CN110631721B - Granary heat insulation judgment method based on grain condition big data - Google Patents

Granary heat insulation judgment method based on grain condition big data Download PDF

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CN110631721B
CN110631721B CN201910890622.9A CN201910890622A CN110631721B CN 110631721 B CN110631721 B CN 110631721B CN 201910890622 A CN201910890622 A CN 201910890622A CN 110631721 B CN110631721 B CN 110631721B
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高树成
曹毅
吴文福
王赫
韩峰
王启阳
崔宏伟
兰天忆
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LIAONING RESEARCH INSTITUTE OF GRAIN SCIENCE
Jilin University
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Jilin University
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Abstract

The invention discloses a method for generating a grain condition big dataThe granary heat preservation judging method comprises the following steps: step one, collecting historical grain condition data, and selecting a grain temperature detection time range delta tau; step two, calculating the temperature difference change rate delta V between the outside environment temperature and the bin temperature and the temperature difference change rate delta V (i, j, k) between the outside environment temperature and the grain temperature collected by each sensor; step three, when the temperature difference change rate delta V (i, j, k) result is judged to be normal distribution, establishing a fitting function Y (i, j, k, delta tau) of the position of the temperature measuring sensor and the temperature difference change rate delta V (i, j, k); step four, calculating coefficients A (i, j, k, delta tau), B (i, j, k, delta tau) and C (i, j, k, delta tau) of the fitting function by adopting a least square method, and calculating a decision coefficient R of the fitting function2Calculating judgment parameters D (i, j, k, delta tau) and F (i, j, k, delta tau); step five, judging the heat preservation of the top and the wall of the granary; and step six, summarizing the normal conditions to obtain the heat preservation performance of the whole granary.

Description

Granary heat insulation judgment method based on grain condition big data
Technical Field
The invention relates to the field of grain storage, in particular to a granary heat preservation judgment method based on grain condition big data.
Background
With the improvement of the living standard of people, the requirements on the quality of grains are improved, the quality, nutrition, taste and sanitation of the grains are studied, the requirements on the grain storage of warehouses are continuously improved, people demand high-quality, high-nutrition and low-pollution of the grains, and a new problem of green grain storage engineering research is provided for people. The low-temperature grain storage is an effective green grain storage measure, the deterioration of the grain quality can be effectively delayed at low temperature (below 15 ℃), the harm of pests and microorganisms to the grain is inhibited, the chemical agent consumption of the stored grain is reduced, even no chemical agent is used, the purposes of quality guarantee and freshness preservation are achieved, the grain pollution is reduced, and meanwhile, the environmental protection and energy conservation are facilitated. The key of low-temperature grain storage is the heat insulation condition of the warehouse, particularly in the month with high air temperature, the air temperature and the warehouse temperature have serious influence on the surface layer temperature of the grain pile, which is caused by that accumulated heat in the warehouse is not removed in time, and is a main factor influencing the quality of the stored grain. In order to enable the barn to be in a stable low-temperature state, the air tightness, the heat preservation and the heat insulation of the barn are important influencing factors except for the technical means of natural ventilation, mechanical refrigeration and the like, the energy consumption can be saved and the barn temperature can be kept only by improving the heat preservation and the heat insulation performance of a granary maintenance structure, and the method plays a key role in realizing safe, economic and high-quality low-temperature grain storage.
The heat preservation and heat insulation of the granary are influenced by a plurality of factors, but according to the existing literature research and the heat preservation and heat insulation technical test of each granary, the heat source of the granary mainly comes from the top and the wall of the granary, a large number of students carry out deep research on the problem of heat preservation and heat insulation of the granary for a long time, the heat preservation and heat insulation performance of the granary is improved by technically modifying the top and the wall of the granary by adopting heat preservation and heat insulation materials with excellent performance, and the effect of the heat insulation materials on slowing down the grain temperature rising rate in high-temperature seasons is verified through a real-bin test. However, factors influencing the heat preservation and insulation of the granary are multiple and complex, and no relevant research is found on how to effectively monitor the heat preservation performance of the granary in real time and determine the heat preservation performance of the top and the wall of the granary through analysis of historical grain situation data in the grain storage process. Therefore, the invention provides a method for analyzing and judging the heat preservation performance of the wall surface and the top of the granary based on the grain condition data in the historical storage process, so that the real-time monitoring of the heat preservation performance of the granary and the detection of the heat preservation performance of the granary in the historical storage process are realized, and the grain storage safety is effectively improved.
Disclosure of Invention
The invention designs and develops a granary heat insulation judging method based on big data of grain conditions, the invention judges whether the granary is in an empty state or not by analyzing the change of the historical grain temperature and the temperature outside the granary according to the grain condition data in the historical storage process, detects the heat insulation performance of the wall surface of the granary by analyzing the change of the historical grain temperature and the temperature outside the granary, detects the heat insulation performance of the top of the granary by analyzing the change of the historical grain temperature and the temperature outside the granary, and realizes the detection of the granary heat insulation in the historical storage process by combining with a set judging threshold value.
The technical scheme provided by the invention is as follows:
a granary heat preservation judgment method based on grain situation big data comprises the following steps:
step one, collecting historical grain condition data, and selecting a grain temperature detection time range delta tau;
step two, calculating the temperature difference change rate delta V of the outside environment temperature and the bin temperature:
Figure BDA0002208632480000021
and
calculating the temperature difference change rate delta V (i, j, k) of the environment temperature outside the barn and the grain temperature collected by each sensor:
Figure BDA0002208632480000022
wherein i, j, k respectively represent the position parameters of the temperature measuring sensor, T (tau)outAmbient temperature outside the warehouse on day τ, T (τ + Δ τ)outIs the environment temperature outside the grain pile on the (tau + delta tau) th day, T (tau) is the grain pile temperature on the (tau + delta tau) th day, T (tau + delta tau) is the grain pile temperature on the (tau + delta tau) th day, T (tau) (i, j, k) is the temperature of the sensor on the (i, j, k) th position inside the grain pile on the (tau + delta tau) th day,
t (tau + delta tau) (i, j, k) is the temperature of the (i, j, k) th position sensor in the (tau + delta tau) th grain pile;
step three, when the temperature difference change rate delta V (i, j, k) result is judged to be normal distribution, establishing a fitting function Y (i, j, k, delta tau) of the position of the temperature measuring sensor and the temperature difference change rate delta V (i, j, k):
Y(i,j,k,Δτ)＝A(i,j,k,Δτ)·i2+B(i,j,k,Δτ)·i+C(i,j,k,Δτ)；
wherein A, B, C is a variation parameter;
step four, calculating coefficients A (i, j, k, delta tau), B (i, j, k, delta tau) and C (i, j, k, delta tau) of the fitting function by adopting a least square method, and calculating a decision coefficient R of the fitting function2(ii) a And
calculating judgment parameters D (i, j, k, delta tau) and F (i, j, k, delta tau);
wherein the content of the first and second substances,
Figure BDA0002208632480000031
Figure BDA0002208632480000032
step five, judging the heat preservation of the top and the wall of the granary:
when | delta V | ≦ delta VmThe heat preservation performance of the bin top is good; when | Δ V | > Δ VmIn time, the heat insulating property of the bin top is poor;
when | A (i, j, k, Δ τ) | < AeJudging that the granary is in an empty state;
when A (i, j, k,. DELTA.tau) > AmaxOr A (i, j, k, Δ τ) < AminJudging that the grain condition at the test point is abnormal;
when D (i, j, k, Δ τ) > DmaxOr D (i, j, k, Δ τ) < DminJudging the abnormal change of the grain temperature of the test points;
when F (i, j, k,. DELTA.tau) > FmaxOr F (i, j, k, Δ τ) < FminJudging the abnormal change of the grain temperature of the test points;
wherein, is Δ Vm、Ae、Amin、Dmin、Fmin、Amax、Dmax、FmaxRespectively setting threshold values;
normal grain temperature ratio of the wall of the barn
Figure BDA0002208632480000033
When gamma is less than P, the heat insulating property grade of the bin wall is excellent;
if alpha is more than or equal to P and less than gamma, the heat preservation grade of the bin wall is good;
if the beta is not less than the P and less than the alpha, the heat insulating property grade of the bin wall is middle;
if P is less than beta, the heat insulating property grade of the bin wall is poor;
wherein gamma, alpha and beta are respectively grade threshold values, N is the number of times of normal change of the grain temperature determined by the result, and N isTIs the total number of detections;
and step six, summarizing the normal conditions to obtain the heat preservation performance of the whole granary.
Preferably, Δ τ ≧ 10.
Preferably, in the fourth step, determining the coefficient and the judgment parameter by a least square process includes:
calculating the deviation epsilon of the fitting function value and the actual grain temperature valuex＝Tx-FxMaking said deviation sum of squares
Figure BDA0002208632480000034
Determining the coefficient and the judgment parameter when the minimum value is reached;
wherein the content of the first and second substances,
Figure BDA0002208632480000035
in the formula, TxIs the actual grain temperature value, FxAnd fitting the grain temperature value of the fitting function at the x-axis position, wherein x is the coordinate value of i or j and the corresponding temperature value.
Preferably, the temperature measuring sensors are arranged in the vertical direction, and the data of each layer of sensors are analyzed; and
determining the number of layers of all sensors adjacent to the bin wall to be detected, dividing the number of layers into two directions according to the arrangement of the sensors, wherein the two directions are mutually vertical and are respectively a row and a column, and the number of rows is greater than the number of columns;
and respectively determining position parameters in the two directions, fixing the parameters in any one direction, and sequentially increasing the parameters in the other direction to be used as test points for testing.
Preferably, in the fourth step, the deviation of the fitting function at the spatial position of the thermometric sensor and the coefficients a (i, j, k, Δ τ), B (i, j, k, Δ τ) and C (i, j, k, Δ τ) of the fitting function when the sum of squares of the deviations reaches the minimum value are calculated by using a least square method;
the calculation formula of the deviation square sum is that E is (Y-delta V (i, j, k))2；
Preferably, in the fourth step, the coefficient of determination R of the fitting function2Is composed of
Figure BDA0002208632480000041
In the formula, R2≥0.3，i＝1,2,3,...,N，
Figure BDA0002208632480000042
Is the mean value.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention judges whether the granary is empty or not according to the change rate of the difference between the historical grain temperature and the temperature outside the granary by analyzing the change rule of the grain condition data in the historical storage process;
2. the method has real-time detection performance, and the granary is likely to age along with the increase of storage time, so that the method can detect and evaluate the heat insulation performance of each position of the granary in the grain storage process, and when the heat insulation performance of the granary is not good, certain measures can be taken in real time to ensure the safe storage of grains;
3. according to the invention, no hardware equipment is required to be added, the supervision of grain storage can be realized only by adding a software analysis module to the existing system, and the evaluation of the heat preservation performance of the granary can be realized by analyzing and counting the grain temperature change rule;
4. the invention realizes the rapid detection of the grain storage state and the heat preservation of the granary, reduces the workload of manual inspection, improves the working efficiency, and the detection method is based on grain temperature data in the historical storage process, the data can not be easily modified by people, the detection result is strong in objectivity, and a custodian can conveniently find the position with poor heat preservation of the granary in time and take measures to strengthen the heat preservation, so as to ensure the grain storage safety;
5. the invention detects whether the grain exists, the heat preservation performance of the top of the granary and the heat preservation performance of the wall surface of the granary according to two statistical characteristics, and the detection result is real-time and reliable.
Drawings
Fig. 1 is a flow chart of the granary thermal insulation judging method based on grain situation big data.
FIG. 2 is a schematic diagram of the rate of change of the top temperature of the warehouse.
Fig. 3 is a longitudinal-section isotherm diagram of 2018, 5/month/25/day with m being 7 and an external temperature of 22.3 ℃.
Fig. 4 is a longitudinal-section isotherm diagram of 2018, 6/month, 3/day, m-7, and an external temperature of 26.5 ℃.
Fig. 5 is a longitudinal-section isotherm diagram of 2018, 6, 24, m ═ 7, and an external temperature of 32 ℃.
Fig. 6 is a longitudinal-section isotherm diagram of 7/3/7 in 2018 with an external temperature of 28.8 ℃.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
As shown in fig. 1, the present invention provides a granary thermal insulation judging method based on grain situation big data, comprising:
step one, calling historical grain situation data, and finishing data cleaning: removing abnormal grain temperature and complement missing grain temperature data;
selecting a standard granary and a grain temperature detection time range delta tau of the granary, wherein the delta tau is more than or equal to 10;
step three, calculating the temperature difference change rate delta V of the outside environment temperature and the bin temperature:
Figure BDA0002208632480000051
wherein, T (tau)outThe ambient temperature outside the chamber on day τ, T (τ) is the chamber temperature on day τ, T (τ + Δ τ)outThe ambient temperature outside the warehouse on the (tau + delta tau) th day, and the temperature T (tau + delta tau) on the (tau + delta tau) th day;
step four, calculating the temperature difference change rate delta V (i, j, k) of the environment temperature outside the barn and the grain temperature:
determining the relevant information of the wall surface to be detected of the grain storage bin, including the outside temperature, the positions of all sensors in the grain pile and the collected temperature, and calculating the temperature difference change rate delta V (i, j, k) of the outside environment temperature and the grain temperature collected by all the sensors:
Figure BDA0002208632480000052
wherein i, j, k respectively represent the position parameters of the temperature measuring sensor, T (tau)outThe ambient temperature outside the warehouse on day Tth, T (tau) (i, j, k) is the temperature of the sensor at the (i, j, k) th position inside the grain pile on day Tth, T (tau + delta tau)outThe ambient temperature outside the warehouse on the (tau + delta tau) th day, and T (tau + delta tau) (i, j, k) is the temperature of the (i, j, k) th position sensor inside the (tau + delta tau) th grain pile.
Step five, if the temperature difference change rate delta V (i, j, k) result in the step four is judged to be normal distribution, establishing a fitting function of the position of the temperature measuring sensor and the temperature difference change rate delta V (i, j, k):
Y(i,j,k,Δτ)＝A(i,j,k,Δτ)·i2+B(i,j,k,Δτ)·i+C(i,j,k,Δτ)；
wherein A, B, C is a variation parameter.
Calculating the deviation of the fitting function at the space position of the temperature measuring sensor by adopting a least square method, and calculating coefficients A (i, j, k, delta tau), B (i, j, k, delta tau) and C (i, j, k, delta tau) of the fitting function when the sum of squares of the deviation reaches a minimum value, wherein the sum of squares of the deviations is calculated by a formula:
E＝(Y-ΔV(i,j,k))2；
calculating a fitting function decision coefficient R2：
Figure BDA0002208632480000061
Wherein R is2≥0.3，i＝1,2,3,...,N，
Figure BDA0002208632480000062
Is an average value;
step six, calculating a judgment parameter D (i, j, k, delta tau) and a parameter E (i, j, k, delta tau), wherein the calculation method comprises the following steps:
Figure BDA0002208632480000063
Figure BDA0002208632480000064
step seven, judging the heat insulating property of the bin top and the bin wall, comprising the following steps:
step 1, judging and analyzing the coefficients and parameters in the step three, comprising the following steps:
when | delta V | ≦ delta VmIn time, the heat preservation of the bin top is better; when | Δ V | > Δ VmIn time, the heat preservation of the bin top is poor;
step 2, judging and analyzing the coefficients and parameters in the step five and the step six, comprising the following steps:
when | A (i, j, k, Δ τ) | < AeJudging that the granary is in an empty state;
when A (i, j, k,. DELTA.tau) > AmaxOr A (i, j, k, Δ τ) < AminJudging that the grain condition at the test point is abnormal;
when D (i, j, k, Δ τ) > DmaxOr D (i, j, k, Δ τ) < DminJudging the abnormal change of the grain temperature at the test point;
when F (i, j, k,. DELTA.tau) > FmaxOr F (i, j, k, Δ τ) < FminJudging the abnormal change of the grain temperature at the test point;
wherein, is Δ Vm、Ae、Amin、Dmin、Fmin、Amax、Dmax、FmaxRespectively setting threshold values;
step 3, respectively calculating the normal grain temperature proportion P of the wall of the granary judged in the step 2:
Figure BDA0002208632480000071
in the formula, N is the number of times of normal change of grain temperature, NTIs the total number of detections;
step 4, counting the results of the step 3:
if gamma is less than P, detecting that the heat retaining property grade of the wall surface is excellent;
if the alpha is more than or equal to P and less than gamma, detecting that the heat preservation level of the wall surface is good;
if the beta is not more than the P and less than the alpha, detecting that the heat retaining property grade of the wall surface is middle;
if P is less than beta, detecting that the grade of the heat retaining property of the wall surface is poor;
wherein, gamma, alpha and beta are grade threshold values respectively;
step eight, summarizing the normal conditions appearing in the step seven to obtain the heat preservation performance of the whole warehouse.
In another embodiment, the granary sensors are arranged and divided into h layers in the vertical direction, and the data of each layer of sensors are analyzed; the data selection process of the single test direction test point comprises the following steps:
determining the number of layers of all sensors adjacent to the wall surface of the granary to be detected, dividing the layers into two directions according to the arrangement of the sensors, wherein the two directions are mutually vertical and are respectively a row and a column, and the number of the rows is larger than the number of the columns; and respectively determining position parameters in the two directions, fixing the parameters in any one direction, and sequentially increasing the parameters in the other direction to be used as test points for testing.
And determining the coefficients in the step five by adopting a least square method:
Fxfitting the grain temperature value of the fitting function at the x-position of the abscissa, wherein x is a coordinate value, and x is the coordinate value of i or j and the corresponding temperature value; the deviation of the fitting function value and the actual grain temperature value is epsilonx＝Tx-FxTo be made ofThe fitting function can more accurately reflect the change trend of the data, and the square sum of the deviation of the fitting function and the grain temperature
Figure BDA0002208632480000072
Should be minimized.
Figure BDA0002208632480000073
To make it possible to
Figure BDA0002208632480000074
And (3) obtaining the minimum, simultaneously and respectively calculating partial derivatives of a, b and c on two sides of the equal sign of the formula, and simplifying the equation into that:
Figure BDA0002208632480000075
Figure BDA0002208632480000081
Figure BDA0002208632480000082
the above three equations are transformed into matrix form as follows:
Figure BDA0002208632480000083
a (i, j, k, Δ τ), B (i, j, k, Δ τ), C (i, j, k, Δ τ), D (i, j, k, Δ τ), and F (i, j, k, Δ τ) are obtained by solving the above matrix.
Examples
Taking the grain situation data of a grain bin in a certain reserve warehouse in Henan as an example, the size of the grain bin is about 61m multiplied by 23m multiplied by 8m, the grain height is about 6m, 112 temperature measuring cables are arranged in the grain bin, the number of rows is 14, the number of columns is 8, the temperature measuring cables are arranged in a 14 multiplied by 8 rectangle, and four temperature measuring points are arranged on each cable for 448 temperature measuring points in total; the grain seed to be stored is corn. The data was started at 8/18 in 2017 and ended at 29/5 in 2019. In this case, the detection time range is selected to be 10 days, i.e., the heat retention of the chamber is analyzed at intervals of 10 days.
1. Firstly, judging the heat preservation of the top of the warehouse, wherein the change rate of the temperature difference of the top of the warehouse is shown in figure 2, and the change rates of the temperature difference between the warehouse temperature and the external temperature of the warehouse which is stored for 20 months are lower than the threshold value of 1.5 ℃/d, so that the heat preservation of the top of the warehouse can be judged to be better.
2. Selecting a layer section, and dividing a point set according to the coordinate axis direction and the grain condition sensor arrangement line rows and columns; and selecting the first layer of the granary as an analysis object.
3. For a row point set P (m, 1, 1) of a single temperature difference change rate, the space coordinates are { (1, 1, 1), (2, 1, 1) … (m, 1, l) }, the column point set P (1, n, 1) and the space coordinates are { (1, 1, 1), (1, 2, 1) … (1, n, l) }, the least square method is used for fitting the row and column grain temperature functions of the first layer, and A, B, C, D, F and R are obtained2A value; the statistical analysis of historical data shows that the threshold values are shown in the following table 1, wherein m is 1-14, and n is 1-8;
TABLE 1 statistical analysis of historical data for various thresholds
Figure BDA0002208632480000084
Figure BDA0002208632480000091
4. Selecting a fitting decision coefficient R2Counting the point sets of not less than 0.3, taking 10 days as an interval, wherein the initial time is 8 and 18 days in 2017, the end time is 7 and 23 days in 2018, and the total time is 360 days, and the statistics are shown in Table 2:
TABLE 2
Figure BDA0002208632480000092
Figure BDA0002208632480000101
The A values of the sequence numbers 27 and 30 are less than the threshold value 10-3And the corresponding dates are 5 and 14 days in 2018 and 6 and 13 days in 2018, and the operation records are checked to know that the grain depot carries out grain surface turning operation at the time, and the temperature measuring cable is pulled out of the grain pile to be placed, so that the empty bin state is judged.
5. Selecting the time period with larger temperature change difference as follows: from 15/2018/4/2018/12/8/2018, the total number of the fitting equation decision coefficients is counted to be greater than or equal to 0.3, and the normal point judgment statistics are shown in table 3:
TABLE 3
Figure BDA0002208632480000102
From the above table, it can be seen that: normal ratio of 6 and 7 months in 2018: the condition that the temperature of the wall of the bin is lower than 0.6 exists in both rows and columns, so that the heat preservation grade of the wall of the bin can be judged to be poor, and measures need to be taken. And selecting a longitudinal section with m being 7, and selecting time with low normal proportion as a temperature contour map as shown in figures 3-6.
As can be seen from FIGS. 3 to 6, when the longitudinal section isotherm graphs of 5 and 25 days in 2018 and 3 and 6 months in 2018 are compared, the isotherm change is very obvious when the interval is 10 days, the surface layer of the isotherm is increased from 18 ℃ to 20 ℃, and the temperature increase speed is higher than the normal speed. Similarly, the normal ratio is low between 24 days in 2018 and 3 days in 2018, and the change of the isotherm cloud pictures is large.
The investigation finds that the warehouse is an old house type warehouse, the infrastructure is old, the heat preservation performance of the warehouse is really insufficient, and the warehouse is not beneficial to grain storage safety.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.

Claims (6)

1. A granary heat-insulating property judgment method based on grain condition big data is characterized by comprising the following steps:
step one, collecting historical grain condition data, and selecting a grain temperature detection time range delta tau;
step two, calculating the temperature difference change rate delta V of the outside environment temperature and the bin temperature:
Figure FDA0002208632470000011
and
calculating the temperature difference change rate delta V (i, j, k) of the environment temperature outside the barn and the grain temperature collected by each sensor:
Figure FDA0002208632470000012
wherein i, j, k respectively represent the position parameters of the temperature measuring sensor, T (tau)outAmbient temperature outside the warehouse on day τ, T (τ + Δ τ)outThe temperature of the environment outside the grain pile on the (tau + delta tau) th day, T (tau) the temperature of the grain pile on the (tau + delta tau) th day, T (tau) (i, j, k) the temperature of the sensor on the (i, j, k) th position inside the grain pile on the (tau + delta tau) th day, and T (tau + delta tau) (i, j, k) the temperature of the sensor on the (i, j, k) th position inside the grain pile on the (tau + delta tau) th day;
step three, when the temperature difference change rate delta V (i, j, k) result is judged to be normal distribution, establishing a fitting function Y (i, j, k, delta tau) of the position of the temperature measuring sensor and the temperature difference change rate delta V (i, j, k):
Y(i,j,k,Δτ)＝A(i,j,k,Δτ)·i2+B(i,j,k,Δτ)·i+C(i,j,k,Δτ)；
wherein A, B, C is a variation parameter;
step four, calculating coefficients A (i, j, k, delta tau), B (i, j, k, delta tau) and C (i, j, k, delta tau) of the fitting function by adopting a least square method, and calculating a decision coefficient R of the fitting function2(ii) a And
calculating judgment parameters D (i, j, k, delta tau) and F (i, j, k, delta tau);
wherein the content of the first and second substances,
Figure FDA0002208632470000013
Figure FDA0002208632470000014
step five, judging the heat preservation of the top and the wall of the granary:
when | delta V | ≦ delta VmThe heat preservation performance of the bin top is good; when | Δ V | > Δ VmIn time, the heat insulating property of the bin top is poor;
when | A (i, j, k, Δ τ) < AeJudging that the granary is in an empty state;
when A (i, j, k,. DELTA.tau) > AmaxOr A (i, j, k, Δ τ) < AminJudging that the grain condition at the test point is abnormal;
when D (i, j, k, Δ τ) > DmaxOr D (i, j, k, Δ τ) < DminJudging the abnormal change of the grain temperature of the test points;
when F (i, j, k,. DELTA.tau) > FmaxOr F (i, j, k, Δ τ) < FminJudging the abnormal change of the grain temperature of the test points;
wherein, is Δ Vm、Ae、Amin、Dmin、Fmin、Amax、Dmax、FmaxRespectively setting threshold values;
normal grain temperature ratio of the wall of the barn
Figure FDA0002208632470000021
When gamma is less than P, the heat insulating property grade of the bin wall is excellent;
if alpha is more than or equal to P and less than gamma, the heat preservation grade of the bin wall is good;
if the beta is not less than the P and less than the alpha, the heat insulating property grade of the bin wall is middle;
if P is less than beta, the heat insulating property grade of the bin wall is poor;
wherein, gamma, alpha and beta are respectively grade threshold values, and N isThe result judges the number of times of normal change of the grain temperature, NTIs the total number of detections;
and step six, summarizing the normal conditions to obtain the heat preservation performance of the whole granary.
2. The method for determining the thermal insulation of a grain depot based on grain situation big data as claimed in claim 1, wherein Δ τ is greater than or equal to 10.
3. The method for judging the heat retaining property of the grain bin based on the grain situation big data as claimed in claim 1 or 2, wherein in the fourth step, the step of determining the coefficient and the judgment parameter by using a least square process comprises the following steps:
calculating the deviation epsilon of the fitting function value and the actual grain temperature valuex＝Tx-FxMaking said deviation sum of squares
Figure FDA0002208632470000022
Determining the coefficient and the judgment parameter when the minimum value is reached;
wherein the content of the first and second substances,
Figure FDA0002208632470000023
in the formula, TxIs the actual grain temperature value, FxAnd fitting the grain temperature value of the fitting function at the x-axis position, wherein x is the coordinate value of i or j and the corresponding temperature value.
4. The grain bin thermal insulation judging method based on the grain situation big data as claimed in claim 3, characterized in that the temperature measuring sensors are arranged in a vertical direction, and the data of each layer of sensors are analyzed; and
determining the number of layers of all sensors adjacent to the bin wall to be detected, dividing the number of layers into two directions according to the arrangement of the sensors, wherein the two directions are mutually vertical and are respectively a row and a column, and the number of rows is greater than the number of columns;
and respectively determining position parameters in the two directions, fixing the parameters in any one direction, and sequentially increasing the parameters in the other direction to be used as test points for testing.
5. The method for determining the thermal insulation of the grain bin according to claim 4, wherein in the fourth step, the deviation of the fitting function in the spatial position of the temperature measuring sensor and the coefficients A (i, j, k, Δ τ), B (i, j, k, Δ τ) and C (i, j, k, Δ τ) of the fitting function when the sum of squares of the deviations reaches the minimum value are calculated by a least square method;
the calculation formula of the deviation square sum is that E is (Y-delta V (i, j, k))2。
6. The method for determining the thermal insulation of a grain bin according to claim 5, wherein in the fourth step, the coefficient of determination R of the fitting function is2Is composed of
Figure FDA0002208632470000031
In the formula, R2≥0.3，i＝1,2,3,...,N，
Figure FDA0002208632470000032
Is the mean value.
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