Temperature control method and system based on AI Internet of Things

Abstract

The invention discloses a temperature control method and system based on AI Internet of Things, and the method comprises the steps: collecting the parameter information of corresponding equipment through a controller in each piece of equipment, and judging whether the starting time and closing time of a compressor in a single control period in the corresponding equipment are effective or not through each controller; the average value of the opening time and the closing time of the compressors of the N continuous and effective control periods of each device is calculated, the Internet of Things platform classifies the devices according to the set control temperature value St of each device, and the opening time and the closing time of the classified compressors after classification are calculated; when the temperature collection is normal, the Internet of Things platform issues the classified compressor starting time Ton (StT) and compressor closing time Toff (StT) to corresponding equipment for compressor control; and when the temperature acquisition of the controller fails, the Internet of Things platform issues the average value Toni of the starting time of the compressor and the average value Tofi of the closing time of the compressor to the failed equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control, and particularly to a temperature control method and system combining the Internet of Things and artificial intelligence, which is applicable to scenarios requiring precise temperature control such as cold chain equipment and industrial refrigeration devices. Background Art

[0002] Cold chain equipment includes freezers or display cabinets. The temperature control of existing freezers or display cabinets has fixed control parameters and cannot adapt to dynamic environmental changes. Moreover, data between devices is isolated, and group optimization of freezers or display cabinets cannot be achieved. When a device fails, it is impossible to effectively control the device in the fault state. Summary of the Invention

[0003] To solve the above technical problems, the present invention provides an AI-based Internet of Things temperature control method and system.

[0004] The specific solutions are as follows: An AI-based Internet of Things temperature control method includes the following steps: S1): The controller in each device collects the parameter information of the corresponding device. The parameter information of the device includes the set control temperature value St, the compressor on-time To in a single control cycle, the compressor off-time Tc in a single control cycle, the highest temperature value TH in a single control cycle, and the lowest temperature value TL in a single control cycle; S2): Each controller respectively determines whether the compressor on-time To in a single control cycle in the corresponding device is valid and whether the compressor off-time Tc in a single control cycle is valid; if valid, the controller uploads the corresponding compressor on-time To in a single control cycle and the compressor off-time Tc in a single control cycle of the device to the Internet of Things platform; S3): The Internet of Things platform calculates the average value Ton of the compressor on-time of N consecutive valid control cycles of each device i , and calculates the average value Toff of the compressor off-time of N consecutive valid control cycles of each device i , where N is greater than or equal to 100; S4): The Internet of Things platform classifies the devices according to the set control temperature value St of each device, and calculates the classified compressor on-time Ton(St T ) and the classified compressor off-time Toff(St T ) after classification; S5): When the temperature acquisition of the controller is normal, the Internet of Things platform sends the classified compressor on-time Ton(St T ) and the classified compressor off-time Toff(St T ) to the corresponding device for compressor control; S6): When a controller temperature acquisition failure occurs, the Internet of Things platform will send the average value Ton of the compressor on-time i and the average value Toff of the compressor off-time i to the faulty device.

[0005] In step S2), the method for the controller to determine whether the compressor on-time To in a single control cycle of the corresponding device is valid is as follows: The controller calculates whether the difference between the set control temperature value St and the lowest temperature value TL in a single control cycle is greater than the minimum design temperature difference. If it is greater, the compressor on-time To in a single control cycle is valid; if not, it is invalid and the data is discarded; The minimum design temperature difference is 1°C.

[0006] In step S2), the method for the controller to determine whether the compressor off-time Tc in a single control cycle is valid is as follows: The controller calculates whether the difference between the highest temperature value TH in a single control cycle and the set control temperature value St is greater than the minimum design temperature difference. If it is greater, the compressor off-time Tc in a single control cycle is valid; if not, it is invalid and the data is discarded; The minimum design temperature difference is 1°C.

[0007] In step S3), the Internet of Things platform calculates the average value Ton of the compressor on-time for N consecutive valid control cycles of each device i using the formula: Ton i =(To i 1 + To i 2 + To i 3 + … To i N) / N; where N ≥ 100, and i represents the i-th device; Ton i represents the average value of the compressor on-time for N consecutive control cycles of the i-th device, and To i N represents the valid value of the compressor on-time for the N-th control cycle of the i-th device; In step S3), the Internet of Things platform calculates the average value Toff of the compressor off-time for N consecutive valid control cycles of each device i using the formula: Toff i =(Toff i 1 + Toff i 2 + Toff i 3 + … Toff i N) / N; Among them, N≥100, and i represents the i-th device; Toff i represents the average value of the compressor shutdown time in N consecutive control cycles of the i-th device, Toff i N represents the effective value of the compressor shutdown time in the N-th control cycle of the i-th device.

[0008] In step S4), the IoT platform calculates the classified compressor startup time Ton(St T ) with the formula: Ton(St T ) = (Ton1 + Ton2 + Ton3 + … Ton x ) / x, where T is the set temperature value, i.e., St = T; x represents that among the 1 to i devices, there are x devices with the set control temperature value St all being T; The IoT platform calculates the classified compressor shutdown time Toff(St T ) with the formula: Toff(St T ) = (Toff1 + Toff2 + Toff3 + … Toff x ) / x, where T is the set temperature value, i.e., St = T; x represents that among the 1 to i devices, there are x devices with the set control temperature value St all being T; A system for an AI IoT temperature control method includes an IoT platform and at least two cold cabinet devices. Each cold cabinet device is provided with a controller, and the controller is wirelessly communicatively connected to the IoT platform. The IoT platform is a server.

[0009] The controller is wirelessly communicatively connected to the IoT platform by any one of wifi, NB-IoT, ZigBee, LoraWAN, or CAT1.

[0010] The present invention discloses an AI IoT temperature control method and system. It collects the effective values of the compressor startup time and the compressor shutdown time of each refrigeration device, calculates the average value of the compressor startup time and the average value of the compressor shutdown time, and at the same time classifies each device according to the set control temperature value to obtain the classified compressor startup time and the classified compressor shutdown time respectively, and finely regulates the compressor startup time and shutdown time of each device, which is beneficial to energy conservation. Moreover, when the temperature sensor fails or the temperature acquisition fails, the IoT platform can send the average value of the compressor startup time and the average value of the compressor shutdown time to the faulty device, realizing effective regulation of the device during a fault. Description of the Drawings

[0011] Figure 1 It is a schematic diagram of the overall structure of the present invention. Specific embodiments

[0012] 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 present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0013] As Figure 1 shown, an AI Internet of Things-based temperature control method includes the following steps: S1): The controller in each device collects the parameter information of the corresponding device. The parameter information of the device includes the set control temperature value St, the compressor on time To in a single control cycle, the compressor off time Tc in a single control cycle, the highest temperature value TH in a single control cycle, and the lowest temperature value TL in a single control cycle; S2): Each controller respectively determines whether the compressor on time To in a single control cycle in the corresponding device is valid and whether the compressor off time Tc in a single control cycle is valid; if valid, the controller uploads the corresponding compressor on time To in a single control cycle and the compressor off time Tc in a single control cycle of the device to the Internet of Things platform; S3): The Internet of Things platform calculates the average value Ton of the compressor on times of N consecutive valid control cycles of each device i , and calculates the average value Toff of the compressor off times of N consecutive valid control cycles of each device i , where N is greater than or equal to 100; S4): The Internet of Things platform classifies the devices according to the set control temperature value St of each device, and calculates the classified compressor on time Ton(St T ) and the classified compressor off time Toff(St T ) after classification; S5): When the temperature acquisition of the controller is normal, the Internet of Things platform sends the classified compressor on time Ton(St T ) and the classified compressor off time Toff(St T ) to the corresponding device for compressor control; S6): When the temperature acquisition of the controller fails, the Internet of Things platform sends the average value Ton of the compressor on time i and the average value Toff of the compressor off time iSend the faulty device. If among the five devices, the fourth device has a temperature acquisition failure, the average value Ton4 of the compressor start time and the average value Toff4 of the compressor shutdown time of the fourth device will be sent to the faulty device.

[0014] In step S2), the method for the controller to determine whether the compressor start time To in a single control cycle of the corresponding device is valid is as follows: The controller calculates whether the difference between the set control temperature value St and the lowest temperature value TL in a single control cycle is greater than the minimum design temperature difference. If it is greater, the compressor start time To in a single control cycle is valid; if not, it is invalid and the data is discarded. The minimum design temperature difference is 1°C; that is, if St - TL > the minimum design temperature difference, the compressor start time To in a single control cycle is valid, and the valid value is transmitted to the Internet of Things platform. In step S2), the method for the controller to determine whether the compressor shutdown time Tc in a single control cycle is valid is as follows: The controller calculates whether the difference between the highest temperature value TH in a single control cycle and the set control temperature value St is greater than the minimum design temperature difference. If it is greater, the compressor shutdown time Tc in a single control cycle is valid; if not, it is invalid and the data is discarded. The minimum design temperature difference is 1°C; that is, if TH - St > the minimum design temperature difference, the compressor shutdown time Tc in a single control cycle is valid, and the valid value is transmitted to the Internet of Things platform.

[0015] In step S3), the Internet of Things platform calculates the average value Ton of the compressor start time for N consecutive valid control cycles of each device. i The formula is: Ton i =(To i 1 + To i 2 + To i 3 + … To i N) / N; Among them, N ≥ 100, and i represents the i-th device; Ton i represents the average value of the compressor start time for N consecutive control cycles of the i-th device, and To i N represents the valid value of the compressor start time for the N-th control cycle of the i-th device. In step S3), the Internet of Things platform calculates the average value Toff of the compressor shutdown time for N consecutive valid control cycles of each device. i The formula is: Toff i =(Toff i 1 + Toff i 2 + Toffi 3 + … Toff i N) / N where N ≥ 100, and i represents the i-th device; Toff i represents the average value of the compressor shutdown time of the N consecutive control cycles of the i-th device, Toff i N represents the effective value of the compressor shutdown time of the N-th control cycle of the i-th device.

[0016] In this embodiment, if there are 5 devices, then i = 5 and N = 100; Then the average value of the compressor startup time of the N consecutive valid control cycles of each of the first to fifth devices is: Ton1 = (To11 + To12 + To13 + … To1100) / 100; Ton2 = (To21 + To22 + To23 + … To2100) / 100; Ton3 = (To31 + To32 + To33 + … To3100) / 100; Ton4 = (To41 + To42 + To43 + … To4100) / 100; Ton5 = (To51 + To52 + To53 + … To5100) / 100; Then the average value of the compressor shutdown time of the N consecutive valid control cycles of each of the first to fifth devices is: Toff1 = (Toff11 + Toff12 + Toff13 + … Toff1100) / 100; Toff2 = (Toff21 + Toff22 + Toff23 + … Toff2100) / 100; Toff3 = (Toff31 + Toff32 + Toff33 + … Toff3100) / 100; Toff4 = (Toff41 + Toff42 + Toff43 + … Toff4100) / 100; Toff5 = (Toff51 + Toff52 + Toff53 + … Toff5100) / 100; In step S4), the formula for the Internet of Things platform to calculate the classified compressor startup time Ton(St T ) is: Ton(St T ) = (Ton1 + Ton2 + Ton3 + … Ton x ) / x, Among them, T is the set temperature value, that is, St = T; x represents that among 1 to i devices, there are x devices whose set control temperature values St are all T; The formula for the IoT platform to calculate the classified compressor shutdown time Toff(St T ) is: Toff(St T ) = (Toff1 + Toff2 + Toff3 + … Toff x ) / x, Among them, T is the set temperature value, that is, St = T; x represents that among 1 to i devices, there are x devices whose set control temperature values St are all T; In this embodiment, if i = 5, there are 3 devices with the control temperature value St = 5°C and 2 devices with the control temperature value St = 1°C; Then, Ton(St5) = (Ton1 + Ton2 + Ton3) / 3; Toff(St5) = (Toff1 + Toff2 + Toff3) / 3; Ton(St1) = (Ton1 + Ton2) / 2; Toff(St1) = (Toff1 + Toff2) / 2; Send the values of Ton(St5) and Toff(St5) to the 3 devices with the control temperature value St = 5°C, and send the values of Ton(St1) and Toff(St1) to the 2 devices with the control temperature value St = 1°C, so as to further optimize the compressor start-up time and compressor shutdown time of the devices; A system for an AI IoT temperature control method, including an IoT platform and at least two cold cabinet devices. A controller is provided in each cold cabinet device, and the controller is wirelessly communicatively connected to the IoT platform, and the IoT platform is a server.

[0017] The controller is wirelessly communicatively connected to the IoT platform by any one of wifi, NB-IoT, ZigBee, LoraWAN or CAT1.

[0018] The present invention discloses an AI-based Internet of Things temperature control method and system, which collects the effective values of the compressor start time and the compressor stop time of each refrigeration device, calculates the average value of the compressor start time and the average value of the compressor stop time, and at the same time classifies each device according to the set control temperature value, and respectively obtains the classified compressor start time and the compressor stop time after classification, and finely regulates the compressor start time and stop time of each device, which is beneficial to energy conservation. Moreover, when the temperature sensor fails or the temperature acquisition fails, the Internet of Things platform can send the average value of the compressor start time and the average value of the compressor stop time to the faulty device, realizing the effective regulation of the device during the fault.

[0019] The technical means disclosed in the solution of the present invention are not limited to the technical means disclosed in the above embodiments, but also include technical solutions composed of any combination of the above technical features. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. An AI-based Internet of Things temperature control method, characterized in that: It includes the following steps: S1): The controller in each device collects the parameter information of the corresponding device. The parameter information of the device includes the set control temperature value St, the compressor on-time To in a single control cycle, the compressor off-time Tc in a single control cycle, the highest temperature value TH in a single control cycle, and the lowest temperature value TL in a single control cycle; S2): Each controller respectively determines whether the compressor on-time To in a single control cycle in the corresponding device is valid and whether the compressor off-time Tc in a single control cycle is valid; if valid, the controller uploads the corresponding compressor on-time To in a single control cycle and the compressor off-time Tc in a single control cycle of the device to the Internet of Things platform; S3): The IoT platform calculates the average value Ton of the compressor on-time for N consecutive valid control cycles of each device respectively i , and calculates the average value Toff of the compressor off-time for N consecutive valid control cycles of each device i , where N is greater than or equal to 100, and i is the number of devices; S4): The IoT platform classifies the devices according to the set control temperature value St of each device, and calculates the classified compressor start time Ton(St T ) and the classified compressor stop time Toff(St T ); S5): When the temperature acquisition of the controller is normal, the IoT platform will send the classified compressor on-time Ton(St T ) and the classified compressor off-time Toff(St T ) to the corresponding device for compressor control; S6): When there is a failure in the controller's temperature acquisition, the IoT platform will send the average value Ton of the compressor's on-time i and the average value Toff of the compressor's off-time i to the faulty device.

2. The AIoT-based temperature control method according to claim 1, wherein: In step S2), the method for the controller to determine whether the compressor on-time To in a single control cycle in the corresponding device is valid is: The controller calculates whether the difference between the set control temperature value St and the lowest temperature value TL in a single control cycle is greater than the minimum design temperature difference. If it is greater, the compressor on-time To in a single control cycle is valid; if not, it is invalid and the data is discarded; The minimum design temperature difference is 1°C.

3. The AIoT-based temperature control method according to claim 1, characterized in that: In step S2), the method for the controller to determine whether the compressor off-time Tc in a single control cycle is valid is: The controller calculates whether the difference between the highest temperature value TH in a single control cycle and the set control temperature value St is greater than the minimum design temperature difference. If it is greater, the compressor off-time Tc in a single control cycle is valid; if not, it is invalid and the data is discarded; The minimum design temperature difference is 1°C.

4. The AIoT-based temperature control method according to claim 1, wherein: In step S3), the IoT platform calculates the average value Ton of the compressor start-up time for N consecutive valid control cycles of each device respectively i The formula for which is: Ton i =(To i 1 + To i 2 + To i 3 + … To i N) / N; Among them, N≥100, and i represents the i-th device; Ton i represents the average value of the compressor on-time of the continuous N control cycles of the i-th device, To i N represents the effective value of the compressor on-time of the N-th control cycle of the i-th device; In step S3), the IoT platform calculates the average value Toff of the compressor shutdown time for N consecutive and valid control cycles of each device respectively i The formula for which is: Toff i =(Toff i 1 + Toff i 2 + Toff i 3 + … Toff i N) / N; where N ≥ 100, and i represents the i-th device; Toff i represents the average compressor off-time of N consecutive control cycles of the i-th device, Toff i N represents the effective value of the compressor off-time of the N-th control cycle of the i-th device.

5. The AIoT-based temperature control method according to claim 1, characterized in that: In step S4), the formula for the Internet of Things platform to calculate the start time Ton (St T ) of the classified compressor is as follows: Ton (St T ) = (Ton1 + Ton2 + Ton3 + … Ton x ) / x, Wherein, T is the set temperature value, that is, St = T; x represents that among 1 to i devices, there are x devices whose set control temperature values St are all T; The formula for the shutdown time Toff (St) of the classified compressor after classification calculation by the Internet of Things platform is: T ). Toff (St T ) = (Toff1 + Toff2 + Toff3 + … Toff x ) / x, Wherein, T is the set temperature value, that is, St = T; x represents that among 1 to i devices, there are x devices whose set control temperature values St are all T.

6. A system for an AI Internet of Things temperature control method according to any one of claims 1 to 5, characterized in that: It includes an Internet of Things platform and at least two freezer devices. A controller is provided in each freezer device. The controller is wirelessly connected to the Internet of Things platform, and the Internet of Things platform is a server.

7. The system of the AIoT temperature control method according to claim 6, characterized in that: The controller is wirelessly connected to the Internet of Things platform by any one of wifi, NB-IoT, ZigBee, LoraWAN or CAT1.

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