CN115876258A - Livestock and poultry breeding environment abnormity monitoring and alarming system based on multi-source data - Google Patents
Livestock and poultry breeding environment abnormity monitoring and alarming system based on multi-source data Download PDFInfo
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- 244000144972 livestock Species 0.000 title claims abstract description 215
- 244000144977 poultry Species 0.000 title claims abstract description 170
- 238000009395 breeding Methods 0.000 title claims abstract description 84
- 230000001488 breeding effect Effects 0.000 title claims abstract description 84
- 238000012544 monitoring process Methods 0.000 title claims abstract description 46
- 230000002159 abnormal effect Effects 0.000 claims abstract description 67
- 238000005070 sampling Methods 0.000 claims abstract description 43
- 230000008859 change Effects 0.000 claims abstract description 36
- 230000002776 aggregation Effects 0.000 claims abstract description 34
- 238000004220 aggregation Methods 0.000 claims abstract description 34
- 238000001514 detection method Methods 0.000 claims abstract description 33
- 238000012545 processing Methods 0.000 claims abstract description 7
- 239000007789 gas Substances 0.000 claims description 251
- 238000000034 method Methods 0.000 claims description 35
- 230000005856 abnormality Effects 0.000 claims description 28
- 238000003064 k means clustering Methods 0.000 claims description 10
- 238000003708 edge detection Methods 0.000 claims description 7
- 238000010606 normalization Methods 0.000 claims description 6
- 230000011218 segmentation Effects 0.000 claims description 6
- 230000007613 environmental effect Effects 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 238000009826 distribution Methods 0.000 abstract description 8
- 230000008569 process Effects 0.000 description 9
- 238000013528 artificial neural network Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 3
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 description 2
- MWUXSHHQAYIFBG-UHFFFAOYSA-N Nitric oxide Chemical compound O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 description 2
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 1
- 229910021529 ammonia Inorganic materials 0.000 description 1
- 229910002091 carbon monoxide Inorganic materials 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
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Abstract
The invention relates to the technical field of data processing, in particular to a livestock and poultry breeding environment abnormity monitoring and alarming system based on multi-source data, which comprises: the data acquisition unit is used for acquiring the temperature of the livestock and poultry breeding environment, the gas content of each type of harmful gas and the gray level image of the livestock and poultry; the temperature detection unit is used for acquiring a temperature abnormal index according to the temperature, acquiring an aggregation value according to the distribution of the livestock and poultry in the gray level image, and acquiring a temperature judgment index according to the aggregation value and the temperature abnormal index; the harmful gas content detection unit is used for taking the harmful gas with the gas content exceeding the gas standard exceeding threshold as target gas and obtaining an exceeding index from the target gas; acquiring a gas change index according to the gas content difference at adjacent sampling moments; acquiring a gas abnormity index according to the exceeding index and the gas change index; and the abnormity monitoring and alarming unit is used for carrying out abnormity monitoring and alarming on the livestock and poultry breeding environment by combining the temperature judgment index and the gas abnormity index, so that the monitoring is more accurate.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a livestock and poultry breeding environment abnormity monitoring and alarming system based on multi-source data.
Background
The importance of the livestock and poultry breeding environment to livestock and poultry breeding is proved by a great deal of scientific research and production practice at home and abroad, particularly the closed livestock and poultry breeding environment has limited illumination, large fluctuation of temperature, humidity and the like, and harmful gas is not easy to be emitted, which all have great influence on the growth and reproduction of livestock and poultry. The environmental quality in the livestock and poultry breeding environment has been raised as a primary problem affecting the health state and production performance of livestock and poultry.
In the prior art, various livestock and poultry breeding environment index parameters such as air temperature, humidity and ammonia concentration are automatically monitored in real time through sensors, acquired monitoring data are compared with normal threshold values, when the monitoring data of the livestock and poultry breeding environment are abnormal, an alarm signal is sent to a mobile intelligent terminal, however, the monitoring data obtained by each type of sensor are independent, namely, the monitoring data corresponding to different environment index parameters are not linked, the alarm signal is generated only according to the abnormality of a certain type of monitoring data, the condition that the alarm signal is deviated can exist, and misleading is generated for workers.
Disclosure of Invention
In order to solve the technical problem that alarm signals are deviated due to inaccurate monitoring data, the invention aims to provide a livestock and poultry breeding environment abnormity monitoring and alarm system based on multi-source data, and the adopted technical scheme is as follows:
the embodiment of the invention provides a livestock and poultry breeding environment abnormity monitoring and alarming system based on multi-source data, which comprises the following components:
the data acquisition unit is used for acquiring the temperature of the livestock and poultry breeding environment, the gas content of each type of harmful gas and the gray level image of the livestock and poultry at the current sampling moment;
the temperature detection unit is used for acquiring a temperature abnormity index according to the temperature; inputting the gray level image into a semantic segmentation network to obtain a livestock head image, carrying out edge detection on the livestock head image to obtain an edge line, obtaining the number of livestock according to the contour information of the edge line, dividing the gray level image into at least two regions, and counting the number of livestock contained in each region to obtain an aggregation value; acquiring a temperature judgment index according to the aggregation value and the temperature abnormity index;
the harmful gas content detection unit is used for acquiring harmful gas with the gas content exceeding the gas standard exceeding threshold of the corresponding harmful gas as target gas, and acquiring an exceeding index according to the difference value between the gas content of the target gas and the gas standard exceeding threshold of the corresponding harmful gas; obtaining a gas change index according to the gas content difference of the same type of harmful gas at the current sampling moment and the adjacent sampling moment; acquiring a gas abnormity index according to the exceeding index and the gas change index;
and the abnormity monitoring and alarming unit is used for carrying out abnormity monitoring and alarming on the livestock and poultry breeding environment by combining the temperature judgment index and the gas abnormity index.
Further, the method for obtaining the number of the livestock and poultry according to the contour information of the edge line in the temperature detection unit comprises the following steps:
taking closed edge lines corresponding to the heads of the livestock and poultry as target edge lines, dividing the center of mass of the target edge lines into two parts, dividing each target edge line into a left edge line and a right edge line, obtaining contour similarities of the left edge line, the right edge line and other edge lines, clustering the left edge line, the right edge line and other edge lines based on the contour similarities to obtain cluster clusters, calculating a contour similarity mean value of each cluster, taking the two cluster clusters corresponding to the minimum difference between the two contour similarity mean values as target cluster, obtaining the number of edge lines in each target cluster, and taking the number of edge lines in any one target cluster as the number of livestock and poultry when the number is equal; and when the number is not equal, taking the number of the edge lines in the largest target cluster as the number of the livestock and poultry.
Further, the method for acquiring the closed edge line corresponding to the head of the livestock and poultry in the temperature detection unit comprises the following steps:
clustering the closed edge lines according to the lengths of the closed edge lines in the edge lines by a K-means clustering algorithm to obtain cluster clusters, calculating the length mean value of the closed edge lines in the cluster clusters, obtaining the cluster corresponding to the maximum closed edge line length mean value as a livestock head cluster, and taking the closed edge lines in the livestock head cluster as the closed edge lines corresponding to the livestock heads.
Further, the method for counting the number of the livestock and poultry contained in each area in the temperature detection unit to obtain the aggregation value comprises the following steps:
acquiring the number of the areas, taking the ratio of the number of the livestock and the poultry to the number of the areas as the average number of the livestock and the poultry in each area, and taking the result obtained by summing the absolute difference value between the number of the livestock and the poultry in each area and the average number of the livestock and the poultry as an aggregation value.
Further, the method for acquiring the temperature judgment index according to the aggregation value and the temperature abnormality index in the temperature detection unit includes:
when the temperature abnormity index is larger than or equal to a preset temperature abnormity threshold value, taking the normalization value of the temperature abnormity index as a temperature judgment index; and when the temperature abnormality index is smaller than the preset temperature abnormality threshold value, acquiring the ratio of the aggregation value to the absolute value of the temperature abnormality index, and performing negative correlation mapping on the ratio and normalizing the obtained result to serve as a temperature judgment index.
Further, the method for acquiring the excess indexes in the harmful gas content detection unit comprises the following steps:
and acquiring the quantity of the target gas, and taking the result of the sum obtained by adding the difference values corresponding to each type of target gas and the quantity of the target gas as an exceeding index.
Further, the method for obtaining the index of the change of the gas in the harmful gas content detection unit includes:
based on the gas content of the same type of harmful gas at the current sampling moment and each sampling moment in the preset time period before the current sampling moment, calculating the absolute value of the difference value of the gas content at every two adjacent sampling moments as a variation value, taking the addition result of all the variation values as the second result of the same type of harmful gas, obtaining the second result corresponding to each type of harmful gas, and taking the sum of all the second results as a gas variation index.
Further, the method for acquiring the abnormal index of the gas in the harmful gas content detection unit comprises the following steps:
and calculating the product of the exceeding index and the gas change index as a gas abnormity index.
Further, the method for carrying out abnormity monitoring and alarming on the livestock and poultry breeding environment by combining the temperature judgment index and the gas abnormity index in the abnormity monitoring and alarming unit comprises the following steps:
setting a temperature abnormal threshold, and directly alarming when the temperature judgment index is greater than the temperature abnormal threshold; when the temperature judgment index is smaller than or equal to the temperature abnormity threshold, carrying out normalization processing on the gas abnormity index to obtain a normalized gas abnormity index, setting a harmful gas content abnormity threshold, and carrying out abnormity alarm if the normalized gas abnormity index is larger than the harmful gas content abnormity threshold; and if the normalized gas abnormal index is less than or equal to the abnormal threshold of the content of the harmful gas, determining that the livestock and poultry breeding environment is not abnormal.
Further, the method for acquiring the temperature anomaly index according to the temperature in the temperature detection unit comprises the following steps:
and setting a standard temperature in the livestock and poultry breeding environment, and taking the difference value between the temperature and the standard temperature as an abnormal temperature index.
The invention has the following beneficial effects: the temperature of the livestock and poultry breeding environment, the gas content of each type of harmful gas and the gray level image of the livestock and poultry at the current sampling moment are obtained, so that the data in the livestock and poultry breeding environment can be obtained in time and the distribution condition of the livestock and poultry can be visually observed; acquiring the temperature abnormality index temperature according to the temperature so as to preliminarily predict whether the temperature in the livestock and poultry breeding environment is abnormal and be beneficial to judging whether the temperature is abnormal or not; inputting the gray level image into a semantic segmentation network to obtain a livestock head image, carrying out edge detection on the livestock head image to obtain edge lines, and obtaining the livestock quantity according to the contour information of the edge lines, so that the livestock quantity is more accurate and closer to the actual livestock quantity; dividing the gray level image into at least two areas, counting the number of livestock and poultry contained in each area to obtain an aggregation value, intuitively feeling the distribution condition of the livestock and poultry, indirectly verifying whether the temperature of the livestock and poultry breeding environment is abnormal at the current sampling moment through the distribution condition of the livestock and poultry, further acquiring a temperature judgment index according to the aggregation value and the temperature abnormal index, and performing abnormal detection on the temperature in the livestock and poultry breeding environment through the temperature judgment index, so that the detection on whether the temperature is abnormal can be more accurate, and the influence caused by the abnormality of a temperature sensor is reduced; harmful gas with the gas content exceeding the gas standard exceeding threshold of the corresponding harmful gas is obtained as target gas, the target gas which has visual influence on whether the harmful gas in the livestock and poultry breeding environment is abnormal or not is directly obtained, and the efficiency of abnormal detection of the harmful gas in the livestock and poultry breeding environment is improved; acquiring an exceeding index according to the difference value between the gas content of the target gas and the gas standard exceeding threshold value of the corresponding harmful gas, and performing preliminary analysis on whether the harmful gas abnormality exists in the livestock and poultry breeding environment according to the exceeding index; obtaining a gas change index according to the gas content difference of the same type of harmful gas at the current sampling moment and the adjacent sampling moment, judging whether the situation of the sudden change of the harmful gas occurs or not according to the gas change index, and further analyzing whether the situation of the abnormal harmful gas exists or not; acquiring a gas abnormal index according to the exceeding index and the gas change index, and improving the accuracy of abnormal detection on harmful gas in the livestock and poultry breeding environment through the gas abnormal index; the temperature judgment index and the gas abnormity index are combined to carry out abnormity monitoring and alarming on the livestock and poultry breeding environment, so that the abnormity monitoring and alarming accuracy of the livestock and poultry breeding environment is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the embodiments or the description of 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 other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a block diagram of a system for monitoring and alarming abnormality of a livestock and poultry breeding environment based on multi-source data according to an embodiment of the present invention.
Detailed Description
In order to further illustrate the technical means and effects of the present invention adopted to achieve the predetermined invention purpose, the following detailed description, with reference to the accompanying drawings and preferred embodiments, describes specific implementation, structure, features and effects of a livestock and poultry breeding environment anomaly monitoring and warning system based on multi-source data according to the present invention. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The specific scheme of the livestock and poultry breeding environment abnormity monitoring and alarming system based on multi-source data is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a block diagram of a system for monitoring and alarming abnormality of livestock and poultry breeding environment based on multi-source data according to an embodiment of the present invention is shown, the system includes: the device comprises a data acquisition unit, a temperature detection unit, a harmful gas content detection unit and an abnormity monitoring and alarming unit.
And the data acquisition unit 10 is used for acquiring the temperature of the livestock and poultry breeding environment, the gas content of each type of harmful gas and the gray level image of the livestock and poultry at the current sampling moment.
Specifically, the existing equipment, such as a temperature sensor, a humidity sensor and various harmful gas sensors, are used for acquiring various data in the livestock and poultry breeding environment, wherein the acquired specific data are as follows: collecting harmful gases such as time, temperature, humidity, carbon monoxide, nitric oxide, sulfur dioxide and the like; shooting a livestock and poultry breeding environment by using an industrial camera to obtain livestock and poultry images; the data and the livestock and poultry images are continuously acquired once every hour.
The livestock and poultry images are subjected to graying processing through a weighted average value method to obtain grayscale images, and the grayscale images are subjected to denoising through a Gaussian filtering algorithm, so that the grayscale images are clearer, and the noise points are prevented from interfering the grayscale images. The weighted average method and the gaussian filtering algorithm are well known techniques, and are not described in detail herein.
A temperature detection unit 20 for acquiring a temperature abnormality index according to the temperature; inputting the gray level image into a semantic segmentation network to obtain a livestock head image, carrying out edge detection on the livestock head image to obtain edge lines, obtaining the number of the livestock according to the contour information of the edge lines, dividing the gray level image into at least two areas, and counting the number of the livestock contained in each area to obtain an aggregation value; and acquiring a temperature judgment index according to the aggregation value and the temperature abnormity index.
Particularly, the temperature is an important environmental index in the livestock and poultry breeding process, especially for the early growth stage of some livestock and poultry, the self-regulation capability of some young livestock and poultry is not perfect, and the survival rate of the young livestock and poultry can be influenced by slight change of the temperature. Therefore, the embodiment of the invention firstly judges whether the temperature of the livestock and poultry breeding environment is abnormal or not, and simultaneously combines the aggregation degree of the livestock and poultry, namely when the temperature is too low, the livestock and poultry can be relatively piled and aggregated together for heating, so that the temperature of the livestock and poultry breeding environment can be judged to be abnormal or not by combining the aggregation condition of the livestock and poultry.
The embodiment of the invention detects whether the temperature in the livestock and poultry breeding environment is abnormal or not through the temperature judgment index, and the method for acquiring the temperature judgment index comprises the following steps:
(1) And acquiring a temperature abnormity index through the temperature.
And setting a standard temperature in the livestock and poultry breeding environment, and taking the difference value between the temperature and the standard temperature as an abnormal temperature index. The specific process of acquiring the temperature anomaly index is as follows:
acquiring the temperature of a temperature sensor at the current sampling moment, setting standard temperature in a livestock and poultry breeding environment according to factors such as the types, sizes and the like of livestock and poultry under actual conditions, taking the difference value of the temperature and the standard temperature as a temperature abnormity index, and acquiring the temperature abnormity index a according to the formula:
wherein,
is the temperature; />
Is the standard temperature; a is a temperature abnormality index.
It should be noted that when
The larger a is, the higher the temperature in the livestock and poultry breeding environment is; when/is>
The smaller a is, the lower the temperature in the livestock and poultry breeding environment is; when/is>
The closer is the>
In the process, the closer to 0, the more normal the temperature in the livestock and poultry breeding environment is.
(2) And calculating an aggregation value by acquiring the number of the livestock and poultry.
Inputting the gray level image into a semantic segmentation network to obtain a livestock head image, and carrying out edge detection on the livestock head image to obtain an edge line. Clustering the closed edge lines according to the lengths of the closed edge lines in the edge lines by a K-means clustering algorithm to obtain cluster clusters, calculating the length mean value of the closed edge lines in the cluster clusters, obtaining the cluster corresponding to the maximum closed edge line length mean value as a livestock head cluster, and taking the closed edge lines in the livestock head cluster as the closed edge lines corresponding to the livestock heads.
Taking closed edge lines corresponding to the heads of the livestock and poultry as target edge lines, dividing the center of mass of the target edge lines into two parts, dividing each target edge line into a left edge line and a right edge line, obtaining contour similarities of the left edge line, the right edge line and other edge lines, clustering the left edge line, the right edge line and other edge lines based on the contour similarities to obtain cluster clusters, calculating a contour similarity mean value of each cluster, taking the two cluster clusters corresponding to the minimum difference between the two contour similarity mean values as target cluster, obtaining the number of edge lines in each target cluster, and taking the number of edge lines in any one target cluster as the number of livestock and poultry when the number is equal; and when the number is not equal, taking the number of the edge lines in the largest target cluster as the number of the livestock and poultry. The specific process for acquiring the number of the livestock and the poultry is as follows:
and performing semantic segmentation on the gray level image by using a Resnet neural network, wherein the input of the Resnet neural network is the gray level image, and the output of the Resnet neural network is the livestock head image.
The label printing mode of Resnet neural network training is as follows: marking the area of the livestock and poultry head image as 1 and other areas as 0 in the Resnet neural network training process; the Resnet neural network loss function is a cross-entropy loss function.
The Resnet neural network is a well-known technology, and will not be described in detail herein.
The method comprises the steps of obtaining edge lines of images of the heads of the livestock and poultry by using a canny edge detection algorithm, clustering closed edge lines in the edge lines, clustering the closed edge lines by using a K-means clustering algorithm based on the length of the closed edge lines, and setting a K value in the K-means clustering algorithm to be 2, so that two clustering clusters can be obtained, calculating the length mean value of the closed edge lines of the two clustering clusters, obtaining a clustering cluster corresponding to the maximum closed edge line length mean value as a livestock and poultry head clustering cluster, and taking the closed edge line in the livestock and poultry head clustering cluster as the closed edge line corresponding to the heads of the livestock and poultry.
The canny edge detection algorithm and the K-means clustering algorithm are well known technologies, and are not described in detail herein.
The method comprises the steps of taking closed edge lines corresponding to the heads of livestock and poultry as target edge lines, dividing the centers of mass of the target edge lines into two parts, dividing each target edge line into a left edge line and a right edge line, using a shape context algorithm to obtain contour similarity of the left edge line, the right edge line and other edge lines, and using a K-means clustering algorithm to cluster the edge lines according to the contour similarity, wherein the K value in the K-means clustering algorithm is determined to be 3, so that 3 clustering clusters can be obtained through the K-means clustering algorithm, and the three clustering clusters respectively correspond to the left edge line clustering cluster, the right edge line clustering cluster and other edge line clustering clusters; respectively obtaining contour similarity mean values in the three cluster clusters, subtracting every two contour similarity mean values to obtain a difference value, and taking the two cluster clusters corresponding to the minimum difference value as target cluster clusters, wherein the two cluster clusters corresponding to the target cluster clusters are respectively a left edge line cluster and a right edge line cluster.
In the livestock head image, the condition of livestock head shielding exists, in order to enable the obtained quantity of livestock to be more accurate according to the livestock head, the embodiment of the invention analyzes the position of the edge line in the target cluster in the livestock head image, when the target edge line does not belong to the same target cluster, namely belongs to a left edge line cluster and a right edge line cluster respectively, the corresponding livestock head is not shielded, and the corresponding target edge line represents one head of livestock, namely one livestock; when the target edge lines belong to the same target cluster, the left side and the right side of the target edge lines belong to the same target cluster only if the corresponding livestock heads are shielded, and the corresponding target edge lines represent two heads of livestock, namely two livestock, so that the number of the livestock in the images of the livestock heads can be obtained according to the number of the edge lines in the target cluster; when the number of the edge lines in the target cluster is equal, the condition that the livestock and poultry head is shielded does not exist in the livestock and poultry head image, and the number of the edge lines in any one target cluster can be used as the number of the livestock and poultry; when the number of the edge lines in the target cluster is not equal, the condition that the livestock and poultry head is shielded exists in the livestock and poultry head image is shown, and at the moment, the number of the edge lines in the maximum target cluster is used as the number of the livestock and poultry.
The shape context algorithm and the K-means clustering algorithm are well known technologies, and are not described in detail here.
Acquiring the number of the areas, taking the ratio of the number of the livestock and the poultry to the number of the areas as the average number of the livestock and the poultry in each area, and taking the result obtained by summing the absolute difference value between the number of the livestock and the poultry in each area and the average number of the livestock and the poultry as an aggregation value. The specific operations for obtaining the aggregation value are as follows:
dividing the gray level image into n regions, wherein n is a positive integer, taking the ratio of the number of the livestock and the poultry to the number n of the regions as the average number of the livestock and the poultry in each region in the gray level image, obtaining the number of the livestock and the poultry in each region, taking the result obtained by summing the absolute value of the difference between the number of the livestock and the poultry in each region and the average number of the livestock and the poultry as an aggregation value, and obtaining the aggregation value
The formula of (1) is as follows: />
Wherein n is the number of regions;
the number of the livestock and poultry contained in the ith area; />
The average number of livestock and poultry; />
Is an aggregate value; />
As a function of absolute values.
It should be noted that, in the following description,
the smaller the sum is, the closer the number of the livestock and poultry contained in the ith area is to the average number of the livestock and poultry, the more uniform the distribution of the livestock and poultry in the ith area is, and the aggregation value is greater or less>
The smaller; />
The larger the description isThe larger the difference between the number of the livestock and the average number of the livestock and poultry contained in the i areas is, the more uneven the distribution of the livestock and poultry in the i area is, and the accumulation value is greater or less than>
The larger; thus, the aggregate value->
The larger the size, the more uneven the distribution of the livestock in the gray scale image, and the more abnormal the temperature in the livestock breeding environment.
(3) And acquiring a temperature judgment index according to the aggregation value and the temperature abnormity index.
When the temperature abnormity index is larger than or equal to a preset temperature abnormity threshold value, taking the normalization value of the temperature abnormity index as a temperature judgment index; and when the temperature abnormality index is smaller than the preset temperature abnormality threshold, acquiring the ratio of the aggregation value to the absolute value of the temperature abnormality index, and performing negative correlation mapping on the ratio and normalizing the obtained result to serve as a temperature judgment index. The specific operation of obtaining the temperature judgment index is as follows:
the embodiment of the invention sets the preset temperature abnormal threshold value as 0, when the temperature abnormal index is more than or equal to the preset temperature abnormal threshold value, the condition that the temperature in the livestock and poultry breeding environment is overhigh is shown, and at the moment, the temperature judgment index can be directly obtained according to the temperature abnormal index: substituting the opposite number of the temperature abnormity index into an exponential function with a natural constant e as a base number to obtain a result as a first result, and taking the difference value between the constant 1 and the first result as a temperature judgment index; when the temperature anomaly index is less than the preset temperature anomaly threshold, the temperature in the livestock and poultry breeding environment is shown to have a too low condition, only the influence of the temperature in the monitoring livestock and poultry breeding environment in which the temperature cannot be accurately analyzed is analyzed at the moment, because the aggregation degree of the livestock and poultry is influenced by the temperature, when the temperature is lower, the aggregation value is larger, and the temperature judgment index is obtained through the aggregation value and the temperature anomaly index together: and acquiring a ratio of the aggregation value to an absolute value of the temperature abnormality index, and substituting the opposite number of the ratio into an exponential function with a natural constant e as a base number to obtain a result serving as the temperature judgment index. ObtainingTemperature judgment index
The formula of (1) is as follows:
wherein,
is an index of temperature anomaly; />
Is an aggregate value; />
Is a temperature judgment index; />
As a function of absolute value; />
Is an exponential function with a natural constant e as a base.
In addition, when the temperature is abnormal, the index
When the temperature is greater than or equal to 0, the temperature abnormality index->
The greater the temperature, the higher the temperature, the greater the temperature decision criterion->
The greater the temperature abnormality criterion->
And the temperature judgment index>
Is in positive correlation; when the temperature is abnormal, the mark is changed>
Less than 0, the accumulation value->
Is subjected to a temperature abnormality indicator>
When the temperature abnormality index>
The smaller the temperature is, the lower the temperature is, the more the livestock and poultry gather, and the gathering value is->
The larger; accordingly, is present>
The smaller the temperature judgment criterion is>
The greater, the greater>
The greater the temperature judgment index->
The smaller the>
And a temperature judgment index>
Is a negative correlation relationship.
The harmful gas content detection unit 30 is configured to obtain a harmful gas with a gas content exceeding a gas standard exceeding threshold of a corresponding type of harmful gas as a target gas, and obtain an exceeding index according to a difference between the gas content of the target gas and the gas standard exceeding threshold of the corresponding type of harmful gas; obtaining a gas change index according to the gas content difference of the same type of harmful gas at the current sampling moment and the adjacent sampling moment; and acquiring a gas abnormity index according to the exceeding index and the gas change index.
Specifically, certain harmful gas can be produced in the process of breeding livestock and poultry, and when the gas content of the harmful gas exceeds a set gas standard-exceeding threshold value, the harmful gas can cause harm to the growth of the livestock and poultry, so that whether the harmful gas in the livestock and poultry breeding environment is abnormal or not needs to be detected in time.
The embodiment of the invention detects whether harmful gas in the livestock and poultry breeding environment is abnormal or not through the gas abnormal index, and the method for acquiring the gas abnormal index comprises the following steps:
(1) And acquiring the exceeding index.
And subtracting the gas content of each type of harmful gas from the gas standard exceeding threshold of the corresponding type of harmful gas to obtain a difference value, and taking the harmful gas corresponding to the difference value larger than 0 as the target gas.
And acquiring the quantity of the target gases, and taking the result of multiplying the sum obtained by adding the difference values corresponding to each type of target gases and the quantity of the target gases as an exceeding index. Obtaining an excess indicator
The formula of (1) is:
wherein,
is the amount of the target gas; />
Is the gas content of the ith target gas; />
A gas standard exceeding threshold corresponding to the ith target gas; />
Is out of specification.
It should be noted that, in the following description,
the greater, the greater>
The greater, the greater the exceeding of the criterion->
The larger the gas content is, the more the gas content of the target gas in the livestock and poultry breeding environment is, the more the harmful gas in the livestock and poultry breeding environment is abnormal; />
The smaller the>
The smaller, the more the index is exceeded>
The smaller the gas content is, the closer the gas content of the target gas in the livestock and poultry breeding environment is to the gas standard exceeding threshold value, the more normal the harmful gas in the livestock and poultry breeding environment is; accordingly, are combined>
The larger the size, the more abnormal the harmful gas in the livestock and poultry raising environment.
(2) And acquiring a gas change index.
Based on the gas content of the same type of harmful gas at the current sampling moment and each sampling moment in the preset time period before the current sampling moment, calculating the absolute value of the difference value of the gas content at every two adjacent sampling moments as a variation value, taking the addition result of all the variation values as the second result of the same type of harmful gas, obtaining the second result corresponding to each type of harmful gas, and taking the sum of all the second results as a gas variation index. The specific process for obtaining the gas change index is as follows:
in the embodiment of the invention, the gas content of each type of harmful gas at each sampling moment in the previous 24 hours of the current sampling moment is obtained, the gas contents of the same type of harmful gas at the adjacent sampling moments are subtracted to obtain a difference absolute value as a change value, all the change values of the type of harmful gas in 24 hours are obtained, and all the change values of the type of harmful gas are obtainedAdding to obtain a second result corresponding to the harmful gas; to a first order
Taking harmful gas as an example, the fifth or fifth judgment is obtained>
The gas content at each sampling moment will be ^ based on the harmful-like gas in the first 24 hours up to the current sampling moment>
The gas content and the ^ th ^ taken at each sampling moment>
The absolute value of the difference between the gas contents acquired at a respective sampling instant is taken as the ^ th ^ or ^ th ^>
The change value of the harmful gas is acquired>
All changes of the noxious-like gas over a 24-hour period are summed up and taken as the ^ th->
A second result of the noxious-like gases; upon a first determination>
The method for obtaining the second result of each harmful gas type comprises the steps of obtaining the second result corresponding to each harmful gas type, and taking the sum of all the second results as a gas change index which is used for judging whether the harmful gas type is harmful or not>
The formula of (1) is as follows:
wherein,
the types and the quantity of harmful gases in the livestock and poultry breeding environment are shown; />
Is a first->
Harmful-like gases are/is/are treated in the first 24 hours up to the current sampling moment>
Gas content obtained at each sampling time; />
Is the first->
Harmful-like gases in the preceding 24 hours up to the current sampling moment>
The gas content obtained at each sampling time; />
Is an index of gas change; />
As a function of absolute value.
It should be noted that, when the value is changed
The larger the result is, the greater the ^ th ^ is>
Harmful gas is at the fifth place>
The gas content and the ^ th ^ taken at each sampling moment>
The greater the change between the gas contents acquired at a respective sampling instant, the corresponding second result->
The greater, the greater>
The larger; when the change value->
The smaller the result, the greater the ^ th>
Harmful gas is at the fifth place>
The gas content and the ^ th ^ taken at each sampling moment>
The smaller the change between the gas contents acquired at a respective sampling instant, the corresponding second result->
The smaller the>
The smaller; accordingly, the change value->
And the gas change indicator->
Is in a positive correlation.
(3) And acquiring a gas abnormity index according to the exceeding index and the gas change index.
And calculating the product of the exceeding index and the gas change index as a gas abnormity index. Obtaining gas anomaly indicators
The formula of (1) is:
wherein,
is an out-of-specification index; />
Is an index of gas abnormality; />
Is an index of gas abnormality.
It should be noted that, in the following description,
the larger the air content is, the more the target gas content in the livestock and poultry raising environment is, the more the gas in the livestock and poultry raising environment is abnormal, and the more the abnormality is existed in the gas in the livestock and poultry raising environment>
The larger; />
The larger the change is, the larger the change of the gas content of the harmful gas in the livestock and poultry raising environment is, the more possible abnormality is indirectly shown in the harmful gas in the livestock and poultry raising environment, and the judgment result shows that the harmful gas in the livestock and poultry raising environment is abnormal>
The larger; accordingly, is present>
The larger the size, the more abnormal the harmful gas in the livestock and poultry breeding environment.
And the abnormity monitoring and alarming unit 40 is used for carrying out abnormity monitoring and alarming on the livestock and poultry breeding environment by combining the temperature judgment index and the gas abnormity index.
Setting a temperature abnormal threshold, and directly alarming when the temperature judgment index is greater than the temperature abnormal threshold; when the temperature judgment index is smaller than or equal to the temperature abnormity threshold, carrying out normalization processing on the gas abnormity index to obtain a normalized gas abnormity index, setting a harmful gas content abnormity threshold, and carrying out abnormity alarm if the normalized gas abnormity index is larger than the harmful gas content abnormity threshold; and if the normalized gas abnormal index is less than or equal to the abnormal threshold of the content of the harmful gas, determining that the livestock and poultry breeding environment is not abnormal.
In the embodiment of the invention, the temperature abnormity threshold value is set to be 0.7, when the temperature judgment index is greater than the temperature abnormity threshold value, the temperature abnormity in the livestock and poultry breeding environment at the current moment is shown, abnormity alarm can be directly carried out, and workers can adjust the temperature in the livestock and poultry breeding environment in time; when the temperature judgment index is smaller than or equal to the abnormal temperature threshold value, the temperature in the livestock and poultry breeding environment is normal at the present moment, and in order to prevent abnormal detection errors, further analysis is carried out on harmful gas in the livestock and poultry breeding environment.
The obtained gas abnormal index is normalized to obtain a normalized gas abnormal index, the embodiment of the invention sets the abnormal threshold value of the content of the harmful gas to be 0.7, and when the normalized gas abnormal index is larger than the abnormal threshold value of the content of the harmful gas, the abnormal index indicates that the harmful gas in the livestock and poultry breeding environment is abnormal at the present moment, so that the abnormal alarm can be directly carried out, and the staff timely processes the gas content of the harmful gas in the livestock and poultry breeding environment; when the normalized gas abnormal index is less than or equal to the harmful gas content abnormal threshold, the abnormality does not exist in the livestock and poultry breeding environment at the present moment.
Thus, the embodiment of the present invention is completed.
To sum up, the embodiments of the present invention include: the data acquisition unit is used for acquiring the temperature of the livestock and poultry breeding environment, the gas content of each type of harmful gas and the gray level image of the livestock and poultry; the temperature detection unit is used for acquiring a temperature abnormal index according to the temperature, acquiring an aggregation value according to the distribution of the livestock and poultry in the gray level image, and acquiring a temperature judgment index according to the aggregation value and the temperature abnormal index; the harmful gas content detection unit is used for taking the harmful gas with the gas content exceeding the gas standard exceeding threshold as target gas and obtaining an exceeding index from the target gas; acquiring a gas change index according to the gas content difference at adjacent sampling moments; acquiring a gas abnormity index according to the exceeding index and the gas change index; and the abnormity monitoring and alarming unit is used for carrying out abnormity monitoring and alarming on the livestock and poultry breeding environment by combining the temperature judgment index and the gas abnormity index, so that the monitoring is more accurate.
It should be noted that: the sequence of the above embodiments of the present invention is only for description, and does not represent the advantages or disadvantages of the embodiments. The processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
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 are within the spirit of the present invention are intended to be included therein.
Claims (10)
1. Beasts and birds breed environmental anomaly monitoring and alarm system based on multisource data, its characterized in that, this system includes the following step:
the data acquisition unit is used for acquiring the temperature of the livestock and poultry breeding environment at the current sampling moment, the gas content of each type of harmful gas and the gray level image of the livestock and poultry;
the temperature detection unit is used for acquiring a temperature abnormity index according to the temperature; inputting the gray level image into a semantic segmentation network to obtain a livestock head image, carrying out edge detection on the livestock head image to obtain an edge line, obtaining the number of livestock according to the contour information of the edge line, dividing the gray level image into at least two regions, and counting the number of livestock contained in each region to obtain an aggregation value; acquiring a temperature judgment index according to the aggregation value and the temperature abnormity index;
the harmful gas content detection unit is used for acquiring harmful gas with the gas content exceeding the gas standard exceeding threshold of the corresponding harmful gas as target gas, and acquiring an exceeding index according to the difference value between the gas content of the target gas and the gas standard exceeding threshold of the corresponding harmful gas; obtaining a gas change index according to the gas content difference of the same type of harmful gas at the current sampling moment and the adjacent sampling moment; acquiring a gas abnormity index according to the exceeding index and the gas change index;
and the abnormity monitoring and alarming unit is used for carrying out abnormity monitoring and alarming on the livestock and poultry breeding environment by combining the temperature judgment index and the gas abnormity index.
2. The multi-source data-based livestock and poultry breeding environment abnormity monitoring and alarming system according to claim 1, wherein the method for obtaining the number of livestock and poultry in the temperature detection unit according to the contour information of the edge line comprises the following steps:
taking closed edge lines corresponding to the heads of the livestock and poultry as target edge lines, dividing the mass center of the target edge lines into two parts, dividing each target edge line into a left edge line and a right edge line, obtaining contour similarities of the left edge line, the right edge line and other edge lines, clustering the left edge line, the right edge line and other edge lines based on the contour similarities to obtain cluster clusters, calculating a contour similarity mean value of each cluster, taking two cluster corresponding to the minimum difference between the two contour similarity mean values as the target cluster, obtaining the number of the edge lines in each target cluster, and taking the number of the edge lines in any one target cluster as the number of the livestock and poultry when the number is equal; and when the number is not equal, taking the number of the edge lines in the largest target cluster as the number of the livestock and poultry.
3. The multi-source data-based livestock and poultry breeding environment abnormity monitoring and alarming system according to claim 2, wherein the method for acquiring the closed margin line corresponding to the head of the livestock and poultry in the temperature detection unit comprises the following steps:
clustering the closed edge lines according to the lengths of the closed edge lines in the edge lines by a K-means clustering algorithm to obtain cluster clusters, calculating the length mean value of the closed edge lines in the cluster clusters, obtaining the cluster corresponding to the maximum closed edge line length mean value as a livestock head cluster, and taking the closed edge lines in the livestock head cluster as the closed edge lines corresponding to the livestock heads.
4. The multi-source data-based livestock and poultry breeding environment abnormity monitoring and alarming system according to claim 1, wherein the method for counting the number of livestock and poultry contained in each area in the temperature detection unit to obtain an aggregation value comprises the following steps:
acquiring the number of the areas, taking the ratio of the number of the livestock and the poultry to the number of the areas as the average number of the livestock and the poultry in each area, and taking the result obtained by summing the absolute difference value between the number of the livestock and the poultry in each area and the average number of the livestock and the poultry as an aggregation value.
5. The multi-source data-based livestock and poultry breeding environment abnormity monitoring and alarming system of claim 1, wherein the method for obtaining the temperature judgment index according to the aggregation value and the temperature abnormity index in the temperature detection unit comprises the following steps:
when the temperature abnormity index is larger than or equal to a preset temperature abnormity threshold value, taking the normalization value of the temperature abnormity index as a temperature judgment index; and when the temperature abnormality index is smaller than the preset temperature abnormality threshold value, acquiring the ratio of the aggregation value to the absolute value of the temperature abnormality index, and performing negative correlation mapping on the ratio and normalizing the obtained result to serve as a temperature judgment index.
6. The multi-source data-based livestock and poultry breeding environment abnormity monitoring and alarming system according to claim 1, wherein the method for acquiring the excess indexes in the harmful gas content detection unit comprises the following steps:
and acquiring the quantity of the target gases, and taking the result of multiplying the sum obtained by adding the difference values corresponding to each type of target gases and the quantity of the target gases as an exceeding index.
7. The multi-source data-based livestock and poultry breeding environment abnormity monitoring and alarming system according to claim 1, wherein the method for acquiring the gas change index in the harmful gas content detection unit comprises the following steps:
based on the gas content of the same type of harmful gas at the current sampling moment and each sampling moment in the preset period before the current sampling moment, calculating the absolute value of the difference value of the gas content at every two adjacent sampling moments as a variation value, taking the addition result of all the variation values as the second result of the same type of harmful gas, obtaining the second result corresponding to each type of harmful gas, and taking the sum of all the second results as a gas variation index.
8. The multi-source data-based livestock and poultry breeding environment abnormity monitoring and alarming system according to claim 1, wherein the method for acquiring the gas abnormity index in the harmful gas content detection unit comprises the following steps:
and calculating the product of the exceeding index and the gas change index as a gas abnormity index.
9. The multi-source data-based livestock and poultry breeding environment abnormity monitoring and alarming system according to claim 1, wherein a method for carrying out abnormity monitoring and alarming on the livestock and poultry breeding environment by combining a temperature judgment index and a gas abnormity index in the abnormity monitoring and alarming unit comprises the following steps:
setting a temperature abnormal threshold, and directly alarming when the temperature judgment index is greater than the temperature abnormal threshold; when the temperature judgment index is smaller than or equal to the temperature abnormity threshold, carrying out normalization processing on the gas abnormity index to obtain a normalized gas abnormity index, setting a harmful gas content abnormity threshold, and carrying out abnormity alarm if the normalized gas abnormity index is larger than the harmful gas content abnormity threshold; and if the normalized gas abnormal index is less than or equal to the abnormal threshold of the content of the harmful gas, determining that the livestock and poultry breeding environment is not abnormal.
10. The multi-source data-based livestock and poultry breeding environment abnormity monitoring and alarming system according to claim 1, wherein a method for acquiring a temperature abnormity index according to temperature in the temperature detection unit comprises the following steps:
and setting a standard temperature in the livestock and poultry breeding environment, and taking the difference value between the temperature and the standard temperature as an abnormal temperature index.
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