Garbage classification efficiency optimization method and system based on data prediction

Abstract

The invention relates to the technical field of garbage classification prediction, in particular to a garbage classification efficiency optimization method and system based on data prediction. According to the method, the load imbalance index is obtained according to the difference between the in-vehicle load and the total load of each putting point and the difference between the volume and the total volume, and then the imbalance route is screened out; obtaining an unbalance state value according to the number of putting points on the route and different load volume data; the routes are classified; the unbalance contribution degree is obtained by combining the load and the volume of each throwing point, and then the throwing points with the prominent contribution are screened out; acquiring a concentrated increment coefficient for the garbage quantity change in the throwing point; time intervals are obtained, and the garbage amount of each prominent contribution throwing point in different time intervals is predicted. According to the method, the garbage amount of each prominent contribution throwing point in different time intervals can be accurately predicted.

Description

Technical Field

[0001] The present invention relates to the technical field of garbage classification prediction, and particularly to a method and system for optimizing garbage classification efficiency based on data prediction. Background Art

[0002] During the garbage collection process, there are problems such as resource waste, load or volume imbalance. Traditional methods are difficult to accurately match the changes in collection demand caused by the fluctuations in garbage volume and density differences. Through data prediction, the distribution law and growth trend of garbage volume can be identified in advance, and the collection route, frequency, and vehicle type can be dynamically adjusted to ensure that the collection process is close to the full-load state, reducing the empty driving rate and resource waste. At the same time, based on the data imbalance contribution analysis and centralized increment coefficient evaluation, the high-imbalance delivery points can be accurately located, the resource allocation can be optimized, the collection efficiency can be improved, and the operation cost can be reduced.

[0003] In actual situations, due to the density differences of different garbage types, it often occurs that the load or volume of the collection vehicle is not fully utilized during the collection process, affecting the overall collection resource allocation and scheduling optimization, and thus it is impossible to accurately predict the garbage volume at each prominent contribution delivery point in different time intervals. Summary of the Invention

[0004] In order to solve the technical problem that due to the density differences of different garbage types, the load or volume of the collection vehicle is not fully utilized during the collection process, affecting the overall collection resource allocation and scheduling optimization, and thus it is impossible to accurately predict the garbage volume at each prominent contribution delivery point in different time intervals, the purpose of the present invention is to provide a method and system for optimizing garbage classification efficiency based on data prediction, and the specific technical solutions adopted are as follows: A method for optimizing garbage classification efficiency based on data prediction, the method comprising: Obtaining the garbage load data and vehicle content volume data at each garbage delivery point during each complete collection process of the collection vehicle in different preset periods; Select a complete waste collection process within an optional preset period as the reference waste collection process; obtain the load imbalance index of the waste collection vehicle during the reference waste collection process based on the difference between all the waste load data of the waste collection vehicle and the rated total load of the waste collection vehicle during the reference waste collection process, and the difference between all the internal volume data of the vehicle and the total volume of the waste collection vehicle; screen the complete waste collection process according to the load imbalance index to obtain all the load imbalance routes; obtain the imbalance state value of each load imbalance route based on the number of waste disposal points within each load imbalance route, and the waste load data and internal volume data of each waste disposal point; obtain the type of load imbalance to which each load imbalance route belongs according to the magnitude difference of the imbalance state values; obtain the degree of imbalance contribution of each waste disposal point in each load imbalance route according to the type of load imbalance to which each load imbalance route belongs, and the waste load data and internal volume data of each waste disposal point; screen out the prominent contribution disposal points in each load imbalance route according to the degree of imbalance contribution; Obtain the concentration increment coefficient of each prominent contribution disposal point each time the waste collection vehicle passes by according to the change in the amount of waste at each prominent contribution disposal point each time the waste collection vehicle passes by; cluster the concentration increment coefficients of the prominent contribution disposal points each time the waste collection vehicle passes by within each preset period to obtain all the time intervals within each preset period; predict the amount of waste at each prominent contribution disposal point in different time intervals according to the distribution characteristics of the waste load data and internal volume data of different prominent contribution disposal points in different time intervals.

[0005] Furthermore, the method for obtaining the load imbalance index includes: Obtain the load imbalance index according to the load imbalance index calculation formula, and the load imbalance index calculation formula is as follows: In the formula, represents the load imbalance index of the waste collection vehicle during the reference waste collection process; represents the total sum of the waste load data of the waste collection vehicle during the reference waste collection process; represents the rated total load of the waste collection vehicle; represents the total sum of the internal volume data of the vehicle during the reference waste collection process; represents the total volume of the waste collection vehicle; represents the maximum value function; represents the absolute value function; represents the Iverson bracket, when the condition inside the bracket holds, the value inside the bracket is 1, when the condition inside the bracket does not hold, the value inside the bracket is 0; represents the logical OR symbol.

[0006] Further, the method for obtaining the load imbalance route includes: Regarding the complete waste collection process with a load imbalance index greater than a preset first threshold as the load imbalance route of the waste collection vehicle.

[0007] Further, the method for obtaining the imbalance state value includes: Taking the difference between the waste load data of the waste collection vehicle at the current waste disposal point and the waste load data at the previous waste disposal point as the waste load increment at the current waste disposal point; Taking the difference between the vehicle volume data of the waste collection vehicle at the current waste disposal point and the vehicle volume data at the previous waste disposal point as the vehicle volume increment at the current waste disposal point; Obtaining the imbalance state value according to the imbalance state value calculation formula, and the imbalance state value calculation formula is as follows: In the formula, represents the imbalance state value of each load imbalance route; represents the number of waste disposal points of each load imbalance route; represents the th waste load increment at the waste disposal point of each load imbalance route; represents the maximum waste load data of the waste collection vehicle; represents the th vehicle volume increment at the waste disposal point of each load imbalance route; represents the maximum volume of the waste collection vehicle.

[0008] Further, the method for obtaining the type of load imbalance to which each load imbalance route belongs includes: Clustering all load imbalance routes according to the imbalance state value to obtain 2 clustering clusters; the clustering clusters are divided into a volume-dominated imbalance cluster and a load-dominated imbalance cluster; The types of load imbalance to which the load imbalance routes belong are divided into volume-dominated imbalance and load-dominated imbalance.

[0009] Further, the method for obtaining the degree of imbalance contribution includes: In each load imbalance route, taking the waste disposal point serial number as the horizontal axis and the waste load data and vehicle volume data as the vertical axis, respectively establishing a waste load coordinate system and a vehicle volume coordinate system, and respectively obtaining the waste load curve and vehicle volume curve corresponding to each load imbalance route; Taking the curve corresponding to each load imbalance route as the main curve according to the type of load imbalance, and taking the other curve as the secondary curve; Obtaining the increment value corresponding to each waste disposal point in each curve; Obtain the imbalance contribution degree according to the calculation formula of the imbalance contribution degree. The calculation formula of the imbalance contribution degree is as follows: In the formula, represents the imbalance contribution degree of the th garbage disposal point in each load imbalance route; represents the increment value of the main curve at the th garbage disposal point in each load imbalance route; represents the increment value of the secondary curve at the th garbage disposal point in each load imbalance route; represents the increment value of the main curve at the th garbage disposal point in each load imbalance route; represents the distance between the th garbage disposal point and the previous garbage disposal point in the load imbalance route; represents the increment value of the main curve at the th garbage disposal point in each load imbalance route; represents the distance between the th garbage disposal point and the next garbage disposal point in the load imbalance route; represents the Iverson bracket. When the condition in the bracket holds, the value in the bracket is 1; when the condition in the bracket does not hold, the value in the bracket is 0.

[0010] Further, the method for obtaining the prominent contribution disposal points includes: Sort the garbage disposal points in each load imbalance route in descending order according to the imbalance contribution degree to obtain a sequence of disposal points; In the sequence of disposal points, starting from the imbalance contribution degree of the first garbage disposal point, add them up in turn until the sum is greater than the preset second threshold for the first time. Then, all the garbage disposal points participating in the addition are used as the prominent contribution disposal points in the load imbalance route.

[0011] Further, the method for obtaining the concentrated increment coefficient includes: Optionally select a prominent contribution disposal point as the reference disposal point; Obtain the number of times the garbage truck passes through the reference disposal point and the garbage increment each time it passes through the reference disposal point in different preset periods; Take the ratio of the garbage increment each time the garbage truck passes through the reference disposal point to the interval time between every two adjacent passes through the reference disposal point as the concentrated increment coefficient of the reference disposal point at the corresponding moment each time the garbage truck passes through.

[0012] Further, according to the distribution characteristics of the garbage load data and the vehicle volume data at different prominent contribution delivery points within different time intervals, predict the garbage volume at each prominent contribution delivery point in different time intervals, including: Taking the corresponding moment when the reference delivery point is passed by the garbage truck each time as the horizontal axis and the corresponding concentration increment coefficient as the vertical axis to establish a coordinate system, scatter all coordinate points within all preset periods in the coordinate system to obtain the concentration increment coefficient distribution map of the reference delivery point; Cluster all the coordinate points in the coordinate system to obtain all clusters. Each cluster has a maximum value and a minimum value at the corresponding moment. Projecting onto the horizontal axis where time is located gives the leftmost and rightmost sides of the cluster. Both the leftmost and rightmost sides are time interpolation points. Project the time interval corresponding to each cluster onto the horizontal axis, and all time interpolation points can be obtained on the horizontal axis; after obtaining the time interpolation points, each interval between two time difference points is a time interval; Use the STL decomposition algorithm to obtain the reference type garbage volume trend term of the reference delivery point in each time interval within each preset period as the reference trend term; calculate the standard deviation of the reference type garbage volume of the reference delivery point in this time interval as the reference standard deviation; normalize the product between the reference trend term and the reference standard deviation to obtain the window expansion coefficient of this time interval; Round up the product of the preset initial window length and the window expansion coefficient to obtain the time window length of this time interval; Use the moving average method to predict the reference type garbage volume in the corresponding time interval within each next preset period; Traverse all time intervals of all prominent contribution delivery points to obtain the prediction results of the reference type garbage volume at each prominent contribution delivery point in each time interval within each next preset period.

[0013] A garbage classification efficiency optimization system based on data prediction, the system includes a memory, a processor, and a computer program stored in the memory and operable on the processor. When the processor executes the computer program, it implements the steps of the above-mentioned garbage classification efficiency optimization method based on data prediction.

[0014] The present invention has the following beneficial effects: Due to problems such as unbalanced garbage load or unbalanced vehicle volume during garbage collection, and there are significant differences in the amount of garbage at the same garbage disposal point during weekdays, weekends and holidays, the present invention obtains the garbage load data and vehicle volume data of each garbage disposal point during each complete garbage collection process of the garbage truck within different preset periods; since there is a situation where the vehicle volume and the load inside the vehicle are not fully utilized during the garbage collection process, the load imbalance index of the garbage truck during the reference garbage collection process is obtained; since there are significant differences in the density of different types of garbage, all complete garbage collection processes of the garbage truck within each preset period are screened to select the garbage collection processes that cannot achieve load balance for the garbage truck, and subsequent analysis is performed on them; since different types of load imbalance lead to different imbalance states of the garbage truck, the imbalance state value in each load imbalance route is calculated, and an analysis is performed on which situation of the type of load imbalance that is likely to be triggered by this route; in order to find out the garbage disposal points that significantly affect the complete garbage collection process and thus make the complete garbage collection process close to the garbage disposal points belonging to the type of load imbalance, the garbage disposal points in the complete garbage collection processes belonging to the type of load imbalance are screened to obtain the prominent contribution disposal points in each load imbalance route; since there are large differences in the garbage increment in different time intervals, first, according to the change in the garbage load data of each prominent contribution disposal point each time the garbage truck passes by, the concentrated increment coefficient of each prominent contribution disposal point each time the garbage truck passes by is obtained, and all time nodes are divided into different time intervals according to the concentrated increment coefficient; and the amount of garbage at each prominent contribution disposal point in different time intervals is predicted. The present invention takes into account the situation where the load or volume of the garbage truck is not fully utilized during the garbage collection process, and can accurately predict the amount of garbage at each prominent contribution disposal point in different time intervals. Brief Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is a flowchart of a method for optimizing the efficiency of garbage classification based on data prediction provided by an embodiment of the present invention. Detailed Embodiments

[0017] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manner, structure, features and effects of a garbage classification efficiency optimization method and system based on data prediction proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0019] The following specifically describes the specific solution of a garbage classification efficiency optimization method and system based on data prediction provided by the present invention with reference to the accompanying drawings.

[0020] Please refer to Figure 1 , which shows a garbage classification efficiency optimization method based on data prediction provided by an embodiment of the present invention. The method includes: Step S1: Obtain the garbage load data and vehicle volume data of each garbage disposal point during each complete garbage collection process by the garbage collection vehicle within different preset periods.

[0021] The embodiment of the present invention is mainly applied to the garbage volume prediction scenario of garbage disposal points. Due to problems such as garbage load imbalance or vehicle volume imbalance during the garbage collection process, the embodiment of the present invention obtains the garbage load data and vehicle volume data of each garbage disposal point during each complete garbage collection process by the garbage collection vehicle. Since the garbage volume at the same garbage disposal point varies greatly during weekdays, weekends and holidays, the garbage load data and vehicle volume data of each garbage disposal point within different preset periods are statistically analyzed and subsequent time intervals are divided.

[0022] In one embodiment of the present invention, the preset period is set to 1 day. It should be noted that the preset period can be set by oneself and is not limited herein.

[0023] In the embodiment of the present invention, the description is mainly based on weekdays. Since weekends and holidays only differ in category and do not affect the analysis process, the analysis process for weekends and holidays is the same as that for weekdays.

[0024] Step S2: Select a complete garbage collection process within an arbitrarily selected preset period as the reference collection process; obtain the load imbalance index of the garbage truck during the reference collection process based on the difference between all the garbage load data of the garbage truck during the reference collection process and the rated total load of the garbage truck, and the difference between all the internal volume data of the truck and the total volume of the garbage truck; screen the complete collection process according to the load imbalance index to obtain all the load imbalance routes; obtain the imbalance state value of each load imbalance route based on the number of garbage disposal points within each load imbalance route, and the garbage load data and internal volume data of each garbage disposal point; obtain the type of load imbalance to which each load imbalance route belongs based on the magnitude difference of the imbalance state values; obtain the imbalance contribution degree of each garbage disposal point in each load imbalance route based on the type of load imbalance to which each load imbalance route belongs, and the garbage load data and internal volume data of each garbage disposal point; screen out the prominent contribution disposal points in each load imbalance route according to the imbalance contribution degree.

[0025] For each garbage truck, there is a maximum load that can be carried and a maximum volume that can be accommodated during a garbage collection process. When the vehicle is defined as load imbalance, it means the following situation: that is, after completing a collection operation, the difference between the cumulative garbage load data of the garbage truck and the rated total load of the garbage truck is large, and the difference between the cumulative internal volume data of the truck and the total volume of the garbage truck is large. The occurrence of this situation indicates that the garbage truck is not fully utilized during the current collection process. Therefore, in the embodiments of the present invention, the load imbalance index of the garbage truck during the reference collection process is obtained based on the difference between all the garbage load data of the garbage truck during the reference collection process and the rated total load of the garbage truck, and the difference between all the internal volume data of the truck and the total volume of the garbage truck.

[0026] Preferably, in an embodiment of the present invention, the method for obtaining the load imbalance index includes: Obtain the load imbalance index according to the load imbalance index calculation formula, and the load imbalance index calculation formula is as follows: In the formula, represents the load imbalance index of the garbage truck during the reference collection process; represents the sum of the garbage load data of the garbage truck during the reference collection process; represents the rated total load of the garbage truck; represents the sum of the internal volume data of the truck during the reference collection process; represents the total volume of the garbage truck; represents the maximum value function; represents the absolute value function; Denotes the Iverson bracket. When the condition inside the bracket holds, the value inside the bracket is 1; when the condition inside the bracket does not hold, the value inside the bracket is 0; Denotes the logical OR symbol.

[0027] In the calculation formula of the load imbalance index, when the condition inside the Iverson bracket holds, it indicates that the waste collection vehicle does not end in a fully loaded state during the reference waste collection process. At this time the value is 1, and at this time, the load imbalance index of the waste collection vehicle during the reference waste collection process is calculated; and when or is larger, it indicates that the load imbalance state of the waste collection vehicle after the reference waste collection process is more serious, and at this time, the load imbalance index of the waste collection vehicle during the reference waste collection process is larger.

[0028] The occurrence of load imbalance of the waste collection vehicle during the waste collection process is due to the load contradiction caused by the physical property differences of different classified wastes received. Specifically, there are significant differences in the density of different types of wastes. Recyclable wastes, such as plastics and papers, which are low-density wastes, are easy to reach the volume limit, while wet wastes have high-density characteristics and are therefore more likely to reach the load limit. All complete waste collection processes of the waste collection vehicle within each preset period are screened to select the waste collection processes that cannot make the waste collection vehicle achieve load balance, and subsequent analysis is carried out on them.

[0029] Preferably, in an embodiment of the present invention, the method for obtaining the load imbalance route includes: Regarding the complete waste collection process with a load imbalance index greater than a preset first threshold as the load imbalance route of the waste collection vehicle. In an embodiment of the present invention, the preset first threshold is set to 0.8. It should be noted that the preset first threshold can be set by oneself and is not limited herein.

[0030] In reality, the load imbalance of the waste collection vehicle is related to the type of waste being collected, that is, high-density wastes, such as wet wastes, are prone to trigger load-dominated imbalance, while low-density wastes, such as dry wastes, are prone to trigger volume-dominated imbalance. Therefore, in the embodiment of the present invention, the imbalance state value in each load imbalance route is calculated to analyze which of the two imbalances the route is prone to trigger.

[0031] Preferably, in the embodiment of the present invention, the method for obtaining the imbalance state value includes: Taking the difference between the waste load data of the waste collection vehicle at the current waste disposal point and the waste load data at the previous waste disposal point as the waste load increment at the current waste disposal point.

[0032] Taking the difference between the vehicle volume data of the waste collection vehicle at the current waste disposal point and the vehicle volume data at the previous waste disposal point as the vehicle volume increment at the current waste disposal point.

[0033] Obtain the imbalance state value according to the imbalance state value calculation formula, and the imbalance state value calculation formula is as follows: In the formula, represents the imbalance state value of each load imbalance route; represents the number of garbage disposal points on each load imbalance route; represents the th garbage load increment of the th garbage disposal point on each load imbalance route; represents the maximum garbage load data of the garbage truck; represents the vehicle volume increment of the th garbage disposal point on each load imbalance route;

[0034] In the imbalance state value calculation formula, represents the garbage load growth rate of the th garbage disposal point on each load imbalance route, represents the vehicle volume growth rate of the th garbage disposal point on each load imbalance route; at the th garbage disposal point, if the garbage load growth rate is greater than the vehicle volume growth rate, it means that the th garbage disposal point is more inclined to be load-dominated imbalance, while the garbage load growth rate is less than the vehicle volume growth rate, indicating that the th garbage disposal point is more inclined to be volume-dominated imbalance; analyze all garbage disposal points to obtain the imbalance state value of each load imbalance route.

[0035] Preferably, in the embodiment of the present invention, the method for obtaining the type of load imbalance to which each load imbalance route belongs includes: The imbalance state value reflects which dominant imbalance state the corresponding garbage collection route tends to be in the current garbage collection process. The larger the imbalance state value, the more inclined it is to be load-dominated imbalance, and the smaller the imbalance state value, the more inclined it is to be volume-dominated imbalance. Therefore, the K-means clustering algorithm is used here, and the number of clustering clusters is set to 2. After clustering the imbalance state values, two clustering clusters are obtained as the types of load imbalance to which the load imbalance routes belong. The type of load imbalance corresponding to the imbalance state value in the clustering cluster with the smaller mean belongs to volume-dominated imbalance, while the type of load imbalance corresponding to the imbalance state value in the other cluster belongs to load-dominated imbalance.

[0036] Next, the garbage disposal points in the complete waste collection process with the corresponding types of load imbalance are screened. The purpose is to find out the garbage disposal points that significantly affect the complete waste collection process and make the complete waste collection process close to the corresponding types of load imbalance. That is, the reason for the load imbalance or volume imbalance in the complete waste collection process is that the amount of garbage of the corresponding type in some garbage disposal points is excessive. Therefore, in the embodiments of the present invention, according to the type of load imbalance to which each load imbalance route belongs, as well as the garbage load data and vehicle volume data of each garbage disposal point, the imbalance contribution degree of each garbage disposal point in each load imbalance route is obtained; the prominent contribution disposal points in each load imbalance route are screened according to the imbalance contribution degree.

[0037] Preferably, in the embodiments of the present invention, the method for obtaining the imbalance contribution degree includes: In each load imbalance route, taking the garbage disposal point number as the horizontal axis and the garbage load data and vehicle volume data as the vertical axis, a garbage load coordinate system and a vehicle volume coordinate system are respectively established, and the garbage load curve and the vehicle volume curve corresponding to each load imbalance route are respectively obtained, and the starting point and the ending point of the garbage load curve and the vehicle volume curve are the same.

[0038] Since there are two types of garbage in any load imbalance route, it is necessary to analyze the changes of the garbage load curve and the vehicle volume curve simultaneously. According to the type of load imbalance, the curve corresponding to each load imbalance route is used as the main curve, and the other curve is used as the secondary curve; that is, for the load imbalance route with load-dominated imbalance, the garbage load curve is used as the main curve and the vehicle volume curve is used as the secondary curve, and for the load imbalance route with volume-dominated imbalance, the vehicle volume curve is used as the main curve and the garbage load curve is used as the secondary curve.

[0039] Calculate the difference between all the garbage of the garbage type corresponding to each curve of each garbage disposal point and all the garbage of the garbage type corresponding to each curve of the previous garbage disposal point, as the incremental value corresponding to each garbage disposal point in each curve.

[0040] The imbalance contribution degree is obtained according to the imbalance contribution degree calculation formula. The imbalance contribution degree calculation formula is as follows: In the formula, represents the imbalance contribution degree of the th garbage disposal point in each load imbalance route; represents the incremental value of the main curve in the th garbage disposal point in each load imbalance route; represents the incremental value of the secondary curve in the th garbage disposal point in each load imbalance route; represents the incremental value of the main curve at the th garbage disposal point in each load imbalance route; represents the distance between the th garbage disposal point and the previous garbage disposal point in the load imbalance route; represents the incremental value of the main curve at the th garbage disposal point in each load imbalance route; represents the distance between the th garbage disposal point and the next garbage disposal point in the load imbalance route; represents the Iverson bracket. When the condition inside the bracket holds, the value inside the bracket is 1; when the condition inside the bracket does not hold, the value inside the bracket is 0.

[0041] In the calculation formula of the imbalance contribution degree, when the condition inside the Iverson bracket holds and the incremental value of the main curve is greater than the incremental value of the secondary curve, the imbalance contribution degree of each garbage disposal point in each load imbalance route is calculated at this time; and the difference between the incremental value of the main curve at the th garbage disposal point and the incremental value of the secondary curve at the th garbage disposal point The larger it is, the more it indicates that the th garbage disposal point is more inclined to collect the garbage type corresponding to the main curve. At this time, the imbalance contribution degree of the th garbage disposal point is greater; in addition, the generation and classification of garbage types have significant geographical location clustering. For example, commercial areas mostly generate wet garbage, and areas closer to commercial areas are more likely to generate more wet garbage due to the radiation of commercial areas. Then, when carrying out the cleaning work in this area, it is more likely to trigger load imbalance. Therefore The larger it is, the more it indicates that this route is more likely to be in the area near the commercial area and is affected by the radiation of the commercial area, and is prone to generate wet garbage, that is, it is easily affected by load imbalance. At this time, the area near the th garbage disposal point is more inclined to have the garbage type corresponding to the main curve.

[0042] Preferably, in the embodiment of the present invention, the method for obtaining the prominent contribution disposal points includes: Sort the garbage disposal points in each load imbalance route according to the imbalance contribution degree from large to small to obtain a sequence of disposal points.

[0043] In the sequence of disposal points, starting from the imbalance contribution degree of the first garbage disposal point, add them up in turn until the sum is greater than the preset second threshold for the first time. All the garbage disposal points participating in the addition are used as the prominent contribution disposal points in the load imbalance route. In an embodiment of the present invention, the preset second threshold is set to 50%. It should be noted that the preset second threshold can be set by oneself and is not limited here.

[0044] Step S3: According to the change in the amount of garbage at each prominent contribution dropping point each time the garbage truck passes by, obtain the centralized increment coefficient of each prominent contribution dropping point each time the garbage truck passes by; cluster the centralized increment coefficients of each prominent contribution dropping point each time the garbage truck passes by within each preset period to obtain all time intervals within each preset period; predict the amount of garbage at each prominent contribution dropping point in different time intervals according to the distribution characteristics of the garbage load data and the vehicle content volume data of different prominent contribution dropping points in different time intervals.

[0045] Based on the above steps, prominent contribution dropping points can be obtained, and then the amount of garbage at the prominent contribution dropping points can be predicted. The object of prediction is the amount of garbage corresponding to the main curve type among the prominent contribution dropping points. In the embodiments of the present invention, the garbage generation time is divided into weekdays, weekends and holidays, and only the garbage types corresponding to the main curve are discussed. Therefore, long-term trends and seasonal factors are avoided. In the embodiments of the present invention, the moving average method is used to predict the amount of garbage at each prominent contribution dropping point in different time intervals, which can effectively smooth the noise, reflect the basic fluctuation law, and is suitable for short-term prediction to support real-time scheduling optimization.

[0046] Since there are large differences in the garbage increments in different time intervals, it is necessary to make the time window length required by the moving average method adaptive. Therefore, in the embodiments of the present invention, first, according to the change in the garbage load data of each prominent contribution dropping point each time the garbage truck passes by, obtain the centralized increment coefficient of each prominent contribution dropping point each time the garbage truck passes by, and divide all time nodes into three time intervals: weekdays, weekends and holidays according to the centralized increment coefficient; and predict the amount of garbage at each prominent contribution dropping point in different time intervals.

[0047] Preferably, in an embodiment of the present invention, the method for obtaining the centralized increment coefficient includes: Optionally select a prominent contribution dropping point as a reference dropping point; obtain the number of times the garbage truck passes by the reference dropping point in different preset periods and the garbage increment each time it passes by the reference dropping point.

[0048] Take the garbage type corresponding to the main curve when the garbage truck passes by the reference dropping point as the reference type; Take the ratio between the garbage increment each time the garbage truck passes by the reference dropping point and the interval time between every two adjacent passes by the reference dropping point as the centralized increment coefficient of the reference dropping point at the corresponding moment each time the garbage truck passes by. In the embodiments of the present invention, the calculation formula of the centralized increment coefficient is as follows: In the formula, represents the centralized increment coefficient of the reference dropping point at the corresponding moment each time the garbage truck passes by; represents the increment of the reference type of garbage when the garbage truck passes the reference delivery point for the th time; represents the time interval between the corresponding time when the garbage truck passes the reference delivery point for the th time and the corresponding time when it passes the reference delivery point for the th time.

[0049] In the calculation formula of the centralized increment coefficient, if more increment of the reference type of garbage is generated at the reference delivery point in a relatively short time, that is is smaller, is larger, it indicates that the centralized increment coefficient at the corresponding time when the garbage truck passes the reference delivery point for the th time is larger.

[0050] Preferably, an embodiment of the present invention provides a prediction method, and the specific steps are as follows: Taking the corresponding time when the reference delivery point is passed by the garbage truck each time as the horizontal axis and the corresponding centralized increment coefficient as the vertical axis to establish a coordinate system, and spreading all the coordinate points within all preset periods in the coordinate system to obtain the centralized increment coefficient distribution map of the reference delivery point.

[0051] Using the LOF algorithm to cluster all the coordinate points in the coordinate system to obtain all the clustering clusters. Each cluster has a maximum value and a minimum value at the corresponding time. Projecting them onto the horizontal axis where time is located is the leftmost and rightmost of the clustering cluster. Both the leftmost and rightmost are time interpolation points. Projecting the time interval corresponding to each cluster onto the horizontal axis, all the time interpolation points can be obtained on the horizontal axis. After obtaining the time interpolation points, the interval between every two time difference points is a time interval.

[0052] So far, the acquisition of all time intervals of the reference delivery point within each preset period is completed.

[0053] Using the STL decomposition algorithm to obtain the reference type garbage quantity trend item of the reference delivery point under each time interval within each preset period as the reference trend item; calculating the standard deviation of the reference type garbage quantity of the reference delivery point under this time interval as the reference standard deviation; normalizing the product between the reference trend item and the reference standard deviation to obtain the window expansion coefficient of this time interval.

[0054] Rounding up the product of the preset initial window length and the window expansion coefficient to obtain the time window length of this time interval.

[0055] Using the moving average method to predict the reference type garbage quantity of the corresponding time interval within each next preset period.

[0056] Traverse all time intervals of all prominent contribution delivery points, and obtain the predicted results of the reference type of garbage volume for each time interval of each prominent contribution delivery point in each next preset period.

[0057] In summary, obtain the garbage load data and vehicle content volume data of each garbage delivery point during each complete garbage collection process in different preset periods; select a complete garbage collection process in a preset period as the reference garbage collection process; according to the difference between all the garbage load data of the garbage truck during the reference garbage collection process and the rated total load of the garbage truck, and the difference between all the vehicle content volume data and the total volume of the garbage truck, obtain the load imbalance index of the garbage truck during the reference garbage collection process; screen the complete garbage collection process according to the load imbalance index to obtain all load imbalance routes; according to the number of garbage delivery points in each load imbalance route, and the garbage load data and vehicle content volume data of each garbage delivery point, obtain the imbalance state value of each load imbalance route; according to the size difference of the imbalance state values, obtain the load imbalance types to which each load imbalance route belongs; according to the load imbalance types to which each load imbalance route belongs, and the garbage load data and vehicle content volume data of each garbage delivery point, obtain the imbalance contribution degree of each garbage delivery point in each load imbalance route; screen out the prominent contribution delivery points in each load imbalance route according to the imbalance contribution degree; according to the change of the garbage load data of each prominent contribution delivery point every time the garbage truck passes by, obtain the concentration increment coefficient of each prominent contribution delivery point every time the garbage truck passes by; cluster the concentration increment coefficients of each prominent contribution delivery point every time the garbage truck passes by in each preset period to obtain all time intervals in each preset period; predict the garbage volume of each prominent contribution delivery point in different time intervals according to the distribution characteristics of the garbage load data and vehicle content volume data of different prominent contribution delivery points in different time intervals.

[0058] An embodiment of the present invention provides a garbage classification efficiency optimization system based on data prediction. The system includes a memory, a processor, and a computer program, where the memory is used to store the corresponding computer program, the processor is used to run the corresponding computer program, and when the computer program runs in the processor, it can implement the method described in steps S1 - S3.

[0059] It should be noted that: the above sequence of embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0060] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized respectively.

Claims

1. A method for optimizing garbage classification efficiency based on data prediction, characterized in that: The method comprises: Obtain the garbage load data and vehicle volume data of each garbage drop point during each complete garbage collection process of the garbage collection vehicle in different preset periods; A complete cleaning process within a preset period is selected as a reference cleaning process; based on the difference between all garbage load data of the cleaning vehicle in the reference cleaning process and the rated total load of the cleaning vehicle, as well as the difference between all vehicle volume data and the total volume of the cleaning vehicle, the load imbalance index of the cleaning vehicle in the reference cleaning process is obtained; based on the load imbalance index, the complete cleaning process is screened to obtain all load imbalance routes; based on the number of garbage delivery points in each load imbalance route, as well as the garbage load data and vehicle volume data of each garbage delivery point, the imbalance state value of each load imbalance route is obtained; based on the difference in the size of the imbalance state value, the load imbalance type of each load imbalance route is obtained; based on the load imbalance type of each load imbalance route, as well as the garbage load data and vehicle volume data of each garbage delivery point, the imbalance contribution degree of each garbage delivery point in each load imbalance route is obtained; based on the imbalance contribution degree, the outstanding contribution delivery points in each load imbalance route are screened out; According to the change in the amount of garbage at each outstanding contribution placement point each time the garbage collection truck passes by, the concentrated incremental coefficient of each outstanding contribution placement point each time the garbage collection truck passes by is obtained; the concentrated incremental coefficients of the outstanding contribution placement points each time the garbage collection truck passes by in each preset period are clustered to obtain all time intervals in each preset period; according to the distribution characteristics of garbage load data and vehicle volume data of different outstanding contribution placement points in different time intervals, the garbage volume of each outstanding contribution placement point in different time intervals is predicted.

2. The method for optimizing garbage classification efficiency based on data prediction according to claim 1, characterized in that: The method for obtaining the load imbalance indicator includes: The load imbalance index is obtained according to the load imbalance index calculation formula, and the load imbalance index calculation formula is as follows: In the formula, Indicates the load imbalance index of the cleaning vehicle during the reference cleaning process; It represents the total garbage load data of the garbage collection vehicle during the reference collection process; Indicates the rated total load of the truck; It indicates the total volume data of the cleaning vehicle during the reference cleaning process; Indicates the total volume of the cleaning vehicle; represents the maximum value function; represents the absolute value function; Indicates Iverson brackets. When the condition in the brackets is met, the value in the brackets is 1. When the condition in the brackets is not met, the value in the brackets is 0. Represents a logical or symbol.

3. The method for optimizing garbage classification efficiency based on data prediction according to claim 1 is characterized in that: The method for obtaining the load imbalance route includes: The complete cleaning process in which the load imbalance index is greater than the preset first threshold is used as the load imbalance route of the cleaning vehicle.

4. The method for optimizing garbage classification efficiency based on data prediction according to claim 1 is characterized in that: The method for obtaining the imbalance state value includes: The difference between the garbage load data of the garbage collection vehicle at the current garbage collection point and the garbage load data of the previous garbage collection point is used as the garbage load increment of the current garbage collection point; The difference between the vehicle volume data of the current garbage disposal point and the vehicle volume data of the previous garbage disposal point is used as the vehicle volume increment of the current garbage disposal point; The imbalance state value is obtained according to the imbalance state value calculation formula, and the imbalance state value calculation formula is as follows: In the formula, Indicates the imbalance status value of each load imbalance route; Indicates the number of garbage drop points on each load imbalance route; Indicates the first The increase in garbage load at each garbage drop-off point; Indicates the maximum garbage load data of the garbage collection truck; Indicates the first The increase in vehicle volume per garbage disposal point; Indicates the maximum volume of the truck.

5. The method for optimizing garbage classification efficiency based on data prediction according to claim 1 is characterized in that: The method for obtaining the load imbalance type to which each load imbalance route belongs includes: Clustering all load imbalance routes according to the imbalance state value to obtain two clusters; the clusters are divided into a volume-dominated imbalance cluster and a load-dominated imbalance cluster; The load imbalance types to which the load imbalance route belongs are divided into volume-dominated imbalance and load-dominated imbalance.

6. The method for optimizing garbage classification efficiency based on data prediction according to claim 1 is characterized in that: The method for obtaining the imbalance contribution degree includes: In each load imbalance route, a garbage load coordinate system and a vehicle volume coordinate system are established with the garbage placement point number as the horizontal axis and the garbage load data and the vehicle volume data as the vertical axis, and the garbage load curve and the vehicle volume curve corresponding to each load imbalance route are obtained respectively; According to the type of load imbalance, the curve corresponding to each load imbalance route is used as the main curve, and the other curve is used as the secondary curve; Get the incremental value corresponding to each garbage placement point in each curve; The imbalance contribution degree is obtained according to the imbalance contribution degree calculation formula, and the imbalance contribution degree calculation formula is as follows: In the formula, Indicates the first The degree of imbalance contribution of each garbage disposal point; Indicates that the main curve in each load imbalance route is The incremental value of each garbage disposal point; Indicates the secondary curve in each load imbalance route The incremental value of each garbage disposal point; Indicates that the main curve in each load imbalance route is The incremental value of each garbage disposal point; Indicates the load imbalance route The distance between a garbage disposal point and the previous garbage disposal point; Indicates that the main curve in each load imbalance route is The incremental value of each garbage disposal point; Indicates the load imbalance route The distance between the first garbage disposal point and the next garbage disposal point; Represents Iverson brackets. When the condition in the brackets is met, the value in the brackets is 1. When the condition in the brackets is not met, the value in the brackets is 0.

7. The method for optimizing garbage classification efficiency based on data prediction according to claim 1 is characterized in that: The method for obtaining the outstanding contribution delivery point includes: Sort the garbage disposal points in each load imbalance route from large to small according to the degree of imbalance contribution to obtain a disposal point sequence; In the sequence of garbage disposal points, the imbalance contribution degrees of the first garbage disposal point are added in sequence until the sum is greater than a preset second threshold for the first time, and all garbage disposal points participating in the addition are regarded as outstanding contribution disposal points in the load imbalance route.

8. The method for optimizing garbage classification efficiency based on data prediction according to claim 1 is characterized in that: The method for obtaining the concentrated increment coefficient includes: Choose any outstanding contribution delivery point as a reference delivery point; Obtain the number of times the garbage collection vehicle passes through the reference delivery point in different preset periods and the garbage increment each time it passes through the reference delivery point; The ratio of the garbage increment each time the garbage collection truck passes through the reference delivery point to the interval time between two adjacent passes through the reference delivery point is used as the concentrated increment coefficient of the reference delivery point at the corresponding moment each time the garbage collection truck passes through.

9. The method for optimizing garbage classification efficiency based on data prediction according to claim 1, characterized in that: According to the distribution characteristics of garbage load data and vehicle volume data of different outstanding contribution delivery points in different time intervals, the garbage volume of each outstanding contribution delivery point in different time intervals is predicted, including: A coordinate system is established with the corresponding time of the reference delivery point each time the cleaning vehicle passes by as the horizontal axis and the corresponding concentrated increment coefficient as the vertical axis, and all coordinate points within all preset periods are scattered in the coordinate system to obtain a distribution diagram of the concentrated increment coefficient of the reference delivery point; Cluster all coordinate points in the coordinate system to obtain all clusters. Each cluster has the maximum and minimum values ​​at the corresponding time. The leftmost and rightmost points of the cluster are projected onto the horizontal axis where time is located. Both the leftmost and rightmost points are time interpolation points. Project the time interval corresponding to each cluster onto the horizontal axis to obtain all time interpolation points on the horizontal axis. After obtaining the time interpolation points, the time interval between every two time difference points is a time interval. The STL decomposition algorithm is used to obtain the reference type garbage quantity trend item of the reference delivery point in each time interval in each preset period as the reference trend item; the standard deviation of the reference type garbage quantity of the reference delivery point in the time interval is calculated as the reference standard deviation; the product between the reference trend item and the reference standard deviation is normalized to obtain the window expansion coefficient of the time interval; The product of the preset initial window length and the window expansion coefficient is rounded up to obtain the time window length of the time interval; Use the moving average method to predict the amount of reference type garbage in the corresponding time interval in each next preset period; Traverse all time intervals of all outstanding contribution delivery points to obtain the reference type garbage volume prediction results for each outstanding contribution delivery point in each time interval in each next preset period.

10. A garbage classification efficiency optimization system based on data prediction, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of a method for optimizing garbage sorting efficiency based on data prediction as described in any one of claims 1 to 9 are implemented.

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