Garbage recycling method, device and electronic equipment

Abstract

The present application discloses a garbage recycling method, device and electronic device. After obtaining the time length required for the overflow degree of the garbage bins at each stacking position to reach a preset overflow degree, the method adjusts the quantity value of the garbage bins at each stacking position according to each time length, and then calculates the target transportation route for the current transportation of each garbage bin according to each adjusted quantity value. Through the above method, the utilization rate of the garbage bins can be improved, and the waste of resources caused by unreasonable garbage recycling paths during garbage transportation can be reduced, and the subsequent waste of resources can be monitored.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular to a garbage recycling method, device and electronic equipment. Background Art

[0002] With the advancement of urbanization and the improvement of urban economic level, more and more people choose to live and work in cities, resulting in a large amount of garbage generated in cities every day. After being collected through trash cans, the garbage is transported to garbage disposal stations for centralized treatment.

[0003] During the garbage recycling process, if some places have a lot of garbage generated but a small number of garbage bins, the garbage bins will often overflow and garbage will be thrown around randomly. If some places have a little garbage generated but a large number of garbage bins, the utilization rate of the garbage bins will be low, causing waste of resources. Summary of the invention

[0004] The present application provides a garbage recycling method, device and electronic equipment, which not only improve the utilization rate of garbage bins during the garbage recycling process, but also reduce the waste of resources caused by unreasonable garbage recycling paths during garbage transportation.

[0005] In one aspect, the present application provides a garbage collection method, the method comprising:

[0006] Obtaining the length of time required for the overflow degree of the trash can at each stacking position to reach a preset overflow degree, wherein the overflow degree represents the amount of trash stacked in the trash can;

[0007] Adjust the number of trash cans at each stacking location according to the length of time;

[0008] Based on the respective adjusted quantity values, the target transport route including the respective storage locations is recalculated.

[0009] Through the above method, the utilization rate of the garbage bin can be improved, and at the same time, the waste of resources caused by unreasonable garbage recycling paths during garbage transportation can be reduced.

[0010] In a possible design, obtaining the time length required for the overflow degree of the trash bins at each stacking position to reach a preset overflow degree includes:

[0011] According to a first relationship between the current weight of any trash can at each stacking position and the maximum bearing weight of any trash can, it is determined whether the overflow degree of any trash can reaches the preset overflow degree.

[0012] According to a second relationship between the current garbage storage height in any one of the garbage bins and the height of any one of the garbage bins, determining whether the overflow degree of any one of the garbage bins reaches the preset overflow degree;

[0013] When the overflow degree of any one of the trash cans reaches the preset overflow degree, a time length for the overflow degree of any one of the trash cans to reach the preset overflow degree is determined.

[0014] Through the above method, the time length required for the overflow degree of the garbage bins at each stacking position to reach the preset overflow degree is obtained, so that the number value of the garbage bins at each stacking position can be further adjusted according to each time length, which can improve the utilization rate of the garbage bins.

[0015] In a possible design, the determining whether the overflow degree of any of the trash cans reaches the preset overflow degree includes:

[0016] According to the first relationship or the second relationship, determining whether the overflow degree of any of the trash cans reaches a first preset value;

[0017] Input the collected image data of the trash cans at each stacking position into a preset model to obtain a predicted value of the overflow degree of the trash cans at each stacking position;

[0018] When the predicted value reaches the second preset value and the overflow degree of any one of the trash cans reaches the first preset value, the time length for the overflow degree of any one of the trash cans to reach the preset overflow degree is determined.

[0019] Through the above method, the accuracy of the time length for the overflow degree of any trash can to reach the preset overflow degree can be improved, so that when the number value of the trash cans at each stacking position is further adjusted according to each time length, the utilization rate of the trash cans can be improved.

[0020] In a possible design, adjusting the quantity of trash cans at each stacking location according to each time length includes:

[0021] Sort each time length to obtain the maximum time length and the minimum time length;

[0022] Determine the difference between the maximum time length and the minimum time length;

[0023] When the difference is less than the preset time length value, the number of trash cans at the stacking position corresponding to the maximum time length is reduced to the first target number value, and the number of trash cans at the stacking position corresponding to the minimum time length is increased to the second target number value, so as to obtain the adjusted number of trash cans at each stacking position.

[0024] Through the above method, based on the length of time required for the overflow degree of the garbage bins at each stacking position to reach a preset overflow degree, the number of garbage bins at each stacking position is dynamically adjusted, thereby improving the utilization rate of the garbage bins.

[0025] In a possible design, the target transportation route for transporting each trash bin is calculated based on each adjusted quantity value, including:

[0026] Calculate the proportion corresponding to each stacking position, wherein the proportion is characterized by the ratio between the overflow degree of the trash can at any position and the number of the trash cans at any position;

[0027] The weight of each driving route is calculated based on each weight and traffic network information, wherein the traffic network information at least includes the current congestion level value of each driving route and the current driving distance value of each driving route;

[0028] The target transportation route is calculated based on the weight.

[0029] Through the above method, the target transportation route is obtained based on the weights of each driving route, which can reduce the waste of resources caused by unreasonable garbage recycling paths during garbage transportation.

[0030] In a possible design, after the target transportation route for transporting the garbage bins at each stacking position is calculated, the following steps are performed:

[0031] Inputting each adjusted quantity value and the target transportation route into a preset analysis model to obtain an analysis result;

[0032] Classifying the analysis results and obtaining optimization parameters according to the classification results;

[0033] Optimizing the adjusted quantity value and the target transportation route using the optimization parameters;

[0034] The optimized quantity value and the optimized target transportation route are sent to the preset terminal.

[0035] Through the above method, the garbage collection method can be further optimized, so as to further improve the utilization rate of garbage bins and reduce the waste of resources caused by unreasonable garbage collection paths during garbage transportation.

[0036] In a second aspect, the present application provides a garbage collection device, the device comprising:

[0037] An acquisition module, for acquiring the length of time required for the overflow degree of the trash cans at each stacking position to reach a preset overflow degree, wherein the overflow degree represents the amount of trash stacked in the trash can;

[0038] The adjustment module adjusts the number of trash bins at each stacking location according to the length of time;

[0039] The calculation module calculates the target transportation route for the garbage bins at each stacking location according to the adjusted quantity values.

[0040] In one possible design, the acquisition module includes:

[0041] The judging unit is configured to judge whether the overflow degree of any trash can at each stacking position reaches the preset overflow degree according to a first relationship between the current weight of any trash can at each stacking position and the maximum bearing weight of any trash can.

[0042] According to a second relationship between the current garbage storage height in any one of the garbage bins and the height of any one of the garbage bins, determining whether the overflow degree of any one of the garbage bins reaches the preset overflow degree;

[0043] The determination unit determines, when the overflow degree of any one of the trash cans reaches the preset overflow degree, a time length for the overflow degree of any one of the trash cans to reach the preset overflow degree.

[0044] In a possible design, the judging unit is specifically configured to:

[0045] According to the first relationship or the second relationship, determining whether the overflow degree of any of the trash cans reaches a first preset value;

[0046] Input the collected image data of the trash cans at each stacking position into a preset model to obtain a predicted value of the overflow degree of the trash cans at each stacking position;

[0047] When the predicted value reaches the second preset value and the overflow degree of any one of the trash cans reaches the first preset value, the time length for the overflow degree of any one of the trash cans to reach the preset overflow degree is determined.

[0048] In a possible design, the adjustment module is specifically used for:

[0049] Sort each time length to obtain the maximum time length and the minimum time length;

[0050] Determine the difference between the maximum time length and the minimum time length;

[0051] When the difference is less than the preset time length value, the number of trash cans at the stacking position corresponding to the maximum time length is reduced to the first target number value, and the number of trash cans at the stacking position corresponding to the minimum time length is increased to the second target number value, so as to obtain the adjusted number of trash cans at each stacking position.

[0052] In one possible design, the computing module includes:

[0053] A first calculation unit calculates a specific gravity corresponding to each stacking position, wherein the specific gravity is represented by a ratio between the overflow degree of the trash can at any position and the number of the trash cans at any position;

[0054] A second calculation unit calculates the weight of each driving route according to each weight and traffic network information, wherein the traffic network information at least includes a current congestion level value of each driving route and a current driving distance value of each driving route;

[0055] The third calculation unit calculates the target transportation route according to the weight.

[0056] In a possible design, the third computing unit is specifically used for:

[0057] Inputting each adjusted quantity value and the target transportation route into a preset analysis model to obtain an analysis result;

[0058] Classifying the analysis results and obtaining optimization parameters according to the classification results;

[0059] Optimizing the adjusted quantity value and the target transportation route using the optimization parameters;

[0060] The optimized quantity value and the optimized target transportation route are sent to the preset terminal.

[0061] In a third aspect, the present application provides an electronic device, including:

[0062] Memory, used to store computer programs;

[0063] The processor is used to implement the above-mentioned garbage collection method steps when executing the computer program stored in the memory.

[0064] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned garbage collection method steps are implemented.

[0065] Based on the above method, after obtaining the time length required for the overflow degree of the garbage bins at each stacking position to reach the preset overflow degree, the number value of the garbage bins at each stacking position is adjusted according to each time length, and then the target transportation route for currently transporting each garbage bin is calculated based on each adjusted number value. This can improve the utilization rate of the garbage bins and reduce the waste of resources caused by unreasonable garbage recycling paths during garbage transportation.

[0066] The technical effects that can be achieved in each of the above-mentioned second to fourth aspects and each of the above-mentioned aspects refer to the technical effects that can be achieved in the above-mentioned first aspect or various possible schemes in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A flowchart of a garbage collection method provided for this application;

[0068] Figure 2 A schematic diagram of an application scenario of a garbage collection method provided in this application;

[0069] Figure 3 A schematic diagram of the structure of a garbage recycling device provided in this application;

[0070] Figure 4 A schematic diagram of the structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The specific operating methods in the method embodiments can also be applied to device embodiments or system embodiments. It should be noted that in the description of the present application, "multiple" is understood as "at least two". "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, and A and B exist, and B exists alone. A is connected to B, which can represent: A is directly connected to B and A is connected to B through C. In addition, in the description of the present application, words such as "first" and "second" are only used to distinguish the purpose of description, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order.

[0072] The embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0073] With the advancement of urbanization and the improvement of urban economic level, more and more people choose to live and work in cities, resulting in a large amount of garbage generated in cities every day. After being collected through trash cans, the garbage is transported to garbage disposal stations for centralized treatment.

[0074] During the garbage recycling process, if some places have a lot of garbage generated but a small number of garbage bins, the garbage bins will often overflow and garbage will be thrown around randomly. If some places have a little garbage generated but a large number of garbage bins, the utilization rate of the garbage bins will be low, causing waste of resources.

[0075] In order to solve the above problems, the example of this application provides a garbage recycling method, which can not only improve the utilization rate of garbage bins, but also reduce the waste of resources caused by garbage transportation by optimizing the garbage transportation path, and monitor the subsequent resource waste. Among them, the method and device described in the embodiment of this application are based on the same technical concept. Since the principles of the problems solved by the method and the device are similar, the embodiments of the device and the method can refer to each other, and the repeated parts will not be repeated.

[0076] In order to make the objectives, technical solutions and advantages of the present application more clear, the present application is further described in detail below in conjunction with the accompanying drawings.

[0077] like Figure 1 As shown, it is a flowchart of a garbage collection method provided by the present application, which specifically includes the following steps:

[0078] S11, obtaining the length of time required for the overflow degree of the garbage bins at each stacking position to reach a preset overflow degree;

[0079] In order to solve the problem of low utilization rate of trash cans, in the embodiment of the present application, the overflow degree is used to characterize the amount of trash piled in the trash can, and the time length required for the overflow degree of the trash cans at each stacking position to reach the preset overflow degree is obtained, and the number of trash cans at each stacking position is further adjusted according to each time length, so as to improve the utilization rate of the trash cans. Among them, the method of obtaining each time length can be:

[0080] First, determine whether the overflow degree of any trash can at each stacking position reaches a preset overflow degree, wherein the preset overflow degree may be 1, indicating that the amount of garbage stacked in the trash can reaches the maximum carrying capacity of the trash can. Of course, the preset overflow degree may also be 0.9, 0.8, etc. The specific value depends on the situation and is not specifically limited here.

[0081] Specifically, it is possible to determine whether the overflow degree of any trash can reaches a preset overflow degree based on a first relationship between the current weight of any trash can at each stacking position and the maximum bearing weight of any trash can. The first relationship can be expressed by formula (1):

[0082] X ≥ k 1 *Y (1)

[0083] In formula (1), X represents the current weight of the trash can, Y represents the maximum load of the trash can, and k 1 Indicates the first preset multiple, k 1 The value of can be 0.9, 0.8 or 0.7, etc., depending on the situation. 1times, it is determined that the overflow degree of any one of the trash cans reaches a preset overflow degree.

[0084] In addition, it is also possible to determine whether the overflow degree of any of the trash cans reaches a preset overflow degree based on a second relationship between the current trash storage height in any of the trash cans and the height of any of the trash cans. The second relationship can be expressed by formula (2):

[0085] h≥k 2 *H (2)

[0086] In formula (2), h represents the current garbage storage height in the trash can, H represents the height of the trash can, and k 2 is the second preset multiple, k 2 The value of can be 0.9, 0.8 or 0.7, etc., depending on the situation. When the current garbage storage height h of the trash can is greater than or equal to the height H of the trash can, k 2 times, it is determined that the overflow degree of any of the trash cans reaches a preset overflow degree.

[0087] Then, when the overflow degree of any one of the trash cans reaches a preset overflow degree, the time length for the overflow degree of any one of the trash cans to reach the preset overflow degree is determined.

[0088] By using the above method, the time length required for the overflow degree of the trash cans at each stacking position to reach a preset overflow degree is obtained, and the number of trash cans at each stacking position is further adjusted according to each time length, thereby improving the utilization rate of the trash cans.

[0089] In a possible application scenario, in order to ensure the accuracy of the time length for the overflow degree of any trash can to reach the preset overflow degree, the above judgment method can be further optimized. Specifically:

[0090] First, according to the first relationship shown in formula (1) or the second relationship shown in formula (2), it is determined whether the overflow degree of any of the trash cans reaches a first preset value, wherein the first preset value may be 1, indicating that the amount of garbage piled in the trash can reaches the maximum carrying capacity of the trash can. Of course, the first preset value may also be 0.9, 0.8, etc. The specific value depends on the situation and is not specifically limited here.

[0091] When the overflow degree of any of the trash cans reaches a first preset value, further, the collected image data of the trash cans at each stacking position is input into a preset model. For example, the preset model can be a trained deep learning model (Residual Neural Network, ResNet), to obtain the predicted value of the overflow degree of the trash cans at each stacking position.

[0092] Specifically, the image data in the trash cans at each stacking position is preprocessed, for example, affine transformation, gray value transformation, rotation and other operations are performed on the collected image data to eliminate image noise. The preprocessed image data is then input into the preset model for calculation. The specific calculation process mainly includes the following aspects:

[0093] First, the residual is obtained by the residual block in the forward network propagation The specific calculation method can be:

[0094]

[0095] In formula (3), x i is the output of the i-th layer neural network and the input of the i+1-th layer neural network, w i is the weight corresponding to the feature of the neural network in layer i, is the predicted value of the i-th layer neural network, that is, the residual.

[0096] Furthermore, the loss function ∈ is obtained based on the residual, and the specific calculation method can be:

[0097]

[0098] In formula (4), P represents the actual label of the image, and the loss function ∈ is about x i The gradient of can be expressed as:

[0099]

[0100] In getting the loss function ∈ about x i After obtaining the gradient, the neural network is back-propagated according to the stochastic gradient descent method to determine the weight value w of each layer of network neurons and the offset b of each layer of network.

[0101] Furthermore, according to the weight value w and the offset b, the predicted value of the overflow degree of any trash can is calculated. The specific calculation formula is:

[0102] y=wx+b (6)

[0103] In formula (6), y is the predicted value of the overflow degree of any trash can, x is the image data obtained after preprocessing, that is, the input of the first layer of the neural network, and w and b are both vectors.

[0104] Further, after obtaining the predicted value y of the overflow degree of any trash can, it is determined whether the predicted value y reaches a second preset value, wherein the second preset value may be 1, indicating that the amount of garbage piled in the trash can reaches the maximum carrying capacity of the trash can. Of course, the second preset value may also be 0.9, 0.8, etc. The specific value depends on the situation and is not specifically limited here.

[0105] When the predicted value y reaches the second preset value, it is determined that the overflow degree of any of the trash cans reaches the preset overflow degree. Further, the length of time required for the overflow degree of the trash cans at each stacking position to reach the preset overflow degree can be calculated.

[0106] S12, adjusting the number of trash cans at each stacking location according to each time length;

[0107] In the embodiment of the present application, after obtaining each time length, the time lengths are sorted to obtain a maximum time length and a minimum time length, and then the difference between the maximum time length and the minimum time length is determined;

[0108] Furthermore, when the difference is less than the preset time length value, the number of trash cans at the stacking position corresponding to the maximum time length is reduced to the first target number value, and the number of trash cans at the stacking position corresponding to the minimum time length is increased to the second target number value, so as to obtain the adjusted number of trash cans at each stacking position.

[0109] For example, there are 4 trash cans at stacking location A, and the total time required for the overflow degree of the 4 trash cans to reach the preset overflow degree is T A ; There are 3 trash cans at stacking position B, and the total time required for the overflow degree of the 3 trash cans to reach the preset overflow degree is T B ; There are 2 trash cans at stacking position C, and the total time required for the overflow degree of the 2 trash cans to reach the preset overflow degree is T C , T A , T B , T C Sort and get the maximum value T MAX =T B , minimum value T MIN =T A , further determine T MAX -T MIN Is it less than the preset time length value T? p , where the preset time length value T p It can be the median time length T C The specific value depends on the circumstances.

[0110] If T MAX -T MIN ≥T p , then T MAX The number of trash cans at the corresponding stacking position B is reduced to a first target number, wherein the first target number may be 1 or 2, and the specific value depends on the situation and is not specifically limited here; and T MINThe number of trash cans at the corresponding stacking position A increases to a second target number, wherein the second target number may be 2 or 3, and the specific value depends on the situation and is not specifically limited here;

[0111] If T MAX -T MIN <T p , the operation is stopped.

[0112] Through the above method, based on the length of time required for the overflow degree of the garbage bins at each stacking position to reach a preset overflow degree, the number of garbage bins at each stacking position is dynamically adjusted, thereby improving the utilization rate of the garbage bins.

[0113] S13, recalculating the target transportation route including each stacking location according to the adjusted quantity value of the trash bins at each stacking location;

[0114] In the embodiment of the present application, the specific gravity corresponding to each stacking position is first calculated, wherein the specific gravity is represented by the ratio between the overflow degree of the trash can at any position and the number of the trash cans at any position, as shown in formula (7).

[0115]

[0116] In formula (7), D represents the overflow degree of the trash can at any location, a represents the number of trash cans at any location, and z represents the specific gravity.

[0117] After obtaining each proportion, the weight of each driving route is calculated. The specific calculation formula can be:

[0118] W=0.5(1-z)+0.25c+0.25d (8)

[0119] In formula (8), c represents the current congestion level of each driving route, d represents the current driving distance of each driving route, and W represents the weight of each driving route.

[0120] Further, according to the weight W, the target transportation route is calculated, wherein the target transportation route can be calculated using the Dijkstra algorithm, and the specific steps are as follows:

[0121] Determine the coordinates of the starting point and the end point of the driving, and mark the trash cans at various locations that do not require garbage collection, and then obtain the first array and the second array, wherein the first array only contains the coordinates of the starting point and the end point, which are used to record the vertex coordinates of the path with the smallest weight. The second array contains the coordinates of the trash can placement points other than the starting point and the end point, which are used to record the vertex coordinates of various paths whose weights have not yet been calculated, and the distance from each vertex coordinate to the starting point coordinate, wherein the vertex coordinate is the starting point coordinate of the path. If the vertex coordinate is not adjacent to the starting point coordinate, the distance is infinite, wherein the trash can placement point coordinates in the second array do not include the marked trash can coordinates at various locations that do not require garbage collection.

[0122] Then, after finding the target vertex coordinates adjacent to the starting point coordinates in the path with the smallest weight from the second array, add the target vertex coordinates to the first array, and use the target vertex coordinates as the new starting point coordinates to recalculate the weight of each route. At the same time, remove the target vertex coordinates from the second array. Next, update the distance from each vertex coordinate in the second array to the new starting point coordinates, and repeat the above operation until the target vertex coordinates adjacent to the starting point coordinates in the path with the smallest weight cannot be found in the second array.

[0123] Finally, the starting point coordinates, each target vertex coordinates, and the end point coordinates in the first array are connected to obtain the target transportation route. The connection order between each target vertex is determined according to the selection order of each target vertex coordinate.

[0124] For example, the first array S is [q, z], which represents the starting point coordinates and the end point coordinates. The second array U is [e, f, g], which represents the coordinates of the trash can placement points other than the starting point coordinates and the end point coordinates. According to the route weight, each target vertex coordinate is selected from U and put into S. If the selection order is q, f, e, g, z, then the target transportation route is to connect q, f, e, g, z.

[0125] Through the above method, the target transportation route is obtained based on the weights of each driving route, which can reduce the waste of resources caused by unreasonable garbage recycling paths during garbage transportation.

[0126] Furthermore, after obtaining each adjusted quantity value and the target transportation route through the above method, each adjusted quantity value and the target transportation route are input into a preset analysis model to obtain an analysis result, and then the analysis result is classified. Specifically, the analysis result can be divided into two types of events. The first type of event is characterized by the number value of the trash cans at each stacking location being adjusted, and the second type of event is characterized by the number value of the trash cans at each stacking location not being adjusted, so as to subsequently monitor the first type of event to confirm whether the waste of resources is improved.

[0127] And obtain optimization parameters according to the classification results, further use the optimization parameters to optimize each adjusted quantity value and target transportation route, and send the optimized quantity value and optimized target transportation route to a preset terminal.

[0128] Based on the above garbage recycling method, not only can the utilization rate of garbage bins be improved, but also the waste of resources caused by unreasonable garbage recycling paths during garbage transportation can be reduced, and subsequent resource waste can be monitored.

[0129] In order to elaborate on the garbage collection method provided by the present application in more detail, a specific application scenario is used below to illustrate. In this application scenario, there are mainly three modules interacting: data acquisition module, AI analysis and processing module, and scheduling processing module.

[0130] like Figure 2 The figure shows the interaction process between the above three modules, which specifically includes:

[0131] The data acquisition module is used to collect video data, that is, the video data inside the trash cans at each stacking position; collect sensor data, that is, collect data through sensors built into the bottom of the trash can and the edge of the trash can mouth, wherein the bottom sensor is responsible for collecting the current weight of any trash can at each stacking position, and the sensor on the edge of the mouth of the trash can obtains the current garbage storage height in any trash can through infrared scanning, thereby obtaining the garbage status information of each trash can in the stored garbage can; it is also used to obtain dynamic information, that is, the current congestion level value of each driving route and the current driving distance value of each driving route.

[0132] AI analysis and processing module, including:

[0133] The AI ​​strategy output is used to obtain the time length required for the overflow degree of the trash cans at each stacking location to reach the preset overflow degree, and further adjust the number of trash cans at each stacking location according to each time length, so as to improve the utilization rate of the trash cans. The method for obtaining each time length is:

[0134] Determine whether the overflow degree of any trash can at each stacking position has reached a preset overflow degree, that is, the maximum tolerable capacity of the trash can.

[0135] Specifically, it is possible to determine whether the overflow degree of any trash can reaches a preset overflow degree based on the first relationship between the current weight of any trash can at each stacking position collected by the data collection module and the maximum bearing weight of any trash can. The first relationship is expressed by formula (1). When the current weight X of the trash can is greater than or equal to 0.9 times the maximum bearing weight Y of the trash can, it is determined that the overflow degree of any trash can reaches the preset overflow degree.

[0136] In addition, it is also possible to determine whether the overflow degree of any of the trash cans reaches a preset overflow degree based on the second relationship between the current trash storage height in any of the trash cans and the height of any of the trash cans collected by the data collection module. The second relationship is expressed by formula (2). When the current trash storage height h of the trash can is greater than or equal to 1.1 times the height H of the trash can, it is determined that the overflow degree of any of the trash cans reaches the preset overflow degree, wherein the value of the preset overflow degree is 1.

[0137] Then, when the overflow degree of any one of the trash cans reaches a preset overflow degree, the time length for the overflow degree of any one of the trash cans to reach the preset overflow degree is determined.

[0138] In a possible application scenario, in order to ensure the accuracy of the time length for the overflow degree of any trash can to reach the preset overflow degree, the above judgment method can be further optimized. Specifically:

[0139] First, according to the first relationship shown in formula (1) or the second relationship shown in formula (2), it is determined whether the overflow degree of any trash can reaches a first preset value of the trash can, wherein the first preset value is 0.8.

[0140] It is also used to, when the overflow degree of any of the trash cans reaches a first preset value, cause the data acquisition module to collect video data in the trash cans at each stacking position and divide it into single-frame pictures to obtain image data in the trash cans at each stacking position, and then input the image data into a trained ResNet model to obtain a predicted value of the overflow degree of the trash cans at each stacking position.

[0141] Specifically, the image data in the trash cans at each stacking position is preprocessed, that is, the collected image data is subjected to affine transformation, gray value transformation, rotation and other operations to eliminate image noise. The preprocessed image data is then input into the preset model for calculation. The specific calculation process mainly includes the following aspects:

[0142] First, the residual is obtained by the residual block in the forward network propagation The specific calculation method is as shown in formula (3), and the loss function ∈ is obtained based on the residual, and the specific calculation method is as shown in formula (4).

[0143] After obtaining the loss function ∈, calculate the loss function ∈ with respect to x i The specific calculation method is as shown in formula (5). Then, the neural network is back-propagated according to the stochastic gradient descent method to determine the weight value w of each layer of the network neuron and the offset b of each layer of the network.

[0144] Furthermore, based on the weight value w and the offset b, the predicted value y of the overflow degree of any trash can is calculated. The specific calculation formula is as shown in formula (6). After obtaining the predicted value y of the overflow degree of any trash can, it is determined whether the predicted value y reaches a second preset value, where the value of the second preset value is 0.9.

[0145] When the predicted value y reaches the second preset value, it is determined that the overflow degree of any of the trash cans reaches the preset overflow degree. Further, the length of time required for the overflow degree of the trash cans at each stacking position to reach the preset overflow degree can be calculated.

[0146] Furthermore, after obtaining each time length, each time length is processed by an AI algorithm to obtain an adjusted number value of the trash cans at each stacking position.

[0147] Specifically, each time length is sorted to obtain the maximum time length and the minimum time length, and then the difference between the maximum time length and the minimum time length is determined. When the difference is less than the preset time length value, the number of trash cans at the stacking location corresponding to the maximum time length is reduced to the first target number value, and the number of trash cans at the stacking location corresponding to the minimum time length is increased to the second target number value to obtain the adjusted number of trash cans at each stacking location, and based on the adjusted number of trash cans at each stacking location, the target transportation route including each stacking location is recalculated.

[0148] Specifically, firstly, the proportion corresponding to each stacking position is calculated, wherein the proportion is represented by the ratio between the overflow degree of the trash can at any position and the number of the trash cans at any position, as shown in formula (7), and according to each proportion, the weight of each driving route is calculated, and the specific calculation formula is shown in formula (8).

[0149] Further, according to the weight, the target transportation route is calculated, wherein the target transportation route can be calculated using the Dijkstra algorithm, and the specific steps are as follows:

[0150] Determine the coordinates of the starting point and the end point of the driving, and mark the trash cans at various locations that do not require garbage collection, and then obtain the first array and the second array, wherein the first array only contains the coordinates of the starting point and the end point, which are used to record the vertex coordinates of the path with the smallest weight. The second array contains the coordinates of the trash can placement points other than the starting point and the end point, which are used to record the vertex coordinates of various paths whose weights have not yet been calculated, and the distance from each vertex coordinate to the starting point coordinate, wherein the vertex coordinate is the starting point coordinate of the path. If the vertex coordinate is not adjacent to the starting point coordinate, the distance is infinite, wherein the trash can placement point coordinates in the second array do not include the marked trash can coordinates at various locations that do not require garbage collection.

[0151] Then, after finding the target vertex coordinates adjacent to the starting point coordinates in the path with the smallest weight from the second array, add the target vertex coordinates to the first array, and use the target vertex coordinates as the new starting point coordinates to recalculate the weight of each route. At the same time, remove the target vertex coordinates from the second array. Next, update the distance from each vertex coordinate in the second array to the new starting point coordinates, and repeat the above operation until the target vertex coordinates adjacent to the starting point coordinates in the path with the smallest weight cannot be found in the second array.

[0152] Finally, the starting point coordinates, each target vertex coordinates, and the end point coordinates in the first array are connected to obtain the target transportation route. The connection order between each target vertex is determined according to the selection order of each target vertex coordinate.

[0153] Through the above method, the target transportation route is obtained based on the weights of each driving route, which can reduce the waste of resources caused by unreasonable garbage recycling paths during garbage transportation.

[0154] AI intelligent early warning is used to input each adjusted quantity value and the target transportation route into a preset analysis model after obtaining the adjusted quantity values ​​and the target transportation route through the above method, to obtain the analysis results, and then classify the analysis results. Specifically, the analysis results can be divided into two types of events. The first type of event is characterized by the number value of the trash cans at each stacking location being adjusted, and the second type of event is characterized by the number value of the trash cans at each stacking location not being adjusted, so as to subsequently monitor the first type of events to confirm whether the waste of resources has been improved.

[0155] And obtain optimization parameters according to the classification results, and further use the optimization parameters to optimize each adjusted quantity value and target transportation route to obtain the optimized quantity value and the optimized target transportation route.

[0156] Scheduling processing module, including:

[0157] AI strategy scheduling execution is used to issue strategy instructions to the system, APP or SMS through the scheduling center for the optimized quantity value and the optimized target transportation route, so as to realize the issuance of tasks and instructions from the dimensions of work lines and work areas.

[0158] Strategy execution and statistics are used for front-line personnel to feedback the execution status through the system, APP or SMS after the tasks or instructions are issued through AI strategy scheduling, and collect data again through the data collection module to confirm the execution results.

[0159] Based on the same inventive concept, the present application also provides a garbage collection device, such as Figure 3FIG. 1 is a schematic diagram of a garbage collection device in the present application, and the device includes:

[0160] An acquisition module 31 is used to acquire the length of time required for the overflow degree of the trash cans at each stacking position to reach a preset overflow degree, wherein the overflow degree represents the amount of trash stacked in the trash can;

[0161] An adjustment module 32 adjusts the quantity of trash cans at each stacking location according to each time length;

[0162] The calculation module 33 recalculates the target transportation route including each stacking location according to each adjusted quantity value.

[0163] In a possible design, the acquisition module 31 includes:

[0164] The judging unit is configured to judge whether the overflow degree of any trash can at each stacking position reaches the preset overflow degree according to a first relationship between the current weight of any trash can at each stacking position and the maximum bearing weight of any trash can.

[0165] According to a second relationship between the current garbage storage height in any one of the garbage bins and the height of any one of the garbage bins, determining whether the overflow degree of any one of the garbage bins reaches the preset overflow degree;

[0166] The determining unit determines, when the overflow degree of any one of the trash cans reaches the preset overflow degree, a time length for the overflow degree of any one of the trash cans to reach the preset overflow degree.

[0167] In a possible design, the judging unit is specifically configured to:

[0168] According to the first relationship or the second relationship, determining whether the overflow degree of any of the trash cans reaches a first preset value;

[0169] Input the collected image data of the trash cans at each stacking position into a preset model to obtain a predicted value of the overflow degree of the trash cans at each stacking position;

[0170] When the predicted value reaches the second preset value and the overflow degree of any one of the trash cans reaches the first preset value, the time length for the overflow degree of any one of the trash cans to reach the preset overflow degree is determined.

[0171] In a possible design, the adjustment module 32 is specifically used for:

[0172] Sort each time length to obtain the maximum time length and the minimum time length;

[0173] Determine the difference between the maximum time length and the minimum time length;

[0174] When the difference is less than the preset time length value, the number of trash cans at the stacking position corresponding to the maximum time length is reduced to the first target number value, and the number of trash cans at the stacking position corresponding to the minimum time length is increased to the second target number value, so as to obtain the adjusted number of trash cans at each stacking position.

[0175] In a possible design, the calculation module 33 includes:

[0176] A first calculation unit calculates a specific gravity corresponding to each stacking position, wherein the specific gravity is represented by a ratio between the overflow degree of the trash can at any position and the number of the trash cans at any position;

[0177] A second calculation unit calculates the weight of each driving route according to each weight and traffic network information, wherein the traffic network information at least includes a current congestion level value of each driving route and a current driving distance value of each driving route;

[0178] The third calculation unit calculates the target transportation route according to the weight.

[0179] In a possible design, the third computing unit is specifically used for:

[0180] Inputting each adjusted quantity value and the target transportation route into a preset analysis model to obtain an analysis result;

[0181] Classifying the analysis results and obtaining optimization parameters according to the classification results;

[0182] Optimizing the adjusted quantity value and the target transportation route using the optimization parameters;

[0183] The optimized quantity value and the optimized target transportation route are sent to the preset terminal.

[0184] Based on the above-mentioned garbage recycling device, the utilization rate of the garbage bin can be improved, and the waste of resources caused by unreasonable garbage recycling paths during garbage transportation can be reduced, and the subsequent waste of resources can be monitored.

[0185] Based on the same inventive concept, an electronic device is also provided in the embodiment of the present application, and the electronic device can realize the function of the garbage collection method and device, referring to Figure 4 , the electronic device comprises:

[0186] At least one processor 41, and a memory 42 connected to the at least one processor 41. The specific connection medium between the processor 41 and the memory 42 is not limited in the embodiment of the present application. Figure 4In the example, the processor 41 and the memory 42 are connected via the bus 40. The bus 40 is Figure 4 The connection between other components is shown by bold lines, and is not intended to be limiting. The bus 40 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. Alternatively, the processor 41 can also be called a controller, and there is no limitation on the name.

[0187] In the embodiment of the present application, the memory 42 stores instructions that can be executed by at least one processor 41. The at least one processor 41 can execute the garbage collection method discussed above by executing the instructions stored in the memory 42. The processor 41 can implement Figure 3 The functions of each module in the device shown.

[0188] Among them, the processor 41 is the control center of the device, and can use various interfaces and lines to connect the various parts of the entire control device. By running or executing instructions stored in the memory 42 and calling data stored in the memory 42, the various functions of the device and processing data, the device can be monitored as a whole.

[0189] In a possible design, the processor 41 may include one or more processing units, and the processor 41 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the modem processor may not be integrated into the processor 41. In some embodiments, the processor 41 and the memory 42 may be implemented on the same chip, and in some embodiments, they may also be implemented separately on separate chips.

[0190] The processor 41 may be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the garbage collection method disclosed in the embodiments of the present application may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0191] The memory 42 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The memory 42 may include at least one type of storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (Random Access Memory, RAM), a static random access memory (Static Random Access Memory, SRAM), a programmable read-only memory (Programmable Read Only Memory, PROM), a read-only memory (Read Only Memory, ROM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), a magnetic memory, a disk, an optical disk, etc. The memory 42 is any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 42 in the embodiment of the present application can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0192] By programming the processor 41, the code corresponding to the garbage collection method described in the above embodiment can be fixed into the chip, so that the chip can execute the garbage collection method when running. Figure 1 The steps of the garbage collection method of the embodiment shown are as follows: How to design and program the processor 41 is a technique well known to those skilled in the art and will not be described in detail here.

[0193] Based on the same inventive concept, an embodiment of the present application further provides a storage medium, which stores computer instructions. When the computer instructions are executed on a computer, the computer executes the garbage collection method discussed above.

[0194] In some possible implementations, various aspects of the garbage collection method provided in the present application can also be implemented in the form of a program product, which includes program code. When the program product is run on an apparatus, the program code is used to enable the control device to execute the steps of the garbage collection method according to various exemplary embodiments of the present application described above in this specification.

[0195] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0196] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0197] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0198] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0199] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A garbage collection method, characterized in that: The method comprises: Obtaining the length of time required for the overflow degree of the trash can at each stacking position to reach a preset overflow degree, wherein the overflow degree represents the amount of trash stacked in the trash can; Adjust the number of trash cans at each stacking location according to the length of time; Calculate the proportion corresponding to each stacking position, wherein the proportion is characterized by the ratio between the overflow degree of the trash can at any position and the number of the trash cans at any position; The weight of each driving route is calculated based on each proportion, the congestion level of each driving route, and the driving distance of each driving route; According to the weights and the Dijkstra algorithm, the target transportation route is calculated.

2. The method according to claim 1, characterized in that The step of obtaining the time length required for the overflow degree of the trash bins at each stacking position to reach a preset overflow degree includes: Determining whether the overflow degree of any trash can reaches the preset overflow degree according to a first relationship between the current weight of any trash can at each stacking position and the maximum bearing weight of any trash can; or According to a second relationship between the current garbage storage height in any one of the garbage bins and the height of any one of the garbage bins, determining whether the overflow degree of any one of the garbage bins reaches the preset overflow degree; When the overflow degree of any one of the trash cans reaches the preset overflow degree, a time length for the overflow degree of any one of the trash cans to reach the preset overflow degree is determined.

3. The method according to claim 2, characterized in that The determining whether the overflow degree of any of the trash cans reaches the preset overflow degree includes: According to the first relationship or the second relationship, determining whether the overflow degree of any of the trash cans reaches a first preset value; Input the collected image data of the trash cans at each stacking position into a preset model to obtain a predicted value of the overflow degree of the trash cans at each stacking position; When the predicted value reaches the second preset value and the overflow degree of any one of the trash cans reaches the first preset value, the time length for the overflow degree of any one of the trash cans to reach the preset overflow degree is determined.

4. The method according to claim 1, characterized in that The method of adjusting the number of trash cans at each stacking location according to each time length includes: Sort each time length to obtain the maximum time length and the minimum time length; Determine the difference between the maximum time length and the minimum time length; When the difference is less than the preset time length value, the number of trash cans at the stacking position corresponding to the maximum time length is reduced to the first target number value, and the number of trash cans at the stacking position corresponding to the minimum time length is increased to the second target number value, so as to obtain the adjusted number of trash cans at each stacking position.

5. The method according to claim 1, characterized in that After the target transportation route is obtained by calculation, the method further comprises: Inputting each adjusted quantity value and the target transportation route into a preset analysis model to obtain an analysis result; Classifying the analysis results and obtaining optimization parameters according to the classification results; Optimizing the adjusted quantity value and the target transportation route using the optimization parameters; The optimized quantity value and the optimized target transportation route are sent to the preset terminal.

6. A garbage collection device, characterized in that: The device comprises: An acquisition module, for acquiring the length of time required for the overflow degree of the trash cans at each stacking position to reach a preset overflow degree, wherein the overflow degree represents the amount of trash stacked in the trash can; The adjustment module adjusts the number of trash bins at each stacking location according to the length of time; A calculation module calculates the proportion corresponding to each stacking position, wherein the proportion is characterized by the ratio between the overflow degree of the garbage bin at any position and the number of the garbage bins at any position; the weight of each driving route is calculated based on each proportion, the congestion level of each driving route, and the driving distance value of each driving route; and the target transportation route is calculated based on the weight and the Dijkstra algorithm.

7. The device according to claim 6, characterized in that The acquisition module comprises: a judgment unit, for judging whether the overflow degree of any trash can at each stacking position reaches the preset overflow degree according to a first relationship between the current weight of any trash can at each stacking position and the maximum bearing weight of any trash can; or According to a second relationship between the current garbage storage height in any one of the garbage bins and the height of any one of the garbage bins, determining whether the overflow degree of any one of the garbage bins reaches the preset overflow degree; The determination unit determines, when the overflow degree of any one of the trash cans reaches the preset overflow degree, a time length for the overflow degree of any one of the trash cans to reach the preset overflow degree.

8. The device according to claim 7, characterized in that The judging unit is specifically used for: According to the first relationship or the second relationship, determining whether the overflow degree of any of the trash cans reaches a first preset value; Input the collected image data of the trash cans at each stacking position into a preset model to obtain a predicted value of the overflow degree of the trash cans at each stacking position; When the predicted value reaches the second preset value and the overflow degree of any one of the trash cans reaches the first preset value, the time length for the overflow degree of any one of the trash cans to reach the preset overflow degree is determined.

9. The device according to claim 6, characterized in that The adjustment module is specifically used for: Sort each time length to obtain the maximum time length and the minimum time length; Determine the difference between the maximum time length and the minimum time length; When the difference is less than the preset time length value, the number of trash cans at the stacking position corresponding to the maximum time length is reduced to the first target number value, and the number of trash cans at the stacking position corresponding to the minimum time length is increased to the second target number value, so as to obtain the adjusted number of trash cans at each stacking position.

10. The device according to claim 6, characterized in that The calculation module is specifically used for: Inputting each adjusted quantity value and the target transportation route into a preset analysis model to obtain an analysis result; Classifying the analysis results and obtaining optimization parameters according to the classification results; Optimizing the adjusted quantity value and the target transportation route using the optimization parameters; The optimized quantity value and the optimized target transportation route are sent to the preset terminal.

11. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to implement the method steps of any one of claims 1 to 5 when executing the computer program stored in the memory.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps of any one of claims 1 to 5 are implemented.

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