Enterprise digital upgrading and transformation method and system based on big data mining

Abstract

The invention discloses an enterprise digital upgrading and transformation method and system based on big data mining, and relates to the technical field of enterprise upgrading and transformation. Comprising the steps that basic information of target enterprise audiences is acquired, basic information items are obtained, and the basic information items are used for representing portrait information of the target enterprise audiences; and based on the basic information item, performing terminal deployment on the target path, obtaining interaction data of the audience, analyzing the data to obtain an interaction information item, and based on the interaction information item, the basic information item and the associated account system, obtaining cross-platform behavior data of the audience. According to the method, the expectation of the masses and the cost required for coping with the expectation are comprehensively considered, on the premise of maintaining the old users, the combination with the expectation and the cost required for coping with the expectation having the best adaptability is selected as the upgrading and transformation direction of the enterprise, and the risk of the enterprise in the upgrading and transformation process is greatly reduced.

Description

Technical Field

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

[0002] Enterprise digital upgrading and transformation refers to the use of digital technology by enterprises to comprehensively transform and improve their business processes, management models, organizational structures and even business models. By introducing automation, data analysis and other digital tools, the daily operating processes of enterprises 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 large data sets. This process combines statistics, machine learning, data analysis and other technologies to identify patterns, trends and correlations in the data. In the process of digital upgrading and transformation of enterprises, the information mined from big data will be used as the basis for upgrading.

[0003] The enterprise digital transformation assessment system and method based on big data, with patent publication number CN118521177A, collects basic information of the enterprise, analyzes the basic information to determine the type and scale of the enterprise, integrates the type, scale and organizational business information of the enterprise into a transformation estimate sequence according to the type and scale of the enterprise, as well as the organizational business information in the basic information, and uses the transformation prediction model to analyze the transformation estimate sequence to obtain transformation information; it can make the analysis results more accurate, which is conducive to providing a basis for subsequent evaluation of the enterprise transformation; and it can predict the transformation cost and transformation period, and can provide transformation information for the enterprise, which is conducive to avoiding low profits due to transformation.

[0004] When the above and similar technical solutions are used in enterprises for digital upgrades and transformations, due to the diversity of the directions of upgrades and transformations and the different costs required for upgrades and transformations in different directions, when the data obtained through big data is used as a reliance for upgrades and transformations, it is easy to lead to a situation where the rate of return after the upgrade and transformation cannot reach the required rate of return, thus causing the failure of the upgrade 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 technology.

[0006] To achieve the above objectives, the present invention provides the following technical solutions: a method for digital upgrading and transformation of an enterprise based on big data mining, comprising: 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, the upgrade and transformation direction of the target enterprise is determined to obtain the target transformation item.

[0007] Furthermore, 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, and judging whether the page dwell time information reaches the time threshold. When the time threshold is reached, the target audience is set as the interaction item to obtain the interaction information item.

[0008] Furthermore, the method for obtaining 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.

[0009] Furthermore, the method for obtaining the audience expected items includes: 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; Get the proportion data of the split data item based on the split data set, get the target expected rate, and combine the target expected rates to get the audience expected item.

[0010] Furthermore, 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.

[0011] Furthermore, 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.

[0012] Furthermore, the method for obtaining the conventional desired 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.

[0013] Furthermore, 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.

[0014] Furthermore, a system for digital upgrading and transformation of enterprises based on big data mining uses the above-mentioned method for digital upgrading and transformation of enterprises based on big data mining, including: 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.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The enterprise digital upgrade and transformation method and system based on big data mining obtains the basic information of the target enterprise's audience and the expectation information of the audience to obtain the audience expectation items, and at the same time obtains the cost data of the target enterprise when making improvements based on the audience expectation items to obtain the audience expectation cost items, sorts the audience expectation items and the audience expectation cost items, and according to the sorting results, selects the audience expectation item with the lowest serial number combination result as the first target upgrade item, determines the upgrade and transformation direction of the target enterprise, and after comprehensively considering the expectations of the masses and the costs required to meet the expectations, selects the combination with the best adaptability of expectations and the costs required to meet the expectations as the upgrade and transformation direction of the enterprise on the premise of maintaining old users, thereby greatly reducing the risks of the enterprise in the upgrade and transformation process.

[0016] Not only that, it also combines multi-source data to obtain the expectation information of the general public, obtains general expectation items, adjusts the general expectation items through matching, obtains general combination items, obtains the combination results of general combination items and 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, the upgrade and transformation direction of the target enterprise is re-determined. On the premise of maintaining old users, the needs of new users are used as auxiliary combination items that affect upgrades and transformations, which further improves the success rate of upgrades and transformations. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the overall process of the present invention; Figure 2 A schematic diagram of the audience group of the present invention; Figure 3 is a schematic diagram of interactive information items of the present invention; Figure 4 A schematic diagram of sorting audience expectations of the present invention; Figure 5 A schematic diagram of sorting the audience expected cost items of the present invention; Figure 6 It is a schematic diagram of the sequence number combination result of the present invention. DETAILED DESCRIPTION

[0018] 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.

[0019] There are many different directions for digital transformation. When choosing an upgrade path, companies need to carefully consider their own situation and market environment. Each business field may have its own unique digital needs. For example, the manufacturing industry may focus on the combination of smart manufacturing and the Internet of Things; while the retail industry may prefer 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 direction of transformation, companies 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 companies must face. Whether it is investing in advanced technology and equipment or training employees to master new systems, the cost of transformation is often huge. In this case, companies need to establish a comprehensive cost control and expected profit measurement system. As an important tool for the digital transformation of modern enterprises, big data is not absolute in its effectiveness and reliability. It may lead to the data that companies rely on when making decisions not accurately reflecting the actual market situation. The technical solution provided by the present application first obtains the basic information of the target enterprise's audience to obtain basic information items, obtains the expected information of the audience based on the basic information items, obtains the audience's expected items, the audience's expected items include at least one expected rate of expected information, and at the same time obtains the cost data of the target enterprise when making improvements based on the audience's expected items to obtain the audience's expected cost items, sorts the audience's expected items and the audience's expected cost items, the audience's expected cost items correspond to the audience's expected sorting items respectively, and according to the sorting results, selects the audience's expected item with the lowest serial number combination result as the first target upgrade item, and at the same time combines multi-source data to obtain the expected information of the general public to obtain the general expected item, adjusts the general expected item by matching to obtain the general combination item, obtains the combination result of the general combination item and the audience's expected item, reselects the audience's expected item with the lowest serial number combination result as the second target upgrade item, and determines the upgrade and transformation direction of the target enterprise based on the first target upgrade item and the second target upgrade item. Specifically in the present application, Figure 1 As shown, steps S100-S900 are included.

[0020] Step S100: Obtain basic information of the target enterprise audience and obtain basic information items.

[0021] 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 method for obtaining the basic information items includes: obtaining the sales data of the target enterprise, locating the sales group information based on the sales data, and obtaining the sales information items; setting a partition threshold, where the partition threshold is a number threshold, and judging whether the sales information item reaches the partition threshold based on the combination of the partition threshold and the sales information item, setting the sales group information in the sales information item that reaches the partition threshold as the audience group, and obtaining the audience group item; and performing ID tagging based on the audience group item to obtain the basic information item.

[0022] Specifically, after obtaining the sales data of the target enterprise, the customer information of the sales can be tracked and located based on the sales data, including the payment path, payer information, etc. The number of purchases by the masses can be obtained based on these data. At the same time, a division threshold is set, and the division threshold is 2. That is, when the number of purchases reaches two, the sales information item is determined to have reached the division threshold. At this time, this part of the sales group is determined as the audience group, and these audience groups are marked with IDs to obtain basic information items.

[0023] Embodiment 1 like Figure 2 As shown, in the specific implementation process, a company sells products offline, and the company's sales data is obtained. From the beginning of its establishment to the date of acquisition, a total of 1,000 items were sold. According to the payment path, payer information and other information, it was obtained that 200 people bought once, 265 people bought twice, and 90 people bought three times. At this time, according to the set division threshold, the sales group with two purchases is determined as the audience group, that is, the audience group has a total of 355 people. The 355 people are marked with IDs to obtain basic information items.

[0024] Step S200: Based on the basic information items, terminals are deployed on the target path, interaction data of the audience is obtained, and the data is analyzed to obtain interaction information items.

[0025] It should be noted that the interaction data includes the page dwell time. The methods for obtaining the interaction information items include: based on the target path, integrating the configurable tracking SDK, collecting the page dwell time information, setting the judgment threshold, the judgment threshold is the time threshold, the judgment threshold is 10s, and judging whether the page dwell time information reaches the time threshold. When the time threshold is reached, the target audience is set as the interaction item to obtain the interaction information item.

[0026] like Figure 3 As shown, specifically, the target path is set to the official website address of the target company. By integrating the configurable tracking SDK, the page residence time information is collected. According to the set judgment threshold, when the basic information item is 355 people, 43 people stay for 10-20s, 98 people stay for 20-30s, 87 people stay for 30-40s, and 72 people stay for more than 40s. A total of 300 people stay on the official website address of the target company for more than 10s. At this time, the interactive information item is 300 people.

[0027] Step S300: Based on the interactive information items and the basic information items, the account system is associated to obtain the cross-platform behavior data of the audience and obtain a behavior data set.

[0028] It should be noted that the method for obtaining the behavioral data set includes: obtaining the specific platform information of the collected data to obtain the target platform item; obtaining the required behavioral data indicators to obtain the target indicator items; based on the interactive information items, obtaining the relevant active data of the audience based on the target platform items to obtain the active information items; based on the target indicator items, obtaining the target indicator information in the active information items to obtain the behavioral data set.

[0029] Specifically, you first need to determine the specific platform of the information you want to collect, such as Douyin, Kuaishou, and Xiaohongshu, to obtain the target platform item, and then obtain the specific behavioral data indicators that need to be collected, such as likes and comments, to obtain the target indicator item, and according to the obtained interactive information items, obtain the active data of the audience based on the target platform item, specifically referring to all active data of the audience in Douyin, Kuaishou, and Xiaohongshu, such as likes, comments, shares, collections, etc., and then according to the target indicator item, obtain the target indicator information in the active information item to obtain the behavioral data set.

[0030] Embodiment 2 In the specific implementation process, by integrating the configurable tracking SDK, the page residence time information is collected. According to the set judgment threshold, when the basic information item is 355 people, a total of 300 people stay on the target company's official website address for more than 10 seconds. At this time, the interactive information item is 300 people. By associating the account system of these 300 people, the specific platform for collecting data is Douyin, and the specific behavioral indicator data that needs to be collected is likes. At this time, all the active data of these 300 people are obtained first, and then the target indicator item of likes related to the company's products is obtained according to the specific behavioral indicator data, and the behavioral data set is the likes information of these 300 people.

[0031] Step S400: Based on the behavior data set, the expected information of the audience is obtained to obtain the audience's expected items.

[0032] It should be noted that the audience expectation item includes at least one expected rate of expected information, and the method for obtaining the audience expectation item includes: based on the behavior data set, setting a first division standard, splitting the behavior data set based on the first division standard 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 item.

[0033] Specifically, the first classification standard is set as a category standard, such as price category and quality category. The behavior data set is split based on the first classification standard to obtain a split data set. The proportion data of the split data items based on the split data set is obtained to obtain the target expected rate. The target expected rate is combined to obtain the audience expected item.

[0034] Embodiment 3 In the specific implementation process, according to the set first division standard, the first division standard is the quality type, and all active data of 300 people are obtained. 150 people like the video that they hope the quality of the target product will be improved by 10%, 100 people like the video that they hope the quality of the target product will be improved by 15%, and 50 people like the video that they hope the quality of the target product will be improved by 20%. At this time, the split data sets are respectively hoping the quality of the target product will be improved by 10%, hoping the quality of the target product will be improved by 15%, and hoping the quality of the target product will be improved by 20%. At this time, the target expectation rates are 50%, 33.33%, and 16.67%, and the audience expectation items are obtained by combination.

[0035] Step S500: Obtain the cost data of the target enterprise when making improvements based on the audience expectation items, and obtain the audience expectation cost items.

[0036] It should be noted that when making improvements based on the obtained audience expectation items, different audience expectation items require different costs, and the cost information under different audience expectation items is obtained to obtain the audience expectation cost items.

[0037] In the specific implementation process, when the split data sets are respectively hoping that the target product quality will be improved by 10%, 15%, and 20%, the cost data increases by 10%, 20%, and 30%, respectively. At this time, the audience expects the cost items to be the increase of 10%, 20%, and 30% of the cost data, respectively.

[0038] Step S600: sorting the audience expectation items and the audience expectation cost items, and selecting the audience expectation item with the lowest sequence number combination result as the first target upgrade item.

[0039] It should be noted that the method for obtaining the first target upgrade item includes: sorting the audience expectation items in order from high to low, obtaining the first sorting item based on the sorting result, sorting the audience expected cost items in order from low to high, and obtaining the second sorting item based on the sorting result; based on the correspondence between the audience expected cost item and the audience expected item, matching the first sorting item and the second sorting item to obtain a combination information item; based on the combination information item, obtaining the combination with the lowest serial number combination result to obtain the target combination item, determining the audience expected item based on the target combination item, and obtaining the first target upgrade item.

[0040] like Figure 4-Figure 6As shown, in the specific implementation process, the audience expectation items are sorted from high to low. When the split data sets are the target product quality improvement of 10%, the target product quality improvement of 15%, and the target product quality improvement of 20%, the target expectation rates are 50%, 33.33%, and 16.67%. At this time, the sorting result is that the target product quality improvement of 10% is No. 1, the target product quality improvement of 15% is No. 2, and the target product quality improvement of 20% is No. 3. The audience expectation cost items are sorted from low to high. When the split data sets are the target product quality improvement of 10%, the target product quality improvement of 15%, and the target product quality improvement of 20%, the target expectation rates are 50%, 33.33%, and 16.67%. The sorting result is that the target product quality improvement of 10% is No. 1, the target product quality improvement of 15% is No. 2, and the target product quality improvement of 20% is No. 3. When the target product quality is expected to improve by 20%, the cost data increases by 10%, 20% and 30% respectively. At this time, the audience expects the cost items to be the cost data increases of 10%, 20% and 30% respectively. At this time, the sorting results are No. 1 for the cost data increased by 10%, No. 2 for the cost data increased by 20%, and No. 3 for the cost data increased by 30%. The first sorting item and the second sorting item are matched to obtain the combination information item, and the combination with the lowest serial number combination result is obtained. At this time, the combination with the lowest serial number is the target product quality improvement of 10% and the cost data increase of 10%. The audience expectation item corresponding to this combination is the target product quality improvement of 10%, and the first target upgrade item is obtained.

[0041] Step S700: Combine multi-source data to obtain the expected information of the general public and obtain the general expected items.

[0042] It should be noted that the method for obtaining conventional expected items includes: obtaining the specific platform information of collected data to obtain the target platform item; obtaining the behavioral data indicators of the requirements to obtain the target indicator items; obtaining the total acquisition data of the target indicator items in the target platform items to obtain the active total items; based on the active total items, the active information items are eliminated to obtain a conventional data set, a second division standard is set, and the active total items are split based on the second division standard to obtain a conventional split set, the conventional split set includes at least one standard conventional split item, and the proportion data of the conventional split items based on the conventional split set is obtained to obtain the conventional expectation rate, and the conventional expectation rates are combined to obtain the conventional expected items.

[0043] Specifically, the second classification standard is also a category standard, such as price category and quality category. The active total amount item is split based on the second classification standard, and the specific platform for collecting data is Douyin. The specific behavioral indicator data to be collected is likes. The active total amount item is 3,000 people. The active information item is eliminated based on the active total amount item to obtain a regular data set. When the active information item is the active information of 300 people, the regular data set is the active information of 2,700 people. Among them, a total of 1,500 people liked the video expressing the hope that the quality of the target product will be improved by 10%, 500 people liked the video expressing the hope that the quality of the target product will be improved by 15%, and 700 people liked the video expressing the hope that the quality of the target product will be improved by 20%. At this time, the split data sets are respectively expressing the hope that the quality of the target product will be improved by 10%, the hope that the quality of the target product will be improved by 15%, and the hope 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 obtained by combination.

[0044] Step S800: adjusting the conventional expected items to obtain conventional combination items, obtaining the combination result of the conventional combination items and the audience expected items, and reselecting the audience expected item with the lowest serial number combination result as the second target upgrade item.

[0045] 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 a combination result of a regular expected item and the combination value, and obtaining a regular combination item.

[0046] 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. The combination results of the regular combination items and the audience expectation items are obtained. The audience expectation items are 50%, 33.33% and 16.67%, and the combination 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 to hope 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.

[0047] 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.

[0048] A system for digital upgrading and transformation of enterprises based on big data mining uses the above-mentioned method for digital upgrading and transformation of enterprises based on big data mining, including: an acquisition module: acquiring basic information of the target enterprise audience, obtaining basic information items, deploying terminals on the target path based on the basic information items, acquiring interactive data of the audience, analyzing the data to obtain interactive information items, associating the account system based on the interactive information items and the basic information items, acquiring the cross-platform behavior data of the audience, obtaining a behavior data set, acquiring the expected information of the audience based on the behavior data set, obtaining the audience expected items, and the audience expected items at least including the expected rate of one expected information; an analysis module: acquiring the target enterprise's improvement based on the audience expected items Cost data is used to obtain audience expected cost items, and audience expected items and audience expected cost items are sorted. The audience expected cost items correspond to the audience expected items respectively. According to the sorting results, the audience expected item with the lowest serial number combination result is selected as the first target upgrade item; Expansion module: Combine multi-source data to obtain the expectation information of the general public and obtain conventional expectation items. Through matching, the conventional expectation items are adjusted to obtain conventional combination items, and the combination results of the conventional combination items and the audience expected items are obtained to obtain the combined expectation items. The audience expected item with the lowest serial number combination result is re-selected as the second target upgrade item. Combination module: Based on the first target upgrade item and the second target upgrade item, the upgrade and transformation direction of the target enterprise is determined to obtain the target transformation item.

[0049] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the attached 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, the upgrade and transformation direction of the target enterprise is determined to obtain the target transformation item.

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 digital upgrading and transformation of enterprises based on big data mining is characterized by: The method for obtaining the audience expectation item includes: 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; Get the proportion data of the split data item based on the split data set, get the target expected rate, and combine the target expected rates to get the audience expected item.

5. The enterprise digital upgrade and transformation method based on big data mining according to claim 1 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 based on the target combination item, the audience expectation item is determined to obtain the first target upgrade item.

6. The enterprise digital upgrade and transformation method based on big data mining according to claim 1 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.

7. The enterprise digital upgrade and transformation method based on big data mining according to claim 3 is characterized by: The method for obtaining the conventional desired 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.

8. 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.

9. 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 8 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.

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