User product demand calculation method based on LBS position big data

Abstract

The invention relates to the technical field of big data analysis and user behavior research, and discloses an LBS position big data-based user product demand calculation method, which comprises the following steps: S1, data acquisition, S2, cleaning of LBS position data, S3, calculation of the preference degree of a user for each interest point, S4, calculation of user product demand distribution in combination with the product layout of the user interest points, and S5, calculation of the user product demand distribution. According to the method, the access behaviors of the user to the interest points can be captured and analyzed in real time, so that the user preferences are accurately evaluated, personalized services are provided for the user for product recommendation, and through multi-dimensional analysis, the insight in consumer behaviors is enhanced, and enterprises are helped to deeply understand the market and user demands; the method can process a large number of samples, is wide in coverage, can be applied to a plurality of industries such as real estate, retail, catering, entertainment, tourism and the like, promotes the formulation of subdivided market strategies, optimizes resource allocation, improves user experience, and guides enterprises to carry out commodity innovation.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis and user behavior research, and specifically to a method for calculating user product demand based on LBS location big data. Background Art

[0002] Big data analysis and user behavior research, especially the application of in-depth mining and analysis of user behavior data under the location-based service (LBS) technology framework.

[0003] At present, the user product demand calculations on the market mainly include the following:

[0004] User needs analysis methods: including Maslow's hierarchy of needs theory, which is used to observe the most fundamental and basic level of human needs; and the MVP (minimum viable product) method, which continuously optimizes the product based on user feedback through rapid iteration;

[0005] User research methods: including situational surveys, user interviews, questionnaires, A / B testing, usability testing, etc. These methods help understand user needs and optimize product design;

[0006] KANO model: a tool for classifying and ranking user needs. By analyzing the user's satisfaction with product functions, it divides the needs into basic needs, expected needs, exciting needs, indifferent needs, and reverse needs, helping to determine the priority in the product implementation process;

[0007] User behavior analysis: By analyzing the user behaviors on your products and the data behind the behaviors, you can build a user behavior model to achieve refined operations and business growth.

[0008] Although the above technologies can observe users’ product needs to a certain extent, they have the following problems:

[0009] Limitations: User behavior analysis can generally only analyze and summarize the data of its own products; user needs and satisfaction change over time and in the market, and the KANO model may not be able to reflect these changes in a timely manner;

[0010] Privacy protection issues: Collecting and analyzing user data may raise concerns about privacy protection and data security;

[0011] Data coverage issues: improper sampling can easily lead to research results that cannot represent the entire user group; poorly designed questionnaires may not accurately capture the user's real needs and feelings.

[0012] In order to solve the above problems, academia and industry have conducted a lot of research and applied for a series of related patents, which are mainly concentrated in the following aspects:

[0013] 1. User demand data processing: involves obtaining the demand data of the target object, standardizing the demand data, calibrating the data and clustering the data to improve the accuracy of service fee calculation.

[0014] 2. Prediction of personalized user needs: This includes obtaining user data, product data, and user interest data to form historical data, building a model based on this data, and predicting personalized user needs.

[0015] 3. User demand prediction based on patent statistical analysis: Use explicit user needs in Web data as keywords, search for relevant patents, mine patent knowledge units, combine the law of demand evolution and Maslow's hierarchy of needs to predict user needs, and determine user demand weights based on Kano attributes.

[0016] 4. Research on user demand prediction methods: Combining patent knowledge units and the law of demand evolution, using SAS software to mine patent knowledge, building a user demand prediction model, and determining demand weights through the Kano questionnaire to classify and predict user needs.

[0017] That is, there is currently no method on the market that combines user location data and point of interest product structure to calculate product demand for different user groups. Therefore, we need to propose a method for calculating user product demand based on LBS location big data. Summary of the invention

[0018] The purpose of the present invention is to provide a user product demand calculation method based on LBS location big data, which can capture and analyze the user's visit behavior to points of interest in real time, so as to accurately evaluate user preferences, provide users with personalized services and product recommendations, and enhance the insight into consumer behavior through multi-dimensional analysis, so as to help enterprises deeply understand the market and user needs; it can process a large number of samples and cover a wide range, and can be applied to multiple industries such as real estate, retail, catering, entertainment, and tourism, promote the formulation of market segmentation strategies, optimize resource allocation, improve user experience, and guide enterprises to carry out product innovation to solve the problems raised in the above background technology.

[0019] To achieve the above object, the present invention provides the following technical solution: a method for calculating user product demand based on LBS location big data, comprising the following steps:

[0020] S1. Data collection: Collect customer’s LBS location data and product layout of points of interest;

[0021] S2. Cleaning the LBS location data, including removing duplicate data and removing LBS location data of passers-by and staff;

[0022] S3. Calculate the user's preference for each POI: There are two indicators for evaluating the user's preference for POIs. The primary indicator is the frequency of visit, and the secondary indicator is the duration of visit. Under the same visit frequency, the longer the duration, the higher the user's preference for the POI.

[0023] S4. Calculate the user product demand distribution based on the product layout of the user's interest points: filter the interest points with 80% quantile preference for each user, and calculate the demand probability of each user for each product based on the product structure of the interest points.

[0024] Preferably, in step S1, the LBS location data of the customer includes the user anonymous ID, the ID of the visited point of interest, and the user visit details, and the user visit details include the visit date, start time, and end time.

[0025] Preferably, in step S1, the product layout of the point of interest includes the point of interest ID, product type, product transaction / supply structure, and the point of interest ID is associated with the visited point of interest ID.

[0026] Preferably, in step S1, within any time period, the LBS location data of the customer's visit to the point of interest includes the universal unique identification code uuid, the stay location, and the stay duration.

[0027] Preferably, in step S3, when calculating the preference for the point of interest, the features of visit frequency and duration are extracted, the visit frequency is recorded as L, the duration is recorded as T, and the calculation formula is as follows:

[0028] L=COUNT(day)

[0029] T=∑(end_time-start_time).

[0030] Preferably, in step S3, the user preference model is performed after calculating the user's preference for each point of interest, and the preference model is calculated according to the LBS location data within the target time period, and the formula is as follows:

[0031] p=f(L,T).

[0032] Preferably, in step S3, the step of calculating the user's preference for each point of interest is as follows:

[0033] S31. Calculate the score S of the main indicator visit frequency L , for S L Perform range standardization:

[0034]

[0035] S32, S LAccording to the standardized scores, the scores are divided into 10 levels, namely 0-1, 1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10;

[0036] S33, calculate the secondary indicator duration score S, which is the user's preference for each point of interest. L The duration is long and the preference is high.

[0037] Preferably, in step S31 and step S32, the frequency of each user visiting each point of interest is counted, and the frequency of the visit is standardized to obtain S L , and all S L The cluster analysis was divided into ten levels, and the S L The maximum value S L_max and minimum value S L_min , maximum value S L_max and minimum value S L_min That is S L The two boundary values ​​of the normalized fraction range.

[0038] Preferably, in step S33, after the visit frequency is standardized, each S L The duration of the visit is standardized and placed in [S L_max , S L_min ], the standardized score S of the continuous visit time is recorded as the user's preference score for each point of interest.

[0039] Preferably, in step S4, the user's product demand probability is calculated in combination with the product combination and layout of the point of interest, and the formula is as follows:

[0040] pro=f(S,product).

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. The present invention can capture and analyze users' visit behaviors to points of interest in real time, so as to accurately evaluate users' preferences, provide users with personalized services and product recommendations, and enhance the insight into consumer behaviors through multi-dimensional analysis, helping enterprises to deeply understand the market and user needs;

[0043] 2. The present invention can process a large number of samples with a wide coverage, and can be applied to multiple industries such as real estate, retail, catering, entertainment, and tourism. It can promote the formulation of market segmentation strategies, optimize resource allocation, enhance user experience, and guide enterprises to carry out product innovation. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flow chart of the present invention;

[0045] Figure 2 It is a flowchart of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] See also Figure 1-2 The present invention provides a technical solution: a method for calculating user product demand based on LBS location big data, which combines multiple technical sub-fields such as geographic information system (GIS) and data mining, and aims to reveal the user's behavioral preferences through the user's location data and predict the demand trend for various products or services, including the following steps:

[0048] S1. Data collection: Collect customer’s LBS location data and product layout of points of interest;

[0049] In step S1, the LBS location data of the customer includes the user anonymous ID, the ID of the visited point of interest (POI), and the user's visit details, and the user's visit details include the visit date, start time, and end time.

[0050] In step S1, the product layout of the point of interest includes the point of interest ID, product type, product transaction / supply structure, and the point of interest ID is associated with the visited point of interest ID, that is, the points of interest (POI) in the LBS location data and the product layout of the point of interest are in a corresponding relationship.

[0051] In step S1, within any time period, the LBS location data of a customer visiting a point of interest includes a universal unique identifier uuid, a stay location (latitude and longitude), and a stay duration (the time difference between the start time and the end time of the visit).

[0052] S2. Cleaning the LBS location data, including removing duplicate data and removing LBS location data of passers-by and staff;

[0053] To remove duplicate data: you can use a database or data processing tool. Specifically, you can use SQL's DISTINCT, GROUP BY combined with the DELETE statement, or use the drop_duplicates() method in Pandas to remove duplicate location records.

[0054] The location data of passers-by are processed as follows:

[0055] Defining the characteristics of passers-by: passers-by usually pass through an area in a short time and then leave quickly without staying in the area for a long time;

[0056] Analyze the dwell time: Calculate the dwell time of each user in each area. If the dwell time is very short (a threshold can be set based on actual conditions, such as less than 5 minutes), the user may be considered a passer-by.

[0057] Application rules: Based on the dwell time threshold, possible passers-by are screened out and their location records are removed from the dataset.

[0058] The LBS location data of staff members is processed as follows:

[0059] Define worker characteristics: Worker location data may show specific time patterns (such as fixed work and rest times on weekdays), high frequency of visits to specific areas (such as offices, warehouses, etc.), etc.

[0060] Combine external information: If possible, obtain employee identity information or work area information and match it directly with location data to eliminate the location data of the staff member;

[0061] Behavioral pattern analysis: If it is not possible to directly obtain employee information, you can try to identify staff members through the behavioral patterns of location data. For example, analyze the user's daily activity range, visit frequency, etc., and consider users who frequently visit certain specific areas and have fixed activity patterns as staff members;

[0062] Apply rules: Based on the analyzed behavior patterns or external information, apply corresponding rules to eliminate the location data of the staff member.

[0063] S3. Calculate the user's preference for each POI: There are two indicators for evaluating the user's preference for POIs. The primary indicator is the frequency of visit, and the secondary indicator is the duration of visit. Under the same visit frequency, the longer the duration, the higher the user's preference for the POI.

[0064] In step S3, when calculating the preference for the point of interest, the features of visit frequency and duration are extracted, the visit frequency is recorded as L, the duration is recorded as T, and the calculation formula is as follows:

[0065] L=COUNT(day)

[0066] T=∑(end_time-start_time).

[0067] In step S3, the user preference degree for each point of interest is calculated to perform user preference modeling, and the preference model is calculated based on the LBS location data within the target time period. The formula is as follows:

[0068] p=f(L,T).

[0069] In step S3, the steps of calculating the user's preference for each point of interest are as follows:

[0070] S31. Calculate the score S of the main indicator visit frequency L , for S L Perform range standardization:

[0071]

[0072] max(L) is the maximum value of the visit frequency, and main(L) is the minimum value of the visit frequency.

[0073] S32, S L The standardized scores are divided into 10 levels, namely 0-1 points, 1-2 points, 2-3 points, 3-4 points, 4-5 points, 5-6 points, 6-7 points, 7-8 points, 8-9 points, and 9-10 points; cluster analysis can also be used for grading.

[0074] For example, if the user's visit frequency is as low as 1 time and as high as 20 times, then when standardized, a visit frequency of 1 time is recorded as 0.5 points, a visit frequency of 2 times is recorded as 1 point, a visit frequency of 3 times is recorded as 1.5 points, a visit frequency of 4 times is recorded as 2 points... a visit frequency of 20 times is recorded as 10 points, and so on.

[0075] In step S31 and step S32, the frequency of each user visiting each point of interest is counted, and the frequency of each user visiting is standardized to obtain S L , and all S L The cluster analysis was divided into ten levels, and the S L The maximum value S L_max and minimum value S L_min , maximum value S L_max and minimum value S L_min That is S L The two boundary values ​​of the normalized fraction range.

[0076] S33, calculate the secondary indicator duration score S, which is the user's preference for each point of interest. L The duration is long and the preference is high.

[0077] In step S33, after the visit frequency is standardized, the number of visits per file is counted. LThe duration of the visit is standardized and placed in [S L_max , S L_min ], the standardized score S of the continuous visit time is recorded as the user's preference score for each point of interest.

[0078] S4. Calculate the user product demand distribution based on the product layout of the user's interest points: filter the interest points with 80% quantile preference for each user, standardize them into percentages based on the degree of preference, and calculate the probability of each user's demand for each product based on the product structure of the interest points.

[0079] In step S4, the user's product demand probability is calculated based on the product combination and layout of the point of interest, and the formula is as follows:

[0080] pro=f(S,product).

[0081] Through the above steps, the present invention not only provides an effective method for calculating user product demand, but also provides a scientific basis for business decision-making, enhances the market competitiveness of enterprises, ensures the protection of user privacy, and has broad potential for cross-industry applications.

[0082] This method has a wide range of applications, spanning real estate, retail, catering, entertainment, tourism and other industries, providing enterprises with decision support based on actual user behavior. Through refined analysis of user access patterns to specific points of interest;

[0083] The present invention can not only enhance enterprises' understanding of market dynamics, but also provide data-driven solutions for business links such as personalized marketing, inventory management and product development. In addition, the design of this technology fully considers the requirements of user privacy protection, ensuring strict compliance with relevant laws and regulations during data analysis and application, and protecting the security of user information.

[0084] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for calculating user product demand based on LBS location big data, characterized in that: The following steps are involved: S1. Data collection: Collect customer’s LBS location data and product layout of points of interest; S2. Cleaning the LBS location data, including removing duplicate data and removing LBS location data of passers-by and staff; S3. Calculate the user's preference for each POI: There are two indicators for evaluating the user's preference for POIs. The primary indicator is the frequency of visit, and the secondary indicator is the duration of visit. Under the same visit frequency, the longer the duration, the higher the user's preference for the POI. S4. Calculate the user product demand distribution based on the product layout of the user's interest points: filter the interest points with 80% quantile preference for each user, and calculate the demand probability of each user for each product based on the product structure of the interest points.

2. The method for calculating user product demand based on LBS location big data according to claim 1, characterized in that: In step S1, the LBS location data of the customer includes the user's anonymous ID, the ID of the visited point of interest, and the user's visit details, and the user's visit details include the visit date, start time, and end time.

3. The method for calculating user product demand based on LBS location big data according to claim 1, characterized in that: In step S1, the product layout of the point of interest includes the point of interest ID, product type, product transaction / supply structure, and the point of interest ID is associated with the visited point of interest ID.

4. The method for calculating user product demand based on LBS location big data according to claim 1, characterized in that: In step S1, within any time period, the LBS location data of the customer's visit to the point of interest includes the universal unique identifier uuid, the stay location, and the stay duration.

5. The method for calculating user product demand based on LBS location big data according to claim 1, characterized in that: In step S3, when calculating the preference for the point of interest, the features of visit frequency and duration are extracted, the visit frequency is recorded as L, the duration is recorded as T, and the calculation formula is as follows: L=COUNT(day) T=∑(end_time-start_time).

6. The method for calculating user product demand based on LBS location big data according to claim 5, characterized in that: In step S3, the user preference degree for each point of interest is calculated to perform user preference modeling, and the preference model is calculated based on the LBS location data within the target time period. The formula is as follows: p=f(L,T).

7. The method for calculating user product demand based on LBS location big data according to claim 6, characterized in that: In step S3, the steps of calculating the user's preference for each point of interest are as follows: S31. Calculate the score S of the main indicator visit frequency L , for S L Perform range standardization: S32, SL is divided into 10 levels according to the standardized scores, namely 0-1 points, 1-2 points, 2-3 points, 3-4 points, 4-5 points, 5-6 points, 6-7 points, 7-8 points, 8-9 points, 9-10 points; S33, calculate the secondary indicator duration score S, which is the user's preference for each point of interest. L The duration is long and the preference is high.

8. The method for calculating user product demand based on LBS location big data according to claim 7, characterized in that: In step S31 and step S32, the frequency of each user visiting each point of interest is counted, and the frequency of each user visiting is standardized to obtain S L , and all S L The cluster analysis was divided into ten levels, and the S L The maximum value S L_max and minimum value S L_min , maximum value S L_max and minimum value S L_min That is S L The two boundary values ​​of the normalized fraction range.

9. The method for calculating user product demand based on LBS location big data according to claim 7, characterized in that: In step S33, after the visit frequency is standardized, the number of visits per file S is counted. L The duration of the visit is standardized and placed in [S L_max , S L_min ], the standardized score S of the continuous visit time is recorded as the user's preference score for each point of interest.

10. The method for calculating user product demand based on LBS location big data according to claim 1, characterized in that: In step S4, the user's product demand probability is calculated based on the product combination and layout of the point of interest, and the formula is as follows: pro=f(S,product).

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