An enterprise digital upgrade and transformation method and system based on big data mining

Abstract

The present invention discloses a method and system for enterprise digital upgrade and transformation based on big data mining, which relates to the technical field of enterprise upgrade and transformation. It includes: obtaining the basic information of the target enterprise's audience to obtain basic information items, where the basic information items are used to represent the portrait information of the target enterprise's audience; based on the basic information items, deploying terminals on the target path, obtaining the interaction data of the audience, analyzing the data to obtain interaction information items, and associating with the account system based on the interaction information items and the basic information items to obtain the cross-platform behavior data of the audience. When comprehensively considering the expectations of the masses and the costs required to meet these expectations, the present invention, with the premise of maintaining old users, selects the combination with the best adaptability of expectations and the costs required to meet these expectations as the enterprise's upgrade and transformation direction, greatly reducing the risks during the enterprise's upgrade and transformation process.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise upgrading and transformation, and particularly to a method and system for enterprise digital upgrading and transformation based on big data mining. Background Art

[0002] Enterprise digital upgrading and transformation refer to the comprehensive transformation and improvement of an enterprise's business processes, management models, organizational structures, and even business models by using digital technologies. By introducing automation, data analysis, and other digital tools, the daily operation processes of the enterprise are simplified and optimized, efficiency is improved, and costs are reduced. Big data mining refers to the process of extracting valuable information and knowledge from a large amount of data sets. This process combines various technologies such as statistics, machine learning, and data analysis, aiming to identify patterns, trends, and correlation relationships in the data. During the process of enterprise digital upgrading and transformation, the information mined from big data is used as the basis for upgrading.

[0003] A system and method for enterprise digital transformation evaluation based on big data with the patent publication number CN118521177A collect the basic information of an enterprise, analyze the basic information to determine the type and scale of the enterprise, and integrate the type, scale, and organizational business information of the enterprise into a transformation prediction sequence according to the type and scale of the enterprise and the organizational business information in the basic information, and use a transformation prediction model to analyze the transformation prediction sequence to obtain transformation information; it can make the analysis results more accurate, which is beneficial to providing a basis for subsequent evaluation of enterprise transformation; and predicts the transformation cost and transformation period, which can provide transformation information for the enterprise and is beneficial to avoiding low returns caused by enterprise transformation.

[0004] When the above-mentioned and similar technical solutions are used for enterprise digital upgrading and transformation, due to the diversity of the directions of upgrading and transformation, and the costs required for upgrading and transformation in different directions are different, when relying on the data obtained through big data for upgrading and transformation, it is easy to cause the situation that the rate of return after upgrading and transformation cannot reach the required return, resulting in the failure of upgrading and transformation. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for enterprise digital upgrading and transformation based on big data mining to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solution: A method for enterprise digital upgrading and transformation based on big data mining, including:

[0007] Obtain the basic information of the target enterprise's audience to obtain basic information items, and the basic information items are used to represent the portrait information of the target enterprise's audience;

[0008] Based on basic information items, terminal deployment is carried out on the target path to obtain the interaction data of the audience. The data is analyzed to obtain interaction information items. Based on the interaction information items and basic information items, the account system is associated to obtain the cross-platform behavior data of the audience, and a behavior data set is obtained. Based on the behavior data set, the expected information of the audience is obtained to get the audience expectation items. The audience expectation items include at least the expectation rate of one piece of expected information.

[0009] Obtain the cost data when the target enterprise makes improvements based on the audience expectation items to get the audience expectation cost items.

[0010] Sort the audience expectation items and the audience expectation cost items. The audience expectation cost items correspond to the audience expectation items respectively. According to the sorting results, select the audience expectation item with the lowest serial number combination result as the first target upgrade item.

[0011] Combined with multi-source data, obtain the expected information of the general public to get the general expectation items.

[0012] Adjust the general expectation items through a matching method to get the general combination items. Obtain the combination result of the general combination items and the audience expectation items to get the combined expectation items. Re-select the audience expectation item with the lowest serial number combination result as the second target upgrade item.

[0013] Based on the first target upgrade item and the second target upgrade item, determine the upgrade and transformation direction of the target enterprise to get the target transformation items.

[0014] Furthermore, the interaction data includes the page stay time. The acquisition method of the interaction information items includes: based on the target path, integrating a configurable buried point SDK, collecting the page stay time information, setting a judgment threshold. The judgment threshold is a time threshold. Determine whether the page stay time information reaches the time threshold. When it reaches the time threshold, set the target audience as the interaction item to obtain the interaction information items.

[0015] Furthermore, the acquisition method of the behavior data set includes:

[0016] Obtain the specific platform information of the collected data to get the target platform items.

[0017] Obtain the behavior data indicators of the requirements to get the target indicator items.

[0018] Based on the interaction information items, obtain the relevant active data of the audience on the target platform items to get the active information items.

[0019] Based on the target indicator items, obtain the target indicator information in the active information items to get the behavior data set.

[0020] Furthermore, the acquisition method of the audience expectation items includes:

[0021] Based on the behavior dataset, set the first division criterion, and split the behavior dataset based on the first division criterion to obtain a split dataset. The split dataset includes at least one standard split data item;

[0022] Obtain the proportion data of the split data item based on the split dataset to obtain the target expected rate, and combine the target expected rate to obtain the audience expected item.

[0023] Furthermore, the method for obtaining the first target upgrade item includes:

[0024] Sort the audience expected items in descending order, and based on the sorting result, obtain the first sorted item;

[0025] Sort the audience expected cost items in ascending order, and based on the sorting result, obtain the second sorted item;

[0026] Based on the corresponding results of the audience expected cost items and the audience expected items, correspond the first sorted item and the second sorted item to obtain the combined information item;

[0027] Based on the combined information item, obtain the combination with the lowest serial number combination result to obtain the target combination item, and determine the audience expected item based on the target combination item to obtain the first target upgrade item.

[0028] Furthermore, the basic information of the target enterprise audience includes ID information, and the method for obtaining the basic information item includes:

[0029] Obtain the sales data of the target enterprise, and based on the sales data, locate the sales group information to obtain the sales information item;

[0030] Set a division threshold, where the division threshold is the number threshold. Based on the combined result of the division threshold and the sales information item, determine whether the sales information item reaches the division threshold, and set the sales group information that reaches the division threshold in the sales information item as the audience group to obtain the audience group item;

[0031] Perform ID marking based on the audience group item to obtain the basic information item.

[0032] Furthermore, the method for obtaining the regular expected item includes:

[0033] Obtain the specific platform information of the collected data to obtain the target platform item;

[0034] Obtain the behavioral data indicators of the requirements to obtain the target indicator item;

[0035] Obtain the total acquisition data of the target indicator item in the target platform item to obtain the active total item;

[0036] Based on the elimination result of the active information items with respect to the total active items, a regular data set is obtained. A second division criterion is set, and based on the second division criterion, the total active items are split to obtain a regular split set. The regular split set includes at least one standard regular split item. The proportion data of the regular split item based on the regular split set is obtained to get a regular expected rate, and the regular expected items are combined from the regular expected rate.

[0037] Furthermore, the matching method includes: setting a combined value, where the combined value is fixed data, obtaining the combined result of the regular expected item and the combined value to get a regular combined item.

[0038] Furthermore, an enterprise digital upgrade and transformation system based on big data mining uses the above-mentioned enterprise digital upgrade and transformation method based on big data mining, and includes:

[0039] Acquisition module: Acquire the basic information of the target enterprise's audience to obtain basic information items. Based on the basic information items, terminal deployment is performed on the target path to obtain the interaction data of the audience. The data is analyzed to obtain interaction information items. Based on the interaction information items and the basic information items, the account system is associated to obtain the cross-platform behavior data of the audience to get a behavior data set. Based on the behavior data set, the expected information of the audience is obtained to get audience expected items, and the audience expected items include at least the expected rate of one piece of expected information;

[0040] Analysis module: Obtain the cost data when the target enterprise makes improvements based on the audience expected items to get audience expected cost items. Sort the audience expected items and the audience expected cost items, where the audience expected cost items correspond to the audience expected items respectively. According to the sorting result, select the audience expected item with the lowest serial number combination result as the first target upgrade item;

[0041] Expansion module: Combine multi-source data to obtain the expected information of regular people to get regular expected items. Through the matching method, the regular expected items are adjusted to get regular combined items. Obtain the combined result of the regular combined items and the audience expected items to get combined expected items. Re-select the audience expected item with the lowest serial number combination result as the second target upgrade item;

[0042] Combination module: Based on the first target upgrade item and the second target upgrade item, determine the upgrade and transformation direction of the target enterprise to get a target transformation item.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] The enterprise digital upgrade and transformation method and system based on big data mining obtain the basic information of the target enterprise's audience and the expected information of the audience to obtain the audience expectation items. At the same time, it obtains the cost data when the target enterprise makes improvements based on the audience expectation items to obtain the audience expectation cost items. It sorts the audience expectation items and the audience expectation cost items. According to the sorting results, it selects the audience expectation item with the lowest serial number combination result as the first target upgrade item to determine the upgrade and transformation direction of the target enterprise. When comprehensively considering the expectations of the masses and the costs required to meet these expectations, on the premise of maintaining old users, it selects the combination with the best adaptability between the expectations and the costs required to meet these expectations as the upgrade and transformation direction of the enterprise, greatly reducing the risks during the upgrade and transformation process of the enterprise.

[0045] Moreover, it also combines multi-source data to obtain the expected information of ordinary masses to obtain the ordinary expectation items. Through matching, it adjusts the ordinary expectation items to obtain the ordinary combination items. It obtains the combination result of the ordinary combination items and the audience expectation items, and re-selects the audience expectation item with the lowest serial number combination result as the second target upgrade item. Based on the first target upgrade item and the second target upgrade item, it re-determines the upgrade and transformation direction of the target enterprise. On the premise of maintaining old users, it takes the needs of new users as the auxiliary combination item affecting the upgrade and transformation, further improving the success rate of the upgrade and transformation. Brief Description of the Drawings

[0046] Figure 1 It is the overall process schematic diagram of the present invention;

[0047] Figure 2 It is the schematic diagram of the audience group of the present invention;

[0048] Figure 3 It is the schematic diagram of the interactive information items of the present invention;

[0049] Figure 4 It is the schematic diagram of the sorting of the audience expectation items of the present invention;

[0050] Figure 5 It is the schematic diagram of the sorting of the audience expectation cost items of the present invention;

[0051] Figure 6 It is the schematic diagram of the serial number combination result of the present invention. Detailed Embodiments

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0053] The directions of digital transformation are diverse. When enterprises choose an upgrade path, they need to carefully consider their own situation and the market environment. Each business area may have its unique digital needs. For example, the manufacturing industry may attach importance to the combination of intelligent manufacturing and the Internet of Things; while the retail industry may be more inclined to the construction of e-commerce platforms and data-driven market analysis. Such diversity means rapid market changes and the continuous evolution of consumer preferences. When determining the transformation direction, enterprises must ensure that their choices are highly consistent with the overall strategic goals. The cost issue in the digital transformation process is also a key factor that enterprises must face. Whether it is investing in advanced technical equipment or training employees to master new systems, the costs required for transformation are often huge. In this case, enterprises need to establish a comprehensive cost control and expected revenue calculation system. However, as an important tool for modern enterprise digital transformation, the effectiveness and reliability of big data are not absolute, which may lead to the data relied on by enterprises in decision-making not accurately reflecting the real market situation. The technical solution provided in this application first obtains the basic information of the target enterprise's audience to obtain basic information items. Based on the basic information items, it obtains the expected information of the audience to obtain audience expectation items. The audience expectation items at least include the expectation rate of at least one piece of expected information. At the same time, it obtains the cost data when the target enterprise makes improvements based on the audience expectation items to obtain audience expectation cost items. It sorts the audience expectation items and the audience expectation cost items, and the audience expectation cost items correspond to the audience expectation sorting items respectively. According to the sorting results, it selects the audience expectation item with the lowest serial number combination result as the first target upgrade item. At the same time, it combines multi-source data to obtain the expected information of the general public to obtain general expectation items. Through a matching method, it adjusts the general expectation items to obtain general combination items. It obtains the combination result of the general combination items and the audience expectation items, and re-selects the audience expectation item with the lowest serial number combination result as the second target upgrade item. Based on the first target upgrade item and the second target upgrade item, it determines the upgrade and transformation direction of the target enterprise. Specifically, in this application, as Figure 1 shown, it includes steps S100 - S900.

[0054] Step S100: Obtain the basic information of the target enterprise's audience to obtain basic information items.

[0055] It should be noted that the basic information items are used to represent the portrait information of the target enterprise's audience. The basic information of the target enterprise's audience includes ID information. The acquisition methods of the basic information items are as follows: obtain the sales data of the target enterprise, based on the sales data, locate the sales group information to obtain the sales information items; set a division threshold, the division threshold is the number of times threshold, based on the combination result of the division threshold and the sales information items, judge whether the sales information items reach the division threshold, and set the sales group information that reaches the division threshold in the sales information items as the audience group to obtain the audience group items; perform ID marking based on the audience group items to obtain the basic information items.

[0056] Specifically, when obtaining the sales data of the target enterprise, the customer information of the sales can be traced and located according to the sales data, including the payment path, payer information, etc. According to these data, the purchase times information of the masses can be obtained. At the same time, a division threshold is set, and the division threshold is 2, that is, when the purchase times reach two times, it is determined that the sales information items reach the division threshold. At this time, this part of the sales group is determined as the audience group, and ID marking is performed on these audience groups to obtain the basic information items.

[0057] Embodiment 1

[0058] As Figure 2 shown, in the specific implementation process, the products sold by a certain company are sold offline. The sales data of the company is obtained. From the establishment to the acquisition date, a total of 1000 products are sold. According to the payment path, payer information and other information, it is obtained that 200 people have purchased once, 265 people have purchased twice, and 90 people have purchased three times. At this time, according to the set division threshold, the sales group with the purchase times reaching two times is determined as the audience group, that is, the total number of the audience group is 355 people. ID marking is performed on these 355 people to obtain the basic information items.

[0059] Step S200: Based on the basic information items, deploy terminals on the target path, obtain the interaction data of the audience, and analyze the data to obtain the interaction information items.

[0060] It should be noted that the interaction data includes the page stay time. The acquisition method of the interaction information items includes: based on the target path, integrate the configurable buried point SDK to collect the page stay time information, set a judgment threshold, the judgment threshold is the time threshold, the judgment threshold is 10s, judge whether the page stay time information reaches the time threshold, and when it reaches the time threshold, set the target audience as the interaction item to obtain the interaction information items.

[0061] As Figure 3As shown in the figure, specifically, the set target path is the official website address of the target company. By integrating the configurable buried point SDK, the page stay time information is collected. According to the set judgment threshold, when the basic information item is 355 people, 43 people have a stay time of 10 - 20s, 98 people have a stay time of 20 - 30s, 87 people have a stay time of 30 - 40s, and 72 people have a stay time of more than 40s. A total of 300 people have a stay time of more than 10s on the official website address of the target company. At this time, the interaction information item is 300 people.

[0062] Step S300: Based on the interaction information item and the basic information item, associate with the account system to obtain the cross - platform behavior data of the audience and get the behavior data set.

[0063] It should be noted that the method for obtaining the behavior data set includes: obtaining the specific platform information for collecting data to get the target platform item; obtaining the required behavior data indicators to get the target indicator item; based on the interaction information item, obtaining the relevant active data of the audience based on the target platform item to get the active information item; based on the target indicator item, obtaining the target indicator information in the active information item to get the behavior data set.

[0064] Specifically, first, it is necessary to determine the specific platform for the information to be collected, such as Douyin, Kuaishou, and Xiaohongshu, to get the target platform item. Then, obtain the specific behavior data indicators to be collected, such as likes and comments, to get the target indicator item. According to the obtained interaction information item, obtain the active data of the audience based on the target platform item, specifically referring to all the active data of the audience on platforms such as Douyin, Kuaishou, and Xiaohongshu, such as likes, comments, shares, and collections. Then, according to the target indicator item, obtain the target indicator information in the active information item to get the behavior data set.

[0065] Embodiment 2

[0066] In the specific implementation process, by integrating the configurable buried point SDK, the page stay time information is collected. According to the set judgment threshold, when the basic information item is 355 people, a total of 300 people have a stay time of more than 10s on the official website address of the target company. At this time, the interaction information item is 300 people. By associating the account systems of these 300 people, the specific platform for collecting data is obtained as Douyin, and the specific behavior index data to be collected is obtained as likes. At this time, first obtain all the active data of these 300 people, and then according to the target indicator item of the specific behavior index data being likes related to the company's products, the behavior data set obtained is the like information of these 300 people.

[0067] Step S400: Based on the behavior data set, obtain the expected information of the audience to get the audience expectation item.

[0068] It should be noted that the expected rate of the audience expectation items includes at least the expected rate of one piece of expected information. The method for obtaining the audience expectation items includes: based on the behavior data set, setting a first division criterion, splitting the behavior data set based on the first division criterion to obtain a split data set, and the split data set includes at least one standard split data item; obtaining the proportion data of the split data item based on the split data set to obtain the target expected rate, and combining the target expected rates to obtain the audience expectation items.

[0069] Specifically, the set first division criterion is the type criterion, such as price type, quality type. Split the behavior data set based on the first division criterion to obtain a split data set, obtain the proportion data of the split data item based on the split data set to obtain the target expected rate, and combine the target expected rates to obtain the audience expectation items.

[0070] Embodiment III

[0071] In the specific implementation process, according to the set first division criterion, which is the quality type, all active data of 300 people are obtained. 150 people liked the video hoping that the quality of the target product would be improved by 10%, 100 people liked the video hoping that the quality of the target product would be improved by 15%, and 50 people liked the video hoping that the quality of the target product would be improved by 20%. At this time, the split data sets are respectively hoping that the quality of the target product will be improved by 10%, hoping that the quality of the target product will be improved by 15%, and hoping that the quality of the target product will be improved by 20%. At this time, the target expected rates are 50%, 33.33%, and 16.67% respectively, and the audience expectation items are combined.

[0072] Step S500: Obtain the cost data when the target enterprise makes improvements based on the audience expectation items to obtain the audience expectation cost items.

[0073] It should be noted that when making improvements based on the obtained audience expectation items, different audience expectation items require different costs. Obtain the cost information under different audience expectation items to obtain the audience expectation cost items.

[0074] In the specific implementation process, when the split data sets are respectively hoping that the quality of the target product will be improved by 10%, hoping that the quality of the target product will be improved by 15%, and hoping that the quality of the target product will be improved by 20%, the cost data increases by 10%, 20%, and 30% respectively. At this time, the audience expectation cost items are 10%, 20%, and 30% of the increased cost data respectively.

[0075] Step S600: Sort the audience expectation items and the audience expectation cost items, and select the audience expectation item with the lowest combined serial number result as the first target upgrade item.

[0076] It should be noted that the method for obtaining the first target upgrade item includes: sorting the audience expectation items in descending order, obtaining the first sorted item based on the sorting result, sorting the audience expectation cost items in ascending order, and obtaining the second sorted item based on the sorting result; corresponding the first sorted item and the second sorted item based on the corresponding result of the audience expectation cost item and the audience expectation item to obtain a combined information item; obtaining the combination with the lowest serial number combination result based on the combined information item to obtain a target combination item, and determining the audience expectation item based on the target combination item to obtain the first target upgrade item.

[0077] As Figures 4 - 6 shown, in the specific implementation process, the audience expectation items are sorted in descending order. When the split data sets are respectively that the quality of the target commodity is expected to increase by 10%, the quality of the target commodity is expected to increase by 15%, and the quality of the target commodity is expected to increase by 20%, the target expectation rates at this time are 50%, 33.33%, and 16.67%. At this time, the sorting result is that the quality of the target commodity is expected to increase by 10% as the first, the quality of the target commodity is expected to increase by 15% as the second, and the quality of the target commodity is expected to increase by 20% as the third. The audience expectation cost items are sorted in ascending order. When the split data sets are respectively that the quality of the target commodity is expected to increase by 10%, the quality of the target commodity is expected to increase by 15%, and the quality of the target commodity is expected to increase by 20%, the cost data increases by 10%, 20%, and 30% respectively. At this time, the audience expectation cost items are the 10%, 20%, and 30% respectively increased in the cost data. At this time, the sorting result is that the cost data increases by 10% as the first, the cost data increases by 20% as the second, and the cost data increases by 30% as the third. Corresponding the first sorted item and the second sorted item to obtain a combined information item, and obtaining the combination with the lowest serial number combination result. At this time, the combination with the lowest serial number is that the quality of the target commodity is expected to increase by 10% and the cost data increases by 10%. The audience expectation item corresponding to this combination is that the quality of the target commodity is expected to increase by 10%, and the first target upgrade item is obtained.

[0078] Step S700: Combine multi-source data to obtain the expectation information of the general public and obtain the general expectation item.

[0079] It should be noted that the method for obtaining the general expectation item includes: obtaining the specific platform information of the collected data to obtain the target platform item; obtaining the behavioral data indicators of the requirements to obtain the target indicator item; obtaining the total acquisition data of the target indicator item in the target platform item to obtain the active total amount item; obtaining the general data set based on the elimination result of the active information item by the active total amount item, setting the second division standard, splitting the active total amount item based on the second division standard to obtain the general split set. The general split set includes at least one standard general split item, obtaining the proportion data of the general split item based on the general split set to obtain the general expectation rate, and combining the general expectation rates to obtain the general expectation item.

[0080] Specifically, the second classification criterion is also a classification criterion by type, such as price type, quality type. Based on the second classification criterion, the total active item is split. The specific platform for collecting data is Douyin. The specific behavioral index data to be collected is the number of likes. The total active item obtained is 3,000 people. Based on the elimination result of the active information item for the total active item, a regular data set is obtained. When the active information item is the active information of 300 people, the regular data set is the active information of 2,700 people at this time. Among them, a total of 1,500 people liked the video hoping that the quality of the target product would be improved by 10%, 500 people liked the video hoping that the quality of the target product would be improved by 15%, and 700 people liked the video hoping that the quality of the target product would be improved by 20%. At this time, the split data sets are respectively hoping that the quality of the target product will be improved by 10%, hoping that the quality of the target product will be improved by 15%, and hoping that the quality of the target product will be improved by 20%. At this time, the regular expectation rates are 55.55%, 18.52%, and 25.92%, and the regular expectation items are combined.

[0081] Step S800: Adjust the regular expectation item to obtain a regular combination item. Obtain the combination result of the regular combination item and the audience expectation item, and reselect the audience expectation item with the lowest serial number combination result as the second target upgrade item.

[0082] It should be noted that the matching method includes: setting a combination value, the combination value is 50%, the combination value is fixed data, obtaining the combination result of the regular expectation item and the combination value, and obtaining the regular combination item.

[0083] Specifically, when the regular expectation rates are 55.55%, 18.52%, and 25.92%, the regular combination items are 27.775%, 9.26%, and 12.96% respectively at this time. Obtain the combination result of the regular combination item and the audience expectation item. The audience expectation items are 50%, 33.33%, and 16.67%, and the combined expectation items are 77.775%, 42.59, and 29.63 respectively. At this time, the audience expectation item with the lowest serial number combination result is still hoping that the quality of the target product will be improved by 10%, that is, the first target item and the second target item are the same.

[0084] Step S900: Based on the first target upgrade item and the second target upgrade item, determine the upgrade and transformation direction of the target enterprise to obtain the target transformation item.

[0085] An enterprise digital upgrade and transformation system based on big data mining, which uses the above-mentioned enterprise digital upgrade and transformation method based on big data mining, includes: Acquisition module: Obtain the basic information of the target enterprise's audience, obtain basic information items, and based on the basic information items, deploy terminals on the target path, obtain the interaction data of the audience, analyze the data to obtain interaction information items, based on the interaction information items and basic information items, associate the account system, obtain the cross-platform behavior data of the audience, obtain a behavior data set, and based on the behavior data set, obtain the expected information of the audience, obtain audience expectation items, and the audience expectation items include at least the expectation rate of one piece of expected information; Analysis module: Obtain the cost data when the target enterprise makes improvements based on the audience expectation items, obtain audience expectation cost items, sort the audience expectation items and audience expectation cost items, the audience expectation cost items correspond to the audience expectation items respectively, and according to the sorting results, select the audience expectation item with the lowest serial number combination result as the first target upgrade item; Expansion module: Combine multi-source data, obtain the expected information of the general public, obtain general expectation items, adjust the general expectation items through matching to obtain general combination items, obtain the combination result of the general combination items and the audience expectation items, obtain combined expectation items, and re-select the audience expectation item with the lowest serial number combination result as the second target upgrade item. Combining module: Based on the first target upgrade item and the second target upgrade item, determine the upgrade and transformation direction of the target enterprise to obtain target transformation items.

[0086] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended embodiments and their equivalents.

Claims

1. A method for enterprise digital upgrading and transformation based on big data mining, characterized in that: include: Obtain basic information of the target enterprise audience and obtain basic information items, which are used to represent the portrait information of the target enterprise audience; Based on the basic information items, terminal deployment is performed on the target path, interaction data of the audience is obtained, the data is analyzed to obtain interaction information items, based on the interaction information items and the basic information items, the account system is associated to obtain cross-platform behavior data of the audience to obtain a behavior data set, based on the behavior data set, the expected information of the audience is obtained to obtain audience expectation items, and the audience expectation items include at least one expected rate of expected information; Obtain the cost data of the target enterprise when making improvements based on the audience's expectations, and obtain the audience's expected cost items; The audience expectation items and the audience expectation cost items are sorted, the audience expectation cost items correspond to the audience expectation items respectively, and according to the sorting result, the audience expectation item with the lowest sequence number combination result is selected as the first target upgrade item; Combine multi-source data to obtain the expected information of the general public and obtain the general expected items; By matching, adjusting the conventional expectation items to obtain the conventional combination items, obtaining the combination results of the conventional combination items and the audience expectation items to obtain the combination expectation items, and reselecting the audience expectation item with the lowest sequence number combination result as the second target upgrade item; Based on the first target upgrade item and the second target upgrade item, determine the upgrade and transformation direction of the target enterprise to obtain the target transformation item; Methods for obtaining audience expectations include: Based on the behavior data set, a first division standard is set, and the behavior data set is split based on the first division standard to obtain a split data set, where the split data set includes at least one standard split data item; Obtain the proportion data of the split data item based on the split data set, obtain the target expected rate, and combine the target expected rates to obtain the audience expected item; The method for obtaining the conventional expected items includes: Get the specific platform information of the collected data and get the target platform item; Obtain the required behavioral data indicators and obtain the target indicator items; Get the total acquired data of the target indicator item in the target platform item to get the total active amount item; Based on the result of eliminating active information items from active total items, a regular data set is obtained, a second division standard is set, and the active total items are split based on the second division standard to obtain a regular split set, the regular split set includes at least one standard regular split item, and the proportion data of the regular split items based on the regular split set is obtained to obtain the regular expected rate, and the regular expected items are obtained by combining the regular expected rates.

2. According to claim 1, a method for digital upgrading and transformation of enterprises based on big data mining is characterized by: The interaction data includes page dwell time, and the method for obtaining the interaction information item includes: based on the target path, integrating a configurable tracking SDK, collecting page dwell time information, setting a judgment threshold, the judgment threshold is a time threshold, judging whether the page dwell time information reaches the time threshold, and when the time threshold is reached, setting the target audience as the interaction item to obtain the interaction information item.

3. The enterprise digital upgrade and transformation method based on big data mining according to claim 1 is characterized by: The method for acquiring the behavior data set includes: Get the specific platform information of the collected data and get the target platform item; Obtain the required behavioral data indicators and obtain the target indicator items; Based on the interactive information items, relevant active data of the audience based on the target platform items are obtained to obtain active information items; Based on the target indicator item, the target indicator information in the active information item is obtained to obtain the behavior data set.

4. According to claim 1, a method for enterprise digital upgrading and transformation based on big data mining is characterized by: The method for obtaining the first target upgrade item includes: Sorting the audience expectation items in descending order, and obtaining the first ranking item based on the sorting result; Sorting the audience expected cost items in order from low to high, and obtaining a second sorting item based on the sorting result; Based on the correspondence result between the audience expected cost item and the audience expected item, the first sorting item and the second sorting item are corresponded to obtain a combined information item; Based on the combination information item, the combination with the lowest serial number combination result is obtained to obtain the target combination item, and the audience expectation item is determined based on the target combination item to obtain the first target upgrade item.

5. According to claim 1, a method for digital upgrading and transformation of enterprises based on big data mining is characterized by: The basic information of the target enterprise audience includes ID information, and the method for obtaining the basic information items includes: Obtain sales data of the target enterprise, locate sales group information based on the sales data, and obtain sales information items; A segmentation threshold is set, where the segmentation threshold is a frequency threshold, and based on a combination result of the segmentation threshold and the sales information item, whether the sales information item reaches the segmentation threshold is determined, and the sales group information that reaches the segmentation threshold in the sales information item is set as the audience group, thereby obtaining the audience group item; ID tagging is performed based on the audience group item to obtain basic information items.

6. The enterprise digital upgrade and transformation method based on big data mining according to claim 1 is characterized by: The matching method includes: setting a combination value, where the combination value is fixed data, obtaining a combination result of a conventional expected item and the combination value, and obtaining a conventional combination item.

7. An enterprise digital upgrade and transformation system based on big data mining, characterized by: A method for digital upgrading and transformation of an enterprise based on big data mining as described in any one of claims 1 to 6 is used, comprising: Acquisition module: acquire basic information of the target enterprise audience, obtain basic information items, deploy terminals on the target path based on the basic information items, acquire interaction data of the audience, analyze the data to obtain interaction information items, associate account systems based on interaction information items and basic information items, acquire cross-platform behavior data of the audience, obtain behavior data sets, acquire expected information of the audience based on the behavior data sets, obtain audience expected items, and the audience expected items include at least one expected rate of expected information; Analysis module: Obtain the cost data of the target enterprise when making improvements based on the audience expectation items, obtain the audience expectation cost items, sort the audience expectation items and the audience expectation cost items, the audience expectation cost items correspond to the audience expectation items respectively, and according to the sorting results, select the audience expectation item with the lowest sequence number combination result as the first target upgrade item; Expansion module: Combine multi-source data to obtain the expectation information of the general public, obtain the general expectation items, adjust the general expectation items through matching, obtain the general combination items, obtain the combination results of the general combination items and the audience expectation items, obtain the combination expectation items, and re-select the audience expectation item with the lowest serial number combination result as the second target upgrade item; Combination module: Based on the first target upgrade item and the second target upgrade item, determine the upgrade and transformation direction of the target enterprise and obtain the target transformation item.

Images