Business analysis method and device based on position information, equipment and storage medium

Abstract

The invention relates to the technical field of data processing and the field of financial science and technology, and discloses a service analysis method based on location information, which comprises the following steps: acquiring a residence address, a working address, a residence age limit and a working age limit of a target user according to an identifier; calculating a commuting distance between the residence address and the working address, determining a residence group category according to the first preset type information, and performing calculation according to the residence group category, the commuting distance and the residence year to obtain a first analysis value; determining an income group category according to second preset type information, and performing calculation according to the income group category and the working age limit to obtain a second analysis value; and obtaining a third analysis value according to the identifier, generating a target qualification analysis value according to the first to third analysis values, and selecting service analysis to the target user. According to the invention, the privacy of the user is protected, the data collection process is simplified, the processing time is reduced, and the business operation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the fields of data processing technology and financial technology, and in particular to a business analysis method, device, equipment and storage medium based on location information. Background Art

[0002] With the development of information technology and the popularization of the Internet, all industries are seeking to improve service quality and customer satisfaction through data-driven methods. Especially in the financial services and retail industries, accurate customer qualifications are crucial to providing personalized services. However, traditional customer qualification methods have many challenges, which not only affect business efficiency, but also raise concerns about user privacy protection.

[0003] In the field of financial services, traditional customer qualifications usually rely on a large amount of personal information, including but not limited to proof of income, occupational information, social security payment records, and credit reports from the People's Bank of China. The acquisition of this information often involves the privacy of users, requires time to communicate with customers, and must obtain authorization from customers before use. This process is not only time-consuming, but may also lead to inefficient services due to incomplete or delayed data collection.

[0004] Similarly, in the retail industry, retailers evaluate customers’ qualifications by analyzing their online browsing history, shopping history, and personal profiles in order to provide personalized product recommendations. However, this approach also faces similar problems: in order to accurately evaluate user qualifications and make appropriate recommendations, the system needs to accumulate a large amount of user behavior data. This reliance on large amounts of data not only increases the time cost of processing, but may also cause the query results of the recommendation system to slow down. At the same time, collecting and storing such a large amount of personal data also brings risks to data security and privacy protection.

[0005] Therefore, under the premise of minimizing reliance on customer personal information, how to quickly determine the customer's qualifications and conduct business analysis on the customer is a technical problem that needs to be solved urgently. Summary of the invention

[0006] In view of the above, it is necessary to provide a business analysis method based on location information, the purpose of which is to qualify customers based on their location information and a small amount of customer data while protecting user privacy, and to make personalized business analysis for customers accordingly.

[0007] In a first aspect, a business analysis method based on location information is provided, comprising:

[0008] Receive a request for business analysis of a target user sent by a client, and obtain the residential address, work address, years of residence at the residential address, and years of work at the work address of the target user from a preset database according to an identifier of the target user included in the request;

[0009] Calculating the commuting distance between the residential address and the work address, determining the residential group category to which the target user belongs according to first preset type information of the residential place corresponding to the residential address, and performing calculation according to the residential group category, the commuting distance, and the years of residence to obtain a first analysis value of the target user;

[0010] Determine the income group category to which the target user belongs according to the second preset type information of the work unit corresponding to the work address, and calculate according to the income group category and the working years to obtain a second analysis value of the target user;

[0011] A third analysis value of the target user is obtained from a preset credit platform according to the identifier of the target user, a target qualification analysis value of the target user is generated according to the first analysis value, the second analysis value and the third analysis value, and a target business to be recommended is analyzed according to the target qualification analysis value.

[0012] In a second aspect, a business analysis device based on location information is provided, comprising:

[0013] A receiving module, configured to receive a request sent by a client for performing business analysis on a target user, and obtain, from a preset database, the residential address, work address, years of residence at the residential address, and years of work at the work address of the target user according to an identifier of the target user included in the request;

[0014] A first calculation module is used to calculate the commuting distance between the residential address and the work address, determine the residential group category to which the target user belongs according to the first preset type information of the residential place corresponding to the residential address, and perform calculation according to the residential group category, the commuting distance and the years of residence to obtain a first analysis value of the target user;

[0015] A second calculation module is used to determine the income group category to which the target user belongs according to the second preset type information of the work unit corresponding to the work address, and to calculate according to the income group category and the working years to obtain a second analysis value of the target user;

[0016] A recommendation module is used to obtain a third analysis value of the target user from a preset credit platform according to the identifier of the target user, generate a target qualification analysis value of the target user according to the first analysis value, the second analysis value and the third analysis value, and analyze a target business to be recommended according to the target qualification analysis value.

[0017] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned location information-based business analysis method when executing the computer program.

[0018] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned location information-based business analysis method are implemented.

[0019] Compared with the prior art, the present invention generates a target qualification analysis value for the target user by comprehensively analyzing the target user's residence and work location, commuting distance and other location-related information and a small amount of other data (such as credit score). Based on the target qualification analysis value, more personalized services or product recommendations are provided to the target user. Compared with the traditional method that relies on a large amount of personal detailed information (such as proof of income, occupational information, etc.), it can reduce the need for sensitive personal information of the user and help protect the user's privacy. Using relatively easy-to-obtain location information and other non-sensitive data for qualification calculation can simplify the data collection process, reduce processing time, and thus improve business operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a schematic diagram of an application environment of a business analysis method based on location information in one embodiment of the present invention;

[0021] Figure 2 A schematic diagram of a flow chart of a method for analyzing business based on location information provided by an embodiment of the present invention;

[0022] Figure 3 A schematic diagram of modules of a location information-based business analysis device provided by an embodiment of the present invention;

[0023] Figure 4 A schematic diagram of the structure of a device for implementing a method for analyzing business based on location information provided by an embodiment of the present invention;

[0024] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0026] It should be noted that the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0027] The location information-based service analysis method provided by the embodiment of the present invention can be applied in the following aspects: Figure 1 In the application environment, the client communicates with the server through the network. The server can send a request for business analysis of the target user through the client; calculate the commuting distance between the residential address and the work address, determine the residential group category to which the target user belongs according to the first preset type information of the residential place corresponding to the residential address, substitute the residential group category, commuting distance and living years into the preset first calculation formula for calculation, and obtain the first analysis value of the target user; determine the income group category to which the target user belongs according to the second preset type information of the work unit corresponding to the work address, substitute the income group category and working years into the preset second calculation formula for calculation, and obtain the second analysis value of the target user; obtain the third analysis value of the target user from the preset credit platform according to the identifier of the target user, generate the target qualification analysis value of the target user according to the first analysis value, the second analysis value and the third analysis value, and select the corresponding business analysis for the target user according to the target qualification analysis value. The present invention is aimed at financial service enterprises, retail industries and other fields. By comprehensively analyzing the location-related information such as the residence and work place, commuting distance and a small amount of other data (such as credit score) of the target user, the method can provide more personalized services or product recommendations for the target user. The client may be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, and portable wearable devices. The server may be implemented as an independent server or a server cluster consisting of multiple servers. The present invention is described in detail below through specific embodiments.

[0028] Reference Figure 2FIG. 1 is a flow chart of a method for analyzing business based on location information provided by an embodiment of the present invention. The method is executed by a device.

[0029] In this embodiment, the service analysis method based on location information includes:

[0030] S1. Receive a request for business analysis of a target user sent by a client, and obtain the residential address, work address, years of residence at the residential address, and years of work at the work address of the target user from a preset database based on an identifier of the target user contained in the request.

[0031] In this embodiment, the server receives a request sent by the client, the purpose of which is to perform business analysis on the target user. The client can be an application or web service currently being used by a user in the financial services field, or an application or web service currently being used by a user in the retail industry. The present invention does not impose any limitation on the client.

[0032] The request contains a unique identifier for the target user, which can be a user ID, mobile phone number, email address, ID number, or other information that can uniquely identify the user.

[0033] Based on the identifier provided by the user, the server queries a pre-set database. The preset database can be a relational database (such as MySQL, Oracle) or a NoSQL database (such as MongoDB). The preset database stores the information provided by the target user when registering, such as residential address, work address, years of residence, years of work, etc.

[0034] Extract the following key information about the target user from the preset database:

[0035] Residential address: The user's current residential address.

[0036] Work address: the user's office location or workplace location.

[0037] Residence length: How long the user has lived at the current address.

[0038] Years of employment: How long the user has worked at the current work location.

[0039] In step S1, the target user's residential address, work address, years of residence at the residential address, and years of work at the work address are obtained to evaluate the target user's stability, lifestyle, and potential needs, so as to make a more accurate business analysis. For example, in the financial services field, this information can help banks better understand the target user's financial status; while in the retail industry, this information helps retailers understand the target user's consumption habits and preferences, so as to provide more personalized marketing strategies.

[0040] S2. Calculate the commuting distance between the residential address and the work address, determine the residential group category to which the target user belongs based on the first preset type information of the residential place corresponding to the residential address, and calculate based on the residential group category, the commuting distance and the years of residence to obtain a first analysis value of the target user.

[0041] In this embodiment, a preset map service (such as a third-party map API) is used to convert the target user's residential address and work address into corresponding longitude and latitude coordinates, and then the road network data provided by the map service is used to calculate the actual commuting distance from the residential address to the work address.

[0042] According to the user's residential address, the server searches for information related to the residential location of the address, that is, the first preset type of information. The first preset type of information includes house price, house rent, business district location, etc.

[0043] The first preset type information is compared with a pre-built residential grade type table, which contains data standards for classifying different communities according to factors such as housing prices, rents, and commercial activities. Through the comparison, the residential group category to which the user belongs can be determined, such as a high-income group, a middle-income group, or a low-income group.

[0044] The three factors of residential group category, commuting distance and user's years of residence are used as variables, and a certain score is assigned to these three variables, and each score has a weight coefficient. Finally, the three factors with assigned scores and weight coefficients are calculated using the preset first calculation formula to obtain the first analysis value of the target user.

[0045] For example, a bank wants to provide customized loan products to its potential customers. The bank uses the scoring system of the present invention to evaluate the qualifications of the customers.

[0046] The customer submits a loan application through the bank's mobile application, which contains the customer's personal information. The bank server extracts the customer's residential address, work address and related years of service information from the internal database based on the identifier provided by the customer (such as ID number).

[0047] Use the map API to calculate the customer's commuting distance, classify the customer into a specific living group category based on information such as the unit price of the house where they live, apply the preset first calculation formula, and combine the commuting distance, years of residence, and group category to calculate the customer's first analysis value.

[0048] In one embodiment, the calculating the commuting distance between the residential address and the work address comprises:

[0049] Using a preset map application service to convert the residential address into a first longitude and latitude, and convert the work address into a second longitude and latitude;

[0050] The preset map application service is used to determine the commuting distance between the first longitude and latitude and the second longitude and latitude.

[0051] The preset map application service may be a third-party map API, such as an online map service such as Google Maps, Baidu Maps or Amap.

[0052] The map service application converts the residential address and work address into geographic coordinates, i.e. longitude and latitude. The map service application uses its built-in road network data to calculate the actual commuting distance from the residential location to the work location. The actual commuting distance can be based on the shortest route, the fastest route or other specific conditions.

[0053] Road network data includes information such as the location, length, direction and possible traffic conditions of roads, which makes the calculated distance not just the straight-line distance between two points, but the real driving distance taking into account the actual road path and possible traffic conditions.

[0054] The residential address and work address are used to accurately calculate the user's daily commuting distance. Commuting distance is very important for evaluating the user's stability and daily life pattern, and can help companies understand the user's travel habits, which in turn affects the user's service recommendation or credit score.

[0055] For example, in financial services, a shorter commute distance may indicate that the user has a more stable life and may therefore receive better loan terms; in the retail industry, commute distance may also affect the selection and delivery of promotional activities.

[0056] In one embodiment, determining the residential group category to which the target user belongs based on the first preset type information of the residential place corresponding to the residential address includes:

[0057] Comparing the first preset type information with a pre-constructed residential grade type table;

[0058] The residential group category to which the target user belongs is determined according to the comparison result of the first preset type information.

[0059] The residential class type table is a pre-built table or database that is used to classify residents into different residential group categories based on factors such as housing unit price, housing rent, and business district location in different communities. For example, the residential class type table defines different residential group categories such as high-income group, middle-income group, and low-income group, and sets corresponding standards for each category. For example, a high-income group may correspond to a community with high housing unit price, expensive rent, and located in a prosperous business district.

[0060] The first preset type information (such as housing unit price, housing rent, business district location) is compared with the standards in the residential grade type table, and based on the comparison results, it can be determined which residential group category the target user belongs to. During the comparison process, check whether the various indicators of the target user's community meet the standards of a specific residential group category. For example, if the target user's community housing unit price is higher than a certain threshold, the rent is also at a high level, and it is close to the main commercial center, then the target user will be classified as a high-income group.

[0061] In one embodiment, the calculation based on the residential group category, the commuting distance and the years of residence to obtain the first analysis value of the target user includes:

[0062] From a preset first scoring standard table, respectively obtain a first score corresponding to the residential group category, a second score corresponding to the commuting distance, and a third score corresponding to the length of residence;

[0063] Weight coefficients are respectively configured for the first score, the second score, and the third score, and the scores after the weight coefficients are configured are summed to obtain a first analysis value of the target user.

[0064] The first calculation formula is preset as:

[0065] V1=w1×S1+w2×S2+w3×S3

[0066] Among them, V1 is the first analysis value, S1 is the first score, S2 is the second score, S3 is the third score, w1 is the weight coefficient of the first score, w2 is the weight coefficient of the second score, and w3 is the weight coefficient of the third score.

[0067] The first scoring criteria table is a predefined table or database that sets corresponding data standards for different residential group categories, commuting distances and years of residence.

[0068] According to the residential group category to which the target user belongs (such as a high-income group, a middle-income group or a low-income group), the corresponding first score is searched from a preset first scoring standard table.

[0069] According to the commuting distance between the target user's residential address and work address, find the corresponding second score from the scoring criteria table. Generally, a shorter commuting distance corresponds to a higher score, because it indicates that the target user's life is more stable and less expensive.

[0070] According to the target user's residence years at the current residential address, the corresponding third score is obtained from the scoring standard table. A longer residence period may mean greater stability and thus may have a higher score.

[0071] In step S2, the residential group category, commuting distance and years of residence are substituted into the preset first calculation formula to obtain the first analysis value of the target user. By comprehensively considering multiple factors (such as residential group category, commuting distance, years of residence), the user's multiple attributes can be converted into a single first analysis value. The first analysis value reflects the customer's stability, economic ability and potential consumption ability. This quantitative method helps to simplify the complex decision-making process and provides an objective standard.

[0072] S3. Determine the income group category to which the target user belongs according to the second preset type information of the work unit corresponding to the work address, and calculate according to the income group category and the working years to obtain a second analysis value of the target user.

[0073] In this embodiment, according to the work unit corresponding to the work address, the second preset type information of the work unit is obtained, and the second preset type information includes the industry to which the work unit belongs, the enterprise attributes (such as state-owned enterprises, private enterprises, foreign enterprises, etc.), the annual income of the enterprise, etc. The second preset type information reflects the scale, nature and profitability of the work unit where the target user is located.

[0074] Pre-build an income scoring standard table, which is a pre-defined table or database used to classify users into different income groups according to the attributes of different work units. For example, the income scoring standard table defines different income group categories such as high-income group, middle-income group and low-income group, and sets corresponding data standards for each category. For example, the high-income group may correspond to employees working in large foreign companies or high-profit industries.

[0075] Each information of the second preset type of information is compared with the income scoring standard table, and the income group category to which the target user belongs is determined according to the comparison result. For example, if the target user works in a large foreign company and the company belongs to a high-profit industry, the target user will be classified as a high-income group.

[0076] Convert the income group category and years of work experience into a quantitative score, substitute it into the preset second calculation formula for calculation, and obtain the second analysis value of the target user.

[0077] In one embodiment, determining the income group category to which the target user belongs according to the second preset type information of the work unit corresponding to the work address includes:

[0078] Comparing the second preset type information with a pre-constructed income scoring standard table, wherein the income scoring standard table records data standards of income group categories corresponding to different work unit attributes;

[0079] The income group category to which the target user belongs is determined according to the comparison result of the second preset type information.

[0080] The second preset type of information refers to various attributes related to the target user's work unit, such as: industry (such as finance, IT, manufacturing, etc.), enterprise attributes (such as state-owned enterprises, private enterprises, foreign enterprises, etc.), annual enterprise income, enterprise scale, and enterprise geographical location.

[0081] The income scoring standard table records the data standards of income group categories corresponding to different work unit attributes. For example: certain industries (such as finance, IT) may correspond to higher income group categories. Large enterprises or highly profitable enterprises may correspond to higher income group categories. State-owned enterprises or foreign enterprises may correspond to higher income group categories.

[0082] The second preset type of information of the work unit (such as the industry, enterprise attributes, annual enterprise income, etc.) obtained from the work address of the target user is compared with the standards in the pre-constructed income scoring standard table. If the target user works in a large financial company with a high annual income, then the corresponding income group category is found in the income scoring standard table. Based on the comparison results, it can be determined which income group category the target user belongs to. Income group categories include: high-income group, middle-income group and low-income group.

[0083] In one embodiment, the calculation based on the income group category and the working years to obtain the second analysis value of the target user includes:

[0084] From the preset second scoring standard table, respectively obtain the fourth score corresponding to the income group category and the fifth score corresponding to the working years;

[0085] A weight coefficient is configured for each of the fourth score and the fifth score, and each score after the weight coefficient is configured is summed to obtain a second analysis value of the target user.

[0086] The preset second calculation formula is:

[0087] V2=w4×S4+w5×S5

[0088] Among them, V2 is the second analysis value, S4 is the fourth score, S5 is the fifth score, w4 is the weight coefficient of the fourth score, and w5 is the weight coefficient of the fifth score.

[0089] The preset second scoring standard table is a predefined table or database that contains data standards corresponding to different income group categories and different working years. For example, different income group categories (such as high-income group, middle-income group, low-income group) correspond to a specific score range. Different working years (such as less than 1 year, 1-3 years, 3-5 years, more than 5 years) correspond to a specific score range.

[0090] In step S3, the income group category reflects the target user's potential income level, and the working years reflect the target user's career stability and experience accumulation. By combining these two factors, the second analysis value of the target user is obtained, which can more comprehensively evaluate the user's economic situation and provide more accurate service recommendations.

[0091] S4. Obtain a third analysis value of the target user from a preset credit platform according to the identifier of the target user, generate a target qualification analysis value of the target user according to the first analysis value, the second analysis value and the third analysis value, and analyze a target business to be recommended according to the target qualification analysis value.

[0092] In this embodiment, the pre-set credit evaluation platform can be a third-party credit evaluation agency or an internal credit evaluation system of an enterprise. The preset credit platform is queried according to the identifier of the target user, and the credit evaluation platform calculates the third analysis value according to the historical credit behavior of the target user. The historical credit behavior includes the repayment record, credit card usage, loan record, etc. of the target user.

[0093] A target qualification analysis value of the target user is generated according to the first analysis value, the second analysis value and the third analysis value. For example, the three analysis values ​​are summed or weighted averaged to generate a final target qualification analysis value.

[0094] Based on the target qualification analysis value, the server selects the most suitable business for the target user for recommendation. For example, in the field of financial services, different types of loan products, credit card limits or investment products can be recommended based on the analysis value.

[0095] In the retail industry, products or promotions that match the spending power and preferences of target users can be recommended based on the analysis values.

[0096] In one embodiment, the step of obtaining the third analysis value of the target user from a preset credit platform according to the identifier of the target user includes:

[0097] Constructing a query request for querying the credit score of the target user according to the identifier of the target user;

[0098] Sending the query request to the preset credit platform;

[0099] The query result returned by the preset credit platform according to the query request is received, and the query result is parsed to obtain a third analysis value of the target user.

[0100] The application scenarios of the present invention are illustrated by way of example:

[0101] A bank wants to provide customized loan products to its potential customers. The bank uses the scoring system of the present invention to evaluate the qualifications of the customers.

[0102] The customer submits a loan application through the bank's mobile application, which contains the customer's personal information. The bank server extracts the customer's residential address, work address and related years of service information from the internal database based on the identifier provided by the customer (such as ID number).

[0103] Use the map API to calculate the customer's commuting distance, classify the customer into a specific living group category based on information such as the unit price of the house where they live, apply the preset first calculation formula, and combine the commuting distance, years of residence, and group category to calculate the customer's first analysis value.

[0104] Based on the information of the work unit (such as the industry to which they belong), the client's income group category is determined, and the calculation formula is applied again to obtain the client's second analysis value based on the income group category and years of work experience.

[0105] The third analysis value of the customer is obtained from the third-party credit platform. The third score reflects the user's historical credit status. The three scores are combined to generate the user's target qualifications. Based on the final score, the bank decides what type of loan product to recommend to the customer.

[0106] In step S4, by integrating the first analysis value (reflecting stability and economic ability), the second analysis value (reflecting career stability and income level) and the third analysis value (reflecting credit history) of the target user, a comprehensive and objective target qualification analysis value can be generated to more accurately understand the overall qualifications of the target user and provide personalized business analysis accordingly, thereby improving the pertinence and effectiveness of the service.

[0107] Based on the above analysis steps S1-S4, the present invention generates a target qualification analysis value for the target user by comprehensively analyzing the target user's residence and work location, commuting distance and other location-related information and a small amount of other data (such as credit score). Based on the target qualification analysis value, more personalized services or product recommendations are provided to the target user. Compared with the traditional method that relies on a large amount of personal detailed information (such as proof of income, occupational information, etc.), it can reduce the demand for sensitive personal information of the user and help protect the privacy of the user. Using relatively easy-to-obtain location information and other non-sensitive data for qualification calculation can simplify the data collection process, reduce processing time, and thus improve business operation efficiency.

[0108] like Figure 3 FIG. 1 is a schematic diagram of a module of a location information-based business analysis device provided in one embodiment of the present invention.

[0109] The business analysis device 100 based on location information of the present invention can be installed in a device. According to the functions implemented, the business analysis device 100 based on location information can include a receiving module 110, a first calculation module 120, a second calculation module 130 and a recommendation module 140. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by a device processor and can complete fixed functions, which are stored in the memory of the device.

[0110] In this embodiment, the functions of each module / unit are as follows:

[0111] The receiving module 110 is used to receive a request sent by a client to perform business analysis on a target user, and obtain the residential address, work address, years of residence at the residential address, and years of work at the work address of the target user from a preset database according to an identifier of the target user included in the request;

[0112] A first calculation module 120 is used to calculate the commuting distance between the residential address and the work address, determine the residential group category to which the target user belongs according to the first preset type information of the residential place corresponding to the residential address, and perform calculation according to the residential group category, the commuting distance and the years of residence to obtain a first analysis value of the target user;

[0113] A second calculation module 130 is used to determine the income group category to which the target user belongs according to the second preset type information of the work unit corresponding to the work address, and to perform calculation according to the income group category and the working years to obtain a second analysis value of the target user;

[0114] The recommendation module 140 is used to obtain a third analysis value of the target user from a preset credit platform according to the identifier of the target user, generate a target qualification analysis value of the target user according to the first analysis value, the second analysis value and the third analysis value, and analyze the target business to be recommended according to the target qualification analysis value.

[0115] In one embodiment, the first calculation module 120 is specifically configured to:

[0116] Using a preset map application service to convert the residential address into a first longitude and latitude, and convert the work address into a second longitude and latitude;

[0117] The preset map application service is used to determine the commuting distance between the first longitude and latitude and the second longitude and latitude.

[0118] In one embodiment, the first calculation module 120 is specifically configured to:

[0119] Comparing the first preset type information with a pre-constructed residential grade type table;

[0120] The residential group category to which the target user belongs is determined according to the comparison result of the first preset type information.

[0121] In one embodiment, the first calculation module 120 is specifically configured to:

[0122] From a preset first scoring standard table, respectively obtain a first score corresponding to the residential group category, a second score corresponding to the commuting distance, and a third score corresponding to the length of residence;

[0123] Weight coefficients are respectively configured for the first score, the second score, and the third score, and the scores after the weight coefficients are configured are summed to obtain a first analysis value of the target user.

[0124] In one embodiment, the second calculation module 130 is specifically configured to:

[0125] Comparing the second preset type information with a pre-constructed income scoring standard table;

[0126] The income group category to which the target user belongs is determined according to the comparison result of the second preset type information.

[0127] In one embodiment, the second calculation module 130 is specifically configured to:

[0128] From the preset second scoring standard table, respectively obtain the fourth score corresponding to the income group category and the fifth score corresponding to the working years;

[0129] A weight coefficient is configured for each of the fourth score and the fifth score, and each score after the weight coefficient is configured is summed to obtain a second analysis value of the target user.

[0130] In one embodiment, the recommendation module 140 is specifically configured to:

[0131] Constructing a query request for querying the credit score of the target user according to the identifier of the target user;

[0132] Sending the query request to the preset credit platform;

[0133] The query result returned by the preset credit platform according to the query request is received, and the query result is parsed to obtain a third analysis value of the target user.

[0134] like Figure 4 , which is a schematic diagram of the structure of a device for implementing a method for business analysis based on location information provided by an embodiment of the present invention.

[0135] In this embodiment, the device 1 includes, but is not limited to, a memory 11, a processor 12, and a network interface 13, which can be interconnected and communicated via a system bus. The memory 11 stores a business analysis program 10 based on location information, and the business analysis program 10 based on location information can be executed by the processor 12. Figure 4 Only the device 1 having components 11-13 and the business analysis program 10 based on location information is shown. It can be understood by those skilled in the art that Figure 4 The structure shown does not constitute a limitation on the device 1, and the device 1 may include fewer or more components than shown, or combine certain components, or arrange the components differently.

[0136] The memory 11 includes a memory and at least one type of readable storage medium. The memory provides a cache for the operation of the device 1; the readable storage medium may be a non-volatile storage medium such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the readable storage medium may be an internal storage unit of the device 1; in other embodiments, the non-volatile storage medium may also be an external storage device of the device 1, such as a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), etc. equipped on the device 1. In this embodiment, the readable storage medium of the memory 11 is generally used to store the operating system and various application software installed on the device 1, such as storing the code of the business analysis program 10 based on location information in an embodiment of the present invention. In addition, the memory 11 may also be used to temporarily store various types of data that have been output or are to be output.

[0137] The processor 12 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 12 is generally used to control the overall operation of the device 1, such as performing control and processing related to data interaction or communication with other devices. In this embodiment, the processor 12 is used to run the program code stored in the memory 11 or process data, such as running the business analysis program 10 based on location information.

[0138] The network interface 13 may include a wireless network interface or a wired network interface, and the network interface 13 is used to establish a communication connection between the device 1 and a terminal (not shown in the figure).

[0139] Optionally, the device 1 may further include a user interface, which may include a display (Display), an input unit such as a keyboard (Keyboard), and the optional user interface may also include a standard wired interface and a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device, etc. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the device 1 and to display a visual user interface.

[0140] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0141] The location information-based business analysis program 10 stored in the memory 11 in the device 1 is a combination of multiple instructions. When running in the processor 12, it can achieve:

[0142] Receive a request for business analysis of a target user sent by a client, and obtain the residential address, work address, years of residence at the residential address, and years of work at the work address of the target user from a preset database according to an identifier of the target user included in the request;

[0143] Calculating the commuting distance between the residential address and the work address, determining the residential group category to which the target user belongs according to first preset type information of the residential place corresponding to the residential address, and performing calculation according to the residential group category, the commuting distance, and the years of residence to obtain a first analysis value of the target user;

[0144] Determine the income group category to which the target user belongs according to the second preset type information of the work unit corresponding to the work address, and calculate according to the income group category and the working years to obtain a second analysis value of the target user;

[0145] A third analysis value of the target user is obtained from a preset credit platform according to the identifier of the target user, a target qualification analysis value of the target user is generated according to the first analysis value, the second analysis value and the third analysis value, and a target business to be recommended is analyzed according to the target qualification analysis value.

[0146] Specifically, the specific implementation method of the processor 12 for the above-mentioned location information-based business analysis program 10 can refer to Figure 2 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0147] Furthermore, if the module / unit integrated in the device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable medium can be non-volatile or non-volatile. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0148] The computer-readable storage medium stores a business analysis program 10 based on location information, and the business analysis program 10 based on location information can be executed by one or more processors. The specific implementation method of the computer-readable storage medium of the present invention is basically the same as the above-mentioned embodiments of the business analysis method based on location information, and will not be repeated here.

[0149] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0150] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0151] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0152] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0153] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0154] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The second and other words are used to indicate names, but not to indicate any particular order.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention. If software tools or components other than those of the Company appear in the embodiments of the present application, they are only used for example and do not represent actual use.

Claims

1. A business analysis method based on location information, characterized in that: The method comprises: Receive a request for business analysis of a target user sent by a client, and obtain the residential address, work address, years of residence at the residential address, and years of work at the work address of the target user from a preset database according to an identifier of the target user included in the request; Calculating the commuting distance between the residential address and the work address, determining the residential group category to which the target user belongs according to first preset type information of the residential place corresponding to the residential address, and performing calculation according to the residential group category, the commuting distance, and the years of residence to obtain a first analysis value of the target user; Determine the income group category to which the target user belongs according to the second preset type information of the work unit corresponding to the work address, and calculate according to the income group category and the working years to obtain a second analysis value of the target user; A third analysis value of the target user is obtained from a preset credit platform according to the identifier of the target user, a target qualification analysis value of the target user is generated according to the first analysis value, the second analysis value and the third analysis value, and a target business to be recommended is analyzed according to the target qualification analysis value.

2. The method for analyzing business based on location information according to claim 1, characterized in that: The calculating the commuting distance between the residential address and the work address comprises: Using a preset map application service to convert the residential address into a first longitude and latitude, and convert the work address into a second longitude and latitude; The preset map application service is used to determine the commuting distance between the first longitude and latitude and the second longitude and latitude.

3. The business analysis method based on location information according to claim 1, characterized in that: The determining the residential group category to which the target user belongs according to the first preset type information of the residential place corresponding to the residential address includes: Comparing the first preset type information with a pre-constructed residential grade type table; The residential group category to which the target user belongs is determined according to the comparison result of the first preset type information.

4. The method for analyzing business based on location information according to claim 1, characterized in that: The calculating according to the residential group category, the commuting distance and the years of residence to obtain the first analysis value of the target user includes: From a preset first scoring standard table, respectively obtain a first score corresponding to the residential group category, a second score corresponding to the commuting distance, and a third score corresponding to the length of residence; Weight coefficients are respectively configured for the first score, the second score, and the third score, and the scores after the weight coefficients are configured are summed to obtain a first analysis value of the target user.

5. The method for analyzing business based on location information according to claim 1, characterized in that: The determining the income group category to which the target user belongs according to the second preset type information of the work unit corresponding to the work address includes: Comparing the second preset type information with a pre-constructed income scoring standard table; The income group category to which the target user belongs is determined according to the comparison result of the second preset type information.

6. The method for analyzing business based on location information according to claim 1, characterized in that: The calculating according to the income group category and the working years to obtain the second analysis value of the target user includes: From the preset second scoring standard table, respectively obtain the fourth score corresponding to the income group category and the fifth score corresponding to the working years; A weight coefficient is configured for each of the fourth score and the fifth score, and each score after the weight coefficient is configured is summed to obtain a second analysis value of the target user.

7. The method for analyzing business based on location information according to claim 1, characterized in that: The step of acquiring the third analysis value of the target user from a preset credit platform according to the identifier of the target user includes: Constructing a query request for querying the credit score of the target user according to the identifier of the target user; Sending the query request to the preset credit platform; A query result returned by the preset credit platform according to the query request is received, and the query result is parsed to obtain a third analysis value of the target user.

8. A business analysis device based on location information, characterized in that: The device comprises: A receiving module, configured to receive a request sent by a client for performing business analysis on a target user, and obtain, from a preset database, the residential address, work address, years of residence at the residential address, and years of work at the work address of the target user according to an identifier of the target user included in the request; A first calculation module is used to calculate the commuting distance between the residential address and the work address, determine the residential group category to which the target user belongs according to the first preset type information of the residential place corresponding to the residential address, and perform calculation according to the residential group category, the commuting distance and the years of residence to obtain a first analysis value of the target user; A second calculation module is used to determine the income group category to which the target user belongs according to the second preset type information of the work unit corresponding to the work address, and to calculate according to the income group category and the working years to obtain a second analysis value of the target user; A recommendation module is used to obtain a third analysis value of the target user from a preset credit platform according to the identifier of the target user, generate a target qualification analysis value of the target user according to the first analysis value, the second analysis value and the third analysis value, and analyze a target business to be recommended according to the target qualification analysis value.

9. A device, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a business analysis program based on location information that can be executed by the at least one processor, and the business analysis program based on location information is executed by the at least one processor so that the at least one processor can execute the business analysis method based on location information as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a business analysis program based on location information, and the business analysis program based on location information can be executed by one or more processors to implement the business analysis method based on location information as described in any one of claims 1 to 7.

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