Monitoring method of municipal solid waste incineration emissions based on atmospheric ecological control

Abstract

The present invention discloses a method for monitoring the emission of domestic waste incineration based on atmospheric ecological control, which relates to the field of solid waste treatment technology, including: establishing a sample list of waste incineration pollutant detection based on historical experience and a waste incineration pollutant detection database; calculating the information gain of each feature item and the sample list based on the ID3 algorithm; screening the maximum value as a leaf node based on information gain comparison; building a decision tree model based on information gain and leaf nodes, and screening the optimal detection method through the decision tree model; optimizing the decision tree model based on a post-pruning strategy to improve the efficiency and accuracy of screening waste incineration pollutant detection methods; establishing a waste incineration pollutant detection ledger management platform for recording, analyzing and managing waste incineration pollutant detection data. The advantages of the present invention are: improving detection efficiency, making rapid warnings for pollutant concentrations around samples, and making timely processing decisions.

Description

Technical Field

[0001] The present invention relates to the technical field of solid waste treatment, and in particular to a method for monitoring emissions from the incineration of domestic waste based on atmospheric ecological control. Background Art

[0002] The most efficient way to treat solid stock waste is incineration, but during the treatment process, it is easy to ignore the amount of emissions resulting from the treatment of solid stock waste, causing a large amount of emissions to be discharged into the air, causing environmental pollution.

[0003] Existing waste incineration pollutant detection mainly focuses on detection equipment and optimization of detection methods. It lacks the ability to systematically match the detection purpose, detection indicators, sample type, detection time, cost-effectiveness and detection method of samples during the detection process. It mainly relies on subjective experience for judgment, which leads to high detection experience requirements, disordered operations, detection results exceeding expected arrangements, and inability to effectively provide early warnings during the actual detection process. Summary of the invention

[0004] In order to solve the above-mentioned technical problems, a method for monitoring the incineration emissions of domestic waste based on atmospheric ecological control is provided. The technical solution solves the problem raised in the above-mentioned background technology that there is a lack of system matching ability for the detection purpose, detection indicators, sample type, detection time, cost-effectiveness and detection method of the samples during the detection process, and it mainly relies on subjective experience for judgment, which leads to high detection experience requirements, disordered operations, detection results exceeding expected arrangements, and inability to efficiently provide early warnings in the actual detection process.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A method for monitoring emissions from incineration of domestic waste based on atmospheric ecological control, comprising:

[0007] Based on historical experience and the waste incineration pollutant detection database, a sample list of waste incineration pollutant detection is established;

[0008] Based on the ID3 algorithm, the information gain of each feature item and sample list is calculated;

[0009] Based on the information gain comparison, the maximum value is selected as the leaf node;

[0010] Based on information gain and leaf nodes, a decision tree model is constructed, and the optimal detection method is selected through the decision tree model;

[0011] The decision tree model is optimized based on the post-pruning strategy to improve the efficiency and accuracy of the detection method for waste incineration pollutants;

[0012] Establish a waste incineration pollutant detection ledger management platform to record, analyze and manage waste incineration pollutant detection data.

[0013] Preferably, the establishment of a waste incineration pollutant detection sample list based on historical experience and a waste incineration pollutant detection database specifically includes:

[0014] According to the detection purpose, detection index, sample type, detection time, cost-effectiveness and detection method of the waste incineration pollutant detection samples, a list of waste incineration pollutant detection samples is formulated based on historical experience and the waste incineration pollutant detection database; among which, the detection purpose, detection index, sample type, detection time and cost-effectiveness are characteristic items, and the detection method is the result item;

[0015] Refine the testing purpose, testing indicators, sample types, testing time, and cost-effectiveness content, set intervals according to the degree and type of treatment, and classify them;

[0016] The expression for the number of sample list data is: ,

[0017] in, is the minimum number of sample list data for waste incineration pollutant detection. For the total number of test purposes, test indicators, sample types, test time, cost-effectiveness and test methods, For the The number of levels corresponding to each feature item.

[0018] Preferably, the calculation of the information gain of each feature item and sample list based on the ID3 algorithm specifically includes:

[0019] Based on the sample list of waste incineration pollutant detection, calculate the information entropy of the sample list;

[0020] Based on the sample list of waste incineration pollutant detection, the information entropy of each feature item is calculated;

[0021] Based on the sample list and the information entropy of each feature item, calculate the information gain of each feature item;

[0022] The information entropy expression of the waste incineration pollutant detection sample list is: ,

[0023] in, is the value of information entropy of the sample list of waste incineration pollutant detection, is the number of detection method categories, To select The probability of a solution, For the scheme;

[0024] The information entropy expression of each characteristic item in the waste incineration pollutant detection sample list is: ,

[0025] in, The sample list for waste incineration pollutant detection is The information entropy of feature items is For the The feature item corresponds to The number of sample data for each solution, is the minimum number of sample list data for waste incineration pollutant detection. is the number of detection method categories;

[0026] The information gain expression of each feature item is: ,

[0027] in, For the The information gain value corresponding to the feature item.

[0028] Preferably, the step of selecting the maximum value as the leaf node based on information gain comparison specifically includes:

[0029] Based on the information gain value of each feature item, the maximum value is selected as the leaf node through comparison;

[0030] The leaf node screening expression is: ,

[0031] in, is the maximum information gain of each feature item, For the The information gain value corresponding to the feature item is is the number of feature items.

[0032] Preferably, the step of constructing a decision tree model based on information gain and leaf nodes, and screening the optimal detection method through the decision tree model specifically includes:

[0033] Calculate the information gain of each feature item and filter the leaf nodes. Based on the leaf nodes, calculate the information gain of the feature items in the subset in turn, and continue to filter the sub-leaf nodes, splitting them down in turn until they can no longer be split, forming a complete decision tree model;

[0034] By inputting the waste incineration pollutant detection samples to be detected, the decision tree model is used to screen out the optimal waste incineration pollutant detection method.

[0035] Preferably, the optimization of the decision tree model based on the post-pruning strategy to improve the efficiency and accuracy of the method for screening waste incineration pollutant detection specifically includes:

[0036] According to the requirements of waste incineration pollutant detection, the value of the anti-overfitting balance item is set;

[0037] Calculate the loss value based on information gain and sample size;

[0038] By judging whether the loss value of the leaf node is greater than the loss value of the leaf node after splitting, if so, it means that pruning operation is needed, if not, it means that pruning operation is not needed;

[0039] The loss value expression is: ,

[0040] in, is the loss value of the leaf node, For the The information gain value corresponding to the feature item is For the leaf node corresponding to The number of sample data for feature items, To prevent overfitting the value of the equilibrium term, is the number of leaf nodes after splitting.

[0041] Preferably, the establishment of a garbage incineration pollutant detection ledger management platform for recording, analyzing and managing garbage incineration pollutant detection data specifically includes:

[0042] Based on the historical experience of waste incineration pollutant detection and the waste incineration pollutant detection database, a waste incineration pollutant detection ledger management platform is established to record, analyze and manage waste incineration pollutant detection data;

[0043] Based on new feature items and new detection methods, update the sample list of waste incineration pollutant detection and refresh the decision tree model;

[0044] Record the detection standards and concentration requirements for waste incineration pollutants, and judge whether the waste incineration pollutants exceed the standards by comparing the test results. If so, mark it as unqualified and issue a warning. If not, mark it as qualified.

[0045] For samples with early warning warnings for waste incineration pollutant detection, it is necessary to collect multiple sets of samples from different areas and depths for re-testing;

[0046] Compare each set of waste incineration pollutant test results with the standards and test concentration requirements:

[0047] When the test result is greater than the standard and the test concentration requirement exceeds the sample volume When the pollutants from garbage incineration in the area exceed the standard, it can be predicted that timely treatment is needed;

[0048] When the test result is greater than the standard and the test concentration requirement, the sample volume does not exceed And the ratio of the pollutant concentration in the test results to the standard and test concentration requirements exceeds When the pollutants from garbage incineration in the area are about to exceed the standard, it can be predicted that preventive measures need to be taken;

[0049] When the test result is greater than the standard and the test concentration requirement, the sample volume does not exceed And the ratio of the pollutant concentration in the test results to the standard and test concentration requirements does not exceed When the data is obtained, it can be predicted that the waste incineration pollutants in the area will be within the qualified range in the future.

[0050] Furthermore, this scheme proposes a domestic waste incineration emission monitoring system based on atmospheric ecological control, which is used to implement the domestic waste incineration emission monitoring method based on atmospheric ecological control as described above, including:

[0051] Optimal detection method module: The optimal detection method module is used to establish a sample list of waste incineration pollutant detection based on historical experience and waste incineration pollutant detection database; calculate the information gain of each feature item and sample list based on the ID3 algorithm; select the maximum value as the leaf node based on the information gain comparison; build a decision tree model based on the information gain and leaf nodes, and select the optimal detection method through the decision tree model; optimize the decision tree model based on the post-pruning strategy to improve the efficiency and accuracy of screening waste incineration pollutant detection methods;

[0052] The ledger management module is used to establish a garbage incineration pollutant detection ledger management platform for recording, analyzing and managing garbage incineration pollutant detection data.

[0053] Preferably, the optimal detection mode module specifically includes:

[0054] A sample list unit, wherein the sample list unit is used to establish a sample list of waste incineration pollutant detection based on historical experience and a waste incineration pollutant detection database;

[0055] An information gain calculation unit, wherein the information gain calculation unit is used to calculate the information gain of each feature item and the sample list based on the ID3 algorithm;

[0056] A leaf node screening unit is used to screen the maximum value as a leaf node based on information gain comparison;

[0057] An optimal detection method unit, wherein the optimal detection method unit is used to construct a decision tree model based on information gain and leaf nodes, and screen the optimal detection method through the decision tree model;

[0058] A decision tree model optimization unit is used to optimize the decision tree model based on a post-pruning strategy to improve the efficiency and accuracy of the method for screening waste incineration pollutant detection.

[0059] Preferably, the ledger management module specifically includes:

[0060] A ledger management platform unit, wherein the ledger management platform unit is used to establish a garbage incineration pollutant detection ledger management platform based on the historical experience of garbage incineration pollutant detection and the garbage incineration pollutant detection database, and is used to record, analyze and manage garbage incineration pollutant detection data;

[0061] A refresh model unit, wherein the refresh model unit is used to update the waste incineration pollutant detection sample list based on new feature items and new detection methods, and refresh the decision tree model;

[0062] A unit for judging whether the waste incineration pollutants exceed the standard. The unit is used to record the detection standard and detection concentration requirements of waste incineration pollutants. By comparing the detection results, it is judged whether the waste incineration pollutants exceed the standard. If yes, it is marked as unqualified. If not, it is marked as qualified.

[0063] A prediction unit, which is used to collect multiple groups of samples from different areas and depths for re-testing samples with early warning warnings for waste incineration pollutants;

[0064] Compare each set of waste incineration pollutant test results with the standards and test concentration requirements:

[0065] When the test result is greater than the standard and the test concentration requirement exceeds the sample volume When the pollutants from garbage incineration in the area exceed the standard, it can be predicted that timely treatment is needed;

[0066] When the test result is greater than the standard and the test concentration requirement, the sample volume does not exceed And the ratio of the pollutant concentration in the test results to the standard and test concentration requirements exceeds When the pollutants from garbage incineration in the area are about to exceed the standard, it can be predicted that preventive measures need to be taken;

[0067] When the test result is greater than the standard and the test concentration requirement, the sample volume does not exceed And the ratio of the pollutant concentration in the test results to the standard and test concentration requirements does not exceed When the data is obtained, it can be predicted that the waste incineration pollutants in the area will be within the qualified range in the future.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] By analyzing the detection purpose, detection indicators, sample type, detection time, cost-effectiveness and detection method of the samples of waste incineration pollutants to be detected, and utilizing the predictive ability of the decision tree algorithm, a decision tree model for screening the optimal detection method for waste incineration pollutants is constructed. The decision tree model is optimized through post-pruning decisions to reduce the poor generalization ability of the model caused by overfitting and the poor effect of predicting new data, thereby improving the accuracy of the model prediction. At the same time, by comparing the results, it is predicted whether the pollutant concentration in the surrounding area of ​​the waste incineration pollutant sample exceeds the standard, so as to achieve the purpose of accurately matching the optimal detection method, improving the detection efficiency, making a rapid warning of the pollutant concentration around the sample, and making timely treatment decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is a flow chart of a method for monitoring emissions from incineration of domestic waste based on atmospheric ecological control of the present invention;

[0071] Figure 2 A decision tree model and an optimization method flow chart for the present invention;

[0072] Figure 3 The present invention provides a waste incineration pollutant detection ledger management platform for recording, analyzing and managing waste incineration pollutant detection data flow chart. DETAILED DESCRIPTION

[0073] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0074] Reference Figure 1 As shown, a method for monitoring emissions from incineration of domestic waste based on atmospheric ecological control includes:

[0075] Based on historical experience and the waste incineration pollutant detection database, a sample list of waste incineration pollutant detection is established;

[0076] Based on the ID3 algorithm, the information gain of each feature item and sample list is calculated;

[0077] Based on the information gain comparison, the maximum value is selected as the leaf node;

[0078] Based on information gain and leaf nodes, a decision tree model is constructed, and the optimal detection method is selected through the decision tree model;

[0079] The decision tree model is optimized based on the post-pruning strategy to improve the efficiency and accuracy of the detection method for waste incineration pollutants;

[0080] Establish a waste incineration pollutant detection ledger management platform to record, analyze and manage waste incineration pollutant detection data.

[0081] It can be explained that this scheme analyzes the detection purpose, detection index, sample type, detection time, cost-effectiveness and detection method of the samples of waste incineration pollutants to be detected, and uses the prediction ability of the decision tree algorithm to build a decision tree model for screening the optimal detection method of waste incineration pollutants, and optimizes the decision tree model through post-pruning decisions to reduce the poor generalization ability of the model caused by overfitting and the poor effect of predicting new data, thereby improving the accuracy of model prediction, and at the same time, by comparing the results, predict whether the pollutant concentration in the surrounding area of ​​the waste incineration pollutant sample exceeds the standard. Among them, the detection purposes include: air pollution investigation, environmental quality monitoring, risk assessment, detection indicators include: total pollutants, key pollutants, sample types include: agricultural land, industrial land, mining area, and detection methods include: hydride-atomic fluorescence spectrometry, diethyldithiocarbamate silver colorimetry, inductively coupled plasma-atomic emission spectrometry, high performance liquid chromatography-hydride generation-inductively coupled plasma-mass spectrometry.

[0082] Reference Figure 2 As shown, the decision tree model construction and optimization method specifically include:

[0083] Calculate the information gain of each feature item and filter the leaf nodes. Based on the leaf nodes, calculate the information gain of the feature items in the subset in turn, and continue to filter the sub-leaf nodes, splitting them down in turn until they can no longer be split, forming a complete decision tree model;

[0084] By inputting the waste incineration pollutant detection samples to be detected, the decision tree model is used to screen out the optimal waste incineration pollutant detection method;

[0085] According to the requirements of waste incineration pollutant detection, the value of the anti-overfitting balance item is set;

[0086] Calculate the loss value based on information gain and sample size;

[0087] By judging whether the loss value of the leaf node is greater than the loss value of the leaf node after splitting, if so, it means that pruning operation is needed, if not, it means that pruning operation is not needed;

[0088] The loss value expression is: ,

[0089] in, is the loss value of the leaf node, For the The information gain value corresponding to the feature item is For the leaf node corresponding to The number of sample data for feature items, To prevent overfitting the value of the equilibrium term, is the number of leaf nodes after splitting.

[0090] It can be explained that in order to ensure the accuracy and generalization of the decision tree model, the sample list of waste incineration pollutant detection must contain every feature item, and the number of samples must be at least greater than Otherwise, the model will have low generalization ability and overfitting. In order to prevent overfitting caused by updating the sample list when adding new features and new detection methods, the model needs to be optimized by post-pruning. The value of determines the prediction bias. The larger the value, the greater the loss value, which tends to be pruning operation, focusing on preventing overfitting; The smaller the value, the smaller the loss value, which tends to be non-pruning and focuses on improving prediction accuracy.

[0091] Reference Figure 3 As shown, the establishment of a garbage incineration pollutant detection ledger management platform for recording, analyzing and managing garbage incineration pollutant detection data specifically includes:

[0092] Based on the historical experience of waste incineration pollutant detection and the waste incineration pollutant detection database, a waste incineration pollutant detection ledger management platform is established to record, analyze and manage waste incineration pollutant detection data;

[0093] Based on new feature items and new detection methods, update the sample list of waste incineration pollutant detection and refresh the decision tree model;

[0094] Record the detection standards and concentration requirements for waste incineration pollutants, and judge whether the waste incineration pollutants exceed the standards by comparing the test results. If so, mark it as unqualified and issue a warning. If not, mark it as qualified.

[0095] For samples with early warning warnings for waste incineration pollutant detection, it is necessary to collect multiple sets of samples from different areas and depths for re-testing;

[0096] Compare each set of waste incineration pollutant test results with the standards and test concentration requirements:

[0097] When the test result is greater than the standard and the test concentration requirement exceeds the sample volume When the pollutants from garbage incineration in the area exceed the standard, it can be predicted that timely treatment is needed;

[0098] When the test result is greater than the standard and the test concentration requirement, the sample volume does not exceed And the ratio of the pollutant concentration in the test results to the standard and test concentration requirements exceeds When the pollutants from garbage incineration in the area are about to exceed the standard, it can be predicted that preventive measures need to be taken;

[0099] When the test result is greater than the standard and the test concentration requirement, the sample volume does not exceed And the ratio of the pollutant concentration in the test results to the standard and test concentration requirements does not exceed When the data is obtained, it can be predicted that the waste incineration pollutants in the area will be within the qualified range in the future.

[0100] It can be explained that this plan, by establishing a waste incineration pollutant detection ledger management platform, realizes the dynamic balance of the decision tree model through data collection and updating, maintains the accuracy and reliability of the model prediction, and at the same time, based on the comparison of the waste incineration pollutant detection results with the standards and detection concentration requirements, predicts whether the waste incineration pollutant concentration in the sample area exceeds the standard, and responds in a timely manner based on the prediction results.

[0101] Further, based on the same inventive concept as the above-mentioned method for monitoring the incineration emissions of domestic waste based on atmospheric ecological control, this solution proposes a system for monitoring the incineration emissions of domestic waste based on atmospheric ecological control, including:

[0102] Optimal detection method module: The optimal detection method module is used to establish a sample list of waste incineration pollutant detection based on historical experience and waste incineration pollutant detection database; calculate the information gain of each feature item and sample list based on the ID3 algorithm; select the maximum value as the leaf node based on the information gain comparison; build a decision tree model based on the information gain and leaf nodes, and select the optimal detection method through the decision tree model; optimize the decision tree model based on the post-pruning strategy to improve the efficiency and accuracy of screening waste incineration pollutant detection methods;

[0103] The ledger management module is used to establish a garbage incineration pollutant detection ledger management platform for recording, analyzing and managing garbage incineration pollutant detection data.

[0104] The optimal detection mode module includes:

[0105] A sample list unit, wherein the sample list unit is used to establish a sample list of waste incineration pollutant detection based on historical experience and a waste incineration pollutant detection database;

[0106] An information gain calculation unit, wherein the information gain calculation unit is used to calculate the information gain of each feature item and the sample list based on the ID3 algorithm;

[0107] A leaf node screening unit is used to screen the maximum value as a leaf node based on information gain comparison;

[0108] An optimal detection method unit, wherein the optimal detection method unit is used to construct a decision tree model based on information gain and leaf nodes, and screen the optimal detection method through the decision tree model;

[0109] A decision tree model optimization unit is used to optimize the decision tree model based on a post-pruning strategy to improve the efficiency and accuracy of the method for screening waste incineration pollutant detection.

[0110] The ledger management module includes: a ledger management platform unit, which is used to establish a garbage incineration pollutant detection ledger management platform based on the historical experience of garbage incineration pollutant detection and the garbage incineration pollutant detection database, and is used to record, analyze and manage garbage incineration pollutant detection data;

[0111] A refresh model unit, wherein the refresh model unit is used to update the waste incineration pollutant detection sample list based on new feature items and new detection methods, and refresh the decision tree model;

[0112] A unit for judging whether the waste incineration pollutants exceed the standard. The unit is used to record the detection standard and detection concentration requirements of waste incineration pollutants. By comparing the detection results, it is judged whether the waste incineration pollutants exceed the standard. If yes, it is marked as unqualified. If not, it is marked as qualified.

[0113] A prediction unit, which is used to collect multiple groups of samples from different areas and depths for re-testing samples with early warning warnings for waste incineration pollutants;

[0114] Compare each set of waste incineration pollutant test results with the standards and test concentration requirements:

[0115] When the test result is greater than the standard and the test concentration requirement exceeds the sample volume When the pollutants from garbage incineration in the area exceed the standard, it can be predicted that timely treatment is needed;

[0116] When the test result is greater than the standard and the test concentration requirement, the sample volume does not exceed And the ratio of the pollutant concentration in the test results to the standard and test concentration requirements exceeds When the pollutants from garbage incineration in the area are about to exceed the standard, it can be predicted that preventive measures need to be taken;

[0117] When the test result is greater than the standard and the test concentration requirement, the sample volume does not exceed And the ratio of the pollutant concentration in the test results to the standard and test concentration requirements does not exceed When the data is obtained, it can be predicted that the waste incineration pollutants in the area will be within the qualified range in the future.

[0118] To sum up, the advantages of the present invention are: by analyzing the detection purpose, detection indicators, sample type, detection time, cost-effectiveness and detection method of the samples to be detected of waste incineration pollutants, and utilizing the predictive ability of the decision tree algorithm, a decision tree model for screening the optimal detection method for waste incineration pollutants is constructed, and the decision tree model is optimized through post-pruning decisions, thereby reducing the poor generalization ability of the model caused by overfitting and the poor effect of predicting new data, thereby improving the accuracy of model prediction, and at the same time, by comparing the results, predicting whether the pollutant concentration in the surrounding area of ​​the waste incineration pollutant sample exceeds the standard, so as to achieve the purpose of accurately matching the optimal detection method, improving detection efficiency, making rapid warnings on the pollutant concentration around the sample, and making timely processing decisions.

[0119] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for monitoring emissions from incineration of domestic waste based on atmospheric ecological control, characterized in that: include: Based on historical experience and the waste incineration pollutant detection database, a sample list of waste incineration pollutant detection is established; Based on the ID3 algorithm, the information gain of each feature item and sample list is calculated; Based on the information gain comparison, the maximum value is selected as the leaf node; Based on information gain and leaf nodes, a decision tree model is constructed, and the optimal detection method is selected through the decision tree model; The decision tree model is optimized based on the post-pruning strategy to improve the efficiency and accuracy of the detection method for waste incineration pollutants; Establish a waste incineration pollutant detection ledger management platform to record, analyze and manage waste incineration pollutant detection data; The waste incineration pollutant detection sample list established based on historical experience and the waste incineration pollutant detection database specifically includes: According to the detection purpose, detection index, sample type, detection time, cost-effectiveness and detection method of the waste incineration pollutant detection samples, a list of waste incineration pollutant detection samples is formulated based on historical experience and the waste incineration pollutant detection database; among which, the detection purpose, detection index, sample type, detection time and cost-effectiveness are characteristic items, and the detection method is the result item; Refine the testing purpose, testing indicators, sample types, testing time, and cost-effectiveness content, set intervals according to the degree and type of treatment, and classify them; The expression for the number of sample list data is: , in, is the minimum number of sample list data for waste incineration pollutant detection. For the total number of test purposes, test indicators, sample types, test time, cost-effectiveness and test methods, For the The number of levels corresponding to each feature item; The optimization of the decision tree model based on the post-pruning strategy to improve the efficiency and accuracy of the waste incineration pollutant detection method specifically includes: According to the requirements of waste incineration pollutant detection, the value of the anti-overfitting balance item is set; Calculate the loss value based on information gain and sample size; By judging whether the loss value of the leaf node is greater than the loss value of the leaf node after splitting, if so, it means that pruning operation is needed, if not, it means that pruning operation is not needed; The loss value expression is: , in, is the loss value of the leaf node, For the The information gain value corresponding to the feature item is For the leaf node corresponding to The number of sample data for feature items, To prevent overfitting the value of the equilibrium term, is the number of leaf nodes after splitting; The establishment of a garbage incineration pollutant detection ledger management platform for recording, analyzing and managing garbage incineration pollutant detection data specifically includes: Based on the historical experience of waste incineration pollutant detection and the waste incineration pollutant detection database, a waste incineration pollutant detection ledger management platform is established to record, analyze and manage waste incineration pollutant detection data; Based on new feature items and new detection methods, update the sample list of waste incineration pollutant detection and refresh the decision tree model; Record the detection standards and concentration requirements for waste incineration pollutants, and judge whether the waste incineration pollutants exceed the standards by comparing the test results. If so, mark it as unqualified and issue a warning. If not, mark it as qualified. For samples with early warning warnings for waste incineration pollutant detection, it is necessary to collect multiple sets of samples from different areas and depths for re-testing; Compare each set of waste incineration pollutant test results with the standards and test concentration requirements: When the test result is greater than the standard and the test concentration requirement exceeds the sample volume When the pollutants from garbage incineration in the area exceed the standard, it can be predicted that timely treatment is needed; When the test result is greater than the standard and the test concentration requirement, the sample volume does not exceed And the ratio of the pollutant concentration in the test results to the standard and test concentration requirements exceeds When the pollutants from garbage incineration in the area are about to exceed the standard, it can be predicted that preventive measures need to be taken; When the test result is greater than the standard and the test concentration requirement, the sample volume does not exceed And the ratio of the pollutant concentration in the test results to the standard and test concentration requirements does not exceed When the data is obtained, it can be predicted that the waste incineration pollutants in the area will be within the qualified range in the future.

2. A method for monitoring emissions from incineration of domestic waste based on atmospheric ecological control according to claim 1, characterized in that: The calculation of the information gain of each feature item and sample list based on the ID3 algorithm specifically includes: Based on the sample list of waste incineration pollutant detection, calculate the information entropy of the sample list; Based on the sample list of waste incineration pollutant detection, the information entropy of each feature item is calculated; Based on the sample list and the information entropy of each feature item, calculate the information gain of each feature item; The information entropy expression of the waste incineration pollutant detection sample list is: , in, is the value of information entropy of the sample list of waste incineration pollutant detection, is the number of detection method categories, To select The probability of a solution, For the scheme; The information entropy expression of each characteristic item in the waste incineration pollutant detection sample list is: , in, The sample list for waste incineration pollutant detection is The information entropy of feature items is For the The feature item corresponds to The number of sample data for each solution, is the minimum number of sample list data for waste incineration pollutant detection. is the number of detection method categories; The information gain expression of each feature item is: , in, For the The information gain value corresponding to the feature item.

3. A method for monitoring emissions from incineration of domestic waste based on atmospheric ecological control according to claim 2, characterized in that: The method of selecting the maximum value as the leaf node based on the information gain comparison specifically includes: Based on the information gain value of each feature item, the maximum value is selected as the leaf node through comparison; The leaf node screening expression is: , in, is the maximum information gain of each feature item, For the The information gain value corresponding to the feature item is is the number of feature items.

4. A method for monitoring emissions from incineration of domestic waste based on atmospheric ecological control according to claim 3, characterized in that: The method of constructing a decision tree model based on information gain and leaf nodes and screening the optimal detection method through the decision tree model specifically includes: Calculate the information gain of each feature item and filter the leaf nodes. Based on the leaf nodes, calculate the information gain of the feature items in the subset in turn, and continue to filter the sub-leaf nodes, splitting them down in turn until they can no longer be split, forming a complete decision tree model; By inputting the waste incineration pollutant detection samples to be detected, the decision tree model is used to screen out the optimal waste incineration pollutant detection method.

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