Method and device for automatically recommending multi-tenant online car-hailing models

Abstract

The present invention provides a method and device for automatically recommending models of online car-hailing vehicles for multiple tenants, and relates to the technical field of online car-hailing vehicles. Information is collected through a data acquisition module and a platform APP to obtain the electric vehicle mileage coefficient KLD or the oil vehicle mileage coefficient KLY of the electric vehicle online car-hailing vehicle, as well as the historical evaluation coefficient LPx, and a recommendation strategy is determined according to the size of the coefficient. The present invention collects wind resistance, temperature, and air density information of the order area according to the data acquisition module, and performs digital processing based on the online car-hailing vehicle information in the order area. The data is analyzed and processed by a first data processing module to obtain the electric vehicle mileage coefficient or the oil vehicle mileage coefficient. At the same time, the relevant evaluation data is analyzed and processed according to a second data processing module to obtain the online car-hailing vehicle historical evaluation coefficient, thereby improving the success matching rate of the order, and giving priority to online car-hailing vehicles with high real reputations, thereby improving the user experience of passengers and online car-hailing vehicle drivers.

Description

Technical Field

[0001] The present invention relates to the technical field of online car-hailing, and specifically to a method and device for automatically recommending models of online car-hailing for multiple tenants. Background Art

[0002] Online ride-hailing is a booking service provided through an online platform, allowing passengers to book and use online ride-hailing services through mobile devices anytime and anywhere. The rise of this service is mainly due to the development of technology and the rise of the sharing economy, which enables people to access travel services more conveniently and flexibly. Passengers can use a dedicated platform APP to book online ride-hailing. This method usually provides a convenient user interface, allowing users to easily select the starting point and destination, view vehicle information, and track the vehicle location in real time. In order to ensure the quality of service, online ride-hailing platforms usually have an evaluation and feedback system. Passengers can rate drivers and vehicles, which helps to improve the level of service and also provides a reference for other passengers.

[0003] Generally, after a passenger places an order on the platform APP, the platform will only list recommended car models based on the specific estimated price. It will not consider whether the online car-hailing vehicle has the fuel or power information to successfully complete the order. The order-accepting algorithm is extremely one-sided, causing some online car-hailing drivers to find that the estimated distance of the order is greater than the drivable distance after accepting the order, and they need to re-match, which affects the user experience of both the online car-hailing vehicle and the passengers. At the same time, the online car-hailing vehicle's reputation evaluation can only display average data after the online car-hailing vehicle driver accepts the order, and cannot reflect the quality of the online car-hailing vehicle's reputation, resulting in high-quality online car-hailing vehicles. Drivers find it difficult to receive orders quickly, affecting the user experience of online car-hailing drivers.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to provide a method and device for automatically recommending multi-tenant online car-hailing models to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The method for automatically recommending online car-hailing models for multiple tenants includes the following specific steps:

[0008] S1. Users generate orders on the platform APP through mobile phones or other location-capable devices. The orders include the boarding location, the alighting location, the number of passengers, and the pre-selected vehicle type. The platform APP obtains the boarding location of the order and obtains all online car-hailing information within a circular area of ​​three kilometers specified by the user;

[0009] S2. Collect the wind resistance, temperature and air density information of the current area through the data acquisition module;

[0010] S3, performing data processing on the online car-hailing information through a first data processing module to obtain a mileage coefficient of an electric vehicle or a mileage coefficient of a gasoline vehicle;

[0011] S4. Processing the online car-hailing information by a second data processing module to obtain a historical evaluation coefficient;

[0012] S5. When the mileage coefficient reaches the threshold, the online car-hailing models are sorted from top to bottom according to the historical evaluation coefficients. The online car-hailing models on the upper side have a higher recommendation priority than the online car-hailing models on the lower side. The online car-hailing models are displayed in sequence on the user selection interface according to the sorting, and the user manually selects the preferred model.

[0013] Furthermore, in S1, the platform APP pre-selects a travel route based on the boarding and alighting locations of the order, and determines the mileage WM of the online car-hailing vehicle based on the travel route. The online car-hailing information includes the model of the online car-hailing vehicle, the average mileage M of the empty online car-hailing vehicle corresponding to a full charge or full fuel state, and the front projection area V of the online car-hailing vehicle. The online car-hailing information is filled in by the online car-hailing driver to the platform APP according to the model of the online car-hailing vehicle. The online car-hailing information also includes the evaluation information of the online car-hailing vehicle's historical orders.

[0014] Furthermore, in S1, if the platform APP fails to obtain qualified online car-hailing information within the circular area within three kilometers of the user-specified boarding location, the area for obtaining the online car-hailing information will be changed to six kilometers, nine kilometers, and twelve kilometers respectively.

[0015] Furthermore, in S2, the average values ​​of wind resistance, temperature, and air density information in the order area of ​​the day released by the meteorological station are collected through the data collection module, and are defined as KF, t, and ρ respectively.

[0016] Furthermore, in S3, the mileage coefficient of the electric vehicle is KLD, which is specifically:

[0017]

[0018] The mileage coefficient of a gasoline vehicle is KLY, which is:

[0019]

[0020] In the above formula, CD is the initial power of the online car-hailing electric vehicle, expressed as a percentage, CY is the initial fuel volume of the online car-hailing gasoline vehicle, ZY is the maximum fuel tank storage volume of the corresponding model of the online car-hailing gasoline vehicle, M is the average mileage of the online car-hailing empty vehicle, obtained by querying the official website of the corresponding model, My is the effective average driving distance coefficient, Mw is the invalid average driving distance coefficient, CJx is the wrong driving coefficient, FZx is the wind resistance coefficient, WDx is the temperature coefficient, τ is the dead weight coefficient, the value range is [1,3.6], representing the overall gravity coefficient of the online car-hailing after the passenger gets on the car, ω is the weight coefficient, the value range is [0.5,1.6], representing the weight of the empty car oil and electricity consumption of the online car-hailing on that day;

[0021] When the electric vehicle mileage coefficient KLD or the gasoline vehicle mileage coefficient KLY of the electric vehicle online car-hailing vehicle is greater than 1, it means that the mileage of the online car-hailing vehicle has a very high reliability in completing the order, and it is recommended to be included in the automatic recommendation pre-selected model;

[0022] When the electric vehicle mileage coefficient KLD or the gasoline vehicle mileage coefficient KLY of the electric vehicle online car-hailing vehicle is less than or equal to 1 and greater than 0, it means that the mileage of the online car-hailing vehicle has a general reliability of completing orders, and it is recommended to be included in the alternative recommended pre-selected models;

[0023] When the electric vehicle mileage coefficient KLD or the gasoline vehicle mileage coefficient KLY of the electric vehicle online-hailing car is less than 0, it means that the mileage of the online-hailing car is not reliable enough to complete the order, and it is not recommended to be included in the recommended pre-selected models.

[0024] Furthermore, the error driving coefficient is specifically:

[0025]

[0026] The specific drag coefficient is:

[0027]

[0028] The temperature coefficient is:

[0029]

[0030] The effective average driving distance coefficient My is specifically the passenger mileage that the online car-hailing vehicle has traveled on that day, and the invalid average driving distance coefficient Mw is specifically the empty vehicle mileage that the online car-hailing vehicle has traveled on that day, both of which are calculated through the platform APP;

[0031] In the above formula, M i is the actual mileage of historical online ride-hailing orders, MG i is the estimated mileage of the historical online car-hailing orders, i is a positive integer, representing the specific batch of the corresponding single order, M i MGi It is recorded by the platform APP and stored in the database. V represents the front projection area of ​​the online car-hailing vehicle, Cr is the discharge capacity at non-standard temperature, and K is the temperature coefficient. When the discharge rate is 10h, K = 0.006 / ℃; when the discharge rate is 3h, K = 0.008 / ℃.

[0032] Furthermore, in S4, the historical evaluation coefficient is defined as LPx, specifically:

[0033]

[0034] in,

[0035]

[0036] In the above formula, YPP i is the average user evaluation weight coefficient, YDP i is the number of stars of the user rating of the online car-hailing service with rating number i, X y The number of stars corresponding to the historical evaluation serial number y of the user who made the evaluation. y is the serial number corresponding to the historical evaluation of the user, and the value is a positive integer. The evaluation submitted by the user for the online car-hailing service can be divided into 1, 2, 3, 4, and 5 stars, from small to large, which respectively represent the degree of satisfaction of the user's ride.

[0037] When LPx is greater than 0, it means that the online car-hailing service has a good user reputation, and the larger the value, the better the reputation of the online car-hailing service;

[0038] When LPx is less than 0, it means that the online car-hailing service has a poor reputation, and the smaller the value, the worse the online car-hailing service has a poor reputation;

[0039] Therefore, it is recommended to sort the online car-hailing models from top to bottom according to the historical evaluation coefficients and make automatic recommendations.

[0040] The present invention also provides a multi-tenant online car-hailing model automatic recommendation device, which is used to execute a multi-tenant online car-hailing model automatic recommendation device method, including:

[0041] The data collection module is used to collect the wind resistance, temperature, and air density information in the order area published on the meteorological station website;

[0042] The first data processing module is used to process the online car-hailing information and obtain the electric car mileage coefficient or the gasoline car mileage coefficient;

[0043] The second data processing module is used to process the historical evaluation information of the online car-hailing service and the user evaluation information to obtain the historical evaluation coefficient of the online car-hailing service;

[0044] The comparison module compares the mileage coefficient of the electric vehicle or the mileage coefficient of the gasoline vehicle with the threshold value to determine whether the corresponding online-hailing vehicle meets the basic mileage conditions for accepting orders, and compares the historical evaluation coefficient of the corresponding online-hailing vehicle that meets the basic mileage conditions for accepting orders with the threshold value to determine the ordering order of automatically recommended vehicle models.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention collects the information of wind resistance, temperature and air density in the order area according to the data collection module, and performs data processing according to the online car-hailing information in the order area, and obtains the electric vehicle mileage coefficient or the oil vehicle mileage coefficient through the first data processing module to analyze and process the data. When the electric vehicle mileage coefficient KLD or the oil vehicle mileage coefficient KLY of the electric vehicle online car-hailing is greater than 1, it means that the mileage of the online car-hailing has extremely high reliability of completing the order, and it is recommended to be included in the automatically recommended pre-selected model; when the electric vehicle mileage coefficient KLD or the oil vehicle mileage coefficient KLY of the electric vehicle online car-hailing is less than or equal to 1, and greater than 0, it means that the mileage of the online car-hailing has general reliability of completing the order, and it is recommended to be included in the alternative recommended pre-selected model; when the electric vehicle mileage coefficient KLD or the oil vehicle mileage coefficient KLY of the electric vehicle online car-hailing is less than or equal to 1, and greater than 0, it means that the mileage of the online car-hailing has general reliability of completing the order, and it is recommended to be included in the alternative recommended pre-selected model; When the mileage coefficient KLD or the mileage coefficient KLY of a gasoline vehicle is less than 0, it means that the mileage of the online car-hailing vehicle is not reliable enough to complete the order, and it is not recommended to be included in the recommended pre-selected models. At the same time, the relevant evaluation data is analyzed and processed according to the second data processing module to obtain the historical evaluation coefficient of the online car-hailing vehicle. When LPx is greater than 0, it means that the online car-hailing vehicle has a good user reputation, and the larger the value, the better the reputation of the online car-hailing vehicle. When LPx is less than 0, it means that the online car-hailing vehicle has a poor reputation, and the smaller the value, the worse the reputation of the online car-hailing vehicle. Therefore, it is recommended to sort the online car-hailing vehicle models from top to bottom according to the historical evaluation coefficients and automatically recommend them, which improves the successful matching rate of orders, and gives priority to online car-hailing vehicles with high real reputations, thereby improving the user experience of passengers and online car-hailing drivers. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0048] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0049] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0050] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0051] Example:

[0052] See also Figure 1 , the present invention provides a technical solution:

[0053] The method for automatically recommending online car-hailing models for multiple tenants includes the following specific steps:

[0054] S1. Users generate orders on the platform APP through mobile phones or other locatable devices. The orders include the boarding location, alighting location, number of passengers, and pre-selected vehicle models. The platform APP obtains the boarding location of the order and obtains all online car-hailing information within a circular area of ​​three kilometers within the user-specified boarding location; the platform APP pre-selects the recommended route for the online car-hailing based on the boarding location and alighting location of the order, and determines the mileage WM of the online car-hailing vehicle based on the recommended route. In order to determine the corresponding drivable distance of the online car-hailing in the area and the reputation of the online car-hailing, the collected online car-hailing information includes the model of the online car-hailing, the average mileage M of the online car-hailing when fully charged or fully fueled, and the front projection area V of the online car-hailing. The online car-hailing information is filled in by the online car-hailing driver on the platform APP according to the model of the online car-hailing. The online car-hailing information also includes the evaluation information of the online car-hailing historical orders. In order to ensure that users can receive orders smoothly, if the platform APP fails to obtain information on online ride-hailing models that meet the standards within the circular area within three kilometers of the user's designated pick-up location, the area for obtaining online ride-hailing information will be changed to six kilometers, nine kilometers, and twelve kilometers respectively, until an online ride-hailing model that meets the standards is found.

[0055] S2. Collect the wind resistance, temperature, and air density information of the current area through the data acquisition module; collect the average values ​​of the wind resistance, temperature, and air density information in the order area of ​​the day released by the meteorological station through the data acquisition module, and define them as KF, t, and ρ respectively. The above are important parameters that affect the specific driving distance of the online car-hailing vehicle. The mileage coefficient can be calculated by these parameters, and the size of the mileage coefficient can be used to determine whether the corresponding online car-hailing vehicle has the conditions to reliably complete the order.

[0056] S3, performing data processing on the online car-hailing information through a first data processing module to obtain a mileage coefficient of an electric vehicle or a mileage coefficient of a gasoline vehicle;

[0057] The mileage coefficient of a tram is KLD, which is:

[0058]

[0059] The mileage coefficient of a gasoline vehicle is KLY, which is:

[0060]

[0061] In the above formula, CD is the initial power of the online car-hailing electric vehicle, expressed as a percentage, CY is the initial fuel volume of the online car-hailing gasoline vehicle, ZY is the maximum fuel tank storage volume of the corresponding model of the online car-hailing gasoline vehicle, M is the average mileage of the online car-hailing empty vehicle, obtained by querying the official website of the corresponding model, My is the effective average driving distance coefficient, Mw is the invalid average driving distance coefficient, CJx is the wrong driving coefficient, FZx is the wind resistance coefficient, WDx is the temperature coefficient, τ is the dead weight coefficient, the value range is [1,3.6], representing the overall gravity coefficient of the online car-hailing after the passenger gets on the car, ω is the weight coefficient, the value range is [0.5,1.6], representing the weight of the empty car oil and electricity consumption of the online car-hailing on that day;

[0062] When making a comparison, when the electric vehicle mileage coefficient KLD or the gasoline vehicle mileage coefficient KLY of the electric vehicle online car-hailing vehicle is greater than 1, it means that the mileage of the online car-hailing vehicle has a very high reliability in completing the order, and it is recommended to be included in the automatic recommendation pre-selected model;

[0063] When the electric vehicle mileage coefficient KLD or the gasoline vehicle mileage coefficient KLY of the electric vehicle online car-hailing vehicle is less than or equal to 1 and greater than 0, it means that the mileage of the online car-hailing vehicle has a general reliability of completing orders, and it is recommended to be included in the alternative recommended pre-selected models;

[0064] When the electric vehicle mileage coefficient KLD or the gasoline vehicle mileage coefficient KLY of the electric vehicle online-hailing car is less than 0, it means that the mileage of the online-hailing car is not reliable enough to complete the order, and it is not recommended to be included in the recommended pre-selected models.

[0065] S4. Processing the online car-hailing information by a second data processing module to obtain a historical evaluation coefficient;

[0066] The historical evaluation coefficient is defined as LPx, specifically:

[0067]

[0068] in,

[0069]

[0070] In the above formula, YPP i is the average user evaluation weight coefficient, YDP i is the number of stars of the user rating of the online car-hailing service with rating number i, X y is the number of stars corresponding to the historical evaluation number y of the user, and y is the number corresponding to the historical evaluation of the user, which is a positive integer. When LPx is greater than 0, it means that the online car-hailing vehicle has a good user reputation, and the larger the value, the better the reputation of the online car-hailing vehicle; when LPx is less than 0, it means that the online car-hailing vehicle has a poor reputation, and the smaller the value, the worse the reputation of the online car-hailing vehicle; therefore, it is recommended to sort the online car-hailing vehicle models from top to bottom according to the historical evaluation coefficient and make automatic recommendations.

[0071] S5. When the mileage coefficient reaches the threshold, when making a comparison, the online car-hailing models are sorted from top to bottom according to the historical evaluation coefficients. The online car-hailing models on the upper side have a higher recommendation priority than the online car-hailing models on the lower side. The online car-hailing models are displayed in sequence on the user selectable interface according to the sorting, and the user manually selects the preferred model. This method can display the real online car-hailing models with good reputation in the front row, improve the order-taking efficiency of online car-hailing models with good reputation, thereby improving the user experience of online car-hailing drivers. In addition, since online car-hailing models with higher reputations are easily selected by passengers, the user experience on the passenger side can also be improved.

[0072] Specifically, when calculating, the error driving coefficient is:

[0073]

[0074] The specific drag coefficient is:

[0075]

[0076] The temperature coefficient is:

[0077]

[0078] The effective average driving distance coefficient My is specifically the passenger mileage that the online car-hailing vehicle has traveled on that day, and the invalid average driving distance coefficient Mw is specifically the empty vehicle mileage that the online car-hailing vehicle has traveled on that day, both of which are calculated through the platform APP;

[0079] In the above formula, M i is the actual mileage of historical online ride-hailing orders, MG i is the estimated mileage of the historical online car-hailing orders, i is a positive integer, representing the specific batch of the corresponding single order, M i MG i It is recorded by the platform APP and stored in the database. V represents the front projection area of ​​the online car-hailing vehicle, Cr is the discharge capacity at non-standard temperature, and K is the temperature coefficient. When the discharge rate is 10h, K = 0.006 / ℃; when the discharge rate is 3h, K = 0.008 / ℃.

[0080] See also Figure 2 The present invention also provides a multi-tenant online car-hailing model automatic recommendation device, which is used to execute a multi-tenant online car-hailing model automatic recommendation device method, including:

[0081] The data collection module is used to collect the wind resistance, temperature and air density information in the order area published on the meteorological station website;

[0082] The first data processing module is used to process the online car-hailing information and obtain the electric car mileage coefficient or the gasoline car mileage coefficient;

[0083] The second data processing module is used to process the historical evaluation information of the online car-hailing service and the user evaluation information to obtain the historical evaluation coefficient of the online car-hailing service;

[0084] The comparison module compares the mileage coefficient of the electric vehicle or the mileage coefficient of the gasoline vehicle with the threshold value to determine whether the corresponding online-hailing vehicle meets the basic mileage conditions for accepting orders, and compares the historical evaluation coefficient of the corresponding online-hailing vehicle that meets the basic mileage conditions for accepting orders with the threshold value to determine the ordering order of automatically recommended vehicle models.

[0085] In summary, the present invention collects the driving distance of user orders, user evaluation information, wind resistance, temperature, air density information of the order area, daily driving data of online-hailing vehicles, and historical evaluation information of online-hailing vehicles through the data acquisition module and the platform APP, and processes and analyzes these information through the first data processing module and the second data processing module to obtain the electric vehicle mileage coefficient KLD or the gasoline vehicle mileage coefficient KLY of the electric vehicle online-hailing vehicle, as well as the historical evaluation coefficient LPx, and compares the above coefficients with the threshold value, and determines whether to recommend the corresponding online-hailing vehicle model according to the mileage coefficient:

[0086] When the electric vehicle mileage coefficient KLD or the gasoline vehicle mileage coefficient KLY of the electric vehicle online car-hailing vehicle is greater than 1, it means that the mileage of the online car-hailing vehicle has a very high reliability in completing the order, and it is recommended to be included in the automatic recommendation pre-selected model;

[0087] When the electric vehicle mileage coefficient KLD or the gasoline vehicle mileage coefficient KLY of the electric vehicle online car-hailing vehicle is less than or equal to 1 and greater than 0, it means that the mileage of the online car-hailing vehicle has a general reliability of completing orders, and it is recommended to be included in the alternative recommended pre-selected models;

[0088] When the electric vehicle mileage coefficient KLD or the gasoline vehicle mileage coefficient KLY of the electric vehicle online car-hailing vehicle is less than 0, it means that the mileage of the online car-hailing vehicle is not reliable enough to complete the order, and it is not recommended to be included in the recommended pre-selected models;

[0089] The online ride-hailing vehicles included in the recommended models are ranked according to the size of the historical evaluation coefficient LPx:

[0090] When LPx is greater than 0, it means that the online car-hailing vehicle has a good user reputation, and the larger the value is, the better the reputation of the online car-hailing vehicle; when LPx is less than 0, it means that the online car-hailing vehicle has a poor reputation, and the smaller the value is, the worse the reputation of the online car-hailing vehicle.

[0091] It improves the success matching rate of orders, and gives priority to recommending online ride-hailing vehicles with high real reputation, thereby improving the user experience for passengers and online ride-hailing drivers.

[0092] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0093] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by computer software, electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0094] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] The above description is only a specific implementation manner 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 changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for automatically recommending multi-tenant online car-hailing models, characterized in that: The specific steps include: S1. Users generate orders on the platform APP through mobile phones or other location-capable devices. The orders include the boarding location, the alighting location, the number of passengers, and the pre-selected vehicle type. The platform APP obtains the boarding location of the order and obtains all online car-hailing information within a circular area of ​​three kilometers specified by the user; S2. Collect the wind resistance, temperature and air density information of the current area through the data acquisition module; S3, performing data processing on the online car-hailing information through a first data processing module to obtain a mileage coefficient of an electric vehicle or a mileage coefficient of a gasoline vehicle; In S1, the platform APP pre-selects a travel route according to the pick-up location and the drop-off location of the order, and determines the mileage WM of the online car-hailing vehicle according to the travel route. The online car-hailing information includes the model of the online car-hailing vehicle, the average mileage M of the online car-hailing vehicle with full charge or full fuel, and the front projection area V of the online car-hailing vehicle. The online car-hailing information is filled in by the online car-hailing driver to the platform APP according to the model of the online car-hailing vehicle. The online car-hailing information also includes the evaluation information of the online car-hailing vehicle's historical orders; In S2, the average values ​​of wind resistance, temperature, and air density information in the order area of ​​the day released by the meteorological station are collected through the data collection module, and are defined as KF, t, and ρ respectively; In S3, the mileage coefficient of the electric vehicle is KLD, which is specifically: The mileage coefficient of a gasoline vehicle is KLY, which is: In the above formula, CD is the initial power of the online car-hailing electric vehicle, expressed as a percentage, CY is the initial fuel volume of the online car-hailing gasoline vehicle, ZY is the maximum fuel tank storage volume of the corresponding model of the online car-hailing gasoline vehicle, M is the average mileage of the online car-hailing empty vehicle, obtained by querying the official website of the corresponding model, My is the effective average driving distance coefficient, Mw is the invalid average driving distance coefficient, CJx is the wrong driving coefficient, FZx is the wind resistance coefficient, WDx is the temperature coefficient, τ is the dead weight coefficient, the value range is [1,3.6], representing the overall gravity coefficient of the online car-hailing after the passenger gets on the car, ω is the weight coefficient, the value range is [0.5,1.6], representing the weight of the empty car oil and electricity consumption of the online car-hailing on that day; When the electric vehicle mileage coefficient KLD or the gasoline vehicle mileage coefficient KLY of the electric vehicle online car-hailing vehicle is greater than 1, it means that the mileage of the online car-hailing vehicle has a very high reliability of completing the order, and it is recommended to be included in the automatic recommendation pre-selected model; When the electric vehicle mileage coefficient KLD or the gasoline vehicle mileage coefficient KLY of the electric vehicle online car-hailing vehicle is less than or equal to 1 and greater than 0, it means that the mileage of the online car-hailing vehicle has a general reliability of completing orders, and it is recommended to be included in the alternative recommended pre-selected models; When the electric vehicle mileage coefficient KLD or the gasoline vehicle mileage coefficient KLY of the electric vehicle online car-hailing vehicle is less than 0, it means that the mileage of the online car-hailing vehicle is not reliable enough to complete the order, and it is not recommended to be included in the recommended pre-selected models; The specific error driving coefficient is: The specific drag coefficient is: The temperature coefficient is: The effective average driving distance coefficient My is specifically the passenger mileage that the online car-hailing vehicle has traveled on that day, and the invalid average driving distance coefficient Mw is specifically the empty vehicle mileage that the online car-hailing vehicle has traveled on that day, both of which are calculated through the platform APP; In the above formula, M i is the actual mileage of historical online ride-hailing orders, MG i is the estimated mileage of historical online car-hailing orders, i is a positive integer, representing the specific batch of the corresponding single order, M i MG i The data is recorded by the platform APP and stored in the database. V represents the front projection area of ​​the online car-hailing vehicle, Cr is the discharge capacity at non-standard temperature, and K is the temperature coefficient. When the discharge rate is 10h, K = 0.006 / ℃; when the discharge rate is 3h, K = 0.008 / ℃. S4. Processing the online car-hailing information by a second data processing module to obtain a historical evaluation coefficient; S5. When the mileage coefficient reaches the threshold, the online car-hailing models are sorted from top to bottom according to the historical evaluation coefficients. The online car-hailing models on the upper side have a higher recommendation priority than the online car-hailing models on the lower side. The online car-hailing models are displayed in sequence on the user selection interface according to the sorting, and the user manually selects the preferred model.

2. The method for automatically recommending multi-tenant online car-hailing models according to claim 1 is characterized in that: In S1, if the platform APP fails to obtain qualified online car-hailing information within the circular area within three kilometers of the user-specified boarding location, the area for obtaining the online car-hailing information will be changed to six kilometers, nine kilometers, and twelve kilometers respectively.

3. The method for automatically recommending multi-tenant online car-hailing models according to claim 1, characterized in that: In S4, the historical evaluation coefficient is defined as LPx, specifically: in, In the above formula, YPP i is the average user evaluation weight coefficient, YDP i is the number of stars of the user rating of the online car-hailing service with rating number i, X y The number of stars corresponding to the historical evaluation serial number y of the user who evaluated the evaluation. y is the serial number corresponding to the historical evaluation of the user, and the value is a positive integer.

4. The method for automatically recommending multi-tenant online car-hailing models according to claim 3 is characterized by: When LPx is greater than 0, it means that the online car-hailing service has a good user reputation, and the larger the value, the better the reputation of the online car-hailing service; When LPx is less than 0, it means that the online car-hailing service has a poor reputation, and the smaller the value, the worse the online car-hailing service has a poor reputation; Therefore, it is recommended to sort the online car-hailing models from top to bottom according to the historical evaluation coefficients and make automatic recommendations.

5. A multi-tenant online car-hailing vehicle model automatic recommendation device, used to execute the multi-tenant online car-hailing vehicle model automatic recommendation method according to any one of claims 1 to 4, characterized in that: include: The data collection module is used to collect the wind resistance, temperature and air density information in the order area published on the meteorological station website; The first data processing module is used to process the online car-hailing information and obtain the electric car mileage coefficient or the gasoline car mileage coefficient; The second data processing module is used to process the historical evaluation information of the online car-hailing service and the user evaluation information to obtain the historical evaluation coefficient of the online car-hailing service; The comparison module compares the mileage coefficient of the electric vehicle or the mileage coefficient of the gasoline vehicle with the threshold value to determine whether the corresponding online-hailing vehicle meets the basic mileage conditions for accepting orders, and compares the historical evaluation coefficient of the corresponding online-hailing vehicle that meets the basic mileage conditions for accepting orders with the threshold value to determine the ordering order of automatically recommended vehicle models.

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