A method for displaying a visualization graphical interface of a business management big data quantitative analysis statistical table

Abstract

The present invention discloses a method for displaying a visualization graphical interface of a business management big data quantitative analysis statistical table, including the following steps: S1, obtaining product information data, product feature information data, cost estimation information data, target price information data and competitive product information data associated with the enterprise's products; S2, obtaining the enterprise's customer information data, contract information data, customer market information data and customer product demand information data; S3, obtaining the enterprise's business opportunity information data; S4, obtaining the enterprise's management software user information data; S5, screening the competitive product information data associated with the enterprise's products, obtaining effective competitive product information data and other steps. The advantages are: the method can improve the efficiency of competitive product analysis and customer demand analysis, improve work efficiency through algorithms, and assist customers in making product route decisions, finding competitive advantages, and improving product functions to greatly increase bidding or market competitiveness.

Description

Technical Field

[0001] The present invention relates to the technical field of data visualization, and in particular to a method for displaying a visualization graphic interface of a business management big data quantitative analysis statistical table. Background Art

[0002] It is impossible with existing technologies to organize structured data on company products, market competitors, business opportunities and customer product needs through a visual graphical interface, and it is impossible to form professional reports, visual interfaces and analytical report evaluations through horizontal and vertical comparisons between company products and competitors to assist companies in making business decisions and technical route decisions. Summary of the invention

[0003] The purpose of the present invention is to provide a method for displaying a visualized graphical interface of a business management big data quantitative analysis statistical table, thereby solving the aforementioned problems existing in the prior art.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0005] A method for displaying a visualization graphical interface of a business management big data quantitative analysis statistical table comprises the following steps:

[0006] S1. Obtain the enterprise's product information data, product feature information data, cost estimation information data, target selling price information data, and competitive product information data associated with the enterprise's products;

[0007] S2. Obtain the enterprise's customer information data, contract information data, customer market information data, and customer product demand information data;

[0008] S3. Obtaining business opportunity information data of the enterprise;

[0009] S4. Obtaining the enterprise's management software user information data;

[0010] S5. Screen the competitive product information data associated with the enterprise's products to obtain valid competitive product information data;

[0011] S6. Screen the business opportunity information data of the enterprise to obtain valid business opportunity information data;

[0012] S7. Based on the user information data of the enterprise management software, the effective competitive product information data and effective business opportunity information data are divided into levels and permissions and displayed.

[0013] Preferably, the product information data includes product category, product name and product introduction;

[0014] Competitive product information data includes competitive product categories, competitive product names, and competitive product introductions.

[0015] Preferably, the customer information data includes industry categories, company profiles, news updates, external activity information, contact information, communication records, and customer-related business opportunities;

[0016] Contract information data includes contract name, contract amount, product name, delivery plan information, delivery status, and acceptance information;

[0017] Customer product demand information data includes customer name and demand content; the demand content includes demand category, demand number, demand name, demand content, cycle requirements, demand source, and estimated sales.

[0018] Preferably, the business opportunity information data includes demand number, demand name, business department, customer name, whether it is a business opportunity, person who submitted the demand, estimated sales, and business opportunity progress data.

[0019] Preferably, the management software user information data includes employee name, employee position, employee role and organizational structure.

[0020] Preferably, step S5 specifically includes, taking the enterprise's products as a benchmark, performing similarity calculation on the competitive product information data associated with the enterprise's products, and screening out effective competitive product data information; including the following steps,

[0021] S51, digitize and classify all competitive product information data associated with the enterprise's products, so as to separate and collect mixed data, text data, and digital data in all associated competitive product information data;

[0022] S52, performing similarity calculation on the mixed data, text data, and digital data of all related competing products based on the enterprise product, so as to extract the mixed data, text data, and digital data that are higher than a first preset threshold;

[0023] S53. Based on the extracted mixed data, text data, and digital data, query the competing products to which they belong, and count the data information of these competing products, which is valid competing product data information.

[0024] Preferably, in step S52, the similarity of the mixed data is calculated using the following formula:

[0025]

[0026] Among them, sim(O 1 , O 2 ) is the similarity between a certain mixed data of the enterprise product and a certain mixed data of the competitor product; 1 is a mixed data of enterprise products, O 2 A mixed data corresponding to the competitor; 1iis the i-th attribute of a mixed data of enterprise products; 2i is the i-th attribute of a mixed data corresponding to the competitor; i = 1, 2, 3, ..., m;

[0027] If O 1 , O 2 If one of them does not have attribute i, then δx is equal to 0; if O 1 , O 2 The attribute i of is an asymmetric binary attribute, and the corresponding attribute values ​​are all 0, then δ i Equal to 0; except for the above two cases, δ i is equal to 1;

[0028] Use the following formula to calculate the similarity of text data:

[0029]

[0030] Wherein, sim(x, y) is the similarity between a certain text data of an enterprise product and a certain text data of a competitor product; x is a certain text data of an enterprise product, and y is a certain text data of a competitor product;

[0031] The similarity of digital data is calculated using the following formula:

[0032]

[0033] Among them, sim(v 1 , v 2 ) is the similarity between a certain digital data of the enterprise product and a certain digital data of the competitor product; v 1 is a digital data of the enterprise's products; 2 A digital data corresponding to the competitor; v 1j is the jth attribute of a digital data of an enterprise product; v 2j is the j-th attribute of a certain digital data corresponding to the competitor; j = 1, 2, 3, ..., n.

[0034] Preferably, step S6 specifically includes filtering out demand content from business opportunity information data according to customer product demand information data, and obtaining information data of products required by customers based on the demand content; performing similarity calculation on information data of products required by customers based on the company's products, and filtering out valid business opportunity information data.

[0035] Preferably, the similarity calculation is performed on the information data of the products required by the customer based on the products of the enterprise; the steps include:

[0036] S61, digitizing and classifying the information data of the products required by the customers, so as to separate and collect the text data and digital data in the information data of the products required by all the customers;

[0037] S62, taking the enterprise products as the benchmark, performing similarity calculation on the text data and the digital data of the information data of all the products required by the customers, so as to extract the text data and the digital data which are higher than the second preset threshold value;

[0038] S63, searching for products needed by the customers according to the extracted text data and digital data, and collecting data information of these products to obtain valid business opportunity information data;

[0039] The similarity calculation method in step S62 is the same as that in step S52.

[0040] Preferably, step S7 specifically includes aggregating effective competitive product information data and effective business opportunity information data, dividing the effective data into display levels and user viewing permissions according to the enterprise organizational structure, and finally displaying the effective information in a graphical manner.

[0041] The beneficial effects of the present invention are: improving the efficiency of competitive product analysis and customer demand analysis, improving work efficiency through algorithms, and assisting customers in making product route decisions, finding competitive advantages, and improving product functions to greatly increase bidding or market competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a flow chart of a display method in an embodiment of the present invention;

[0043] Figure 2 is a schematic diagram of business opportunity information data in an embodiment of the present invention;

[0044] Figure 3 It is a schematic diagram showing effective data in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation methods described herein are only used to explain the present invention and are not used to limit the present invention.

[0046] like Figure 1 and Figure 2 As shown, in this embodiment, a method for displaying a visualization graphical interface of a business management big data quantitative analysis statistical table is provided, comprising the following steps:

[0047] S1. Obtain the enterprise's product information data, product feature information data, cost estimation information data, target selling price information data, and competitive product information data associated with the enterprise's products;

[0048] S2. Obtain the enterprise's customer information data, contract information data, customer market information data, and customer product demand data;

[0049] S3. Obtaining business opportunity information data of the enterprise;

[0050] S4. Obtaining the enterprise's management software user information data;

[0051] S5. Screen the competitive product information data associated with the enterprise's products to obtain valid competitive product information data;

[0052] S6. Screen the business opportunity information data of the enterprise to obtain valid business opportunity information data;

[0053] S7. Based on the user information data of the enterprise management software, the authority division and hierarchical display of valid competitor information data and valid business opportunity information data are carried out.

[0054] In this embodiment, the product information data includes product category, product name and product introduction;

[0055] Competitive product information data includes competitive product categories, competitive product names, and competitive product introductions.

[0056] For product categories and competing product categories, please refer to the Industrial Classification for National Economic Activities (GB / T 4754-2017).

[0057] Cost estimation information data includes the individual cost of products and the average cost of products. These data come from the enterprise EPR system. Cost estimation is used to determine subsequent business analysis recommendations. Target selling price information data comes from historical contract product selling price information.

[0058] In this embodiment, the customer information data includes industry categories, company profiles, news updates, external activity information, contact information, communication records, and customer-related business opportunities;

[0059] Contract information data includes contract name, contract amount, product name, delivery plan information, delivery status, and acceptance information;

[0060] Customer market information data mainly includes industry information and the category of products required by customers.

[0061] Customer product demand information data includes customer name and demand content; the demand content includes demand category, demand number, demand name, demand content, cycle requirements, demand source, and estimated sales.

[0062] In this embodiment, the business opportunity information data includes demand number, demand name, business department, customer name, whether it is a business opportunity, demand submitter, estimated sales, and business opportunity progress data.

[0063] In this embodiment, the management software user information data includes employee name, employee position, employee role and organizational structure.

[0064] In this embodiment, step S5 specifically includes calculating the similarity of competitive product information data associated with the enterprise's products based on the enterprise's products, and screening out valid competitive product data information; including the following steps:

[0065] S51, digitize and classify all competitive product information data associated with the enterprise's products, so as to separate and collect mixed data, text data, and digital data in all associated competitive product information data;

[0066] S52, performing similarity calculation on the mixed data, text data, and digital data of all related competing products based on the enterprise product, so as to extract the mixed data, text data, and digital data that are higher than a first preset threshold;

[0067] S53. Based on the extracted mixed data, text data, and digital data, query the competing products to which they belong, and count the data information of these competing products, which is valid competing product data information.

[0068] In this embodiment, in step S52, the similarity calculation of the mixed data is performed using the following formula:

[0069]

[0070] Among them, sim(O 1 , O 2 ) is the similarity between a certain mixed data of the enterprise product and a certain mixed data of the competitor product; 1 is a mixed data of enterprise products, O 2 A mixed data corresponding to the competitor; 1i is the i-th attribute of a mixed data of enterprise products; 2i is the i-th attribute of a mixed data corresponding to the competitor; i = 1, 2, 3, ..., m;

[0071] If O 1 , O 2 If one of them does not have attribute i, then δ i Equal to 0; if O 1 , O 2 The attribute i of is an asymmetric binary attribute, and the corresponding attribute values ​​are all 0, then δ i Equal to 0; except for the above two cases, δ i is equal to 1;

[0072] Use the following formula to calculate the similarity of text data:

[0073]

[0074] Wherein, sim(x, y) is the similarity between a certain text data of an enterprise product and a certain text data of a competitor product; x is a certain text data of an enterprise product, and y is a certain text data of a competitor product;

[0075] The similarity of digital data is calculated using the following formula:

[0076]

[0077] Among them, sim(v 1 , v 2 ) is the similarity between a certain digital data of the enterprise product and a certain digital data of the competitor product; v 1 is a digital data of the enterprise's products; 2 A digital data corresponding to the competitor; v 1j is the jth attribute of a digital data of an enterprise product; v 2j is the j-th attribute of a certain digital data corresponding to the competitor; j = 1, 2, 3, ..., n.

[0078] The mixed data is digital data mixed with text attributes, for example: length, width and height are 12*24*32cm. The first preset threshold can be selected according to actual conditions to better meet actual needs.

[0079] In this embodiment, step S6 specifically includes filtering out demand content from business opportunity information data based on customer product demand information data, and obtaining information data of products required by customers based on the demand content; performing similarity calculation on information data of products required by customers based on the company's products, and filtering out valid business opportunity information data.

[0080] In step S6, similarity calculation is performed on the information data of the products required by the customer based on the products of the enterprise; the steps include:

[0081] S61, digitizing and classifying the information data of the products required by the customers, so as to separate and collect the text data and digital data in the information data of the products required by all the customers;

[0082] S62, taking the enterprise products as the benchmark, performing similarity calculation on the text data and the digital data of the information data of all the products required by the customers, so as to extract the text data and the digital data which are higher than the second preset threshold value;

[0083] S63, searching for products needed by the customers according to the extracted text data and digital data, and collecting data information of these products to obtain valid business opportunity information data;

[0084] The similarity calculation method in step S62 is the same as that in step S52.

[0085] The second preset threshold can be selected according to actual conditions to better meet actual needs.

[0086] like Figure 3 As shown, in this embodiment, step S7 specifically includes aggregating valid competitor information data and valid business opportunity information data, dividing the valid data into display levels and user viewing permissions according to the enterprise organizational structure, and finally displaying the valid information in a graphical manner.

[0087] Enterprise employees can view valid competitor information data and valid business opportunity information data according to the corresponding display level and viewing permissions, thereby helping to improve the efficiency of competitor analysis and customer demand analysis, assisting customers in making product route decisions, finding competitive advantages, and improving product functions to increase bidding or market competitiveness.

[0088] The graph can be a pie chart, a bar chart, a line chart or a scatter chart. You can choose the one based on the actual situation to better meet the actual needs.

[0089] By adopting the above technical solution disclosed in the present invention, the following beneficial effects are obtained:

[0090] The present invention provides a method for displaying a visualized graphical interface of a business management big data quantitative analysis statistical table. The method can improve the efficiency of competitive product analysis and customer demand analysis, improve work efficiency through algorithms, and assist customers in making product route decisions, finding competitive advantages, and improving product functions, thereby greatly increasing bidding or market competitiveness.

[0091] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be considered as the scope of protection of the present invention.

Claims

1. A method for displaying a visualization graphical interface of a business management big data quantitative analysis statistical table, characterized by: The following steps are included: S1. Obtain the enterprise's product information data, product feature information data, cost estimation information data, target selling price information data, and competitive product information data associated with the enterprise's products; S2. Obtain the enterprise's customer information data, contract information data, customer market information data, and customer product demand information data; S3. Obtaining business opportunity information data of the enterprise; S4. Obtaining the enterprise's management software user information data; S5. Screen the competitive product information data associated with the enterprise's products to obtain valid competitive product information data; Step S5 specifically includes calculating the similarity of competitive product information data associated with the enterprise's products based on the enterprise's products, and screening out valid competitive product data information; it includes the following steps: S51, digitize and classify all competitive product information data associated with the enterprise's products, so as to separate and collect mixed data, text data, and digital data in all associated competitive product information data; S52, performing similarity calculation on the mixed data, text data, and digital data of all related competing products based on the enterprise product, so as to extract the mixed data, text data, and digital data that are higher than a first preset threshold; In step S52, the similarity of the mixed data is calculated using the following formula: Among them, sim(O1,O2) is the similarity between a certain mixed data of the enterprise product and a certain mixed data of the competitor product; O1 is a certain mixed data of the enterprise product, O2 is a certain mixed data of the competitor product; 1i is the i-th attribute of a mixed data of enterprise products; 2i is the i-th attribute of a mixed data corresponding to the competitor; i = 1, 2, 3, ..., m; If one of O1 and O2 does not have attribute i, then δ i is equal to 0; if the attribute i of O1 and O2 is an asymmetric binary attribute, and the corresponding attribute values ​​are all 0, then δ i =0; except for the above two cases, δ i is equal to 1; Use the following formula to calculate the similarity of text data: Wherein, sim(x,y) is the similarity between a certain text data of an enterprise product and a certain text data of a competitor product; x is a certain text data of an enterprise product, and y is a certain text data of a competitor product; The similarity of digital data is calculated using the following formula: Where sim(v1,v2) is the similarity between a certain digital data of the enterprise product and a certain digital data of the competitor product; v1 is a certain digital data of the enterprise product; v2 is a certain digital data of the competitor product; v 1j is the jth attribute of a digital data of an enterprise product; v 2j is the jth attribute of a digital data corresponding to the competitor; j = 1, 2, 3, ..., n; S53, query the competing products according to the extracted mixed data, text data, and digital data, and collect data information of the competing products, which is valid competing product data information; S6, screen the business opportunity information data of the enterprise to obtain valid business opportunity information data; S7. Based on the user information data of the enterprise management software, the effective competitive product information data and effective business opportunity information data are divided into levels and permissions and displayed.

2. The method for displaying a visualization graphical interface of a business management big data quantitative analysis statistical table according to claim 1, characterized in that: Product information data includes product category, product name and product introduction; Competitive product information data includes competitive product categories, competitive product names, and competitive product introductions.

3. The method for displaying a visualization graphical interface of a business management big data quantitative analysis statistical table according to claim 2 is characterized by: Customer information data includes industry categories, company profiles, news updates, external activity information, contact information, communication records, and customer-related business opportunities; Contract information data includes contract name, contract amount, product name, delivery plan information, delivery status, and acceptance information; Customer product demand information data includes customer name and demand content; the demand content includes demand category, demand number, demand name, cycle requirements, demand source, and estimated sales.

4. The method for displaying a visualization graphical interface of a business management big data quantitative analysis statistical table according to claim 3 is characterized by: Business opportunity information data includes demand number, demand name, business department, customer name, whether it is a business opportunity, person who submitted the demand, estimated sales, and business opportunity progress data.

5. The method for displaying a visualization graphical interface of a business management big data quantitative analysis statistical table according to claim 4 is characterized by: Management software user information data includes employee name, employee position, employee role and organizational structure.

6. The method for displaying a visualization graphical interface of a business management big data quantitative analysis statistical table according to claim 1, characterized in that: Step S6 specifically includes filtering out demand content from business opportunity information data according to customer product demand information data, and obtaining product information data required by the customer based on the demand content; performing similarity calculation on the product information data required by the customer based on the company's products, and filtering out valid business opportunity information data.

7. The method for displaying a visualization graphical interface of a business management big data quantitative analysis statistical table according to claim 6 is characterized by: The similarity calculation is performed on the information data of the products required by the customer based on the company's products; the steps include: S61, digitizing and classifying the information data of the products required by the customers, so as to separate and collect the text data and digital data in the information data of the products required by all the customers; S62, taking the enterprise products as the benchmark, performing similarity calculation on the text data and the digital data of the information data of all the products required by the customers, so as to extract the text data and the digital data that are higher than the second preset threshold value; S63, searching for products needed by the customers according to the extracted text data and digital data, and collecting data information of these products to obtain valid business opportunity information data; The similarity calculation method in step S62 is the same as that in step S52.

8. The method for displaying a visualization graphical interface of a business management big data quantitative analysis statistical table according to claim 6, characterized in that: Step S7 specifically includes aggregating effective competitive product information data and effective business opportunity information data, dividing the effective data into display levels and user viewing permissions according to the enterprise organizational structure, and finally displaying the effective information in a graphical manner.

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