Evaluation system about environmental sanitation service

Abstract

The invention discloses a sanitation service evaluation system, and the system obtains a sanitation vehicle punch-in record and the location information of each dustbin in a target city, and generates sanitation vehicle scheduling data based on the sanitation vehicle punch-in record and the location information. Sanitation vehicle dispatching evaluation indexes are calculated based on sanitation vehicle dispatching data so as to achieve sanitation service evaluation of a target city, and a sanitation vehicle clock-in function is set in each intelligent dustbin in the city, so that clock-in data can be generated while garbage in the intelligent dustbins is collected and transported by sanitation vehicles; and finally, the scheduling evaluation indexes are calculated according to the scheduling data of the environmental sanitation vehicles, so that evaluation of environmental sanitation services can be realized based on a scheduling scheme of the environmental sanitation vehicles by means of an intelligent means of a modern computer.

Description

Technical Field

[0001] The present invention relates to the technical field of sanitation services, and in particular to a sanitation service evaluation system. Background Art

[0002] In recent years, with the rapid growth of population and the continuous development of economy, the amount of domestic waste has shown a continuous increasing trend. Correct waste classification and recycling is of great significance to reducing environmental pollution, improving resource recycling rate, reducing energy waste and building an eco-friendly environment. my country's current waste classification adopts the standard GB / T 19095-2019, which divides domestic waste into four categories: recyclables, kitchen waste, hazardous waste and other waste. The implementation of the domestic waste classification system is an important measure to promote the green and low-carbon development of cities. However, it also brings considerable challenges to the operation of the urban domestic waste management system. Compared with the problem of mixed garbage collection and transportation, the problem of garbage classification collection and transportation is more complicated. It is necessary to include various types of domestic waste into the urban domestic waste collection and transportation system, and plan the collection and transportation plan for each type of domestic waste. Due to the wide variety of domestic waste, a city often needs to organize multiple sanitation vehicles to provide services for collection points. If the scheduling plan of sanitation vehicles is unreasonable, it is easy to cause conflicts in the operation time of sanitation vehicles at garbage collection points, and there will be no sanitation vehicles to collect and transport garbage at collection points for a long time. This will lead to a decrease in collection and transportation efficiency, traffic congestion and other negative effects, greatly reducing the quality of urban sanitation services. Therefore, the sanitation management department hopes to use the intelligent means of modern computers to realize the evaluation of sanitation services based on the scheduling plan of sanitation vehicles, but there is still a lack of relevant technology in the existing technology. Summary of the invention

[0003] The technical problem to be solved by the present invention is how to evaluate sanitation services based on a scheduling scheme for sanitation vehicles by means of intelligent means of modern computers.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a sanitation service evaluation system, the system comprising: The acquisition module is used to obtain the sanitation vehicle check-in records and location information of each garbage bin in the target city.

[0005] A generation module is used to generate sanitation vehicle scheduling data based on the sanitation vehicle punch-in record and the location information.

[0006] A calculation module is used to calculate sanitation vehicle scheduling evaluation indicators based on the sanitation vehicle scheduling data to achieve sanitation service evaluation for the target city.

[0007] The calculation module calculates the sanitation vehicle scheduling evaluation index based on the sanitation vehicle scheduling data, including: Calculating sanitation vehicle operation cost data based on the sanitation vehicle scheduling data; Calculating the total operating cost of the sanitation vehicle based on the sanitation vehicle operating cost data; Calculating sanitation vehicle travel utilization data based on the sanitation vehicle scheduling data; Calculating the sanitation vehicle trip utilization rate based on the sanitation vehicle trip utilization rate data; Calculate sanitation vehicle task allocation balance data based on the sanitation vehicle scheduling data; The sanitation vehicle task allocation balance is calculated based on the sanitation vehicle task allocation balance data.

[0008] In the first aspect of the present invention, the calculation module calculates the total operating cost of the sanitation vehicle based on the sanitation vehicle operating cost data, including: The total cost of sanitation vehicle operation is calculated using the following formula: Among them, a is the total number of sanitation vehicles used in the city, b is the total number of road sections in the city, The purchase cost of sanitation vehicles. is the average driving cost of sanitation vehicles, is the total distance traveled by sanitation vehicle k on section o on the road network in the city's sanitation area.

[0009] In the first aspect of the present invention, the calculation module calculates the sanitation vehicle travel utilization rate based on the sanitation vehicle travel utilization rate data, including: The trip utilization rate of sanitation vehicles is calculated by the following formula: in, is the total distance required for sanitation vehicle k to travel through road section o in the city's sanitation area road network under working conditions, E is the sanitation vehicle travel utilization rate, is the total distance travelled by the sanitation vehicle in working condition, The total distance required for the sanitation vehicle to actually complete the task.

[0010] In the first aspect of the present invention, the calculation module calculates the sanitation vehicle task allocation balance based on the sanitation vehicle task allocation balance data, including: The balance of sanitation vehicle task allocation is calculated by the following formula: in, is the amount of work done by sanitation vehicle k, is the total task volume, is the average task volume, c is the total number of sanitation vehicles, .

[0011] In the first aspect of the present invention, the sanitation service evaluation system is also used for: Determine whether the sanitation vehicle dispatch evaluation index meets the preset conditions; When the sanitation vehicle scheduling evaluation index does not meet the preset conditions, calculating the sanitation vehicle scheduling optimization data based on the sanitation vehicle scheduling data; A sanitation vehicle scheduling plan for the target city is generated based on the sanitation vehicle scheduling optimization data.

[0012] In the first aspect of the present invention, the sanitation service evaluation system generates a sanitation vehicle scheduling plan for the target city based on the sanitation vehicle scheduling optimization data, including: The sanitation vehicle dispatching plan is generated by the following formula: , , ,∀j∈F, ,∀i∈F, ,∀k∈A, , , , , in, is the distance of the sanitation vehicle from work point i to work point j, is the judgment result of whether sanitation vehicle k drives from work point i to work point j. If sanitation vehicle k drives directly to work point j after completing work at work point i, then =1, otherwise =0, h is a specific working point in the model, A={1,2,...,c} represents the set of sanitation vehicles, with a total of c sanitation vehicles, F={1,2,...,n} represents the set of working points, with a total of n working points, m is the number of services that need to be satisfied at each preset working point, and Q is the maximum endurance of the sanitation vehicle.

[0013] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: In an embodiment of the present invention, the sanitation vehicle punch-in record and location information of each garbage bin in the target city are obtained, sanitation vehicle dispatching data is generated based on the sanitation vehicle punch-in record and location information, and sanitation vehicle dispatching evaluation indicators are calculated based on the sanitation vehicle dispatching data to realize the sanitation service evaluation of the target city. By setting the sanitation vehicle punch-in function at each smart garbage bin in the city, the sanitation vehicle can generate punch-in data while collecting and transporting garbage from the smart garbage bin, and the sanitation vehicle dispatching data is generated accordingly. Finally, the dispatching evaluation indicators are calculated based on the sanitation vehicle dispatching data. With the help of the intelligent means of modern computers, the sanitation service can be evaluated based on the sanitation vehicle dispatching plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 It is a flow chart of a method for evaluating environmental sanitation services disclosed in an embodiment of the present invention; Figure 2 is a visualization diagram of the dispatching path of the sanitation vehicle disclosed in the embodiment of the present invention; Figure 3 is a schematic diagram of a smart trash can involved in an embodiment of the present invention; Figure 4 is a schematic diagram of model training involved in an embodiment of the present invention; Figure 5 is a schematic diagram of a GhostNet module involved in an embodiment of the present invention; Figure 6 is a schematic diagram of the CBAM attention mechanism involved in an embodiment of the present invention; Figure 7 It is a schematic diagram of the cascade structure of the Ghost convolution and CBAM attention mechanism involved in an embodiment of the present invention; Figure 8 It is a structural diagram of a sanitation service evaluation system disclosed in an embodiment of the present invention; Fig. 9 It is a structural schematic diagram of a sanitation service evaluation terminal disclosed in an embodiment of the present invention; Fig.10 It is a structural schematic diagram of a computer storage medium disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0018] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0019] The present invention discloses a method, system, terminal and storage medium for evaluating sanitation services, which obtains the punch-in record and location information of sanitation vehicles of each garbage bin in a target city, generates sanitation vehicle dispatching data based on the punch-in record and location information of sanitation vehicles, and calculates sanitation vehicle dispatching evaluation indicators based on the sanitation vehicle dispatching data to realize the evaluation of sanitation services in the target city. By setting a sanitation vehicle punch-in function at each smart garbage bin in the city, punch-in data can be generated while the sanitation vehicles collect and transport garbage in the smart garbage bins, and the dispatching data of the sanitation vehicles can be generated accordingly. Finally, the dispatching evaluation indicators are calculated based on the dispatching data of the sanitation vehicles, and the evaluation of sanitation services can be realized based on the dispatching scheme of sanitation vehicles by means of intelligent means of modern computers. The following are detailed descriptions.

[0020] Example 1: Please refer to Figure 1 , Figure 1 FIG. 1 is a flow chart of a method for evaluating environmental sanitation services disclosed in an embodiment of the present invention. Figure 1 As shown, the sanitation service evaluation method may include the following operations: 101. Obtain the check-in records and location information of sanitation vehicles for each garbage bin in the target city.

[0021] 102. Generate sanitation vehicle dispatch data based on the sanitation vehicle punch-in record and the location information.

[0022] 103. Calculate sanitation vehicle dispatch evaluation indicators based on the sanitation vehicle dispatch data to achieve sanitation service evaluation for the target city.

[0023] In an embodiment of the present invention, in order to carry out the sanitation work in the city, smart trash bins are usually set up in various locations in the city to recycle the residents' garbage, and sanitation vehicles regularly go to the smart trash bins scattered around to transport the garbage away. A sanitation vehicle punch-in function is set up in each smart trash bin in the city. Every time a sanitation vehicle arrives at a smart trash bin, it punches in at the smart trash bin to form a corresponding punch-in record. The punch-in record may generally include: the unique identification of the sanitation vehicle that punched in, the punch-in time, and the location information of the smart trash bin that punched in. Based on the punch-in record of the sanitation vehicle, the itinerary of the sanitation vehicle can be inferred, that is, the sanitation vehicle scheduling data can be generated. Finally, the itinerary of the sanitation vehicle is analyzed according to a specific calculation method to obtain different evaluation indicators, thereby realizing the evaluation of the sanitation vehicle scheduling plan. For example Figure 2 As shown, the dispatching route of the sanitation vehicle can be generated according to the clock-in record of the sanitation vehicle.

[0024] In an optional embodiment, the calculating of the sanitation vehicle scheduling evaluation index based on the sanitation vehicle scheduling data includes: Calculating sanitation vehicle operation cost data based on the sanitation vehicle scheduling data; The total operating cost of the sanitation vehicle is calculated based on the sanitation vehicle operating cost data.

[0025] In an optional embodiment, the calculating the total operating cost of the sanitation vehicle based on the sanitation vehicle operating cost data includes: The total cost of sanitation vehicle operation is calculated using the following formula: Among them, a is the total number of sanitation vehicles used in the city, b is the total number of road sections in the city, The purchase cost of sanitation vehicles. is the average driving cost of sanitation vehicles, is the total distance traveled by sanitation vehicle k on section o on the road network in the city's sanitation area.

[0026] In the embodiment of the present invention, the location of each smart trash bin is regarded as a point on the road network, and the path between every two smart trash bins can be regarded as a road section on the road network. According to the dispatching data of sanitation vehicles, the total number of road sections in the city, the total distance traveled by each road section and other sanitation vehicle operation cost data can be generated. Finally, the calculated operation cost of the sanitation vehicle includes the purchase cost of the sanitation vehicle and the travel cost of the sanitation vehicle, wherein the travel cost of the sanitation vehicle depends on the cumulative value of the product of the total distance traveled by the sanitation vehicle and the unit operation cost.

[0027] It can be seen that the embodiment of the present invention calculates the purchase cost and travel cost of the sanitation vehicle through the sanitation vehicle scheduling data, and finally obtains the total operating cost of the sanitation vehicle, so that the operating cost of the sanitation vehicle can be used as a sanitation vehicle scheduling evaluation indicator, and the sanitation service can be evaluated from the dimension of operating cost.

[0028] In an optional embodiment, the calculating of the sanitation vehicle scheduling evaluation index based on the sanitation vehicle scheduling data includes: Calculating sanitation vehicle travel utilization data based on the sanitation vehicle scheduling data; The sanitation vehicle trip utilization rate is calculated based on the sanitation vehicle trip utilization rate data.

[0029] In an optional embodiment, the calculating the sanitation vehicle trip utilization rate based on the sanitation vehicle trip utilization rate data includes: The trip utilization rate of sanitation vehicles is calculated by the following formula: in, is the total distance required for sanitation vehicle k to travel through road section o in the city's sanitation area road network under working conditions, E is the sanitation vehicle travel utilization rate, is the total distance travelled by the sanitation vehicle in working condition, The total distance required for the sanitation vehicle to actually complete the task.

[0030] In the embodiment of the present invention, the clock-in data of the sanitation vehicle may also include an identifier for indicating whether the sanitation vehicle is in working condition, so that it is possible to obtain whether the sanitation vehicle is in working condition when traveling on a certain road section. The travel utilization rate E is the ratio of the total distance L1 traveled by the sanitation vehicle in working condition when completing the task to the total distance L2 actually required to complete the task.

[0031] It can be seen that the embodiment of the present invention calculates the total distance traveled by the sanitation vehicle in the working state and the total distance required for the sanitation vehicle to actually complete the task through the sanitation vehicle scheduling data, and finally obtains the trip utilization rate of the sanitation vehicle. The trip utilization rate of the sanitation vehicle can be used as an evaluation indicator for sanitation vehicle scheduling, and the sanitation service can be evaluated from the dimension of trip utilization rate.

[0032] In an optional embodiment, the calculating of the sanitation vehicle scheduling evaluation index based on the sanitation vehicle scheduling data includes: Calculate sanitation vehicle task allocation balance data based on the sanitation vehicle scheduling data; The sanitation vehicle task allocation balance is calculated based on the sanitation vehicle task allocation balance data.

[0033] In an optional embodiment, the calculating the sanitation vehicle task allocation balance degree based on the sanitation vehicle task allocation balance degree data includes: The balance of sanitation vehicle task allocation is calculated by the following formula: in, is the amount of work done by sanitation vehicle k, is the total task volume, is the average task volume, c is the total number of sanitation vehicles, .

[0034] In an embodiment of the present invention, the workload of a sanitation vehicle can be the number of kilometers traveled by the sanitation vehicle, or the number of smart garbage bins reached by the sanitation vehicle. Reasonable task allocation balance means that, under the premise of successful completion of the task, through reasonable task allocation and scheduling, the amount of tasks that each sanitation vehicle is responsible for is made as even as possible, thereby improving the efficiency and fairness of task allocation. The difference between the actual workload completed by each sanitation vehicle and the average workload is calculated through the sanitation vehicle scheduling data, and finally the task allocation balance is obtained based on this, so that the task allocation balance of the sanitation vehicle can be used as a sanitation vehicle scheduling evaluation indicator, and the sanitation service can be evaluated from the dimension of task allocation balance.

[0035] In an optional embodiment, the method further includes: Determine whether the sanitation vehicle dispatch evaluation index meets the preset conditions; When the sanitation vehicle scheduling evaluation index does not meet the preset conditions, calculating the sanitation vehicle scheduling optimization data based on the sanitation vehicle scheduling data; A sanitation vehicle scheduling plan for the target city is generated based on the sanitation vehicle scheduling optimization data.

[0036] In an optional embodiment, the generating the sanitation vehicle dispatching scheme of the target city based on the sanitation vehicle dispatching optimization data includes: The sanitation vehicle dispatching plan is generated by the following formula: , (1) , (2) ,∀j∈F,(3) ,∀i∈F,(4) ,∀k∈A,(5) , (6) , (7) , (8) , (9) in, is the distance of the sanitation vehicle from work point i to work point j, is the judgment result of whether sanitation vehicle k drives from work point i to work point j. If sanitation vehicle k drives directly to work point j after completing work at work point i, then =1, otherwise =0, h is a specific working point in the model, A={1,2,...,c} represents the set of sanitation vehicles, with a total of c sanitation vehicles, F={1,2,...,n} represents the set of working points, with a total of n working points (i.e., garbage bins), m is the number of services that each preset working point needs to meet, and Q is the maximum endurance of the sanitation vehicle.

[0037] In the embodiment of the present invention, the following assumptions are made: 1) the parameters of sanitation vehicles are the same, such as fuel consumption, load, etc.; 2) the road conditions are good and there is no traffic jam; 3) the sanitation vehicle base is fixed; 4) the sanitation vehicles maintain the same speed during driving. Based on the above assumptions, a mathematical model for sanitation vehicle scheduling is established through the above formula, and the operation tasks of each sanitation vehicle are planned so that the sanitation vehicle starts from a given node in the road network of the sanitation area and traverses the specified operation section to complete the operation task. The constraints are as follows: each work point meets the service number m; the maximum cruising capacity of the sanitation vehicle is Q, and the driving distance of each sub-path is less than the maximum cruising distance of the sanitation vehicle; the sanitation vehicle maintains the same speed during driving; the sanitation vehicle can enter from the starting node of the road network of the sanitation area and exit at the termination node. There are three final goals: 1) the number of sanitation vehicles required is the minimum; 2) the total distance traveled by all sanitation vehicles is the minimum; 3) the difference in the amount of tasks between each sanitation vehicle is the minimum. Formulas (1), (2), and (3) respectively guarantee the three ultimate goals, namely, the minimum number of sanitation vehicles required to minimize the total distance traveled by all sanitation vehicles and the minimum difference in the amount of tasks between each sanitation vehicle. Formula (4) indicates that each work point satisfies the service number constraint. Formula (5) indicates that all sanitation vehicles meet the maximum endurance constraint. Formulas (6), (7), and (8) ensure that the sanitation vehicle enters from the starting node and exits from the ending node after completing the task. Formula (9) is an integer constraint. Furthermore, based on the above mathematical model of sanitation vehicle scheduling, the set of sub-paths with the minimum total distance traveled can be obtained through the saving algorithm, and then the task allocation scheme with the highest task balance can be found through task allocation optimization, and finally the further optimization of sanitation vehicle scheduling can be achieved. Different thresholds can be set for sanitation vehicle scheduling evaluation indicators such as total operating cost of sanitation vehicles, sanitation vehicle trip utilization rate, and sanitation vehicle task allocation balance. When it is greater than or less than the threshold, the current vehicle scheduling plan is judged to be unqualified. At this time, the mathematical model of sanitation vehicle scheduling is used to regenerate a new scheduling plan, thereby optimizing the scheduling plan, so that the sanitation service evaluation system has the ability to optimize sanitation services on the basis of evaluation.

[0038] It can be seen that implementation Figure 1The described method for evaluating sanitation services obtains the sanitation vehicle punch-in records and location information of each garbage bin in the target city, generates sanitation vehicle dispatching data based on the sanitation vehicle punch-in records and location information, and calculates sanitation vehicle dispatching evaluation indicators based on the sanitation vehicle dispatching data to realize sanitation service evaluation of the target city. By setting a sanitation vehicle punch-in function at each smart garbage bin in the city, punch-in data can be generated while sanitation vehicles collect and transport garbage from the smart garbage bins, and sanitation vehicle dispatching data can be generated accordingly. Finally, the dispatching evaluation indicators are calculated based on the sanitation vehicle dispatching data. With the help of intelligent means of modern computers, the sanitation service evaluation based on the sanitation vehicle dispatching plan can be realized.

[0039] In an optional embodiment, the garbage classification of the garbage bin can also be evaluated. Specifically, the sanitation service evaluation method can also include: Obtain a garbage image corresponding to garbage in a target garbage bin used by a user when throwing away garbage.

[0040] In the above steps, my country divides domestic waste into four categories according to GB / T 19095-2019 standard: recyclables, kitchen waste, hazardous waste and other waste. In order to achieve waste classification, sanitation management departments usually set up four types of trash cans in residential communities, which are used to recycle the four major categories of waste. When throwing away garbage, residents can first identify the type of garbage, and then throw different types of garbage into the corresponding types of trash cans. Figure 3 As shown in Figure 1, there are some smart trash cans that can automatically sort garbage. Whether it is done manually by residents or automatically by machines, the sanitation management department hopes to use the intelligent means of modern computers to evaluate the garbage classification. To this end, a camera can be set up in the trash can. Every time garbage enters the trash can, the thrown garbage is photographed, and then the collected garbage image is identified to determine whether the type of garbage is consistent with the type of the trash can, thereby realizing the evaluation of the garbage classification.

[0041] The garbage classification model is called to identify the garbage image to obtain a garbage classification result.

[0042] In the above steps, the garbage classification model can use the YOLO model. The YOLO series of algorithms have been widely used in the field of target detection. The YOLO model has a small model size and less computational effort, and has a considerable speed. It is particularly suitable for real-time target detection tasks, and has a high cost-effectiveness without considering whether a GPU exists, especially the CPU occupancy of the PC side is small. The YOLO model is more suitable for the detection of small objects, and the equipment usage cost is low, which is more suitable for the design of the garbage classification evaluation system. The YOLO model mainly consists of four components: input, trunk, neck, and head. You can use architectures such as YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. These architectures differ in network depth and feature map width, allowing users to choose the architecture that best meets their specific needs. In order to meet the actual needs of garbage classification evaluation, such as real-time detection and simple deployment, YOLOv5s with a smaller model size and faster detection speed can be selected as the basic algorithm. It can provide higher detection accuracy and faster processing speed, providing a reliable foundation for the efficient operation and accurate classification of the intelligent garbage classification evaluation system.

[0043] It is determined whether the waste classification result includes other waste categories different from the target waste category, wherein the target waste category refers to the waste category that the target waste bin is designated to be disposed of.

[0044] In the above steps, the garbage classification model identifies the garbage image, and after obtaining the garbage classification result, it can be used to judge whether the garbage classification behavior is accurate, thereby realizing the evaluation of garbage classification. For example, when throwing garbage, the garbage is placed in a recyclable garbage bin. After the garbage classification model identifies the garbage image, it is found that the types of garbage are recyclables and kitchen waste. It is determined that there are other types of garbage, and the evaluation result of the user's garbage throwing behavior is recorded as unqualified.

[0045] When it is determined that the garbage classification result includes the other garbage categories, the evaluation result of the user's garbage throwing behavior is recorded as unqualified.

[0046] When it is determined that the garbage classification result does not include the other garbage categories, a specific garbage category detection model corresponding to the other garbage categories is called to detect whether there is garbage of the other garbage categories in the garbage image.

[0047] In the above steps, due to technical limitations, the classification accuracy of the garbage classification model is usually limited. In order to further improve the accuracy of garbage classification evaluation, when it is determined that there are no other garbage categories in the garbage classification results, the specific garbage category detection model corresponding to other garbage categories is called to detect whether there is garbage of other garbage categories in the garbage image. For example, the type of the garbage bin is recyclable, and the classification result of the garbage classification model is also recyclable. At this time, the specific garbage category detection model corresponding to the three categories of kitchen waste, hazardous waste and other garbage can be called to detect the garbage image. If kitchen waste, hazardous waste and other garbage are detected in the garbage image, the evaluation result of the user's garbage throwing behavior will be recorded as unqualified. The specific garbage category detection model can also use the YOLO model. However, the training data set of the specific garbage category detection model can be different from that of the garbage classification model, so that the specific garbage category detection model and the garbage classification model have different recognition capabilities. For example, for the specific garbage category detection model for recyclables, its training data set mainly contains images of recyclable garbage, so that the trained specific garbage category detection model can more accurately identify images of recyclable garbage and has stronger recognition capabilities for images of recyclable garbage. The training data set for the garbage classification model contains four types of garbage images, aiming to make the garbage classification model have stronger generalization capabilities and be able to more accurately identify various types of garbage images. The coordination of the garbage classification model and the specific garbage category detection model can improve the accuracy of the garbage classification evaluation results.

[0048] When it is detected that garbage of other garbage categories exists in the garbage image, the evaluation result of the user's garbage throwing behavior is recorded as unqualified.

[0049] When it is detected that there is no garbage of other garbage categories in the garbage image, the evaluation result of the user's garbage throwing behavior is recorded as qualified.

[0050] It can be seen that the garbage image corresponding to the target garbage bin is obtained, and then the garbage classification model and the specific garbage category detection model are used to identify the garbage category in the garbage image. By judging whether the identified garbage category corresponds to the target garbage bin, the user's garbage throwing behavior can be evaluated. In addition, by making the garbage classification model and the specific garbage category detection model have different garbage classification recognition capabilities, and through the cooperation between the two, the accuracy of the garbage classification evaluation results can be effectively improved.

[0051] In an optional embodiment, the sanitation service evaluation method further includes: Acquire local training data of the target trash bin, and train the trash classification model based on the local training data; Uploading the trained local model parameters of the garbage classification model to the global server; The global server aggregates the local model parameters to obtain global model parameters of the global garbage classification model; Determine whether a preset model update condition is met; when it is determined that the model update condition is met, the global server sends the global model parameters to the target trash bin locally to update the trash classification model.

[0052] In this optional embodiment, due to the complexity of garbage images, in order to maintain a good recognition accuracy of the garbage classification model and the specific garbage category detection model, more training data is needed to train the model. To this end, the garbage images that are constantly generated in practice can be used as nutrients for the classification model and used for model training. Specifically, Figure 4 As shown, garbage bins distributed in various places can use the garbage images collected locally to train the model. After the training is completed, the local model parameters are uploaded to the global server. Then the global server aggregates the local model parameters to obtain the global model parameters of the global garbage classification model. When the local model needs to be updated, the global server sends the global garbage classification model to garbage bins in various places, thereby updating the local models of garbage bins in various places. In this way, it is possible to avoid transmitting a large amount of training data between the local and global servers, ensuring the efficiency and security of data transmission, and to use the computing power resources of garbage bins distributed in various places to provide computing power resource support for the training of the classification model. At the same time, since the global garbage classification model aggregates the local model parameters of garbage bins distributed in various places, it is equivalent to aggregating the training effects of garbage bins in various places, so that the generalization ability and accuracy of the global garbage classification model recognition are higher, which can avoid overfitting of data on a single device and improve the accuracy and stability of the training model.

[0053] In an optional embodiment, determining whether a preset model update condition is met includes: Calculating a deviation value between the garbage classification model and the global garbage classification model based on the local model parameters and the global model parameters; When the deviation value is greater than a preset deviation threshold, determining that a model update condition is met; When the deviation value is not greater than the deviation threshold, it is determined that the model update condition is not satisfied.

[0054] In this optional embodiment, during the continuous training process, the model parameters of the local model and the global model will inevitably be continuously updated. If the local model and the global model are kept consistent at all times, a large amount of data transmission will inevitably be generated between the local and global models, which is obviously not realistic. Therefore, the conditions for model update can be set. When the conditions for model update are met, the global model will be sent to the local to update the local model. This can avoid a large amount of data transmission and constrain the local model parameters of each device to not be too far away from the global model parameters, which helps to avoid the generation of local optimal solutions and improve the convergence speed and generalization performance of the global model.

[0055] In an optional embodiment, calculating the deviation value between the garbage classification model and the global garbage classification model based on the local model parameters and the global model parameters includes: in, is the deviation value, is the coefficient, is the number of trash bins, For the The local model parameters uploaded by each trash bin, and w are the global model parameters.

[0056] In this optional embodiment, by introducing a regularization term, it is possible to better adapt to unbalanced and non-independent data distribution, prevent over-reliance on the data of certain participants, and enable the global model to better reflect the overall characteristics of the participants.

[0057] In an optional embodiment, calculating the deviation value between the garbage classification model and the global garbage classification model based on the local model parameters and the global model parameters includes: in, is the deviation value, is the shallow parameter coefficient of the local model, is the deep parameter coefficient of the local model, Less than , is the number of trash bins, For the Shallow parameters of local models uploaded from trash bins, For the The local model deep parameters uploaded by the trash bin, is the shallow parameter of the global model, is the deep parameter of the global model.

[0058] In this optional embodiment, during the continuous training process, the shallow parameters of the model are usually updated more frequently than the deep parameters, and the change of the deep parameters has a greater impact on the model recognition ability. Therefore, different weight coefficients can be set for the shallow parameters and deep parameters of the model, so that the weight of the deep parameters is higher than the weight of the shallow parameters, so that the deviation value can more accurately reflect the conditions for model update.

[0059] In an optional embodiment, the preset model update condition may also be the number of training rounds or the amount of training data.

[0060] In this optional embodiment, the number of rounds of model training or the amount of training data can also be used as a condition for model update. Usually, after the model has undergone multiple rounds of training, or the amount of trained data has reached a certain amount, it can be inferred that the model has undergone significant changes, and it is necessary to synchronize the local model and the global model to prevent the local model parameters from being too far away from the global model parameters.

[0061] In an optional embodiment, the feature extraction network in the garbage classification model is replaced by a GhostNet module.

[0062] In this optional embodiment, if Figure 5 As shown in the figure, GhostNet first uses a small number of convolution kernels to extract features from the input feature map, then further performs linear transformation operations on this part of the feature map, and finally generates the final feature map through splicing operations. For the input image, , where c, h, w are the number of input channels, the input image height, and the input image width, respectively. In general, the operation of any convolutional layer that produces n feature maps can be written as in, represents the convolution operation, b represents the bias, , which means the output feature map has n channels, represents the filter of this convolution layer. and is the height and width map of the output features, is the kernel size of the convolution filter f, and the number of FLOPs required in this convolution process can be calculated as , usually hundreds of thousands, because the number of filters n and the number of channels c are usually very large. When using Ghost convolution, the amount of computation is The computational compression ratio is Among them, s is the kernel area of ​​the convolution filter f, and d*d is the kernel size of the second convolution filter f.

[0063] This method reduces the learning cost of non-critical features: that is, by combining a small number of convolution kernels with cheaper linear transformation operations instead of conventional convolution methods, the amount of convolution operations is effectively reduced without affecting the performance of the model.

[0064] In an optional embodiment, the garbage classification model introduces a CBAM attention mechanism.

[0065] In this optional embodiment, CBAM aims to address the limitations of traditional convolutional neural networks in processing information of different scales, shapes, and directions. It introduces two attention mechanisms: channel attention and spatial attention. Channel attention is used to enhance the feature representation of different channels, while spatial attention is used to extract key information at different locations in space. Figure 6 As shown in Figure 2, CBAM consists of two key parts: Channel Attention Module (CAM) and Spatial Attention Module (SAM). These two modules can be embedded in different layers of Convolutional Neural Network (CNN) to enhance feature representation.

[0066] In the channel attention module, the global feature representation of each channel is first calculated through a global average pooling operation. Then, the importance weights of each channel are learned through a fully connected layer (or convolutional layer). Finally, these weights are applied to the feature map of each channel to enhance useful channel information. CAM emphasizes key features, while SAM emphasizes the spatial localization of these key features. The formula of the channel attention module is: in It is a nonlinear The spatial attention module is composed of two linear layers and a ReLU activation function. in, Indicates that the convolution kernel size is 7x7, and Concat represents the concatenation operation. Thus, CBAM can dynamically adjust the importance of each channel and spatial position in the feature map, thereby improving the representation ability and performance of the convolutional neural network.

[0067] like Figure 7As shown in the figure, Ghost convolution and CBAM attention mechanism can be implemented through a cascade structure. The CBAM channel attention mechanism is used to obtain the global average of each channel through global average pooling of the input feature map, and then a set of fully connected layers are used to generate channel attention weights. Then, new weight parameters are generated through the Sigmoid function. These weight parameters are applied to each channel of the input feature map, so that the linear operation makes a greater contribution to the final target feature, thereby obtaining a more effective feature map. The cascade structure of Ghost convolution and CBAM attention mechanism effectively combines the advantages of Ghost convolution and CBAM attention mechanism.

[0068] Example 2: Please refer to Figure 8 , Figure 8 Schematic diagram of the structure of a sanitation service evaluation system disclosed in an embodiment of the present invention. Figure 8 As shown, the sanitation service evaluation system may include: The acquisition module 801 is used to obtain the check-in record and location information of the sanitation vehicle for each garbage bin in the target city.

[0069] The generation module 802 is used to generate sanitation vehicle scheduling data based on the sanitation vehicle punch-in record and the location information.

[0070] The calculation module 803 is used to calculate the sanitation vehicle dispatch evaluation index based on the sanitation vehicle dispatch data to achieve the sanitation service evaluation of the target city.

[0071] In an optional embodiment, the specific method in which the calculation module 803 calculates the sanitation vehicle scheduling evaluation index based on the sanitation vehicle scheduling data is: Calculating sanitation vehicle operation cost data based on the sanitation vehicle scheduling data; The total operating cost of the sanitation vehicle is calculated based on the sanitation vehicle operating cost data.

[0072] In an optional embodiment, the calculation module 803 calculates the total operating cost of the sanitation vehicle based on the sanitation vehicle operating cost data, including: The total cost of sanitation vehicle operation is calculated using the following formula: Among them, a is the total number of sanitation vehicles used in the city, b is the total number of road sections in the city, The purchase cost of sanitation vehicles. is the average driving cost of sanitation vehicles, is the total distance traveled by sanitation vehicle k on section o on the road network in the city's sanitation area.

[0073] In an optional embodiment, the calculation module 803 calculates the sanitation vehicle scheduling evaluation index based on the sanitation vehicle scheduling data, including: Calculating sanitation vehicle travel utilization data based on the sanitation vehicle scheduling data; The sanitation vehicle trip utilization rate is calculated based on the sanitation vehicle trip utilization rate data.

[0074] In an optional embodiment, the calculation module 803 calculates the sanitation vehicle trip utilization rate based on the sanitation vehicle trip utilization rate data, including: The trip utilization rate of sanitation vehicles is calculated by the following formula: in, is the total distance required for sanitation vehicle k to travel through road section o in the city's sanitation area road network under working conditions, E is the sanitation vehicle travel utilization rate, is the total distance travelled by the sanitation vehicle in working condition, The total distance required for the sanitation vehicle to actually complete the task.

[0075] In an optional embodiment, the calculation module 803 calculates the sanitation vehicle scheduling evaluation index based on the sanitation vehicle scheduling data, including: Calculate sanitation vehicle task allocation balance data based on the sanitation vehicle scheduling data; The sanitation vehicle task allocation balance is calculated based on the sanitation vehicle task allocation balance data.

[0076] In an optional embodiment, the calculation module 803 calculates the sanitation vehicle task allocation balance based on the sanitation vehicle task allocation balance data, including: The balance of sanitation vehicle task allocation is calculated by the following formula: in, is the amount of work done by sanitation vehicle k, is the total task volume, is the average task volume, c is the total number of sanitation vehicles, .

[0077] In an optional embodiment, the sanitation service evaluation system is also used for: Determine whether the sanitation vehicle dispatch evaluation index meets the preset conditions; When the sanitation vehicle scheduling evaluation index does not meet the preset conditions, calculating the sanitation vehicle scheduling optimization data based on the sanitation vehicle scheduling data; A sanitation vehicle scheduling plan for the target city is generated based on the sanitation vehicle scheduling optimization data.

[0078] In an optional embodiment, the sanitation service evaluation system generates a sanitation vehicle scheduling plan for the target city based on the sanitation vehicle scheduling optimization data, including: The sanitation vehicle dispatching plan is generated by the following formula: , (1) , (2) ,∀j∈F,(3) ,∀i∈F,(4) ,∀k∈A,(5) , (6) , (7) , (8) , (9) in, is the distance of the sanitation vehicle from work point i to work point j, is the judgment result of whether sanitation vehicle k drives from work point i to work point j. If sanitation vehicle k drives directly to work point j after completing work at work point i, then =1, otherwise =0, h is a specific working point in the model, A={1,2,...,c} represents the set of sanitation vehicles, with a total of c sanitation vehicles, F={1,2,...,n} represents the set of working points, with a total of n working points (i.e., garbage bins), m is the number of services that each preset working point needs to meet, and Q is the maximum endurance of the sanitation vehicle.

[0079] For the specific description of the above-mentioned sanitation service evaluation system, please refer to the specific description of the above-mentioned sanitation service evaluation method, which will not be repeated here.

[0080] Example 3: Please refer to Fig. 9 , Fig. 9 Schematic diagram of the structure of a sanitation service evaluation terminal disclosed in an embodiment of the present invention. Fig. 9 As shown, the sanitation service evaluation terminal may include: A memory 901 storing executable program codes; A processor 902 coupled to the memory 901; The processor 902 calls the executable program code stored in the memory 901 to execute the steps in the sanitation service evaluation method disclosed in the first embodiment of the present invention.

[0081] Embodiment 4: Fig.10 It is a structural schematic diagram of a computer storage medium disclosed in an embodiment of the present invention.

[0082] An embodiment of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps in the sanitation service evaluation method disclosed in the first embodiment of the present invention.

[0083] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0084] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0085] Finally, it should be noted that the environmental sanitation service evaluation method, system, terminal and storage medium disclosed in the embodiments of the present invention are only preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A sanitation service evaluation system, characterized in that: The system comprises: The acquisition module is used to obtain the check-in records and location information of sanitation vehicles for each garbage bin in the target city; A generation module, used to generate sanitation vehicle dispatching data based on the sanitation vehicle punch-in record and the location information; A calculation module, used to calculate the sanitation vehicle dispatch evaluation index based on the sanitation vehicle dispatch data, so as to realize the sanitation service evaluation of the target city; The calculation module calculates the sanitation vehicle scheduling evaluation index based on the sanitation vehicle scheduling data, including: Calculating sanitation vehicle operation cost data based on the sanitation vehicle scheduling data; Calculating the total operating cost of the sanitation vehicle based on the sanitation vehicle operating cost data; Calculating sanitation vehicle travel utilization data based on the sanitation vehicle scheduling data; Calculating the sanitation vehicle trip utilization rate based on the sanitation vehicle trip utilization rate data; Calculate sanitation vehicle task allocation balance data based on the sanitation vehicle scheduling data; The sanitation vehicle task allocation balance is calculated based on the sanitation vehicle task allocation balance data.

2. According to claim 1, the sanitation service evaluation system is characterized in that: The calculation module calculates the total operating cost of the sanitation vehicle based on the sanitation vehicle operating cost data, including: The total cost of sanitation vehicle operation is calculated using the following formula: Among them, a is the total number of sanitation vehicles used in the city, b is the total number of road sections in the city, The purchase cost of sanitation vehicles. is the average driving cost of sanitation vehicles, is the total distance traveled by sanitation vehicle k on section o on the road network in the city's sanitation area.

3. The sanitation service evaluation system according to claim 2 is characterized in that: The calculation module calculates the travel utilization rate of the sanitation vehicle based on the travel utilization rate data of the sanitation vehicle, including: The trip utilization rate of sanitation vehicles is calculated by the following formula: in, is the total distance required for sanitation vehicle k to travel through road section o in the city's sanitation area road network under working conditions, E is the sanitation vehicle travel utilization rate, is the total distance travelled by the sanitation vehicle in working condition, The total distance required for the sanitation vehicle to actually complete the task.

4. The sanitation service evaluation system according to claim 3 is characterized in that: The calculation module calculates the sanitation vehicle task allocation balance based on the sanitation vehicle task allocation balance data, including: The balance of sanitation vehicle task allocation is calculated by the following formula: in, is the amount of work done by sanitation vehicle k, is the total task volume, is the average task volume, c is the total number of sanitation vehicles, .

5. The sanitation service evaluation system according to any one of claims 1 to 4, characterized in that: The sanitation service evaluation system is also used for: Determine whether the sanitation vehicle dispatch evaluation index meets the preset conditions; When the sanitation vehicle scheduling evaluation index does not meet the preset conditions, calculating the sanitation vehicle scheduling optimization data based on the sanitation vehicle scheduling data; A sanitation vehicle scheduling plan for the target city is generated based on the sanitation vehicle scheduling optimization data.

6. The sanitation service evaluation system according to claim 5 is characterized in that: The sanitation service evaluation system generates a sanitation vehicle dispatching plan for the target city based on the sanitation vehicle dispatching optimization data, including: The sanitation vehicle dispatching plan is generated by the following formula: ， ， ,∀j∈F, ,∀i∈F, ,∀k∈A, ， ， ， ， in, is the distance of the sanitation vehicle from work point i to work point j, is the judgment result of whether sanitation vehicle k drives from work point i to work point j. If sanitation vehicle k drives directly to work point j after completing work at work point i, then =1, otherwise =0, h is a specific working point in the model, A={1,2,...,c} represents the set of sanitation vehicles, with a total of c sanitation vehicles, F={1,2,...,n} represents the set of working points, with a total of n working points, m is the number of services that need to be satisfied at each preset working point, and Q is the maximum endurance of the sanitation vehicle.

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