CN115834642B - Intelligent silkworm co-rearing room data transmission method based on Internet of things technology - Google Patents
Intelligent silkworm co-rearing room data transmission method based on Internet of things technology Download PDFInfo
- Publication number
- CN115834642B CN115834642B CN202310108338.8A CN202310108338A CN115834642B CN 115834642 B CN115834642 B CN 115834642B CN 202310108338 A CN202310108338 A CN 202310108338A CN 115834642 B CN115834642 B CN 115834642B
- Authority
- CN
- China
- Prior art keywords
- data
- fluctuation amplitude
- value
- amplitude value
- sequence
- 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.)
- Active
Links
Images
Landscapes
- Indication And Recording Devices For Special Purposes And Tariff Metering Devices (AREA)
Abstract
The invention relates to the technical field of data transmission, in particular to a smart silkworm co-rearing room data transmission method based on the Internet of things technology. The invention improves the compression rate and ensures the authenticity of the transmission data.
Description
Technical Field
The invention relates to the technical field of data transmission, in particular to a data transmission method of an intelligent silkworm rearing room based on the internet of things technology.
Background
Since ancient times, we have traditional culture of silk reeling of silkworm rearing, the key of the silkworm rearing is that environmental conditions suitable for survival of young silkworms are included, the environmental conditions include but are not limited to temperature and humidity, the traditional method for controlling the room temperature and humidity of the silkworm rearing is to adjust the room temperature and humidity of the silkworm rearing through some manual means, for example, a ground fire cage or a coal stove is adopted to raise the temperature and humidity through manual sprinkling, but the method too depends on subjective ideas and rearing experience of people and cannot accurately control the room temperature and humidity. In the prior art, a temperature sensor is generally adopted to collect corresponding temperature data, the collected temperature data is transmitted to a receiving end through a communication network system based on the technology of the Internet of things, and the temperature of the co-cultivation room is further adjusted.
The inventors have found in practice that the above prior art has the following drawbacks:
in the prior art, the acquired temperature data is transmitted to a receiving end through a communication network system based on the Internet of things technology, the communication network based on the Internet of things technology is easy to receive the influence of external interference and network fluctuation, and the acquired data of the young silkworm co-rearing room is discrete data, and although a data curve corresponding to the discrete data can reflect the real characteristics of the temperature data in trend, the acquired temperature data has serious data distortion compared with the real data of the temperature of the young silkworm co-rearing room due to the fluctuation and noise influence of the temperature data of the young silkworm co-rearing room, so that the intelligent young silkworm co-rearing room data transmission method based on the Internet of things in the prior art has serious data distortion.
Disclosure of Invention
In order to solve the technical problem of serious data distortion of an intelligent silkworm co-rearing room data transmission method based on the Internet of things in the prior art, the invention aims to provide the intelligent silkworm co-rearing room data transmission method based on the Internet of things, and the adopted technical scheme is as follows:
the invention provides a data transmission method of an intelligent silkworm co-rearing room based on the technology of the Internet of things, which comprises the following steps:
acquiring temperature data of the young silkworm co-rearing chamber within a preset time length according to a preset sampling frequency, and fitting a temperature change curve according to the temperature data;
obtaining more than two data fluctuation amplitude values according to the slope change of temperature data in a temperature change curve, and carrying out cluster analysis on the data fluctuation amplitude values according to the distribution difference of the data fluctuation amplitude values to obtain more than two fluctuation amplitude value segmentation sequences;
obtaining correction probability according to the spatial distribution of the fluctuation amplitude values in each fluctuation amplitude value segmentation sequence, denoising all the fluctuation amplitude value segmentation sequences according to the correction probability to obtain corrected fluctuation amplitude value segmentation sequences, and correcting the temperature change curve according to the corrected fluctuation amplitude value sequences to obtain a corrected temperature change curve;
and fitting the corrected temperature change curve through a revolving door compression method to obtain corrected temperature data, and storing the corrected temperature data to a receiving end to finish data transmission.
Further, the obtaining more than two data fluctuation amplitude values according to the slope change of the temperature data in the temperature change curve comprises:
in the temperature change curve, taking the ratio of the amplitude value difference of the target temperature data to the amplitude value difference of the previous temperature data to the time difference as the slope of the target temperature data; when the slope of the previous temperature data of the target temperature data is a positive value and the slope of the next temperature data is a negative value, recording the target temperature data as peak point data; when the slope of the previous temperature data of the target temperature data is a negative value and the slope of the next temperature data is a positive value, recording the target temperature data as valley point data;
changing target temperature data to obtain all peak point data and valley point data, taking adjacent peak point and valley point data as a group of calculated peak point and valley point data corresponding amplitude value differences, marking the calculated peak point and valley point data as data fluctuation amplitude values, and obtaining more than two data fluctuation amplitude values according to all the peak point and valley point data.
Further, performing cluster analysis on the data fluctuation amplitude values according to the distribution difference of the data fluctuation amplitude values to obtain more than two segmentation sequences of the fluctuation amplitude values comprises:
iterative selection of k values is carried out through a k-means clustering algorithm to obtain optimal clustering effects, and clustering analysis is carried out on data fluctuation amplitude values according to the k values of the optimal clustering effects selected through the k-means clustering algorithm to obtain more than two fluctuation amplitude value segmentation sequences; k is a positive integer and is at least equal to 3.
Further, the method for obtaining the k value of the optimal clustering effect comprises the following steps:
performing iteration of a preset step length on a preset initial k value, calculating a clustering effect value corresponding to the initial k value and the k value after each iteration, wherein the process of calculating the clustering effect value comprises the following steps:
k fluctuation amplitude value segmentation sequences obtained through clustering analysis of the target k values are used for counting the number of fluctuation amplitude values in each fluctuation amplitude value segmentation sequence and recording the number as the fluctuation amplitude value segmentation sequence data quantity, and the mean square error of the fluctuation amplitude values in each fluctuation amplitude value segmentation sequence is calculated; calculating the sum of the mean square error of each fluctuation amplitude value segmentation sequence and the value of the negative correlation normalization of the fluctuation amplitude value segmentation sequence data quantity, marking the sum as a characteristic value of the fluctuation amplitude value segmentation sequence, calculating the accumulated value of all the fluctuation amplitude value segmentation sequence characteristic values corresponding to a target k value, and marking the accumulated value as a clustering effect value corresponding to the target k value;
changing the clustering effect values corresponding to all k values of the target k values, and selecting k corresponding to the minimum value of the clustering effect values as the k value of the optimal clustering effect.
Further, the method for acquiring the correction probability comprises the following steps:
and sequentially arranging all peak value point data in the fluctuation amplitude value segmentation sequence, calculating the difference value between the corresponding time of all adjacent peak value points to obtain a peak value time difference value sequence, and calculating the correction probability of each fluctuation amplitude value segmentation sequence according to the data distribution of the peak value time difference value sequence.
Further, the calculating the correction probability of each fluctuation amplitude value division sequence according to the data distribution of the peak time difference value sequence comprises:
taking the same data in the peak time difference value corresponding to the target fluctuation amplitude value segmentation sequence as a data type, counting the number of the data types corresponding to each peak time difference value in the peak time difference value sequence and the data quantity of each data type, recording the difference value between the data quantity of the data type and the total data quantity in the peak time difference value sequence as a data type probability value, calculating the distribution entropy of the probability of the data types corresponding to all the data types in the peak time difference value sequence, and carrying out negative correlation normalization on the distribution entropy to obtain a reality correction coefficient corresponding to the target fluctuation amplitude value segmentation sequence;
calculating the quantity ratio of the quantity of data in the target fluctuation amplitude value segmentation sequence and all the fluctuation amplitude value segmentation sequences, and recording the product of the quantity ratio and the authenticity correction coefficient as the correction probability of the target fluctuation amplitude value segmentation sequence; and changing the target fluctuation amplitude value segmentation sequence to obtain the correction probability of each fluctuation amplitude value segmentation sequence.
Further, denoising all the fluctuation amplitude value segmentation sequences according to the correction probability to obtain corrected fluctuation amplitude value segmentation sequences comprises the following steps:
calculating the ratio of the data quantity in all the fluctuation amplitude value segmentation sequences to the fluctuation amplitude value segmentation sequence quantity to obtain average fluctuation amplitude value quantity, sequentially arranging the fluctuation amplitude value segmentation sequences, rounding and rounding the product of the correction probability accumulated value corresponding to the previous a fluctuation amplitude value segmentation sequences and the average fluctuation amplitude value quantity to obtain correction mapping values corresponding to the a-th fluctuation amplitude value segmentation sequence, and sequentially calculating the correction mapping values corresponding to all the fluctuation amplitude value segmentation sequences, wherein a is a positive integer and is smaller than or equal to the quantity of the fluctuation amplitude value segmentation sequences;
when the correction mapping value of the target fluctuation amplitude value segmentation sequence is equal to the correction mapping value of the previous fluctuation amplitude value segmentation sequence, the data corresponding to the target fluctuation amplitude value segmentation sequence are recorded as noise data, and the noise data are screened to obtain the correction fluctuation amplitude value segmentation sequence.
The invention has the following beneficial effects:
considering that the acquired distortion data of the small silkworm co-rearing chamber is discrete data, the discrete data is represented as a continuous meandering line segment on a temperature data curve, the numerical characteristic change of corresponding individual data is obvious, and if the temperature condition of the small silkworm co-rearing chamber is adjusted in real time according to the data reflected by the temperature data curve, namely, the temperature condition of the small silkworm co-rearing chamber is adjusted at a high frequency, the survival condition of the small silkworm can be seriously influenced. According to the invention, the data fluctuation amplitude value corresponding to the continuous meandering line segment is used as basic data for cluster analysis, the fluctuation assignment segmentation sequence obtained by the cluster analysis is denoised to obtain a corrected temperature change curve, the corrected temperature change curve is further fitted by a revolving door compression method to obtain corrected temperature data, and compared with the original temperature data change curve, the meandering and fluctuation of the corrected temperature data are smaller, so that the integral temperature characteristics of the silkworm rearing chamber can be reflected more. The method eliminates the noise influence under the condition of not influencing the real change of the ambient temperature, and maximally eliminates the data distortion problem in the data transmission process. In addition, because the temperature data in the historical data are stored in the receiving end in the prior art, the storage pressure of the receiving end is continuously increased along with the accumulation of time, and the temperature data are stored to the receiving end by adopting a revolving door compression method for the corrected temperature data, the data transmission authenticity is ensured, the length of single compression fitting is furthest improved, and the compression rate is improved. Therefore, the invention ensures the authenticity of the transmitted data while improving the compression rate.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a diagram of a smart silkworm co-breeding room data transmission method based on the internet of things technology according to an embodiment of the present invention.
Detailed Description
In order to further explain the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of the intelligent silkworm rearing room data transmission method based on the internet of things technology according to the invention by combining the accompanying drawings and the preferred embodiment. 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 invention provides a specific scheme of an intelligent silkworm rearing room data transmission method based on the Internet of things technology, which is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a smart silkworm rearing room data transmission method based on the internet of things technology according to an embodiment of the present invention is shown, where the method includes:
step S1: and acquiring temperature data of the young silkworm co-rearing chamber within a preset time length according to a preset sampling frequency, and fitting a temperature change curve according to the temperature data.
The invention aims to transmit the co-cultivation room temperature data to a receiving end for storage and adaptively adjust the co-cultivation room temperature according to the temperature data, but the acquired temperature data is discrete data, so that the individual data characteristics of partial data are obvious, the temperature change trend of the whole data is influenced, and the acquired data is seriously distorted due to the influence of noise data. Firstly, collecting environmental characteristic data of a co-rearing room, selecting a corresponding sensor according to the data to be collected, and collecting the environmental characteristic data of the co-rearing room of the young silkworms within a preset time length through a preset sampling frequency. In the embodiment of the invention, temperature data of the young silkworm co-rearing chamber within a preset time length is acquired according to a preset sampling frequency, the preset sampling frequency is set once per second, and the preset acquisition time length is set to be 30 minutes. It should be noted that, for different environmental characteristic data, the subsequent analysis and processing modes are the same, and the invention only analyzes the temperature data.
After the temperature data of the co-rearing room temperature of the young silkworms within the preset time length are collected, a temperature change curve is fitted according to the collected discrete data. However, due to serious data distortion, the compression rate of the receiving end is reduced due to excessive data overoveroveroveroveroveroveroveroveroverrule when the data corresponding to the temperature change curve is directly transmitted to the receiving end. In addition, since the temperature of the young silkworm breeding room is regulated for a certain time and is not data which can be regulated in real time, the regulation according to each data corresponding to the temperature change curve is not realistic. Therefore, the temperature change curve is corrected by analyzing the trend and the characteristic of the temperature change curve, the corrected temperature change curve which can accurately reflect the temperature change trend of the co-rearing chamber of the young silkworms is obtained, and the temperature of the co-rearing chamber of the young silkworms is further adjusted according to the corrected temperature change curve.
After the corrected temperature change curve is obtained, the data in the corrected temperature change curve is compressed and stored by adopting a revolving door compression method. Because the revolving door compression method can obtain the overall trend of the data, and the temperature data is used as a continuous and diffuse adjusting parameter, the information quantity and the reality of the overall data are far higher than those of the individual data, and the revolving door compression method can well inhibit the numerical characteristics of the individual data. On the other hand, the revolving door compression method can keep the data characteristics to the maximum extent and increase the data compression rate at the same time, and can obviously reduce the pressure of data storage at a receiving end. The revolving door compression method is specifically as follows: selecting a starting point, setting a preset tolerance, marking the positions, which are respectively separated from the starting point by the preset tolerance, of the upper part and the lower part as two fulcrums, establishing two doors, wherein the length of the doors can be extended, closing the door when one point is started, gradually opening the door along with the increase of data until the two doors are parallel or the inner angle sum is larger than 180 degrees, and taking the last data as a cut-off point. By adopting the revolving door compression method, each piece of data obtained by the revolving door compression method is surrounded by two parallel straight lines, and all data in the corresponding data section are further replaced by the straight lines between the starting point and the cut-off point corresponding to each piece of data.
Step S2: obtaining more than two data fluctuation amplitude values according to the slope change of temperature data in a temperature change curve, and carrying out cluster analysis on the data fluctuation amplitude values according to the distribution difference of the data fluctuation amplitude values to obtain more than two fluctuation amplitude value segmentation sequences.
So far, the temperature change curve of the young silkworm co-breeding chamber is obtained through the step S1, and the acquired temperature change curve visually presents a tiny saw-tooth shape instead of a smooth curve due to the reasons of elastic fatigue, voltage fluctuation, element aging, semiconductor element parameter change and the like of sensitive element materials in the sensor and noise influence, namely, the fluctuation of the temperature change curve is larger, and the temperature change curve cannot represent accurate and practical temperature change characteristics. Therefore, in order to obtain a curve capable of showing the temperature change characteristics, the invention analyzes the fluctuation of the temperature change curve and screens out the temperature data related to the temperature change of the co-rearing temperature of the young silkworms as real temperature data.
Firstly, obtaining more than two data fluctuation amplitude values according to the slope change of temperature data in a temperature change curve, and specifically: and (3) obtaining peak points and valley points according to the slope of each temperature data in the temperature change curve, traversing all the peak points and valley points by taking the adjacent peak points and valley points as a group, calculating the difference of the temperature data values between the adjacent peak points and valley points, and recording the difference as a data fluctuation amplitude value, wherein the data fluctuation amplitude value can clearly represent the change condition of the individual data in the whole temperature data. It should be noted that, since the peak point and the valley point are obtained according to the temperature change curve, the peak point and the valley point are necessarily represented as intersecting distribution in time sequence, and when the number of the peak points and the valley points is odd, the last peak point or the valley point is discarded.
Preferably, in the temperature change curve, a ratio of a difference between an amplitude corresponding to the target temperature data and an amplitude value of the previous temperature data and a time difference is taken as a slope of the target temperature data; when the slope of the previous temperature data of the target temperature data is a positive value and the slope of the next temperature data is a negative value, recording the target temperature data as peak point data; when the slope of the previous temperature data of the target temperature data is a negative value and the slope of the next temperature data is a positive value, recording the target temperature data as valley point data; changing target temperature data to obtain all peak point data and valley point data, taking adjacent peak point and valley point data as a group of calculated peak point and valley point data corresponding amplitude value differences, marking the calculated peak point and valley point data as data fluctuation amplitude values, and obtaining more than two data fluctuation amplitude values according to all the peak point and valley point data.
In consideration of the subsequent need to store temperature data in a compressed manner by the revolving door compression method, the revolving door compression method is cut off due to noise fluctuation and fluctuation generated when the temperature changes, thereby generating a plurality of data segments generated by the revolving door algorithm, and further causing a great reduction in compression rate. Therefore, in order to enable the compression efficiency of the subsequent revolving door compression method to be higher, the invention performs cluster analysis on the data fluctuation amplitude values.
Considering that the distribution of the amplitude values of the acquired data fluctuation is different due to the fact that the temperature data is affected by different external influences, namely, the distribution of the amplitude values of the data fluctuation corresponding to the three reasons for affecting the temperature data is different due to the fact that the data fluctuation caused by data errors is generated due to the influence of the sensor, the actual temperature change is generated due to the fact that the data fluctuation is generated and the data fluctuation caused by noise is generated. It is necessary to further classify the different data fluctuation amplitude values.
And carrying out cluster analysis on the data fluctuation amplitude values according to the distribution difference of the data fluctuation amplitude values to obtain more than two fluctuation amplitude value segmentation sequences. Specific: and counting all the data fluctuation amplitude values to form a data fluctuation amplitude value sequence, carrying out cluster analysis on the data fluctuation amplitude values in the data fluctuation amplitude value sequence through a k-means clustering algorithm, and carrying out cluster analysis on the data fluctuation amplitude values in the data fluctuation amplitude value sequence according to the k value of the optimal clustering effect to obtain more than two fluctuation amplitude value segmentation sequences. Preferably, k values are iteratively selected through a k-means clustering algorithm to obtain optimal clustering effects, and clustering analysis is carried out on data fluctuation amplitude values according to the k values of the optimal clustering effects selected through the k-means clustering algorithm to obtain more than two fluctuation amplitude value segmentation sequences; k is a positive integer and is at least equal to 3.
The k value of the best clustering effect is obtained through the following steps: performing iteration of a preset step length on a preset initial k value, calculating a clustering effect value corresponding to the initial k value and the k value after each iteration, wherein the process of calculating the clustering effect value comprises the following steps: k fluctuation amplitude value segmentation sequences obtained through clustering analysis of the target k values are used for counting the number of fluctuation amplitude values in each fluctuation amplitude value segmentation sequence and recording the number as the fluctuation amplitude value segmentation sequence data quantity, and the mean square error of the fluctuation amplitude values in each fluctuation amplitude value segmentation sequence is calculated; calculating the sum of the mean square error of each fluctuation amplitude value segmentation sequence and the value of the negative correlation normalization of the fluctuation amplitude value segmentation sequence data quantity, marking the sum as a characteristic value of the fluctuation amplitude value segmentation sequence, calculating the accumulated value of all the fluctuation amplitude value segmentation sequence characteristic values corresponding to a target k value, and marking the accumulated value as a clustering effect value corresponding to the target k value; changing the clustering effect values corresponding to all k values of the target k values, and selecting k corresponding to the minimum value of the clustering effect values as the k value of the optimal clustering effect.
The process of calculating the cluster effect value is expressed as the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
for the target k value, +.>
Dividing sequence numbers for fluctuation amplitude values obtained by cluster analysis under the condition of target k value, +.>
To be +.>
A sequence of wave amplitude value divisions, < >>
To be +.>
Mean square error of the individual wave amplitude value division sequences, < >>
To be +.>
Data volume of a sequence of wave amplitude value divisions, < >>
For the clustering effect value corresponding to the target k value, < >>
Is a natural constant. When the mean square error of the fluctuation amplitude value segmentation sequences corresponding to the k values is smaller, the data volume in each fluctuation amplitude value segmentation sequence is larger, and the corresponding clustering effect value is smaller. And changing the target k value to calculate the clustering effect value obtained by all the iterative k values, and selecting the smallest clustering effect value as the optimal clustering effect.
The calculation formula of the clustering effect value characterizes the clustering effect according to the distribution condition of the data in the fluctuation amplitude value segmentation sequence obtained by the clustering analysis, but only the distribution condition of the data in the fluctuation amplitude value segmentation sequence is adopted to characterize the clustering effect, so that when the k value is equal to the total temperature data, the corresponding clustering effect is the best, namely, only one data exists in each fluctuation amplitude value segmentation sequence. In order to avoid the occurrence of the situation, the invention introduces the quantity of each fluctuation amplitude value segmentation sequence to limit the situation, so that the obtained optimal clustering effect ensures that the data in each fluctuation amplitude value segmentation sequence is similar enough as far as possible while the data quantity of each fluctuation amplitude value segmentation sequence is enough.
Step S3: obtaining correction probability according to the spatial distribution of the fluctuation amplitude values in each fluctuation amplitude value segmentation sequence, denoising all the fluctuation amplitude value segmentation sequences according to the correction probability to obtain corrected fluctuation amplitude value segmentation sequences, and correcting the temperature change curve according to the corrected fluctuation amplitude value sequences to obtain a corrected temperature change curve.
To this end, two or more wave amplitude value division sequences are obtained through step S2, which effectively eliminate the data fluctuation due to the data error caused by the influence of the sensor itself, but the actual temperature change is such that the generated data fluctuation and the influence of the data fluctuation due to noise are not numerically distinguished, so that it is necessary to further exclude the influence of noise data.
In order to eliminate the influence of noise data, it is preferable to select a distinction that can distinguish between noise data and actual temperature change data. Considering noise data as accidentally generated data, fewer than actual temperature change data appear on the time-series temperature data sequence as: the actual temperature change data continues to appear for a short period of time, and the noise data is randomly distributed in the overall time-series temperature data sequence. Therefore, according to the invention, noise data are screened out according to the distribution condition of the interval difference between the fluctuation amplitude values in each fluctuation amplitude value segmentation sequence between temperature change curves.
Firstly, obtaining correction probability according to the spatial distribution of the fluctuation amplitude values in each fluctuation amplitude value segmentation sequence, and specifically: taking the same data in the peak time difference value corresponding to the target fluctuation amplitude value segmentation sequence as a data type, counting the number of the data types corresponding to each peak time difference value in the peak time difference value sequence and the data quantity of each data type, recording the difference value between the data quantity of the data type and the total data quantity in the peak time difference value sequence as a data type probability value, calculating the distribution entropy of the probability of the data types corresponding to all the data types in the peak time difference value sequence, and carrying out negative correlation normalization on the distribution entropy to obtain a reality correction coefficient corresponding to the target fluctuation amplitude value segmentation sequence; calculating the quantity ratio of the quantity of data in the target fluctuation amplitude value segmentation sequence and all the fluctuation amplitude value segmentation sequences, and recording the product of the quantity ratio and the authenticity correction coefficient as the correction probability of the target fluctuation amplitude value segmentation sequence; and changing the target fluctuation amplitude value segmentation sequence to obtain the correction probability of each fluctuation amplitude value segmentation sequence. The calculation process of the correction probability is expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is->
Correction probability of individual wave amplitude value division sequences, < ->
Is->
The data amount of the data in the sequence is divided by the fluctuation amplitude value,/->
Is the total number of temperature data in the temperature profile, +.>
For peak time difference sequence +.>
Data amount of individual data types +.>
For the number of data types in the peak time difference sequence, < >>
Sequence number for data type in peak time difference sequence, < >>
Is a natural constant. />
For the distribution entropy of the probability of the data type in the sequence of peak time difference values, the degree of confusion of the data is characterized by +.>
The ratio of the total data of the data quantity stations in the fluctuation amplitude value segmentation sequence is characterized, and the smaller the data quantity in the fluctuation amplitude value segmentation sequence is, the more chaotic the data distribution is, and the smaller the correction probability of the corresponding fluctuation amplitude value segmentation sequence is.
The calculation formula of the correction probability combines the difference between the noise data and the actual temperature change data to obtain the correction probability of each fluctuation amplitude value segmentation sequence. Because the noise data is randomly and discretely distributed, in the same fluctuation amplitude value segmentation sequence, the distance between the noise data in a temperature data fitting temperature change curve is also random, so that the corresponding distribution entropy is larger; for the actual temperature change data, the actual temperature change data continuously appears in a time sequence temperature data sequence in a short time, and the corresponding temperature data in the same fluctuation amplitude value segmentation sequence are distributed in the middle distance of the temperature data fitting temperature change curve, so that the corresponding distribution entropy is smaller. In addition, the correction probability obtained by introducing the ratio of the total number of data of the data number station in the fluctuation amplitude value division sequence can be represented as a correction probability value obtained by correcting the total data amount of the data amount in the fluctuation amplitude value division sequence.
Denoising all the fluctuation amplitude value segmentation sequences according to the correction probability to obtain corrected fluctuation amplitude value segmentation sequences, and specifically: and obtaining a correction mapping value of each fluctuation amplitude value segmentation sequence according to the correction probability of each fluctuation segmentation sequence and the average data quantity corresponding to the data in each fluctuation amplitude value segmentation sequence, namely the data quantity of each fluctuation amplitude value segmentation sequence after correction, and screening the data when the data quantity of the fluctuation amplitude value segmentation sequence is larger than the correction mapping value, wherein the screened data is noise data.
Preferably, calculating the ratio of the number of data in all the fluctuation amplitude value division sequences to the number of the fluctuation amplitude value division sequences to obtain the average fluctuation amplitude value number, sequentially arranging the fluctuation amplitude value division sequences, rounding and rounding the product of the correction probability accumulated value corresponding to the first a fluctuation amplitude value division sequences and the average fluctuation amplitude value number to obtain correction mapping values corresponding to the a-th fluctuation amplitude value division sequence, and sequentially calculating the correction mapping values corresponding to all the fluctuation amplitude value division sequences, wherein a is a positive integer and a is smaller than or equal to the number of the fluctuation amplitude value division sequences; when the correction mapping value of the target fluctuation amplitude value segmentation sequence is equal to the correction mapping value of the previous fluctuation amplitude value segmentation sequence, the data corresponding to the target fluctuation amplitude value segmentation sequence are recorded as noise data, and the noise data are screened to obtain the correction fluctuation amplitude value segmentation sequence. The process of screening noise data is expressed in terms of the formula:
wherein the method comprises the steps of
To add to->
Mapping values of the sequence of wave amplitude value divisions, < >>
Is->
Correction probability values of the individual wave amplitude value division sequences, < >>
Is the total number of temperature data in the temperature profile, +.>
The number of sequences is divided for the fluctuation amplitude value. The characteristic value corresponding to the fluctuation amplitude value segmentation sequence after each accumulation can be obtained through accumulation, when the corresponding correction mapping value changes little, the fluctuation amplitude value segmentation sequence corresponding to the accumulation is considered to be noise data, and the fluctuation amplitude value corresponding to the noise data is eliminated. Correcting the temperature change curve according to the corrected fluctuation amplitude value sequence to obtain a corrected temperature change curve, wherein the specific correction process comprises the following steps: and ignoring the pivot corresponding to the noise data on the temperature change curve, and directly fitting the next data pivot.
Step S4: and fitting the corrected temperature change curve through a revolving door compression method to obtain corrected temperature data, and storing the corrected temperature data to a receiving end to finish data transmission.
Thus, the corrected temperature change curve is obtained in step S3. And further compressing and storing the corrected temperature change curve to a receiving end to finish data transmission. The revolving door compression method is considered to utilize the trend after fitting to control the temperature, the corresponding feedback speed is high, the reality is high, and the data distortion is less likely to occur on the premise of ensuring the compression rate. Therefore, the corrected temperature change curve is compressed and stored to a receiving end specifically: and fitting the corrected temperature change curve through a revolving door compression method to obtain corrected temperature data, and storing the corrected temperature data to a receiving end to finish data transmission.
In summary, the data fluctuation amplitude value is obtained through slope change of the temperature change curve, k value with the best clustering effect is selected according to the data fluctuation value, the data fluctuation amplitude value is classified by adopting a k-means clustering algorithm to obtain a fluctuation amplitude value segmentation sequence, noise data is screened out according to spatial distribution characteristics of data in the fluctuation amplitude value segmentation sequence to obtain a corrected temperature change curve, and the data in the corrected temperature change curve is compressed and stored to a receiving end through a revolving door compression method to finish data transmission. The invention improves the compression rate and ensures the authenticity of the transmission data.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (5)
1. The intelligent silkworm co-rearing room data transmission method based on the internet of things technology is characterized by comprising the following steps of:
acquiring temperature data of the young silkworm co-rearing chamber within a preset time length according to a preset sampling frequency, and fitting a temperature change curve according to the temperature data;
obtaining more than two data fluctuation amplitude values according to the slope change of temperature data in a temperature change curve, and carrying out cluster analysis on the data fluctuation amplitude values according to the distribution difference of the data fluctuation amplitude values to obtain more than two fluctuation amplitude value segmentation sequences;
obtaining correction probability according to the spatial distribution of the fluctuation amplitude values in each fluctuation amplitude value segmentation sequence, denoising all the fluctuation amplitude value segmentation sequences according to the correction probability to obtain corrected fluctuation amplitude value segmentation sequences, and correcting the temperature change curve according to the corrected fluctuation amplitude value sequences to obtain a corrected temperature change curve;
fitting the corrected temperature change curve through a revolving door compression method to obtain corrected temperature data, and storing the corrected temperature data to a receiving end to complete data transmission;
the obtaining more than two data fluctuation amplitude values according to the slope change of the temperature data in the temperature change curve comprises the following steps:
in the temperature change curve, taking the ratio of the amplitude value difference of the target temperature data to the amplitude value difference of the previous temperature data to the time difference as the slope of the target temperature data; when the slope of the previous temperature data of the target temperature data is a positive value and the slope of the next temperature data is a negative value, recording the target temperature data as peak point data; when the slope of the previous temperature data of the target temperature data is a negative value and the slope of the next temperature data is a positive value, recording the target temperature data as valley point data;
changing target temperature data to obtain all peak point data and valley point data, taking adjacent peak point and valley point data as a group of calculated peak point and valley point data corresponding amplitude value differences, marking the calculated peak point and valley point data as data fluctuation amplitude values, and obtaining more than two data fluctuation amplitude values according to all the peak point and valley point data;
the method for acquiring the correction probability comprises the following steps:
and sequentially arranging all peak value point data in the fluctuation amplitude value segmentation sequence, calculating the difference value between the corresponding time of all adjacent peak value points to obtain a peak value time difference value sequence, and calculating the correction probability of each fluctuation amplitude value segmentation sequence according to the data distribution of the peak value time difference value sequence.
2. The internet of things-based intelligent silkworm co-breeding room data transmission method according to claim 1, wherein the clustering analysis of the data fluctuation amplitude values according to the distribution difference of the data fluctuation amplitude values to obtain more than two fluctuation amplitude value segmentation sequences comprises:
iterative selection of k values is carried out through a k-means clustering algorithm to obtain optimal clustering effects, and clustering analysis is carried out on data fluctuation amplitude values according to the k values of the optimal clustering effects selected through the k-means clustering algorithm to obtain more than two fluctuation amplitude value segmentation sequences; k is a positive integer and is at least equal to 3.
3. The intelligent silkworm co-breeding room data transmission method based on the internet of things technology according to claim 2, wherein the k value obtaining method of the optimal clustering effect comprises the following steps:
performing iteration of a preset step length on a preset initial k value, calculating a clustering effect value corresponding to the initial k value and the k value after each iteration, wherein the process of calculating the clustering effect value comprises the following steps:
k fluctuation amplitude value segmentation sequences obtained through clustering analysis of the target k values are used for counting the number of fluctuation amplitude values in each fluctuation amplitude value segmentation sequence and recording the number as the fluctuation amplitude value segmentation sequence data quantity, and the mean square error of the fluctuation amplitude values in each fluctuation amplitude value segmentation sequence is calculated; calculating the sum of the mean square error of each fluctuation amplitude value segmentation sequence and the value of the negative correlation normalization of the fluctuation amplitude value segmentation sequence data quantity, marking the sum as a characteristic value of the fluctuation amplitude value segmentation sequence, calculating the accumulated value of all the fluctuation amplitude value segmentation sequence characteristic values corresponding to a target k value, and marking the accumulated value as a clustering effect value corresponding to the target k value;
changing the clustering effect values corresponding to all k values of the target k values, and selecting k corresponding to the minimum value of the clustering effect values as the k value of the optimal clustering effect.
4. The internet of things-based intelligent silkworm co-breeding room data transmission method according to claim 1, wherein the calculating of the correction probability of each fluctuation amplitude value division sequence according to the data distribution of the peak time difference sequence comprises:
taking the same data in the peak time difference value corresponding to the target fluctuation amplitude value segmentation sequence as a data type, counting the number of the data types corresponding to each peak time difference value in the peak time difference value sequence and the data quantity of each data type, recording the difference value between the data quantity of the data type and the total data quantity in the peak time difference value sequence as a data type probability value, calculating the distribution entropy of the probability of the data types corresponding to all the data types in the peak time difference value sequence, and carrying out negative correlation normalization on the distribution entropy to obtain a reality correction coefficient corresponding to the target fluctuation amplitude value segmentation sequence;
calculating the quantity ratio of the quantity of data in the target fluctuation amplitude value segmentation sequence and all the fluctuation amplitude value segmentation sequences, and recording the product of the quantity ratio and the authenticity correction coefficient as the correction probability of the target fluctuation amplitude value segmentation sequence; and changing the target fluctuation amplitude value segmentation sequence to obtain the correction probability of each fluctuation amplitude value segmentation sequence.
5. The internet of things-based intelligent silkworm co-breeding room data transmission method according to claim 1, wherein denoising all fluctuation amplitude value segmentation sequences according to the correction probability to obtain the corrected fluctuation amplitude value segmentation sequences comprises:
calculating the ratio of the data quantity in all the fluctuation amplitude value segmentation sequences to the fluctuation amplitude value segmentation sequence quantity to obtain average fluctuation amplitude value quantity, sequentially arranging the fluctuation amplitude value segmentation sequences, rounding and rounding the product of the correction probability accumulated value corresponding to the previous a fluctuation amplitude value segmentation sequences and the average fluctuation amplitude value quantity to obtain correction mapping values corresponding to the a-th fluctuation amplitude value segmentation sequence, and sequentially calculating the correction mapping values corresponding to all the fluctuation amplitude value segmentation sequences, wherein a is a positive integer and is smaller than or equal to the quantity of the fluctuation amplitude value segmentation sequences;
when the correction mapping value of the target fluctuation amplitude value segmentation sequence is equal to the correction mapping value of the previous fluctuation amplitude value segmentation sequence, the data corresponding to the target fluctuation amplitude value segmentation sequence are recorded as noise data, and the noise data are screened to obtain the correction fluctuation amplitude value segmentation sequence.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310108338.8A CN115834642B (en) | 2023-02-14 | 2023-02-14 | Intelligent silkworm co-rearing room data transmission method based on Internet of things technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310108338.8A CN115834642B (en) | 2023-02-14 | 2023-02-14 | Intelligent silkworm co-rearing room data transmission method based on Internet of things technology |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115834642A CN115834642A (en) | 2023-03-21 |
| CN115834642B true CN115834642B (en) | 2023-04-21 |
Family
ID=85521142
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310108338.8A Active CN115834642B (en) | 2023-02-14 | 2023-02-14 | Intelligent silkworm co-rearing room data transmission method based on Internet of things technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115834642B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117233645B (en) * | 2023-11-15 | 2024-01-26 | 深圳鹏城新能科技有限公司 | Energy storage inverter battery abnormality judging method, system and medium |
| CN117725540B (en) * | 2024-02-07 | 2024-05-07 | 宇恒数智(北京)科技有限公司 | Method, system, equipment and medium for calculating dynamic baseband |
| CN117879769B (en) * | 2024-03-11 | 2024-05-28 | 陕西惠延机械有限公司 | Data pushing and transmitting method for tunnel lining trolley cloud platform system |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW201245909A (en) * | 2011-01-18 | 2012-11-16 | Tokyo Electron Ltd | Unusual judging system and unusual judging method of processing apparatus |
| CN104706337A (en) * | 2015-02-11 | 2015-06-17 | 华东师范大学 | Automatic pulse wave crest and trough detection method |
| CN108181948A (en) * | 2018-03-20 | 2018-06-19 | 中国工程物理研究院化工材料研究所 | A kind of accurate follow-up control method of temperature and control system |
| WO2019094729A1 (en) * | 2017-11-09 | 2019-05-16 | Strong Force Iot Portfolio 2016, Llc | Methods and systems for the industrial internet of things |
| CN109903179A (en) * | 2019-02-21 | 2019-06-18 | 苏州旅联智能科技有限公司 | Seedlings of cereal crops wisdom pipe supports method and system |
| CN110995275A (en) * | 2019-11-20 | 2020-04-10 | 安徽省安泰科技股份有限公司 | Improved algorithm for revolving door compression algorithm |
| CN112686512A (en) * | 2020-12-24 | 2021-04-20 | 泰州可以信息科技有限公司 | System and method for identifying variation amplitude bearing degree |
| CN112883536A (en) * | 2020-11-30 | 2021-06-01 | 广西大学 | Bushing insulating oiled paper temperature correction and activation energy prediction method based on dielectric modulus |
| CN113940218A (en) * | 2021-09-30 | 2022-01-18 | 上海易航海芯农业科技有限公司 | Intelligent heat supply method and system for greenhouse |
| CN114971426A (en) * | 2022-07-28 | 2022-08-30 | 南通金立电气工程有限公司 | Heating equipment constant temperature intelligent analysis method based on multi-mode perception |
| CN115032888A (en) * | 2022-06-20 | 2022-09-09 | 浙江中烟工业有限责任公司 | Loose leaf moistening machine outlet temperature control method based on temperature division zone delay accumulation strategy |
| CN115409253A (en) * | 2022-08-24 | 2022-11-29 | 浙江天演维真网络科技股份有限公司 | Soil temperature adjusting method and device, storage medium and electronic equipment |
| CN115459782A (en) * | 2022-08-29 | 2022-12-09 | 北京思维实创科技有限公司 | Industrial Internet of things high-frequency data compression method based on time sequence segmentation and clustering |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050149264A1 (en) * | 2003-12-30 | 2005-07-07 | Schlumberger Technology Corporation | System and Method to Interpret Distributed Temperature Sensor Data and to Determine a Flow Rate in a Well |
| US10417806B2 (en) * | 2018-02-15 | 2019-09-17 | JJK Holdings, LLC | Dynamic local temporal-consistent textured mesh compression |
-
2023
- 2023-02-14 CN CN202310108338.8A patent/CN115834642B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW201245909A (en) * | 2011-01-18 | 2012-11-16 | Tokyo Electron Ltd | Unusual judging system and unusual judging method of processing apparatus |
| CN104706337A (en) * | 2015-02-11 | 2015-06-17 | 华东师范大学 | Automatic pulse wave crest and trough detection method |
| WO2019094729A1 (en) * | 2017-11-09 | 2019-05-16 | Strong Force Iot Portfolio 2016, Llc | Methods and systems for the industrial internet of things |
| CN108181948A (en) * | 2018-03-20 | 2018-06-19 | 中国工程物理研究院化工材料研究所 | A kind of accurate follow-up control method of temperature and control system |
| CN109903179A (en) * | 2019-02-21 | 2019-06-18 | 苏州旅联智能科技有限公司 | Seedlings of cereal crops wisdom pipe supports method and system |
| CN110995275A (en) * | 2019-11-20 | 2020-04-10 | 安徽省安泰科技股份有限公司 | Improved algorithm for revolving door compression algorithm |
| CN112883536A (en) * | 2020-11-30 | 2021-06-01 | 广西大学 | Bushing insulating oiled paper temperature correction and activation energy prediction method based on dielectric modulus |
| CN112686512A (en) * | 2020-12-24 | 2021-04-20 | 泰州可以信息科技有限公司 | System and method for identifying variation amplitude bearing degree |
| CN113940218A (en) * | 2021-09-30 | 2022-01-18 | 上海易航海芯农业科技有限公司 | Intelligent heat supply method and system for greenhouse |
| CN115032888A (en) * | 2022-06-20 | 2022-09-09 | 浙江中烟工业有限责任公司 | Loose leaf moistening machine outlet temperature control method based on temperature division zone delay accumulation strategy |
| CN114971426A (en) * | 2022-07-28 | 2022-08-30 | 南通金立电气工程有限公司 | Heating equipment constant temperature intelligent analysis method based on multi-mode perception |
| CN115409253A (en) * | 2022-08-24 | 2022-11-29 | 浙江天演维真网络科技股份有限公司 | Soil temperature adjusting method and device, storage medium and electronic equipment |
| CN115459782A (en) * | 2022-08-29 | 2022-12-09 | 北京思维实创科技有限公司 | Industrial Internet of things high-frequency data compression method based on time sequence segmentation and clustering |
Non-Patent Citations (2)
| Title |
|---|
| W. Dong等."An Efficient Nonlinear Online Data Compression Algorithm".《 2021 International Conference on Intelligent Computing, Automation and Systems (ICICAS)》.2022,全文. * |
| 于洋等."基于改进旋转门算法的变电站数据压缩存储方法".《中国电力》.2022,全文. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115834642A (en) | 2023-03-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115834642B (en) | Intelligent silkworm co-rearing room data transmission method based on Internet of things technology | |
| CN115987295B (en) | Crop monitoring data efficient processing method based on Internet of things | |
| CN116760908B (en) | Agricultural information optimization management method and system based on digital twin | |
| CN108540136B (en) | Compression method suitable for agricultural sensing data | |
| CN111898591B (en) | Modulation signal identification method based on pruning residual error network | |
| CN115219067B (en) | Real-time state monitoring method for garlic storage | |
| CN115802056B (en) | User data compression storage method for mobile terminal | |
| CN115359807B (en) | Noise online monitoring system for urban noise pollution | |
| CN111275132A (en) | Target clustering method based on SA-PFCM + + algorithm | |
| CN117271987B (en) | Intelligent acquisition and processing method for environmental state data of power distribution equipment | |
| CN113883674B (en) | Air conditioner sleep curve correction method and device, air conditioner and readable storage medium | |
| CN111401140B (en) | Offline learning method of intelligent video monitoring system in edge computing environment | |
| CN117496359B (en) | Plant planting layout monitoring method and system based on three-dimensional point cloud | |
| CN113051237A (en) | Compression storage method of time series remote sensing data | |
| CN117764290B (en) | BIM model data management method for hydraulic engineering construction | |
| CN117316301B (en) | Intelligent compression processing method for gene detection data | |
| CN108667463B (en) | Monitoring data compression method | |
| CN110533111A (en) | A kind of adaptive K mean cluster method based on local density Yu ball Hash | |
| CN116753562B (en) | Graphene electric heating intelligent temperature control system based on data analysis | |
| CN117435873A (en) | Data management method based on intelligent spraying dust fall | |
| CN117692012B (en) | Remote monitoring and transmitting method for temperature data of intelligent sleeping bag | |
| CN112164468B (en) | Method for processing missing data of pregnancy examination data | |
| CN111753792B (en) | Calculated efficient SEVI (sequence independent variable) adjustment factor optimization method | |
| CN117785818B (en) | Gas chromatograph data optimized storage method and system | |
| CN117767960B (en) | Sensor data optimization acquisition and storage method |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |