Garbage monitoring method and system for fixed garbage sites

Abstract

The present invention relates to the field of data processing technology, and in particular to a garbage monitoring method and system for a fixed garbage station. The method includes the following steps: obtaining garbage weight monitoring data of each garbage station; calculating the degree of placement of any category of garbage at any time; obtaining the correlation of any category of garbage; calculating the concentration of garbage at any moment according to the correlation and placement degree of all categories of garbage at any garbage station at any moment; for any moment that needs to be predicted, calculating the smoothing parameter of the moment according to the standard deviation of the concentration of garbage at the same moment in the historical day, and using the exponential smoothing method to predict the garbage weight monitoring data at the moment, so that the staff can process according to the prediction results. The present invention makes the prediction results of garbage weight monitoring data more accurate and improves the efficiency of garbage removal.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a garbage monitoring method and system for a fixed garbage station. Background Art

[0002] With the acceleration of urbanization and population growth, urban domestic waste treatment has become an important public management task. Traditional garbage collection and transportation usually lacks intelligent management and often faces problems such as insufficient resource utilization and low transportation efficiency. Existing garbage collection points are often designated fixed garbage stations distributed in various areas of the city. The garbage treatment and recycling of many garbage stations rely on manual monitoring and regular cleaning. This method is not only inefficient, but also easily leads to garbage accumulation and environmental pollution. Even in some areas where the amount of garbage put in is somewhat volatile, the capacity of the garbage station cannot meet the demand in time, resulting in garbage overflow or space waste, which affects the surrounding environment.

[0003] For example, a Chinese patent application document with publication number CN115687957A discloses a method for monitoring overflow of a garbage bin, which points out that by predicting garbage data and dividing the overflow levels according to the prediction results, the staff can deal with the overflow information in a timely manner, thereby improving the garbage cleaning efficiency.

[0004] As a prediction algorithm, the exponential smoothing method predicts future data based on preset fixed smoothing parameters. However, due to the different garbage disposal amounts at different times, if the smoothing parameter is too large, the predicted value will be easily affected by noise data. If the smoothing parameter is too small, the predicted value cannot respond to the changes in the real-time garbage disposal amount in time. Therefore, the fixed smoothing parameters meet the different changes in different garbage disposal amounts, resulting in inaccurate prediction results. Summary of the invention

[0005] The object of the present invention is to provide a garbage monitoring method and system for a fixed garbage site, so as to solve the technical problem of inaccurate garbage monitoring results for the fixed garbage site.

[0006] In a first aspect, the present invention provides a garbage monitoring method for a fixed garbage station, which adopts the following technical solution:

[0007] Obtain garbage weight monitoring data for each garbage station;

[0008] At any time of any garbage station, obtain the garbage weight monitoring data of any category and the mean of the garbage weight monitoring data of the garbage of this category at all garbage stations, obtain the difference between the garbage weight monitoring data and the mean, and calculate the degree of placement of the garbage of this category at this time according to the ratio of the difference to the standard deviation of the garbage weight monitoring data;

[0009] For the degree of placement of any category of garbage at any time, the Pearson correlation coefficient of the degree of placement of this category of garbage and other categories of garbage at all times is used to obtain the correlation of this category of garbage; the concentration of garbage at any time is calculated based on the mean of the product of the correlation of all categories of garbage at any garbage station at any time and the degree of placement;

[0010] For any moment that needs to be predicted, according to the history The standard deviation of the concentration of garbage at the same time of the day is used to calculate the smoothing parameter at that moment, and the exponential smoothing method and the smoothing parameter corresponding to that moment are used to predict the garbage weight monitoring data at that moment, so that the staff can process according to the prediction results.

[0011] For any garbage weight monitoring data at a certain moment that needs to be predicted, the present invention calculates the smoothing parameter at that moment by the change in the concentration of garbage at the same moment in history for multiple days, and uses the calculated smoothing parameter to predict the garbage weight monitoring data at that moment, thereby making the prediction result more accurate and improving the garbage removal efficiency.

[0012] Furthermore, the present invention calculates the degree of garbage disposal of any category at any moment, and calculates the concentration of garbage at that moment based on the degree of garbage disposal and the correlation between each category of garbage, thereby making the calculation result of the garbage concentration degree more accurate and the prediction result of the garbage weight monitoring data more accurate.

[0013] Preferably, the garbage weight monitoring data includes:

[0014] For any fixed garbage station, use weight sensors to The garbage weight monitoring data of the fixed garbage station is collected once every hour; the garbage weight monitoring data refers to the The increased weight of the garbage within an hour.

[0015] Preferably, the delivery degree satisfies the expression:

[0016] ;

[0017] In the formula, Indicates that the fixed garbage station Moment The level of waste disposal; Indicates the preset number of days; Indicates the ordinal number of the day; Indicates the ordinal number of the day at the current time; Indicates Day's Moment Monitoring data on the weight of garbage of the following categories; Indicates All the time of the day The mean value of garbage weight monitoring data for the following types of garbage; Indicates the total number of garbage categories; Indicates the time sequence number; Indicates the ordinal number of garbage category; represents the hyperbolic tangent function.

[0018] The present invention analyzes the past The difference between the monitoring data of garbage weight of each category at each moment of the day and the average level of this type of garbage at all moments of the day can be used to obtain the degree of disposal of this type of garbage at any moment, so as to facilitate the subsequent acquisition of the concentration of garbage disposal at any moment based on the calculated degree of disposal, making the calculation result more accurate.

[0019] Preferably, the correlation is obtained in the following manner:

[0020] Obtain the degree of disposal of any category of garbage at all times on any day, and use it as the degree sequence of disposal of this category of garbage on that day, calculate the Pearson correlation coefficient between the degree sequence of disposal of this category of garbage on that day and the degree sequence of disposal of any other category of garbage on that day; obtain the mean of the Pearson correlation coefficients of the degree sequence of disposal of this category of garbage on any day and all other categories of garbage; and use the mean of the Pearson correlation coefficients of the degree sequence of disposal of this category of garbage on all days and all other categories of garbage as the correlation of this category of garbage.

[0021] By obtaining the correlation between any category of garbage and other categories of garbage, we can determine whether this type of garbage has a large similarity change with other garbage at the same time. Based on the similarity change, we can determine whether the degree of garbage placement at any moment is affected by the similarity, thereby making subsequent calculation results more accurate.

[0022] Preferably, the concentration degree satisfies the expression:

[0023] ;

[0024] In the formula, Indicates The concentration of garbage at any given moment; Indicates the total number of garbage categories; Indicates that the fixed garbage station Moment The level of waste disposal; Indicates that the fixed garbage station relevance of category junk; Indicates the time sequence; Indicates the ordinal number of garbage category; Represents the standard normalization function.

[0025] According to the degree of disposal of any category of garbage at any moment and the correlation of such garbage, the present invention can more accurately understand the concentration of all categories of garbage at that moment, judge the amount of garbage disposed by the garbage station at that moment, and thus facilitate the prediction of future data.

[0026] Preferably, the smoothing parameter satisfies the expression:

[0027] ;

[0028] In the formula, Indicates smoothing parameter at time instant; Represents the preset initial smoothing parameter; Indicates the preset number of historical days; Representation History Tianzhongdi The standard deviation of garbage concentration at each moment; Indicates the time sequence number; Indicated by natural constant The exponential function of base .

[0029] The present invention calculates the smoothing parameter at the moment according to the change of the concentration degree at the same moment for multiple consecutive days in history, so that the smoothing parameter can better meet the change of historical data, thereby making the prediction result more accurate.

[0030] Preferably, the method of using the exponential smoothing method and the smoothing parameter corresponding to the moment to predict the garbage weight monitoring data at the moment includes:

[0031] For the garbage weight monitoring data at any time that needs to be predicted, obtain the historical The garbage weight monitoring data at that moment of the day is used as the prediction sample of the exponential smoothing method, and the garbage weight monitoring data at that moment is predicted using the exponential smoothing method and the smoothing parameters corresponding to that moment.

[0032] In a second aspect, the present invention provides a garbage monitoring system for a fixed garbage station, which adopts the following technical solution:

[0033] A garbage monitoring system for a fixed garbage site 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 monitoring method for the fixed garbage site is implemented.

[0034] By adopting the above technical solution, the above-mentioned garbage monitoring method for a fixed garbage station is generated into a computer program and stored in a memory to be loaded and executed by a processor, so that a terminal device is made according to the memory and the processor for easy use.

[0035] The present invention has the following technical effects:

[0036] For any garbage weight monitoring data at a certain moment that needs to be predicted, the present invention calculates the smoothing parameter at that moment by the change in the concentration of garbage at the same moment in history for multiple days, and uses the calculated smoothing parameter to predict the garbage weight monitoring data at that moment, thereby making the prediction result more accurate and improving the garbage removal efficiency.

[0037] Furthermore, the present invention calculates the degree of garbage disposal of any category at any moment, and calculates the concentration of garbage at that moment based on the degree of garbage disposal and the correlation between each category of garbage, thereby making the calculation result of the garbage concentration degree more accurate and the prediction result of the garbage weight monitoring data more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] 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 invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0039] Figure 1 The present invention is a flowchart of a method for monitoring garbage at a fixed garbage station according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0041] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention 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.

[0042] The embodiment of the present invention discloses a garbage monitoring method for a fixed garbage station, referring to Figure 1 , comprising steps S1 to S4:

[0043] S1: Obtain garbage weight monitoring data;

[0044] It should be noted that garbage weight monitoring data refers to monitoring the weight of each category of garbage in fixed garbage stations. By monitoring the weight of each category of garbage in fixed garbage stations, the changes in the weight of garbage in the fixed garbage stations can be analyzed, and predictions can be made based on the changes in the weight of garbage, so as to issue early warnings on the garbage disposal platform, so that the staff can handle the garbage in the fixed garbage station in a timely manner and reduce the impact of garbage on the environment.

[0045] In one embodiment, for any fixed garbage station, a weight sensor is used to measure the garbage every The garbage weight monitoring data of the fixed garbage station is collected once every hour. The garbage weight monitoring data refers to the The added weight of garbage in one hour, It is a preset collection cycle, which can be adjusted by the implementers according to the specific implementation situation, for example For example, The garbage weight monitoring data collected at the time refers to the The weight of garbage collected at the time is The difference in the weight of garbage collected at each moment.

[0046] At this point, the garbage weight monitoring data has been acquired.

[0047] S2: For any fixed garbage station, obtain the concentration of garbage at any time;

[0048] It should be noted that fixed garbage stations are usually set up near some public places such as communities, residential areas, and commercial areas to facilitate residents and businesses to put out garbage. Since these fixed garbage stations belong to different areas, the peak flow of people in these areas is different in different time periods. For example, fixed garbage stations in residential areas may have a larger amount of garbage put out in the morning and evening after residents go home, while fixed garbage stations in commercial areas may have a larger amount of garbage put out during the day and on weekdays. Therefore, it is necessary to understand the peak time of garbage put out at different fixed garbage stations, which can help optimize resource allocation. Therefore, the present invention needs to calculate the concentration of garbage at any fixed garbage station at any time.

[0049] It should be further explained that the amount of garbage placed in different types of fixed garbage stations in different places is different, and the amount of garbage placed in different types of fixed garbage stations will affect the concentration of garbage at any time in the fixed garbage station. Therefore, for any fixed garbage station, it is necessary to analyze the amount of garbage placed in any type of fixed garbage station at any time. In order to make the calculation result more accurate, the present invention uses the garbage placement station in the past All the data collected within the day are analyzed to ensure the accuracy of the calculation results.

[0050] In one embodiment, for any fixed garbage station, the past information of the fixed garbage station is obtained. All the garbage weight monitoring data collected during the day are used as reference data for the current day, so as to facilitate subsequent prediction of the garbage weight monitoring data for the current day. It represents the preset empirical value of the number of days, which is not specifically limited in the present invention and can be limited by the implementer according to the specific implementation situation, for example .

[0051] It should be noted that in obtaining the past All garbage weight monitoring data collected during the day does not include the garbage weight monitoring data of the current day. That is, the current day is recorded as , get the Tianzhidi The garbage weight monitoring data collected at all times of the day.

[0052] In one embodiment, for any fixed garbage station, according to its past All the garbage weight monitoring data collected during the day are used to calculate the degree of garbage placement of any category at any time at the fixed garbage station. The specific calculation formula is as follows:

[0053] ;

[0054] In the formula, Indicates that the fixed garbage station Moment The level of waste disposal; Indicates the preset number of days; Indicates the ordinal number of the day; Indicates the ordinal number of the day at the current time; Indicates Day's Moment Monitoring data on the weight of garbage of the following categories; Indicates All the time of the day The mean value of garbage weight monitoring data for the following types of garbage; Indicates the total number of garbage categories; Indicates the time sequence number; Indicates the ordinal number of garbage category; represents the hyperbolic tangent function.

[0055] in, Indicates Tiandi Moment The larger the difference between the garbage weight monitoring data of a category of garbage and the average level of garbage weight monitoring data of this category of garbage at all times, the more garbage of this category is put in at that time, that is, the higher the level of garbage put in at that time; Indicates All the time of the day The volatility of garbage weight monitoring data of garbage-like waste also represents the molecular The confidence level, The smaller the value, the All the time of the day The smaller the difference in garbage weight monitoring data of different types of garbage, the The higher the confidence level; The purpose is to The value mapping is The value range is between .

[0056] At this point, the calculation of the placement degree of any category of garbage at any time in the fixed garbage station is completed.

[0057] It should be noted that the above steps are used to calculate the level of garbage placement of any category at any time in the fixed garbage station. When the level of garbage placement of a category is high at a certain time, it is impossible to determine whether the high level of garbage placement of this category is caused by external factors. For example, for commercial blocks, the flow of people is large during the daytime, and the amount of garbage placed in each category is also large. If such external factors occur, the levels of garbage placement of different categories at the same time will show a high correlation. Therefore, for any garbage station, the present invention calculates the concentration of garbage at that moment based on the correlation and placement level of all categories of garbage at that moment.

[0058] In one embodiment, the placement degree of any category of garbage at all times on any day is obtained and used as the placement degree sequence of the category of garbage on that day, and the Pearson correlation coefficient between the placement degree sequence of the category of garbage on that day and the placement degree sequence of any other category of garbage on that day is calculated; the mean of the Pearson correlation coefficients of the placement degree sequence of the category of garbage on any day and all other categories of garbage is obtained; and the mean of the Pearson correlation coefficients of the placement degree sequence of the category of garbage on all days with all other categories of garbage is used as the correlation of the category of garbage.

[0059] The concentration of garbage at a given moment is calculated based on the correlation of any category of garbage and the degree of placement of any category of garbage at a given moment. The specific calculation formula is as follows:

[0060] ;

[0061] In the formula, Indicates The concentration of garbage at any given moment; Indicates the total number of garbage categories; Indicates that the fixed garbage station Moment The level of waste disposal; Indicates that the fixed garbage station relevance of category junk; Indicates the time sequence number; Indicates the ordinal number of garbage category; Represents the standard normalization function.

[0062] in, Indicates Moment The concentration of garbage of different categories, the larger the value, the Moment The greater the concentrated amount of garbage of a certain category, the more concentrated the garbage is. The concentration of garbage at that moment is obtained by calculating the concentration of all categories of garbage at that moment.

[0063] It should be noted that, for any time of any day, the garbage concentration level at that time can be obtained by using step S2.

[0064] At this point, the concentration of garbage at any moment is calculated.

[0065] S3: Calculate the smoothing parameter at the same moment based on the change in garbage concentration at the same moment for multiple consecutive days;

[0066] It should be noted that the concentration of garbage at a certain moment represents the amount of garbage of all categories at that moment. The greater the concentration, the greater the amount of garbage at that moment. When predicting the amount of garbage at all moments of the current day, it is necessary to refer to the changes in the concentration of garbage at that moment for many consecutive days in history. If the fluctuation of the garbage level at that moment is greater over many consecutive days, it means that the garbage concentration at that moment is more susceptible to external factors; conversely, if the fluctuation of the garbage level at that moment is smaller over many consecutive days, it means that the garbage concentration at that moment is less susceptible to external factors.

[0067] It should be further explained that the exponential smoothing method is a statistical method for time series prediction. Its basic principle is to perform weighted averaging on the historical values ​​in the time series data to generate future prediction values. Among them, the smoothing parameter controls the weight distribution of historical data when calculating the prediction value. Different smoothing parameters will affect the sensitivity of the prediction value to the historical data. If the smoothing parameter is larger, the weight of the closer historical data will be greater, so that the prediction value can adapt to the change of data more quickly; if the smoothing parameter is smaller, the weight of the closer historical data will be smaller, so that the prediction value is insensitive to the change of data. Therefore, the present invention needs to calculate the smoothing parameter at any moment according to the change of the garbage concentration degree of the historical data at any moment.

[0068] In one embodiment, for any time that needs to be predicted, obtain the past The concentration of garbage at the same time of the day is calculated, and the smoothing parameter is calculated based on all the concentrations obtained. The specific calculation formula is as follows:

[0069] ;

[0070] In the formula, Indicates smoothing parameter at time instant; Represents the preset initial smoothing parameter; Indicates the preset number of historical days; Representation History Tianzhongdi The standard deviation of garbage concentration at each moment; Indicates the time sequence number; Expressed as a natural constant The exponential function of base .

[0071] in, Indicates that for consecutive days The smaller the value, the smaller the fluctuation of the concentration of garbage at the moment, which means that the concentration of garbage at this moment remains basically unchanged. At this time, a smaller smoothing parameter is needed for prediction to reduce the impact of noise data on it; conversely, the larger the value, the greater the fluctuation, which means that the concentration of garbage at this moment changes greatly. At this time, a larger smoothing parameter is needed for prediction so that the prediction result can respond to the rapid changes in the amount of garbage put in in a timely manner. The purpose is to The value mapping is between the ranges.

[0072] It should be noted that is the preset initial smoothing parameter, It is a preset number of historical days, which is not specifically limited in the present invention and can be adjusted by implementers according to specific implementation conditions, for example ; .

[0073] S4: For any moment, the garbage weight monitoring data at that moment is predicted using the smoothing parameters at that moment, so that the staff can process according to the monitoring results.

[0074] In one embodiment, for the garbage weight monitoring data at any time that needs to be predicted, obtain the historical The garbage weight monitoring data at that moment of the day is used as the prediction sample of the exponential smoothing method. The exponential smoothing method and the smoothing parameters corresponding to that moment are used to predict the garbage weight monitoring data at that moment, which is convenient for the staff to process according to the monitoring results, thereby improving the garbage treatment efficiency of fixed garbage stations and avoiding environmental pollution caused by untimely garbage transportation.

[0075] Through the above steps, garbage monitoring of fixed garbage sites is completed.

[0076] An embodiment of the present invention further discloses a garbage monitoring system for a fixed garbage site, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a garbage monitoring method for a fixed garbage site according to the present invention is implemented.

[0077] 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.

[0078] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory, a dynamic random access memory, a static random access memory, an enhanced dynamic random access memory, a high bandwidth memory, a hybrid storage 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 the device or accessible or connectable to the device.

[0079] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0080] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A garbage monitoring method for a fixed garbage station, characterized in that: Includes steps: Obtain garbage weight monitoring data for each garbage station; At any time of any garbage station, obtain the garbage weight monitoring data of any category and the mean of the garbage weight monitoring data of the garbage of this category at all garbage stations, obtain the difference between the garbage weight monitoring data and the mean, and calculate the degree of placement of the garbage of this category at this time according to the ratio of the difference to the standard deviation of the garbage weight monitoring data; The delivery level satisfies the expression: ； In the formula, Indicates that the fixed garbage station Moment The level of waste disposal; Indicates the preset number of days; Indicates the ordinal number of the day; Indicates the ordinal number of the day at the current time; Indicates Day's Moment Monitoring data on the weight of garbage of the following categories; Indicates All the time of the day The mean value of garbage weight monitoring data for the following types of garbage; Indicates the total number of garbage categories; Indicates the time sequence number; Indicates the ordinal number of garbage category; represents the hyperbolic tangent function; For the level of garbage placement of any category at any time, the Pearson correlation coefficient of the level of garbage placement of this category at all times and other categories of garbage is used to obtain the correlation of this category of garbage; The concentration of garbage at any garbage station at any time is calculated based on the average of the product of the correlation of all types of garbage and the degree of placement; The concentration satisfies the expression: ； In the formula, Indicates The concentration of garbage at any given moment; Indicates that the fixed garbage station Moment The level of waste disposal; Indicates that the fixed garbage station relevance of category junk; represents the standard normalization function; For any moment that needs to be predicted, according to the history The standard deviation of the concentration of garbage at the same time of the day is used to calculate the smoothing parameter at that time, and the garbage weight monitoring data at that time is predicted using the exponential smoothing method and the smoothing parameter corresponding to that time, so that the staff can process according to the prediction results; The smoothing parameter satisfies the expression: ； In the formula, Indicates smoothing parameter at time instant; Represents the preset initial smoothing parameter; Indicates the preset number of historical days; Representation History Tianzhongdi The standard deviation of garbage concentration at each moment; Indicated by natural constant The exponential function of base .

2. The garbage monitoring method for a fixed garbage station according to claim 1, characterized in that: The garbage weight monitoring data includes: For any fixed garbage station, use weight sensors to The garbage weight monitoring data of the fixed garbage station is collected once every hour; the garbage weight monitoring data refers to the The increased weight of the garbage within an hour.

3. The garbage monitoring method for a fixed garbage station according to claim 1, characterized in that: The method of obtaining the correlation includes: Obtain the degree of disposal of any category of garbage at all times on any day, and use it as the degree sequence of disposal of this category of garbage on that day, calculate the Pearson correlation coefficient between the degree sequence of disposal of this category of garbage on that day and the degree sequence of disposal of any other category of garbage on that day; obtain the mean of the Pearson correlation coefficients of the degree sequence of disposal of this category of garbage on any day and all other categories of garbage; and use the mean of the Pearson correlation coefficients of the degree sequence of disposal of this category of garbage on all days and all other categories of garbage as the correlation of this category of garbage.

4. The garbage monitoring method for a fixed garbage station according to claim 1, characterized in that: The method of using the exponential smoothing method and the smoothing parameter corresponding to the moment to predict the garbage weight monitoring data at the moment includes: For the garbage weight monitoring data at any time that needs to be predicted, obtain the historical The garbage weight monitoring data at that moment of the day is used as the prediction sample of the exponential smoothing method, and the garbage weight monitoring data at that moment is predicted using the exponential smoothing method and the smoothing parameters corresponding to that moment.

5. A garbage monitoring system for a fixed garbage station, 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, a garbage monitoring method for a fixed garbage site according to any one of claims 1 to 4 is implemented.

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