Watering vehicle operation path planning method

Abstract

The invention belongs to the technical field of operation path planning, and particularly discloses a watering vehicle operation path planning method, which comprises the following steps: importing initial watering plan information of a vehicle; positioning real-time traffic data of each planned watering road section in the current time period from a GIS geographic map; monitoring current environment information of each planned watering road section; performing path planning based on a preset path planning algorithm to obtain a recommended path; and carrying out adaptability verification on the recommended path, taking the recommended path as the recommended path of the vehicle when the verification is passed, and setting a target optimization function, carrying out recommended path adjustment and feeding back to the vehicle when the verification is not passed. According to the method, the defects existing in a current static path planning mode are effectively overcome, the dynamic change of traffic is fully considered, the difference between watering path planning and an actual scene is further reduced, and therefore path planning is more targeted.

Description

Technical Field

[0001] The invention belongs to the technical field of operation path planning and relates to an operation path planning method for a sprinkler vehicle. Background Art

[0002] Sprinkler vehicles are commonly used for road cleaning and environmental protection work in areas such as highways, urban roads, and industrial parks. They are mainly used to spray detergents or water to clean road surfaces, reduce dust, cool down, and prevent dust. Therefore, in order to ensure the operating efficiency of sprinkler vehicles, their operating routes need to be planned.

[0003] Currently, the path planning of watering operations is mainly based on the shortest path or the fastest path, and the factors affecting traffic along the watering path are not fully considered.

[0004] Prior art, such as the Chinese invention patent application with application publication number CN113074745B, discloses a method and device for planning the path of a city road cleaning vehicle, which includes: obtaining the section information and geographic information of all road sections within the area under cleaning. Dividing all road sections into multiple directed segments based on the section information and geographic information. Obtaining the time taken by the cleaning vehicle to perform cleaning operations and not perform cleaning operations in all directed segments. Obtaining the cleaning methods of all directed segments within the area under cleaning and the number of cleanings corresponding to each cleaning method. Allocating cleaning vehicles and planning paths for all directed segments that need cleaning based on the number of cleanings and the time taken to operate. This minimizes the number of non-operating sections that need to be detoured in the planned path, greatly reducing the vehicle's idle running rate, and thus improving work efficiency.

[0005] Obviously, the above technical solutions still have the following problems in planning the watering path: 1. It is a static path planning method, which does not take into account the dynamic changes in traffic, which may lead to inflexible path planning and failure to adapt to changes in traffic conditions in a timely manner.

[0006] 2. The sprinkler configuration is relatively fixed. The watering demand of different road sections is affected by various environmental factors such as temperature and dust. The current fixed sprinkler configuration cannot fully reflect the actual demand of each road section, nor can it reflect the watering time of each path, which in turn affects the effectiveness of the water replenishment setting.

[0007] 3. There is a lack of dynamic adjustment mechanism. Currently, after the planning is completed, the path adaptability is not verified and adjusted according to traffic conditions and environmental conditions, resulting in certain deviations in the reliability and rationality of the planning. Summary of the Invention

[0008] In view of this, in order to solve the problems raised in the above background technology, a method for planning an operation path of a watering vehicle is proposed.

[0009] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides a method for planning the operation path of a watering vehicle, which includes: S1, importing the initial watering plan: recording the vehicle currently to be watered as the target vehicle, and importing the initial watering plan information of the target vehicle.

[0010] S2. Extracting road section traffic information: Extracting the location of each planned watering section from the initial watering plan information of the target vehicle, and then locating the real-time traffic data of each planned watering section in the current time period from the GIS geographic map.

[0011] S3. Road section environmental information monitoring: monitor the current environmental information of each planned watering section.

[0012] S4. Preliminary planning of the operation path: performing path planning based on the initial watering plan information and a preset path planning algorithm to obtain a recommended path.

[0013] S5. Recommended route adaptation verification: Based on the real-time traffic data and current environmental information of each planned watering section, the recommended route is adapted and verified. If the verification passes, step S7 is executed; if the verification fails, step S6 is executed.

[0014] S6. Recommended path adjustment confirmation: setting a target optimization function, and obtaining an adjusted recommended path according to the target optimization function.

[0015] S7. Feedback on the watering path: Feedback the recommended path or the adjusted recommended path to the target vehicle, so that the watering operation is performed according to the recommended path.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention verifies and adjusts the adaptability of the recommended path according to the real-time traffic data and current environmental information of each planned watering section, effectively solving the shortcomings of the current static path planning method, fully considering the dynamic changes of traffic, and thus reducing the difference between the watering path planning and the actual scene, which helps to adapt to the changes in traffic conditions in a timely manner, thereby making the path planning more targeted.

[0017] (2) The present invention sets traffic delay weights and watering delay weights, and then calculates the traffic delay interference degree and watering delay interference degree, and verifies the adaptability of the recommended path. This can more accurately weigh different factors, intuitively display the traffic status and watering demand, and thus ensure the validity and accuracy of the recommended path adaptability verification results. It also provides a reliable data basis for the subsequent adjustment of the recommended path.

[0018] (3) The present invention sets the target optimization function by combining the real-time traffic data and current environmental information of each planned watering section, thereby obtaining the adjusted recommended path, thereby improving the flexibility of watering path planning, and also filling the current gap in the setting of dynamic adjustment mechanism. It also effectively integrates the influence of traffic status and road section environment on watering path planning, realizes the precise adjustment of watering path planning, and thus ensures the reliability, rationality and effectiveness of watering path planning.

[0019] (4) When setting the target optimization function, the present invention sets multiple optimization constraints by combining the traffic delay weight and the watering delay weight, avoiding the imbalance problem caused by single-target optimization, improving the overall efficiency and scientificity of path planning, and also improving the robustness and stability of path planning, thereby making path planning more in line with actual needs, thereby effectively ensuring the efficiency of watering operations while reducing the loss of watering operation resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing 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.

[0021] Figure 1 The figure is a flow chart of the steps for implementing the method of the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments 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 are within the scope of protection of the present invention.

[0023] See also Figure 1 As shown, the present invention provides a method for planning an operation path of a watering vehicle, which includes: S1, importing an initial watering plan: recording a vehicle to be watered as a target vehicle, and importing initial watering plan information of the target vehicle.

[0024] Specifically, the initial watering plan information includes but is not limited to watering capacity, the locations of each set water replenishment point and the location of each planned watering section, the set permission delay time, the set warning delay time, the planned watering volume, the planned watering time and the planned total passage time.

[0025] S2. Extracting road section traffic information: Extracting the location of each planned watering section from the initial watering plan information of the target vehicle, and then locating the real-time traffic data of each planned watering section in the current time period from the GIS geographic map.

[0026] Specifically, traffic data consists of traffic volume and vehicle density.

[0027] S3. Road section environmental information monitoring: monitor the current environmental information of each planned watering section.

[0028] Specifically, the current environmental information consists of the current temperature, current humidity and current dust concentration. The current temperature is monitored by a temperature sensor installed in the corresponding environmental point of the watering section, the humidity is monitored by a humidity sensor installed in the corresponding environmental point of the watering section, and the dust concentration is monitored by a particulate matter sensor installed in the corresponding environmental point of the watering section.

[0029] S4. Preliminary planning of the operation path: performing path planning based on the initial watering plan information and a preset path planning algorithm to obtain a recommended path.

[0030] It should be added that the pre-set path planning algorithm can be a shortest path algorithm, that is, using the Dijkstra algorithm or the A* algorithm, or a heuristic algorithm, that is, combining the simulated annealing algorithm, the genetic algorithm, etc. In the absence of restrictions, the pre-set path planning algorithm defaults to the shortest path algorithm, that is, only considering the path length. The default watering time of each road section meets the expected requirements. Therefore, the traffic delay weight and the watering delay weight are set to perform the recommended path adaptability verification analysis.

[0031] It is understandable that the shortest path algorithm and the heuristic algorithm are relatively mature existing algorithms, and their specific execution process, that is, the specific path planning process, will not be described in detail here.

[0032] S5. Recommended route adaptation verification: Based on the real-time traffic data and current environmental information of each planned watering section, the recommended route is adapted and verified. If the verification passes, step S7 is executed; if the verification fails, step S6 is executed.

[0033] Specifically, the adaptability verification of the recommended route includes: S5-1, extracting real-time traffic flow and real-time vehicle density from the real-time traffic data of each planned watering section, and setting the traffic delay weight of each planned watering section accordingly, which is recorded as , Indicates the planned watering section number. .

[0034] S5-2, based on the current environmental information of each planned watering section, set the watering delay weight of each planned watering section, recorded as .

[0035] S5-3, based on , calculate the traffic delay interference, recorded as , while following The calculation method of the sprinkler delay interference degree is similar to that of the sprinkler delay interference degree, which is recorded as .

[0036] S5-4, will As verification condition 1, As verification condition 2.

[0037] S5-5. If verification condition 1 or verification condition 2 is met, verification failure is regarded as the adaptability verification result; if both verification condition 1 and verification condition 2 are not met, verification pass is regarded as the adaptability verification result.

[0038] The embodiment of the present invention sets traffic delay weights and watering delay weights, then calculates the traffic delay interference degree and watering delay interference degree, and verifies the adaptability of the recommended path. This can more accurately weigh different factors, intuitively display the traffic status and watering needs, and thus ensure the validity and accuracy of the recommended path adaptability verification results. It also provides a reliable data basis for subsequent adjustments to the recommended path.

[0039] Furthermore, in step S5-1, the traffic delay weight of each planned watering section is set, including: U1, with time as the horizontal coordinate and traffic flow and vehicle density as the vertical coordinates, respectively, to construct the traffic flow curve and vehicle density curve of each planned watering section.

[0040] U2. Extract the slope from the traffic flow curve, set a correction factor based on the traffic flow curve, correct the slope, record the corrected slope as the traffic flow change rate, and confirm the vehicle density change rate in the same way as the vehicle change rate.

[0041] It can be understood that setting the correction factor includes: constructing a reference line in the traffic flow curve based on the slope of the traffic flow curve.

[0042] Extract the total length of the curve above the reference line and the total length of the curve below the reference line, and compare them with the total length of the traffic flow curve respectively. The ratios are recorded as and .

[0043] like or ,Will and The difference is used as the correction factor.

[0044] like , taking 0 as the correction factor, we can get the correction factor and record it as , The value is 0 or .

[0045] It should be added that the slope is corrected according to a correction formula, wherein the correction formula is: corrected slope = (1 + correction factor) × slope.

[0046] In a specific embodiment, the slope of the extracted curve described in the present invention refers to the slope of the regression line corresponding to the extracted curve, and by setting a correction factor based on the total length of the curve above the regression line and the total length of the curve below the regression line, the extracted slope is corrected, which can significantly improve the stability and accuracy of the slope estimation, thereby better reflecting the real change trend of the data and helping to improve the accuracy of future data predictions.

[0047] U3. Extract the maximum values ​​from the real-time traffic flow and the real-time vehicle density respectively to obtain the maximum traffic flow and the maximum vehicle density of each planned watering section.

[0048] U4. Normalize the traffic flow change rate, vehicle density change rate, maximum traffic flow, and maximum vehicle density of each planned watering section. Based on the processed results, set the traffic flow delay weight and vehicle density delay weight of each planned watering section.

[0049] It should be added that the traffic flow change rate, vehicle density change rate, maximum traffic flow and maximum vehicle density of each planned watering section are normalized respectively, including: 1) subtracting the traffic flow change rate of each planned watering section from the set reference interference traffic flow change rate to obtain the traffic flow change rate difference of each planned watering section, and comparing it with the set reference interference traffic flow change rate, and using the ratio as the traffic flow change deviation of each planned watering section.

[0050] 2) Subtract the vehicle density change rate of each planned watering section from the set reference interference vehicle density change rate to obtain the vehicle density change rate difference of each planned watering section, and compare it with the set reference interference vehicle density change rate. The ratio is used as the vehicle density change deviation of each planned watering section.

[0051] 3) Subtract the maximum traffic flow of each planned watering section from the reference interference traffic flow of each planned watering section, compare the difference with the reference interference traffic flow of each planned watering section, and use the ratio as the traffic flow excess degree of each planned watering section.

[0052] 4) Subtract the maximum vehicle density of each planned watering section from the reference interference vehicle density of each planned watering section, compare the difference with the reference interference vehicle density of each planned watering section, and use the ratio as the vehicle density excess degree of each planned watering section.

[0053] 5) The traffic flow change deviation, vehicle density change deviation, traffic flow excess and vehicle density excess of each planned watering section are taken as the results after normalization.

[0054] It should also be added that setting the traffic flow delay weight and vehicle density delay weight of each planned watering section includes taking the traffic flow change deviation and traffic flow excess of each planned watering section as traffic flow delay evaluation independent variables, and then obtaining the traffic flow delay weight of each planned watering section through Sigmoid function setting. At the same time, taking the vehicle density change deviation and vehicle density excess of each planned watering section as vehicle density delay evaluation independent variables, and obtaining the vehicle density delay weight of each planned watering section through Sigmoid function setting.

[0055] In a specific embodiment, when the traffic flow change rate is 0, it indicates that the traffic flow tends to be stable, that is, it no longer grows, and when the traffic flow change rate is greater than 0, it indicates that the vehicles tend to grow, that is, they may further increase, so the probability of the target vehicle's passage delay will also increase further. Therefore, the traffic flow change rate and the maximum traffic flow are used as two indicators to set the traffic flow delay weight. Specifically, in order to facilitate analysis, the reference interference traffic flow change rate involved in the normalization processing can be taken as an intermediate value of 0. For example, the reference interference traffic flow change rate can be taken as 0.1, and the specific value of the reference interference traffic flow is mainly extracted from the traffic assessment specification of the traffic plan watering section.

[0056] In another specific embodiment, the specific setting values ​​of the reference interference vehicle density change rate and the reference interference vehicle density are the same as the setting methods and principles of the reference interference vehicle flow change rate and the reference interference vehicle flow in the case of traffic delay, so the examples will not be expanded here.

[0057] U5. Set the weights of traffic flow and vehicle density, and then perform weighted sum calculation on the traffic flow delay weight and vehicle density delay weight of each planned watering section, and use the calculation result as the traffic delay weight of each planned watering section.

[0058] It should be added that traffic volume refers to the number of vehicles passing a certain observation point per unit time, which usually directly affects traffic delay, because high traffic volume will lead to an increase in the number of vehicles on the road, thereby increasing the possibility of congestion, while vehicle density refers to the number of vehicles per unit road length. Vehicle density has a more direct impact on traffic delay, which describes the distribution of vehicles on the road. High density usually means that the distance between vehicles is small, which can easily cause traffic congestion and delays. Therefore, under high-density traffic conditions, even if the traffic volume is not high, traffic delays may increase significantly. Therefore, the weight of vehicle density is set to be greater than the weight of traffic volume. Specifically, the weight of vehicle density can be taken as 0.6, and the weight of traffic volume can be taken as 0.4.

[0059] Furthermore, the watering delay weights of each planned watering section are set in step S5-2, including: F1, extracting the current temperature, current humidity and current dust concentration from the current environmental information of each planned watering section, and recording them as 、 and .

[0060] F2. Set the evaluation weights of temperature, humidity and dust concentration, which are respectively 、 and , .

[0061] In a specific embodiment, when the temperature is high, the evaporation rate of water on the road surface after watering may be faster, so it may be necessary to increase the watering delay time. It is possible to consider setting the temperature weight between 0.3 and 0.5, and specifically 0.4. When the humidity is high, there is more water in the air, and the road surface may be more likely to remain moist after watering. Therefore, a shorter watering delay time may be required. It is possible to consider setting the temperature weight between 0.2 and 0.4, and specifically 0.25. When the dust concentration on the road surface is high, the demand for watering is greater. It is possible to consider setting the dust concentration weight between 0.3 and 0.5, and specifically 0.35, that is, 、 and It can be 0.4, 0.25 and 0.35 respectively.

[0062] F3. Standardize the current temperature, humidity and dust concentration of each planned watering section, and obtain the temperature factor, humidity factor and dust concentration factor of each planned watering section after standardization, which are recorded as 、 and .

[0063] In a specific embodiment, the temperature, humidity and dust concentration of each planned watering section are respectively standardized to convert them into the interval [0, 1]. Specifically, a minimum-maximum normalization method can be used.

[0064] The historical maximum temperature, minimum temperature, maximum humidity, minimum humidity, maximum dust concentration and minimum dust concentration of each planned watering section are extracted from the traffic information database and recorded as 、 、 、 、 and ,Among them, the traffic information database is a pre-set data storage database for storing ,traffic data and environmental information of each planned watering section.

[0065] Standardize the temperature of each planned watering section: .

[0066] Standardize the humidity of each planned watering section: .

[0067] Standardize the dust concentration for each planned watering section: .

[0068] F4, As the comprehensive watering influencing factor of each planned watering section, it is recorded as .

[0069] F5. Extract the permission delay time and warning delay time for each planned watering section from the initial watering plan information of the target vehicle, and record them as and .

[0070] F6. Set the watering delay weight for each planned watering section , , The delay adjustment coefficient is set.

[0071] It should be added that Indicates the The predicted sprinkling delay time of each planned sprinkling section is used. When the predicted sprinkling delay time is greater than or equal to the warning delay time, 1 is used as the sprinkling delay weight. When the predicted sprinkling delay time is less than the warning delay time, the ratio of the predicted sprinkling delay time to the warning delay time is used as the sprinkling delay weight. The default value is 1 and can be adjusted accordingly based on actual scenario requirements.

[0072] In a specific embodiment, assuming that the historical maximum temperature of a planned watering section is 40°C, the historical minimum temperature is 20°C, the historical maximum humidity is 0.7, the historical minimum humidity is 0.3, the historical maximum dust concentration is 100 micrograms per cubic meter, the historical minimum dust concentration is 10 micrograms per cubic meter, the current temperature is 30°C, the current humidity is 0.5, the current dust concentration is 50 micrograms per cubic meter, the permitted delay time and the warning delay time are set to 10 minutes and 15 minutes respectively, and the delay adjustment coefficient is 1, the watering delay weights of the planned watering section are set as follows: L1: Normalized temperature, humidity and dust concentration: , , .

[0073] L2: Calculate the comprehensive sprinkler impact factor: .

[0074] L3: Calculate the watering delay weight: .

[0075] Furthermore, the traffic delay interference degree is calculated in step S5-3, including: R1, locating the watering order of each planned watering section from the recommended path, sorting each planned watering section according to its watering order, and locating the planned watering section with the middle position in the sorting as the split section.

[0076] R2. Record the planned watering sections that are ranked before the split sections as priority watering sections. Extract the traffic delay weights of the priority watering sections from the traffic delay weights of the planned watering sections, and sum them up to get the total traffic delay weights of the priority watering sections. .

[0077] R3. Sum the traffic delay weights of each planned watering section and record it as , and then set the interference traffic delay weight , .

[0078] R4. Sort each planned watering section according to the traffic delay weight from large to small to obtain the traffic delay ranking of each planned watering section, and record the planned watering section with the middle ranking position as the standard section.

[0079] R5. Traverse the traffic delay ranking of each planned watering section, count the number of priority watering sections whose ranking position is before the standard section, and compare it with the number of priority watering sections, as the priority traffic delay watering section ratio, recorded as .

[0080] R6. Calculate traffic delay interference , , To set the reference ratio of the road section with delayed watering due to traffic interference, is a natural constant, Indicates the floor symbol.

[0081] In a specific embodiment, The value is set according to the number of priority watering sections. Assuming that the number of priority watering sections is 10, 2 can be set as the number of priority watering sections that can carry traffic delays and are ranked before standard sections, that is, The specific value can be 0.2.

[0082] It should be explained that the sum of traffic delay weights reflects the severity of the overall traffic situation, while the ranking position reflects the relative importance of each road section in the overall traffic. By calculating the traffic delay interference degree from two dimensions: the sum of traffic delay weights of priority watering sections and the ratio of the number of priority watering sections whose traffic delay weight ranking position is before the standard sections, a dual analysis of the whole and the part is achieved, which can provide quantitative indicators to measure the traffic delay interference degree, making the evaluation more objective and specific, and thus can more comprehensively evaluate the impact of traffic delays in different sections on watering interference.

[0083] S6. Recommended path adjustment confirmation: setting a target optimization function, and obtaining an adjusted recommended path according to the target optimization function.

[0084] Specifically, the target optimization function is set, including: S6-1, based on the location of each planned watering section, construct a watering section set, denoted as .

[0085] S6-2, extract the locations of each set water replenishment point from the initial watering plan information, and construct a water replenishment location set, recorded as .

[0086] S6-3. Extract the watering capacity, the planned watering volume, the planned watering duration and the planned total travel time of each planned watering section from the initial watering plan information of the target vehicle, and record them as as well as 、 and .

[0087] S6-4. Set the constraints of the target optimization function.

[0088] S6-5. Taking distance and time as optimization items, based on the traffic delay weight and watering delay weight of each planned watering section, determine the optimization indicators of the distance optimization item and the time optimization item, which are recorded as and , set the weights of distance and time, respectively and , and then as the target optimization function.

[0089] It should be added that the present invention plans the watering path for the purpose of improving the watering efficiency. Therefore, when considering distance and time, the time weight is set to be greater. Specifically, and The values ​​can be 0.55 and 0.45 respectively.

[0090] It should also be added that the target optimization function described in the present invention is optimized based on the cost function of path planning, that is, it is specifically optimized according to the actual traffic conditions and environmental information of the planned watering section.

[0091] Furthermore, the constraint conditions for setting the target optimization function in step S6-4 include setting the watering demand constraint: , , Indicates the The required watering volume for each planned watering section is: .

[0092] Set the total watering capacity constraint for vehicles: .

[0093] Set up segment continuity constraints: , represents the forward travel decision variable, indicating that if the target vehicle Drive to the planned watering section The location of the water replenishment point is ,otherwise , Indicates that the target vehicle is from Drive to the planned watering section The distance between the water filling points, Indicates that the target vehicle is from Drive to the planned watering section The corresponding path between the water replenishment point locations The distance between the middle node positions, Indicates the water filling point number, , Represents an arbitrary proposition symbol.

[0094] It needs to be added that It may represent a watering point in the middle of the path, or it may represent a planned watering section in the middle of the path. Indicates that the target vehicle is from Drive to the planned watering section The distance between the water filling points shall not exceed the target vehicle's Drive to the planned watering section The corresponding path between the water replenishment point locations The distance between the middle node positions.

[0095] Set up watering order constraints: , , Represents the negative travel decision variable, which means if the target vehicle comes from Drive to the water filling point If there are three planned watering sections, ,otherwise .

[0096] Set water replenishment point constraints: , Represents the water replenishment decision variable, which means if the target vehicle is in If water is replenished at each water replenishment point, ,otherwise , Indicates the rounding symbol.

[0097] It should be added that Represents the water replenishment location set Selected The sum of the values, i.e. Indicates all The sum of must be at least The threshold is determined by the sum of the required watering volumes for all planned watering sections.

[0098] Set the time window constraint: , Indicates the The actual total length of stay on the planned watering section, .

[0099] When setting the target optimization function, the embodiment of the present invention sets multiple optimization constraints by combining the traffic delay weight and the watering delay weight, thereby avoiding the imbalance problem caused by single-target optimization, improving the overall efficiency and scientificity of path planning, and also improving the robustness and stability of path planning, thereby making path planning more in line with actual needs, thereby effectively ensuring the efficiency of watering operations while reducing the loss of watering operation resources.

[0100] Furthermore, the specific confirmation process of the optimization index of the distance optimization item in step S6-5 is as follows: compare the locations of each planned watering section and each set water replenishment point, and obtain the distance between each planned watering section and each set water replenishment point, which is recorded as .

[0101] Will As an optimization metric for distance optimization , Represents the summation operator symbol, Represents the union operator symbol.

[0102] Furthermore, the specific confirmation process of the optimization index of the time optimization item in step S6-5 is as follows: B1. Set the optimization index of the planned watering section, recorded as , , is the sprinkler flow time coefficient, and Respectively represent the target vehicle from The duration of traffic delay and watering delay on each planned watering section.

[0103] It should be added that , , the sprinkler flow time coefficient is a constant and is set accordingly based on actual specific needs.

[0104] B2. Set the optimization index of the water replenishment point, recorded as , , Indicates that the target vehicle is in The refilling time of each refilling point.

[0105] It should be added that the water replenishment time is mainly based on the water replenishment amount of the target vehicle and the water replenishment speed set at the water replenishment point. The specific data can be extracted from the operation technical table of the water replenishment point. Among them, the water replenishment speed, water replenishment amount and water replenishment time data can be referred to as shown in Table 1.

[0106] Table 1 Schematic diagram of some data on water replenishment speed, water replenishment amount and water replenishment time

[0107]

[0108] B3. Set the optimization index for driving from the planned watering section to the watering point, recorded as , , Indicates that the target vehicle is from Drive to the planned watering section The total duration of each water filling point.

[0109] It should be added that the present invention analyzes the average speed of the target vehicle and Drive to the planned watering section The ratio of the distance to the water filling point and the average driving speed is used as the target vehicle's Drive to the planned watering section The total duration of each water filling point.

[0110] B4. 、 and The sum of the three is used as the optimization indicator of the time optimization item.

[0111] In another specific embodiment, obtaining an adjusted recommended path according to the target optimization function specifically includes: G1: selecting a path optimization algorithm.

[0112] In a specific embodiment, the path optimization algorithm includes but is not limited to a greedy algorithm, a dynamic programming algorithm, a genetic algorithm, a simulated annealing algorithm, an ant colony algorithm, and the like.

[0113] G2: Execute the optimization process according to the selected path optimization algorithm and output the adjusted recommended path.

[0114] In a specific embodiment, assuming that a genetic algorithm is selected for path optimization, the specific execution steps are as follows: Step 1: Encode each watering path into a chromosome, that is, a path sequence, and randomly generate a group of initial paths as a population.

[0115] Step 2: Calculate the fitness value of each path according to the target optimization function.

[0116] Step 3: Select the optimal path for reproduction based on the fitness value.

[0117] Step 4: Generate a new path through crossover operation.

[0118] Step 5: Randomly mutate some paths.

[0119] Step 6: Repeat steps 3 to 5 until the predetermined number of iterations is reached or the optimization effect is no longer significantly improved.

[0120] Step 7: Select the path with the highest fitness value from the final population as the adjusted recommended path.

[0121] The embodiment of the present invention sets a target optimization function by combining the real-time traffic data and current environmental information of each planned watering section, thereby obtaining an adjusted recommended path, thereby improving the flexibility of watering path planning, and also filling the current gap in the setting of dynamic adjustment mechanisms. It also effectively integrates the influence of traffic conditions and road environment on watering path planning, realizes precise adjustment of watering path planning, and thus ensures the reliability, rationality and effectiveness of watering path planning.

[0122] S7. Feedback on the watering path: Feedback the recommended path or the adjusted recommended path to the target vehicle, so that the watering operation is performed according to the recommended path.

[0123] The embodiment of the present invention effectively solves the shortcomings of the current static path planning method by verifying and adjusting the adaptability of the recommended path based on the real-time traffic data and current environmental information of each planned watering section, fully considers the dynamic changes of traffic, and thus reduces the difference between the watering path planning and the actual scene, which helps to adapt to changes in traffic conditions in a timely manner, thereby making the path planning more targeted.

[0124] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. A method for planning an operation path of a watering vehicle, characterized by: include: S1. Record the vehicle currently to be watered as the target vehicle and import the initial watering plan information of the target vehicle; S2. Extracting the location of each planned watering section from the initial watering plan information of the target vehicle, and then locating the real-time traffic data of each planned watering section in the current time period from the GIS geographic map; S3. Monitor the current environmental information of each planned watering section; S4. Performing path planning based on the initial watering plan information and a preset path planning algorithm to obtain a recommended path; S5, verifying the suitability of the recommended route based on the real-time traffic data and current environmental information of each planned watering section, and if the verification passes, executing step S7; if the verification fails, executing step S6; S6. Setting a target optimization function, and obtaining an adjusted recommended path according to the target optimization function; S7. Feedback the recommended path or the adjusted recommended path to the target vehicle, and then perform the watering operation according to the recommended path.

2. A method for planning an operation path of a watering vehicle according to claim 1, characterized in that: The performing adaptability verification on the recommended path includes: Extract the real-time traffic volume and real-time vehicle density from the real-time traffic data of each planned watering section, and set the traffic delay weight of each planned watering section accordingly; Based on the current environmental information of each planned watering section, the watering delay weight of each planned watering section is set; The traffic delay interference degree is calculated based on the traffic delay weight, and the watering delay interference degree is calculated in the same way as the traffic delay interference degree; The traffic delay interference degree is greater than or equal to 1 as verification condition 1, and the watering delay interference degree is greater than or equal to 1 as verification condition 2; If verification condition 1 or verification condition 2 is met, verification failure is regarded as the adaptability verification result; if both verification condition 1 and verification condition 2 are not met, verification passing is regarded as the adaptability verification result.

3. A method for planning an operation path of a watering vehicle according to claim 2, characterized in that: The setting of the traffic delay weight of each planned watering section includes: With time as the horizontal axis and traffic volume and vehicle density as the vertical axis, the traffic volume curve and vehicle density curve of each planned watering section are constructed; Extracting a slope from the traffic flow curve, setting a correction factor according to the traffic flow curve, correcting the slope, recording the corrected slope as the traffic flow change rate, and confirming the vehicle density change rate in the same manner as confirming the vehicle change rate; Extracting the maximum values ​​from the real-time traffic flow and the real-time vehicle density respectively to obtain the maximum traffic flow and the maximum vehicle density of each planned watering section; The traffic flow change rate, vehicle density change rate, maximum traffic flow, and maximum vehicle density of each planned watering section are normalized respectively. Based on the processed results, the traffic flow delay weight and vehicle density delay weight of each planned watering section are set; Set the weights of traffic flow and vehicle density, and then perform weighted sum calculation on the traffic flow delay weight and vehicle density delay weight of each planned watering section, and use the calculation result as the traffic delay weight of each planned watering section.

4. A method for planning an operation path of a watering vehicle according to claim 3, characterized in that: The setting correction factor includes: constructing a reference line in the traffic flow curve based on the slope of the traffic flow curve; Extract the total length of the curve above the reference line and the total length of the curve below the reference line, and compare them with the total length of the traffic flow curve respectively. The ratios are recorded as and ; like or ,Will and The difference is used as the correction factor; like , taking 0 as the correction factor, we can get the correction factor and record it as , The value is 0 or .

5. A method for planning an operation path of a watering vehicle according to claim 2, characterized in that: The method of setting the watering delay weight of each planned watering section includes: Extracting current temperature, current humidity and current dust concentration from current environmental information of each planned watering section; Set the evaluation weights for temperature, humidity, and dust concentration; The current temperature, current humidity and current dust concentration of each planned watering section are respectively standardized to obtain the standardized temperature factor, humidity factor and dust concentration factor of each planned watering section; Based on the temperature factor, humidity factor, dust concentration factor and corresponding evaluation weight, the comprehensive watering influence factor of each planned watering section is calculated by linear weighting method and recorded as ; Extract the permission delay time and warning delay time of each planned watering section from the initial watering plan information of the target vehicle, and record them as and ; Set the watering delay weight for each planned watering section , , The delay adjustment coefficient is set.

6. A method for planning an operation path of a watering vehicle according to claim 2, characterized in that: The calculation of the traffic delay interference degree includes: Locate the watering order of each planned watering section from the recommended path, sort each planned watering section according to its watering order, and locate the planned watering section with the middle position in the sorting order as the split section; The planned watering sections that are ranked before the split section are recorded as priority watering sections, and the traffic delay weights of the priority watering sections are extracted from the traffic delay weights of the planned watering sections, and the sum of the traffic delay weights of the priority watering sections is obtained; Sum up the traffic delay weights of each planned watering section, and use half of the sum as the interference traffic delay weight; Sort each planned watering section by traffic delay weight from large to small to obtain the traffic delay ranking of each planned watering section, and record the planned watering section with the middle ranking position as the standard section; Traverse the traffic delay ranking of each planned watering section, count the number of priority watering sections whose ranking position is before the standard section, and compare it with the number of priority watering sections to obtain the priority traffic delay watering section ratio; The traffic delay interference degree is calculated comprehensively based on the sum of the traffic delay weights of the priority watering sections, the interference traffic delay weights, the priority traffic delay watering section ratio and the set reference interference traffic delay watering section ratio.

7. A method for planning an operation path of a watering vehicle according to claim 5, characterized in that: The setting target optimization function includes: Based on the location of each planned watering section, a watering section set is constructed; Extract the locations of each set water replenishment point from the initial watering plan information to construct a water replenishment location set; Extract the watering capacity, planned watering volume, planned watering duration, and planned total travel time of each planned watering section from the initial watering plan information of the target vehicle; Set the constraints of the target optimization function; Taking distance and time as optimization items, the optimization indicators of distance optimization items and time optimization items are confirmed based on the traffic delay weight and watering delay weight of each planned watering section. According to the optimization indicators of distance optimization items and time optimization items and the weights of distance and time, the results of linear weighted calculation are used as the target optimization function.

8. A method for planning an operation path of a watering vehicle according to claim 7, characterized in that: The constraint conditions for setting the target optimization function include: Set up watering demand constraints: , , Indicates the The required watering volume for each planned watering section; Set the total watering capacity constraint for vehicles: ; Set up segment continuity constraints: , represents the forward travel decision variable, indicating that if the target vehicle Drive to the planned watering section The location of the water replenishment point is ,otherwise , Indicates that the target vehicle is from Drive to the planned watering section The distance between the water filling points, Indicates that the target vehicle is from Drive to the planned watering section The corresponding path between the water replenishment point locations The distance between the middle node positions, Indicates the water filling point number, , represents any proposition symbol; Set up watering order constraints: , , Represents the negative travel decision variable, which means if the target vehicle comes from Drive to the water filling point If there are three planned watering sections, ,otherwise ; Set water replenishment point constraints: , Represents the water replenishment decision variable, which means if the target vehicle is in If water is replenished at each water replenishment point, ,otherwise , Indicates the rounding symbol; Set the time window constraint: , Indicates the The actual total length of stay on the planned watering section, .

9. A method for planning an operation path of a watering vehicle according to claim 8, characterized in that: The specific confirmation process of the optimization index of the distance optimization item is as follows: Compare the locations of each planned watering section and each set water replenishment point to obtain the distance between each planned watering section and each set water replenishment point, which is recorded as ; Will As an optimization metric for distance optimization , Represents the summation operator symbol, Represents the union operator symbol.

10. The method for planning an operation path of a watering vehicle according to claim 8, characterized in that: The specific confirmation process of the optimization index of the time optimization item is as follows: Set the optimization index of the planned watering section, denoted as , , is the sprinkler flow time coefficient, and Respectively represent the target vehicle from The duration of traffic delays and watering delays on each planned watering section; Set the optimization index of the water replenishment point, denoted as , , Indicates that the target vehicle is in The duration of water replenishment at each water replenishment point; Set the optimization index of the planned watering section to the watering point, denoted as , , Indicates that the target vehicle is from Drive to the planned watering section Total duration of each water filling point; Will 、 and The sum of the three is used as the optimization indicator of the time optimization item.

Images