Commercial vehicle subdivision market identification method, device and equipment and storage medium

Abstract

The invention relates to the technical field of Internet of Vehicles data analysis, in particular to a commercial vehicle subdivision market identification method, device and equipment and a storage medium, and the method comprises the steps: firstly obtaining parking point data in a journey from the Internet of Vehicles, and looking up an electronic map information look-up library to obtain the publicity information of the parking points; the method comprises the following steps of: determining the working range of a parking spot, further determining the working range of the parking spot, determining a permanent parking spot related to operation business through clustering analysis, then determining the operation characteristics of the vehicle by combining the carrying attribute of the vehicle, and finally matching with a plurality of preset subdivided markets one by one according to the characteristics. And determining the subdivided market to which the vehicle belongs according to the sequence of the matching degrees. According to the scheme, the accuracy and efficiency of commercial vehicle market subdivision are improved through analysis of the Internet of Vehicles data, an enterprise can better understand the proportion and dynamic change of each detailed market in the whole market, and the enterprise can plan and adjust the market business based on the proportion and dynamic change.

Description

Technical Field

[0001] The present invention relates to the field of vehicle networking data analysis technology, and in particular to a commercial vehicle market segmentation identification method, device, equipment and storage medium. Background Art

[0002] With the deepening development of globalization and rapid economic growth, the commercial vehicle market plays an increasingly important role worldwide. Commercial vehicles, including trucks, buses and special engineering vehicles, are an indispensable part of the modern logistics system. They support the transportation of goods and the flow of people, and are crucial to economic development and social operations. However, as the market scale expands, the commercial vehicle industry also faces challenges such as the diversification of customer needs and the improvement of operational efficiency. These challenges require commercial vehicle manufacturers and service providers to more accurately identify market needs, optimize product design, improve operational efficiency, and achieve sustainable development.

[0003] In the context of the commercial vehicle market, market segmentation has become a key strategy for companies to gain competitive advantages. Through market segmentation, companies can more accurately identify the specific needs of different customer groups, develop products that meet these needs, and provide customized services, thereby improving market response speed and customer satisfaction. However, traditional market segmentation methods often rely on limited research data and qualitative analysis, making it difficult to process and analyze large-scale, real-time, and dynamic data. With the development of big data technology, especially the application of vehicle networking technology, a large amount of dynamic vehicle operation data has become possible, providing new opportunities for the segmentation of the commercial vehicle market.

[0004] Therefore, how to extract valuable information from massive amounts of vehicle dynamic operation data and conduct accurate market segmentation has become a technical problem that the commercial vehicle industry urgently needs to solve. Summary of the invention

[0005] The main purpose of the present invention is to provide a commercial vehicle market segmentation identification method, device, equipment and storage medium, aiming to solve the technical problem of how to extract valuable information from massive vehicle dynamic operation data in the prior art and perform accurate market segmentation.

[0006] To achieve the above object, the present invention provides a commercial vehicle market segmentation identification method, the method comprising the following steps: Determine the permanent parking spots for operations based on the IoV trip data; Obtaining vehicle operation characteristics according to the operational permanent parking points and vehicle transport attributes; According to the vehicle operation characteristics, a market segment identification result of the vehicle is determined.

[0007] Optionally, determining the operational permanent parking point according to the Internet of Vehicles travel data includes: Obtaining original parking point data according to the Internet of Vehicles travel data; Performing cluster analysis on the original parking point data to obtain parking point cluster analysis results; Determine the permanent parking points of the trip according to the matching results of the parking point cluster analysis and the electronic map information lookup database; The operational permanent parking point is determined according to the screening conditions of the permanent parking point and the operational parking point of the trip.

[0008] Optionally, the determining the operational permanent parking point according to the screening conditions of the trip permanent parking point and the operational parking point includes: Based on the electronic map information reference library, obtain the business publicity information of the permanent parking point of the trip; Determine the business attributes of the permanent stop at the trip according to the business public information; Performing term and semantic statistics on the business attributes of the regular parking spots under the same trip, and obtaining the original operating parking spots according to the statistical results; According to the operating parking point screening condition, non-operating parking points and low-frequency operating parking points in the original operating parking points are screened out to determine the operating permanent parking points.

[0009] Optionally, the vehicle operation characteristics are obtained according to the operational permanent parking points and vehicle transport attributes, including: Based on the electronic map information reference database, obtain the business publicity information of the operating permanent parking spot; Extracting detailed market keywords from the business publicity information of the operating permanent parking spot to obtain segmented market keyword extraction results; The segmented market keyword extraction results are matched with the vehicle transport attributes to obtain the vehicle operation characteristics.

[0010] Optionally, before obtaining the vehicle operation characteristics according to the operational permanent parking point and the vehicle transport attribute, the method further includes: According to the IoV trip data, trip characteristics are obtained, wherein the trip characteristics include one-way distance, annual operating mileage, and starting and ending place types; The vehicle transport attribute is obtained according to the travel characteristics and the vehicle model number.

[0011] Optionally, determining the market segment identification result of the vehicle according to the vehicle operation characteristics includes: Obtaining a detailed market initial identification result of the vehicle according to the vehicle operation characteristics; Determining deep recognition associated words based on the primary recognition results of the market segments and the vehicle transport attributes; The market segment identification result of the vehicle is obtained according to the vehicle operation characteristics, the deep identification associated words and the primary identification result of the market segment.

[0012] Optionally, obtaining a detailed market initial identification result of the vehicle according to the vehicle operation characteristics includes: According to the vehicle operation characteristics, cargo transportation characteristics, operation characteristics and operation route characteristics are obtained; Using the cargo transportation characteristics, operation characteristics and operation route characteristics as input data of a refined market matching model, and obtaining a matching correlation result between the vehicle and each preset refined market; According to the matching correlation results between the vehicle and each preset refined market, an initial refined market identification result of the vehicle is obtained.

[0013] In addition, to achieve the above-mentioned purpose, the present invention further proposes a commercial vehicle market segment identification device, the commercial vehicle market segment identification device comprising: The operational parking spot identification module is used to determine the operational permanent parking spots based on the IoV trip data; An operation feature extraction module, used to obtain vehicle operation features according to the operation permanent parking points and vehicle transport attributes; The refined market identification module is used to determine the segmented market identification result of the vehicle according to the vehicle operation characteristics.

[0014] In addition, to achieve the above-mentioned purpose, the present invention also proposes a commercial vehicle market segment identification device, which includes: a memory, a processor, and a commercial vehicle market segment identification program stored in the memory and executable on the processor, and the commercial vehicle market segment identification program is configured to implement the steps of the commercial vehicle market segment identification method described above.

[0015] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which a commercial vehicle market segment identification program is stored. When the commercial vehicle market segment identification program is executed by a processor, the steps of the commercial vehicle market segment identification method described above are implemented.

[0016] One or more technical solutions proposed in this application have at least the following technical effects: This application obtains parking point data during a trip through the Internet of Vehicles, obtains the public information of these parking points by consulting the electronic map information reference library, determines the working scope of the parking points based on the public information, and determines the permanent parking points related to the operating business through cluster analysis, and then determines the operating characteristics of the vehicle in combination with the vehicle's carrying attributes, and finally matches these characteristics with multiple pre-set market segments one by one, and determines the market segment to which the vehicle belongs based on the ranking of the matching degree. This solution improves the accuracy and efficiency of commercial vehicle market segmentation through the analysis of Internet of Vehicles data, which helps companies better understand the proportion and dynamic changes of each detailed market in the overall market, and companies can plan and adjust their market business based on this. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 It is a schematic diagram of the flow chart of the first embodiment of the commercial vehicle market segmentation identification method of the present invention; Figure 2 It is a flow chart of the second embodiment of the commercial vehicle market segmentation identification method of the present invention; Figure 3 It is a schematic diagram of the flow chart of the third embodiment of the commercial vehicle market segmentation identification method of the present invention; Figure 4 It is a structural block diagram of the first embodiment of the commercial vehicle market segment identification device of the present invention; Figure 5 It is a structural diagram of a commercial vehicle market segmentation identification device in a hardware operating environment involved in an embodiment of the present invention.

[0020] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0022] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solution of the embodiment of the present application is: determine the operating permanent parking points based on the Internet of Vehicles travel data; obtain the vehicle operating characteristics based on the operating permanent parking points and the vehicle carrying attributes; and determine the market segment identification result of the vehicle based on the vehicle operating characteristics.

[0024] Due to the differences in customer target groups and customer needs between the commercial vehicle field and the passenger vehicle field, the commercial vehicle field has more segmented markets and more complex user needs. Therefore, market data statistics and user demand research in the commercial vehicle field are also more difficult. Commercial vehicle companies use traditional market research methods, such as questionnaires, telephone interviews or face-to-face interviews, which are not only costly but also difficult to cover a wide range of customer groups and complex usage scenarios. In addition, these methods often lack real-time and dynamic features, making it difficult to capture rapidly changing market trends and user behaviors.

[0025] The present application provides a solution, which obtains parking point data in a trip from real-time Internet of Vehicles data, obtains public information of these parking points by consulting an electronic map information reference library, and further determines the working scope of the parking points, and determines the permanent parking points related to the operating business through cluster analysis, and then determines the operating characteristics of the vehicle in combination with the vehicle's carrying attributes, and finally matches these characteristics with multiple pre-set market segments one by one, and determines the market segment to which the corresponding vehicle under the Internet of Vehicles data belongs according to the ranking of the matching degree. Based on this solution, the market segments of all open Internet of Vehicles vehicles can be identified and counted, and the proportion and dynamic changes of each refined market in the overall market can be obtained, and the company can carry out subsequent market business planning and adjustments based on this.

[0026] Based on this, the embodiment of the present invention provides a commercial vehicle market segmentation identification method, referring to Figure 1 , Figure 1 The figure is a flow chart of a first embodiment of a commercial vehicle market segment identification method according to the present invention.

[0027] In this embodiment, the commercial vehicle market segment identification method comprises the following steps: Step S10: Determine the operational permanent parking spot based on the Internet of Vehicles travel data.

[0028] It should be noted that the overall idea of ​​the present application needs to be introduced here. Since commercial vehicle companies can obtain the location information of vehicles that have established Internet of Vehicles communications in real time, and the market segment to which the same vehicle belongs will basically not change over a long period of time, for any single vehicle, its travel data over a long period of time can be used to identify parking points. Due to the characteristics of commercial vehicles, most parking points are directly related to their business types. For example, when a vehicle is at a port and a logistics transshipment center related to seafood products many times within a few months, combining these two parking points can preliminarily determine that the market segment of the vehicle is seafood transshipment. For another example, when the travel data of a vehicle is statistically shown to appear many times in coal mines and thermal power stations, it can be preliminarily determined that the market segment of the vehicle is coal power generation transportation. By analogy, the parking points of the Internet of Vehicles data of the same vehicle and the parking points related to operations can be associated and derived to determine the market segment results of the vehicle, and then the same operation can be performed on other vehicles in the database to obtain the statistical results of the market segment.

[0029] It is understandable that not all parking spots for commercial vehicles are related to the business operations themselves. For example, parking at gas stations, public toilets, food and lodging spots, and vehicle repair spots can be considered as unrelated to the business operations. Therefore, these parking spots need to be screened out. On the other hand, some parking spots related to operations appear less frequently and are not suitable for judging the vehicle's operational characteristics based on the comprehensive characteristics of these parking spots. For example, some commercial vehicles serve specific customers outside their business scope. These customers have private loading and unloading spots, which may only be visited when specific goods need to be loaded and unloaded. Therefore, the visit frequency is not high. For another example, some transportation businesses are not point-to-point transportation, but pick up goods at the same location and transport them to different customers. At this time, the probability of duplication of parking spots on the customer side is low, while the probability of duplication of parking spots on the pickup side is high. Therefore, in the process of data derivation, in order to ensure accurate identification of market segments, this part of the content needs to be screened out.

[0030] It should be understood that in order to determine whether a parking spot is related to the operation of the vehicle, in addition to the business attributes of the parking spots themselves, it is also necessary to further determine the transportation attributes of the vehicle itself. For example, a gas station is a parking spot that all vehicles go to during operation, and it does not have special business attributes. However, when the operating vehicle is a fuel transport vehicle, the gas station may not be a parking spot unrelated to the operation business. In this case, the gas station is not only a place for vehicles to refuel, but also a distribution destination or loading point for fuel transport vehicles. Therefore, for the operation-related parking spots, the business attributes of the parking spots and the transportation attributes of the vehicles need to be comprehensively considered. This comprehensive analysis method can provide more accurate market segmentation and vehicle operation characteristics.

[0031] In one embodiment, determining the operational permanent parking point based on the Internet of Vehicles travel data includes: obtaining original parking point data based on the Internet of Vehicles travel data; performing cluster analysis on the original parking point data to obtain parking point cluster analysis results; determining the travel permanent parking point based on the matching results of the parking point cluster analysis results and the electronic map information lookup library; and determining the operational permanent parking point based on the screening conditions of the travel permanent parking point and the operational parking point.

[0032] It should be noted that the original information of parking points extracted from the travel data collected by the Internet of Vehicles includes the vehicle's location (latitude and longitude), parking time, etc. Generally speaking, considering the amount of data processing and the positioning accuracy of the Internet of Vehicles, only the location where the vehicle's position remains unchanged for more than 5 to 10 minutes is regarded as the original parking point when reading the parking point. Generally speaking, for parking points that constitute business operations, the same vehicle should generate multiple parking records within a long sampling time. Therefore, in order to reduce the amount of data processing and avoid data noise, the number of parking times at parking points in the same area will be counted during the actual parking point analysis, and parking points with a number lower than the preset value will not be included in the statistics of subsequent operation-related parking points.

[0033] It is understandable that since the location information of parking spots is the latitude and longitude coordinates of a certain accuracy, and the parking locations in the same business operation site are an area, for some industry-intensive areas, it is likely that multiple different business sites are close to each other. Therefore, in order to ensure accurate identification, cluster analysis will be performed on parking spots within a certain distance range. Cluster analysis usually sets a distance threshold, that is, it is considered that parking spots within this distance range belong to the same cluster. The setting of this threshold needs to be determined according to actual business needs and geographical environment. For example, for the city center area, this threshold may be smaller than that of the suburbs, because the business sites in the city center are more dense. After the cluster analysis is completed, this group of coordinate points will be classified as permanent parking spots for the same business trip.

[0034] In one embodiment, determining the operational permanent parking point based on the screening conditions of the trip permanent parking point and the operational parking point includes: acquiring business disclosure information of the trip permanent parking point based on an electronic map information reference library; determining business attributes of the trip permanent parking point based on the business disclosure information; performing entry and semantic statistics on the business attributes of the trip permanent parking points under the same trip, and obtaining original operational parking points based on the statistical results; and determining the operational permanent parking point by screening out non-operational parking points and low-frequency operational parking points from the original operational parking points based on the screening conditions of the operational parking points.

[0035] It should be understood that the original parking points are a large number of discrete, non-overlapping points. After cluster analysis, the parking points in the same place will be divided into the same family of parking points, and some isolated parking points that have not formed a family will be screened out according to the business attributes of the place. In this way, the discrete points can be grouped according to their geographical location and business attributes, and the clustering results corresponding to these points can be used as permanent parking points for the trip. By mining the information related to the permanent parking points for the trip, we can better understand the connection between parking points and business.

[0036] Step S20: Obtaining vehicle operation characteristics according to the operating permanent parking points and vehicle transport attributes.

[0037] It should be noted that operational permanent parking points refer to places where vehicles carry out operational activities with a certain frequency. These places may include transportation hubs such as stations, docks, and airports, or specific cargo loading and unloading points, such as refineries, mines, and seafood markets. The vehicle's transport attributes include the vehicle's load capacity, volume, type (such as buses, trucks, special vehicles, etc.) and other characteristics that may affect its operating efficiency and safety. For some special vehicles, the parking points related to their business will be determined in reverse based on their specific operating business. For example, transport vehicles loaded with oil tanks have a certain business relevance to parking points such as gas stations. In addition, according to different load weight levels, they can be further divided into light trucks, medium trucks, and heavy trucks to correspond to more detailed transport attributes.

[0038] It is understandable that different transport attributes have a strong potential correlation with the permanent parking points related to operations. For example, when cold storage and aquaculture bases appear many times among the permanent parking points related to operations, and on the other hand, the vehicle has the relevant attributes of long-distance transportation and temperature control, then the corresponding vehicle operation special diagnosis is long-distance transportation related to aquatic products.

[0039] It should be understood that vehicle operating characteristics refer to a series of characteristics and attributes exhibited by vehicles during transportation and logistics activities. These characteristics and attributes can reflect the purpose, efficiency, safety, economy and environmental impact of the vehicle, such as the types of goods mainly transported by the vehicle, such as dry goods, refrigerated goods, dangerous goods, liquids, heavy machinery and other cargo characteristics, the vehicle's carrying capacity characteristics such as load weight and volume capacity, the distance the vehicle usually travels, which may be short-distance, medium-distance or long-distance transportation distance characteristics, and the routes that the vehicle frequently travels, which may include urban roads, highways, rural roads and other transportation route characteristics. This series of detailed characteristics together constitute the vehicle operating characteristics.

[0040] Step S30: Determine the market segment identification result of the vehicle according to the vehicle operation characteristics.

[0041] It should be noted that the segmented market can be divided into three levels of market segments according to the degree of segmentation, among which the first-level segmented market includes traction, cargo loading, and dumping, etc., the second-level segmented market refers to trunk transportation, long-distance transportation, medium- and long-distance transportation, regional distribution, etc., and the third-level segmented scene includes express delivery, cold chain, green access, etc. It can be seen that the relationship between the vehicle operation characteristics and the segmented market is mutually influential. The vehicle's operation characteristics determine its positioning in the segmented market, and the needs and characteristics of the segmented market in turn affect the design and configuration of the vehicle. By analyzing all the vehicle's operation characteristics in detail, including cargo type, carrying capacity, transportation distance, transportation route, etc., the degree of match between the current vehicle and multiple preset market segments can be determined, and the vehicle's market segment identification result can be determined based on the sorting results of the matching degree.

[0042] It is understandable that by analyzing all the operational characteristics of a vehicle in detail, including cargo type, carrying capacity, transportation distance, transportation route, etc., it is possible to determine the degree of match between the current vehicle and multiple pre-set market segments. For example, if a vehicle often transports dry goods and mainly conducts medium and long-distance transportation between regions, then its highest matching market segments may be medium and long-distance transportation and regional distribution. If the relevant entries of a vehicle's parking points repeatedly contain coal mines, thermal power generation, etc., then its highest matching market segments may be the coal transportation and energy transportation markets.

[0043] It should be understood that by comprehensively considering the above factors, the vehicle market segment identification results can be determined more accurately, thereby providing guidance for vehicle manufacturing, sales and operation. This detailed market analysis helps vehicle manufacturers and logistics companies optimize resource allocation, improve market competitiveness, and achieve better economic benefits.

[0044] This embodiment determines the operating permanent parking points based on the Internet of Vehicles travel data; obtains the vehicle operating characteristics based on the operating permanent parking points and the vehicle carrying attributes; and determines the market segmentation identification result of the vehicle based on the vehicle operating characteristics.

[0045] In summary, in this embodiment, the parking point data during the vehicle operation process is obtained through the Internet of Vehicles, and the public information of these parking points is obtained by consulting the electronic map information reference library, and the working scope of the parking points is further determined, and the permanent parking points related to the operation business are determined through cluster analysis, and then the operation characteristics of the vehicle are determined in combination with the vehicle's carrying attributes, and finally these characteristics are matched one by one with multiple pre-set market segments, and the market segment to which the vehicle belongs is determined according to the ranking of the matching degree. This solution improves the accuracy and efficiency of commercial vehicle market segmentation through the analysis of Internet of Vehicles data, which helps companies better understand the proportion and dynamic changes of each detailed market in the overall market, and companies can plan and adjust their market business based on this.

[0046] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 , the step S20 further includes: Step S201: Based on the electronic map information reference library, obtain the business publicity information of the operating permanent parking spot.

[0047] It should be noted that the electronic map information reference library is a resource that contains rich geographic information and business data, and can provide detailed business disclosure information for operating permanent parking spots. The business disclosure information includes but is not limited to the name of the parking spot and business information related to the name. For example, the common business content of the parking spot can be determined based on semantic analysis of the name or through online channels, or whether the parking spot serves a specific type of vehicle or provides specific logistics services can be determined based on the disclosure information.

[0048] Step S202: extracting market keywords from the business publicity information of the operating permanent parking spot to obtain segmented market keyword extraction results.

[0049] It is understandable that the use of text analysis technology, such as the keyword extraction tool AIKTP KeywordExtraction Tool, can automatically extract the most commonly used and important words and phrases from the text, and help summarize the text content and identify the main topics. The identified topic keywords are the keywords about market segmentation. On the other hand, since there is more than one permanent parking point in operation, the business disclosure information corresponding to all permanent parking points in operation is summarized, and the high-frequency words in the summarized text content can be detected. Commonly used methods include count vectorization and TF-IDF. TF-IDF (Term Frequency-Inverse Document Frequency) is a commonly used weighted technology for information retrieval and text mining. It is used to reflect the importance of a word to a document set or one of the documents in a corpus, so it is often used to identify key words in documents, so as to be used for tasks such as document classification, clustering and information retrieval.

[0050] It should be understood that the results of keyword extraction for market segments refer to a set of keywords that can represent the characteristics of specific market segments extracted from the business disclosure information of the operating permanent parking spots through text analysis technology. These keywords can reveal the main themes and important concepts in the text, and play an important role in understanding and analyzing the operating behavior of the vehicle and the corresponding market segments.

[0051] Step S203: Match the market segment keyword extraction result with the vehicle transport attribute to obtain the vehicle operation characteristics.

[0052] It should be noted that the service type in the keyword extraction results (such as "express delivery" and "delivery") can help identify the service type characteristics of the vehicle, such as whether it is suitable for fast delivery or scheduled delivery services. Combining the vehicle's carrying capacity (such as load weight and volume) with the type of goods identified in the keywords can evaluate whether the vehicle is suitable for transporting these goods; for example, if the vehicle has a larger load weight and volume, the vehicle is more suitable for transporting bulk goods. The geographic location information (such as "terminal") and transportation-related terms (such as "long-distance") that appear in the keywords can help determine the vehicle's transportation distance and route characteristics, which helps to identify whether the vehicle is suitable for long-distance transportation tasks. The semantic results formed by matching the keyword extraction results with the vehicle's carrying attributes can clarify the type of market segment it points to or the key characteristics of the operation.

[0053] In one embodiment, before obtaining the vehicle operation characteristics based on the operating permanent parking points and the vehicle transportation attributes, it also includes: obtaining travel characteristics based on the Internet of Vehicles travel data, the travel characteristics including one-way distance, annual operating mileage and starting and ending place types; obtaining the vehicle transportation attributes based on the travel characteristics and the vehicle model number.

[0054] This embodiment obtains the business publicity information of the operating permanent parking spot; extracts detailed market keywords from the business publicity information of the operating permanent parking spot to obtain segmented market keyword extraction results; matches the segmented market keyword extraction results with the vehicle carrying attributes to obtain the vehicle operation characteristics.

[0055] In summary, in this embodiment, the accuracy of market segmentation is improved through the above method, so that enterprises can more accurately locate the market area of ​​vehicle services; secondly, by analyzing the vehicle travel characteristics and carrying attributes, the vehicle configuration and scheduling are optimized to improve transportation efficiency; thirdly, this solution helps enterprises to deeply understand market demand and formulate more accurate marketing strategies. Overall, this solution provides a scientific and systematic analysis method for vehicle operation management, which helps to achieve optimal resource allocation and maximize operational efficiency.

[0056] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 3 , step S30, comprising: Step S301: Obtaining a detailed market initial identification result of the vehicle according to the vehicle operation characteristics.

[0057] It should be noted that vehicle operating characteristics at least include cargo transportation characteristics, operation characteristics and operation route characteristics. Cargo transportation characteristics include cargo categories, cargo transportation requirements and characteristics related to the transport vehicle; operation characteristics include the operating scope of the vehicle, such as whether it is limited to intra-city distribution, or is capable of inter-regional or even international transportation, and the time characteristics of the operation, such as seasonal transportation or transportation during a specific time period; operation route characteristics include the characteristics of the starting and ending points, the type of route the vehicle mainly travels, such as highways, urban roads, country roads or specific transport corridors, and the average distance of a single vehicle transport, which corresponds to short-distance, medium-distance or long-distance transportation types.

[0058] In one embodiment, the initial identification result of the vehicle's refined market is obtained based on the vehicle's operating characteristics, including: obtaining cargo transportation characteristics, operation characteristics and operation route characteristics based on the vehicle's operating characteristics; using the cargo transportation characteristics, operation characteristics and operation route characteristics as input data of a refined market matching model to obtain a matching correlation result between the vehicle and each preset refined market; and obtaining the initial identification result of the vehicle's refined market based on the matching correlation result between the vehicle and each preset refined market.

[0059] It is understandable that before this, a detailed market matching model needs to be built in advance, and then the extracted cargo transportation characteristics, operation characteristics and operation route characteristics are used as input data. The model can match the vehicle characteristics with the preset detailed market and calculate the matching correlation results between the vehicle and each preset detailed market. The model uses data mining technologies such as association rule algorithms to calculate the matching correlation between the vehicle and each preset detailed market to determine the adaptability and correlation strength of the vehicle in different market segments.

[0060] Step S302: Determine deep recognition associated words based on the primary recognition results of the market segments and vehicle transport attributes.

[0061] It should be noted that since some market segments with similar business content may have some business overlap, deep identification of associated words can help distinguish the subtle differences between these markets.

[0062] It can be understood that after obtaining the deep recognition associated words, the original input data and the previous market segment recognition results are used as new input data again to obtain the matching results with each lower-level market segment, and the matching results can be used as the market segment recognition results corresponding to the vehicle's Internet of Vehicles travel data.

[0063] Step S303: Obtain the market segment recognition result of the vehicle according to the vehicle operation characteristics, the deep recognition associated words and the market segment primary recognition result.

[0064] This embodiment obtains the detailed market initial recognition result of the vehicle based on the vehicle operation characteristics; determines the deep recognition associated words based on the segmented market primary recognition result and the vehicle carrying attributes; and obtains the segmented market recognition result of the vehicle based on the vehicle operation characteristics, the deep recognition associated words and the segmented market primary recognition result.

[0065] In summary, in this embodiment, by comprehensively analyzing vehicle operation characteristics, deeply identifying associated words and primary identification results of market segments, accurate identification of vehicle refined markets is achieved, the accuracy of market segmentation is improved, and enterprises can more accurately locate the market areas of vehicle services, deeply understand market demand, and formulate more accurate marketing strategies to enhance the company's ability to respond to market changes and improve competitiveness. Overall, this solution provides a scientific and systematic analysis method for vehicle operation management, which helps enterprises achieve refined management and market expansion.

[0066] This application also provides a commercial vehicle market segment identification device, please refer to Figure 4 , the commercial vehicle market segment identification device comprises: The operating parking spot identification module 10 is used to determine the operating permanent parking spot based on the Internet of Vehicles travel data; An operation feature extraction module 20, for obtaining vehicle operation features according to the operation permanent parking points and vehicle transport attributes; The refined market identification module 30 is used to determine the segmented market identification result of the vehicle according to the vehicle operation characteristics.

[0067] In one embodiment, the operational parking point identification module 10 is further used to obtain original parking point data based on the Internet of Vehicles travel data; perform cluster analysis on the original parking point data to obtain parking point cluster analysis results; determine the itinerary permanent parking point based on the matching results of the parking point cluster analysis results and the electronic map information lookup library; determine the operational permanent parking point based on the itinerary permanent parking point and the operational parking point screening conditions.

[0068] In one embodiment, the operating parking point identification module 10 is further used to obtain the business publicity information of the permanent parking point of the trip based on the electronic map information reference library; determine the business attributes of the permanent parking point of the trip according to the business publicity information; perform entry and semantic statistics on the business attributes of the permanent parking points of the trip under the same trip, and obtain the original operating parking point according to the statistical results; according to the operating parking point screening conditions, screen out non-operating parking points and low-frequency operating parking points in the original operating parking points to determine the operating permanent parking point.

[0069] In one embodiment, the operation feature extraction module 20 is also used to obtain the business publicity information of the operating permanent parking spot based on the electronic map information reference library; perform refined market keyword extraction on the business publicity information of the operating permanent parking spot to obtain segmented market keyword extraction results; match the segmented market keyword extraction results with the vehicle carrying attributes to obtain the vehicle operation characteristics.

[0070] In one embodiment, the operating feature extraction module 20 is also used to obtain travel characteristics based on the Internet of Vehicles travel data, and the travel characteristics include one-way distance, annual operating mileage, and starting and ending location types; based on the travel characteristics and the vehicle model number, the vehicle transportation attributes are obtained.

[0071] In one embodiment, the refined market identification module 30 is further used to obtain the refined market initial identification result of the vehicle based on the vehicle operation characteristics; determine the deep identification associated words based on the segmented market primary identification result and the vehicle carrying attributes; and obtain the segmented market identification result of the vehicle based on the vehicle operation characteristics, the deep identification associated words and the segmented market primary identification result.

[0072] In one embodiment, the refined market identification module 30 is further used to obtain cargo transportation characteristics, operation characteristics and operation route characteristics based on the vehicle operation characteristics; use the cargo transportation characteristics, operation characteristics and operation route characteristics as input data of the refined market matching model to obtain the matching correlation results between the vehicle and each preset refined market; and obtain the initial refined market identification result of the vehicle based on the matching correlation results between the vehicle and each preset refined market.

[0073] This embodiment obtains the parking point data of the vehicle during a long period of travel through the Internet of Vehicles, obtains the public information of these parking points by consulting the electronic map information reference library, and further determines the working scope of the parking points, and determines the permanent parking points related to the operating business through cluster analysis, and then determines the operating characteristics of the vehicle in combination with the vehicle's carrying attributes, and finally matches these characteristics with multiple pre-set market segments one by one, and determines the market segment to which the vehicle belongs based on the ranking of the matching degree. This solution improves the accuracy and efficiency of commercial vehicle market segmentation through the analysis of Internet of Vehicles data, which helps companies better understand the proportion and dynamic changes of each detailed market in the overall market, and companies can plan and adjust their market business based on this.

[0074] The commercial vehicle market segmentation identification device provided by the present application adopts the commercial vehicle market segmentation identification method in the above-mentioned embodiment, which can solve the technical problem of how to extract valuable information from massive vehicle dynamic operation data and perform accurate market segmentation. Compared with the prior art, the beneficial effects of the commercial vehicle market segmentation identification device provided by the present application are the same as the beneficial effects of the commercial vehicle market segmentation identification method provided by the above-mentioned embodiment, and the other technical features of the commercial vehicle market segmentation identification device are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0075] The present application provides a commercial vehicle market segment identification device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the commercial vehicle market segment identification method in the above-mentioned embodiment one.

[0076] Reference below Figure 5 , which shows a schematic diagram of the structure of a commercial vehicle market segment identification device suitable for implementing the embodiment of the present application. The commercial vehicle market segment identification device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The commercial vehicle market segment identification device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0077] like Figure 5As shown, the commercial vehicle market segment identification device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM) 1004. In the RAM 1004, various programs and data required for the operation of the commercial vehicle market segment identification device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the commercial vehicle segment identification device to communicate with other devices wirelessly or by wire to exchange data. Although the commercial vehicle segment identification device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0078] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0079] The commercial vehicle market segmentation identification device provided by the present application adopts the commercial vehicle market segmentation identification method in the above embodiment, which can solve the technical problem of how to extract valuable information from massive vehicle dynamic operation data and perform accurate market segmentation. Compared with the prior art, the beneficial effects of the commercial vehicle market segmentation identification device provided by the present application are the same as the beneficial effects of the commercial vehicle market segmentation identification method provided by the above embodiment, and the other technical features in the commercial vehicle market segmentation identification device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0080] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0081] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0082] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the commercial vehicle market segment identification method in the above-mentioned embodiment.

[0083] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0084] The computer-readable storage medium may be included in the commercial vehicle market segment identification device; or may exist independently without being assembled into the commercial vehicle market segment identification device.

[0085] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the commercial vehicle market segment identification device, the commercial vehicle market segment identification device performs commercial vehicle market segment identification.

[0086] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0087] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0088] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0089] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned commercial vehicle market segmentation identification method, and can solve the technical problem of how to extract valuable information from massive vehicle dynamic operation data and perform accurate market segmentation. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the commercial vehicle market segmentation identification method provided in the above-mentioned embodiment, and will not be repeated here.

[0090] The computer program product provided by this application can solve the technical problem of identifying commercial vehicle market segments. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the commercial vehicle market segment identification method provided by the above embodiment, which will not be repeated here.

[0091] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A commercial vehicle market segmentation identification method, characterized in that: The commercial vehicle market segment identification method comprises: Determine the permanent parking spots for operations based on the IoV trip data; Obtaining vehicle operation characteristics according to the operational permanent parking points and vehicle transport attributes; According to the vehicle operation characteristics, a market segment identification result of the vehicle is determined.

2. The commercial vehicle market segment identification method according to claim 1, characterized in that: Determining the operational permanent parking point based on the Internet of Vehicles travel data includes: Obtaining original parking point data according to the Internet of Vehicles travel data; Performing cluster analysis on the original parking point data to obtain parking point cluster analysis results; Determine the permanent parking points of the trip according to the matching results of the parking point cluster analysis and the electronic map information lookup database; The operational permanent parking point is determined according to the screening conditions of the permanent parking point and the operational parking point of the trip.

3. The commercial vehicle market segment identification method according to claim 2, characterized in that: The step of determining the operational permanent parking point according to the screening conditions of the permanent parking point and the operational parking point during the trip includes: Based on the electronic map information reference library, obtain the business publicity information of the permanent parking point of the trip; Determine the business attributes of the permanent stop at the trip according to the business public information; Performing term and semantic statistics on the business attributes of the regular parking spots under the same trip, and obtaining the original operating parking spots according to the statistical results; According to the operating parking point screening condition, non-operating parking points and low-frequency operating parking points in the original operating parking points are screened out to determine the operating permanent parking points.

4. The commercial vehicle market segment identification method according to claim 1, characterized in that: The vehicle operation characteristics are obtained according to the operational permanent parking points and vehicle transport attributes, including: Based on the electronic map information reference database, obtain the business publicity information of the operating permanent parking spot; Extracting detailed market keywords from the business publicity information of the operating permanent parking spot to obtain segmented market keyword extraction results; The segmented market keyword extraction results are matched with the vehicle transport attributes to obtain the vehicle operation characteristics.

5. The commercial vehicle market segment identification method according to claim 4, characterized in that: Before obtaining the vehicle operation characteristics according to the operational permanent parking point and the vehicle transport attribute, the method further includes: According to the IoV trip data, trip characteristics are obtained, wherein the trip characteristics include one-way distance, annual operating mileage, and starting and ending place types; The vehicle transport attribute is obtained according to the travel characteristics and the vehicle model number.

6. The commercial vehicle market segment identification method according to claim 1, characterized in that: Determining the market segment identification result of the vehicle according to the vehicle operation characteristics includes: Obtaining a detailed market initial identification result of the vehicle according to the vehicle operation characteristics; Determining deep recognition associated words based on the primary recognition results of the market segments and the vehicle transport attributes; The market segment identification result of the vehicle is obtained according to the vehicle operation characteristics, the deep identification associated words and the primary identification result of the market segment.

7. The commercial vehicle market segment identification method according to claim 6, characterized in that: The obtaining of the detailed market initial identification result of the vehicle according to the vehicle operation characteristics includes: According to the vehicle operation characteristics, cargo transportation characteristics, operation characteristics and operation route characteristics are obtained; Using the cargo transportation characteristics, operation characteristics and operation route characteristics as input data of a refined market matching model, and obtaining a matching correlation result between the vehicle and each preset refined market; According to the matching correlation results between the vehicle and each preset refined market, an initial refined market identification result of the vehicle is obtained.

8. A commercial vehicle market segment identification device, characterized in that: The commercial vehicle market segment identification device comprises: The operational parking spot identification module is used to determine the operational permanent parking spots based on the IoV trip data; An operation feature extraction module, used to obtain vehicle operation features according to the operation permanent parking points and vehicle transport attributes; The refined market identification module is used to determine the segmented market identification result of the vehicle according to the vehicle operation characteristics.

9. A commercial vehicle market segmentation identification device, characterized in that: The commercial vehicle market segment identification device comprises: a memory, a processor, and a commercial vehicle market segment identification program stored in the memory and executable on the processor, wherein the commercial vehicle market segment identification program is configured to implement the steps of the commercial vehicle market segment identification method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a commercial vehicle market segment identification program, and when the commercial vehicle market segment identification program is executed by the processor, the steps of the commercial vehicle market segment identification method according to any one of claims 1 to 7 are implemented.

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