Carbon emission evaluation method and system applied to waste incineration power plants

Abstract

The present invention relates to the field of data processing technology, and in particular to a carbon emission evaluation method and system applied to a waste incineration power plant, including: collecting carbon dioxide concentration data, carbon monoxide and nitrogen oxide concentration data, and exhaust gas overall concentration data emitted by the waste incineration power plant at each moment, obtaining the first special degree of each carbon dioxide concentration data according to the concentration data difference at the same moment, obtaining the second special degree of each carbon dioxide concentration data, thereby screening out normal data from all carbon dioxide concentration data, and performing carbon emission evaluation of the waste incineration power plant according to the normal data. The present invention improves the accuracy of normal data detection by adaptively adjusting the update diameter of the data class to which each carbon dioxide concentration data belongs, uses normal data to perform carbon emission evaluation, and improves the accuracy of carbon emission evaluation of the waste incineration power plant.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a carbon emission evaluation method and system applied to a waste incineration power plant. Background Art

[0002] When evaluating the carbon emissions of waste incineration power plants, it is inevitable that the carbon dioxide emissions will exceed the standard. If the carbon dioxide emissions exceed the standard during industrial production, it will aggravate global climate change, leading to environmental problems such as climate anomalies and extreme weather. At the same time, it will also have a negative impact on human health, such as respiratory diseases and cardiovascular diseases. The existing method for anomaly detection of carbon dioxide emissions is the Fisher optimal solution algorithm, which can minimize the misjudgment rate, improve the sensitivity and accuracy of detection, and can quickly process large amounts of data, so that it can maximize the detection effect under uncertain conditions.

[0003] Existing problems: The Fisher optimal solution algorithm defines the diameter of a class by taking the mean of all the data in each divided class, and is also used to analyze the discreteness of the class. Since the detection of carbon dioxide concentration may be affected by changes in wind speed and sensor accuracy, generating noise data, if there is noise data in each class, the mean value will not be accurate enough as a standard for calculating the discreteness, making the abnormal data detection results inaccurate, thereby reducing the accuracy of carbon emission evaluation of waste incineration power plants. Summary of the invention

[0004] The present invention provides a carbon emission evaluation method and system applied to a waste incineration power plant to solve the existing problems.

[0005] The carbon emission evaluation method and system applied to waste incineration power plants of the present invention adopt the following technical solutions:

[0006] An embodiment of the present invention provides a carbon emission evaluation method applied to a waste incineration power plant, the method comprising the following steps:

[0007] Collect the carbon dioxide concentration data, carbon monoxide and nitrogen oxide concentration data and overall exhaust gas concentration data emitted by a waste incineration power plant at every moment over a period of time, and obtain the carbon dioxide concentration data series, carbon monoxide and nitrogen oxide concentration data series and overall exhaust gas concentration data series respectively;

[0008] In the carbon dioxide concentration data sequence, the carbon monoxide and nitrogen oxide concentration data sequence, and the exhaust gas overall concentration data sequence, according to the concentration data differences at the same time, obtain the first specific degree of each carbon dioxide concentration data in the carbon dioxide concentration data sequence;

[0009] In the carbon dioxide concentration data sequence, according to the carbon dioxide concentration data, its time and the first special degree, the second special degree of each carbon dioxide concentration data is obtained;

[0010] According to the carbon dioxide concentration data in the carbon dioxide concentration data sequence and its second special degree, obtaining an updated diameter of the data class to which each carbon dioxide concentration data belongs;

[0011] According to the update diameter of the data class to which each carbon dioxide concentration data in the carbon dioxide concentration data sequence belongs, a number of normal data are screened out from all the carbon dioxide concentration data; based on the normal data, the carbon emission of the waste incineration power plant is evaluated.

[0012] Furthermore, in the carbon dioxide concentration data sequence, the carbon monoxide and nitrogen oxide concentration data sequence, and the exhaust gas overall concentration data sequence, according to the concentration data differences at the same moment, obtaining the first special degree of each carbon dioxide concentration data in the carbon dioxide concentration data sequence includes the following specific steps:

[0013] Record each carbon dioxide concentration data in the carbon dioxide concentration data sequence as target data;

[0014] Record each carbon monoxide and nitrogen oxide concentration data in the carbon monoxide and nitrogen oxide concentration data sequence as reference data;

[0015] Record each exhaust gas overall concentration data in the exhaust gas overall concentration data sequence as total concentration data;

[0016] According to multiple target data and reference data engraved at the same time, the credibility of each target data is obtained;

[0017] According to the credibility of each target data and the target data, reference data and total concentration data at each moment, the first specificity of each target data is obtained.

[0018] Furthermore, the specific calculation formula corresponding to the credibility of each target data obtained based on the target data and reference data engraved at the same time is:

[0019]

[0020] in For the Target data, For the Reference data, For the The credibility of the target data, For the Target data, For the Reference data, is the preset quantity threshold, is the absolute value function.

[0021] Furthermore, the specific calculation formula corresponding to the first special degree of each target data is obtained according to the credibility of each target data and the target data, reference data and total concentration data at each moment:

[0022]

[0023] in For the The first degree of specificity of the target data, For the Target data, For the Reference data, For the Total concentration data, For the The credibility of the target data, is a linear normalization function.

[0024] Furthermore, in the carbon dioxide concentration data sequence, the second special degree of each carbon dioxide concentration data is obtained according to the carbon dioxide concentration data, its time and the first special degree, including the following specific steps:

[0025] In the carbon dioxide concentration data series, from Starting from the target data, count the The target data before the target data is obtained non-repeating data segments; the number of target data in each data segment is ; is a preset data segment quantity threshold; is the preset length threshold;

[0026] Use the first-order derivative method to calculate each data segment and obtain several local extreme points in each data segment;

[0027] According to the target data difference and time difference corresponding to the adjacent local extreme value points in each data segment, the trend change degree of each data segment is obtained;

[0028] The second special degree of each target data is obtained according to the difference between the trend change degrees of the data segments and the first special degree of each target data.

[0029] Furthermore, the specific calculation formula corresponding to the second special degree of each target data is obtained according to the difference between the trend change degrees of the data segments and the first special degree of each target data:

[0030]

[0031] in For the The second degree of specificity of the target data, For the The first degree of specificity of the target data, and The first and The degree of trend change of each data segment, is the preset data segment quantity threshold, and Respectively In the data segment and The target data corresponding to the local extreme points, and Respectively In the data segment and The time corresponding to the local extreme point is For the The number of local extreme points in a data segment, is the absolute value function, is a linear normalization function.

[0032] Furthermore, the step of obtaining the updated diameter of the data class to which each carbon dioxide concentration data belongs according to the carbon dioxide concentration data in the carbon dioxide concentration data sequence and its second special degree includes the following specific steps:

[0033] The Fisher optimal solution algorithm is used to classify the carbon dioxide concentration data in the carbon dioxide concentration data sequence to obtain several data classes;

[0034] No. The specific calculation formula corresponding to the update diameter of the data class to which the target data belongs is:

[0035]

[0036] in For the The update diameter of the data class to which the target data belongs, For the The target data belongs to the data class Target data, For the The target data belongs to the data class The second degree of specificity of the target data, For the The number of target data in the data class to which the target data belongs.

[0037] Furthermore, the method of selecting a plurality of normal data from all carbon dioxide concentration data according to the update diameter of the data class to which each carbon dioxide concentration data in the carbon dioxide concentration data sequence belongs includes the following specific steps:

[0038] According to the update diameter of the data class to which each target data belongs, the carbon dioxide concentration data in the carbon dioxide concentration data sequence is classified using the Fisher optimal solution algorithm to obtain a number of updated data classes;

[0039] In any updated data class, the mean of all target data is calculated, and the product of the mean and a preset threshold coefficient is recorded as a judgment threshold, and target data less than the judgment threshold is recorded as normal data.

[0040] Furthermore, the carbon emission evaluation of the waste incineration power plant is performed based on normal data, and the specific steps include the following:

[0041] Among all normal data in all update data classes, normal data with a concentration greater than or equal to a preset concentration threshold value is recorded as exceeding-standard data;

[0042] When there is excessive data, determining that the carbon emissions of the waste incineration power plant in the period of time exceed the standard;

[0043] When there is no excessive data, it is determined that the carbon emissions of the waste incineration power plant during the period of time are qualified.

[0044] The present invention also proposes a carbon emission assessment system applied to a waste incineration power plant, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the aforementioned carbon emission assessment method applied to a waste incineration power plant.

[0045] The beneficial effects of the technical solution of the present invention are:

[0046] In the embodiment of the present invention, the carbon dioxide concentration data, carbon monoxide and nitrogen oxide concentration data and the overall exhaust gas concentration data emitted by the waste incineration power plant at each moment are collected, and the first special degree of each carbon dioxide concentration data is obtained according to the concentration data difference at the same moment, which improves the accuracy of noise interference data detection according to the multi-type data analysis at the same moment. The second special degree of each carbon dioxide concentration data is obtained to obtain the update diameter of the data class to which each carbon dioxide concentration data belongs, which further improves the accuracy of noise interference data detection through the data change of the same type of data within a period of time, ensures the accuracy of data clustering, and makes the normal data detection result more credible. Thus, a number of normal data are screened out from all carbon dioxide concentration data, and the carbon emission evaluation of the waste incineration power plant is performed according to the normal data. So far, the present invention improves the accuracy of data classification by adaptively adjusting the update diameter of the data class to which each carbon dioxide concentration data belongs, thereby ensuring the accuracy of normal data detection, using normal data for carbon emission evaluation, and improving the accuracy of carbon emission evaluation of the waste incineration power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 The present invention is a flowchart of the steps of the carbon emission evaluation method applied to a waste incineration power plant. DETAILED DESCRIPTION

[0049] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the carbon emission evaluation method and system applied to the waste incineration power plant proposed by the present invention, its specific implementation method, structure, characteristics and effects are described in detail as follows in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0050] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0051] The specific scheme of the carbon emission evaluation method and system for waste incineration power plants provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0052] See also Figure 1 , which shows a flowchart of a carbon emission evaluation method for a waste incineration power plant provided by an embodiment of the present invention, the method comprising the following steps:

[0053] Step S001: Over a period of time, collect the carbon dioxide concentration data, carbon monoxide and nitrogen oxide concentration data and the overall exhaust gas concentration data emitted by a waste incineration power plant at each moment, and obtain the carbon dioxide concentration data sequence, carbon monoxide and nitrogen oxide concentration data sequence and the overall exhaust gas concentration data sequence respectively.

[0054] The main purpose of this embodiment is to analyze the change characteristics of the carbon dioxide concentration data and the carbon monoxide and nitrogen oxide concentration data at the same time, to obtain the special degree of the corresponding carbon dioxide data point at each moment, and then to achieve adaptation through the special degree combined with the calculation of the class diameter in the original Fisher optimal solution algorithm, thereby using the adapted class diameter to complete data segmentation, realize the detection of abnormal data, and finally use the remaining data after excluding the abnormal data to evaluate the carbon emissions of the waste incineration power plant.

[0055] Over a period of time, a multi-parameter gas analyzer is used to collect data on the carbon dioxide concentration, carbon monoxide and nitrogen oxide concentrations, and overall exhaust gas concentration data emitted by a waste incineration power plant at each moment, and a carbon dioxide concentration data series, a carbon monoxide and nitrogen oxide concentration data series, and an overall exhaust gas concentration data series are obtained respectively.

[0056] It should be noted that the sampling time set in this embodiment is 30 minutes, the sampling frequency is once per second, and the value range of the concentration data is 0% to 100%. This is taken as an example for description. Other implementations can be set to other values, which are not limited in this embodiment. The overall exhaust gas concentration data is the sum of the concentrations of all exhaust gases emitted by the waste incineration power plant at each moment. And for subsequent data analysis, this embodiment will also collect data for a period of time before the current 30 minutes as historical data. The historical data includes carbon dioxide concentration data, carbon monoxide and nitrogen oxide concentration data, and overall exhaust gas concentration data at each moment in a period of time before the current 30 minutes.

[0057] Step S002: In the carbon dioxide concentration data sequence, the carbon monoxide and nitrogen oxide concentration data sequence and the exhaust gas overall concentration data sequence, according to the concentration data differences at the same moment, obtain the first specificity of each carbon dioxide concentration data in the carbon dioxide concentration data sequence.

[0058] This embodiment analyzes the change characteristics of the concentration data of carbon dioxide, carbon monoxide and nitrogen oxides at the same time to obtain the special degree of each carbon dioxide data, and then uses the special degree of each carbon dioxide data, combined with the position of each carbon dioxide data, to obtain the diameter of the class to which each carbon dioxide data belongs, that is, the data discrete degree of the class to which the data point belongs. Therefore, firstly, based on the change relationship between the carbon dioxide concentration data and the concentration data of the mixture of carbon monoxide and nitrogen oxides at the same time, the first special degree of each carbon dioxide data is calculated. Then, the data change characteristics before each carbon dioxide data to be analyzed are analyzed, and then combined with the true degree of the carbon dioxide data, the second special degree of the data is obtained. Finally, based on the second special degree of each carbon dioxide data, the diameter of the class to which the data belongs is obtained in combination with the original value corresponding to the data.

[0059] In actual scenarios, due to the influence of wind speed, the measurement of carbon dioxide concentration data may not be accurate enough. Therefore, this embodiment calculates the first special degree of each carbon dioxide data according to the change relationship between the carbon dioxide concentration data and the concentration data of the mixture of carbon monoxide and nitrogen oxides at the same time. It can be seen from objective common sense that the carbon dioxide emissions can be reflected by the combustion efficiency in the garbage incinerator, and the combustion efficiency can be reflected by the specific gravity relationship between the carbon dioxide content in the exhaust gas and the mixture content of carbon monoxide and nitrogen oxides. The logic of calculating the first special degree of each carbon dioxide data is that the greater the difference between each carbon dioxide concentration data and the concentration of the mixture of carbon monoxide and nitrogen oxides at the same time, the higher the combustion efficiency corresponding to the moment, the higher the combustion efficiency, the more carbon dioxide theoretically generated at the moment, and the greater the first special degree of the carbon dioxide concentration data at the moment. That is to say, at this moment, the concentration of carbon dioxide is more special compared to other moments.

[0060] The quantity threshold preset in this embodiment The value is 10, and this is taken as an example for description. Other values ​​may be set in other implementation modes, and this embodiment does not limit this.

[0061] Each carbon dioxide concentration data in the carbon dioxide concentration data sequence is recorded as target data.

[0062] Each carbon monoxide and nitrogen oxide concentration data in the carbon monoxide and nitrogen oxide concentration data sequence is recorded as reference data.

[0063] Each exhaust gas overall concentration data in the exhaust gas overall concentration data sequence is recorded as total concentration data.

[0064] First For example, the target data The first degree of specificity of the target data The calculation formula is:

[0065]

[0066]

[0067] in For the The first degree of specificity of the target data, For the Target data, For the Reference data, For the Total concentration data, For the The credibility of the target data, For the Target data, For the Reference data, is the preset quantity threshold, is the absolute value function, It is a linear normalization function that normalizes the data values ​​to the interval [0,1].

[0068] What needs to be explained is: If Less than or equal to When the historical data of the previous time of the currently collected period of time is selected for data supplement, and the collected gas concentration data is generally not 0. If the collected gas concentration data is 0, 0.1 is added to the denominator of the above formula to ensure that the denominator is not 0. This is used as an example for description. Other implementation methods can be set to other values, which are not limited in this embodiment. Indicates The total concentration ratio of target data is Indicates The total concentration percentage of reference data is The larger it is, the greater the proportion of carbon dioxide content in the exhaust gas to the mixture of carbon monoxide and nitrogen oxides at the same time, that is, the more complete the combustion, the more carbon dioxide is produced. The data at this time should be paid special attention to, so the first special degree of the data at this time is greater. and Respectively represent The ratio of the carbon dioxide content to the mixture of carbon monoxide and nitrogen oxides at the same time and the previous period of time is The smaller the value, the more stable the ratio of carbon dioxide content to the mixture of carbon monoxide and nitrogen oxides is over a period of time. The more reliable the data at the moment, the more reliable the and The product of The first degree of specificity of the target data.

[0069] Step S003: In the carbon dioxide concentration data sequence, the second special degree of each carbon dioxide concentration data is obtained according to the carbon dioxide concentration data, its time and the first special degree.

[0070] It is not enough to analyze the special degree of the concentration data only by the change relationship between the carbon dioxide concentration data and other gas content data at a moment. This embodiment also needs to analyze the data change characteristics of each carbon dioxide concentration data in a period of time before, and obtain the second special degree of each carbon dioxide concentration data. The logic of calculating the second special degree of each carbon dioxide concentration data is to compare the data segment adjacent to the current data with other data segments, and analyze the transformation of the data change trend in each data segment, the time the same data change trend is maintained, and the characteristics of the change amplitude of the same data change trend. The greater the difference in these characteristics between the data segments, the more frequent and drastic the data changes during this period, then the greater the probability of abnormal data appearing in the segment of data, and correspondingly, the greater the second special degree corresponding to the current data.

[0071] The quantity threshold preset in this embodiment The preset length threshold is 5. The value is 50, which is described by taking this as an example. Other values ​​may be set in other implementation modes, and this embodiment does not limit this.

[0072] Still For example, in the carbon dioxide concentration data series, from the Starting from the target data, count the The target data before the target data is obtained non-repeating data segments, where the number of target data in each data segment is .

[0073] What needs to be explained is: If Less than When the data is collected, the historical data at the previous time of the current period is selected for data supplement. The first data in the first data segment of the non-repeating data segment is the first data segment mentioned above. target data.

[0074] The first-order derivative method is used to calculate each data segment to obtain several local extreme value points in each data segment. The first-order derivative method is a well-known technology and the specific method is not introduced here.

[0075] From this we can know that The second degree of specificity of the target data The calculation formula is:

[0076]

[0077] in For the The second degree of specificity of the target data, For the The first degree of specificity of the target data, and The first and The degree of trend change of each data segment, is the preset data segment quantity threshold, and Respectively In the data segment and The target data corresponding to the local extreme points, and Respectively In the data segment and The time corresponding to the local extreme point is For the The number of local extreme points in a data segment, is the absolute value function, It is a linear normalization function that normalizes the data values ​​to the interval [0,1].

[0078] It should be noted that the data between two adjacent local extreme points in the data segment is increasing or decreasing. It indicates the data difference between two adjacent local extreme points in the data segment. The larger the data difference, the greater the increase or decrease. Represents the time difference between two adjacent local extreme points in the data segment, so The larger the value is, the greater the increase or decrease in the data over a period of time. The larger the value, the more dramatic the data changes in the data segment. and The greater the difference, the more abnormal the data change, so The normalized value of The second degree of specificity of the target data, The bigger, the The more abnormal the target data is.

[0079] According to the above method, the second specificity of each target data in the carbon dioxide concentration data sequence is obtained.

[0080] Step S004: According to the carbon dioxide concentration data in the carbon dioxide concentration data sequence and its second special degree, an updated diameter of the data class to which each carbon dioxide concentration data belongs is obtained.

[0081] Therefore, this embodiment can use each carbon dioxide concentration data and its second special degree to calculate the diameter of the class to which each carbon dioxide concentration data belongs. The greater the second special degree of each carbon dioxide concentration data, the greater the difference between the data and other data, and the greater the possibility of abnormality of the data, so the weight of the data should be smaller when weighting.

[0082] The Fisher optimal solution algorithm is used to classify the carbon dioxide concentration data in the carbon dioxide concentration data sequence to obtain several data classes.

[0083] It should be noted that the Fisher optimal solution algorithm is a well-known technology, and the specific method is not introduced here. The algorithm is an ordered clustering algorithm, which divides the data sequence into several sequence segments, each sequence segment is a data class. The diameter of the data class is the main parameter of the Fisher optimal solution algorithm, and the diameter of the data class is the mean of all data in the data class.

[0084] Still For example, the target data Update diameter of the data class to which the target data belongs The calculation formula is:

[0085]

[0086] in For the The update diameter of the data class to which the target data belongs, For the The target data belongs to the data class Target data, For the The target data belongs to the data class The second degree of specificity of the target data, For the The number of target data in the data class to which the target data belongs.

[0087] What needs to be explained is: The larger the value, the more abnormal the data is, so it should be given a smaller weight. for The weight of Indicates The update diameter of the data class to which the target data belongs.

[0088] According to the above method, the updated diameter of the data class to which each target data in the carbon dioxide concentration data sequence belongs is obtained.

[0089] Step S005: According to the update diameter of the data class to which each carbon dioxide concentration data in the carbon dioxide concentration data sequence belongs, a number of normal data are screened out from all carbon dioxide concentration data; based on the normal data, carbon emission evaluation of the waste incineration power plant is performed.

[0090] According to the update diameters of the data classes to which all target data belong, the Fisher optimal solution algorithm is used to classify the carbon dioxide concentration data in the carbon dioxide concentration data sequence to obtain several updated data classes.

[0091] The preset threshold coefficient of this embodiment is 1.2, and the preset concentration threshold is 12%. This is described by taking this as an example. Other implementations may set it to other values, which are not limited in this embodiment.

[0092] It should be noted that according to industry standards, the carbon dioxide emission concentration standard of waste incineration power plants is not more than 12% of carbon dioxide concentration per cubic meter of exhaust gas. Therefore, the preset concentration threshold in this embodiment is 12%.

[0093] In each update data class, the mean of all target data is calculated, and 1.2 times of the mean is recorded as the judgment threshold. Target data less than the judgment threshold is recorded as normal data, and target data greater than or equal to the judgment threshold is recorded as abnormal data.

[0094] In this way, abnormal data affected by noise can be detected, and normal data can be used to evaluate the carbon emissions of the waste incineration power plant to ensure the credibility of the evaluation.

[0095] One possible implementation method of carbon emission assessment for waste incineration power plants is:

[0096] Among all the normal data in all updated data classes, the normal data that is greater than or equal to 12% will be recorded as exceeding the standard data.

[0097] When there is data exceeding the standard, it is determined that the carbon emissions of the waste incineration power plant exceed the standard during the period of time.

[0098] When there is no data exceeding the standard, it is determined that the carbon emissions of the waste incineration power plant during the period of time are qualified.

[0099] Another possible way to implement carbon emission assessment of waste incineration power plants is:

[0100] In the carbon dioxide concentration data sequence, the quotient of the number of exceeding data divided by the number of target data is recorded as the carbon emission evaluation value of the waste incineration power plant during the period of time. The larger the carbon emission evaluation value, the more serious the waste incineration pollution of the waste incineration power plant during the period of time.

[0101] So far, the present invention is completed.

[0102] In summary, in an embodiment of the present invention, within a period of time, the carbon dioxide concentration data, carbon monoxide and nitrogen oxide concentration data and the overall exhaust gas concentration data emitted by a waste incineration power plant at each moment are collected, and the first special degree of each carbon dioxide concentration data is obtained according to the concentration data difference at the same moment, and the second special degree of each carbon dioxide concentration data is obtained, thereby obtaining the update diameter of the data class to which each carbon dioxide concentration data belongs, thereby screening out a number of normal data from all carbon dioxide concentration data, and performing carbon emission evaluation of the waste incineration power plant based on the normal data. The present invention improves the accuracy of data classification by adaptively adjusting the update diameter of the data class to which each carbon dioxide concentration data belongs, thereby ensuring the accuracy of normal data detection, using normal data for carbon emission evaluation, and improving the accuracy of carbon emission evaluation of waste incineration power plants.

[0103] The present invention also provides a carbon emission assessment system applied to a waste incineration power plant, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the aforementioned carbon emission assessment method applied to a waste incineration power plant.

[0104] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A carbon emission evaluation method applied to a waste incineration power plant, characterized in that: The method comprises the following steps: Collect the carbon dioxide concentration data, carbon monoxide and nitrogen oxide concentration data and overall exhaust gas concentration data emitted by a waste incineration power plant at every moment over a period of time, and obtain the carbon dioxide concentration data series, carbon monoxide and nitrogen oxide concentration data series and overall exhaust gas concentration data series respectively; In the carbon dioxide concentration data sequence, the carbon monoxide and nitrogen oxide concentration data sequence, and the exhaust gas overall concentration data sequence, according to the concentration data differences at the same time, obtain the first specific degree of each carbon dioxide concentration data in the carbon dioxide concentration data sequence; In the carbon dioxide concentration data sequence, according to the carbon dioxide concentration data, its time and the first special degree, the second special degree of each carbon dioxide concentration data is obtained; According to the carbon dioxide concentration data in the carbon dioxide concentration data sequence and its second special degree, obtaining an updated diameter of the data class to which each carbon dioxide concentration data belongs; According to the update diameter of the data class to which each carbon dioxide concentration data belongs in the carbon dioxide concentration data sequence, a number of normal data are screened out from all carbon dioxide concentration data; based on the normal data, the carbon emission of the waste incineration power plant is evaluated; The step of obtaining the first specific degree of each carbon dioxide concentration data in the carbon dioxide concentration data sequence according to the concentration data differences at the same time in the carbon dioxide concentration data sequence, the carbon monoxide and nitrogen oxide concentration data sequence and the exhaust gas overall concentration data sequence includes the following specific steps: Record each carbon dioxide concentration data in the carbon dioxide concentration data sequence as target data; Record each carbon monoxide and nitrogen oxide concentration data in the carbon monoxide and nitrogen oxide concentration data sequence as reference data; Record each exhaust gas overall concentration data in the exhaust gas overall concentration data sequence as total concentration data; According to multiple target data and reference data engraved at the same time, the credibility of each target data is obtained; According to the credibility of each target data and the target data, reference data and total concentration data at each moment, a first special degree of each target data is obtained; The specific calculation formula corresponding to the credibility of each target data obtained based on the target data and reference data engraved at the same time is: in For the Target data, For the Reference data, For the The credibility of the target data, For the Target data, For the Reference data, is the preset quantity threshold, is the absolute value function; According to the credibility of each target data and the target data, reference data and total concentration data at each moment, the specific calculation formula corresponding to the first special degree of each target data is obtained as follows: in For the The first degree of specificity of the target data, For the Target data, For the Reference data, For the Total concentration data, For the The credibility of the target data, is a linear normalization function; In the carbon dioxide concentration data sequence, the second special degree of each carbon dioxide concentration data is obtained according to the carbon dioxide concentration data, its time and the first special degree, and the specific steps include the following: In the carbon dioxide concentration data series, from Starting from the target data, count the The target data before the target data is obtained non-repeating data segments; the number of target data in each data segment is ; is the number threshold of preset data segments; is the preset length threshold; Use the first-order derivative method to calculate each data segment and obtain several local extreme points in each data segment; According to the target data difference and time difference corresponding to the adjacent local extreme value points in each data segment, the trend change degree of each data segment is obtained; According to the difference between the trend change degrees of the data segments and the first special degree of each target data, the second special degree of each target data is obtained; The specific calculation formula corresponding to the second special degree of each target data is obtained according to the difference between the trend change degrees of the data segments and the first special degree of each target data: in For the The second degree of specificity of the target data, For the The first degree of specificity of the target data, and The first and The degree of trend change of each data segment, is the preset data segment quantity threshold, and Respectively In the data segment and The target data corresponding to the local extreme points, and Respectively In the data segment and The time corresponding to the local extreme point is For the The number of local extreme points in a data segment, is the absolute value function, is a linear normalization function.

2. The carbon emission evaluation method for a waste incineration power plant according to claim 1 is characterized in that: The step of obtaining the updated diameter of the data class to which each carbon dioxide concentration data belongs according to the carbon dioxide concentration data in the carbon dioxide concentration data sequence and its second special degree includes the following specific steps: The Fisher optimal solution algorithm is used to classify the carbon dioxide concentration data in the carbon dioxide concentration data sequence to obtain several data classes; No. The specific calculation formula corresponding to the update diameter of the data class to which the target data belongs is: in For the The update diameter of the data class to which the target data belongs, For the The target data belongs to the data class Target data, For the The target data belongs to the data class The second degree of specificity of the target data, For the The number of target data in the data class to which the target data belongs.

3. The carbon emission evaluation method for a waste incineration power plant according to claim 1 is characterized in that: The specific steps of selecting a number of normal data from all carbon dioxide concentration data according to the update diameter of the data class to which each carbon dioxide concentration data belongs in the carbon dioxide concentration data sequence are as follows: According to the update diameter of the data class to which each target data belongs, the carbon dioxide concentration data in the carbon dioxide concentration data sequence is classified using the Fisher optimal solution algorithm to obtain a number of updated data classes; In any updated data class, the mean of all target data is calculated, and the product of the mean and a preset threshold coefficient is recorded as a judgment threshold, and target data less than the judgment threshold is recorded as normal data.

4. The carbon emission evaluation method for waste incineration power plants according to claim 2 is characterized in that: The carbon emission evaluation of the waste incineration power plant based on normal data includes the following specific steps: Among all normal data in all update data classes, normal data with a concentration greater than or equal to a preset concentration threshold value is recorded as exceeding-standard data; When there is excessive data, determining that the carbon emissions of the waste incineration power plant in the period of time exceed the standard; When there is no excessive data, it is determined that the carbon emissions of the waste incineration power plant during the period of time are qualified.

5. A carbon emission evaluation system for a waste incineration power plant, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of the carbon emission evaluation method applied to a waste incineration power plant as described in any one of claims 1 to 4 are implemented.

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