CN116913057A - Livestock-raising abnormal early warning system based on thing networking - Google Patents
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Abstract
The invention relates to the field of data processing, in particular to an abnormal livestock breeding early warning system based on the Internet of things, which comprises the following steps: collecting temperature time sequence data; constructing a Huffman tree, acquiring coded data, acquiring coding redundancy according to the code element length and the code element conversion times of the coded data, and acquiring a first ordering result according to the coding redundancy; acquiring all the adjacent data in the equal-profile data, obtaining the priority of the equal-profile data according to the coding length and the frequency of the adjacent data of each equal-profile data, and obtaining a third ordering result according to the priority of the equal-profile data and the frequency of the data; obtaining a new coding result according to the first sorting result and the third sorting result; compressing the new coding result to obtain a compressed result, and carrying out cluster analysis difference on the compressed data to carry out early warning. The invention uses a data processing mode to carry out recombination coding on the data so as to improve the compression efficiency of temperature time sequence data.
Description
Technical Field
The invention relates to the field of data processing, in particular to an abnormal livestock breeding early warning system based on the Internet of things.
Background
Livestock breeding is an important component of agriculture, and has important significance for guaranteeing the safety of human grains and promoting the modernization of agriculture. However, there are many problems in the livestock breeding industry, such as serious consequences of illness, death, etc. of animals caused by environmental factors such as temperature, humidity, etc. Therefore, it is necessary to monitor the animal state through the internet of things. By analyzing historical data of the animal state, abnormal data in the animal state is found, and early warning is carried out on the user according to the abnormal data.
When the historical data is analyzed, the larger the historical data quantity is, the more accurate the abnormal data analysis result is. Thus, there is a need for lossless compression of historical data to facilitate analysis of anomalous data, and the more and better the historical data compression.
In the huffman coding in the prior art, the processing means for characters with the same coding length are arranged in sequence from left to right according to the magnitude of the occurrence frequency, and if compressed data is wanted to be recompressed by using the run Cheng Bian code, the compression effect is not good. The original coding rule is destroyed, characters with the same coding length are recoded, so that the coding redundancy with higher frequency is larger, the data after the data are subjected to Huffman coding is better, and the effect of recompression is better by the running coding again.
Disclosure of Invention
The invention provides an abnormal livestock breeding early warning system based on the Internet of things, which aims to solve the existing problems.
The livestock breeding abnormal early warning system based on the Internet of things adopts the following technical scheme:
the embodiment of the invention provides an abnormal livestock breeding early warning system based on the Internet of things, which comprises the following modules:
the data acquisition module acquires temperature time sequence data;
the data coding module is used for constructing a Huffman tree according to the data frequency, marking any layer in the Huffman tree as a target layer, marking temperature time sequence data on the target layer as target data, wherein the target data have the same coding length, obtaining the coding redundancy according to the coding code element length and the code element conversion times of the target data, and obtaining a first sequencing result according to the coding redundancy;
recording the target data with the same frequency as equal-profile data, acquiring the adjacent data of the equal-profile data in the temperature time sequence data, and acquiring the priority of the equal-profile data according to the coding length of the adjacent data of each equal-profile data and the frequency of the adjacent data of the equal-profile data; according to the sequence from big to small of the frequency of the target data and the sequence from big to small of the priority of the equal probability data, sequencing the target data to obtain a third sequencing result; obtaining a new coding result of the target data according to the first sorting result and the third sorting result;
the data compression early warning module compresses the temperature time sequence data according to the new coding result to obtain a compressed result, clusters the compressed data to obtain normal data and abnormal data, obtains the difference between the normal data and the abnormal data according to the normal data and the abnormal data, and carries out early warning according to the difference between the normal data and the abnormal data and a preset threshold value.
Further, the specific method for obtaining the redundancy of the code comprises the following steps:
the formula of the redundancy of the code is:
where R represents redundancy of the code, C represents the number of symbol conversions of the code, and L represents the length of the code.
Further, the specific method for obtaining the first sorting result comprises the following steps:
and sequencing the codes of the target data according to the code redundancy from large to small to obtain a first sequencing result.
Further, the obtaining the ortho-position data of the isocratic data in the temperature time sequence data comprises the following specific operations:
the method comprises the steps of recording any one of the object data and the like as data A, acquiring data adjacent to the data A on the left and the right in temperature time sequence data, and recording adjacent data adjacent to the data A on all the data adjacent to the data A on the left and the right as ortho data because the data A possibly appear many times in the temperature time sequence data.
Further, the specific method for acquiring the priority of the equipotential data comprises the following steps:
the formula of the priority of the equipotential data is:
b represents the priority of the equipotential data; n represents the number of ortho-data of the isochronal data; i represents the ith ortho data of the isostatically data;the coding length of the ith adjacent data representing the isostatically data; />Indicating the frequency of the ith neighbor of the isocratic data.
Further, the specific method for obtaining the third sorting result comprises the following steps:
sorting the second sorting resultReordering the data with the same medium frequency from big to small according to the priority of the equal probability data to obtain +.>Marking as a third sorting result; wherein (1)>Represents the nth target data in the second ranking result, n represents the lengths of the second ranking result and the third ranking result, +.>Representing the nth destination data in the third ranking result.
Further, the specific method for obtaining the second sorting result comprises the following steps:
data is processedOrdering according to the occurrence frequency from big to small to obtain +.>Marking as a second sorting result; wherein (1)>Represents the nth target data in the second ranking result, n represents the lengths of the second ranking result and the third ranking result, +.>Representing the nth destination data in the third ranking result.
Further, the specific method for obtaining the new coding result of the target data according to the first sorting result and the third sorting result comprises the following steps:
the first sorting result is recorded asThe third ranking result is marked +.>Target data->The result of the encoding is->Target data->The result of the encoding is->… …, target data->The result of the encoding is->The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Represents the nth code in the first ordering result, n represents the length of the first ordering result and the third ordering result,/->Representing the nth destination data in the third ranking result.
Further, the specific method for acquiring the difference between the normal data and the abnormal data comprises the following steps:
the formula of the difference between normal data and abnormal data is:
wherein Q represents the difference between normal data and abnormal data;a cluster center representing normal data; />A cluster center representing abnormal data; />Representing the maximum of all data; />Representing the minimum of all data.
Further, the early warning is performed according to the difference between the normal data and the abnormal data and a preset threshold, and the specific steps include:
when the difference between normal data and abnormal dataWhen the difference between the normal data and the abnormal data is larger than a preset threshold T, early warning is carried out, and the difference between the normal data and the abnormal data is +.>And if the preset threshold value T is smaller than or equal to the preset threshold value T, no early warning is carried out.
The technical scheme of the invention has the beneficial effects that: the recoding is carried out on the data with the same coding length in the same layer in the Huffman tree, so that the recoding effect of the codes is better, each data is needed to be recoded under the condition that the Huffman coding compression effect is not changed, the coding redundancy corresponding to the data with higher frequency is larger, the data with the same frequency cannot be well determined in sequence position due to the fact that the same frequency exists among the data, the data is not unique, therefore, the data has a better sequencing result through the priority when the frequency is determined through the relation between the calculation of the coding redundancy and the adjacent data with the same frequency, and finally the recoding is carried out on the data on the Huffman tree, so that the compression efficiency of the data is improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of steps of the livestock breeding abnormality early warning system based on the internet of things.
Detailed Description
In order to further explain the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description is given below of the livestock breeding abnormal early warning system based on the internet of things according to the invention, and the detailed implementation, structure, characteristics and effects thereof are given below. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
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 breeding abnormal early warning system based on the Internet of things provided by the invention is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of steps of an abnormal livestock breeding early warning system based on internet of things according to an embodiment of the present invention is shown, where the system includes the following modules:
module 101: and a data acquisition module.
Firstly, a data acquisition module is required to be arranged to monitor the environmental change of livestock. The data acquisition module comprises a temperature sensor and a humidity sensor. The temperature sensor is used for acquiring data in one day at intervals of ten minutes for monitoring the temperature in the greenhouse to obtain a group of temperature time sequence data; the humidity sensor is used for acquiring data in one day at intervals of ten minutes aiming at the humidity in the greenhouse and monitoring the humidity in the greenhouse to obtain a group of humidity time sequence data.
The temperature time sequence data and the humidity time sequence data are all environment data of the livestock, the temperature time sequence data of the environment of the livestock are taken as an example for analysis, and the acquired temperature data are reserved in one decimal.
So far, the data acquisition is completed.
Module 102: and a data encoding module.
When data is compressed by huffman coding, the frequencies of the data corresponding to the same coding length are not all equal. The conventional huffman coding is to arrange the data from left to right to large according to the frequency, and if the data is to be recompressed in this order, the compression effect is not good. In the embodiment, the recoding is performed on the data with the same coding length, so that the coding redundancy corresponding to the data with larger frequency is larger, and the recompression effect of the recoded data by using the run-length coding is better.
(1) And constructing a Huffman tree according to the data frequency, and acquiring the coding length of each data.
The frequencies of the respective data are different, and the encoding lengths thereof after huffman encoding are also different. The code length with higher data occurrence frequency is shorter; the code length is longer when the frequency of data occurrence is lower. But it may occur during the encoding process that the data encoding length is the same, but the frequency of the data is not equal. These data need to be recoded.
Specifically, firstly, counting each data frequency, constructing a Huffman tree according to the frequency, and obtaining the coding length of each data.
Firstly, counting the frequency of each data in the temperature data; then, constructing a Huffman tree according to the frequency; finally, all data with the same coding length are obtained according to the Huffman tree.
So far, all the data with the same coding length are obtained.
(2) And re-encoding the data with the same encoding length to ensure that the encoding redundancy with higher frequency is higher.
In the huffman tree, the encoding lengths of the same layer are the same. The original coding rule for the same layer is as follows: coding is given by sequencing from left to right and from small to large. Under this coding rule: the higher the frequency of occurrence of the data, the larger the corresponding code value is, and the redundancy of the code is irrelevant. If the re-compression effect of the encoding is desired to be better, the encoding redundancy corresponding to the data with higher frequency is required to be greater by re-encoding each data (i.e. re-encoding at the same layer) without changing the huffman encoding compression effect.
It should be further noted that, in the huffman tree, the higher the frequency of occurrence of the data of the upper layer, the greater the influence of the redundancy thereof on the compression result. Thus, the data is recoded sequentially from the upper layer to the lower layer of the huffman tree.
Specifically, a set of data of the same code length is obtained as，/>Data with the same coding length is represented by an nth number, and n represents the same number of coding lengths; this set of data +.>The codes respectively corresponding to are，/>For data->And corresponding codes, and calculating redundancy of each code.
The redundancy of the code is calculated as follows:
wherein R represents redundancy of codes, and the value range of R is 0, 1; c represents the number of symbol transforms of the code, and L represents the length of the code.
For example, the number of symbol changes of "10010" is 3; l denotes a symbol length, the symbol length of "10010" is 5,=5, (L-1) represents the maximum number of changes of the symbol.
The method for acquiring the code element conversion times comprises the following steps: since only 1 and 0 are used in the coding, the number of positions adjacent to 1 and 0 is counted and is referred to as the number of symbol transitions.
The longer the code length, the fewer the number of symbol changes, and the greater the redundancy of the code. The greater the coding redundancy, the better the choice of coding.
Will encodeOrdering according to the redundancy to obtain +.>And recording as a first sorting result. Data +.>Ordering according to the occurrence frequency from big to small to obtain +.>And recording as a second sorting result. Will->And->One-to-one matching is performed, i.e. data->Matching code->Data->Matching code… …, data->Matching code->。
The above recoding of the data through the redundancy and the frequency ensures that the code corresponding to the data with high frequency has higher redundancy, and when the code corresponding to the data with high frequency has higher redundancy, the higher compression rate can be further ensured when the subsequent run-length coding is performed, thereby ensuring that the storage space of the data is saved.
So far, each data with the same coding length of the same layer is obtained to correspond to a new code.
(3) And analyzing the characteristics of the left and right adjacent data of the data with the same occurrence frequency in the same coding layer, and recoding the data.
It should be noted that, in the previous step, the data frequency is equal in the same coding layer, so that when the ordering is performed according to the probability, the ordering results are multiple, and in this embodiment, a proper ordering result needs to be selected to ensure further compression efficiency. In the whole data sample, since each data is randomly distributed, coding is given to the data which probably appears, so that the redundancy of the data combination coding of the adjacent data is larger as much as possible, and therefore, the proper sequencing result is required to be obtained according to the adjacent data of the data with equal frequency.
It should be noted that when the redundancies are the same, multiple sorting results of the redundancies are obtained, but the influence of different sorting results on the final compression rate is the same, and in this embodiment, only the codes with the same redundancies need to be randomly sorted.
By analyzing the characteristics of the left and right adjacent data of the data with the same occurrence frequency in the same coding layer, the adjacent data coding redundancy is larger, and the data probably appearing in the same coding layer is prioritized.
Specifically, taking an arbitrary coding layer as an example, all data with the same frequency of the coding layer are acquired and are denoted as equipotential data, and any one of the data is denoted as data a.
The data adjacent to the data a on the left and the right are acquired from the temperature time sequence data, and since the data a may appear multiple times in the temperature time sequence data, there are multiple data adjacent to the data a on the left and the right, and in this embodiment, all the data adjacent to the data a on the left and the right are referred to as adjacent data of the data a, and are denoted as ortho data.
Thus, the number of adjacent data and the frequency of adjacent data of each equipotential data are obtained.
It should be noted that, for these data appearing in the same layer, their priorities are the same only in view of the frequency of appearance. Therefore, other factors need to be considered on a frequency basis to prioritize these data that appear probably. The directions considered in this embodiment are: the length of the adjacent data codes of the probably occurring data. Since data needs to be recompressed with run-length encoding, the ideal data for run-length encoding compression is: the data is long and the number of symbol changes of the data is small. In which case a greater compression ratio can be achieved. Therefore, the data with the long adjacent data coding length is preferentially selected, so that the data with the longer coding length is preferentially selected for coding with larger redundancy.
(4) Calculating the sum of the coding lengths of the equal general data and all the adjacent data, and recording the sum as the priority characteristic of the equal general data.
The priority calculation formula of the equipotential data is as follows:
wherein B represents the priority of the equipotential data; n represents the number of ortho data of the summary data; i represents the ith ortho data of the summary data;representing the encoded length of the ith neighbor data of the summary data; />Indicating the frequency of the ith neighbor data of the summary data. />I.e. the sum of all the coded lengths of the ortho data representing the summary data.
The longer the encoding length of the adjacent data is, the higher the frequency is, the larger the sum of the encoding lengths of the adjacent data is. The greater the sum of the encoding lengths of the ortho data, the higher the order priority in which the encoding is selected.
The layer of the uniform data is sequentially selected to be encoded according to the priority order, and the higher the priority is, the higher the redundancy of the selected encoding is.
Specifically, the second sorting result is obtainedReordering the data with the same medium frequency from big to small according to the priority of the equal probability data to obtain +.>The third sorting result is recorded as a third sorting result according to whichAnd first ranking result->And performing one-to-one correspondence to obtain codes corresponding to each data, and obtaining a new coding result.
In the process of selecting codes, codes with the same redundancy are generated, when the codes are compressed, the data with the same redundancy are compressed by using run-length codes, and the compression sizes are also the same. Therefore, the same code can be arbitrarily selected without affecting the compression result.
And according to the result of selecting the codes, on the premise of not changing the shape of the original Huffman tree, changing the codes corresponding to the data, and reconstructing the Huffman tree.
Thus, the reconstructed Huffman tree is obtained.
Module 103: and the data compression early warning module.
And performing primary compression on the original data according to the reconstructed Huffman tree, and then recompressing the compressed data by using run-length coding. Uploading the compressed data to the cloud, and performing differential analysis on the data in the cloud platform. And dividing the data into normal data and abnormal data by using k-means clustering, and obtaining a clustering center and the clustering quantity of the two groups of data by clustering.
Calculating the difference between abnormal data and normal data:
wherein Q represents the difference between normal data and abnormal data;a cluster center representing normal data; />A cluster center representing abnormal data; />Representing the maximum of all data; />Representing the minimum of all data.
Q represents the distance between two clustering centers, and the farther the distance between the two clustering centers is, the larger the difference between the normal data and the abnormal data is, and the higher the early warning degree of the user is.
An early warning threshold T is preset, where the embodiment is described by taking t=0.5 as an example, and the embodiment is not specifically limited, where T may be determined according to the specific implementation situation. Since the difference of the data describes the difference between the temperatures, when Q > T, the early warning is performed, otherwise, the early warning is not performed.
This embodiment is completed.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. Livestock-raising abnormal early warning system based on thing networking, its characterized in that, this system includes following module:
the data acquisition module acquires temperature time sequence data;
the data coding module is used for constructing a Huffman tree according to the data frequency, marking any layer in the Huffman tree as a target layer, marking temperature time sequence data on the target layer as target data, wherein the target data have the same coding length, obtaining the coding redundancy according to the coding code element length and the code element conversion times of the target data, and obtaining a first sequencing result according to the coding redundancy;
recording the target data with the same frequency as equal-profile data, acquiring the adjacent data of the equal-profile data in the temperature time sequence data, and acquiring the priority of the equal-profile data according to the coding length of the adjacent data of each equal-profile data and the frequency of the adjacent data of the equal-profile data; according to the sequence from big to small of the frequency of the target data and the sequence from big to small of the priority of the equal probability data, sequencing the target data to obtain a third sequencing result; obtaining a new coding result of the target data according to the first sorting result and the third sorting result;
the data compression early warning module compresses the temperature time sequence data according to the new coding result to obtain a compressed result, clusters the compressed data to obtain normal data and abnormal data, obtains the difference between the normal data and the abnormal data according to the normal data and the abnormal data, and carries out early warning according to the difference between the normal data and the abnormal data and a preset threshold value.
2. The abnormal livestock breeding early warning system based on the internet of things according to claim 1, wherein the formula of the redundancy of the codes is as follows:
where R represents redundancy of the code, C represents the number of symbol conversions of the code, and L represents the length of the code.
3. The abnormal livestock breeding early warning system based on the internet of things according to claim 1, wherein the specific acquisition method of the first sequencing result is as follows:
and sequencing the codes of the target data according to the code redundancy from large to small to obtain a first sequencing result.
4. The abnormal livestock breeding early warning system based on the internet of things according to claim 1, wherein the acquiring of the ortho-position data of the isochronal data in the temperature time sequence data comprises the following specific operations:
the method comprises the steps of recording any one of the object data and the like as data A, acquiring data adjacent to the data A on the left and the right in temperature time sequence data, and recording adjacent data adjacent to the data A on all the data adjacent to the data A on the left and the right as ortho data because the data A possibly appear many times in the temperature time sequence data.
5. The abnormal livestock breeding early warning system based on the internet of things according to claim 1, wherein the equation of the priority of the equipotential data is:
b represents the priority of the equipotential data; n represents the number of ortho-data of the isochronal data; i represents the ith ortho data of the isostatically data;the coding length of the ith adjacent data representing the isostatically data; />Indicating the frequency of the ith neighbor of the isocratic data.
6. The abnormal livestock breeding early warning system based on the internet of things according to claim 1, wherein the specific acquisition method of the third sequencing result is as follows:
sorting the second sorting resultReordering the data with the same medium frequency from big to small according to the priority of the equal probability data to obtain +.>Marking as a third sorting result; wherein (1)>Represents the nth target data in the second ranking result, n represents the lengths of the second ranking result and the third ranking result, +.>Representing the nth destination data in the third ranking result.
7. The abnormal livestock breeding early warning system based on the internet of things according to claim 6, wherein the specific acquisition method of the second sequencing result is as follows:
data is processedOrdering according to the occurrence frequency from big to small to obtain +.>Recorded as the second ranking resultThe method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Represents the nth target data in the second ranking result, n represents the lengths of the second ranking result and the third ranking result, +.>Representing the nth destination data in the third ranking result.
8. The abnormal livestock breeding early warning system based on the internet of things according to claim 1, wherein the specific acquisition method for obtaining the new coding result of the target data according to the first sorting result and the third sorting result is as follows:
the first sorting result is recorded asThe third ranking result is marked +.>Target data->The result of the encoding is->Target data->The result of the encoding is->… …, target data->The result of the encoding is->The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Represents the nth code in the first ordering result, n represents the length of the first ordering result and the third ordering result,/->Representing the nth destination data in the third ranking result.
9. The abnormal livestock breeding early warning system based on the internet of things according to claim 1, wherein the formula of the difference between the normal data and the abnormal data is:
wherein Q represents the difference between normal data and abnormal data;a cluster center representing normal data; />A cluster center representing abnormal data; />Representing the maximum of all data; />Representing the minimum of all data.
10. The abnormal livestock breeding early warning system based on the internet of things according to claim 1, wherein the early warning is performed according to the difference between the normal data and the abnormal data and a preset threshold value, and the method comprises the following specific steps:
when the difference between normal data and abnormal dataWhen the difference between the normal data and the abnormal data is larger than a preset threshold T, early warning is carried out, and the difference between the normal data and the abnormal data is +.>And if the preset threshold value T is smaller than or equal to the preset threshold value T, no early warning is carried out.
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| CN202311167053.8A CN116913057B (en) | 2023-09-12 | 2023-09-12 | Livestock-raising abnormal early warning system based on thing networking |
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