 with D2, if D1>D2, it means that the user tends to use professional terms in terms of words, if D1<D2, it means that the user tends to use Internet buzzwords in terms of words, if D1=D2, then calculate the fourth weight value d=0; if the user tends to use professional terms in terms of words, calculate the fourth weight value d=(D1 / fourth preset value)*100, where D1 and the fourth preset value are both greater than zero; if the user tends to use professional terms in terms of words, calculate the fourth weight value d=(-D2 / fourth preset value)*100, where D2>0. This setting can quickly determine whether the user tends to use professional terms or Internet buzzwords in terms of words.

[0064] In this embodiment, the specific calculation process of the fifth weight value e is: identify the number of punctuation types from the collected punctuation and mark them as E1; identify the total number of punctuation from the collected punctuation and mark them as E2; calculate the fifth weight value e=(E1*E2 / fifth preset value)*100, where E1, E2 and the fifth preset value are all greater than zero. This setting can quickly determine the customer's habit of using punctuation.

[0065] In this embodiment, the specific screening process of the visualization scheme is as follows: mark the preset visualization schemes in the database as Qi, i=1···n, where n is a positive integer; mark the pentagonal pattern corresponding to Qi as Pi, i=1···n; match the overlapping area of ​​P in real time with each Pi, and screen out the top 100 visualization schemes with the largest overlapping area with P in real time from Pi, and mark them as pj, j=1···100; then mark the chart type of pj as k1, the color scheme as k2, and the layout as k3; divide the pj of the same group with the same chart type k1 and the largest number into the first sequence; divide the pj of the same group with the same color scheme k2 and the largest number into the second sequence; and divide the pj of the same layout k3 and the largest number into the second sequence. The pj in the same group is divided into the third sequence; three visualization schemes are randomly selected from the first sequence. If there are less than three visualization schemes in the first sequence, the insufficient visualization schemes are randomly selected from outside the first, second and third sequences; three visualization schemes are randomly selected from the second sequence. If there are less than three visualization schemes in the second sequence, the insufficient visualization schemes are randomly selected from outside the first, second and third sequences; three visualization schemes are randomly selected from the third sequence. If there are less than three visualization schemes in the third sequence, the insufficient visualization schemes are randomly selected from outside the first, second and third sequences; the nine selected visualization schemes are reduced in proportion, shuffled and displayed in the form of a nine-square grid for the user to make the final selection. After selecting one hundred visualization schemes that are more in line with the user's personality preference, in order to save the user's selection time, they are divided according to the chart type, color scheme and layout of the visualization scheme, and the nine most representative visualization schemes are selected from them. These nine visualization schemes are shuffled and displayed to the user in the form of a nine-square grid, so that the user can compare and select the visualization scheme that he or she is most satisfied with.

[0066] In this embodiment, if there are two or more groups of pj in the first sequence, when selecting visualization solutions from the first sequence, all pj in the first sequence are randomly selected; if there are two or more groups of pj in the second sequence, when selecting visualization solutions from the second sequence, all pj in the second sequence are randomly selected; if there are two or more groups of pj in the third sequence, when selecting visualization solutions from the third sequence, all pj in the third sequence are randomly selected. In addition, when selecting nine visualization solutions, if there are identical visualization solutions among them, the duplicate visualization solutions are deleted and re-selected.

[0067] In this embodiment, the customer's satisfaction score is marked as V. If V is greater than or equal to the sixth preset value, it means that the user is satisfied with the visualization result, and the visualization scheme and pentagonal pattern corresponding to the visualization result are stored in the database; if V is less than the sixth preset value, it means that the user is not satisfied with the visualization result, and the user optimizes the visualization result by himself, and then the visualization scheme and pentagonal pattern corresponding to the optimized visualization result are stored in the database. This setting ensures that the final visualization result can meet the needs of the user, and the visualization scheme and pentagonal pattern corresponding to the visualization result that the user is satisfied with are stored in the database for later screening as a case.

[0068] In this embodiment, the user identity verification method includes face recognition, fingerprint recognition and password verification. This setting can effectively prevent others from stealing identity and querying data.

[0069] The present invention can quickly screen out visualization solutions that are more in line with the user's personality preferences for selection, without the need for the user to find a satisfactory visualization solution by themselves, saving time and effort. After screening out one hundred visualization solutions that are more in line with the user's personality preferences, in order to save the user's selection time, the visualization solutions are divided according to the chart type, color scheme, and layout, and the nine most representative visualization solutions are screened out, and these nine visualization solutions are shuffled and presented to the user in a nine-square grid format, so that the user can compare and select the visualization solution that he or she is most satisfied with.

[0070] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0071] What is described above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A data warehouse management system based on artificial intelligence, characterized in that: include: Authentication module, used to verify the user's identity; Demand input and analysis module, used to input visualization requirements and analyze the user's personality preferences during the input process; The data collection and processing module is used to collect the big data that needs to be visualized according to the input visualization requirements, and clean and process the data to remove duplicate, erroneous or inconsistent data; The scheme screening module is used to screen out nine visualization schemes for the user to make the final selection based on the user's personality preference; The visualization realization module is used to realize the visualization results of the collected big data according to the final selected visualization scheme; The scoring module is used to rate the satisfaction of the visualization results.

2. The data warehouse management system based on artificial intelligence according to claim 1 is characterized in that: The specific analysis process of user personality preference is as follows: The typing speed of the user when inputting the visualization requirement is collected and marked as A, and then A is compared and calculated with the first preset value to obtain a first weight value a=(A / first preset value)*100, where A and the first preset value are both greater than zero; The typing error rate of the user when inputting the visualization requirement is collected and marked as B, and then B is compared and calculated with the second preset value to obtain a second weight value b=(B / second preset value)*100, where both B and the second preset value are greater than zero; The total number of words when the user inputs the visualization requirement is collected, marked as C, and then C is compared and calculated with the third preset value to obtain a third weight value c=(C / third preset value)*100, where both C and the third preset value are greater than zero; Collect the words used by the user when inputting the visualization requirements, identify and analyze them, and calculate the fourth weight value d; The punctuation marks when the user inputs the visualization requirements are collected, and the punctuation marks are identified and analyzed to calculate the fifth weight value e; Generating a pentagonal pattern P in real time according to the first weight value a, the second weight value b, the third weight value c, the fourth weight value d, and the fifth weight value e; P is output in real time as the analysis result of the user's personality tendency.

3. The data warehouse management system based on artificial intelligence according to claim 2 is characterized in that: The specific calculation process of the fourth weight value d is: Identify the number of professional terms and Internet buzzwords from the collected words and mark them as D1 and D2 respectively; Compare D1 with D2. If D1>D2, it means that the user tends to use professional terms. If D1<D2, it means that the user tends to use Internet buzzwords. If D1=D2, calculate the fourth weight value d=0. If the user tends to use professional terms, a fourth weight value d=(D1 / fourth preset value)*100 is calculated, where D1 and the fourth preset value are both greater than zero; If the user tends to use professional terms, the fourth weight value d=(-D2 / fourth preset value)*100 is calculated, where D2>0.

4. The data warehouse management system based on artificial intelligence according to claim 3 is characterized in that: The specific calculation process of the fifth weight value e is: Identify the types and number of punctuation marks from the collected punctuation marks and mark them as E1; Identify the total number of punctuation marks from the collected punctuation marks and mark it as E2; The fifth weight value e=(E1*E2 / fifth preset value)*100 is calculated, wherein E1, E2 and the fifth preset value are all greater than zero.

5. The data warehouse management system based on artificial intelligence according to claim 4 is characterized in that: The specific screening process of the visualization scheme is as follows: The preset visualization schemes in the database are marked as Qi, i=1···n, where n is a positive integer; The pentagonal pattern corresponding to Qi is marked as Pi, i = 1···n; Match the overlapping areas of P real-time with each Pi, and select the top 100 visualization schemes with the largest overlapping areas with P real-time from Pi, and mark them as pj, j = 1···100; Then mark the chart type of pj as k1, the color scheme as k2, and the layout as k3; The largest number of the same group pj belonging to the same chart type k1 is divided into the first sequence; The largest number of groups pj belonging to the same color scheme k2 are divided into the second sequence; The pj of the same group with the largest number belonging to the same layout k3 is divided into the third sequence; Randomly select three visualization schemes from the first sequence. If there are less than three visualization schemes in the first sequence, randomly select the insufficient visualization schemes from outside the first sequence, the second sequence, and the third sequence; Randomly select three visualization schemes from the second sequence. If there are less than three visualization schemes in the second sequence, randomly select the insufficient visualization schemes from outside the first sequence, the second sequence, and the third sequence; Randomly select three visualization solutions from the third sequence. If there are less than three visualization solutions in the third sequence, randomly select the insufficient visualization solutions from outside the first sequence, the second sequence, and the third sequence. The nine selected visualization schemes are scaled down in equal proportions, shuffled and displayed in the form of a nine-square grid for users to make their final selection.

6. The data warehouse management system based on artificial intelligence according to claim 5 is characterized in that: If there are two or more groups of pj in the first sequence, then when screening visualization solutions from the first sequence, all pj in the first sequence are shuffled and randomly screened; if there are two or more groups of pj in the second sequence, then when screening visualization solutions from the second sequence, all pj in the second sequence are shuffled and randomly screened; if there are two or more groups of pj in the third sequence, then when screening visualization solutions from the third sequence, all pj in the third sequence are shuffled and randomly screened.

7. The data warehouse management system based on artificial intelligence according to claim 6 is characterized in that: The customer satisfaction score is marked as V. If V is greater than or equal to the sixth preset value, it means that the user is satisfied with the visualization result, and the visualization scheme and the pentagonal pattern corresponding to the visualization result are stored in the database; If V is less than the sixth preset value, it means that the user is not satisfied with the visualization result. The user optimizes the visualization result by himself, and then stores the visualization scheme and the pentagonal pattern corresponding to the optimized visualization result in the database.

8. The data warehouse management system based on artificial intelligence according to claim 7 is characterized in that: Ways to verify user identity include face recognition, fingerprint recognition, and password verification.

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