Method and device for determining the driving route of freight vehicles

Abstract

The present invention discloses a method and device for determining the driving route of a freight vehicle. First, after determining that the target freight vehicle is currently in an initial area, all routes of the target freight vehicle driving out of the preset target area are determined, and a scenario is established for each route in combination with historical shipping orders and the expected probability of the scenario is determined, and then the expected value of each route is obtained using relevant information such as shipping orders, and then the driving route of the target freight vehicle is determined according to the expected value of the route. The present application uses historical shipping orders to predict the generation of future orders and the expected value of the route, and realizes the determination of the optimal route from all drivable routes, so as to maximize the freight benefits of the freight vehicle, thereby reducing the economic losses of users.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics information processing, and in particular to a method and device for determining a travel route of a freight vehicle. Background Art

[0002] Currently, in the field of logistics, especially in the field of truck freight, there is the concept of "desert city", which has a huge impact on freight. A "desert city" is a city where the incoming volume is far greater than the outgoing volume. If vehicles deliver goods to a "desert city", not only will they not be profitable, but they will also incur losses. Moreover, "desert cities" are not fixed. It is very likely that changes in seasonal climate and other factors will cause certain regions or cities to form "desert cities" or cause certain "desert cities" to disappear.

[0003] When a vehicle delivers goods to a "desert city", it is very likely that the vehicle will have no subsequent connection orders in the city and will have to leave the city empty-handed; in this case, the greater the cost of empty-handed driving, the greater the loss to the carrier. In addition, even if some vehicles find a connection order in the city, the delivery time of the connection order is a few days later, which means that the vehicle needs to wait in place, and the suspension of the vehicle during this period will also bring certain losses.

[0004] However, for vehicles that have entered the "desert city", how to choose a suitable route to leave the "desert city" is an urgent problem to be solved. Summary of the invention

[0005] The main purpose of the present invention is to provide a method for determining the driving route of a freight vehicle, which is characterized by comprising: after determining that a target freight vehicle is currently in an initial area, using a preset strategy to determine all routes of the target freight vehicle driving out of a preset target area; if there is a to-be-processed route among all the routes determined, for each to-be-processed route, after obtaining the shipping order information of all the areas passed by the to-be-processed route in the past N days, according to the shipping orders of each day in the N days, N scenarios are correspondingly established, wherein each of the N scenarios contains the shipping order information of all the areas passed by the to-be-processed route within one day; the target freight vehicle is matched with the shipping orders in the N scenarios one by one, and all matching schemes of the to-be-processed route are traversed; the expected value of each matching scheme corresponding to the to-be-processed route is calculated, and the maximum expected value of all matching schemes corresponding to the to-be-processed route is obtained; the maximum expected value of all matching schemes corresponding to the to-be-processed route is determined as the route expected value of the corresponding route; based on the size of the route expected value of each route among all the routes, the driving route of the target freight vehicle is determined.

[0006] Optionally, after determining that the target freight vehicle is currently in the initial area, a preset strategy is used to determine all routes for the target freight vehicle to exit the preset target area, including: after determining that the target freight vehicle is currently in the initial area, determining a preset target area with the center of the initial area as the center of the circle or the current position of the target freight vehicle as the center of the circle and a preset distance as the radius, wherein the preset target area includes a boundary of the preset target area; based on the intersection of the boundary of the preset target area with each road, determining all routes from the current position of the target freight vehicle to the intersection; and determining all routes from the current position of the target freight vehicle to the intersection as all routes exiting the preset target area.

[0007] Optionally, the expected value of each matching scheme corresponding to the route to be processed is calculated to obtain the maximum expected value of all matching schemes corresponding to the route to be processed, including: wherein, the expected value of the matching scheme = (price of the matching order - cost of empty driving for accepting the order) × expected probability of the scenario in which the matching order is located.

[0008] Optionally, the calculating of the expected value of each matching scheme corresponding to the line to be processed to obtain the maximum expected value of all matching schemes corresponding to the line to be processed includes: determining the occurrence probability of each scene in N scenes according to a probability model; regularizing the occurrence probability of the N scenes to obtain the expected probability of each scene in the N scenes.

[0009] The optional method of determining the travel route of the target freight vehicle based on the size of the route expected value of each route among all routes includes: judging whether there is a to-be-processed route among all routes, and if so, calculating the route expected value of the to-be-processed route; if not, determining the travel route of the target freight vehicle based on the size of the route expected values ​​of all routes.

[0010] According to a second aspect of the present invention, there is provided a device for determining a route of a freight vehicle, characterized in that it comprises: a determination unit, after determining that a target freight vehicle is currently in an initial area, determines all routes of the target freight vehicle out of a preset target area using a preset strategy; a processing unit, if it is determined that there is a to-be-processed route among all the routes obtained, for each to-be-processed route, after obtaining the shipping order information of all the areas passed by the to-be-processed route in the past N days, establishes N scenarios correspondingly according to the shipping orders of each day in the N days, wherein each of the N scenarios contains the shipping order information of all the areas passed by the to-be-processed route in one day. order information; a matching unit, for each route to be processed, matches the target freight vehicle with the shipping orders in N scenarios one by one, and traverses all matching schemes for the route to be processed; a calculation unit, for each route to be processed, calculates the expected value of each matching scheme corresponding to the route to be processed, and obtains the maximum expected value of all matching schemes corresponding to the route to be processed; a route expected value unit, for each route to be processed, determines the maximum expected value of all matching schemes corresponding to the route to be processed as the route expected value of the corresponding route; a judgment unit, configured to judge the size of the expected values ​​of all routes, and determine the route with the largest expected value as the route for the freight vehicle.

[0011] Optionally, the determination unit is configured to include: a first determination module, configured to, after determining that the target freight vehicle is currently in the initial area, determine a preset target area with the center of the initial area as the center of the circle or the current position of the target freight vehicle as the center of the circle and a preset distance as the radius, wherein the preset target area includes the boundary of the preset target area; a second determination module, configured to determine all routes from the current position of the target freight vehicle to the intersection based on the intersection of the boundary of the preset target area with each road; a third determination module, configured to determine all routes from the current position of the target freight vehicle to the intersection as all routes out of the preset target area.

[0012] Optionally, the calculation unit includes: wherein the expected value of the matching solution = (the price of the matching order - the cost of empty driving for accepting the order) × the expected probability of the scenario in which the matching order is located.

[0013] According to a third aspect of the present invention, there is provided a computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions are used to cause the computer to execute the method for determining a travel route of a freight vehicle as described in any one of the embodiments of the first aspect.

[0014] According to a fourth aspect of the present invention, there is provided an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the method for determining the travel route of a freight vehicle as described in any one embodiment of the first aspect.

[0015] In the embodiment of the present invention, after first determining that the target freight vehicle is currently in the initial area, all routes for the target freight vehicle to drive out of the preset target area are determined, and scenarios are established for each route in combination with historical shipping orders and the expected probability of the scenarios is determined, and then the expected value of each route is obtained using shipping orders and other related information, and then the driving route of the target freight vehicle is determined according to the expected value of the route. The present application uses historical shipping orders to predict the generation of future orders and the expected value of the route, and realizes the determination of the optimal route from all drivable routes to maximize the freight benefits of the freight vehicle, thereby reducing the economic losses of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 is a flow chart of a method for determining a travel route of a freight vehicle according to an embodiment of the present invention;

[0018] Figure 2 a is a diagram of a pre-established probability model application scenario according to an embodiment of the present invention;

[0019] Figure 2 b is another application scenario diagram of the pre-established probability model according to an embodiment of the present invention

[0020] Figure 3 is another application scenario diagram of the pre-established probability model according to an embodiment of the present invention;

[0021] Figure 4 is another application scenario diagram of the pre-established probability model according to an embodiment of the present invention;

[0022] Figure 5 2 is a schematic diagram of a device for determining a travel route of a freight vehicle according to an embodiment of the present invention.

[0023] Figure 6is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only embodiments of a part of the present disclosure, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so as to describe the embodiments of the present invention described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0027] According to an embodiment of the present invention, a method for determining a travel route of a freight vehicle is provided. Figure 1 As shown, the method includes the following steps 101 to 106:

[0028] Step 101: After determining that the target freight vehicle is currently in the initial area, using a preset strategy, determine all routes for the target freight vehicle to drive out of the preset target area.

[0029] Region refers to an artificially defined area such as a city, region or administrative area, and the initial region can be a "desert city" region, or a region where the current available shipment order volume is 0 and the ratio of historical purchase volume to historical shipment volume is greater than a preset multiple, which can be set according to actual conditions. The preset target region can be preset or determined based on the initial region.

[0030] In this embodiment, the execution subject of the method for determining the driving route of the target freight vehicle may be a processing device such as a server. Before determining all routes out of the preset target area, the server may pre-determine whether the target freight vehicle is currently in the initial area. If it is determined that the target freight vehicle is currently in the initial area, then all routes out of the preset target area may be further determined.

[0031] Specifically, for the process of determining whether the target freight vehicle is currently in the initial area, the execution entity can directly receive the target freight vehicle currently in the initial area sent by the user terminal, and request to obtain the route information to determine that the target freight vehicle is currently in the initial area; the execution entity can also first receive the current positioning information of the target freight vehicle sent by the user terminal, and determine whether the current location of the target freight vehicle is within the initial area. If it is determined that the current location of the target freight vehicle is within the initial area, it can be determined that the target freight vehicle is currently within the initial area.

[0032] As an optional implementation method of this embodiment, after determining that the target freight vehicle is currently in the initial area, using a preset strategy to determine all routes for the target freight vehicle to exit the preset target area includes: after determining that the target freight vehicle is currently in the initial area, determining a preset target area with the center of the initial area as the center of the circle or the current position of the target freight vehicle as the center of the circle and a preset distance as the radius, wherein the preset target area includes a boundary of the preset target area; based on the intersection of the boundary of the preset target area with each road, determining all routes from the current position of the target freight vehicle to the intersection; determining all routes from the current position of the target freight vehicle to the intersection as all routes exiting the preset target area.

[0033] In this embodiment, after determining that the target freight vehicle is currently in the initial area, the execution subject can determine on the map a preset target area with the center of the initial area where the target freight vehicle is located as the center of the circle and the preset distance as the radius; the execution subject can also use the current positioning information of the target freight vehicle, and then determine on the map a preset target area with the point corresponding to the positioning information as the center of the circle and the preset distance as the radius. When determining the preset distance as the radius, it can be determined based on the expected order receiving distance or fixed distance uploaded by the user of the target freight vehicle. For example, the user of the target freight vehicle expects to find an order within a driving range of 200KM, then the preset target area is a circular preset target area on the map with the center of the initial area where the target freight vehicle is located as the center of the circle, or the current location of the target freight vehicle as the center of the circle, and 200KM as the radius. The circumference of the circular preset target area is the boundary of the preset target area.

[0034] Specifically, after determining the preset target area, the intersections of the preset target area and various roads on the map can be determined according to the boundaries of the preset target area, and the coordinates of the intersections can be obtained; then, route acquisition technology or vehicle navigation technology can be used to determine all drivable routes between the current location of the target freight vehicle and the intersection coordinates, and all drivable routes are determined as all routes out of the target area.

[0035] Step 102: If there is a pending route among all the routes determined, for each pending route, after obtaining the shipping order information of all the areas passed by the pending route in the past N days, N scenarios are established according to the shipping orders of each day in the N days, wherein each of the N scenarios contains the shipping order information of all the areas passed by the pending route in one day. The pending route is a route for which the route expectation value has not been calculated.

[0036] In this embodiment, the number of routes to be processed may be one or more. When determining the route expected value of each route to be processed, all areas passed by each route to be processed may be first determined, and the shipping order information of all areas passed by each route to be processed in the past N days is obtained. Then, for each route to be processed, N scenarios are established according to the shipping orders of each day in N days, wherein each of the N scenarios contains the shipping order information of all areas passed by a route to be processed in one day.

[0037] In addition, all the areas through which the to-be-processed route passes may be one or more areas divided according to administrative area standards.

[0038] Specifically, the execution entity can obtain the shipping order information of all regions that each route to be processed passes through in the past N days (for example, the first 52 weeks in history) from the database or other storage devices. The shipping order information includes various related information such as the price, loading place, unloading place, loading time, unloading time, etc. of the shipping order. The shipping order refers to the freight order starting from the region or city on the route and leaving the region or city.

[0039] For example, on one of all the pending routes, the areas (or cities) passed by when leaving the preset target area are Jiuquan City, Zhangye City and Jinchang City. Then the execution entity can obtain the historical shipment order information of Jiuquan City, Zhangye City and Jinchang City for N days respectively. For ease of understanding, take the historical shipment order information of Jiuquan City, Zhangye City and Jinchang City from August 20 to 22, 2020 as an example, and establish three corresponding scenarios as follows:

[0040] According to the shipping orders for each of the three days, three scenarios are established:

[0041] On August 20, 2020, Jiuquan City has a shipment order A, but Zhangye City and Jinchang City have no shipment orders. Then the corresponding scenario can be established as follows:

[0042] Scenario 1 (the scenario corresponding to August 20, 2020):

[0043] Jiuquan City, Order A: from Jiuquan City to Dunhuang City, the order price is 1,000 yuan;

[0044] It should be noted that the scenario can be created in the form of a list. In addition, the information of the shipping order in the above scenario is only for the convenience of explanation. In practice, more information, even all the information of the shipping order, can be included when creating the scenario.

[0045] On August 21, 2020, Jiuquan City has one shipment order, Jinchang City has two shipment orders, and Zhangye City has no shipment order. Then the corresponding scenario can be established as follows:

[0046] Scenario 2 (the scenario corresponding to August 21, 2020):

[0047] Jiuquan City, Order B: From Jiuquan City to Jiayuguan City, the order price is 100 yuan;

[0048] Jinchang City, Order C: From Jinchang City to Wuwei City, the order price is 200 yuan;

[0049] Jinchang City, Order D: From Jinchang City to Wuwei City, the order price is 200 yuan;

[0050] On August 22, 2020, Jiuquan City had two shipment orders, but Zhangye City and Jinchang City had no shipment orders. Then the corresponding scenario can be established as follows:

[0051] Scenario 3 (the scenario corresponding to August 22, 2020):

[0052] Jiuquan City, Order E: from Jiuquan City to Jiayuguan City, the order price is 100 yuan;

[0053] Jiuquan City, Order F: from Jiuquan City to Jiayuguan City, the order price is 100 yuan;

[0054] In the above example, three scenarios are established based on the shipment orders for each of the three days from August 20 to 22, 2020. Each scenario contains the shipment order information of the three regions of Jiuquan City, Zhangye City and Jinchang City within one day. That is, each scenario contains the shipment order information of the three cities of Jiuquan City, Zhangye City and Jinchang City (that is, all regions) passed by this route to be processed within one day.

[0055] It should be noted that the process of establishing a scenario in this step is performed for each line to be processed, so each line to be processed has N scenarios corresponding to it.

[0056] Step 103: For each route to be processed, the target freight vehicle is matched with the shipping orders in N scenarios one by one, and all matching solutions for the route to be processed are traversed.

[0057] Through the above method, after establishing the corresponding N scenarios for each route to be processed, the target freight vehicle and the shipping orders in the N scenarios can be matched one by one to traverse all matching solutions, for example:

[0058] For example, on one of all pending routes, assuming that there are currently 6 shipping orders in N scenarios, order A to order F, a total of 6 solutions can be matched. The matching solutions after matching are as follows:

[0059] Solution 1: The target delivery vehicle is matched with order A;

[0060] Solution 2: The target delivery vehicle is matched with order B; .......

[0062] Solution 5, the target delivery vehicle is matched with order E;

[0063] Solution 6, the target delivery vehicle is matched with order F.

[0064] It should be noted that the number of actual matching solutions may be relatively large, and the algorithm in the prior art or the technology of matching and traversing all solutions by computer can be used to save matching time, which will not be described in detail here. In addition, this step is performed for each line to be processed, and each line to be processed has its own corresponding N scenarios, so each line to be processed has its own corresponding multiple matching solutions.

[0065] Step 104: For each route to be processed, calculate the expected value of each matching solution corresponding to the route to be processed, and obtain the maximum expected value of all matching solutions corresponding to the route to be processed; wherein, the expected value of the matching solution = (price of the matching order - cost of empty driving for accepting the order) × the expected probability of the scenario in which the matching order is located.

[0066] Specifically, the matching order price is the price of the shipment order matched in the solution. Taking order A matched in Solution 1 as an example, the matching order price is 1,000 yuan.

[0067] The cost of driving an order without a driver is the cost incurred when the vehicle drives to the loading place without a driver in order to accept an order. The distance from the vehicle to the loading place is known, that is, the distance from the current location of the vehicle to the loading place of a certain delivery order (the relevant value can be obtained by using existing technologies such as navigation), and the cost incurred by the vehicle per unit distance can also be obtained based on existing data, such as the vehicle's fuel consumption per 100 kilometers, fuel prices, and other data; the cost of driving an order without a driver can then be obtained based on the above data. For example, the cost of driving a vehicle per unit distance can be combined to set the cost per unit distance to a fixed value, and then the product of the driving distance and the fixed value can be used as the cost of driving an order without a driver.

[0068] The expected probability of the scene where the matching order is located is the expected probability of the scene where the shipping order matched in the solution is located. The expected probability of the N scenes corresponding to the pending route is obtained by regularizing the occurrence probability of the N scenes corresponding to the pending route, and the occurrence probability of the scene is obtained by using the probability model, that is, the occurrence probability of each scene in the N scenes can be determined according to the probability model; the probability model is a model established based on the shipping order information of all areas passed by the pending route in the past N days to determine the occurrence probability of the N scenes in history tomorrow. That is, for each pending route, the occurrence probability of each scene in the N scenes is determined according to the probability model, and then the occurrence probability of the N scenes is regularized to obtain the expected probability of each scene in the N scenes, and then the expected probability of the scene where the matching order is located is obtained.

[0069] This embodiment provides a detailed description of the establishment of the model.

[0070] First of all, it should be noted that the model is obtained before the expected value of the matching solution is calculated, but its establishment is not limited to being performed before or after a certain step before the expected value of the matching solution is calculated; the probability model can also be established in advance. In addition, the model can also be established separately in an independent system or device, and then directly used by the system through acquisition.

[0071] As an exemplary embodiment, the method for establishing the model may be: establishing a probability model according to the distance between each scene and tomorrow in the time dimension; or establishing a probability model according to the distance between each scene and tomorrow in the period dimension; or establishing a probability model according to the distance between each scene and tomorrow in the time dimension and the period dimension. Each case is described below:

[0072] 1. According to the relationship between each scene and tomorrow in the time dimension, a probability model is established, and the probability of occurrence of N scenes is obtained according to the probability model (it can also be understood as the probability of occurrence or recurrence of N scenes tomorrow). Among them, under the time dimension, the expected probability of occurrence of the scene is negatively correlated with the number of days from the actual occurrence date of the scene to tomorrow. Specifically, the farther the actual occurrence date of the scene is from tomorrow, the lower the probability of occurrence; the closer the actual occurrence date of the scene is to tomorrow, the higher the probability of occurrence. Figure 2 As shown in the figure, the closer the actual occurrence date of the scene is to tomorrow, the higher the probability of occurrence is. In the figure, since scene 2 is closer to tomorrow than scene 1, the probability of occurrence of scene 2 is higher than that of scene 1.

[0073] 2. Establish a probability model based on the relationship between each scenario and tomorrow in the cycle dimension, and obtain the probability of occurrence of N scenarios tomorrow based on the probability model. Among them, under the cycle dimension, the expected probability of occurrence of the scenario is positively correlated with the number of days from the actual occurrence date of the scenario in the cycle to tomorrow. Specifically, the farther the position of the actual occurrence date of the scenario in the cycle is from the position of tomorrow in the cycle, the lower the probability of occurrence; the closer the position of the actual occurrence date of the scenario in the cycle is to the position of tomorrow in the cycle, the higher the probability of occurrence. It should be noted here that the cycle can be a week, month, quarter, year, or other cycle, or a cycle such as the alternating time between the peak and off-peak seasons of logistics and freight. For example Figure 3 As shown, if scenario 2 and tomorrow are both on Wednesday, and scenario 1 is on Sunday, from the periodic dimension, scenario 2 is closer to tomorrow, while scenario 1 is farther away, so scenario 2 is more likely to occur than scenario 1.

[0074] 3. According to the distance relationship between each scene and tomorrow in the time dimension and cycle dimension, a probability model is established, and the occurrence probability of N scenes tomorrow is obtained according to the probability model. It can be said that the farther the actual occurrence date of the scene is from tomorrow, and the farther the position of the actual occurrence date of the scene in the cycle is from the position of tomorrow in the cycle, the lower the occurrence probability; the closer the actual occurrence date of the scene is to tomorrow, and the closer the position of the actual occurrence date of the scene in the cycle is from the position of tomorrow in the cycle, the higher the occurrence probability; if Figure 4 As shown, by combining the time dimension and the period dimension, a probability model can be established to obtain the occurrence probability of scenario 2 and scenario 1.

[0075] Furthermore, a more specific model is given to illustrate the establishment of the third probability model mentioned above, as follows.

[0076] The probability model is established based on the distance between the scene and tomorrow in the time dimension and cycle dimension:

[0077]

[0078] Among them, T x It represents the value corresponding to the scene x in the time dimension. This value increases as the difference between the date where the scene is located and tomorrow increases.

[0079] C x Indicates the value corresponding to scene x in the period dimension. This value can be set manually, such as Figure 4 As shown in , the two scenarios involve a total of 4 cycles, so the C in one cycle with tomorrow x is 0.9, which is one cycle of C x is 0.8, which is two cycles of C x is 0.7, a difference of three cycles of C x is 0.6, then finally C2=0.8, C1=0.6; this value can be directly obtained using historical data, such as The formula becomes, Among them, D0 represents the date of tomorrow, D x Indicates the date of scene x. For example, since the difference between scene 1 and tomorrow is 21 days, D0-D1=21; the difference between scene 2 and tomorrow is 6 days, D0-D2=6. The following also uses N scenes corresponding to a certain line to be processed among all the lines to be processed to illustrate. After obtaining the occurrence probability of each scene in the N scenes, the N occurrence probabilities can be regularized to obtain the expected probability of each N scene, so that the sum of the expected probabilities of the N scenes can be guaranteed to be 1.

[0080] The expected probability of matching the scene where the order is located is the expected probability of the scene where the shipping order matched in the plan is located. Since there is a corresponding relationship between the shipping order and the scene in the process of establishing the scene, when the expected probability of the scene is obtained, the expected probability of the scene where the matching order is located is also obtained.

[0081] Corresponding to the above example description, the expected probability of matching the order scenario is as follows:

[0082]

[0083] It should be noted that this step is performed for each line to be processed. In addition, each line to be processed has its own corresponding N scenarios, so each N scenario corresponding to each line to be processed has its own occurrence probability and expected probability.

[0084] Step 105: for each line to be processed, determining the maximum expected value among all matching solutions corresponding to the line to be processed as the line expected value of the corresponding line;

[0085] The expected value with the largest value in the matching solution is used as the line expected value of the line corresponding to the line to be processed. It should be noted that the expected value and the line expected value obtained can be negative values. For example, when the empty driving cost of receiving an order is greater than the price of the matching order, a negative value may be generated.

[0086] Step 106: Determine the travel route of the target freight vehicle based on the route expectation value of each route among all routes.

[0087] In this embodiment, the execution entity can sort the route expectation values ​​of all routes, determine the routes corresponding to the top M route expectation values ​​as the driving routes of the target freight vehicle, and then send the determined M target freight vehicle driving routes to the user terminal, so that the user can select one of the M target freight vehicle driving routes as the driving route of the target freight vehicle through the user terminal.

[0088] More specifically, when determining the driving route of the target freight vehicle, the executing entity may also sort the sizes of the route expectation values, determine the route corresponding to the maximum route expectation value as the driving route of the target freight vehicle, and then send the driving route of the target freight vehicle to the user terminal so that the user can drive along the route through the user terminal.

[0089] As an optional implementation method of this embodiment, determining the driving route of the target freight vehicle based on the size of the route expected values ​​of all routes includes: judging whether there is a to-be-processed route among all routes, and if so, calculating the route expected value of the to-be-processed route; if not, determining the driving route of the target freight vehicle based on the size of the route expected values ​​of all routes.

[0090] In this embodiment, after determining the expected value of the route to be processed based on steps 101 to 105, the executing entity may further determine whether there are any routes to be processed among all routes. If so, the expected value of the route to be processed may continue to be calculated according to the method of steps 102 to 105 until there are no routes to be processed among all routes, and then determine the travel route of the target freight vehicle based on the size of the expected value of each route among all routes.

[0091] From the above description, it can be seen that the present invention achieves the following technical effects: historical shipping orders are used to predict the generation of future orders and the expected value of the route, and the optimal route is determined from all drivable routes to maximize the freight benefits of the freight vehicles, thereby reducing the economic losses of users.

[0092] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0093] According to an embodiment of the present invention, a device for implementing the above-mentioned method of determining a driving route of a freight vehicle is also provided. Figure 5 As shown, the device comprises:

[0094] The determination unit 501, after determining that the target freight vehicle is currently in the initial area, uses a preset strategy to determine all routes for the target freight vehicle to drive out of the preset target area.

[0095] Processing unit 502, if it is determined that there is a to-be-processed route among all the routes obtained, for each to-be-processed route, after obtaining the shipping order information of all areas passed by the to-be-processed route in the past N days, N scenarios are established accordingly according to the shipping orders of each day in the N days, wherein each of the N scenarios contains the shipping order information of all areas passed by the to-be-processed route within one day.

[0096] The matching unit 503 matches the target freight vehicle with the shipping orders in N scenarios one by one for each route to be processed, and traverses all matching solutions for the route to be processed.

[0097] The calculation unit 504 calculates the expected value of each matching scheme corresponding to each route to be processed, and obtains the maximum expected value of all matching schemes corresponding to the route to be processed; wherein, the expected value of the matching scheme = (the price of the matching order - the cost of empty driving when accepting the order) × the expected probability of the scenario in which the matching order is located.

[0098] The line expectation value unit 505 determines, for each line to be processed, the maximum expected value among all matching solutions corresponding to the line to be processed as the line expectation value of the corresponding line.

[0099] The judgment unit 506 is configured to judge the expected values ​​of all routes and determine the route with the largest expected value as the route for the freight vehicle to travel.

[0100] As an optional implementation of this embodiment, the determination unit 501 is configured to include: a first determination module, configured to, after determining that the target freight vehicle is currently in the initial area, determine a preset target area with the center of the initial area as the center of the circle or the current location of the target freight vehicle as the center of the circle and a preset distance as the radius, wherein the preset target area includes the boundary of the preset target area; a second determination module, configured to determine all routes from the current location of the target freight vehicle to the intersection based on the intersection of the boundary of the preset target area with each road; a third determination module, configured to determine all routes from the current location of the target freight vehicle to the intersection as all routes out of the preset target area.

[0101] As an optional implementation method of this embodiment, the process of establishing the pre-established probability model includes: establishing based on the distance relationship between each scene and tomorrow in the time dimension; and / or establishing based on the distance relationship between each scene and tomorrow in the period dimension.

[0102] An embodiment of the present invention provides an electronic device, such as Figure 6 As shown, the electronic device includes one or more processors 61 and a memory 62. Figure 6 A processor 61 is taken as an example.

[0103] The controller may further include: an input device 63 and an output device 64 .

[0104] The processor 61, the memory 62, the input device 63 and the output device 64 may be connected via a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0105] The processor 61 may be a central processing unit (CPU). The processor 61 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips. A general-purpose processor may be a microprocessor or the processor may be any conventional processor.

[0106] The memory 62 is a non-transitory computer-readable storage medium that can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the control method in the embodiment of the present disclosure. The processor 61 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions and modules stored in the memory 62, that is, the method for determining the driving route of the freight vehicle in the above method embodiment is implemented.

[0107] The memory 62 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of a processing device operated by the server, etc. In addition, the memory 62 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 62 may optionally include a memory remotely arranged relative to the processor 61, and these remote memories may be connected to a network connection device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0108] The input device 63 can receive input digital or character information and generate key signal input related to user settings and function control of the processing device of the server. The output device 64 can include display devices such as a display screen.

[0109] One or more modules are stored in the memory 62, and when executed by one or more processors 61, the execution is as follows: Figure 1 The method shown.

[0110] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment method can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned motor control methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0111] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for determining a travel route of a freight vehicle, characterized in that: include: After determining that the target freight vehicle is currently in the initial area, using a preset strategy, all routes for the target freight vehicle to exit the preset target area are determined, including: after determining that the target freight vehicle is currently in the initial area, determining a preset target area with the center of the initial area as the center of the circle or the current location of the target freight vehicle as the center of the circle and a preset distance as the radius, wherein the preset target area includes a boundary of the preset target area; based on the intersection of the boundary of the preset target area and each road, all routes from the current location of the target freight vehicle to the intersection are determined; all routes from the current location of the target freight vehicle to the intersection are determined as all routes exiting the preset target area; If there is a pending route among all the routes obtained, for each pending route, after obtaining the shipping order information of all the areas passed by the pending route in the past N days, N scenarios are established according to the shipping orders of each day in the N days, wherein each of the N scenarios contains the shipping order information of all the areas passed by the pending route in one day; Match the target freight vehicles with the shipping orders in N scenarios one by one, and traverse all matching solutions for the routes to be processed; Calculate the expected value of each matching solution corresponding to the line to be processed, and obtain the maximum expected value of all matching solutions corresponding to the line to be processed; The maximum expected value among all matching solutions corresponding to the line to be processed is determined as the line expected value of the corresponding line; Determining the travel route of the target freight vehicle based on the route expectation value of each route among all routes; Among them, the expected value of the matching plan = (the price of the matching order - the cost of empty driving when accepting the order) × the expected probability of the scenario in which the matching order is located.

2. The method for determining the travel route of a freight vehicle according to claim 1, characterized in that: The step of calculating the expected value of each matching solution corresponding to the line to be processed to obtain the maximum expected value of all matching solutions corresponding to the line to be processed includes: The occurrence probability of each of the N scenes is determined according to the probability model, and the occurrence probability of the N scenes is regularized to obtain the expected probability of each of the N scenes.

3. The method for determining the travel route of a freight vehicle according to claim 1, characterized in that: Determining the driving route of the target freight vehicle based on the route expectation value of each route among all routes includes: Determine whether there is a line to be processed among all the lines, and if so, calculate the line expected value of the line to be processed; If it does not exist, the driving route of the target freight vehicle is determined based on the size of the route expectation values ​​of all routes.

4. A device for determining a travel route of a freight vehicle, characterized in that: include: The determination unit, after determining that the target freight vehicle is currently in the initial area, determines all routes for the target freight vehicle to exit the preset target area using a preset strategy, wherein the determination unit is configured to include: a first determination module, configured to determine a preset target area with the center of the initial area as the center of a circle or the current location of the target freight vehicle as the center of a circle and a preset distance as the radius after determining that the target freight vehicle is currently in the initial area, wherein the preset target area includes a boundary of the preset target area; a second determination module, configured to determine all routes from the current location of the target freight vehicle to the intersection based on the intersection of the boundary of the preset target area and each road; a third determination module, configured to determine all routes from the current location of the target freight vehicle to the intersection as all routes exiting the preset target area; The processing unit, if there is a to-be-processed route among all the routes determined, for each to-be-processed route, after obtaining the historical shipping order information of all the areas passed by the to-be-processed route in N days, establish N scenarios according to the shipping orders of each day in the N days, wherein each of the N scenarios contains the shipping order information of all the areas passed by the to-be-processed route in one day; The matching unit matches the target freight vehicle with the shipping orders in N scenarios one by one for each route to be processed, and traverses all matching solutions for the route to be processed; A calculation unit, for each line to be processed, calculates the expected value of each matching solution corresponding to the line to be processed, and obtains the maximum expected value of all matching solutions corresponding to the line to be processed; A line expectation value unit, for each line to be processed, determines the maximum expected value among all matching solutions corresponding to the line to be processed as the line expectation value of the corresponding line; A judging unit is configured to judge the expected values ​​of all routes and determine the route with the largest expected value as the route for the freight vehicle to travel; Among them, the expected value of the matching plan = (the price of the matching order - the cost of empty driving when accepting the order) × the expected probability of the scenario in which the matching order is located.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method for determining the travel route of a freight vehicle as described in any one of claims 1-3.

6. An electronic device, characterized in that: include: at least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the method for determining the travel route of a freight vehicle as described in any one of claims 1-3.

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