Remote control system for temperature and humidity of flour storage based on internet of things

Abstract

The application relates to the technical field of warehouse temperature regulation, and provides a flour warehouse temperature and humidity remote regulation system based on the Internet of Things, which comprises a state stability acquisition module, a flour complexity acquisition module and a delicacy acquisition module, wherein the state stability of each batch of flour is determined first, then the flour complexity of a flour storage room is obtained according to the state stability of each batch of flour stored in the flour storage room and the difference between the batches, and finally the flour temperature and humidity regulation delicacy of the flour storage room is obtained by combining the diversity of the shelf life of each batch of flour stored in the flour storage room, so that the accuracy and reliability of the temperature and humidity regulation delicacy are improved. Moreover, the temperature and humidity regulation strategy obtained according to the temperature and humidity regulation delicacy is more in line with the actual situation of the flour storage room, and the reliability and accuracy of the temperature and humidity regulation of the flour storage room are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of warehouse temperature regulation, and particularly relates to a flour warehouse temperature and humidity remote regulation system based on Internet of Things. BACKGROUND

[0002] As an important staple food raw material in daily life, the storage condition of flour has a decisive influence on quality, and the flour storage industry pays more attention to temperature and humidity regulation. As a new network technology, Internet of Things connects objects and the Internet through various sensor devices to realize the intelligentization of objects, and it has great application potential in the warehouse field.

[0003] Generally, the same type of flour is placed in the same flour storage room, such as high-gluten flour and low-gluten flour. In addition, multiple batches of flour are usually stored in the same flour storage room, and the shelf life and process control of different batches of flour are not completely different, so that the complex situation of flour in different flour storage rooms is different, and the temperature and humidity regulation demand in the storage process should be different. The more complex the situation of flour in the flour storage room, the more precise the temperature and humidity regulation strategy needs to be. Therefore, different flour storage rooms should require different and corresponding temperature and humidity regulation precision, and finally different temperature and humidity regulation strategies are determined according to different temperature and humidity regulation precision, to ensure the reliability and accuracy of temperature and humidity regulation of the flour storage room. However, in the prior art, the specific situation of flour in different flour storage rooms is not considered, and the temperature and humidity regulation precision of different flour storage rooms is not considered, but the temperature and humidity regulation precision of all flour storage rooms is set to the same value, so that the temperature and humidity regulation strategies of different flour storage rooms are completely the same, which will make the temperature and humidity regulation strategy not adapt to the actual complex situation of flour in the flour storage room, and affect the reliability and accuracy of temperature and humidity regulation of the flour storage room. SUMMARY

[0004] In order to solve the technical problem that the temperature and humidity regulation precision of the flour storage room is not adapted to the actual situation of the flour in the flour storage room, the purpose of the present application is to provide a flour warehouse temperature and humidity remote regulation system based on Internet of Things, and the technical solution is as follows:

[0005] The present application provides a flour warehouse temperature and humidity remote regulation system based on Internet of Things, comprising:

[0006] A state stability acquisition module is configured to determine the state stability of each batch of flour, which is obtained from the temperature and humidity of each batch of flour in the process and the humidity characteristics of the flour after the process is completed.

[0007] a flour complexity obtaining module, configured to obtain a flour complexity of the flour storage chamber according to the state stability of each batch of flour stored in the flour storage chamber and the difference between the batches of flour;

[0008] a flour temperature and humidity control fineness obtaining module, configured to determine a diversity of shelf life of each batch of flour stored in the flour storage chamber, and obtain a flour temperature and humidity control fineness of the flour storage chamber in combination with the flour complexity, the flour temperature and humidity control fineness being used to determine a flour temperature and humidity control strategy.

[0009] In an exemplary embodiment, the state stability obtaining module is specifically configured to:

[0010] obtain a temperature fluctuation degree and a humidity fluctuation degree of any batch of flour in a processing process, and obtain a temperature and humidity stability degree of the batch of flour;

[0011] determine a difference between the humidity feature of the batch of flour and a preset standard humidity feature, and obtain a state stability of the batch of flour in combination with the temperature and humidity stability degree.

[0012] In an exemplary embodiment, the flour complexity obtaining module is specifically configured to:

[0013] obtain a state stability fluctuation degree and a state stability overall level of each batch of flour stored in the flour storage chamber;

[0014] obtain the flour complexity according to the state stability fluctuation degree and the state stability overall level.

[0015] In an exemplary embodiment, the diversity obtaining process comprises:

[0016] obtain a shelf life remaining duration of each batch of flour according to the shelf life of each batch of flour, and obtain a shelf life remaining duration fluctuation degree;

[0017] cluster each batch of flour according to the shelf life remaining duration, and obtain a plurality of clusters;

[0018] obtain the diversity according to the shelf life remaining duration fluctuation degree and the number of clusters.

[0019] In an exemplary embodiment, the flour storage temperature and humidity remote control system based on the Internet of Things further comprises:

[0020] a correction module, configured to correct an initial parameter warning fluctuation of the flour storage chamber according to the flour temperature and humidity control fineness of the flour storage chamber, and obtain a target parameter warning fluctuation; the parameter is temperature or humidity;

[0021] The control module is configured to control the parameter of the flour storage chamber according to the target parameter early warning fluctuation and the early warning parameter value of the flour storage chamber.

[0022] In an exemplary embodiment, the candidate flour storage chamber is set as any one of the flour storage chambers, and the initial parameter early warning fluctuation of the candidate flour storage chamber includes:

[0023] determining the minimum initial parameter early warning fluctuation from the initial parameter early warning fluctuations of the plurality of flour storage chambers;

[0024] obtaining a correction coefficient according to the fine degree of flour temperature and humidity control of the candidate flour storage chamber;

[0025] obtaining a warning fluctuation correction amount according to the correction coefficient and the minimum initial parameter early warning fluctuation;

[0026] obtaining the target parameter early warning fluctuation according to the initial parameter early warning fluctuation of the candidate flour storage chamber and the warning fluctuation correction amount.

[0027] In an exemplary embodiment, the correction coefficient is obtained according to the fine degree of flour temperature and humidity control of the candidate flour storage chamber, and includes:

[0028] normalizing the fine degree of flour temperature and humidity control of the candidate flour storage chamber by using a maximum-minimum normalization method, and then performing a negative correlation on the normalized result to obtain the correction coefficient.

[0029] In an exemplary embodiment, the initial parameter early warning fluctuation of the candidate flour storage chamber includes:

[0030] obtaining each parameter active control time in a historical parameter fluctuation curve of the candidate flour storage chamber;

[0031] determining adjacent parameter values of a plurality of time points adjacent to the parameter active control time, and obtaining parameter fluctuation characteristics of the adjacent parameter values;

[0032] fusing the parameter fluctuation characteristics of all parameter active control times to obtain the initial parameter early warning fluctuation of the candidate flour storage chamber.

[0033] In an exemplary embodiment, the early warning parameter value of the candidate flour storage chamber includes:

[0034] obtaining parameter characteristics of the parameter active control time according to the adjacent parameter values of the parameter active control time of the candidate flour storage chamber;

[0035] fusing the parameter characteristics of all parameter active control times of the candidate flour storage chamber to obtain the early warning parameter value of the candidate flour storage chamber.

[0036] In one exemplary embodiment, the control module is specifically used for:

[0037] If the actual parameter of the flour storage room deviates from the early warning parameter value, and the actual parameter fluctuation is greater than the target parameter early warning fluctuation, the parameter of the flour storage room is controlled.

[0038] The present application has the following beneficial effects: The present application simultaneously analyzes the processing process and storage process of various batches of flour, analyzes the temperature and humidity in the processing process to determine the state stability of each batch of flour, and then obtains the flour complexity of the flour storage room. Then, according to the different shelf life of different batches of flour, the flour temperature and humidity control precision degree corresponding to each batch of flour stored in the flour storage room is obtained. Therefore, the temperature and humidity control precision degree is adapted to the actual situation of each batch of flour stored in the flour storage room, so that the temperature and humidity control precision degree corresponding to different flour storage rooms is not completely the same, and the accuracy and reliability of the temperature and humidity control precision degree are improved. Moreover, the temperature and humidity control strategy obtained according to the temperature and humidity control precision degree is more in line with the actual situation of the flour storage room, thereby improving the reliability and accuracy of the temperature and humidity control of the flour storage room. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 is a module composition schematic diagram of the flour storage temperature and humidity remote control system based on the Internet of Things provided by an embodiment of the present application;

[0040] Figure 2 is a method flowchart corresponding to each module of the flour storage temperature and humidity remote control system based on the Internet of Things provided by an embodiment of the present application;

[0041] Figure 3 is an acquisition flowchart of state stability provided by an embodiment of the present application;

[0042] Figure 4 is an acquisition flowchart of flour complexity provided by an embodiment of the present application;

[0043] Figure 5 is an acquisition flowchart of diversity provided by an embodiment of the present application;

[0044] Figure 6 is a module composition schematic diagram of the flour storage temperature and humidity remote control system based on the Internet of Things provided by an embodiment of the present application;

[0045] Figure 7 is a method flowchart corresponding to each module of the flour storage temperature and humidity remote control system based on the Internet of Things provided by an embodiment of the present application;

[0046] Figure 8 is a flowchart of the acquisition of the initial parameter early warning fluctuation provided by one embodiment of the present application;

[0047] Figure 9 is a flowchart of the correction of the initial parameter early warning fluctuation provided by one embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to further clarify the technical means and effects taken by the present application to achieve the predetermined inventive purpose, the specific embodiments, structures, features and effects of the present application are described in detail below in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The data information collected by the present application is obtained with full authorization.

[0050] The embodiment provides a flour storage temperature and humidity remote control system based on Internet of Things, which is used for remote control of the temperature and humidity of flour storage of a flour mill. The flour storage environment needs to be in an optimal temperature range and humidity range. When the temperature is too high or too low, temperature control is needed; when the humidity is too high or too low, humidity control is needed. The flour mill includes a flour production and processing workshop and a flour warehouse. The flour production and processing workshop is used for producing and processing flour, and the flour warehouse is used for storing finished flour. The flour warehouse is provided with a plurality of flour storage rooms, and the temperature and humidity control of each flour storage room is independent. Any flour storage room stores a plurality of batches of flour, and all batches of flour stored in the flour storage room are flour of the same type, such as high-gluten flour or low-gluten flour. In addition, since the production date of the same batch of flour is usually the same day, the embodiment takes the production date of the same batch of flour as the same day as an example.

[0051] At the hardware level, temperature sensors and humidity sensors are arranged in the flour production and processing workshop to detect the temperature and humidity of each batch of flour during the processing process. The temperature and humidity are the environmental temperature and humidity of the flour production and processing workshop. The arrangement position of the temperature sensor and the humidity sensor in the flour production and processing workshop is preferably selected to be close to the flour production and processing equipment. The sampling frequency of the temperature sensor and the humidity sensor is set according to actual needs. In this embodiment, it is collected once every 5 seconds. During the processing of the flour, the temperature and humidity need to be monitored to activate the target reaction (such as protein hydration, starch gelatinization, etc.). If the temperature and humidity control of the processing process is unreasonable, it will cause protein hydrolysis and fat oxidation during storage, which will increase the risk of flour corruption. Therefore, the temperature and humidity data during the processing process are obtained through the temperature and humidity sensors during the processing process.

[0052] After the flour processing is completed, that is, after the finished flour (i.e., bagged flour) is made, the humidity characteristics of the flour are detected, such as by a near-infrared spectrometer to scan the bag surface to obtain the humidity value of the finished flour, which is defined as the humidity characteristics. It should be understood that in order to improve the detection efficiency, the same batch of flour can be sampled, and the average value of the humidity characteristics obtained by sampling is used as the humidity characteristics of the batch of flour.

[0053] Temperature sensors and humidity sensors are arranged in each flour storage room to detect the environmental temperature and humidity in the flour storage room. The arrangement position of the temperature sensor and the humidity sensor in the flour storage room is set according to actual needs, such as being arranged at the middle roof of the flour storage room. The collection frequency of the temperature sensor and the humidity sensor is set according to actual needs. In this embodiment, it is collected once every 30 seconds.

[0054] In this embodiment, each collection device in the above is provided with a wireless communication module for remote wireless communication with the data processing device of the background monitoring center through the Internet of Things. The data processing device processes to realize remote regulation and control of the flour storage temperature and humidity.

[0055] The temperature and humidity of each batch of flour during the production and processing process are recorded to obtain the temperature fluctuation curve and the humidity fluctuation curve of each batch of flour during the production and processing process. For any flour storage room, the temperature fluctuation curve and the humidity fluctuation curve of each batch of flour stored in the flour storage room during the production and processing process are determined. Moreover, the humidity characteristics of each batch of flour stored in the flour storage room are obtained.

[0056] The flour storage temperature and humidity remote regulation and control system based on the Internet of Things provided in the embodiment can be configured as a hardware device, specifically, the data processing device of the background monitoring center in the above, or as a software system, and the embodiment is not limited to the specific form of the system.

[0057] As shown in the above Figure 1 The flour storage temperature and humidity remote regulation and control system based on the Internet of Things provided in the embodiment includes a state stability acquisition module, a flour complexity acquisition module, and a delicacy acquisition module. As shown in the above Figure 2 The method steps corresponding to each module are as follows:

[0058] The state stability acquisition module is configured to determine the state stability of each batch of flour, which is obtained from the temperature and humidity of each batch of flour in the processing process and the humidity characteristics of the flour after the processing is completed.

[0059] The flour complexity acquisition module is configured to obtain the flour complexity of the flour storage room according to the state stability of each batch of flour stored in the flour storage room and the difference therebetween.

[0060] The delicacy acquisition module is configured to determine the diversity of the shelf life of each batch of flour stored in the flour storage room, and obtain the flour temperature and humidity regulation delicacy of the flour storage room in combination with the flour complexity.

[0061] The specific implementation process of each module is described below in combination with the accompanying drawings.

[0062] The state stability acquisition module is configured to determine the state stability of each batch of flour, which is obtained from the temperature and humidity of each batch of flour in the processing process and the humidity characteristics of the flour after the processing is completed.

[0063] For any batch of flour stored in the flour storage room, the more stable the temperature and humidity of the batch of flour in the processing process, the more stable the state of the batch of flour after the processing is completed, and the smaller the difference between the humidity characteristics of the batch of flour after the processing is completed and the preset standard humidity characteristics, indicating that the state of the batch of flour after the processing is completed is more stable. Therefore, the state stability of the batch of flour is obtained from the temperature and humidity of the batch of flour in the processing process and the humidity characteristics of the flour after the processing is completed. In an exemplary embodiment, as shown in the above Figure 3 A specific acquisition process of the state stability is given as follows:

[0064] Step 11: Obtain the temperature fluctuation degree and the humidity fluctuation degree of any batch of flour in the processing process to obtain the temperature and humidity stability degree of the batch of flour.

[0065] The temperature fluctuation degree of the temperature fluctuation curve of the batch of flour in the processing process is obtained. In an exemplary embodiment, the variance is used to represent the fluctuation degree, and then the variance of the temperature values in the temperature fluctuation curve of the batch of flour in the processing process is calculated as the temperature fluctuation degree. It should be understood that if the processing process includes multiple processing stages, the temperature fluctuation degree of the temperature fluctuation curve of each processing stage is obtained respectively, and then the average value of the temperature fluctuation degrees is calculated as the temperature fluctuation degree of the batch of flour in the processing process.

[0066] Similarly, the humidity fluctuation degree of the humidity fluctuation curve of the batch of flour in the processing process is obtained. In an exemplary embodiment, the variance is used to represent the fluctuation degree, and then the variance of the humidity values in the humidity fluctuation curve of the batch of flour in the processing process is calculated as the humidity fluctuation degree. It should be understood that if the processing process includes multiple processing stages, the humidity fluctuation degree of the humidity fluctuation curve of each processing stage is obtained respectively, and then the average value of the humidity fluctuation degrees is calculated as the humidity fluctuation degree of the batch of flour in the processing process.

[0067] The temperature fluctuation degree represents the instability degree of temperature, and the humidity fluctuation degree represents the instability degree of humidity. Then, according to the temperature fluctuation degree and the humidity fluctuation degree of the batch of flour in the processing process, the temperature and humidity stability degree of the batch of flour is obtained. The temperature and humidity stability degree is inversely related to both the temperature fluctuation degree and the humidity fluctuation degree. In an exemplary embodiment, a specific quantitative way of the temperature and humidity stability degree is as follows:

[0068] ;

[0069] wherein, represents the temperature and humidity stability degree of the mth batch of flour, and represents the temperature and humidity effectiveness in the processing process; represents the temperature fluctuation degree of the mth batch of flour; represents the humidity fluctuation degree of the mth batch of flour; represents the exponential function with the natural constant as the base.

[0070] Step 12: Determine the difference between the humidity characteristic of the batch of flour and the preset standard humidity characteristic, and combine the temperature and humidity stability degree to obtain the state stability of the batch of flour.

[0071] For the batch of flour, a standard humidity characteristic value is preset. The preset standard humidity characteristic value is the optimal humidity required by the batch of flour, which can be obtained through a large number of experiments in the laboratory. It should be understood that for the same type of flour, the corresponding preset standard humidity characteristic can be the same.

[0072] The difference between the humidity characteristic of the batch of flour and the preset standard humidity characteristic is calculated, specifically, the absolute value of the difference, the smaller the difference, the closer the humidity of the batch of flour to the optimal humidity, the higher the state stability of the batch of flour. Therefore, the state stability of the batch of flour is inversely related to the difference between the humidity characteristic of the batch of flour and the preset standard humidity characteristic. The higher the temperature and humidity stability degree of the batch of flour, the higher the state stability of the batch of flour, so the state stability of the batch of flour is positively related to the temperature and humidity stability degree of the batch of flour. In an exemplary embodiment, a specific quantification method of state stability is given as follows:

[0073] ;

[0074] wherein, represents the state stability of the mth batch of flour, represents the humidity characteristic value of the mth batch of flour, represents the preset standard humidity characteristic value.

[0075] The flour complexity obtaining module is configured to obtain the flour complexity of the flour storage room according to the state stability of each batch of flour stored in the flour storage room and the difference between them.

[0076] Through the above process, the state stability of each batch of flour stored in the flour storage room is obtained. The lower the state stability of each batch of flour stored in the same flour storage room, the more complex the flour storage condition in the flour storage room, i.e. the higher the flour complexity of the flour storage room; the greater the difference between the state stability of each batch of flour stored in the flour storage room, the more complex the flour storage condition in the flour storage room, i.e. the higher the flour complexity of the flour storage room. Therefore, the flour complexity of the flour storage room is obtained according to the state stability of each batch of flour stored in the flour storage room and the difference between them. In an exemplary embodiment, as shown in Figure 4 , a specific acquisition process of flour complexity is given as follows:

[0077] Step 21: Obtain the state stability fluctuation degree and the overall level of state stability of each batch of flour stored in the flour storage room.

[0078] The state stability fluctuation degree is obtained according to the state stability of each batch of flour stored in the flour storage room. In an exemplary embodiment, the fluctuation degree is represented by variance, i.e. the variance of the state stability of each batch of flour stored in the flour storage room is calculated, and the variance is the state stability fluctuation degree.

[0079] According to the state stability of each batch of flour stored in the flour storage room, an overall level of state stability is obtained. In an exemplary embodiment, the average value of the state stability of each batch of flour stored in the flour storage room is calculated, which is the overall level of state stability.

[0080] Step 22: According to the state stability fluctuation degree and the overall level of state stability, the flour complexity degree is obtained.

[0081] Through the above analysis, the higher the state stability fluctuation degree, the higher the flour complexity degree; the lower the overall level of state stability, the higher the flour complexity degree. Therefore, the flour complexity degree is positively correlated with the state stability fluctuation degree and is inversely correlated with the overall level of state stability. In an exemplary embodiment, a specific quantification method of the flour complexity degree is as follows:

[0082] ;

[0083] Among them, represents the flour complexity degree of the flour storage room, represents the state stability fluctuation degree of the flour storage room, represents the overall level of state stability of the flour storage room.

[0084] The fine degree acquisition module is configured to determine the diversity of the shelf life of each batch of flour stored in the flour storage room, and obtain the flour temperature and humidity regulation fine degree of the flour storage room in combination with the flour complexity degree.

[0085] The shelf life of each batch of flour stored in the flour storage room is obtained. The more diversified the shelf life of each batch of flour stored in the flour storage room, the more fine the flour temperature and humidity of the flour storage room needs to be regulated, that is, the higher the flour temperature and humidity regulation fine degree of the flour storage room. In addition, the higher the flour complexity degree of the flour storage room, the more fine the flour temperature and humidity of the flour storage room needs to be regulated, that is, the higher the flour temperature and humidity regulation fine degree of the flour storage room. Therefore, according to the diversity of the shelf life of each batch of flour stored in the flour storage room and the flour complexity degree, the flour temperature and humidity regulation fine degree of the flour storage room is obtained.

[0086] In an exemplary embodiment, as shown in Figure 5 , a specific acquisition process of the diversity of the shelf life of each batch of flour stored in the flour storage room is as follows:

[0087] Step 31: According to the shelf life of each batch of flour, the shelf life remaining time of each batch of flour is obtained, and the shelf life remaining time fluctuation degree is obtained.

[0088] For any batch of flour, according to the shelf life of the batch of flour and the production date, a deadline, i.e. the last day of the shelf life, is obtained, so as to obtain the time interval between the current time and the deadline as the shelf life remaining length of the batch of flour. Thus, the shelf life remaining lengths of each batch of flour stored in the flour storage room are obtained.

[0089] Then, the fluctuation degree of the shelf life remaining lengths of each batch of flour stored in the flour storage room is obtained, and here the fluctuation degree is represented by variance. Then, the variance of the shelf life remaining lengths of each batch of flour stored in the flour storage room is calculated, and the variance is the fluctuation degree of the shelf life remaining lengths. The higher the fluctuation degree of the shelf life remaining lengths, the more non-uniform the shelf life of each batch of flour stored in the flour storage room, the more dispersed the distribution of the shelf life remaining lengths, the more diversified, i.e. the stronger the diversity, the batch of flour stored in the flour storage room, the more the need for fine control of temperature and humidity.

[0090] Step 32: clustering each batch of flour according to the shelf life remaining length of each batch of flour to obtain a plurality of clusters.

[0091] According to the shelf life remaining length of each batch of flour, the batches of flour are clustered, so that the batches of flour with similar shelf life remaining lengths are divided into a cluster, and the shelf life remaining lengths of the batches of flour in different clusters are far apart. In an exemplary embodiment, the absolute value of the difference between the shelf life remaining lengths of any two batches of flour is obtained, and then the K-means clustering algorithm is used to cluster each batch of flour stored in the flour storage room according to the absolute value of the difference between the shelf life remaining lengths of any two batches of flour. Wherein, the number of clusters K is determined by the silhouette coefficient method. The more the number of clusters obtained by clustering, the more dispersed the distribution of the shelf life remaining lengths of each batch of flour stored in the flour storage room, the more diversified, i.e. the stronger the diversity, the batch of flour stored in the flour storage room, the more the need for fine control of temperature and humidity.

[0092] Step 33: obtaining diversity according to the fluctuation degree of the shelf life remaining length and the number of clusters.

[0093] From the above analysis, it can be seen that the diversity is positively correlated with the fluctuation degree of the shelf life remaining length and the number of clusters. According to the fluctuation degree of the shelf life remaining length and the number of clusters, the diversity of the shelf life of each batch of flour stored in the flour storage room is obtained.

[0094] In an example embodiment, the fluctuation degree of the shelf life remaining duration and the cluster quantity corresponding to the flour storage room are normalized respectively, and the normalization manner is the maximum-minimum normalization manner. In order to facilitate the description, the candidate flour storage room is set as an arbitrary flour storage room. For the fluctuation degree of the shelf life remaining duration corresponding to the candidate flour storage room, the maximum value and the minimum value in the fluctuation degrees of the shelf life remaining duration corresponding to all the flour storage rooms are obtained, and then the fluctuation degree of the shelf life remaining duration corresponding to the candidate flour storage room is normalized by using the maximum-minimum normalization manner. For the cluster quantity corresponding to the candidate flour storage room, the maximum value and the minimum value in the cluster quantities corresponding to all the flour storage rooms are obtained, and then the cluster quantity corresponding to the candidate flour storage room is normalized by using the maximum-minimum normalization manner.

[0095] A specific quantification manner of the diversity of the shelf life of the batches of flour stored in the candidate flour storage room is as follows: the product of the normalized fluctuation degree of the shelf life remaining duration corresponding to the candidate flour storage room and the normalized cluster quantity corresponding to the candidate flour storage room is calculated, and the product is the diversity of the shelf life of the batches of flour stored in the candidate flour storage room. Through the above manner, the diversity of the shelf life corresponding to each flour storage room is obtained.

[0096] According to the diversity corresponding to the candidate flour storage room and the flour complexity of the candidate flour storage room, the flour temperature and humidity regulation refinement degree of the candidate flour storage room is obtained. The higher the flour complexity of the candidate flour storage room is, the more refined the flour temperature and humidity regulation of the candidate flour storage room needs to be, and the higher the flour temperature and humidity regulation refinement degree of the candidate flour storage room is. The stronger the diversity corresponding to the candidate flour storage room is, the more refined the flour temperature and humidity regulation of the candidate flour storage room needs to be, and the higher the flour temperature and humidity regulation refinement degree of the candidate flour storage room is. In an example embodiment, the product of the diversity corresponding to the candidate flour storage room and the flour complexity of the candidate flour storage room is calculated, and the product is the flour temperature and humidity regulation refinement degree of the candidate flour storage room.

[0097] Through the above process, the flour temperature and humidity regulation refinement degree of each flour storage room is obtained, and the flour temperature and humidity regulation refinement degree is used to determine the flour temperature and humidity regulation strategy. The higher the flour temperature and humidity regulation refinement degree is, the more refined the flour temperature and humidity regulation strategy needs to be, so that the flour temperature and humidity regulation refinement degree is adapted to the actual situation of the batches of flour stored in the corresponding flour storage room, and the accuracy and reliability of the flour temperature and humidity regulation refinement degree are improved. Moreover, the flour temperature and humidity regulation strategy is determined according to the flour temperature and humidity regulation refinement degree, so that the flour temperature and humidity regulation strategy is more in line with the actual situation of the flour storage room, and the reliability and accuracy of the flour temperature and humidity regulation of the flour storage room are improved.

[0098] In an example embodiment, as Figure 6As shown, the flour storage and humidity remote regulation and control system based on the Internet of Things provided in the embodiment further includes a correction module and a regulation and control module. Figure 7 As shown, the method steps corresponding to the correction module and the regulation and control module are shown.

[0099] The correction module is configured to correct the initial parameter warning fluctuation of the candidate flour storage room according to the flour temperature and humidity regulation and control fineness of the candidate flour storage room, to obtain the target parameter warning fluctuation.

[0100] For the candidate flour storage room, the temperature and humidity regulation and control of the candidate flour storage room are based on the flour temperature and humidity regulation and control fineness of the candidate flour storage room, and the temperature and humidity regulation and control process is independent. In order to facilitate the description, the parameter is used to represent the temperature or humidity. Therefore, the initial parameter warning fluctuation is the initial temperature warning fluctuation or the initial humidity warning fluctuation, and the target parameter warning fluctuation is the target temperature warning fluctuation or the target humidity warning fluctuation.

[0101] According to the flour temperature and humidity regulation and control fineness of the candidate flour storage room, the initial parameter warning fluctuation of the candidate flour storage room is corrected to obtain the target parameter warning fluctuation. When the parameter is temperature, the initial temperature warning fluctuation of the candidate flour storage room is corrected according to the flour temperature and humidity regulation and control fineness of the candidate flour storage room to obtain the target temperature warning fluctuation. When the parameter is humidity, the initial humidity warning fluctuation of the candidate flour storage room is corrected according to the flour temperature and humidity regulation and control fineness of the candidate flour storage room to obtain the target humidity warning fluctuation.

[0102] First, the initial parameter warning fluctuation of the candidate flour storage room is obtained, as shown in Figure 8 As shown, a specific acquisition process is given as follows:

[0103] Step 41: Obtain each parameter active regulation time in the historical parameter fluctuation curve of the candidate flour storage room.

[0104] The historical parameter fluctuation curve of the candidate flour storage room in the historical preset time period is obtained, wherein the length of the historical preset time period is set according to actual needs. The historical parameter fluctuation curve is a historical temperature fluctuation curve or a historical humidity fluctuation curve.

[0105] In the historical preset time period, there are several parameter active regulation times, such as when the parameter in the candidate flour storage room does not meet the condition, the regulation and control condition is triggered, and the parameter in the candidate flour storage room is regulated and controlled. Map each parameter active regulation time to the historical parameter fluctuation curve and mark it in the historical parameter fluctuation curve.

[0106] Step 42: determine the adjacent parameter values of the parameter active regulation time point and obtain the parameter fluctuation characteristics of the adjacent parameter values.

[0107] For any parameter active regulation time point, obtain a plurality of time points adjacent to the parameter active regulation time point and before the parameter active regulation time point, which are reference time points of the parameter active regulation time point, and obtain the parameter values of the reference time points of the parameter active regulation time point in the historical parameter fluctuation curve, which are adjacent parameter values. The number of reference time points is set according to actual needs, such as 6.

[0108] Obtain the parameter fluctuation characteristics of the adjacent parameter values of the parameter active regulation time point. The parameter fluctuation characteristics represent the degree of parameter change of the adjacent parameter values. In an exemplary embodiment, the absolute values of the slopes of the adjacent parameter values in the historical parameter fluctuation curve are obtained, and then the average of the absolute values of the slopes is calculated as the parameter fluctuation characteristics of the adjacent parameter values of the parameter active regulation time point. The absolute value of the slope represents the degree of change of the adjacent parameter values. The larger the absolute value of the slope, the greater the degree of change of the adjacent parameter values, and the more obvious the parameter fluctuation characteristics.

[0109] At the same time, the values of the adjacent parameter values of the parameter active regulation time point of the candidate flour storage room are fused, specifically, the average of the values of the adjacent parameter values of the parameter active regulation time point is calculated as the parameter characteristics of the parameter active regulation time point.

[0110] Step 43: fuse the parameter fluctuation characteristics of all parameter active regulation time points to obtain the initial parameter warning fluctuation of the candidate flour storage room.

[0111] Fuse the parameter fluctuation characteristics of each parameter active regulation time point in the historical parameter fluctuation curve of the candidate flour storage room, specifically, calculate the average of the parameter fluctuation characteristics of each parameter active regulation time point in the historical parameter fluctuation curve of the candidate flour storage room as the initial parameter warning fluctuation of the candidate flour storage room.

[0112] At the same time, fuse the parameter characteristics of all parameter active regulation time points of the candidate flour storage room, specifically, calculate the average of the parameter characteristics of all parameter active regulation time points of the candidate flour storage room as the warning parameter value of the candidate flour storage room.

[0113] The following describes the acquisition process of the initial parameter warning fluctuation and the warning parameter value by taking the parameters of temperature and humidity as examples, respectively.

[0114] Using the nth flour storage chamber as a candidate flour storage chamber, obtain its historical temperature fluctuation curve over a preset historical time period, and determine several active temperature control moments within that period. Since active temperature control includes both active temperature reduction and active temperature increase, for example, if the temperature of the nth flour storage chamber is higher than the optimal temperature range, the temperature needs to be reduced; if the temperature is lower than the optimal temperature range, the temperature needs to be increased. Therefore, obtain several active temperature reduction moments and active temperature increase moments respectively. Let Y be the number of active controls performed, then the historical temperature fluctuation curve of the nth flour storage chamber will have Y marked temperature values. Let there be... The second method is to actively reduce the temperature. The second method involves actively increasing the temperature.

[0115] For the temperature value of the y-th marker, the first 6 temperature data points are recorded as reference temperature data, and the average of these 6 reference temperature data points is calculated and recorded as the temperature characteristic of the y-th marker. The absolute values ​​of the slopes of these 6 reference temperature data points in the historical temperature fluctuation curve are obtained, and then the average of the absolute values ​​of the slopes of these 6 reference temperature data points is calculated as the temperature fluctuation characteristic of the y-th marker.

[0116] Then, The average of the temperature characteristics of each proactive temperature reduction is denoted as the warning temperature value at which the nth flour storage room needs to be lowered; The average value of the temperature fluctuation characteristics of each proactive temperature reduction is denoted as the initial temperature warning fluctuation for the nth flour storage room requiring temperature reduction. The average value of the temperature characteristics of each proactive temperature increase is denoted as the warning temperature value for the nth flour storage room that requires a temperature increase. The average value of the temperature fluctuation characteristics of each active temperature increase is denoted as the initial temperature warning fluctuation of the nth flour storage room that requires a temperature increase.

[0117] Obtain the historical humidity fluctuation curve of the nth flour storage room within a preset historical time period, and determine several active humidity control moments within that period. Since active humidity control includes actively reducing humidity and actively increasing humidity, for example, if the humidity of the nth flour storage room is higher than the optimal humidity range, it needs to be reduced; if the humidity is lower than the optimal humidity range, it needs to be increased. Therefore, obtain several moments of actively reducing humidity and moments of actively increasing humidity. Assume there are Z active control actions, then there are Z marked humidity values ​​in the historical humidity fluctuation curve of the nth flour storage room. Assume that among these Z active control actions, there are... The second method is to actively reduce humidity. The second method is to actively increase humidity.

[0118] For the zth humidity value, the first 6 humidity data are recorded as reference humidity data, and the average of the 6 reference humidity data is calculated as the humidity characteristic of the zth humidity value. The absolute value of the slope of the 6 reference humidity data in the historical humidity fluctuation curve is obtained, and then the average of the absolute values of the slopes of the 6 reference humidity data is calculated as the humidity fluctuation characteristic of the zth humidity value.

[0119] Then, the average of the humidity characteristics of the n times of active humidity reduction is recorded as the pre-warning humidity value of the nth flour storage room requiring humidity reduction; the average of the humidity fluctuation characteristics of the n times of active humidity reduction is recorded as the initial humidity pre-warning fluctuation of the nth flour storage room requiring humidity reduction. The average of the humidity characteristics of the n times of active humidity reduction is recorded as the pre-warning humidity value of the nth flour storage room requiring humidity reduction; the average of the humidity fluctuation characteristics of the n times of active humidity reduction is recorded as the initial humidity pre-warning fluctuation of the nth flour storage room requiring humidity reduction. The average of the humidity characteristics of the n times of active humidity reduction is recorded as the pre-warning humidity value of the nth flour storage room requiring humidity reduction; the average of the humidity fluctuation characteristics of the n times of active humidity reduction is recorded as the initial humidity pre-warning fluctuation of the nth flour storage room requiring humidity reduction. The average of the humidity characteristics of the n times of active humidity reduction is recorded as the pre-warning humidity value of the nth flour storage room requiring humidity reduction; the average of the humidity fluctuation characteristics of the n times of active humidity reduction is recorded as the initial humidity pre-warning fluctuation of the nth flour storage room requiring humidity reduction. The average of the humidity characteristics of the n times of active humidity reduction is recorded as the pre-warning humidity value of the nth flour storage room requiring humidity reduction; the average of the humidity fluctuation characteristics of the n times of active humidity reduction is recorded as the initial humidity pre-warning fluctuation of the nth flour storage room requiring humidity reduction.

[0120] In an exemplary embodiment, as shown in FIG. 6, a specific correction process for the initial parameter pre-warning fluctuation of the candidate flour storage room is as follows: Figure 9

[0121] Step 44: Determine the minimum initial parameter pre-warning fluctuation from the initial parameter pre-warning fluctuations of the plurality of flour storage rooms.

[0122] Since each flour storage room is provided with an initial parameter pre-warning fluctuation, the minimum initial parameter pre-warning fluctuation is determined therefrom.

[0123] Step 45: Obtain a correction coefficient according to the flour temperature and humidity regulation fineness of the candidate flour storage room.

[0124] When the fineness of the temperature and humidity regulation is greater, the temperature and humidity regulation needs to be more refined, and the correction coefficient is smaller, so that the pre-warning fluctuation correction amount is smaller, and finally the target parameter pre-warning fluctuation is smaller. In an exemplary embodiment, the maximum-minimum normalization method is used to normalize the fineness of the flour temperature and humidity regulation of the candidate flour storage room, and then the negative correlation of the normalized result is obtained to obtain the correction coefficient, and the formula is as follows:

[0125] ;

[0126] wherein, represents the correction coefficient of the nth flour storage room, represents the fineness of the flour temperature and humidity regulation of the nth flour storage room, ​Minimun flour temperature and humidity control fineness degree in flour temperature and humidity control fineness degrees of each flour storage room, Maximun flour temperature and humidity control fineness degree in flour temperature and humidity control fineness degrees of each flour storage room. It should be noted that, in order to ensure the calculation result is meaningful, when performing fractional operation, if the denominator is 0, a parameter adjustment factor greater than 0 is added to the denominator to prevent the denominator from being 0. The value of the parameter adjustment factor is set by the implementer according to the actual situation, and the present application is set to 0.1.

[0127] Step 46: obtaining a warning fluctuation correction amount according to the correction coefficient and the minimum initial parameter warning fluctuation.

[0128] Step 45: multiplying the correction coefficient of the candidate flour storage room by the minimum initial parameter warning fluctuation to obtain the warning fluctuation correction amount of the candidate flour storage room.

[0129] Step 47: obtaining a target parameter warning fluctuation according to the initial parameter warning fluctuation of the candidate flour storage room and the warning fluctuation correction amount.

[0130] Step 46: obtaining a warning fluctuation correction amount according to the correction coefficient and the minimum initial parameter warning fluctuation.

[0131] ;

[0132] Wherein, represents the target parameter warning fluctuation of the nth flour storage room, represents the initial parameter warning fluctuation of the nth flour storage room, represents the minimum initial parameter warning fluctuation.

[0133] According to the above calculation process of the target parameter warning fluctuation, when the initial parameter warning fluctuation is the initial temperature warning fluctuation that needs to be reduced, the target temperature warning fluctuation that needs to be reduced of each flour storage room is calculated according to the above process; when the initial parameter warning fluctuation is the initial temperature warning fluctuation that needs to be increased, the target temperature warning fluctuation that needs to be increased of each flour storage room is calculated according to the above process; when the initial parameter warning fluctuation is the initial humidity warning fluctuation that needs to be reduced, the target humidity warning fluctuation that needs to be reduced of each flour storage room is calculated according to the above process; when the initial parameter warning fluctuation is the initial humidity warning fluctuation that needs to be increased, the target humidity warning fluctuation that needs to be increased of each flour storage room is calculated according to the above process.

[0134] The control module is configured to control the parameters of the flour storage room according to the target parameter warning fluctuation and the warning parameter value of the flour storage room.

[0135] For example, the current actual parameters of the candidate flour storage room are obtained, and the actual parameter fluctuation is obtained. If the current actual parameters of the candidate flour storage room deviate from the early warning parameter value of the candidate flour storage room, and the actual parameter fluctuation is greater than the target parameter early warning fluctuation, the parameters of the candidate flour storage room are regulated.

[0136] For temperature regulation of the candidate flour storage room:

[0137] The current actual temperature of the candidate flour storage room is obtained, and the absolute value of the slope of the current actual temperature is obtained. If the current actual temperature of the candidate flour storage room is less than or equal to the early warning temperature value of the temperature to be raised, and the absolute value of the slope of the current actual temperature is greater than or equal to the initial temperature early warning fluctuation of the temperature to be raised, a temperature raising regulation instruction is output, and the temperature rise per unit time cannot exceed a preset value, so as to ensure that the temperature rise speed is less than the initial temperature early warning fluctuation of the temperature to be raised, and ensure that the temperature rises smoothly. If the current actual temperature of the candidate flour storage room is greater than or equal to the early warning temperature value of the temperature to be reduced, and the absolute value of the slope of the current actual temperature is greater than or equal to the initial temperature early warning fluctuation of the temperature to be reduced, a temperature reducing regulation instruction is output, and the temperature reduction per unit time cannot exceed a preset value, so as to ensure that the temperature reduction speed is less than the initial temperature early warning fluctuation of the temperature to be reduced, and ensure that the temperature reduces smoothly.

[0138] Similarly, the current actual humidity of the candidate flour storage room is obtained, and the absolute value of the slope of the current actual humidity is obtained. If the current actual humidity of the candidate flour storage room is less than or equal to the early warning humidity value of the humidity to be raised, and the absolute value of the slope of the current actual humidity is greater than or equal to the initial humidity early warning fluctuation of the humidity to be raised, a humidity raising regulation instruction is output, and the humidity rise per unit time cannot exceed a preset value, so as to ensure that the humidity rise speed is less than the initial temperature early warning fluctuation of the humidity to be raised, and ensure that the humidity rises smoothly. If the current actual humidity of the candidate flour storage room is greater than or equal to the early warning humidity value of the humidity to be reduced, and the absolute value of the slope of the current actual humidity is greater than or equal to the initial humidity early warning fluctuation of the humidity to be reduced, a humidity reducing regulation instruction is output, and the humidity reduction per unit time cannot exceed a preset value, so as to ensure that the humidity reduction speed is less than the initial humidity early warning fluctuation of the humidity to be reduced, and ensure that the humidity reduces smoothly.

[0139] In an exemplary embodiment, the flour storage room is provided with an air conditioner, a humidifier and a dehumidifier, the temperature in the flour storage room is regulated by regulating the air conditioner, and the humidity in the flour storage room is regulated by regulating the humidifier and the dehumidifier; a heater and a refrigeration device can also be used to replace the air conditioner, which will not be described further here.

[0140] It is to be noted that the sequential order of the above-described embodiments of the present application only for the purpose of description, but not the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0141] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments.

Claims

1. An Internet of Things-based flour warehouse temperature and humidity remote regulation system, characterized in that, The method comprises the following steps: The state stability acquisition module is configured to determine the state stability of each batch of flour, which is obtained based on the temperature and humidity of each batch of flour during the processing and the humidity characteristics of the flour after the processing is completed; The flour complexity acquisition module is configured to obtain the flour complexity of the flour storage room based on the state stability of each batch of flour stored in the flour storage room and the difference between the state stability; The delicacy acquisition module is configured to determine the diversity of the shelf life of each batch of flour stored in the flour storage room, and obtain the flour temperature and humidity control delicacy of the flour storage room based on the flour complexity, wherein the flour temperature and humidity control delicacy is used to determine the flour temperature and humidity control strategy; The diversity acquisition process comprises the following steps: The shelf life remaining duration of each batch of flour is obtained based on the shelf life of each batch of flour, and the shelf life remaining duration fluctuation degree is obtained; Each batch of flour is clustered based on the shelf life remaining duration, and a plurality of clusters is obtained; The diversity is obtained based on the shelf life remaining duration fluctuation degree and the number of clusters. The flour storage and humidity remote control system based on the Internet of Things further comprises: The correction module is configured to correct the initial parameter warning fluctuation of the flour storage room based on the flour temperature and humidity control delicacy of the flour storage room, and obtain the target parameter warning fluctuation; the parameter is temperature or humidity; The control module is configured to control the parameter of the flour storage room based on the target parameter warning fluctuation and the warning parameter value of the flour storage room. The initial parameter warning fluctuation correction process of the candidate flour storage room comprises the following steps: The minimum initial parameter warning fluctuation is determined from the initial parameter warning fluctuations of the plurality of flour storage rooms; The correction coefficient is obtained based on the flour temperature and humidity control delicacy of the candidate flour storage room; The warning fluctuation correction amount is obtained based on the correction coefficient and the minimum initial parameter warning fluctuation; The target parameter warning fluctuation is obtained based on the initial parameter warning fluctuation of the candidate flour storage room and the warning fluctuation correction amount. The initial parameter warning fluctuation acquisition process of the candidate flour storage room comprises the following steps: Each parameter active control time point in the historical parameter fluctuation curve of the candidate flour storage room is obtained; The adjacent parameter values of the previous several time points adjacent to the parameter active control time point are determined, and the parameter fluctuation characteristics of the adjacent parameter values are obtained; The parameter fluctuation characteristics of all parameter active control time points are fused to obtain the initial parameter warning fluctuation of the candidate flour storage room.

2. The remote temperature and humidity control system for flour storage based on Internet of Things according to claim 1, characterized in that, The state stability acquisition module is configured to: Obtain the temperature fluctuation degree and the humidity fluctuation degree of any batch of flour during the processing to obtain the temperature and humidity stability degree of the batch of flour; Determine the difference between the humidity characteristics of the batch of flour and the preset standard humidity characteristics, and obtain the state stability of the batch of flour based on the temperature and humidity stability degree.

3. The remote temperature and humidity control system for flour storage based on Internet of Things according to claim 1, characterized in that, The flour complexity acquisition module is configured to: Obtain the state stability fluctuation degree and the overall level of the state stability of each batch of flour stored in the flour storage room; According to the state stability fluctuation degree and the state stability overall level, the flour complexity degree is obtained.

4. The remote temperature and humidity control system for flour storage based on Internet of Things according to claim 1, characterized in that, The correction coefficient is obtained according to the flour temperature and humidity regulation precision of the candidate flour storage room, comprising: The flour temperature and humidity regulation precision of the candidate flour storage room is normalized by using the maximum and minimum normalization method, and then the normalized result is negatively correlated to obtain the correction coefficient.

5. The remote temperature and humidity control system for flour storage based on Internet of Things according to claim 1, characterized in that, The process of obtaining the early warning parameter value of the candidate flour storage room comprises: According to the adjacent parameter values of the parameter active regulation time of the candidate flour storage room, the parameter characteristics of the parameter active regulation time are obtained. The early warning parameter value of the candidate flour storage room is obtained by fusing the parameter characteristics of all parameter active regulation times of the candidate flour storage room.

6. The Internet of Things based flour storage temperature and humidity remote regulation and control system according to claim 1, characterized in that, The regulation module is specifically used for: If the actual parameter of the flour storage room deviates from the early warning parameter value, and the actual parameter fluctuation is greater than the target parameter early warning fluctuation, the parameter of the flour storage room is regulated.

Images