Wireless Closed-Loop Control System and Method for Heating Stations Based on Edge Computing Controller

Abstract

This application relates to a wireless closed-loop control system and method for heating stations based on an edge computing controller, belonging to the field of smart heating. It includes an edge computing controller running a heating AI control algorithm; a wireless coordination module for establishing and managing a wireless communication network and configuring the priority of transmission resources, with transmission resources for transmitting control commands having a higher priority than those for transmitting sensor data; multiple terminal devices connected to the wireless coordination module via a wireless communication protocol to form a wireless communication network; the heating edge computing controller collects sensor data from the terminal devices through the wireless communication network, runs the heating AI control algorithm to generate control commands locally, and sends the control commands to the corresponding terminal devices through the wireless communication network based on priority; the terminal devices execute the control commands, forming a closed-loop control. This application achieves real-time wireless closed-loop control of heating stations.

Description

Technical Field

[0001] This application relates to the field of smart heating, and in particular to a wireless closed-loop control system and method for heating stations based on an edge computing controller. Background Technology

[0002] Heating stations such as heat exchange stations and boiler rooms are equipped with numerous sensors and actuators, including temperature sensors, pressure sensors, flow sensors, electric regulating valves, and frequency converters for circulating pumps. In traditional solutions, these sensors and actuators are connected to the PLC via RS485 bus, 4-20mA analog hardwired connections, or digital switching cables. This wired connection method requires laying a large number of cables, cable trays, and conduits, resulting in long construction periods and high material and labor costs. Especially in the renovation of older sites, wiring work must be carried out during heating shutdowns, severely impacting residents' normal heating supply. Furthermore, cables are prone to aging and poor contact after long-term operation, leading to time-consuming troubleshooting and repairs. When new monitoring and control points are needed, rewiring is required, resulting in extremely low system expansion flexibility.

[0003] While existing wireless communication technologies such as LoRa, ZigBee, and Bluetooth can achieve wireless data transmission, they cannot meet the requirements of closed-loop control. Their MAC layers generally employ CSMA / CA mechanisms, requiring devices to compete for the channel before transmitting data. This results in random fluctuations in end-to-end latency, ranging from several milliseconds to hundreds of milliseconds, and low communication reliability. Therefore, a long-standing and widely accepted technological bias exists in the industrial control field: wireless communication technology can be used for non-real-time data acquisition and status monitoring, but it cannot be used for closed-loop control requiring deterministic real-time responses, especially for the direct control of actuators such as electric regulating valves and water pump frequency converters. Summary of the Invention

[0004] To address the issues of high construction costs, long construction periods, and difficult maintenance caused by wired connections in existing technologies, as well as the technical challenge that existing wireless technologies cannot meet the real-time requirements of industrial closed-loop control, this application provides a wireless closed-loop control system and method for heating stations based on an edge computing controller.

[0005] In a first aspect, this application provides a wireless closed-loop control system for heating stations based on an edge computing controller, employing the following technical solution: A wireless closed-loop control system for a heating station based on an edge computing controller includes: An edge computing controller is deployed within the heating station, and the heating edge computing controller runs a heating AI control algorithm; A wireless coordination module, integrated within the edge computing controller, is used to establish and manage the wireless communication network and configure the priority of transmission resources, wherein the priority of transmission resources used for transmitting control commands is higher than the priority of transmission resources used for transmitting sensor data. Multiple terminal devices are deployed at various monitoring and control points in the heating station and are connected to the wireless coordination module through a wireless communication protocol to form a wireless communication network. The heating edge computing controller collects sensor data from the terminal device through the wireless communication network, runs the heating AI regulation algorithm to generate control commands locally, and sends the control commands to the corresponding terminal device through the wireless communication network based on the priority. The terminal device executes the control command to form a closed-loop control.

[0006] By adopting the above technical solution, the edge computing controller and terminal devices are connected via a wireless communication network, replacing the traditional RS485 bus and hard wiring. The terminal devices are distributed at various monitoring and control points in the heating station, eliminating the need for laying signal cables, effectively shortening construction time, reducing wiring costs, and ensuring the continuity of heating for residents during the renovation of old stations without interrupting heating. The control decisions and execution of the edge computing controller are completed within the same device, without relying on the cloud, eliminating the network latency of data transmission to the cloud and back. The control response time is reduced from seconds to milliseconds, and the control accuracy is equivalent to that of wired methods, which can meet the requirements of industrial process control. To meet the real-time requirements, the edge computing controller, in conjunction with the terminal equipment, constructed a fully wireless closed-loop control architecture for the heating station. The wireless coordination module ensures that control commands are always transmitted before sensor data by configuring the priority of transmission resources, avoiding random delays caused by channel contention when multiple devices are running concurrently. Without the need for contention and waiting, deterministic low-latency transmission under wireless communication is achieved, meeting the real-time requirements of heating closed-loop control. This eliminates random backoff delays caused by channel contention under the traditional CSMA / CA mechanism, and ensures that the end-to-end delay of control commands is deterministic, avoiding control overshoot or oscillation caused by delay jitter.

[0007] In one specific implementation, the transmission resources include dedicated time slots within a communication cycle; The wireless coordination module allocates the dedicated time slot to the terminal device; The first time slot allocated to the control command has a higher priority than the second time slot allocated to the sensing data.

[0008] By adopting the above technical solution, the wireless coordination module divides the communication cycle into multiple dedicated time slots, allocates an independent transmission time window to each terminal device, allocates control commands to the first time slot, and allocates sensor data to the second time slot. This separates the two types of data in time and allocates independent transmission resources to different types of data. Multiple terminal devices can send data sequentially within the same communication cycle, completely eliminating channel contention from the physical transmission mechanism. This eliminates interference and competition between data transmissions, and the transmission time of control commands can be accurately predicted. The latency jitter is controlled at the microsecond level, ensuring the determinism and real-time performance of control command transmission.

[0009] In one specific implementation, the first time slot is located at the beginning of the communication cycle.

[0010] By adopting the above technical solution, the first time slot is set at the beginning of the communication cycle. Once the edge computing controller generates a control command, it can send it immediately at the start of the next communication cycle without waiting for other time slots in the current cycle to end. This minimizes the waiting time from command generation to transmission, further reducing the end-to-end latency of closed-loop control. Especially for algorithms such as dual-network balancing that require simultaneous control commands to multiple valves, concentrating the control command time slots at the beginning of the cycle allows all commands to be issued within one communication cycle, controlling the time difference between valve actions to within milliseconds and avoiding hydraulic oscillations in the pipeline network caused by dispersed command issuance times.

[0011] In one specific implementation, the wireless coordination module is further configured to dynamically adjust the number or frequency of the first time slots according to the execution cycle of the heating AI control algorithm.

[0012] By adopting the above technical solution, the heating AI control algorithm has a fixed execution cycle. Within the algorithm's adjustment window, batch control commands need to be issued rapidly, while during non-adjustment periods, control commands are scarce. Therefore, the number of first time slots is temporarily increased within the adjustment window to meet the need for rapid batch command issuance; while during non-adjustment periods, the number of first time slots is reduced to allocate more resources to sensor data reporting, optimizing communication resource utilization. If a fixed time slot allocation is used, the number of first time slots needs to be determined based on peak demand, resulting in a large number of time slots being idle during non-adjustment periods. The dynamic adjustment mechanism allows time slot resources to be allocated on demand, ensuring real-time control while increasing the frequency of sensor data reporting and enhancing the overall information perception capability of the system.

[0013] In one specific implementation, the terminal device includes at least one of a wireless sensing device and a wireless execution device; The wireless sensing device is used to collect heating operation parameters and upload them to the edge computing controller via a wireless communication network; The wireless execution device is used to receive the control commands and execute control actions through a wireless communication network.

[0014] In one specific implementation, the closed-loop control period is ≤100ms.

[0015] By adopting the above technical solution, the closed-loop cycle of traditional wireless technology is usually more than a second, which cannot be used for process control. However, the closed-loop control cycle in this application is ≤100ms, which means that the complete cycle from sensor data acquisition and AI algorithm calculation to the completion of actuator action is completed within 100ms. For heating systems, this cycle is fast enough to respond quickly to load changes caused by cold waves and changes in user heating behavior, avoid room temperature fluctuations caused by response lag, and achieve an equivalent level to the typical control cycle of wired PLC systems.

[0016] In one specific implementation scheme, the wireless coordination module is a StarSpark coordinator module, the terminal device is a StarSpark terminal device, and the StarSpark terminal device communicates with the StarSpark coordinator module through the StarSpark wireless protocol to form a StarSpark wireless closed-loop control network.

[0017] By adopting the above technical solutions, XingShan's ultra-low latency characteristic enables the time slot priority scheduling mechanism to truly achieve a closed-loop cycle of ≤100ms, and its ultra-high reliability ensures that control commands are delivered on the first attempt, with a packet loss rate of ≤0.001%. XingShan employs a frequency hopping spread spectrum mechanism and Polar channel coding, enabling stable communication even in environments with strong electromagnetic interference such as inverters and motors within the heat exchange station, thus ensuring the stable operation of unattended stations during the heating season.

[0018] In one specific implementation, the edge computing controller is further configured to: When the connection with the cloud platform is interrupted, closed-loop control continues to be executed independently through the wireless communication network.

[0019] By adopting the above technical solution, the connection between the edge computing controller and the cloud platform automatically switches to local autonomous mode when the signal is interrupted. The closed-loop control is completed entirely within the local wireless network, which enables the heating system to maintain intelligent regulation during network outages. The room temperature fluctuation is always controlled within the set range, avoiding the decline in heating quality or safety accidents caused by network interruption. Moreover, the cloud does not need to process the AI ​​computing tasks of each site in real time, which greatly reduces the concurrent computing pressure on the cloud.

[0020] In one specific implementation, the edge computing controller is further configured to: The runtime data during the network outage is cached in the local storage module; Once the network is restored, the operational data will be synchronized to the cloud platform.

[0021] By adopting the above technical solution, the local storage module continuously caches all operating data during network outages, and automatically resumes transmission to the cloud after network recovery, ensuring the integrity of historical heating operation data and providing complete data for subsequent data analysis and fault tracing.

[0022] In one specific implementation, the edge computing controller further includes a wired interface module; The wired interface module is configured to connect to a wired device; The edge computing controller is also configured to perform fusion processing on the data collected by the wired device and the data collected by the terminal device.

[0023] By adopting the above technical solutions, the renovation of heating stations often involves existing wired equipment, and complete replacement is costly. However, wired interface modules can enable these devices to continue to be used. During the renovation, only wireless terminal devices need to be added. Furthermore, the edge computing controller integrates the data from the wired and wireless devices and uses them together as input for the heating AI control algorithm. The control strategy can comprehensively refer to the data from both the old and new devices to achieve unified optimization control of the entire station and avoid control blind spots caused by scattered data sources.

[0024] In one specific implementation scheme, the heating AI control algorithm includes at least one of the following: load prediction algorithm, secondary network hydraulic balance algorithm, pump and valve linkage optimization algorithm, fault diagnosis algorithm, and room temperature feedback optimization algorithm.

[0025] Secondly, this application provides a wireless closed-loop control method for heating stations based on an edge computing controller, employing the following technical solution: A wireless closed-loop control method for heating stations based on an edge computing controller is applied to the aforementioned wireless closed-loop control system for heating stations. The system includes an edge computing controller and terminal equipment, comprising: The wireless coordination module inside the edge computing controller establishes a wireless communication network and configures the priority of transmission resources, wherein the priority of transmission resources used for transmitting control commands is higher than the priority of transmission resources used for transmitting sensor data. The terminal device is connected to the wireless communication network; The edge computing controller collects sensor data from the terminal device through the wireless communication network; The edge computing controller runs the heating AI regulation algorithm to generate control commands locally; Based on the priority, the edge computing controller sends the control command to the corresponding terminal device through the wireless communication network; The terminal device executes the control command to form a closed-loop control.

[0026] In summary, this application includes at least one of the following beneficial technical effects: By configuring the priority of transmission resources, the transmission priority of control commands is made higher than that of sensor data. This eliminates channel contention when multiple devices are running concurrently from the perspective of communication scheduling mechanism, solves the problem of uncertain wireless communication latency, and realizes deterministic low-latency transmission under wireless communication. By adopting a dedicated time slot scheduling mechanism, independent transmission resources are allocated to different types of data, further eliminating interference and competition between data transmissions and ensuring the determinism and real-time performance of control command transmission. By setting the control command time slot at the beginning of the communication cycle, the waiting time after the control command is generated is minimized, further reducing the end-to-end delay of the closed-loop control. By dynamically adjusting time slot resources, the scheduling of wireless communication resources is matched with the execution rhythm of the control algorithm, which not only meets the need for rapid issuance of batch control commands, but also optimizes the utilization rate of communication resources. The control node generates and issues control commands locally, creating a localized closed-loop control architecture that ensures control continuity even when the network is down. Attached Figure Description

[0027] Figure 1 This is a system architecture diagram of the wireless closed-loop control system for heating stations based on edge computing controllers, as described in this application. Figure 2 This is a flowchart illustrating the wireless closed-loop control method for heating stations based on an edge computing controller, as described in this application. Detailed Implementation

[0028] The following is in conjunction with the appendix Figure 1-2 This application will be described in further detail. Embodiments of this application provide a wireless closed-loop control system for heating stations based on an edge computing controller.

[0029] like Figure 1 As shown, the wireless closed-loop control system for heating stations based on an edge computing controller includes an edge computing controller and multiple terminal devices. The edge computing controller is deployed within the heating station and runs a heating AI control algorithm. The edge computing controller integrates a wireless coordination module. Multiple terminal devices are deployed at various monitoring and control points within the heating station and connect to the wireless coordination module via a wireless communication protocol to form a wireless communication network.

[0030] Specifically, the edge computing controller is the core processing unit of the heating station, typically deployed in the control cabinet of the heat exchange station or boiler room. Unlike traditional solutions where the controller and wireless gateway are separate, this embodiment integrates the wireless coordination module directly into the edge computing controller, with the two interacting at high speed via an internal bus (such as SPI, SDIO, or PCIe). This integrated design eliminates the serial port or Ethernet conversion link between the controller and the external gateway in traditional architectures, significantly reducing data forwarding latency and providing a hardware foundation for achieving millisecond-level closed-loop control.

[0031] The wireless coordination module is used to establish and manage the wireless communication network and configure the priority of transmission resources. The transmission resources used for transmitting control commands have a higher priority than the transmission resources used for transmitting sensor data. The deterministic transmission of control commands is guaranteed through a priority scheduling mechanism.

[0032] Furthermore, during the initialization phase, the wireless coordination module scans the surrounding wireless environment, selects a channel with less interference to establish a network, and divides the communication cycle into different transmission resources according to a preset strategy. By setting the transmission resources of control commands to high priority, the wireless coordination module can ensure that control commands are always transmitted before sensor data when wireless channel resources are scarce, thereby guaranteeing the real-time performance of closed-loop control. This approach breaks the Carrier Sense Multiple Access / Collision Avoidance (CSMA / CA) mechanism commonly used in traditional wireless communications (such as Wi-Fi and Bluetooth). Under the CSMA / CA mechanism, all devices compete fairly for the channel. When sensor data and control commands are generated simultaneously, random backoff caused by channel contention is likely to occur, introducing unpredictable latency jitter. In this embodiment, by preset priority, control commands can be transmitted before sensor data, eliminating the uncertainty caused by channel contention from the underlying logic of communication scheduling, and ensuring that control commands always obtain the highest priority transmission channel.

[0033] The edge computing controller internally runs heating AI control algorithms, such as load forecasting algorithms, secondary network hydraulic balance algorithms, and pump-valve linkage optimization algorithms, which can generate control commands based on collected heating operation parameters. The wireless coordination module, as the master node of the wireless communication network, is responsible for the establishment, management, and resource scheduling of the wireless network. Terminal devices are deployed at various monitoring and control points in the heating station, including wireless sensing devices (such as temperature sensors, pressure sensors, and flow sensors) and wireless actuators (such as electric regulating valves and circulating pump frequency converters).

[0034] The edge computing controller collects sensor data from terminal devices through a wireless communication network, runs a heating AI regulation algorithm to generate control commands locally, and sends the control commands to the corresponding terminal devices through the wireless communication network based on priority. The terminal devices execute the control commands, forming a closed-loop control.

[0035] Specifically, the heating AI control algorithm integrated within the edge computing controller can run directly locally without needing to upload to a cloud server for feedback. Based on collected heating operation parameters and the heating AI control algorithm, the edge computing controller performs real-time calculations to generate control commands for wireless actuators, such as the calculated target valve opening or target circulation pump frequency. Because the control logic is completed within the edge computing controller, a localized closed-loop control architecture is constructed. This approach not only reduces communication link dependence but also significantly shortens the decision path, providing a foundation for achieving millisecond-level closed-loop control. Upon receiving the control command, the wireless actuator parses the command content and drives the actuator to perform actions. For example, after receiving a control command, the wireless electric regulating valve adjusts the valve core position, thereby changing the physical state of the heating system. After execution, the terminal device can selectively feed back the execution result to the edge computing controller, thus completing a full "perception-decision-execution" closed-loop control process.

[0036] This embodiment utilizes a priority scheduling mechanism to construct a deterministic control path on an unreliable wireless channel, enabling wireless technology to truly replace wired connections in real-time closed-loop control scenarios for heating. It achieves a localized closed loop from perception to execution, allowing the edge computing controller to complete decision-making logic without relying on a cloud server, thus ensuring the determinism and continuity of control.

[0037] In another embodiment of this application, the transmission resources include dedicated time slots within a communication cycle, and the wireless coordination module allocates dedicated time slots to the terminal device. The first time slot allocated to control commands has a higher priority than the second time slot allocated to sensor data.

[0038] In wireless communication, a communication cycle refers to a complete time scheduling unit defined by the system, with a typically fixed duration, such as 10 milliseconds or 20 milliseconds. A dedicated time slot is a pre-divided time segment of fixed duration within this cycle, each dedicated to a specific device or type of data transmission, and they do not interfere with each other. In this embodiment, the wireless coordination module, as the network's master node, is responsible for dividing the communication cycle into multiple dedicated time slots and allocating these time slots to various terminal devices in the network. Through this dedicated time slot allocation mechanism, each terminal device has its own exclusive transmission time window, thereby completely eliminating the random backoff delay caused by multiple devices competing for the channel in traditional wireless communication.

[0039] Furthermore, in this embodiment, the first time slot allocated to the control command is set at the beginning of the communication cycle. During closed-loop control, control commands often correspond to urgent adjustment actions, such as rapid adjustment of valve opening or immediate change of circulating pump frequency. These actions are extremely sensitive to latency. If the control command time slot is arranged at the end of the communication cycle, the command must wait for the sensor data time slot to complete transmission before it can be sent, introducing uncontrollable waiting latency. By setting the first time slot at the beginning of the communication cycle, the edge computing controller can send the control command immediately at the beginning of the nearest communication cycle once it generates it, without waiting for other data transmissions, thus minimizing the waiting latency of the control command. It should be understood that although this embodiment preferably sets the first time slot at the beginning, in other application scenarios with relatively relaxed latency requirements, the first time slot can also be set at other positions within the cycle, as long as its priority is higher than the second time slot.

[0040] Furthermore, the wireless coordination module is configured to dynamically adjust the number or frequency of the first time slot based on the execution cycle of the heating AI control algorithm. Different heating AI control algorithms running within the edge computing controller have different execution rhythms. For example, a load forecasting algorithm might execute every relatively long interval (e.g., 15 minutes), but requires issuing a large number of control commands in batches; while conventional PID control might execute at a higher frequency (e.g., every 100 milliseconds), issuing only a small number of commands each time. This embodiment achieves dynamic optimization of resource allocation by coupling the scheduling of wireless communication resources with the execution cycle of the heating AI control algorithm.

[0041] Specifically, when the edge computing controller detects that the heating AI control algorithm is about to enter a high-frequency adjustment phase or a batch command issuance phase, it can temporarily increase the number of first time slots or increase their frequency to ensure the rapid passage of a large number of control commands. When the heating AI control algorithm is in a low-frequency adjustment or standby phase, the number of first time slots can be reduced, allocating more resources to the uploading of sensor data. This optimizes the overall utilization of communication resources while ensuring real-time control. This dynamic adjustment mechanism avoids resource waste or congestion that may result from fixed time slot allocation, enabling the wireless communication network to flexibly adapt to the needs of different heating control scenarios.

[0042] In contrast, if the traditional CSMA / CA (Carrier Sense Multiple Access / Collision Avoidance) contention mechanism is used, all terminal devices need to listen to the channel status before sending data. If the channel is busy, it will randomly back off for a period of time before trying again. This mechanism performs adequately when the number of devices is small, but in a heating closed-loop control scenario, when dozens of sensors and actuators are working simultaneously, channel collisions are very likely to occur, causing the transmission delay of control commands to fluctuate drastically in the range of milliseconds or even hundreds of milliseconds, which cannot meet the stringent deterministic requirements of heating control. This embodiment, through the combination of dedicated time slots and priority scheduling, fundamentally avoids channel contention and ensures the predictability and stability of control command transmission delay.

[0043] In another embodiment of this application, the terminal device includes at least one of a wireless sensing device and a wireless execution device. The wireless sensing device is used to collect heating operation parameters and upload them to the edge computing controller via a wireless communication network. The wireless execution device is used to receive control commands and execute control actions via the wireless communication network. The wireless sensing device includes temperature sensors, pressure sensors, flow sensors, etc., for collecting operating parameters such as the supply and return water temperature, pressure, and flow rate of the heating system. The wireless execution device includes electric regulating valves, circulating pump frequency converters, etc., for executing control actions such as valve opening adjustment and pump frequency adjustment. Through the cooperation of the wireless sensing device and the wireless execution device, the entire link of the heating closed-loop control is made wireless.

[0044] Furthermore, the closed-loop control cycle is ≤100ms. The closed-loop control cycle refers to the complete time from sensor data acquisition and heating AI regulation algorithm calculation to the issuance and execution of control commands. This embodiment ensures that the closed-loop control cycle is ≤100ms through dedicated time slot scheduling, priority configuration, and localized processing by the edge computing controller, meeting the real-time requirements of heating process control, and achieving control accuracy equivalent to wired methods.

[0045] In another embodiment of this application, the wireless coordination module is a StarSpark coordinator module, and the terminal device is a StarSpark terminal device. The StarSpark terminal device communicates with the StarSpark coordinator module via the StarSpark wireless protocol to form a StarSpark wireless closed-loop control network. StarSpark technology features ultra-low latency, ultra-high reliability, strong anti-interference capabilities, and high concurrent access. This embodiment, by employing StarSpark technology and utilizing its dedicated time slot scheduling mechanism, allocates high-priority time slots to control commands, ensuring deterministic low-latency transmission of control commands, thereby realizing wireless closed-loop control of the heating station.

[0046] Furthermore, in heating sites, the network environment is often complex and variable, and the wide area network connection between the edge computing controller and the cloud platform may be interrupted due to operator failures, signal interference, or equipment maintenance. To ensure the continuity of control, this embodiment provides an autonomous solution for network outages.

[0047] Specifically, when the connection between the edge computing controller and the cloud platform is interrupted, the edge computing controller continues to independently execute closed-loop control via the wireless communication network. When the edge computing controller detects a disconnection in the link between its wide-area communication interface (such as a 4G / 5G module or Ethernet interface) and the cloud platform, or a heartbeat timeout, the edge computing controller does not stop working or wait for the cloud to recover; instead, it automatically switches to local autonomous mode. This functionality is based on the local closed-loop control architecture adopted in this application, meaning the heating AI control algorithm is deployed locally on the edge computing controller, not in the cloud. The wireless communication network formed by the edge computing controller and the terminal devices is an independent local area network (LAN), and its operation does not depend on the wide area network. Therefore, even if the communication link with the cloud is disconnected, the edge computing controller can still collect sensor data through the wireless communication network, run the heating AI control algorithm locally to generate control commands, and then send them to the terminal devices for execution, thereby maintaining the normal operation of the closed-loop control. This architecture design decouples the local control network from the wide area network, greatly improving the robustness and reliability of the system and avoiding heating service interruptions or safety incidents caused by network fluctuations.

[0048] Furthermore, during autonomous operation without network access, the edge computing controller caches operational data generated during the outage in a local storage module. This local storage module can be an SD card, eMMC memory, or other non-volatile storage media integrated within the edge computing controller. Operational data includes, but is not limited to, collected sensor data, generated control commands, feedback status from terminal devices, and system logs. The edge computing controller establishes a circular buffer or a timestamp-indexed database in the local storage module, continuously writing real-time generated operational data, thus solving the problem of data loss during network outages. Compared to solutions relying on cloud caching, local storage offers lower write latency and higher reliability, unaffected by fluctuations in wide area network bandwidth. It should be understood that the capacity of the local storage module is limited; therefore, the edge computing controller can configure data overwrite strategies, such as prioritizing the overwriting of the oldest data when storage space is insufficient, or tiered storage based on data importance.

[0049] Once the network is restored, the edge computing controller synchronizes its operational data to the cloud platform. Specifically, the edge computing controller monitors the connection status of the wide area network in real time, and initiates the data synchronization process as soon as it detects a connection restoration. The edge computing controller reads the data cached in its local storage module and uploads it to the cloud platform via the wide area communication interface. To ensure data integrity and consistency, the synchronization process can employ mechanisms such as breakpoint resumption and data verification. For example, the edge computing controller can record the timestamp of synchronized data and only upload new data after that timestamp. After receiving the data, the cloud platform archives and stores it for subsequent big data analysis, report generation, or remote monitoring. Through this mechanism, even after a network outage, the cloud platform can still obtain a complete system operation record, ensuring data continuity and providing comprehensive decision support for operations and maintenance personnel.

[0050] By clearly defining the types of terminal devices, limiting the closed-loop control cycle, applying star-flash technology, and establishing a network outage autonomy and data synchronization mechanism, the wireless closed-loop control system for heating stations has been fully optimized in terms of real-time performance, reliability, compatibility, and robustness, demonstrating the significant advantages of localized architecture in heating control scenarios.

[0051] In another embodiment of this application, the heating AI control algorithm includes at least one of the following: a load prediction algorithm, a secondary network hydraulic balance algorithm, a pump-valve linkage optimization algorithm, a fault diagnosis algorithm, and a room temperature feedback optimization algorithm. The load prediction algorithm is used to predict the heating load for a future period based on historical heating data, meteorological data, etc., thereby adjusting heating parameters in advance. The secondary network hydraulic balance algorithm is used to calculate the target opening degree of each electric regulating valve based on data such as the supply and return water temperature and flow rate of each building, achieving hydraulic balance in the secondary network. The pump-valve linkage optimization algorithm is used to optimize the coordination of circulating pump frequency and valve opening degree based on heating load and network characteristics, improving heating efficiency. The fault diagnosis algorithm is used to identify abnormal states of the heating system based on sensor data, providing early warnings of potential faults. The room temperature feedback optimization algorithm is used to dynamically adjust heating parameters based on user room temperature feedback data, improving user comfort. The heating AI control algorithm runs internally in the edge computing controller, which can intelligently generate control commands based on the collected heating operation parameters, realizing intelligent control of the heating process.

[0052] In addition, the edge computing controller also includes a wired interface module. This module is configured to connect to wired devices. The edge computing controller is also configured to fuse data collected by wired devices with data collected by terminal devices. In actual renovation projects of heating stations, there are often a large number of existing wired devices (such as traditional RS485 pressure transmitters and Modbus flow meters), and completely replacing them with wireless devices is costly and impractical. This embodiment achieves compatible access to existing wired devices by setting a wired interface module (such as an RS485 interface, Ethernet interface, CAN interface, etc.) in the edge computing controller. The edge computing controller runs a multi-protocol parsing engine that can automatically identify the communication protocols of wired devices and uniformly map the parsed wired data and wireless sensor data into its internal data model. When generating control commands, the edge computing controller can comprehensively refer to both wired and wireless data, thereby achieving hybrid control of wired and wireless devices. This fusion processing mechanism greatly lowers the threshold for retrofitting existing heating systems, enabling them to smoothly transition from wired control to fully wireless control.

[0053] This embodiment uses the wireless transformation of a heat exchange station in an old residential community as an example to describe in detail the application of the aforementioned wireless closed-loop control system for heating stations in a real heating scenario. It should be understood that this embodiment aims to verify the industrial applicability of the technical solution, rather than to limit the scope of protection.

[0054] In this application scenario, the edge computing controller is specifically manifested as a heating edge computing controller deployed within the control cabinet of the heat exchange station. The terminal devices include StarScan wireless temperature sensors and StarScan wireless electric regulating valves deployed at on-site monitoring and control points. The heating edge computing controller integrates a wireless coordination module that supports the StarScan wireless communication protocol for building a local wireless communication network. The heating edge computing controller runs heating AI control algorithms, such as secondary network hydraulic balance algorithms and load forecasting algorithms.

[0055] Specifically, during the network establishment phase, after the heating edge computing controller is powered on, its built-in wireless coordination module automatically scans the surrounding wireless environment, selects a channel with less interference to establish a wireless communication network, and configures the priority of transmission resources. After the StarSpark wireless temperature sensor and StarSpark wireless electric regulating valve are powered on, they automatically scan and request access to the network. The wireless coordination module verifies the identity of the requesting device, assigns a network address after successful verification, and completes the configuration-free self-organizing network process. This process completely eliminates the cumbersome wiring and manual configuration work of traditional wired solutions, and the renovation does not require heating shutdowns, greatly reducing the impact on residents' lives.

[0056] Once the system is in normal operation, the heating edge computing controller collects sensor data from the StarShock wireless temperature sensors via the wireless communication network. Because the wireless coordination module is configured to prioritize control commands over sensor data transmission, the sensor data acquisition process does not block the control command transmission channel. The heating edge computing controller runs heating AI control algorithms locally, such as the secondary network hydraulic balance algorithm. This algorithm calculates the target opening degree of each electric regulating valve in real time based on the collected return water temperature data for each building, thereby generating control commands locally.

[0057] Once a control command is generated, the heating edge computing controller immediately sends it out via the wireless coordination module. The wireless coordination module uses a high-priority dedicated time slot (i.e., the first time slot) to send the control command to the corresponding star-flash wireless electric control valve. Upon receiving the command, the star-flash wireless electric control valve immediately executes the valve opening adjustment action and feeds back the execution result to the heating edge computing controller, thus forming a complete closed-loop control.

[0058] Furthermore, in this application scenario, the heating edge computing controller is also connected to the existing wired pressure transmitters within the station via a wired interface module. The controller fuses the wired pressure data with the wireless temperature data, using both as inputs to the heating AI control algorithm, thus achieving mixed access and data fusion between new and old equipment.

[0059] Meanwhile, if the wide area network connection between the heating edge computing controller and the cloud platform is interrupted during operation, the controller will automatically switch to the offline autonomous mode to continue to maintain local closed-loop control and cache the operating data during the offline period in the local storage module. It will be automatically synchronized after the network is restored, ensuring the continuity of heating services.

[0060] Another embodiment of this application provides a wireless closed-loop control method for heating stations based on an edge computing controller.

[0061] like Figure 2 As shown, the edge computing controller-based wireless closed-loop control method for heating stations is applied to the aforementioned wireless closed-loop control system for heating stations. The system includes an edge computing controller and terminal devices. The edge computing controller is deployed within the heating station and runs a heating AI control algorithm. The edge computing controller integrates a wireless coordination module. Multiple terminal devices are deployed at various monitoring and control points within the heating station. The wireless closed-loop control method for heating stations includes the following steps: S1: The wireless coordination module inside the edge computing controller establishes a wireless communication network and configures the priority of transmission resources, wherein the priority of transmission resources used for transmitting control commands is higher than the priority of transmission resources used for transmitting sensor data.

[0062] Specifically, the edge computing controller is the core processing unit of the heating station, typically deployed in the control cabinet of the heat exchange station or boiler room. The wireless coordination module, as the master node of the wireless communication network, is responsible for the establishment, management, and resource scheduling of the wireless network. During the initialization phase, the wireless coordination module scans the surrounding wireless environment, selects a channel with less interference to establish the network, and divides the communication cycle into different transmission resources according to a preset strategy. By setting the transmission resources of control commands as high priority, the wireless coordination module ensures that control commands are always transmitted before sensor data when wireless channel resources are scarce, thereby guaranteeing the real-time performance of closed-loop control.

[0063] In another embodiment of this application, the transmission resources include dedicated time slots within a communication cycle, and the wireless coordination module allocates dedicated time slots to the terminal device. The first time slot allocated to control commands has a higher priority than the second time slot allocated to sensor data.

[0064] Furthermore, in this embodiment, the first time slot allocated to the control command is set at the beginning of the communication cycle. Once the edge computing controller generates the control command, it can send it immediately at the beginning of the nearest communication cycle without waiting for other data transmission, thereby minimizing the waiting latency of the control command.

[0065] S2: Terminal equipment accesses the wireless communication network.

[0066] Specifically, the terminal equipment includes at least one of wireless sensing devices and wireless actuators. The wireless sensing devices collect heating operation parameters and upload them to the edge computing controller via a wireless communication network. The wireless actuators receive control commands and execute control actions via the wireless communication network. The wireless sensing devices include temperature sensors, pressure sensors, flow sensors, etc., used to collect operating parameters such as the supply and return water temperature, pressure, and flow rate of the heating system. The wireless actuators include electric regulating valves, circulating pump frequency converters, etc., used to execute control actions such as valve opening adjustment and pump frequency adjustment. Through the cooperation of wireless sensing devices and wireless actuators, the entire link of the heating closed-loop control is made wireless.

[0067] In another embodiment of this application, the wireless coordination module is a StarSpark coordinator module, and the terminal device is a StarSpark terminal device. The StarSpark terminal device communicates with the StarSpark coordinator module through the StarSpark wireless protocol to form a StarSpark wireless closed-loop control network.

[0068] S3: The edge computing controller collects sensor data from the terminal device through a wireless communication network.

[0069] Wireless sensing devices collect operating parameters such as supply and return water temperature, pressure, and flow rate of the heating system and transmit them to the edge computing controller via a wireless communication network.

[0070] S4: The edge computing controller runs the heating AI regulation algorithm to generate control commands locally.

[0071] Specifically, the edge computing controller runs heating AI control algorithms, such as load prediction algorithms, secondary network hydraulic balance algorithms, and pump-valve linkage optimization algorithms, which can generate control commands based on the collected heating operation parameters.

[0072] The heating AI control algorithm includes at least one of the following: load prediction algorithm, secondary network hydraulic balance algorithm, pump-valve linkage optimization algorithm, fault diagnosis algorithm, and room temperature feedback optimization algorithm. The load prediction algorithm predicts the heating load for a future period based on historical heating data and meteorological data, thereby adjusting heating parameters in advance. The secondary network hydraulic balance algorithm calculates the target opening degree of each electric regulating valve based on data such as the supply and return water temperature and flow rate of each building, achieving hydraulic balance in the secondary network. The pump-valve linkage optimization algorithm optimizes the coordination of circulating pump frequency and valve opening based on heating load and network characteristics, improving heating efficiency. The fault diagnosis algorithm identifies abnormal states of the heating system based on sensor data, providing early warnings of potential faults. The room temperature feedback optimization algorithm dynamically adjusts heating parameters based on user room temperature feedback data, improving user comfort. The edge computing controller internally runs the heating AI control algorithm, which can intelligently generate control commands based on collected heating operation parameters, achieving intelligent control of the heating process.

[0073] S5: The edge computing controller sends control commands to the corresponding terminal devices via a wireless communication network based on priority.

[0074] Specifically, the heating AI control algorithm integrated within the edge computing controller can run directly locally without needing to upload to a cloud server for feedback. Based on collected heating operation parameters and the heating AI control algorithm, the edge computing controller performs real-time calculations to generate control commands for wireless actuators, such as calculated target valve openings or target circulation pump frequencies. Because the control logic is completed within the edge computing controller, a localized closed-loop control architecture is constructed. This approach not only reduces communication link dependence but also significantly shortens the decision-making path, providing a foundation for achieving millisecond-level closed-loop control.

[0075] Furthermore, the wireless coordination module is configured to dynamically adjust the number or frequency of the first time slot based on the execution cycle of the heating AI control algorithm. When the edge computing controller detects that the heating AI control algorithm is about to enter a high-frequency adjustment phase or a batch command issuance phase, it can temporarily increase the number of the first time slot or increase its frequency to ensure the rapid passage of a large number of control commands. When the heating AI control algorithm is in a low-frequency adjustment or standby phase, it can reduce the number of the first time slot and allocate more resources to the uploading of sensor data, thereby optimizing the overall utilization of communication resources while ensuring the real-time performance of control.

[0076] S6: The terminal device executes control commands to form a closed-loop control.

[0077] After receiving a control command, the wireless actuator parses the command content and drives the actuator to move. For example, after receiving a control command, the wireless electric regulating valve adjusts the valve core position, thereby changing the physical state of the heating system. After execution, the terminal device can selectively feed back the execution result to the edge computing controller, thus completing a complete "perception-decision-execution" closed-loop control process.

[0078] Furthermore, the closed-loop control cycle is ≤100ms. The closed-loop control cycle refers to the complete time from sensor data acquisition and heating AI regulation algorithm calculation to the issuance and execution of control commands. This embodiment ensures that the closed-loop control cycle is ≤100ms through dedicated time slot scheduling, priority configuration, and localized processing by the edge computing controller, meeting the real-time requirements of heating process control, and achieving control accuracy equivalent to wired methods.

[0079] When the connection between the edge computing controller and the cloud platform is interrupted, the edge computing controller continues to independently execute closed-loop control via the wireless communication network. When the edge computing controller detects a disconnection in the link between its wide-area communication interface (such as a 4G / 5G module or Ethernet interface) and the cloud platform, or a heartbeat timeout, the edge computing controller does not stop working or wait for the cloud to restore its function; instead, it automatically switches to local autonomous mode. The wireless communication network formed by the edge computing controller and the terminal devices is an independent local area network (LAN), and its operation does not depend on the wide area network. Therefore, even if the communication link with the cloud is lost, the edge computing controller can still collect sensor data through the wireless communication network, run the heating AI control algorithm locally to generate control commands, and then send them to the terminal devices for execution, thereby maintaining the normal operation of the closed-loop control.

[0080] Furthermore, during autonomous operation without network connectivity, the edge computing controller caches operational data from the outage period in its local storage module. This local storage module can be an SD card, eMMC memory, or other non-volatile storage media integrated within the edge computing controller. Operational data includes, but is not limited to, collected sensor data, generated control commands, feedback status from terminal devices, and system logs. Once the network is restored, the edge computing controller synchronizes the operational data to the cloud platform. Specifically, the edge computing controller monitors the connection status of the wide area network in real time, and initiates a data synchronization process once connection restoration is detected. The edge computing controller reads the cached data from the local storage module and uploads it to the cloud platform via the wide area communication interface.

[0081] In addition, the edge computing controller also includes a wired interface module. This module is configured to connect to wired devices. The edge computing controller is also configured to fuse data collected by wired devices with data collected by terminal devices. In actual renovation projects of heating stations, there are often a large number of existing wired devices (such as traditional RS485 pressure transmitters and Modbus flow meters), and completely replacing them with wireless devices is costly and impractical. This embodiment achieves compatible access to existing wired devices by setting a wired interface module (such as an RS485 interface, Ethernet interface, CAN interface, etc.) in the edge computing controller. The edge computing controller runs a multi-protocol parsing engine that can automatically identify the communication protocols of wired devices and uniformly map the parsed wired data and wireless sensor data into its internal data model. When generating control commands, the edge computing controller can comprehensively refer to both wired and wireless data, thereby achieving hybrid control of wired and wireless devices.

[0082] Figure 2 This is a flowchart illustrating the wireless closed-loop control method for heating stations based on an edge computing controller, as described in this application. It should be understood that, although... Figure 2 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows; unless explicitly stated otherwise, there is no strict order requirement for the execution of these steps, and they can be executed in other orders; and Figure 2 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0083] This specific embodiment is merely an explanation of the present invention and is not intended to limit the invention. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of the claims of the present invention.

Claims

1. A wireless closed-loop control system for a heating station based on an edge computing controller, characterized in that, include: An edge computing controller is deployed within the heating station, and the heating edge computing controller runs a heating AI control algorithm; A wireless coordination module, integrated within the edge computing controller, is used to establish and manage the wireless communication network and configure the priority of transmission resources, wherein the priority of transmission resources used for transmitting control commands is higher than the priority of transmission resources used for transmitting sensor data. Multiple terminal devices are deployed at various monitoring and control points in the heating station and are connected to the wireless coordination module through a wireless communication protocol to form a wireless communication network. The heating edge computing controller collects sensor data from the terminal device through the wireless communication network, runs the heating AI regulation algorithm to generate control commands locally, and sends the control commands to the corresponding terminal device through the wireless communication network based on the priority. The terminal device executes the control command to form a closed-loop control.

2. The wireless closed-loop control system for heating stations based on an edge computing controller according to claim 1, characterized in that, The transmission resources include dedicated time slots within the communication cycle; The wireless coordination module allocates the dedicated time slot to the terminal device; The first time slot allocated to the control command has a higher priority than the second time slot allocated to the sensing data.

3. The wireless closed-loop control system for heating stations based on an edge computing controller according to claim 2, characterized in that, The first time slot is located at the beginning of the communication cycle.

4. The wireless closed-loop control system for heating stations based on an edge computing controller according to claim 2, characterized in that, The wireless coordination module is also configured to dynamically adjust the number or frequency of the first time slot according to the execution cycle of the heating AI control algorithm.

5. The wireless closed-loop control system for heating stations based on an edge computing controller according to claim 1, characterized in that, The terminal device includes at least one of a wireless sensing device and a wireless execution device; The wireless sensing device is used to collect heating operation parameters and upload them to the edge computing controller via a wireless communication network; The wireless execution device is used to receive the control commands and execute control actions through a wireless communication network.

6. The wireless closed-loop control system for heating stations based on an edge computing controller according to claim 1, characterized in that, The closed-loop control period is ≤100ms.

7. The wireless closed-loop control system for heating stations based on an edge computing controller according to claim 1, characterized in that, The wireless coordination module is a StarSpark coordinator module, and the terminal device is a StarSpark terminal device. The StarSpark terminal device communicates with the StarSpark coordinator module through the StarSpark wireless protocol to form a StarSpark wireless closed-loop control network.

8. The wireless closed-loop control system for heating stations based on an edge computing controller according to claim 1, characterized in that, The edge computing controller is also configured to: When the connection with the cloud platform is interrupted, closed-loop control continues to be executed independently through the wireless communication network.

9. The wireless closed-loop control system for heating stations based on an edge computing controller according to claim 8, characterized in that, The edge computing controller is also configured to: The runtime data during the network outage is cached in the local storage module; Once the network is restored, the operational data will be synchronized to the cloud platform.

10. A wireless closed-loop control method for a heating station based on an edge computing controller, applied to the wireless closed-loop control system for a heating station based on an edge computing controller as described in any one of claims 1 to 9, wherein the system includes an edge computing controller and terminal equipment, characterized in that, include: The wireless coordination module inside the edge computing controller establishes a wireless communication network and configures the priority of transmission resources, wherein the priority of transmission resources used for transmitting control commands is higher than the priority of transmission resources used for transmitting sensor data. The terminal device is connected to the wireless communication network; The edge computing controller collects sensor data from the terminal device through the wireless communication network; The edge computing controller runs the heating AI regulation algorithm to generate control commands locally; Based on the priority, the edge computing controller sends the control command to the corresponding terminal device through the wireless communication network; The terminal device executes the control command to form a closed-loop control.

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