Garbage throwing data intelligent analysis method and system

Abstract

The invention provides a garbage throwing data intelligent analysis method and system, and belongs to the technical field of data processing, and the method specifically comprises the steps: determining a throwing monitoring optimization site in target sites according to the change condition of garbage throwing personnel, and obtaining the analysis result of the historical throwing data of the throwing monitoring optimization site according to the analysis result of the historical throwing data of the throwing monitoring optimization site; according to the method, classification deviation data of different garbage throwing persons in different types of garbage is determined, detection target persons in a throwing monitoring optimization site are determined based on the classification deviation data, and only the garbage thrown by the detection target persons in the throwing monitoring optimization site is subjected to classification result identification processing. The secondary verification strategy of garbage classification of the throwing monitoring optimization site is determined according to the detection target personnel of the throwing monitoring optimization site in the current date and the garbage throwing data of other personnel, so that the accuracy of the verification processing result of the garbage classification result is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a method and system for intelligent analysis of garbage disposal data. Background Art

[0002] In order to achieve intensive management of garbage, the invention patent application CN202411744164.5 "An intelligent garbage collection system based on an Internet integrated scheduling platform" predicts abnormal data over a period of time, so that reference data for garbage collection resource scheduling can be used to propose a recycling plan, which effectively improves the efficiency of garbage collection, optimizes resource allocation, and promptly responds to and handles abnormal changes in garbage volume, accumulation status, or delivery behavior. However, there are the following technical defects:

[0003] In the process of analyzing and processing garbage disposal data, existing technical solutions often use cameras to identify and process garbage classification results. Due to the large amount of garbage classification, it will inevitably lead to difficulty in analyzing and processing the identification and processing data of garbage classification results and high data storage pressure. Therefore, how to generate differentiated garbage classification identification and processing strategies and reduce the difficulty of analyzing and processing garbage classification identification and processing has become a technical problem that needs to be solved urgently.

[0004] In response to the above technical problems, the present application specifically provides a method and system for intelligent analysis of garbage disposal data. Summary of the invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0006] Specifically, in a first aspect, the present application provides a method for intelligent analysis of garbage disposal data, which specifically includes:

[0007] S1 determines the abnormality of the garbage classification data of different target sites based on the analysis results of the garbage placement data, and proceeds to the next step when it is determined that the classification monitoring demand coefficient of the target site is within a preset demand coefficient range according to the abnormality;

[0008] S2: obtaining garbage disposal personnel according to the camera device of the target site, obtaining the change of garbage disposal personnel at the target site between different dates, and determining the garbage disposal monitoring optimization site in the target site according to the change of the garbage disposal personnel;

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

[0010] S4 only identifies and processes the classification results of the garbage placed by the detection target personnel at the placement monitoring optimization site, and determines the secondary verification strategy for the garbage classification of the placement monitoring optimization site based on the garbage placement data of the detection target personnel and other personnel at the placement monitoring optimization site on the current date.

[0011] The beneficial effects of the present invention are:

[0012] In the present application, the target persons for detection in the monitoring and optimization site are determined based on the classification deviation data, thereby avoiding the classification and identification processing of the garbage placed by all the target persons for detection, which leads to the problem of greater difficulty in classification and identification processing and higher storage space requirements. At the same time, the classification deviation data is further combined to ensure the reliability of the detection and processing of the classification processing results of the garbage placers with a higher degree of deviation in garbage classification, thereby achieving accurate identification of the garbage classification processing results.

[0013] In the present application, the secondary verification strategy for garbage classification at the placement monitoring optimization site is determined based on the garbage disposal data of the target personnel and other personnel at the placement monitoring optimization site on the current date. This not only takes into account the differences in the detection reliability of garbage classification processing at the placement monitoring optimization nodes due to differences in the amount of garbage disposal of the target personnel, but also takes into account the differences in the monitoring reliability of garbage classification processing at the placement monitoring optimization nodes due to differences in the proportion of the target personnel in the number of garbage disposal personnel. By determining differentiated secondary verification strategies, the reliability of the verification processing results is guaranteed while also reducing the difficulty and resource investment of the secondary verification processing.

[0014] A further technical solution is that the garbage disposal data includes garbage disposal amounts and garbage classification data of different target sites on different dates.

[0015] A further technical solution is that the abnormal conditions of the garbage classification data of the target site include the amount of garbage disposed with deviations in different types of garbage classification and the number of garbage disposal personnel with deviations in garbage classification in the target site.

[0016] A further technical solution is that the method for determining the classification monitoring demand coefficient of the target site is:

[0017] Based on the abnormal situation of the garbage classification data of the target site, determine the garbage delivery amount of the target site with deviation in garbage classification on different dates, and use it as the abnormal classification delivery amount;

[0018] Determine the abnormal delivery date of the target site according to the abnormal classified delivery amounts on different dates;

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

[0020] A further technical solution is that the abnormal delivery date is a date when the abnormal classification delivery amount is within a preset abnormal delivery amount range.

[0021] A further technical solution is that the method for determining the secondary verification strategy of the garbage classification of the monitoring optimization site is:

[0022] Based on the garbage placement data of the detection target personnel placed at the monitoring optimization site on the current date, determine the proportion of the garbage placement data of the detection target personnel in the garbage placement data on the current date, and use it as the placement data proportion;

[0023] According to the number of detection target personnel and other personnel placed at the monitoring optimization site on the current date, determine the proportion of the detection target personnel in the garbage disposal personnel on the current date, and use it as the detection target proportion;

[0024] Based on the detection target ratio and the delivery data ratio, the monitoring reliability coefficient of the garbage classification of the delivery monitoring optimization site is determined, and the secondary verification strategy of the garbage classification of the delivery monitoring optimization site is determined using the monitoring reliability coefficient.

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

[0026] A further technical solution is to use the monitoring reliability coefficient to determine the secondary verification strategy for the garbage classification of the monitoring optimization site, which specifically includes:

[0027] When the monitoring reliability coefficient of the placement monitoring optimization site is greater than the preset reliability coefficient threshold, it is determined that there is no need to perform secondary verification processing of garbage classification on the target site;

[0028] When the monitoring reliability coefficient of the placement monitoring optimization site is not greater than a preset reliability coefficient threshold, it is determined that a secondary verification process of garbage classification of the target site is required.

[0029] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned method for intelligent analysis of garbage disposal data when running the computer program.

[0030] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0031] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0033] Figure 1 It is a flow chart of a method for intelligent analysis of garbage disposal data;

[0034] Figure 2 is a flow chart of a method for determining a classification monitoring demand factor for a target site;

[0035] Figure 3 It is a flow chart of a method for determining a delivery monitoring optimization site in a target site;

[0036] Figure 4 is a flow chart of a method for detecting a determination of a target person;

[0037] Figure 5 It is a framework diagram of a computer system. DETAILED DESCRIPTION

[0038] In order 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 in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0039] In the present application, the deviations in the historical garbage sorting and processing by garbage disposal personnel are utilized to determine the target personnel among the garbage disposal personnel who need to undergo garbage sorting inspection, thereby reducing a large number of unnecessary inspections, reducing the waste of storage space, and also reducing the difficulty of detection and processing for classification processing.

[0040] Example 1

[0041] like Figure 1 As shown, the present application provides a method for intelligent analysis of garbage placement data, which specifically includes:

[0042] S1 determines the abnormality of the garbage classification data of different target sites based on the analysis results of the garbage placement data, and proceeds to the next step when it is determined that the classification monitoring demand coefficient of the target site is within a preset demand coefficient range according to the abnormality;

[0043] The amount of garbage placed in the target site on different dates with deviations in garbage classification is taken as the abnormal classification amount, and the date with the abnormal classification amount within the preset abnormal amount range is taken as the abnormal placement date. Based on the proportion of the number of abnormal placement dates of the target site, the classification monitoring demand coefficient of the target site is determined.

[0044] S2: obtaining garbage disposal personnel according to the camera device of the target site, obtaining the change of garbage disposal personnel at the target site between different dates, and determining the garbage disposal monitoring optimization site in the target site according to the change of the garbage disposal personnel;

[0045] The dates in which the number of deviations of garbage disposal personnel is within a preset range are divided into the same date group. Based on the proportion of the number of dates in different date groups, the distribution clustering coefficients of different date groups are determined. When the maximum value of the distribution clustering coefficients of different date groups is less than 0.2, it is determined that the target site does not belong to the disposal monitoring optimization site.

[0046] S3 determines the classification deviation data of different garbage disposal personnel for different types of garbage based on the analysis results of the historical disposal data of the disposal monitoring and optimization site, and determines the detection target personnel in the disposal monitoring and optimization site based on the classification deviation data;

[0047] The target persons for detection are those who dispose of garbage and whose number of dates with garbage classification deviation accounts for more than 0.7.

[0048] S4 only identifies and processes the classification results of the garbage placed by the detection target personnel at the placement monitoring optimization site, and determines the secondary verification strategy for the garbage classification of the placement monitoring optimization site based on the garbage placement data of the detection target personnel and other personnel at the placement monitoring optimization site on the current date.

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

[0050] Based on the average value of the detection target ratio and the delivery data ratio, the monitoring reliability coefficient of the garbage classification of the delivery monitoring optimization site is determined. When the monitoring reliability coefficient of the delivery monitoring optimization site is greater than 0.7, it is determined that the target site does not need to be subjected to secondary verification of garbage classification. When the monitoring reliability coefficient of the delivery monitoring optimization site is not greater than 0.7, it is determined that the target site needs to be subjected to secondary verification of garbage classification.

[0051] Furthermore, the garbage disposal data includes garbage disposal amounts and garbage classification data of different target sites on different dates.

[0052] Specifically, the abnormal conditions of the garbage classification data of the target site include the amount of garbage disposed with deviations in different types of garbage classification and the number of garbage disposal personnel with deviations in garbage classification in the target site.

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

[0054] Based on the abnormal situation of the garbage classification data of the target site, determine the garbage delivery amount of the target site with deviation in garbage classification on different dates, and use it as the abnormal classification delivery amount;

[0055] Determine the abnormal delivery date of the target site according to the abnormal classified delivery amounts on different dates;

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

[0057] It should be noted that the abnormal delivery date is the date when the abnormal classification delivery amount is within the preset abnormal delivery amount range.

[0058] It can be understood 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 the garbage disposal will be identified for all garbage disposal personnel. If not, the garbage disposal personnel will not be subject to garbage classification processing, and only the target site will be subject to secondary verification of garbage classification.

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

[0060] Specifically, Figure 3 As shown, the method for determining the delivery monitoring optimization site in the target site is:

[0061] Determine the number of changes in garbage disposal personnel between different adjacent dates based on the changes in the garbage disposal personnel;

[0062] Based on the changed number of garbage disposal personnel between adjacent dates, determine the ratio of the changed number of garbage disposal personnel on the date, and use it as the changed number ratio;

[0063] Whether the target site is a delivery monitoring optimization site is determined based on the average value of the percentage of change quantities on different dates.

[0064] Furthermore, when the average value of the percentage of the change quantity of the target site on different dates is greater than a preset threshold value of the percentage of the change quantity, it is determined that the target site does not belong to the delivery monitoring optimization site.

[0065] It should also be noted that when the target site does not belong to the garbage placement monitoring optimization site, the classification results of the garbage placed by all garbage placement personnel are identified and processed.

[0066] Optionally, the method for determining the delivery monitoring optimization site in the target site is:

[0067] Determine the deviation number of garbage disposal personnel between different dates based on the change of the garbage disposal personnel, and classify the dates with deviation numbers within a preset range into the same date group;

[0068] Based on the proportion of the number of dates in different date groups, determine the distribution clustering coefficients of different date groups;

[0069] According to the maximum values ​​of the distribution clustering coefficients of different date groups, it is determined whether the target site is a delivery monitoring optimization site.

[0070] Furthermore, when the maximum value of the distribution clustering coefficients of different date groups is less than a preset clustering coefficient threshold, it is determined that the target site does not belong to the delivery monitoring optimization site.

[0071] Optionally, the method for determining the delivery monitoring optimization site in the target site is:

[0072] According to the change of the garbage delivery personnel, the deviation number of the garbage delivery personnel between different dates is determined, and the dates with the deviation number within a preset range are divided 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 delivery monitoring optimization site;

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

[0074] Based on the proportion of the number of dates in different date groups, the distribution clustering coefficients of different date groups are determined, and when the maximum value of the distribution clustering coefficients of different date groups is less than a preset clustering coefficient threshold, it is determined that the target site does not belong to the delivery monitoring optimization site;

[0075] When the maximum value of the distribution 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 a preset clustering coefficient interval, it is determined that the target site belongs to a delivery monitoring optimization site;

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

[0078] Determine the number of changes in the garbage disposal personnel between different adjacent dates based on the changes in the garbage disposal personnel; when the average number of changes in the number of garbage disposal personnel between different adjacent dates does not meet the requirements, determine that the target site does not belong to the garbage disposal monitoring optimization site;

[0079] When the average value of the change in the number of garbage disposal personnel between different adjacent dates meets the requirements:

[0080] Based on the changed number of garbage disposal personnel between adjacent dates, the ratio of the changed number of garbage disposal personnel on the date is determined, and it is used as the changed number ratio. When the changed number ratio is greater than the preset changed number ratio and the date number ratio does not meet the requirement, it is determined that the target site does not belong to the placement monitoring optimization site;

[0081] When the percentage of the number of dates whose percentage of change is greater than the preset percentage of change meets the requirement:

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

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

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

[0085] Specifically, Figure 4 As shown, the method for determining the detection target person is:

[0086] Determine the number of classification deviations of the garbage disposing personnel in different types of garbage based on the classification deviation data of the garbage disposing personnel in different types of garbage;

[0087] The type of garbage whose classification deviation times are within a preset classification deviation times interval is regarded as the classification deviation type;

[0088] According to the number of classification deviation types of the garbage disposal personnel, it is determined whether the garbage disposal personnel is a detection target person.

[0089] Furthermore, when the number of classification deviation types of the garbage disposal person is greater than the preset number of deviation types, the garbage disposal person is determined to be a detection target person.

[0090] Optionally, the method for determining the detection target person is:

[0091] S41 determines the number of classification deviations of the garbage disposing personnel for different types of garbage and the classification deviation amounts of the garbage with different classification deviations based on the classification deviation data of the garbage disposing personnel for different types of garbage;

[0092] S42: obtaining the interval between the date corresponding to the different classification deviation times of the garbage disposal personnel and the current date, and combining the classification deviation amounts of the garbage with different classification deviation times to determine the classification processing deviation coefficient of the garbage disposal personnel for different types of garbage;

[0093] S43 determines the classification and processing deviation of the garbage disposal personnel according to the classification and processing deviation coefficient of the garbage disposal personnel for different types of garbage, and uses the classification and processing deviation to determine whether the garbage disposal personnel is a detection target person.

[0094] Optionally, when the classification processing deviation of the garbage disposal person is greater than a preset classification deviation threshold, the garbage disposal person is determined to be a detection target person.

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

[0096] S411 determines the sum of the classification deviations of the garbage disposing personnel in different types of garbage based on the classification deviation data of the garbage disposing personnel in different types of garbage. When the sum of the classification deviations of the garbage disposing personnel in different types of garbage does not meet the requirements, the garbage disposing personnel is determined as a detection target person. When the sum of the classification deviations of the garbage disposing personnel in different types of garbage meets the requirements, the process proceeds to step S412.

[0097] S412: based on the classification deviation of the garbage disposal personnel in different types of garbage, when it is determined that the garbage disposal personnel has a type of garbage whose classification deviation does not meet the requirements, the process proceeds to step S413: when the classification deviation of the garbage disposal personnel in different types of garbage meets the requirements, it is determined that the garbage disposal personnel does not belong to the detection target personnel;

[0098] S413 obtains the number of types of garbage for which the classification deviation of the garbage disposal personnel does not meet the requirements. When the number of types of garbage for which the classification deviation of the garbage disposal personnel does not meet the requirements is greater than the preset garbage type number threshold, the garbage disposal personnel is determined to be a detection target person. When the number of types of garbage for which the classification deviation of the garbage disposal personnel does not meet the requirements is not greater than the preset garbage type number threshold, the process proceeds to step S414.

[0099] S414 obtains the number of classification deviations of the garbage disposal personnel for different types of garbage. When the sum of the number of classification deviations of the garbage disposal personnel for different types of garbage is greater than the preset classification deviation number setting value, the garbage disposal personnel is determined to be a detection target person. When the sum of the number of classification deviations of the garbage disposal personnel for different types of garbage is not greater than the preset classification deviation number setting value, the process proceeds to step S42.

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

[0101] S421 obtains the interval between the date corresponding to the different classification deviation times of the garbage disposal personnel and the current date, and uses the interval to determine the classification deviation times of the garbage disposal personnel within a preset time period. When the classification deviation times of the garbage disposal personnel within the preset time period do not meet the requirements, the garbage disposal personnel are determined to be the detection target personnel. When the classification deviation times of the garbage disposal personnel within the preset time period meet the requirements, the process proceeds to step S422;

[0102] S422: When the classification deviation of the garbage disposal personnel within the preset time period is within the preset deviation range, it is determined that the garbage disposal personnel does not belong to the detection target personnel; when the classification deviation of the garbage disposal personnel within the preset time period is within the preset deviation range, the process proceeds to step S423;

[0103] S423 obtains the interval between the date corresponding to the different classification deviation times of the garbage disposal personnel and the current date, and determines the classification processing deviation coefficient of the garbage disposal personnel for different types of garbage in combination with the classification deviation amounts of the garbage with different classification deviation times. When the classification processing deviation coefficient of the garbage disposal personnel for different types of garbage meets the requirements, the process proceeds to step S43. When the garbage disposal personnel has a type of garbage whose classification processing deviation coefficient does not meet the requirements, the process proceeds to step S424.

[0104] S424 When the garbage disposal personnel's classification and processing deviation coefficient does not meet the requirement and the number of garbage types is within the preset type quantity range, it is determined that the garbage disposal personnel does not belong to the detection target personnel; when the garbage disposal personnel's classification and processing deviation coefficient does not meet the requirement and the number of garbage types is not within the preset type quantity range, go to step S43.

[0105] Furthermore, the other personnel are the garbage disposal personnel excluding the detection target personnel on the current date.

[0106] Specifically, the method for determining the secondary verification strategy for garbage classification of the placement monitoring optimization site is as follows:

[0107] Based on the garbage placement data of the detection target personnel placed at the monitoring optimization site on the current date, determine the proportion of the garbage placement data of the detection target personnel in the garbage placement data on the current date, and use it as the placement data proportion;

[0108] According to the number of detection target personnel and other personnel placed at the monitoring optimization site on the current date, determine the proportion of the detection target personnel in the garbage disposal personnel on the current date, and use it as the detection target proportion;

[0109] Based on the detection target ratio and the delivery data ratio, the monitoring reliability coefficient of the garbage classification of the delivery monitoring optimization site is determined, and the secondary verification strategy of the garbage classification of the delivery monitoring optimization site is determined using the monitoring reliability coefficient.

[0110] Furthermore, the monitoring reliability coefficient of the garbage classification of the placement monitoring optimization site is the average value of the detection target proportion and the placement data proportion.

[0111] It should also be noted that the secondary verification strategy for the garbage classification of the monitoring optimization site is determined by using the monitoring reliability coefficient, specifically including:

[0112] When the monitoring reliability coefficient of the placement monitoring optimization site is greater than the preset reliability coefficient threshold, it is determined that there is no need to perform secondary verification processing of garbage classification on the target site;

[0113] When the monitoring reliability coefficient of the placement monitoring optimization site is not greater than a preset reliability coefficient threshold, it is determined that a secondary verification process of garbage classification of the target site is required.

[0114] Example 2

[0115] Second, as Figure 5 As shown, the present invention provides a computer system, comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned method for intelligent analysis of garbage disposal data when running the computer program.

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

[0117] Based on the abnormal situation of the garbage classification data of the target site, determine the garbage delivery amount of the target site with deviation in garbage classification on different dates, and use it as the abnormal classification delivery amount;

[0118] According to the abnormal classification delivery amounts on different dates, the preset monitoring demand coefficients corresponding to the abnormal classification delivery amounts on different dates are determined;

[0119] Based on the average values ​​of the preset monitoring requirement coefficients of the target site on different dates, the classified monitoring requirement coefficient of the target site is determined.

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

[0121] Based on the abnormal situation of the garbage classification data of the target site, the garbage delivery volume with deviations in garbage classification of the target site on different dates is determined, and it is used as the abnormal classification delivery volume, and the sum of the abnormal classification delivery volume of the target site within a preset time period is obtained. When the sum of the abnormal classification delivery volume of the target site within the preset time period is greater than the preset abnormal delivery volume threshold, the classification results of the garbage delivery of all garbage delivery personnel are identified and processed;

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

[0123] When there is no date when the amount of abnormal classified delivery is not within the preset abnormal delivery amount range, the garbage delivery personnel will not be required to classify the garbage, and only the target site will be subject to secondary verification of the garbage classification;

[0124] When there is a date when the abnormal category delivery volume is not within the preset abnormal delivery volume range:

[0125] The date when the abnormal classification delivery amount is not within the preset abnormal delivery amount range is regarded as the abnormal delivery date. When the number ratio of the abnormal delivery date of the target site is not within the preset abnormal date number ratio range, the classification results of the garbage delivery are identified for all garbage delivery personnel;

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

[0127] Based on the abnormal classified delivery amounts of different types of garbage in the target site on different days, the site classification deviation coefficients of different types of garbage are calculated. When there are types of garbage whose site classification deviation coefficients do not meet the requirements, the classification results of the garbage delivery are identified for all garbage delivery personnel;

[0128] When there is no type of garbage whose site classification deviation coefficient does not meet the requirements:

[0129] The classification monitoring requirement coefficient of the target site is determined based on the average value of the site classification deviation coefficients of the target site for different types of garbage.

[0130] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0131] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0132] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A method for intelligent analysis of garbage disposal data, characterized in that: Specifically include: Based on the analysis results of the garbage placement data, determine the abnormal conditions of the garbage classification data of different target sites, and when it is determined that the classification monitoring demand coefficient of the target site is within a preset demand coefficient range according to the abnormal conditions, proceed to the next step; Acquire the garbage disposal personnel according to the camera device of the target site, acquire the change of the garbage disposal personnel of the target site between different dates, and determine the garbage disposal monitoring optimization site in the target site according to the change of the garbage disposal personnel; Determine the classification deviation data of different garbage disposal personnel for different types of garbage based on the analysis results of the historical disposal data of the disposal monitoring and optimization site, and determine the detection target personnel in the disposal monitoring and optimization site based on the classification deviation data; Only the classification results of the garbage placed by the target personnel at the monitoring and optimization site are identified and processed, and the secondary verification strategy for the garbage classification of the monitoring and optimization site is determined based on the garbage placement data of the target personnel and other personnel at the monitoring and optimization site on the current date.

2. The method for intelligent analysis of garbage disposal data according to claim 1, characterized in that: The garbage disposal data includes garbage disposal amounts and garbage classification data of different target sites on different dates.

3. The method for intelligent analysis of garbage disposal data according to claim 1, characterized in that: The abnormal conditions of the garbage classification data of the target site include the amount of garbage disposed with deviations in different types of garbage classification and the number of garbage disposal personnel with deviations in garbage classification in the target site.

4. The method for intelligent analysis of garbage disposal data according to claim 1, characterized in that: The method for determining the classification monitoring demand coefficient of the target site is: Based on the abnormal situation of the garbage classification data of the target site, determine the garbage delivery amount of the target site with deviation in garbage classification on different dates, and use it as the abnormal classification delivery amount; Determine the abnormal delivery date of the target site according to the abnormal classified delivery amounts on different dates; Based on the proportion of the number of abnormal delivery dates of the target site, the classification monitoring demand coefficient of the target site is determined.

5. The method for intelligent analysis of garbage disposal data according to claim 4, characterized in that: The abnormal delivery date is the date when the abnormal classification delivery amount is within the preset abnormal delivery amount range.

6. The method for intelligent analysis of garbage disposal data according to 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 the garbage disposed by all garbage disposal personnel will be identified and processed. If not, the garbage disposal personnel will not be subject to garbage classification processing, and only the target site will be subject to secondary verification of garbage classification.

7. The method for intelligent analysis of garbage disposal data according to claim 1, characterized in that: The other personnel are the garbage disposal personnel excluding the detection target personnel on the current date.

8. The method for intelligent analysis of garbage disposal data according to claim 1, characterized in that: The method for determining the secondary verification strategy of the garbage classification of the placement monitoring optimization site is: Based on the garbage placement data of the detection target personnel placed at the monitoring optimization site on the current date, determine the proportion of the garbage placement data of the detection target personnel in the garbage placement data on the current date, and use it as the placement data proportion; According to the number of detection target personnel and other personnel placed at the monitoring optimization site on the current date, determine the proportion of the detection target personnel in the garbage disposal personnel on the current date, and use it as the detection target proportion; Based on the detection target ratio and the delivery data ratio, the monitoring reliability coefficient of the garbage classification of the delivery monitoring optimization site is determined, and the secondary verification strategy of the garbage classification of the delivery monitoring optimization site is determined using the monitoring reliability coefficient.

9. The method for intelligent analysis of garbage disposal data according to claim 8, characterized in that: The secondary verification strategy for the garbage classification of the monitoring optimization site is determined by using the monitoring reliability coefficient, specifically including: When the monitoring reliability coefficient of the placement monitoring optimization site is greater than the preset reliability coefficient threshold, it is determined that there is no need to perform secondary verification processing of garbage classification on the target site; When the monitoring reliability coefficient of the placement monitoring optimization site is not greater than a preset reliability coefficient threshold, it is determined that a secondary verification process of garbage classification of the target site is required.

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

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