An intelligent control system for grain drying tower

Abstract

The present invention relates to the technical field of grain drying towers, and specifically to an intelligent control system for grain drying towers, comprising: a main control terminal, a perception layer, a modeling layer and a control layer; the main control terminal is a control switch of the system, and is used to control the start and stop of the system operation; the grain drying state parameters of each layer of the grain drying tower are collected and recorded through the perception layer, the modeling layer receives the grain drying state parameters recorded in the perception layer in real time, and decides whether to build a grain drying state control model based on the grain drying state parameters. The present invention collects temperature information through the deployment of temperature sensors and obtains the operating parameters of the grain drying tower itself, designs a reasonable temperature information collection path for the drying station of the drying tower, and further combines the analysis of the operating parameters of the grain drying tower itself to provide real-time and continuous temperature control and transmission control effects for the grain drying tower, so that the grain being dried in the grain drying tower can obtain real-time drying temperature and transmission speed control.

Description

Technical Field

[0001] The present invention relates to the technical field of grain drying towers, and in particular to an intelligent control system for a grain drying tower. Background Art

[0002] Grain drying towers are important agricultural equipment. They dry damp grain by heating air, quickly reducing its moisture content, preventing mold and facilitating storage. Their structure typically includes a feed inlet, drying chamber, and heating device. They are easy to operate, highly efficient, and energy-efficient, ensuring the proper handling of harvested grain.

[0003] The invention patent with application number 201810153380.0 discloses a grain dryer control system, which is mainly composed of a material level sensor, a humidity sensor, a temperature sensor, a moisture meter, a DSP, a converter, a host computer, a grain feeding control module and a grain discharging control module. It is characterized in that: the material level sensor, the humidity sensor, the temperature sensor and the moisture meter are connected to the DSP, the grain feeding control module and the grain discharging control module are arranged at the input end of the DSP, the converter is connected to the DSP, and the host computer is connected to the converter.

[0004] The application aims to solve the problem that "after harvest, grain cannot be dried in time due to weather reasons or the grain does not meet the safety storage standards after drying, resulting in grain mold during storage, processing, drying, etc. The losses caused by this cause 5% of the total grain output, and the economic losses can reach 30 billion to 60 billion yuan."

[0005] However, after receiving the grain, the grain drying tower usually performs three consecutive drying operations at different temperatures in the drying station. Due to the difference in moisture content of grain particles, a continuous fixed drying temperature obviously cannot guarantee a good grain drying effect.

[0006] To this end, we proposed an intelligent control system for grain drying tower. Summary of the Invention

[0007] In view of the above-mentioned shortcomings of the prior art, the present invention provides an intelligent control system for a grain drying tower, which solves the technical problems raised in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] An intelligent control system for a grain drying tower, comprising: a main control terminal, a perception layer, a modeling layer and a control layer;

[0010] The main control terminal is the control switch of the system, which is used to control the start and stop of the system operation;

[0011] The grain drying state parameters of each layer of the grain drying tower are collected and recorded by the perception layer. The modeling layer receives the grain drying state parameters recorded in the perception layer in real time and decides whether to build a grain drying state control model based on the grain drying state parameters. If the decision result is yes and the construction of the grain drying state control model is completed, the control layer synchronously runs the grain drying state control model constructed by the modeling layer, controls the grain drying tower based on the grain drying state control model, and controls the system to refresh and run;

[0012] The modeling layer includes a receiving module, a decision module and a construction module. The receiving module is used to receive the grain drying state parameters collected in the perception layer. The decision module is used to traverse the grain drying state parameters received by the receiving module and decide whether to trigger the construction module to run based on the grain drying state parameters. The construction module is used to build a grain drying state control model.

[0013] The logic of the decision module to decide whether to trigger the construction module to run is expressed as follows:

[0014]

[0015] Where: P is the judgment value of whether to trigger the operation of the construction module; n is the drying layer set of the drying station of the grain drying tower; f[·] is the cumulative function; T last 、T next RH is the temperature of drying layer a in the previous set of grain drying state parameters and the temperature of drying layer a in the next set of grain drying state parameters; last RH next V is the humidity of drying layer a in the previous set of grain drying state parameters and the humidity of drying layer a in the next set of grain drying state parameters; last 、V next is the transmission speed of the drying layer a at the transmission station in the previous set of grain drying state parameters, and the transmission speed of the drying layer a at the transmission station in the next set of grain drying state parameters; δ is the grain drying state safety judgment value;

[0016] Among them, the safety judgment value of grain drying state is customized by the system end user, and the cumulative function f[·] is expressed as The value of the cumulative function f[·] is determined based on formula (2), where a, b, and c represent the upper drying station, the middle drying station, and the lower drying station. Based on formula (1), the values ​​of the cumulative function f[·] corresponding to the upper drying station, the middle drying station, and the lower drying station are calculated and summed. When the judgment value P ≥ 1, the construction module is triggered to run; otherwise, the construction module is not triggered to run.

[0017] Furthermore, the perception layer includes a configuration module, a monitoring module and a recording module. The configuration module is used to upload the thermal imaging image of the grain drying tower, configure the deployment path of the monitoring module based on the thermal imaging image of the grain drying tower, the monitoring module is used to monitor the temperature information of the drying tower in real time and receive the operating parameters of the drying tower, and the recording module is used to receive the temperature information of the drying tower detected by the monitoring module and receive the operating parameters of the drying tower, and distinguish and record the temperature information of the drying tower and the operating parameters of the drying tower based on the monitoring timestamps of the drying tower temperature information and the operating parameters of the drying tower received;

[0018] Among them, the drying tower temperature information and the received drying tower operating parameters are synchronously marked with their monitoring timestamps before the recording operation is performed based on the recording module. The drying tower is integrated with a feeding station, a drying station, a discharging station and a control station. The drying station includes the drying station itself and also includes a transmission station. The drying station is composed of an upper drying station, a middle drying station and a lower drying station. The monitoring module and the transmission station in the drying station run through the upper, middle and lower groups of drying stations. The drying tower operating parameters include: humidity values ​​of the upper drying station, the middle drying station and the lower drying station; transmission speed of the transmission station. The drying tower runs synchronously with the monitoring module and feeds back its own operating parameters to the monitoring module in real time.

[0019] Furthermore, the thermal imaging images of the grain drying tower uploaded in the configuration module are collected by the thermal imager on the outside of the grain drying tower. When the grain drying tower is a rectangular parallelepiped, the thermal imaging images of the grain drying tower are four groups of side thermal imaging images of the grain drying tower. When the grain drying tower is a cylinder, the thermal imaging images of the grain drying tower are two groups of side thermal imaging images from the side view of the grain drying tower. After the acquisition is completed, the thermal imaging images of the grain drying tower are further spliced ​​horizontally in sequence to form a thermal imaging image of the grain drying tower, which is recorded as the entire thermal imaging image of the grain drying tower. When the entire thermal imaging image of the grain drying tower is horizontally divided, the division width is customized by the system user to obtain several groups of long strip thermal imaging images of the grain drying tower, and the medium temperature point is captured on the horizontal center line of each group of long strip thermal imaging images of the grain drying tower as the deployment point of the monitoring module;

[0020] Among them, the monitoring module is integrated by several groups of temperature sensors, and each monitoring module deployment point is installed with a group of temperature sensors.

[0021] Furthermore, the logic for capturing the lowest temperature point on the horizontal midline of each set of thermal imaging images of the long grain drying tower is expressed as follows:

[0022]

[0023] Where: G(x,y) is the grayscale value of the point (x,y) on the horizontal midline of each group of thermal imaging images of the long grain drying tower; t is the acquisition time of the thermal imaging image of the grain drying tower from which the thermal imaging image of the long grain drying tower is derived; K is the adjustment coefficient;

[0024] Among them, the adjustment coefficient K is used to control G(x, y) to always be in the range of 0 to 255. Based on the above formula, the grayscale value of each pixel on the horizontal center line of the thermal imaging image of the long grain drying tower is calculated, and then each pixel is further arranged in descending order based on the grayscale value calculation results. A group of pixels in the middle position of the descending order queue is used as the deployment point of the monitoring module.

[0025] Furthermore, after the monitoring module deployment points are determined, corresponding points are further determined in the thermal imaging image of the grain drying tower, and then corresponding points of the corresponding points in the thermal imaging image of the grain drying tower are further determined on the surface of the grain drying tower. The corresponding points determined on the surface of the grain drying tower are adjacent to each other along the surface of the grain drying tower to obtain a set of monitoring module deployment paths. Based on the monitoring module deployment paths, temperature sensors are evenly deployed so that the temperature sensors are equidistant from the surface of the grain drying tower, and temperature sensors are deployed at all points constituting the monitoring module deployment paths.

[0026] Furthermore, the grain drying state parameters received by the receiving module are derived from the recording module in the perception layer, and the grain drying state parameters received by the receiving module include the drying tower temperature information and the drying tower operation parameters;

[0027] The decision module decides whether to trigger the construction module to run, and makes a decision by referring to the two sets of grain drying state parameters recorded most recently;

[0028] The decision module is also provided with several groups of trigger judgment thresholds, and the several groups of trigger judgment thresholds correspond one-to-one to the grain drying state parameters. When any one of the two groups of grain drying state parameters recorded for reference meets the corresponding trigger judgment threshold, the construction module is triggered to run.

[0029] Furthermore, during the operation phase of the construction module, the two sets of grain drying state parameters with the latest record for reference are obtained in the decision module, and the model length is calculated based on the parameters corresponding to the upper drying station, the middle drying station and the lower drying station in the set of grain drying state parameters with the earliest record time. This is recorded as The lengths are The three groups of line segments form a group of grain drying state control models, grain drying state control model A;

[0030] Based on the above logic, the latest set of grain drying state parameters recorded in the two sets of grain drying state parameters is obtained. The lengths are The three groups of line segments form a grain drying state control model, which is recorded as grain drying state control model B.

[0031] Furthermore, the control layer includes an identification module, an adjustment module and a refresh module. The identification module is used to receive the grain drying state control model constructed by the modeling layer and identify the differences of the grain drying state control model. The adjustment module is used to receive the differences of the grain drying state control model identified in the identification module, determine the adjustment target based on the differences of the grain drying state control model, and adjust and control the determined target. The refresh module is used to monitor the operation status of the adjustment module and control the system to refresh and run after monitoring that the adjustment module has finished running.

[0032] Among them, the identification module identifies the operations of the differences in the grain drying state control model, namely: the difference between the model length value corresponding to the upper drying station a of the grain drying state control model A and the model length value corresponding to the upper drying station a of the grain drying state control model B, the difference between the model length value corresponding to the middle drying station b of the grain drying state control model A and the model length value corresponding to the upper middle drying station b of the grain drying state control model B, and the difference between the model length values ​​corresponding to the upper and lower drying stations c of the grain drying state control model A and the model length values ​​corresponding to the upper and lower drying stations c of the grain drying state control model B.

[0033] Furthermore, the logic for adjusting and controlling the determined target in the adjustment module is expressed as follows:

[0034] Logic 1:

[0035] The adjustment logic of the upper drying station is: adjust the target temperature to

[0036] The adjustment logic of the drying station is: adjust the target temperature to

[0037] The adjustment logic of the lower drying station is: adjust the target temperature to

[0038] Logic 2:

[0039] The adjustment logic of the upper drying station is: adjust the transmission speed of the target transmission station to

[0040] The adjustment logic of the drying station is: adjust the target transmission station transmission speed to

[0041] The adjustment logic of the lower drying station is: adjust the target transmission station transmission speed to

[0042] Furthermore, the main control terminal is interactively connected to a configuration module via a wireless network, the configuration module is interactively connected to a monitoring module and a recording module via a wireless network, the recording module is interactively connected to a receiving module via a wireless network, the receiving module is interactively connected to a decision module and a construction module via a wireless network, the construction module is interactively connected to an identification module via a wireless network, and the identification module is interactively connected to an adjustment module and a refresh module via a wireless network.

[0043] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0044] The present invention provides an intelligent control system for a grain drying tower. During operation, the system collects temperature information through the deployment of temperature sensors and obtains the operating parameters of the grain drying tower itself. A reasonable temperature information collection path for the drying stations of the drying tower is designed, and further combined with the analysis of the operating parameters of the grain drying tower itself, real-time and continuous temperature control and transmission control effects are provided for the grain drying tower, so that the grain being dried in the grain drying tower can obtain real-time drying temperature and transmission speed control, ensuring that the moisture content of the grain dried and output by the grain drying tower can better meet the storage conditions, thereby improving the drying function effect of the grain drying tower and making the drying function of the grain drying tower more real-time, continuous and intelligent. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0046] Figure 1 This is a structural diagram of an intelligent control system for a grain drying tower;

[0047] Figure 2 Schematic diagram of the target and combination state of thermal imaging image acquisition on the surface of the grain drying tower in the present invention;

[0048] Figure 3 This is a schematic diagram of the process of determining the deployment points of the surface monitoring module of a rectangular or cylindrical grain drying tower in the present invention;

[0049] Figure 4 This is a schematic diagram of an example of a grain drying state control model constructed by applying the latest two sets of grain drying state parameters in the present invention;

[0050] The numbers in the figure represent: a, the corresponding line segment length of the upper drying station a in the grain drying state control model; b, the corresponding line segment length of the middle drying station b in the grain drying state control model; c, the corresponding line segment length of the lower drying station c in the grain drying state control model. DETAILED DESCRIPTION

[0051] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0052] The present invention will be further described below with reference to the embodiments.

[0053] Example 1:

[0054] An intelligent control system for a grain drying tower in this embodiment, such as Figure 1 As shown, it includes: main control terminal, perception layer, modeling layer and control layer;

[0055] The main control terminal is the control switch of the system, which is used to control the start and stop of the system operation;

[0056] The grain drying state parameters of each layer of the grain drying tower are collected and recorded by the perception layer. The modeling layer receives the grain drying state parameters recorded in the perception layer in real time and decides whether to build a grain drying state control model based on the grain drying state parameters. If the decision result is yes and the construction of the grain drying state control model is completed, the control layer synchronously runs the grain drying state control model constructed by the modeling layer, controls the grain drying tower based on the grain drying state control model, and controls the system to refresh and run;

[0057] The perception layer includes a configuration module, a monitoring module, and a recording module. The configuration module is used to upload thermal imaging images of the grain drying tower and configure the deployment path of the monitoring module based on the thermal imaging images of the grain drying tower. The monitoring module is used to monitor the temperature information of the drying tower in real time and receive the operating parameters of the drying tower. The recording module is used to receive the temperature information of the drying tower detected by the monitoring module and receive the operating parameters of the drying tower. The temperature information of the drying tower and the operating parameters of the drying tower are distinguished and recorded based on the monitoring timestamps of the drying tower temperature information and the operating parameters received;

[0058] Among them, the drying tower temperature information and the received drying tower operating parameters are synchronously marked with their monitoring timestamps before the recording module performs the distinguishing recording operation. The drying tower is integrated with the feeding station, drying station, discharging station and control station. The drying station includes the drying station itself and the transmission station. The drying station is composed of the upper drying station, the middle drying station and the lower drying station. The monitoring module and the transmission station in the drying station run through the upper, middle and lower drying stations. The drying tower operating parameters include: the humidity value of the upper drying station, the middle drying station and the lower drying station; the transmission speed of the transmission station. The drying tower runs synchronously with the monitoring module and feeds back its own operating parameters to the monitoring module in real time.

[0059] The modeling layer includes a receiving module, a decision module, and a construction module. The receiving module is used to receive the grain drying state parameters collected in the perception layer. The decision module is used to traverse the grain drying state parameters received by the receiving module and decide whether to trigger the construction module to run based on the grain drying state parameters. The construction module is used to build a grain drying state control model.

[0060] The logic of whether to trigger the construction module to run in the decision module is expressed as follows:

[0061]

[0062] Where: P is the judgment value of whether to trigger the operation of the construction module; n is the drying layer set of the drying station of the grain drying tower; f[·] is the cumulative function; T last 、T next RH is the temperature of drying layer a in the previous set of grain drying state parameters and the temperature of drying layer a in the next set of grain drying state parameters; last RH next V is the humidity of drying layer a in the previous set of grain drying state parameters and the humidity of drying layer a in the next set of grain drying state parameters; last 、V next is the transmission speed of the drying layer a at the transmission station in the previous set of grain drying state parameters, and the transmission speed of the drying layer a at the transmission station in the next set of grain drying state parameters; δ is the grain drying state safety judgment value;

[0063] Among them, the safety judgment value of grain drying state is customized by the system end user, and the cumulative function f[·] is expressed as The value of the cumulative function f[·] is determined based on formula (2), where a, b, and c represent the upper drying station, the middle drying station, and the lower drying station. Based on formula (1), the values ​​of the cumulative function f[·] corresponding to the upper drying station, the middle drying station, and the lower drying station are calculated and summed. When the judgment value P ≥ 1, the construction module is triggered to run; otherwise, the construction module is not triggered to run.

[0064] The control layer includes an identification module, an adjustment module and a refresh module. The identification module is used to receive the grain drying state control model constructed by the modeling layer and identify the differences in the grain drying state control model. The adjustment module is used to receive the differences in the grain drying state control model identified in the identification module, determine the adjustment target based on the differences in the grain drying state control model, and adjust and control the determined target. The refresh module is used to monitor the operating status of the adjustment module. After monitoring that the adjustment module has completed operation, the system refreshes and runs.

[0065] Among them, the recognition module recognizes the operation of the difference of the grain drying state control model, namely: the difference between the model length value corresponding to the upper drying station a of the grain drying state control model A and the model length value corresponding to the upper drying station a of the grain drying state control model B, the difference between the model length value corresponding to the middle drying station b of the grain drying state control model A and the model length value corresponding to the middle drying station b of the grain drying state control model B, and the difference between the model length value corresponding to the upper and lower drying stations c of the grain drying state control model A and the model length value corresponding to the upper and lower drying stations c of the grain drying state control model B;

[0066] The main control terminal is interactively connected to the configuration module through a wireless network, the configuration module is interactively connected to the monitoring module and the recording module through a wireless network, the recording module is interactively connected to the receiving module through a wireless network, the receiving module is interactively connected to the decision module and the construction module through a wireless network, the construction module is interactively connected to the identification module through a wireless network, and the identification module is interactively connected to the adjustment module and the refresh module through a wireless network.

[0067] In this embodiment, the main control terminal controls system operation. The configuration module runs to upload a thermal imaging image of the grain drying tower. The monitoring module deployment path is configured based on the thermal imaging image of the grain drying tower. The monitoring module monitors the drying tower temperature information and receives the drying tower operating parameters in real time. The recording module further receives the drying tower temperature information and the received drying tower operating parameters detected by the monitoring module and distinguishes and records the drying tower temperature information and the received drying tower operating parameters based on the monitoring timestamps of the drying tower temperature information and the received drying tower operating parameters. The receiving module then receives the grain drying state parameters collected by the perception layer. The decision module runs post-processing to traverse the grain drying state parameters received by the receiving module and decides whether to trigger the construction module to run based on the grain drying state parameters. The construction module simultaneously builds a grain drying state control model. The identification module runs to receive the grain drying state control model constructed by the modeling layer and identifies differences in the grain drying state control model. Finally, the adjustment module receives the differences in the grain drying state control model identified in the identification module, determines an adjustment target based on the differences in the grain drying state control model, and adjusts and controls the determined target. The refresh module synchronously monitors the operating status of the adjustment module. After monitoring that the adjustment module has completed operation, the system refreshes and runs.

[0068] Through the system in the above embodiment, an intelligent, continuous and real-time control effect is brought to the grain drying tower, so that the grain dried in the grain drying tower can be dried more stably based on temperature control and transmission speed control, ensuring the grain drying quality and improving the working performance of the grain drying tower.

[0069] See also Figure 2 、 Figure 3 As shown, based on the arrow indication, the acquisition source of thermal imaging images of the rectangular and cylindrical grain drying tower surfaces is displayed, and combined with Figure 3 The content shown further demonstrates the process of determining the deployment points of the temperature sensors (see the positions marked with black dots in the figure). By designing the deployment positions of the temperature sensors, the system operation can be supported by more accurate temperature data, ensuring that the system operation in the above-mentioned Example 1 has better accuracy and real-time performance in controlling the temperature of the grain drying tower.

[0070] See also Figure 4 As shown in the figure, the grain drying state control model is displayed, and based on the markings in the figure, the grain drying state control model is further displayed.

[0071] Example 2:

[0072] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The intelligent control system for a grain drying tower in Example 1 is further described in detail:

[0073] The thermal imaging images of the grain drying tower uploaded in the configuration module are collected by the thermal imager on the outside of the grain drying tower. When the grain drying tower is a rectangular parallelepiped, the thermal imaging images of the grain drying tower are four sets of side thermal imaging images of the grain drying tower. When the grain drying tower is a cylinder, the thermal imaging images of the grain drying tower are two sets of side thermal imaging images from the side view of the grain drying tower. After the thermal imaging images of the grain drying tower are collected, they are further spliced ​​horizontally in sequence to form a thermal imaging image of the grain drying tower, which is recorded as the whole thermal imaging image of the grain drying tower. When the whole thermal imaging image of the grain drying tower is horizontally divided, the division width is customized by the system end user to obtain several sets of long strip thermal imaging images of the grain drying tower. The medium temperature point is captured on the horizontal midline of each set of long strip thermal imaging images of the grain drying tower as the deployment point of the monitoring module;

[0074] Among them, the monitoring module is integrated by several groups of temperature sensors, and each monitoring module deployment point is installed with a group of temperature sensors;

[0075] The logical expression for capturing the lowest temperature point on the horizontal midline of each set of thermal imaging images of the long grain drying tower is:

[0076]

[0077] Where: G(x,y) is the grayscale value of the point (x,y) on the horizontal midline of each group of thermal imaging images of the long grain drying tower; t is the acquisition time of the thermal imaging image of the grain drying tower from which the thermal imaging image of the long grain drying tower is derived; K is the adjustment coefficient;

[0078] The adjustment coefficient K is used to control G(x, y) to always be within the range of 0 to 255. Based on the above formula, the grayscale value of each pixel on the horizontal center line of the thermal imaging image of the long grain drying tower is calculated. The pixels are further sorted in descending order based on the grayscale value calculation results. A group of pixels in the middle position of the descending order queue is used as the deployment point of the monitoring module.

[0079] After the monitoring module deployment points are determined, corresponding points are further determined in the thermal imaging image of the grain drying tower, and then corresponding points of the corresponding points in the thermal imaging image of the grain drying tower are further determined on the surface of the grain drying tower. The corresponding points determined on the surface of the grain drying tower are adjacent to each other along the surface of the grain drying tower to obtain a set of monitoring module deployment paths. Based on the monitoring module deployment paths, temperature sensors are evenly deployed so that the temperature sensors are equidistant from the surface of the grain drying tower, and temperature sensors are deployed at all points constituting the monitoring module deployment path.

[0080] In this embodiment, through the above settings, further operation data support is provided for the operation of the system in Example 1, and in this embodiment, the grayscale value calculation based on the thermal imaging image is further used to determine the deployment position of the temperature sensor in the monitoring module to ensure stable deployment of the temperature sensor and accurate collection of temperature information.

[0081] Example 3:

[0082] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The intelligent control system for a grain drying tower in Example 1 is further described in detail:

[0083] The grain drying state parameters received by the receiving module are derived from the recording module in the perception layer. The grain drying state parameters received by the receiving module include the drying tower temperature information and the receiving drying tower operation parameters.

[0084] When the decision module decides whether to trigger the construction module to run, it makes a decision by referring to the two sets of grain drying status parameters recorded most recently;

[0085] The decision module is also provided with several sets of trigger determination thresholds, which correspond one-to-one to grain drying state parameters. When any one of the two sets of grain drying state parameters recorded for reference meets its corresponding trigger determination threshold, the construction module is triggered to run;

[0086] During the construction module operation phase, the two most recently recorded sets of grain drying state parameters for reference are obtained in the decision module. The model length is calculated using the parameters corresponding to the upper drying station, middle drying station, and lower drying station in the set of grain drying state parameters with the earliest recording time. This is recorded as The lengths are The three groups of line segments form a group of grain drying state control models, grain drying state control model A;

[0087] Based on the above logic, the latest set of grain drying state parameters recorded in the two sets of grain drying state parameters is obtained. The lengths are The three groups of line segments form a grain drying state control model, which is recorded as grain drying state control model B.

[0088] Through the above settings, further construction logic is provided for the construction of the grain drying state control model.

[0089] like Figure 1 As shown, the logic of adjusting and controlling the target in the adjustment module is expressed as follows:

[0090] Logic 1:

[0091] The adjustment logic of the upper drying station is: adjust the target temperature to

[0092] The adjustment logic of the drying station is: adjust the target temperature to

[0093] The adjustment logic of the lower drying station is: adjust the target temperature to

[0094] Logic 2:

[0095] The adjustment logic of the upper drying station is: adjust the transmission speed of the target transmission station to

[0096] The adjustment logic of the drying station is: adjust the target transmission station transmission speed to

[0097] The adjustment logic of the lower drying station is: adjust the target transmission station transmission speed to

[0098] Through the above settings, the control logic of the grain drying tower of the adjustment module of the system in Example 1 is further limited;

[0099] It should be noted that the adjustment target in the adjustment module, namely the temperature control or the control of the transmission speed of the transmission station, can be customized by the system end user as a specific adjustment target during specific implementation.

[0100] In summary, during the operation of the system in the above embodiment, the temperature information is collected by deploying temperature sensors and the operating parameters of the grain drying tower itself are obtained. A reasonable temperature information collection path for the drying station of the drying tower is designed, and further combined with the analysis of the operating parameters of the grain drying tower itself, the grain drying tower is provided with real-time continuous temperature control and transmission control effects, so that the grain being dried in the grain drying tower can obtain real-time drying temperature and transmission speed control, ensuring that the moisture content of the grain dried and output by the grain drying tower can better meet the storage conditions, thereby improving the drying function of the grain drying tower and making the drying function of the grain drying tower more real-time, continuous and intelligent.

[0101] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An intelligent control system for a grain drying tower, characterized in that: include: Main control terminal, perception layer, modeling layer and control layer; The main control terminal is the control switch of the system, which is used to control the start and stop of the system operation; The grain drying state parameters of each layer of the grain drying tower are collected and recorded by the perception layer. The modeling layer receives the grain drying state parameters recorded in the perception layer in real time and decides whether to build a grain drying state control model based on the grain drying state parameters. If the decision result is yes and the construction of the grain drying state control model is completed, the control layer synchronously runs the grain drying state control model constructed by the modeling layer, controls the grain drying tower based on the grain drying state control model, and controls the system to refresh and run; The modeling layer includes a receiving module, a decision module and a construction module. The receiving module is used to receive the grain drying state parameters collected in the perception layer. The decision module is used to traverse the grain drying state parameters received by the receiving module and decide whether to trigger the construction module to run based on the grain drying state parameters. The construction module is used to build a grain drying state control model. The logic of the decision module to decide whether to trigger the construction module to run is expressed as follows: Where: P is the judgment value of whether to trigger the operation of the construction module; n is the drying layer set of the drying station of the grain drying tower; f[·] is the cumulative function; T last 、T next RH is the temperature of drying layer a in the previous set of grain drying state parameters and the temperature of drying layer a in the next set of grain drying state parameters; last RH next V is the humidity of drying layer a in the previous set of grain drying state parameters and the humidity of drying layer a in the next set of grain drying state parameters; last 、V next is the transmission speed of the drying layer a at the transmission station in the previous set of grain drying state parameters, and the transmission speed of the drying layer a at the transmission station in the next set of grain drying state parameters; δ is the grain drying state safety judgment value; Among them, the safety judgment value of grain drying state is customized by the system end user, and the cumulative function f[·] is expressed as The value of the cumulative function f[·] is determined based on formula (2), where a, b, and c represent the upper drying station, the middle drying station, and the lower drying station. Based on formula (1), the values ​​of the cumulative function f[·] corresponding to the upper drying station, the middle drying station, and the lower drying station are calculated and summed. When the judgment value P ≥ 1, the construction module is triggered to run; otherwise, the construction module is not triggered to run.

2. The intelligent control system for a grain drying tower according to claim 1, characterized in that: The perception layer includes a configuration module, a monitoring module and a recording module. The configuration module is used to upload the thermal imaging image of the grain drying tower, configure the deployment path of the monitoring module based on the thermal imaging image of the grain drying tower, the monitoring module is used to monitor the temperature information of the drying tower in real time and receive the operating parameters of the drying tower, and the recording module is used to receive the temperature information of the drying tower detected by the monitoring module and receive the operating parameters of the drying tower, and distinguish and record the temperature information of the drying tower and the operating parameters of the drying tower based on the monitoring timestamps of the drying tower temperature information and the operating parameters of the drying tower received; Among them, the drying tower temperature information and the received drying tower operating parameters are synchronously marked with their monitoring timestamps before the recording operation is performed based on the recording module. The drying tower is integrated with a feeding station, a drying station, a discharging station and a control station. The drying station includes the drying station itself and also includes a transmission station. The drying station is composed of an upper drying station, a middle drying station and a lower drying station. The monitoring module and the transmission station in the drying station run through the upper, middle and lower groups of drying stations. The drying tower operating parameters include: humidity values ​​of the upper drying station, the middle drying station and the lower drying station; transmission speed of the transmission station. The drying tower runs synchronously with the monitoring module and feeds back its own operating parameters to the monitoring module in real time.

3. The intelligent control system for a grain drying tower according to claim 2, characterized in that: The thermal imaging images of the grain drying tower uploaded in the configuration module are collected by the thermal imager on the outside of the grain drying tower. When the grain drying tower is a rectangular parallelepiped, the thermal imaging images of the grain drying tower are four groups of side thermal imaging images of the grain drying tower. When the grain drying tower is a cylinder, the thermal imaging images of the grain drying tower are two groups of side thermal imaging images from the side view of the grain drying tower. After the acquisition is completed, the thermal imaging images of the grain drying tower are further spliced ​​horizontally in sequence to form a thermal imaging image of the grain drying tower, which is recorded as the entire thermal imaging image of the grain drying tower. When the entire thermal imaging image of the grain drying tower is horizontally divided, the division width is customized by the system end user to obtain several groups of long strip thermal imaging images of the grain drying tower, and the medium temperature point is captured on the horizontal midline of each group of thermal imaging images of the long strip grain drying tower as the deployment point of the monitoring module; Among them, the monitoring module is integrated by several groups of temperature sensors, and each monitoring module deployment point is installed with a group of temperature sensors.

4. The intelligent control system for a grain drying tower according to claim 2, characterized in that: The logical expression for capturing the lowest temperature point on the horizontal midline of each set of thermal imaging images of the long grain drying tower is: Where: G(x,y) is the grayscale value of the point (x,y) on the horizontal midline of each group of thermal imaging images of the long grain drying tower; t is the acquisition time of the thermal imaging image of the grain drying tower from which the thermal imaging image of the long grain drying tower is derived; K is the adjustment coefficient; Among them, the adjustment coefficient K is used to control G(x, y) to always be in the range of 0 to 255. Based on the above formula, the grayscale value of each pixel on the horizontal center line of the thermal imaging image of the long grain drying tower is calculated, and then each pixel is further arranged in descending order based on the grayscale value calculation results. A group of pixels in the middle position of the descending order queue is used as the deployment point of the monitoring module.

5. A grain drying tower intelligent control system according to claim 3 or 4, characterized in that: After the monitoring module deployment points are determined, corresponding points are further determined in the thermal imaging image of the grain drying tower, and then corresponding points of the corresponding points in the thermal imaging image of the grain drying tower are further determined on the surface of the grain drying tower. The corresponding points determined on the surface of the grain drying tower are adjacent to each other along the surface of the grain drying tower to obtain a set of monitoring module deployment paths. Temperature sensors are evenly deployed based on the monitoring module deployment paths, so that the temperature sensors are deployed at equal distances from the surface of the grain drying tower, and temperature sensors are deployed at all points constituting the monitoring module deployment path.

6. The intelligent control system for a grain drying tower according to claim 1, characterized in that: The grain drying state parameters received by the receiving module are derived from the recording module in the perception layer, and the grain drying state parameters received by the receiving module include the drying tower temperature information and the receiving drying tower operation parameters; The decision module decides whether to trigger the construction module to run, and makes a decision by referring to the two sets of grain drying state parameters recorded most recently; The decision module is also provided with several groups of trigger judgment thresholds, and the several groups of trigger judgment thresholds correspond one-to-one to the grain drying state parameters. When any one of the two groups of grain drying state parameters recorded for reference meets the corresponding trigger judgment threshold, the construction module is triggered to run.

7. The intelligent control system for a grain drying tower according to claim 1, characterized in that: During the operation phase of the construction module, the decision module obtains two sets of the latest recorded grain drying state parameters for reference, and uses the set of grain drying state parameters with the earliest recording time among the two latest sets of grain drying state parameters, and the parameters corresponding to the upper drying station, the middle drying station and the lower drying station in the set of grain drying state parameters to calculate the model length, which is recorded as The lengths are The three groups of line segments form a group of grain drying state control models, grain drying state control model A; Based on the above logic, the latest set of grain drying state parameters recorded in the two sets of grain drying state parameters is obtained. The lengths are The three groups of line segments form a grain drying state control model, which is recorded as grain drying state control model B.

8. The intelligent control system for a grain drying tower according to claim 1, characterized in that: The control layer includes an identification module, an adjustment module and a refresh module. The identification module is used to receive the grain drying state control model constructed by the modeling layer and identify the differences of the grain drying state control model. The adjustment module is used to receive the differences of the grain drying state control model identified in the identification module, determine the adjustment target based on the differences of the grain drying state control model, and adjust and control the determined target. The refresh module is used to monitor the operation status of the adjustment module and control the system to refresh and run after monitoring that the adjustment module has finished running. Among them, the identification module identifies the operations of the differences in the grain drying state control model, namely: the difference between the model length value corresponding to the upper drying station a of the grain drying state control model A and the model length value corresponding to the upper drying station a of the grain drying state control model B, the difference between the model length value corresponding to the middle drying station b of the grain drying state control model A and the model length value corresponding to the upper middle drying station b of the grain drying state control model B, and the difference between the model length values ​​corresponding to the upper and lower drying stations c of the grain drying state control model A and the model length values ​​corresponding to the upper and lower drying stations c of the grain drying state control model B.

9. The intelligent control system for a grain drying tower according to claim 8, characterized in that: The logic of adjusting and controlling the determined target in the adjustment module is expressed as follows: Logic 1: The adjustment logic of the upper drying station is: adjust the target temperature to The adjustment logic of the drying station is: adjust the target temperature to The adjustment logic of the lower drying station is: adjust the target temperature to Logic 2: The adjustment logic of the upper drying station is: adjust the transmission speed of the target transmission station to The adjustment logic of the drying station is: adjust the target transmission station transmission speed to The adjustment logic of the lower drying station is: adjust the target transmission station transmission speed to 10. The intelligent control system for a grain drying tower according to claim 1, characterized in that: The main control terminal is interactively connected to a configuration module via a wireless network, the configuration module is interactively connected to a monitoring module and a recording module via a wireless network, the recording module is interactively connected to a receiving module via a wireless network, the receiving module is interactively connected to a decision module and a construction module via a wireless network, the construction module is interactively connected to an identification module via a wireless network, and the identification module is interactively connected to an adjustment module and a refresh module via a wireless network.

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