Path planning method and device based on carbon sequestration efficiency, equipment and storage medium

Abstract

The application discloses a path planning method and device based on carbon sequestration efficiency, equipment and storage medium. The method comprises the following steps: obtaining first request information of all requested path planning in a target area, wherein the first request information at least comprises a path starting node and a path ending node; for each first request information, based on a first planning algorithm, a target planning path meeting a preset condition is determined according to the path starting node and the path ending node, wherein the first planning algorithm is obtained by improving a path planning algorithm based on path carbon sequestration efficiency, and the preset condition comprises that the total cost of travel time and carbon sequestration efficiency is minimum, so as to realize multi-objective collaborative optimization of traffic efficiency and ecological value, greatly improve the overall carbon sequestration efficiency of the target area, and provide path decision with traffic efficiency and ecological sustainability for a smart city.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vegetation maintenance planning, and particularly relates to a path planning method and device based on carbon sequestration efficiency, equipment and a storage medium. BACKGROUND

[0002] The existing path planning system (such as navigation technology based on A* algorithm) takes the travel time or distance as a single optimization target, and does not quantify the influence of ecological parameters such as green belt carbon sequestration efficiency and plant stress resistance on path selection. Although the open source map platform (such as OpenStreetMap) provides road geometric properties, it lacks dynamic data of ecological efficiency of green belts (such as real-time carbon sequestration amount and pollution adsorption capacity), and the navigation algorithm cannot guide the vehicle to select a path with high ecological value.

[0003] Traditional path planning algorithms (such as Dijkstra and A*) take the travel efficiency as the only optimization target, and cannot balance the time cost and ecological gain in path selection. At the same time, the path planning system and the vegetation maintenance database are completely separated: the navigation decision does not depend on the plant health state data (such as photosynthesis efficiency and soil pollution level), and the green maintenance department also cannot use traffic parameters such as vehicle flow density and emission level for plant stress resistance prediction. This separation leads to a vicious cycle of "high ecological value road section with low utilization rate and high loss road section with overload operation" in urban road network, hindering the deep integration of smart traffic and ecological city. SUMMARY

[0004] The present application provides a path planning method and device based on carbon sequestration efficiency, equipment and a storage medium, to realize multi-objective collaborative optimization of traffic efficiency and ecological value, greatly improve the overall carbon sequestration efficiency of the target area, and provide path decision with both travel efficiency and ecological sustainability for smart city.

[0005] According to one aspect of the present application, a path planning method based on carbon sequestration efficiency is provided. The method comprises:

[0006] Obtaining first request information of all requested path planning in a target area, wherein the first request information at least includes a path starting node and a path ending node;

[0007] For each first request information, based on a first planning algorithm, a target planning path meeting a preset condition is determined according to the path starting node and the path ending node, wherein the first planning algorithm is obtained by improving a path planning algorithm based on path carbon sequestration efficiency, and the preset condition includes that the total cost of travel time and carbon sequestration efficiency is minimum.

[0008] According to another aspect of the present application, a path planning device based on carbon sequestration efficiency is provided. The method comprises:

[0009] a planning request determination module configured to obtain first request information of all requested path planning in a target area, wherein the first request information at least includes a path starting node and a path ending node;

[0010] a path planning determination module configured to, for each of the first request information, determine a target planning path meeting a preset condition based on a first planning algorithm and according to the path starting node and the path ending node, wherein the first planning algorithm is obtained by improving a path planning algorithm based on path carbon fixation efficiency, and the preset condition includes that a total cost of travel time and carbon fixation efficiency is minimum.

[0011] According to another aspect of the present application, an electronic device is provided, which comprises:

[0012] at least one processor; and

[0013] a memory connected to the at least one processor in communication; wherein,

[0014] 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 path planning method based on carbon fixation efficiency according to any one of the embodiments of the present application.

[0015] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to implement the path planning method based on carbon fixation efficiency according to any one of the embodiments of the present application when executed.

[0016] The technical scheme of the embodiments of the present application obtains first request information of all requested path planning in a target area, wherein the first request information at least includes a path starting node and a path ending node. For each of the first request information, a target planning path meeting a preset condition is determined based on a first planning algorithm and according to the path starting node and the path ending node, wherein the first planning algorithm is obtained by improving a path planning algorithm based on path carbon fixation efficiency, and the preset condition includes that a total cost of travel time and carbon fixation efficiency is minimum. The present application realizes dynamic balance optimization of traffic efficiency and ecological benefit by quantifying path carbon fixation efficiency and embedding the improved first planning algorithm. The planning path minimizes travel time while preferentially selecting high carbon fixation efficiency sections, thereby driving the matching of traffic distribution and ecological resources, forming a positive cycle of "traffic diversion-carbon fixation gain", and providing path decisions with both traffic efficiency and ecological sustainability for smart cities.

[0017] It is to be understood that the details set forth in the description contained herein do not limit the scope of the application. Other embodiments of the application will be readily apparent to those skilled in the art from the description herein. With reference to the drawings, embodiments of the application are hereinafter described in detail. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0019] Figure 1 is a flow chart of a path planning method based on carbon sequestration efficiency according to an embodiment of the present application;

[0020] Figure 2 is a flow chart of a path planning method based on carbon sequestration efficiency according to an embodiment of the present application;

[0021] Figure 3 is a structural diagram of a path planning device based on carbon sequestration efficiency according to an embodiment of the present application;

[0022] Figure 4 is a structural diagram of an electronic device for implementing a path planning method based on carbon sequestration efficiency according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the technical personnel in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort should be within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", and the like in the description and claims of the application and the above drawings are used to distinguish between similar objects, and are not necessarily used to describe a particular sequential or chronological order. It should be understood that the data thus used can be interchanged, where appropriate, so that the embodiments of the application described herein can be carried out in other than the order shown or described herein. Furthermore, the terms "comprise" and "have", and any variations thereof, are intended to cover non-exclusive inclusion, for example, processes, methods, systems, products, or devices that comprise a list of steps or units are not necessarily limited to those clearly listed, but can include other steps or units not clearly listed or inherent to such processes, methods, products, or devices.

[0025] Embodiment one

[0026] Figure 1 A flowchart of a path planning method based on carbon sequestration efficiency provided for the first embodiment of the application. This embodiment can be applied to the case of planning a travel path according to the carbon sequestration capacity and path efficiency of a road. The method can be executed by a path planning device based on carbon sequestration efficiency, which can be realized in the form of hardware and / or software, and can be configured in an electronic device. As shown in the figure, the method comprises: Figure 1

[0027] S101, obtaining first request information for all requested path planning in a target area.

[0028] The target area can be an area to be maintained by road vegetation, for example, a certain city. The first request information can be the request information for path planning by a user. The first request information at least includes a path starting node and a path ending node. It should be noted that the path starting node and the path ending node can be generally referred to as a certain road in the target area, and different positions in the same road can be regarded as the same path starting node or path ending node.

[0029] For example, the application can be applied to an open source map platform. The first request information for path planning triggered by each user in the target area for travel demand is received through the open source map platform. The path starting node can be determined according to the starting point information input by the user in the starting point box of the open source map platform, or can be determined according to the current position information of the user. The path ending node can be determined according to the terminal point information input by the user in the terminal point box of the open source map platform.

[0030] S102, for each first request information, determining a target planning path meeting a preset condition based on a first planning algorithm according to the path starting node and the path ending node. ​

[0031] The first planning algorithm is an algorithm for implementing path planning for the first request information. The first planning algorithm is obtained by improving a path planning algorithm (such as an A* algorithm) based on path carbon fixation efficiency. The first planning algorithm embeds path carbon fixation efficiency on the basis of a traditional path planning algorithm, and can achieve multi-objective collaborative optimization of traffic efficiency (travel time) and ecological value (carbon fixation efficiency). Although the first planning algorithm needs to sacrifice a small part of path planning efficiency, it can enhance the carbon fixation benefit in the target area. The preset conditions include that the total cost of travel time and carbon fixation efficiency is minimum.

[0032] Specifically, for each first request information, the first planning algorithm is used to calculate the optimal path between the path starting node and the path ending node with the constraint condition that the total cost of travel time and carbon fixation efficiency is minimum, so as to obtain the target planning path meeting the preset conditions.

[0033] Exemplarily, the target planning path meeting the preset conditions is determined according to the path starting node and the path ending node based on the first planning algorithm, including:

[0034] determining a first actual cost between the path starting node and a current path node; and determining a second predicted cost between the current path node and the path ending node;

[0035] determining a planning path with minimum total cost of travel time and carbon fixation efficiency between the path starting node and the path ending node according to the first actual cost and the second predicted cost, and determining the planning path with minimum total cost as the target planning path.

[0036] It should be noted that the current path node can be the latest node of the determined path in path planning, and the current path node is different following different path planning progress. Exemplarily, at the beginning of path planning, the current path node can be the path starting node; at the beginning of path planning, the current path node can be the path ending node.

[0037] The first actual cost can be the actual cost that has been determined, i.e., the actual cost between the path starting node and the current path node. The second predicted cost can be the cost predicted according to the current path node and the path ending node. It should be noted that the first planning algorithm is obtained by improving a path planning algorithm based on path carbon fixation efficiency, and therefore, the first actual cost and the second predicted cost both include the total cost of travel time and carbon fixation efficiency.

[0038] Specifically, after obtaining the first actual cost and the second predicted cost, the planning path with the minimum total cost of travel time and carbon sequestration efficiency is determined as the target planning path. Exemplarily, the total cost of the first actual cost and the second predicted cost is as follows:

[0039] f(n) = h(n) + g(n);

[0040] wherein f(n) refers to the total cost, h(n) refers to the second predicted cost, and g(n) refers to the first actual cost.

[0041] Exemplarily, the determining of the second predicted cost between the current path node and the path end node comprises: determining a time heuristic function according to the current path node and the path end node; determining an ecological heuristic function according to the current node carbon sequestration efficiency corresponding to the current path node, and the second maximum carbon sequestration efficiency and the node shortest distance between the current path node and the path end node; and determining the second predicted cost according to the time heuristic function and the ecological heuristic function, and a time coefficient and an ecological coefficient.

[0042] Exemplarily, the time heuristic function is determined according to the current path node and the path end node, and is as follows:

[0043]

[0044] wherein h geo (n) refers to the time heuristic function, x n refers to the horizontal coordinate of the current path node, x goal refers to the horizontal coordinate of the path end node, y n refers to the vertical coordinate of the current path node, y goal refers to the vertical coordinate of the path end node, and v avg refers to the average travel speed between the current path node and the path end node.

[0045] Exemplarily, the ecological heuristic function is determined as follows:

[0046]

[0047] wherein h carbon (n) refers to the ecological heuristic function, C current refers to the current node carbon sequestration efficiency, ∑p∈Path opt C eff (p) refers to the second maximum carbon sequestration efficiency, d(n, goal) refers to the node shortest distance, and v avg refers to the average travel speed between the current path node and the path end node.

[0048] According to the time heuristic function and the ecological heuristic function, and the time coefficient and the ecological coefficient, the second prediction cost is determined.

[0049] h(n)=α·h geo (n)+β·h carbon (n);

[0050] Wherein, h(n) refers to the second prediction cost, h geo (n) refers to the time heuristic function, h carbon (n) refers to the ecological heuristic function, α refers to the time coefficient, and β refers to the ecological coefficient.

[0051] According to the time heuristic function and the ecological heuristic function, and the time coefficient and the ecological coefficient, the second prediction cost is determined.

[0052]

[0053] Wherein, the formula is a weight formula adjusted in real time according to traffic pressure, α refers to the time coefficient, R represents a real-time traffic pressure comprehensive index, R0 is a baseline pressure threshold (default 0.7) depending on urban road traffic efficiency, and k is an adjustment sensitivity coefficient (default 3.0).

[0054]

[0055] Wherein, η represents a congestion index weight (default 0.2), I t is a congestion index, from three-party navigation platform data (0-10 points), I max represents a maximum congestion index (default 10), Q is a traffic flow in a statistical period, and Q max is a road design capacity.

[0056]

[0057] β satisfies the formula condition. Wherein, refers to the maximum theoretical road carbon fixation efficiency between the current path node and the path end node, d min refers to the shortest straight line distance between the current path node and the path end node.

[0058] According to the time heuristic function and the ecological heuristic function, and the time coefficient and the ecological coefficient, the second prediction cost is determined.

[0059] According to the time heuristic function and the ecological heuristic function, and the time coefficient and the ecological coefficient, the second prediction cost is determined.

[0060] The road planning information can be planning information that each regional road in the target region is planned to be a driving road.

[0061] Specifically, all target planning paths in the target region are analyzed, so that the road planning information that each regional road in the target region is planned to be a driving road can be obtained, and then the road planning information is analyzed and processed, that is, the number of times and the proportion of each regional road being planned are determined, and then the vegetation maintenance of each regional road in the target region is performed according to the road planning information. For example, when a regional road is planned to be an ecological driving path for 30 consecutive days, the maintenance level is automatically triggered to be improved (drip irrigation frequency + 50%, pruning period - 30%).

[0062] In addition, the present application can also modify the travel road according to other conditions. For example, when the carbon fixation efficiency of a regional road decreases by 15% or more, a detour suggestion is generated and a maintenance work order is pushed.

[0063] On the basis of the above-mentioned embodiments, the vegetation maintenance of each regional road in the target region according to the road planning information comprises: for each regional road, determining the road planning proportion of the regional road being planned to be a driving road under the same type of first request information according to the road planning information; when the road planning proportion is greater than a first preset proportion, planting a preset carbon fixation vegetation in the regional road based on a preset interval; when the road planning proportion is less than a second preset proportion, reducing the ecological coefficient of the regional road; otherwise, no vegetation maintenance of the regional road is performed.

[0064] The first preset proportion and the second preset proportion can be set according to actual conditions, and the second preset proportion is less than the first preset proportion. The road planning proportion can be the proportion of being planned to be a driving road under the same type of first request information. The same type of first request information can mean that in a plurality of first request information, the path starting node of each first request information is located in the same road, and the path ending node is also located in the same road.

[0065] Specifically, the road planning information corresponding to the same type of first request information is compared and processed, so that the road planning proportion of each regional road being planned to be a driving road under the same type of first request information can be determined, and when the road planning proportion is greater than a first preset proportion, a preset carbon fixation vegetation is planted in the regional road based on a preset interval, such as planting the same type of high carbon fixation plant every 50 meters. When the road planning proportion is less than a second preset proportion, the ecological coefficient of the regional road is reduced. Otherwise, no vegetation maintenance of the regional road is performed.

[0066] The technical scheme of the embodiment of the present application acquires first request information of all request path planning of a target area, wherein the first request information at least includes a path starting node and a path ending node. For each first request information, a target planning path meeting a preset condition is determined based on the path starting node and the path ending node based on a first planning algorithm, wherein the first planning algorithm is obtained based on path carbon fixation efficiency and path planning algorithm improvement, and the preset condition includes that the total cost of travel time and carbon fixation efficiency is minimum. The present application realizes dynamic balance optimization of traffic efficiency and ecological benefit by quantifying path carbon fixation efficiency and embedding the improved first planning algorithm. The planning path minimizes the travel time while preferentially selecting high carbon fixation efficiency sections, thereby driving the matching of traffic distribution and ecological resources, forming a positive cycle of "traffic diversion-carbon fixation gain", and providing path decision with both traffic efficiency and ecological sustainability for a smart city.

[0067] Embodiment two

[0068] Figure 2 A flowchart of a path planning method based on carbon fixation efficiency provided by the second embodiment of the present application is shown in the figure. The embodiment further refines the determination process of carbon fixation efficiency of each road based on the above-mentioned embodiments. As shown in the figure, the method comprises: Figure 2

[0069] S201, acquiring vegetation species information, traffic flow information, noise information and vegetation growth information corresponding to a target path to be determined carbon fixation efficiency.

[0070] Specifically, the vegetation species, planting density and growth cycle can be obtained by integrating the municipal greening database; the real-time traffic flow monitoring data of the traffic management platform includes traffic flow, vehicle type and emission level; the noise information of the noise sensor is obtained through the environmental monitoring station, and the vegetation growth microenvironment parameters collected by the Internet of Things equipment deployed in the green belt, etc. The accurate association and real-time update of multi-dimensional data can also be realized through a space-time matching algorithm.

[0071] S202, determining vegetation theoretical carbon fixation data of the target path based on the vegetation species information.

[0072] Exemplarily, the vegetation theoretical carbon fixation data determination process is shown as follows:

[0073] C base = LAI x P max x φ light x t active ;

[0074] Wherein, LAI is the leaf area index, which depends on the plant species, P max is the maximum photosynthetic rate, which also depends on the species, φ light ​is the light energy utilization efficiency, t active is the annual effective photosynthesis time length, which depends on the region.

[0075] S203, determining a vehicle flow exhaust inhibition factor of the target path based on the vehicle flow information.

[0076] Exemplarily, the vehicle flow exhaust inhibition factor determination process is as follows:

[0077]

[0078] wherein γ is an exhaust sensitivity coefficient, Q is the vehicle flow in the statistical period, Q max is the road design capacity.

[0079] S204, determining an environmental noise interference factor of the target path based on the noise information.

[0080] Exemplarily, the environmental noise interference factor determination process is as follows:

[0081] λ noise = 1-0.015x max(0, N-65);

[0082] wherein N is the average noise decibel in the statistical period.

[0083] S205, determining a vegetation growth period factor of the target path based on the vegetation growth information.

[0084] Exemplarily, the vegetation growth period factor determination process is as follows:

[0085] λ growth = 1-e -k·t ;

[0086] wherein k is a growth rate constant, which depends on the species, and t is the vegetation planting age.

[0087] S206, determining the carbon sequestration efficiency corresponding to the target path according to the vegetation theoretical carbon sequestration data, the vehicle flow exhaust inhibition factor, the environmental noise interference factor, and the vegetation growth period factor.

[0088] Exemplarily, the carbon sequestration efficiency determination process is as follows:

[0089] C eff = C base x λ traffic x λ noise x λ growth ;

[0090] wherein C base is the vegetation theoretical carbon sequestration data, λ traffic , λ noise, lambda growth are dynamic performance correction factors, respectively representing vehicle exhaust suppression factors, environmental noise interference factors and vegetation growth cycle factors.

[0091] S207, acquire first request information of all request path planning of a target area.

[0092] S208, for each first request information, based on the first planning algorithm, according to the path starting node and the path ending node, determine the target planning path meeting the preset condition.

[0093] The technical scheme of the embodiment of the application determines the carbon sequestration efficiency corresponding to the target path through the vegetation theory carbon sequestration data, the vehicle exhaust suppression factor, the environmental noise interference factor and the vegetation growth cycle factor, breaks through the limitation of traditional static evaluation, quantifies the real-time loss of traffic pollution and noise on the carbon sequestration capacity of plants, and combines the growth cycle to predict the carbon sequestration potential of vegetation, thereby providing high-resolution ecological parameters for path planning and maintenance decision-making.

[0094] Embodiment three

[0095] Figure 3 A structure schematic diagram of a path planning device based on carbon sequestration efficiency provided by the third embodiment of the application is shown in FIG. 3. Figure 3 As shown in the figure, the device comprises:

[0096] The planning request determination module 301 is configured to acquire first request information of all request path planning of a target area, wherein the first request information at least includes a path starting node and a path ending node.

[0097] The planning path determination module 302 is configured to determine, for each first request information, a target planning path meeting a preset condition based on a first planning algorithm according to the path starting node and the path ending node, wherein the first planning algorithm is obtained by improving the path planning algorithm based on the path carbon sequestration efficiency, and the preset condition includes that the total cost of driving time and carbon sequestration efficiency is minimum.

[0098] The technical scheme of the embodiment of the application obtains first request information of all request path planning of a target area, wherein the first request information at least includes a path starting node and a path ending node. For each first request information, a target planning path meeting a preset condition is determined based on a first planning algorithm according to the path starting node and the path ending node, wherein the first planning algorithm is obtained based on path carbon fixation efficiency and path planning algorithm improvement, and the preset condition includes that the total cost of travel time and carbon fixation efficiency is minimum. The application realizes dynamic balance optimization of traffic efficiency and ecological benefit by quantifying path carbon fixation efficiency and embedding the improved first planning algorithm. The planning path minimizes the travel time and preferentially selects a high carbon fixation efficiency section, thereby driving the matching of traffic flow distribution and ecological resources and forming a positive cycle of 'traffic flow diversion-carbon fixation gain', thereby providing a path decision with both traffic efficiency and ecological sustainability for a smart city.

[0099] Optionally, the planning path determination module 302 includes:

[0100] A first actual cost determination unit is configured to determine a first actual cost between the path starting node and a current path node, wherein the first actual cost includes the total cost of travel time and carbon fixation efficiency.

[0101] A second prediction cost determination unit is configured to determine a second prediction cost between the current path node and the path ending node, wherein the second prediction cost includes the total cost of travel time and carbon fixation efficiency.

[0102] A target planning path determination unit is configured to determine a planning path with the minimum total cost of travel time and carbon fixation efficiency between the path starting node and the path ending node according to the first actual cost and the second prediction cost, and determine the planning path with the minimum total cost as the target planning path.

[0103] Optionally, the second prediction cost determination unit includes:

[0104] A time heuristic function determination subunit is configured to determine a time heuristic function according to the current path node and the path ending node.

[0105] An ecological heuristic function determination subunit is configured to determine an ecological heuristic function according to the current node carbon fixation efficiency corresponding to the current path node, and the second maximum carbon fixation efficiency and the node shortest distance between the current path node and the path ending node.

[0106] A second prediction cost determination subunit is configured to determine the second prediction cost according to the time heuristic function and the ecological heuristic function, and a time coefficient and an ecological coefficient.

[0107] Optionally, the ecological heuristic function determining subunit is configured to:

[0108]

[0109] wherein h carbon (n) refers to the ecological heuristic function, C current refers to the current node carbon fixation efficiency, ∑p∈Path opt C eff (p) refers to the second maximum carbon fixation efficiency, d(n, goal) refers to the node shortest distance, v avg refers to the average driving speed between the current path node and the path end node.

[0110] Optionally, the second prediction cost determining subunit is configured to:

[0111] h(n) = a · h geo (n) + β · h carbon (n);

[0112] wherein h(n) refers to the second prediction cost, h geo (n) refers to the time heuristic function, h carbon (n) refers to the ecological heuristic function, a refers to the time coefficient, and β refers to the ecological coefficient.

[0113] Optionally, the device further comprises a carbon fixation efficiency determining module, wherein

[0114] The carbon fixation efficiency determining module is configured to:

[0115] obtain vegetation species information, traffic flow information, noise information, and vegetation growth information corresponding to a target path for which carbon fixation efficiency is to be determined;

[0116] determine theoretical carbon fixation data of the vegetation of the target path based on the vegetation species information;

[0117] determine a vehicle exhaust suppression factor of the target path based on the traffic flow information;

[0118] determine an environmental noise interference factor of the target path based on the noise information;

[0119] determine a vegetation growth cycle factor of the target path based on the vegetation growth information;

[0120] determine the carbon fixation efficiency corresponding to the target path according to the theoretical carbon fixation data of the vegetation, the vehicle exhaust suppression factor, the environmental noise interference factor, and the vegetation growth cycle factor.

[0121] Optionally, the device further comprises a vegetation maintenance executing module, wherein

[0122] The vegetation maintenance execution module is configured to:

[0123] According to all the target planning paths in the target area, determine road planning information of each regional road in the target area that is planned to be traveled, and perform vegetation maintenance on each regional road in the target area according to the road planning information.

[0124] The path planning device based on carbon sequestration efficiency provided by the embodiment of the application can execute the path planning method based on carbon sequestration efficiency provided by any embodiment of the application, and has a corresponding function module and beneficial effects of the execution method.

[0125] Embodiment four

[0126] Figure 4 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the application described and / or claimed in this document.

[0127] As shown in Figure 4 The electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0128] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0129] The processor 11 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the carbon fixation performance-based path planning method.

[0130] In some embodiments, the carbon fixation performance-based path planning method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of the carbon fixation performance-based path planning method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the carbon fixation performance-based path planning method by any other appropriate means, such as by means of firmware.

[0131] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0132] Computer programs for implementing the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, can cause instructions defined in the flow charts and / or block diagrams to be implemented. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, and partially on a remote machine or entirely on a remote machine or server.

[0133] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0134] To provide for interaction with a user, the systems and techniques described here 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 a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; 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 acoustic, speech, or tactile input.

[0135] The systems and techniques described herein can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein, or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0136] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0137] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in different orders, as long as the desired results of the technical solutions of the present disclosure can be achieved, and the present disclosure is not limited herein.

[0138] The above detailed description does not constitute a limitation on the protection scope of the present application. 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 replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A path planning method based on carbon sequestration efficiency, characterized in that, include: Obtain the first request information for all request path planning in the target area, wherein the first request information includes at least the path start node and the path end node; For each of the first request messages, based on the first planning algorithm, a target planning path that meets preset conditions is determined according to the path starting node and the path ending node. The first planning algorithm is obtained by improving the path planning algorithm based on the path carbon sequestration efficiency. The preset conditions include minimizing the total cost of travel time and carbon sequestration efficiency. The step of determining a target planning path that meets preset conditions based on the first planning algorithm, according to the path's starting node and the path's ending node, includes: Determine the first actual cost between the starting node of the path and the current path node, wherein the first actual cost includes the total cost of travel time and carbon sequestration efficiency; Determine a second predicted cost between the current path node and the path end node, wherein the second predicted cost includes the total cost of travel time and carbon sequestration efficiency; Based on the first actual cost and the second predicted cost, determine the planned path between the starting node and the ending node of the path that minimizes the total cost of travel time and carbon sequestration efficiency, and determine the planned path with the minimum total cost as the target planned path. The determination of the second prediction cost between the current path node and the path end node includes: Determine the time heuristic function based on the current path node and the path end node; An ecological heuristic function is determined based on the carbon sequestration efficiency of the current path node, the second maximum carbon sequestration efficiency between the current path node and the path end node, and the shortest distance between the nodes. The second prediction cost is determined based on the time heuristic function and the ecological heuristic function, as well as the time coefficient and the ecological coefficient.

2. The method according to claim 1, characterized in that, The determination of the ecological heuristic function includes: ； in, This refers to the ecological heuristic function, This refers to the carbon sequestration efficiency of the current node. This refers to the second maximum carbon sequestration efficiency. This refers to the shortest distance between the nodes. This refers to the average travel speed between the current path node and the path end node.

3. The method according to claim 1, characterized in that, The step of determining the second prediction cost based on the time heuristic function and the ecological heuristic function, as well as the time coefficient and the ecological coefficient, includes: ； in, This refers to the second prediction cost, This refers to the time heuristic function, This refers to the ecological heuristic function, This refers to the time coefficient, This refers to the ecological coefficient.

4. The method according to any one of claims 1-3, characterized in that, The process for determining the carbon sequestration efficiency includes: Obtain vegetation species information, traffic flow information, noise information, and vegetation growth information corresponding to the target path for which carbon sequestration efficiency needs to be determined; Based on the vegetation species information, the theoretical carbon sequestration data of the target route are determined. Based on the traffic flow information, determine the exhaust emission suppression factor of the target path; Based on the noise information, the environmental noise interference factor of the target path is determined; Based on the vegetation growth information, the vegetation growth cycle factor of the target path is determined. Based on the theoretical carbon sequestration data of vegetation, the vehicle exhaust emission suppression factor, the environmental noise interference factor, and the vegetation growth cycle factor, the carbon sequestration efficiency corresponding to the target path is determined.

5. The method according to claim 1, characterized in that, After determining the target planning path that meets the preset conditions, the following is also included: Based on all the target planned paths within the target area, determine the road planning information for each regional road within the target area that is planned to be traveled, and carry out vegetation maintenance on each regional road within the target area based on the road planning information.

6. A path planning device based on carbon sequestration efficiency, characterized in that, include: The planning request determination module is used to obtain the first request information of all request path planning in the target area, wherein the first request information includes at least the path start node and the path end node; The route planning and determination module is used to determine a target planned route that meets preset conditions for each first request information, based on a first planning algorithm and according to the starting node and ending node of the route. The first planning algorithm is obtained by improving the route planning algorithm based on the carbon sequestration efficiency of the route. The preset conditions include minimizing the total cost of travel time and carbon sequestration efficiency. The path planning and determination module includes: The first actual cost determination unit is used to determine the first actual cost between the path starting node and the current path node, wherein the first actual cost includes the total cost of travel time and carbon sequestration efficiency. The second prediction cost determination unit is used to determine the second prediction cost between the current path node and the path end node, wherein the second prediction cost includes the total cost of travel time and carbon sequestration efficiency; The target planning path determination unit is used to determine the planning path with the minimum total cost of travel time and carbon sequestration efficiency between the path starting node and the path ending node based on the first actual cost and the second predicted cost, and to determine the planning path with the minimum total cost as the target planning path. The second prediction cost determination unit includes: The time heuristic function determines the sub-unit, which is used to determine the time heuristic function based on the current path node and the path end node; An ecological heuristic function determines a sub-unit, which is used to determine the ecological heuristic function based on the current node carbon sequestration efficiency corresponding to the current path node, the second maximum carbon sequestration efficiency between the current path node and the path end node, and the shortest distance between the nodes. The second prediction cost determination subunit is used to determine the second prediction cost based on the time heuristic function and the ecological heuristic function, as well as the time coefficient and the ecological coefficient.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the path planning method based on carbon sequestration efficiency as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the path planning method based on carbon sequestration efficiency as described in any one of claims 1-5.

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