Carbon sequestration efficiency-based path planning method, apparatus and device, and storage medium

Abstract

The invention discloses a path planning method and device based on carbon sequestration efficiency, equipment and a storage medium. The method comprises the steps that first request information of all request path planning in a target area is acquired, and the first request information at least comprises a path starting node and a path ending node; for each piece of first request information, based on a first planning algorithm, according to the path starting node and the path ending node, determining a target planning path meeting a preset condition, the first planning algorithm being obtained by improving a path planning algorithm based on path carbon sequestration efficiency, and the path planning algorithm being obtained by improving the path planning algorithm based on the path carbon sequestration efficiency; the preset condition comprises that the total cost of the driving time and the carbon sequestration efficiency is minimum, so that multi-target collaborative optimization of the traffic efficiency and the ecological value is realized, the overall carbon sequestration efficiency of the target area is greatly improved, and a path decision with both the passing efficiency and the ecological sustainability is provided for the smart city.

Description

Technical Field

[0001] The present invention relates to the technical field of vegetation maintenance planning, and in particular to a path planning method, device, equipment and storage medium based on carbon sequestration efficiency. Background Art

[0002] Existing route planning systems (such as navigation technology based on the A* algorithm) use travel time or distance as a single optimization objective and fail to quantify the impact of ecological parameters such as green belt carbon sequestration efficiency and plant stress tolerance on route selection. Open-source mapping platforms (such as OpenStreetMap) provide road geometry, but lack dynamic data on the ecological performance of green belts (such as real-time carbon sequestration and pollution absorption capacity). This makes navigation algorithms unable to guide vehicles to choose routes with high ecological value.

[0003] Traditional path planning algorithms (such as Dijkstra and A*) prioritize efficiency as their sole optimization objective, failing to balance time costs and ecological gains in path selection. Furthermore, path planning systems are completely disconnected from vegetation maintenance databases: navigation decisions do not rely on plant health data (such as photosynthesis efficiency and soil contamination levels), and landscaping maintenance departments are unable to use traffic parameters such as traffic density and emission levels to predict plant stress resistance. This disconnect has trapped urban road networks in a vicious cycle of low utilization of high-ecological-value sections and overloaded high-loss sections, hindering the deep integration of smart transportation and ecological cities. Summary of the Invention

[0004] The present invention provides a path planning method, device, equipment and storage medium based on carbon sequestration efficiency to achieve multi-objective coordinated optimization of traffic efficiency and ecological value, greatly improving the overall carbon sequestration efficiency of the target area, and providing smart cities with path decisions that combine traffic efficiency and ecological sustainability.

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

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

[0007] For each of the first request information, based on the first planning algorithm, according to the path starting node and the path ending node, a target planning path that meets the preset conditions is determined, wherein the first planning algorithm is obtained by improving the path planning algorithm based on the path carbon sequestration efficiency, and the preset conditions include minimizing the total cost of driving time and carbon sequestration efficiency.

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

[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 includes at least a path starting node and a path ending node;

[0010] A planning path determination module is used to determine, for each of the first request information, a target planning path that meets preset conditions 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 conditions include minimizing the total cost of driving time and carbon sequestration efficiency.

[0011] According to another aspect of the present invention, an electronic device is provided, comprising:

[0012] at least one processor; and

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

[0014] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the path planning method based on carbon sequestration efficiency described in any embodiment of the present invention.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the path planning method based on carbon sequestration efficiency described in any embodiment of the present invention when executed.

[0016] The technical solution of the embodiment of the present invention is to obtain the first request information of all requested path planning in the target area, wherein the first request information includes at least the path starting node and the path ending node. For each of the first request information, based on the first planning algorithm, according to the path starting node and the path ending node, a target planning path that meets the preset conditions is determined, wherein the first planning algorithm is obtained by improving the path planning algorithm based on the path carbon sequestration efficiency, and the preset conditions include minimizing the total cost of driving time and carbon sequestration efficiency. The present invention realizes the dynamic balance optimization of traffic efficiency and ecological benefits by quantifying the path carbon sequestration efficiency and embedding it into the improved first planning algorithm. While minimizing the travel time, the planned path gives priority to sections with high carbon sequestration efficiency, thereby driving the matching of traffic distribution and ecological resources, forming a positive cycle of "traffic diversion-carbon sequestration gain", and providing smart cities with path decisions that have both traffic efficiency and ecological sustainability.

[0017] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 is a flow chart of a path planning method based on carbon sequestration efficiency provided in accordance with the first embodiment of the present invention;

[0020] Figure 2 is a flow chart of a path planning method based on carbon sequestration efficiency provided in accordance with the second embodiment of the present invention;

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

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

[0023] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] Example 1

[0026] Figure 1 This is a flow chart of a path planning method based on carbon sequestration efficiency provided in the first embodiment of the present invention. This embodiment is applicable to situations where travel paths are planned based on the carbon sequestration capacity and path efficiency of roads. This method can be executed by a path planning device based on carbon sequestration efficiency. The path planning device based on carbon sequestration efficiency can be implemented in the form of hardware and / or software. The path planning device based on carbon sequestration efficiency can be configured in an electronic device. Figure 1 As shown, the method includes:

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

[0028] The target area may be an area where road vegetation maintenance is to be performed, for example, a city. The first request information may be request information for a user to plan a travel route. The first request information includes at least a path starting node and a path ending node. It should be noted that the path starting node and the path ending node may respectively refer to a road in the target area, and different locations on the same road may be considered the same path starting node or path ending node.

[0029] For example, the present invention can be applied to an open source map platform, where the open source map platform receives first request information for route planning triggered by travel needs of users within a target area. The route's starting node can be determined based on the starting point information entered by the user in a starting point box of the open source map platform, or based on the user's current location information. The route's ending node can be determined based on the end point information entered by the user in an end point box of the open source map platform.

[0030] S102: For each first request information, determine a target planning path that meets preset conditions 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. It should be noted that the first planning algorithm is obtained by improving the path planning algorithm (such as the A* algorithm) based on the path carbon sequestration efficiency. The first planning algorithm embeds the path carbon sequestration efficiency on the basis of the traditional path planning algorithm, which can achieve multi-objective coordinated optimization of traffic efficiency (driving time) and ecological value (carbon sequestration efficiency). Although the first planning algorithm requires sacrificing a small amount of path planning efficiency, it can enhance the carbon sequestration benefit in the target area. The preset conditions include minimizing the total cost of driving time and carbon sequestration efficiency.

[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 minimum total cost of driving time and carbon sequestration efficiency as the constraint condition, so as to obtain the target planning path that meets the preset conditions.

[0033] Exemplarily, determining the target planning path that meets preset conditions based on the path starting node and the path ending node based on the first planning algorithm includes:

[0034] Determine a first actual cost between the path originating node and the current path node; determine a second predicted cost between the current path node and the path ending node;

[0035] Based on the first actual cost and the second predicted cost, a planned path with the minimum total cost of travel time and carbon sequestration efficiency between the path starting node and the path ending node is determined, and the planned path with the minimum total cost is determined as the target planned path.

[0036] It should be noted that the current path node can refer to the latest node on the determined path in path planning. The current path node varies with the progress of path planning. For example, at the beginning of path planning, the current path node can be the path starting node; at the end of path planning, the current path node can be the path ending node.

[0037] The first actual cost may refer to the currently determined actual cost, i.e., the actual cost between the starting node and the current node of the route. The second predicted cost may refer to the cost predicted based on the current node and the end node of the route. It is worth noting that the first planning algorithm is an improvement to the path planning algorithm based on the carbon sequestration efficiency of the route. Therefore, both the first actual cost and the second predicted cost include the total cost of travel time and carbon sequestration efficiency.

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

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

[0040] Among them, 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, determining the second predicted cost between the current path node and the path end node includes: determining a time heuristic function based on the current path node and the path end node; determining an ecological heuristic function based on the current node carbon sequestration efficiency corresponding to the current path node, and the second maximum carbon sequestration efficiency and the shortest node distance between the current path node and the path end node; determining the second predicted cost based on the time heuristic function and the ecological heuristic function, as well as the time coefficient and the ecological coefficient.

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

[0043]

[0044] Among them, 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 end node of the path, y n Refers to the current path node ordinate, y goal Refers to the vertical coordinate of the end node of the path, v avg It 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] Among them, h carbon (n) refers to the ecological heuristic function, C current Refers to the carbon sequestration efficiency of the current node, ∑p∈Path opt C eff (p) refers to the second maximum carbon sequestration efficiency, d(n,goal) refers to the shortest distance between the nodes, v avg It refers to the average travel speed between the current path node and the path end node.

[0048] Exemplarily, determining the second prediction cost according to the time heuristic function and the ecological heuristic function, as well as the time coefficient and the ecological coefficient, includes:

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

[0050] Among them, 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] For example,

[0052]

[0053] Among them, this formula is a real-time weight adjustment formula based on traffic pressure, α refers to the time coefficient, R represents the real-time traffic pressure comprehensive index, R0 is the baseline pressure threshold (default 0.7) depends on the traffic efficiency of urban roads, and k is the adjustment sensitivity coefficient (default 3.0).

[0054]

[0055] Where η represents the congestion index weight (default 0.2), I t Is the congestion index, from the third-party navigation platform data (0-10 points), I max Indicates the maximum value of the congestion index (default is 10), Q is the traffic volume during the statistical period, Q max is the road design capacity.

[0056]

[0057] β only needs to satisfy the conditions of this formula. It refers to the maximum theoretical road carbon sequestration efficiency between the current path node and the path end node, d min It refers to the shortest straight-line distance between the current path node and the path end node.

[0058] Exemplarily, after determining the target planning path that meets the preset conditions, the method further includes:

[0059] According to all the target planned paths in the target area, road planning information of each regional road in the target area is determined, and vegetation maintenance is performed on each regional road in the target area according to the road planning information.

[0060] The road planning information may be planning information of each regional road in the target area being planned as a driving road.

[0061] Specifically, by analyzing all planned routes within a target area, we can obtain road planning information for each regional road within the target area. This road planning information is then analyzed and processed to determine the number and proportion of planned routes for each regional road. Based on this road planning information, vegetation maintenance is then performed on each regional road within the target area. For example, if a regional road is designated as an ecological route for 30 consecutive days, an upgrade in maintenance level (drip irrigation frequency +50%, pruning cycle -30%) is automatically triggered.

[0062] Furthermore, the present invention can modify travel routes based on other conditions. For example, when the carbon sequestration efficiency of a road in a certain area drops by 15% or more, a detour suggestion can be generated and a maintenance work order can be pushed.

[0063] On the basis of the above embodiments, vegetation maintenance is performed on each regional road in the target area according to the road planning information, including: for each regional road, determining the road planning proportion of the regional road planned as 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, replanting preset carbon sequestration vegetation in the regional road based on preset intervals; when the road planning proportion is less than a second preset proportion, reducing the ecological coefficient of the regional road; otherwise, no vegetation maintenance is performed on the regional road.

[0064] The first preset ratio and the second preset ratio can be set according to actual conditions, and the second preset ratio is smaller than the first preset ratio. The road planning ratio may refer to the ratio of roads planned as travelable roads under the same type of first request information. The same type of first request information may mean that, in multiple first request information, the originating node of each first request information path is located on the same road, and the ending node of the path is also located on the same road.

[0065] Specifically, the road planning information corresponding to similar first request information is compared to determine the proportion of roads in each region that are planned as travelable roads based on the same first request information. If the planned road ratio is greater than a first preset ratio, pre-set carbon-sequestering vegetation is replanted within the regional roads at preset intervals, such as replanting similar high-carbon-sequestering plants every 50 meters. If the planned road ratio is less than a second preset ratio, the ecological coefficient of the regional roads is reduced. Otherwise, vegetation maintenance is not performed on the regional roads.

[0066] The technical solution of the embodiment of the present invention is to obtain the first request information of all requested path planning in the target area, wherein the first request information includes at least the path starting node and the path ending node. For each of the first request information, based on the first planning algorithm, according to the path starting node and the path ending node, a target planning path that meets the preset conditions is determined, wherein the first planning algorithm is obtained by improving the path planning algorithm based on the path carbon sequestration efficiency, and the preset conditions include minimizing the total cost of driving time and carbon sequestration efficiency. The present invention realizes the dynamic balance optimization of traffic efficiency and ecological benefits by quantifying the path carbon sequestration efficiency and embedding it into the improved first planning algorithm. While minimizing the travel time, the planned path gives priority to sections with high carbon sequestration efficiency, thereby driving the matching of traffic distribution and ecological resources, forming a positive cycle of "traffic diversion-carbon sequestration gain", and providing smart cities with path decisions that have both traffic efficiency and ecological sustainability.

[0067] Example 2

[0068] Figure 2 This is a flow chart of a route planning method based on carbon sequestration efficiency provided by the second embodiment of the present invention. Based on the above embodiments, this embodiment further refines the process of determining the carbon sequestration efficiency of each road. Figure 2 As shown, the method includes:

[0069] S201. Obtain vegetation species information, traffic flow information, noise information, and vegetation growth information corresponding to a target path for which carbon sequestration efficiency is to be determined.

[0070] Specifically, municipal greening databases can be integrated to obtain information on vegetation species, planting density, and growth cycles; real-time traffic flow monitoring data, including volume, vehicle type, and emission levels, can be obtained from traffic management platforms; noise information from noise sensors can be obtained from environmental monitoring stations; and vegetation microenvironmental parameters collected by IoT devices deployed in green belts can be collected. Furthermore, spatiotemporal matching algorithms can be used to accurately correlate and update multi-dimensional data in real time.

[0071] S202: Determine theoretical vegetation carbon sequestration data for the target path based on the vegetation species information.

[0072] For example, the process of determining theoretical vegetation carbon sequestration data is as follows:

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

[0074] Where 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, lightis the light energy utilization efficiency, t active It is the effective photosynthetic duration of the year, which depends on the region.

[0075] S203: Determine a traffic exhaust suppression factor for the target path based on the traffic information.

[0076] Exemplarily, the process of determining the vehicle exhaust suppression factor is as follows:

[0077]

[0078] Among them, γ is the exhaust gas sensitivity coefficient, Q is the traffic volume in the statistical period, and Q max is the road design capacity.

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

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

[0081] λ noise =1-0.015×max(0,N-65);

[0082] Where N is the average noise decibel during the statistical period.

[0083] S205: Determine a vegetation growth cycle factor of the target path based on the vegetation growth information.

[0084] For example, the process of determining the vegetation growth cycle factor is as follows:

[0085] λ growth =1-e -k·t ;

[0086] where k is the growth rate constant, which depends on the species, and t is the number of years the vegetation has been planted.

[0087] S206. Determine the carbon sequestration efficiency corresponding to the target path based on the vegetation theoretical carbon sequestration data, the vehicle exhaust suppression factor, the environmental noise interference factor, and the vegetation growth cycle factor.

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

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

[0090] Among them, C base is the theoretical carbon sequestration data of vegetation, λ traffic ,λ noise,λ growth They are all dynamic performance correction factors, representing the traffic exhaust suppression factor, environmental noise interference factor and vegetation growth cycle factor respectively.

[0091] S207: Obtain first request information for all requested path planning in the target area.

[0092] S208: For each first request information, based on a first planning algorithm, determine a target planning path that meets preset conditions according to the path starting node and the path ending node.

[0093] The technical solution of the embodiment of the present invention determines the carbon sequestration efficiency corresponding to the target path through theoretical vegetation carbon sequestration data, the vehicle exhaust suppression factor, the environmental noise interference factor and the vegetation growth cycle factor, breaking through the limitations of traditional static evaluation. It not only quantifies the real-time loss of plant carbon sequestration capacity caused by traffic pollution and noise, but also predicts the carbon sequestration potential of vegetation in combination with the growth cycle, providing high-resolution ecological parameters for path planning and maintenance decisions.

[0094] Example 3

[0095] Figure 3 This is a structural diagram of a path planning device based on carbon sequestration efficiency provided in the third embodiment of the present invention. Figure 3 As shown, the device includes:

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

[0097] The planning path determination module 302 is used to determine, for each of the first request information, a target planning path that meets preset conditions 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 conditions include minimizing the total cost of driving time and carbon sequestration efficiency.

[0098] The technical solution of the embodiment of the present invention is to obtain the first request information of all requested path planning in the target area, wherein the first request information includes at least the path starting node and the path ending node. For each of the first request information, based on the first planning algorithm, according to the path starting node and the path ending node, a target planning path that meets the preset conditions is determined, wherein the first planning algorithm is obtained by improving the path planning algorithm based on the path carbon sequestration efficiency, and the preset conditions include minimizing the total cost of driving time and carbon sequestration efficiency. The present invention realizes the dynamic balance optimization of traffic efficiency and ecological benefits by quantifying the path carbon sequestration efficiency and embedding it into the improved first planning algorithm. While minimizing the travel time, the planned path gives priority to sections with high carbon sequestration efficiency, thereby driving the matching of traffic distribution and ecological resources, forming a positive cycle of "traffic diversion-carbon sequestration gain", and providing smart cities with path decisions that have both traffic efficiency and ecological sustainability.

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

[0100] A first actual cost determining unit, configured to determine a first actual cost between the path origin node and the current path node, wherein the first actual cost includes a total cost of travel time and carbon sequestration efficiency;

[0101] a second predicted cost determining unit, configured to determine a second predicted cost between the current path node and the path end node, wherein the second predicted cost includes a total cost of travel time and carbon sequestration efficiency;

[0102] 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 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, configured to determine a time heuristic function according to the current path node and the path end node;

[0105] an ecological heuristic function determination subunit, configured to determine 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 shortest node distance between the current path node and the path end node;

[0106] The second prediction cost determination subunit is used to determine the second prediction cost according to the time heuristic function and the ecological heuristic function, as well as the time coefficient and the ecological coefficient.

[0107] Optionally, an ecologically inspired function determines the subunits for:

[0108]

[0109] Among them, h carbon (n) refers to the ecological heuristic function, C current Refers to the carbon sequestration efficiency of the current node, ∑p∈Path opt C eff (p) refers to the second maximum carbon sequestration efficiency, d(n,goal) refers to the shortest distance between the nodes, v avg It refers to the average travel speed between the current path node and the path end node.

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

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

[0112] Among them, 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.

[0113] Optionally, the device further comprises a carbon sequestration efficiency determination module.

[0114] Carbon sequestration efficiency determination module, used to:

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

[0116] Determining theoretical vegetation carbon sequestration data for the target path based on the vegetation species information;

[0117] Determining a traffic exhaust suppression factor for the target path based on the traffic information;

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

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

[0120] The carbon sequestration efficiency corresponding to the target path is determined based on the vegetation theoretical carbon sequestration data, the vehicle exhaust suppression factor, the environmental noise interference factor, and the vegetation growth cycle factor.

[0121] Optionally, the device further includes a vegetation maintenance execution module, wherein:

[0122] Vegetation maintenance execution module, used to:

[0123] According to all the target planned paths in the target area, road planning information of each regional road in the target area is determined, and vegetation maintenance is performed 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 in an embodiment of the present invention can execute the path planning method based on carbon sequestration efficiency provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0125] Example 4

[0126] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, 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 processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0127] like Figure 4 As shown, 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. 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. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0128] Multiple 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 via a computer network such as the Internet and / or various telecommunication networks.

[0129] Processor 11 can be any general-purpose and / or specialized processing component 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 specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. Processor 11 executes the various methods and processes described above, such as the carbon sequestration efficiency-based path planning method.

[0130] In some embodiments, the carbon sequestration efficiency-based path planning method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the carbon sequestration efficiency-based path planning method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the carbon sequestration efficiency-based path planning method in any other appropriate manner (e.g., by means of firmware).

[0131] Various embodiments of the systems and techniques described above 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 embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0132] Computer programs for implementing 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 the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0133] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0134] 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 can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the 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 input, voice input, or tactile input).

[0135] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, 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] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0137] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0138] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A path planning method based on carbon sequestration efficiency, characterized in that: include: Obtaining first request information for all path planning requests in the 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, based on the first planning algorithm, according to the path starting node and the path ending node, a target planning path that meets the preset conditions is determined, wherein the first planning algorithm is obtained by improving the path planning algorithm based on the path carbon sequestration efficiency, and the preset conditions include minimizing the total cost of driving time and carbon sequestration efficiency.

2. The method according to claim 1, characterized in that The determining, based on the first planning algorithm and according to the path starting node and the path ending node, a target planning path that meets preset conditions includes: Determining a first actual cost between the starting node of the path and the current node of the path, wherein the first actual cost includes a total cost of travel time and carbon sequestration efficiency; Determining a second predicted cost between the current path node and the path end node, wherein the second predicted cost includes a total cost of travel time and carbon sequestration efficiency; Based on the first actual cost and the second predicted cost, a planned path with the minimum total cost of travel time and carbon sequestration efficiency between the path starting node and the path ending node is determined, and the planned path with the minimum total cost is determined as the target planned path.

3. The method according to claim 2, characterized in that The determining of the second predicted cost between the current path node and the path end node includes: 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 shortest node distance between the current path node and the path end node; The second prediction cost is determined according to the time heuristic function and the ecological heuristic function, as well as the time coefficient and the ecological coefficient.

4. The method according to claim 3, characterized in that Determining the ecological heuristic function includes: Among them, h carbon (n) refers to the ecological heuristic function, C current Refers to the carbon sequestration efficiency of the current node, ∑p∈Path opt C eff (p) refers to the second maximum carbon sequestration efficiency, d(n,goal) refers to the shortest distance between the nodes, v avg It refers to the average travel speed between the current path node and the path end node.

5. The method according to claim 3, characterized in that The determining the second prediction cost according to the time heuristic function and the ecological heuristic function, as well as the time coefficient and the ecological coefficient, includes: h(n)=α·h geo (n)+β·h carbon (n); Among them, 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.

6. The method according to any one of claims 1 to 5, characterized in that The process of 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 is to be determined; Determining theoretical vegetation carbon sequestration data for the target path based on the vegetation species information; Determining a traffic exhaust suppression factor for the target path based on the traffic information; Determining an environmental noise interference factor of the target path based on the noise information; Determining a vegetation growth cycle factor of the target path based on the vegetation growth information; The carbon sequestration efficiency corresponding to the target path is determined based on the vegetation theoretical carbon sequestration data, the vehicle exhaust suppression factor, the environmental noise interference factor, and the vegetation growth cycle factor.

7. The method according to claim 1, characterized in that After determining the target planning path that meets the preset conditions, the method further includes: According to all the target planned paths in the target area, road planning information of each regional road in the target area is determined, and vegetation maintenance is performed on each regional road in the target area according to the road planning information.

8. A path planning device based on carbon sequestration efficiency, characterized in that: include: 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 includes at least a path starting node and a path ending node; A planning path determination module is used to determine, for each of the first request information, a target planning path that meets preset conditions 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 conditions include minimizing the total cost of driving time and carbon sequestration efficiency.

9. An electronic device, characterized in that: The electronic device comprises: 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, 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 sequestration efficiency according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the path planning method based on carbon sequestration efficiency according to any one of claims 1 to 7 when executed.

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