A driving subsidy cost pushing method, device and terminal

Abstract

The application discloses a truck driving subsidy fee pushing method and device and a terminal, and relates to the field of truck driving subsidy fee pushing. The method comprises the following steps: obtaining the safe driving behavior data and the fee evasion behavior data of each truck in an initial truck set which is pre-counted; inputting the safe driving behavior data and the fee evasion behavior data into a truck negative credit value calculation model to output the negative credit value of each truck; determining a to-be-diverted vehicle set according to the negative credit value of each truck; calculating the subsidy fee of each to-be-diverted vehicle in the target to-be-diverted vehicle set according to a truck fee subsidy model, and pushing the subsidy fee of each to-be-diverted vehicle to a corresponding client. The application calculates the credit value of the to-be-diverted vehicle set by using the safe driving behavior data and the fee evasion behavior data of the truck, and calculates the subsidy fee of each to-be-diverted vehicle by combining the truck fee subsidy model to push the subsidy fee, so as to attract truck drivers to choose to drive on the highway, and improve the truck transportation efficiency.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, and in particular to a method, device and terminal for pushing out driving subsidy fees. Background Technology

[0002] With the continuous construction of highways and the increasing number of vehicles, implementing differentiated tolling on highways is a very important matter. The main purpose of implementing differentiated tolling is to use price levers to balance the distribution of traffic flow on the road network, improve the overall operating efficiency of the regional road network, and promote cost reduction and efficiency improvement in regional logistics transportation. The key target is freight vehicles, which have a significant impact on road capacity and service level.

[0003] In existing technologies, differentiated toll collection on highways typically involves adjusting rates based on road segment, time of day, and vehicle type. Highway management departments primarily consider variables such as road network structure, traffic flow characteristics, and passenger / freight volume when formulating differentiated toll strategies. However, factors such as freight truck travel patterns, driving behavior, and vehicle ownership are not taken into account. This results in insufficient attractiveness of rate adjustments for freight trucks, ultimately reducing their transportation efficiency. Summary of the Invention

[0004] This application provides a method, apparatus, and terminal for pushing out driving subsidy fees. To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general description, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0005] In a first aspect, embodiments of this application provide a method for pushing out driving subsidy fees, the method including:

[0006] Obtain safe driving behavior data and toll payment / evasion behavior data for each truck in the pre-statistical initial truck set;

[0007] Input safe driving behavior data and toll evasion behavior data into the truck negative credit value calculation model, and output the negative credit value of each truck;

[0008] The target set of vehicles to be diverted is determined based on the negative credit score of each truck;

[0009] The subsidy amount for each vehicle in the target vehicle pool is calculated based on the truck subsidy model, and the subsidy amount for each vehicle is pushed to the corresponding client.

[0010] Optionally, before obtaining the safe driving behavior data and toll payment / evasion behavior data for each truck in the pre-statistically collected initial truck set, the following steps are also included:

[0011] Identify the target highway for traffic diversion;

[0012] Identify at least one non-highway target road segment based on the target highway;

[0013] Load truck trajectory data from at least one non-highway target road segment within a preset period;

[0014] An initial set of trucks was determined based on truck driving trajectory data.

[0015] Optionally, the subsidy cost for each vehicle in the target vehicle pool to be redirected is calculated based on the truck negative credit score calculation model, including:

[0016] Obtain the highway mileage, highway fuel consumption per unit price, and highway toll standard for each of the target highways;

[0017] Obtain the mileage of the non-highway target road segment and the unit price of fuel consumption in the non-highway segment;

[0018] The target value of the subsidy coefficient for the truck cost subsidy model is determined based on each vehicle to be diverted, and the pre-set subsidy constant is obtained.

[0019] Input the target value of the subsidy coefficient, highway mileage, highway fuel consumption unit price, highway toll standard, non-highway mileage and non-highway fuel consumption unit price, and subsidy constant into the truck cost subsidy model, and output the subsidy cost for each vehicle to be diverted.

[0020] Optionally, a target value for the subsidy coefficient of the truck cost subsidy model is determined based on each vehicle to be diverted, including:

[0021] The net revenue of highways is calculated based on highway mileage, highway fuel consumption per unit price, highway toll standards, non-highway mileage, and non-highway fuel consumption per unit price.

[0022] The net revenue of the expressway is set to be greater than 0, and the first range of values ​​for the subsidy coefficient is determined.

[0023] The target value of the subsidy coefficient is obtained based on the first value range.

[0024] Optionally, the traffic flow saturation of the target highway is obtained, and a second range of values ​​for the subsidy coefficient is determined based on the traffic flow saturation.

[0025] Calculate the intersection of the first and second value ranges to obtain the target value range of the subsidy coefficient;

[0026] The target value of the subsidy coefficient is obtained based on the target value range.

[0027] Optionally, at least one non-highway target road segment may be identified based on the target highway, including:

[0028] Find at least one non-highway segment that corresponds to the target highway and denote it as the first target diversion segment set;

[0029] Calculate the difference between the mileage of each segment in each non-highway segment and the mileage of the target segment, and generate a difference sequence.

[0030] The road segments corresponding to the differences before the median difference in the difference sequence are determined as the second target diversion road segment set.

[0031] Optionally, an initial set of trucks can be calculated based on truck driving trajectory data, including:

[0032] The frequency of passage for each truck is calculated from the truck's driving trajectory data;

[0033] The truck identifiers with a passage frequency greater than a preset value are saved to obtain the initial truck set.

[0034] Optionally, a target set of vehicles to be redirected can be determined based on the negative credit score of each truck, including:

[0035] Determine whether the negative credit score of each truck is less than the preset credit score;

[0036] If so, then it is identified as a vehicle to be diverted;

[0037] If not, then it is determined to be a non-diversion vehicle;

[0038] Once all the negative credit values ​​for each truck have been determined, all the vehicles to be redirected will be identified as the target vehicle set.

[0039] Secondly, embodiments of this application provide a device for pushing out driving subsidy fees, the device comprising:

[0040] The data acquisition module is used to acquire safe driving behavior data and toll payment evasion behavior data for each truck in the pre-statistical initial truck set;

[0041] The credit score output module is used to input safe driving behavior data and toll evasion behavior data into the truck negative credit score calculation model and output the negative credit score for each truck.

[0042] The module for determining the set of vehicles to be diverted is used to determine the target set of vehicles to be diverted based on the negative credit value of each truck.

[0043] The subsidy fee push module is used to calculate the subsidy fee for each vehicle in the target vehicle set to be redirected according to the truck fee subsidy model, and push the subsidy fee for each vehicle to be redirected to the corresponding client.

[0044] Thirdly, embodiments of this application provide a terminal that may include a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and executed by the above-described method steps.

[0045] The technical solutions provided in this application embodiment may include the following beneficial effects:

[0046] In this embodiment, the vehicle subsidy push device first acquires safe driving behavior data and toll evasion behavior data for each truck in a pre-statistically collected initial truck set. Then, it inputs this data into a truck negative credit value calculation model, outputting a negative credit value for each truck. Based on these negative credit values, it determines a set of vehicles to be redirected. Finally, it calculates the subsidy for each vehicle in the target set based on the truck subsidy model and pushes the subsidy for each vehicle to the corresponding client. Because this application uses safe driving behavior data and toll evasion behavior data to calculate credit values ​​to determine the set of vehicles to be redirected, and combines this with the truck subsidy model to calculate and push the subsidy for each vehicle, it attracts truck drivers to choose highways as much as possible, thereby improving highway utilization and increasing truck transportation efficiency.

[0047] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit the invention. Attached Figure Description

[0048] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0049] Figure 1 This is a flowchart illustrating a method for pushing out driving subsidy fees according to an embodiment of this application;

[0050] Figure 2 This is a schematic diagram of several non-highway sections corresponding to the same starting highway, provided in an embodiment of this application.

[0051] Figure 3 This is a flowchart illustrating a method for pushing out driving subsidy fees according to an embodiment of this application;

[0052] Figure 4This is a schematic diagram of the structure of a driving subsidy payment push device provided in an embodiment of this application;

[0053] Figure 5 This is a schematic diagram of another driving subsidy payment push device provided in an embodiment of this application;

[0054] Figure 6 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application. Detailed Implementation

[0055] The following description and accompanying drawings fully illustrate specific embodiments of the invention to enable those skilled in the art to practice them.

[0056] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0057] In the following description, when referring to the accompanying drawings, the same numbers in different drawings denote the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0058] In the description of this invention, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of these terms in this invention based on the specific circumstances. Furthermore, in the description of this invention, unless otherwise stated, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0059] This application provides a method, apparatus, and terminal for pushing out driving subsidies to address the problems existing in the aforementioned related technologies. In the technical solution provided by this application, a negative credit value is calculated based on safe driving behavior data and toll evasion behavior data of trucks to determine the set of vehicles to be redirected. The subsidy amount for each vehicle to be redirected is then calculated and pushed out using a truck subsidy model, thereby attracting truck drivers to choose highways as much as possible, thus improving the transportation efficiency of trucks. The following is a detailed description using exemplary embodiments.

[0060] The following will be combined with the appendix Figure 1 -Appendix Figure 3 This application provides a detailed description of the method for pushing out driving subsidies according to embodiments. This method can be implemented using a computer program and can run on a driving subsidy push device based on the von Neumann architecture. This computer program can be integrated into an application or run as a standalone utility application.

[0061] Please see Figure 1 This is a flowchart illustrating a method for pushing out driving subsidies, as provided in this application embodiment. Figure 1 As shown, the method in this application embodiment may include the following steps:

[0062] S101, Obtain safe driving behavior data and toll payment evasion behavior data for each truck in the pre-statistically collected initial truck set;

[0063] Typically, most freight vehicles (such as large trucks) are equipped with vehicle positioning terminals that can record vehicle travel routes, driving behavior, and vehicle ownership. At the same time, highway departments have deployed toll collection and verification systems to monitor toll payment and evasion behavior of trucks.

[0064] The safe driving behavior data for each truck is obtained through real-time reporting from the vehicle's positioning terminal to the national public platform for road freight vehicles, and includes the number of times the driver was fatigued (NF) and the number of times the driver violated regulations (NV). The toll evasion behavior data is recorded by the toll audit and evidence collection system, and includes the number of times toll evasion occurred (NE).

[0065] In this embodiment of the application, when generating the pre-statistical initial truck set, the initial truck set is selected and determined from the truck driving data of the non-highway sections for diversion. First, the target highway to be diverted is determined, such as target highway A. Then, multiple non-highway sections corresponding to the highway section are searched, such as multiple sections B (B1, B2, B3...Bn) of national and provincial trunk lines. Then, based on the principle that the distance from the target highway is less than a preset threshold d1, at least one non-highway target road section, such as Bi and Bx, is determined based on the target highway A, and is denoted as the first target diversion road section.

[0066] Next, load truck driving trajectory data from at least one non-highway target road segment (first target diversion road segment) within a preset period, such as the past week, and statistically analyze the truck driving trajectory data to form an initial set of trucks.

[0067] Specifically, when determining at least one non-highway target road segment based on the target highway, first query at least one non-highway segment corresponding to the target highway. Here, "corresponding" means a non-highway segment that is parallel or approximately parallel to the target highway segment and is less than a preset threshold d1 away from the target highway segment, such as national or provincial trunk road segments. The obtained at least one non-highway segment that meets the conditions is recorded as the first target diversion road segment set.

[0068] Furthermore, the difference between the mileage of each segment in each non-highway segment of the first target diversion route set and the mileage of the target highway segment is calculated, generating a difference sequence. This difference sequence is then sorted in ascending order, and the segments corresponding to the differences before the median difference are determined as the preferred set of non-highway segments where the trucks to be diverted are located, denoted as the second target diversion route set.

[0069] Specifically, when calculating the initial set of trucks based on truck driving trajectory data, the frequency of each truck's passage is first counted from the truck driving trajectory data. Then, the trucks with a passage frequency greater than a preset value are saved to obtain the initial set of trucks to be diverted.

[0070] In one possible implementation, after obtaining the initial set of trucks, the system first queries the national public platform for road freight vehicles to determine the number of times each truck in the initial set has been fatigued (NF) and the number of times it has violated traffic rules (NV). NF and NV are then identified as safe driving behavior data. Finally, the system queries the toll audit and evidence collection system to determine the number of times each truck in the initial set has evaded tolls (NE). NE is then identified as toll evasion behavior data.

[0071] For example Figure 2 As shown, the target expressway for traffic diversion identified by the expressway management unit is AB (Beijing-Shanghai Expressway). There are two non-expressways (such as two national and provincial trunk roads) with the same starting and ending points as AB. When the traffic saturation of the Beijing-Shanghai Expressway is less than the preset saturation, vehicles with better credit ratings on the two national and provincial trunk roads can be guided to enter the Beijing-Shanghai Expressway through preferential subsidies.

[0072] S102, Input safe driving behavior data and toll evasion behavior data into the truck negative credit value calculation model, and output the negative credit value of each truck;

[0073] The truck negative credit score calculation model is a mathematical model used to calculate the negative credit score of each truck based on parameters.

[0074] Typically, the formula for calculating the negative credit score of trucks is: C = (NF + NV) * α1 + NE * α2. Here, α1 represents the weight of safe driving behavior, for example, 70%, and α2 represents the weight of toll evasion behavior, for example, 30%.

[0075] In one possible implementation, after obtaining the number of times each truck is fatigued driving (NF), the number of times it violates traffic rules (NV), and the number of times it evades tolls (NE), the first weight value of the safe driving behavior data is first determined, and then the second weight value of the toll payment and toll evasion behavior data is determined. Then, the NF, NV, NE, α1, and α2 of each truck are substituted into the formula of the truck negative credit value calculation model for calculation to obtain the negative credit value of each truck.

[0076] S103, determine the target set of vehicles to be diverted based on the negative credit score of each truck;

[0077] In one possible implementation, when determining the target set of vehicles to be redirected, it is first determined whether the negative credit value of each truck is less than a preset credit value; if so, it is determined as a vehicle to be redirected; if not, it is determined as a non-redirecting vehicle; finally, after all the negative credit values ​​of each truck have been determined, all the vehicles to be redirected are determined as the target set of vehicles to be redirected.

[0078] For example, each freight vehicle has a negative credit value of C, with a preset negative credit value of 1. When C>=1, the vehicle has poor credit and is excluded from targeted traffic redirection. When C<1, the vehicle's identifier is cached as a targeted traffic redirection target. After all the judgments are completed, a set of target vehicles to be redirected is obtained.

[0079] In this embodiment, a set of target vehicles to be diverted is determined by credit value and the subsidy amount is pushed out. This can enable toll highways to use subsidy incentives to target trucks, making toll rate adjustments more accurate and applicable, increasing the attractiveness to trucks, and encouraging trucks to drive safely and reduce toll evasion.

[0080] S104: Calculate the subsidy fee for each vehicle in the target vehicle set to be diverted according to the truck cost subsidy model, and push the subsidy fee for each vehicle to be diverted to the corresponding client.

[0081] In this embodiment, the following steps are taken: First, the highway mileage, average speed SA, fuel consumption unit price, and toll standard of the target highway are obtained. Then, the non-highway mileage, average speed SB, and fuel consumption unit price of the non-highway target road segment are obtained. Next, the target value of the subsidy coefficient is determined, and a pre-set subsidy constant is obtained. Finally, the target value of the subsidy coefficient, highway mileage, fuel consumption unit price, toll standard, non-highway mileage, fuel consumption unit price, and subsidy constant are input into the truck cost subsidy model, and the subsidy cost for each vehicle to be diverted is output.

[0082] Understandably, the subsidy is calculated based on the driving routes on the target highway section after the diversion and the non-highway section before the diversion. If the target highway section is the same, the amount of subsidy given to vehicles traveling on different routes may differ in order to achieve the diversion.

[0083] Specifically, when determining the target value of the subsidy coefficient, the net revenue of the expressway is first calculated based on the expressway mileage, expressway fuel consumption unit price, expressway toll standard, non-expressway mileage, and non-expressway fuel consumption unit price. Then, based on the principle that the net revenue of the expressway is greater than 0, the first range of values ​​for the subsidy coefficient is determined. A target value is then determined from the first range as the target value of the subsidy coefficient, which is denoted as the first target value of the subsidy coefficient.

[0084] In a preferred embodiment, to avoid excessive traffic diversion causing congestion on highway sections, the traffic flow saturation of the target highway section should be taken into account when providing traffic diversion subsidies. Specifically, the traffic flow saturation of the target highway is obtained, and a second range of values ​​for the subsidy coefficient is determined based on the traffic flow saturation. The intersection of the first and second ranges is calculated to obtain the target range of values ​​for the subsidy coefficient. Finally, the target value of the subsidy coefficient is obtained based on the target range, and is denoted as the second target value of the subsidy coefficient.

[0085] Specifically, when determining the second range of subsidy coefficients based on traffic flow saturation, if the highway traffic flow saturation is lower than the first preset value (κ>1), the subsidy amount is increased, exceeding the increased cost for freight vehicles using the highway, thus guiding freight vehicles to use the highway. If the highway traffic flow saturation is greater than the first preset threshold but less than the second preset threshold (κ<1), the subsidy amount is less than the increased cost for freight vehicles using the highway, guiding some vehicles to use the highway to increase traffic flow during off-peak hours and improve highway utilization.

[0086] For example, for the trucks diverted, it is necessary to calculate the current net revenue of the highway. It is necessary to ensure that the net revenue of the highway is positive. This is a constraint on the subsidy coefficient. Therefore, the highway mileage LA, highway fuel consumption unit price PA, highway toll standard TA, non-highway mileage LB, and non-highway fuel consumption unit price PB are substituted into the highway net revenue calculation formula.

[0087] The net revenue N of the expressway can be calculated as follows: N = TA*LA - κ*(PA*LA + TA*LA - PB*LB) - β) = (1 - κ)TA*LA - κ*(PA*LA - PB*LB) - β.

[0088] It should be noted that N = P - DelaC, where P is the highway toll and DelaC is the subsidy fee.

[0089] To improve the profitability of expressways, the proposed solution sets N>0 when diverting traffic to the expressway, determines the first value range of the subsidy coefficient κ, obtains the traffic flow saturation of the target expressway, determines the second value range based on the traffic flow saturation, and finally obtains the target value range of the subsidy coefficient κ based on the first and second value ranges.

[0090] Wherein, (toll fee and travel time for the target highway) = (PA*LA+TA*LA, LA / SA), (toll fee and travel time for the non-target highway section) = (PB*LB, LB / SB).

[0091] The formula for calculating the subsidy amount DelaC in the truck expense subsidy model is as follows:

[0092] DelaC = κ*(PA*LA + TA*LA - PB*LB) + β, where β is the subsidy constant. The formula for calculating the time saved is: DeltaT = LA / SA - LB / SB.

[0093] Furthermore, in some preferred embodiments, the subsidy coefficient κ value is also related to the number of vehicles to be diverted in the diversion road segment set. When the number of vehicles to be diverted is large, the road segment closest to the target highway segment is preferred for diversion. Based on the diversion effect, the diversion range is expanded outward in a stepwise manner, and the diversion range is limited to the non-highway segments in the first target diversion path set.

[0094] In some preferred embodiments, the subsidy amount is also related to the vehicle type. For hazardous chemical transport vehicles, the target value κ is calculated and then multiplied by a proportional coefficient λ, which is greater than λ>1, in order to guide hazardous chemical vehicles to take the highway as much as possible, improve traffic efficiency, and reduce their potential impact on non-highway sections such as national highways.

[0095] For special types of vehicles, such as those carrying hazardous chemicals, the subsidy amount (DelaC) is:

[0096] DelaC=λ*κ*(PA*LA+TA*LA-PB*LB)+β1, where β1 is the subsidy constant for hazardous chemical vehicles.

[0097] Furthermore, after receiving the subsidy for each vehicle to be diverted, the information on highway subsidy offers is pushed to the target vehicles through the truck's onboard terminal and mobile app on the national public platform for road freight vehicles. Vehicle owners of these target vehicles can download the mini-program or app operated by the highway company to obtain corresponding targeted subsidy coupons. The coupon amount is DeltaC, making the toll fees for choosing highway segment A equal to those for choosing parallel national / provincial trunk line segment B. Since highway speeds are higher than those on national / provincial trunk lines, truck drivers can save DeltaT of travel time while paying the same toll fees, which is attractive to trucks and encourages trucks with high credit ratings to use highways.

[0098] In this embodiment, the vehicle subsidy push device first acquires safe driving behavior data and toll evasion behavior data for each truck in a pre-statistically collected initial truck set. Then, it inputs this data into a truck negative credit value calculation model, outputting a negative credit value for each truck. Based on these negative credit values, it determines a set of vehicles to be redirected. Finally, it calculates the subsidy for each vehicle in the target set based on the truck subsidy model and pushes the subsidy for each vehicle to the corresponding client. Because this application uses safe driving behavior data and toll evasion behavior data to calculate credit values ​​to determine the set of vehicles to be redirected, and combines this with the truck subsidy model to calculate and push the subsidy for each vehicle, it attracts truck drivers to choose highways as much as possible, thereby improving truck transportation efficiency.

[0099] Please see Figure 3 This document provides a flowchart illustrating another method for pushing out driving subsidy fees, as illustrated in the embodiments of this application. Figure 3 As shown, the method in this application embodiment may include the following steps:

[0100] S201, identify the target expressway for traffic diversion;

[0101] S202, based on the target expressway, at least one non-expressway target road segment is identified;

[0102] S203, load truck driving trajectory data from at least one non-highway target road segment within a preset period;

[0103] S204, The initial set of trucks is calculated based on truck driving trajectory data;

[0104] S205, Obtain safe driving behavior data and toll payment evasion behavior data for each truck in the pre-statistical initial truck set;

[0105] S206, input safe driving behavior data and toll evasion behavior data into the truck negative credit value calculation model, and output the negative credit value of each truck;

[0106] S207, determine the target set of vehicles to be diverted based on the negative credit score of each truck;

[0107] S208, obtain the highway mileage, highway fuel consumption unit price, and highway toll standard of the target highway;

[0108] S209, obtain the non-highway mileage and non-highway fuel consumption unit price of the non-highway target road segment;

[0109] S210, determine the target value of the subsidy coefficient and obtain the pre-set subsidy constant;

[0110] S211 inputs the target value of the subsidy coefficient, highway mileage, highway fuel consumption unit price, highway toll standard, non-highway mileage and non-highway fuel consumption unit price, and subsidy constant into the truck cost subsidy model, outputs the subsidy cost for each vehicle to be diverted, and pushes the subsidy cost for each vehicle to be diverted to the corresponding client.

[0111] In this embodiment, the vehicle subsidy push device first acquires safe driving behavior data and toll evasion behavior data for each truck in a pre-statistically collected initial truck set. Then, it inputs this data into a truck negative credit value calculation model, outputting a negative credit value for each truck. Based on these negative credit values, it determines a set of vehicles to be redirected. Finally, it calculates the subsidy for each vehicle in the target set based on the truck subsidy model and pushes the subsidy for each vehicle to the corresponding client. Because this application uses safe driving behavior data and toll evasion behavior data to calculate credit values ​​to determine the set of vehicles to be redirected, and combines this with the truck subsidy model to calculate and push the subsidy for each vehicle, it attracts truck drivers to choose highways as much as possible, thereby improving truck transportation efficiency.

[0112] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the embodiments of the apparatus of the present invention, please refer to the embodiments of the method of the present invention.

[0113] Please see Figure 4This diagram illustrates the structure of a driving subsidy payment push device provided in an exemplary embodiment of the present invention. The driving subsidy payment push device can be implemented as all or part of a terminal through software, hardware, or a combination of both. The device 1 includes a data acquisition module 10, a credit value output module 20, a vehicle set determination module 30, and a subsidy payment push module 40.

[0114] Data acquisition module 10 is used to acquire safe driving behavior data and toll payment evasion behavior data of each truck in the pre-statistical initial truck set;

[0115] The credit score output module 20 is used to input safe driving behavior data and toll evasion behavior data into the truck negative credit score calculation model and output the negative credit score of each truck.

[0116] The vehicle set determination module 30 is used to determine the target vehicle set to be diverted based on the negative credit value of each truck.

[0117] The subsidy fee push module 40 is used to calculate the subsidy fee for each vehicle in the target vehicle set to be diverted according to the truck fee subsidy model, and push the subsidy fee for each vehicle to be diverted to the corresponding client.

[0118] Optional, such as Figure 5 As shown, device 1 also includes:

[0119] Highway determination module 50 is used to determine the target highway to be diverted;

[0120] The non-highway target road segment determination module 60 is used to determine at least one non-highway target road segment based on the target highway;

[0121] The trajectory data loading module 70 is used to load truck driving trajectory data on at least one non-highway target road segment within a preset period;

[0122] The initial truck set statistics module 80 is used to calculate the initial truck set based on truck driving trajectory data.

[0123] It should be noted that the driving subsidy payment push device provided in the above embodiments is only illustrated by the division of the above functional modules when executing the driving subsidy payment push method. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the driving subsidy payment push device and the driving subsidy payment push method embodiments provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.

[0124] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0125] In this embodiment, the vehicle subsidy push device first acquires safe driving behavior data and toll evasion behavior data for each truck in a pre-statistically collected initial truck set. Then, it inputs this data into a truck negative credit value calculation model, outputting a negative credit value for each truck. Based on these negative credit values, it determines a set of vehicles to be redirected. Finally, it calculates the subsidy for each vehicle in the target set based on the truck subsidy model and pushes the subsidy for each vehicle to the corresponding client. Because this application uses safe driving behavior data and toll evasion behavior data to calculate credit values ​​to determine the set of vehicles to be redirected, and combines this with the truck subsidy model to calculate and push the subsidy for each vehicle, it attracts truck drivers to choose highways as much as possible, thereby improving truck transportation efficiency.

[0126] The present invention also provides a computer-readable medium having program instructions stored thereon, which, when executed by a processor, implement the method for pushing out driving subsidy fees provided in the above-described method embodiments.

[0127] The present invention also provides a computer program product containing instructions that, when run on a computer, causes the computer to execute the method for pushing out driving subsidy fees according to the various method embodiments described above.

[0128] Please see Figure 6 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application. Figure 6 As shown, terminal 1000 may include: at least one processor 1001, at least one network interface 1004, user interface 1003, memory 1005, and at least one communication bus 1002.

[0129] The communication bus 1002 is used to realize the connection and communication between these components.

[0130] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0131] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0132] The processor 1001 may include one or more processing cores. The processor 1001 connects to various parts within the electronic device 1000 using various interfaces and lines. It executes various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005. Optionally, the processor 1001 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 1001 may integrate one or a combination of several of the following: a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor 1001.

[0133] The memory 1005 may include random access memory (RAM) or read-only memory. Optionally, the memory 1005 may include a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1005 may also be at least one storage device located remotely from the aforementioned processor 1001. Figure 6 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a push application for driving subsidy payments.

[0134] exist Figure 6In the terminal 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and to obtain the user's input data; while the processor 1001 can be used to call the driving subsidy fee push application stored in the memory 1005, and specifically perform the following operations:

[0135] Obtain safe driving behavior data and toll payment / evasion behavior data for each truck in the pre-statistical initial truck set;

[0136] Input safe driving behavior data and toll evasion behavior data into the truck negative credit value calculation model, and output the negative credit value of each truck;

[0137] The target set of vehicles to be diverted is determined based on the negative credit score of each truck;

[0138] The subsidy amount for each vehicle in the target vehicle pool is calculated based on the truck subsidy model, and the subsidy amount for each vehicle is pushed to the corresponding client.

[0139] In one embodiment, before the processor 1001 performs the following operations: obtains the safe driving behavior data and toll payment / evasion behavior data for each truck in the pre-statistically collected initial set of trucks:

[0140] Identify the target highway for traffic diversion;

[0141] Identify at least one non-highway target road segment based on the target highway;

[0142] Load truck trajectory data from at least one non-highway target road segment within a preset period;

[0143] An initial set of trucks was determined based on truck driving trajectory data.

[0144] In one embodiment, when the processor 1001 calculates the subsidy cost for each vehicle in the target vehicle referral set based on the truck negative credit value calculation model, it specifically performs the following operations:

[0145] Obtain the highway mileage, highway fuel consumption per unit price, and highway toll standard of the target highway;

[0146] Obtain the mileage of the non-highway target road segment and the unit price of fuel consumption in the non-highway segment;

[0147] Determine the target value of the subsidy coefficient and obtain the pre-set subsidy constant;

[0148] Input the target value of the subsidy coefficient, highway mileage, highway fuel consumption unit price, highway toll standard, non-highway mileage and non-highway fuel consumption unit price, and subsidy constant into the truck cost subsidy model, and output the subsidy cost for each vehicle to be diverted.

[0149] In one embodiment, when the processor 1001 executes the target value for determining the subsidy coefficient, it specifically performs the following operations:

[0150] The net revenue of highways is calculated based on highway mileage, highway fuel consumption per unit price, highway toll standards, non-highway mileage, and non-highway fuel consumption per unit price.

[0151] Based on the principle that the net revenue of the expressway is greater than 0, the first range of values ​​for the subsidy coefficient is determined.

[0152] Obtain the traffic flow saturation of the target highway, and determine a second range of values ​​for the subsidy coefficient based on the traffic flow saturation;

[0153] Calculate the intersection of the first and second value ranges to obtain the target value range of the subsidy coefficient;

[0154] The target value of the subsidy coefficient is obtained based on the target value range.

[0155] In one embodiment, when the processor 1001 performs the operation of determining at least one non-highway target road segment based on the target highway, it specifically performs the following operations:

[0156] Find at least one non-highway segment corresponding to the target highway;

[0157] Calculate the difference between the segment mileage of each segment in at least one non-highway segment and the segment mileage of the target segment, and generate at least one difference.

[0158] Sort at least one difference in ascending order to generate at least one sorted difference.

[0159] From the starting position of at least one difference after sorting, a preset number of differences are obtained sequentially, and the road segments corresponding to the preset number of differences are determined as at least one non-highway target road segment.

[0160] In one embodiment, when the processor 1001 calculates the initial set of trucks based on truck trajectory data, it specifically performs the following operations:

[0161] The frequency of passage for each truck is calculated from the truck's driving trajectory data;

[0162] The truck identifiers with a passage frequency greater than a preset value are saved to obtain the initial truck set.

[0163] In one embodiment, when processor 1001 determines the target set of vehicles to be diverted based on the negative credit value of each truck, it specifically performs the following operations:

[0164] Determine whether the negative credit score of each truck is less than the preset negative credit score;

[0165] If so, then it is identified as a vehicle to be diverted;

[0166] If not, then it is determined to be a non-diversion vehicle;

[0167] Once all the negative credit values ​​for each truck have been determined, all the vehicles to be redirected will be identified as the target vehicle set.

[0168] In this embodiment, the vehicle subsidy push device first acquires safe driving behavior data and toll evasion behavior data for each truck in a pre-statistically collected initial truck set. Then, it inputs this data into a truck negative credit value calculation model, outputting a negative credit value for each truck. Based on these negative credit values, it determines a set of vehicles to be redirected. Finally, it calculates the subsidy for each vehicle in the target set based on the truck subsidy model and pushes the subsidy for each vehicle to the corresponding client. Because this application uses safe driving behavior data and toll evasion behavior data to calculate credit values ​​to determine the set of vehicles to be redirected, and combines this with the truck subsidy model to calculate and push the subsidy for each vehicle, it attracts truck drivers to choose highways as much as possible, thereby improving truck transportation efficiency.

[0169] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program for pushing out driving subsidy fees can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory, or random access memory, etc.

[0170] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A method for distributing driving subsidies, characterized in that, For vehicle traffic diversion, applied to a public platform for freight vehicles, the method includes: Obtain safe driving behavior data and toll payment / evasion behavior data for each truck in the pre-statistical initial truck set; The safe driving behavior data and toll evasion behavior data are input into the truck negative credit value calculation model, and the negative credit value of each truck is output. The target set of vehicles to be diverted is determined based on the negative credit score of each truck. The subsidy fee for each vehicle in the target vehicle set to be diverted is calculated based on the truck cost subsidy model, and the subsidy fee for each vehicle to be diverted is pushed to the corresponding client. Before obtaining the safe driving behavior data and toll payment / evasion behavior data of each truck in the pre-statistically collected initial truck set, the process also includes: Identify the target highway for traffic diversion; Based on the target expressway, at least one non-expressway target road segment shall be identified; Load truck trajectory data from at least one non-highway target road segment within a preset period; An initial set of trucks is calculated based on the truck driving trajectory data; When calculating the diversion subsidy, the traffic saturation of the target highway section should be taken into account, based on the principle that the net revenue of the highway is greater than 0.

2. The method according to claim 1, characterized in that, The calculation of the subsidy cost for each vehicle in the target vehicle pool to be redirected, based on the truck negative credit value calculation model, includes: Obtain the highway mileage, highway fuel consumption per unit price, and highway toll standard of the target highway; Obtain the non-highway mileage and non-highway fuel consumption unit price for each of the aforementioned non-highway target road segments; The target value of the subsidy coefficient for the truck cost subsidy model is determined based on each vehicle to be diverted, and the pre-set subsidy constant is obtained. The target value of the subsidy coefficient, the highway mileage, the highway fuel consumption unit price, the highway toll standard, the non-highway mileage and the non-highway fuel consumption unit price, and the subsidy constant are input into the truck cost subsidy model, and the subsidy cost for each vehicle to be diverted is output.

3. The method according to claim 2, characterized in that, The target value of the subsidy coefficient for the truck cost subsidy model determined based on each vehicle to be diverted includes: The net revenue of the expressway is calculated based on the expressway mileage, expressway fuel consumption unit price, expressway toll standard, non-expressway mileage, and non-expressway fuel consumption unit price. The net revenue of the expressway is set to be greater than 0, and the first range of values ​​for the subsidy coefficient is determined. The target value of the subsidy coefficient is obtained based on the first value range.

4. The method according to claim 3, characterized in that, Also includes: Obtain the traffic flow saturation of the target highway, and determine a second value range of the subsidy coefficient based on the traffic flow saturation; Calculate the intersection of the first value range and the second value range to obtain the target value range of the subsidy coefficient; The target value of the subsidy coefficient is obtained based on the target value range.

5. The method according to claim 1, characterized in that, The determination of at least one non-highway target road segment based on the target highway includes: Find at least one non-highway segment that corresponds to the target highway and denote it as the first target diversion segment set; Calculate the difference between the mileage of each non-highway segment and the mileage of the target segment, and generate a difference sequence; The road segments corresponding to the differences before the median difference in the difference sequence are determined as the second target diversion road segment set.

6. The method according to claim 1, characterized in that, The initial set of trucks is calculated based on the truck driving trajectory data, including: The frequency of passage of each truck is calculated from the truck's driving trajectory data; The truck identifiers with a passage frequency greater than a preset value are saved to obtain an initial truck set.

7. The method according to claim 1, characterized in that, The step of determining the target set of vehicles to be diverted based on the negative credit value of each truck includes: Determine whether the negative credit value of each truck is less than a preset negative credit value; If so, then it is identified as a vehicle to be diverted; If not, then it is determined to be a non-diversion vehicle; Once the negative credit values ​​of each truck have been determined, all the vehicles to be redirected will be identified as the target vehicle set.

8. A device for pushing out driving allowance payments using the method described in any one of claims 1-7, characterized in that, The device includes: The data acquisition module is used to acquire safe driving behavior data and toll payment evasion behavior data for each truck in the pre-statistical initial truck set; The negative credit value output module is used to input the safe driving behavior data and toll evasion behavior data into the truck negative credit value calculation model and output the negative credit value of each truck. The module for determining the set of vehicles to be diverted is used to determine the target set of vehicles to be diverted based on the negative credit value of each truck. The subsidy fee push module is used to calculate the subsidy fee for each vehicle in the target vehicle set to be redirected according to the truck fee subsidy model, and push the subsidy fee for each vehicle to be redirected to the corresponding client.

9. A terminal, characterized in that, include: A processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and executed the method steps as claimed in any one of claims 1-7.

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