Marketing information identification method and device based on vehicle positioning, equipment and medium

Abstract

The invention provides a marketing information identification method and device based on vehicle positioning, equipment and a medium, and relates to the technical field of financial science and technology. According to the method, the parking characteristics and the parking information of the vehicle can be accurately obtained by obtaining the vehicle positioning data. By analyzing the vehicle parking characteristics, the vehicle parking information is identified, so that high-frequency parking points in the use process of the vehicle can be identified, and potential collecting and distributing places can be identified. A clustering algorithm is used to analyze the vehicle parking information, and the target distribution place with concentrated vehicle rental demands can be effectively identified. By identifying the target marketing object in the preset radiation range of the target distribution place, the potential customer group can be more accurately positioned, so that the identification accuracy and the identification efficiency of the marketing information are improved, the customer obtaining cost is reduced, more accurate market positioning and customer service are provided, the pertinence and the effectiveness of the marketing activity are enhanced, and the marketing efficiency is improved. And the marketing efficiency and the conversion rate are improved.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and in particular to a marketing information identification method, device, equipment and medium based on vehicle positioning. Background Art

[0002] Because the price of vehicles is high, car buyers will purchase vehicles from financial companies in installments (including bank credit loan installments, financial leasing monthly rent payments, etc.). Taking financial leasing companies as an example, the vehicle buyer (i.e., the lessee) provides funds to purchase the vehicle in one lump sum, and the lessee needs to pay a certain amount of money as a down payment and pay rent every month.

[0003] In order to ensure the safety of leased assets, leasing companies will install GPS positioning devices on vehicles with the consent of the lessee to continuously track the location and usage of vehicle assets. Leasing companies can use positioning devices to analyze vehicle location, vehicle operation trajectory, vehicle mileage and duration, etc., to discover abnormal vehicle use behavior or overall industry risks, and take corresponding measures in advance, such as requiring the lessee to repay the loan in advance, etc. to avoid risks.

[0004] A distribution center refers to a place where a certain type of vehicle gathers, especially commercial vehicles. The transportation loading and unloading of commercial vehicles is centralized. The transportation status of commercial vehicles can be obtained through the changes in the distribution center.

[0005] At present, the sales staff of financial leasing companies mainly obtain new commercial vehicle customers through dealer referrals, thereby providing financing services to customers, and the channel is relatively single. Leasing companies need to provide more commission incentives to dealers' customers, and the cost of acquiring customers is high.

[0006] Therefore, how to improve the recognition efficiency of vehicle rental marketing information has become a technical problem that needs to be solved urgently. Summary of the invention

[0007] The present application provides a marketing information identification method, device, equipment and medium based on vehicle positioning, aiming to improve the identification efficiency of vehicle rental marketing information.

[0008] In a first aspect, the present application provides a marketing information identification method based on vehicle positioning, the marketing information identification method based on vehicle positioning comprising the following steps:

[0009] obtaining vehicle location data of at least one vehicle;

[0010] Based on the vehicle positioning data, analyzing the vehicle stop characteristics to obtain the vehicle stop information;

[0011] Based on a clustering algorithm, cluster analysis is performed on the vehicle stop information to determine at least one target distribution center;

[0012] Based on the target distribution center, at least one target marketing object within a preset radiation range is identified, and marketing information of the target marketing object is obtained.

[0013] In a second aspect, the present application further provides a marketing information identification device based on vehicle positioning, the marketing information identification device based on vehicle positioning comprising:

[0014] A positioning data acquisition module, used to acquire vehicle positioning data of at least one vehicle;

[0015] A stop information analysis module, used to analyze the vehicle stop characteristics based on the vehicle positioning data and obtain the vehicle stop information;

[0016] A distribution center determination module, used for performing cluster analysis on the vehicle stop information based on a clustering algorithm to determine at least one target distribution center;

[0017] The marketing information collection module is used to identify at least one target marketing object within a preset radiation range based on the target distribution center, and obtain the marketing information of the target marketing object.

[0018] In a third aspect, the present application also provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the marketing information identification method based on vehicle positioning as described above are implemented.

[0019] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the marketing information identification method based on vehicle positioning as described above are implemented.

[0020] The present application provides a marketing information identification method, device, computer equipment and storage medium based on vehicle positioning. The method of the present application includes obtaining vehicle positioning data of at least one vehicle; parsing vehicle stop features based on the vehicle positioning data to obtain vehicle stop information; performing cluster analysis on the vehicle stop information based on a clustering algorithm to determine at least one target distribution center; identifying at least one target marketing object within a preset radiation range based on the target distribution center to obtain marketing information of the target marketing object. In the above manner, the present application can accurately obtain the stop features and stop information of the vehicle by obtaining vehicle positioning data. By parsing the vehicle stop features and identifying the vehicle stop information, it is helpful to identify high-frequency stop points during vehicle use, thereby identifying potential distribution centers. By using a clustering algorithm to analyze the vehicle stop information, the target distribution center where vehicle rental demand is concentrated can be effectively identified. By identifying the target marketing object within the preset radiation range of the target distribution center, the potential customer group can be more accurately located, thereby improving the recognition accuracy and recognition efficiency of marketing information, reducing customer acquisition costs, and providing more accurate market positioning and customer service, enhancing the pertinence and effectiveness of marketing activities, and improving marketing efficiency and conversion rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 A flowchart of a first embodiment of a marketing information identification method based on vehicle positioning provided by the present application;

[0023] Figure 2 A schematic diagram of the marketing information push process provided in the embodiment of the present application;

[0024] Figure 3 A schematic diagram of a marketing information identification process based on vehicle GPS data provided in an embodiment of the present application;

[0025] Figure 4 It is a structural schematic diagram of a first embodiment of a marketing information identification device based on vehicle positioning provided by the present application;

[0026] Figure 5 It is a schematic block diagram of the structure of a computer device provided in an embodiment of the present application.

[0027] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0030] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0031] Please refer to Figure 1 , Figure 1 A flowchart of a first embodiment of a marketing information identification method based on vehicle positioning provided in this application.

[0032] like Figure 1 As shown, the marketing information identification method based on vehicle positioning includes steps S101 to S104.

[0033] S101, obtaining vehicle positioning data of at least one vehicle;

[0034] In one embodiment, in a financial leasing scenario, the vehicle is owned by the leasing company. To ensure the asset security of the leasing company, the leasing company may obtain the vehicle's GPS positioning information with the user's authorization.

[0035] Furthermore, vehicle information of at least one vehicle is obtained, wherein the vehicle information includes vehicle owner information, vehicle number and vehicle identification information; based on a vehicle positioning sensor, the vehicle positioning information is collected once every preset interval, wherein the positioning information includes positioning time and positioning coordinates; based on the vehicle information and the positioning information, the vehicle positioning data is generated.

[0036] In one embodiment, the vehicle information includes but is not limited to the vehicle owner information, vehicle number and vehicle identification information (VIN). Such information is usually recorded in the vehicle financing lease contract, and its accuracy and timeliness need to be ensured.

[0037] Specifically, the owner information may include the owner's name, contact information and address, while the vehicle identification information is the unique identifier of the vehicle, which is crucial for tracking and managing the vehicle.

[0038] In one embodiment, the vehicle GPS may report the latitude and longitude of the positioning point at regular intervals (eg, 15 seconds), and the positioning point may be parsed using public tools to obtain address information near the positioning point.

[0039] Exemplarily, vehicle GPS data may be stored in the form of:

[0040] Table 1 Vehicle GPS data record table

[0041]

[0042] In one embodiment, by combining the vehicle information and the collected positioning information, the rental company can generate detailed vehicle positioning data, which includes not only the real-time location of the vehicle, but also the vehicle's driving track, speed, driving time, etc.

[0043] S102, analyzing the vehicle stop characteristics based on the vehicle positioning data to obtain vehicle stop information;

[0044] In one embodiment, by analyzing the speed change and location information of the vehicle, it is possible to detect when the vehicle changes from a moving state to a stopped state. When a vehicle stop event is detected, relevant stop features, such as the concentrated area of ​​the stop coordinates, the stop frequency, etc., can be extracted to generate vehicle stop information.

[0045] In one embodiment, the vehicle stop information includes stop coordinates, stop period, stop frequency and stop duration.

[0046] Specifically, the stop coordinates refer to the geographic coordinates of the vehicle when it stops. The stop period refers to the start and end time of the vehicle's stop. The stop frequency refers to the number of times the vehicle stops at the same location within a specific event period. The stop duration refers to the duration of each stop.

[0047] Exemplarily, the vehicle stop information may be stored using fields such as those shown in Table 2:

[0048] Table 2 Vehicle stop information record table

[0049]

[0050] In one embodiment, the vehicle stop information can be monitored over a long period of time to facilitate more accurate analysis of the vehicle's driving patterns and stop information. Specifically, as shown in Table 2, each day can be used as a recording period to record the vehicle's stop information for each day, where the customer ID and the vehicle frame number are the unique identifier of the vehicle owner or user and the unique identification number of the vehicle, respectively.

[0051] Furthermore, based on the vehicle positioning data, at least one stop point of the vehicle is identified to obtain the vehicle stop features corresponding to each stop point, wherein the vehicle stop features include the coordinate repetition rate of the stop points and the stop point similarity features; based on preset information extraction rules, the vehicle stop features are screened and classified to obtain the vehicle stop information.

[0052] In one embodiment, the trajectory point sequence is analyzed through the vehicle positioning data to determine whether the vehicle stays at a certain location for more than a preset time threshold, thereby identifying the vehicle's stop point within a specific time.

[0053] In one embodiment, the vehicle stop information may only record event information when the vehicle stops for a longer time. For example, when the vehicle stops temporarily for a few minutes, it does not need to be recorded to avoid generating too much useless redundant information.

[0054] Exemplarily, a time threshold can be set to determine whether a vehicle stop event needs to be identified and recorded. For example, if the vehicle's stop time from the start time to the end time exceeds fifteen minutes, the vehicle stop event can be considered a valid stop event, and the vehicle stop information for this period of time can be recorded; if the vehicle's stop duration is less than fifteen minutes, it can be considered that the owner or user is only temporarily staying at the stop location, and the vehicle stop event is an invalid stop event and no record is required.

[0055] In one embodiment, for each identified stop point, multiple features may be extracted, including the coordinate repetition rate of the stop point and the stop point similarity feature. The coordinate repetition rate of the stop point refers to the frequency of the vehicle stopping at a specific location, and the stop point similarity feature may involve similarity metrics such as the distance and time interval between the stop points.

[0056] In one embodiment, the preset information extraction rules may include time threshold, speed threshold, coordinate change threshold, etc., which are used to judge and filter out effective stay events. These rules are helpful to extract valuable stay information from a large amount of positioning data.

[0057] Specifically, based on the extracted vehicle stop features and the preset information extraction rules, the stop events can be screened and classified. For example, work-related stop points (such as warehouses or customer locations frequently visited by vehicles) and non-work-related stop points (such as vehicle stops at restaurants or gas stations) can be identified.

[0058] By recording effective vehicle stop information, this embodiment can ensure that the recorded data has high information value and can be effectively used in various analysis and decision support scenarios. For example, a rental company can identify the common stop locations of vehicles to provide more targeted services or monitoring. At the same time, this also helps to reduce the cost of data storage and processing and improve the efficiency of data processing.

[0059] S103, performing cluster analysis on the vehicle stop information based on a clustering algorithm to determine at least one target distribution center;

[0060] In one embodiment, cluster analysis is performed on vehicle stop information based on a clustering algorithm to determine target distribution centers, which can help identify locations where vehicles frequently stop. These locations may have specific functions or attributes, such as logistics distribution centers, commercial activity centers, or transportation hubs.

[0061] In one embodiment, before data analysis is performed, the vehicle positioning data needs to be preprocessed, including filtering and smoothing to remove noise, and formatting timestamp and geographic coordinate data to ensure the quality and accuracy of the vehicle positioning data.

[0062] In one embodiment, a suitable clustering algorithm can be selected according to the characteristics of the vehicle stop information. For example, the ST-DBSCAN algorithm is a clustering method suitable for time series data. It adds a time threshold as a restriction condition based on the traditional DBSCAN algorithm and is more suitable for processing vehicle trajectory data.

[0063] Specifically, the selected clustering algorithm is used to cluster the vehicle stop information and identify the clusters where the vehicles stop, each cluster representing a target distribution center. The clustering results will show the gathering of vehicles at specific times and locations. Based on the clustering results, the locations where vehicles frequently stop are identified as target distribution centers. The target distribution centers may have high coordinate repetition rates and similar characteristics of the stop points, indicating that the vehicles have a clear purpose of staying at these locations. According to the preset information extraction rules, the vehicle stop characteristics obtained by cluster analysis (including the stop duration, stop frequency and other characteristics of the stop points) are screened and classified to determine which stop points are important distribution centers.

[0064] Furthermore, based on the clustering algorithm, cluster analysis is performed on the vehicle stop information to obtain at least one candidate distribution center; potential business information of each candidate distribution center within a first preset range is collected; and based on the business type corresponding to each potential business information, at least one target distribution center is screened out from each candidate distribution center.

[0065] Understandably, the amount of characteristic data of stay points in various regions and industries is very large. In order to effectively improve the accuracy of marketing and the convenience of customer managers, it is necessary to give priority to areas with concentrated stay characteristics, obvious stay characteristics, and obvious industry characteristics for feature extraction and classification.

[0066] For example, if the screening rule of the target distribution center is to select the stopover points of the dangerous goods industry in Shaanxi to form a distribution center, you can select all the stopover points in various cities in Shaanxi in the past three months, perform cluster analysis on these stopover points with a radius of 1 km, form several cluster groups, and take the center of the group as the distribution center.

[0067] In one embodiment, clustering algorithms such as K-means, DBSCAN or hierarchical clustering are used to cluster vehicle stop information. These algorithms can divide data points into several clusters based on the similarity of the stop points. For example, the DBSCAN algorithm forms clusters by identifying high-density areas, which can effectively handle clusters with noise and complex shapes. After the cluster analysis is completed, the center or representative point of each cluster can be regarded as a candidate distribution center. These locations may represent areas where vehicles frequently stop, such as loading and unloading points, warehouses or checkpoints in the dangerous goods industry.

[0068] In one embodiment, for each candidate distribution center, it is necessary to collect its potential business information within a preset range, such as business activities, facility types, service scope, etc. in the area. Based on the collected potential business information, the candidate distribution centers can be screened according to the business type. For example, if the goal is to analyze the stopover points of the hazardous goods industry, then those distribution centers that are closely related to the storage, transportation or use of hazardous goods can be screened.

[0069] S104: Based on the target distribution center, identify at least one target marketing object within a preset radiation range, and obtain marketing information of the target marketing object.

[0070] In one embodiment, the precise geographic location of the target distribution center is determined, and data of all potential marketing targets within a preset radiation range of the location is collected. This may include commercial institutions, residential areas, public places, etc. Feature extraction is performed on each identified group to determine their common characteristics. These features may include geographic location, industry type, customer group, etc. Based on the extracted features and business type, target marketing targets are screened out from candidate distribution centers. For example, if the target is the hazardous goods industry, then marketing targets related to the industry can be screened out, such as chemical warehouses, gas stations, etc. The screened target marketing targets are analyzed in depth, and their marketing information, such as customer evaluations, sales data, market trends, etc., is collected.

[0071] In one embodiment, the latitude and longitude of the target distribution center are interacted with the map company, and POIs (Point of Interest) of the company type within 1 km of the latitude and longitude are collected as target marketing objects. These target marketing objects are then connected to the National Enterprise Credit Publicity System to obtain the company's industry and contact number, and then generate the target distribution center.

[0072] Furthermore, based on the target distribution center, at least one of the candidate marketing objects within the preset radiation range is identified to obtain first identification information of each of the candidate marketing objects; based on the first identification information, the business type of each of the candidate marketing objects and the number of marketing objects corresponding to each of the business types are identified; based on each of the business types and the number of marketing objects corresponding to each of the business types, at least one of the target marketing objects is screened out from each of the candidate marketing objects; second identification information of each of the target marketing objects is collected to obtain marketing information of each of the target marketing objects.

[0073] In one embodiment, the marketing information includes the distribution center address, marketing object name, business type, marketing contact information, and business demand information of the target marketing object.

[0074] Specifically, through interaction with map service providers (such as Amap API, Baidu Map API, etc.), all POI (Point of Interest) data within the preset radiation range (such as 1 km nearby) of the target distribution center are collected. These data usually include information such as name, address, longitude and latitude coordinates, and category. For example, Amap API provides the function of obtaining POI data, which can be used to obtain POI data within a specific range by keyword, surrounding search, etc. Filter out company-type POIs from the collected POI data. These companies may include various commercial organizations, industrial enterprises, etc.

[0075] Specifically, the screened company information is connected to the National Enterprise Credit Information Publicity System to obtain detailed information such as the company's industry and contact number. Based on the first identification information (such as company name, address, etc.), analyze the company's business scope, industry classification and other information to identify the business type of each candidate marketing object and the number of marketing objects corresponding to each business type. Based on the business type and the number of marketing objects, select the target marketing objects from the candidate marketing objects, such as companies that dominate a specific industry, or companies with a large number within the preset radiation range. For the screened target marketing objects, collect more detailed second identification information, such as the company's financial status, market share, customer evaluation, etc., to obtain more comprehensive marketing information. Develop targeted marketing strategies based on the collected marketing information. This may include customized promotional activities, targeted advertising, content marketing, etc.

[0076] Exemplarily, the storage method of the marketing information field of the target distribution center is shown in Table 3:

[0077] Table 3 Marketing information statistics of target distribution centers

[0078]

[0079] In one embodiment, considering that a salesperson (such as an account manager) may not be able to find the person in charge of financing in the company through the address and phone number, such as Figure 2 As shown, the Internet of Vehicles system can be connected with the salesperson's rental system to generate a list of contact numbers of car owners and dealers who have rented cars at the distribution center, which is convenient for the salesperson's marketing conversion.

[0080] For example, the marketing information pushed to the salesperson may be as shown in Table 4 below:

[0081] Table 4 Marketing information business push table of target distribution center

[0082]

[0083] Specifically, the Internet of Vehicles system generates a list of contact numbers of car owners and dealers who have rented cars at distribution centers by integrating vehicle GPS positioning data and rental system information. This helps salespeople quickly identify potential marketing targets and conduct effective communication and marketing activities. Salespeople can develop personalized marketing strategies based on the pushed marketing information and the company's products and services. For example, for companies in the dangerous goods transportation industry, specific logistics solutions or safety training services can be provided.

[0084] In this embodiment, based on Figure 3 The distribution center marketing information identification process based on vehicle GPS shown in the figure can expand the customer base at a low cost by mining the stay data of existing customers, analyzing the stay points and distribution centers of the vehicle GPS positioning information itinerary, and obtaining the information of new customers. Through the technical solution of this application, the account manager of the leasing company can obtain the location of the transportation type company near the stay point, etc., and conduct visits or marketing to achieve conversion. As a supplementary way for daily dealers to acquire customers, it gets rid of the business model that relies entirely on dealer recommendations, saves commission incentives for dealers, and achieves low-cost customer acquisition.

[0085] This embodiment provides a marketing information identification method based on vehicle positioning. By acquiring vehicle positioning data, the method of this application can accurately obtain the vehicle's stop characteristics and stop information. By analyzing the vehicle's stop characteristics and identifying the vehicle's stop information, it is helpful to identify high-frequency stop points during vehicle use, thereby identifying potential distribution centers. By analyzing the vehicle's stop information using a clustering algorithm, the target distribution centers where vehicle rental demand is concentrated can be effectively identified. By identifying the target marketing objects within the preset radiation range of the target distribution center, potential customer groups can be more accurately located, thereby improving the recognition accuracy and efficiency of marketing information, reducing customer acquisition costs, and providing more accurate market positioning and customer service, enhancing the pertinence and effectiveness of marketing activities, and improving marketing efficiency and conversion rates.

[0086] See also Figure 4 , Figure 4 It is a structural schematic diagram of a first embodiment of a marketing information identification device based on vehicle positioning provided in the present application, and the marketing information identification device based on vehicle positioning is used to execute the aforementioned marketing information identification method based on vehicle positioning.

[0087] like Figure 4 As shown, the marketing information identification device 200 based on vehicle positioning includes: a positioning data acquisition module 201, a stay information analysis module 202, a distribution center determination module 203 and a marketing information collection module 204.

[0088] A positioning data acquisition module 201 is used to acquire vehicle positioning data of at least one vehicle;

[0089] A stop information analysis module 202 is used to analyze the vehicle stop characteristics based on the vehicle positioning data to obtain the vehicle stop information;

[0090] A distribution center determination module 203 is used to perform cluster analysis on the vehicle stop information based on a clustering algorithm to determine at least one target distribution center;

[0091] The marketing information collection module 204 is used to identify at least one target marketing object within a preset radiation range based on the target distribution center, and obtain the marketing information of the target marketing object.

[0092] In one embodiment, the positioning data acquisition module 201 includes:

[0093] A vehicle information acquisition unit, used to acquire vehicle information of at least one vehicle, wherein the vehicle information includes vehicle owner information, vehicle number and vehicle identification information;

[0094] A positioning information acquisition unit, used to collect the positioning information of the vehicle once at a preset interval based on the vehicle positioning sensor, wherein the positioning information includes the positioning time and the positioning coordinates;

[0095] A positioning data generating unit is used to generate the vehicle positioning data based on the vehicle information and the positioning information.

[0096] In one embodiment, the vehicle stop information includes stop coordinates, stop period, stop frequency and stop duration.

[0097] In one embodiment, the distribution center determination module 203 includes:

[0098] A candidate distribution center determining unit, configured to perform cluster analysis on each of the vehicle stop information based on the clustering algorithm to obtain at least one candidate distribution center;

[0099] A potential business information collection unit, used to collect potential business information of each candidate distribution center within a first preset range;

[0100] The target distribution center screening unit is used to screen at least one target distribution center from each of the candidate distribution centers based on the business type corresponding to each of the potential business information.

[0101] In one embodiment, the stay information analysis module 202 includes:

[0102] A stay point identification unit, used to identify at least one stay point of the vehicle based on the vehicle positioning data, and obtain the vehicle stay features corresponding to each stay point, wherein the vehicle stay features include the coordinate repetition rate of the stay point and the stay point similarity features;

[0103] The vehicle stop information obtaining unit is used to screen and classify the vehicle stop features based on a preset information extraction rule to obtain the vehicle stop information.

[0104] In one embodiment, the marketing information collection module 204 includes:

[0105] A candidate marketing object identification unit, configured to identify at least one candidate marketing object within a preset radiation range based on the target distribution center, and obtain first identification information of each candidate marketing object;

[0106] A business type identification unit, configured to identify the business type of each of the candidate marketing objects and the number of marketing objects corresponding to each of the business types based on the first identification information;

[0107] A target marketing object screening unit, configured to screen out at least one target marketing object from each of the candidate marketing objects based on each of the business types and the number of marketing objects corresponding to each of the business types;

[0108] The marketing information collection unit is used to collect the second identification information of each target marketing object and obtain the marketing information of each target marketing object.

[0109] In one embodiment, the marketing information includes the distribution center address, marketing object name, business type, marketing contact information, and business demand information of the target marketing object.

[0110] It should be noted that technical personnel in the relevant field can clearly understand that, for the convenience and conciseness of description, the specific working process of the above-described device and each module can refer to the corresponding process in the aforementioned marketing information identification method based on vehicle positioning, and will not be repeated here.

[0111] The apparatus provided in the above embodiment may be implemented in the form of a computer program. The computer program may be Figure 5 Runs on the computer device shown.

[0112] See also Figure 5 , Figure 5 1 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device may be a server.

[0113] See also Figure 5 The computer device includes a processor, a memory and a network interface connected through a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0114] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any one of the marketing information identification methods based on vehicle positioning.

[0115] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0116] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any marketing information identification method based on vehicle positioning.

[0117] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 5The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0118] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0119] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps:

[0120] obtaining vehicle location data of at least one vehicle;

[0121] Based on the vehicle positioning data, analyzing the vehicle stop characteristics to obtain the vehicle stop information;

[0122] Based on a clustering algorithm, cluster analysis is performed on the vehicle stop information to determine at least one target distribution center;

[0123] Based on the target distribution center, at least one target marketing object within a preset radiation range is identified, and marketing information of the target marketing object is obtained.

[0124] In one embodiment, when the processor implements the acquisition of vehicle positioning data of at least one vehicle, it is used to implement:

[0125] Acquire vehicle information of at least one vehicle, wherein the vehicle information includes vehicle owner information, vehicle number, and vehicle identification information;

[0126] Based on the vehicle positioning sensor, the vehicle positioning information is collected once at a preset interval, and the positioning information includes the positioning time and positioning coordinates;

[0127] The vehicle positioning data is generated based on the vehicle information and the positioning information.

[0128] In one embodiment, the vehicle stop information includes stop coordinates, stop period, stop frequency and stop duration.

[0129] In one embodiment, when the processor implements the clustering algorithm-based clustering analysis of the vehicle stop information to determine at least one target distribution center, it is used to implement:

[0130] Based on the clustering algorithm, cluster analysis is performed on each of the vehicle stop information to obtain at least one candidate distribution center;

[0131] Collecting potential business information of each candidate distribution center within a first preset range;

[0132] Based on the business type corresponding to each of the potential business information, at least one of the target distribution centers is screened out from each of the candidate distribution centers.

[0133] In one embodiment, when the processor implements the step of parsing the vehicle stop feature based on the vehicle positioning data and obtaining the vehicle stop information, it is used to implement:

[0134] Based on the vehicle positioning data, identifying at least one stop point of the vehicle, and obtaining the vehicle stop features corresponding to each stop point, wherein the vehicle stop features include the coordinate repetition rate of the stop points and the stop point similarity features;

[0135] Based on preset information extraction rules, the vehicle stop features are screened and classified to obtain the vehicle stop information.

[0136] In one embodiment, when the processor implements the step of identifying at least one target marketing object within a preset radiation range based on the target distribution center and obtaining marketing information of the target marketing object, the processor is used to implement:

[0137] Based on the target distribution center, identifying at least one candidate marketing object within a preset radiation range, and obtaining first identification information of each candidate marketing object;

[0138] Based on the first identification information, identifying the business type of each of the candidate marketing objects and the number of marketing objects corresponding to each of the business types;

[0139] Based on each of the business types and the number of marketing objects corresponding to each of the business types, at least one of the target marketing objects is selected from each of the candidate marketing objects;

[0140] The second identification information of each of the target marketing objects is collected to obtain the marketing information of each of the target marketing objects.

[0141] In one embodiment, the marketing information includes the distribution center address, marketing object name, business type, marketing contact information, and business demand information of the target marketing object.

[0142] A computer-readable storage medium is also provided in an embodiment of the present application, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and the processor executes the program instructions to implement any one of the marketing information identification methods based on vehicle positioning provided in the embodiments of the present application.

[0143] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc., equipped on the computer device.

[0144] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A marketing information identification method based on vehicle positioning, characterized in that: The method comprises: obtaining vehicle location data of at least one vehicle; Based on the vehicle positioning data, analyzing the vehicle stop characteristics to obtain the vehicle stop information; Based on a clustering algorithm, cluster analysis is performed on the vehicle stop information to determine at least one target distribution center; Based on the target distribution center, at least one target marketing object within a preset radiation range is identified, and marketing information of the target marketing object is obtained.

2. The marketing information identification method based on vehicle positioning according to claim 1, characterized in that: The obtaining of vehicle positioning data of at least one vehicle comprises: Acquire vehicle information of at least one vehicle, wherein the vehicle information includes vehicle owner information, vehicle number, and vehicle identification information; Based on the vehicle positioning sensor, the vehicle positioning information is collected once at a preset interval, and the positioning information includes the positioning time and positioning coordinates; The vehicle positioning data is generated based on the vehicle information and the positioning information.

3. The marketing information identification method based on vehicle positioning according to claim 1, characterized in that: The vehicle stop information includes stop coordinates, stop period, stop frequency and stop duration.

4. The marketing information identification method based on vehicle positioning according to claim 1, characterized in that: The step of performing cluster analysis on the vehicle stop information based on a clustering algorithm to determine at least one target distribution center includes: Based on the clustering algorithm, cluster analysis is performed on each of the vehicle stop information to obtain at least one candidate distribution center; Collecting potential business information of each candidate distribution center within a first preset range; Based on the business type corresponding to each of the potential business information, at least one of the target distribution centers is screened out from each of the candidate distribution centers.

5. The marketing information identification method based on vehicle positioning according to claim 1, characterized in that: The vehicle stop characteristics are analyzed based on the vehicle positioning data. Get vehicle stop information, including: Based on the vehicle positioning data, identifying at least one stop point of the vehicle, and obtaining the vehicle stop features corresponding to each stop point, wherein the vehicle stop features include the coordinate repetition rate of the stop points and the stop point similarity features; Based on preset information extraction rules, the vehicle stop features are screened and classified to obtain the vehicle stop information.

6. The marketing information identification method based on vehicle positioning according to claim 1, characterized in that: The step of identifying at least one target marketing object within a preset radiation range based on the target distribution center and obtaining marketing information of the target marketing object includes: Based on the target distribution center, identifying at least one candidate marketing object within a preset radiation range, and obtaining first identification information of each candidate marketing object; Based on the first identification information, identifying the business type of each of the candidate marketing objects and the number of marketing objects corresponding to each of the business types; Based on each of the business types and the number of marketing objects corresponding to each of the business types, at least one of the target marketing objects is selected from each of the candidate marketing objects; The second identification information of each of the target marketing objects is collected to obtain the marketing information of each of the target marketing objects.

7. The marketing information identification method based on vehicle positioning according to claim 6, characterized in that: The marketing information includes the distribution center address, marketing object name, business type, marketing contact information and business demand information of the target marketing object.

8. A marketing information recognition device based on vehicle positioning, characterized in that: The marketing information identification device based on vehicle positioning includes: A positioning data acquisition module, used to acquire vehicle positioning data of at least one vehicle; A stop information analysis module, used to analyze the vehicle stop characteristics based on the vehicle positioning data and obtain the vehicle stop information; A distribution center determination module, used for performing cluster analysis on the vehicle stop information based on a clustering algorithm to determine at least one target distribution center; The marketing information collection module is used to identify at least one target marketing object within a preset radiation range based on the target distribution center, and obtain the marketing information of the target marketing object.

9. A computer device, characterized in that: The computer device includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the marketing information identification method based on vehicle positioning as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the marketing information identification method based on vehicle positioning as described in any one of claims 1 to 7 are implemented.

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