Service management method and device for insurance industry, equipment and medium

Abstract

The invention relates to the technical field of insurance services, and discloses a service management method, device and equipment for the insurance industry and a medium, and the method comprises the steps: obtaining the geographic position of a user for accessing an insurance service platform, carrying out the clustering of the geographic position, and obtaining a full-amount user cluster; according to the clustering cluster information of the total user clustering cluster, determining a total user clustering grid, the clustering cluster information comprising at least one user coordinate; obtaining optimized service store information according to the total user clustering grid; according to order information of the to-be-served user, target clustering grids to which the to-be-served user belongs are determined from the total user clustering grids, and the target clustering grids comprise common grids and real-time grids; and according to the service store information in the target clustering grid, recommending a service store for the order information to the to-be-served user, so that the user can conveniently find a proper insurance service store, thereby improving the insurance service experience of the user.

Description

Technical Field

[0001] The present invention relates to the field of insurance service technology, and in particular to a service management method, device, equipment and medium for the insurance industry. Background Art

[0002] In the full-link operation of user car services in the insurance industry, providing users with efficient and convenient services is the key factor in improving user satisfaction. In order to provide users with better services, the industry is currently beginning to adopt a grid model for user car services.

[0003] However, the current grid-based car service operation in the industry is basically resource-oriented, such as dividing the service area into smaller grids based on service stores. Although this model helps insurance companies to manage and optimize internal resources to a certain extent, for users, the resource allocation of this model may be more inclined to key stores or high-density areas, resulting in some users who are far away from key stores or in low-density areas having a poor experience when seeking insurance services. Summary of the invention

[0004] The present invention provides a service management method, device, equipment and medium for the insurance industry to solve the technical problem of how to facilitate users to find suitable insurance service stores, thereby improving the users' insurance service experience.

[0005] In a first aspect, a service management method for the insurance industry is provided, comprising: Obtaining the geographical location of the user visiting the insurance service platform, clustering the geographical location, and obtaining clusters of all users; Determining a full user clustering grid according to cluster information of the full user clustering cluster, wherein the cluster information includes at least one user coordinate; According to the full user clustering grid, obtaining optimized service store information; According to the order information of the user to be served, determining the target clustering grid to which the user to be served belongs from the full user clustering grids, wherein the target clustering grid includes a commonly used grid and a real-time grid; According to the service store information in the target clustering grid, a service store corresponding to the order information is recommended to the user to be served.

[0006] In a second aspect, a service management device for the insurance industry is provided, comprising: A user clustering module is used to obtain the geographical location of users visiting the insurance service platform, cluster the geographical location, and obtain clusters of all users; A grid division module, used to determine a full user clustering grid according to cluster information of the full user clustering clusters, wherein the cluster information includes at least one user coordinate; A store acquisition module, used to acquire optimized service store information according to the full user clustering grid; A grid determination module, configured to determine, according to order information of the user to be served, a target clustering grid to which the user to be served belongs from the full user clustering grids, wherein the target clustering grid includes a commonly used grid and a real-time grid; A store recommendation module is used to recommend a service store corresponding to the order information to the user to be served according to the service store information of the target clustering grid.

[0007] In a third aspect, a computer device is provided, comprising 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 service management method for the insurance industry when executing the computer program.

[0008] 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 service management method for the insurance industry are implemented.

[0009] In the scheme implemented by the above-mentioned service management method, device, equipment and medium for the insurance industry, users can be clustered by obtaining the geographical location of the user accessing the insurance service platform terminal, and the full user clustering grid can be divided within the region. According to the full user clustering grid, the user density in different grids can be intuitively understood, and the service store can be optimized according to the user density. It is user-oriented, convenient for users to find insurance service stores, and more humane; when the user needs insurance service, the user's commonly used grid and real-time grid can be determined according to the order information of the user to be served, and the service store for the commonly used grid and the service store for the real-time grid can be provided to the user to be served, thereby providing the user to be served with multiple choices, so that the user can choose the appropriate service store according to their own needs, which greatly improves the user's insurance service experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0011] Figure 1This is a schematic diagram of an application environment of a service management method for the insurance industry provided by the first embodiment of the present invention; Figure 2 This is a flow chart of a service management method for the insurance industry provided in Embodiment 2 of the present invention; Figure 3 This is a flow chart of a service management method for the insurance industry provided by Embodiment 3 of the present invention; Figure 4 This is a flow chart of a service management method for the insurance industry provided by Embodiment 4 of the present invention; Figure 5 This is a flow chart of a service management method for the insurance industry provided by Embodiment 5 of the present invention; Figure 6 This is a flow chart of a service management method for the insurance industry provided by Embodiment 6 of the present invention; Figure 7 This is a flow chart of a service management method for the insurance industry provided by Embodiment 7 of the present invention; Figure 8 This is a flow chart of a service management method for the insurance industry provided by Embodiment 8 of the present invention; Fig. 9 It is a structural diagram of a service management device for the insurance industry provided by an embodiment of the present invention.

[0012] Fig.10 is a structural schematic diagram of a computer device provided by an embodiment of the present invention; Fig.11 It is another structural schematic diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0013] 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 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.

[0014] A service management method for the insurance industry provided in the first embodiment of the present invention can be applied in the following aspects: Figure 1In an application environment, the client communicates with the server through a network. The server can obtain the geographical location of the user using the insurance service platform through the client, and after receiving the geographical location, the server executes the service management method of the present invention. The client can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and portable wearable devices. The server can be implemented with an independent server or a server cluster composed of multiple servers. In this embodiment, the insurance service platform is taken as an example for explanation.

[0015] In the present invention, a service management method for the insurance industry is provided. The insurance service platform clusters users by obtaining the geographical location of the terminal through which the user accesses the insurance service platform, and divides the full user clustering grids within the region. Based on the full user clustering grids, the user density in different grids can be intuitively understood, and the service stores can be optimized based on the user density. This is user-oriented, convenient for users to seek insurance service stores, and more humane. When a user needs insurance services, the user's commonly used grid and real-time grid can be determined based on the order information of the user to be served, and service stores for the commonly used grid and service stores for the real-time grid can be provided to the user to be served, thereby providing the user to be served with multiple choices so that the user can choose the appropriate service store according to his or her own needs, greatly improving the user's insurance service experience.

[0016] like Figure 2 As shown, Figure 2 A flow chart of a service management method for the insurance industry provided in Embodiment 2 of the present invention includes: Step S201: Obtain the geographical location of the user visiting the insurance service platform, cluster the geographical location, and obtain clusters of all users.

[0017] The actual application scenario of the service management method for the insurance industry provided by the present invention can be embodied in the car insurance service industry in a city-level area or a county-level area. In this area, when a user uses or purchases insurance services of a car owner insurance service platform through a terminal (mobile phone or other terminal), the insurance service platform obtains the user's geographical location. In one embodiment, the geographical location can be LBS positioning. The insurance service platform clusters the geographical locations according to the geographical locations of car owners in the area to obtain a cluster of all users.

[0018] Step S202: determining a full user clustering grid according to cluster information of the full user clusters, where the cluster information includes at least one user coordinate.

[0019] The insurance service platform obtains cluster information of all user clusters, including the coordinates of all users in the cluster. The shape and range of the grid of all user clusters, i.e., the cluster outline and cluster area, are determined based on the coordinates of all users in the cluster. Thus, the region is divided into different grids of all user clusters based on users, which makes it easier to optimize service stores according to the clustering of users, so as to improve the users' insurance service experience.

[0020] Step S203: Obtain optimized service store information based on the full user clustering grid.

[0021] After determining the full user clustering grid, based on the full user clustering grid, we can intuitively understand the user clustering characteristics of different full user clustering grids, optimize service stores based on different user clustering characteristics, and provide optimization suggestions for suppliers or outlets in different grids, so that suppliers or service outlets can optimize service stores according to the optimization suggestions.

[0022] For example, for grids with a relatively high concentration of users, insurance companies can expand stores based on the user data and order data of the grid; for grids with fewer users, the number of service stores in the grid can be appropriately reduced to focus on improving service quality.

[0023] In one embodiment, the service store includes at least one of a customer service center store, a maintenance cooperation store, a physical examination center store, an emergency rescue store, and a claim service center store. The service store information includes the store contribution value, service quality, and deep cooperation potential of the service store.

[0024] Step S204: According to the order information of the user to be served, a target clustering grid to which the user to be served belongs is determined from all user clustering grids, and the target clustering grid includes a commonly used grid and a real-time grid.

[0025] After the user to be served places an order on the terminal, the insurance service platform obtains the real-time location and common location of the user to be served based on the order information of the user to be served, and determines the real-time grid to which the user to be served belongs based on the real-time location, and determines the common grid to which the user to be served belongs based on the common location.

[0026] Step S205: recommending service stores corresponding to the order information to the user to be served according to the service store information in the target clustering grid.

[0027] Furthermore, after obtaining the target clustering grid of the user to be served, the insurance service platform obtains the service store information within the target clustering grid, and selects the best service store from the user's usual grid and real-time grid according to the service store information, and recommends it to the user to be served, so that the user to be served can choose a service store that is more suitable for him according to his own needs.

[0028] The embodiment of the present invention obtains the geographical location of the user visiting the insurance service platform terminal, performs user clustering, divides the full user clustering grid within the region, and can intuitively understand the user density in different grids based on the full user clustering grid, optimize the service store based on the user density, and is user-oriented and more humane; when the user needs insurance service, the user's commonly used grid and real-time grid can be determined based on the order information of the user to be served, and the service store for the commonly used grid and the service store for the real-time grid can be provided to the user to be served, thereby providing the user to be served with multiple choices, so that the user can choose the appropriate service store according to his own needs, which greatly improves the user's insurance service experience.

[0029] like Figure 3 FIG. 2 is a flow chart of a service management method for the insurance industry provided by Embodiment 3 of the present invention. In step S201, the geographical locations are clustered to obtain a cluster of all users, including: Step S301: Acquire the longitude and latitude information of the geographic location as user coordinates.

[0030] The latitude and longitude information of the user's geographical location when accessing the insurance service platform is obtained. Since the latitude and longitude information is two sets of data, the latitude and longitude information can be directly used as the user coordinates of the user, and all user coordinates are used as the data set of the clustering algorithm. For example, one of the user coordinates is (116.424722, 39.904200).

[0031] Step S302: Perform a first clustering on the user coordinates according to the K-means clustering algorithm to obtain an initial cluster.

[0032] In one embodiment, a K-means clustering algorithm is selected to perform a first clustering on all user coordinates to obtain an initial clustering cluster.

[0033] First, the user coordinates in the data set are preprocessed and converted into a format that can be input by the K-means clustering algorithm. For example, the user's latitude and longitude coordinates are converted into a two-dimensional array and normalized.

[0034] Specifically, in this embodiment, the MiniBatchKMeans clustering algorithm in the K-means clustering algorithm is used to perform the first clustering on the user coordinates.

[0035] It is understandable that the MiniBatchKMeans clustering algorithm has the following advantages: 1. Can handle large-scale data sets: The MiniBatchKMeans algorithm significantly reduces the amount of computation by using only a small batch of the data set to update the cluster center in each iteration, making it suitable for processing large-scale data sets.

[0036] 2. Faster calculation speed: Since each iteration only processes a small part of the data, the MiniBatchKMeans algorithm usually has a faster calculation speed than the standard KMeans algorithm.

[0037] 3. Better clustering effect: Despite the use of small batches of data, the MiniBatchKMeans algorithm still attempts to optimize the objective function, so its clustering effect is better.

[0038] However, the MiniBatchKMeans clustering algorithm also has the following disadvantages: 1. Reduced accuracy: Since only part of the data is used to update the cluster centers in each iteration, the clustering accuracy of the MiniBatchKMeans algorithm may be slightly lower than that of the standard KMeans algorithm.

[0039] 2. Convergence speed may be affected: Although the MiniBatchKMeans algorithm generally has a faster calculation speed, the convergence speed may be affected to a certain extent because small batches of data are used in its iteration process.

[0040] 3. The ability to process noise points is relatively limited: Noise points may interfere with the selection of cluster centers and the formation of clustering results, so the MiniBatchKMeans algorithm is more sensitive to noise points.

[0041] The MiniBatchKMeans clustering algorithm is used to cluster the user coordinates for the first time, and the hyperparameters of the MiniBatchKMeans clustering algorithm are appropriately adjusted according to the clustering situation to obtain the optimal initial clustering cluster. For example, the hyperparameters of minBatchKMeans such as n_clusters, batch_size, and init can solve the problems of large number of users, slow performance, and cluster area control.

[0042] However, due to the shortcomings of the MiniBatchKMeans clustering algorithm, the initial clustering clusters cannot fully achieve the desired effect of the present invention, so other clustering algorithms are needed to optimize the initial clustering clusters.

[0043] Step S303: Perform a second clustering on the initial clusters according to the density clustering algorithm to obtain clusters of all users.

[0044] In one embodiment, the present invention uses a density clustering algorithm to perform a second clustering on the initial clusters, thereby obtaining a full user cluster. Specifically, the density clustering algorithm used is a DBscan clustering algorithm.

[0045] It is understandable that the DBscan clustering algorithm has the following advantages: 1. No need to preset the number of clusters: The DBscan algorithm does not need to know the number of clusters to be formed in advance, which is an advantage over the MiniBatchKMeans clustering algorithm.

[0046] 2. Discover clusters of arbitrary shapes: The DBscan clustering algorithm can discover clusters of arbitrary shapes, including non-convex clusters.

[0047] 3. Processing noise points: The DBscan algorithm has good robustness to noise points and can identify and process noise points.

[0048] Similarly, the DBscan clustering algorithm has the following disadvantages: 1. Difficulty in parameter selection: The performance of the DBscan algorithm is highly dependent on the selection of two hyperparameters (Eps and MinPts), which usually need to be selected based on experience and may vary on different datasets.

[0049] 2. High memory requirement: When processing large data sets, the memory requirement of the DBscan algorithm is high because it needs to store the neighborhood information of all points.

[0050] 3. Not suitable for datasets with uneven density: When the density of the dataset is uneven, the performance of the DBscan algorithm may deteriorate because it is difficult to select a suitable global parameter to adapt to different density areas.

[0051] It can be seen that for the initial clustering clusters, using the DBscan clustering algorithm for the second clustering can combine the respective advantages of the MiniBatchKMeans clustering algorithm and the DBscan clustering algorithm, thereby improving the quality and accuracy of clustering. For example, the MiniBatchKMeans algorithm is suitable for processing large-scale data sets and has a high computing speed, while the DBscan algorithm can find clusters of any shape and process noise points. Therefore, the combination of these two clustering algorithms can find clusters of any shape while processing large-scale data sets. For another example, the MiniBatchKMeans algorithm is first used to perform preliminary clustering of user coordinates to quickly identify the main clustering centers, and then the DBscan algorithm is used to further optimize and adjust the initial clustering clusters to find more refined clusters and better process noise points.

[0052] In one embodiment, the insurance service platform performs secondary clustering on user coordinates, which can improve the clustering effect of user coordinates, thereby improving the accuracy of clustering grid division of all users.

[0053] Furthermore, in order to make the grid division of clustering of all users more accurate, factors such as preset grid area, number of users, number of historical orders, etc. can be added during clustering to obtain a clustering of all users that is more in line with regional conditions.

[0054] like Figure 4 FIG. 2 is a flow chart of a service management method for the insurance industry provided by Embodiment 4 of the present invention. In step S202, cluster information of all user clusters is obtained, and all user cluster grids are divided according to the cluster information, including: Step S401: obtaining standard coordinates of all user coordinates in all user clusters after standardization processing.

[0055] First, the insurance service platform standardizes all user coordinates in all user clusters, thereby converting all user coordinates in all user clusters into WGS84 standard coordinates so as to perform grid division according to WGS84 standard coordinates.

[0056] Specifically, in this embodiment, geopandas and CRS algorithm are used to convert user coordinates into WGS84 standard coordinates. Other algorithms may also be used in other embodiments, which are not limited here.

[0057] Step S402: According to the standard coordinates, obtain the central standard coordinates, cluster outline and cluster area of ​​any full user cluster.

[0058] In one embodiment, the cluster information also includes the central standard coordinates, cluster outline and cluster area obtained according to the standard coordinates. According to all the standard coordinates in the full user cluster clusters, the central standard coordinates, cluster outline and cluster area of ​​any full user cluster cluster are obtained to divide the full user cluster grid.

[0059] Specifically, in this embodiment, the ConvexHull algorithm can be used to calculate the cluster outline and cluster area of ​​the full user cluster according to the standard coordinates to form a convex full user cluster grid that can conduct business and is easy to operate. Other algorithms can also be used in other embodiments, which are not limited here.

[0060] Step S403: Determine the clustering grid of all users based on the standard center coordinates, cluster outlines and cluster areas.

[0061] Specifically, the insurance service platform can divide all users into clustering grids based on the standard coordinates of the cluster center combined with the cluster outline and cluster area calculated above.

[0062] In one embodiment, dividing the full user clustering grid according to the clustering information of the full user clustering clusters can make the division of the full user clustering grid more accurate, facilitate service store optimization according to the full user clustering grid, and thus facilitate providing insurance services to users.

[0063] like Figure 5 FIG. 2 is a flow chart of a service management method for the insurance industry provided by Embodiment 5 of the present invention. In step S205, based on the service store information in the target clustering grid, a service store for order information is recommended to the user to be served, including: Step S501: According to the service store information in the target clustering grid, the service scores of all service stores in the target clustering grid are obtained.

[0064] From the above, it can be seen that the service store information includes the store contribution value, service quality and deep cooperation potential. When calculating the service score of the service store, the insurance service platform assigns a weight to each service store information to calculate the service score based on the weight.

[0065] For example, a weight of 50% is assigned to the store contribution value, a weight of 30% is assigned to the service quality, and a weight of 20% is assigned to the potential for deep cooperation.

[0066] Each indicator of each service store is then scored, and the total score is calculated based on the weight.

[0067] For example, the value contribution score of store A = sales / customer flow; Store A’s service quality score = average customer evaluation score; Store A’s cooperation potential score = evaluation score based on historical cooperation records and future cooperation potential.

[0068] Therefore, the comprehensive service score of Store A = (value contribution score* 50%) + (service quality score*30%) + (cooperation potential score* 20%).

[0069] Step S502: sorting the service stores according to the service scores, and obtaining an initial order list of the commonly used grid and an initial order list of the real-time grid.

[0070] The insurance service platform sorts the service stores according to the calculated service scores of all service stores in the frequently used grid of the user to be served, and obtains an initial order distribution list of the frequently used grid. In the initial order distribution list, service stores with higher service scores are ranked at the front.

[0071] Similarly, the insurance service platform sorts the service stores according to the calculated service scores of all service stores in the real-time grid to which the user to be served belongs, and obtains an initial order list of the real-time grid.

[0072] Step S503: Based on the initial order list, recommend service stores for order information in the frequently used grid and service stores for order information in the real-time grid to the user to be served.

[0073] The insurance service platform recommends the top N service stores to the users to be served based on the initial order distribution list of the frequently used grids, and recommends the top N service stores to the users to be served based on the initial order distribution list of the real-time grids for the users to choose from.

[0074] In one embodiment, by calculating the service scores of service stores in the user's frequently used grid and the real-time grid, service stores with better service quality can be provided to the user to be served. At the same time, service stores in the frequently used grid and the real-time grid are provided to the user to be served, allowing the user to choose a suitable service store according to his or her own needs, thereby improving the user's service experience.

[0075] In one embodiment, the order dispatching logic for the user to be served can also obtain customer preferences, transaction records and other content based on the user's order information, and obtain service stores related to the customer preferences and transaction records in the initial order dispatching list corresponding to the user's frequently used grid and the real-time grid, and make recommendations based on actual conditions. The service stores involved in the customer preference information can also be used as service score bonus items for the service stores, and the initial order dispatching list can be re-sorted.

[0076] like Figure 6 FIG. 2 is a flow chart of a service management method for the insurance industry provided in Embodiment 6 of the present invention. After step S204, the method further includes: Step S601: Obtain the order volume of the service store in the initial order distribution list, and determine whether the service store has reached the target order volume; Step S602: If yes, the service stores that have reached the target order volume are removed from the initial order splitting list.

[0077] For steps S601-S602, since there may be a situation where the service store orders are full in the full user clustering grid, the insurance service platform obtains the order volume of the service store in the initial order list after obtaining the initial order list, and determines whether the target order volume has been reached. At this time, if the service store reaches the target order volume, the service store is removed from the initial order list to ensure the user's insurance service experience, bring customers to other service stores, and optimize resource allocation.

[0078] like Figure 7 FIG. 2 is a flow chart of a service management method for the insurance industry provided by Embodiment 7 of the present invention. In step S201, after obtaining the geographical locations of all users using the insurance service platform, the method further includes: Step S701: Obtain the service distance between any user and the nearest service store, and mark the user whose service distance exceeds the service range of the service store as a service-uncovered user.

[0079] Step S702: obtaining geographical locations of users not covered by the service, clustering the geographical locations according to the K-means clustering algorithm, and obtaining clusters of users not covered by the service.

[0080] Step S703: Obtain a service-uncovered user clustering grid according to the cluster information of the service-uncovered user clustering cluster, and generate a store expansion suggestion based on the service-uncovered clustering grid.

[0081] For steps S701-S703, due to the differences in the areas where users are located, there is a possibility that some users are far away from the service store. In this embodiment, the insurance service platform determines whether the user has exceeded the service range of the nearest service store based on the service distance between the user and the nearest service store and the service range of the service store. For example, the service range of the service store (1.5km for car washing, 3km for maintenance / painting, 10km for emergency rescue, etc.). If exceeded, the user is marked as a user not covered by the service.

[0082] For the service-uncovered users, the K-means clustering algorithm is also used for clustering to obtain the service-uncovered user clusters. The specific clustering method has been described in detail in the above step S302 and will not be repeated here.

[0083] The insurance service platform divides the service uncovered user clustering grids according to the service uncovered user clustering clusters to indicate that there are gaps in service stores in the service uncovered user clustering grids, and generates store expansion suggestions based on the service uncovered user clustering grids.

[0084] like Figure 8FIG. 2 is a flow chart of a service management method for the insurance industry provided by Embodiment 8 of the present invention. In step S202, after determining the full user clustering grid, the following is further included: Step S801: Divide the grid levels for all user clustering grids based on order data, user data and service store information in the grids for all user clustering.

[0085] Step S802: Based on the grid level, generate grid optimization suggestions for all user clustering grids.

[0086] For steps S801-S802, the insurance service platform divides the grid levels for all user clustering grids according to the order data, user data and service store information of the grids. Experts can use grid management input allocation according to different grid levels, analyze the cooperation within the grid, and give reasonable grid optimization suggestions.

[0087] For example, based on the service target radius of the service store (1.5km for car washing, 3km for maintenance / painting, etc.), store order preferences (new stores increase customer flow, old stores increase profits, etc.), and future service demand forecasts, the optimal network layout for each clustering grid is calculated, and store expansion tasks are generated based on the grid optimization suggestions. In the form of task flows, suppliers are encouraged to introduce new stores and expand services at old stores.

[0088] In one embodiment, in order to facilitate further management of insurance services, the following contents are also provided in the embodiment of the present invention: (1) Value / order matching progress management: Goal: Convert the customer acquisition value, premium income, free resources, etc. of the service stores into specific orders, and ensure the achievement of target progress through real-time management, so as to realize the conversion of offline value into online output value.

[0089] Implementation method: 1. Establish a value assessment model to quantify the value indicators of service stores.

[0090] 2. Develop an order management system to achieve a one-to-one correspondence between value and orders.

[0091] 3. Monitor order progress in real time, adjust strategies through data analysis, and ensure the achievement of output value targets.

[0092] 4. Generate value conversion reports regularly to provide decision-making basis for management.

[0093] (2) Grid operation status monitoring: Objective: To understand the operation status within the grid in real time, including order volume, store status, customer evaluation, etc., so as to adjust the operation strategy in time.

[0094] Implementation method: 1. Develop a real-time monitoring platform to display operational data within the grid.

[0095] 2. Analyze customer reviews, identify strengths and weaknesses, and propose improvement measures.

[0096] 3. Develop optimization strategies based on grid operation data to improve operational efficiency.

[0097] (3) One-click task push function: Objective: To achieve efficient communication between suppliers, outlets and ABC ends (End A: headquarters, End B: suppliers, End C: consumers) by automatically generating and pushing tasks through the system.

[0098] Implementation steps: 1. Design task templates, including store expansion, store inspection, coupon configuration, customer acquisition and other task types.

[0099] 2. Develop a task push system to ensure that tasks can be delivered to relevant personnel quickly and accurately.

[0100] 3. Establish a task feedback mechanism to ensure timely feedback on task execution.

[0101] 4. Regularly evaluate the effectiveness of task execution and optimize task push strategies.

[0102] Through the above measures, intelligent management of service store operations can be achieved, management efficiency can be improved, and business growth can be promoted.

[0103] The embodiment of the present invention obtains the geographical location of the user visiting the insurance service platform terminal, clusters the users, divides the full user clustering grids in the region, and can intuitively understand the user density in different grids based on the full user clustering grids, optimize the service stores based on the user density, and is user-oriented, which is convenient for users to seek insurance service stores and more humane. When the user needs insurance service, the user's commonly used grid and real-time grid can be determined based on the order information of the user to be served, and the service stores for the commonly used grid and the service stores for the real-time grid can be provided to the user to be served, thereby providing the user to be served with multiple choices, so that the user can choose the appropriate service store according to his own needs, which greatly improves the user's insurance service experience.

[0104] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0105] like Fig. 9As shown, it is a schematic diagram of the structure of a service management device for the insurance industry provided by an embodiment of the present invention, and the service management device includes a user clustering module 91, a grid division module 92, a store acquisition module 93, a grid determination module 94 and a store recommendation module 95. The functional modules are described in detail as follows: The user clustering module 91 is used to obtain the geographical location of the user using the insurance service platform, cluster the geographical location, and obtain the clusters of all users; A grid division module 92, configured to determine a full user clustering grid according to cluster information of the full user clusters, wherein the cluster information includes at least one user coordinate; The store acquisition module 93 is used to obtain optimized service store information according to the full user clustering grid; A grid determination module 94 is used to determine the target clustering grid to which the user to be served belongs from all user clustering grids according to the order information of the user to be served, and the target clustering grid includes a common grid and a real-time grid; The store recommendation module 95 is used to recommend service stores corresponding to order information to the user to be served according to the service store information of the target clustering grid.

[0106] In one embodiment, the user clustering module 91 includes: A first acquisition unit, used to acquire the latitude and longitude information of the geographic location as the user coordinates; A first clustering unit, used to perform a first clustering on the user coordinates according to a K-means clustering algorithm to obtain an initial clustering cluster; The second clustering unit is used to perform a second clustering on the initial clusters according to a density clustering algorithm to obtain clusters of all users.

[0107] In one embodiment, the grid division module 92 includes: A second acquisition unit is used to acquire standard coordinates after standardizing the coordinates of all users in the cluster of all users; A third acquisition unit is used to acquire the central standard coordinates, cluster outline and cluster area of ​​any full user cluster according to the standard coordinates; The grid division unit is used to determine the clustering grid of all users based on the center standard coordinates, cluster outlines and cluster areas.

[0108] In one embodiment, the store recommendation module 95 includes: A score calculation unit, used to obtain the service scores of all service stores in the target clustering grid according to the service store information in the target clustering grid; A list acquisition unit is used to sort the service stores according to the service scores, and obtain an initial order list of the commonly used grid and an initial order list of the real-time grid; The store recommendation unit is used to recommend service stores for order information in the frequently used grid and service stores for order information in the real-time grid to the user to be served based on the initial order list.

[0109] In one embodiment, the store recommendation module 95 further includes: An order determination unit is used to obtain the order volume of the service store in the initial order distribution list and determine whether the service store has reached the target order volume; The store removal unit is used to remove the service stores that have reached the target order volume from the initial order distribution list based on the judgment result of the target order volume.

[0110] In one embodiment, the user clustering module 91 further includes: A distance acquisition unit, used to acquire the service distance between any user and the nearest service store, and mark the user whose service distance exceeds the service range of the service store as a user not covered by the service; A third clustering unit is used to obtain geographic locations of users not covered by the service, cluster the geographic locations according to a K-means clustering algorithm, and obtain clusters of users not covered by the service; The store expansion suggestion unit is used to obtain the service-uncovered user cluster grid according to the cluster information of the service-uncovered user cluster cluster, and generate store expansion suggestions based on the service-uncovered cluster grid.

[0111] In one embodiment, the grid determination module 94 further includes: A level division unit, used for dividing the grid levels for all user clustering grids based on order data, user data and service store information in all user clustering grids; The optimization suggestion unit is used to generate grid optimization suggestions for all user clustering grids based on the grid level.

[0112] The specific definition of the service management device for the insurance industry can be found in the definition of the service management method for the insurance industry above, which will not be repeated here. Each module in the above-mentioned service management device for the insurance industry can be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0113] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig.10As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a service management method for the insurance industry on the service side.

[0114] In one embodiment, a computer device is provided. The computer device may be a client, and its internal structure diagram may be as follows: Fig.11 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server through a network connection. When the computer program is executed by the processor, it realizes the functions or steps of the client side of a service management method for the insurance industry.

[0115] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant descriptions on the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0116] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0117] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0118] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

[0119] The non-Company software tools or components appearing in the embodiments of this application are merely examples and do not represent actual use.

Claims

1. A service management method for the insurance industry, characterized in that: include: Obtaining the geographical location of the user visiting the insurance service platform, clustering the geographical location, and obtaining clusters of all users; Determining a full user clustering grid according to cluster information of the full user clustering cluster, wherein the cluster information includes at least one user coordinate; According to the full user clustering grid, obtaining optimized service store information; According to the order information of the user to be served, determining the target clustering grid to which the user to be served belongs from the full user clustering grids, wherein the target clustering grid includes a commonly used grid and a real-time grid; According to the service store information in the target clustering grid, a service store corresponding to the order information is recommended to the user to be served.

2. The service management method for the insurance industry according to claim 1, characterized in that: The clustering of the geographical locations to obtain a cluster of all users includes: Obtaining the latitude and longitude information of the geographic location as user coordinates; Performing a first clustering on the user coordinates according to a K-means clustering algorithm to obtain an initial clustering cluster; According to the density clustering algorithm, the initial clusters are subjected to a second clustering to obtain clusters of all users.

3. The service management method for the insurance industry according to claim 1, characterized in that: The obtaining of cluster information of the full user clusters, and dividing the full user cluster grids according to the cluster information, includes: Obtaining standard coordinates of all user coordinates in the cluster of all users after standardization processing; According to the standard coordinates, obtaining the central standard coordinates, cluster outline and cluster area of ​​any of the full user clusters; A full user clustering grid is determined based on the central standard coordinates, the cluster outline and the cluster area.

4. The service management method for the insurance industry according to claim 1, characterized in that: The recommending, for the user to be served, a service store corresponding to the order information according to the service store information in the target clustering grid, includes: According to the service store information in the target clustering grid, obtaining the service scores of all service stores in the target clustering grid; The service stores are sorted according to the service scores to obtain an initial order list of the commonly used grid and an initial order list of the real-time grid; Based on the initial order list, a service store for the order information in a frequently used grid and a service store for the order information in a real-time grid are recommended to the user to be served.

5. The service management method for the insurance industry according to claim 4, characterized in that: After obtaining the initial order list, the method further includes: Obtaining the order volume of the service store in the initial order split list, and determining whether the service store has reached the target order volume; If so, the service stores that have reached the target order volume are removed from the initial order splitting list.

6. The service management method for the insurance industry according to any one of claims 1 to 5, characterized in that: After obtaining the geographical locations of all users using the insurance service platform, the method further includes: Obtain the service distance between any user and the nearest service store, and mark the user whose service distance exceeds the service range of the service store as a user not covered by the service; Obtaining geographic locations of users not covered by the service, clustering the geographic locations according to a K-means clustering algorithm, and obtaining clusters of users not covered by the service; According to the cluster information of the service-uncovered user cluster, a service-uncovered user cluster grid is obtained, and based on the service-uncovered cluster grid, a store expansion suggestion is generated.

7. The service management method for the insurance industry according to any one of claims 1 to 5, characterized in that: After determining the full user clustering grid, the method further includes: Based on the order data, user data and service store information in the full user clustering grid, dividing the full user clustering grid into grid levels; Based on the grid level, a grid optimization suggestion is generated for the full set of user clustering grids.

8. A service management device for the insurance industry, characterized in that: include: A user clustering module is used to obtain the geographical location of users visiting the insurance service platform, cluster the geographical location, and obtain clusters of all users; A grid division module, used to determine a full user clustering grid according to cluster information of the full user clustering clusters, wherein the cluster information includes at least one user coordinate; A store acquisition module, used to acquire optimized service store information according to the full user clustering grid; A grid determination module, configured to determine, according to order information of the user to be served, a target clustering grid to which the user to be served belongs from the full user clustering grids, wherein the target clustering grid includes a commonly used grid and a real-time grid; A store recommendation module is used to recommend a service store corresponding to the order information to the user to be served according to the service store information of the target clustering grid.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the service management method for the insurance industry as claimed in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the service management method for the insurance industry as claimed in any one of claims 1 to 7 are implemented.

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