A garbage throwing data intelligent analysis method and system

Abstract

The application provides a garbage throwing data intelligent analysis method and system, and belongs to the technical field of data processing, and specifically comprises the following steps: determining a throwing monitoring optimization site in the target site according to the change of garbage throwing personnel; determining the classification deviation data of different garbage throwing personnel for different types of garbage based on the analysis result of the historical throwing data of the throwing monitoring optimization site; determining the detection target personnel in the throwing monitoring optimization site based on the classification deviation data; only identifying the classification result of the garbage thrown by the detection target personnel in the throwing monitoring optimization site; and determining the secondary verification strategy of the garbage classification of the throwing monitoring optimization site based on the garbage throwing data of the detection target personnel and other personnel in the throwing monitoring optimization site on the current date, so as to ensure the accuracy of the verification processing result of the garbage classification result.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular relates to an intelligent analysis method and system for waste disposal data. Background Technology

[0002] To achieve intensive waste management, the invention patent application CN202411744164.5, "An Intelligent Waste Recycling System Based on an Internet-Integrated Scheduling Platform," predicts abnormal data over a period of time, providing reference data for waste recycling resource scheduling to propose recycling plans. This effectively improves waste recycling efficiency, optimizes resource allocation, and promptly responds to and handles abnormal changes in waste volume, accumulation status, or disposal behavior. However, it has the following technical shortcomings:

[0003] In the process of analyzing and processing waste disposal data, existing technical solutions often use cameras to identify and process waste sorting results. Due to the large volume of waste disposal, it is inevitable that the analysis and processing of waste sorting results will be difficult and the data storage pressure will be high. Therefore, how to generate differentiated waste sorting identification and processing strategies and reduce the difficulty of waste sorting identification and processing has become an urgent technical problem to be solved.

[0004] To address the aforementioned technical issues, this application specifically provides a method and system for intelligent analysis of waste disposal data. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted:

[0006] Specifically, in the first aspect, this application provides an intelligent analysis method for waste disposal data, which specifically includes:

[0007] S1 uses the analysis results of waste disposal data to determine the abnormalities in waste sorting data at different target sites. Based on the abnormalities, if the classification monitoring demand coefficient of the target site is within the preset demand coefficient range, proceed to the next step.

[0008] S2 acquires information about waste disposal personnel using the camera device at the target site, obtains information about changes in waste disposal personnel at the target site between different dates, and determines the waste disposal monitoring and optimization sites in the target site based on the changes in waste disposal personnel.

[0009] S3 uses the analysis results of historical waste disposal data from the waste disposal monitoring and optimization site to determine the classification deviation data of different waste disposal personnel for different types of waste, and determines the target personnel for detection in the waste disposal monitoring and optimization site based on the classification deviation data;

[0010] S4 only identifies and processes the classification results of the garbage disposed of by the target personnel at the garbage disposal monitoring and optimization site. Based on the garbage disposal data of the target personnel at the garbage disposal monitoring and optimization site and other personnel on the current date, a secondary verification strategy for garbage classification at the garbage disposal monitoring and optimization site is determined.

[0011] The beneficial effects of this invention are as follows:

[0012] In this application, the target personnel in the waste disposal monitoring optimization site are determined based on classification deviation data, thereby avoiding the problems of high difficulty in classification and identification and high storage space requirements caused by classifying and identifying the waste disposed of by all target personnel. At the same time, by combining classification deviation data, the reliability of the detection and processing of waste disposal results of waste disposal personnel with a high degree of waste classification deviation is ensured, and the accurate identification of waste classification results is achieved.

[0013] In this application, a secondary verification strategy for waste sorting at the waste sorting monitoring and optimization site is determined based on the waste disposal data of the target personnel and other personnel at the current date. This strategy not only considers the differences in the detection reliability of waste sorting at the waste sorting and optimization node due to differences in the amount of waste disposed of by the target personnel, but also the differences in the monitoring reliability of waste sorting at the waste sorting and optimization node due to differences in the proportion of the target personnel among the waste disposal personnel. By determining a differentiated secondary verification strategy, the reliability of the verification results is ensured while also reducing the difficulty and resource investment of the secondary verification process.

[0014] A further technical solution is that the waste disposal data includes the amount of waste disposed of at different target sites on different days, as well as waste sorting data.

[0015] A further technical solution is that the abnormal situations in the waste sorting data of the target site include the amount of waste disposed of at the target site that deviates from different types of waste sorting, and the number of people who dispose of waste with deviations in waste sorting.

[0016] A further technical solution involves determining the classification monitoring demand coefficient for the target site using the following method:

[0017] Based on the anomalies in the waste sorting data of the target site, determine the amount of waste disposed of at the target site on different dates where waste sorting is deviated, and treat it as the abnormal waste disposal amount;

[0018] Based on the abnormal delivery volume in different dates, the abnormal delivery dates for the target site are determined;

[0019] Based on the proportion of abnormal deployment dates at the target site, the classification monitoring demand coefficient for the target site is determined.

[0020] A further technical solution is that the abnormal delivery date is the date when the abnormal category delivery volume falls within the preset abnormal delivery volume range.

[0021] A further technical solution is that the method for determining the secondary verification strategy for waste sorting at the waste disposal monitoring and optimization site is as follows:

[0022] Based on the waste disposal data of the target personnel at the monitoring and optimization stations on the current date, determine the proportion of the waste disposal data of the target personnel on the current date, and use it as the proportion of the disposal data;

[0023] Based on the number of target personnel and other personnel at the monitoring and optimization sites on the current date, determine the proportion of target personnel among waste disposal personnel on the current date, and use this proportion as the target proportion.

[0024] Based on the proportion of the detection targets and the proportion of the disposal data, the monitoring reliability coefficient of the disposal monitoring optimization site is determined, and the secondary verification strategy of the disposal monitoring optimization site is determined using the monitoring reliability coefficient.

[0025] A further technical solution is that the monitoring reliability coefficient of the waste classification at the waste disposal monitoring and optimization site is the average of the proportion of the detection target and the proportion of disposal data.

[0026] A further technical solution involves using the monitoring reliability coefficient to determine a secondary verification strategy for waste sorting at the optimized waste disposal monitoring site, specifically including:

[0027] When the monitoring reliability coefficient of the optimized monitoring site is greater than the preset reliability coefficient threshold, it is determined that no secondary verification process for garbage classification is required for the target site.

[0028] When the monitoring reliability coefficient of the optimized monitoring site is not greater than the preset reliability coefficient threshold, it is determined that the target site needs to undergo secondary verification of waste classification.

[0029] Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described intelligent analysis method for waste disposal data when running the computer program.

[0030] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0031] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0032] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0033] Figure 1 This is a flowchart of a method for intelligent analysis of waste disposal data;

[0034] Figure 2 This is a flowchart illustrating the method for determining the classification monitoring demand coefficients for target sites;

[0035] Figure 3 This is a flowchart illustrating the method for determining the target sites for monitoring and optimization.

[0036] Figure 4 This is a flowchart illustrating the method for identifying target personnel.

[0037] Figure 5 It is a framework diagram of a computer system. Detailed Implementation

[0038] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0039] In this application, the historical deviations in waste sorting and disposal by waste disposal personnel are used to identify the target personnel who need to be tested for waste sorting, thereby reducing a large number of unnecessary tests, reducing the waste of storage space, and also reducing the difficulty of sorting and disposal testing.

[0040] Example 1

[0041] like Figure 1 As shown, this application provides an intelligent analysis method for waste disposal data, specifically including:

[0042] S1 uses the analysis results of waste disposal data to determine the abnormalities in waste sorting data at different target sites. Based on the abnormalities, if the classification monitoring demand coefficient of the target site is within the preset demand coefficient range, proceed to the next step.

[0043] The amount of waste disposed of at the target site on different dates with deviations in waste sorting is taken as the abnormal disposal amount. Dates with abnormal disposal amounts within the preset abnormal disposal amount range are taken as abnormal disposal dates. Based on the proportion of abnormal disposal dates at the target site, the classification monitoring demand coefficient of the target site is determined.

[0044] S2 acquires information about waste disposal personnel using the camera device at the target site, and obtains information about changes in waste disposal personnel at the target site between different dates, and uses the changes in waste disposal personnel to determine the waste disposal monitoring and optimization sites in the target site;

[0045] Dates with deviations in the number of waste disposal personnel within a preset range are grouped into the same date group. Based on the proportion of dates in different date groups, the distribution clustering coefficient of different date groups is determined. When the maximum value of the distribution clustering coefficient of different date groups is less than 0.2, the target site is determined not to belong to the waste disposal monitoring and optimization site.

[0046] S3 uses the analysis results of historical waste disposal data from the waste disposal monitoring and optimization site to determine the classification deviation data of different waste disposal personnel for different types of waste, and determines the target personnel for detection at the waste disposal monitoring and optimization site based on the classification deviation data;

[0047] The target personnel for testing are those who disposed of garbage on dates with a classification error that accounted for more than 0.7% of the total number of such disposals.

[0048] S4 only identifies and processes the classification results of the garbage disposed of by the target personnel at the garbage disposal monitoring and optimization site. Based on the garbage disposal data of the target personnel at the garbage disposal monitoring and optimization site and other personnel on the current date, a secondary verification strategy for garbage classification at the garbage disposal monitoring and optimization site is determined.

[0049] Determine the proportion of the waste disposal data of the target personnel in the current date, and use it as the proportion of the disposal data; determine the proportion of the number of the target personnel in the waste disposal personnel in the current date, and use it as the proportion of the target personnel.

[0050] Based on the average of the target detection rate and the disposal data rate, the monitoring reliability coefficient of the waste sorting at the waste disposal monitoring optimization site is determined. When the monitoring reliability coefficient of the waste disposal monitoring optimization site is greater than 0.7, it is determined that no secondary verification processing of waste sorting is required for the target site. When the monitoring reliability coefficient of the waste disposal monitoring optimization site is not greater than 0.7, it is determined that secondary verification processing of waste sorting is required for the target site.

[0051] Furthermore, the waste disposal data includes the amount of waste disposed of at different target sites on different days, as well as waste sorting data.

[0052] Specifically, the abnormal situations in the waste sorting data of the target site include the amount of waste disposed of at the target site that deviates from the different types of waste sorting, and the number of people who disposed of waste with deviations in waste sorting.

[0053] Specifically, such as Figure 2 As shown, the method for determining the classification monitoring demand coefficient of the target site is as follows:

[0054] Based on the anomalies in the waste sorting data of the target site, determine the amount of waste disposed of at the target site on different dates where waste sorting is deviated, and treat it as the abnormal waste disposal amount;

[0055] Based on the abnormal delivery volume in different dates, the abnormal delivery dates for the target site are determined;

[0056] Based on the proportion of abnormal deployment dates at the target site, the classification monitoring demand coefficient for the target site is determined.

[0057] It should be noted that the abnormal delivery date is the date when the abnormal category delivery volume falls within the preset abnormal delivery volume range.

[0058] It is understandable that when the classification monitoring demand coefficient of the target site is not within the preset demand coefficient range, it is also necessary to determine whether the classification monitoring demand coefficient of the target site is greater than the preset monitoring demand coefficient threshold. If so, the classification results of all garbage disposal personnel will be identified and processed. If not, the garbage disposal personnel will not be processed for garbage classification, and only the target site will be processed for secondary verification of garbage classification.

[0059] Furthermore, the changes in waste disposal personnel between different dates include the number of changes in waste disposal personnel between different dates.

[0060] Specifically, such as Figure 3 As shown, the method for determining the target site for monitoring and optimization is as follows:

[0061] Based on the changes in the garbage disposal personnel, determine the number of changes in garbage disposal personnel between different adjacent dates;

[0062] Based on the change in the number of people disposing of garbage between adjacent dates, the percentage of the number of people disposing of garbage on that date is determined and used as the percentage of the change.

[0063] Based on the average percentage of changes on different dates, determine whether the target site is a site for monitoring and optimization.

[0064] Furthermore, if the average percentage of changes in the target site on different dates is greater than a preset threshold for percentage of changes, then the target site is determined not to be a site for monitoring and optimization.

[0065] It should also be noted that when the target site is not a waste disposal monitoring and optimization site, the waste disposal classification results of all waste disposal personnel will be identified and processed.

[0066] Optionally, the method for determining the target sites for monitoring and optimization is as follows:

[0067] Based on the changes in the garbage disposal personnel, determine the deviation in the number of garbage disposal personnel between different dates, and group the dates with deviations within a preset range into the same date group;

[0068] Based on the proportion of dates in different date groups, the distribution clustering coefficient of different date groups is determined;

[0069] The target site is determined as an optimized site for monitoring based on the maximum value of the distribution clustering coefficient for different date groups.

[0070] Furthermore, if the maximum value of the distribution clustering coefficient of different date groups is less than the preset clustering coefficient threshold, then the target site is determined not to belong to the monitoring and optimization site.

[0071] Optionally, the method for determining the target sites for monitoring and optimization is as follows:

[0072] Based on the changes in the garbage disposal personnel, the deviation of garbage disposal personnel between different dates is determined, and the dates with deviations within a preset range are grouped into the same date group. When the number of the date groups is greater than the preset number of groups, it is determined that the target site does not belong to the garbage disposal monitoring and optimization site.

[0073] When the number of date groups is not greater than the preset number of groups:

[0074] Based on the proportion of dates in different date groups, the distribution clustering coefficient of different date groups is determined. When the maximum value of the distribution clustering coefficient of different date groups is less than the preset clustering coefficient threshold, it is determined that the target site does not belong to the site for monitoring and optimization.

[0075] When the maximum value of the clustering coefficient of different date groups is not less than the preset clustering coefficient threshold:

[0076] When the maximum value of the distribution clustering coefficient is within the preset clustering coefficient range, the target site is determined to be a monitoring and optimization site.

[0077] When the maximum value of the distribution clustering coefficient is not within the preset clustering coefficient range:

[0078] Based on the changes in the number of waste disposal personnel, determine the number of changes in waste disposal personnel between different adjacent dates. If the average number of changes in the number of waste disposal personnel between different adjacent dates does not meet the requirements, then the target site is determined not to be a waste disposal monitoring and optimization site.

[0079] When the average number of changes in the number of waste disposal personnel between different adjacent dates meets the requirements:

[0080] Based on the change in the number of people disposing of garbage between adjacent dates, the percentage of the number of people disposing of garbage on that date is determined and used as the percentage of the change. If the percentage of the change is greater than the percentage of the number of days on which the percentage of the change is greater than the preset percentage of the change does not meet the requirements, then the target site is determined not to be a garbage disposal monitoring and optimization site.

[0081] When the percentage of days with a change in quantity is greater than the preset percentage of change in quantity, the requirement is met:

[0082] The variation coefficient of waste disposal personnel at the target site is determined by the percentage of changes on different dates and the variation between adjacent dates, and the variation coefficient is used to determine whether the target site is a waste disposal monitoring and optimization site.

[0083] Furthermore, when the variation coefficient of the garbage disposal personnel at the target site is greater than a preset variation coefficient threshold, the target site is determined to be a garbage disposal monitoring and optimization site.

[0084] It should be noted that the data on the classification deviation of waste disposal personnel for different types of waste includes the amount of classification deviation of waste disposal personnel for different types of waste.

[0085] Specifically, such as Figure 4 As shown, the method for determining the target personnel is as follows:

[0086] Based on the data on the waste disposal personnel's classification deviations for different types of waste, determine the number of classification deviations made by the waste disposal personnel for different types of waste.

[0087] The type of waste whose number of classification deviations falls within a preset range is used as the classification deviation type;

[0088] Based on the number of classification deviation types of the waste disposal personnel, it is determined whether the waste disposal personnel are the target personnel for detection.

[0089] Furthermore, when the number of classification deviation types of the waste disposal personnel is greater than the preset number of deviation types, the waste disposal personnel are determined to be the target personnel for detection.

[0090] Optionally, the method for determining the target personnel is as follows:

[0091] S41 uses the waste disposal personnel's classification deviation data for different types of waste to determine the number of classification deviations for different types of waste and the amount of classification deviation for different numbers of classification deviations;

[0092] S42 obtains the interval between the date and the current date corresponding to different sorting deviations of the waste disposal personnel, and determines the sorting and processing deviation coefficient of the waste disposal personnel for different types of waste by combining the sorting deviation amount of waste with different sorting deviations;

[0093] S43 determines the sorting deviation of the waste disposal personnel based on the sorting deviation coefficients of the waste disposal personnel for different types of waste, and uses the sorting deviation to determine whether the waste disposal personnel is a target person for detection.

[0094] Optionally, when the waste disposal personnel's sorting deviation exceeds a preset sorting deviation threshold, the waste disposal personnel are identified as the target personnel for detection.

[0095] Optionally, step S41 above includes the following:

[0096] S411 uses the waste disposal personnel's classification deviation data for different types of waste to determine the total amount of classification deviation for different types of waste. If the total amount of classification deviation for different types of waste by the waste disposal personnel does not meet the requirements, then the waste disposal personnel is identified as the detection target personnel. If the total amount of classification deviation for different types of waste by the waste disposal personnel meets the requirements, proceed to step S412.

[0097] S412 Based on the classification deviation of the waste disposal personnel for different types of waste, if it is determined that the waste disposal personnel have a type of waste whose classification deviation does not meet the requirements, proceed to step S413. If the waste disposal personnel meet the requirements for classification deviation in different types of waste, then it is determined that the waste disposal personnel do not belong to the detection target personnel.

[0098] S413 Obtain the number of types of waste for which the waste disposal personnel do not meet the classification deviation requirements. When the number of types of waste for which the waste disposal personnel do not meet the classification deviation requirements is greater than a preset waste type quantity threshold, the waste disposal personnel are determined to be the detection target personnel. When the number of types of waste for which the waste disposal personnel do not meet the classification deviation requirements is not greater than the preset waste type quantity threshold, proceed to step S414.

[0099] S414 obtains the number of sorting deviations of the waste disposal personnel in different types of waste. When the total number of sorting deviations of the waste disposal personnel in different types of waste is greater than the preset sorting deviation number setting value, the waste disposal personnel is determined to be the detection target personnel. When the total number of sorting deviations of the waste disposal personnel in different types of waste is not greater than the preset sorting deviation number setting value, the process proceeds to step S42.

[0100] Optionally, step S42 above includes the following:

[0101] S421 Obtain the interval between the date corresponding to different sorting deviations of the waste disposal personnel and the current date, and use the interval to determine the sorting deviation number of the waste disposal personnel within a preset time period. If the sorting deviation number of the waste disposal personnel within the preset time period does not meet the requirements, then the waste disposal personnel is determined to be the detection target personnel. If the sorting deviation number of the waste disposal personnel within the preset time period meets the requirements, then proceed to step S422.

[0102] S422 When the waste disposal personnel's classification deviation within a preset time period is within a preset deviation range, it is determined that the waste disposal personnel are not the target personnel for detection. When the waste disposal personnel's classification deviation within a preset time period is within a preset deviation range, proceed to step S423.

[0103] S423 obtains the interval between the date corresponding to different sorting deviations of the waste disposal personnel and the current date, and combines the sorting deviation amount of waste with different sorting deviations to determine the sorting and processing deviation coefficient of the waste disposal personnel for different types of waste. When the sorting and processing deviation coefficient of the waste disposal personnel for different types of waste meets the requirements, proceed to step S43. When there is a type of waste for which the waste disposal personnel does not meet the requirements for sorting and processing deviation coefficient, proceed to step S424.

[0104] S424 When the number of types of waste whose classification and processing deviation coefficient of the waste disposal personnel does not meet the requirements is within the preset range of types, it is determined that the waste disposal personnel does not belong to the detection target personnel. When the number of types of waste whose classification and processing deviation coefficient of the waste disposal personnel does not meet the requirements is not within the preset range of types, proceed to step S43.

[0105] Furthermore, the other personnel refer to the waste disposal personnel other than the target personnel being detected as of the current date.

[0106] Specifically, the method for determining the secondary verification strategy for waste sorting at the waste disposal monitoring and optimization sites is as follows:

[0107] Based on the waste disposal data of the target personnel at the monitoring and optimization stations on the current date, determine the proportion of the waste disposal data of the target personnel on the current date, and use it as the proportion of the disposal data;

[0108] Based on the number of target personnel and other personnel at the monitoring and optimization sites on the current date, determine the proportion of target personnel among waste disposal personnel on the current date, and use this proportion as the target proportion.

[0109] Based on the proportion of the detection targets and the proportion of the disposal data, the monitoring reliability coefficient of the disposal monitoring optimization site is determined, and the secondary verification strategy of the disposal monitoring optimization site is determined using the monitoring reliability coefficient.

[0110] Furthermore, the monitoring reliability coefficient of the waste sorting at the waste disposal monitoring and optimization site is the average of the proportion of the detection target and the proportion of disposal data.

[0111] Additionally, it should be noted that the secondary verification strategy for determining the waste sorting at the optimized waste disposal monitoring sites using the aforementioned monitoring reliability coefficient specifically includes:

[0112] When the monitoring reliability coefficient of the optimized monitoring site is greater than the preset reliability coefficient threshold, it is determined that no secondary verification process for garbage classification is required for the target site.

[0113] When the monitoring reliability coefficient of the optimized monitoring site is not greater than the preset reliability coefficient threshold, it is determined that the target site needs to undergo secondary verification of waste classification.

[0114] Example 2

[0115] Secondly, such as Figure 5 As shown, the present invention provides a computer system, including: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described intelligent analysis method for waste disposal data when running the computer program.

[0116] Optionally, the method for determining the classification monitoring demand coefficient of the target site is as follows:

[0117] Based on the anomalies in the waste sorting data of the target site, determine the amount of waste disposed of at the target site on different dates where waste sorting is deviated, and treat it as the abnormal waste disposal amount;

[0118] Based on the abnormal classification and delivery volume on different dates, determine the preset monitoring demand coefficient corresponding to the abnormal classification and delivery volume on different dates;

[0119] Based on the average value of the preset monitoring demand coefficients of the target site on different dates, the classification monitoring demand coefficient of the target site is determined.

[0120] Optionally, the method for determining the classification monitoring demand coefficient of the target site is as follows:

[0121] Based on the abnormalities in the waste sorting data of the target site, determine the amount of waste disposal with deviations in waste sorting on different dates at the target site, and take it as the abnormal disposal amount. Obtain the sum of the abnormal disposal amounts of the target site within a preset time period. When the sum of the abnormal disposal amounts of the target site within the preset time period is greater than the preset abnormal disposal amount threshold, then identify and process the waste sorting results of all waste disposal personnel.

[0122] When the total abnormal category delivery volume of the target site within a preset time period is not greater than a preset abnormal delivery volume threshold:

[0123] If there are no days with abnormal waste disposal volumes outside the preset abnormal disposal volume range, then no waste disposal personnel will be subject to waste sorting processing; only the target site will be subject to secondary verification of waste sorting.

[0124] When there are dates when the abnormal category delivery volume is outside the preset abnormal delivery volume range:

[0125] Dates with abnormal sorting and disposal volumes outside the preset abnormal disposal volume range are designated as abnormal disposal dates. When the percentage of abnormal disposal dates at the target site is outside the preset percentage range of abnormal dates, the waste sorting results of all waste disposal personnel are identified and processed.

[0126] When the percentage of abnormal delivery dates for the target site is within a preset range for the percentage of abnormal delivery dates:

[0127] Based on the abnormal sorting and disposal volume of different types of waste at the target site on different days, the site classification deviation coefficient of different types of waste is calculated. When there are types of waste whose site classification deviation coefficient does not meet the requirements, the waste sorting results of all waste disposal personnel are identified and processed.

[0128] When there is no type of waste for which the site classification deviation coefficient does not meet the requirements:

[0129] The classification monitoring requirement coefficient of the target site is determined by the average value of the site classification deviation coefficients for different types of waste at the target site.

[0130] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0131] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0132] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for intelligent analysis of waste disposal data, characterized in that, Specifically, it includes: Based on the analysis results of waste disposal data, identify any anomalies in waste sorting data at different target sites. If the classification monitoring demand coefficient of the target site is within a preset demand coefficient range based on the anomalies, proceed to the next step. The system acquires information about waste disposal personnel using camera devices at the target site, and obtains information about changes in waste disposal personnel at the target site between different dates. Based on these changes in waste disposal personnel, the system identifies waste disposal monitoring and optimization sites within the target site. Based on the analysis results of historical waste disposal data from the waste disposal monitoring and optimization sites, the classification deviation data of different waste disposal personnel for different types of waste is determined, and the target personnel for detection in the waste disposal monitoring and optimization sites are determined based on the classification deviation data. The classification results of waste disposed of by the target personnel at the waste disposal monitoring and optimization site are identified and processed only. Based on the waste disposal data of the target personnel at the waste disposal monitoring and optimization site and other personnel on the current date, a secondary verification strategy for waste classification at the waste disposal monitoring and optimization site is determined.

2. The intelligent analysis method for waste disposal data as described in claim 1, characterized in that, The waste disposal data includes the amount of waste disposed of at different target sites on different days, as well as waste sorting data.

3. The intelligent analysis method for waste disposal data as described in claim 1, characterized in that, The abnormalities in the waste sorting data of the target site include the amount of waste disposed of at the target site that deviates from the different types of waste sorting, and the number of people who disposed of waste with deviations in waste sorting.

4. The intelligent analysis method for waste disposal data as described in claim 1, characterized in that, The method for determining the classification monitoring demand coefficient of the target site is as follows: Based on the anomalies in the waste sorting data of the target site, determine the amount of waste disposed of at the target site on different dates where waste sorting is deviated, and treat it as the abnormal waste disposal amount; Based on the abnormal delivery volume in different dates, the abnormal delivery dates for the target site are determined; Based on the proportion of abnormal deployment dates at the target site, the classification monitoring demand coefficient for the target site is determined.

5. The intelligent analysis method for waste disposal data as described in claim 4, characterized in that, The abnormal delivery date is the date on which the abnormal category delivery volume falls within the preset abnormal delivery volume range.

6. The intelligent analysis method for waste disposal data as described in claim 4, characterized in that, When the classification monitoring demand coefficient of the target site is not within the preset demand coefficient range, it is also necessary to determine whether the classification monitoring demand coefficient of the target site is greater than the preset monitoring demand coefficient threshold. If so, the classification results of all garbage disposal personnel will be identified and processed. If not, the garbage disposal personnel will not be processed for garbage classification, and only the target site will be processed for secondary verification of garbage classification.

7. The intelligent analysis method for waste disposal data as described in claim 1, characterized in that, The other personnel refer to the waste disposal personnel other than the personnel targeted for detection on the current date.

8. The intelligent analysis method for waste disposal data as described in claim 1, characterized in that, The method for determining the secondary verification strategy for waste sorting at the aforementioned waste disposal monitoring and optimization sites is as follows: Based on the waste disposal data of the target personnel at the monitoring and optimization stations on the current date, determine the proportion of the waste disposal data of the target personnel on the current date, and use it as the proportion of the disposal data; Based on the number of target personnel and other personnel at the monitoring and optimization sites on the current date, determine the proportion of target personnel among waste disposal personnel on the current date, and use this as the target proportion. Based on the proportion of the detection targets and the proportion of the disposal data, the monitoring reliability coefficient of the disposal monitoring optimization site is determined, and the secondary verification strategy of the disposal monitoring optimization site is determined using the monitoring reliability coefficient.

9. The intelligent analysis method for waste disposal data as described in claim 8, characterized in that, The secondary verification strategy for waste sorting at the optimized waste disposal monitoring site is determined using the monitoring reliability coefficient, specifically including: When the monitoring reliability coefficient of the optimized monitoring site is greater than the preset reliability coefficient threshold, it is determined that no secondary verification process for garbage classification is required for the target site. When the monitoring reliability coefficient of the optimized monitoring site is not greater than the preset reliability coefficient threshold, it is determined that the target site needs to undergo secondary verification of waste classification.

10. A computer system, comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a method for intelligent analysis of waste disposal data as described in any one of claims 1-9.

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