A driving route recommendation method, device, electronic device and storage medium

Abstract

The present invention provides a driving route recommendation method, device, electronic device and storage medium, which relate to the field of cargo transportation technology, and solve the technical problem in the prior art that truck drivers may travel a large amount of invalid distance before arriving at a cargo source point and after leaving a destination point, resulting in low cargo transportation efficiency and a large amount of waste of transportation resources. The method comprises: in response to a processing request sent by a user, determining at least one direct route combination corresponding to the user; determining at least one direct route combination to be recommended from the at least one direct route combination corresponding to the user; determining the direct route to be recommended as the driving route corresponding to the user, and recommending the direct route to be recommended to the user.

Description

Technical Field

[0001] The present invention relates to the technical field of cargo transportation, and in particular to a method, device, electronic equipment and storage medium for recommending a travel route. Background Art

[0002] Currently, truck drivers can drive trucks to load corresponding goods from a certain source point, and then transport the goods to the destination point and complete the corresponding unloading process.

[0003] However, the truck driver may travel a lot of ineffective distances before arriving at the above-mentioned cargo source point and after leaving the above-mentioned destination point. For example, the type of cargo that the truck can load is different from the type of cargo provided by the cargo source point, that is, the truck driver finds that he needs to change the cargo source point after arriving at the cargo source point. This leads to low efficiency of cargo transportation and wastes a lot of transportation resources. Summary of the invention

[0004] The present invention provides a driving route recommendation method, device, electronic device and storage medium, which solves the technical problem in the prior art that truck drivers may travel a large amount of invalid distance before arriving at a cargo source point and after leaving a destination point, resulting in low cargo transportation efficiency and a large amount of waste of transportation resources.

[0005] In a first aspect, the present invention provides a method for recommending a travel route, comprising: in response to a processing request sent by a user, determining at least one direct route combination corresponding to the user, wherein the user request includes the type of goods to be transported and the address information of the target destination point, a direct route combination includes the address information of a source point and the address information of a destination point, and the type of goods corresponding to the direct route combination corresponding to the user includes the type of goods to be transported; determining at least one direct route combination to be recommended from the at least one direct route combination corresponding to the user, wherein the first distance corresponding to each direct route combination to be recommended in the at least one direct route combination to be recommended The distance is less than a first distance threshold, and the second distance corresponding to each to-be-recommended direct route combination is less than the first distance threshold, the first distance corresponding to each to-be-recommended direct route combination is the distance between the source point included in each to-be-recommended direct route combination and the current location of the user, and the second distance corresponding to each to-be-recommended direct route combination is the distance between the destination point included in each to-be-recommended direct route combination and the target destination point; the to-be-recommended direct route is determined as the driving route corresponding to the user, and the to-be-recommended direct route is recommended to the user, and the to-be-recommended direct route is the driving route corresponding to the at least one to-be-recommended direct route combination.

[0006] Optionally, the above-mentioned driving route recommendation method also includes: when the at least one direct route combination to be recommended does not exist in the at least one direct route combination corresponding to the user, determining multiple splicing route combinations, wherein each splicing route combination includes at least two routes, the cargo type corresponding to the starting route in each splicing route combination includes the cargo type to be transported, the distance between the cargo source point corresponding to the starting route and the current location of the user is less than the first distance threshold, the distance between the destination point corresponding to the ending route in each splicing route combination and the target destination point is less than the first distance threshold, and the distance between the destination point corresponding to the nth route of the at least two routes and the cargo source point corresponding to the n+1th route is less than the first distance threshold, n≥1; determining the deviation between each splicing route combination and the expected route, the expected route is the route characterized between the current location of the user and the target destination point; determining the m splicing route combinations with the smallest deviations among the multiple splicing route combinations as the splicing route combinations to be recommended corresponding to the user, and recommending the m splicing route combinations to the user, m≥1.

[0007] Optionally, the above-mentioned determination of at least one direct route combination corresponding to the user specifically includes: obtaining driving data of multiple vehicles in a historical time period, wherein the driving data of a vehicle includes the vehicle status of the vehicle in each time interval and the load change of the vehicle in each time interval, and each time interval is a time interval included in the historical time period; determining the operation event corresponding to the vehicle in each time interval according to the vehicle status of the vehicle in each time interval and the load change of the vehicle in each time interval, and the operation event is a loading event, a transportation event or an unloading event; determining the location corresponding to each loading event as a cargo source point, and determining the location corresponding to each unloading event as a destination point; determining multiple direct route combinations according to each cargo source point and each destination point, and determining at least one direct route combination corresponding to the user from the multiple direct route combinations according to the type of cargo to be transported.

[0008] Optionally, the above-mentioned driving route recommendation method also includes: obtaining address information of the first cargo source point, the first cargo source point is a cargo source point included in the first direct route combination, and the first direct route combination is one of the multiple direct route combinations; determining whether there is a preset keyword in the address information of the first cargo source point; when the preset keyword exists in the address information of the first cargo source point, determining the cargo type corresponding to the first cargo source point from a preset correspondence relationship based on the preset keyword, and the preset correspondence relationship includes multiple preset keywords and the cargo types corresponding to each of the multiple preset keywords.

[0009] Optionally, the above-mentioned driving route recommendation method also includes: when the preset keyword does not exist in the address information of the first cargo source point, determining at least one second direct route combination, the at least one second direct route combination is a direct route combination in the direct route combination corresponding to the target category except the first direct route combination, and the address information of the cargo source point included in each of the at least one second direct route combination contains the preset keyword, and the target category is the category to which the first direct route combination belongs; according to the cargo type corresponding to the at least one second cargo source point and the cargo type corresponding to at least one preset point of interest (POI), determining the cargo type corresponding to the first cargo source point, the at least one second cargo source point is the cargo source point included in each of the at least one second direct route combination.

[0010] Optionally, the above-mentioned driving route recommendation method also includes: determining the feature vector of each direct route combination based on the address information of the cargo source point included in each direct route combination in the multiple direct route combinations, the address information of the destination point included in each direct route, and the driving data of each vehicle corresponding to each direct route combination; performing a clustering operation on the feature vectors of the multiple direct route combinations to obtain the category to which the feature vector of each direct route combination belongs.

[0011] In a second aspect, the present invention provides a travel route recommendation device, comprising: a determination module and a recommendation module; the determination module is used to determine at least one direct route combination corresponding to the user in response to a processing request sent by the user, wherein the user request includes the type of goods to be transported and the address information of the target destination point, a direct route combination includes the address information of a source point and the address information of a destination point, and the type of goods corresponding to the direct route combination corresponding to the user includes the type of goods to be transported; the determination module is also used to determine at least one direct route combination to be recommended from the at least one direct route combination corresponding to the user, each of the at least one direct route combination to be recommended The corresponding first distance is less than the first distance threshold, and the second distance corresponding to each to-be-recommended direct route combination is less than the first distance threshold, the first distance corresponding to each to-be-recommended direct route combination is the distance between the source point included in each to-be-recommended direct route combination and the current location of the user, and the second distance corresponding to each to-be-recommended direct route combination is the distance between the destination point included in each to-be-recommended direct route combination and the target destination point; the determination module is also used to determine the to-be-recommended direct route as the driving route corresponding to the user, and the to-be-recommended direct route is the driving route corresponding to the at least one to-be-recommended direct route combination; the recommendation module is used to recommend the to-be-recommended direct route to the user.

[0012] Optionally, the determination module is further configured to determine a plurality of spliced ​​route combinations when the at least one direct route combination to be recommended does not exist in the at least one direct route combination corresponding to the user, wherein each spliced ​​route combination includes at least two routes, the cargo type corresponding to the start route in each spliced ​​route combination includes the cargo type to be transported, the distance between the cargo source point corresponding to the start route and the current location of the user is less than the first distance threshold, the distance between the destination point corresponding to the end route in each spliced ​​route combination and the target destination point is less than the first distance threshold, and the at least two The distance between the destination point corresponding to the nth route and the cargo source point corresponding to the n+1th route among the routes is less than the first distance threshold, n≥1; the determination module is further used to determine the deviation between each splicing route combination and the expected route, where the expected route is a route represented between the current location of the user and the target destination point; the determination module is further used to determine the m splicing route combinations with the smallest deviations among the multiple splicing route combinations as the splicing route combinations to be recommended corresponding to the user, m≥1; the recommendation module is further used to recommend the m splicing route combinations to the user.

[0013] Optionally, the driving route recommendation device also includes an acquisition module; the acquisition module is used to acquire driving data of multiple vehicles within a historical time period, wherein the driving data of a vehicle includes the vehicle status of the vehicle in each time interval and the load change of the vehicle in each time interval, and each time interval is a time interval included in the historical time period; the determination module is specifically used to determine the operation event corresponding to the vehicle in each time interval according to the vehicle status of the vehicle in each time interval and the load change of the vehicle in each time interval, and the operation event is a loading event, a transportation event or an unloading event; the determination module is also specifically used to determine the location corresponding to each loading event as a cargo source point, and determine the location corresponding to each unloading event as a destination point; the determination module is also specifically used to determine multiple direct route combinations according to each cargo source point and each destination point, and determine at least one direct route combination corresponding to the user from the multiple direct route combinations according to the type of cargo to be transported.

[0014] Optionally, the acquisition module is used to acquire the address information of the first source point, the first source point is a source point included in the first direct line combination, and the first direct line combination is one of the multiple direct line combinations; the determination module is also used to determine whether there is a preset keyword in the address information of the first source point; the determination module is also used to determine the type of goods corresponding to the first source point from a preset correspondence relationship according to the preset keyword when the preset keyword exists in the address information of the first source point, and the preset correspondence relationship includes multiple preset keywords and the types of goods corresponding to each of the multiple preset keywords.

[0015] Optionally, the determination module is also used to determine at least one second direct route combination when the preset keyword does not exist in the address information of the first source point, the at least one second direct route combination being a direct route combination other than the first direct route combination in the direct route combination corresponding to the target category, and the preset keyword exists in the address information of the source point included in each of the at least one second direct route combination, and the target category is the category to which the first direct route combination belongs; the determination module is also used to determine the type of goods corresponding to the first source point based on the type of goods corresponding to the at least one second source point and the type of goods corresponding to at least one preset POI, the at least one second source point being the source point included in each of the at least one second direct route combination.

[0016] Optionally, the driving route recommendation device also includes a processing module; the determination module determines the feature vector of each direct route combination based on the address information of the cargo source point included in each of the multiple direct route combinations, the address information of the destination point included in each direct route, and the driving data of each vehicle corresponding to each direct route combination; the processing module is used to perform a clustering operation on the feature vectors of the multiple direct route combinations to obtain the category to which the feature vector of each direct route combination belongs.

[0017] In a third aspect, the present invention provides an electronic device comprising: a processor and a memory configured to store processor executable instructions; wherein the processor is configured to execute the instructions to implement any one of the optional driving route recommendation methods in the first aspect above.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium having instructions stored thereon. When the instructions in the computer-readable storage medium are executed by an electronic device, the electronic device is enabled to execute any one of the optional driving route recommendation methods in the first aspect described above.

[0019] The driving route recommendation method, device, electronic device and storage medium provided by the present invention can determine at least one direct route combination corresponding to the user in response to a processing request sent by a user. Since the cargo type corresponding to the one direct route combination corresponding to the user includes the cargo type to be transported, the electronic device can determine at least one direct route combination whose corresponding cargo type includes the cargo type to be transported from multiple direct route combinations. Then the electronic device can determine at least one direct route combination to be recommended from the at least one direct route combination corresponding to the user, and the driving route corresponding to the at least one direct route combination to be recommended (i.e., the direct route to be recommended) is determined as the driving route corresponding to the user, and the direct route to be recommended is recommended to the user. In an embodiment of the present invention, since the cargo source point included in each of the at least one direct route combination to be recommended is closer to the user's current location, and the cargo source point included in each recommended direct route combination is closer to the target destination point, the electronic device can determine for the user a driving route that can provide the corresponding cargo type (i.e., including the cargo type to be transported) and has a shorter distance except for the effective transportation distance (i.e., the distance from a cargo source point to a destination point), which can improve the efficiency of cargo transportation and enhance the utilization rate of transportation resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0021] Figure 1 A hardware schematic diagram of an electronic device provided by an embodiment of the present invention;

[0022] Figure 2 A schematic diagram of a flow chart of a driving route recommendation method provided by an embodiment of the present invention;

[0023] Figure 3 A schematic flow chart of another driving route recommendation method provided by an embodiment of the present invention;

[0024] Figure 4 A schematic diagram of a splicing line combination provided by an embodiment of the present invention;

[0025] Figure 5 A schematic flow chart of another driving route recommendation method provided by an embodiment of the present invention;

[0026] Figure 6 A schematic flow chart of another driving route recommendation method provided by an embodiment of the present invention;

[0027] Figure 7 A schematic flow chart of another driving route recommendation method provided by an embodiment of the present invention;

[0028] Figure 8 A schematic flow chart of another driving route recommendation method provided by an embodiment of the present invention;

[0029] Fig. 9 A schematic diagram of the structure of a driving route recommendation device provided by an embodiment of the present invention;

[0030] Fig.10 A schematic diagram of the structure of another driving route recommendation device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The driving route recommendation method, device, electronic device and storage medium provided by the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0032] The terms "first" and "second" in the specification and drawings of this application are used to distinguish different objects rather than to describe the specific order of the objects. For example, the first source point and the second source point are used to distinguish different source points rather than to describe the specific order of the source points.

[0033] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0034] It should be noted that, in the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0035] The term "and / or" used in the present application includes using either or both methods at the same time.

[0036] In the description of the present application, unless otherwise specified, “plurality” means two or more.

[0037] Based on the description in the background technology, in the prior art, before a truck driver arrives at a cargo source point and after leaving a destination point, he may travel a large amount of invalid distance, resulting in low efficiency of cargo transportation and a large amount of waste of transportation resources. Based on this, an embodiment of the present invention provides a driving route recommendation method, device, electronic device and storage medium. Since the cargo source point included in each of at least one direct route combination to be recommended is close to the user's current location, and the cargo source point included in each recommended direct route combination is close to the target destination point, the electronic device can determine for the user a driving route that can provide the corresponding cargo type (i.e., including the cargo type to be transported) and has a shorter distance except for the effective transportation distance (i.e., the distance from a cargo source point to a destination point), which can improve the efficiency of cargo transportation and increase the utilization rate of transportation resources.

[0038] For example, Figure 1 A schematic diagram of the hardware structure of an electronic device for executing the driving route recommendation method provided in an embodiment of the present invention. Figure 1 As shown, the electronic device 10 includes a processor 101, a memory 102, a network interface 103, and the like.

[0039] Among them, the processor 101 is the core component of the electronic device 10, and the processor 101 is used to run the operating system of the electronic device 10 and the applications on the electronic device 10 (including system applications and third-party applications) to implement the driving route recommendation method of the electronic device 10.

[0040] In the embodiment of the present invention, the processor 101 can be a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof, which can implement or execute the various exemplary logic blocks, modules and circuits described in combination with the contents disclosed in the embodiment of the present invention; the processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of DSP and microprocessors, etc.

[0041] Optionally, the processor 101 of the electronic device 10 includes one or more CPUs, which are single-core CPUs (single-CPU) or multi-core CPUs (multi-CPU).

[0042] The memory 102 includes, but is not limited to, a random access memory (RAM), a read only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, or an optical memory, etc. The memory 102 stores operating system codes.

[0043] Optionally, the processor 101 implements the driving route recommendation method in the embodiment of the present invention by reading instructions stored in the memory 102, or the processor 101 implements the driving route recommendation method provided in the embodiment of the present invention by internally stored instructions. In the case where the processor 101 implements the driving route recommendation method provided in the embodiment of the present invention by reading instructions stored in the memory, the memory stores instructions for implementing the driving route recommendation method provided in the embodiment of the present invention.

[0044] The network interface 103 is a wired interface, such as a fiber distributed data interface (FDDI) or a gigabit Ethernet (GE) interface. Alternatively, the network interface 103 is a wireless interface. The network interface 103 is used for the electronic device 10 to communicate with other devices.

[0045] The memory 102 is used to store at least one direct line combination corresponding to the user. At least one processor 101 further executes the method described in the embodiment of the present invention according to the at least one direct line combination corresponding to the user stored in the memory 102. For more details on how the processor 101 implements the above functions, please refer to the description of each method embodiment below.

[0046] Optionally, the electronic device 10 further includes a bus, and the processor 101 and the memory 102 are interconnected via the bus 104, or are interconnected in other ways.

[0047] Optionally, the electronic device 10 further includes an input / output interface 105, which is used to connect to an input device and receive a processing request input by a user through the input device. The input device includes but is not limited to a keyboard, a touch screen, a microphone, etc. The input / output interface 105 is also used to connect to an output device and output the processing result of the processor 101 (i.e., the direct line to be recommended). The output device includes but is not limited to a display, a printer, etc.

[0048] The driving route recommendation method, device, electronic device and storage medium provided in the embodiments of the present invention are applied to the application scenario of recommending driving routes to users (specifically, truck drivers). When a user wants to transport certain types of goods by truck, a processing request can be sent to an electronic device based on the method provided in the embodiments of the present invention, so that the electronic device can determine a direct route to be recommended and recommend the direct route to be recommended to the user.

[0049] like Figure 2 As shown, the driving route recommendation method provided by the embodiment of the present invention may include S101-S103.

[0050] S101. The electronic device determines at least one direct line combination corresponding to the user in response to a processing request sent by the user.

[0051] Among them, the user request includes the type of goods to be transported and the address information of the target destination point, a direct route combination includes the address information of a source point and the address information of a destination point, and the type of goods corresponding to a direct route combination corresponding to the user includes the type of goods to be transported.

[0052] It should be understood that the electronic device may store multiple direct route combinations. When the electronic device responds to the processing request sent by the user, it may determine at least one direct route combination corresponding to the user from the multiple direct route combinations based on the type of goods to be transported included in the processing request.

[0053] It can be understood that the type of goods to be transported is the type of goods that can be loaded (or transported) by the truck driven by the user (which can also be understood as the truck driver), and the truck is currently in an empty state.

[0054] S102: The electronic device determines at least one direct line combination to be recommended from at least one direct line combination corresponding to the user.

[0055] Among them, the first distance corresponding to each direct route combination to be recommended in the at least one direct route combination to be recommended is less than the first distance threshold, and the second distance corresponding to each direct route combination to be recommended is less than the first distance threshold, the first distance corresponding to each direct route combination to be recommended is the distance between the source point included in each direct route combination to be recommended and the current location of the user, and the second distance corresponding to each direct route combination to be recommended is the distance between the destination point included in each direct route combination to be recommended and the above-mentioned target destination point.

[0056] It should be understood that the current location of the user is the current location of the truck, and the target destination is the destination that the user expects to reach. When the first distance corresponding to each direct route combination to be recommended is less than the first distance threshold, and the second distance corresponding to each direct route combination to be recommended is less than the first distance threshold, it means that the source point included in each direct route combination to be recommended in the at least one direct route combination to be recommended is closer to the current location of the user, and the destination point included in each direct route combination to be recommended is closer to the target destination point. That is, it can ensure that the user can load the corresponding type of goods within a shorter distance, and can also ensure that the user can reach the destination within a shorter distance after completing the unloading of the goods, thereby improving the efficiency of cargo transportation.

[0057] Optionally, the first distance threshold may be 5 km (kilometer).

[0058] S103: The electronic device determines the direct route to be recommended as the driving route corresponding to the user, and recommends the direct route to be recommended to the user.

[0059] The direct route to be recommended is a driving route corresponding to the at least one direct route combination to be recommended.

[0060] It can be understood that, for each of the at least one direct route combination to be recommended, the driving route corresponding to each direct route combination to be recommended is the route from the cargo source point included in the direct route combination to the destination point included in the direct route combination to be recommended. Specifically, the user can start from the current location to reach the cargo source point, complete the loading of related cargo, and reach the destination point from the cargo source point. After unloading at the destination point, continue driving to reach the target destination point.

[0061] The technical solution provided by the above embodiment can at least bring the following beneficial effects: As can be seen from S101-S103, the electronic device can determine at least one direct route combination corresponding to the user in response to the processing request sent by the user. Since the cargo type corresponding to the one direct route combination corresponding to the user includes the cargo type to be transported, the electronic device can determine at least one direct route combination whose cargo type includes the cargo type to be transported from multiple direct route combinations. Then the electronic device can determine at least one direct route combination to be recommended from the at least one direct route combination corresponding to the user, and the driving route corresponding to the at least one direct route combination to be recommended (i.e., the direct route to be recommended) is determined as the driving route corresponding to the user, and the direct route to be recommended is recommended to the user. In an embodiment of the present invention, since the cargo source point included in each of the at least one direct route combination to be recommended is closer to the user's current location, and the cargo source point included in each recommended direct route combination is closer to the target destination point, the electronic device can determine for the user a driving route that can provide the corresponding cargo type (i.e., including the cargo type to be transported) and has a shorter distance except for the effective transportation distance (i.e., the distance from a cargo source point to a destination point), which can improve the efficiency of cargo transportation and enhance the utilization rate of transportation resources.

[0062] Combination Figure 2 ,like Figure 3 As shown, the driving route recommendation method provided in the embodiment of the present invention also includes S104-S106.

[0063] S104: When at least one direct line combination corresponding to the user does not include at least one direct line combination to be recommended, the electronic device determines a plurality of spliced ​​line combinations.

[0064] Among them, each splicing route combination includes at least two routes, the cargo type corresponding to the starting route in each splicing route combination includes the above-mentioned cargo type to be transported, the distance between the cargo source point corresponding to the starting route and the current location of the user is less than the first distance threshold, the distance between the destination point corresponding to the ending route in each splicing route combination and the above-mentioned target destination point is less than the first distance threshold, and the distance between the destination point corresponding to the nth route in the at least two routes and the cargo source point corresponding to the n+1th route is less than the first distance threshold, and n≥1.

[0065] It should be understood that the starting route in a splicing route combination is the first route included in the splicing route combination, and the distance between the starting point of the first route (i.e., the source of goods included in the first route) and the current location of the user is less than the first distance threshold. The ending route in a splicing route combination is the last route included in the splicing route combination, and the distance between the end point of the last route (i.e., the destination point included in the last route) and the target destination point is less than the first distance threshold.

[0066] The distance between the destination point corresponding to the nth route and the cargo source point corresponding to the n+1th route is less than the first distance threshold, which can also be understood as the distance between the destination point corresponding to the current route among the at least two routes and the cargo source point corresponding to the next route is less than the first distance threshold.

[0067] S105. The electronic device determines the deviation between each spliced ​​line combination and the expected line.

[0068] The expected route is a route between the user's current location and the target destination.

[0069] In an implementation of the embodiment of the present invention, the deviation between each splicing line combination and the expected line is used to characterize the distance between a target point in each splicing line combination and the expected line, and the target point is the point in each splicing line combination with the largest distance from the expected line.

[0070] For example, Figure 4 As shown, it is a schematic diagram of a splicing line combination provided by an embodiment of the present invention. Specifically, point A is the current location of the user, and point B is the target destination point. That is, the line represented by point A-point B is the expected line. Splicing line combination 1 includes line 1 and line 2, the source point corresponding to line 1 is point C, the destination point corresponding to line 1 is point D, the source point corresponding to line 2 is point E, and the destination point corresponding to line 2 is point F. Specifically, the distance between point A and point C is less than the first distance threshold, the distance between point D and point E is less than the first distance threshold, and the distance between point F and point B is less than the first distance threshold. Since point E is the point with the largest distance from the expected line in the splicing line combination 1 (that is, point E is the target point), the electronic device can determine the distance between point E and the expected line as the deviation between the splicing line combination 1 and the expected line.

[0071] S106. The electronic device determines m splicing line combinations with the smallest deviations among the multiple splicing line combinations as the splicing line combinations to be recommended corresponding to the user, and recommends the m splicing line combinations to the user.

[0072] It can be understood that the electronic device recommends the m splicing route combinations to the user, that is, recommends to the user the driving route corresponding to each of the m splicing route combinations.

[0073] Optionally, the electronic device may also determine at least one splicing line combination among the multiple splicing line combinations whose corresponding deviation is less than a deviation threshold as the splicing line combination to be recommended corresponding to the user.

[0074] Combination Figure 2 ,like Figure 5 As shown, in an implementation manner of the embodiment of the present invention, the above-mentioned determination of at least one direct line combination corresponding to the user includes S1011-S1014.

[0075] S1011. The electronic device obtains driving data of multiple vehicles within a historical time period.

[0076] The driving data of a vehicle includes the vehicle state of the vehicle in each time interval and the load change of the vehicle in each time interval, and each time interval is a time interval included in the historical time period.

[0077] It should be understood that the vehicle state may include a driving state and a parking state, and the load change may include no load change, load increase, and load decrease.

[0078] S1012: The electronic device determines an operation event corresponding to the vehicle in each time interval according to the vehicle state in each time interval and the load change of the vehicle in each time interval.

[0079] The operation event is a loading event, a transportation event or an unloading event.

[0080] Specifically, when the vehicle state of the vehicle in a certain time interval is a driving state, and the load change of the vehicle in the time interval is no load change, the electronic device determines that the operation event corresponding to the vehicle in the time interval is a transportation event; when the vehicle state of the vehicle in the time interval is a parking state, and the load change of the vehicle in the time interval is an increase in load, the electronic device can determine that the operation event corresponding to the vehicle in the time interval is a loading event; when the vehicle state of the vehicle in the time interval is a parking state, and the load change of the vehicle in the time interval is a decrease in load, the electronic device can determine that the operation event corresponding to the vehicle in the time interval is an unloading event.

[0081] S1013. The electronic device determines the location corresponding to each loading event as a cargo source point, and determines the location corresponding to each unloading event as a target point.

[0082] It should be understood that when the electronic device performs a loading event (or an unloading event), a location will be corresponding, specifically loading (or unloading) is performed at the location. In the embodiment of the present invention, the electronic device determines the location where the loading (or supply of goods) is performed as the source point, and determines the location where the unloading (or the need for goods) is performed as the destination point.

[0083] In an optional implementation, the vehicle driving data may also include the latitude and longitude information corresponding to the vehicle in each time interval. The location represented by the latitude and longitude information corresponding to the vehicle in a time interval is the location corresponding to the operation event corresponding to the vehicle in the time interval.

[0084] S1014. The electronic device determines a plurality of direct route combinations according to each cargo source point and each destination point, and determines at least one direct route combination corresponding to the user from the plurality of direct route combinations according to the type of cargo to be transported.

[0085] In combination with the description of the above embodiment, it should be understood that a direct route combination includes the address information of a cargo source point and the address information of a destination point, and it can also be understood that a direct route combination is composed of a cargo source point and a destination point. The electronic device can determine at least one direct route combination corresponding to the user from the multiple direct route combinations based on the processing request sent by the above user (specifically, the type of cargo to be transported included in the request), and specifically, the cargo type corresponding to at least one direct route combination corresponding to the user includes the type of cargo to be transported.

[0086] It is understandable that the cargo type corresponding to a direct route combination may be one or more, and the embodiment of the present invention does not specifically limit this.

[0087] Combination Figure 2 ,like Figure 6 As shown, the driving route recommendation method provided in the embodiment of the present invention also includes S107-S109.

[0088] S107. The electronic device obtains address information of the first supply source point.

[0089] Among them, the first cargo source point is a cargo source point included in the first direct line combination, and the first direct line combination is one of the above-mentioned multiple direct line combinations.

[0090] In combination with the description of the above embodiments, it should be understood that a direct line combination may also include address information of a source point, so that the electronic device can directly obtain the address information of the first source point from the first direct line combination.

[0091] S108. The electronic device determines whether there is a preset keyword in the address information of the first supply source point.

[0092] It should be understood that the preset keywords may be keywords related to the type of goods, such as chicken, duck, ore, coal, fish, shrimp, etc.

[0093] Optionally, the electronic device can process the address information of the first source point by segmentation, that is, obtain at least one segmented word corresponding to (or including) the address information of the first source point, and then determine whether the at least one segmented word is the same as a preset keyword, so as to determine whether the preset keyword exists in the address information of the first source point.

[0094] S109: When there are preset keywords in the address information of the first source of goods, determine the type of goods corresponding to the first source of goods from a preset corresponding relationship according to the preset keywords.

[0095] The preset corresponding relationship includes a plurality of preset keywords and the types of goods corresponding to the plurality of preset keywords.

[0096] Exemplarily, the following Table 1 is an example of a preset correspondence provided by an embodiment of the present invention. As shown in Table 1, the cargo types may include four categories: poultry (livestock), minerals (sand), vehicles, and fresh food (refrigerated).

[0097] Table 1

[0098] Preset keywords The type of goods corresponding to the preset keywords Chicken, duck, goose, poultry, eggs, cattle, sheep, pigs Poultry, livestock Minerals, coal, sand, cement, copper, iron, stone, soil Minerals, sand car, sedan, car vehicle Seafood, fish, shrimp, crab, meat Fresh, refrigerated

[0099] Assuming that the address information of the above-mentioned first source point is Qianyuan Egg Chicken Ecological Farm in Taiping Town, Zhao'an County, Zhangzhou City, Fujian Province, the electronic device determines that there are preset keywords (ie "eggs", "chickens") in the address information of the first source point, and then the electronic device determines that the type of goods corresponding to the first source point is poultry (livestock).

[0100] Combination Figure 6 ,like Figure 7 As shown, the driving route recommendation method provided in the embodiment of the present invention also includes S110-S111.

[0101] S110: When there is no preset keyword in the address information of the first supply source point, the electronic device determines at least one second direct route combination.

[0102] Among them, the at least one second direct route combination is a direct route combination among the direct route combinations corresponding to the target category except the first direct route combination, and there are preset keywords in the address information of the source points included in each of the at least one second direct route combinations, and the target category is the category to which the first direct route combination belongs.

[0103] It should be understood that the electronic device can first determine the category (ie, target category) to which the first direct line combination belongs, then determine the direct line combination corresponding to the target category, and then determine the at least one second direct line combination from the direct line combinations corresponding to the target category.

[0104] S111. The electronic device determines the type of goods corresponding to the first source point according to the type of goods corresponding to at least one second source point and the type of goods corresponding to at least one preset POI.

[0105] Among them, the at least one second source of goods point is the source of goods point included in the at least one second direct route combination mentioned above.

[0106] It is understandable that the electronic device can determine the type of goods corresponding to the at least one second source of goods based on the description in S109 above. Also, the electronic device can determine the at least one preset POI based on relevant map software and obtain the type of goods corresponding to the at least one preset POI.

[0107] Optionally, the at least one preset POI may include at least one of a logistics center, a cargo distribution center, and a mine. In the case of determining at least one preset POI, the embodiment of the present invention may exclude service areas and gas stations in the map software.

[0108] In one implementation of the embodiment of the present invention, the electronic device may determine the type of goods corresponding to the first source point based on the number of occurrences of the type of goods corresponding to the at least one second source point, and the type of goods corresponding to at least one preset POI that is closest to the position corresponding to the first source point among the at least one preset POI. Specifically, the electronic device may assign a value to the type of goods corresponding to the at least one second source point based on the number of occurrences of the type of goods corresponding to the at least one second source point (for example, if a certain type of goods appears once, the value is assigned +1), and assign a value to the type of goods corresponding to the at least one nearest preset POI (for example, the closer the distance, the higher the value), and then determine the several (for example, 3) types of goods with the highest values ​​finally obtained as the types of goods corresponding to the first source point.

[0109] Exemplarily, it is assumed that the address information of the first source of goods is Xiaoming Town, Tieling City, Liaoning Province. The types of goods corresponding to the at least one second source of goods include iron, coal, corn and rice, and the number of occurrences of the types of goods corresponding to the at least one second source of goods is 50 times, 70 times, 60 times and 40 times, respectively. Then the electronic device determines that the values ​​of the types of goods corresponding to the at least one second source of goods (i.e., iron, coal, corn and rice) are 50, 70, 60 and 40, respectively. Then the electronic device determines that the types of goods corresponding to the at least one nearest preset POI (specifically, the order of the at least one nearest preset POI from near to far) are coal, corn and iron, respectively, and the values ​​assigned to them by the electronic device are 100, 50 and 20, respectively. Combined with the values ​​assigned by the electronic device to the types of goods corresponding to the at least one second source of goods, the final scores of iron, coal, corn and rice are determined to be 70 (50+20), 170 (70+100), 110 (60+50) and 40, respectively. So far, the electronic device determines that the types of goods corresponding to the first source of goods are coal, corn and iron.

[0110] Combination Figure 2 ,like Figure 8 As shown, the driving route recommendation method provided in the embodiment of the present invention also includes S112-S113.

[0111] S112. The electronic device determines a feature vector of each direct route combination according to the address information of the cargo source point included in each direct route combination, the address information of the destination point included in each direct route combination, and the driving data of each vehicle corresponding to each direct route combination.

[0112] In combination with the description of the above embodiment, it should be understood that the driving data of the above vehicle includes the vehicle state of the vehicle in each time interval and the load change of the vehicle in each time interval.

[0113] It is understandable that a direct route combination may correspond to at least one vehicle, specifically, the at least one vehicle performs loading events and unloading events at the cargo source point and the destination point included in the direct route combination during the above historical time period. In this way, the electronic device may determine the feature vector of each direct route combination according to the address information of the cargo source point included in each direct route combination, the address information of the destination point included in each direct route, the vehicle state of at least one vehicle corresponding to each direct route combination in each time interval, and the load change of the at least one vehicle in each time interval.

[0114] In one implementation of an embodiment of the present invention, the driving data of each vehicle corresponding to each of the above-mentioned direct line combinations is digital data, for example, the driving speed represents the driving status, and the real-time load represents the load change. At this time, the electronic device can directly use the digital data as part of the feature vector of each direct line combination.

[0115] In another implementation of the embodiment of the present invention, the address information of the source point included in each direct route combination and the address information of the destination point included in each direct route combination are text data. The electronic device can perform word segmentation on the text data, and then use a neural network model (such as word2vec) to obtain the vector of each word segment after the word segmentation as another part of the feature vector of each direct route combination. Furthermore, the electronic device can splice the above digital data and the vector of each word segment to obtain the feature vector of each direct route combination.

[0116] Optionally, the driving data of the vehicle may also include the license plate number of the vehicle, the maximum load of the vehicle, the brand model of the vehicle, the engine model of the vehicle, and the average speed of the vehicle. That is, when determining the feature vector of each direct route combination, the electronic device may also combine the license plate number of each vehicle corresponding to each direct route combination, the maximum load of each vehicle, the brand model of each vehicle, the engine model of each vehicle, and the average speed of each vehicle.

[0117] S113. Perform a clustering operation on the feature vectors of multiple direct line combinations to obtain the category to which the feature vector of each direct line combination belongs.

[0118] It should be understood that the category to which the feature vector of a direct line combination belongs is the category to which the direct line combination belongs. That is, the above target category can also be understood as the category to which the feature vector of the above first direct line combination belongs.

[0119] In one implementation of the embodiment of the present invention, the electronic device can implement the above process of performing clustering operations on the feature vectors of multiple direct line combinations through the following steps A to E.

[0120] Step A: determining T feature vectors from a plurality of feature vectors, the distance between which and the last first feature vector is less than a second distance threshold.

[0121] The multiple feature vectors are feature vectors of the above-mentioned multiple direct line combinations, the previous first feature vector is one of the multiple feature vectors, and T≥1.

[0122] Specifically, the electronic device may determine any feature vector (i.e., the last first feature vector) that is not marked as belonging to any category as the center point C0. Then, the distance between other feature vectors (i.e., feature vectors other than the last first feature vector in the multiple feature vectors) and the last first feature vector (or the center point C0) is determined, and the feature vectors whose distance is less than the second distance threshold are determined as the T feature vectors.

[0123] Step B: The electronic device adds a preset value to the probability that the T feature vectors belong to the first category.

[0124] The first category is the category corresponding to the previous first eigenvector.

[0125] Optionally, the preset value may be 1.

[0126] Step C: The electronic device determines a distance mean vector between the last first eigenvector and T eigenvectors.

[0127] Specifically, the electronic device can determine the distance vector between the previous first eigenvector and each of the T eigenvectors, and then determine the average value of the distance vectors between the T eigenvectors and the previous first eigenvector as the distance mean vector between the previous first eigenvector and the T eigenvectors.

[0128] Step D: The electronic device determines a current first eigenvector based on a previous first eigenvector and a distance mean vector.

[0129] Specifically, the electronic device may determine the sum of the previous first eigenvector and the distance mean vector as the current first eigenvector (or the center point C1).

[0130] Step E, iteratively execute the above steps A to D, determine the current first feature vector with the largest number of corresponding T feature vectors, determine it as the target first feature vector, and determine the type of the T feature vectors corresponding to each previous feature vector in the iteration process as the first category.

[0131] It can be understood that the first category is also the category corresponding to the target first feature vector, and the target first feature vector is the center point (ie, C2) that the first category finally corresponds to.

[0132] In one implementation of an embodiment of the present invention, the electronic device can determine the distance between the target first feature vector (or the center point C2 that ultimately corresponds to the first category) and other target feature vectors of determined categories (for example, the target second feature vector, which can also be understood as the center point that ultimately corresponds to other categories, such as C3). When the distance is less than or equal to the above-mentioned second distance threshold, the category to which the target first feature vector belongs can be merged with the category to which the target feature vector belongs to obtain a new category.

[0133] In another implementation of the embodiment of the invention, when the distance between the target first feature vector and other target feature vectors of determined categories is greater than the second distance threshold, it can be determined that the first category and the other categories are different categories.

[0134] Optionally, the electronic device may determine the probability of the feature vector of each direct line combination in the above-mentioned multiple direct line combinations in multiple categories, and then determine the category with the highest probability as the category to which each direct line combination (or the feature vector of each direct line combination) belongs.

[0135] The embodiment of the present invention can divide the functional modules of the electronic device etc. according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0136] In the case of dividing each functional module into corresponding functional modules, Fig. 9 A possible structural diagram of the driving route recommendation device involved in the above embodiment is shown. Fig. 9 As shown, the driving route recommendation device 20 may include: a determination module 201 and a recommendation module 202 .

[0137] Determination module 201 is used to determine at least one direct route combination corresponding to the user in response to a processing request sent by the user, wherein the user request includes the type of goods to be transported and the address information of the target destination point, a direct route combination includes the address information of a source point and the address information of a destination point, and the type of goods corresponding to a direct route combination corresponding to the user includes the type of goods to be transported.

[0138] The determination module 201 is also used to determine at least one direct route combination to be recommended from at least one direct route combination corresponding to the user, the first distance corresponding to each direct route combination to be recommended in the at least one direct route combination to be recommended is less than the first distance threshold, and the second distance corresponding to each direct route combination to be recommended is less than the first distance threshold, the first distance corresponding to each direct route combination to be recommended is the distance between the source point included in each direct route combination to be recommended and the current location of the user, and the second distance corresponding to each direct route combination to be recommended is the distance between the destination point included in each direct route combination to be recommended and the target destination point.

[0139] The determination module 201 is further configured to determine the to-be-recommended direct route as the driving route corresponding to the user, the to-be-recommended direct route being the driving route corresponding to the at least one to-be-recommended direct route combination;

[0140] The recommendation module 202 is used to recommend the direct route to be recommended to the user.

[0141] Optionally, the determination module 201 is further used to determine multiple splicing route combinations when the at least one direct route combination to be recommended does not exist in the at least one direct route combination corresponding to the user, wherein each splicing route combination includes at least two routes, the cargo type corresponding to the starting route in each splicing route combination includes the cargo type to be transported, the distance between the cargo source point corresponding to the starting route and the current location of the user is less than the first distance threshold, the distance between the destination point corresponding to the ending route in each splicing route combination and the target destination point is less than the first distance threshold, and the distance between the destination point corresponding to the nth route and the cargo source point corresponding to the n+1th route of the at least two routes is less than the first distance threshold, and n≥1.

[0142] The determination module 201 is further used to determine the deviation between each spliced ​​route combination and an expected route, where the expected route is a route represented between the current location of the user and the target destination point.

[0143] The determination module 201 is further configured to determine m splicing route combinations with the smallest deviation among the multiple splicing route combinations as the splicing route combinations to be recommended corresponding to the user, where m≥1.

[0144] The recommendation module 202 is further configured to recommend the m splicing line combinations to the user.

[0145] Optionally, the driving route recommendation device 20 further includes an acquisition module 203 .

[0146] The acquisition module 203 is used to acquire the driving data of multiple vehicles within a historical time period, wherein the driving data of a vehicle includes the vehicle state of the vehicle in each time interval and the load change of the vehicle in each time interval, and each time interval is a time interval included in the historical time period.

[0147] The determination module 201 is specifically used to determine the operation event corresponding to the vehicle in each time interval according to the vehicle state of the vehicle in each time interval and the load change of the vehicle in each time interval, and the operation event is a loading event, a transportation event or an unloading event.

[0148] The determination module 201 is further specifically used to determine the location corresponding to each loading event as a cargo source point, and to determine the location corresponding to each unloading event as a destination point.

[0149] The determination module 201 is further specifically used to determine a plurality of direct route combinations according to each cargo source point and each destination point, and to determine at least one direct route combination corresponding to the user from the plurality of direct route combinations according to the type of cargo to be transported.

[0150] Optionally, the acquisition module 203 is used to obtain address information of a first source point, where the first source point is a source point included in a first direct line combination, and the first direct line combination is one of the multiple direct line combinations.

[0151] The determination module 201 is further used to determine whether there is a preset keyword in the address information of the first supply source point.

[0152] The determination module 201 is also used to determine the type of goods corresponding to the first source point from a preset correspondence relationship according to the preset keyword when the preset keyword exists in the address information of the first source point. The preset correspondence relationship includes multiple preset keywords and the types of goods corresponding to each of the multiple preset keywords.

[0153] Optionally, the determination module 201 is also used to determine at least one second direct line combination when the preset keyword does not exist in the address information of the first source point, and the at least one second direct line combination is a direct line combination other than the first direct line combination in the direct line combination corresponding to the target category, and the preset keyword exists in the address information of the source point included in each of the at least one second direct line combinations, and the target category is the category to which the first direct line combination belongs.

[0154] The determination module 201 is also used to determine the cargo type corresponding to the first cargo source point based on the cargo type corresponding to at least one second cargo source point and the cargo type corresponding to at least one preset POI, and the at least one second cargo source point is the cargo source point included in the at least one second direct route combination.

[0155] Optionally, the driving route recommendation device 20 further includes a processing module 204 .

[0156] The determination module 201 determines the feature vector of each direct route combination according to the address information of the cargo source point included in each direct route combination, the address information of the destination point included in each direct route, and the driving data of each vehicle corresponding to each direct route combination.

[0157] The processing module 204 is used to perform a clustering operation on the feature vectors of the multiple direct line combinations to obtain the category to which the feature vector of each direct line combination belongs.

[0158] In the case of an integrated unit, Fig.10 FIG. 2 shows a possible structural diagram of the driving route recommendation device involved in the above embodiment. Fig.10 As shown, the driving route recommendation device 30 may include: a processing module 301 and a communication module 302. The processing module 301 may be used to control and manage the actions of the driving route recommendation device 30. The communication module 302 may be used to support the communication between the driving route recommendation device 30 and other entities. Fig.10 As shown, the driving route recommendation device 30 may further include a storage module 303 for storing program codes and data of the driving route recommendation device 30 .

[0159] The processing module 301 may be a processor or a controller (for example, the above-mentioned Figure 1 The communication module 302 may be a transceiver, a transceiver circuit or a communication interface, etc. (for example, it may be the processor 101 as described above). Figure 1 The storage module 303 may be a memory (for example, the network interface 103 as shown above). Figure 1 Memory 102 shown).

[0160] When the processing module 301 is a processor, the communication module 302 is a transceiver, and the storage module 303 is a memory, the processor, the transceiver, and the memory may be connected via a bus. The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc.

[0161] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0162] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0163] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0164] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0165] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using a software program, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, data center, etc. that contains one or more media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium (eg, a solid state disk (SSD)).

[0166] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A driving route recommendation method, characterized in that: include: In response to a processing request sent by a user, at least one direct route combination corresponding to the user is determined, wherein the user request includes the type of goods to be transported and the address information of the target destination point, a direct route combination includes the address information of a source point and the address information of a destination point, and the type of goods corresponding to the direct route combination corresponding to the user includes the type of goods to be transported; Determine at least one direct route combination to be recommended from at least one direct route combination corresponding to the user, wherein a first distance corresponding to each direct route combination to be recommended in the at least one direct route combination to be recommended is less than a first distance threshold, and a second distance corresponding to each direct route combination to be recommended is less than the first distance threshold, the first distance corresponding to each direct route combination to be recommended is the distance between a source point included in each direct route combination to be recommended and a current location of the user, and the second distance corresponding to each direct route combination to be recommended is the distance between a destination point included in each direct route combination to be recommended and the target destination point; Determine the direct route to be recommended as the driving route corresponding to the user, and recommend the direct route to be recommended to the user, wherein the direct route to be recommended is the driving route corresponding to the at least one direct route combination to be recommended; The step of determining, in response to a processing request sent by a user, at least one direct line combination corresponding to the user comprises: Determine at least one direct route combination corresponding to the user from a plurality of stored direct route combinations based on the type of goods to be transported included in the processing request sent by the user; The determining of at least one direct line combination corresponding to the user comprises: Acquire driving data of multiple vehicles in a historical time period, wherein the driving data of a vehicle includes a vehicle state of the vehicle in each time interval and a load change of the vehicle in each time interval, and each time interval is a time interval included in the historical time period; Determine, according to the vehicle state of the vehicle in each time interval and the load change of the vehicle in each time interval, an operation event corresponding to the vehicle in each time interval, wherein the operation event is a loading event, a transport event or an unloading event; The location corresponding to each loading event is determined as the source point, and the location corresponding to each unloading event is determined as the destination point; A plurality of direct route combinations are determined according to each cargo source point and each destination point, and at least one direct route combination corresponding to the user is determined from the plurality of direct route combinations according to the type of cargo to be transported.

2. The driving route recommendation method according to claim 1, characterized in that: The method further comprises: When at least one direct route combination to be recommended does not exist in at least one direct route combination corresponding to the user, a plurality of spliced ​​route combinations are determined, wherein each spliced ​​route combination includes at least two routes, the cargo type corresponding to the start route in each spliced ​​route combination includes the cargo type to be transported, the distance between the cargo source point corresponding to the start route and the current location of the user is less than the first distance threshold, the distance between the destination point corresponding to the end route in each spliced ​​route combination and the target destination point is less than the first distance threshold, and the distance between the destination point corresponding to the nth route and the cargo source point corresponding to the n+1th route in the at least two routes is less than the first distance threshold, and n≥1; Determine the deviation between each spliced ​​route combination and an expected route, where the expected route is a route represented between the current location of the user and the target destination point; The m splicing line combinations with the smallest deviations among the multiple splicing line combinations are determined as the splicing line combinations to be recommended corresponding to the user, and the m splicing line combinations are recommended to the user, where m≥1.

3. The driving route recommendation method according to claim 1, characterized in that: The method further comprises: Acquire address information of a first source of goods, where the first source of goods is a source of goods included in a first direct route combination, and the first direct route combination is one of the multiple direct route combinations; Determining whether a preset keyword exists in the address information of the first supply source point; When the preset keyword exists in the address information of the first source of goods, the type of goods corresponding to the first source of goods is determined from a preset correspondence relationship based on the preset keyword, and the preset correspondence relationship includes multiple preset keywords and the types of goods corresponding to each of the multiple preset keywords.

4. The driving route recommendation method according to claim 3, characterized in that: The method further comprises: When the preset keyword does not exist in the address information of the first source of goods, at least one second direct route combination is determined, wherein the at least one second direct route combination is a direct route combination other than the first direct route combination in the direct route combination corresponding to the target category, and the address information of the source of goods included in each of the at least one second direct route combination contains the preset keyword, and the target category is the category to which the first direct route combination belongs; According to the cargo type corresponding to at least one second cargo source point and the cargo type corresponding to at least one preset point of interest POI, the cargo type corresponding to the first cargo source point is determined, and the at least one second cargo source point is the cargo source point included in each of the at least one second direct route combination.

5. The driving route recommendation method according to any one of claims 1 to 4, characterized in that: The method further comprises: Determine a feature vector of each direct route combination according to address information of a cargo source point included in each direct route combination among the multiple direct route combinations, address information of a destination point included in each direct route, and travel data of each vehicle corresponding to each direct route combination; A clustering operation is performed on the feature vectors of the multiple direct line combinations to obtain the category to which the feature vector of each direct line combination belongs.

6. A driving route recommendation device, characterized in that: include: Identify modules and recommend modules; The determination module is used to determine at least one direct route combination corresponding to the user in response to a processing request sent by the user, wherein the user request includes the type of goods to be transported and the address information of the target destination point, a direct route combination includes the address information of a source point and the address information of a destination point, and the type of goods corresponding to the direct route combination corresponding to the user includes the type of goods to be transported; The determination module is further used to determine at least one direct route combination to be recommended from at least one direct route combination corresponding to the user, wherein the first distance corresponding to each direct route combination to be recommended in the at least one direct route combination to be recommended is less than the first distance threshold, and the second distance corresponding to each direct route combination to be recommended is less than the first distance threshold, the first distance corresponding to each direct route combination to be recommended is the distance between the source point included in each direct route combination to be recommended and the current location of the user, and the second distance corresponding to each direct route combination to be recommended is the distance between the destination point included in each direct route combination to be recommended and the target destination point; The determination module is further configured to determine the to-be-recommended direct route as the driving route corresponding to the user, wherein the to-be-recommended direct route is the driving route corresponding to the at least one to-be-recommended direct route combination; The recommendation module is used to recommend the direct route to be recommended to the user; The step of determining, in response to a processing request sent by a user, at least one direct line combination corresponding to the user comprises: Determine at least one direct route combination corresponding to the user from a plurality of stored direct route combinations based on the type of goods to be transported included in the processing request sent by the user; The driving route recommendation device also includes an acquisition module; The acquisition module is used to acquire the driving data of multiple vehicles in a historical time period, wherein the driving data of a vehicle includes the vehicle state of the vehicle in each time interval and the load change of the vehicle in each time interval, and each time interval is a time interval included in the historical time period; The determination module is specifically used to determine the operation event corresponding to the vehicle in each time interval according to the vehicle state of the vehicle in each time interval and the load change of the vehicle in each time interval, wherein the operation event is a loading event, a transportation event or an unloading event; The determination module is further specifically used to determine the location corresponding to each loading event as the cargo source point, and to determine the location corresponding to each unloading event as the destination point; The determination module is specifically used to determine multiple direct route combinations according to each cargo source point and each destination point, and to determine at least one direct route combination corresponding to the user from the multiple direct route combinations according to the type of cargo to be transported.

7. The driving route recommendation device according to claim 6, characterized in that: The determination module is further configured to determine a plurality of spliced ​​route combinations when at least one direct route combination to be recommended does not exist in at least one direct route combination corresponding to the user, wherein each spliced ​​route combination includes at least two routes, the cargo type corresponding to the start route in each spliced ​​route combination includes the cargo type to be transported, the distance between the cargo source point corresponding to the start route and the current location of the user is less than the first distance threshold, the distance between the destination point corresponding to the end route in each spliced ​​route combination and the target destination point is less than the first distance threshold, and the distance between the destination point corresponding to the nth route and the cargo source point corresponding to the n+1th route in the at least two routes is less than the first distance threshold, and n≥1; The determination module is further used to determine the deviation between each spliced ​​route combination and an expected route, where the expected route is a route represented between the current location of the user and the target destination point; The determination module is further configured to determine m splicing line combinations with the smallest deviation among the multiple splicing line combinations as the splicing line combinations to be recommended for the user, where m≥1; The recommendation module is further used to recommend the m splicing line combinations to the user.

8. The driving route recommendation device according to claim 6, characterized in that: The driving route recommendation device also includes an acquisition module; The acquisition module is used to acquire address information of a first source of goods, where the first source of goods is a source of goods included in a first direct route combination, where the first direct route combination is one of the multiple direct route combinations; The determination module is further used to determine whether there is a preset keyword in the address information of the first supply source point; The determination module is also used to determine the type of goods corresponding to the first source point from a preset correspondence relationship according to the preset keyword when the preset keyword exists in the address information of the first source point, and the preset correspondence relationship includes multiple preset keywords and the types of goods corresponding to each of the multiple preset keywords.

9. The driving route recommendation device according to claim 8, characterized in that: The determination module is further configured to determine, when the preset keyword does not exist in the address information of the first source of goods, at least one second direct route combination, wherein the at least one second direct route combination is a direct route combination other than the first direct route combination in the direct route combination corresponding to the target category, and the address information of the source of goods included in each of the at least one second direct route combination contains the preset keyword, and the target category is the category to which the first direct route combination belongs; The determination module is also used to determine the type of goods corresponding to the first source point based on the type of goods corresponding to at least one second source point and the type of goods corresponding to at least one preset point of interest POI, and the at least one second source point is the source point included in the at least one second direct route combination.

10. The driving route recommendation device according to any one of claims 6 to 9, characterized in that: The driving route recommendation device also includes a processing module; The determination module determines a feature vector of each direct route combination according to address information of a cargo source point included in each direct route combination, address information of a destination point included in each direct route, and travel data of each vehicle corresponding to each direct route combination; The processing module is used to perform a clustering operation on the feature vectors of the multiple direct line combinations to obtain the category to which the feature vector of each direct line combination belongs.

11. An electronic device, characterized in that: The electronic device comprises: processor; a memory configured to store instructions executable by the processor; The processor is configured to execute the instructions to implement the driving route recommendation method as described in any one of claims 1-5.

12. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions in the computer-readable storage medium are executed by an electronic device, the electronic device is enabled to execute the driving route recommendation method according to any one of claims 1 to 5.

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