CN117422200B - Intelligent monitoring and early warning method and system for power plant - Google Patents
Intelligent monitoring and early warning method and system for power plant Download PDFInfo
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 114
- 238000000034 method Methods 0.000 title claims abstract description 53
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims abstract description 280
- 239000013618 particulate matter Substances 0.000 claims abstract description 167
- 229910002092 carbon dioxide Inorganic materials 0.000 claims abstract description 140
- 239000001569 carbon dioxide Substances 0.000 claims abstract description 140
- 238000004458 analytical method Methods 0.000 claims abstract description 120
- 230000005856 abnormality Effects 0.000 claims abstract description 98
- 239000011159 matrix material Substances 0.000 claims abstract description 50
- 238000010248 power generation Methods 0.000 claims abstract description 41
- 239000002245 particle Substances 0.000 claims abstract description 40
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 49
- 238000012360 testing method Methods 0.000 claims description 34
- 238000012545 processing Methods 0.000 claims description 28
- 238000012549 training Methods 0.000 claims description 16
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- 239000013598 vector Substances 0.000 claims description 8
- 238000005303 weighing Methods 0.000 claims description 7
- 238000013507 mapping Methods 0.000 claims description 5
- 238000010801 machine learning Methods 0.000 claims description 4
- 238000000691 measurement method Methods 0.000 claims description 4
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- 230000002159 abnormal effect Effects 0.000 description 17
- 230000008569 process Effects 0.000 description 6
- 238000004422 calculation algorithm Methods 0.000 description 5
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- 238000012706 support-vector machine Methods 0.000 description 2
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Abstract
The application discloses an intelligent monitoring and early warning method and system for a power plant, which belong to the field of power systems, wherein the method comprises the following steps: collecting the concentration of particulate matters and the concentration of carbon dioxide in a power plant; performing compensation analysis on the plurality of particle concentrations to obtain a plurality of compensation particle concentrations; obtaining a particulate matter concentration characteristic field and a carbon dioxide characteristic field according to the plurality of compensated particulate matter concentrations, the plurality of carbon dioxide concentrations and the plurality of position coordinates; acquiring an operation abnormality analysis result of the power plant according to the characteristic field; constructing a power generation emission data matrix according to the concentration field, and acquiring a plurality of emission scores of a plurality of positions; and generating an operation monitoring result of the target power plant according to the operation abnormality analysis result and the emission scores, and carrying out early warning according to the operation monitoring result based on the early warning station. The application solves the technical problems of low accuracy and poor effect of monitoring and early warning of the operation of the power plant in the prior art, and achieves the technical effect of improving the accuracy and effect of monitoring and early warning of the operation of the power plant.
Description
Technical Field
The invention relates to the field of power systems, in particular to an intelligent monitoring and early warning method and system for a power plant.
Background
With the development of society, the power demand is continuously increased, and the specific gravity of thermal power generation is continuously increased. A large amount of discharged substances can be generated in the thermal power generation process, and the operation state of the thermal power plant is reflected. Therefore, the monitoring and early warning of the emission of the thermal power plant are very important. In the prior art, various monitoring devices are used for monitoring the emission of the power plant, but the devices are often used in an isolated and dispersed way, so that the effective monitoring on the whole operation condition of the power plant is difficult to realize, and the abnormal operation position of the power plant is difficult to accurately identify. In addition, the early warning function of the existing monitoring equipment is simpler, and the early warning effect on the power plant emergency is still limited. Therefore, the operation monitoring and early warning precision of the power plant in the prior art is low, the early warning effect is poor, and the requirements of safe production of the power plant cannot be met.
Disclosure of Invention
The application provides an intelligent monitoring and early warning method and system for a power plant, and aims to solve the technical problems of low monitoring and early warning precision and poor effect for power plant operation in the prior art.
In view of the above problems, the application provides an intelligent monitoring and early warning method and system for a power plant.
The first aspect of the application discloses a power plant intelligent monitoring and early warning method, which comprises the following steps: the method comprises the steps that through testing devices which are arranged at a plurality of positions in a target power plant in an emission data acquisition station, the particle concentration and the carbon dioxide concentration of the plurality of positions are acquired, and the target power plant is a thermal power plant; collecting water vapor concentrations at a plurality of positions through a plurality of testing devices, and performing compensation analysis on a plurality of particle concentrations according to the plurality of water vapor concentrations to obtain a plurality of compensation particle concentrations; respectively constructing a particulate matter concentration field and a carbon dioxide concentration field according to the coordinates of the plurality of compensation particulate matter concentrations, the plurality of carbon dioxide concentrations and the plurality of positions, and performing processing discrimination to obtain a particulate matter concentration characteristic field and a carbon dioxide characteristic field; the method comprises the steps of identifying and acquiring an operation abnormality analysis result of a target power plant according to a particulate matter concentration characteristic field and a carbon dioxide characteristic field by a power plant operation analysis station, wherein the operation abnormality analysis result comprises whether abnormality occurs or not and an abnormality position when the abnormality occurs; constructing a power generation emission data matrix according to the particulate matter concentration field and the carbon dioxide concentration field through an emission analysis station, and calculating a plurality of emission scores of a plurality of positions according to the power generation emission data matrix; and generating an operation monitoring result of the target power plant according to the operation abnormality analysis result and the emission scores, and carrying out early warning according to the operation monitoring result based on the early warning station, wherein the operation monitoring result comprises event description information of an operation abnormality event.
In another aspect of the present disclosure, an intelligent monitoring and early warning system for a power plant is provided, the system includes: the power plant data acquisition module is used for acquiring the concentration of particulate matters and the concentration of carbon dioxide at a plurality of positions through the testing devices arranged at a plurality of positions in the target power plant in the emission data acquisition station, and the target power plant is a thermal power plant; the concentration compensation analysis module is used for collecting the water vapor concentrations at a plurality of positions through a plurality of testing devices, and carrying out compensation analysis on the plurality of particle concentrations according to the plurality of water vapor concentrations to obtain a plurality of compensation particle concentrations; the data processing and distinguishing module is used for respectively constructing a particulate matter concentration field and a carbon dioxide concentration field according to the coordinates of a plurality of compensation particulate matter concentrations, a plurality of carbon dioxide concentrations and a plurality of positions, and processing and distinguishing the particulate matter concentration field and the carbon dioxide concentration field to obtain a particulate matter concentration characteristic field and a carbon dioxide characteristic field; the operation abnormality analysis module is used for identifying and acquiring operation abnormality analysis results of the target power plant according to the particulate matter concentration characteristic field and the carbon dioxide characteristic field through the power plant operation analysis work station, wherein the operation abnormality analysis results comprise whether abnormality occurs or not and an abnormality position when the abnormality occurs; the position emission scoring module is used for constructing a power generation emission data matrix according to the particulate matter concentration field and the carbon dioxide concentration field through the emission analysis station and calculating a plurality of emission scores of a plurality of positions according to the power generation emission data matrix; and the monitoring result early warning module is used for generating an operation monitoring result of the target power plant according to the operation abnormality analysis result and the emission scores, and carrying out early warning according to the operation monitoring result based on the early warning station, wherein the operation monitoring result comprises event description information of operation abnormal events.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
due to the adoption of the multi-point layout testing device in the target power plant, multi-source data are collected so as to realize comprehensive monitoring of the overall operation condition of the power plant; the data of the concentration of the particulate matters, the concentration of the carbon dioxide and the concentration of the water vapor are collected, and the accuracy of data collection can be improved by carrying out compensation analysis on the concentration of the particulate matters; constructing a particulate matter concentration field and a carbon dioxide concentration field by using the acquired data, and processing, distinguishing and acquiring a characteristic field so as to accurately locate the abnormal operation condition of the power plant; according to the characteristic field analysis, the abnormal operation condition of the power plant is identified, and the monitoring precision of the abnormal operation condition of the power plant is improved; calculating emission scores of a plurality of positions, and intuitively reflecting emission conditions of different parts of the power plant; the technical scheme for early warning according to the monitoring result and generating the operation monitoring result, early warning the power plant emergency in time and improving the monitoring effect solves the technical problems of low operation monitoring early warning precision and poor effect of the power plant in the prior art, and achieves the technical effect of improving the operation monitoring early warning precision and effect of the power plant.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
FIG. 1 is a schematic flow chart of an intelligent monitoring and early warning method for a power plant according to an embodiment of the application;
FIG. 2 is a schematic flow chart of a method for generating operation monitoring results in an intelligent monitoring and early warning method of a power plant according to an embodiment of the application;
fig. 3 is a schematic structural diagram of an intelligent monitoring and early warning system for a power plant according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a power plant data acquisition module 11, a concentration compensation analysis module 12, a data processing discrimination module 13, an operation anomaly analysis module 14, a position emission scoring module 15 and a monitoring result early warning module 16.
Description of the embodiments
The technical scheme provided by the application has the following overall thought:
the embodiment of the application provides an intelligent monitoring and early warning method and system for a power plant. The multi-source data is collected by multi-point arrangement of the testing device in the target power plant, and the collected data comprise the concentration of particulate matters, the concentration of carbon dioxide and the concentration of water vapor. And in order to improve the data acquisition precision, the concentration of the particulate matters is compensated and analyzed. And then, respectively constructing a particulate matter concentration field and a carbon dioxide concentration field by utilizing the acquired data, and acquiring respective characteristic fields by processing and distinguishing so as to accurately locate the abnormal operation condition of the power plant, thereby improving the monitoring precision of the abnormal operation condition of the power plant. Meanwhile, emission scores of a plurality of positions are calculated to reflect the emission conditions of different parts of the power plant. And finally, early warning is carried out on the power plant according to the monitoring result, and an operation monitoring result is generated so as to realize intelligent monitoring and early warning on the operation condition of the power plant.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Examples
As shown in FIG. 1, the embodiment of the application provides an intelligent monitoring and early warning method of a power plant, which is applied to intelligent monitoring and early warning equipment of the power plant, wherein the equipment comprises an emission data acquisition station, a power plant operation analysis station, an emission analysis station and an early warning station.
Specifically, the embodiment of the application relates to an intelligent monitoring and early warning method for a power plant, which is used for realizing intelligent monitoring and early warning of the operation process of the power plant by collecting and analyzing the emission data of the power plant. The method in the embodiment of the application is applied to intelligent monitoring and early warning equipment of a power plant, and the equipment comprises an emission data acquisition station, a power plant operation analysis station, an emission analysis station and an early warning station.
The emission data acquisition station is a device for acquiring emission monitoring data of a plurality of positions of a power plant, and comprises a testing device arranged at different positions of the power plant and used for acquiring emission data such as particulate matter concentration, carbon dioxide concentration and the like; the power plant operation analysis station is a device for analyzing and judging the operation condition of the power plant according to the collected emission data and is used for identifying the abnormal condition in the operation process of the power plant; the emission analysis station is a device for scoring and analyzing the emission conditions of different positions of the power plant according to the data of the emission concentration field, and evaluates the emission quality grade of the power plant; the early warning station is a device for carrying out early warning on abnormal conditions in the operation process of the power plant according to the operation monitoring result of the power plant, and sends early warning information to avoid or reduce the influence of the abnormal operation on the power plant. The emission data acquisition station, the power plant operation analysis station, the emission analysis station and the early warning station form intelligent monitoring and early warning equipment of the power plant together, so that comprehensive monitoring, intelligent analysis and early warning of the emission data of the power plant are realized, and the safe operation level of the power plant is effectively improved.
The monitoring and early warning method comprises the following steps:
the method comprises the steps that through testing devices which are arranged at a plurality of positions in a target power plant in an emission data acquisition station, the particle concentration and the carbon dioxide concentration of the plurality of positions are acquired, and the target power plant is a thermal power plant;
In an embodiment of the present application, the emission data collection station includes a plurality of test devices that are deployed at a plurality of locations within the target thermal power plant to collect emission data at different locations, for example. The testing device adopts a light scattering sensor to monitor and record the concentration of the particles at the target position in real time; and simultaneously, a non-dispersive infrared gas analyzer is arranged to monitor and record the carbon dioxide concentration of the target position.
The plurality of testing devices are distributed at different positions of the thermal power plant, such as a chimney peripheral area, an inlet and an outlet of the power plant and the like, so as to comprehensively monitor the emission condition of the thermal power plant. The plurality of test devices are connected to the emission data collection station by data lines and transmit data of the detected particulate matter concentration and carbon dioxide concentration to the emission data collection station in real time to provide base data for subsequent emission analysis.
The emission levels of different areas of the thermal power plant are comprehensively reflected by acquiring the concentrations of particulate matters and carbon dioxide at a plurality of positions of the thermal power plant, so that basis is provided for concentration field analysis, anomaly identification and the like.
Collecting water vapor concentrations at a plurality of positions through a plurality of testing devices, and performing compensation analysis on a plurality of particle concentrations according to the plurality of water vapor concentrations to obtain a plurality of compensation particle concentrations;
Further, the method specifically comprises the following steps:
Acquiring a sample particulate matter concentration information record according to a test data record tested by a light scattering measurement method;
According to the test data record measured by sampling weighing, acquiring a sample compensation particulate matter concentration information record, and according to the water vapor concentration monitoring record, acquiring a sample water vapor concentration record;
Calculating and obtaining a sample compensation coefficient record according to the sample particulate matter concentration information record and the sample compensation particulate matter concentration information record;
sampling a sample water vapor concentration record and a sample compensation coefficient record, and training to obtain a compensation correction analysis channel;
And acquiring a plurality of compensation coefficients, and performing compensation correction on the plurality of particulate matter concentration information, wherein the plurality of compensation coefficients are acquired by inputting a plurality of water vapor concentrations into a compensation correction analysis channel for processing.
In a preferred embodiment, to compensate for the effects of water vapor in order to increase the accuracy of particulate matter concentration, a compensation calibration analysis channel needs to be established. Firstly, detecting a large number of space particulate matter samples by using a light scattering sensor, recording the obtained particulate matter concentration data as sample particulate matter concentration information record, reflecting the particulate matter concentration level in the sample without water vapor influence compensation, and laying a foundation for comparing and calibrating the accurate concentration obtained by using a standard sampling weighing method. Secondly, aiming at the same batch of samples, except for light scattering measurement, a spatial particulate matter sample corresponding to the particulate matter concentration of the sample is acquired through a sampler, the particulate matters are weighed by a precision electronic balance, the particulate matter mass concentration in the sample volume is calculated and acquired, and the particulate matter mass concentration is recorded as sample compensation particulate matter concentration information record. Meanwhile, aiming at the same batch of samples, the water vapor concentration monitoring record corresponding to the samples is measured and recorded through a temperature and humidity sensor and is used as the sample water vapor concentration record. And then, comparing and calculating the sample particulate matter concentration information record and the sample compensation particulate matter concentration information record, and determining the proportional relation between the original particulate matter concentration and the compensation particulate matter concentration under the same sample condition, wherein the proportional relation is the sample compensation coefficient record. Then, using machine learning algorithm, taking sample water vapor concentration record as input and sample compensation coefficient record as output, training to build up nonlinear mapping model between water vapor concentration and compensation coefficient, namely compensation correction analysis channel.
The testing device is provided with a temperature and humidity sensor except for detecting the concentration of particulate matters and the concentration of carbon dioxide, and synchronously collects water vapor concentration data of a plurality of positions. After the water vapor concentration data of a plurality of positions are obtained, inputting the water vapor concentrations into a compensation correction analysis channel to obtain a plurality of corresponding compensation coefficients, multiplying the corresponding particle concentrations by the plurality of compensation coefficients to realize concentration compensation, and eliminating the influence of the water vapor on the particle concentration test result, thereby obtaining more accurate compensated particle concentration, namely a plurality of compensated particle concentrations.
By compensating the concentration of the water vapor, the monitoring precision of the emission of the particulate matters of the thermal power plant is improved, and more accurate particulate matter concentration data is provided for the subsequent concentration field analysis so as to improve the early warning precision.
Respectively constructing a particulate matter concentration field and a carbon dioxide concentration field according to the coordinates of the plurality of compensation particulate matter concentrations, the plurality of carbon dioxide concentrations and the plurality of positions, and performing processing discrimination to obtain a particulate matter concentration characteristic field and a carbon dioxide characteristic field;
further, the method specifically comprises the following steps:
coordinate information of a plurality of positions is obtained, and a particulate matter concentration field and a carbon dioxide concentration field are constructed by respectively combining the plurality of coordinate information with a plurality of carbon dioxide concentrations and a plurality of compensation particulate matter concentrations;
dividing a particulate matter concentration field and a carbon dioxide concentration field by adopting a local processing operator to obtain a plurality of first local areas and a plurality of second local areas;
In each first local area, judging whether the concentration of the particulate matters at other positions is larger than, smaller than or falls into an error range of the threshold value by taking the concentration of the particulate matters at the central position as the threshold value, and respectively marking the concentration as 1, -1 and 0 to generate a plurality of local vectors so as to obtain a particulate matter concentration characteristic field;
and judging each second local area to obtain a carbon dioxide characteristic field.
In a preferred embodiment, the particle concentration field and the carbon dioxide concentration field are constructed according to the acquired compensated particle concentration, carbon dioxide concentration and corresponding position coordinates of each position, and the corresponding characteristic fields are extracted through processing.
Firstly, carrying out 3D modeling on a target power plant, generating a coordinate map of the target power plant, marking the position of a testing device on the map, mapping and corresponding a plurality of position coordinates of the testing device with the corresponding compensation particulate matter concentration and carbon dioxide concentration, and generating continuous distribution scenes of the particulate matter concentration and the carbon dioxide concentration, namely a particulate matter concentration field and a carbon dioxide concentration field, in the coordinate map of the target power plant by utilizing a spatial interpolation algorithm. The particle concentration field represents the distribution and change conditions of the particle concentration in the target power plant; the carbon dioxide concentration field represents the distribution of carbon dioxide concentration. And secondly, defining a local window on the coordinate map by adopting a local processing operator, dividing the local area of the particulate matter concentration field and the carbon dioxide concentration field based on the window, dividing the particulate matter concentration field into a plurality of first local areas, and dividing the carbon dioxide concentration field into a plurality of second local areas. These local areas reflect the characteristic information of the concentration field in the local areas.
And then, for each first local area, judging the relation between the concentration of the particulate matters at other positions in the local area and the threshold value by taking the concentration value of the particulate matters at the central position of the local area as the threshold value, and distinguishing according to the error range which is larger than, smaller than or equal to the threshold value. If the concentration of the particulate matter at a certain position is higher than the positive error range of the concentration of the central position, the particulate matter is marked as 1; if the negative error range is lower than the concentration of the central position, the mark is-1; if the concentration is within the positive and negative error range of the center position, the mark is 0. And sequentially carrying out the treatment on each first local area, extracting the characteristics of the relation between the concentration of the particulate matters in the first local areas and the concentration of the particulate matters at the central position, and forming local vectors of each local area, wherein the local vectors form characteristic expression of a particulate matter concentration field, namely the particulate matter concentration characteristic field. Meanwhile, for each second local area, an average carbon dioxide concentration in the local area is calculated, and the average carbon dioxide concentration is compared with the carbon dioxide concentration at the central position of the local area. If the average carbon dioxide concentration is higher than the center position concentration, then the local area is marked as 1; if the concentration is lower than the concentration at the central position, the mark is-1; if the center position concentration is approached, the flag is 0. Wherein the degree of proximity may be set based on the error range for carbon dioxide concentration testing by one skilled in the art. Repeating the above process for all second local areas, extracting the carbon dioxide concentration distribution characteristics in each local area, and forming a plurality of local vectors, wherein the local vectors form characteristic expression of a carbon dioxide concentration field, namely a carbon dioxide characteristic field.
The operation abnormality analysis result of the target power plant is identified and obtained according to the particulate matter concentration characteristic field and the carbon dioxide characteristic field by a power plant operation analysis workstation, wherein the operation abnormality analysis result comprises whether abnormality occurs and an abnormality position when abnormality occurs;
further, the method specifically comprises the following steps:
According to historical emission monitoring data of a target power plant, processing and acquiring a plurality of sample particulate matter concentration characteristic fields and a plurality of sample carbon dioxide characteristic fields, and acquiring a plurality of sample operation abnormality analysis results through operation abnormality monitoring data;
The method comprises the steps of adopting a plurality of sample particulate matter concentration characteristic fields and a plurality of sample carbon dioxide characteristic fields as input training data, adopting a plurality of sample operation abnormality analysis results as output training data, and training to obtain an operation abnormality recognition channel in an operation analysis station of a power plant based on machine learning;
And acquiring an operation abnormality analysis result, wherein the operation abnormality analysis result is acquired by adopting a particulate matter concentration characteristic field and a carbon dioxide characteristic field to input an operation abnormality identification channel for operation abnormality analysis.
In a preferred embodiment, first, emission monitoring data of a target power plant is collected over a period of time, including concentration monitoring results of the power plant at different periods of time; and constructing concentration fields and extracting features according to the historical monitored data, and processing to obtain a plurality of corresponding sample particulate matter concentration feature fields and a plurality of sample carbon dioxide feature fields. And meanwhile, acquiring operation abnormality analysis results corresponding to the historical samples, namely, marking results of whether the power plant operation is abnormal or not in the period of time in which the historical data are located, and taking the marking results as a plurality of sample operation abnormality analysis results. And then taking the obtained sample particulate matter concentration characteristic field and the sample carbon dioxide characteristic field as input data and the corresponding sample operation abnormality analysis result as output data, and performing model training by adopting a supervised learning algorithm such as SVM (support vector machine), random forest algorithm and the like in a power plant operation analysis workstation. Through training of a large number of historical samples, a model capable of judging operation abnormality of a new scene, namely an operation abnormality identification channel, is obtained.
And then, after the particulate matter concentration characteristic field and the carbon dioxide characteristic field of the target power plant are obtained, the two characteristic fields are used as new inputs by a power plant operation analysis workstation and are input into an operation abnormality recognition channel, the channel analyzes and judges the input particulate matter concentration characteristic field and carbon dioxide characteristic field, judges whether an operation abnormality exists or not, outputs a corresponding operation abnormality analysis result, and gives out the operation state of each position of the current target power plant, thereby judging which position has the operation abnormality, and realizing intelligent analysis of the emission data of the power plant.
Constructing a power generation emission data matrix according to the particulate matter concentration field and the carbon dioxide concentration field through an emission analysis station, and calculating a plurality of emission scores of a plurality of positions according to the power generation emission data matrix;
further, the method specifically comprises the following steps:
and (3) carrying out maximization treatment on concentration data in the particulate matter concentration field and the carbon dioxide concentration field, wherein the formula is as follows:
；
Wherein y is maximized concentration data, and x is original concentration data;
Based on the data in the maximized particulate matter concentration field and the maximized carbon dioxide concentration field, a power generation emission data matrix is constructed, and the power generation emission data matrix has the following formula:
；
wherein P is a power generation emission data matrix, For the first location of the maximized particulate matter concentration data,For maximum particulate matter concentration data for the nth location, n is the number of locations,For the maximized carbon dioxide concentration data for the first location,Maximized carbon dioxide concentration data for the nth position.
Further, the method further comprises the following steps:
according to the power generation emission data matrix, a plurality of emission scores of a plurality of positions are calculated and acquired, wherein the emission scores are represented by the following formula:
；
Wherein, For the emissions score for the ith location,、AndAs the weight of the material to be weighed,AndIs the data of the target power plant after maximizing the particulate matter concentration standard and the carbon dioxide concentration standard,AndFor the maximized particulate matter concentration data and maximized carbon dioxide concentration data for the ith location,For generating the data of the j-th column and i-th row in the emission data matrix,To generate the minimum value of the j-th column in the emission data matrix,Weights are assigned to the extent to which the particulate matter concentration and the carbon dioxide concentration affect the target plant emission quality.
In a preferred embodiment, in an emission analysis workstation, a power generation emission data matrix is constructed according to a particulate matter concentration field and a carbon dioxide concentration field, emission scores of each position of a target power plant are obtained according to the power generation emission data matrix, emission states of different areas of the power plant are quantitatively evaluated, and support is provided for early warning of subsequent abnormal operation. First, for each position of concentration data in a particulate matter concentration field and a carbon dioxide concentration field, raw concentration data is obtained according to the formulaAnd performing maximization, wherein y is the maximized concentration data, and x is the original concentration data. When the original concentration x is smaller, the calculated maximized concentration y is larger; when the original concentration x is larger, the maximized concentration y is smaller, the effect of compressing the dynamic range of the background concentration is achieved, and the influence of the background concentration value on subsequent calculation is reduced. Secondly, maximizing the data in the particle concentration field and the carbon dioxide concentration field after treatment according to a matrixAnd (3) combining the position correspondence of the particles to construct a power generation emission data matrix, wherein each row of the matrix represents a position, the first column represents maximized particulate matter concentration data, and the second column represents maximized carbon dioxide concentration data. Wherein, In order to generate the matrix of emission data,For the first location of the maximized particulate matter concentration data,For maximum particulate matter concentration data for the nth location, n is the number of locations,For the maximized carbon dioxide concentration data for the first location,Maximized carbon dioxide concentration data for the nth position. The data of the two indexes of the particulate matters and the carbon dioxide at all positions in the target power plant are integrated in a matrix form, so that the emission score can be conveniently calculated according to the matrix.
Then, according to the constructed power generation emission data matrixAn emissions score is calculated for each monitored location in the target power plant. Wherein, For the emissions score for the ith location,、AndAs the weight of the material to be weighed,AndIs the data of the target power plant after maximizing the particulate matter concentration standard and the carbon dioxide concentration standard,AndFor the maximized particulate matter concentration data and maximized carbon dioxide concentration data for the ith location,For generating the data of the j-th column and i-th row in the emission data matrix,To generate the minimum value of the j-th column in the emission data matrix,Weights are assigned to the extent to which the particulate matter concentration and the carbon dioxide concentration affect the target plant emission quality. The emission condition of each position is quantitatively scored through an emission score calculation algorithm, a plurality of emission scores of a plurality of positions are obtained, the emission condition of each position is clear, and support is provided for subsequent evaluation and early warning.
And generating an operation monitoring result of the target power plant according to the operation abnormality analysis result and the emission scores, and carrying out early warning according to the operation monitoring result based on the early warning station, wherein the operation monitoring result comprises event description information of an operation abnormality event.
Further, as shown in fig. 2, the steps specifically include:
according to emission monitoring data of a target power plant, extracting data and calculating and obtaining a sample emission score record and a sample emission quality grade record;
adopting a sample emission score record and a sample emission quality grade record to construct an emission quality comparison table;
Matching based on the plurality of emission scores to obtain a plurality of emission quality levels;
And generating event description information as an operation monitoring result when an abnormality occurs in the operation abnormality analysis result or any emission quality level is smaller than the qualified emission quality level.
In one possible embodiment, first, emission monitoring data is collected for a target power plant over a past period of time, including emission monitoring results for different locations of the power plant under normal and abnormal operating conditions. And processing and calculating the historical monitoring data to obtain emission scores corresponding to each sample data, and forming a sample emission score record. And meanwhile, corresponding emission quality grades are determined according to the grading results of the samples, and sample emission quality grade records are formed, for example, grading is carried out according to the quality, the quality and the slight exceeding standard. Then, using these sample emission score records and emission quality grade records, a mapping relationship between emission score and quality grade is established, forming an emission quality comparison table. For example, a plurality of scoring intervals are preset according to expert experience, and then the quality grades corresponding to the different intervals are determined, so that the emission quality grades corresponding to the emission scores can be realized. Then, a plurality of emission quality levels corresponding to the plurality of emission scores are acquired based on the plurality of emission scores of the plurality of positions currently acquired, and matching is performed with the emission quality comparison table.
And then, carrying out risk early warning according to the operation abnormality analysis and a plurality of emission quality grades. When the operation abnormality analysis result shows that the operation abnormality exists in the power plant area or the emission quality level of any position in a plurality of emission quality levels is lower than the qualified level, the operation abnormality exists in the target power plant, and an operation monitoring result is generated, wherein the operation monitoring result comprises event description information such as details of abnormality type, abnormality position, exceeding standard degree, occurrence time and the like. When the operation monitoring result is displayed and abnormal is detected, the early warning station displays the operation monitoring result in the form of early warning information, and simultaneously sends early warning notification to power plant supervisory personnel in the form of sound and light and the like, the supervisory personnel checks event description details in the early warning information based on the early warning station, judges the risk degree according to the event description details, adopts countermeasures such as adjusting operation parameters and repairing equipment faults and the like, so that the influence of the abnormality on the power plant is reduced or avoided, intelligent monitoring and early warning of the power plant process are realized, the reliability and efficiency of the power plant are improved, and stable and efficient operation of the power plant is ensured.
In summary, the intelligent monitoring and early warning method for the power plant provided by the embodiment of the application has the following technical effects:
Through arranging the testing arrangement in a plurality of positions in the target power plant in the emission data collection workstation, gather particulate matter concentration and the carbon dioxide concentration in a plurality of positions, the target power plant is the thermal power plant, realizes the comprehensive monitoring to the overall operation condition of power plant. Through a plurality of testing arrangement, gather the vapor concentration in a plurality of positions, carry out compensation analysis to a plurality of particulate matter concentrations according to a plurality of vapor concentrations, obtain a plurality of compensation particulate matter concentrations, improve data acquisition's accuracy. And respectively constructing a particulate matter concentration field and a carbon dioxide concentration field according to the coordinates of the plurality of compensation particulate matter concentrations, the plurality of carbon dioxide concentrations and the plurality of positions, and processing and judging to obtain a particulate matter concentration characteristic field and a carbon dioxide characteristic field so as to accurately locate the abnormal operation condition of the power plant. And identifying and acquiring an operation abnormality analysis result of the target power plant according to the particulate matter concentration characteristic field and the carbon dioxide characteristic field by using a power plant operation analysis workstation, wherein the operation abnormality analysis result comprises whether abnormality occurs or not and an abnormality position when the abnormality occurs, so as to monitor the operation abnormality of the power plant. And constructing a power generation emission data matrix according to the particulate matter concentration field and the carbon dioxide concentration field through an emission analysis station, calculating and acquiring a plurality of emission scores of a plurality of positions according to the power generation emission data matrix, and quantifying the emission conditions of different parts of the power plant. Generating an operation monitoring result of the target power plant according to the operation abnormality analysis result and the emission scores, and carrying out early warning according to the operation monitoring result based on the early warning station, wherein the operation monitoring result comprises event description information of operation abnormal events, intelligent monitoring and early warning of the operation condition of the power plant are realized, and the accuracy and effect of monitoring and early warning are comprehensively improved.
Examples
Based on the same inventive concept as the power plant intelligent monitoring and early warning method in the foregoing embodiment, as shown in fig. 3, an embodiment of the present application provides a power plant intelligent monitoring and early warning system, where the system is applied to a power plant intelligent monitoring and early warning device, and the device includes an emission data acquisition station, a power plant operation analysis station, an emission analysis station and an early warning station, and the system includes:
the power plant data acquisition module 11 is used for acquiring the concentration of particulate matters and the concentration of carbon dioxide at a plurality of positions through the testing devices arranged at a plurality of positions in a target power plant in an emission data acquisition station, wherein the target power plant is a thermal power plant;
The concentration compensation analysis module 12 is configured to collect water vapor concentrations at a plurality of locations through a plurality of test devices, perform compensation analysis on a plurality of particulate matter concentrations according to the plurality of water vapor concentrations, and obtain a plurality of compensated particulate matter concentrations;
The data processing and distinguishing module 13 is configured to respectively construct a particulate matter concentration field and a carbon dioxide concentration field according to coordinates of a plurality of compensated particulate matter concentrations, a plurality of carbon dioxide concentrations and a plurality of positions, and perform processing and distinguishing to obtain a particulate matter concentration characteristic field and a carbon dioxide characteristic field;
An operation abnormality analysis module 14, configured to identify and obtain an operation abnormality analysis result of a target power plant according to the particulate matter concentration feature field and the carbon dioxide feature field by using a power plant operation analysis workstation, where the operation abnormality analysis result includes whether an abnormality occurs and an abnormality position when the abnormality occurs;
a position emission scoring module 15 for constructing a power generation emission data matrix from the particulate matter concentration field and the carbon dioxide concentration field by an emission analysis station, and calculating a plurality of emission scores for a plurality of positions from the power generation emission data matrix;
And the monitoring result early warning module 16 is used for generating an operation monitoring result of the target power plant according to the operation abnormality analysis result and the emission scores, and carrying out early warning according to the operation monitoring result based on the early warning station, wherein the operation monitoring result comprises event description information of operation abnormal events.
Further, the concentration compensation analysis module 12 includes the following steps:
Acquiring a sample particulate matter concentration information record according to a test data record tested by a light scattering measurement method;
According to the test data record measured by sampling weighing, acquiring a sample compensation particulate matter concentration information record, and according to the water vapor concentration monitoring record, acquiring a sample water vapor concentration record;
Calculating and obtaining a sample compensation coefficient record according to the sample particulate matter concentration information record and the sample compensation particulate matter concentration information record;
sampling a sample water vapor concentration record and a sample compensation coefficient record, and training to obtain a compensation correction analysis channel;
And acquiring a plurality of compensation coefficients, and performing compensation correction on the plurality of particulate matter concentration information, wherein the plurality of compensation coefficients are acquired by inputting a plurality of water vapor concentrations into a compensation correction analysis channel for processing.
Further, the job anomaly analysis module 14 includes the following execution steps:
coordinate information of a plurality of positions is obtained, and a particulate matter concentration field and a carbon dioxide concentration field are constructed by respectively combining the plurality of coordinate information with a plurality of carbon dioxide concentrations and a plurality of compensation particulate matter concentrations;
dividing a particulate matter concentration field and a carbon dioxide concentration field by adopting a local processing operator to obtain a plurality of first local areas and a plurality of second local areas;
In each first local area, judging whether the concentration of the particulate matters at other positions is larger than, smaller than or falls into an error range of the threshold value by taking the concentration of the particulate matters at the central position as the threshold value, and respectively marking the concentration as 1, -1 and 0 to generate a plurality of local vectors so as to obtain a particulate matter concentration characteristic field;
and judging each second local area to obtain a carbon dioxide characteristic field.
Further, the job anomaly analysis module 14 further includes the following execution steps:
According to historical emission monitoring data of a target power plant, processing and acquiring a plurality of sample particulate matter concentration characteristic fields and a plurality of sample carbon dioxide characteristic fields, and acquiring a plurality of sample operation abnormality analysis results through operation abnormality monitoring data;
The method comprises the steps of adopting a plurality of sample particulate matter concentration characteristic fields and a plurality of sample carbon dioxide characteristic fields as input training data, adopting a plurality of sample operation abnormality analysis results as output training data, and training to obtain an operation abnormality recognition channel in an operation analysis station of a power plant based on machine learning;
And acquiring an operation abnormality analysis result, wherein the operation abnormality analysis result is acquired by adopting a particulate matter concentration characteristic field and a carbon dioxide characteristic field to input an operation abnormality identification channel for operation abnormality analysis.
Further, the location emissions scoring module 15 includes the following execution steps:
and (3) carrying out maximization treatment on concentration data in the particulate matter concentration field and the carbon dioxide concentration field, wherein the formula is as follows:
；
Wherein y is maximized concentration data, and x is original concentration data;
Based on the data in the maximized particulate matter concentration field and the maximized carbon dioxide concentration field, a power generation emission data matrix is constructed, and the power generation emission data matrix has the following formula:
；
wherein P is a power generation emission data matrix, For the first location of the maximized particulate matter concentration data,For maximum particulate matter concentration data for the nth location, n is the number of locations,For the maximized carbon dioxide concentration data for the first location,Maximized carbon dioxide concentration data for the nth position.
Further, the location emission scoring module 15 further includes the following execution steps:
according to the power generation emission data matrix, a plurality of emission scores of a plurality of positions are calculated and acquired, wherein the emission scores are represented by the following formula:
；
Wherein, For the emissions score for the ith location,、AndAs the weight of the material to be weighed,AndIs the data of the target power plant after maximizing the particulate matter concentration standard and the carbon dioxide concentration standard,AndFor the maximized particulate matter concentration data and maximized carbon dioxide concentration data for the ith location,For generating the data of the j-th column and i-th row in the emission data matrix,To generate the minimum value of the j-th column in the emission data matrix,Weights are assigned to the extent to which the particulate matter concentration and the carbon dioxide concentration affect the target plant emission quality.
Further, the monitoring result early warning module 16 includes the following steps:
according to emission monitoring data of a target power plant, extracting data and calculating and obtaining a sample emission score record and a sample emission quality grade record;
adopting a sample emission score record and a sample emission quality grade record to construct an emission quality comparison table;
Matching based on the plurality of emission scores to obtain a plurality of emission quality levels;
And generating event description information as an operation monitoring result when an abnormality occurs in the operation abnormality analysis result or any emission quality level is smaller than the qualified emission quality level.
Any of the steps of the methods described above may be stored as computer instructions or programs in a non-limiting computer memory and may be called by a non-limiting computer processor to identify any method for implementing an embodiment of the present application, without unnecessary limitations.
Further, the first or second element may not only represent a sequential relationship, but may also represent a particular concept, and/or may be selected individually or in whole among a plurality of elements. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.
Claims (4)
1. The utility model provides a power plant intelligent monitoring early warning method which is characterized in that the method is applied to a power plant intelligent monitoring early warning equipment, and the equipment includes emission data acquisition workstation, power plant operation analysis workstation, emission analysis workstation and early warning workstation, and the method includes:
the method comprises the steps that through testing devices which are arranged at a plurality of positions in a target power plant in an emission data acquisition station, the particle concentration and the carbon dioxide concentration of the plurality of positions are acquired, and the target power plant is a thermal power plant;
collecting water vapor concentrations at a plurality of positions through a plurality of testing devices, and performing compensation analysis on a plurality of particle concentrations according to the plurality of water vapor concentrations to obtain a plurality of compensation particle concentrations;
respectively constructing a particulate matter concentration field and a carbon dioxide concentration field according to the coordinates of the plurality of compensation particulate matter concentrations, the plurality of carbon dioxide concentrations and the plurality of positions, and performing processing discrimination to obtain a particulate matter concentration characteristic field and a carbon dioxide characteristic field;
The operation abnormality analysis result of the target power plant is identified and obtained according to the particulate matter concentration characteristic field and the carbon dioxide characteristic field by a power plant operation analysis workstation, wherein the operation abnormality analysis result comprises whether abnormality occurs and an abnormality position when abnormality occurs;
constructing a power generation emission data matrix according to the particulate matter concentration field and the carbon dioxide concentration field through an emission analysis station, and calculating a plurality of emission scores of a plurality of positions according to the power generation emission data matrix;
Generating an operation monitoring result of the target power plant according to the operation abnormality analysis result and the emission scores, and carrying out early warning according to the operation monitoring result based on the early warning station, wherein the operation monitoring result comprises event description information of an operation abnormality event;
wherein the method comprises the following steps:
and (3) carrying out maximization treatment on concentration data in the particulate matter concentration field and the carbon dioxide concentration field, wherein the formula is as follows:
Wherein y is maximized concentration data, and x is original concentration data;
Based on the data in the maximized particulate matter concentration field and the maximized carbon dioxide concentration field, a power generation emission data matrix is constructed, and the power generation emission data matrix has the following formula:
Wherein, P is a power generation emission data matrix, y k1 is the maximized particulate matter concentration data of the first position, y kn is the maximized particulate matter concentration data of the nth position, n is the number of the plurality of positions, y c1 is the maximized carbon dioxide concentration data of the first position, and y cn is the maximized carbon dioxide concentration data of the nth position;
wherein the method comprises the following steps:
according to the power generation emission data matrix, a plurality of emission scores of a plurality of positions are calculated and acquired, wherein the emission scores are represented by the following formula:
Wherein G i is the emission score of the ith position, ω 1、ω2 and ω 3 are weights, y kb and y cb are data of maximized particulate matter concentration standard and carbon dioxide concentration standard of the target power plant, y ki and y ci are maximized particulate matter concentration data and maximized carbon dioxide concentration data of the ith position, y ji is the data of the ith row of the jth column in the power generation emission data matrix, y jmin is the minimum value of the jth column in the power generation emission data matrix, and w j is the weight distributed according to the particulate matter concentration and carbon dioxide concentration influence degree of the target power plant emission quality;
wherein the method comprises the following steps:
Acquiring a sample particulate matter concentration information record according to a test data record tested by a light scattering measurement method;
According to the test data record measured by sampling weighing, acquiring a sample compensation particulate matter concentration information record, and according to the water vapor concentration monitoring record, acquiring a sample water vapor concentration record;
According to the sample particulate matter concentration information record and the sample compensation particulate matter concentration information record, calculating an obtained sample compensation coefficient record, wherein the step of obtaining the sample compensation coefficient record comprises the following steps: detecting a large number of space particle samples by using a light scattering sensor, and recording the obtained particle concentration data as sample particle concentration information record; the method comprises the steps that a sampler is used for collecting space particulate matter samples corresponding to sample particulate matter concentration for the same batch of samples, weighing is carried out to calculate and obtain particulate matter mass concentration in a sample volume, and the particulate matter mass concentration is recorded as sample compensation particulate matter concentration information record; for the same batch of samples, measuring and recording a water vapor concentration monitoring record corresponding to the samples by a temperature and humidity sensor, and taking the water vapor concentration monitoring record as a sample water vapor concentration record; determining a proportional relation between the original particle concentration and the compensation particle concentration under the same sample condition as a sample compensation coefficient record by comparing and calculating the sample particle concentration information record and the sample compensation particle concentration information record;
Sampling a sample water vapor concentration record and a sample compensation coefficient record, training and establishing a nonlinear mapping model between the water vapor concentration and the compensation coefficient to obtain a compensation correction analysis channel;
Acquiring a plurality of compensation coefficients, performing compensation correction on the plurality of particulate matter concentration information, wherein the plurality of compensation coefficients are acquired by inputting a plurality of water vapor concentrations into a compensation correction analysis channel for processing;
wherein the method comprises the following steps:
coordinate information of a plurality of positions is obtained, and a particulate matter concentration field and a carbon dioxide concentration field are constructed by respectively combining the plurality of coordinate information with a plurality of carbon dioxide concentrations and a plurality of compensation particulate matter concentrations;
dividing a particulate matter concentration field and a carbon dioxide concentration field by adopting a local processing operator to obtain a plurality of first local areas and a plurality of second local areas;
In each first local area, judging whether the concentration of the particulate matters at other positions is larger than, smaller than or falls into an error range of the threshold value by taking the concentration of the particulate matters at the central position as the threshold value, and respectively marking the concentration as 1, -1 and 0 to generate a plurality of local vectors so as to obtain a particulate matter concentration characteristic field;
and judging each second local area to obtain a carbon dioxide characteristic field.
2. The method according to claim 1, characterized in that the method comprises:
According to historical emission monitoring data of a target power plant, processing and acquiring a plurality of sample particulate matter concentration characteristic fields and a plurality of sample carbon dioxide characteristic fields, and acquiring a plurality of sample operation abnormality analysis results through operation abnormality monitoring data;
The method comprises the steps of adopting a plurality of sample particulate matter concentration characteristic fields and a plurality of sample carbon dioxide characteristic fields as input training data, adopting a plurality of sample operation abnormality analysis results as output training data, and training to obtain an operation abnormality recognition channel in an operation analysis station of a power plant based on machine learning;
And acquiring an operation abnormality analysis result, wherein the operation abnormality analysis result is acquired by adopting a particulate matter concentration characteristic field and a carbon dioxide characteristic field to input an operation abnormality identification channel for operation abnormality analysis.
3. The method according to claim 1, characterized in that the method comprises:
according to emission monitoring data of a target power plant, extracting data and calculating and obtaining a sample emission score record and a sample emission quality grade record;
adopting a sample emission score record and a sample emission quality grade record to construct an emission quality comparison table;
Matching based on the plurality of emission scores to obtain a plurality of emission quality levels;
And generating event description information as an operation monitoring result when an abnormality occurs in the operation abnormality analysis result or any emission quality level is smaller than the qualified emission quality level.
4. A power plant intelligent monitoring and early warning system for implementing the power plant intelligent monitoring and early warning method as set forth in any one of claims 1 to 3, the system being applied to a power plant intelligent monitoring and early warning device, the device including an emission data acquisition station, a power plant operation analysis station, an emission analysis station, and an early warning station, the system comprising:
The power plant data acquisition module is used for acquiring the concentration of particulate matters and the concentration of carbon dioxide at a plurality of positions through the testing devices arranged at the plurality of positions in the target power plant in the emission data acquisition station, and the target power plant is a thermal power plant;
The concentration compensation analysis module is used for collecting water vapor concentrations at a plurality of positions through a plurality of testing devices, carrying out compensation analysis on a plurality of particle concentrations according to the water vapor concentrations, and obtaining a plurality of compensation particle concentrations;
the data processing and distinguishing module is used for respectively constructing a particulate matter concentration field and a carbon dioxide concentration field according to the coordinates of a plurality of compensating particulate matter concentrations, a plurality of carbon dioxide concentrations and a plurality of positions, and processing and distinguishing the particulate matter concentration field and the carbon dioxide concentration field to obtain a particulate matter concentration characteristic field and a carbon dioxide characteristic field;
The operation abnormality analysis module is used for identifying and acquiring operation abnormality analysis results of the target power plant according to the particulate matter concentration characteristic field and the carbon dioxide characteristic field through a power plant operation analysis station, wherein the operation abnormality analysis results comprise whether abnormality occurs and an abnormality position when the abnormality occurs;
the position emission scoring module is used for constructing a power generation emission data matrix according to the particulate matter concentration field and the carbon dioxide concentration field through an emission analysis station and calculating a plurality of emission scores of a plurality of positions according to the power generation emission data matrix;
The monitoring result early warning module is used for generating an operation monitoring result of the target power plant according to the operation abnormality analysis result and the emission scores, and carrying out early warning according to the operation monitoring result based on the early warning station, wherein the operation monitoring result comprises event description information of operation abnormality events;
The concentration compensation analysis module 12 specifically further includes the following steps:
Acquiring a sample particulate matter concentration information record according to a test data record tested by a light scattering measurement method;
According to the test data record measured by sampling weighing, acquiring a sample compensation particulate matter concentration information record, and according to the water vapor concentration monitoring record, acquiring a sample water vapor concentration record;
According to the sample particulate matter concentration information record and the sample compensation particulate matter concentration information record, calculating an obtained sample compensation coefficient record, wherein the step of obtaining the sample compensation coefficient record comprises the following steps: detecting a large number of space particle samples by using a light scattering sensor, and recording the obtained particle concentration data as sample particle concentration information record; the method comprises the steps that a sampler is used for collecting space particulate matter samples corresponding to sample particulate matter concentration for the same batch of samples, weighing is carried out to calculate and obtain particulate matter mass concentration in a sample volume, and the particulate matter mass concentration is recorded as sample compensation particulate matter concentration information record; for the same batch of samples, measuring and recording a water vapor concentration monitoring record corresponding to the samples by a temperature and humidity sensor, and taking the water vapor concentration monitoring record as a sample water vapor concentration record; determining a proportional relation between the original particle concentration and the compensation particle concentration under the same sample condition as a sample compensation coefficient record by comparing and calculating the sample particle concentration information record and the sample compensation particle concentration information record;
Sampling a sample water vapor concentration record and a sample compensation coefficient record, training and establishing a nonlinear mapping model between the water vapor concentration and the compensation coefficient to obtain a compensation correction analysis channel;
Acquiring a plurality of compensation coefficients, performing compensation correction on the plurality of particulate matter concentration information, wherein the plurality of compensation coefficients are acquired by inputting a plurality of water vapor concentrations into a compensation correction analysis channel for processing;
the job anomaly analysis module 14 specifically further includes the following execution steps:
coordinate information of a plurality of positions is obtained, and a particulate matter concentration field and a carbon dioxide concentration field are constructed by respectively combining the plurality of coordinate information with a plurality of carbon dioxide concentrations and a plurality of compensation particulate matter concentrations;
dividing a particulate matter concentration field and a carbon dioxide concentration field by adopting a local processing operator to obtain a plurality of first local areas and a plurality of second local areas;
In each first local area, judging whether the concentration of the particulate matters at other positions is larger than, smaller than or falls into an error range of the threshold value by taking the concentration of the particulate matters at the central position as the threshold value, and respectively marking the concentration as 1, -1 and 0 to generate a plurality of local vectors so as to obtain a particulate matter concentration characteristic field;
Judging each second local area to obtain a carbon dioxide characteristic field;
The location emission scoring module 15 specifically further includes the following execution steps:
and (3) carrying out maximization treatment on concentration data in the particulate matter concentration field and the carbon dioxide concentration field, wherein the formula is as follows:
Wherein y is maximized concentration data, and x is original concentration data;
Based on the data in the maximized particulate matter concentration field and the maximized carbon dioxide concentration field, a power generation emission data matrix is constructed, and the power generation emission data matrix has the following formula:
Wherein, P is a power generation emission data matrix, y k1 is the maximized particulate matter concentration data of the first position, y kn is the maximized particulate matter concentration data of the nth position, n is the number of the plurality of positions, y c1 is the maximized carbon dioxide concentration data of the first position, and y cn is the maximized carbon dioxide concentration data of the nth position;
The location emission scoring module 15 specifically further includes the following execution steps:
according to the power generation emission data matrix, a plurality of emission scores of a plurality of positions are calculated and acquired, wherein the emission scores are represented by the following formula:
Wherein G i is the emission score at the i-th position, ω 1、ω2 and ω 3 are weights, y kb and y cb are data obtained by maximizing the particulate matter concentration standard and the carbon dioxide concentration standard of the target power plant, y ki and y ci are maximized particulate matter concentration data and maximized carbon dioxide concentration data at the i-th position, y ji is data of the j-th row in the power generation emission data matrix, y jmin is the minimum value of the j-th row in the power generation emission data matrix, and w j is a weight distributed according to the influence degree of the particulate matter concentration and the carbon dioxide concentration on the emission quality of the target power plant.
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