Control platform and method based on air conditioning system, electronic device and storage medium

Abstract

The embodiment of the application discloses a control platform and method based on an air conditioning system, an electronic device and a storage medium, which comprise a business private module, a general business module, a scheduling module and a platform data service module; the scheduling module is connected with the general business module, and the scheduling module is used for sending a trigger operation signal to the general business module according to a control signal of a client; the general business module is connected with the business private module and the platform data service module, and the general business module is used for performing top-layer algorithm processing and bottom-layer algorithm processing on business data acquired from the business private module and the platform data service module according to the trigger operation signal, and then outputting control information and sending the control information to the platform data service module; and the platform data service module is used for being connected with a controlled device, sending the control information to the corresponding controlled device, and controlling the operation state of the controlled device through the control information, so that the problem of poor energy-saving effect of the air conditioning system can be solved, and the energy-saving effect of the air conditioning system is improved.

Description

Technical Field

[0001] This application relates to the field of air conditioning control technology, and in particular to a control platform, method, electronic device and storage medium based on an air conditioning system. Background Technology

[0002] With societal development and increased environmental awareness, energy conservation and carbon emission reduction have become crucial aspects of environmental protection. Statistics show that the total carbon emissions from the entire building process account for a staggering 51.3% of national carbon emissions, with the building operation phase accounting for up to 22%. Therefore, carbon reduction during building operation is a vital part of energy conservation. Of the energy consumed during building operation, central air conditioning systems account for 50%-60%, making it particularly important to reduce the energy consumption of central air conditioning systems to achieve energy emission reductions during this phase.

[0003] For traditional central air conditioning (HVAC) systems, the control program is typically written into a programmable logic controller (PLC). The PLC executes the control logic to achieve automatic control of the central air conditioning system. However, due to the limitations of the PLC's computing power, the control algorithms written in the PLC are relatively simple and cannot achieve global optimization of the central air conditioning system's operating parameters.

[0004] Currently, central air conditioning systems typically upload collected data to a cloud platform, which serves only as a data storage device. The control program of the programmable logic controller (PLC) in the central air conditioning control system is usually set during project construction. After the project is completed, the control program in the PLC is generally not iterated or upgraded. If an update is needed, engineers need to go to the site to conduct relevant tests and perform manual upgrades. This results in a lag in the energy-saving effect of the central air conditioning system, and the overall energy-saving effect of the central air conditioning system in terms of operating time is poor. Summary of the Invention

[0005] This application provides a control platform, method, electronic device, and storage medium based on an air conditioning system, which can solve the problem of poor energy-saving effect of air conditioning systems, improve the energy-saving effect of air conditioning systems, and improve the timeliness of energy-saving control parameter updates.

[0006] In a first aspect, embodiments of this application provide a control platform based on an air conditioning system, including a private business module, a general business module, a scheduling module, and a platform data service module;

[0007] The scheduling module is connected to the general business module, and the scheduling module is used to send a trigger operation signal to the general business module according to the control signal of the client.

[0008] The general business module is connected to the business private module and the platform data service module. The general business module is used to obtain business data from the business private module and the platform data service module according to the trigger operation signal, and output control information after performing top-level algorithm processing and bottom-level algorithm processing on the business data, and send the control information to the platform data service module.

[0009] The platform data service module is used to connect with the controlled device and send the control information to the corresponding controlled device so as to control the operating status of the controlled device through the control information.

[0010] Furthermore, the general business module includes a top-level algorithm module, a bottom-level algorithm module, and a data processing module;

[0011] The scheduling module is connected to the top-level algorithm module and the bottom-level algorithm module. The scheduling module is used to send a trigger run signal to the top-level algorithm module according to the control signal from the client.

[0012] The top-level algorithm module is connected to the data processing module, the business private module, and the bottom-level algorithm module. The top-level algorithm module is used to receive business data sent by the business private module and the data processing module, perform corresponding top-level algorithm processing on the business data, output result data, and send the result data to the bottom-level algorithm module.

[0013] The underlying algorithm module is connected to the data processing module and the business private module. The underlying algorithm module is used to perform corresponding underlying algorithm processing based on the result data transmitted by the top-level algorithm module and the business data to obtain corresponding control information, and send the control information to the data processing module.

[0014] The data processing module is connected to the platform data service module. The data processing module is used to obtain corresponding business data from the platform data service module, and to transcode the control information received from the underlying algorithm module, and send the transcoded control information to the platform data service module.

[0015] Furthermore, the top-level algorithm module includes a first control-type algorithm submodule and a first non-control-type algorithm submodule;

[0016] The top-level algorithm module is used to control the first control-type algorithm submodule and / or the first non-control-type algorithm submodule to perform top-level algorithm processing according to the trigger operation signal sent by the scheduling module, and output the result data.

[0017] Furthermore, the first control algorithm submodule includes a model prediction control algorithm unit and a deep reinforcement learning algorithm unit;

[0018] The first non-control algorithm submodule includes a load forecasting algorithm unit, a PID self-tuning algorithm unit, and a first simulation algorithm unit;

[0019] The top-level algorithm module is used to control the model prediction control algorithm unit, the deep reinforcement learning algorithm unit, the load prediction algorithm unit, the PID self-tuning algorithm unit and / or the first simulation algorithm unit to perform corresponding algorithm processing according to the trigger operation signal sent by the scheduling module, and output the result data.

[0020] Furthermore, the underlying algorithm module includes a second control-type algorithm submodule and a second non-control-type algorithm submodule;

[0021] The underlying algorithm module is used to control the second control-type algorithm submodule and / or the second non-control-type algorithm submodule to perform underlying algorithm processing based on the result data sent by the top-level algorithm module, and output the control information.

[0022] Furthermore, the control information includes device control parameters, status data, and device configuration parameters;

[0023] The second control algorithm submodule includes a chiller parameter control algorithm unit, a cooling water pump parameter control algorithm unit, a cooling tower parameter control algorithm unit, a chilled water pump parameter control unit, a PID algorithm unit, a water system mode control algorithm unit, a two-way valve control algorithm unit, a bypass valve control algorithm unit, a fan parameter control algorithm module, a wind system mode control algorithm unit, and an interlock control algorithm unit;

[0024] The second non-control algorithm submodule includes a thermal comfort algorithm unit and a second simulation algorithm unit;

[0025] The underlying algorithm module is used to control the chiller parameter control algorithm unit, the cooling water pump parameter control algorithm unit, the cooling tower parameter control algorithm unit, the chilled water pump parameter control unit, the PID algorithm unit, the water system mode control algorithm unit, the two-way valve control algorithm unit, the bypass valve control algorithm unit, the fan parameter control algorithm module, the air system mode control algorithm unit, and / or the interlock control algorithm unit to perform corresponding algorithm processing based on the result data sent by the top-level algorithm module, and output the equipment control parameters;

[0026] Alternatively, the underlying algorithm module is used to control the thermal comfort algorithm unit and / or the second simulation algorithm unit to perform corresponding algorithm processing based on the result data sent by the top-level algorithm module, and output status data and device configuration parameters.

[0027] Furthermore, the control platform also includes a communication module and a logging module;

[0028] The communication module is connected to the data processing module and the platform data service module. The communication module is used to send the control information after transcoding by the data processing module to the platform data service module, and to send the business data obtained from the platform data service module to the data processing module.

[0029] The log module is connected to the business private module, the scheduling module, the platform data service module, the top-level algorithm module, the bottom-level algorithm module, the data processing module, and the communication module. The log module is used to store log data from all modules.

[0030] In a second aspect, embodiments of this application provide a control method based on an air conditioning system, using the control platform described in the first aspect, including:

[0031] The business data is processed by a top-level algorithm based on the trigger operation signal to obtain the result data;

[0032] Based on the resulting data, the business data is processed using underlying algorithms to obtain control information;

[0033] The control information is transcoded and then sent to the corresponding controlled device to control the operating status of the controlled device.

[0034] In a third aspect, embodiments of this application provide a control device based on an air conditioning system, comprising:

[0035] Memory and one or more processors;

[0036] The memory is used to store one or more programs;

[0037] When the one or more programs are executed by the one or more processors, the one or more processors implement the control method based on the air conditioning system as described in the second aspect.

[0038] In a fourth aspect, embodiments of this application provide a storage medium for storing computer-executable instructions, which, when executed by a computer processor, are used to perform the control method based on an air conditioning system as described in the second aspect.

[0039] This embodiment connects a scheduling module and a general business module. The general business module is connected to a business-specific module and a platform data service module. This allows for top-level and bottom-level algorithm processing of business data obtained from the business-specific module and the platform data service module to obtain control information. The platform data service module then sends this control information to the corresponding controlled devices to control their operating status. By employing this technique, the general business module can perform top-level and bottom-level algorithm processing on business data, thereby avoiding the problem of poor energy efficiency in air conditioning systems and improving their energy-saving performance. Furthermore, by periodically controlling the operating status of the controlled devices using the control information obtained from the top-level and bottom-level algorithm processing through the control platform, the timeliness of energy-saving control parameter updates is improved. Attached Figure Description

[0040] Figure 1 This is a first schematic diagram of a control platform based on an air conditioning system provided in an embodiment of this application;

[0041] Figure 2 This is a schematic diagram of a business private module provided in an embodiment of this application;

[0042] Figure 3 This is a second schematic diagram of a control platform based on an air conditioning system provided in an embodiment of this application;

[0043] Figure 4 This is a schematic diagram of a top-level algorithm module provided in an embodiment of this application;

[0044] Figure 5 This is a schematic diagram of a low-level algorithm module provided in an embodiment of this application;

[0045] Figure 6 This is a schematic diagram of a multi-site task scheduling provided in an embodiment of this application;

[0046] Figure 7 This is a flowchart of a control method based on an air conditioning system provided in an embodiment of this application;

[0047] Figure 8 This is a schematic diagram of the structure of a control device based on an air conditioning system provided in an embodiment of this application. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0049] According to relevant research reports, the total carbon emissions from the entire building process account for as much as 51.3% of the national total carbon emissions, with the building operation phase accounting for up to 22%. Therefore, carbon reduction during the building operation phase is a crucial link in achieving the "dual carbon" goals (carbon reduction and emission reduction). For public building energy consumption, central air conditioning systems account for 50%-60% of energy consumption, and most central air conditioning systems suffer from inefficient operation. To reduce building energy consumption, decrease building carbon emissions, and build green and low-carbon buildings, it is urgent to optimize and upgrade the control systems of building central air conditioning systems.

[0050] Traditional HVAC systems typically use programmable logic controllers (PLCs) to execute control logic, achieving automatic control. However, due to the limitations of PLC computing power, their control algorithms are relatively simple and cannot achieve global optimization of system operating parameters. Therefore, there is room for further energy reduction in PLC-controlled HVAC systems. Traditional technologies generally upload collected data to cloud platforms, preventing remote project operation. After project completion, feedback on control effectiveness and on-site equipment status cannot be obtained promptly. Furthermore, once a project is completed, the PLC control program is generally not iterated upon; updates require on-site engineering, limiting the applicability of iterative control technology to older projects.

[0051] To address the problem of insufficient computing resources in local programmable logic controllers (PLCs), improve the energy-saving performance of central air conditioning control systems, and enhance the long-term operational service quality of projects, embodiments of this application provide a control platform, method, electronic device, and storage medium based on an air conditioning system.

[0052] This application provides a control platform, method, electronic device, and storage medium for air conditioning systems. The aim is to improve the energy efficiency of air conditioning systems by performing top-level and bottom-level algorithm processing on business data through a general business module during control, thereby avoiding the problem of poor energy-saving performance. Furthermore, the control platform periodically processes the control information obtained from the top-level and bottom-level algorithm processing to control the operating status of the controlled equipment, improving the timeliness of energy-saving control parameter updates. Compared to traditional air conditioning control methods, which typically write control programs into a programmable logic controller (PLC) and execute the control logic locally to achieve automatic control of the central air conditioning system, traditional methods suffer from limitations in computational power. The control algorithms written in PLCs are relatively simple and cannot achieve global optimization of the operating parameters of the central air conditioning system. Central air conditioning systems typically upload collected data to a cloud platform, which merely serves as a data storage hub. The control program of the programmable logic controller (PLC) in the central air conditioning control system is usually set during project construction. After project completion, the PLC's control program generally doesn't undergo iterative upgrades. Updates require on-site testing and manual processing by engineers, leading to delayed energy-saving effects and poor overall energy efficiency during operation. Therefore, this application provides a control platform based on an air conditioning system to address the problem of poor energy efficiency in air conditioning systems.

[0053] Figure 1 This is a first schematic diagram of a control platform based on an air conditioning system provided in an embodiment of this application, referring to... Figure 1The air conditioning system-based control platform provided in this application embodiment is applied to public buildings using central air conditioning systems, such as subway stations, office buildings, museums, airports, and factories. This air conditioning system-based control platform includes a private business module 10, a general business module 20, a scheduling module 30, and a platform data service module 40. The scheduling module 30 is connected to the general business module 20 and is used to send trigger operation signals to the general business module 20 based on control signals from the client. The general business module 20 is connected to both the private business module 10 and the platform data service module 40. The general business module 20 is used to obtain business data from the private business module 10 and the platform data service module 40 based on the trigger operation signals, and after performing top-level and bottom-level algorithm processing on the business data, output control information and send the control information to the platform data service module 40. The platform data service module 40 is used to connect to the controlled device 50 and send the control information to the corresponding controlled device 50 to control the operating status of the controlled device 50 through the control information.

[0054] Figure 2 This is a schematic diagram of a business private module 10 provided in an embodiment of this application, with reference to... Figure 2 The business-private module 10 includes a business configuration module 101 and a business-private algorithm module 102. The business configuration module 101 primarily handles configuration information that is not yet suitable for generalization, such as water system mode tables, equipment operation status mapping tables, and missing parameter tables. The business-private algorithm module 102 primarily handles custom algorithms involved in various business processes. The business-private module 10 is a temporary module. Once some business configuration modules or algorithm modules can be abstracted into general modules, this part can be deleted and replaced with functions from general business modules.

[0055] The general business module 20 includes a top-level algorithm module 21, a bottom-level algorithm module 22, and a data processing module 23. The data processing module 23 is mainly used for data cleaning, missing data imputation, resampling of time series data, target data retrieval, standardization of collected parameters, and anomaly detection data screening.

[0056] Figure 3 This is a second schematic diagram of a control platform based on an air conditioning system provided in an embodiment of this application, referring to... Figure 3The scheduling module 30 is connected to the top-level algorithm module 21 and the bottom-level algorithm module 22. The scheduling module 30 is used to send a trigger operation signal to the top-level algorithm module 21 according to the control signal from the client. The top-level algorithm module 21 is connected to the data processing module 23, the business private module 10, and the bottom-level algorithm module 22. The top-level algorithm module 21 is used to receive business data sent by the business private module 10 and the data processing module 23, and perform corresponding top-level algorithm processing based on the business data to output result data, which is then sent to the bottom-level algorithm module 22. The bottom-level algorithm module 22 is connected to the data processing module 23 and the business private module 10. The bottom-level algorithm module 22 is used to perform corresponding bottom-level algorithm processing based on the result data transmitted by the top-level algorithm module 21 and the business data to obtain corresponding control information, which is then sent to the data processing module 23. The data processing module 23 is connected to the platform data service module 40. The data processing module 23 is used to obtain corresponding business data from the platform data service module 40, and to transcode the control information received from the bottom-level algorithm module 22, sending the transcoded control information to the platform data service module 40.

[0057] Among them, reference Figure 4 The top-level algorithm module 21 includes a first control-type algorithm submodule 24 and a first non-control-type algorithm submodule 25. The top-level algorithm module 21 is used to control the first control-type algorithm submodule 24 and / or the first non-control-type algorithm submodule 25 to perform top-level algorithm processing according to the trigger operation signal sent by the scheduling module 30, and output the result data. (Refer to...) Figure 5 The underlying algorithm module 22 includes a second control-type algorithm submodule 26 and a second non-control-type algorithm submodule 27. The underlying algorithm module 22 is used to control the second control-type algorithm submodule 26 and / or the second non-control-type algorithm submodule 27 to perform underlying algorithm processing based on the result data sent by the top-level algorithm module 21, and output the control information.

[0058] Figure 4 This is a schematic diagram of a top-level algorithm module 21 provided in an embodiment of this application, with reference to... Figure 4 The first control-type algorithm submodule 24 includes a model predictive control algorithm unit 241 and a deep reinforcement learning algorithm unit 242. The first non-control-type algorithm submodule 25 includes a load prediction algorithm unit 251, a PID self-tuning algorithm unit 252, and a first simulation algorithm unit 253. The top-level algorithm module 21 is used to control the model predictive control algorithm unit 241, deep reinforcement learning algorithm unit 242, load prediction algorithm unit 251, PID self-tuning algorithm unit 252, and / or the first simulation algorithm unit 253 to perform corresponding algorithm processing according to the trigger operation signal sent by the scheduling module 30, and output the result data.

[0059] Figure 5 This is a schematic diagram of a low-level algorithm module 22 provided in an embodiment of this application, with reference to... Figure 5 The control information includes equipment control parameters, status data, and equipment configuration parameters. The second control algorithm submodule 26 includes a chiller parameter control algorithm unit 260, a cooling water pump parameter control algorithm unit 261, a cooling tower parameter control algorithm unit 262, a chilled water pump parameter control unit 263, a PID algorithm unit 264, a water system mode control algorithm unit 265, a two-way valve control algorithm unit 266, a bypass valve control algorithm unit 267, a fan parameter control algorithm module 268, a wind system mode control algorithm unit 269, and an interlocking control algorithm unit 270. The second non-control algorithm submodule 27 includes a thermal comfort algorithm unit 271 and a second simulation algorithm unit 272. The bottom-level algorithm module 22 is used to control the chiller parameter control algorithm unit 260, cooling water pump parameter control algorithm unit 261, cooling tower parameter control algorithm unit 262, chilled water pump parameter control unit 263, PID algorithm unit 264, water system mode control algorithm unit 265, two-way valve control algorithm unit 266, bypass valve control algorithm unit 267, fan parameter control algorithm module 268, air system mode control algorithm unit 269 and / or interlock control algorithm unit 270 to perform corresponding algorithm processing based on the result data sent by the top-level algorithm module 21, and output equipment control parameters; or, the bottom-level algorithm module 22 is used to control the thermal comfort algorithm unit 271 and / or the second simulation algorithm unit 272 to perform corresponding algorithm processing based on the result data sent by the top-level algorithm module 21, and output status data and equipment configuration parameters.

[0060] For example, the first control algorithm submodule 24 includes a Model Predictive Control (MPC) unit and a Deep Reinforcement Learning (DRL) unit. Compared to the second control algorithm submodule 26 in the lower-level algorithm module 22, the first control algorithm submodule 24 in the top-level algorithm module 21 can dynamically combine and globally optimize the control parameters involved in the operation of the central air conditioning system equipment. Therefore, the top-level control algorithm module can achieve high efficiency and energy saving. The first non-control algorithm submodule 25 includes a load prediction algorithm unit 251, a PID self-tuning algorithm unit 252, and a first simulation algorithm unit 253, etc., wherein the first simulation algorithm unit 253 is a data-driven HVAC simulation unit. The load prediction algorithm unit 251 can be used to predict short-term building cooling load to achieve the purpose of "predictive control," and can also be used to predict long-term power consumption to determine the optimal electricity billing method for the building. The PID self-tuning algorithm unit 252 can optimize the coefficients in the PID algorithm. The HVAC simulation unit can be used as an environment for interacting with control algorithms. It is a crucial module in the process of global optimization of control parameters. It can be divided into two categories: data-driven and physical model-driven. When the amount of data is small in the early stage of equipment operation, the physical model-based HVAC simulation model (second simulation algorithm unit 272) can be used. Otherwise, it switches to the data-driven simulation model (first simulation algorithm unit 253).

[0061] In one embodiment, the second control algorithm submodule 26 in the bottom-level algorithm module 22 mainly uses traditional control algorithms, including expert rule control algorithms and fuzzy control algorithms. The second control algorithm submodule is primarily used to ensure the robustness and completeness of the control algorithm; that is, for controllable devices whose control parameters, status data, or configuration parameters are not yet considered by the top-level algorithm module 21, the bottom-level algorithm can be used for control. When the bottom-level algorithm module 22 receives result data from the top-level algorithm module 21 and the corresponding controllable device's control parameters, status data, or configuration parameters are empty, it needs to perform algorithmic processing through the bottom-level algorithm to obtain the corresponding control parameters, status data, or configuration parameters. If the received result data includes the corresponding controllable device's control parameters, status data, or configuration parameters, the bottom-level algorithm module 22 sends the corresponding control parameters, status data, or configuration parameters (control information) to the platform data service module 40, so that the platform data service module 40 can forward the corresponding control parameters, status data, or configuration parameters to the corresponding controlled device 50. The second control algorithm submodule 26 mainly includes a chiller parameter control algorithm unit 260, a cooling water pump parameter control algorithm unit 261, a cooling tower parameter control algorithm unit 262, a chilled water pump parameter control unit 263, a PID algorithm unit 264, a water system mode control algorithm unit 265, a two-way valve control algorithm unit 266, a bypass valve control algorithm unit 267, a fan parameter control algorithm module 268, a wind system mode control algorithm unit 269, and an interlock control algorithm unit 270. Among these, the water system mode control algorithm unit 265 and the wind system mode control algorithm unit 269 only perform mode analysis and selection, while the equipment interlock control process during mode control is implemented by the interlock control algorithm unit 270. Unlike the parameter control algorithm and the mode control algorithm, the PID algorithm unit 264 is an auxiliary algorithm unit. It uses the total flow rate output by the top-level algorithm module 21 (model predictive control algorithm unit 241) as the target value and adjusts the corresponding water pump frequency through PID control to achieve the target flow rate. The second non-control algorithm submodule 27 mainly includes a thermal comfort algorithm unit 271 and a second simulation algorithm unit 272. The thermal comfort algorithm unit 271 is a dynamic thermal comfort algorithm unit, and the second simulation algorithm unit 272 is a physical model-based HVAC simulation unit. The dynamic thermal comfort algorithm unit 271 dynamically analyzes whether the setpoints for indoor temperature and humidity in the building are within a reasonable range under the current outdoor temperature, thereby correcting the temperature and humidity to achieve both meeting the end-point temperature and humidity targets and reducing energy consumption. The thermal comfort algorithm unit 271 can output environmental thermal comfort state assessment results and upper and / or lower limits for end-point temperature and humidity setpoints.The thermal comfort algorithm unit 271 performs corresponding algorithm processing and outputs status data and equipment configuration parameters. The status data output by the thermal comfort algorithm unit 271 is the environmental thermal comfort status assessment result, which includes parameters assessing whether the environment is suitable or comfortable. The equipment configuration parameters output by the thermal comfort algorithm unit 271 are the upper and / or lower limits of the terminal temperature and humidity setpoints. The second simulation algorithm unit 272 performs corresponding algorithm processing and outputs status data. The status data output by the second simulation algorithm unit 272 is the operating parameters of the simulated equipment, which are the output results of the HVAC simulation model. The controllable parameters of the control algorithms in the second control algorithm submodule 26 of the underlying algorithm module 22 include chilled water temperature, chiller load rate, fan frequency, water pump frequency, water system mode (number of each device in the water system), and air system mode (number of each device in the air system), etc.

[0062] In one embodiment, reference is made to Figure 3 The control platform also includes a communication module 28 and a log module (not shown in the figure). The communication module 28 is connected to the data processing module 23 and the platform data service module 40. The communication module 28 is used to send the control information transcoded by the data processing module 23 to the platform data service module 40, and to send the business data obtained from the platform data service module 40 to the data processing module 23. The data processing module 23 is connected to the platform data service module 40 through the communication module 28, so as to transmit the transcoded control information to the platform data service module 40 through the communication module 28. The log module is connected to the business private module 10, the scheduling module 30, the platform data service module 40, the top-level algorithm module 21, the bottom-level algorithm module 22, the data processing module 23, and the communication module 28. The log module is used to store the log data of all modules.

[0063] For example, the log module is mainly used to record the operation logs of each component of the central air conditioning system control algorithm and to perform log rotation. The communication module 28 is mainly used to realize communication between the top-level algorithm module 21, the bottom-level algorithm module 22 and the platform data service module 40, including the acquisition of algorithm calculation data (business data) and the distribution of algorithm analysis results. The scheduling module 30 is mainly used to realize the algorithm scheduling of all controlled sites (controlled devices 50), the asynchronous scheduling of different bottom-level algorithms, and the hierarchical scheduling between the top-level algorithm in the top-level algorithm module 21 and the bottom-level algorithms in the bottom-level algorithm module 22. By scheduling the division of labor between the top-level algorithm module 21 and the bottom-level algorithm module 22 through the scheduling module 30, the working efficiency of algorithm processing is improved, thereby improving the working efficiency of equipment control.

[0064] In one embodiment, reference is made to Figure 3This application provides an embodiment of a central air conditioning system control algorithm data flow and a fusion implementation of top-level and bottom-level algorithms. The algorithms in the top-level algorithm module 21 or the bottom-level algorithm module 22 communicate with the platform data service module 40 via the communication module 28 to obtain calculation-related business data. This business data is then processed by the data processing module 23 and enters the top-level algorithm module 21 and / or the bottom-level algorithm module 22 for algorithm analysis and processing. The first control-type algorithm submodule 24 in the top-level algorithm module 21 only performs global optimization of the controllable parameters of the central air conditioning system equipment and does not directly participate in instruction issuance. If the top-level algorithm module 21 obtains equipment control parameters, status data, or equipment configuration parameters through algorithm processing, these are transmitted to the bottom-level algorithm module 22, and then to the data processing module 23. The data processing module 23 transcodes the equipment control parameters, status data, or equipment configuration parameters and then transmits the transcoded equipment control parameters, status data, or equipment configuration parameters to the platform data service module 40 via the communication module 28. Since the top-level algorithm module 21 performs global optimization control, it only outputs some device control parameters, status data, or device configuration parameters. Other device control parameters, status data, or device configuration parameters that are not output need to be processed by the bottom-level algorithm module 22 before being output. The top-level algorithm module 21 outputs result data and passes it to the bottom-level algorithm module 22. The bottom-level algorithm module 22 determines whether the top-level algorithm module 21 should control the target device based on the result data to distinguish the action execution. The bottom-level algorithm module 22 directly issues commands for the device control parameters, status data, or device configuration parameters obtained from the top-level algorithm module 21; otherwise, it analyzes the operating data (business data) of the target controlled device 50 and issues corresponding control commands.

[0065] For example, if the underlying algorithm module 22 receives result data from the top-level algorithm module 21 containing no device control parameters, status data, or device configuration parameters for a certain controlled device 50, then the underlying algorithm needs to perform algorithm analysis processing on the device control parameters, status data, or device configuration parameters of the controlled device 50. If the result data contains device control parameters, status data, or device configuration parameters for a certain controlled device 50, then the underlying algorithm module 22 does not need to perform algorithm analysis processing on the device control parameters, status data, or device configuration parameters of the controlled device 50. The underlying algorithm module 22 can send the corresponding device control parameters, status data, or device configuration parameters obtained by the top-level algorithm module 21 to the platform data service module 40 via the data processing module 23 and the communication module 28.

[0066] Furthermore, during the algorithm execution phase, the top-level algorithm module 21 or the bottom-level algorithm module 22 will obtain business data from the business private module 10 in real time, thereby realizing the transformation of the general algorithm towards privatization. The data-driven algorithm (top-level algorithm) and the traditional control algorithm (bottom-level algorithm) can be integrated through hierarchical association.

[0067] Figure 6 This is a schematic diagram of a multi-site task scheduling provided in an embodiment of this application, with reference to... Figure 6 Taking a subway station as an example, in the diagram, S1 (Status 1) represents controlled station 1, SN (Status N) represents controlled station N; HLA (High Level Algorithm) represents the top-level algorithm; and LLA (Low Level Algorithm) represents the bottom-level algorithm. As shown in the diagram, the task scheduler periodically triggers the top-level algorithm in the top-level algorithm module 21. Only after the top-level algorithm completes its calculations will the scheduling of the bottom-level algorithm task in the bottom-level algorithm module 22 be activated. After the bottom-level algorithm completes its execution and issues instructions, the current round of control algorithm task is completed, and the task awaits the next triggering of the top-level algorithm task by the scheduler. By periodically triggering the top-level algorithm module 21 and the bottom-level algorithm module 22 to cooperate, the efficiency of algorithm processing is improved, while the continuous optimization of equipment control parameters, status data, and equipment configuration parameters is achieved, thereby improving the timeliness of energy-saving control parameter updates.

[0068] As described above, by deploying the central air conditioning system's control algorithm on a cloud platform, abundant computing resources are available, enabling data-driven algorithms with high computing power requirements. This results in more precise control and more significant energy savings. The central air conditioning control system's equipment operation data is collected in real-time and stored in a cloud database, facilitating online analysis by operators to identify anomalies in the control algorithm, whether the energy-saving effect at the control station meets expectations, whether field sensors are offline, and whether field equipment is malfunctioning. Because the algorithm is deployed on the control platform, the iterative update function of the control algorithm can be quickly implemented and applied to field equipment. Centralized project management reduces labor costs and the workload of on-site maintenance personnel. The above can be applied to all buildings using central air conditioning control systems, regardless of the type or number of field equipment. Modular, universal algorithms allow for the activation or deactivation of specific algorithms to adapt to different building scenarios, thereby improving implementation flexibility.

[0069] As described above, the scheduling module 30 and the general business module 20 are connected. The general business module 20 is connected to the business private module 10 and the platform data service module 40 to perform top-level and bottom-level algorithm processing on the business data obtained from the business private module 10 and the platform data service module 40 to obtain control information. The platform data service module 40 is used to send the control information to the corresponding controlled device 50 to control the operating status of the controlled device 50. By adopting the above technical means, the general business module 20 can perform top-level and bottom-level algorithm processing on the business data, thereby improving the energy-saving effect of the air conditioning system. In addition, by controlling the operating status of the controlled device 50 through the control information obtained from the top-level and bottom-level algorithm processing at regular intervals by the control platform, the timeliness of energy-saving control parameter updates is improved.

[0070] Based on the above embodiments, Figure 7 A flowchart of a control method based on an air conditioning system provided in this application embodiment is given. The control method based on the air conditioning system provided in this embodiment can be executed by a control device based on the air conditioning system. This control device can be implemented by software and / or hardware, and can consist of two or more physical entities, or a single physical entity. Generally, the control device based on the air conditioning system can be a terminal device, such as a computer device.

[0071] The following description uses a computer device as the main entity executing a control method based on an air conditioning system. (Refer to...) Figure 7 The control method based on the air conditioning system applies the aforementioned control platform based on the air conditioning system, and specifically includes:

[0072] S101. Perform top-level algorithm processing on the business data according to the trigger operation signal to obtain the result data.

[0073] The system automatically generates trigger operation signals at regular intervals, or generates trigger operation signals based on control signals from the client. The trigger operation signals then trigger the top-level algorithm module of the control platform to perform top-level algorithm processing and obtain the result data.

[0074] S102. Based on the result data, perform underlying algorithm processing on the business data to obtain control information.

[0075] The top-level algorithm module determines whether to control the target device based on the result data to distinguish the action execution. The bottom-level algorithm module directly issues instructions for the device control parameters, status data or device configuration parameters obtained by the top-level algorithm module. Conversely, the bottom-level algorithm module analyzes the target controlled device's operating data (business data) and issues corresponding control instructions.

[0076] For example, if the underlying algorithm module receives result data from the top-level algorithm module containing empty device control parameters, status data, or device configuration parameters for a certain controlled device, then the underlying algorithm needs to perform algorithmic analysis and processing on those parameters. If the result data contains device control parameters, status data, or device configuration parameters for a certain controlled device, then the underlying algorithm module does not need to perform algorithmic analysis and processing on those parameters; it can directly issue the corresponding instructions.

[0077] The control information includes equipment control parameters, status data, and equipment configuration parameters. During the underlying algorithm processing, the chiller parameter control algorithm unit, cooling water pump parameter control algorithm unit, cooling tower parameter control algorithm unit, chilled water pump parameter control unit, PID algorithm unit, water system mode control algorithm unit, two-way valve control algorithm unit, bypass valve control algorithm unit, fan parameter control algorithm module, air system mode control algorithm unit, and / or interlock control algorithm unit are controlled according to the result data sent by the top-level algorithm module to perform corresponding algorithm processing and output equipment control parameters; or, the underlying algorithm module is used to control the thermal comfort algorithm unit and / or the second simulation algorithm unit to perform corresponding algorithm processing according to the result data sent by the top-level algorithm module, and output status data and equipment configuration parameters.

[0078] S103. The control information is transcoded and the transcoded control information is sent to the corresponding controlled device.

[0079] The control information is transcoded and then sent to the corresponding controlled device via the platform data service module, so as to control the operation of the controlled device through the control information.

[0080] As described above, the business data is processed by a top-level algorithm based on the trigger operation signal to obtain result data. Then, the business data is processed by a bottom-level algorithm based on the result data to obtain control information. This control information is then transcoded and sent to the corresponding controlled device. By employing this technique, the energy-saving effect of the air conditioning system can be improved through the collaborative processing of the top-level and bottom-level algorithms. Furthermore, by using a control platform to periodically update the control information obtained from both the top-level and bottom-level algorithms to control the operating status of the controlled device, the timeliness of energy-saving control parameter updates is improved.

[0081] The control platform based on the air conditioning system provided in this application embodiment can be used to execute the control method based on the air conditioning system provided in the above embodiment, and has corresponding functions and beneficial effects.

[0082] This application provides a control device based on an air conditioning system, referring to... Figure 8 The control device based on the air conditioning system includes: a processor 31, a memory 32, a communication module 33, an input device 34, and an output device 35. The number of processors and the number of memories in the control device can be one or more. The processor, memory, communication module, input device, and output device of the control device can be connected via a bus or other means.

[0083] The memory 32, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as program instructions / modules corresponding to the control method based on the air conditioning system described in any embodiment of this application (e.g., business-private modules, general business modules, scheduling modules, and platform data service modules in the control platform based on the air conditioning system). The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the device, etc. Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0084] The communication module 33 is used for data transmission.

[0085] The processor 31 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory, thereby realizing the above-mentioned control method based on the air conditioning system.

[0086] Input device 34 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the device. Output device 35 may include display devices such as a display screen.

[0087] The control device based on the air conditioning system provided above can be used to execute the control method based on the air conditioning system provided in the above embodiments, and has corresponding functions and beneficial effects.

[0088] This application embodiment also provides a storage medium for storing computer-executable instructions. When executed by a computer processor, the computer-executable instructions are used to execute a control method based on an air conditioning system. The control method based on the air conditioning system includes: performing top-level algorithm processing on business data according to a trigger operation signal to obtain result data; performing low-level algorithm processing on the business data according to the result data to obtain control information; transcoding the control information; and sending the transcoded control information to a corresponding controlled device to control the operating state of the controlled device through the control information.

[0089] Storage medium – any type of memory device or storage device. The term “storage medium” is intended to include: mounting media, such as CD-ROM, floppy disk, or magnetic tape devices; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. Storage medium may also include other types of memory or combinations thereof. Furthermore, storage medium may reside in a first computer system in which the program is executed, or it may reside in a different second computer system connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term “storage medium” can include two or more storage media residing in different locations (e.g., in different computer systems connected via a network). Storage medium may store program instructions (e.g., specifically implemented as a computer program) executable by one or more processors.

[0090] Of course, the computer-executable instructions stored in the storage medium provided in the embodiments of this application are not limited to the control method based on the air conditioning system as described above, but can also execute related operations in the control method based on the air conditioning system provided in any embodiment of this application.

[0091] The air conditioning system-based control device, storage medium, and air conditioning system-based control equipment provided in the above embodiments can execute the air conditioning system-based control method provided in any embodiment of this application. For technical details not described in detail in the above embodiments, please refer to the air conditioning system-based control method provided in any embodiment of this application.

[0092] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the claims.

Claims

1. A control platform based on an air conditioning system, characterized by, The system comprises a business private module, a general business module, a scheduling module and a platform data service module; The scheduling module is connected with the general business module, and the scheduling module is configured to send a trigger running signal to the general business module according to a control signal of a client; The general business module is connected with the business private module and the platform data service module, and the general business module is configured to acquire business data from the business private module and the platform data service module according to the trigger running signal, perform top-level algorithm processing and bottom-level algorithm processing on the business data, and output control information, and send the control information to the platform data service module; The platform data service module is configured to be connected with controlled devices, and send the control information to the corresponding controlled devices to control the running state of the controlled devices through the control information. The general business module comprises a top-level algorithm module, a bottom-level algorithm module and a data processing module. The scheduling module is connected with the top-level algorithm module and the bottom-level algorithm module, and the scheduling module is configured to send a trigger running signal to the top-level algorithm module according to a control signal of a client. The top-level algorithm module is connected with the data processing module, the business private module and the bottom-level algorithm module, and the top-level algorithm module is configured to receive business data sent by the business private module and the data processing module, perform corresponding top-level algorithm processing on the business data to output result data, and send the result data to the bottom-level algorithm module. The bottom-level algorithm module is connected with the data processing module and the business private module, and the bottom-level algorithm module is configured to perform corresponding bottom-level algorithm processing on the result data transmitted by the top-level algorithm module and the business data to obtain corresponding control information, and send the control information to the data processing module. The data processing module is connected with the platform data service module, and the data processing module is configured to acquire corresponding business data from the platform data service module, and perform transcoding processing on the control information received from the bottom-level algorithm module, and send the transcoded control information to the platform data service module.

2. The control platform of claim 1, wherein, The top-level algorithm module comprises a first control-type algorithm submodule and a first non-control-type algorithm submodule. The top-level algorithm module is configured to control the first control-type algorithm submodule and / or the first non-control-type algorithm submodule to perform top-level algorithm processing according to the trigger running signal sent by the scheduling module, and output the result data.

3. The control platform of claim 2, wherein, The first control-type algorithm submodule comprises a model predictive control algorithm unit and a deep reinforcement learning algorithm unit. The first non-control-type algorithm submodule comprises a load prediction algorithm unit, a PID self-tuning algorithm unit and a first simulation algorithm unit. The top-level algorithm module is configured to control the model predictive control algorithm unit, the deep reinforcement learning algorithm unit, the load prediction algorithm unit, the PID self-tuning algorithm unit and / or the first simulation algorithm unit to perform corresponding algorithm processing according to the trigger running signal sent by the scheduling module, and output the result data.

4. The control platform of claim 1, wherein, The bottom algorithm module includes a second control algorithm submodule and a second non-control algorithm submodule; The bottom algorithm module is configured to control the second control algorithm submodule and / or the second non-control algorithm submodule to perform bottom algorithm processing according to the result data sent by the top algorithm module, and output the control information.

5. The control platform of claim 4, wherein, The control information includes device control parameters, state data, and device configuration parameters. The second control algorithm submodule includes a chiller parameter control algorithm unit, a cooling water pump parameter control algorithm unit, a cooling tower parameter control algorithm unit, a chilled water pump parameter control unit, a PID algorithm unit, a water system mode control algorithm unit, a two-way valve control algorithm unit, a bypass valve control algorithm unit, a fan parameter control algorithm module, a wind system mode control algorithm unit, and a interlock control algorithm unit. The second non-control algorithm submodule includes a thermal comfort algorithm unit and a second simulation algorithm unit. The bottom algorithm module is configured to control the chiller parameter control algorithm unit, the cooling water pump parameter control algorithm unit, the cooling tower parameter control algorithm unit, the chilled water pump parameter control unit, the PID algorithm unit, the water system mode control algorithm unit, the two-way valve control algorithm unit, the bypass valve control algorithm unit, the fan parameter control algorithm module, the wind system mode control algorithm unit, and / or the interlock control algorithm unit to perform corresponding algorithm processing according to the result data sent by the top algorithm module, and output device control parameters. Alternatively, the bottom algorithm module is configured to control the thermal comfort algorithm unit and / or the second simulation algorithm unit to perform corresponding algorithm processing according to the result data sent by the top algorithm module, and output state data and device configuration parameters.

6. The control platform of claim 1, wherein, The control platform further includes a communication module and a log module; The communication module is connected with the data processing module and the platform data service module, and is configured to send the control information processed by the data processing module to the platform data service module, and send the service data obtained from the platform data service module to the data processing module; The log module is connected with the service private module, the scheduling module, the platform data service module, the top algorithm module, the bottom algorithm module, the data processing module, and the communication module, and is configured to store log data of all modules.

7. A control method based on an air conditioning system, applying the control platform of any one of claims 1-6, characterized in that, The method includes: performing top algorithm processing on the service data according to a trigger running signal to obtain result data; performing bottom algorithm processing on the service data according to the result data to obtain control information; performing transcoding processing on the control information, and sending the control information processed by transcoding to corresponding controlled devices, so as to control the running state of the controlled devices through the control information.

8. A control device based on an air conditioning system, characterized by The apparatus includes: a memory and one or more processors; the memory is configured to store one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method in claim 7.

9. A storage medium storing computer-executable instructions, wherein: The computer executable instructions, when executed by the processor, are adapted to perform the method as claimed in claim 7.

Images