Pure electric heavy truck route planning method, device, equipment, medium and program product

Abstract

The invention discloses a pure electric heavy truck route planning method, device and equipment, a medium and a program product, and the method comprises the steps: determining a starting point and an ending point according to the position information of a pure electric heavy truck, and carrying out the planning to obtain at least two candidate driving routes; collecting vehicle internal associated data, external environment data and road associated data corresponding to the pure electric heavy truck; based on the vehicle internal associated data, the external environment data and the road associated data, respectively evaluating the power consumption, the endurance support amount, the charging duration and the passing cost of each candidate driving route to obtain a quantitative evaluation result; and processing the quantitative evaluation result through a preset model so as to screen out a target recommended route from the at least two candidate driving routes according to the processing result. The route of the pure electric heavy truck is planned by integrating the vehicle internal associated data, the external environment data and the road associated data, the accuracy and rationality of route planning are greatly improved, and the demand of a user for timeliness or cost is effectively met.

Description

Technical Field

[0001] This invention relates to the field of route planning technology for pure electric heavy-duty trucks, and in particular to a route planning method, apparatus, equipment, medium, and program product for pure electric heavy-duty trucks. Background Technology

[0002] With the advancement of the green and low-carbon concept, new energy tractor vehicles are gradually being demonstrated and applied in medium- and long-distance scenarios. However, due to the influence of driving range, charging infrastructure layout and charging speed, many users are still hesitant to use new energy pure electric heavy trucks in medium- and long-distance scenarios based on timeliness considerations, which seriously affects the promotion and application of new energy heavy trucks.

[0003] Currently, the main considerations for planning the operation routes of pure electric heavy-duty trucks are mainly focused on internal factors of the heavy-duty trucks and limited external factors (such as the location of charging piles). It is impossible to accurately assess the driving range, and therefore it is impossible to support pure electric heavy-duty trucks to meet the high timeliness requirements comparable to traditional fuel vehicles. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, medium, and program product for route planning of pure electric heavy trucks, which can significantly improve the accuracy and rationality of route planning and effectively meet users' core needs for timeliness or cost.

[0005] According to one aspect of the present invention, a route planning method for pure electric heavy-duty trucks is provided, comprising: Based on the location information of the pure electric heavy truck, the starting point and the destination are determined, and at least two candidate driving routes are planned. Collect the vehicle internal data, external environment data, and road-related data corresponding to the pure electric heavy-duty truck; Based on the vehicle's internal data, external environment data, and road data, the power consumption, range support, charging time, and toll fees of each candidate driving route are evaluated to obtain quantitative evaluation results. The quantitative evaluation results are processed by a preset model to select a target recommended route from at least two candidate driving routes based on the processing results.

[0006] According to another aspect of the present invention, a route planning device for pure electric heavy-duty trucks is provided, comprising: The route planning module is used to determine the starting point and the destination based on the location information of the pure electric heavy truck, and to plan at least two candidate driving routes. The data acquisition module is used to collect the vehicle internal data, external environment data, and road data associated with the pure electric heavy truck. The quantitative evaluation module is used to evaluate the power consumption, range support, charging time and toll of each candidate driving route based on the vehicle internal correlation data, external environment data and road correlation data, and obtain the quantitative evaluation results. The route recommendation module is used to process the quantitative evaluation results through a preset model, so as to select a target recommended route from at least two candidate driving routes based on the processing results.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and memory that is communicatively connected to at least one processor; The memory stores a computer program that can be executed by at least one processor, which is then executed by the at least one processor to enable the at least one processor to execute the pure electric heavy truck route planning method of any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute the pure electric heavy truck route planning method of any embodiment of the present invention.

[0009] According to another aspect of the present invention, a computer program product is provided, the computer program product including a computer program, which, when executed by a processor, implements the pure electric heavy truck route planning method of any embodiment of the present invention.

[0010] The technical solution of this invention determines the starting point and ending point through the location information of a pure electric heavy-duty truck, and plans at least two candidate driving routes; it collects the vehicle's internal correlation data, external environment data, and road correlation data corresponding to the pure electric heavy-duty truck; based on the vehicle's internal correlation data, external environment data, and road correlation data, it evaluates the power consumption, range support, charging time, and toll fees of each candidate driving route to obtain quantitative evaluation results; it processes the quantitative evaluation results through a preset model to select a target recommended route from at least two candidate driving routes based on the processing results. This solves the problems of existing pure electric heavy-duty truck route planning that only considers limited factors, cannot accurately evaluate range and timeliness, and is difficult to meet the needs of medium and long-distance transportation. It realizes multi-dimensional and accurate quantitative evaluation of pure electric heavy-duty truck routes, selects the target route with the best timeliness or the lowest cost, improves the practicality and competitiveness of pure electric heavy-duty trucks in medium and long-distance operation, and helps the large-scale promotion of new energy heavy-duty trucks.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart of a route planning method for pure electric heavy trucks provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a pure electric heavy truck route planning device provided in an embodiment of the present invention; Figure 3 A schematic diagram of the electronic device used to implement the pure electric heavy truck route planning method of this invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] Figure 1 This is a flowchart illustrating a route planning method for a pure electric heavy-duty truck according to an embodiment of the present invention. This embodiment is applicable to situations where the optimal driving route for a pure electric heavy-duty truck is comprehensively evaluated based on both internal and external factors of the vehicle. This method can be executed by a pure electric heavy-duty truck route planning device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1As shown, the method specifically includes the following steps: S110. Based on the location information of the pure electric heavy truck, determine the starting point and the destination, and plan at least two candidate driving routes.

[0017] Location information can be understood as data that can determine the starting point and destination of the pure electric heavy truck. Candidate driving routes can be driving routes planned based on the starting point and destination, and there can be more than two candidate driving routes.

[0018] Specifically, the location information of the pure electric heavy-duty truck can be obtained, and the starting point and destination of the journey can be determined through this location information. The route of the pure electric heavy-duty truck can be planned to obtain at least two candidate routes.

[0019] S120. Collect the vehicle internal association data, external environment data and road association data corresponding to the pure electric heavy truck.

[0020] Among them, the vehicle internal associated data can be data related to the status of the pure electric heavy truck itself and affecting the route assessment, the external environment data can be data related to the driving environment of the pure electric heavy truck, and the road associated data can be data related to the driving road.

[0021] Specifically, targeted data can be collected on the internal vehicle data, external environment data, and road data of pure electric heavy-duty trucks, which will facilitate subsequent comprehensive evaluation of the optimal driving route for pure electric heavy-duty trucks based on both internal and external factors.

[0022] S130. Based on the vehicle's internal correlation data, external environment data, and road correlation data, evaluate the power consumption, range support, charging time, and toll for each candidate driving route to obtain quantitative evaluation results.

[0023] The quantitative assessment results can include specific values ​​for power consumption, range support, charging time, and toll fees.

[0024] Specifically, based on the three types of data collected, the power consumption, range support, charging time, and toll fees of each candidate driving route can be evaluated to obtain quantitative evaluation results.

[0025] S140. The quantitative evaluation results are processed by a preset model to select a target recommended route from at least two candidate driving routes based on the processing results.

[0026] The preset model can be a pre-built algorithm model that can be used to process quantitative evaluation results; the target recommended route can be the optimal route selected from multiple candidate driving routes.

[0027] Specifically, the quantitative evaluation results can be input into a preset model, processed by the preset model, and the target recommended route can be selected from at least two candidate driving routes based on the processing results.

[0028] The technical solution of this invention determines the starting point and ending point through the location information of a pure electric heavy-duty truck, and plans at least two candidate driving routes; it collects the vehicle's internal correlation data, external environment data, and road correlation data corresponding to the pure electric heavy-duty truck; based on the vehicle's internal correlation data, external environment data, and road correlation data, it evaluates the power consumption, range support, charging time, and toll fees of each candidate driving route to obtain quantitative evaluation results; it processes the quantitative evaluation results through a preset model to select a target recommended route from at least two candidate driving routes based on the processing results. This solves the problems of existing pure electric heavy-duty truck route planning that only considers limited factors, cannot accurately evaluate range and timeliness, and is difficult to meet the needs of medium and long-distance transportation. It realizes multi-dimensional and accurate quantitative evaluation of pure electric heavy-duty truck routes, selects the target route with the best timeliness or the lowest cost, improves the practicality and competitiveness of pure electric heavy-duty trucks in medium and long-distance operation, and helps the large-scale promotion of new energy heavy-duty trucks.

[0029] In some embodiments, the vehicle internal associated data includes vehicle weight data, electric drive system health status data, and driver's historical driving habit data. The collection of the vehicle internal associated data corresponding to the pure electric heavy truck includes: acquiring vehicle weight data and electric drive system health status data through the sensors of the pure electric heavy truck; and retrieving and acquiring the driver's historical driving habit data corresponding to the pure electric heavy truck through a big data platform.

[0030] Among them, the electric drive system health status data is used to indicate whether the electric drive system of the pure electric heavy truck is operating well or whether its performance is stable; the driver's historical driving habit data can be data such as acceleration, braking, and constant speed driving during the driver's past driving process.

[0031] Specifically, the sensors on the pure electric heavy-duty truck can be activated to collect vehicle weight data and electric drive system health status data in real time; it can also be connected to a big data platform to retrieve the driver's historical driving habit data corresponding to the pure electric heavy-duty truck, so as to achieve comprehensive collection of related data inside the vehicle.

[0032] In some embodiments, evaluating the energy consumption of each candidate driving route based on the vehicle's internal correlation data includes: calculating energy consumption impact parameters based on the vehicle weight data; calculating energy consumption impact parameters based on the electric drive system health status data; calculating energy consumption impact parameters based on the driver's historical driving habit data; and quantifying the energy consumption value of each candidate driving route by combining the energy consumption impact parameters.

[0033] Among them, the power consumption impact parameter can be a quantitative parameter that reflects the effect of various types of vehicle internal correlation data on power consumption, and the quantitative power consumption value can refer to the specific power consumption value corresponding to each candidate driving route obtained through comprehensive calculation.

[0034] Specifically, based on the vehicle weight data, electric drive system health status data, and driver historical driving habit data collected above, the correlation between each data and power consumption can be established, and the power consumption impact parameter corresponding to each data can be calculated through the correlation. Furthermore, according to the importance of each power consumption impact parameter, corresponding weights can be assigned, and all power consumption impact parameters can be comprehensively calculated to quantify the power consumption value of each candidate driving route.

[0035] In some embodiments, the collection of external environmental data and road-related data corresponding to the pure electric heavy truck includes: acquiring current temperature data through vehicle temperature sensors; acquiring predicted temperature data, current and predicted wind speed and direction data, road slope data, road congestion data, charging facility layout and historical queuing data for the same time period, road network information data, vehicle speed data of vehicles passing through the corresponding road section, as well as highway mileage data and national highway toll station location data through the Internet of Vehicles.

[0036] In this embodiment, the current temperature data can be collected by a temperature sensor installed on the vehicle; the vehicle network system can also be used to obtain predicted temperature data, current and predicted wind speed and direction data, road slope data, road congestion data, charging facility layout and historical queuing data for the same time period, road network information data, vehicle speed data of vehicles passing through the corresponding road section, as well as highway mileage data and national highway toll station location data, so as to achieve comprehensive coverage of external environment and road-related data collection.

[0037] In some embodiments, the evaluation of the power consumption, range support, charging time, and toll fees of each candidate driving route based on the external environment data and road association data includes: calculating the discharge capacity of the power battery based on the current temperature data and predicted temperature data, as the range support; calculating the vehicle operating speed based on the current and predicted wind speed and direction data, road network information data, and vehicle speed data of vehicles traveling through the corresponding road segments, and calculating the power consumption value in combination with the vehicle operating speed; calculating the power consumption increment and the range replenishment corresponding to braking energy recovery based on the road slope data and road congestion data, respectively; calculating the charging queuing time and recharging time based on the charging facility layout and historical queuing data for the same time period, and determining the charging time; and calculating the toll fees based on the highway mileage data and national highway toll station location data.

[0038] Among them, the discharge capacity of the power battery can refer to the total amount of electrical energy that the power battery can release under the current and predicted environment; the vehicle operating speed can refer to the expected driving speed of the pure electric heavy truck on the candidate driving route; the increase in power consumption can refer to the additional increase in power consumption caused by factors such as road slope and congestion. The range replenishment corresponding to regenerative braking can be the driving range corresponding to the amount of electricity that regenerative braking technology can replenish for a vehicle.

[0039] Specifically, based on the collected current temperature data and predicted temperature data, the discharge capacity of the power battery can be calculated using a power battery discharge characteristic model, and this can be directly used as the range support. Combining current and predicted wind speed and direction data, road network information data, and vehicle speed data of vehicles traveling on the corresponding road sections, the vehicle's operating speed can be calculated using a speed prediction model, and then the power consumption value can be calculated based on the correlation between speed and power consumption.

[0040] For road slope data and road congestion data, corresponding calculation models are established to calculate the increase in power consumption and the corresponding range replenishment amount corresponding to braking energy recovery; based on the layout of charging facilities and historical queuing data for the same time period, the charging queuing time is estimated, and combined with the basic time required for recharging, the charging duration is determined; based on highway mileage data and national highway toll station location data, the toll for the candidate route is calculated according to the corresponding toll standards.

[0041] In some embodiments, the step of processing the quantitative evaluation results using a preset model includes: using a pre-trained multi-dimensional trade-off model to perform weighted calculations on the quantitative evaluation results of the power consumption, range support, charging time, and toll fees of each candidate driving route to obtain a comprehensive score for each candidate route; and selecting the target recommended route with the best timeliness or the lowest cost based on the comprehensive score.

[0042] The multi-dimensional trade-off model can be an algorithm model pre-trained with a large amount of data, capable of comprehensively weighing power consumption, range support, charging time, and toll fees. Specifically, a pre-trained multi-dimensional trade-off model can be used, inputting the quantitative evaluation results of power consumption, range support, charging time, and toll fees for each candidate route into the model. The model performs a weighted calculation on the quantitative evaluation results according to the preset weights of each indicator, obtaining a comprehensive score for each candidate route. Furthermore, the comprehensive scores of all candidate routes are ranked, and the route with the best timeliness or lowest cost is selected as the target recommended route, ensuring that the recommended route can meet the core needs of users and improve the operational practicality of pure electric heavy trucks.

[0043] The technical solution of this invention determines the starting point and ending point through the location information of a pure electric heavy-duty truck, and plans at least two candidate driving routes; it collects vehicle internal correlation data, external environment data, and road correlation data corresponding to the pure electric heavy-duty truck; based on the vehicle internal correlation data, external environment data, and road correlation data, it evaluates the power consumption, range support, charging time, and toll fees of each candidate driving route to obtain quantitative evaluation results; it processes the quantitative evaluation results through a preset model to select a target recommended route from at least two candidate driving routes based on the processing results. This solves the problems of existing pure electric heavy-duty truck route planning that only considers limited factors, cannot accurately evaluate range and timeliness, and is difficult to meet the needs of medium and long-distance transportation. It achieves multi-dimensional and accurate quantitative evaluation of pure electric heavy-duty truck routes, selects the target route with the best timeliness or lowest cost, improves the practicality and competitiveness of pure electric heavy-duty trucks in medium and long-distance operation, and helps the large-scale promotion of new energy heavy-duty trucks. In some optional embodiments, Figure 2 This is a schematic diagram of a route planning device for a pure electric heavy-duty truck provided in an embodiment of the present invention. Figure 2 As shown, the device includes: The route planning module 210 is used to determine the starting point and the destination based on the location information of the pure electric heavy truck, and to plan at least two candidate driving routes. The data acquisition module 220 is used to collect the vehicle internal associated data, external environment data and road associated data corresponding to the pure electric heavy truck; The quantitative evaluation module 230 is used to evaluate the power consumption, range support, charging time and toll of each candidate driving route based on the vehicle internal correlation data, external environment data and road correlation data, and obtain the quantitative evaluation results. The route recommendation module 240 is used to process the quantitative evaluation results through a preset model, so as to select a target recommended route from at least two candidate driving routes based on the processing results.

[0044] The technical solution of this invention determines the starting point and ending point through the location information of a pure electric heavy-duty truck, and plans at least two candidate driving routes; it collects the vehicle's internal correlation data, external environment data, and road correlation data corresponding to the pure electric heavy-duty truck; based on the vehicle's internal correlation data, external environment data, and road correlation data, it evaluates the power consumption, range support, charging time, and toll fees of each candidate driving route to obtain quantitative evaluation results; it processes the quantitative evaluation results through a preset model to select a target recommended route from at least two candidate driving routes based on the processing results. This solves the problems of existing pure electric heavy-duty truck route planning that only considers limited factors, cannot accurately evaluate range and timeliness, and is difficult to meet the needs of medium and long-distance transportation. It realizes multi-dimensional and accurate quantitative evaluation of pure electric heavy-duty truck routes, selects the target route with the best timeliness or the lowest cost, improves the practicality and competitiveness of pure electric heavy-duty trucks in medium and long-distance operation, and helps the large-scale promotion of new energy heavy-duty trucks.

[0045] In some embodiments, the vehicle internal associated data includes vehicle weight data, electric drive system health status data, and driver's historical driving habit data, and the data acquisition module 220 is specifically used for: The vehicle weight data and electric drive system health status data are obtained through the sensors of the pure electric heavy truck. The historical driving habit data of the drivers corresponding to the pure electric heavy trucks is retrieved and obtained through a big data platform.

[0046] In some embodiments, the quantitative evaluation module 230 is specifically used for: Calculate the energy consumption impact parameters based on the vehicle weight data; Calculate power consumption impact parameters based on the health status data of the electric drive system; Calculate the energy consumption impact parameters based on the driver's historical driving habit data; The power consumption value of each candidate driving route is quantified by combining various power consumption impact parameters.

[0047] In some embodiments, the data acquisition module 220 is further configured to: The current air temperature data is obtained through the vehicle's temperature sensor; The vehicle network acquires data such as predicted temperature, current and predicted wind speed and direction, road slope, road congestion, charging facility layout and historical queuing data for the same period, road network information, vehicle speed data of vehicles passing through the corresponding road sections, as well as highway mileage data and national highway toll station location data.

[0048] In some embodiments, the quantitative evaluation module 230 is further configured to: The discharge capacity of the power battery is calculated based on the current temperature data and the predicted temperature data, and used as the range support amount. The vehicle's operating speed is calculated based on the current and predicted wind speed and direction data, road network information data, and vehicle speed data of vehicles traveling through the corresponding road sections, and the power consumption value is calculated in combination with the vehicle's operating speed. Based on the road slope data and road congestion data, calculate the increase in power consumption and the corresponding range replenishment amount corresponding to brake energy recovery. Based on the layout of the charging facilities and historical queuing data for the same time period, the charging queuing time and recharging time are calculated to determine the charging duration. The toll fee is calculated based on the highway mileage data and the location data of national highway toll stations.

[0049] In some embodiments, the route recommendation module 240 is specifically used for: A pre-trained multi-dimensional trade-off model is used to perform weighted calculations on the quantitative evaluation results of power consumption, range support, charging time and toll fees of each candidate driving route to obtain a comprehensive score for each candidate route. Based on the comprehensive score, the recommended routes with the best timeliness or the lowest cost are selected.

[0050] The pure electric heavy truck route planning device provided in this embodiment of the invention can execute the pure electric heavy truck route planning method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0051] Figure 3 This is a schematic diagram of the structure of an electronic device for implementing the pure electric heavy-duty truck route planning method according to embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0052] like Figure 3As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0053] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0054] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the route planning method for pure electric heavy trucks.

[0055] In some embodiments, the pure electric heavy-duty truck route planning method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the pure electric heavy-duty truck route planning method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the pure electric heavy-duty truck route planning method by any other suitable means (e.g., by means of firmware).

[0056] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0057] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0058] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0059] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0060] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0061] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0062] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0063] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A pure electric heavy truck route planning method, characterized in that, The method comprises the following steps: determining the starting point and the ending point according to the position information of the pure electric heavy truck, and planning at least two candidate driving routes; collecting vehicle internal related data, external environment data and road related data corresponding to the pure electric heavy truck; based on the vehicle internal related data, external environment data and road related data, respectively evaluating the power consumption, the endurance support amount, the charging time and the toll of each candidate driving route to obtain a quantitative evaluation result; processing the quantitative evaluation result through a preset model to screen out a target recommended route from the at least two candidate driving routes according to the processing result.

2. The method of claim 1, wherein, The vehicle internal related data includes vehicle weight data, electric drive system health state data and driver historical driving habit data, and the collection of the vehicle internal related data corresponding to the pure electric heavy truck includes: obtaining the vehicle weight data and the electric drive system health state data through the sensors of the pure electric heavy truck; obtaining the driver historical driving habit data corresponding to the pure electric heavy truck through a big data platform.

3. The method of claim 2, wherein, Based on the vehicle internal related data, the power consumption of each candidate driving route is evaluated, which includes: calculating the power consumption influence parameter based on the vehicle weight data; calculating the power consumption influence parameter based on the electric drive system health state data; calculating the power consumption influence parameter based on the driver historical driving habit data; combining each power consumption influence parameter to quantify the power consumption value of each candidate driving route.

4. The method of claim 1, wherein, The collection of the external environment data and the road related data corresponding to the pure electric heavy truck includes: obtaining the current temperature data through a vehicle temperature sensor; obtaining the predicted temperature data, the current and predicted wind speed and direction data, the road slope data, the road congestion situation data, the charging facility layout and the historical same time period queuing data, the road network information data, the vehicle speed data of the vehicles passing through the corresponding road section, the highway mileage data and the national highway toll station location data through the Internet of Vehicles.

5. The method of claim 4, wherein, Based on the external environment data and the road related data, the power consumption, the endurance support amount, the charging time and the toll of each candidate driving route are evaluated, which includes: calculating the power consumption influence parameter based on the vehicle weight data; calculating the power consumption value based on the vehicle running speed calculated by combining the current and predicted wind speed and direction data, the road network information data and the vehicle speed data of the vehicles passing through the corresponding road section; calculating the power consumption increment and the braking energy recovery corresponding endurance supplement amount based on the road slope data and the road congestion situation data; calculating the charging queuing time and the energy supplement time based on the charging facility layout and the historical same time period queuing data to determine the charging time; calculating the toll based on the highway mileage data and the national highway toll station location data.

6. The method of claim 1, wherein, The processing of the quantitative evaluation result through the preset model includes: using a pre-trained multi-dimensional trade-off model to perform weighted operation on the quantitative evaluation results of the power consumption, the endurance support amount, the charging time and the toll of each candidate driving route to obtain a comprehensive score of each candidate route; screening out the target recommended route with the optimal time efficiency or the lowest cost according to the comprehensive score.

7. A pure electric heavy truck route planning device, characterized in that, The method comprises the following steps: A route planning module is configured to determine a start point and an end point according to position information of the pure electric heavy truck, and to plan at least two candidate driving routes; A data collection module is configured to collect vehicle internal related data, external environment data and road related data corresponding to the pure electric heavy truck; A quantitative evaluation module is configured to evaluate power consumption, endurance support, charging time and passing cost of each candidate driving route based on the vehicle internal related data, the external environment data and the road related data, and to obtain a quantitative evaluation result; A route recommendation module is configured to process the quantitative evaluation result through a preset model, and to select a target recommended route from the at least two candidate driving routes according to a processing result.

8. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected in communication with 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 to enable the at least one processor to execute the pure electric heavy truck route planning method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to execute the pure electric heavy truck route planning method of any one of claims 1-6 when executed.

10. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program, when executed by the processor, implements the pure electric heavy truck route planning method according to any one of claims 1-6.

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