Intelligent environmental sanitation management method, system, medium and equipment

Abstract

The invention discloses an intelligent environmental sanitation management method and system, a medium and equipment, and relates to the technical field of intelligent environmental sanitation. The method comprises the steps that the garbage volume corresponding to each garbage can in a target area is obtained; target garbage cans needing to be cleaned and garbage cleaning modes corresponding to the target garbage cans are determined on the basis of the garbage volumes; determining a corresponding target cleaning vehicle based on the position and the garbage type of each target garbage can; in the garbage cleaning mode, calculating a target route for the target cleaning vehicle to reach a corresponding target garbage can; and all the target cleaning vehicles are controlled to move to the corresponding target garbage cans according to the target routes to conduct garbage cleaning. By implementing the technical scheme provided by the invention, the cleaning efficiency and quality are improved, and the resource waste is avoided, so that the environmental sanitation service quality is improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent sanitation technology, and specifically to an intelligent sanitation management method, system, medium and equipment. Background Art

[0002] In modern urban management, the efficiency and intelligence level of the sanitation system directly affects the cleanliness of the city and the quality of life of residents. With the acceleration of urbanization, traditional sanitation management methods face many challenges, especially garbage removal.

[0003] Traditional sanitation management systems usually rely on fixed garbage collection routes and timed cleaning strategies, that is, garbage collection is carried out according to a preset schedule, and the cleaning vehicles usually travel along fixed routes when performing tasks. However, this traditional garbage collection method cannot adapt well to the dynamic changes of the environment. For example, some garbage bins in high-traffic areas may be full before the unscheduled cleaning time, while garbage bins in low-traffic areas may not be full for a long time, resulting in untimely garbage collection or waste of sanitation resources, resulting in low quality of sanitation services. Summary of the invention

[0004] The present application provides a smart sanitation management method, system, medium and equipment, which improves cleaning efficiency and quality, avoids waste of resources, and thus improves the quality of sanitation services.

[0005] In a first aspect, the present application provides a smart sanitation management method, the method comprising: Get the garbage volume corresponding to each garbage bin in the target area; Determine the target trash cans that need to be cleaned based on the volume of each trash, and the trash cleaning mode corresponding to each target trash can; Determine a corresponding target cleaning vehicle based on the location and type of garbage of each target garbage bin; In the garbage cleaning mode, calculating a target route for the target cleaning vehicle to reach a corresponding target garbage bin; Control each of the target cleaning vehicles to clean up garbage in the corresponding target garbage bin along the target route.

[0006] By adopting the above technical solution, by obtaining the real-time garbage volume data of each garbage bin in the target area, the target garbage bin that needs to be cleaned and the corresponding cleaning mode are determined according to the garbage volume, and then the target cleaning vehicle corresponding to each target garbage bin is dynamically determined in combination with the location of the garbage bin and the type of garbage. At the same time, the optimal target route is planned. Compared with the existing technology, it breaks through the traditional fixed route and scheduled cleaning mode, realizes dynamic intelligent scheduling based on the real-time status of the garbage bin and environmental factors, improves the cleaning efficiency and quality, avoids waste of resources, and improves the quality of sanitation services.

[0007] Optionally, the determining of the target trash cans to be cleaned based on the trash volumes, and the trash cleaning modes corresponding to the target trash cans, includes: Determining whether the volume of each of the garbage is greater than a first preset volume; Using trash cans larger than the first preset volume as target trash cans, and obtaining environmental information of locations of the target trash cans; Calculating a cleaning score of each target trash can based on the trash volume of each target trash can and the corresponding environmental information; The garbage cleaning mode corresponding to each of the target garbage bins is determined based on the cleaning score.

[0008] By adopting the above technical solution, the garbage bins with garbage volume exceeding the first preset volume are preliminarily determined as the target garbage bins that need to be cleaned first, so as to quickly screen out the full garbage bins with overflow risks. Then, based on the real-time garbage volume and environmental information of the target garbage bins, a comprehensive cleaning score is calculated. The score not only takes into account the fullness of the garbage bin, but also incorporates the influence of environmental factors. Finally, according to the calculated cleaning score, the system can flexibly assign different cleaning modes to each target garbage bin, breaking through the single judgment mode that only relies on a fixed volume threshold, and improving the accuracy of cleaning demand assessment.

[0009] Optionally, the environmental information includes temperature, humidity, and average human flow, and the calculating of the cleaning score of each target trash can based on the trash volume of each target trash can and the corresponding environmental information includes: Substituting the temperature, humidity, average flow of people and the volume of garbage in the target garbage bin into a preset calculation formula to obtain a cleaning score for each target garbage bin; The calculation formula is: In the formula, S i is the cleaning score of the i-th target trash can, α is the volume factor, β is the humidity factor, γ is the human flow factor, δ is the human flow adjustment coefficient, V i is the volume of garbage in the i-th target garbage bin, T i is the temperature of the i-th target trash can, H i is the humidity of the i-th target trash can, H0 is the reference humidity, P i is the average flow of people at the location of the i-th target trash can, D i is the time interval between the last cleaning of the i-th target trash can.

[0010] By adopting the above technical solution, by taking multiple key environmental factors and status factors such as temperature, humidity, average traffic flow and garbage volume as variable inputs, the cleaning score of each target trash can can be accurately calculated, and it can dynamically and truly reflect the actual situation of the trash can, significantly improving the scientificity, accuracy and comprehensiveness of the cleaning needs assessment.

[0011] Optionally, determining a garbage cleaning mode corresponding to each of the target garbage bins based on the cleaning score includes: Determining whether the cleaning score is greater than a score threshold; If the cleaning score is greater than the scoring threshold, the garbage cleaning mode of the target garbage bin is determined to be an emergency cleaning mode; if the cleaning score is not greater than the scoring threshold, the garbage cleaning mode of the target garbage bin is determined to be a normal cleaning mode.

[0012] By adopting the above technical solution, the target trash cans are automatically classified and managed according to the cleaning scores, and limited sanitation resources are allocated preferentially to the trash cans that need urgent treatment most, thus avoiding waste of resources, improving cleaning efficiency, and realizing dynamic hierarchical management of cleaning needs. It breaks through the single mode of traditional fixed-route cleaning, so that cleaning resources can be flexibly allocated according to the real-time status of the trash cans, thereby improving the quality of sanitation services.

[0013] Optionally, determining a corresponding target cleaning vehicle based on the location and garbage type of each target garbage bin includes: Determine a first set of cleaning vehicles within a preset range of the location of each target trash can, wherein the first set of cleaning vehicles includes idle cleaning vehicles in the parking lot and busy cleaning vehicles that are working; Determining the remaining capacity of each cleaning vehicle in the first set of cleaning vehicles; Screening out a second set of cleaning vehicles from the first set of cleaning vehicles, the second set of cleaning vehicles having the same type of garbage as the target garbage bin and a remaining capacity greater than the capacity of the target garbage bin; The cleaning vehicle closest to the target trash can in the second cleaning vehicle set is determined as the target cleaning vehicle.

[0014] By adopting the above technical solution, firstly, by limiting the geographical location range, unnecessary capacity allocation for vehicles that are too far away can be avoided; then, secondary screening is performed in combination with the remaining capacity of the vehicle and the garbage type matching to ensure that the dispatched vehicle has the actual garbage loading capacity; finally, the target vehicle is designated according to the principle of closest distance, which can shorten the operation time and improve the response efficiency. Through a reasonable optimization strategy, the operation capacity and efficiency of the dispatched vehicles are maximized, the utilization rate of limited cleaning resources is improved, and the operating cost is reduced.

[0015] Optionally, in the garbage cleaning mode, calculating a target route for the target cleaning vehicle to reach a corresponding target garbage bin includes: Taking the position of the target cleaning vehicle as the starting point and the position of the target garbage bin as the end point, and planning multiple routes from the starting point to the end point based on a preset electronic map; If the garbage cleaning mode is the emergency cleaning mode, the shortest route is selected from the multiple routes as the target route; if the garbage cleaning mode is the normal cleaning mode, the route that passes through the most garbage bins to be cleaned and the sum of the garbage volumes of the garbage bins to be cleaned is selected as the target route, and the garbage bins to be cleaned are target garbage bins in the normal cleaning mode and of the same garbage type.

[0016] By adopting the above technical solution, if it is an emergency cleaning mode, the shortest route is selected from multiple route options as the target route, which can ensure that the target cleaning vehicle reaches the target trash can at the fastest speed and solves possible serious environmental hazards such as overflow and odor as soon as possible. If it is a normal cleaning mode, the system's route selection will consider the improvement of operating efficiency more, so that the target cleaning vehicle can clean the trash cans in normal state along the way while heading to the designated target trash can, thereby maximizing the single-vehicle operating efficiency, reducing the need for repeated scheduling and secondary transportation, and further optimizing resource utilization.

[0017] Optionally, the method further includes: Obtaining the total historical cleaning frequency of all garbage bins in each sub-area within a preset time period, wherein the target area includes multiple sub-areas; and determining the cleaning vehicle demand corresponding to each sub-area based on the total historical cleaning frequency; Based on the demand for cleaning vehicles, cleaning vehicles are allocated to parking lots corresponding to each of the sub-areas.

[0018] By adopting the above technical solution, based on the statistical analysis of historical data, it is possible to more accurately predict the changes in cleaning pressure in various areas within a certain period of time in the future, thereby guiding the reasonable deployment of vehicles in advance, overcoming the problem of excess or shortage of vehicle resources in different areas under the traditional fixed configuration mode, ensuring that the cleaning needs of each sub-area are met, while avoiding waste or shortage of resources.

[0019] In a second aspect of the present application, a smart sanitation management system is provided, the system comprising: A garbage volume acquisition module is used to obtain the garbage volume corresponding to each garbage bin in the target area; A cleaning mode determination module, used to determine the target trash cans to be cleaned based on the volume of each trash, and the trash cleaning mode corresponding to each target trash can; A cleaning vehicle determination module is used to determine the corresponding target cleaning vehicle based on the location and garbage type of each target garbage bin; a target route determination module is used to calculate the target route for the target cleaning vehicle to reach the corresponding target garbage bin in the garbage cleaning mode; The control cleaning module is used to control each of the target cleaning vehicles to clean garbage in the corresponding target garbage bin along the target route.

[0020] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above method steps.

[0021] In a fourth aspect of the present application, an electronic device is provided, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0022] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: The present application obtains the real-time garbage volume data of each garbage bin in the target area, determines the target garbage bins that need to be cleaned and the corresponding cleaning mode according to the garbage volume, and dynamically determines the target cleaning vehicles corresponding to each target garbage bin in combination with the location of the garbage bin and the type of garbage, and plans the optimal target route at the same time. Compared with the existing technology, it breaks through the traditional fixed route and scheduled cleaning mode, realizes dynamic intelligent scheduling based on the real-time status of the garbage bin and environmental factors, improves the cleaning efficiency and quality, avoids waste of resources, and improves the quality of sanitation services. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of a smart sanitation management method provided in an embodiment of the present application; Figure 2 It is a module schematic diagram of a smart sanitation management system provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0024] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0025] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0026] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0027] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0028] The following will provide a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0029] The technical solution of the embodiments of the present application can be applied to garbage collection in commercial areas, residential areas, tourist areas, etc. For example, in busy commercial areas, shops, restaurants and shopping malls frequently generate a large amount of garbage. Especially during holidays or promotional activities, the amount of garbage generated may increase sharply. The intelligent sanitation management method of the present application can monitor the garbage volume in the trash can in real time and dynamically adjust the cleaning plan to ensure that the garbage in these areas can be processed in time.

[0030] Please refer to Figure 1 , a flow chart of a smart sanitation management method is proposed, the method can be implemented by a computer program, can be implemented by a single-chip microcomputer, and can also be run on a smart sanitation management system. The computer program can be integrated in a computer device, or can be run as an independent tool application. Specifically, the method includes steps 10 to 50, and the above steps are as follows: Step 10: Get the garbage volume corresponding to each garbage bin in the target area.

[0031] In the embodiment of the present application, the target area refers to a specific area or range where garbage management is required, such as a city, community, park, etc.

[0032] In the embodiment of the present application, the garbage volume refers to the capacity or filling level of the garbage in the garbage bin.

[0033] Specifically, a weight sensor and an ultrasonic sensor are installed inside each trash can. The weight sensor is used to monitor the weight change of the trash in the trash can in real time, while the ultrasonic sensor is used to measure the distance from the surface of the trash to the top of the trash can. The data of these two sensors are comprehensively analyzed by the built-in data processing unit to obtain a more accurate estimate of the volume of the trash. At the same time, in order to improve the reliability of the data, an image acquisition device is also installed on the top of the trash can to regularly take images of the garbage accumulation status. These images are processed in real time by edge computing devices, and computer vision algorithms are used to assist in the evaluation of the garbage volume. Especially in the case of uneven garbage accumulation, this method can provide more accurate volume estimates.

[0034] In addition, considering the density differences of different types of garbage, the system also applies different calculation parameters according to the type of garbage can (such as recyclables, kitchen waste, other garbage, etc.). For example, for kitchen waste, due to its high density and easy sedimentation, the system will rely more on weight data; while for recyclables, it will refer more to the results of ultrasonic ranging and image analysis. In the embodiment of the present application, an ultrasonic rangefinder is mainly used to monitor the distance between the upper surface of each type of garbage and the upper edge of the garbage can, and then the garbage volume corresponding to each garbage can is calculated in combination with parameters such as the bottom area of ​​each garbage can.

[0035] Step 20: Determine the target trash cans that need to be cleaned based on the volume of each trash, and the trash cleaning mode corresponding to each target trash can.

[0036] In the embodiment of the present application, the target trash can refers to the trash can that is determined to be required to be cleaned. The trash cleaning mode refers to the specific cleaning method determined by the system for each target trash can. In the embodiment of the present application, there are mainly two cleaning modes: emergency cleaning mode and normal cleaning mode.

[0037] Specifically, the volume of garbage in each trash can will first be compared with the preset first preset volume. When the volume of garbage exceeds this first preset volume, the trash can will be preliminarily determined as a target trash can. This preliminary screening can effectively identify those trash cans that are highly filled and need to be cleaned in time. However, relying solely on the volume of garbage to determine the cleaning needs may not be comprehensive enough, because the urgency of garbage cleaning is also affected by a variety of environmental factors, such as temperature, humidity, and average traffic flow. Based on the garbage volume of each target trash can and the corresponding environmental information, the cleaning score of each target trash can can be calculated; after calculating the cleaning score, the system will compare this cleaning score with the preset scoring threshold. If the cleaning score is greater than the scoring threshold, the garbage cleaning mode of the target trash can is determined as the emergency cleaning mode; otherwise, it is determined as the normal cleaning mode.

[0038] Based on the above embodiment, as an optional embodiment, the step of determining the target trash cans to be cleaned based on the volume of each trash and the trash cleaning mode corresponding to each target trash can may further include the following steps: Step 201: Determine whether the volume of each garbage is greater than a first preset volume.

[0039] Specifically, all trash cans in the target area are traversed, and a judgment operation is performed on each trash can to determine whether the volume of each trash is greater than a first preset volume. The first preset volume is usually set to a reasonable proportion of the trash can capacity, for example, it may be 70% or 80% of the total capacity. The specific value can be adjusted according to actual operating experience and regional characteristics.

[0040] Step 202: trash cans with a volume larger than a first preset volume are taken as target trash cans, and environmental information of the locations of the target trash cans is obtained.

[0041] Specifically, the trash can with a garbage volume greater than the first preset volume is marked as a target trash can. This process is completed through a data screening algorithm to ensure fast and accurate target identification. For each target trash can, the system will immediately start the environmental information collection program. The environmental information mainly includes key parameters such as temperature, humidity and average human flow. The acquisition of these environmental data can be achieved in a variety of ways: temperature and humidity data can be collected in real time by a micro-meteorological station installed on the trash can; the average human flow can be estimated based on the historical regional human flow. Because it is not comprehensive enough to determine the cleaning priority based solely on the volume of garbage, the actual impact of garbage and the urgency of cleaning not only depend on its volume, but also closely related to the surrounding environmental conditions. For example, in a high temperature environment, even if the amount of garbage is not large, it may need to be treated first because of accelerated corruption; and in areas with dense traffic, even if the trash can is not full, it may need to be cleaned more frequently to maintain environmental hygiene. By obtaining this environmental information, the system can more comprehensively evaluate the actual situation of each target trash can, providing more abundant and accurate input data for subsequent cleaning score calculation and cleaning mode determination.

[0042] Step 203: Calculate the cleaning score of each target trash can based on the trash volume of each target trash can and the corresponding environmental information.

[0043] Specifically, the environmental information includes temperature, humidity, and average human flow. The temperature, humidity, average human flow, and the volume of garbage in the target garbage bin are substituted into a preset calculation formula to obtain the cleaning score of each target garbage bin. The calculation formula is: In the formula, S i is the cleaning score of the i-th target trash can, α is the volume factor, β is the humidity factor, γ is the human flow factor, δ is the human flow adjustment coefficient, V i is the volume of garbage in the i-th target garbage bin, T i is the temperature of the i-th target trash can, H i is the humidity of the i-th target trash can, H0 is the reference humidity, P i is the average flow of people at the location of the i-th target trash can, D i is the time interval between the last cleaning of the i-th target trash can.

[0044] The calculation formula consists of four parts. The first part is the garbage volume part, which represents the impact of garbage volume on the garbage bin cleaning score. i Take the logarithm to prevent V iWhen it is 0, the logarithm is undefined and the effect of volume growth is smoothed. α is the volume factor, which is used to adjust the intensity of the impact of garbage volume, and then the impact is further adjusted through the exponential function, so that the score responds nonlinearly to the volume, reflecting the urgent cleaning needs of large-volume garbage bins.

[0045] The second part is the temperature and humidity part, which reflects the joint influence of temperature and humidity on the trash can cleaning score. i is the temperature effect, using a logistic function Humidity H i Processing, H0 is the reference humidity, when H i When it is greater than H0, the denominator decreases and the overall expression value increases, reflecting that garbage under high humidity may need to be processed more promptly (for example, to avoid corruption and odor).

[0046] The third part is the human flow part, which considers the impact of human flow on the demand for trash can cleaning. A logistic function is used to smooth the impact of human flow on the score, and it is adjusted by δ to reflect the nonlinear impact of human flow on the cleaning demand. This takes into account that in areas with very high human flow, the frequency of use of trash cans and the urgency of cleaning are also higher.

[0047] The third part is the time interval part, which reflects the impact of the length of time since the last cleaning on the cleaning priority. The impact of time is smoothed by a logarithmic function, so that the increase in time interval contributes gradually to the score rather than linearly.

[0048] This calculation formula is used to determine the cleaning priority of urban garbage bins. It combines factors such as garbage volume, temperature and humidity, human flow and time interval to calculate a comprehensive score S. i , a score that reflects the urgency of cleaning each trash can.

[0049] For example, the following parameters are assumed: garbage volume V i =50 units, temperature T i =35 degrees Celsius, humidity H i =75%, base humidity H0 = 50%, average flow rate P i = 100 people / hour, the time interval from the last cleaning is D i = 24 hours, parameter settings α = 0.5, β = 1, γ = 0.3, δ = 2. Substituting the above parameters into the calculation formula, the garbage volume part is: Temperature and humidity section: Traffic flow part: Time interval part: ln(1+D i )≈3.218, then the cleaning score of the target trash can is calculated as: Si =7.073×35+0.294×3.218=248.501.

[0050] Step 204: Determine the garbage cleaning mode corresponding to each target garbage bin based on the cleaning score.

[0051] Specifically, a scoring threshold is first set in advance. This scoring threshold is a key parameter determined based on historical data analysis, actual operating experience and environmental management requirements. For example, in the embodiment of the present application, the scoring threshold can be 200. The cleaning scores of each target trash can are traversed, and the conditional judgment algorithm is used to compare with the scoring threshold. When the cleaning score of a target trash can is greater than the scoring threshold, the cleaning mode of the target trash can is marked as an emergency cleaning mode, which means that the target trash can needs to be processed first, and the route of the cleaning vehicle may need to be adjusted or additional cleaning resources may need to be arranged. On the contrary, if the cleaning score is not greater than the scoring threshold, the cleaning mode of the target trash can is determined as a normal cleaning mode, indicating that it can be processed according to the conventional plan. The cleaning mode division method based on the scoring threshold can effectively balance the cleaning efficiency and resource utilization. The emergency cleaning mode ensures that the trash cans that need the most attention can be processed in time, effectively preventing possible garbage overflow, odor or other environmental problems. The normal cleaning mode allows the system to arrange the cleaning route and time more flexibly while ensuring the cleaning quality, thereby optimizing the overall cleaning efficiency. It should be noted that in special circumstances, the scoring threshold can also be adjusted according to actual conditions. For example, during special weather conditions or large-scale events, the scoring threshold can be temporarily adjusted to enable the system to more sensitively identify trash cans that require emergency disposal, thereby better responding to possible surges in garbage.

[0052] Step 30: Determine the corresponding target cleaning vehicle based on the location and garbage type of each target garbage bin.

[0053] Specifically, the precise location information and garbage type data of all target trash cans will be obtained first. The location information is usually obtained through the GPS module installed on the trash can, while the garbage type may be pre-set or determined by the intelligent recognition system. At the same time, the system will also obtain information about the currently available cleaning vehicles, including their location, type, remaining capacity, and working status. This information is uploaded to the central management system in real time through the on-board terminal. Next, the system will execute a multi-step matching algorithm. First, a preset range will be determined for each target trash can, which may usually be within a certain radius centered on the trash can. Then, all available cleaning vehicles will be searched within this range to form a preliminary first set of cleaning vehicles, which includes idle cleaning vehicles in the parking lot and cleaning vehicles that are working but not yet fully loaded.

[0054] Then, garbage type matching is performed, and the cleaning vehicles that can handle the garbage type in the target garbage bin are selected from the first cleaning vehicle set. Because different types of garbage (such as recyclables, kitchen waste, other garbage, etc.) usually require different types of cleaning vehicles to handle, and the remaining capacity of the cleaning vehicle is also considered on the basis of type matching, and the cleaning vehicles with a remaining capacity greater than the current garbage volume of the target garbage bin are further selected to ensure that the selected cleaning vehicle can fully accommodate the garbage in the target garbage bin, and obtain the second cleaning vehicle set. Finally, in the second cleaning vehicle set that meets all the above conditions, the one closest to the target garbage bin is selected as the final target cleaning vehicle to minimize the driving distance of the cleaning vehicle and improve the cleaning efficiency.

[0055] Based on the above embodiment, as an optional embodiment, the step of determining the corresponding target cleaning vehicle based on the location and garbage type of each target garbage bin may further include the following steps: Step 301: Determine a first set of cleaning vehicles within a preset range of the location of each target trash can, where the first set of cleaning vehicles includes idle cleaning vehicles in the parking lot and busy cleaning vehicles that are working.

[0056] Specifically, a preset location range is first defined for each target trash can. This range is usually a circular area with a predetermined radius (such as 8 kilometers) centered on the location of the target trash can. The size of the preset range can be dynamically adjusted according to factors such as the size of the city, traffic conditions, and the distribution of cleaning vehicle resources. Next, the current location information of all cleaning vehicles is obtained based on real-time positioning technology. This information is usually uploaded to the central management system in real time through a GPS device installed on the cleaning vehicle. Through the spatial query algorithm, all cleaning vehicles within the preset range of each target trash can are screened out, and these cleaning vehicles together constitute the first cleaning vehicle set. It should be noted that this set includes not only idle cleaning vehicles on standby in the parking lot, but also busy cleaning vehicles that are performing tasks but are not yet fully loaded. Taking busy cleaning vehicles into consideration greatly expands the range of available resources and improves the flexibility and efficiency of the system. For example, a cleaning vehicle that is performing a task can handle a new target trash can along the way, thereby improving the overall cleaning efficiency.

[0057] Step 302: Determine the remaining capacity of each cleaning vehicle in the first cleaning vehicle set.

[0058] Specifically, it will first interact with the intelligent weighing system installed on each cleaning vehicle in real time. These weighing systems usually use high-precision pressure sensors to accurately measure the weight of the garbage currently loaded on the cleaning vehicle. The system will obtain the latest weight data from the on-board terminal of each cleaning vehicle, and then compare this data with the rated maximum load of the cleaning vehicle to calculate the remaining capacity of each cleaning vehicle. The cleaning vehicle in the embodiment of the present application is equipped with a garbage compression device, which can significantly increase the actual load. Therefore, the system will adjust the calculation result of the remaining capacity according to the compression capacity of different cleaning vehicles and apply the corresponding compression coefficient. In addition, the density difference of different types of garbage will be considered. For example, the weight of the same volume of kitchen waste and recyclables may be very different, and these factors will be included in the accurate calculation of the remaining capacity. For those busy cleaning vehicles that are performing cleaning tasks, the system will not only consider their current actual load, but also estimate the amount of garbage that may increase before reaching the new target trash can. This estimate is based on the current task route of the cleaning vehicle and the expected amount of garbage in the trash cans along the way, so that the system can more accurately evaluate the actual available capacity of the cleaning vehicle when it arrives at the new task point. By accurately determining the remaining capacity of the cleaning vehicle, cleaning tasks can be allocated more reasonably to ensure that each assigned task can be completed smoothly, avoiding the situation where the cleaning vehicle is fully loaded midway and cannot complete the task.

[0059] Step 303: Filter out a second set of cleaning vehicles from the first set of cleaning vehicles, which have the same garbage type as the target garbage bin and whose remaining capacity is greater than the capacity of the target garbage bin.

[0060] Specifically, the garbage type information of the target trash can and the capacity of the current trash can are first obtained. The garbage type information can be preset or determined by the intelligent recognition system. The current garbage capacity can be measured in real time by the sensor installed in the trash can, or recorded and saved in advance. Next, the system will traverse the first cleaning vehicle set determined previously, and perform two key judgments on each cleaning vehicle: first, check whether the cleaning vehicle can handle the garbage type in the target trash can; second, compare whether the remaining capacity of the cleaning vehicle is greater than the current garbage capacity of the target trash can. In terms of garbage type matching, the system will compare the garbage processing type of the cleaning vehicle with the garbage type of the target trash can. It should be noted that some cleaning vehicles may also have multiple garbage processing capabilities, and the system will take this into account. For the judgment of the remaining capacity, the system will compare the real-time remaining capacity of the cleaning vehicle with the current garbage capacity of the target trash can to ensure that the selected cleaning vehicle has enough space to accommodate all the garbage in the target trash can, and the cleaning vehicles that meet the above two conditions will form the second cleaning vehicle set. By matching the garbage type, the system avoids unsuitable cleaning vehicles being assigned to garbage types that cannot be processed, thereby preventing potential garbage mixing or secondary processing problems. At the same time, by ensuring that the cleaning vehicle has sufficient remaining capacity, the system can avoid garbage overflow during the cleaning process or the cleaning vehicle needing to return to the processing center to unload, greatly improving the cleaning efficiency.

[0061] Step 304: Determine in the second set of cleaning vehicles the cleaning vehicle closest to the target trash can as the target cleaning vehicle.

[0062] Specifically, the real-time location information of each cleaning vehicle in the second cleaning vehicle set is obtained, and the actual distance from each cleaning vehicle in the second cleaning vehicle set to the target trash can is calculated using a distance calculation algorithm, such as the Haversine formula or a road network distance algorithm. When calculating the distance, not only the straight-line distance is considered, but also real-time traffic data and road conditions are combined. This includes factors such as current traffic flow, road construction conditions, and temporary traffic control. By integrating this information, the system can more accurately estimate the actual driving distance and estimated time for the cleaning vehicle to reach the target trash can. All distances are compared, and the cleaning vehicle with the shortest distance is selected as the final target cleaning vehicle. If there are multiple cleaning vehicles with similar distances, the system will further consider other factors, such as the remaining power of the cleaning vehicle (for electric cleaning vehicles), the working time of the cleaning vehicle, etc., to ensure that the optimal cleaning vehicle is selected.

[0063] Step 40: In the garbage collection mode, the target route for the target collection vehicle to reach the corresponding target garbage bin is calculated.

[0064] Specifically, first, based on the preset electronic map, the current position of the target cleaning vehicle is set as the starting point, and the position of the corresponding target trash can is set as the end point. Then, different route calculation strategies are adopted according to different garbage cleaning modes. For the emergency cleaning mode, the system will give priority to the shortest path as the target route to ensure that the cleaning vehicle can reach the target trash can in the shortest time. The improved Dijkstra algorithm or A* algorithm can be used to quickly calculate the shortest path. For the ordinary cleaning mode, the system will use a more complex path planning algorithm, which not only considers the path length, but also considers other trash cans to be cleaned along the way. In the ordinary cleaning mode, an attempt will be made to plan a route that can cover multiple target trash cans, and an improved traveling salesman problem (TSP) algorithm or a vehicle routing problem (VRP) algorithm may be used here. Considering the location of each trash can to be cleaned, the type of trash, and the remaining capacity of the cleaning vehicle, as many trash cans as possible are cleaned in one trip, but at the same time, it is ensured that the remaining capacity of the cleaning vehicle is sufficient to accommodate the total volume of trash in all trash cans to be cleaned along the way. In addition, the system will take into account factors such as real-time traffic conditions, traffic restrictions, construction information, etc., and dynamically adjust the route to avoid congested or impassable sections of the road.

[0065] Based on the above embodiment, as an optional embodiment, in the garbage cleaning mode, the step of calculating the target route for the target cleaning vehicle to reach the corresponding target garbage bin further includes the following steps: Step 401: The location of the target cleaning vehicle is taken as the starting point, the location of the target garbage bin is taken as the end point, and multiple routes from the starting point to the end point are planned based on a preset electronic map.

[0066] Specifically, the real-time GPS location information of the target cleaning vehicle is obtained as the starting point, and the location of the target trash can is used as the end point. Then the preset electronic map database is called. This electronic map not only contains detailed road network information of the target area, but also integrates dynamic factors such as real-time traffic data, road construction information, and temporary traffic control. Based on this comprehensive information, a path planning algorithm, such as the improved Dijkstra algorithm, the A* algorithm, or a more complex heuristic algorithm, is used to generate multiple feasible routes from the starting point to the end point. The system will consider a variety of factors. The first is the distance factor. The algorithm will calculate the total length of each route. The second is the expected driving time. The system will estimate the expected driving time for each route based on the current traffic flow data and historical statistics. In addition, the system will also consider factors such as road type (such as main roads, secondary roads, small roads, etc.), number of turns, number of traffic lights, etc., which will affect the driving efficiency of the cleaning vehicle. For electric cleaning vehicles, the system will also consider the slope of the route to optimize the use of electricity. Usually, the system will generate 3-5 alternative routes. These routes may include the shortest distance route, the fastest expected arrival route, the most energy-saving route, etc. Each route comes with detailed road information, estimated driving time, possible traffic conditions and other data.

[0067] Step 402: If the garbage cleaning mode is the emergency cleaning mode, the shortest route among multiple routes is selected as the target route.

[0068] Specifically, if the garbage cleaning mode is the emergency cleaning mode, the route selection algorithm is called immediately. The algorithm will traverse the multiple routes generated before and compare the total length of each route. In this process, the system mainly focuses on the geometric distance of the route without considering other factors such as the estimated driving time or energy consumption.

[0069] Use a sorting algorithm, such as quick sort or heap sort, to quickly find the shortest route. The "shortest" here refers to the shortest actual road distance, not the shortest straight-line distance. The system will consider factors such as the actual direction of the road, one-way road restrictions, etc. to ensure that the selected route is the truly feasible shortest path, and use the shortest route as the target route. In another feasible embodiment, the route with the shortest time can also be used as the target route.

[0070] Step 403: If the garbage cleaning mode is the normal cleaning mode, then the route that passes through the most garbage bins to be cleaned and the sum of the garbage volumes of the garbage bins to be cleaned is selected as the target route, and the garbage bins to be cleaned are in the normal cleaning mode and are of the same garbage type as the target garbage bins.

[0071] Specifically, if the garbage collection mode is the normal collection mode, a specific route will be selected from the multiple planned routes as the target route. This route needs to meet two key conditions: first, it should pass through the most garbage bins to be cleaned; second, the sum of the garbage volumes of these garbage bins to be cleaned must be less than the remaining capacity of the target cleaning vehicle. The garbage bins to be cleaned here specifically refer to other target garbage bins that are in the normal collection mode and have the same garbage type as the current target garbage bin.

[0072] First, check all the trash cans passed by each route, filter out those in normal cleaning mode and with the same trash type as the current target trash can, and mark them as trash cans to be cleaned. For each trash can to be cleaned identified on each route, the system will accumulate their current trash volume to get a sum. Compare the calculated sum of trash volume with the remaining capacity of the target cleaning vehicle to ensure that the sum does not exceed the remaining capacity. For multiple routes that meet the capacity requirements, the system will give a score. This score is mainly based on the number of trash cans to be cleaned. The more the number, the higher the score. Among all routes that meet the conditions, the system will select the route with the highest score as the final target route.

[0073] Step 50: Control each target cleaning vehicle to clean up garbage at the corresponding target garbage bin along the target route.

[0074] Specifically, the previously determined target route information is sent to the on-board terminal of the corresponding target cleaning vehicle. This information includes detailed route navigation, location coordinates of each trash can to be cleaned, estimated arrival time, type of trash and estimated volume, etc. After receiving the task information, the intelligent control system of the target cleaning vehicle will automatically start the navigation function to guide the driver along the designated target route. During the driving process, the on-board system will monitor the location, speed and surrounding road conditions of the cleaning vehicle in real time, and maintain continuous communication with the central management system. If there is an emergency such as traffic congestion or road construction on the way, the system will immediately calculate alternative routes and provide suggestions to the driver to ensure that the cleaning task can proceed smoothly.

[0075] For tasks in normal cleaning mode, the cleaning vehicle will process multiple garbage bins to be cleaned in sequence according to the predetermined route. The system will monitor the remaining capacity of the cleaning vehicle in real time. If it is found that the remaining capacity is not enough to process the next garbage bin on the way, it will adjust the route in time and guide the cleaning vehicle to the nearest garbage disposal station for unloading. For tasks in emergency cleaning mode, the cleaning vehicle will go directly to the target garbage bin as quickly as possible, giving priority to emergency situations. After handling the emergency task, the system will immediately re-evaluate the cleaning needs of the surrounding area and decide whether to dispatch the cleaning vehicle to other emergency tasks or return to normal cleaning mode.

[0076] Based on the above embodiment, as an optional embodiment, a smart sanitation management method also includes a cleaning vehicle allocation process, which is as follows: Specifically, a suitable preset time period will be set first, such as the past half month or one month. The selection of this time period needs to take into account factors such as seasonal changes and the impact of special events to ensure the representativeness of the data. Then, the system will access the database to extract the cleaning records of all garbage bins in each sub-area during the time period. These records include detailed information such as the time, location, and amount of garbage each time. The system will perform statistical analysis on these data and calculate the total historical cleaning frequency of all garbage bins in each sub-area. Each sub-area can be divided based on the geographical streets or total amount of garbage accumulated in the target area. Based on these historical cleaning total frequency data, a preset algorithm model is used to determine the corresponding cleaning vehicle demand for each sub-area. This algorithm model may consider multiple factors, such as the relationship between cleaning frequency and garbage generation, the average working efficiency of the cleaning vehicle, the area and population density of the sub-area, etc. By comprehensively analyzing these factors, the system calculates a reasonable number of cleaning vehicles required for each sub-area.

[0077] Suppose there is a commercial area sub-area A and sub-area B. Data from the past month show that the average daily cleaning frequency of area A is 20 times, and that of area B is 15 times. The cleaning frequency of area A is significantly higher on weekdays than on weekends, while that of area B is relatively stable. The amount of garbage collected in a single cleaning in area A is about 30% higher than that in area B. 25% of the cleaning in area A occurs during lunch time and after work, indicating that commercial activities have a significant impact on garbage generation. Although the cleaning frequency in area A is 33% higher than that in area B, the actual demand for cleaning vehicles may be 50% higher or more. This is because area A needs more mobility to cope with the peak period on weekdays, and the workload of a single cleaning is larger. Convert these complex analysis results into specific cleaning vehicle requirements. For example, it may be recommended to configure 3 cleaning vehicles for area A and 2 for area B. This recommendation not only takes into account the average cleaning demand, but also takes into account demand fluctuations and peak response capabilities.

[0078] After determining the demand for cleaning vehicles in each sub-area, the system will consider information such as the location of each parking lot, the number of existing cleaning vehicles, and the type of cleaning vehicles. It will then use optimization algorithms (such as integer programming or heuristic algorithms) to allocate cleaning vehicles to the parking lots corresponding to each sub-area. This allocation process must not only meet the needs of each sub-area, but also take into account the balance of overall resources and the convenience of scheduling to ensure that the cleaning needs of each sub-area are met while avoiding waste or shortage of resources.

[0079] See also Figure 2, is a module diagram of a smart sanitation management system provided in an embodiment of the present application, the smart sanitation management system may include: a garbage volume acquisition module, a cleaning mode determination module, a cleaning vehicle determination module, a target route determination module and a control cleaning module, wherein: A garbage volume acquisition module is used to obtain the garbage volume corresponding to each garbage bin in the target area; A cleaning mode determination module, used to determine the target trash cans to be cleaned based on the volume of each trash, and the trash cleaning mode corresponding to each target trash can; A cleaning vehicle determination module is used to determine the corresponding target cleaning vehicle based on the location and garbage type of each target garbage bin; a target route determination module is used to calculate the target route for the target cleaning vehicle to reach the corresponding target garbage bin in the garbage cleaning mode; The control cleaning module is used to control each of the target cleaning vehicles to clean garbage in the corresponding target garbage bin along the target route.

[0080] Optionally, the cleaning mode determination module is also used to determine whether the volume of each garbage is greater than a first preset volume; to take a garbage can that is greater than the first preset volume as a target garbage can, and to obtain environmental information about the location of each target garbage can; to calculate a cleaning score for each target garbage can based on the garbage volume of each target garbage can and the corresponding environmental information; and to determine the garbage cleaning mode corresponding to each target garbage can based on the cleaning score.

[0081] Optionally, the cleaning mode determination module is further used to substitute the temperature, the humidity, the average flow of people and the volume of garbage in the target garbage bin into a preset calculation formula to obtain a cleaning score for each of the target garbage bins; The calculation formula is: In the formula, S i is the cleaning score of the i-th target trash can, α is the volume factor, β is the humidity factor, γ is the human flow factor, δ is the human flow adjustment coefficient, V i is the volume of garbage in the i-th target garbage bin, T i is the temperature of the i-th target trash can, H i is the humidity of the i-th target trash can, H0 is the reference humidity, P i is the average flow of people at the location of the i-th target trash can, D i is the time interval between the last cleaning of the i-th target trash can.

[0082] Optionally, the cleaning mode determination module is also used to determine whether the cleaning score is greater than a scoring threshold; if the cleaning score is greater than the scoring threshold, the garbage cleaning mode of the target trash can is determined to be an emergency cleaning mode; if the cleaning score is not greater than the scoring threshold, the garbage cleaning mode of the target trash can is determined to be a normal cleaning mode.

[0083] Optionally, the cleaning vehicle determination module is also used to determine a first set of cleaning vehicles within a preset range of the location of each target garbage bin, the first set of cleaning vehicles including idle cleaning vehicles in the parking lot and busy cleaning vehicles that are working; determine the remaining capacity of each cleaning vehicle in the first set of cleaning vehicles; screen out a second set of cleaning vehicles from the first set of cleaning vehicles that have the same garbage type as the target garbage bin and whose remaining capacity is greater than the capacity of the target garbage bin; and determine the cleaning vehicle from the second set of cleaning vehicles that is closest to the location of the target garbage bin as the target cleaning vehicle.

[0084] Optionally, the target route determination module is also used to take the position of the target cleaning vehicle as the starting point and the position of the target garbage bin as the end point, and plan multiple routes from the starting point to the end point based on a preset electronic map; if the garbage cleaning mode is the emergency cleaning mode, the shortest route among the multiple routes is selected as the target route; if the garbage cleaning mode is the normal cleaning mode, the route that passes through the most garbage bins to be cleaned and the sum of the garbage volumes of each garbage bin to be cleaned is selected as the target route, and the garbage bin to be cleaned is a target garbage bin in the normal cleaning mode and of the same garbage type.

[0085] Optionally, a smart sanitation management system also includes a cleaning vehicle allocation module, which is used to obtain the total historical cleaning frequency of all garbage bins in each sub-area within a preset time period, where the target area includes multiple sub-areas; determine the cleaning vehicle demand corresponding to each sub-area based on the total historical cleaning frequency; and allocate cleaning vehicles to parking lots corresponding to each sub-area based on the cleaning vehicle demand.

[0086] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0087] An embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded by a processor and executing a smart sanitation management method of the above embodiment. The specific execution process can be found in the specific description of the above embodiment, which will not be repeated here.

[0088] Please refer to Figure 3 The application also discloses an electronic device. Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0089] The communication bus 302 is used to realize the connection and communication between these components.

[0090] The user interface 303 may include a display screen (Display) and a camera (Camera). The optional user interface 303 may also include a standard wired interface and a wireless interface.

[0091] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0092] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0093] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a smart sanitation management method.

[0094] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program storing a smart sanitation management method in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0095] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0096] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0097] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0098] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0099] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0100] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0101] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A smart sanitation management method, characterized in that: include: Get the garbage volume corresponding to each garbage bin in the target area; Determine the target trash cans that need to be cleaned based on the volume of each trash, and the trash cleaning mode corresponding to each target trash can; Determine a corresponding target cleaning vehicle based on the location and type of garbage of each target garbage bin; In the garbage cleaning mode, calculating a target route for the target cleaning vehicle to reach a corresponding target garbage bin; Control each of the target cleaning vehicles to clean up garbage in the corresponding target garbage bin along the target route.

2. A smart sanitation management method according to claim 1, characterized in that: The determining of the target trash cans to be cleaned based on the trash volumes, and the trash cleaning modes corresponding to the target trash cans, includes: Determining whether the volume of each of the garbage is greater than a first preset volume; Using trash cans larger than the first preset volume as target trash cans, and obtaining environmental information of locations of the target trash cans; Calculating a cleaning score of each target trash can based on the trash volume of each target trash can and the corresponding environmental information; The garbage cleaning mode corresponding to each of the target garbage bins is determined based on the cleaning score.

3. A smart sanitation management method according to claim 2, characterized in that: The environmental information includes temperature, humidity, and average human flow. The calculation of the cleaning score of each target trash can based on the trash volume of each target trash can and the corresponding environmental information includes: Substituting the temperature, humidity, average flow of people and the volume of garbage in the target garbage bin into a preset calculation formula to obtain a cleaning score for each target garbage bin; The calculation formula is: In the formula, S i is the cleaning score of the i-th target trash can, α is the volume factor, β is the humidity factor, γ is the human flow factor, δ is the human flow adjustment coefficient, V i is the volume of garbage in the i-th target garbage bin, T i is the temperature of the i-th target trash can, H i is the humidity of the i-th target trash can, H0 is the reference humidity, P i is the average flow of people at the location of the i-th target trash can, D i is the time interval between the last cleaning of the i-th target trash can.

4. A smart sanitation management method according to claim 2, characterized in that: The determining of the garbage cleaning mode corresponding to each of the target garbage bins based on the cleaning score includes: Determining whether the cleaning score is greater than a score threshold; If the cleaning score is greater than the scoring threshold, the garbage cleaning mode of the target garbage bin is determined to be an emergency cleaning mode; if the cleaning score is not greater than the scoring threshold, the garbage cleaning mode of the target garbage bin is determined to be a normal cleaning mode.

5. The intelligent sanitation management method according to claim 1, characterized in that: The determining of the corresponding target cleaning vehicle based on the location and garbage type of each target garbage bin includes: Determine a first set of cleaning vehicles within a preset range of the location of each target trash can, wherein the first set of cleaning vehicles includes idle cleaning vehicles in the parking lot and busy cleaning vehicles that are working; Determining the remaining capacity of each cleaning vehicle in the first set of cleaning vehicles; Screening out a second set of cleaning vehicles from the first set of cleaning vehicles, the second set of cleaning vehicles having the same type of garbage as the target garbage bin and a remaining capacity greater than the capacity of the target garbage bin; The cleaning vehicle closest to the target trash can in the second cleaning vehicle set is determined as the target cleaning vehicle.

6. A smart sanitation management method according to claim 4, characterized in that: In the garbage cleaning mode, calculating the target route for the target cleaning vehicle to reach the corresponding target garbage bin includes: Taking the position of the target cleaning vehicle as the starting point and the position of the target garbage bin as the end point, and planning multiple routes from the starting point to the end point based on a preset electronic map; If the garbage cleaning mode is the emergency cleaning mode, the shortest route is selected from the multiple routes as the target route; if the garbage cleaning mode is the normal cleaning mode, the route that passes through the most garbage bins to be cleaned and the sum of the garbage volumes of the garbage bins to be cleaned is selected as the target route, and the garbage bins to be cleaned are target garbage bins in the normal cleaning mode and of the same garbage type.

7. A smart sanitation management method according to claim 1, the method further comprising: Obtaining the total historical cleaning frequency of all trash cans in each sub-area within a preset time period, wherein the target area includes multiple sub-areas; Determine the cleaning vehicle demand corresponding to each of the sub-areas based on the total historical cleaning frequency; Based on the demand for cleaning vehicles, cleaning vehicles are allocated to parking lots corresponding to each of the sub-areas.

8. A smart sanitation management system, characterized in that: The system comprises: A garbage volume acquisition module is used to obtain the garbage volume corresponding to each garbage bin in the target area; A cleaning mode determination module, used to determine the target trash cans to be cleaned based on the volume of each trash, and the trash cleaning mode corresponding to each target trash can; A cleaning vehicle determination module, used to determine a corresponding target cleaning vehicle based on the location and type of garbage of each target garbage bin; A target route determination module, used to calculate the target route for the target cleaning vehicle to reach the corresponding target garbage bin in the garbage cleaning mode; The control cleaning module is used to control each of the target cleaning vehicles to clean garbage in the corresponding target garbage bin along the target route.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

Images