An urban sanitation vehicle cooperative perception management system based on an internet of things
By using IoT and cloud data mining technologies, the transportation routes of sanitation vehicles are dynamically planned, which solves the problems of subjectivity and lack of accuracy in traditional sanitation vehicle transportation route planning, and realizes efficient, balanced transportation capacity allocation and long-term accuracy of sanitation vehicles.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANGHAI PUHUI ZHITU TRANSPORTATION TECHNOLOGY CO LTD
- Filing Date
- 2026-01-14
- Publication Date
- 2026-05-29
AI Technical Summary
Traditional sanitation vehicle route planning is highly subjective and lacks precision, making it impossible to accurately grasp the characteristics of waste generation on each route, leading to waste accumulation or resource waste.
By leveraging IoT technology and cloud data mining, historical waste parameter ranges are extracted. Combined with real-time monitoring and positioning sensors, the optimal collection route is dynamically planned, and capacity allocation is optimized using the total available capacity and the shortest travel distance.
It achieves precise binding between sanitation vehicles and cleaning areas, reduces unnecessary driving, improves collection efficiency, balances transport capacity allocation, dynamically adapts to changes in waste, and ensures the accuracy of long-term planning.
Smart Images
Figure CN122114444A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of sanitation cleaning technology, specifically to an Internet of Things-based collaborative sensing and management system for urban sanitation vehicles. Background Technology
[0002] With the continued acceleration of urbanization, the built-up area of cities is constantly expanding, and street networks are becoming increasingly complex. The difficulty and workload of urban environmental sanitation and waste collection are surging simultaneously, placing higher demands on the precision and efficiency of sanitation management. Currently, urban sanitation vehicle collection and management still largely relies on traditional methods, and there are many technical challenges that urgently need to be addressed, as follows:
[0003] Traditional sanitation vehicle route planning is highly subjective and lacks precision. Existing route plans are mostly drafted by staff based on past experience, failing to fully consider the actual patterns and dynamic changes in waste generation in each street. This leads to problems such as waste accumulation and untimely collection in some sections where there is a large volume of waste but insufficient collection capacity, while other sections experience inefficient waste management due to excessive collection frequency or redundant capacity. Furthermore, the lack of systematic analysis and mining of historical collection data makes it impossible to accurately grasp the waste generation characteristics of each route, hindering the formation of a scientific basis for route planning and further exacerbating the haphazardness of route planning.
[0004] Against this backdrop, leveraging IoT and cloud data mining technologies to achieve collaborative perception and intelligent management of sanitation vehicles, accurately identifying the patterns of garbage generation on various road sections, and dynamically optimizing collection routes and capacity allocation has become an inevitable trend for improving urban sanitation management and addressing the pain points of traditional waste collection. Based on this, developing an IoT-based collaborative perception and management system for urban sanitation vehicles has significant practical implications and application value. Summary of the Invention
[0005] To address the shortcomings of existing technologies, this invention provides an Internet of Things-based collaborative sensing and management system for urban sanitation vehicles, which solves the problem of lacking systematic analysis and mining of historical collection data and being unable to accurately grasp the characteristics of waste generation intervals for each single route.
[0006] To achieve the above objectives, the present invention provides the following technical solution: a collaborative sensing and management system for urban sanitation vehicles based on the Internet of Things, comprising:
[0007] The associated interval determination end identifies the historical data associated with each single route within the route area from cloud data, and extracts and records the garbage parameter intervals associated with each single route from the historical data. The specific method is as follows:
[0008] Each single route is identified within the extracted route area. A set of traceability cycles is identified with the current time as the base time. The traceability cycle is a preset cycle. Historical data associated with each single route is extracted from cloud data. The total amount of waste associated with a single collection process is identified from the historical data. Several sets of total waste amounts are sorted in ascending order of value to identify the total waste amount sequence.
[0009] Randomly select numerical segments from the total waste sequence, confirm the numerical density associated with each segment, extract the maximum and minimum values associated with each segment, lock the numerical range associated with the corresponding segment, and then confirm the total number of values associated with each segment. Use the formula: numerical range ÷ total number of values = numerical density to confirm the different numerical densities associated with different segments in the total waste sequence. Then select the maximum value from the confirmed sets of numerical densities, record the numerical segment associated with the maximum value as the standard segment, and use the numerical range associated with the standard segment as the waste parameter interval for the corresponding single route. Confirm the waste parameter intervals associated with different single routes in turn.
[0010] On the zoning planning end, the cleaning area associated with the sanitation vehicle is determined based on the vehicle number associated with the sanitation vehicle in different cities.
[0011] The real-time monitoring terminal monitors the total amount of garbage in a single city's sanitation vehicle in real time, and simultaneously monitors the real-time location of the city's sanitation vehicle.
[0012] The analysis and processing center, based on the positioning sensors installed in urban sanitation vehicles, confirms the routes that the vehicles have completed cleaning. Based on the total operational capacity associated with each vehicle, it plans subsequent cleaning routes. The specific method is as follows:
[0013] The total amount of garbage LZ associated with urban sanitation vehicles is monitored in real time, and the total amount of operation associated with the corresponding urban sanitation vehicles is generated in real time based on the total amount of operation set for the corresponding urban sanitation vehicles, where: total amount of operation = total amount of operation - LZ.
[0014] Based on the confirmed cleanup area, identify multiple sets of single routes associated with this cleanup area;
[0015] Based on the positioning sensors installed in the urban sanitation vehicles, the single routes that the urban sanitation vehicles have traveled are identified and recorded as processing routes. Other single routes in multiple sets of single routes that are not marked as processing routes are recorded as routes to be planned.
[0016] From the marked groups of routes to be planned, identify the waste parameter intervals associated with each group of routes, extract the median value of each waste parameter interval, and use this median value as the interval feature of the waste parameter interval. Then, randomly combine several groups of routes to be planned to identify the sum of the associated interval features, denoted as ZH. k Where k represents different combinations of processes, the confirmed operable total is denoted as ZL and a total range [80% × ZL, ZL] is generated, which satisfies: ZH k A combined process ∈ [80% × ZL, ZL] is denoted as an optional process;
[0017] The process characteristics associated with different optional processes are confirmed in turn, and the minimum value is selected from the confirmed groups of process characteristics. The optional process associated with the minimum value is recorded as the standard process, and the multiple groups of routes to be planned in the standard process are recorded as the cleaning routes.
[0018] Preferably, the route area is the area that the urban sanitation vehicle needs to clean, and the route area is a preset area.
[0019] Preferably, the method for confirming the process characteristics of the selectable process is as follows:
[0020] Select a single optional process and determine the current location of the urban sanitation vehicle as the starting point. From the multiple planned routes associated with the optional process, identify the planned route with the shortest travel distance from the starting point and record it as the starting route. Record the starting point and ending point of the starting route. Then, using the ending point as the starting point, select the planned route with the shortest travel distance from the other planned routes. In this way, sort and confirm the subsequent associated planned routes in sequence, and sum the travel distances of the confirmed routes. Use the sum value as the process characteristic of the current optional process.
[0021] Preferred options also include:
[0022] The associated route output end transmits the collection route to the receiving terminal based on the marked collection route and the receiving terminal associated with the urban sanitation vehicle.
[0023] The analysis and processing center, based on the real-time monitoring data of the total amount of waste generated by different single routes, compares the total amount of waste generated by the corresponding route with the waste parameter range in real time. Based on the comparison process, it determines whether the waste parameter range needs to be adjusted in real time. The specific method is as follows:
[0024] Based on the real-time collection process of urban sanitation vehicles, the total amount of garbage generated by the corresponding single route is confirmed and marked as Zq, where q represents different single routes. Then, the garbage parameter range associated with the corresponding single route is confirmed simultaneously, and it is identified whether the total amount of garbage Zq generated belongs to the corresponding garbage parameter range. If it does, no processing is required. If not, the overflow ratio is confirmed and an overflow signal is generated.
[0025] The overflow ratio is confirmed as follows: if Zq is less than the minimum value of the garbage parameter range, no processing is required; if Zq is greater than the maximum value of the garbage parameter range, the overflow ratio is confirmed by using: (Zq - maximum value) ÷ maximum value = overflow ratio.
[0026] If three sets of overflow signals are generated consecutively, the overflow ratios of the three sets are averaged to confirm the average ratio. Then, based on the average ratio, the subsequent un-cleaned planned routes are adjusted by interval correction. The maximum value associated with the garbage parameter interval corresponding to the planned route is extracted, and the correction value associated with the corresponding garbage parameter interval is confirmed by using: (maximum value × average ratio) + maximum value = correction value. The correction value is then used as the maximum value of the corresponding garbage parameter interval to complete the correction process.
[0027] This invention provides a collaborative sensing and management system for urban sanitation vehicles based on the Internet of Things (IoT). Compared with existing technologies, it has the following advantages:
[0028] The zoning planning system accurately binds sanitation vehicles to cleaning areas. Combined with the dynamic planning logic of the analysis and processing center based on the total available capacity, it selects route combinations that meet the capacity requirements and determines the optimal collection route based on the "shortest travel distance" as the core sorting principle. This effectively reduces the ineffective mileage of sanitation vehicles, such as empty runs and detours, and improves the collection efficiency per unit time of a single sanitation vehicle. At the same time, through the scientific combination of multiple sets of routes to be planned, it ensures that the sanitation vehicle capacity is fully utilized, avoiding the waste of resources such as some vehicles being overloaded and others being idle, and achieving a balanced allocation of sanitation vehicle capacity within the region.
[0029] Real-time tracking of the total amount of garbage from sanitation vehicles, the amount of garbage generated per route, and the location of vehicles enables the analysis and processing center to dynamically monitor the progress of garbage collection and the remaining capacity. This provides real-time data support for dynamic route adjustments, ensuring that the collection work can respond to special circumstances such as sudden increases in garbage volume. At the same time, the system has a dynamic correction mechanism for garbage parameter ranges. When garbage overflow occurs continuously, the garbage parameter range of the uncleaned routes is corrected by calculating the average overflow ratio. This ensures that the parameter range can continuously adapt to the changing patterns of garbage generation on urban streets, avoiding planning deviations due to historical data lag, and guaranteeing the long-term accuracy of route planning. Attached Figure Description
[0030] Figure 1 This is a schematic diagram of the principle framework of the present invention. Detailed Implementation
[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0032] First Embodiment
[0033] Please see Figure 1 This application provides an Internet of Things-based collaborative sensing and management system for urban sanitation vehicles, including an associated interval determination end, a zoning planning end, an analysis and processing center, a real-time monitoring end, and an associated route output end. The associated interval determination end and the analysis and processing center are bidirectionally connected, and the zoning planning end, the analysis and processing center, and the associated route output end are electrically connected sequentially from the output node to the input node. The real-time monitoring end is electrically connected to the input node of the analysis and processing center.
[0034] In the process of determining the associated interval, the route areas that urban sanitation vehicles need to clean are extracted. These route areas are preset areas, which are planned in advance by relevant personnel based on experience. Then, the historical data associated with each single route within the route area is confirmed from cloud data, and the garbage parameter intervals associated with each single route are extracted from the historical data and recorded. Specifically, in the historical cleaning process, each different route has a different total amount of garbage, but the area covered by each street generally has clustering characteristics. Based on a large amount of historical data, the garbage parameter intervals associated with each street are identified, which facilitates subsequent route planning for sanitation vehicles.
[0035] The specific method for extracting and recording the garbage parameter range is as follows:
[0036] Each single route is identified within the extracted route area. A set of traceability cycles is identified with the current time as the base time. The traceability cycle is a preset cycle, generally 30 days. Historical data associated with each single route is extracted from cloud data. The total amount of waste associated with a single collection process is identified from the historical data. Several sets of total waste amounts are sorted in ascending order of value to identify the total waste amount sequence.
[0037] Randomly select numerical segments from the total waste sequence (the total number of values associated with each segment should not be less than five groups). Confirm the numerical density associated with each segment, extract the maximum and minimum values associated with each segment, lock the numerical range associated with the corresponding segment, and then confirm the total number of values associated with each segment. Use the formula: numerical range ÷ total number of values = numerical density. Confirm the different numerical densities associated with different segments in the total waste sequence. Then select the maximum value from the confirmed numerical densities. Record the numerical segment associated with the maximum value as the standard segment. Use the numerical range associated with the standard segment as the waste parameter interval for the corresponding single route. Confirm the waste parameter intervals associated with different single routes in turn.
[0038] Specifically, within the corresponding route area, there are several different single routes, each associated with different historical data. From the corresponding historical data, the garbage parameter range associated with each single route can be effectively identified. Once identified, the subsequent associated collection routes can be rationally planned.
[0039] In the zoning planning end, based on the vehicle numbers associated with sanitation vehicles in different cities, the cleaning area associated with the corresponding sanitation vehicle is identified, and the identified cleaning area is transmitted to the analysis and processing center.
[0040] Among them, the real-time monitoring terminal monitors the total amount of garbage in a single city sanitation vehicle in real time, and simultaneously monitors the total amount of garbage generated by each single route in real time, and simultaneously monitors the real-time location of the city sanitation vehicle, and transmits it to the analysis and processing center.
[0041] The analysis and processing center, based on the positioning sensors installed in the urban sanitation vehicles, confirms the relevant routes that the urban sanitation vehicles have completed cleaning, and plans the subsequent associated cleaning routes based on the total number of operational vehicles associated with the urban sanitation vehicles.
[0042] The specific methods for planning the waste removal routes are as follows:
[0043] The total amount of garbage LZ associated with urban sanitation vehicles is monitored in real time, and the total amount of operation associated with the corresponding urban sanitation vehicles is generated in real time based on the total amount of operation set for the corresponding urban sanitation vehicles, where: total amount of operation = total amount of operation - LZ.
[0044] Based on the confirmed cleanup area, identify multiple sets of single routes associated with this cleanup area;
[0045] Based on the positioning sensors installed in the urban sanitation vehicles, the single routes that the urban sanitation vehicles have traveled are confirmed (generally, after traveling, it means that the relevant cleaning has been completed), and such single routes are recorded as processing routes. Other single routes in multiple sets of single routes that are not marked as processing routes are recorded as routes to be planned.
[0046] From the marked groups of routes to be planned, identify the waste parameter intervals associated with each group of routes, extract the median value of each waste parameter interval, and use this median value as the interval feature of the waste parameter interval. Then, randomly combine several groups of routes to be planned to identify the sum of the associated interval features, denoted as ZH. k Where k represents different combinations of processes, the confirmed operable total is denoted as ZL and a total range [80% × ZL, ZL] is generated, which satisfies: ZH k A combined process ∈ [80% × ZL, ZL] is denoted as an optional process;
[0047] Feature confirmation is performed on the different optional processes marked: Select a single optional process and determine the current location of the urban sanitation vehicle as the starting point. From the multiple planned routes associated with the optional process, confirm the planned route with the shortest travel distance from the starting point and record it as the starting route. Record the starting point and ending point of the starting route (the starting point is the point closest to the starting point. When confirming the distance, it is necessary to confirm the distance based on the two endpoints of the corresponding single route). Then, using the ending point as the starting point, select the planned route with the shortest travel distance from the other planned routes. In this way, the subsequent associated several planned routes are sorted and confirmed in sequence. The travel distances of the confirmed several groups are summed and the sum value is used as the process feature of the current optional process.
[0048] The process characteristics associated with different optional processes are confirmed in turn, and the minimum value is selected from the confirmed groups of process characteristics. The optional process associated with the minimum value is recorded as the standard process, and the multiple groups of routes to be planned in the standard process are recorded as the cleaning routes.
[0049] Specifically, during the planning and formulation of collection routes, based on the remaining amount of garbage transported by each sanitation vehicle and the garbage parameter range generated by each route in the historical processing process, the total garbage characteristics associated with several routes can be identified in the subsequent route planning process, based on the garbage characteristics associated with each route. Thus, under the premise that the sanitation vehicles can effectively clean up, the subsequent cleaning routes can be rationally formulated, and the problem of long routes can be effectively avoided in the planning process, so as to fully improve the collection speed of sanitation vehicles and improve the overall cleaning effect of the cleaning area.
[0050] The associated route output terminal transmits the cleaning route to the receiving terminal based on the marked cleaning route and the receiving terminal associated with the urban sanitation vehicle. When the staff receives the cleaning route, they carry out the street cleaning process according to the cleaning route.
[0051] Second Embodiment
[0052] This embodiment is a further embodiment of the first embodiment, mainly targeting the overflow characteristics associated with the real-time monitoring terminal and the recorded garbage parameter range. Based on the corresponding overflow characteristics, the parameters are corrected to ensure the accuracy of the next route planning.
[0053] The analysis and processing center, based on the real-time monitoring data of the total amount of waste generated by different single routes, compares the total amount of waste generated by the corresponding route with the waste parameter range in real time. Based on the comparison process, it assesses whether the waste parameter range needs to be adjusted in real time.
[0054] Based on the real-time collection process of urban sanitation vehicles, the total amount of garbage generated by the corresponding single route is confirmed and marked as Zq, where q represents different single routes. Then, the garbage parameter range associated with the corresponding single route is confirmed simultaneously, and it is identified whether the total amount of garbage Zq generated belongs to the corresponding garbage parameter range. If it does, no processing is required. If not, the overflow ratio is confirmed and an overflow signal is generated.
[0055] The overflow ratio is confirmed as follows: if Zq is less than the minimum value of the garbage parameter range, no processing is required; if Zq is greater than the maximum value of the garbage parameter range, the overflow ratio is confirmed by using: (Zq - maximum value) ÷ maximum value = overflow ratio.
[0056] If three sets of overflow signals are generated consecutively (that is, during the actual collection process, three sets of overflow signals appear consecutively according to the collection progress of the corresponding collection route), the average of the three overflow ratios is processed to confirm the average ratio. Then, based on the average ratio, the subsequent uncollected planned routes are adjusted by interval correction. The maximum value associated with the garbage parameter interval of the planned route is extracted, and the correction value associated with the corresponding garbage parameter interval is confirmed by using: (maximum value × average ratio) + maximum value = correction value. The correction value is then used as the maximum value of the corresponding garbage parameter interval to complete the correction process, which facilitates the accuracy of subsequent route planning.
[0057] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.
[0058] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.
Claims
1. A collaborative sensing and management system for urban sanitation vehicles based on the Internet of Things, characterized in that, include: The associated interval determination end identifies the historical data associated with each single route within the route area from cloud data, and extracts and records the garbage parameter interval associated with each single route from the historical data; On the zoning planning end, the cleaning area associated with the sanitation vehicle is determined based on the vehicle number associated with the sanitation vehicle in different cities. The real-time monitoring terminal monitors the total amount of garbage in a single city's sanitation vehicle in real time, and simultaneously monitors the real-time location of the city's sanitation vehicle. The analysis and processing center, based on the positioning sensors installed in the city sanitation vehicles, confirms the relevant routes that the city sanitation vehicles have completed cleaning, and plans the subsequent associated cleaning routes based on the total number of operational vehicles associated with the city sanitation vehicles.
2. The IoT-based collaborative sensing and management system for urban sanitation vehicles according to claim 1, characterized in that, The route area is the area that urban sanitation vehicles need to clean, and the route area is a preset area.
3. The IoT-based collaborative sensing and management system for urban sanitation vehicles according to claim 1, characterized in that, The specific method for extracting and recording the garbage parameter range at the correlation interval determination end is as follows: Each single route is identified within the extracted route area. A set of traceability cycles is identified with the current time as the base time. The traceability cycle is a preset cycle. Historical data associated with each single route is extracted from cloud data. The total amount of waste associated with a single collection process is identified from the historical data. Several sets of total waste amounts are sorted in ascending order of value to identify the total waste amount sequence. Randomly select numerical segments from the total waste sequence, confirm the numerical density associated with each segment, extract the maximum and minimum values associated with each segment, lock the numerical range associated with the corresponding segment, and then confirm the total number of values associated with each segment. Use the formula: numerical range ÷ total number of values = numerical density to confirm the different numerical densities associated with different segments in the total waste sequence. Then select the maximum value from the confirmed sets of numerical densities, and record the numerical segment associated with the maximum value as the standard segment. Use the numerical range associated with the standard segment as the waste parameter interval for the corresponding single route. Confirm the waste parameter intervals associated with different single routes in turn.
4. The IoT-based collaborative sensing and management system for urban sanitation vehicles according to claim 1, characterized in that, The specific method by which the analysis and processing center confirms the total operational capacity is as follows: The total amount of garbage (LZ) associated with urban sanitation vehicles is monitored in real time, and the total amount of operation associated with the corresponding urban sanitation vehicles is generated in real time based on the total amount of operation set for the corresponding urban sanitation vehicles, where: Total amount of operation = Total amount of operation - LZ.
5. The IoT-based collaborative sensing and management system for urban sanitation vehicles according to claim 4, characterized in that, The analysis and processing center plans the waste removal routes in the following specific way: Based on the confirmed cleanup area, identify multiple sets of single routes associated with this cleanup area; Based on the positioning sensors installed in the urban sanitation vehicles, the single routes that the urban sanitation vehicles have traveled are identified and recorded as processing routes. Other single routes in multiple sets of single routes that are not marked as processing routes are recorded as routes to be planned. From the marked groups of routes to be planned, identify the waste parameter intervals associated with each group of routes, extract the median value of each waste parameter interval, and use this median value as the interval feature of the waste parameter interval. Then, randomly combine several groups of routes to be planned to identify the sum of the associated interval features, denoted as ZH. k Where k represents different combinations of processes, the confirmed operable total is denoted as ZL and a total range [80% × ZL, ZL] is generated, which satisfies: ZH k A combined process ∈ [80% × ZL, ZL] is denoted as an optional process; The process characteristics associated with different optional processes are confirmed in turn, and the minimum value is selected from the confirmed groups of process characteristics. The optional process associated with the minimum value is recorded as the standard process, and the multiple groups of routes to be planned in the standard process are recorded as the cleaning routes.
6. The IoT-based collaborative sensing and management system for urban sanitation vehicles according to claim 5, characterized in that, The method for confirming the process characteristics of the optional process is as follows: Select a single optional process and determine the current location of the urban sanitation vehicle as the starting point. From the multiple planned routes associated with the optional process, identify the planned route with the shortest travel distance from the starting point and record it as the starting route. Record the starting point and ending point of the starting route. Then, using the ending point as the starting point, select the planned route with the shortest travel distance from the other planned routes. In this way, sort and confirm the subsequent associated planned routes in sequence, and sum the travel distances of the confirmed routes. Use the sum value as the process characteristic of the current optional process.
7. The IoT-based collaborative sensing and management system for urban sanitation vehicles according to claim 6, characterized in that, Also includes: The associated route output terminal transmits the collection route to the receiving terminal based on the marked collection route and the receiving terminal associated with the urban sanitation vehicle.
8. The IoT-based collaborative sensing and management system for urban sanitation vehicles according to claim 1, characterized in that, The analysis and processing center compares the total amount of waste generated by different single routes in real time with the waste parameter range based on the real-time monitoring data of the monitoring terminal. Based on the comparison process, it determines whether the waste parameter range needs to be adjusted in real time.
9. The IoT-based collaborative sensing and management system for urban sanitation vehicles according to claim 8, characterized in that, The specific method by which the analysis and processing center adjusts the waste parameter range is as follows: Based on the real-time collection process of urban sanitation vehicles, the total amount of garbage generated by the corresponding single route is confirmed and marked as Zq, where q represents different single routes. Then, the garbage parameter range associated with the corresponding single route is confirmed simultaneously, and it is identified whether the total amount of garbage Zq generated belongs to the corresponding garbage parameter range. If it does, no processing is required. If not, the overflow ratio is confirmed and an overflow signal is generated. The overflow ratio is confirmed as follows: if Zq is less than the minimum value of the garbage parameter range, no processing is required; if Zq is greater than the maximum value of the garbage parameter range, the overflow ratio is confirmed by using: (Zq - maximum value) ÷ maximum value = overflow ratio. If three sets of overflow signals are generated consecutively, the overflow ratios of the three sets are averaged to confirm the average ratio. Then, based on the average ratio, the subsequent un-cleaned planned routes are adjusted by interval correction. The maximum value associated with the garbage parameter interval corresponding to the planned route is extracted, and the correction value associated with the corresponding garbage parameter interval is confirmed by using: (maximum value × average ratio) + maximum value = correction value. The correction value is then used as the maximum value of the corresponding garbage parameter interval to complete the correction process.