CN115267094A - Exhaust emission monitoring and detecting method - Google Patents
Exhaust emission monitoring and detecting method Download PDFInfo
- Publication number
- CN115267094A CN115267094A CN202211194718.XA CN202211194718A CN115267094A CN 115267094 A CN115267094 A CN 115267094A CN 202211194718 A CN202211194718 A CN 202211194718A CN 115267094 A CN115267094 A CN 115267094A
- Authority
- CN
- China
- Prior art keywords
- data
- sampling period
- exhaust gas
- value
- concentration
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 40
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000005070 sampling Methods 0.000 claims abstract description 117
- 230000002159 abnormal effect Effects 0.000 claims abstract description 61
- 230000005856 abnormality Effects 0.000 claims description 18
- 150000001875 compounds Chemical class 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 6
- 238000013144 data compression Methods 0.000 abstract description 5
- 239000007789 gas Substances 0.000 description 52
- 238000004364 calculation method Methods 0.000 description 5
- 239000002912 waste gas Substances 0.000 description 5
- 230000003044 adaptive effect Effects 0.000 description 4
- 238000001514 detection method Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 230000006835 compression Effects 0.000 description 2
- 239000002440 industrial waste Substances 0.000 description 2
- 230000035772 mutation Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0062—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method or the display, e.g. intermittent measurement or digital display
-
- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
- H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
- H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
- H03M7/40—Conversion to or from variable length codes, e.g. Shannon-Fano code, Huffman code, Morse code
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Combustion & Propulsion (AREA)
- Food Science & Technology (AREA)
- Medicinal Chemistry (AREA)
- Theoretical Computer Science (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Sampling And Sample Adjustment (AREA)
Abstract
The invention relates to the field of data compression, in particular to a method for monitoring and detecting exhaust emission, which comprises the following steps: acquiring concentration data of exhaust gas indexes in a sampling period; obtaining the accumulated value of each data by using the difference value of each data and the adjacent data; obtaining the continuity of each data by utilizing the proportion of the accumulated value of each data to the quantity of all data in the sampling period; obtaining the abnormal degree of each data by using the allowable emission concentration standard of the exhaust gas index, each data in the sampling period and the standard deviation of all data in the sampling period; obtaining attention weight values of the data by using the continuity and the abnormal degree of the data; carrying out self-adaptive Huffman coding on the concentration data of the exhaust gas index according to the attention weight value of each data; and comparing the encoded exhaust gas index concentration data with the allowable emission concentration standard of the exhaust gas index, and monitoring the concentration data of the exhaust gas index. The method is used for monitoring and detecting the exhaust emission, and can improve the accuracy of monitoring and detecting.
Description
Technical Field
The invention relates to the field of data compression, in particular to an exhaust emission monitoring and detecting method.
Background
Along with the development of society, the discharge amount of industrial waste gas is gradually increased year by year. The emission of industrial waste gas not only affects the environment, but also seriously harms the health of people. Therefore, monitoring and detecting of exhaust emissions is very necessary.
The existing exhaust emission monitoring and detecting method comprises the following steps: the collected waste gas data is compressed and transmitted by using a traditional Hoffman coding mode, and then the compressed and transmitted data is compared with the national waste gas concentration emission standard, so that the monitoring and detection of waste gas emission are realized.
However, the existing exhaust emission monitoring and detecting method is to compress the exhaust data by using the traditional huffman coding method. The traditional Huffman coding mode is that weight setting is carried out according to the probability of data occurrence, so that important data are easily lost, the compressed and transmitted data are inaccurate, and the accuracy of exhaust emission monitoring and detection is reduced.
Disclosure of Invention
The invention provides an exhaust emission monitoring and detecting method, which aims to solve the problem of low accuracy of the existing exhaust emission monitoring and detecting method.
In order to achieve the purpose, the invention adopts the following technical scheme that the exhaust emission monitoring and detecting method comprises the following steps:
acquiring concentration data of exhaust gas indexes in a sampling period;
obtaining an accumulated value of each data in a sampling period by using a difference value of each data and adjacent data in the sampling period;
calculating the continuity of each data in the sampling period by utilizing the proportion of the accumulated value of each data in the sampling period to the quantity of all data in the sampling period;
calculating to obtain the abnormal degree of each data in the sampling period by using the standard of the allowable emission concentration of the exhaust gas index, each data in the sampling period and the standard deviation of all data in the sampling period;
calculating to obtain an attention weight value of each data in a sampling period by using the continuity and the abnormal degree of each data in the sampling period;
carrying out self-adaptive Huffman coding on the concentration data of the exhaust gas index in the sampling period according to the attention weight value of each datum in the sampling period to obtain the coded exhaust gas index concentration data;
and transmitting the encoded exhaust gas index concentration data to a monitor to be compared with the allowable emission concentration standard of the exhaust gas index, and monitoring the concentration data of the exhaust gas index in a sampling period.
According to the exhaust emission monitoring and detecting method, the accumulated value of each datum in the sampling period is obtained as follows:
optionally selecting one data from the concentration data of the exhaust gas index in the sampling period as a first data, and calculating the absolute value of the difference value between the first data and the adjacent data;
setting a threshold value according to an allowable emission concentration standard of an exhaust gas index;
judging the absolute value of the difference value between the first data and the adjacent data by using a threshold value: when the absolute value of the difference value between the first data and the adjacent data is less than or equal to a threshold value, dividing the adjacent data and the first data into a set to obtain a first set;
taking adjacent data of the first data in the first set as second data, and calculating an absolute value of a difference value between the second data and the adjacent data;
judging the absolute value of the difference value between the second data and the adjacent data: when the absolute value of the difference value between the second data and the adjacent data is smaller than or equal to a threshold value, dividing the adjacent data into a first set to obtain an updated first set;
continuously iterating and updating the first set according to the mode until the absolute value of the difference value between each data and the adjacent data in the updated first set is larger than the threshold value, stopping iteration, and taking the iteratively updated first set as a second set;
counting the number of the data in the second set, and taking the number of the data as an accumulated value of the first data;
and obtaining the accumulated value of each datum in the sampling period according to the mode of obtaining the accumulated value of the first datum.
According to the exhaust emission monitoring and detecting method, the abnormal degree of each datum in the sampling period is obtained as follows:
calculating to obtain the standard deviation of all data in the sampling period by utilizing each data in the sampling period, the quantity of all data in the sampling period and the allowable emission concentration standard of the exhaust gas index;
and calculating the abnormal degree of each data in the sampling period by using the allowable emission concentration standard of the exhaust gas index, each data in the sampling period and the standard deviation of all data in the sampling period.
In the exhaust emission monitoring and detecting method, the expression of the abnormal degree of each datum in the sampling period is as follows:
in the formula (I), the compound is shown in the specification,
denotes the first
The degree of abnormality of the individual data,
is shown as
The number of the data is set to be,
the allowable emission concentration standard that indicates the index of exhaust gas,
representing the standard deviation of all data during the sampling period,
representing a hyperbolic tangent function.
According to the exhaust emission monitoring and detecting method, the attention weight value of each datum in the sampling period is obtained as follows:
acquiring the maximum value and the minimum value of the continuity of all data in a sampling period;
acquiring the maximum value and the minimum value of the abnormal degree of all data in a sampling period;
and calculating the attention weight value of each data in the sampling period by using the continuity and the abnormal degree of each data in the sampling period, the product of the maximum continuity and the maximum abnormal degree of all data in the sampling period and the product of the minimum continuity and the minimum abnormal degree of all data in the sampling period.
In the exhaust emission monitoring and detecting method, the expression of the attention weighted value of each datum in the sampling period is as follows:
in the formula (I), the compound is shown in the specification,
indicating the second within the sampling period
The attention weight value of the individual data,
indicating the first in the sampling period
The degree of continuity of the individual data,
indicating the first in the sampling period
The degree of abnormality of the individual data,
represents the product of the maximum value of the continuity and the maximum value of the degree of abnormality of all the data in the sampling period,
and represents the product of the minimum value of the continuity and the minimum value of the degree of abnormality of all data in the sampling period.
In the exhaust emission monitoring and detecting method, the process of monitoring the concentration data of the exhaust gas index in the sampling period is specifically as follows:
comparing the encoded exhaust gas index concentration data with an allowable emission concentration standard of the exhaust gas index: when the encoded exhaust gas index concentration data is greater than the allowable emission concentration standard of the exhaust gas index, the exhaust gas index concentration data is abnormal; when the encoded exhaust gas index concentration data is less than or equal to the allowable emission concentration standard of the exhaust gas index, the exhaust gas index concentration data is normal;
and (3) judging abnormal exhaust gas index concentration data: when abnormal exhaust gas index concentration data continuously appear and the data are not changed, judging that the data sensor is damaged; when abnormal exhaust gas index concentration data sporadically appears, judging that the data sensor is unstable; and when the abnormal exhaust gas index concentration data continuously appear and show a gradually changing trend, judging that the exhaust gas index concentration exceeds the standard.
The invention has the beneficial effects that: according to the characteristics of the exhaust gas data, the attention weight value of each data is calculated. And performing self-adaptive Huffman coding on the exhaust gas data by using the attention weight of each datum. Compared with the traditional Huffman coding mode, the self-adaptive Huffman coding method is used for carrying out self-adaptive Huffman coding according to the importance degree of the waste gas data, so that the loss of the important data can be prevented, the compressed and transmitted data is more complete and accurate, and the accuracy of waste gas emission monitoring and detection is effectively improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of an exhaust emission monitoring and detecting method according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: according to the characteristics of the acquired sensor data and the national exhaust emission standard, the continuity and the abnormal degree of each data are obtained. And evaluating the attention degree of the data by using the continuity and the abnormal degree of each data. And then, self-adaptive Huffman coding is carried out according to the attention of each data, and the loss of important data is prevented in the data compression coding process. And comparing the compressed and transmitted data with the national exhaust gas concentration discharge standard to realize the monitoring and detection of the exhaust gas discharge.
The distributed sensor has huge data volume and is influenced by network bandwidth, so that important data is easily lost in the process of data compression and transmission. Therefore, the invention provides an exhaust emission monitoring and detecting method, which aims at a self-adaptive data compression method of exhaust emission data, so that the data after compression and transmission is more complete and accurate, and the accuracy of exhaust emission monitoring and detecting is effectively improved.
An embodiment of an exhaust emission monitoring and detecting method of the present invention, as shown in fig. 1, includes:
s101, acquiring concentration data of the exhaust gas index in a sampling period.
Firstly, a plurality of sensors are arranged at an exhaust port of the equipment and are used for acquiring concentration data of exhaust gas indexes in a sampling period. Wherein the quantity of all data in the acquisition period is
。
S102, obtaining an accumulated value of each data in the sampling period by using a difference value between each data and adjacent data in the sampling period.
It should be noted that: in order to realize the adaptive Huffman coding, namely, the weight value in the Huffman coding is distributed to each data according to the data characteristics, the continuity degree and the abnormal degree of the data are firstly calculated for evaluating the importance degree of the data.
According to the characteristics of the sensor data and the national exhaust gas concentration emission standard, the continuity and the abnormal degree of each data are calculated, and a foundation is provided for carrying out self-adaptive Huffman coding on each data in the next step.
Due to the change of the operating environment of the sensor, the unstable performance or the fault of the sensor and the like, abnormal data can be generated in the collected data, and the abnormal data not only comprises important data, such as the abnormal data caused by the increase of the concentration of the sulfur dioxide in the exhaust gas discharged by the equipment, but also comprises other data, such as the abnormal data caused by the unstable or damaged sensor. Because the importance degree of abnormal data caused by different reasons is different, the importance degree of the abnormal data needs to be evaluated before the data adaptive compression is carried out.
Important data, which tend to be continuous, of long duration, and vary widely from the mean of all data. Unlike outlier data, important data is gradually changing. Other data, there are two cases, one is discrete data, and the data is only sporadic and belongs to the unstable generation of the sensor; the other is continuous data, but the continuous data has no difference, namely, the data does not change along with the change of time, and the data belongs to the sensor damage.
Therefore, the present embodiment combines the continuity of the sensor data, the degree of abnormality, and the equipment condition data to identify the important data among the normal data, the abnormal data, and the other data. And calculating the attention weight value of each data in the current sampling period according to the continuity and the abnormal degree.
The continuity of data refers to the proportion of time within a sampling period during which a case where the difference in data value is small between adjacent data lasts. The process of acquiring the continuity of each data in the sampling period is specifically as follows:
optionally the first step
The data being the first data, i.e. the second data
Data at each time is
。
Setting data difference value threshold
Allowable emission concentration according to an exhaust index in a national exhaust emission standard
To this end, the proposed value of this embodiment is
。
If it is first
Data and adjacent second
Difference value between individual data
Then will be
Data and the second
The data is divided into a set to obtain a first set.
Will be first
The data is used as the second data if
Data and adjacent second
Difference value between individual data
Then will be
And dividing the data into a first set to obtain an updated first set.
And repeating the iteration and updating the first set until the absolute value of the difference value between each data and the adjacent data in the updated first set is larger than the data difference value threshold, stopping the iteration, and taking the first set after the iteration and the updating as a second set.
And counting the number of the data in the second set, and taking the number of the data as the accumulated value of the first data. Thus, the first one is obtained
And the accumulated value of the data is used for representing the continuous occurrence times of the data meeting the threshold value condition.
And S103, calculating the continuity of each data in the sampling period by utilizing the proportion of the accumulated value of each data in the sampling period to the quantity of all data in the sampling period.
Then it is first
The computational expression of the continuity of each datum is:
in the formula (I), the compound is shown in the specification,
is shown as
The degree of continuity of the individual data,
is shown as
The cumulative value of the individual data is,
representing the amount of all data in the acquisition cycle. Wherein
Is used for characterizing
Continuity of individual data, i.e. characterisation of
Whether the data is continuous or not is also used to reflect the second
Whether the data is mutation data. The greater the continuity, the more data is indicated
The greater the continuity of (c), the smaller the probability of being mutation data.
And S104, calculating to obtain the abnormal degree of each data in the sampling period by using the allowable emission concentration standard of the exhaust gas index, each data in the sampling period and the standard deviation of all data in the sampling period.
The degree of abnormality of the data refers to the difference between the data and the national exhaust emission standard. The priori knowledge can know that the data collected by the sensor generally conform to Gaussian distribution or approximate Gaussian distribution, and the statistical significance is achieved. According to a Gaussian distribution
The rule can be known that abnormal data in the data are distributed in
In addition to the above, wherein,
is the average of all the data and is,
the standard deviation of all data. The embodiment therefore allows the emission concentration standard by introducing the exhaust gas index
And calculating the abnormal degree of the sensor data. According to
The calculation expression of the standard deviation of all data in a sampling period is as follows:
in the formula (I), the compound is shown in the specification,
is shown as
The number of the data is set to be,
the allowable emission concentration standard representing the index of exhaust gas,
indicating the amount of all data in the acquisition cycle,
representing the standard deviation of all data over the sampling period. SignThe calculation formula of the tolerance is common knowledge, and here, the standard deviation of all data in the sampling period is calculated to obtain the distribution range of normal data, and further obtain the distribution range of abnormal data.
Then it is first
The calculation expression of the degree of abnormality of each data is:
in the formula (I), the compound is shown in the specification,
denotes the first
The degree of abnormality of the individual data,
is shown as
The number of the data is set to be,
the allowable emission concentration standard that indicates the index of exhaust gas,
represents the standard deviation of all data over the sampling period,
representing a hyperbolic tangent function. First, the
The abnormal degree of each data represents the difference between the data and the national exhaust emission standard, if the data is greater than the national exhaust emission standard, the data is abnormal data, and the larger the data is, the more abnormal the data is; if less thanAnd the data is normal data according to the national exhaust emission standard. According to
Rule, abnormal data in sensor data are distributed in
Otherwise, it indicates if
Data and
the larger the difference is, the first
The greater the degree of abnormality of the individual data. If it is first
Data is less than
If the data is within the national exhaust emission standard, the degree of abnormality of the data is 0.0001. Only influences larger than the upper limit are considered in this embodiment.
It should be noted that: according to
Rule that abnormal data in the data should be distributed
Besides, when the distribution range of the normal data is calculated, since the present embodiment is for monitoring and detecting the exhaust emission, and the national exhaust emission standard is the allowable exhaust emission set by the country, the average value of the normal data in the present embodiment should be based on the national standard. It is clearly not appropriate to use the mean of the collected data to calculate the distribution range. For example, if the average value of the collected data is large, the national exhaust emission is already exceededAnd standard, when abnormal data is calculated, the distribution range of the wrong abnormal data can be obtained.
And S105, calculating to obtain the attention weight value of each data in the sampling period by using the continuity and the abnormal degree of each data in the sampling period.
It should be noted that: and calculating attention weight values according to the continuity and the abnormal degree of each datum, wherein the attention weight values are in a direct proportion relation with the product of the continuity and the abnormal degree. The attention degree weight value of the important data is set to be maximum, the attention degree weight value of the normal data is set to be minimum, and the attention degree weight values of other data are set to be second-maximum.
Through the steps, the continuity and the abnormal degree of each data in the sampling period are obtained. In order to implement adaptive huffman coding on data in a sampling period, it is necessary to calculate a weight value of attention of each data in the sampling period, and perform adaptive huffman coding according to the weight value of attention of each data.
First, an attention weight value needs to be assigned to each data in a sampling period, and the attention weight value of the data is related to the importance degree of the data. Distributing the maximum attention weight value to the important data, wherein the continuity and the abnormal degree of the important data are large; distributing a minimum attention weight value to normal data, wherein the continuity of the normal data is high, and the abnormal degree is close to 0; and a smaller attention weight value is distributed to other data, the continuity of the other data is smaller, and the abnormal degree is larger. Through the analysis, the attention weight value distributed to each data is in direct proportion to the product of the continuity and the abnormal degree of the data. Thus for the first
Attention weighting values for data distribution
The calculation expression of (a) is:
in the formula (I), the compound is shown in the specification,
indicating the first in the sampling period
The attention weight value of each data set,
indicating the first in the sampling period
The degree of continuity of the individual data,
indicating the second within the sampling period
The degree of abnormality of the individual data,
represents the product of the maximum value of the continuity and the maximum value of the degree of abnormality of all the data in the sampling period,
representing the product of the minimum value of continuity and the minimum value of anomaly for all data during the sampling period. The attention weight value is used for representing the second
The higher the attention weighted value is, the shorter the code length in the corresponding Huffman coding algorithm is, the less the data is lost. For important data with large continuity and large abnormal degree, the embodiment considers that the data are the most important and cannot be lost, the calculated attention weight value is large, and the shorter the code length is, the less easy the data are lost; for normality with large continuity but with degree of abnormality close to 0Data, which is considered to be unimportant by the embodiment and can be properly lost, the attention weight value obtained by calculation is small, and the longer the code length is, the proper loss can be realized; for other data with small continuity but large abnormal degree, the embodiment considers that the data is the second most important and cannot be lost, and the calculated attention weight value is large, the code length is short and is not easy to be lost.
Thus, the attention weight value of each data in the sampling period is obtained.
And S106, carrying out self-adaptive Huffman coding on the concentration data of the exhaust gas index in the sampling period according to the attention weight value of each data in the sampling period to obtain the coded exhaust gas index concentration data.
And performing self-adaptive Huffman coding according to the attention weight value of each datum. The self-adaptive Huffman coding process comprises the following steps:
replacing the probability weight value of the traditional Huffman coding with the attention weight value to carry out Huffman coding, and carrying out addition operation on the two minimum attention weight values to obtain a new weight value;
carrying out the next minimum value addition operation on the new weight values until the new weight values reach the root node, thereby establishing a Huffman coding tree;
coding is carried out according to all nodes of the Huffman coding tree, and a left node and a right node of each layer of the coding tree are respectively represented by '0' and '1'.
And obtaining the encoded exhaust gas index concentration data.
And S107, transmitting the encoded exhaust gas index concentration data to a monitoring device to be compared with the allowable emission concentration standard of the exhaust gas index, and monitoring the concentration data of the exhaust gas index in a sampling period.
And obtaining the encoded data, and comparing the data with the allowable emission concentration standard of the exhaust gas index so as to monitor the concentration data of the exhaust gas index in the sampling period.
If the coded data is larger than the allowable emission concentration standard of the exhaust gas index and shows a gradually changing trend, carrying out system early warning to indicate that the exhaust gas index concentration exceeds the standard; if the coded data is larger than the allowable emission concentration standard of the exhaust gas index but sporadically appears, performing system early warning to indicate that the sensor has an unstable condition;
if the data after the code is larger than the allowable emission concentration standard of the exhaust gas index, and the data is not changed within a certain time, system early warning is carried out, and the condition that the sensor is damaged exists is indicated.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. An exhaust emission monitoring and detecting method, comprising:
acquiring concentration data of exhaust gas indexes in a sampling period;
obtaining an accumulated value of each data in a sampling period by using a difference value of each data and adjacent data in the sampling period;
calculating the continuity of each data in the sampling period by utilizing the proportion of the accumulated value of each data in the sampling period to the quantity of all data in the sampling period;
calculating the abnormal degree of each data in the sampling period by using the allowable emission concentration standard of the exhaust gas index, each data in the sampling period and the standard deviation of all data in the sampling period;
calculating to obtain an attention weight value of each data in a sampling period by using the continuity and the abnormal degree of each data in the sampling period;
carrying out self-adaptive Huffman coding on the concentration data of the exhaust gas index in the sampling period according to the attention weight value of each data in the sampling period to obtain the coded exhaust gas index concentration data;
and transmitting the encoded exhaust gas index concentration data to a monitor to be compared with the allowable emission concentration standard of the exhaust gas index, and monitoring the concentration data of the exhaust gas index in the sampling period.
2. The exhaust emission monitoring and detecting method according to claim 1, wherein the integrated value of each data in the sampling period is obtained as follows:
optionally selecting one data from the concentration data of the exhaust gas index in the sampling period as a first data, and calculating the absolute value of the difference value between the first data and the adjacent data;
setting a threshold value according to an allowable emission concentration standard of an exhaust gas index;
judging the absolute value of the difference value between the first data and the adjacent data by using a threshold value: when the absolute value of the difference value between the first data and the adjacent data is less than or equal to a threshold value, dividing the adjacent data and the first data into a set to obtain a first set;
taking adjacent data of the first data in the first set as second data, and calculating an absolute value of a difference value between the second data and the adjacent data;
judging the absolute value of the difference value between the second data and the adjacent data: when the absolute value of the difference value between the second data and the adjacent data is less than or equal to a threshold value, dividing the adjacent data into a first set to obtain an updated first set;
continuously iterating and updating the first set according to the mode until the absolute value of the difference value between each datum in the updated first set and the adjacent datum is larger than the threshold value, stopping iteration, and taking the iteratively updated first set as a second set;
counting the number of the data in the second set, and taking the number of the data as an accumulated value of the first data;
and obtaining the accumulated value of each datum in the sampling period according to the mode of obtaining the accumulated value of the first datum.
3. The exhaust emission monitoring and detecting method according to claim 1, wherein the degree of abnormality of each data in the sampling period is obtained as follows:
calculating to obtain the standard deviation of all data in the sampling period by utilizing each data in the sampling period, the quantity of all data in the sampling period and the allowable emission concentration standard of the exhaust gas index;
and calculating the abnormal degree of each data in the sampling period by using the allowable emission concentration standard of the exhaust gas index, each data in the sampling period and the standard deviation of all data in the sampling period.
4. The exhaust emission monitoring and detecting method according to claim 3, wherein the expression of the degree of abnormality of each data in the sampling period is as follows:
in the formula (I), the compound is shown in the specification,
is shown as
The degree of abnormality of the individual data,
is shown as
The number of the data is one,
the allowable emission concentration standard that indicates the index of exhaust gas,
representing the standard deviation of all data during the sampling period,
representing a hyperbolic tangent function.
5. The exhaust emission monitoring and detecting method according to claim 1, wherein the attention weight value of each data in the sampling period is obtained as follows:
acquiring the maximum value and the minimum value of the continuity of all data in a sampling period;
acquiring the maximum value and the minimum value of the abnormal degree of all data in a sampling period;
and calculating to obtain the attention weight value of each data in the sampling period by using the continuity and the abnormal degree of each data in the sampling period, the product of the maximum continuity and the maximum abnormal degree of all data in the sampling period, and the product of the minimum continuity and the minimum abnormal degree of all data in the sampling period.
6. The exhaust emission monitoring and detecting method according to claim 5, wherein the expression of the attention weight value of each data in the sampling period is as follows:
in the formula (I), the compound is shown in the specification,
indicating the second within the sampling period
The attention weight value of each data set,
indicating the first in the sampling period
The degree of continuity of the individual data,
indicating the first in the sampling period
The degree of abnormality of the individual data,
represents the product of the maximum value of the continuity and the maximum value of the degree of abnormality of all the data in the sampling period,
representing the product of the minimum value of continuity and the minimum value of anomaly for all data during the sampling period.
7. The exhaust emission monitoring and detecting method according to claim 1, wherein the process of monitoring the concentration data of the exhaust gas index in the sampling period is as follows:
comparing the encoded exhaust gas index concentration data with an allowable emission concentration standard of the exhaust gas index: when the encoded exhaust gas index concentration data is greater than the allowable emission concentration standard of the exhaust gas index, the exhaust gas index concentration data is abnormal; when the encoded exhaust gas index concentration data is less than or equal to the allowable emission concentration standard of the exhaust gas index, the exhaust gas index concentration data is normal;
and (3) judging abnormal exhaust gas index concentration data: when abnormal exhaust gas index concentration data continuously appear and the data are not changed, judging that the data sensor is damaged; when abnormal exhaust gas index concentration data sporadically appears, judging that the data sensor is unstable; and when the abnormal exhaust gas index concentration data continuously appear and show a gradually changing trend, judging that the exhaust gas index concentration exceeds the standard.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211194718.XA CN115267094A (en) | 2022-09-29 | 2022-09-29 | Exhaust emission monitoring and detecting method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211194718.XA CN115267094A (en) | 2022-09-29 | 2022-09-29 | Exhaust emission monitoring and detecting method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115267094A true CN115267094A (en) | 2022-11-01 |
Family
ID=83757118
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211194718.XA Pending CN115267094A (en) | 2022-09-29 | 2022-09-29 | Exhaust emission monitoring and detecting method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115267094A (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109636613A (en) * | 2018-10-19 | 2019-04-16 | 平安医疗健康管理股份有限公司 | Medical data abnormality recognition method, device, terminal and storage medium |
| CN115063427A (en) * | 2022-08-18 | 2022-09-16 | 中海油天津化工研究设计院有限公司 | Pollutant discharge monitoring image processing method for novel ship |
-
2022
- 2022-09-29 CN CN202211194718.XA patent/CN115267094A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109636613A (en) * | 2018-10-19 | 2019-04-16 | 平安医疗健康管理股份有限公司 | Medical data abnormality recognition method, device, terminal and storage medium |
| CN115063427A (en) * | 2022-08-18 | 2022-09-16 | 中海油天津化工研究设计院有限公司 | Pollutant discharge monitoring image processing method for novel ship |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117093879B (en) | Intelligent operation management method and system for data center | |
| CN107092654B (en) | Based on Change in Mean detection alarm normally with abnormal deviation data examination method and device | |
| CN117235557B (en) | Electrical equipment fault rapid diagnosis method based on big data analysis | |
| CN111669123B (en) | Method and device for fault diagnosis of photovoltaic string | |
| CN102856903A (en) | Micro-grid probability load flow calculation method | |
| CN117375630B (en) | Monitoring data compression method for live working early warning system | |
| CN115801901A (en) | Compression processing method for enterprise production emission data | |
| CN113723716B (en) | Passenger flow classification early warning abnormity warning method, device and storage medium | |
| CN115689393A (en) | Real-time dynamic monitoring system and method for power system based on Internet of things | |
| CN113687176B (en) | Deep neural network-based power consumption abnormity detection method and system | |
| CN114252103B (en) | Fusion power station operation fault prediction method | |
| CN117148045A (en) | Fault studying and judging management system for running state of power distribution network | |
| CN116933734B (en) | Intelligent diagnosis method for cutter faults of shield machine | |
| CN115267094A (en) | Exhaust emission monitoring and detecting method | |
| CN105404709A (en) | Complex network based sensitive monitoring point analysis method for dam health monitoring | |
| CN112612824A (en) | Water supply pipe network abnormal data detection method based on big data | |
| CN115078892B (en) | State remote monitoring system for single-machine large-transmission frequency converter | |
| CN113128482B (en) | Multi-factor-influenced power transmission line abnormal discharge risk grade evaluation method | |
| CN115173865A (en) | Battery data compression processing method for energy storage power station and electronic equipment | |
| CN114064760A (en) | Multi-dimensional early warning analysis and judgment method for data | |
| CN116545591B (en) | BMS battery management system-based data transmission method | |
| CN110929849A (en) | Neural network model compression method and device | |
| CN105391049B (en) | A kind of electrical network parameter method of estimation considering probability distribution | |
| CN117370917B (en) | Urban intelligent street lamp service life prediction method and system | |
| CN117692260B (en) | Network abnormal behavior identification method based on data analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20230608 Address after: Room 801, Building 9, Zone 5, Linhe Street, Economic and Technological Development Zone, Changchun City, Jilin Province, 130000 Applicant after: Changchun Ruijiu Technology Co.,Ltd. Address before: No. 1-3, Hantong Road, Wujie Town, Tongzhou District, Nantong City, Jiangsu Province, 226000 Applicant before: Nantong Meilun Electromechanical Technology Co.,Ltd. |
|
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20221101 |