Cooperative control method for cross-brand air conditioning system

Abstract

The invention relates to the technical field of air conditioner control, in particular to a cooperative control method of a cross-brand air conditioner system, which comprises the following steps of: dividing a protocol conversion module arranged between cross-brand indoor and outdoor units into a base layer module and an optimization layer module, processing a startup and shutdown and mode switching control instruction by the base layer module, and optimizing the optimization layer module; other air conditioner control instructions are processed by the optimization layer module, and the state is fed back to the optimization layer module after the startup and shutdown and mode switching control instructions are executed; an original protocol conversion module is internally divided into a base layer module and an optimization layer module, the base layer module is mainly responsible for executing basic control instructions such as startup and shutdown and mode switching, the control instruction requirement can be quickly met, and the real-time requirement of the basic control instructions is met; the optimization layer module focuses on advanced control instructions such as temperature adjustment and wind speed optimization, efficient cooperative work of cross-brand air conditioner indoor and outdoor units is achieved, and the comfort level of users and the energy efficiency of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of air-conditioning control, and in particular to a collaborative control method for cross-brand air-conditioning systems. Background Art

[0002] As key equipment for indoor environmental regulation, air conditioning systems are becoming increasingly important for their compatibility and interoperability. Air conditioning system retrofits can often lead to brand inconsistencies between indoor and outdoor units. Each brand of air conditioner typically uses its own unique communication protocols, which differ significantly in data format, transmission methods, and control commands. This prevents effective communication and control between indoor and outdoor units of different brands.

[0003] To address communication and control issues between indoor and outdoor units, a protocol conversion device (module) is often used to translate between different brand protocols. This protocol conversion device incorporates a mapping model that maps the commands and data formats of different brand protocols. This model maps the temperature setting command in air conditioner protocol A to the corresponding command location in air conditioner protocol B, converting the data format to ensure both parties can understand and execute each other's control commands. Through this mapping model, the protocol conversion device bridges the gap between different brand protocols, enabling data transmission and control command execution.

[0004] Currently, cross-brand air conditioning systems mostly rely on centralized control architectures, requiring frequent communication of multiple data points and protocol conversion. This results in significant system response delays. Traditional power on / off command response times exceed 500ms, making them incapable of meeting the high real-time requirements of basic operations. Furthermore, during cooling or heating operations, performance differences and operational characteristics of cross-brand air conditioners can lead to high system energy efficiency or failure to achieve the desired user-set temperature. Summary of the Invention

[0005] The purpose of the present invention is to provide a collaborative control method for cross-brand air conditioning systems to solve the problems existing in the prior art. To achieve the above purpose, the present invention solves the problem through the following technical solutions:

[0006] The present invention provides a collaborative control method for cross-brand air-conditioning systems, comprising the following steps:

[0007] The protocol conversion modules set up between cross-brand indoor and outdoor units are divided into basic layer modules and optimization layer modules. The power on / off and mode switching control instructions are processed by the basic layer module, and the remaining air-conditioning control instructions are processed by the optimization layer module. After the power on / off and mode switching control instructions are executed, the status is fed back to the optimization layer module.

[0008] As a further technical solution, when the user issues a power-on command, the basic layer module responds quickly and controls the startup of the indoor and outdoor units of the air conditioner; then, the optimization layer module calculates and sends appropriate frequency adjustment instructions to the outdoor unit of the air conditioner based on the temperature information fed back by the indoor temperature sensor and the temperature value set by the user, so that the compressor runs at an appropriate speed, thereby achieving temperature regulation.

[0009] As a further technical solution, the optimization layer module uses an adaptive learning algorithm to build an energy consumption model based on the historical operating data of the air-conditioning system; the optimization layer module collects the operating data of the air-conditioning system in real time, and automatically adjusts the control parameters through the energy consumption model to ensure that the air-conditioning system is in an optimized operating state under different working conditions.

[0010] As a further technical solution, the operating data of the air-conditioning system includes indoor and outdoor temperature, humidity, operating frequency of the outdoor unit compressor, speed of the indoor unit fan and energy consumption data.

[0011] As a further technical solution, the comfort range is 24~28℃

[0012] As a further technical solution, the optimization layer module continuously collects air-conditioning operation data before and after automatic adjustment of control parameters in real time, and further optimizes the constructed energy consumption model based on the adaptive learning algorithm to achieve dynamic optimization of the air-conditioning system.

[0013] As a further technical solution, the electronic expansion valve opening conversion relationship between outdoor units and indoor units across brands is obtained; the optimization layer module monitors the temperature adjustment changes. If it is found that the cooling effect or heating effect does not meet the expectations, the electronic expansion valve opening is automatically adjusted according to the set number of steps, and the temperature changes are continuously monitored until the expected results are met, and then a new electronic expansion valve opening conversion relationship is formed.

[0014] As a further technical solution, the optimization layer module continuously collects the conversion relationship of the electronic expansion valve before and after adjustment based on the adaptive learning algorithm to form an electronic expansion valve conversion model, so that the optimization layer module can control the opening of the electronic expansion valve based on the electronic expansion valve conversion model under different working conditions.

[0015] The beneficial effects of the present invention are as follows:

[0016] (1) The present invention divides the original protocol conversion module into a basic layer module and an optimization layer module. The basic layer module is mainly responsible for executing basic control instructions such as power on / off and mode switching, which can quickly meet the control instruction requirements and meet the real-time requirements of basic control instructions; while the optimization layer module focuses on advanced control instructions such as temperature adjustment and wind speed optimization. Its purpose is to achieve more refined control based on indoor and outdoor environmental parameters and user needs on the basis of ensuring the basic operation of the air conditioner, and to achieve efficient collaborative work between the indoor and outdoor units of cross-brand air conditioners to improve user comfort and system energy efficiency.

[0017] (2) The optimization layer module of the present invention collects the operating data of the air-conditioning system in real time, including indoor and outdoor temperature, humidity, compressor operating frequency, fan speed, etc., and combines it with historical operating data. Based on the in-depth analysis and learning of the operating data by the adaptive learning algorithm, it can build and continuously optimize the energy consumption model, and then continuously optimize the control parameters of the cross-brand air-conditioning operation, so that the air-conditioning system can operate according to the optimized strategy under this working condition, thereby realizing the dynamic optimization of the cross-brand air-conditioning collaborative operation and improving the overall performance and energy efficiency of the system.

[0018] (3) If the present invention finds that the cooling effect or heating effect does not meet expectations during operation, the electronic expansion valve opening can be automatically adjusted according to the set number of steps, and the temperature change will continue to be monitored until the expected temperature is achieved. After that, a new electronic expansion valve opening conversion relationship will be formed, and the optimization layer module will be able to dynamically adjust the air-conditioning system according to the cooling or heating effect, thereby achieving efficient collaborative operation of the indoor and outdoor units of cross-brand air conditioners. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which form part of the present invention, are provided to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their description are provided to illustrate the present invention and are not intended to limit the present invention. It should also be understood that these drawings are shown for simplicity and clarity and are not necessarily drawn to scale. The present invention will now be described and explained with additional specificity and detail through the use of the accompanying drawings, in which:

[0020] Figure 1 A schematic flow chart of a collaborative control method for cross-brand air-conditioning systems in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0021] The technical solutions in typical 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.

[0022] like Figure 1 As shown, this embodiment provides a collaborative control method for cross-brand air-conditioning systems, including the following steps:

[0023] The protocol conversion modules set up between cross-brand indoor and outdoor units are divided into basic layer modules and optimization layer modules. The power on / off and mode switching control instructions are processed by the basic layer module, and the remaining air-conditioning control instructions are processed by the optimization layer module. After the power on / off and mode switching control instructions are executed, the status is fed back to the optimization layer module.

[0024] It's easy to understand that implementing the above approach requires appropriate configuration of the protocol conversion module. This requires changing the existing centralized control architecture to a layered one. Within the protocol conversion module, the module is divided into a base layer module and an optimization layer module. These two modules can communicate with each other and perform protocol conversion. In addition to protocol conversion, both modules also control the indoor and outdoor units of the air conditioner using built-in algorithms. The base layer module is primarily responsible for executing basic control commands such as power on / off, and mode switching (cooling, heating, sleep, dehumidification, etc.). These commands are fundamental to air conditioner operation and require high real-time performance. The optimization layer module, on the other hand, focuses on advanced control commands such as temperature adjustment and wind speed optimization. Its goal is to ensure basic air conditioner operation while enabling more refined control based on indoor and outdoor environmental parameters and user needs, thereby improving user comfort and system energy efficiency.

[0025] When the user issues a power-on command, the basic layer module responds quickly and controls the startup of the indoor and outdoor units of the air conditioner; then, the optimization layer module calculates and sends appropriate frequency adjustment instructions to the outdoor unit of the air conditioner based on the temperature information fed back by the indoor temperature sensor and the temperature value set by the user, so that the compressor runs at an appropriate speed, thereby achieving temperature regulation.

[0026] Taking the specific boot process as an example, the basic layer module control process is described in detail:

[0027] (1) The user terminal sends a power-on command (JSON format, including parameters such as device ID / mode / target temperature);

[0028] (2) The basic layer module generates a unified control instruction (binary code: 0x01000001);

[0029] (3) The real-time control unit drives the internal and external units to start (relay response time ≤ 15ms) and feeds back the status to the optimization layer module (status code: 0x0010 means "startup completed").

[0030] When operating air conditioners of different brands, high energy consumption can occur due to differences in performance and operating characteristics. The optimization layer module in this embodiment uses an adaptive learning algorithm to construct an energy consumption model based on the air conditioning system's historical operating data. The optimization layer module collects the air conditioning system's operating data in real time and automatically adjusts control parameters based on the energy consumption model to optimize the air conditioning system's operating state under different operating conditions.

[0031] The optimization layer module continuously collects air-conditioning operation data before and after automatic adjustment of control parameters in real time, and further optimizes the constructed energy consumption model based on the adaptive learning algorithm to achieve dynamic optimization of the air-conditioning system.

[0032] The operating data of the air conditioning system includes indoor and outdoor temperature, humidity, operating frequency of the outdoor unit compressor, speed of the indoor unit fan and energy consumption data. In this embodiment, the comfort range is 24-28°C.

[0033] Taking the specific temperature adjustment process as an example, the process of the optimization layer module working based on the energy consumption model is explained in detail:

[0034] (1) The optimization layer module collects real-time operating data through the sensor group (indoor temperature T = 28 °C, set temperature Tset = 24 °C, outdoor temperature Tout = 35 °C);

[0035] (2) The optimization layer module calculates the temperature deviation ΔT = 4°C and calls the energy efficiency model to generate the compressor frequency adjustment sequence (initial frequency f0 = 50 Hz, target frequency ftarget = 70 Hz, adjustment step size 5 Hz / 10s);

[0036] (3) Optimization layer module assignment task: Brand A outdoor unit frequency is increased to 70Hz, and Brand B indoor unit fan speed is adjusted to high speed (wind speed level mapping table: high speed → 1500rpm);

[0037] (4) Data feedback after execution: 10 minutes later, T = 24°C (if it does not meet the expectation, it can be adjusted through subsequent electronic expansion valve opening conversion), energy consumption data (current power P = 2.5kW) and operating data before and after adjustment are stored in the optimization layer module for optimizing the energy efficiency model.

[0038] The optimization layer module of this embodiment collects the operating data of the air-conditioning system in real time, including indoor and outdoor temperature, humidity, compressor operating frequency, fan speed, etc., and combines it with historical operating data. Based on the in-depth analysis and learning of the operating data by the adaptive learning algorithm, it can build and continuously optimize the energy consumption model, and then continuously optimize the control parameters of the cross-brand air-conditioning operation, so that the air-conditioning system operates according to the optimized strategy under the working conditions, thereby realizing the dynamic optimization of the collaborative work of cross-brand air-conditioning and improving the overall performance and energy efficiency of the system.

[0039] To solve the problem that the user-set temperature does not meet expectations, this embodiment solves it by optimizing the electronic expansion valve opening conversion algorithm. Specifically:

[0040] Obtain the electronic expansion valve opening conversion relationship between outdoor and indoor units of different brands; the optimization layer module monitors the temperature adjustment changes. If it is found that the cooling effect or heating effect does not meet the expectations, the electronic expansion valve opening is automatically adjusted according to the set number of steps, and the temperature changes are continuously monitored until the expected temperature is achieved, and then a new electronic expansion valve opening conversion relationship is formed.

[0041] The optimization layer module continuously collects the conversion relationship of the electronic expansion valve before and after adjustment based on the adaptive learning algorithm to form an electronic expansion valve conversion model, so that the optimization layer module can control the opening of the electronic expansion valve based on the electronic expansion valve conversion model under different working conditions.

[0042] It should be noted that the electronic expansion valve opening conversion relationship between cross-brand outdoor and indoor units specifically refers to the following: if the original Brand B indoor unit is paired with a Brand B outdoor unit, no electronic expansion valve opening conversion is required. However, if a Brand A outdoor unit is replaced, the electronic expansion valve opening needs to be converted to match the Brand A outdoor unit. In this case, an opening conversion is required between the two. The opening conversion relationship is often expressed in the form of an electronic expansion valve opening conversion formula. After the air conditioning equipment is modified and the brands of the indoor and outdoor units are determined, the electronic expansion valve opening conversion formula (the initial formula, which is prior art and will not be repeated here) can be obtained from the modification company.

[0043] Let's take an example to illustrate this. Let's assume the electronic expansion valve currently installed in the air conditioning system is brand B (function input X), and we need to convert it to brand A (function output Y). Both brands have a 2000 pls (pulses, representing the number of electrical signal pulses that control the valve's opening), meaning the conversion is 2000 pls -> 2000 pls. The formula for converting the electronic expansion valve's opening is as follows (the applicable formula depends on the cooling capacity Q and the input range; see Tables 1 and 2):

[0044] Y=(X-C1)*K1 / K2+C2

[0045] Among them, K1, K2, C1, and C2 are all parameters.

[0046] The initial formula and parameters for the electronic expansion valve opening conversion are shown in Table 1. In this embodiment, taking 56<Q≤112 as an example, the adjusted formula and parameters for the electronic expansion valve opening conversion are shown in Table 2.

[0047] Table 1 Initial formula and parameters for electronic expansion valve opening conversion

[0048]

[0049] Table 2 Electronic expansion valve opening conversion adjustment formula and parameters

[0050]

[0051] During operation, if it is found that the cooling effect or heating effect does not meet expectations, this embodiment can automatically adjust the opening of the electronic expansion valve according to the set number of steps, and continue to monitor the temperature changes until the expected results are achieved. After that, a new electronic expansion valve opening conversion relationship will be formed, and the optimization layer module will dynamically adjust the air-conditioning system according to the cooling or heating effect, thereby achieving efficient collaborative work between the indoor and outdoor units of cross-brand air conditioners.

[0052] Although the present invention has been disclosed above in terms of preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications to the technical solutions of the present invention by using the methods and technical contents disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall fall within the scope of protection of the technical solutions of the present invention.

Claims

1. A collaborative control method for cross-brand air-conditioning systems, characterized in that: The following steps are involved: The protocol conversion modules set up between cross-brand indoor and outdoor units are divided into basic layer modules and optimization layer modules. The power on / off and mode switching control instructions are processed by the basic layer module, and the remaining air-conditioning control instructions are processed by the optimization layer module. After the power on / off and mode switching control instructions are executed, the status is fed back to the optimization layer module.

2. The collaborative control method for cross-brand air-conditioning systems according to claim 1, characterized in that: When the user issues a power-on command, the basic layer module responds quickly and controls the startup of the indoor and outdoor units of the air conditioner; then, the optimization layer module calculates and sends appropriate frequency adjustment instructions to the outdoor unit of the air conditioner based on the temperature information fed back by the indoor temperature sensor and the temperature value set by the user, so that the compressor runs at an appropriate speed, thereby achieving temperature regulation.

3. The collaborative control method for cross-brand air-conditioning systems according to claim 2, characterized in that: The optimization layer module uses an adaptive learning algorithm to build an energy consumption model based on the historical operating data of the air-conditioning system; the optimization layer module collects the operating data of the air-conditioning system in real time, and automatically adjusts the control parameters through the energy consumption model to ensure that the air-conditioning system is in an optimized operating state under different working conditions.

4. The collaborative control method for cross-brand air-conditioning systems according to claim 3, characterized in that: The operating data of the air-conditioning system includes indoor and outdoor temperature, humidity, operating frequency of the outdoor unit compressor, speed of the indoor unit fan and energy consumption data.

5. The collaborative control method for cross-brand air-conditioning systems according to claim 3, characterized in that: The comfort level range is 24~28℃.

6. The collaborative control method for cross-brand air conditioning systems according to claim 3, characterized in that: The optimization layer module continuously collects air-conditioning operation data before and after automatic adjustment of control parameters in real time, and further optimizes the constructed energy consumption model based on the adaptive learning algorithm to achieve dynamic optimization of the air-conditioning system.

7. The collaborative control method for cross-brand air-conditioning systems according to claim 6, characterized in that: Obtain the electronic expansion valve opening conversion relationship between outdoor and indoor units of different brands; the optimization layer module monitors the temperature adjustment changes. If it is found that the cooling effect or heating effect does not meet the expectations, the electronic expansion valve opening is automatically adjusted according to the set number of steps, and the temperature changes are continuously monitored until the expected temperature is achieved, and then a new electronic expansion valve opening conversion relationship is formed.

8. The collaborative control method for cross-brand air-conditioning systems according to claim 7, characterized in that: The optimization layer module continuously collects the conversion relationship of the electronic expansion valve before and after adjustment based on the adaptive learning algorithm to form an electronic expansion valve conversion model, so that the optimization layer module can control the opening of the electronic expansion valve based on the electronic expansion valve conversion model under different working conditions.

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