A method and system for managing burner monitoring data

Abstract

The present application relates to the technical field of data processing, and particularly relates to a kind of burner monitoring data management method and system.The method comprises the following steps: collecting flue gas monitoring data and flow rate data of each monitoring point at each time, obtaining the pollution degree of each monitoring point at each time according to the ratio of pollution gas to combustion gas, and obtaining the pollution time sequence of each monitoring point based on the pollution degree;According to the pollution time sequence of each monitoring point and its similar monitoring point, the pollution monitoring correlation of each monitoring point is obtained, and then each inaccurate monitoring point is obtained;Difficult to diffuse of each item of pollution gas of each inaccurate monitoring point is obtained;Based on the difficult to diffuse of pollution gas, the correction concentration data of each item of pollution gas of each pollution time of each inaccurate monitoring point is obtained, and the data collected under each monitoring point is compressed, and the accuracy of data collection is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a burner monitoring data management method and system. BACKGROUND

[0002] With the enhancement of global environmental awareness and the continuous pursuit of energy efficiency in industrial production, as one of the core equipment for energy conversion, the burner is more and more widely used in industrial production. The burner not only affects the energy use efficiency, but also directly affects the emission of environmental pollutants, especially the emission of pollutants such as carbon dioxide, nitrogen oxides, sulfur dioxide, etc. In order to reduce environmental pollution and improve energy utilization, the operating state of the burner needs to be monitored in real time, and adjusted and optimized in time according to the pollution data. However, since each monitoring device of the burner can generate a large amount of data, if the data is not compressed, it can quickly consume the storage space and increase the data storage cost. Especially in the case of long-term monitoring or multiple devices working in parallel, compressing the data can significantly reduce the storage requirement and improve the efficiency of data management. Especially in the real-time monitoring scenario, how to efficiently store and process these data has become a big challenge for burner monitoring data management.

[0003] However, due to the bending, narrowing or foreign matter inside the pipeline, the diffusion of the pollution gas is affected, resulting in that when the flue gas under some monitoring points is difficult to diffuse, the concentration data of the pollution gas collected under some monitoring points is inaccurate. When these inaccurate data are included in the data compression system, due to the characteristics of the compression algorithm, these errors can not be effectively identified and removed in the compression process, but are compressed and stored together with other accurate data, thus reducing the overall quality of the data set. Especially in the case of long-term monitoring or multiple monitoring devices working in parallel, the accumulation of such inaccurate data will increase the complexity of subsequent data analysis and decision-making, resulting in that the subsequent pollution control and optimization strategy cannot be accurately adjusted. SUMMARY

[0004] In order to solve the technical problem that the bending, narrowing or foreign matter inside the pipeline affects the diffusion of the gas, resulting in that the concentration data of the pollution gas collected under some monitoring points is inaccurate, the present application provides a burner monitoring data management method and system.

[0005] In a first aspect, the present application provides a burner monitoring data management method, which adopts the following technical scheme:

[0006] A burner monitoring data management method, comprising the steps of:

[0007] Collecting flue gas monitoring data and flow rate data of each monitoring point at each time point;

[0008] According to the proportion of different gases in the flue gas monitoring data, the pollution degree of each monitoring point at each moment is obtained; based on the pollution degree, the pollution moment sequence of each monitoring point is obtained; according to the similarity of the flow rate data between the monitoring points, the similar monitoring points of each monitoring point are obtained;

[0009] All adjacent pollution moments in the pollution moment sequence of each monitoring point form each time period as the pollution period of each monitoring point; the pollution monitoring correlation of each monitoring point is obtained:

[0010] , The pollution monitoring correlation of the i th monitoring point is represented as: The number of all similar monitoring points of the i th monitoring point is represented as: The number of all pollution periods of the i th monitoring point is represented as: The number of all pollution periods of the k th similar monitoring point of the i th monitoring point is represented as: The maximum value in the number of pollution periods of the i th monitoring point and the k th similar monitoring point thereof is represented as: The average value of the pollution degree of all pollution moments in the r th pollution period of the i th monitoring point is represented as: The average value of the pollution degree of all pollution moments in the r th pollution period of the k th similar monitoring point of the i th monitoring point is represented as: The absolute value is represented as; the exponential function with the natural constant as the base is represented as exp(); The preset parameter is represented as; based on the pollution monitoring correlation, each inaccurate monitoring point is obtained;

[0011] According to the change trend between the pollution periods of each inaccurate monitoring point, the pollution gas difficult to diffuse of each inaccurate monitoring point is obtained; based on the pollution gas difficult to diffuse, the correction concentration data of each pollution gas at each pollution moment of each inaccurate monitoring point is obtained; the compressed data of each monitoring point is obtained.

[0012] The innovation of the present application lies in that the flow rate data between the monitoring points is analyzed, the similar monitoring points of each monitoring point are obtained, then the pollution moment sequence of each monitoring point and its similar monitoring points is compared, if the flow rate data is similar but the pollution moment sequence is not similar, the monitoring point is inaccurate, the monitoring point with potential monitoring error is identified; then the diffusion rate of the pollution gas at the inaccurate monitoring point is analyzed, the pollution gas difficult to diffuse of each inaccurate monitoring point is obtained, and the data of the inaccurate monitoring point is corrected, which can further improve the accuracy of the data and ensure the accuracy of each pollutant concentration value.

[0013] Preferably, the obtaining of the pollution degree of each monitoring point at each time point comprises:

[0014] ;

[0015] In the formula, denotes the pollution degree of the i-th monitoring point at the j-th time point; denotes the sum of the concentrations of all pollution gases of the i-th monitoring point at the j-th time point; denotes the sum of the concentrations of all combustion gases of the i-th monitoring point at the j-th time point.

[0016] Preferably, the obtaining of the pollution time point sequence of each monitoring point comprises:

[0017] A pollution threshold parameter T1 is preset, and if the pollution degree of the i-th monitoring point at the j-th time point is greater than or equal to the pollution threshold parameter T1, the j-th time point is recorded as a pollution time point of the i-th monitoring point; a sequence formed by all pollution time points of the i-th monitoring point is taken as the pollution time point sequence of the i-th monitoring point.

[0018] Preferably, the obtaining of the similar monitoring point of each monitoring point comprises:

[0019] A sequence formed by the flow rate data of the i-th monitoring point at all time points is recorded as the flow rate data sequence of the i-th monitoring point; a Pearson correlation coefficient between the flow rate data sequence of the i-th monitoring point and the flow rate data sequence of the k-th monitoring point is taken as a flow rate similarity trend factor between the i-th monitoring point and the k-th monitoring point; the flow rate similarity between the i-th monitoring point and the k-th monitoring point is obtained according to the flow rate similarity trend factor.

[0020] A pollution threshold parameter T2 is preset, and if the flow rate similarity between the i-th monitoring point and the k-th monitoring point is greater than or equal to the pollution threshold parameter T2, the k-th monitoring point is recorded as a similar monitoring point of the i-th monitoring point; all similar monitoring points of the i-th monitoring point are obtained.

[0021] Preferably, the obtaining of the flow rate similarity between the i-th monitoring point and the k-th monitoring point comprises:

[0022] ;

[0023] In the formula, denotes the flow rate similarity between the i-th monitoring point and the k-th monitoring point; denotes the number of all time points; denotes the flow rate data of the i-th monitoring point at the j-th time point; denotes the flow rate data of the k-th monitoring point at the j-th time point; a flow rate similarity trend factor between the i-th monitoring point and the k-th monitoring point; denotes taking an absolute value; exp() denotes an exponential function with a natural constant as a base; and norm() denotes a linear normalization function.

[0024] Preferably, the obtaining of the pollution gas diffusivity of each inaccurate monitoring point comprises:

[0025] a time interval between the last pollution time in the r-th pollution period of the v-th inaccurate monitoring point and the first pollution time in the r+1-th pollution period is recorded as a pollution interval factor of the r-th pollution period; and

[0026] ; wherein, denotes the pollution gas diffusivity of the v-th inaccurate monitoring point; denotes the pollution monitoring correlation of the v-th inaccurate monitoring point; denotes the slope of the pollution fitting straight line of the v-th inaccurate monitoring point; denotes the pollution interval factor of the r-th pollution period of the v-th inaccurate monitoring point; denotes the number of all pollution periods of the v-th inaccurate monitoring point; and norm() denotes a linear normalization function.

[0027] Preferably, the obtaining of the pollution fitting straight line of the v-th inaccurate monitoring point comprises:

[0028] a two-dimensional rectangular coordinate system is constructed with the serial numbers of all pollution periods of the v-th inaccurate monitoring point as abscissas and with the mean values of pollution degrees of all pollution times in the pollution periods as ordinates; the mean values of pollution degrees of all pollution times in all pollution periods of the v-th inaccurate monitoring point are input into the two-dimensional rectangular coordinate system to obtain a plurality of pollution period data points of the v-th inaccurate monitoring point; and a least square method is used to perform straight line fitting on all pollution period data points of the v-th inaccurate monitoring point to obtain the pollution fitting straight line of the v-th inaccurate monitoring point.

[0029] Preferably, the obtaining of the corrected concentration data of each item of pollution gas at each pollution time of each inaccurate monitoring point comprises:

[0030] ;

[0031] In the formula, denotes the corrected concentration data of the o-th item of pollution gas at the j-th pollution time of the v-th inaccurate monitoring point; denotes the pollution gas diffusivity of the v-th inaccurate monitoring point; The concentration data of the oth pollution gas at the jth pollution moment of the vth inaccurate monitoring point.

[0032] In a second aspect, the present application provides a burner monitoring data management system, which adopts the following technical solution:

[0033] A burner monitoring data management system comprises a processor and a memory, and the memory stores computer program instructions which, when executed by the processor, implement the above-mentioned burner monitoring data management method.

[0034] By adopting the above technical solution, the above-mentioned burner monitoring data management method is generated into a computer program and stored in the memory to be loaded and executed by the processor, so that a terminal device is made according to the memory and the processor, and use is facilitated.

[0035] The present application has the following technical effects: The purpose of the present application is to analyze the flow rate data between monitoring points, obtain similar monitoring points of each monitoring point, then compare each monitoring point with the pollution moment sequence of its similar monitoring point to obtain inaccurate monitoring points and identify monitoring points with monitoring errors; then analyze the diffusion rate of the pollution gas at the inaccurate monitoring points to obtain the pollution gas diffusion difficulty of each inaccurate monitoring point, and correct the data of the inaccurate monitoring points according to the pollution gas diffusion difficulty, which can further improve the accuracy of the data and ensure the accuracy of each pollutant concentration value. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 is a method flowchart in a burner monitoring data management method of an embodiment of the present application. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0038] An embodiment of the present application discloses a burner monitoring data management method, which refers to Figure 1 and comprises steps S1-S4:

[0039] S1: Collecting flue gas monitoring data and flow rate data of each monitoring point at each moment.

[0040] In the embodiment of the present application, the preset collection frequency is , the collection duration is one week, a plurality of multi-sensor monitoring points are arranged in the flue part before the flue gas discharged by the burner enters the atmosphere, and the concentration of sulfur dioxide, nitrogen oxides, carbon monoxide, oxygen and other gases and the flow rate data of the flue gas at each moment of each monitoring point are collected;

[0041] The sulfur dioxide, nitrogen oxide and carbon monoxide at each moment of each monitoring point are pollution gases at each moment of each monitoring point, and the sulfur dioxide, nitrogen oxide, carbon monoxide and oxygen at each moment of each monitoring point are combustion gases at each moment of each monitoring point.

[0042] S2: According to the proportion of different gases in the flue gas monitoring data, the pollution degree of each monitoring point at each moment is obtained, and based on the pollution degree, the pollution moment sequence of each monitoring point is obtained; according to the similarity of the flow rate data between the monitoring points, the similar monitoring points of each monitoring point are obtained; according to the pollution moment sequence of each monitoring point and its similar monitoring points, the pollution monitoring correlation of each monitoring point is obtained; and based on the pollution monitoring correlation, each inaccurate monitoring point is obtained.

[0043] It should be noted that since each monitoring device of the burner can generate a large amount of data, if the data is not compressed, the storage space can be rapidly consumed, and the data storage cost is increased, especially in the case of long-term monitoring or multiple devices working in parallel, compressing the data can significantly reduce the storage requirement and improve the efficiency of data management. However, due to the bending, narrowing or foreign matter inside the pipe, the diffusion of the gas in the flue is limited, so the flue gas under some monitoring points is difficult to diffuse, resulting in inaccurate concentration data of the pollution gas collected under some monitoring points. Therefore, the present application firstly obtains the inaccurate monitoring points according to the pollution gas collected by each monitoring data at each moment, and then analyzes the flue gas diffusion difficulty of each inaccurate monitoring point, and further corrects the concentration data of the pollution gas.

[0044] It should be further noted that the ratio of the sum of the concentrations of all pollution gases to the sum of the concentrations of all combustion gases at each moment of each monitoring point can represent the pollution degree of each monitoring point at each moment, and based on the pollution degree, the pollution moment sequence of each monitoring point is obtained.

[0045] In the embodiment of the present application, the calculation expression for obtaining the pollution degree of each monitoring point at each moment is:

[0046] ;

[0047] In the formula, represents the pollution degree of the jth moment of the ith monitoring point; represents the sum of the concentrations of all pollution gases of the ith monitoring point at the jth moment; represents the sum of the concentrations of all combustion gases of the ith monitoring point at the jth moment; It should be noted that the pollution gas is sulfur dioxide, nitrogen oxide and carbon monoxide; and the combustion gas is sulfur dioxide, nitrogen oxide, carbon monoxide and oxygen.

[0048] In the embodiment of the present application, according to the pollution degree of each monitoring point at each time, the specific method for obtaining the pollution time sequence of each monitoring point is as follows:

[0049] A preset pollution threshold parameter T1=0.65 is set, if the pollution degree of the jth time of the ith monitoring point is greater than or equal to the pollution threshold parameter T1, the jth time is recorded as the pollution time of the ith monitoring point; the sequence formed by all the pollution times of the ith monitoring point is taken as the pollution time sequence of the ith monitoring point;

[0050] It should be noted that if the flow rates of two monitoring points in the flue are similar, the environment in which they are located should be relatively close, and the flow pattern of the gas and the diffusion characteristics of the pollutants will also be similar, that is, the pollution time sequences between the monitoring points with similar flow rates are similar; if the pollution time sequences between the monitoring points with similar flow rates are not similar, it indicates that the monitoring points may be located at the bending and narrow places inside the pipeline, which limits the flow of the gas, which may cause the accumulation of the concentration of the pollution gas and slow down the diffusion rate. Therefore, firstly, the similarity of the flow rate data between the monitoring points is analyzed to obtain the similar monitoring points of each monitoring point, and then the pollution time sequences of each monitoring point and its similar monitoring points are compared. If the flow rate data are similar but the pollution time sequences are not similar, it indicates that the monitoring points may be inaccurate.

[0051] It should be further noted that if the cumulative value of the flow rate data difference between the monitoring points at all times is smaller and the change trend of the flow rate data sequence between the monitoring points is more consistent, the flow rate similarity between the monitoring points is greater, and one of the monitoring points is the similar monitoring point of the other monitoring point.

[0052] In the embodiment of the present application, the specific method for obtaining the similar monitoring points of each monitoring point according to the flow rate data between the monitoring points is as follows:

[0053] The sequence formed by the flow rate data of the ith monitoring point at all times is recorded as the flow rate data sequence of the ith monitoring point; the Pearson correlation coefficient between the flow rate data sequence of the ith monitoring point and the flow rate data sequence of the kth monitoring point is taken as the flow rate similarity trend factor between the ith monitoring point and the kth monitoring point.

[0054] The calculation expression of the flow rate similarity between the ith monitoring point and the kth monitoring point is as follows:

[0055] ;

[0056] In the formula, indicates the flow rate similarity between the ith monitoring point and the kth monitoring point. represents the number of all time points; represents the flow rate data of the i th monitoring point at the j th time point; represents the flow rate data of the k th monitoring point at the j th time point; represents the flow rate similarity trend factor between the i th monitoring point and the k th monitoring point; represents taking an absolute value; exp() represents an exponential function with a natural constant as a base; norm() represents a linear normalization function;

[0057] represents the cumulative value of the flow rate data difference between the i th monitoring point and the k th monitoring point at all time points, and the smaller the value is, the greater the flow rate similarity between the i th monitoring point and the k th monitoring point is; The greater the value is, the more consistent the change trend of the flow rate data sequence between the i th monitoring point and the k th monitoring point is.

[0058] A pollution threshold parameter T2=0.85 is preset, if the flow rate similarity between the i th monitoring point and the k th monitoring point is greater than or equal to the pollution threshold parameter T2, the k th monitoring point is recorded as a similar monitoring point of the i th monitoring point; similarly, all similar monitoring points of the i th monitoring point are obtained.

[0059] It should be noted that then, the pollution time point sequence between each monitoring point and its similar monitoring point needs to be compared to judge the accuracy of each monitoring point, if the number of pollution time periods of any monitoring point and its similar monitoring point is similar, and the pollution degree of the monitoring point and its similar monitoring point in the corresponding pollution time period is close, it is indicated that the pollution conditions between the two monitoring points are similar, therefore, if the number of pollution time periods of any monitoring point and each similar monitoring point thereof is similar and the pollution degree in the corresponding pollution time period is small, it is indicated that the pollution time point sequence of the monitoring point and each similar monitoring point thereof is similar, and the monitoring point is more accurate.

[0060] In the embodiment of the application, according to the comparison of the pollution time point sequence between each monitoring point and its similar monitoring point, the specific method for obtaining the pollution monitoring correlation of each monitoring point is:

[0061] All adjacent pollution time points in the pollution time point sequence of the i th monitoring point form each time period, and are recorded as the pollution time period of the i th monitoring point;

[0062] The calculation expression for obtaining the pollution monitoring correlation of the i th monitoring point is:

[0063] ;

[0064] In the formula, represents the pollution monitoring correlation of the i th monitoring point; Number of all similar monitoring points of the ith monitoring point; Number of all pollution time periods of the ith monitoring point; Number of all pollution time periods of the kth similar monitoring point of the ith monitoring point; Maximum value of the number of pollution time periods of the ith monitoring point and the kth similar monitoring point thereof; Mean value of the pollution degree of all pollution time points in the rth pollution time period of the ith monitoring point; Mean value of the pollution degree of all pollution time points in the rth pollution time period of the kth similar monitoring point of the ith monitoring point; Indicates taking the absolute value; exp() indicates the exponential function with the natural constant as the base; Indicates that the preset parameter is 1, and the denominator is prevented from being 0; it should be noted that if the rth pollution time period exists for the ith monitoring point, but the rth pollution time period does not exist for the kth similar monitoring point of the ith monitoring point, then the mean value of the pollution degree of all pollution time points in the rth pollution time period of the kth similar monitoring point of the ith monitoring point is 0; on the contrary, if the rth pollution time period exists for the kth similar monitoring point of the ith monitoring point, but the rth pollution time period does not exist for the ith monitoring point, then the mean value of the pollution degree of all pollution time points in the rth pollution time period of the ith monitoring point is 0;

[0065] In the embodiment of the present application, the specific method for obtaining all inaccurate monitoring points based on pollution monitoring correlation is as follows:

[0066] Obtain the pollution monitoring correlation of each monitoring point, and in the ith monitoring point and all similar monitoring points thereof, the monitoring point with the minimum pollution monitoring correlation is recorded as an inaccurate monitoring point.

[0067] S3: Obtain the pollution gas diffusion difficulty of each inaccurate monitoring point according to the time interval between the pollution time periods of each inaccurate monitoring point and the slope of the pollution fitting straight line of each inaccurate monitoring point; and obtain the correction concentration data of each item of pollution gas of each pollution time point of each inaccurate monitoring point based on the pollution gas diffusion difficulty.

[0068] It should be noted that the inaccurate monitoring point can be located at a bending and narrow place inside the pipeline, which causes the flow of the gas to be limited, and thus the concentration of the contaminated gas to be accumulated and the diffusion rate to be slow, so the smaller the time interval between all pollution periods of the arbitrary inaccurate monitoring point, the slower the diffusion rate of the contaminated gas at the inaccurate monitoring point, and at this time, the contaminated gas at the inaccurate monitoring point is more difficult to diffuse; and the pollution fitting straight line of each inaccurate monitoring point can be obtained by fitting the pollution degree data of all pollution periods of the inaccurate monitoring point through the least square method, and the slope of the pollution fitting straight line reflects the trend of the concentration accumulation of the contaminated gas, and when the concentration of the contaminated gas is accumulated, the slope of the pollution fitting straight line is larger, which means that the contaminated gas at the inaccurate monitoring point is more difficult to diffuse; therefore, the contaminated gas difficult-to-diffuse property of each inaccurate monitoring point is obtained in combination with the above.

[0069] In the embodiment of the application, the specific method for obtaining the contaminated gas difficult-to-diffuse property of each inaccurate monitoring point is as follows:

[0070] a two-dimensional rectangular coordinate system is constructed with the serial numbers of all pollution periods of the vth inaccurate monitoring point as the abscissa and the mean values of the pollution degrees of all pollution time points in the pollution periods as the ordinate; the mean values of the pollution degrees of all pollution time points in all pollution periods of the vth inaccurate monitoring point are input into the two-dimensional rectangular coordinate system to obtain a plurality of pollution period data points of the vth inaccurate monitoring point; and a straight line fitting is performed on all pollution period data points of the vth inaccurate monitoring point by using the least square method to obtain a pollution fitting straight line of the vth inaccurate monitoring point.

[0071] the time interval between the last pollution time point in the rth pollution period of the vth inaccurate monitoring point and the first pollution time point in the r+1th pollution period is recorded as a pollution interval factor of the rth pollution period of the vth inaccurate monitoring point;

[0072] the calculation expression for obtaining the contaminated gas difficult-to-diffuse property of the vth inaccurate monitoring point is as follows:

[0073] ;

[0074] in the formula, difficult-to-diffuse property of the contaminated gas of the vth inaccurate monitoring point; pollution monitoring correlation of the vth inaccurate monitoring point; slope of the pollution fitting straight line of the vth inaccurate monitoring point; pollution interval factor of the rth pollution period of the vth inaccurate monitoring point; number of all pollution periods of the vth inaccurate monitoring point; and norm() represents a linear normalization function. The smaller the value is, the slower the diffusion rate of the pollution gas at the inaccurate monitoring point is, and the greater the pollution gas diffusion difficulty of the inaccurate monitoring point is; The greater the value is, the greater the pollution gas diffusion difficulty of the inaccurate monitoring point is. The smaller the value is, the more different the pollution conditions of the inaccurate monitoring point and the similar monitoring point are, and the greater the pollution gas diffusion difficulty of the inaccurate monitoring point is.

[0075] It should be noted that, according to the pollution gas diffusion difficulty, the concentration of each item of pollution gas at each moment of each inaccurate monitoring point is corrected, and the greater the pollution gas diffusion difficulty is, the smaller the concentration of the pollution gas is.

[0076] In the embodiment of the present application, the specific method for correcting the concentration of each item of pollution gas at each pollution moment of each inaccurate monitoring point according to the pollution gas diffusion difficulty is as follows:

[0077] The calculation expression for obtaining the corrected concentration data of each item of pollution gas at the jth pollution moment of the vth inaccurate monitoring point is as follows:

[0078]

[0079] In the formula, represents the corrected concentration data of the oth item of pollution gas at the jth pollution moment of the vth inaccurate monitoring point; represents the pollution gas diffusion difficulty of the vth inaccurate monitoring point; represents the concentration data of the oth item of pollution gas at the jth pollution moment of the vth inaccurate monitoring point.

[0080] S4: Compress the corrected collected data of each monitoring point.

[0081] It should be noted that, the concentration of each item of pollution gas at each pollution moment of each inaccurate monitoring point is corrected to obtain accurate data, and then the collected data of each monitoring point is compressed.

[0082] In the embodiment of the present application, for any monitoring point, a set composed of the corrected concentration data of all items of pollution gas at all pollution moments of the monitoring point is denoted as the pollution data set of the monitoring point, and a set composed of the concentration data of all items of pollution gas at all other moments of the monitoring point is denoted as the normal data set of the monitoring point; the pollution data set of the monitoring point is losslessly compressed by using the Huffman coding algorithm, and the normal data set of the monitoring point is lossily compressed by using the difference coding algorithm.

[0083] ​It should be noted that the Huffman coding algorithm and the differential coding algorithm are prior art, and in the embodiments of the present application, they are not described in detail.

[0084] The embodiments of the present application also disclose a combustor monitoring data management system, comprising a processor and a memory, wherein the memory stores computer program instructions which, when executed by the processor, implement a combustor monitoring data management method according to the present application.

Claims

1. A method for managing burner monitoring data, characterized in that, Including the following steps: Collect flue gas monitoring data and flow velocity data at each monitoring point at each time point; Based on the proportion of different gases in the flue gas monitoring data, the pollution level of each monitoring point at each time point is obtained; based on the pollution level, the pollution time sequence of each monitoring point is obtained; based on the similarity of flow velocity data between monitoring points, similar monitoring points for each monitoring point are obtained. For each monitoring point, all adjacent pollution times in the pollution time sequence constitute each time period, which is used as the pollution time period for each monitoring point; obtain the pollution monitoring correlation for each monitoring point: , The pollution monitoring correlation of the i-th monitoring point; , These represent the number of all similar monitoring points for the i-th monitoring point and the number of all pollution periods, respectively. This represents the number of all pollution periods at the k-th similar monitoring point for the i-th monitoring point; The maximum value among the number of pollution periods of the i-th monitoring point and its k-th similar monitoring point; This represents the average pollution level at all times during the r-th pollution period at the i-th monitoring point. It is the average pollution level at all pollution moments during the r-th pollution period of the k-th similar monitoring point at the i-th monitoring point; To take the absolute value; exp() is an exponential function with the natural constant as its base; Preset parameters; based on the correlation of pollution monitoring, obtain each inaccurate monitoring point; Based on the changing trend between pollution periods at each inaccurate monitoring point, the dispersion of polluted gas at each inaccurate monitoring point is obtained, including: the time interval between the last pollution moment in the r-th pollution period at the v-th inaccurate monitoring point and the first pollution moment in the (r+1)-th pollution period is denoted as the pollution interval factor for the r-th pollution period; and the pollution fitting line at the v-th inaccurate monitoring point is obtained. , The pollutant gas at the vth inaccurate monitoring point is difficult to disperse. , , , These represent the pollution monitoring correlation of the v-th inaccurate monitoring point, the slope of the pollution fitting line, the number of all pollution periods, and the pollution interval factor of the r-th pollution period, respectively, with norm() being the linear normalization function. Based on the difficulty of polluting gases to diffuse, corrected concentration data of each pollutant gas at each pollution moment are obtained at each inaccurate monitoring point. The corrected collected data at each monitoring point is compressed.

2. The burner monitoring data management method according to claim 1, characterized in that, The acquisition of the pollution level at each monitoring point at each moment includes: ； In the formula, This represents the pollution level at time j of the i-th monitoring point; This represents the sum of the concentrations of all polluting gases at the i-th monitoring point at time j; This represents the sum of the concentrations of all combustion gases at the i-th monitoring point at time j.

3. The burner monitoring data management method according to claim 1, characterized in that, The acquisition of the pollution time sequence for each monitoring point includes: A pollution threshold parameter T1 is preset. If the pollution level of the i-th monitoring point at time j is greater than or equal to the pollution threshold parameter T1, the j-th time is recorded as the pollution time of the i-th monitoring point. The sequence of all pollution times of the i-th monitoring point is used as the pollution time sequence of the i-th monitoring point.

4. The burner monitoring data management method according to claim 1, characterized in that, The process of obtaining similar monitoring points for each monitoring point includes: The sequence of flow velocity data at all times of the i-th monitoring point is denoted as the flow velocity data sequence of the i-th monitoring point; the Pearson correlation coefficient between the flow velocity data sequence of the i-th monitoring point and the flow velocity data sequence of the k-th monitoring point is used as the flow velocity similarity trend factor between the i-th and k-th monitoring points; based on the flow velocity similarity trend factor, the flow velocity similarity between the i-th and k-th monitoring points is obtained; A pollution threshold parameter T2 is preset. If the flow velocity similarity between the i-th monitoring point and the k-th monitoring point is greater than or equal to the pollution threshold parameter T2, the k-th monitoring point is recorded as a similar monitoring point of the i-th monitoring point; all similar monitoring points of the i-th monitoring point are obtained.

5. The burner monitoring data management method according to claim 4, characterized in that, The step of obtaining the flow velocity similarity between the i-th monitoring point and the k-th monitoring point includes: ； In the formula, This indicates the similarity of flow velocity between the i-th monitoring point and the k-th monitoring point; Indicates the number of all moments; This represents the flow velocity data of the i-th monitoring point at time j; This represents the flow velocity data at the k-th monitoring point at time j; This represents the similarity factor in flow velocity between the i-th monitoring point and the k-th monitoring point. The expression represents taking the absolute value; exp() represents an exponential function with the natural constant as the base; norm() represents a linear normalization function.

6. The burner monitoring data management method according to claim 1, characterized in that, The step of obtaining the pollution fitting line for the vth inaccurate monitoring point includes: A two-dimensional rectangular coordinate system is constructed with the sequence number of all pollution time periods of the v-th inaccurate monitoring point as the x-axis and the mean of the pollution degree at all pollution moments within the pollution time period as the y-axis. The mean of the pollution degree at all pollution moments within all pollution time periods of the v-th inaccurate monitoring point is input into the two-dimensional rectangular coordinate system to obtain several pollution time period data points of the v-th inaccurate monitoring point. The least squares method is used to perform linear fitting on all pollution time period data points of the v-th inaccurate monitoring point to obtain the pollution fitting line of the v-th inaccurate monitoring point.

7. The burner monitoring data management method according to claim 1, characterized in that, The acquisition of corrected concentration data for each pollutant gas at each pollution moment for each inaccurate monitoring point includes: ； In the formula, This represents the corrected concentration data of the o-th pollutant gas at the j-th pollution time of the v-th inaccurate monitoring point; This indicates the difficulty in dispersing pollutants at the vth inaccurate monitoring point; This represents the concentration data of the o-th pollutant gas at the j-th pollution time of the v-th inaccurate monitoring point.

8. A burner monitoring data management system, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement a burner monitoring data management method according to any one of claims 1-7.

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