Intelligent data service method based on AI artificial intelligence big data model

Abstract

The invention discloses an intelligent data service method based on an AI artificial intelligence big data model, and relates to the field of data services, and the method comprises the steps: building a service field recognition mechanism, and carrying out the recognition of a target service field of a to-be-served enterprise through the service field recognition mechanism; determining the type of a to-be-served enterprise; at least one actual enterprise bottleneck is obtained during conventional service; calculating to obtain a critical coefficient of the actual bottleneck of the enterprise, and obtaining an actual bottleneck sequence of the enterprise; forming a data service set for the actual bottleneck of the enterprise; calculating the service matching degree of the data service block; filling a data service set corresponding to the actual bottleneck of the enterprise into the visual region; and in an unconventional service, calculating a priority coefficient of a potential development direction. By establishing a service field identification mechanism, the type of an enterprise to be served is determined, different types of data services are carried out according to different enterprise positioning, and sequencing presentation is carried out according to the importance and matching degree of data to the enterprise services.

Description

Technical Field

[0001] The present invention relates to the field of data services, and in particular to an intelligent data service method based on an AI artificial intelligence big data model. Background Art

[0002] Data services are services that provide various types of data to support customers' businesses. Data services are widely used in a variety of industries, including finance, healthcare, industry, and transportation. Services to enterprises are particularly important. Through data services, enterprises can achieve higher work efficiency, expand their operations, and improve their decision-making capabilities, thereby achieving the goal of adding value to their business.

[0003] However, since the company is involved in many businesses, the amount of data that affects the business is very large. If there is no focus on the data, data users will spend a lot of time screening the data. In addition, different companies have different positioning and different purposes of data services. However, when providing data services, there is a lack of distinction between these situations, and the quality of data services needs to be improved. Summary of the invention

[0004] In order to solve the above technical problems, an intelligent data service method based on AI artificial intelligence big data model is provided. This technical solution solves the problems raised in the above background technology.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: An intelligent data service method based on an AI artificial intelligence big data model, comprising: Establish a service field identification mechanism, and use the service field identification mechanism to identify the target service fields of the enterprises to be served; Determine the type of enterprise to be served, and use non-routine services when the enterprise is a leading enterprise, and use regular services when the enterprise is a non-leading enterprise; In conventional services, based on big data, at least one data application scenario in the target service field is obtained, and enterprise bottleneck analysis is performed in the data application scenario to obtain at least one actual bottleneck of the enterprise; Calculate the criticality coefficient of the enterprise's actual bottleneck, sort the enterprise's actual bottlenecks from large to small according to the criticality coefficient, and obtain the enterprise's actual bottleneck sequence; Form a data service set for the actual bottleneck of the enterprise; Evenly divide the data service set to obtain at least one data service block; Calculate the service matching degree of the data service block; In the data service set, sort the data service blocks from largest to smallest according to the service matching degree; At least one visualization area is formed, and the data service set corresponding to the actual bottleneck of the enterprise is filled into the visualization area according to the order of the actual bottleneck sequence of the enterprise; In the case of non-routine services, at least one potential development direction in the target service field is obtained, and the priority coefficient of the potential development direction is calculated; A data call set is formed for potential development directions whose priority coefficients are higher than preset values, and the data call set is visualized according to the priority coefficients from large to small.

[0006] Preferably, the establishment of the service domain identification mechanism comprises the following steps: Based on the big data, at least one service field is obtained, at least one attribute feature is extracted from the service field, and the attribute features extracted from the service field are summarized into an attribute feature set; Acquire at least one enterprise business of the enterprise to be served, and use the enterprise business containing attribute characteristics as a characteristic enterprise business; Use the parallel formula to calculate the feature proportion of the attribute feature, take the average of the feature proportions of the attribute features in the attribute feature set, and obtain the conditional proportion; The service area corresponding to the attribute feature set with the largest condition ratio is taken as the service area of ​​the enterprise to be served; The parallel formula is as follows: Among them, A is the characteristic proportion of the attribute feature, a is the number of characteristic enterprise businesses of the attribute feature, and b is the total number of enterprise businesses of the enterprise to be served.

[0007] Preferably, the step of acquiring at least one data application scenario in the target service field comprises the following steps: Acquire at least one sample business of at least one sample enterprise in the target service field, wherein the business level of the sample business is an average level; Evenly divide the sample business into at least one local business, and obtain the overlap ratio of the types of two local businesses, the overlap ratio of the purposes of the two local businesses, and the overlap ratio of the target customer groups of the two local businesses; Using the first similarity formula, calculate the similarity between two local services; Classify local services with similarity less than a preset ratio into the same local service category; Merge the business processes of local businesses in the local business classification to obtain data application scenarios; The first similarity formula is as follows: Among them, B is the similarity between the two local businesses, c is the overlap ratio of the types of the two local businesses, d is the overlap ratio of the uses of the two local businesses, and e is the overlap ratio of the target customer groups of the two local businesses.

[0008] Preferably, performing enterprise bottleneck analysis in the data application scenario to obtain at least one actual bottleneck of the enterprise comprises the following steps: The revenue of the enterprise business is divided by the scale of the enterprise business to obtain the first operating coefficient of the enterprise business of the enterprise to be served; Take the intersection of enterprise business and local business classification corresponding to data application scenarios to obtain overlapping business; Multiply the proportion of overlapping business in the enterprise's business by the first operating coefficient to obtain the second operating coefficient; Acquire at least one local service constituting the overlapping service, obtain at least one target local service, and acquire a sample service including the target local service as the target sample service; The revenue of the target sample business is divided by the scale of the target sample business to obtain the third operating coefficient; At least one third operating coefficient is multiplied by the proportion of the target local business in the target sample business and then added together to obtain a fourth operating coefficient; When the portion by which the second operating coefficient is smaller than the fourth operating coefficient is greater than a preset gap, the enterprise's business is regarded as the actual bottleneck of the enterprise.

[0009] Preferably, the calculation of the criticality coefficient of the actual bottleneck of the enterprise comprises the following steps: Obtain the revenue and scale of the actual bottleneck of the enterprise, and use the criticality formula to calculate the criticality coefficient of the actual bottleneck of the enterprise; The key formula is as follows: Among them, C is the criticality coefficient of the actual bottleneck of the enterprise, f is the revenue of the actual bottleneck of the enterprise, and g is the scale of the actual bottleneck of the enterprise.

[0010] Preferably, forming a data service set for the actual bottleneck of the enterprise comprises the following steps: Based on historical data, first service data is obtained to show that the revenue of the target local business corresponding to the actual bottleneck of the enterprise has been improved, and second service data is obtained to show that the scale of the target local business corresponding to the actual bottleneck of the enterprise has been improved; The first service data and the second service data are aggregated to obtain a data service set.

[0011] Preferably, the calculation of the service matching degree of the data service block comprises the following steps: Acquire the first service data that overlaps with the data service block as the first target service data, and acquire the proportion of the first target service data in the data service block as the first weight; Acquire second service data that overlaps with the data service block as second target service data, and acquire a proportion of the second target service data in the data service block as a second weight; Obtaining the ratio of the first target service data to the revenue increase of the target local business corresponding to the actual bottleneck of the enterprise as the first ratio; Obtaining the improvement ratio of the second target service data to the scale of the target local business corresponding to the actual bottleneck of the enterprise as the second ratio; Use the service matching formula to calculate the service matching degree of the data service block; The service matching formula is as follows: Where D is the service matching degree of the data service block, i is the subscript, and n is the total number of the first target service data. is the first proportion of the i-th first target service data, is the first weight of the i-th first target service data, j is the subscript, m is the total number of second target service data, The second proportion of the j-th second target service data, A second weight for the j-th second target service data.

[0012] Preferably, the obtaining of at least one potential development direction in the target service field comprises the following steps: Acquire at least one potential business in a service field other than the target service field, and use the overlap ratio of the target customer population of the potential business and the sample business as the similarity between the potential business and the sample business; Potential businesses whose similarity with sample businesses is less than a preset ratio are regarded as potential development directions.

[0013] Preferably, the step of calculating the priority coefficient of the potential development direction comprises the following steps: Obtain the business development costs of potential development directions, obtain the business transformation costs of potential development directions, obtain the business adjustment range of potential development directions, and obtain the business revenue growth rate of potential development directions; Using the analytic hierarchy process, we obtain the weights of business development costs, business transformation costs, business adjustment range, and business revenue growth rate; Use the priority comprehensive formula to calculate the priority coefficient of potential development directions; The priority comprehensive formula is as follows: Among them, E is the priority coefficient of the potential development direction, o is the business development cost, p is the business transformation cost, q is the business adjustment range, r is the business revenue growth rate, O is the weight of the business development cost, P is the weight of the business transformation cost, Q is the weight of the business adjustment range, and R is the weight of the business revenue growth rate.

[0014] Preferably, forming a data call set for potential development directions with priority coefficients higher than a preset value comprises the following steps: Obtain sample businesses whose similarity with potential development directions is less than a preset ratio as calibration sample businesses; Based on the historical data, third service data that improves the revenue of the calibration sample business is obtained, and fourth service data that improves the scale of the calibration sample business is obtained; The third service data and the fourth service data are aggregated to obtain a data call set.

[0015] Compared with the prior art, the present invention has the following beneficial effects: By establishing a service field identification mechanism, determining the types of enterprises to be served, and providing different data services according to the types of enterprises to be served, different types of data services can be provided according to the different positioning of enterprises. At the same time, when providing data services, the data is sorted and presented according to its importance and degree of matching to the enterprise services, so that enterprise personnel can quickly obtain data that is of greatest help to the enterprise, saving personnel time spent on data screening and improving the efficiency of data services. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the intelligent data service method based on the AI ​​artificial intelligence big data model of the present invention; Figure 2 A schematic diagram of a process for establishing a service domain identification mechanism of the present invention; Figure 3 A schematic diagram of a process for acquiring at least one data application scenario in a target service field according to the present invention; Figure 4 A flowchart of performing enterprise bottleneck analysis in a data application scenario of the present invention to obtain at least one actual bottleneck of an enterprise; Figure 5 It is a flow chart of forming a data service set for the actual bottleneck of an enterprise according to the present invention; Figure 6 A schematic diagram of a process for calculating the service matching degree of a data service block of the present invention; Figure 7 A schematic diagram of a process for obtaining at least one potential development direction in a target service field of the present invention; Figure 8 A schematic diagram of a flow chart of calculating a priority coefficient of a potential development direction of the present invention; Fig. 9 The present invention is a flow chart of forming a data call set for potential development directions with priority coefficients higher than a preset value. DETAILED DESCRIPTION

[0017] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0018] Reference Figure 1 As shown, an intelligent data service method based on an AI artificial intelligence big data model includes: Establish a service field identification mechanism, and use the service field identification mechanism to identify the target service fields of the enterprises to be served; Determine the type of enterprise to be served, and use non-routine services when the enterprise is a leading enterprise, and use regular services when the enterprise is a non-leading enterprise; In conventional services, based on big data, at least one data application scenario in the target service field is obtained, and enterprise bottleneck analysis is performed in the data application scenario to obtain at least one actual bottleneck of the enterprise; Calculate the criticality coefficient of the enterprise's actual bottleneck, sort the enterprise's actual bottlenecks from large to small according to the criticality coefficient, and obtain the enterprise's actual bottleneck sequence; Form a data service set for the actual bottleneck of the enterprise; Evenly divide the data service set to obtain at least one data service block; Calculate the service matching degree of the data service block; In the data service set, sort the data service blocks from largest to smallest according to the service matching degree; At least one visualization area is formed, and the data service set corresponding to the actual bottleneck of the enterprise is filled into the visualization area according to the order of the actual bottleneck sequence of the enterprise; In the case of non-routine services, at least one potential development direction in the target service field is obtained, and the priority coefficient of the potential development direction is calculated; A data call set is formed for potential development directions whose priority coefficients are higher than preset values, and the data call set is visualized according to the priority coefficients from large to small.

[0019] When using big data to serve enterprises, the service data is different when the enterprise's field is different. When the enterprise's own positioning is different, the services it needs are also different. Usually, the boundary of enterprise positioning is leading enterprises and non-leading enterprises. For non-leading enterprises, they do not need to expand the industry, but only need to expand their business within the industry, that is, expand their business scale and revenue. However, for leading enterprises, they pay more attention to the development of new businesses, and there is no greater room for improvement in existing businesses. Therefore, the data services required for these two different needs are completely different and need to be distinguished. In this solution, separate data services are provided for these two situations, and when providing services, the provided data is sorted so that enterprise personnel can obtain the data they need more quickly.

[0020] Reference Figure 2 As shown, establishing a service domain identification mechanism includes the following steps: Based on the big data, at least one service field is obtained, at least one attribute feature is extracted from the service field, and the attribute features extracted from the service field are summarized into an attribute feature set; Acquire at least one enterprise business of the enterprise to be served, and use the enterprise business containing attribute characteristics as a characteristic enterprise business; Use the parallel formula to calculate the feature proportion of the attribute feature, take the average of the feature proportions of the attribute features in the attribute feature set, and obtain the conditional proportion; The service area corresponding to the attribute feature set with the largest condition ratio is taken as the service area of ​​the enterprise to be served; The parallel formula is as follows: Among them, A is the characteristic proportion of the attribute feature, a is the number of characteristic enterprise businesses of the attribute feature, and b is the total number of enterprise businesses of the enterprise to be served.

[0021] When an enterprise is in different fields, the content of its data services will vary greatly. Therefore, it is necessary to identify the field of the enterprise so as to provide targeted data services in the target service field. Otherwise, large data deviations are likely to occur.

[0022] Reference Figure 3 As shown, obtaining at least one data application scenario in the target service field includes the following steps: Acquire at least one sample business of at least one sample enterprise in the target service field, wherein the business level of the sample business is an average level; Evenly divide the sample business into at least one local business, and obtain the overlap ratio of the types of two local businesses, the overlap ratio of the purposes of the two local businesses, and the overlap ratio of the target customer groups of the two local businesses; Using the first similarity formula, calculate the similarity between two local services; Classify local services with similarity less than a preset ratio into the same local service category; Merge the business processes of local businesses in the local business classification to obtain data application scenarios; The first similarity formula is as follows: Among them, B is the similarity between the two local businesses, c is the overlap ratio of the types of the two local businesses, d is the overlap ratio of the uses of the two local businesses, and e is the overlap ratio of the target customer groups of the two local businesses.

[0023] In order to provide targeted services to enterprises, it is necessary to analyze the enterprise's data in each data application scenario and provide data services based on the analysis results. Because enterprises involve many businesses, if the analysis is not classified, the businesses will overlap and it will be unclear which business the data service is for. Therefore, there may be deviations in the service objects. Analysis in the data application scenario can avoid deviations in the identification of data service objects.

[0024] Reference Figure 4 As shown, performing enterprise bottleneck analysis in a data application scenario to obtain at least one actual bottleneck of an enterprise includes the following steps: The revenue of the enterprise business is divided by the scale of the enterprise business to obtain the first operating coefficient of the enterprise business of the enterprise to be served; Take the intersection of enterprise business and local business classification corresponding to data application scenarios to obtain overlapping business; Multiply the proportion of overlapping business in the enterprise's business by the first operating coefficient to obtain the second operating coefficient; Acquire at least one local service constituting the overlapping service, obtain at least one target local service, and acquire a sample service including the target local service as the target sample service; The revenue of the target sample business is divided by the scale of the target sample business to obtain the third operating coefficient; At least one third operating coefficient is multiplied by the proportion of the target local business in the target sample business and then added together to obtain a fourth operating coefficient; When the portion by which the second operating coefficient is smaller than the fourth operating coefficient is greater than a preset gap, the enterprise's business is regarded as the actual bottleneck of the enterprise.

[0025] The main goal of an enterprise is revenue and scale. Therefore, when providing data services, its main goal is to increase revenue and scale. For non-leading enterprises, it is necessary to conduct horizontal comparisons within the industry to find out where their businesses are weak, and then provide data services for the weak businesses. However, since enterprises have many businesses and involve many data services, in order to ensure that data that is more important to the enterprise is presented first, it is necessary to calculate the criticality coefficient of the actual bottleneck of the enterprise. The criticality coefficient of the actual bottleneck of the enterprise is determined based on its impact on the revenue and scale of the enterprise.

[0026] Calculating the criticality coefficient of the actual bottleneck of the enterprise includes the following steps: Obtain the revenue and scale of the actual bottleneck of the enterprise, and use the criticality formula to calculate the criticality coefficient of the actual bottleneck of the enterprise; The key formula is as follows: Among them, C is the criticality coefficient of the actual bottleneck of the enterprise, f is the revenue of the actual bottleneck of the enterprise, and g is the scale of the actual bottleneck of the enterprise.

[0027] Reference Figure 5 As shown, forming a data service set for the actual bottleneck of the enterprise includes the following steps: Based on historical data, first service data is obtained to show that the revenue of the target local business corresponding to the actual bottleneck of the enterprise has been improved, and second service data is obtained to show that the scale of the target local business corresponding to the actual bottleneck of the enterprise has been improved; The first service data and the second service data are aggregated to obtain a data service set.

[0028] Reference Figure 6 As shown, calculating the service matching degree of the data service block includes the following steps: Acquire the first service data that overlaps with the data service block as the first target service data, and acquire the proportion of the first target service data in the data service block as the first weight; Acquire second service data that overlaps with the data service block as second target service data, and acquire a proportion of the second target service data in the data service block as a second weight; Obtaining the ratio of the first target service data to the revenue increase of the target local business corresponding to the actual bottleneck of the enterprise as the first ratio; Obtaining the improvement ratio of the second target service data to the scale of the target local business corresponding to the actual bottleneck of the enterprise as the second ratio; Use the service matching formula to calculate the service matching degree of the data service block; The service matching formula is as follows: Where D is the service matching degree of the data service block, i is the subscript, and n is the total number of the first target service data. is the first proportion of the i-th first target service data, is the first weight of the i-th first target service data, j is the subscript, m is the total number of second target service data, The second proportion of the j-th second target service data, A second weight for the j-th second target service data.

[0029] The data service set formed by the actual bottleneck of the enterprise may involve a lot of data. Therefore, it is also necessary to sort the data in the data service set and give priority to more important data. Since the impact of the actual bottleneck of the enterprise involves revenue and scale, it is necessary to sort the data from these two aspects, so as to give priority to data that has a greater impact on revenue and scale.

[0030] Reference Figure 7 As shown, obtaining at least one potential development direction in the target service field includes the following steps: Acquire at least one potential business in a service field other than the target service field, and use the overlap ratio of the target customer population of the potential business and the sample business as the similarity between the potential business and the sample business; Potential businesses whose similarity with sample businesses is less than a preset ratio are regarded as potential development directions.

[0031] The discovery of potential development directions mainly depends on the target customer population. As long as the business involved in the target customer population of the enterprise is the target of analysis, possible businesses are selected from them as potential development directions. When discovering potential development directions, restrictions on the business are lifted, that is, not only considering businesses similar to existing businesses, but also considering businesses different from existing businesses. Therefore, only the objects of the service are restricted, but the attributes of the business are not restricted.

[0032] Reference Figure 8 As shown, calculating the priority coefficient of the potential development direction includes the following steps: Obtain the business development costs of potential development directions, obtain the business transformation costs of potential development directions, obtain the business adjustment range of potential development directions, and obtain the business revenue growth rate of potential development directions; Using the analytic hierarchy process, we obtain the weights of business development costs, business transformation costs, business adjustment range, and business revenue growth rate; Use the priority comprehensive formula to calculate the priority coefficient of potential development directions; The priority comprehensive formula is as follows: Among them, E is the priority coefficient of the potential development direction, o is the business development cost, p is the business transformation cost, q is the business adjustment range, r is the business revenue growth rate, O is the weight of the business development cost, P is the weight of the business transformation cost, Q is the weight of the business adjustment range, and R is the weight of the business revenue growth rate.

[0033] When developing potential development directions, it is necessary to analyze from multiple angles, because the feasibility of development and the potential development space of the business have an important impact on whether it is worth developing. When the company has multiple choices, it needs to give priority to displaying the service data of potential development directions that are more convenient for the company to expand, so as to facilitate decision-making.

[0034] Reference Fig. 9 As shown, forming a data call set for a potential development direction with a priority coefficient higher than a preset value includes the following steps: Obtain sample businesses whose similarity with potential development directions is less than a preset ratio as calibration sample businesses; Based on the historical data, third service data that improves the revenue of the calibration sample business is obtained, and fourth service data that improves the scale of the calibration sample business is obtained; The third service data and the fourth service data are aggregated to obtain a data call set.

[0035] Furthermore, the present solution also proposes a storage medium on which a computer-readable program is stored, and when the computer-readable program is called, the above-mentioned intelligent data service method based on the AI ​​artificial intelligence big data model is executed.

[0036] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state drive (SSD).

[0037] To sum up, the advantages of the present invention are: by establishing a service field identification mechanism, determining the types of enterprises to be served, and providing different data services according to the types of enterprises to be served, different types of data services can be provided according to the different positioning of the enterprises. At the same time, when providing data services, the data is sorted and presented according to its importance and degree of matching to the enterprise services, so that enterprise personnel can quickly obtain data that is of great help to the enterprise, saving the time spent by personnel on data screening and improving the efficiency of data services.

[0038] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. An intelligent data service method based on AI artificial intelligence big data model, characterized in that: include: Establish a service field identification mechanism, and use the service field identification mechanism to identify the target service fields of the enterprises to be served; Determine the type of enterprise to be served, and use non-routine services when the enterprise is a leading enterprise, and use regular services when the enterprise is a non-leading enterprise; In conventional services, based on big data, at least one data application scenario in the target service field is obtained, and enterprise bottleneck analysis is performed in the data application scenario to obtain at least one actual bottleneck of the enterprise; Calculate the criticality coefficient of the enterprise's actual bottleneck, sort the enterprise's actual bottlenecks from large to small according to the criticality coefficient, and obtain the enterprise's actual bottleneck sequence; Form a data service set for the actual bottleneck of the enterprise; Evenly divide the data service set to obtain at least one data service block; Calculate the service matching degree of the data service block; In the data service set, sort the data service blocks from largest to smallest according to the service matching degree; At least one visualization area is formed, and the data service set corresponding to the actual bottleneck of the enterprise is filled into the visualization area according to the order of the actual bottleneck sequence of the enterprise; In the case of non-routine services, at least one potential development direction in the target service field is obtained, and the priority coefficient of the potential development direction is calculated; A data call set is formed for potential development directions whose priority coefficients are higher than preset values, and the data call set is visualized according to the priority coefficients from large to small.

2. According to claim 1, the intelligent data service method based on the AI ​​artificial intelligence big data model is characterized in that: The establishment of the service domain identification mechanism comprises the following steps: Based on the big data, at least one service field is acquired, at least one attribute feature is extracted from the service field, and the attribute features extracted from the service field are aggregated into an attribute feature set; Acquire at least one enterprise business of the enterprise to be served, and use the enterprise business containing attribute characteristics as a characteristic enterprise business; Use the parallel formula to calculate the feature proportion of the attribute feature, take the average of the feature proportions of the attribute features in the attribute feature set, and obtain the conditional proportion; The service area corresponding to the attribute feature set with the largest condition ratio is taken as the service area of ​​the enterprise to be served; The parallel formula is as follows: Among them, A is the characteristic proportion of the attribute feature, a is the number of characteristic enterprise businesses of the attribute feature, and b is the total number of enterprise businesses of the enterprise to be served.

3. The intelligent data service method based on AI artificial intelligence big data model according to claim 2 is characterized in that: The step of acquiring at least one data application scenario in the target service field comprises the following steps: Acquire at least one sample business of at least one sample enterprise in the target service field, wherein the business level of the sample business is an average level; Evenly divide the sample business into at least one local business, and obtain the overlap ratio of the types of two local businesses, the overlap ratio of the purposes of the two local businesses, and the overlap ratio of the target customer groups of the two local businesses; Using the first similarity formula, calculate the similarity between two local services; Classify local services with similarity less than a preset ratio into the same local service category; Merge the business processes of local businesses in the local business classification to obtain data application scenarios; The first similarity formula is as follows: Among them, B is the similarity between the two local businesses, c is the overlap ratio of the types of the two local businesses, d is the overlap ratio of the uses of the two local businesses, and e is the overlap ratio of the target customer groups of the two local businesses.

4. The intelligent data service method based on the AI ​​artificial intelligence big data model according to claim 3 is characterized in that: The performing of enterprise bottleneck analysis in the data application scenario to obtain at least one actual bottleneck of the enterprise comprises the following steps: The revenue of the enterprise business is divided by the scale of the enterprise business to obtain the first operating coefficient of the enterprise business of the enterprise to be served; Take the intersection of enterprise business and local business classification corresponding to data application scenarios to obtain overlapping business; Multiply the proportion of overlapping business in the enterprise's business by the first operating coefficient to obtain the second operating coefficient; Acquire at least one local service constituting the overlapping service, obtain at least one target local service, and acquire a sample service including the target local service as the target sample service; The revenue of the target sample business is divided by the scale of the target sample business to obtain the third operating coefficient; At least one third operating coefficient is multiplied by the proportion of the target local business in the target sample business and then added together to obtain a fourth operating coefficient; When the portion by which the second operating coefficient is smaller than the fourth operating coefficient is greater than a preset gap, the enterprise's business is regarded as the actual bottleneck of the enterprise.

5. The intelligent data service method based on AI artificial intelligence big data model according to claim 4 is characterized in that: The calculation to obtain the criticality coefficient of the actual bottleneck of the enterprise includes the following steps: Obtain the revenue and scale of the actual bottleneck of the enterprise, and use the criticality formula to calculate the criticality coefficient of the actual bottleneck of the enterprise; The key formula is as follows: Among them, C is the criticality coefficient of the actual bottleneck of the enterprise, f is the revenue of the actual bottleneck of the enterprise, and g is the scale of the actual bottleneck of the enterprise.

6. The intelligent data service method based on AI artificial intelligence big data model according to claim 5 is characterized in that: The forming of a data service set for the actual bottleneck of the enterprise comprises the following steps: Based on historical data, first service data is obtained to show that the revenue of the target local business corresponding to the actual bottleneck of the enterprise has been improved, and second service data is obtained to show that the scale of the target local business corresponding to the actual bottleneck of the enterprise has been improved; The first service data and the second service data are aggregated to obtain a data service set.

7. The intelligent data service method based on AI artificial intelligence big data model according to claim 6 is characterized in that: The calculation of the service matching degree of the data service block comprises the following steps: Acquire the first service data that overlaps with the data service block as the first target service data, and acquire the proportion of the first target service data in the data service block as the first weight; Acquire second service data that overlaps with the data service block as second target service data, and acquire a proportion of the second target service data in the data service block as a second weight; Obtaining the ratio of the first target service data to the revenue increase of the target local business corresponding to the actual bottleneck of the enterprise as the first ratio; Obtaining the improvement ratio of the second target service data to the scale of the target local business corresponding to the actual bottleneck of the enterprise as the second ratio; Use the service matching formula to calculate the service matching degree of the data service block; The service matching formula is as follows: Where D is the service matching degree of the data service block, i is the subscript, and n is the total number of the first target service data. is the first proportion of the i-th first target service data, is the first weight of the i-th first target service data, j is the subscript, m is the total number of second target service data, The second proportion of service data for the jth second target, A second weight for the j-th second target service data.

8. The intelligent data service method based on AI artificial intelligence big data model according to claim 7 is characterized in that: The obtaining of at least one potential development direction in the target service field comprises the following steps: Acquire at least one potential business in a service field other than the target service field, and use the overlap ratio of the target customer population of the potential business and the sample business as the similarity between the potential business and the sample business; Potential businesses whose similarity with sample businesses is less than a preset ratio are regarded as potential development directions.

9. The intelligent data service method based on AI artificial intelligence big data model according to claim 8 is characterized in that: The calculation of the priority coefficient of the potential development direction comprises the following steps: Obtain the business development costs of potential development directions, obtain the business transformation costs of potential development directions, obtain the business adjustment range of potential development directions, and obtain the business revenue growth rate of potential development directions; Using the analytic hierarchy process, we obtain the weights of business development costs, business transformation costs, business adjustment range, and business revenue growth rate; Use the priority comprehensive formula to calculate the priority coefficient of potential development directions; The priority comprehensive formula is as follows: Among them, E is the priority coefficient of the potential development direction, o is the business development cost, p is the business transformation cost, q is the business adjustment range, r is the business revenue growth rate, O is the weight of the business development cost, P is the weight of the business transformation cost, Q is the weight of the business adjustment range, and R is the weight of the business revenue growth rate.

10. The intelligent data service method based on AI artificial intelligence big data model according to claim 9 is characterized in that: The forming of a data call set for a potential development direction with a priority coefficient higher than a preset value comprises the following steps: Obtain sample businesses whose similarity with potential development directions is less than a preset ratio as calibration sample businesses; Based on the historical data, third service data that improves the revenue of the calibration sample business is obtained, and fourth service data that improves the scale of the calibration sample business is obtained; The third service data and the fourth service data are aggregated to obtain a data call set.

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