An intelligent temperature control system for assembled metal low-temperature granaries

Abstract

The present invention relates to the field of temperature control technology, and specifically to an intelligent temperature control system for an assembled metal low-temperature granary, comprising a data acquisition module, a regional square bin analysis module, an overall square bin analysis module and an intelligent temperature control module; the data acquisition module acquires first environmental data and second rice data; the regional square bin analysis module acquires a first germination coefficient of each square bin area according to the first environmental data; the overall square bin analysis module is used to acquire a second overall square bin germination coefficient of the overall square bin and a temperature error of the first overall square bin, and obtain a third overall square bin abnormal coefficient; the intelligent temperature control module obtains a first fan adjustment power by comparing the abnormal coefficient with a preset threshold value, so as to control the overall square bin temperature so that it reaches the overall square bin target temperature, thereby realizing intelligent regulation of the metal square bin temperature and further ensuring the quality of rice.

Description

Technical Field

[0001] The invention relates to the technical field of temperature control, and in particular to an intelligent temperature control system for an assembled metal low-temperature granary. Background Art

[0002] Metal granaries are grain warehouses that use metal materials as the main building material. Metal granaries are widely used in granary construction due to their high strength, corrosion resistance and light weight.

[0003] When storing rice, traditional metal square silos do not fully consider the impact of environmental differences in different altitudes on temperature control, nor do they fully consider the impact of rice germination trends on temperature control, resulting in insufficient control efficiency of the overall temperature of the metal square silos, which in turn leads to insufficient storage quality of rice.

[0004] Therefore, an intelligent temperature control system for assembled metal low-temperature granary is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent temperature control system for an assembled metal low-temperature granary. The present invention relates to the field of temperature control technology, specifically to an intelligent temperature control system for an assembled metal low-temperature granary, comprising a data acquisition module, a regional square warehouse analysis module, an overall square warehouse analysis module and an intelligent temperature control module; the data acquisition module acquires first environmental data and second rice data; the regional square warehouse analysis module acquires a first germination coefficient of each square warehouse area according to the first environmental data; the overall square warehouse analysis module is used to acquire a second overall square warehouse germination coefficient of the overall square warehouse and a temperature error of the first overall square warehouse to obtain a third overall square warehouse abnormal coefficient; the intelligent temperature control module obtains a first fan adjustment power by comparing the abnormal coefficient with a preset threshold value to control the overall square warehouse temperature so that it reaches the overall square warehouse target temperature, thereby realizing intelligent regulation of the metal square warehouse temperature and further ensuring the quality of rice.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An intelligent temperature control system for an assembled metal low-temperature granary, comprising:

[0008] The data acquisition module divides the rice storage area of ​​the assembled metal square silo into regions according to the height of the square silo, obtains each square silo area, and obtains the first environmental data and the second rice data of each square silo area; the first environmental data is obtained by arranging multiple monitoring points in each square silo area;

[0009] A regional cubicle analysis module is used to analyze the first environmental data to obtain the first cubicle area temperature, the second cubicle area humidity and the third cubicle area ventilation coefficient of each cubicle area, and obtain the first germination coefficient of each cubicle area according to these three coefficients;

[0010] The overall cubicle analysis module is used to obtain a first overall cubicle temperature according to the first cubicle area temperature of each cubicle area; obtain a first overall cubicle temperature error according to the first overall cubicle temperature and the overall cubicle target temperature; obtain a second overall cubicle germination coefficient according to the first germination coefficient of each cubicle area; and obtain a third overall cubicle abnormality coefficient according to the second overall cubicle germination coefficient and the first overall cubicle temperature error;

[0011] The intelligent temperature control module is used to compare and analyze the abnormal coefficient of the third integral square warehouse and the preset abnormal threshold. If the abnormal coefficient of the third integral square warehouse exceeds the preset abnormal threshold, the first fan adjustment power is obtained according to the abnormal coefficient of the third integral square warehouse, and the temperature of the first integral square warehouse of the assembled metal square warehouse is controlled by the first fan adjustment power to reach the target temperature of the integral square warehouse.

[0012] Preferably, the first environmental data includes temperature, humidity and wind speed of each monitoring point; and the second rice data is rice weight.

[0013] Preferably, the temperature of the first square warehouse area is:

[0014] ;

[0015] in, Expressed as The temperature of the first cubicle area among the cubicle areas; Indicates Square warehouse area The temperature of each monitoring point; It is expressed as the number of monitoring points;

[0016] The humidity of the second warehouse area is:

[0017] ;

[0018] in, Expressed as Humidity of the second cubicle area in the cubicle area; Indicates Square warehouse area Humidity of each monitoring point;

[0019] The ventilation coefficient of the third-party warehouse area is:

[0020] ;

[0021] in, Expressed as Ventilation coefficient of the third warehouse area in each cubic warehouse area; Indicates Square warehouse area The wind speed at each monitoring point.

[0022] Preferably, the first germination coefficient is:

[0023] ;

[0024] in, Expressed as The first germination coefficient of each square warehouse area; represents the regional temperature impact factor; Expressed as The temperature of the first cubicle area among the cubicle areas; Expressed as The reference temperature of each cubicle area; represents the regional humidity impact factor; Expressed as Humidity of the second cubicle area in the cubicle area; Expressed as Reference humidity of each cubic warehouse area; Indicates the influencing factor of regional ventilation coefficient; Expressed as Ventilation coefficient of the third warehouse area in each cubic warehouse area; Expressed as Reference ventilation coefficient for each cubic warehouse area.

[0025] Preferably, the first integral square warehouse temperature is:

[0026] ;

[0027] in, It is represented as the temperature of the first overall cubicle; Expressed as The temperature of the first cubicle area among the cubicle areas; Expressed as the number of square warehouse areas.

[0028] Preferably, the temperature error of the first integral cubicle is:

[0029] ;

[0030] in, It is expressed as the temperature error of the first overall cubicle; It is represented as the temperature of the first overall cubicle; It is expressed as the target temperature of the whole cubicle.

[0031] Preferably, the germination coefficient of the second integral square warehouse is:

[0032] ;

[0033] in, It is expressed as the germination coefficient of the second overall square bin; Expressed as The first germination coefficient of each square warehouse area; Expressed as the number of square warehouse areas.

[0034] Preferably, the abnormal coefficient of the third integral cubicle is:

[0035] ;

[0036] in, It is expressed as the anomaly coefficient of the third overall cubic warehouse; Expressed as the temperature error influence factor; It is expressed as the temperature error of the first overall cubicle; It is expressed as the influencing factor of germination coefficient; Expressed as the germination coefficient of the second overall square warehouse.

[0037] Preferably, the first fan adjustment power is obtained according to the initial fan adjustment power, the third overall cubicle abnormality coefficient and a preset abnormality threshold.

[0038] Preferably, the first fan adjusts its power to:

[0039] ;

[0040] in, Indicates that the power is adjusted for the first fan; It represents the initial fan adjustment power; It is expressed as the overall warehouse abnormal impact factor; It is expressed as the anomaly coefficient of the third overall cubic warehouse; Indicates the preset abnormal threshold.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. The present invention divides the rice storage area of ​​the assembled metal square warehouse into regions according to the height of the square warehouse, obtains each square warehouse area, and obtains the first environmental data and the second rice data of each square warehouse area; analyzes according to the first environmental data to obtain the first square warehouse area temperature, the second square warehouse area humidity and the third warehouse area ventilation coefficient of each square warehouse area, and obtains the first germination coefficient of each square warehouse area according to these three coefficients; the present invention divides the rice storage area of ​​the metal square warehouse, fully considers the impact of the environmental differences of each square warehouse area on temperature control, and obtains the rice germination coefficient of each square warehouse area based on the environmental data of each square warehouse area, further provides a data basis for the temperature control of the metal square warehouse, and analyzes the metal square warehouse in a regionalized manner, thereby effectively improving the overall temperature control efficiency of the metal square warehouse, which is further beneficial to improving the storage quality of rice.

[0043] 2. The present invention obtains the first overall square bin temperature based on the first square bin area temperature of each square bin area, and obtains the second overall square bin germination coefficient based on the first germination coefficient of each square bin area; further obtains the third overall square bin abnormality coefficient, and comprehensively considers the temperature difference and germination trend of the overall square bin, so as to effectively realize the temperature control analysis of the metal square bin, thereby effectively improving the overall temperature control efficiency of the metal square bin, which is further beneficial to improving the storage quality of rice.

[0044] 3. The present invention is based on the third overall square bin abnormality coefficient of the metal square bin, and compares and analyzes it with the preset threshold value, so as to reasonably and effectively adaptively adjust the fan power, and further realize the effective control of the overall temperature of the metal square bin. The present invention effectively improves the control efficiency of the overall temperature of the metal square bin, which is beneficial to improving the storage quality of rice. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic diagram of the structure of an intelligent temperature control system for an assembled metal low-temperature granary provided by an embodiment of the present invention;

[0046] Figure 2 A schematic diagram of the regional division of a rice storage area in a metal square warehouse A provided in Example 1 of the present invention;

[0047] Figure 3 A schematic diagram of obtaining an abnormal coefficient of a third integral cubicle provided by an embodiment of the present invention;

[0048] Figure 4 A schematic diagram of the regional division of the rice storage area in the B metal cube warehouse area provided in Example 2 of the present invention.

[0049] In the figure: H1, first height; H2, second height; H3, third height; H4, fourth height; AB, first line segment; EX, second line segment; FD, third line segment; JC, fourth line segment; G, first center point; EC, first diagonal; BF, second diagonal; HH1, height one; HH2, height two; HH3, height three; A1B1, line segment one; E1X1, line segment two; F1D1, line segment three; J1C1, line segment four; G1, center point one; E1C1, diagonal one; B1F1, diagonal two. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Embodiment 1

[0052] In order to improve the temperature control efficiency of metal square silo A and improve the storage quality of rice, an intelligent temperature control system for assembled metal low-temperature silos was applied;

[0053] refer to Figure 1 , which is a schematic diagram of the structure of an intelligent temperature control system of an assembled metal low-temperature granary provided by an embodiment of the present invention, comprising:

[0054] The data acquisition module divides the rice storage area of ​​the assembled metal square silo into regions according to the height of the square silo, obtains each square silo area, and obtains the first environmental data and the second rice data of each square silo area; the first environmental data is obtained by arranging multiple monitoring points in each square silo area;

[0055] like Figure 2 A schematic diagram of the regional division of a rice storage area in a metal square warehouse A provided in Example 1 of the present invention;

[0056] The rice storage area of ​​metal square warehouse A is divided into four areas from top to bottom according to the first height H1, the second height H2, the third height H3, and the fourth height H4; each monitoring point includes the midpoints of four vertical line segments passing through each area and the center point, which are divided into 5 monitoring points; Figure 2 As shown in , the five monitoring points include the midpoint of the first line segment AB, the midpoint of the second line segment EX, the midpoint of the third line segment FD, the midpoint of the fourth line segment JC and the first center point G; the first center point G is the intersection of the first diagonal line EC and the second diagonal line BF;

[0057] Furthermore, the first environmental data includes the temperature, humidity and wind speed of each monitoring point; the second rice data is the weight of rice;

[0058] The temperature is obtained through the temperature sensor; the humidity is obtained through the humidity sensor; the wind speed is obtained through the anemometer; the weight of the rice is obtained through the gravity sensor;

[0059] A regional cubicle analysis module is used to analyze the first environmental data to obtain the first cubicle area temperature, the second cubicle area humidity and the third cubicle area ventilation coefficient of each cubicle area, and obtain the first germination coefficient of each cubicle area according to these three coefficients;

[0060] Furthermore, the temperature of the first cubicle area is:

[0061] ;

[0062] in, Expressed as The temperature of the first cubicle area among the cubicle areas; Indicates Square warehouse area The temperature of each monitoring point; It is expressed as the number of monitoring points;

[0063] The humidity of the second warehouse area is:

[0064] ;

[0065] in, Expressed as Humidity of the second cubicle area in the cubicle area; Indicates Square warehouse area Humidity of each monitoring point;

[0066] The ventilation coefficient of the third-party warehouse area is:

[0067] ;

[0068] in, Expressed as Ventilation coefficient of the third warehouse area in each cubic warehouse area; Indicates Square warehouse area The wind speed at each monitoring point.

[0069] Further, the first germination coefficient is:

[0070] ;

[0071] in, Expressed as The first germination coefficient of each square warehouse area; represents the regional temperature impact factor; Expressed as The temperature of the first cubicle area among the cubicle areas; Expressed as The reference temperature of each cubicle area; represents the regional humidity impact factor; Expressed as Humidity of the second cubicle area in the cubicle area; Expressed as Reference humidity of each cubic warehouse area; Indicates the influencing factor of regional ventilation coefficient; Expressed as Ventilation coefficient of the third warehouse area in each cubic warehouse area; Expressed as Reference ventilation coefficient for each cubic warehouse area.

[0072] This embodiment divides the rice storage area of ​​the assembled metal square warehouse into regions according to the height of the square warehouse, obtains each square warehouse area, and obtains the first environmental data and the second rice data of each square warehouse area; analyzes according to the first environmental data to obtain the first square warehouse area temperature, the second square warehouse area humidity and the third square warehouse area ventilation coefficient of each square warehouse area, and obtains the first germination coefficient of each square warehouse area according to these three coefficients; the present invention divides the rice storage area of ​​the metal square warehouse, fully considers the impact of the environmental differences of each square warehouse area on temperature control, and obtains the rice germination coefficient of each square warehouse area based on the environmental data of each square warehouse area, further provides a data basis for the temperature control of the metal square warehouse, and analyzes the metal square warehouse in a regionalized manner, thereby effectively improving the overall temperature control efficiency of the metal square warehouse, which is further beneficial to improving the storage quality of rice.

[0073] The overall cubicle analysis module is used to obtain a first overall cubicle temperature according to the first cubicle area temperature of each cubicle area; obtain a first overall cubicle temperature error according to the first overall cubicle temperature and the overall cubicle target temperature; obtain a second overall cubicle germination coefficient according to the first germination coefficient of each cubicle area; and obtain a third overall cubicle abnormality coefficient according to the second overall cubicle germination coefficient and the first overall cubicle temperature error;

[0074] Furthermore, the first integral cubicle temperature is:

[0075] ;

[0076] in, It is represented as the temperature of the first overall cubicle; Expressed as The temperature of the first cubicle area among the cubicle areas; Expressed as the number of square warehouse areas.

[0077] Furthermore, the temperature error of the first integral cubicle is:

[0078] ;

[0079] in, It is expressed as the temperature error of the first overall cubicle; It is represented as the temperature of the first overall cubicle; It is expressed as the target temperature of the whole cubicle.

[0080] Furthermore, the germination coefficient of the second integral square warehouse is:

[0081] ;

[0082] in, It is expressed as the germination coefficient of the second overall square bin; Expressed as The first germination coefficient of each square warehouse area; Expressed as the number of square warehouse areas.

[0083] Furthermore, the abnormal coefficient of the third overall cubicle is:

[0084] ;

[0085] in, It is expressed as the anomaly coefficient of the third overall cubic warehouse; Expressed as the temperature error influence factor; It is expressed as the temperature error of the first overall cubicle; It is expressed as the influencing factor of germination coefficient; Expressed as the germination coefficient of the second overall square warehouse.

[0086] like Figure 3 As shown, it is a schematic diagram of obtaining the abnormal coefficient of a third integral square warehouse provided by an embodiment of the present invention;

[0087] This embodiment obtains the first overall square bin temperature based on the first square bin area temperature of each square bin area, and obtains the second overall square bin germination coefficient based on the first germination coefficient of each square bin area; further obtains the third overall square bin abnormality coefficient, and comprehensively considers the temperature difference of the overall square bin and the germination trend, so as to effectively realize the temperature control analysis of the metal square bin, thereby effectively improving the overall temperature control efficiency of the metal square bin, which is further beneficial to improving the storage quality of rice.

[0088] The intelligent temperature control module is used to compare and analyze the abnormal coefficient of the third integral square warehouse and the preset abnormal threshold. If the abnormal coefficient of the third integral square warehouse exceeds the preset abnormal threshold, the first fan adjustment power is obtained according to the abnormal coefficient of the third integral square warehouse, and the temperature of the first integral square warehouse of the assembled metal square warehouse is controlled by the first fan adjustment power to reach the target temperature of the integral square warehouse.

[0089] Furthermore, the first fan adjustment power is obtained according to the initial fan adjustment power, the third overall cubicle abnormality coefficient and a preset abnormality threshold.

[0090] Furthermore, the first fan adjusts its power to:

[0091] ;

[0092] in, Indicates that the power is adjusted for the first fan; It represents the initial fan adjustment power; It is expressed as the overall warehouse abnormal impact factor; It is expressed as the anomaly coefficient of the third overall cubic warehouse; Indicates the preset abnormal threshold.

[0093] In order to verify the effectiveness of the intelligent temperature control system of an assembled metal low-temperature granary provided in this embodiment, comparative experiments were conducted using system one, system two and system three. System one is an intelligent temperature control system of an assembled metal low-temperature granary provided in this embodiment; system two is based on system one without considering the influence of the abnormal coefficient of the third overall square bin on the temperature control of metal square bin A; system three is based on system one without considering the influence of the adaptive adjustment of the fan power on the temperature control of metal square bin A; the comparison was conducted by comparing the accuracy of the temperature control of multiple comparative experiments; the accuracy was represented by the average value of multiple experiments; the specific results are shown in Table 1;

[0094] Table 1 Comparison of the accuracy of temperature control of metal square warehouse A by different systems

[0095]

[0096] It can be seen from Table 1 that the intelligent temperature control system for an assembled metal low-temperature granary provided in this embodiment has certain effectiveness.

[0097] This embodiment is based on the third overall square bin abnormality coefficient of the metal square bin, and compares and analyzes it with the preset threshold value, so as to reasonably and effectively adaptively adjust the fan power and further realize the effective control of the overall temperature of the metal square bin. The present invention effectively improves the control efficiency of the overall temperature of the metal square bin, which is beneficial to improving the storage quality of rice.

[0098] This embodiment includes a data acquisition module, a regional cube analysis module, an overall cube analysis module and an intelligent temperature control module; the data acquisition module acquires first environmental data and second rice data; the regional cube analysis module acquires a first germination coefficient of each cube area according to the first environmental data; the overall cube analysis module is used to acquire a second overall cube germination coefficient of the overall cube and a temperature error of the first overall cube to obtain a third overall cube abnormality coefficient; the intelligent temperature control module obtains a first fan adjustment power by comparing the abnormality coefficient with a preset threshold to control the overall cube temperature to reach the overall cube target temperature, thereby realizing intelligent regulation of the metal cube temperature and further ensuring the quality of rice.

[0099] Embodiment 2

[0100] In order to improve the temperature control efficiency of metal square silo B and improve the storage quality of rice, an intelligent temperature control system for assembled metal low-temperature silos was applied;

[0101] refer to Figure 1 , which is a schematic diagram of the structure of an intelligent temperature control system of an assembled metal low-temperature granary provided by an embodiment of the present invention, comprising:

[0102] The data acquisition module divides the rice storage area of ​​the assembled metal square silo into regions according to the height of the square silo, obtains each square silo area, and obtains the first environmental data and the second rice data of each square silo area; the first environmental data is obtained by arranging multiple monitoring points in each square silo area;

[0103] like Figure 4 A schematic diagram of the regional division of a rice storage area in a metal square warehouse B provided in the second embodiment of the present invention;

[0104] The rice storage area of ​​metal square warehouse B is divided into three areas from top to bottom according to height 1 HH1, height 2 HH2 and height 3 HH3; each monitoring point consists of the midpoints of four vertical line segments passing through each area and the center point, which is divided into 5 monitoring points; Figure 4 As shown in , the five monitoring points include the midpoint of line segment 1 A1B1, the midpoint of line segment 2 E1X1, the midpoint of line segment 3 F1D1, the midpoint of line segment 4 J1C1 and the center point 1 G1; the center point 1 G1 is the intersection of diagonal line 1 and diagonal line 2;

[0105] Furthermore, the first environmental data includes the temperature, humidity and wind speed of each monitoring point; the second rice data is the weight of rice;

[0106] The temperature is obtained through the temperature sensor; the humidity is obtained through the humidity sensor; the wind speed is obtained through the anemometer; the weight of the rice is obtained through the gravity sensor;

[0107] A regional cubicle analysis module is used to analyze the first environmental data to obtain the first cubicle area temperature, the second cubicle area humidity and the third cubicle area ventilation coefficient of each cubicle area, and obtain the first germination coefficient of each cubicle area according to these three coefficients;

[0108] Furthermore, the temperature of the first cubicle area is:

[0109] ;

[0110] in, Expressed as The temperature of the first cubicle area among the cubicle areas; Indicates Square warehouse area The temperature of each monitoring point; It is expressed as the number of monitoring points;

[0111] The humidity of the second warehouse area is:

[0112] ;

[0113] in, Expressed as Humidity of the second cubicle area in the cubicle area; Indicates Square warehouse area Humidity of each monitoring point;

[0114] The ventilation coefficient of the third-party warehouse area is:

[0115] ;

[0116] in, Expressed as Ventilation coefficient of the third warehouse area in each cubic warehouse area; Indicates Square warehouse area The wind speed at each monitoring point.

[0117] Further, the first germination coefficient is:

[0118] ;

[0119] in, Expressed as The first germination coefficient of each square warehouse area; represents the regional temperature impact factor; Expressed as The temperature of the first cubicle area among the cubicle areas; Expressed as The reference temperature of each cubicle area; represents the regional humidity impact factor; Expressed as Humidity of the second cubicle area in the cubicle area; Expressed as Reference humidity of each cubic warehouse area; Indicates the influencing factor of regional ventilation coefficient; Expressed as Ventilation coefficient of the third warehouse area in each cubic warehouse area; Expressed as Reference ventilation coefficient for each cubic warehouse area.

[0120] This embodiment divides the rice storage area of ​​the assembled metal square warehouse into regions according to the height of the square warehouse, obtains each square warehouse area, and obtains the first environmental data and the second rice data of each square warehouse area; analyzes according to the first environmental data to obtain the first square warehouse area temperature, the second square warehouse area humidity and the third square warehouse area ventilation coefficient of each square warehouse area, and obtains the first germination coefficient of each square warehouse area according to these three coefficients; the present invention divides the rice storage area of ​​the metal square warehouse, fully considers the impact of the environmental differences of each square warehouse area on temperature control, and obtains the rice germination coefficient of each square warehouse area based on the environmental data of each square warehouse area, further provides a data basis for the temperature control of the metal square warehouse, and analyzes the metal square warehouse in a regionalized manner, thereby effectively improving the overall temperature control efficiency of the metal square warehouse, which is further beneficial to improving the storage quality of rice.

[0121] The overall cubicle analysis module is used to obtain a first overall cubicle temperature according to the first cubicle area temperature of each cubicle area; obtain a first overall cubicle temperature error according to the first overall cubicle temperature and the overall cubicle target temperature; obtain a second overall cubicle germination coefficient according to the first germination coefficient of each cubicle area; and obtain a third overall cubicle abnormality coefficient according to the second overall cubicle germination coefficient and the first overall cubicle temperature error;

[0122] Furthermore, the first integral cubicle temperature is:

[0123] ;

[0124] in, It is represented as the temperature of the first overall cubicle; Expressed as The temperature of the first cubicle area among the cubicle areas; Expressed as the number of square warehouse areas.

[0125] Furthermore, the temperature error of the first integral cubicle is:

[0126] ;

[0127] in, It is expressed as the temperature error of the first overall cubicle; It is represented as the temperature of the first overall cubicle; It is expressed as the target temperature of the whole cubicle.

[0128] Furthermore, the germination coefficient of the second integral square warehouse is:

[0129] ;

[0130] in, It is expressed as the germination coefficient of the second overall square bin; Expressed as The first germination coefficient of each square warehouse area; Expressed as the number of square warehouse areas.

[0131] Furthermore, the abnormal coefficient of the third overall cubicle is:

[0132] ;

[0133] in, It is expressed as the anomaly coefficient of the third overall cubic warehouse; Expressed as the temperature error influence factor; It is expressed as the temperature error of the first overall cubicle; It is expressed as the influencing factor of germination coefficient; Expressed as the germination coefficient of the second overall square warehouse.

[0134] like Figure 3 As shown, it is a schematic diagram of obtaining the abnormal coefficient of a third integral square warehouse provided by an embodiment of the present invention;

[0135] This embodiment obtains the first overall square bin temperature based on the first square bin area temperature of each square bin area, and obtains the second overall square bin germination coefficient based on the first germination coefficient of each square bin area; further obtains the third overall square bin abnormality coefficient, and comprehensively considers the temperature difference of the overall square bin and the germination trend, so as to effectively realize the temperature control analysis of the metal square bin, thereby effectively improving the overall temperature control efficiency of the metal square bin, which is further beneficial to improving the storage quality of rice.

[0136] The intelligent temperature control module is used to compare and analyze the abnormal coefficient of the third integral square warehouse and the preset abnormal threshold. If the abnormal coefficient of the third integral square warehouse exceeds the preset abnormal threshold, the first fan adjustment power is obtained according to the abnormal coefficient of the third integral square warehouse, and the temperature of the first integral square warehouse of the assembled metal square warehouse is controlled by the first fan adjustment power to reach the target temperature of the integral square warehouse.

[0137] Furthermore, the first fan adjustment power is obtained according to the initial fan adjustment power, the third overall cubicle abnormality coefficient and a preset abnormality threshold.

[0138] Furthermore, the first fan adjusts its power to:

[0139] ;

[0140] in, Indicates that the power is adjusted for the first fan; It represents the initial fan adjustment power; It is expressed as the overall warehouse abnormal impact factor; It is expressed as the anomaly coefficient of the third overall cubic warehouse; Indicates the preset abnormal threshold.

[0141] In order to verify the effectiveness of the intelligent temperature control system of an assembled metal low-temperature granary provided in this embodiment, comparative experiments were conducted using system one, system two and system three. System one is an intelligent temperature control system of an assembled metal low-temperature granary provided in this embodiment; system two is based on system one without considering the influence of the abnormal coefficient of the third overall square bin on the temperature control of metal square bin B; system three is based on system one without considering the influence of the adaptive adjustment of the fan power on the temperature control of metal square bin B; the comparison was conducted by comparing the accuracy of the temperature control of multiple comparative experiments; the accuracy was represented by the average value of multiple experiments; the specific results are shown in Table 2;

[0142] Table 2 Comparison of the accuracy of temperature control of metal square warehouse B by different systems

[0143]

[0144] It can be seen from Table 2 that the intelligent temperature control system for an assembled metal low-temperature granary provided in this embodiment has certain effectiveness.

[0145] This embodiment is based on the third overall square bin abnormality coefficient of the metal square bin, and compares and analyzes it with the preset threshold value, so as to reasonably and effectively adaptively adjust the fan power and further realize the effective control of the overall temperature of the metal square bin. The present invention effectively improves the control efficiency of the overall temperature of the metal square bin, which is beneficial to improving the storage quality of rice.

[0146] This embodiment includes a data acquisition module, a regional cube analysis module, an overall cube analysis module and an intelligent temperature control module; the data acquisition module acquires first environmental data and second rice data; the regional cube analysis module acquires a first germination coefficient of each cube area according to the first environmental data; the overall cube analysis module is used to acquire a second overall cube germination coefficient of the overall cube and a temperature error of the first overall cube to obtain a third overall cube abnormality coefficient; the intelligent temperature control module obtains a first fan adjustment power by comparing the abnormality coefficient with a preset threshold to control the overall cube temperature to reach the overall cube target temperature, thereby realizing intelligent regulation of the metal cube temperature and further ensuring the quality of rice.

[0147] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent temperature control system for an assembled metal low-temperature granary, characterized in that: include: The data acquisition module divides the rice storage area of ​​the assembled metal square silo into regions according to the height of the square silo, obtains each square silo area, and obtains the first environmental data and the second rice data of each square silo area; the first environmental data is obtained by arranging multiple monitoring points in each square silo area; The regional cubicle analysis module is used to analyze the first environmental data to obtain the first cubicle area temperature, the second cubicle area humidity and the third cubicle area ventilation coefficient of each cubicle area, and obtain the first germination coefficient of each cubicle area according to these three coefficients; the first germination coefficient is: ； in, Expressed as The first germination coefficient of each square warehouse area; represents the regional temperature impact factor; Expressed as The temperature of the first cubicle area among the cubicle areas; Expressed as The reference temperature of each cubicle area; represents the regional humidity impact factor; Expressed as Humidity of the second cubicle area in the cubicle area; Expressed as Reference humidity of each cubic warehouse area; Indicates the influencing factor of regional ventilation coefficient; Expressed as Ventilation coefficient of the third warehouse area in each cubic warehouse area; Expressed as Reference ventilation coefficient for each cubic warehouse area; The overall cubicle analysis module is used to obtain a first overall cubicle temperature according to the first cubicle area temperature of each cubicle area; obtain a first overall cubicle temperature error according to the first overall cubicle temperature and the overall cubicle target temperature; obtain a second overall cubicle germination coefficient according to the first germination coefficient of each cubicle area; and obtain a third overall cubicle abnormality coefficient according to the second overall cubicle germination coefficient and the first overall cubicle temperature error; The intelligent temperature control module is used to compare and analyze the abnormal coefficient of the third integral square warehouse and the preset abnormal threshold. If the abnormal coefficient of the third integral square warehouse exceeds the preset abnormal threshold, the first fan adjustment power is obtained according to the abnormal coefficient of the third integral square warehouse, and the temperature of the first integral square warehouse of the assembled metal square warehouse is controlled by the first fan adjustment power to reach the target temperature of the integral square warehouse.

2. The intelligent temperature control system for an assembled metal low-temperature granary according to claim 1 is characterized in that: The first environmental data includes the temperature, humidity and wind speed of each monitoring point; the second rice data is the weight of rice.

3. The intelligent temperature control system for an assembled metal low-temperature granary according to claim 1 is characterized in that: The temperature of the first square warehouse area is: ； in, Expressed as The temperature of the first cubicle area among the cubicle areas; Indicates Square warehouse area The temperature of each monitoring point; It is expressed as the number of monitoring points; The humidity of the second warehouse area is: ； in, Expressed as Humidity of the second cubicle area in the cubicle area; Indicates Square warehouse area Humidity of each monitoring point; The ventilation coefficient of the third-party warehouse area is: ； in, Expressed as Ventilation coefficient of the third warehouse area in each cubic warehouse area; Indicates Square warehouse area The wind speed at each monitoring point.

4. The intelligent temperature control system for an assembled metal low-temperature granary according to claim 1 is characterized by: The temperature of the first integral square warehouse is: ； in, It is represented as the temperature of the first overall cubicle; Expressed as The temperature of the first cubicle area among the cubicle areas; Expressed as the number of square warehouse areas.

5. The intelligent temperature control system for an assembled metal low-temperature granary according to claim 1 is characterized in that: The temperature error of the first integral cubicle is: ； in, It is expressed as the temperature error of the first overall cubicle; It is represented as the temperature of the first overall cubicle; It is expressed as the target temperature of the whole cubicle.

6. The intelligent temperature control system for an assembled metal low-temperature granary according to claim 1 is characterized by: The germination coefficient of the second integral square warehouse is: ； in, It is expressed as the germination coefficient of the second overall square bin; Expressed as The first germination coefficient of each square warehouse area; Expressed as the number of square warehouse areas.

7. The intelligent temperature control system for an assembled metal low-temperature granary according to claim 1 is characterized by: The abnormal coefficient of the third integral square warehouse is: ； in, It is expressed as the anomaly coefficient of the third overall cubic warehouse; Expressed as the temperature error influence factor; It is expressed as the temperature error of the first overall cubicle; It is expressed as the influencing factor of germination coefficient; Expressed as the germination coefficient of the second overall square warehouse.

8. The intelligent temperature control system for an assembled metal low-temperature granary according to claim 1 is characterized by: The first fan adjustment power is obtained according to the initial fan adjustment power, the third overall cubicle abnormality coefficient and a preset abnormality threshold.

9. The intelligent temperature control system for an assembled metal low-temperature granary according to claim 8 is characterized by: The first fan adjusts its power to: ； in, Indicates that the power is adjusted for the first fan; It represents the initial fan adjustment power; It is expressed as the overall warehouse abnormal impact factor; It is expressed as the anomaly coefficient of the third overall cubic warehouse; Indicates the preset abnormal threshold.

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