Garbage clearance method and system

Abstract

The invention relates to the field of data processing, in particular to a garbage collection method and system, and the method comprises the steps: calculating the periodicity of garbage throwing in a preset time interval; calculating the weight according to the periodicity of garbage throwing and the moment interval, and carrying out throwing prediction by utilizing a weighted moving average method; the periodic calculation method comprises the following steps: clustering throwing moments of historical garbage throwing data by using a density peak clustering algorithm to obtain a plurality of clusters, wherein the clustering center of each cluster is a mean value of all throwing moments in the cluster; taking the clustering cluster of the clustering center of the clustering cluster in the moment interval as the clustering cluster in the moment interval; the stability of the moment interval is calculated according to the throwing moments of all the clustering clusters in the moment interval and the garbage throwing amount; and calculating the periodicity of the moment interval according to the moment distribution of all the clusters in the same moment interval of continuous days and the garbage throwing amount. The method has the effect of improving the accuracy of garbage throwing prediction.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a garbage removal method and system. Background Art

[0002] Fixed garbage stations, as the core nodes of garbage collection, are responsible for the centralized storage and transportation of large amounts of garbage. Traditional garbage collection methods mostly rely on manual scheduling and inspections. The arrangement of collection tasks lacks scientificity and accuracy, leading to problems such as low collection efficiency and garbage accumulation.

[0003] The prior art can predict the user's garbage disposal behavior (garbage site overflow situation) by placing sensors at garbage sites, monitoring the garbage volume at garbage sites in real time, and using the historical data of users' garbage disposal. For example, a Chinese patent with the patent authorization number CN118585852B discloses a garbage classification resource optimization management method based on a cloud platform, which includes the following steps: predicting and analyzing the overflow status of garbage sites based on real-time monitoring data and historical monitoring data of garbage sites.

[0004] The weighted moving average method can be used to predict the weight of garbage at garbage stations, prompting staff to remove garbage in a timely manner. Among them, the traditional weighted moving average method is a statistical method for smoothing time series data. Its working principle is to give different weights to different data points in the time window when calculating the average value of each time point, rather than simply giving equal weights to all data points. The traditional approach is to give higher weights to recent data in order to better reflect the latest trends of the data.

[0005] However, for the data on users' garbage disposal, since the time when users dispose of garbage is usually concentrated around breakfast and dinner, if the traditional weighted moving average method is used to predict the garbage weight, a higher weight will be given, the reference value will be low, and the prediction result will be inaccurate. Summary of the invention

[0006] In order to solve the technical problem of how to accurately predict garbage disposal according to the user's garbage disposal habits using a weighted moving average method, the present application provides a garbage removal method and system.

[0007] In the first aspect, the present application provides a garbage removal method, which adopts the following technical solution: A garbage collection method comprises the following steps: setting time intervals, calculating the periodicity of garbage placement in any time interval; calculating weights according to the periodicity of garbage placement and the time interval, using a weighted moving average method to make a placement prediction, and generating a garbage collection reminder according to the prediction result; a method for calculating the periodicity comprises: obtaining the placement time of historical garbage placement data, clustering the placement time by using a density peak clustering algorithm to obtain a plurality of clusters, wherein the cluster center of the cluster is the mean of all the placement times in the cluster; taking the cluster whose cluster center is in the time interval as the cluster within the time interval; for any time interval, calculating the stability of the time interval according to the placement times of all clusters within the time interval and the amount of garbage placement; and calculating the periodicity of the time interval according to the time distribution of all clusters within the same time interval on consecutive days and the amount of garbage placement.

[0008] By analyzing the garbage placement situation in different time intervals, the periodicity in each time interval is calculated. Specifically, by clustering the time when users place garbage, and dividing the clusters according to the time intervals, the periodicity of the time interval is calculated according to all the clusters in each time interval, so that the periodicity calculation result of any time interval is more accurate. According to the periodicity in different time intervals, the time interval is given a weight, so that the prediction result of garbage placement is more accurate.

[0009] Optionally, the stability calculation formula can also be: , where Indicates Tiandi Stability of the time interval; Indicates Tiandi The total number of clusters in the time interval; Indicates Tiandi In the time interval The standard deviation of the clusters; Indicated by natural constant The exponential function of base .

[0010] The stability of the time interval is quantified by the fluctuation degree of clusters within the time interval and the total number of clusters. Indicates the degree of fluctuation of all clusters within the time interval. The larger the value, the greater the degree of fluctuation, and the weaker the stability within the time interval. Conversely, the stronger the stability within the time interval. Indicates the total number of clusters within the time interval; the smaller its value is, the closer the data in the cluster is, that is, the stronger the stability of the time interval is, and vice versa, the weaker the stability of the time interval is.

[0011] Optionally, the stability calculation formula can also be: , where Indicates Tiandi Stability of the time interval; Indicates Tiandi The total number of clusters in the time interval; Indicated by natural constant The exponential function of base .

[0012] The stability of data quantification in a single dimension of the total number of clusters within the time interval is small in computational complexity and is suitable for application scenarios that require less computational complexity.

[0013] An optional, periodic calculation formula is: , where Indicates The periodicity of the time interval; Indicates the total number of days that it lasts; Indicates Tiandi The garbage disposal weight of all clusters in the time interval; Indicates the number of days in the total number of days The mean of the garbage disposal weight of all clusters in the time interval; Indicates Tiandi Stability of the time interval; Indicated by natural constant The exponential function of base .

[0014] It indicates the difference between the garbage weight put out in this time interval and the average level on each day in the consecutive days. The smaller the value, the more consistent the garbage weight put out in this time interval, and the stronger the periodicity of this time interval. Indicates the stability of the time interval. The larger the value, the stronger the periodicity of the time interval. This embodiment focuses more on evaluating the consistency and stability of the garbage delivery volume on consecutive days.

[0015] Optional, , where Indicates The periodicity of the time interval; Indicates the total number of days that it lasts; Indicates Tiandi The garbage disposal weight of all clusters in the time interval; Indicates Tiandi The garbage disposal weight of all clusters in the time interval; Indicates Tiandi Stability of the time interval; Indicated by natural constant The exponential function of base .

[0016] It indicates the difference between the garbage weight and the average level in the same time interval of consecutive days. This embodiment focuses more on evaluating the periodic changes of garbage volume in a shorter time interval (between days).

[0017] Optionally, the weight is calculated according to the periodicity and time interval of garbage delivery, including the steps of: presetting a sliding window, and calculating the weights corresponding to all time intervals in the sliding window; the weight calculation formula is: , where Indicates the first The weight of each time interval; Indicates the sequence number of the time interval in the sliding window; Indicates the first The periodicity of the time interval; Indicates the first The starting time of the time interval; Indicates the starting time of the time interval that needs to be predicted; Represents the standard normalization function.

[0018] The garbage disposal data in time intervals with stronger periodicity are more stable, so their weights should be higher, and the prediction results are more reliable. On the contrary, time intervals with weaker periodicity may be noise data points, and their weights should be lower.

[0019] Optionally, the predicted value is obtained by using the weighted moving average method. The expression of the predicted value is: , where Indicates the predicted value of the garbage delivery weight at the next moment; Indicates the sliding window size; Indicates the first The weight of each time interval; Indicates the first The weight of garbage put in a time interval.

[0020] In the second aspect, the present application provides a garbage removal system, which adopts the following technical solution: A garbage removal system comprises a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the garbage removal method described above is implemented.

[0021] The above-mentioned garbage removal method is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a system based on the memory and the processor for easy use.

[0022] This application has the following technical effects: By analyzing the garbage placement situation in different time intervals, the periodicity in each time interval is calculated. Specifically, by clustering the time when users place garbage, and dividing the clusters according to the time intervals, the periodicity of the time interval is calculated according to all the clusters in each time interval, so that the periodicity calculation result of any time interval is more accurate. According to the periodicity in different time intervals, the time interval is given a weight, so that the prediction result of garbage placement is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present application will become easily understood. In the accompanying drawings, several embodiments of the present application are shown in an exemplary and non-restrictive manner, and the same or corresponding numbers represent the same or corresponding parts.

[0024] Figure 1 It is a method flow chart of a garbage removal method according to an embodiment of the present application.

[0025] Figure 2 It is a method flow chart of step S1 in a garbage removal method in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0027] It should be understood that when the terms "first", "second", etc. are used in the claims, specification and drawings of the present application, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the specification and claims of the present application indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections.

[0028] The present application embodiment discloses a garbage removal method, referring to Figure 1 , including steps S1-S2, which are as follows: S1: Set the time interval and calculate the periodicity of garbage delivery in any time interval.

[0029] Collect garbage disposal data at garbage sites; garbage disposal data refers to the amount of garbage disposed by residents, businesses and other users at fixed garbage sites, as well as information such as the time of disposal. Monitoring of garbage disposal data is used to help managers understand the amount of garbage disposed at each site, so as to reasonably allocate resources, reduce garbage overflow or premature cleaning, and improve garbage disposal efficiency.

[0030] In one embodiment, when the weight of garbage in the garbage station changes (when the user puts garbage), the current time of putting and the increment of garbage weight are collected as the data point of garbage putting and garbage putting. Each time the user puts garbage, it is recorded once.

[0031] At this point, the collection of garbage disposal data at the garbage site is completed.

[0032] Most people's lives follow certain rules, and the generation of garbage is often closely related to daily activities. For example, the amount of garbage generated by an average household may be the highest during peak hours in the morning and evening, especially after breakfast and dinner, when the amount of garbage is large, resulting in a significant periodicity in the frequency of garbage disposal. By understanding the periodicity of garbage disposal at different time intervals, it is helpful to better predict the peak and trough of garbage volume, and then arrange garbage disposal according to the prediction results.

[0033] Specifically, refer to Figure 2 The periodic calculation method includes steps S10 to S12, which are as follows: S10: Obtain the time of delivery of historical garbage delivery data, cluster the delivery time using the density peak clustering algorithm to obtain multiple clusters, the cluster center of the cluster is the mean of all delivery times in the cluster; the cluster whose cluster center is in the time interval is used as the cluster within the time interval.

[0034] The clustering algorithm of the present application adopts a density peak clustering algorithm, which is an unsupervised learning algorithm based on data point density. It aims to adaptively determine the center of the cluster by finding the "density peak" in the data space, and classify the data points into different clusters according to the center of the cluster.

[0035] Since garbage disposal usually has a certain periodicity and regularity, for example, the amount of garbage will be higher during certain periods of the day (such as after breakfast and dinner). By clustering using the density peak clustering algorithm, these time intervals in the data can be classified into different clusters, thereby identifying the peak and trough periods of garbage disposal.

[0036] By clustering the data samples, we can identify the "lonely data points" in the garbage data, that is, individual data points that do not conform to the overall trend, and separate these data points to avoid inaccurate prediction of garbage weight results.

[0037] For example, starting from 0:00, every two hours is taken as a time interval. For any time interval, if the cluster center of a cluster is within the time interval, the cluster is taken as the cluster within the time interval. Repeat the above steps to obtain each time interval in consecutive days. The empirical value is taken as 7 consecutive days.

[0038] S11: For any time interval, the stability of the time interval is calculated according to the delivery time and the amount of garbage delivered of all clusters within the time interval.

[0039] In one embodiment, the stability is calculated as: , where Indicates Tiandi Stability of the time interval; Indicates Tiandi The total number of clusters in the time interval; Indicates Tiandi In the time interval The standard deviation of the clusters; Indicated by natural constant The exponential function of base .

[0040] Among them, the stability of the time interval is quantified by the fluctuation degree of clusters within the time interval and the total number of clusters. Indicates the degree of fluctuation of all clusters within the time interval. The larger the value, the greater the degree of fluctuation, and the weaker the stability within the time interval. Conversely, the stronger the stability within the time interval. Indicates the total number of clusters within the time interval; the smaller its value is, the closer the data in the cluster is, that is, the stronger the stability of the time interval is, and vice versa, the weaker the stability of the time interval is.

[0041] In one embodiment, the calculation formula of stability can also be: , where Indicates Tiandi Stability of the time interval; Indicates Tiandi The total number of clusters in the time interval; Indicated by natural constant The exponential function of the base is used to quantify the stability of the data in a single dimension by the total number of clusters in the time interval. The calculation amount is small and it is suitable for application scenarios that require a small amount of calculation.

[0042] S12: Calculate the periodicity of the time interval according to the time distribution of all clusters in the same time interval on consecutive days and the amount of garbage placed.

[0043] In one embodiment, the periodicity calculation formula is: , where Indicates The periodicity of the time interval; Indicates the total number of days that it lasts; Indicates Tiandi The garbage disposal weight of all clusters in the time interval; Indicates the number of days in the total number of days The mean of the garbage disposal weight of all clusters in the time interval; Indicates Tiandi Stability of the time interval; Indicated by natural constant The exponential function of base .

[0044] in, It indicates the difference between the garbage weight put out in this time interval and the average level on each day in the consecutive days. The smaller the value, the more consistent the garbage weight put out in this time interval, and the stronger the periodicity of this time interval. Indicates the stability of the time interval. The larger the value, the stronger the periodicity of the time interval. This embodiment focuses more on evaluating the consistency and stability of the garbage delivery volume on consecutive days.

[0045] In one embodiment, the periodicity calculation formula is: , where Indicates The periodicity of the time interval; Indicates the total number of days that it lasts; Indicates Tiandi The garbage disposal weight of all clusters in the time interval; Indicates Tiandi The garbage disposal weight of all clusters in the time interval; Indicates Tiandi Stability of the time interval; Indicated by natural constant The exponential function of base .

[0046] It indicates the difference between the garbage weight and the average level in the same time interval of consecutive days. This embodiment focuses more on evaluating the periodic changes of garbage volume in a shorter time interval (between days).

[0047] S2: Use the weighted moving average method to make predictions based on the periodicity of garbage disposal, and generate garbage removal reminders based on the prediction results.

[0048] The weighted moving average method is a statistical method for smoothing time series data. Its working principle is to give different weights to different data points in the time window when calculating the average value of each time point, rather than simply giving equal weights to all data points. The traditional approach is to give higher weights to more recent data in order to better reflect the latest trends of the data.

[0049] As for the garbage data released by users, since the periodicity strength of users in different time intervals is different, the garbage data in the time intervals with stronger periodicity is more stable, and the reliability of using it for prediction is higher. Conversely, the garbage data in the time intervals with weaker periodicity is more unstable, and it may be a noise data point. Therefore, this application needs to calculate the weight of any time interval according to the periodicity size of different time intervals.

[0050] A sliding window of the weighted moving average method is preset, and the weight of any time interval within the sliding window is calculated. In one embodiment, a prediction is made every two hours to predict the garbage delivery weight at the next moment. The sliding window of the weighted moving average method is preset to 5, and the implementer can set it according to the specific implementation situation.

[0051] According to the weights corresponding to all time intervals in the sliding window, the predicted value is predicted using the weighted moving average method. In one embodiment, the expression formula of the predicted value is: , where Indicates the predicted value of the garbage delivery weight at the next moment; Indicates the sequence number of the time interval in the sliding window; Indicates the sliding window size; Indicates the first The weight of each time interval; Indicates the first The weight of garbage put in a time interval.

[0052] For any data that needs to be predicted, the five data points closest to the time corresponding to the data are used as the data points in the sliding window to predict the data. The weight calculation formula for any time interval in the sliding window is: , where Indicates the first The weight of each time interval; Indicates the sequence number of the time interval in the sliding window; Indicates the first The periodicity of the time interval; Indicates the first The starting time of the time interval; Indicates the starting time of the time interval that needs to be predicted; Represents the standard normalization function.

[0053] The garbage disposal data in time intervals with stronger periodicity are more stable, so their weights should be higher, and the prediction results are more reliable. On the contrary, time intervals with weaker periodicity may be noise data points, and their weights should be lower.

[0054] In one embodiment, the sum of the total weight of garbage at the current garbage station and the predicted weight of garbage at the next moment is taken as the total weight of garbage at the garbage station at the next moment. If the value exceeds 80% of the total weight of the garbage station, the staff is reminded that the garbage needs to be cleared.

[0055] The embodiment of the present application also discloses a garbage removal system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the garbage removal method according to the present application is implemented.

[0056] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0057] In the present application, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high bandwidth memory HBM (High Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device.

[0058] Although this specification has shown and described a plurality of embodiments of the present application, it is obvious to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will think of many changes, modifications and alternatives without departing from the thought and spirit of the present application. It should be understood that in the process of practicing the present application, various alternatives to the embodiments of the present application described herein may be adopted.

[0059] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A garbage removal method, characterized in that: Includes steps: Set time intervals and calculate the periodicity of garbage delivery in any time interval; Calculate the weights based on the periodicity and time interval of garbage disposal, use the weighted moving average method to make a prediction, and generate garbage removal reminders based on the prediction results; The calculation methods of periodicity include: Obtain the time of delivery of historical garbage delivery data, cluster the delivery time using the density peak clustering algorithm to obtain multiple clusters, and the cluster center of the cluster is the mean of all delivery times in the cluster; The cluster whose cluster center is in the time interval is regarded as the cluster within the time interval; for any time interval, the stability of the time interval is calculated according to the delivery time and garbage delivery amount of all clusters within the time interval; The periodicity of the time interval is calculated based on the time distribution of all clusters in the same time interval on consecutive days and the amount of garbage placed.

2. The garbage removal method according to claim 1, characterized in that: The stability calculation formula is: , where Indicates Tiandi Stability of the time interval; Indicates Tiandi The total number of clusters in the time interval; Indicates Tiandi In the time interval The standard deviation of the clusters; Indicated by natural constant The exponential function of base .

3. The garbage removal method according to claim 1, characterized in that: The stability calculation formula is: , where Indicates Tiandi Stability of the time interval; Indicates Tiandi The total number of clusters in the time interval; Indicated by natural constant The exponential function of base .

4. The garbage removal method according to claim 1, characterized in that: The periodicity calculation formula is: , where Indicates The periodicity of the time interval; Indicates the total number of days that it lasts; Indicates Tiandi The garbage disposal weight of all clusters in the time interval; Indicates the number of days in the total number of days The mean of the garbage disposal weight of all clusters in the time interval; Indicates Tiandi Stability of the time interval; Indicated by natural constant The exponential function of base .

5. The garbage removal method according to claim 1, characterized in that: The periodicity calculation formula is: , where Indicates The periodicity of the time interval; Indicates the total number of days that it lasts; Indicates Tiandi The garbage disposal weight of all clusters in the time interval; Indicates Tiandi The garbage disposal weight of all clusters in the time interval; Indicates Tiandi Stability of the time interval; Indicated by natural constant The exponential function of base .

6. The garbage removal method according to any one of claims 1 to 5, characterized in that: The weight is calculated according to the periodicity and time interval of garbage delivery, including the steps of: presetting a sliding window, and calculating the weights corresponding to all time intervals in the sliding window; The weight calculation formula is: , where Indicates the first The weight of each time interval; Indicates the sequence number of the time interval in the sliding window; Indicates the first The periodicity of the time interval; Indicates the first The starting time of the time interval; Indicates the starting time of the time interval that needs to be predicted; Represents the standard normalization function.

7. The garbage removal method according to claim 6, characterized in that: The predicted value is obtained by using the weighted moving average method. The expression of the predicted value is: , where Indicates the predicted value of the garbage delivery weight at the next moment; Indicates the sliding window size; Indicates the first The weight of each time interval; Indicates the first The weight of garbage put in a time interval.

8. A garbage removal system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the garbage removal method according to any one of claims 1 to 7 is implemented.

Images