Pollution source monitoring method and system based on multi-source data collection and analysis

Abstract

The present application relates to the technical field of pollution monitoring, in particular to a pollution source monitoring method and system based on multi-source data acquisition and analysis. The present application first analyzes and obtains fluctuation characteristic parameters of each pollution index, and obtains a working condition correlation coefficient of each pollution index by combining historical time sequence change correlation between emission data under each pollution index and working condition data under each working condition index. Then, by combining short-term change characteristics of emission data under the pollution index and working condition data under the working condition index at the current monitoring moment, an abnormal state coefficient under each pollution index is evaluated to perform abnormal emission early warning. The present application first quantifies the response sensitivity of the pollution index to the working condition change, further quantifies the working condition correlation of the pollution index, and then analyzes the working condition correlation of the pollution index at the current monitoring moment, accurately evaluates the abnormal emission behavior of the pollution source not meeting the working condition, and improves the monitoring accuracy of the pollution source.

Description

Technical Field

[0001] This invention relates to the field of pollution monitoring technology, specifically to a pollution source monitoring method and system based on multi-source data acquisition and analysis. Background Technology

[0002] Accurate monitoring of pollution sources and early warning and control of emissions have become important goals of environmental regulation. With the development of technologies such as the Internet of Things and big data, pollution source monitoring is gradually evolving towards automation, intelligence, and networking. By collecting and integrating multi-source data, various monitoring data can complement and verify each other, thereby reducing the errors and uncertainties that may exist in single monitoring data and improving the accuracy and reliability of pollution source monitoring.

[0003] Traditional monitoring primarily focuses on whether the concentration or content of pollutants at the emission outlet exceeds standards, without considering the dynamic correlation between operating parameters during actual production and pollutant generation. For example, when monitoring factory boiler emissions, the impact of changes in operating parameters such as boiler load, coal quality, and the operational status of treatment facilities on pollutant emissions is not taken into account. When production conditions change, it is impossible to accurately determine whether the change in pollutant concentration is a normal instantaneous fluctuation or an abnormal emission, which can easily lead to false alarms or missed alarms, thus affecting the effectiveness of pollution source monitoring. Summary of the Invention

[0004] To address the technical problem of low accuracy in pollution source monitoring, the present invention aims to provide a pollution source monitoring method and system based on multi-source data acquisition and analysis. The specific technical solution adopted is as follows:

[0005] A pollution source monitoring method based on multi-source data acquisition and analysis, the method comprising:

[0006] Acquire multi-source monitoring data at each monitoring moment within a historical monitoring period. The multi-source monitoring data includes at least emission data for each pollution index and operating condition data for each operating condition index at the pollution source emission outlet.

[0007] During the historical monitoring period, based on the temporal fluctuations of the emission data under each pollution indicator, the fluctuation characteristic parameters of each pollution indicator are obtained, and the working condition correlation coefficient of each pollution indicator is obtained by combining the temporal variation correlation between the emission data under each pollution indicator and the working condition data under each working condition indicator.

[0008] At the current monitoring time, based on the change characteristics of the emission data under each pollution indicator and the operating condition data under each operating condition indicator, the current operating condition correlation coefficient of each pollution indicator within a preset historical period at the current monitoring time is obtained; based on the difference between the current operating condition correlation coefficient and the operating condition correlation coefficient under each pollution indicator, the abnormal state coefficient under each pollution indicator is obtained; and abnormal emission early warning is issued based on the abnormal state coefficient.

[0009] Furthermore, the method for obtaining the fluctuation characteristic parameters includes:

[0010] Divide historical monitoring periods and obtain all time windows; within each time window, obtain the fluctuation parameters of each pollution index based on the fluctuation changes of the emission data for each pollution index.

[0011] Based on the discrete characteristics of the fluctuation parameters of each pollution index within different time windows, the stable characteristic parameters of each pollution index within the historical monitoring period are obtained.

[0012] Based on the fluctuation parameters and the stable characteristic parameters within each time window, the fluctuation characteristic parameters of each pollution index are obtained.

[0013] Furthermore, the method for obtaining the time window includes:

[0014] Historical monitoring periods are evenly divided into preset lengths to determine all time windows.

[0015] Furthermore, the method for obtaining the working condition correlation coefficient includes:

[0016] Within each time window, based on the correlation between the emission data under each pollution index and the operating condition data under each operating condition index, the co-change sub-parameter between each pollution index and each operating condition index is obtained;

[0017] By comprehensively considering the differences among the co-variation sub-parameters within all time windows during the historical monitoring period, the co-variation parameters between each pollution index and each operating condition index are obtained.

[0018] Based on the co-variation parameters between each pollution index and each operating condition index, and the fluctuation characteristic parameters of each pollution index, the operating condition correlation coefficient of each pollution index is obtained.

[0019] Furthermore, the method for obtaining the cooperative change sub-parameters includes:

[0020] At each monitoring moment within each time window, based on the emission data for each pollution indicator and the operating condition data for each operating condition indicator, the correlation characteristic value between each pollution indicator and each operating condition indicator is determined;

[0021] By combining the associated feature values ​​at adjacent monitoring times within the combined time window, the co-variation sub-parameters between each pollution index and each operating condition index are obtained.

[0022] Furthermore, the abnormal state coefficients for each pollution index are obtained as follows:

[0023] For each pollution index, the negative correlation normalization result of the ratio between the current operating condition correlation coefficient and the operating condition correlation coefficient is used as the abnormal state coefficient.

[0024] Furthermore, the abnormal emission early warning based on the aforementioned abnormal state coefficient includes:

[0025] An abnormal emission warning is issued when the abnormal state coefficient under any pollution indicator is greater than a preset threshold.

[0026] Furthermore, the method for obtaining the fluctuation parameters includes:

[0027] For each pollution indicator, within each time window, an emission reference value is obtained based on the concentrated characteristics of the emission data at all monitoring times, and a fluctuation parameter is obtained based on the deviation of the emission data at each monitoring time from the emission reference value.

[0028] A pollution source monitoring system based on multi-source data acquisition and analysis includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the pollution source monitoring method based on multi-source data acquisition and analysis.

[0029] The present invention has the following beneficial effects:

[0030] This invention first acquires emission data for each pollution indicator and operating condition data for each operating condition at the pollution source emission outlet at each monitoring time within a historical monitoring period. Then, based on the temporal fluctuations of the emission data for each pollution indicator within the historical monitoring period, it preliminarily acquires fluctuation characteristic parameters reflecting the sensitivity of each pollution indicator to changes in operating conditions. Combining the temporal correlation between the emission data for each pollution indicator and the operating condition data for each operating condition, it obtains the operating condition correlation coefficient for each pollution indicator. This coefficient quantifies the degree to which each pollution indicator is affected by changes in operating conditions, preparing for subsequent abnormal emission warnings. Furthermore, at the current monitoring time, based on the changing characteristics of the emission data for each pollution indicator and the operating condition data for each operating condition, it preliminarily assesses the operating condition correlation of each pollution indicator at the current monitoring time. Combining this with the historical reference benchmark provided by the operating condition correlation coefficient for each pollution indicator within the historical monitoring period, it accurately assesses and acquires the abnormal state coefficient for each pollution indicator. Finally, it issues abnormal emission warnings based on the abnormal state coefficient. This invention first analyzes the fluctuations in emission data over historical periods to quantify the sensitivity of pollution indicators to changes in operating conditions. It then assesses the correlation between pollution indicators and operating conditions by combining the temporal changes between pollution indicators and operating condition indicators. Finally, it analyzes the correlation between pollution indicators and operating conditions at the current monitoring time to evaluate abnormal emission behavior of pollution sources that does not conform to operating conditions, thereby improving the accuracy of pollution source monitoring. Attached Figure Description

[0031] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a flowchart illustrating a pollution source monitoring method based on multi-source data acquisition and analysis, provided as an embodiment of the present invention. Detailed Implementation

[0033] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a pollution source monitoring method and system based on multi-source data acquisition and analysis proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0034] Unless otherwise defined, 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 pertains.

[0035] The following description, in conjunction with the accompanying drawings, details a specific scheme for a pollution source monitoring method and system based on multi-source data acquisition and analysis provided by the present invention.

[0036] Please see Figure 1 The diagram illustrates a flowchart of a pollution source monitoring method based on multi-source data acquisition and analysis according to an embodiment of the present invention, specifically including:

[0037] Step S1: Obtain multi-source monitoring data for each monitoring moment within the historical monitoring period. The multi-source monitoring data includes at least emission data for each pollution indicator and operating condition data for each operating condition indicator at the pollution source emission outlet.

[0038] It should be noted that the purpose of this embodiment of the invention is to distinguish between normal production fluctuations caused by operating condition adjustments and genuine abnormal emission behavior, thereby providing early warning of anomalies and avoiding interference from changes in operating conditions such as boiler load adjustments and coal quality fluctuations, which could lead to poor reliability of monitoring results. Abnormal emission behavior refers to abnormal behavior in which emissions do not change with operating conditions. Even if the emission data does not exceed the pollution threshold, there is a high probability of abnormal situations such as equipment failure, illegal discharge, or decreased efficiency of treatment facilities.

[0039] This invention uses flue gas from a coal-fired power plant boiler as an example for analysis and description; in other embodiments, the implementer may also use industrial wastewater as a pollution source for monitoring and analysis.

[0040] In one embodiment of the present invention, a fixed continuous emission monitoring system (CEMS) installed at the boiler flue gas (after being treated by flue gas treatment equipment) is first used to monitor real-time emission behavior, and the industrial control system (DCS) of the coal-fired power plant is used to monitor the operating status of the flue gas treatment equipment and the production status of the boiler in real time, thereby obtaining multi-source monitoring data at each monitoring moment within a preset historical monitoring period.

[0041] Historical monitoring period refers to the continuous historical running period at the current monitoring time. In this embodiment, a continuous historical 24-hour period is taken as an example. The implementer can also adjust it according to the specific operating conditions. Multi-source monitoring data includes at least emission data for each pollution index and operating data for each operating condition index at the pollution source emission outlet. Emission data reflects the emission status, and operating data reflects the operating status of the flue gas treatment equipment and the production status of the boiler.

[0042] The pollution indicators include at least sulfur dioxide concentration, nitrogen oxide concentration, particulate matter concentration, flue gas temperature, flue gas pressure, flue gas velocity, flue gas humidity, and flue gas oxygen content; the operating condition indicators include at least the operating load of the flue gas treatment equipment, fan current, desulfurization tower pH value, circulating pump current, ammonia injection flow rate, and boiler production load; the above indicators and parameters are all monitored and collected by existing industrial control systems, and the specific acquisition process will not be described in detail; in other embodiments, the implementer may also adjust them according to the actual situation, such as adding or removing pollution indicators or operating condition indicators.

[0043] It should be noted that the monitoring frequency of the above-mentioned pollution indicators and operating condition indicators is the same and they are monitored synchronously. In this embodiment, it is set to 1Hz. In other embodiments, the implementer may adjust the monitoring frequency according to the actual situation, but the indicator data under different monitoring frequencies need to be resampled to ensure time sequence alignment for subsequent analysis.

[0044] The data for each (pollution or operating condition) indicator is cleaned and standardized, such as removing duplicate values ​​or filling in missing values. Then, the data for each (pollution or operating condition) indicator is stripped of its units and mapped to a preset range, such as 0-10. Data cleaning and standardization are well-known techniques and will not be elaborated further.

[0045] Step S2: During the historical monitoring period, based on the temporal fluctuations of emission data under each pollution indicator, obtain the fluctuation characteristic parameters of each pollution indicator, and combine the temporal correlation between emission data under each pollution indicator and operating data under each operating condition indicator to obtain the operating condition correlation coefficient of each pollution indicator.

[0046] During coal-fired power generation, emission data under various pollution indicators are not static but fluctuate with changes in normal operating conditions. Furthermore, the fluctuations in emission data under different pollution indicators are not entirely the same. For example, under the same operating conditions, some pollution indicators respond significantly to changes in operating conditions and can provide effective signals of changes in pollution emission status; while the responses of other pollution indicators to changes in operating conditions may have a certain lag or coupling relationship, and their values ​​change slowly or are unpredictable due to random noise interference, providing weak and unstable signals of changes in pollution emission status.

[0047] Considering that the more drastic or significant the fluctuations in emission data under pollution indicators, and the more regular or repeatable the fluctuation pattern in time series, the more sensitive the data is to changes in operating conditions, and the higher its reference value for assessing subsequent abnormal emissions; on the other hand, the weaker the fluctuations in emission data under pollution indicators, or the more random and irregular the fluctuations in time series, the more sluggish the data is to changes in operating conditions or the presence of noise interference, and the lower its reference value for assessing subsequent abnormal emissions.

[0048] Based on this, the embodiments of the present invention will first obtain the fluctuation characteristic parameters of each pollution index according to the temporal fluctuation changes of emission data under each pollution index during the historical monitoring period. The fluctuation characteristic parameters characterize the sensitivity level of the pollution index to changes in operating conditions during the historical monitoring period. The larger the fluctuation characteristic parameters, the more sensitive the corresponding pollution index is to changes in operating conditions, the higher the reference value for the assessment of subsequent abnormal emissions, and the greater the reference value for the analysis and correlation between subsequent pollution indexes and operating condition indicators.

[0049] Preferably, in one embodiment of the present invention, considering that long-term time-series analysis may smooth out the fluctuation characteristics of emission data, thereby affecting subsequent analysis of fluctuation information, the historical monitoring period is first divided to obtain all time windows. Then, the local fluctuation characteristics of emission data are analyzed within each time window to help assess its sensitivity or responsiveness to changes in operating conditions. Furthermore, the repeatability or consistency of the local fluctuation characteristics of emission data within different time windows is analyzed to obtain the fluctuation characteristic parameters for each pollution indicator. Therefore, the method for obtaining the fluctuation characteristic parameters includes:

[0050] Divide historical monitoring periods and obtain all time windows; within each time window, obtain the fluctuation parameters of each pollution indicator based on the fluctuation changes of emission data under each pollution indicator.

[0051] Based on the discrete characteristics of the fluctuation parameters of each pollution indicator within different time windows, the stable characteristic parameters of each pollution indicator within the historical monitoring period are obtained.

[0052] Based on the fluctuation parameters and stable characteristic parameters within each time window, the fluctuation characteristic parameters of each pollution index are obtained.

[0053] It should be noted that the analysis method for the fluctuation characteristic parameters of each pollution indicator is the same. Here, we will only take one pollution indicator as an example for analysis and description, and will not go into detail about each one.

[0054] In a preferred embodiment of the present invention, the method for obtaining the time window includes: uniformly dividing the historical monitoring period into a preset length to determine all time windows.

[0055] The preset length is at least 10, which is set to 10 in this embodiment. The implementer can also adjust it according to their own needs, for example, based on the stage of change in working conditions. Starting from the beginning of the historical monitoring period, every 10 monitoring moments are a time window, and the division is carried out sequentially until the entire historical monitoring period is covered, ensuring that the length of each time window is consistent and there is no overlap. This division method is conducive to reducing the interference caused by the boundary effect and improving the stability and comparability of fluctuation feature extraction.

[0056] Furthermore, within each time window, fluctuation parameters are calculated based on the fluctuations in emission data under the pollution indicators. These fluctuation parameters reflect the fluctuations in emission data and may be the instantaneous sensitive response of pollution indicators to changes in operating conditions within a local time window, or they may be occasional instantaneous values ​​such as noise. This prepares the parameters for the subsequent comprehensive evaluation of the fluctuation characteristics of pollution indicators.

[0057] In a preferred embodiment of the present invention, considering that the concentrated characteristics of emission data under pollution indicators within a time window, such as the mean, can characterize average emission behavior, an emission reference value reflecting a continuous and stable emission level can first be obtained; then, based on the deviation of the emission data relative to the emission reference value at each monitoring time, a fluctuation parameter reflecting the instantaneous fluctuation characteristics within the time window is obtained; therefore, the method for obtaining the fluctuation parameter includes:

[0058] For each pollution indicator, within each time window, an emission reference value is obtained based on the concentrated characteristics of emission data at all monitoring times, and a fluctuation parameter is obtained based on the deviation of emission data at each monitoring time from the emission reference value.

[0059] Specifically, within each time window, the mean of emission data at all monitoring times is used as the emission reference value. The sum of the absolute values ​​of the differences between the emission data at each monitoring time and the emission reference value is normalized, for example, by multiplying by a preset scaling factor of 0.1 and then mapping it to the sigmoid function to prevent the output from saturating (approaching 1) due to excessively large input values ​​and losing discriminability. The normalized mapping result is used as the fluctuation parameter.

[0060] In other embodiments, the implementer may also use the normalized result of the fitting error of the least squares linear fitting of the emission data within the time window as the fluctuation parameter, or other normalization methods such as linear normalization may be used. These are all well-known techniques and will not be described in detail here.

[0061] Furthermore, based on the discrete characteristics of the fluctuation parameters of each pollution indicator within different time windows, stable characteristic parameters of each pollution indicator within historical monitoring periods are obtained. Stable characteristic parameters reflect the repeatability or regularity of the fluctuation changes of pollution indicators within different time windows. The higher the repeatability, the more consistent the instantaneous sensitivity response of pollution indicators to changes in operating conditions within different local time windows is, and the greater the possibility of reflecting the true fluctuation of emission data. Conversely, the lower the repeatability, the greater the possibility of occasional instantaneous values ​​such as noise, thus preparing for subsequent comprehensive evaluation of the fluctuation characteristic parameters of pollution indicators.

[0062] Specifically, the absolute value of the difference between the fluctuation parameter and the minimum fluctuation parameter of the pollution index in each time window is calculated. The absolute value of the difference is averaged and then negatively correlated and normalized. For example, the mean of the absolute values ​​of the difference is added to a constant 1 and then the reciprocal is used for negative correlation normalization to obtain stable characteristic parameters.

[0063] In other embodiments, implementers may also use other negative correlation normalization methods, such as mapping to an exponential function exp(-x) with the natural constant e as the base; or they may use the maximum fluctuation parameter, the mean of the fluctuation parameters, etc., instead of the minimum fluctuation parameter.

[0064] Then, based on the fluctuation parameters and stable characteristic parameters within each time window, the fluctuation characteristic parameters of each pollution index are obtained. Specifically, the fluctuation parameters within all time windows are averaged, and then the average fluctuation parameter is multiplied and fused with the stable characteristic parameter. The stable characteristic parameter integrates the repetitive patterns of the fluctuation characteristics of emission data within all time windows, providing a confidence reference for the long-term fluctuation of emission data within historical monitoring periods. The average fluctuation parameter can provide a relevant reference for the instantaneous sensitive response of pollution index to changes in operating conditions within a preset historical monitoring period. Therefore, multiplying and fusing the two yields the fluctuation characteristic parameters of the pollution index under the pollution index.

[0065] After obtaining the fluctuation characteristic parameters of each pollution indicator, this embodiment of the invention further analyzes the temporal change correlation between emission data under each pollution indicator and operating data under each operating condition indicator, and obtains the operating condition correlation coefficient of each pollution indicator. The operating condition correlation coefficient, combined with the fluctuation characteristic parameters that reflect the sensitivity of the pollution indicator to changes in operating conditions, analyzes the actual temporal change correlation between the pollution indicator and the operating condition indicator, quantifies the degree of influence of each pollution indicator on changes in operating conditions, and prepares for subsequent abnormal emission early warning.

[0066] Preferably, in one embodiment of the present invention, considering the correlation between emission data under each pollution index and operating condition data under each operating condition index, comprehensively assessing the synergistic changes of the two across all time windows can help evaluate their coordinated changes; further, the overall coordinated changes of pollution indices with operating conditions can be evaluated by comprehensively considering the synergistic change parameters between each pollution index and all operating condition indices, while combining the fluctuation characteristic parameters reflecting the sensitivity of pollution indices to changes in operating conditions, the degree of influence of changes in operating condition indices on pollution indices is comprehensively evaluated, and the operating condition correlation coefficient is determined; the method for obtaining the operating condition correlation coefficient includes:

[0067] Within each time window, based on the correlation between the changes in emission data under each pollution indicator and the operating condition data under each operating condition indicator, the co-change sub-parameters between each pollution indicator and each operating condition indicator are obtained.

[0068] By integrating the co-variation sub-parameters across all time windows during the historical monitoring period, the co-variation parameters between each pollution index and each operating condition index are obtained.

[0069] Based on the co-variation parameters between each pollution index and each operating condition index, as well as the fluctuation characteristic parameters of each pollution index, the operating condition correlation coefficient of each pollution index is obtained.

[0070] In a preferred embodiment of the present invention, the method for obtaining the cooperative variation sub-parameter includes:

[0071] At each monitoring moment within each time window, based on the emission data for each pollution indicator and the operating data for each operating condition indicator, the correlation characteristic value between each pollution indicator and each operating condition indicator is determined;

[0072] By considering the differences between associated feature values ​​at adjacent monitoring times within a comprehensive time window, we can obtain the co-variation sub-parameters between each pollution index and each operating condition index.

[0073] As an example, we will analyze and describe the situation using any pollution index and any operating condition index.

[0074] Specifically, at each monitoring moment within each time window, the emission data under the pollution index is used as the numerator, the operating condition data under the operating condition index is used as the denominator, and the ratio of the fractions is used as the correlation characteristic value between the pollution index and the operating condition index. To avoid the denominator being 0, a very small positive parameter, such as 0.01, can be added to the operating condition data before using it as the denominator. The correlation characteristic value represents the instantaneous proportional relationship between the pollution index and the operating condition index, preparing for subsequent measurement of changes and correlation.

[0075] Then, the absolute value of the difference between the associated characteristic values ​​at adjacent monitoring times within the time window is calculated. The smaller the absolute value of the difference, the more stable the instantaneous proportional relationship between the pollution index and the operating condition index, and the greater the possibility of coordinated change between the pollution index and the operating condition index. Therefore, the absolute values ​​of the difference between the associated characteristic values ​​at all adjacent monitoring times are averaged and negatively correlated and normalized. For example, the average absolute value of the difference is added to a constant 1 and then the reciprocal is used for negative correlation normalization to obtain the co-change sub-parameter.

[0076] In other embodiments, implementers may also employ other negative correlation normalization methods, such as mapping to an exponential function exp(-x) with the natural constant e as the base.

[0077] Further, by comprehensively analyzing the co-variation sub-parameters within all time windows during the historical monitoring period, the co-variation parameters between each pollution index and each operating condition index are obtained. Specifically, the absolute value of the difference between the co-variation sub-parameter within each time window and the mean of the co-variation sub-parameters across all time windows is calculated. The smaller the absolute value of the difference, the more consistent the co-variation sub-parameters are across different time windows. Then, the absolute values ​​of the differences corresponding to all time windows are summed and negatively correlated and normalized. For example, the sum of the absolute values ​​of the differences is increased by a constant 1 and then the reciprocal is used for negative correlation normalization to obtain the co-variation parameters.

[0078] Then, the co-variance parameter between each pollution index and each operating condition index is multiplied by the fluctuation characteristic parameter of each pollution index, and the product is used as the operating condition correlation coefficient of each pollution index.

[0079] Step S3: At the current monitoring time, based on the change characteristics of emission data under each pollution indicator and operating data under each operating condition indicator, obtain the current operating condition correlation coefficient of each pollution indicator within the preset historical period at the current monitoring time; based on the difference between the current operating condition correlation coefficient and the operating condition correlation coefficient under each pollution indicator, obtain the abnormal state coefficient under each pollution indicator; and conduct abnormal emission early warning based on the abnormal state coefficient.

[0080] Considering that the operating condition correlation coefficient reflects the sensitivity of pollution indicators to changes in operating conditions during historical monitoring periods, it can provide a certain historical benchmark. If the sensitivity of pollution indicators to changes in operating conditions at the current monitoring time is closer to the historical benchmark, it indicates that the operating condition correlation at the current monitoring time has not been disrupted, and the pollution indicators change with the operating conditions, which is consistent with normal emissions. Conversely, it indicates that the operating condition correlation has been disrupted, for example, the operating conditions have not changed but the emission data fluctuates drastically, and the possibility of abnormal emissions is greater.

[0081] Based on this, in a preset historical period within the current monitoring time, this embodiment of the invention preliminarily assesses the correlation between the operating conditions of each pollution indicator and the emission data and operating condition data of each operating condition indicator within the current monitoring time, and obtains the current operating condition correlation coefficient. Furthermore, by combining the operating condition correlation coefficients of each pollution indicator within the historical monitoring period, an abnormal state coefficient is obtained for each pollution indicator. The abnormal state coefficient reflects the change in the operating condition correlation of the corresponding pollution indicator, and further reflects the possibility of abnormal emissions provided under the corresponding pollution indicator, providing a basis for subsequent abnormal emission early warning.

[0082] In one embodiment of the present invention, the current operating condition correlation coefficient of each pollution indicator can be calculated first based on the analysis steps of the operating condition correlation coefficient of each pollution indicator during the historical monitoring period; then the difference between the current operating condition correlation coefficient and the operating condition correlation coefficient is compared to evaluate the consistency of the operating condition correlation, and then the abnormal state coefficient under the pollution indicator is obtained.

[0083] As an example, taking any pollution indicator as an example, firstly, within a preset historical time period of the current monitoring time, obtain the current operating condition correlation coefficient of the pollution indicator; wherein, the preset historical time period is 10-15 minutes of the history of the current monitoring time, and 10 minutes is taken in this embodiment, but the implementer can also adjust it himself; divide the preset historical time period into several time windows for analysis and evaluation. The analysis method of the current operating condition correlation coefficient is the same as that of the operating condition correlation coefficient in the historical monitoring time period, but the analysis and calculation are performed within the preset historical time period of the current monitoring time, and the acquisition process will not be described again.

[0084] In a preferred embodiment of the present invention, under each pollution index, the negative correlation normalization result of the ratio between the current working condition correlation coefficient and the working condition correlation coefficient is used as the abnormal state coefficient; the current working condition correlation coefficient is used as the numerator, the working condition correlation coefficient is used as the denominator, and the ratio of the fraction is negatively normalized, for example, mapped to the exponential function exp(-x) with the natural constant e as the base, and the negative correlation normalization mapping result is used as the abnormal state coefficient.

[0085] When the correlation coefficient of the current operating condition is greater than or equal to the correlation coefficient of the operating condition, the ratio is greater than or equal to 1, indicating that the change of the pollution index with the operating condition in the preset historical period at the current monitoring time is greater than the historical benchmark, the probability of normal emission of the pollution source under the pollution index is relatively higher, and the abnormal state coefficient is smaller; conversely, if the ratio is less than 1, it indicates that the correlation between the pollution index and the operating condition in the preset historical period at the current monitoring time is not strong, there may be abnormal emission behavior, and the abnormal state coefficient is larger.

[0086] Once the abnormal state coefficient for each pollution indicator is determined, abnormal emission warnings can be issued based on the abnormal state coefficient.

[0087] Preferably, in one embodiment of the present invention, considering that when any pollution indicator has a high probability of abnormal emission, the pollution source has the possibility of abnormal emission and it is necessary to issue an abnormal emission warning; therefore, issuing an abnormal emission warning based on the abnormal state coefficient includes: issuing an abnormal emission warning when the abnormal state coefficient of any pollution indicator is greater than a preset threshold.

[0088] The preset threshold is set to 0.7. The preset threshold is an example value calculated under a typical working condition (the equipment is in a healthy / normal operating state, such as 24 hours after the system has just been overhauled). In actual applications, it can be adjusted according to the tolerance for false alarm rate. When the abnormal state coefficient under any pollution indicator is greater than 0.7, an abnormal emission warning is issued. When the abnormal state coefficient under all pollution indicators is less than or equal to 0.7, monitoring continues.

[0089] Based on the same inventive concept, one embodiment of the present invention also proposes a pollution source monitoring system based on multi-source data acquisition and analysis. The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the pollution source monitoring method based on multi-source data acquisition and analysis described in steps S1-S3 above.

[0090] In summary, this invention first acquires multi-source monitoring data at each monitoring moment within a historical monitoring period. Then, based on the temporal fluctuations of emission data for each pollution indicator, it obtains the fluctuation characteristic parameters of each pollution indicator. Furthermore, by combining the temporal correlation between emission data for each pollution indicator and operating condition data for each operating condition indicator, it obtains the operating condition correlation coefficient for each pollution indicator. At the current monitoring moment, based on the change characteristics of emission data for each pollution indicator and operating condition data for each operating condition indicator, it obtains the current operating condition correlation coefficient for each pollution indicator within a preset historical period at the current monitoring moment. Further, by comparing the current operating condition correlation coefficient with the operating condition correlation coefficient for each pollution indicator, it obtains the abnormal state coefficient for each pollution indicator. Based on the abnormal state coefficient, it provides early warning of abnormal emissions. This invention quantifies the response sensitivity of pollution indicators to changes in operating conditions by analyzing the fluctuations of emission data within a historical period. It further assesses the operating condition correlation of pollution indicators by combining the temporal correlation between pollution indicators and operating condition indicators, and then analyzes the operating condition correlation of pollution indicators at the current monitoring moment. This accurately assesses abnormal emission behavior of pollution sources that does not conform to operating conditions, thereby improving the accuracy of pollution source monitoring.

[0091] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0092] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A pollution source monitoring method based on multi-source data acquisition and analysis, characterized in that, The method includes: Acquire multi-source monitoring data at each monitoring moment within a historical monitoring period. The multi-source monitoring data includes at least emission data for each pollution index and operating condition data for each operating condition index at the pollution source emission outlet. During the historical monitoring period, based on the temporal fluctuations of the emission data under each pollution indicator, the fluctuation characteristic parameters of each pollution indicator are obtained, and the working condition correlation coefficient of each pollution indicator is obtained by combining the temporal variation correlation between the emission data under each pollution indicator and the working condition data under each working condition indicator. At the current monitoring time, based on the change characteristics of the emission data under each pollution indicator and the operating condition data under each operating condition indicator, the current operating condition correlation coefficient of each pollution indicator within a preset historical period at the current monitoring time is obtained; based on the difference between the current operating condition correlation coefficient and the operating condition correlation coefficient under each pollution indicator, the abnormal state coefficient under each pollution indicator is obtained; and abnormal emission early warning is issued based on the abnormal state coefficient. Divide historical monitoring periods evenly into preset lengths to determine all time windows; The method for obtaining the working condition correlation coefficient includes: Within each time window, based on the correlation between the emission data under each pollution index and the operating condition data under each operating condition index, the co-change sub-parameter between each pollution index and each operating condition index is obtained; By comprehensively considering the differences among the co-variation sub-parameters within all time windows during the historical monitoring period, the co-variation parameters between each pollution index and each operating condition index are obtained. Based on the co-variation parameters between each pollution index and each operating condition index, and the fluctuation characteristic parameters of each pollution index, the operating condition correlation coefficient of each pollution index is obtained.

2. The pollution source monitoring method based on multi-source data acquisition and analysis according to claim 1, characterized in that, The method for obtaining the fluctuation characteristic parameters includes: Divide historical monitoring periods and obtain all time windows; within each time window, obtain the fluctuation parameters of each pollution index based on the fluctuation changes of the emission data for each pollution index. Based on the discrete characteristics of the fluctuation parameters of each pollution index within different time windows, the stable characteristic parameters of each pollution index within the historical monitoring period are obtained. Based on the fluctuation parameters and the stable characteristic parameters within each time window, the fluctuation characteristic parameters of each pollution index are obtained.

3. The pollution source monitoring method based on multi-source data acquisition and analysis according to claim 1, characterized in that, The method for obtaining the cooperative change sub-parameters includes: At each monitoring moment within each time window, based on the emission data for each pollution indicator and the operating condition data for each operating condition indicator, the correlation characteristic value between each pollution indicator and each operating condition indicator is determined; By combining the associated feature values ​​at adjacent monitoring times within the combined time window, the co-variation sub-parameters between each pollution index and each operating condition index are obtained.

4. The pollution source monitoring method based on multi-source data acquisition and analysis according to claim 1, characterized in that, The abnormal state coefficients for each pollution index include: For each pollution index, the negative correlation normalization result of the ratio between the current operating condition correlation coefficient and the operating condition correlation coefficient is used as the abnormal state coefficient.

5. The pollution source monitoring method based on multi-source data acquisition and analysis according to claim 1, characterized in that, Abnormal emission early warning based on the aforementioned abnormal state coefficient includes: An abnormal emission warning is issued when the abnormal state coefficient under any pollution indicator is greater than a preset threshold.

6. The pollution source monitoring method based on multi-source data acquisition and analysis according to claim 2, characterized in that, The method for obtaining the fluctuation parameters includes: For each pollution indicator, within each time window, an emission reference value is obtained based on the concentrated characteristics of the emission data at all monitoring times, and a fluctuation parameter is obtained based on the deviation of the emission data at each monitoring time from the emission reference value.

7. A pollution source monitoring system based on multi-source data acquisition and analysis, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the pollution source monitoring method based on multi-source data acquisition and analysis as described in any one of claims 1 to 6.

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