CN115219067B - Real-time state monitoring method for garlic storage - Google Patents
Real-time state monitoring method for garlic storage Download PDFInfo
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Abstract
The invention relates to a method for monitoring the real-time state of garlic storage, which belongs to the technical field of data processing and comprises the following steps: collecting temperature time sequence data of each detection position in the garlic storage room in real time, and dividing each data segment into a plurality of sub-data segments according to the importance degree of each temperature value; performing linear fitting on all temperature values in each sub-data segment to obtain a fitting value of each temperature value in each sub-data segment, and subtracting the fitting value of each temperature value from the corresponding temperature value to obtain a first differential sequence corresponding to each sub-data segment; coding and compressing the first differential sequence corresponding to each sub-data segment to obtain compressed data of each sub-data segment; transmitting the compressed data of each subdata segment to a monitoring control center of the garlic storage room; the invention improves the step of obtaining the differential sequence in the differential reversible variable length coding, and avoids the problem of error propagation caused by the abnormity of the whole differential sequence.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a real-time state monitoring method for garlic storage.
Background
In the storage of garlic, a large batch of garlic needs to be stored, wherein the large batch of garlic needs to be monitored periodically during storage, so as to ensure that the garlic is not rotten or damaged during delivery. The storage of garlic receives the temperature influence great, the lower garlic surface frosting that can lead to of temperature, receive the frostbite harm, the higher garlic of temperature sprouts easily, lead to the garlic clove to become dry, influence the quality of garlic, so need monitor the indoor temperature of garlic storage in real time in order to guarantee the quality of garlic, when the monitoring, generally utilize the indoor temperature sensor of storage to carry out the monitoring to the indoor temperature of storage, then the temperature of the indoor different positions of monitoring control center regulation and control garlic storage through the storage.
Because the data of the temperature sensor needs to be transmitted to the monitoring control center and then the temperature in the storage room can be determined by the staff, the transmission method of the temperature data generally adopts the way that the acquired data is directly transmitted to the monitoring control center or the acquired data is compressed by a compression algorithm and then transmitted, but in the prior art, the data compression is generally carried out by adopting differential reversible variable length coding when the data is compressed. In the differential reversible variable length coding in the prior art, a differential method is adopted to obtain a differential sequence by subtracting previous data from next data, if data at a certain position is wrong, the difference between the previous data and the next data can cause the cumulative error of the whole differential sequence, and the error can be propagated in the later period, so that the later-period decoded value is too large to deviate from the original data, and the temperature in the storage room can not be accurately monitored.
Disclosure of Invention
The invention provides a real-time state monitoring method for garlic storage, which improves the step of obtaining a differential sequence in differential reversible variable length coding, adaptively segments data according to the characteristics of collected temperature data in a garlic storage room, obtains a reference value of each segment of data, and subtracts the temperature value of each segment of data from the reference value to obtain the differential sequence of each segment of data, thereby avoiding error propagation caused by accumulated errors in the process of obtaining the differential sequence.
The invention relates to a real-time state monitoring method for garlic storage, which adopts the following technical scheme; the method comprises the following steps:
collecting the temperature value of each detection position in the garlic storage room in real time;
forming a temperature time sequence data sequence by temperature values in the same sampling interval of each detection position, converting the temperature time sequence data sequence into a two-dimensional data matrix, establishing a sliding window by taking each temperature value in the two-dimensional data matrix as a center, and calculating the importance degree of each temperature value by using all the temperature values contained in the sliding window;
calculating the temperature change interval of each temperature value by using the importance degree of each temperature value in the two-dimensional data matrix;
solving an intersection of the temperature change intervals of all the temperature values, and dividing the temperature values corresponding to the temperature change intervals with the common intersection into a data section to obtain a plurality of data sections;
dividing each data segment into a plurality of sub-data segments according to the similarity of adjacent temperature values in each data segment;
performing linear fitting on all temperature values in each sub-data segment to obtain a fitting value of each temperature value in each sub-data segment, subtracting the fitting value of each temperature value from the corresponding temperature value to obtain a first differential value of each temperature value, and arranging the obtained first differential values corresponding to each temperature value in each sub-data segment according to a time sequence order to obtain a first differential sequence corresponding to each sub-data segment;
coding and compressing the first differential sequence corresponding to each subdata segment to obtain compressed data of each subdata segment;
transmitting the compressed data of each subdata segment to a garlic storage chamber monitoring control center, decompressing the compressed data by the garlic storage chamber monitoring control center, and acquiring temperature time sequence data of each detection position in the garlic storage chamber in the same sampling interval.
Further, the step of establishing a sliding window with each temperature value in the two-dimensional data matrix as a center, and calculating the importance degree of each temperature value by using all temperature values contained in the sliding window includes:
selecting any temperature value in the two-dimensional data matrix as a target temperature value, and establishing a target sliding window by taking the target temperature value as a center;
calculating a first temperature average value corresponding to the target sliding window by using all temperature values contained in the target sliding window;
calculating a first difference value between each temperature value in the target sliding window and a first temperature average value corresponding to the target sliding window;
calculating the importance degree of the target temperature value by using all the first difference values corresponding to the target sliding window and the quantity of the temperature values in the target sliding window;
and calculating the importance degree of each temperature value according to the importance degree calculation method of the target temperature value.
Further, the step of calculating the temperature change interval of each temperature value by using the importance degree of each temperature value in the two-dimensional data matrix includes:
calculating a second temperature average value corresponding to the two-dimensional data matrix by using all temperature values in the two-dimensional data matrix;
acquiring a maximum temperature value in the two-dimensional data matrix, and calculating a second difference value between the corresponding maximum temperature value in the two-dimensional data matrix and a second temperature average value;
calculating the temperature change amplitude corresponding to each temperature value by using the second difference and the importance degree of each temperature value;
adding the temperature change amplitude corresponding to each temperature value and the temperature value to be used as the temperature upper limit value of the temperature value, and subtracting the temperature change amplitude corresponding to each temperature value and the temperature value to be used as the temperature lower limit value of the temperature value;
and determining the temperature change interval of each temperature value according to the upper temperature limit value and the lower temperature limit value of each temperature value.
Further, the step of solving an intersection of the temperature change intervals of all the temperature values and dividing the temperature values corresponding to the temperature change intervals with the common intersection into one data segment includes:
acquiring a temperature change interval of a first temperature value and a second temperature value in a temperature time sequence data sequence;
if the temperature change interval of the second temperature value is intersected with the temperature change interval of the first temperature value, dividing the second temperature value and the first temperature value into the same section;
acquiring a temperature change interval of a third temperature value in the temperature time sequence data sequence, and if a common intersection exists between the temperature change interval of the third temperature value and the temperature change intervals of the second temperature value and the first temperature value, dividing the third temperature value, the second temperature value and the first temperature value into the same section;
and traversing each temperature value in the temperature time sequence data sequence in sequence, if the temperature value and each temperature value of the category of the previous temperature value have a common intersection, dividing the temperature value and the previous temperature value into the same segment, otherwise, starting to segment again from the temperature value, and taking the temperature value divided into the same segment in the temperature time sequence data sequence as a data segment.
Further, the step of dividing each data segment into a plurality of sub-data segments according to the similarity of adjacent temperature values in each data segment includes:
obtaining a second difference sequence of each data segment by utilizing the difference between the temperature value of the latter temperature value and the temperature value of the former temperature value in every two adjacent temperature values in each data segment;
calculating the similarity of adjacent temperature values in each data segment according to the difference value of two adjacent differential values in the second differential sequence;
and determining the number of a plurality of continuous and adjacent temperature values with the accumulated similarity larger than the similarity threshold value by utilizing the similarity of the adjacent temperature values in each data segment, dividing the plurality of continuous and adjacent temperature values with the similarity larger than the similarity threshold value into a sub-data segment, and dividing each data segment into a plurality of sub-data segments in the same way.
Further, the step of performing linear fitting on all temperature values in each sub-data segment to obtain a fitting value of each temperature value in each sub-data segment includes:
the position of each temperature value in each sub data segment in the sub data segment is taken as an abscissa, and the temperature value is taken as an ordinate, so as to obtain a coordinate value of each temperature value in each sub data segment in a two-dimensional coordinate system;
fitting all coordinate values in each subdata segment in a two-dimensional coordinate system to obtain a linear fitting equation;
and substituting each temperature value in each sub data segment into a linear fitting equation to obtain a fitting value of each temperature value in each sub data segment.
Further, the step of encoding and compressing the first differential sequence corresponding to each sub-data segment to obtain compressed data of each sub-data segment includes:
and coding and compressing the first difference sequence corresponding to each sub-data segment by adopting run coding, and obtaining compressed data of each sub-data segment under the condition of storing the slope and intercept of the linear fitting equation corresponding to each sub-data segment.
The invention has the beneficial effects that:
the invention provides a real-time state monitoring method for garlic storage, which is characterized in that according to the characteristics of temperature data in a storage chamber, a plurality of sub-data sections are obtained by carrying out adaptive segmentation processing on the temperature data acquired by a sensor, and the purpose of carrying out adaptive segmentation on the temperature data is to divide continuous temperature values with large similarity into one sub-data section.
After a plurality of sub data segments are obtained, fitting all data in each sub data segment to obtain a fitting value of each temperature value in each sub data segment, taking the fitting value of each temperature value in each sub data segment as a reference value, and subtracting the reference value from the temperature value of each sub data segment to obtain a difference sequence of each sub data segment; when the differential sequence of data compression is obtained, the differential sequence is obtained without depending on difference of adjacent temperature values, but a fitting value of each temperature value is determined as a reference value, the temperature value of each sub-data segment is subtracted from the reference value to obtain the differential sequence of each sub-data segment, and the differential sequence is obtained by subtracting the temperature value from the reference value, so that the problem that when data propagation errors occur at a certain position, the integral data are propagated due to accumulated errors of the whole differential sequence, and the decoding value at the later stage is deviated from the original data to be overlarge can be avoided.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic flow chart illustrating the general steps of a real-time status monitoring method for garlic storage according to an embodiment of the present invention;
FIG. 2 is a diagram showing the position of each temperature value in each sub-data segment in a two-dimensional coordinate system according to the present invention;
FIG. 3 shows a linear fitting equation obtained by fitting all coordinate values in each sub-data segment in a two-dimensional coordinate system.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
An embodiment of the invention relates to a method for monitoring the real-time status of garlic storage, as shown in fig. 1, the method comprises the following steps:
s1, collecting the temperature value of each detection position in the garlic storage room in real time.
Owing to deposit regional difference during the garlic storage, the garlic temperature also can have the difference, in order to guarantee that the quality of garlic is the same as far as possible, the temperature of the indoor different positions in regulation and control garlic storage of quick accurate for every position all is in the best storage temperature as long as possible in the garlic storage process, the event arranges temperature sensor in the inside different regions in garlic storage room, gather the temperature data in the garlic storage process, wherein, different detection position both contain the garlic inlayer region and also contain the outer region of garlic. Because a sensor is arranged at each detection position in the garlic storage chamber, the temperature value of each detection position can be collected by using the sensor.
And S2, forming a temperature time sequence data sequence by temperature values in the same sampling interval of each detection position, converting the temperature time sequence data sequence into a two-dimensional data matrix, establishing a sliding window by taking each temperature value in the two-dimensional data matrix as a center, and calculating the importance degree of each temperature value by using all the temperature values contained in the sliding window.
According to the invention, after the temperature time sequence data sequence of each detection position is collected, the importance degree of each temperature value is analyzed. The temperature values in the storage room are similar under the normal condition, the similarity of the temperature values acquired at adjacent moments is larger, in order to increase the relevance between the temperature values, the temperature values in the same sampling interval of each detection position form a temperature time sequence data sequence, and the temperature time sequence data sequence is converted into a two-dimensional data matrix, so that the reference point of the data is prevented from being excessively limited.
When the temperature time sequence data sequence is divided, the temperature time sequence data with the length of N is divided into M temperature substring sequences with the length of N, and the M temperature substring sequences with the length of N after the division are arranged according to the dividing sequence to obtain the temperature substring sequence with the size of N
In the generation of two-dimensional data matrices, i.e. in the generation of
A single analysis is carried out after each temperature time sequence data, i.e. the sampling interval is
And (4) data.
Establishing a sliding window by taking each temperature value in the two-dimensional data matrix as a center, and calculating the importance degree of each temperature value by using all temperature values contained in the sliding window, wherein the steps comprise: selecting any temperature value in the two-dimensional data matrix as a target temperature value, and establishing a target sliding window by taking the target temperature value as a center; calculating a first temperature average value corresponding to the target sliding window by using all temperature values contained in the target sliding window; calculating a first difference value between each temperature value in the target sliding window and a first temperature average value corresponding to the target sliding window; calculating the importance degree of the target temperature value by using all the first difference values corresponding to the target sliding window and the quantity of the temperature values in the target sliding window; and calculating the importance degree of each temperature value according to the importance degree calculation method of the target temperature value.
The calculation formula of the importance degree of the target temperature value is as follows:
wherein, the first and the second end of the pipe are connected with each other,
representing the second one contained in a target sliding window established with the target temperature value as the center
A temperature value;
representing a first temperature average value corresponding to a target sliding window;
representing the number of temperature values within the target sliding window;
expressing an exponential function with a natural constant e as a base;
indicating the importance of the target temperature value. When the difference between the target temperature value and the neighborhood temperature value is larger, the larger the deviation degree of the target temperature value from the normal is, that is, the more important the target temperature value is, the accuracy of the target temperature value needs to be ensured, and the importance degree of each temperature value is calculated according to the importance degree calculation method of the target temperature value.
And S3, calculating the temperature change interval of each temperature value by using the importance degree of each temperature value in the two-dimensional data matrix.
The step of calculating the temperature change interval of each temperature value by using the importance degree of each temperature value in the two-dimensional data matrix comprises the following steps: calculating a second temperature average value corresponding to the two-dimensional data matrix by using all temperature values in the two-dimensional data matrix; acquiring a maximum temperature value in the two-dimensional data matrix, and calculating a second difference value between the corresponding maximum temperature value in the two-dimensional data matrix and a second temperature average value; calculating the temperature change amplitude corresponding to each temperature value by using the second difference and the importance degree of each temperature value; adding the temperature change amplitude corresponding to each temperature value and the temperature value to be used as the temperature upper limit value of the temperature value, and subtracting the temperature change amplitude corresponding to each temperature value and the temperature value to be used as the temperature lower limit value of the temperature value; and determining the temperature change interval of each temperature value according to the upper temperature limit value and the lower temperature limit value of each temperature value.
After the importance degree of each temperature value in the two-dimensional data matrix is determined, the data is subjected to self-adaptive segmentation, the important data is expected to be classified into one class, and in order to increase the subsequent compression rate and accelerate the data transmission rate, the difference sequence is expected to be subjected to recompression, so that the temperature values with similar data importance degrees and similar data change trends are expected to be classified into one class. For the temperature value with large importance degree, the data difference which can be accommodated is small, and for the temperature value with small importance degree, the data difference which can be accommodated is large, so the calculation formula of the temperature change interval of each temperature value is as follows:
wherein the content of the first and second substances,
representing a temperature value of
；
Representing a temperature value
The degree of importance of;
maximum temperature values in the two-dimensional data matrix;
representing a second temperature average value corresponding to the two-dimensional data matrix;
representing a temperature value
Temperature change interval of (1).
The significance of (a) is that a larger significance of a temperature value means that the temperature value is more significant, i.e. the allowable temperature variation interval thereof is smaller, and conversely a smaller significance of a temperature value means that the temperature value is less significant, i.e. the allowable temperature interval thereof is larger. And obtaining the temperature change interval of each temperature value in the two-dimensional matrix.
And S4, solving an intersection of the temperature change intervals of all the temperature values, and dividing the temperature values corresponding to the temperature change intervals with the common intersection into a data section to obtain a plurality of data sections.
The intersection of the temperature change intervals of all the temperature values is solved, and the step of dividing the temperature values corresponding to the temperature change intervals with the common intersection into a data section comprises the following steps: acquiring a temperature change interval of a first temperature value and a second temperature value in a temperature time sequence data sequence; if the temperature change interval of the second temperature value is intersected with the temperature change interval of the first temperature value, dividing the second temperature value and the first temperature value into the same section; acquiring a temperature change interval of a third temperature value in the temperature time sequence data sequence, and if a common intersection exists between the temperature change interval of the third temperature value and the temperature change intervals of the second temperature value and the first temperature value, dividing the third temperature value, the second temperature value and the first temperature value into the same section; and traversing each temperature value in the temperature time sequence data sequence in sequence, if the temperature value and each temperature value of the category of the previous temperature value have a common intersection, dividing the temperature value and the previous temperature value into the same segment, otherwise, starting to segment again from the temperature value, and taking the temperature value divided into the same segment in the temperature time sequence data sequence as a data segment.
The data are pre-segmented according to the temperature change interval of each temperature value, the acquired temperature data are time sequence data, so that the temperature change interval of the first temperature value is acquired by taking the first temperature value in the temperature time sequence data sequence as a reference point, the temperature change interval of the second temperature value is searched according to the time sequence, and if the temperature change interval of the second temperature value is intersected with the temperature change interval of the first temperature value, the second temperature value and the first temperature value are divided into the same segment. And continuously searching for a third temperature value, and if a common intersection exists between the temperature change interval of the third temperature value and the temperature change intervals of the second temperature value and the first temperature value, dividing the third temperature value, the second temperature value and the first temperature value into the same segment.
Continuing to search for a fourth temperature value, and if a common intersection exists between the temperature change interval of the fourth temperature value and the temperature change intervals of the third temperature value, the second temperature value and the first temperature value, dividing the fourth temperature value, the third temperature value, the second temperature value and the first temperature value into the same segment; and if the temperature change interval of the fourth temperature value does not have a common intersection with the temperature change interval of any one of the third temperature value, the second temperature value and the first temperature value, starting the segmentation of the first data segment from the fourth temperature value, stopping the segmentation of the second data segment from the fourth temperature value, and taking the temperature value which is divided into the same segment in the temperature time sequence data sequence as one data segment according to the method.
And S5, dividing each data segment into a plurality of sub-data segments according to the similarity of the adjacent temperature values in each data segment.
The step of dividing each data segment into a plurality of sub-data segments according to the similarity of adjacent temperature values in each data segment comprises the following steps: obtaining a second difference sequence of each data segment by utilizing the difference between the temperature value of the latter temperature value and the temperature value of the former temperature value in every two adjacent temperature values in each data segment; calculating the similarity of adjacent temperature values in each data segment according to the difference value of two adjacent differential values in the second differential sequence; and determining the number of a plurality of continuous and adjacent temperature values with the accumulated similarity larger than the similarity threshold value by utilizing the similarity of the adjacent temperature values in each data segment, dividing the plurality of continuous and adjacent temperature values with the similarity larger than the similarity threshold value into a sub-data segment, and dividing each data segment into a plurality of sub-data segments in the same way.
After the temperature time sequence data sequence is divided into a plurality of data sections, in order to enable the follow-up compression ratio to be larger, each data section needs to be subdivided, as the more similar the variation trends of the data, the more favorable the data recompression is, the more dissimilar the variation trends of the data, the more unfavorable the recompression is, each data section is divided into a plurality of sub-data sections according to the similarity of adjacent temperature values in each data section, and when each data section is divided into a plurality of sub-data sections, the second differential sequence of each data section is firstly obtained as follows:
wherein the content of the first and second substances,
representing the difference between the second temperature value and the first temperature value in any data segment;
representing a difference between the third temperature value and the second temperature value;
is shown as
Individual temperature value and
difference in temperature values. The second difference sequence is obtained in the invention to subdivide each data segment, and the more similar the change trends of the adjacent temperature values, the higher the similarity of the difference values, i.e. the more favorable the recompression is when the temperature values are classified into one class, so the calculation formula of the similarity of the adjacent temperature values in each data segment is as follows:
wherein, the first and the second end of the pipe are connected with each other,
denotes the first
Individual temperature value and
the difference value of the temperature values, namely two differential sequence values are selected according to the sequence of the differential sequences to carry out data similarity calculation, if the requirement is met, the number of the differential sequences is continuously increased, and when the calculated similarity of a plurality of continuous and adjacent data is obtained
In time, a data similarity threshold is set according to empirical values
，
When the calculated similarity of a plurality of continuous and adjacent data is smaller than the similarity threshold value, stopping at the moment, classifying the difference values into one class, namely
The temperature values are subdivided into sub-data segments, each data segment is divided into a plurality of sub-data segments, and the temperature values in the sub-data segments areThe number is at least 2 and at most
At this time, finishing the fine segmentation of each data segment, and dividing each data segment into a plurality of sub-data segments; the similarity of the data calculated by using the difference between two adjacent difference values is that the smaller the difference between two adjacent difference values is, the more similar the temperature values corresponding to the difference values are.
S6, performing linear fitting on all temperature values in each sub-data segment to obtain a fitting value of each temperature value in each sub-data segment, subtracting the fitting value of each temperature value from the corresponding temperature value to obtain a first differential value of each temperature value, and arranging the obtained first differential values corresponding to each temperature value in each sub-data segment according to a time sequence to obtain a first differential sequence corresponding to each sub-data segment.
The step of performing linear fitting on all temperature values in each sub-data segment to obtain a fitting value of each temperature value in each sub-data segment comprises the following steps: the position of each temperature value in each sub data segment in the sub data segment is taken as an abscissa, and the temperature value is taken as an ordinate, so as to obtain a coordinate value of each temperature value in each sub data segment in a two-dimensional coordinate system; fitting all coordinate values in each subdata segment in a two-dimensional coordinate system to obtain a linear fitting equation; and substituting each temperature value in each sub data segment into a linear fitting equation to obtain a fitting value of each temperature value in each sub data segment.
In the prior art, when the differential sequence is obtained by adopting differential reversible variable length coding to carry out data compression, the differential sequence is obtained by depending on adjacent data, so that the invention does not depend on the adjacent data when carrying out differential, and one datum is required to be selected as a differential target for avoiding error propagation. Constructing a two-dimensional coordinate system by taking the position of each temperature value in each sub data segment in the sub data segment as an abscissa and the temperature value as an ordinate, and acquiring the coordinate value of each temperature value in each sub data segment in the two-dimensional coordinate system as a coordinate value
As shown in FIG. 2, the position of each temperature value within each sub-data segment in the two-dimensional coordinate system is determined.
As shown in FIG. 3, fitting all coordinate values in each sub-data segment in a two-dimensional coordinate system to obtain a linear fitting equation, and fitting by using the least square method to obtain a linear equation of
And substituting each temperature value in each sub data segment into a linear fitting equation to obtain a fitting value of each temperature value in each sub data segment. The fitting value of each temperature value is differentiated from the corresponding temperature value to obtain a first differential value corresponding to each temperature value, the obtained first differential values corresponding to each temperature value in each sub-data segment are arranged according to a time sequence to obtain a first differential sequence corresponding to each sub-data segment, and a calculation formula of the first differential value corresponding to each temperature value in the first differential sequence is as follows:
wherein the content of the first and second substances,
representing the Rth temperature value in any sub data segment;
a fitting value representing an Rth temperature value in any sub data segment;
a first differential value representing an R-th temperature value in any one of the sub-data segments. Thus, a first differential value sequence corresponding to each sub-data segment is obtained:
wherein, in the step (A),
the number of the differential values in the first differential value sequence corresponding to the sub data segment is shown.
S7, coding and compressing the first differential sequence corresponding to each sub data segment to obtain compressed data of each sub data segment.
The step of coding and compressing the first differential sequence corresponding to each subdata segment to obtain the compressed data of each subdata segment comprises the following steps: and coding and compressing the first difference sequence corresponding to each sub-data segment by adopting run coding, and obtaining compressed data of each sub-data segment under the condition of storing the slope and intercept of the linear fitting equation corresponding to each sub-data segment.
After the first differential sequence corresponding to each subdata segment is obtained, coding and compressing the first differential sequence corresponding to each subdata segment by adopting run length coding, and simultaneously storing the slope and intercept of a linear fitting equation corresponding to each subdata segment, wherein the slope of the linear fitting equation is used as the slope
For expressing, intercept of, linear fitting equation
And (4) showing. For example: in one of the sub-data sections
The values are respectively:
and the first difference sequence corresponding to the sub data segment is as follows:
the compressed data is
The same compression mode is carried out on other subsections, and the header storage of the subsegment compressed data is
The value, the remaining storage is the first differential sequence value.
S8, transmitting the compressed data of each subdata segment to a garlic storage chamber monitoring control center, decompressing the compressed data by the garlic storage chamber monitoring control center, and acquiring temperature time sequence data of each detection position in the garlic storage chamber in the same sampling interval.
Transmitting the compressed data of each subdata segment to a garlic storage chamber monitoring control center, decompressing the compressed data by the garlic storage chamber monitoring control center, acquiring temperature time sequence data of each detection position in the garlic storage chamber in the same sampling interval, analyzing and calculating the temperature time sequence data of each detection position in the same sampling interval, judging whether the temperature time sequence data of each detection position in the same sampling interval deviates from a normal temperature range, calculating the deviation degree of the normal temperature range, determining whether the temperature of each detection position is abnormal according to the deviation degree and the change trend, determining the detection position with abnormal temperature, and informing garlic storage chamber managers to regulate and control the area with abnormal temperature.
In summary, the present invention provides a real-time status monitoring method for garlic storage, which improves the step of obtaining a difference sequence in a difference reversible variable length code, adaptively segments data according to the characteristics of collected temperature data in a garlic storage room, obtains a reference value of each segment of data, and subtracts the temperature value of each segment of data from the reference value to obtain the difference sequence of each segment of data, thereby avoiding error propagation caused by cumulative errors occurring in obtaining the difference sequence.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. A real-time status monitoring method for garlic storage is characterized by comprising the following steps:
collecting the temperature value of each detection position in the garlic storage room in real time;
forming a temperature time sequence data sequence by temperature values in the same sampling interval of each detection position, converting the temperature time sequence data sequence into a two-dimensional data matrix, establishing a sliding window by taking each temperature value in the two-dimensional data matrix as a center, and calculating the importance degree of each temperature value by using all the temperature values contained in the sliding window;
establishing a sliding window by taking each temperature value in the two-dimensional data matrix as a center, and calculating the importance degree of each temperature value by using all temperature values contained in the sliding window, wherein the steps comprise: selecting any temperature value in the two-dimensional data matrix as a target temperature value, and establishing a target sliding window by taking the target temperature value as a center; calculating a first temperature average value corresponding to the target sliding window by using all temperature values contained in the target sliding window; calculating a first difference value between each temperature value in the target sliding window and a first temperature average value corresponding to the target sliding window; calculating the importance degree of the target temperature value by using all the first difference values corresponding to the target sliding window and the quantity of the temperature values in the target sliding window; calculating the importance degree of each temperature value according to the importance degree calculation method of the target temperature value;
calculating the temperature change interval of each temperature value by using the importance degree of each temperature value in the two-dimensional data matrix;
solving an intersection of the temperature change intervals of all the temperature values, and dividing the temperature values corresponding to the temperature change intervals with the common intersection into a data section to obtain a plurality of data sections;
dividing each data segment into a plurality of sub-data segments according to the similarity of adjacent temperature values in each data segment;
performing linear fitting on all temperature values in each sub-data segment to obtain a fitting value of each temperature value in each sub-data segment, subtracting the fitting value of each temperature value from the corresponding temperature value to obtain a first differential value of each temperature value, and arranging the obtained first differential values corresponding to each temperature value in each sub-data segment according to a time sequence order to obtain a first differential sequence corresponding to each sub-data segment;
coding and compressing the first differential sequence corresponding to each subdata segment to obtain compressed data of each subdata segment;
transmitting the compressed data of each subdata segment to a garlic storage chamber monitoring control center, decompressing the compressed data by the garlic storage chamber monitoring control center, and acquiring temperature time sequence data of each detection position in the garlic storage chamber in the same sampling interval.
2. The method for monitoring the real-time status of garlic storage according to claim 1, wherein the step of calculating the temperature change interval of each temperature value by using the importance degree of each temperature value in the two-dimensional data matrix comprises:
calculating a second temperature average value corresponding to the two-dimensional data matrix by using all temperature values in the two-dimensional data matrix;
acquiring a maximum temperature value in the two-dimensional data matrix, and calculating a second difference value between the corresponding maximum temperature value in the two-dimensional data matrix and a second temperature average value;
calculating the temperature change amplitude corresponding to each temperature value by using the second difference and the importance degree of each temperature value;
adding the temperature change amplitude corresponding to each temperature value and the temperature value to be used as the temperature upper limit value of the temperature value, and subtracting the temperature change amplitude corresponding to each temperature value and the temperature value to be used as the temperature lower limit value of the temperature value;
the temperature change interval of each temperature value is determined by the upper temperature limit value and the lower temperature limit value of each temperature value.
3. The real-time state monitoring method for garlic storage according to claim 1, wherein the step of solving an intersection of temperature change intervals of all temperature values and dividing the temperature values corresponding to the temperature change intervals with the common intersection into a data segment comprises:
acquiring a temperature change interval of a first temperature value and a second temperature value in a temperature time sequence data sequence;
if the temperature change interval of the second temperature value is intersected with the temperature change interval of the first temperature value, dividing the second temperature value and the first temperature value into the same section;
acquiring a temperature change interval of a third temperature value in the temperature time sequence data sequence, and if a common intersection exists between the temperature change interval of the third temperature value and the temperature change intervals of the second temperature value and the first temperature value, dividing the third temperature value, the second temperature value and the first temperature value into the same section;
and traversing each temperature value in the temperature time sequence data sequence in sequence, if the temperature value and each temperature value of the category of the previous temperature value have a common intersection, dividing the temperature value and the previous temperature value into the same segment, otherwise, starting to segment again from the temperature value, and taking the temperature value divided into the same segment in the temperature time sequence data sequence as a data segment.
4. The real-time garlic storage state monitoring method as claimed in claim 1, wherein the step of dividing each data segment into a plurality of sub-data segments according to the similarity of adjacent temperature values in each data segment comprises:
obtaining a second difference sequence of each data segment by utilizing the difference between the temperature value of the latter temperature value and the temperature value of the former temperature value in every two adjacent temperature values in each data segment;
calculating the similarity of adjacent temperature values in each data segment according to the difference value of two adjacent differential values in the second differential sequence;
and determining the number of a plurality of continuous and adjacent temperature values with the accumulated similarity larger than the similarity threshold value by utilizing the similarity of the adjacent temperature values in each data segment, dividing the plurality of continuous and adjacent temperature values with the similarity larger than the similarity threshold value into a sub-data segment, and dividing each data segment into a plurality of sub-data segments in the same way.
5. The method for monitoring the real-time status of garlic storage according to claim 1, wherein the step of performing linear fitting on all temperature values in each sub-data segment to obtain a fitted value of each temperature value in each sub-data segment comprises:
the position of each temperature value in each sub data segment in the sub data segment is taken as an abscissa, and the temperature value is taken as an ordinate, so as to obtain a coordinate value of each temperature value in each sub data segment in a two-dimensional coordinate system;
fitting all coordinate values in each subdata segment in a two-dimensional coordinate system to obtain a linear fitting equation;
and substituting each temperature value in each sub data segment into a linear fitting equation to obtain a fitting value of each temperature value in each sub data segment.
6. The method for monitoring the real-time status of garlic storage according to claim 5, wherein the step of encoding and compressing the first differential sequence corresponding to each sub-data segment to obtain the compressed data of each sub-data segment comprises:
and coding and compressing the first difference sequence corresponding to each sub-data segment by adopting run coding, and obtaining compressed data of each sub-data segment under the condition of storing the slope and intercept of the linear fitting equation corresponding to each sub-data segment.
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