Nursing product push method and system based on address features

Abstract

The present invention discloses a method and system for pushing nursing products based on address features, which relates to the field of artificial intelligence technology. The method and system for pushing nursing products based on address features obtain the user's real-time address data through a geographic information system, and extract the user's address area features in combination with a regional division algorithm. The user's geographic features are deeply matched with the nursing product demand data through a clustering algorithm, and suitable nursing products are screened and a recommendation list is generated. The push priority of the nursing product is evaluated according to the matching strength, and the push order and frequency are adjusted in combination with the user behavior feedback data, thereby improving the accuracy of the push and user satisfaction. It effectively improves the personalization and timeliness of nursing product recommendations, reduces the interference of irrelevant pushes, significantly improves the accuracy and practicality of the traditional push mode, enhances the user experience, and optimizes the market demand matching efficiency of nursing products.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method and system for pushing nursing products based on address features. Background Art

[0002] In today's society, with the improvement of residents' health awareness, the demand for nursing products has increased significantly. In particular, regarding the nursing needs of the elderly, patients with special diseases, and those with limited mobility, the market demand for accurate and personalized nursing product push is increasing. At the same time, the popularization of smart devices and data analysis technology has opened up the possibility of personalized recommendations for nursing products. More and more service platforms use characteristics such as users' basic information, health status, and geographic location to try to push nursing products that meet users' needs at the appropriate time and place. However, traditional push methods rely heavily on basic user data and lack sufficient consideration of users' real-time location, geographical environment, or the timeliness of their needs, which affects the accuracy and practicality of push.

[0003] Traditional care product push models lack in-depth insights into users' living environments and geographic locations, resulting in generalized push content that struggles to meet personalized care needs. Second, existing push methods are mostly based on users' health data or purchase records, lacking effective analytical tools for dynamic changes in their geographic location or product demand preferences in specific regions, leading to recommended care products that don't match actual usage scenarios. Furthermore, existing systems often lack precise control over push timing, making it impossible to adjust based on users' real-time locations or demand differences during specific time periods. This reduces the timeliness of push notifications and reduces user satisfaction. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides a method and system for pushing care products based on address features, which solves the problems of the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for pushing nursing products based on address features, comprising the following steps: S1. obtaining real-time address data of users through a geographic information system, and extracting user address area features in combination with a regional division algorithm; S2. based on user address area features, deeply correlating and matching user address area features with nursing product geographic area demand data through a clustering algorithm, screening nursing products, and generating a nursing product recommendation list; S3. evaluating the nursing product push priority and obtaining a nursing product push index based on the matching strength between user address area features and nursing product geographic area demand data; S4. prioritizing the nursing product recommendation list based on the nursing product push index; S5. monitoring user behavior feedback data and regulating the push order and frequency of nursing products.

[0006] Furthermore, the specific process of extracting user address area characteristics through regional division algorithm is as follows: extract the user's geographic coordinates according to the geographic information system, spatially cluster the user's geographic coordinates through regional division algorithm, and divide the user's geographic location into corresponding geographic area units; based on the division results, extract the socioeconomic characteristics and medical resource distribution characteristics of each geographic area, and construct a regional feature vector; match the user's geographic area according to the regional feature vector to generate user address area characteristics.

[0007] Furthermore, the specific process of deeply associating and matching user address area characteristics with nursing product geographic area demand data through clustering algorithms is as follows: based on user address area characteristics and nursing product geographic area demand data, construct user demand feature matrix and product demand feature matrix; jointly cluster user address area characteristics and nursing product demand characteristics through clustering algorithms, associate and match users and nursing products in the geographic area demand dimension, analyze the similarity between user geographic characteristics and nursing product demand data, and assign users and nursing products with similar demand patterns to the same cluster.

[0008] Furthermore, the specific process of screening nursing products and generating a nursing product recommendation list is as follows: based on the joint clustering results, determine the address area characteristics of each user and the corresponding nursing product demand type; based on the cluster group to which each user belongs, screen out nursing products that meet the needs of the group and evaluate the matching degree of each nursing product; prioritize the screened nursing products and generate a nursing product recommendation list.

[0009] Furthermore, based on the matching strength between the user's address area characteristics and the nursing product geographical area demand data, the specific process of evaluating the nursing product push priority is as follows: Set the user's address area characteristics as , the geographical demand characteristics of care products are ,in and Represents users Address area characteristics and care products Demand feature vector; calculate the matching strength between each care product and the user ,The matching strength formula is as follows: ;in, Indicates computing users and care products Cosine similarity function on the geographic area demand dimension.

[0010] Furthermore, the specific process of obtaining the nursing product push index is as follows: calculate the matching strength between each nursing product and all users, select the best matching product corresponding to each user, and sort all nursing products according to the matching strength to obtain the nursing product push index of each nursing product. , where the formula for the nursing product push index is: ;in, For users The weighted coefficient represents the relative importance of the user's demand for care products. For users and care products matching strength.

[0011] Furthermore, the specific process of prioritizing the care product recommendation list is as follows: based on the care product push index, the care products are sorted in descending order from large to small; for care products with the same push index, secondary sorting is performed based on user behavior feedback data.

[0012] Furthermore, the specific process of monitoring user behavior feedback data and regulating the push order and frequency of nursing products is as follows: based on the user's behavioral feedback data on the pushed nursing products, including click volume, purchase rate, and stay time, calculate the behavior feedback index of each nursing product; based on the behavior feedback index of each nursing product, compare it with the set behavior feedback index threshold range; when the behavior feedback index is greater than or equal to the set feedback index threshold range, increase its push frequency; when the behavior feedback index is less than the set feedback index threshold range, reduce its push frequency and adjust the push order.

[0013] The nursing product push system based on address features includes the following modules: an address feature extraction module, a cluster matching module, a priority evaluation module, a sorting module, and a behavior feedback control module; the address feature extraction module is used to obtain the user's real-time address data through a geographic information system, and extract the user's address area features in combination with a regional division algorithm; the cluster matching module is used to perform deep correlation matching between the user's address area features and the nursing product geographic area demand data based on the user's address area features through a clustering algorithm, screen nursing products, and generate a nursing product recommendation list; the priority evaluation module is used to evaluate the nursing product push priority and obtain the nursing product push index based on the matching strength between the user's address area features and the nursing product geographic area demand data; the sorting module is used to prioritize the nursing product recommendation list based on the nursing product push index; the behavior feedback control module is used to monitor user behavior feedback data and regulate the push order and frequency of nursing products.

[0014] The present invention has the following beneficial effects:

[0015] (1) This method of pushing nursing products based on address features achieves accurate product recommendations by combining the user's address area characteristics with the geographical area demand data of nursing products. It improves the matching degree of nursing products, avoids invalid or irrelevant pushes, and optimizes the user experience. By matching users and products through clustering algorithms, it can quickly identify user groups with similar demand patterns in big data, ensure that the pushed nursing products meet the demand characteristics of specific regions, and improve the relevance and usage rate of products. By calculating the matching strength between users and nursing products, reasonably evaluating the push priority, and sorting according to the nursing product push index, the pushed content is more in line with the actual needs of users, improving the accuracy and effectiveness of the push.

[0016] (2) This address-based care product push system monitors user behavioral feedback data and regulates the order and frequency of care product pushes in real time, enabling the recommendation system to dynamically adjust based on actual user feedback, avoiding excessive or inappropriate product pushes, thereby improving user satisfaction and system adaptability. Combining geographic information and user behavior analysis, it can more quickly identify and respond to changes in user needs. The system can optimize recommendation strategies in a timely manner, reduce user waiting time, and improve user trust and satisfaction with the system.

[0017] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of the method for pushing care products based on address features of the present invention.

[0019] Figure 2 This is a flow chart of the care product push system based on address features of the present invention. DETAILED DESCRIPTION

[0020] The embodiments of the present application solve the problem that the traditional push mode ignores the user's geographical location and the timeliness of demand through a nursing product push method and system based on address features, and achieve more accurate and personalized nursing product recommendations.

[0021] The overall approach to the problems in the embodiments of this application is as follows:

[0022] The user's real-time address data is obtained through the geographic information system, and the user's address area characteristics are extracted by combining the regional division algorithm.

[0023] Based on the user's address area characteristics, a clustering algorithm is used to deeply correlate and match the user's address area characteristics with the geographical area demand data of nursing products, screen nursing products, and generate a recommended list of nursing products.

[0024] Based on the matching strength between the user's address area characteristics and the geographical area demand data of nursing products, the priority of nursing product push is evaluated and the nursing product push index is obtained.

[0025] Based on the nursing product push index, the nursing product recommendation list is prioritized.

[0026] Monitor user behavior feedback data and adjust the order and frequency of pushing care products.

[0027] See also Figure 1 , an embodiment of the present invention provides a technical solution: a method for pushing nursing products based on address features, comprising the following steps: S1. obtaining real-time address data of users through a geographic information system, and extracting user address area features in combination with a regional division algorithm; S2. based on user address area features, performing deep correlation matching between user address area features and nursing product geographic area demand data through a clustering algorithm, screening nursing products, and generating a nursing product recommendation list; S3. evaluating the nursing product push priority and obtaining a nursing product push index based on the matching strength between user address area features and nursing product geographic area demand data; S4. prioritizing the nursing product recommendation list based on the nursing product push index; S5. monitoring user behavior feedback data and regulating the push order and frequency of nursing products.

[0028] In this implementation, S1. Geographic Information System (GIS) is a technology used to capture, store, analyze, and manage geospatial data. It can help obtain users' real-time address data (such as GPS coordinates or other geolocation information). Regionalization Algorithm: This algorithm spatially partitions users' location data into distinct geographic units. This partitioning allows for the extraction of characteristic information for each unit, such as population density, traffic conditions, and climate. User Address Area Characteristics: The characteristics extracted from the regionalization algorithm include the user's geographic location, environmental factors, and socioeconomic characteristics of the region. S2. Clustering Algorithm: A clustering algorithm is an unsupervised learning algorithm that groups elements in a dataset based on certain characteristics, resulting in high similarity within the same group and low similarity between different groups. Here, the clustering algorithm is used to analyze the relationship between users' geographic characteristics and their care product demand characteristics. Care product geographic demand data: This refers to information on demand, preferences, and usage of care products in different geographic regions. For example, certain regions may have a high demand for specific types of care products. Deep Correlation Matching: This involves using a clustering algorithm to comprehensively analyze users' geographic characteristics and their care product needs to identify similarities between users and their care product needs. Based on this analysis, the most suitable care products for the user can be screened and a list of recommended care products can be generated. S3. Match Strength: Match strength refers to the similarity or degree of correlation between the user's geographic region characteristics and the care product needs. Similarity metrics can be used to quantify this degree of match. Push priority ranks the importance of each care product based on the strength of match. Care products with higher match strengths receive higher push priority. Care Product Push Index: The Care Product Push Index is a comprehensive metric used to measure the push priority of a care product for a specific user. A higher push index indicates a higher push priority for the product. S4. Recommended care products are ranked based on the calculated Care Product Push Index. The higher the push index, the higher the priority it will be placed at the top of the recommended list, ensuring that users see the products that best meet their needs first. S5. User Behavior Feedback: User behavior feedback data includes user interaction data with recommended products, such as click-through rate, purchase rate, browsing time, and feedback ratings. Based on this behavioral data, users can analyze their acceptance and interest in recommended products. Push Order and Frequency Control: Based on monitored user behavior data, the push order and frequency of products can be dynamically adjusted. For example, if the push effect of a certain care product is poor, you can reduce its push frequency or optimize the push content.

[0029] Specifically, the specific process of extracting user address area characteristics through the regional division algorithm is as follows: extract the user's geographic coordinates according to the geographic information system, spatially cluster the user's geographic coordinates through the regional division algorithm, and divide the user's geographic location into corresponding geographic area units; based on the division results, extract the socioeconomic characteristics and medical resource distribution characteristics of each geographic area, and construct a regional feature vector; match the user's geographic area according to the regional feature vector to generate the user's address area characteristics.

[0030] In this implementation, a geographic information system (GIS) is used to obtain users' real-time geographic coordinates (e.g., longitude and latitude). These coordinates accurately represent the user's current location. The user's geographic location is essential for targeted care product delivery. Using geographic coordinates, the system can determine the user's actual location and further analyze the characteristics of their region (e.g., environmental, socioeconomic, and healthcare resources) to prepare for subsequent delivery. A regionalization algorithm groups geographic coordinate data (user locations) based on spatial distance or similarity, forming distinct geographic regions. Mapping all users' locations within a specific region provides aggregated data for subsequent analysis. For example, users in a given region may face similar socioeconomic and healthcare resource needs. Regionalization helps identify user groups with similar needs. Based on the user distribution within each geographic region, socioeconomic characteristics (e.g., income level, population density, and educational attainment) and healthcare resource characteristics (e.g., number of hospitals and clinics, accessibility of healthcare services, etc.) are extracted. These characteristics help reflect the care needs of residents within the region. These extracted regional characteristics (e.g., socioeconomic, healthcare resources, etc.) are converted into a "regional feature vector," a set of numerical values ​​that describes the overall characteristics of the region. Each characteristic (such as income level and number of medical facilities) is converted into a vector component using a specific quantitative method. The regional characteristic vector represents a region's complex information (social, economic, and medical) in a simplified manner, enabling efficient subsequent calculations (such as matching users with regional characteristics and recommending recommendations). The characteristic vector provides the system with a standardized and unified way to represent regional characteristics, facilitating comparison and analysis across regions. By matching a user's geographic location with existing regional characteristic vectors, the user's geographic region characteristics are determined, identifying the geographic region that best meets their needs. By matching regional characteristics, the system can accurately identify the specific needs of the user's region and provide personalized recommendations. This process ensures the regional adaptability and demand-driven accuracy of recommended products.

[0031] Specifically, the specific process of deep correlation matching between user address area characteristics and nursing product geographical area demand data through clustering algorithm is as follows: based on user address area characteristics and nursing product geographical area demand data, construct user demand feature matrix and product demand feature matrix; jointly cluster user address area characteristics and nursing product demand characteristics through clustering algorithm, correlate and match users and nursing products in the geographical area demand dimension, analyze the similarity between user geographical characteristics and nursing product demand data, and assign users and nursing products with similar demand patterns to the same cluster.

[0032] In this implementation, the user demand feature matrix: Each user's geographic characteristics (income level, health status, and medical resource distribution) are converted into numerical feature vectors and constructed into a matrix format, with each row representing a user and each column representing a characteristic. The product demand feature matrix: Each care product's demand characteristics (demand intensity, usage frequency) in different geographic regions are converted into numerical feature vectors and constructed into a matrix format, with each row representing a product and each column representing a demand characteristic. By quantifying the geographic demand characteristics of users and products, a standardized data foundation is provided for subsequent clustering analysis, enabling the system to perform matching and recommendation within a unified framework. The user demand feature matrix and the product demand feature matrix are merged, forming a high-dimensional feature vector for each user and product based on their geographic and demand characteristics. A clustering algorithm (K-means) clusters these vectors, grouping users and products with similar needs. By identifying similarities between user and product demand characteristics, the clustering algorithm can group users and care products with similar demand patterns, ensuring that each user is matched with products that match their geographic region and demand characteristics, thereby improving the relevance and accuracy of recommendations. Based on the clustering results, the system selects care products suitable for the user's cluster group and the group's demand characteristics, and matches them with the corresponding users. Through this matching method, the system can ensure that the recommended care products not only meet the user's basic needs, but also adapt to the specific needs of their region, avoid pushing inappropriate products, and improve the accuracy of the recommendation system. During the joint clustering process, the system calculates the similarity between each user and the care product, analyzes the differences in the feature vectors of users and products in different clusters, and identifies which products are most in line with the needs of which users. This similarity analysis can help the system accurately identify the best matching relationship between users and products, thereby optimizing the push strategy, improving user satisfaction and the effectiveness of care product recommendations.

[0033] Specifically, the specific process of screening nursing products and generating a nursing product recommendation list is as follows: based on the joint clustering results, determine the address area characteristics of each user and the corresponding nursing product demand type; based on the cluster group to which each user belongs, screen out nursing products that meet the needs of the group and evaluate the matching degree of each nursing product; prioritize the screened nursing products and generate a nursing product recommendation list.

[0034] In this implementation, after the joint cluster analysis is complete, each user is assigned to a specific cluster based on their location characteristics (geographic location, socioeconomic status, and medical resource distribution). Each cluster represents a group of users with similar needs (high-demand care products in the same area). The system then determines the types of care products relevant to each cluster based on their needs. For example, one cluster may be more interested in care products tailored to the elderly, while another may prefer care products targeting specific conditions. The clustering results clarify the type of care product needs for each user's cluster. This enables the system to analyze user needs holistically and recommend personalized products, rather than simply recommending based on a single user's characteristics. Each user's cluster is then used to identify care products commonly needed by that group. For example, for the elderly, the system prioritizes care products tailored to their needs, such as assistive devices and nursing beds. The system also quantitatively assesses the compatibility between each user and care product. Common methods include scoring models based on geographic needs, product type, and historical demand. The compatibility evaluation formula can provide a comprehensive score based on factors such as geographic region, product functional requirements, and user health status. For example, if a user's area has a strong demand for a certain type of care product, and the user's health condition requires such a product, the degree of match is relatively high. Products that match user characteristics are screened through group demand. Care products that meet user needs are accurately screened to ensure that the recommended care products are more in line with the user's needs, thereby improving the effectiveness of the recommendation. According to the matching score between each care product and the user, the system sorts the screened care products. Products with high matching scores are ranked first, and products with low scores are ranked last. The core purpose of this sorting process is to ensure that the system can recommend care products that best meet the needs and have the highest matching degree to users. Through priority sorting, the products in the recommendation list will be accurately arranged according to the user's needs, increasing the user's acceptance and satisfaction with the recommendation results.

[0035] Specifically, the specific process of evaluating the priority of nursing product push based on the matching strength between the user's address area characteristics and the nursing product geographical area demand data is as follows: Set the user's address area characteristics as , the geographical demand characteristics of care products are ,in and Represents users Address area characteristics and care products Demand feature vector; calculate the matching strength between each care product and the user ,The matching strength formula is as follows: ;in, Indicates computing users and care products Cosine similarity function on the geographic area demand dimension.

[0036] In this implementation scheme, the matching strength is calculated as follows: based on the user's geographical area characteristics and the geographical demand characteristics of the care products, the system calculates the matching strength between each care product and the user. The matching strength reflects the degree of fit between the user's needs and the characteristics of the care product in terms of geographical area needs. By calculating this matching strength, the system can determine which care products best meet the needs of the user's area. For each care product, the system calculates its matching strength with all users and sorts the products according to the matching strength. Based on the relative importance of each user's demand for the care product, the system assigns a push index to each product. The push index indicates the priority of each care product relative to the user's needs, helping the system determine which products should be pushed to the user first.

[0037] Specifically, the specific process of obtaining the nursing product push index is as follows: calculate the matching strength between each nursing product and all users, select the best matching product corresponding to each user, and sort all nursing products according to the matching strength to obtain the nursing product push index of each nursing product. , where the formula for the nursing product push index is: ;in, For users The weighted coefficient represents the relative importance of the user's demand for care products. For users and care products matching strength.

[0038] In this implementation scheme, the calculation of matching strength: calculates the matching strength of each care product with all users. Matching strength reflects the degree of fit between each care product and user needs, taking into account the similarity between geographical area characteristics and care product needs. Select the best matching product: For each user, the system selects the care product that best matches the user's needs. This process ensures that the pushed product each user receives is the most relevant product in terms of their needs dimension. Sorting care products: All care products are sorted according to the matching strength with the user, with products with higher matching strength ranked first. The system assigns a push index to each care product, and the push index reflects the priority of the care product for all users. The role of the weighting coefficient: The user's weighting coefficient plays a key role in the calculation of the push index. This coefficient indicates the importance of the user's demand for a certain care product. The weighting coefficient can be adjusted according to the user's specific needs or historical behavior, so that products that are more demanding for specific users have a higher push priority.

[0039] Specifically, the specific process of prioritizing the care product recommendation list is as follows: based on the care product push index, the care products are sorted in descending order from large to small; for care products with the same push index, secondary sorting is performed based on user behavior feedback data.

[0040] In this implementation plan, preliminary sorting: sorting is based on the push index of each care product. The push index reflects the matching strength between the care product and the user's needs. Care products with a higher push index are considered to be more relevant to the user's needs and are therefore recommended first. Therefore, the system will sort the care products in descending order according to the push index from large to small, ensuring that the products that best meet the user's needs are ranked first. Secondary sorting: For care products with the same push index, the system performs secondary sorting by analyzing the user's behavioral feedback data. Behavioral feedback data includes information such as the user's interaction with the care product, purchase history, and click-through rate. These data can help the system further determine which products are more attractive or in demand for the user, and thus adjust the order of recommendations.

[0041] Specifically, the specific process of monitoring user behavior feedback data and regulating the push order and frequency of care products is as follows: based on the user's behavior feedback data on the pushed care products, including click volume, purchase rate, and stay time, calculate the behavior feedback index of each care product; based on the behavior feedback index of each care product, compare it with the set behavior feedback index threshold range. When the behavior feedback index is greater than or equal to the set feedback index threshold range, increase its push frequency; when the behavior feedback index is less than the set feedback index threshold range, reduce its push frequency and adjust the push order.

[0042] In this implementation plan, behavioral feedback data collection: The system monitors user interaction data on the recommended care products, mainly including: Click volume: The number of times a user clicks on a care product, reflecting the initial interest in the product. Purchase rate: The user's actual purchase behavior of the recommended product, which directly reflects the product's attractiveness and practicality. Dwell time: The length of time a user stays when browsing a product, indicating the user's attention and depth of interest in the product. The formula for calculating the behavioral feedback index for each care product is as follows: in: : Behavioral feedback index of the ath care product. :No. The number of clicks on the ath care product by a user. :No. The purchase rate of the ath care product by users. :No. The length of time a user stays on the ath care product. :No. The number of times a user collects the ath care product. : Indicates the timestamp when product a is pushed. : Represents the weighting coefficients for click volume, purchase rate, dwell time, and number of favorites. Each user's weight factor can be adjusted based on the priority and importance of their behavior. m: Represents the total number of users; the behavior data of all users affects the calculation of the push index. : represents the time weighted attenuation factor, The time decay coefficient reflects the impact of time on the push index. As time passes, the effectiveness of push may decrease, so a decay function is used.

[0043] See also Figure 2 The nursing product push system based on address features includes the following modules: address feature extraction module, cluster matching module, priority evaluation module, sorting module, and behavior feedback control module; the address feature extraction module is used to obtain the user's real-time address data through the geographic information system, and extract the user's address area features in combination with the regional division algorithm; the cluster matching module is used to perform deep correlation matching between the user's address area features and the nursing product geographic area demand data through the clustering algorithm based on the user's address area features, screen nursing products, and generate a nursing product recommendation list; the priority evaluation module is used to evaluate the nursing product push priority and obtain the nursing product push index based on the matching strength between the user's address area features and the nursing product geographic area demand data; the sorting module is used to prioritize the nursing product recommendation list based on the nursing product push index; the behavior feedback control module is used to monitor user behavior feedback data and regulate the push order and frequency of nursing products.

[0044] In this implementation plan, the address feature extraction module: obtains the user's real-time address data through the geographic information system, and extracts the user's geographic area characteristics to provide basic data for subsequent matching. Cluster matching module: uses a clustering algorithm to match the user's address area characteristics with the geographical needs of care products, and screens out care products that meet the user's needs. Priority evaluation module: evaluates and calculates the push priority of each care product based on the matching degree between the user's geographic area characteristics and the care product needs. Sorting module: Sorts the care products according to their push priority to ensure that the products that best meet the needs are displayed first. Behavioral feedback control module: monitors the user's behavioral feedback data, adjusts the push order and frequency of care products, and optimizes the push effect.

[0045] In summary, this application has at least the following effects:

[0046] This address-based care product push method and system combines user location and regional needs to provide care product recommendations that better meet users' actual needs, improving push accuracy. Dynamically adjust the order and frequency of push notifications based on user behavioral feedback to avoid excessive push notifications, thereby increasing user satisfaction and the actual value of the products. Leveraging deep matching of geographic information and regional demand data, personalized care product recommendations are provided to meet diverse care needs. Real-time monitoring and push strategy adjustments ensure that care product push notifications are timely and consistent with users' timeframes. Accurate matching and efficient sorting reduce unnecessary push notifications, optimize resource allocation, and improve overall system efficiency.

[0047] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0048] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0049] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0050] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0051] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0052] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for pushing care products based on address features, characterized in that: The following steps are involved: S1. Obtain user real-time address data through a geographic information system and extract user address area features using a regional division algorithm; S2. Based on the user's address area characteristics, a clustering algorithm is used to deeply correlate and match the user's address area characteristics with the geographical area demand data for nursing products, screen nursing products, and generate a recommended list of nursing products; S3. Evaluate the priority of nursing product push based on the strength of the match between the user's address area characteristics and the geographical area demand data for nursing products, and obtain the nursing product push index; S4. Prioritize the recommended list of nursing products based on the nursing product push index; S5. Monitor user behavior feedback data and adjust the order and frequency of push notifications for care products; The specific process for evaluating the priority of nursing product push based on the matching strength between the user's address area characteristics and the geographical demand data of nursing products is as follows: Set the user address area characteristics to , the geographical demand characteristics of care products are ,in and Represents users Address area characteristics and care products The demand feature vector of Calculate the matching strength between each care product and the user ,The matching strength formula is as follows: ;in, Indicates computing users and care products Cosine similarity function on the geographical area demand dimension; The specific process of obtaining the nursing product push index is as follows: Calculate the matching strength between each care product and all users, select the best matching care product for each user, sort all care products according to the matching strength, and calculate the care product push index for each care product , where the formula for the nursing product push index is: ;in, For users The weighted coefficient, r, represents the total number of users and the relative importance of the user's demand for care products. For users and care products matching strength.

2. The method for pushing care products based on address features according to claim 1, characterized in that: The specific process of extracting user address area features through the area division algorithm is as follows: Extract the user's geographic coordinates from the geographic information system, perform spatial clustering on the user's geographic coordinates using a regional division algorithm, and divide the user's geographic location into corresponding geographic area units; Based on the division results, the socioeconomic characteristics and medical resource distribution characteristics of each geographical area are extracted to construct a regional feature vector; The user's geographical area is matched according to the regional feature vector to generate the user's address regional feature.

3. The method for pushing care products based on address features according to claim 2, characterized in that: The specific process of deep correlation matching between user address area characteristics and geographic area demand data of nursing products through clustering algorithms is as follows: Based on the user's address area characteristics and the geographical area demand data of nursing products, the user demand feature matrix and the product demand feature matrix are constructed; The user address area characteristics and care product demand characteristics are jointly clustered through clustering algorithms, and users and care products are associated and matched in the geographical area demand dimension. The similarity between user geographical characteristics and care product demand data is analyzed, and users and care products with similar demand patterns are assigned to the same cluster.

4. The method for pushing care products based on address features according to claim 3, characterized in that: The specific process of screening care products and generating a recommended list of care products is as follows: Based on the joint clustering results, determine the address area characteristics of each user and the corresponding type of care product demand; According to the cluster group of each user, we screen out the care products that meet the needs of that group and evaluate the matching degree of each care product; Prioritize the screened care products and generate a recommended list of care products.

5. The method for pushing care products based on address features according to claim 4, characterized in that: The specific process for prioritizing the recommended list of care products is as follows: Based on the nursing product push index, the nursing products are sorted in descending order from largest to smallest; For care products with the same push index, secondary sorting is performed based on user behavior feedback data.

6. The method for pushing care products based on address features according to claim 5, characterized in that: The specific process of monitoring user behavior feedback data and adjusting the order and frequency of care product push notifications is as follows: Calculate the behavioral feedback index for each care product based on user behavioral feedback data on the pushed care products, including click volume, purchase rate, and stay time; Based on the behavioral feedback index of each care product, it is compared with the set behavioral feedback index threshold range. When the behavioral feedback index is greater than or equal to the set feedback index threshold range, its push frequency is increased. When the behavioral feedback index is less than the set feedback index threshold range, its push frequency is reduced and the push order is adjusted.

7. A system for pushing care products based on address features, applying the method for pushing care products based on address features according to any one of claims 1 to 6, characterized in that: It includes the following modules: address feature extraction module, cluster matching module, priority evaluation module, sorting module, and behavior feedback control module; The address feature extraction module is used to obtain the user's real-time address data through the geographic information system, and extract the user's address area features in combination with the area division algorithm; The cluster matching module is used to perform deep correlation matching between the user's address area characteristics and the nursing product geographical area demand data based on the user's address area characteristics through a clustering algorithm, screen nursing products, and generate a nursing product recommendation list; The priority evaluation module is used to evaluate the priority of nursing product push and obtain the nursing product push index based on the matching strength between the user's address area characteristics and the nursing product geographical area demand data; The sorting module is used to prioritize the nursing product recommendation list based on the nursing product push index; The behavior feedback control module is used to monitor user behavior feedback data and control the order and frequency of pushing care products.

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