Heat exchange station controller with super channel

Abstract

The invention discloses application of a heat exchange station controller with a super channel, and belongs to the technical field of heating and ventilation control. The method specifically comprises the following steps: S1, obtaining historical big data of task execution of each channel, preprocessing the obtained historical big data, and constructing a channel dynamic adjustment model through the preprocessed historical big data; s2, collecting big data of task execution of a controller in real time, and allocating execution tasks to each channel through a dynamic adjustment model; and S3, performing corresponding operation on each channel according to the execution task allocated by the dynamic adjustment model, and tracking a task execution result of each channel. The current heat supply peak period or stable working condition is determined by collecting big data in real time, and the data and the working condition are input into the model, so that task allocation better meets actual requirements. For example, in the peak period, one channel can preferentially undertake the core adjusting task, the other channel can share the auxiliary function, balanced distribution is achieved under the stable working condition, and the overall operation efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of heating control, and particularly relates to a heat exchange station controller with super channels. BACKGROUND

[0002] In traditional heat exchange station control systems, I / O modules are usually designed as fixed types of input or output interfaces (such as digital input DI, digital output DO, analog input AI, or analog output AO). Although this fixed design is simple and direct, it has the following disadvantages in actual application:

[0003] 1. Low versatility: Each I / O port can only support a single type of signal (such as only supporting digital input), resulting in the need to replace different modules in different application scenarios, increasing system complexity and cost.

[0004] 2. Inconvenient configuration: For different types of signal access, manual jumper or module replacement is often required, increasing the difficulty of system installation and maintenance.

[0005] 3. Poor scalability: When system requirements change, existing I / O modules are difficult to adapt flexibly, and new modules may need to be redesigned or purchased.

[0006] 4. The adaptive signal recognition circuit and ADC / DAC module of the channel may wear out or fail due to frequent switching or high load, affecting the health status of the channel.

[0007] Therefore, it is necessary to develop a hardware design that can flexibly support multiple signal types to improve the versatility and engineering flexibility of I / O modules.

[0008] In a heat exchange station dedicated controller, SuperChannel refers to a hardware design that can flexibly support multiple signal types (DI / DO / AI / AO) through software configuration in a single physical I / O channel. This technology greatly improves the versatility and engineering flexibility of I / O modules. Here is a detailed analysis:

[0009] Core features of SuperChannel:

[0010] Hardware multiplexing: The same physical terminal (or channel) can be switched to digital input (DI), digital output (DO), analog input (AI, such as 4-20mA), or analog output (AO) through software.

[0011] Adaptive signal type: Automatically identify or manually configure the signal type (such as voltage / current / dry contact) accessed, without the need for jumper or module replacement.

[0012] Channel level independent configuration: each channel can be set to different functions (for example, channel 1 is AI temperature measurement, and channel 2 is DO control valve). SUMMARY

[0013] The purpose of the present application is to provide a heat exchange station controller with super channels; to solve the problem that the adaptive signal recognition circuit and the ADC / DAC module of the channel may be worn or malfunction due to frequent switching or high load, affecting the health status of the channel.

[0014] Technical scheme: To solve the above technical problems, according to one aspect of the present application, more specifically, a method for using a heat exchange station controller with super channels, the method specifically includes the following steps:

[0015] S1, obtain historical big data of each channel executing tasks, preprocess the obtained historical big data, and build a channel dynamic adjustment model through the preprocessed historical big data;

[0016] S2, real-time collection of big data of the controller executing tasks, and distribution of each channel executing tasks through the dynamic adjustment model;

[0017] S3, each channel performs corresponding operation according to the execution task allocated by the dynamic adjustment model, and tracks the execution task result of each channel;

[0018] S4, obtaining the big data of each channel executing tasks, and analyzing and processing the obtained big data to obtain the optimization index of the dynamic adjustment model;

[0019] S5, real-time determination of whether to optimize and upgrade the dynamic adjustment model through the optimization index of the dynamic adjustment model.

[0020] Further, the step S1 specifically includes the following steps:

[0021] S11, obtaining historical big data of each channel executing tasks, including task amount and each element wear index of each channel;

[0022] S12, preprocessing the historical big data, including filtering noise, missing value completion and repeated value deletion;

[0023] S13, MIN-MAX normalization processing of each historical big data to obtain standardized data of each historical big data;

[0024] S14, building a channel dynamic adjustment model, dividing each historical big data standardized data into a training set, a validation set and a test set to train the dynamic adjustment model.

[0025] Further, the step S2 specifically includes the following steps:

[0026] S21. Real-time acquisition of big data of tasks executed by the controller, and determination of whether the current period is a peak heating period or a stable operating condition based on the big data of the current tasks executed;

[0027] S22. Dynamically adjust the model by combining the big data collected by the controller with the currently determined operating condition input;

[0028] S23. The dynamic adjustment model allocates execution tasks to each channel based on the big data of the controller's execution tasks and the current determined operating conditions.

[0029] Furthermore, in step S23, when allocating execution tasks, the signal quality is analyzed by a self-test circuit. When the load of the current channel is detected to exceed a preset threshold, the DSP signal processing module automatically switches the high-load task to another channel, and the original channel is switched to a low-load standby state.

[0030] Furthermore, in step S3, the tracking of the task execution results of each channel includes the execution status of each task and the execution response time of each task.

[0031] Furthermore, in step S4, the optimization index of the dynamic adjustment model is obtained through comprehensive analysis based on the acquired loss indicators of each channel and each component, as well as the switching time for each dynamic adjustment.

[0032]

[0033] Where DY is the optimization exponent of the dynamically adjusted model, and ZY i ZS represents the initial lifespan of the i-th element in the first channel. i BY represents the current worn-out lifespan of the i-th element in the first channel. i BS represents the initial lifespan of the i-th element in the second channel. i The current worn-out lifespan of the i-th component in the second channel, where n is the number of components in the first or second channel, and QT l JT is the switching time for each dynamic adjustment, m is the base switching time, and m is the number of times dynamic adjustments are performed.

[0034] Furthermore, in step S5, when the optimization index of the dynamic adjustment model exceeds the threshold of the optimization index of the dynamic adjustment model, the dynamic adjustment model is optimized and upgraded; otherwise, the dynamic adjustment model continues to be used without optimization and upgrade.

[0035] According to another aspect of the present invention, a heat exchange station controller with a superchannel is provided. The heat exchange station controller is used to execute the above-described method of using a heat exchange station controller with a superchannel, comprising: a housing, a fixing plate fixed inside the housing, multiple output interfaces fixed in the middle of the top of the housing, multiple input interfaces fixed on the left side of the bottom of the housing, and two processing units provided in front of the fixing plate; and a heat dissipation unit provided inside the housing.

[0036] Furthermore, the processing unit includes a mounting plate. A CPU processor is fixed to the right side of the front surface of the mounting plate. An ADC / DAC module is fixed to the upper left side of the front surface of the mounting plate. A relay module is fixed to the lower left side of the front surface of the mounting plate, to the right of the relay module. A DSP signal processing module is fixed to the lower left side of the front surface of the mounting plate, to the right of the power module. A first protective plate is fixed around the ADC / DAC module on the front surface of the mounting plate, and a second protective plate is fixed around the front surface of the mounting plate.

[0037] Furthermore, the heat dissipation unit includes a cooling fan, a guide shroud fixed to the right side of the cooling fan, a filter fixed to the right side of the guide shroud, a delivery pipe fixed to the right side of the filter, a jet nozzle fixed to the right side of the delivery pipe, a diverter pipe fixed to the front of the middle section of the outer wall of the delivery pipe, a solenoid valve fixed to the right end of the diverter pipe, air supply pipes fixed to both the upper and lower ends of the solenoid valve, multiple through holes evenly distributed on the outer wall of the air supply pipe inside the second protective plate, a tension spring fixed to the right side of the rear surface of the fixed plate, a guide plate fixed to the rear end of the tension spring, a groove formed in the middle of the front surface of the guide plate, a conductive head fixed to the rear of the guide plate, a warning light embedded in the right side of the rear inner wall of the housing, a contact piece fixed to the front end of the warning light, an air outlet formed at the rear of the right side of the housing, and a dustproof mesh fixed inside the air outlet.

[0038] Beneficial effects:

[0039] 1. By installing a protective structure at the top of the mounting plate in the channel, the electrical components in the channel are isolated to avoid common-mode failure caused by electromagnetic interference. In addition, the installation area and exhaust area are set inside the housing to isolate heat and improve the overall stability of the device. When the heat dissipation unit is running, the temperature sensor monitors the temperature of the channel inside the housing and can change the blowing air flow rate by frequency conversion, which then blows the air onto the guide plate, so that the conductive head contacts the contact piece. The warning light is used to issue a warning to remind the staff of the malfunction.

[0040] 2. By acquiring historical big data of tasks executed through each channel, and after filtering out noise, filling in missing values, removing duplicate values, and performing MIN-MAX normalization, the accuracy and consistency of the data are ensured. The standardized data is divided into training, validation, and test sets to train the model, enabling the dynamic adjustment model to fit the actual operating scenario. This provides a scientific basis for subsequent task allocation and reduces unreasonable allocation problems caused by data errors.

[0041] 3. By collecting big data in real time, the system determines whether the current heating period is peak or stable, and inputs this data and operating condition into the model to make task allocation more aligned with actual needs. For example, during peak periods, one channel can be prioritized for core regulation tasks, while the other channel handles auxiliary functions. Under stable conditions, tasks are distributed evenly to improve overall operating efficiency. When the self-test circuit detects that the load on the current channel exceeds a preset threshold, the DSP signal processing module automatically switches the high-load task to another channel, and the original channel enters a low-load standby state. This mechanism avoids accelerated wear of critical components such as ADC / DAC modules and relays due to overload on each channel, reduces signal distortion and response delay, and ensures control accuracy.

[0042] 4. By tracking the execution status and response time of each task, operational issues in each channel can be identified promptly. For example, if a task's response time is too long, it can be determined that the corresponding channel may be overloaded or have minor component malfunctions, providing a basis for subsequent optimization. The tracking data of task execution results is the foundation for subsequent calculations of optimization indices. By recording actual execution conditions, it ensures that the model's optimization direction aligns with actual operational needs, avoiding optimization that deviates from the real-world scenario.

[0043] 5. By comprehensively analyzing the component loss indicators of the primary and backup channels and dynamically adjusting the switching time, an optimization index is calculated to achieve a quantitative evaluation of model performance. This index considers both the balance of component losses and the efficiency of task switching, providing a more comprehensive evaluation. The value of the optimization index directly reflects the current adaptability of the model, avoiding optimization lag or over-optimization caused by subjective judgment, and making model adjustment more scientific.

[0044] 6. By optimizing the index in real time, the system determines whether to upgrade the model, enabling it to dynamically adjust to changes in operating conditions and component aging, maintaining high-efficiency task allocation capabilities over the long term. For example, when component aging causes changes in loss patterns, the model can be upgraded to adapt to the new loss characteristics. Continuous model optimization ensures that the primary and backup channels are always under reasonable load conditions, reducing component failures caused by improper allocation, extending the overall lifespan of the controller, and lowering maintenance costs. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating the method.

[0046] Figure 2 This is a schematic diagram of the overall structure of a heat exchange station-specific controller with a super channel proposed in this invention;

[0047] Figure 3 This is a top view cross-sectional structural diagram of a heat exchange station dedicated controller with a super channel proposed in this invention;

[0048] Figure 4 This is a partial three-dimensional structural diagram of the processing unit located on the PCB board in a heat exchange station dedicated controller with super channel proposed in this invention;

[0049] Figure 5 yes Figure 4 A schematic diagram of the enlarged structure of region A in the middle;

[0050] Figure 6 This is a side view cross-sectional structural diagram of a heat exchange station dedicated controller with a super channel proposed in this invention;

[0051] Figure 7 yes Figure 6 Enlarged structural diagram of region B in the middle;

[0052] Figure 8 This is a partial planar structural diagram of the heat dissipation unit in a heat exchange station dedicated controller with a super channel proposed in this invention;

[0053] Figure 9 This is a schematic diagram of the gas transmission pipe structure in a heat exchange station dedicated controller with a super channel proposed in this invention.

[0054] In the diagram: 1. Housing; 2. Processing unit; 20. Mounting plate; 200. DSP signal processing module; 21. CPU processor; 22. ADC / DAC module; 23. Electrical module; 24. Power supply module; 25. First protective plate; 26. Second protective plate; 3. Fixing plate; 4. Output interface; 5. Input interface; 6. Heat dissipation unit; 61. Cooling fan; 62. Air guide; 63. Filter; 64. Delivery pipe; 65. Jet nozzle; 66. Diverter pipe; 67. Air delivery pipe; 68. Solenoid valve; 69. Through hole; 71. Tension spring; 72. Air guide plate; 73. Groove; 74. Conductive head; 75. Contact piece; 76. Warning light; 8. Air outlet; 81. Dustproof net Detailed Implementation

[0055] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] Example 1

[0057] Reference Figures 2-9A dedicated controller for heat exchange stations with a superchannel includes a housing 1, with a fixing plate 3 fixed inside the housing 1. Multiple output interfaces 4 are fixed in the middle of the top of the housing 1, and multiple input interfaces 5 are fixed on the left side of the bottom of the housing 1. The output interfaces 4 and input interfaces 5 are configurable via software. Each interface is equipped with a multi-functional conversion chip, flexibly supporting multiple signal types: DI, DO, AI, and AO. It supports DI, DO, AI, and AO functions through software configuration. During system initialization, each output interface 4 and input interface 5 is scanned to detect the currently connected signal type, and the corresponding mode is automatically configured based on the detection results. The controller also includes: a mounting plate 20 fixed between two fixing plates 3, dividing the interior of the housing 1 into an installation area and an exhaust area; symmetrically distributed processing units 2, mounted on the mounting plate 20; a first protective plate 25 and a second protective plate 26, isolating the two processing units 2 and forming two closed chambers; and a heat dissipation unit 6, located inside the housing 1, with one end penetrating the protective structure and extending to the exhaust area inside the housing 1. During operation, the heat dissipation unit generates a portion of the cooling airflow, which is blown towards the bottom of the mounting plate 20.

[0058] Traditional heat exchange station controllers often employ single-channel or simple redundant designs. High-frequency tasks are concentrated on a single channel for extended periods, leading to accelerated aging of critical components such as the ADC / DAC module 22 due to continuous high load operation. This can result in signal distortion or response delays, affecting control accuracy. Furthermore, the dense internal electrical components of the housing 1 cause heat to accumulate in localized areas. Existing heat dissipation structures, such as single-fan direct blowing, cannot effectively guide airflow to high-heat areas and lack targeted heat dissipation for top components, leading to overheating, derating, or malfunctions. In this invention, two processing units 2 are isolated into independent chambers by a first protective plate 25 and a second protective plate 26, forming a two-channel super-channel architecture. Each processing unit 2 has a built-in self-test circuit. When it detects that the current channel's component temperature rise exceeds the limit due to high load (e.g., increased output ripple of the power module 24) or signal abnormalities (e.g., conversion error of the ADC / DAC module 22 exceeding a threshold), the mounting plate 20 automatically transfers some tasks to the other channel via a relay module 23. For example, during peak heating periods, one channel can be responsible for real-time adjustment of the primary-side electric regulating valve, while the other channel can share the frequency control of the secondary-side circulating pump, achieving load balancing. The housing 1 is equipped with components such as a cooling fan 61, a guide shroud 62, a filter 63, and a distribution pipe 66. Cooling air enters the interior of the housing 1 from the outside. After being filtered by the filter 63, it is divided into two paths by the guide shroud 62: one path directly blows onto the bottom area of ​​the mounting plate 20, cooling the bottom area embedded in the mounting plate 20, and then exhausts the hot air through the dust filter 81; the other path is transported through the distribution pipe 66 to the cavity of the protective structure at the top of the mounting plate 20. With the delivery of the cool air, the heat in the cavity is carried away, thereby improving the heat dissipation effect and reducing the occurrence of channel failure. The device features a protective structure at the top of the mounting plate 20, which isolates the electrical components within the channel to prevent common-mode faults caused by electromagnetic interference. Additionally, an installation area and an exhaust area are provided inside the housing 1 to isolate heat and improve the overall stability of the device. When the heat dissipation unit is running, a temperature sensor monitors the temperature of the channel inside the housing 1, allowing for frequency conversion to change the airflow rate. This airflow is then directed to the guide plate 72, causing the conductive head 74 to contact the contact piece 75. A warning signal is then emitted using the warning light 76 to alert the operator to the occurrence of a malfunction.

[0059] Example 2

[0060] Reference Figure 4Similar to Embodiment 1, but further: the processing unit 2 includes a CPU processor 21 fixed on the mounting plate 20, an ADC / DAC module 22 fixed on the mounting plate 20 mounted on one side of the CPU processor 21, a relay module 23 located on one side of the ADC / DAC module 22 mounted on the mounting plate 20, a power module 24 located on one side of the relay module 23 mounted on the mounting plate 20, and a DSP signal processing module 200 located on one side of the power module 24 mounted on the mounting plate 20.

[0061] The symmetrically distributed processing units 2 are fixed inside the housing 1 by the mounting plate 20, forming a main and backup dual channel. The CPU processor 21 integrated in the DSP signal processing module 200 monitors the load status of the current channel in real time, such as the sampling rate of the ADC / DAC module 22 and the number of times the relay module 23 operates. It also analyzes the signal quality, such as noise amplitude and response delay, through a self-test circuit. When it is detected that the load of the current channel exceeds a preset threshold, such as the ADC sampling frequency continuously exceeding 80% of the maximum value, the DSP signal processing module 200 automatically switches the high-load task, such as high-frequency data acquisition or complex control algorithm, to another channel, and the original channel is put into a low-load standby state. For example, during peak heating periods, one channel is responsible for real-time temperature regulation, while the other channel handles historical data storage and communication. Under steady-state conditions, the two channels alternately perform core functions to balance component fatigue. The self-testing circuit uses digital filtering algorithms, such as Kalman filtering, to diagnose the current channel's health status in real time. If an anomaly is detected, such as an ADC signal-to-noise ratio below 60dB or a relay engagement time exceeding the limit, the DSP signal processing module 200 immediately triggers the relay module 23 to switch to the other channel. The switching time is less than 50ms, ensuring uninterrupted control commands. The two channels dynamically perform tasks as needed, reducing aging caused by long-term high-load operation of components. The self-testing circuit, combined with intelligent algorithms, improves fault diagnosis accuracy, and the switching logic is driven by real-time data, avoiding delays caused by human intervention and ensuring the continuity of heating control.

[0062] Example 3

[0063] Reference Figure 5 Similar to Embodiment 2, but further: the protective structure includes a second protective plate 26 fixed on the mounting plate 20, and a first protective plate 25 is fixedly installed inside the second protective plate 26 around the ADC / DAC module 22. The first protective plate 25 and the second protective plate 26 do not contact each other.

[0064] The first protective plate 25 and the second protective plate 26 are nested to form an independent chamber, which isolates the analog circuits of the main and backup channels, and the ADC / DAC module 22 is isolated separately. The inner walls of the first protective plate 25 and the second protective plate 26 are coated with a nickel-copper alloy shielding layer to absorb the electromagnetic pulses generated at the channel. The physical isolation structure suppresses electromagnetic interference in the local chamber and avoids common-mode failure.

[0065] Example 4

[0066] Reference Figure 6 , Figure 7 , Figure 8 , Figure 9 Similar to Embodiment 3, but with a further improvement: a cooling fan 61 is embedded in one side of the housing 1, a guide shroud 62 is fixedly installed on one side of the cooling fan 61, a delivery pipe 64 is fixedly connected to one end of the guide shroud 62, a filter 63 is installed at the connection between the delivery pipe 64 and the guide shroud 62, a jet nozzle 65 is fixedly installed to one end of the delivery pipe 64, one end of the jet nozzle 65 is fixed to the bottom surface of one of the fixing plates 3, and the jet nozzles 65 are arranged at an inclined angle. A diverter pipe 66 is connected to the delivery pipe 64, one end of the diverter pipe 66 passes through one of the fixing plates 3 and is fixedly connected to an air supply pipe 67, a solenoid valve 68 is installed between the air supply pipe 67 and the diverter pipe 66, one end of the air supply pipe 67 passes through the second protective plate 26 and the first protective plate 25, and the other end of the air supply pipe 67 passes through the other fixing plate 3 and extends to the exhaust area. The surface of the gas pipe 67 has through holes 69 located in the first protective plate 25 and the second protective plate 26. The through holes 69 are evenly distributed and set at a 45° angle. A tension spring 71 is fixedly connected to the bottom of another fixed plate 3. A guide plate 72 is fixedly connected to the end of the tension spring 71. The surface of the guide plate 72 has a groove 73. The top surface of the guide plate 72 is set at an angle. A conductive head 74 is fixedly installed at the bottom of the guide plate 72. A warning light 76 is embedded in the bottom surface of the housing 1. A contact piece 75 is installed on one side of the warning light 76. The contact piece 75 is compatible with the conductive head 74. An air outlet 8 is opened on one side of the housing 1. A dustproof net 81 is installed inside the air outlet 8. The inclined surface of the guide plate 72 corresponds to the air outlet 8. Mounting holes are opened on both sides of the housing 1. The mounting holes are elliptical in shape.

[0067] A cooling fan 61 is embedded in one side of the housing 1, and a guide shroud 62 is fixed on its outer side to guide external cold air into the housing after purification by a filter 63. The filter 63 is removable and replaceable to prevent dust from entering. The purified airflow is divided into two paths: one path blows through the delivery pipe 64 to the bottom area of ​​the high-heat-generating components mounted on the mounting plate 20. The jet nozzle 65 is installed at an angle to better blow air onto the bottom surface of the mounting plate 20. When the airflow flows at high speed in the delivery pipe 64, it generates negative pressure, forming a siphon effect through the air supply pipe 67 and the through hole 69, actively removing heat from the electrical components on the mounting plate 20, thereby achieving a better heat dissipation effect. The cooling fan 61 is equipped with a variable frequency motor. When the temperature sensor does not detect an excessively high temperature, it sends a signal to the control system to activate the frequency converter of the cooling fan 61. This changes the airflow rate in the delivery pipe 64, improving the cooling effect. The airflow is then ejected through the air delivery pipe 67 and blown onto the guide plate 72. The guide plate 72, through the force of the strong airflow after frequency conversion, causes the conductive head 74 to move downward and contact the contact piece 75. This causes the warning light 76 to activate and generate a warning signal for easy viewing by staff. The conductive head 74 is connected to the contact piece 75 by the battery end, and the contact piece 75 is electrically connected to the warning light 76. The groove 73 facilitates the diversion of heat from the air delivery pipe 67 to the air outlet 8.

[0068] Example 5

[0069] The first step is to acquire historical big data of tasks executed by each channel, preprocess the acquired historical big data, and construct a dynamic adjustment model for each channel based on the preprocessed historical big data. This specifically includes the following steps:

[0070] 1. Obtain historical big data of tasks executed by each channel, including: the amount of tasks executed and the loss indicators of each component in each channel;

[0071] 2. Preprocess historical big data, including filtering noise, filling in missing values, and deleting duplicate values;

[0072] 3. Perform MIN-MAX normalization on each historical big data to obtain standardized data for each historical big data;

[0073] 4. Construct dynamic adjustment models for each channel, and divide the standardized historical big data into training sets, validation sets, and test sets to train the dynamic adjustment models.

[0074] By acquiring historical big data of tasks executed through each channel, and performing noise filtering, missing value completion, duplicate value removal, and MIN-MAX normalization, the accuracy and consistency of the data are ensured. The standardized data is divided into training, validation, and test sets to train the model, enabling the dynamic adjustment model to fit the actual operating scenario. This provides a scientific basis for subsequent task allocation and reduces unreasonable allocation problems caused by data errors.

[0075] The second step involves real-time acquisition of big data on the tasks executed by the controller, and dynamic adjustment of the model to allocate tasks to each channel. This includes the following steps:

[0076] 1. Collect big data on the tasks executed by the controller in real time, and determine whether the current period is a peak heating period or a stable operating condition based on the big data of the current tasks executed;

[0077] 2. Dynamically adjust the model by combining the big data collected by the controller with the currently determined operating condition input;

[0078] 3. The dynamic adjustment model allocates execution tasks to each channel based on the big data of the controller's execution tasks and the current determined operating conditions.

[0079] By collecting big data in real time to determine whether the current heating period is peak or stable, and inputting the data and operating conditions into the model, task allocation can be more closely aligned with actual needs. For example, during peak periods, one channel can be prioritized for core regulation tasks, while the other channel handles auxiliary functions. Under stable operating conditions, tasks are distributed evenly to improve overall operational efficiency. When the self-test circuit detects that the load on the current channel exceeds a preset threshold, the DSP signal processing module automatically switches the high-load task to another channel, and the original channel enters a low-load standby state. This mechanism can prevent accelerated wear of critical components such as ADC / DAC modules and relays due to overload of each channel, reduce signal distortion and response delay, and ensure control accuracy.

[0080] The third step involves each channel performing corresponding operations based on the tasks assigned by the dynamic adjustment model, and tracking the results of these tasks. Tracking the results includes the execution status and response time of each task. By tracking the execution status and response time of each task, operational problems in each channel can be identified promptly. For example, if a task's response time is too long, it can be determined that the corresponding channel may be overloaded or have a minor component failure, providing a basis for subsequent optimization. The tracking data of task execution results forms the basis for subsequent calculations of optimization indices. By recording actual execution conditions, it ensures that the model's optimization direction aligns with actual operational needs, avoiding optimization that deviates from the real-world scenario.

[0081] The fourth step involves acquiring big data on the tasks executed by each channel, and then analyzing and processing this data to obtain the optimization index of the dynamic adjustment model. The optimization index of the dynamic adjustment model is obtained through comprehensive analysis of the loss indicators of each component in each channel and the switching time for each dynamic adjustment.

[0082]

[0083] Where DY is the optimization exponent of the dynamically adjusted model, and ZY iZS represents the initial lifespan of the i-th element in the first channel. i BY represents the current worn-out lifespan of the i-th element in the first channel. i BS represents the initial lifespan of the i-th element in the second channel. i The current worn-out lifespan of the i-th component in the second channel, where n is the number of components in the first or second channel, and QT l JT is the base switching time for each dynamic adjustment, and m is the number of times dynamic adjustments are performed. Quantify the loss difference between the two to avoid excessive loss on a single channel and achieve load balancing. Mapping the cumulative value of the loss difference to the [0, 1] interval avoids the influence of extreme values ​​and makes the results more stable. This indicates that switching time directly affects system response speed. By comparing the actual switching time with the baseline value, the efficiency of the dynamic adjustment model is evaluated to ensure timely task switching. The smaller the ratio, the closer the switching speed is to the ideal state, and the higher the efficiency.

[0084] By comprehensively analyzing the component loss indicators of the primary and backup channels and dynamically adjusting the switching time, an optimization index is calculated to achieve a quantitative evaluation of model performance. This index considers both the balance of component loss and the efficiency of task switching, providing a more comprehensive evaluation. The value of the optimization index directly reflects the current adaptability of the model, avoiding optimization lag or over-optimization caused by subjective judgment, and making model adjustment more scientific.

[0085] The fifth step involves determining in real-time whether to optimize and upgrade the dynamically adjusted model by using its optimization index. When the optimization index exceeds a threshold, the model is upgraded; otherwise, it continues to be used without upgrade. This real-time determination of the upgrade based on the optimization index allows the model to dynamically adjust to changes in operating conditions and component aging, maintaining high-efficiency task allocation capabilities over the long term. For example, when component aging causes changes in loss patterns, the model can be upgraded to adapt to the new loss characteristics. Continuous model optimization ensures that the primary and backup channels are always under reasonable load conditions, reducing component failures caused by improper allocation, extending the overall lifespan of the controller, and lowering maintenance costs.

[0086] Example 6

[0087] When calculating the optimization index of the dynamically adjusted model, when If m = 3 and JT = 50, then:

[0088]

[0089] If 0.5434 > the threshold of the optimization index of the dynamic adjustment model, then the dynamic adjustment model will be optimized and upgraded.

[0090] If 0.5434 ≤ the threshold of the optimization index of the dynamic adjustment model, then the dynamic adjustment model will continue to be used without optimization or upgrade.

[0091] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method of using a heat exchange station controller with a super channel, characterized in that: The method specifically includes the following steps: S1. Obtain historical big data of tasks executed by each channel, preprocess the obtained historical big data, and construct a channel dynamic adjustment model based on the preprocessed historical big data. S2. Real-time acquisition of big data of tasks executed by the controller, and allocation of tasks to each channel through dynamic adjustment model; S3. Each channel performs corresponding operations according to the execution tasks assigned by the dynamic adjustment model, and tracks the results of each channel's execution tasks; S4. Obtain big data of tasks executed by each channel, and analyze and process the big data to obtain the optimization index of the dynamic adjustment model. S5. Determine in real time whether to optimize or upgrade the dynamic adjustment model by adjusting the optimization index of the dynamic adjustment model.

2. The method of using a heat exchange station controller with a super channel according to claim 1, characterized in that: Step S1 specifically includes the following steps: S11. Obtain historical big data of tasks executed by each channel, including: the amount of tasks executed and the loss indicators of each component in each channel; S12. Preprocess historical big data, including filtering noise, filling in missing values, and deleting duplicate values; S13. Perform MIN-MAX normalization on each historical big data to obtain standardized data for each historical big data. S14. Construct a channel dynamic adjustment model, and divide the standardized historical big data into training set, validation set and test set to train the dynamic adjustment model.

3. The method of using a heat exchange station controller with a super channel according to claim 1, characterized in that: Step S2 specifically includes the following steps: S21. Real-time acquisition of big data of tasks executed by the controller, and determination of whether the current period is a peak heating period or a stable operating condition based on the big data of the current tasks executed; S22. Dynamically adjust the model by combining the big data collected by the controller with the currently determined operating condition input; S23. The dynamic adjustment model allocates execution tasks to each channel based on the big data of the controller's execution tasks and the current determined operating conditions.

4. The method of using a heat exchange station controller with a super channel according to claim 3, characterized in that: In step S23, when allocating execution tasks, the signal quality is analyzed by the self-test circuit. When the load of the current channel is detected to exceed the preset threshold, the DSP signal processing module automatically switches the high-load task to another channel, and the original channel is switched to a low-load standby state.

5. The method of using a heat exchange station controller with a super channel according to claim 1, characterized in that: In step S3, the results of each channel's task execution are tracked, including the execution status of each task and the response time of each task.

6. The method of using a heat exchange station controller with a super channel according to claim 1, characterized in that: In step S4, the optimization index of the dynamic adjustment model is obtained through comprehensive analysis based on the acquired loss indicators of each channel and each component, as well as the switching time for each dynamic adjustment. Where DY is the optimization exponent of the dynamically adjusted model, and ZY i ZS represents the initial lifespan of the i-th element in the first channel. i BY represents the current worn-out lifespan of the i-th element in the first channel. i BS represents the initial lifespan of the i-th element in the second channel. i The current worn-out lifespan of the i-th component in the second channel, where n is the number of components in the first or second channel, and QT l JT is the switching time for each dynamic adjustment, m is the base switching time, and m is the number of times dynamic adjustments are performed.

7. The method of using a heat exchange station controller with a super channel according to claim 1, characterized in that: In step S5, if the optimization index of the dynamic adjustment model exceeds the threshold of the dynamic adjustment model, the dynamic adjustment model is optimized and upgraded; otherwise, the dynamic adjustment model is used without optimization and upgrade.

8. A heat exchange station controller with a super channel, characterized in that, The heat exchange station controller is used to implement the method of using a heat exchange station controller with a super channel as described in any one of claims 1-7, comprising: a housing (1), a fixing plate (3) fixed inside the housing (1), a plurality of output interfaces (4) fixed in the middle of the top of the housing (1), a plurality of input interfaces (5) fixed on the left side of the bottom of the housing (1), and two processing units (2) provided in front of the fixing plate (3); and a heat dissipation unit (6) provided inside the housing (1).

9. A heat exchange station controller with a super channel according to claim 8, characterized in that: The processing unit (2) includes a mounting plate (20). A CPU processor (21) is fixed on the right side of the front surface of the mounting plate (20). An ADC / DAC module (22) is fixed on the upper left side of the front surface of the mounting plate (20). A relay module (23) is fixed on the lower left side of the front surface of the mounting plate (20). A power module (24) is fixed on the lower left side of the front surface of the mounting plate (20) to the right of the relay module (23). A DSP signal processing module (200) is fixed on the lower left side of the front surface of the mounting plate (20) to the right of the power module (24). A first protective plate (25) is fixed around the front surface of the mounting plate (20) around the ADC / DAC module (22). A second protective plate (26) is fixed around the front surface of the mounting plate (20).

10. A heat exchange station controller with a super channel according to claim 9, characterized in that: The heat dissipation unit (6) includes a cooling fan (61). A guide shroud (62) is fixed to the right side of the cooling fan (61). A filter (63) is fixed to the right side of the guide shroud (62). A delivery pipe (64) is fixed to the right side of the filter (63). An air nozzle (65) is fixed to the right side of the delivery pipe (64). A diversion pipe (66) is fixed to the front of the middle part of the outer wall of the delivery pipe (64). A solenoid valve (68) is fixed to the right end of the diversion pipe (66). Air supply pipes (67) are fixed to both the upper and lower ends of the solenoid valve (68). The outer wall of the air supply pipe (67) is located on the second protective plate. (26) has multiple through holes (69) evenly distributed inside. A tension spring (71) is fixed to the right side of the rear surface of the fixing plate (3). A guide plate (72) is fixed to the rear end of the tension spring (71). A groove (73) is opened in the middle of the front surface of the guide plate (72). A conductive head (74) is fixed behind the guide plate (72). A warning light (76) is embedded in the right side of the rear inner wall of the housing (1). A contact piece (75) is fixed to the front end of the warning light (76). An air outlet (8) is opened at the rear of the right side of the housing (1). A dustproof net (81) is fixed inside the air outlet (8).

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