Cold radiation panel refrigerating system with breeze fan

Abstract

The invention relates to the technical field of refrigerating systems, in particular to a cold radiation panel refrigerating system with a breeze fan, which comprises a temperature and humidity gradient extraction module, a condensation trend identification module, an air volume reference generation module, a load analysis module and a linkage control module. According to the method, by obtaining the temperature and humidity space change rate between the discrete measuring points on the surface of the cold radiation panel, the damp air area tending to be saturated can be insighted and accurately positioned in advance, compared with traditional single-point temperature monitoring, conversion from passive response to active prediction is achieved, intervention is conducted before the condensation phenomenon occurs, and the accuracy of the condensation phenomenon is improved. The temperature and humidity gradient changes are weighted and quantified to form a comprehensive risk intensity index, so that the assessment of the condensation risk is not simple threshold triggering any more, but can distinguish the degree of the risk, and meanwhile, the dynamic change of the activity frequency of indoor personnel and the gas concentration is monitored, so that the risk assessment accuracy is improved. And the refrigeration output can be matched with the real damp and hot load generated by personnel activities in real time.

Description

Technical Field

[0001] This invention relates to the field of refrigeration system technology, and in particular to a cold radiant panel refrigeration system with a micro-fan. Background Technology

[0002] The field of refrigeration system technology involves devices and methods for regulating indoor air temperature through heat transfer. Its core aspects include the generation, transport and release of cooling capacity, as well as the coupling and control of air flow and indoor thermal environment.

[0003] Among them, the cold radiant panel cooling system refers to a device that uses cold water as a circulating medium and transfers cold energy to the indoor environment through radiation and partial convection by radiant cold radiant panels arranged on the indoor ceiling or walls, thereby eliminating indoor cooling load and ensuring balanced heat distribution.

[0004] Existing radiant panel cooling systems only focus on maintaining a balanced overall indoor temperature during operation, lacking a refined perception of the microenvironment on the panel surface. They cannot identify uneven surface temperature and humidity distribution caused by poor airflow or localized heat sources, resulting in a lack of predictive ability for condensation risks. Often, they can only mitigate condensation after it has already occurred or by setting overly conservative operating parameters. The former can lead to a damp indoor environment and even property damage, while the latter sacrifices cooling efficiency and increases unnecessary energy consumption. Furthermore, the cooling regulation of traditional systems is usually global, unable to provide differentiated responses based on the actual spatial distribution of indoor load. For example, when there is only activity in a localized area, the system will still provide indiscriminate cooling to the entire space, resulting in significant energy waste. Conversely, when people suddenly gather, the system cannot quickly increase the cooling output of the corresponding area, leading to a decrease in local comfort. This singular and lagging control mode makes it difficult to adapt to the complexity and dynamism of human activity and load changes in modern building spaces, limiting its performance in terms of energy saving and comfort. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides a radiant cooling panel system with a micro-fan. The technical solution is as follows: On the one hand, a radiant cooling panel system with a micro-fan is provided, the system comprising: The temperature and humidity gradient extraction module acquires temperature and humidity data from multiple measuring points on the surface of the cold radiation panel within a specified period, calculates the temperature and humidity gradients of adjacent measuring points, and forms a dataset of temperature and humidity gradients on the surface of the cold radiation panel. The condensation trend recognition module acquires indoor air state parameters within the same period, identifies the condensation trend status of each area on the surface of the cold radiation panel, and marks the area where the condensation trend is initially judged. The air volume reference generation module extracts the initial condensation trend judgment area on the surface of each cold radiation panel, divides the risk level of all initial judgment areas by combining the temperature and humidity gradient dataset of the cold radiation panel surface, establishes a correspondence with the adjustable air volume level of the micro fan, and outputs an air volume adjustment reference list. The load analysis module obtains the frequency of indoor human activity and the characteristics of gas concentration changes, and establishes a cold radiation panel load adjustment dataset by referring to the corresponding relationship in the air volume adjustment reference list. The linkage control module, based on the load adjustment dataset of the cold radiant panel and the air volume adjustment reference list, performs linkage to adjust the air volume output of the micro fan and the refrigerant flow range of the cold radiant panel, thereby obtaining the refrigeration control result.

[0006] As a further aspect of the present invention, the surface temperature and humidity gradient dataset of the cold radiation panel includes the distance between measuring points, temperature gradient value, humidity gradient value, and spatial distribution direction; the condensation trend preliminary judgment area specifically includes the area number, spatial boundary position, and trend identification type; the air volume adjustment reference list includes the area risk level, air volume level correspondence, and air volume output level; the cold radiation panel load adjustment dataset includes personnel activity frequency parameters, gas concentration change, and heat load classification label; and the refrigeration control result specifically refers to the refrigerant flow range, fan air volume output level, and cold radiation panel cooling capacity range.

[0007] As a further aspect of the present invention, the temperature and humidity gradient extraction module includes: The data space construction submodule acquires temperature and humidity data from multiple measuring points on the surface of the cold radiation panel within a specified period using temperature and humidity sensors. It establishes a two-dimensional coordinate space according to the location of the temperature and humidity sensors and matches the real-time temperature and humidity values ​​of each temperature and humidity sensor to the corresponding positions in the coordinate space to generate the spatiotemporal temperature and humidity measurement results of the cold radiation panel surface. The gradient difference calculation submodule aligns the temperature and humidity values ​​at the same time point for all adjacent measuring points in the two-dimensional coordinate space of the spatiotemporal temperature and humidity measurement results of the cold radiation panel surface, and quantifies the temperature gradient and humidity gradient in different directions between adjacent measuring points to obtain discrete temperature and humidity gradient values. The gradient data integration submodule classifies the discrete temperature and humidity gradient values ​​in all directions into a unified dataset of temperature and humidity gradients on the surface of the cold radiation panel.

[0008] As a further aspect of the present invention, the condensation trend identification module includes: The indoor parameter acquisition submodule acquires the average indoor air temperature and average air humidity within the same period and establishes a set of indoor air state parameters. The trend state comparison submodule compares the temperature gradient of each area of ​​the cold radiation panel with the preset temperature gradient enhancement benchmark value based on the indoor air state parameter set of the indoor air temperature and average air humidity. It also determines whether the surface temperature of the cold radiation panel is lower than the indoor air temperature, filters out areas that meet both conditions, and obtains a set of potential condensation areas. The trend area marking submodule, based on the set of potential condensation areas, traverses the position information of all divided areas on the surface of the cold radiation panel, assigns a condensation trend status mark to areas belonging to the set of potential condensation areas, and assigns a normal status mark to other areas, thereby generating a preliminary condensation trend judgment area.

[0009] As a further aspect of the present invention, the air volume reference generation module includes: The regional feature extraction submodule parses the coordinate position and area boundary information of the initial condensation trend judgment area on the surface of each cold radiation panel, and calls the temperature and humidity gradient dataset of the surface of the cold radiation panel to match and obtain the temperature and humidity gradient corresponding to each initial condensation trend judgment area, and establishes the gradient intensity index of the initial judgment area. The risk level classification submodule compares the gradient intensity index of the preliminary judgment area with the preset risk level classification threshold, assigns a corresponding risk level to each preliminary judgment area, and obtains the regional risk level classification result. The airflow level mapping submodule matches each risk level with the preset adjustable airflow level of the micro fan based on the regional risk level classification results, assigns a corresponding airflow output level to the risk level area and establishes a corresponding relationship, and generates an airflow adjustment reference list.

[0010] As a further aspect of the present invention, the process of comparing the gradient intensity index of the preliminary judgment area with a preset risk level classification threshold and assigning a corresponding risk level to each preliminary judgment area is specifically as follows: Preset a temperature gradient weighting coefficient and a humidity gradient weighting coefficient for the temperature gradient and humidity gradient in the temperature and humidity gradient, respectively; The temperature gradient of each of the condensation trend preliminary judgment areas is multiplied by the temperature gradient weighting coefficient to obtain the weighted temperature gradient value, and the humidity gradient is multiplied by the humidity gradient weighting coefficient to obtain the weighted humidity gradient value. The weighted temperature gradient value and the weighted humidity gradient value are summed to generate the gradient intensity index of the preliminary judgment area; Multiple risk level division intervals are set, and each risk level division interval has independent upper and lower limit thresholds and corresponds to a unique risk level; The generated gradient intensity index of the preliminary judgment area is numerically compared with the upper and lower thresholds of the multiple risk level division intervals. Based on the risk level division interval where the initial judgment area gradient intensity index is located, the risk level corresponding to the risk level division interval is assigned to the initial judgment area of ​​the condensation trend.

[0011] As a further aspect of the present invention, the load analysis module includes: The indoor status monitoring submodule uses infrared sensors to monitor the changes in the frequency of movement of people in the room in real time within the same period, and uses carbon dioxide sensors to monitor the rate of change of indoor gas concentration in real time, and establishes a feature set of indoor people and gas changes. The load characteristic matching submodule calls the movement frequency change and gas concentration change rate of the indoor personnel and the personnel in the gas change characteristic set, and matches them with the corresponding relationship in the air volume adjustment reference list to obtain the regional load characteristic matching result. The load dataset creation submodule formats and processes the regional load characteristic matching results, and generates a cold radiant panel load regulation dataset by combining the operating parameters of the cold radiant panel with the regional location information.

[0012] As a further aspect of the present invention, the process of using an infrared sensor to monitor changes in the movement frequency of people indoors in real time specifically includes: The continuous monitoring time is divided into multiple equal monitoring cycles; The infrared sensor is used to count the number of events triggered by human movement within each monitoring cycle, and the number of events is defined as the movement frequency of the current cycle; Obtain the historical movement frequency of the previous monitoring period; The difference between the current movement frequency and the historical movement frequency is calculated to obtain the change in the movement frequency of the personnel.

[0013] As a further aspect of the present invention, the linkage control module includes: The linkage target analysis submodule, based on the cold radiation panel load adjustment dataset and the air volume adjustment reference list, confirms the target air volume output level and refrigerant flow range for each area in the room, and establishes a linkage control target instruction set; The fan air volume linkage execution submodule calls the target air volume output level corresponding to each area in the room in the linkage control target instruction set, generates an air volume adjustment command and transmits it to the micro fan, drives the fan to switch to the target air volume output level, obtains the fan's operating status parameters after switching, and obtains the fan's real-time operating status information. The panel refrigerant linkage execution submodule adjusts the cooling boundary of the cold radiation panel according to the refrigerant flow range adjustment requirements in the linkage control target instruction set and with reference to the real-time operating status information of the fan, and generates refrigeration control results.

[0014] As a further aspect of the present invention, the process of adjusting the cooling boundary of the cold radiation panel in conjunction with the method is specifically as follows: Multiple refrigerant parameter adjustment combinations are preset for the refrigerant flow range adjustment requirements, and each refrigerant parameter adjustment combination includes a refrigerant inlet water temperature target value and a refrigerant flow rate target value. Based on the refrigerant flow range adjustment requirements in the linkage control target instruction set, match and select the corresponding refrigerant parameter adjustment combination; Adjust the inlet water temperature of the refrigerant supply system according to the target value of the refrigerant inlet water temperature in the selected refrigerant parameter adjustment combination; The flow rate of the refrigerant supply system is adjusted according to the target value of the refrigerant flow rate in the selected refrigerant parameter adjustment combination.

[0015] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By acquiring the spatial variation rate of temperature and humidity between discrete measuring points on the surface of the cold radiation panel, it is possible to identify and accurately locate areas of saturated humid air in advance. Compared with traditional single-point temperature monitoring, this achieves a shift from passive response to active prediction, intervening before condensation occurs. Furthermore, by weighting and quantifying the changes in temperature and humidity gradients, a comprehensive risk intensity index is formed. This makes the assessment of condensation risk no longer a simple threshold trigger, but can distinguish the degree of risk, providing a basis for subsequent graded and precise control. At the same time, monitoring the dynamic changes in indoor personnel activity frequency and gas concentration allows the cooling output to match the actual humid and heat load generated by personnel activities in real time, avoiding ineffective cooling in unoccupied areas and insufficient cooling supply in densely populated areas. Finally, based on the quantified risk level and real-time load status of specific areas, corresponding intensity of local micro-wind disturbance and differentiated refrigerant supply adjustment are executed simultaneously, achieving on-demand allocation of cooling capacity and air volume. This effectively suppresses the occurrence of local condensation, ensures thermal comfort in different areas, and significantly reduces overall operating energy consumption. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of a cold radiant panel cooling system with a micro fan provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the system framework of the present invention; Figure 3 This is a flowchart of the temperature and humidity gradient extraction module in this invention; Figure 4 This is a flowchart of the condensation trend identification module in this invention; Figure 5 This is a flowchart of the air volume reference generation module in this invention; Figure 6 This is a flowchart of the load analysis module in this invention; Figure 7 This is a flowchart of the linkage control module in this invention. Detailed Implementation

[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0019] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0020] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0021] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0022] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0023] This invention provides a radiant panel cooling system with a micro-fan, such as... Figure 1 The diagram shown illustrates a radiant cooling panel system with a micro-fan. The system includes: The temperature and humidity gradient extraction module acquires temperature and humidity data from multiple measuring points on the surface of the cold radiation panel within a specified period, calculates the temperature and humidity gradients of adjacent measuring points, and forms a dataset of temperature and humidity gradients on the surface of the cold radiation panel. The condensation trend recognition module acquires indoor air state parameters within the same period, identifies the condensation trend status of each area on the surface of the cold radiation panel, and marks the area where the condensation trend is initially judged. The airflow reference generation module extracts the initial condensation trend assessment area on the surface of each cold radiation panel, combines the temperature and humidity gradient dataset on the surface of the cold radiation panel to classify the risk level of all initial assessment areas, establishes a correspondence with the adjustable airflow level of the micro fan, and outputs an airflow adjustment reference list. The load analysis module obtains the frequency of indoor human activity and the characteristics of gas concentration changes, and establishes a cold radiation panel load regulation dataset by referring to the corresponding relationship in the air volume regulation reference list. The linkage control module, based on the load adjustment dataset of the cold radiant panel and the air volume adjustment reference list, performs linkage to adjust the air volume output of the micro fan and the refrigerant flow range of the cold radiant panel, thereby obtaining the refrigeration control result. The cold radiant panel surface temperature and humidity gradient dataset includes the measurement point spacing, temperature gradient value, humidity gradient value, and spatial distribution direction. The condensation trend preliminary judgment area is specifically defined by the area number, spatial boundary location, and trend identifier type. The air volume adjustment reference list includes the area risk level, air volume level correspondence, and air volume output level. The cold radiant panel load adjustment dataset includes personnel activity frequency parameters, gas concentration changes, and heat load classification labels. The refrigeration control results specifically refer to the refrigerant flow range, fan air volume output level, and cold radiant panel cooling capacity effective range.

[0024] Please see Figure 2 and Figure 3 The temperature and humidity gradient extraction module includes: The data space construction submodule acquires temperature and humidity data from multiple measuring points on the surface of the cold radiation panel within a specified period using temperature and humidity sensors. It establishes a two-dimensional coordinate space according to the location of the temperature and humidity sensors and matches the real-time temperature and humidity values ​​of each temperature and humidity sensor to the corresponding positions in the coordinate space to generate the spatiotemporal temperature and humidity measurement results of the cold radiation panel surface. Data was acquired using nine SHT31 temperature and humidity sensors (accuracy ±2%RH and ±0.3℃) arrayed on a 1m × 1m panel. The sensors formed a 3×3 matrix layout with a horizontal and vertical spacing of 0.5m. A two-dimensional coordinate system was established for each sensor location, starting from the top left corner and numbered sequentially from (0,0), (0,1), (0,2) to (2,2). Temperature and relative humidity data were continuously collected at 10-second sampling intervals within a specified period. For example, at time T0, the sensor at coordinate (1,1) recorded a real-time temperature of 22.5℃ and a real-time humidity of 68.5%RH; simultaneously, the sensor at coordinate (1,2) recorded a real-time temperature of 22.3℃ and a real-time humidity of 69.1%RH. The temperature and humidity readings from all nine points at this moment were then matched to their corresponding positions in the two-dimensional coordinate space, forming a temperature and humidity distribution matrix for a time slice. By continuously collecting data at 6 time points (60 seconds in total), the spatiotemporal temperature and humidity measurement results of the cold radiation panel surface, consisting of 6 temperature and humidity distribution matrices, were generated.

[0025] The gradient difference calculation submodule aligns the temperature and humidity values ​​at the same time point for all adjacent measuring points in the two-dimensional coordinate space of the spatiotemporal temperature and humidity measurement results of the cold radiation panel surface, and quantifies the temperature gradient and humidity gradient in different directions between adjacent measuring points to obtain discrete temperature and humidity gradient values. The temperature and humidity distribution matrix at the same time point T0 in the spatiotemporal temperature and humidity measurement results of the cold radiant panel surface is processed. All physically adjacent measurement point pairs in the two-dimensional coordinate space are selected. For example, measurement point (1,1) and its adjacent measurement points include (0,1), (2,1), (1,0), and (1,2). For measurement points (1,1) and (1,2), the temperature and humidity values ​​of measurement points (1,1) and (1,2) are aligned at time point T0, which are 22.5℃ and 68.5%RH and 22.3℃ and 69.1%RH, respectively. The temperature and humidity gradients between two adjacent measuring points in the vertical direction are quantified. The calculation process is as follows: Vertical temperature gradient = (temperature value of measuring point (1,2) - temperature value of measuring point (1,1)) / distance between the two measuring points = (22.3℃ - 22.5℃) / 0.5m = -0.4℃ / m; Vertical humidity gradient = (humidity value of measuring point (1,2) - humidity value of measuring point (1,1)) / distance between the two measuring points = (69.1%RH - 68.5%RH) / 0.5m = 1.2%RH / m. The same calculation method is used to process the temperature and humidity values ​​of measuring point (1,1) and all other adjacent measuring points (horizontal and vertical directions). This calculation process traverses all 12 pairs of horizontally adjacent measuring points and 12 pairs of vertically adjacent measuring points in the entire two-dimensional coordinate space, thus obtaining the set of discrete temperature and humidity gradient values ​​in different directions between all adjacent measuring points at time point T0.

[0026] The gradient data integration submodule unifies and categorizes discrete temperature and humidity gradient values ​​in all directions to generate a dataset of temperature and humidity gradients on the surface of a cold radiation panel. Discrete temperature and humidity gradient values ​​calculated in all directions between all adjacent measuring points are uniformly categorized. All horizontal temperature and humidity gradient values ​​are grouped into a horizontal gradient subset, and all vertical temperature and humidity gradient values ​​are grouped into a vertical gradient subset. For example, at time point T0, the gradient value {-0.4 ℃ / m, 1.2 %RH / m} calculated between measuring points (1,1) and (1,2) is marked as the vertical gradient; the gradient value {0.2 ℃ / m, -0.8 %RH / m} calculated between measuring points (1,1) and (2,1) is marked as the horizontal gradient. All 24 gradient values, their direction identifiers, corresponding measuring point coordinates, and timestamp information are integrated. Finally, all discrete temperature and humidity gradient values ​​calculated at all time points (T0 to T5) within the specified period are integrated to form a structured dataset. Each entry in the dataset contains a timestamp, measuring point coordinates, direction, temperature gradient value, and humidity gradient value, generating a cold radiation panel surface temperature and humidity gradient dataset.

[0027] Please see Figure 2 and Figure 4The condensation trend recognition module includes: The indoor parameter acquisition submodule acquires the average indoor air temperature and average air humidity within the same period and establishes a set of indoor air state parameters. An independent environmental monitoring instrument (model Testo440) deployed in the center of the room, 1.5 meters above the ground, was used to acquire the average indoor air temperature and humidity over the same period. The monitoring instrument collected data at 60-second intervals, synchronized with the data collection cycle of the cold radiant panel surface. During the same period of panel data acquisition, the monitoring instrument recorded an average indoor air temperature of 26.0℃ and an average air humidity of 70.0%RH. This set of data {26.0℃, 70.0%RH} was used as the current set of indoor air condition parameters.

[0028] The trend status comparison submodule compares the temperature gradient of each area of ​​the cold radiation panel with the preset temperature gradient enhancement benchmark value based on the indoor air temperature and average air humidity status in the indoor air status parameter set, and determines whether the surface temperature of the cold radiation panel is lower than the indoor air temperature. It then filters out areas that meet both conditions and obtains a set of potential condensation areas. The indoor air condition parameter set {26.0℃, 70.0%RH} is invoked. Simultaneously, a preset temperature gradient enhancement benchmark value is set. This benchmark value is based on numerous repetitive experiments conducted in a controlled environment chamber, gradually adjusting the indoor temperature and humidity along with the surface temperature of the cold radiation panel, using a high-speed camera to capture the moment condensation occurs. The temperature gradient of each area on the panel surface is recorded under the critical state before condensation. It is found that the absolute value of the temperature gradient before condensation generally concentrates in the range of 0.7℃ / m to 0.9℃ / m. To ensure the sensitivity of the identification, the lower limit of this range is selected, and a safety margin is set, ultimately determining the temperature gradient enhancement benchmark value to be 0.6℃ / m. The specific comparison and judgment process involves sequentially traversing each area of ​​the cold radiation panel surface (represented by sensor measurement points). Taking the region at coordinates (1,2) as an example, the surface temperature of this region is 22.3℃. The absolute value of the temperature gradient calculated with the adjacent measuring point (1,1) is 0.4℃ / m, and the absolute value of the temperature gradient calculated with the adjacent measuring point (2,2) is 0.7℃ / m. First, it is determined whether the surface temperature of this region is lower than the indoor air temperature. If 22.3℃ is less than 26.0℃, the next step is performed; if the surface temperature is not less than the indoor air temperature, it is directly determined that there is no risk of condensation in this region, and subsequent judgments are skipped, continuing to traverse the next region. In this example, 22.3℃ is less than 26.0℃, the condition is met, so it is then determined whether the absolute value of the temperature gradient in any direction exceeds the temperature gradient enhancement benchmark value. The maximum absolute value of the temperature gradient in this region is 0.7℃ / m, which is greater than 0.6℃ / m, so the judgment result is yes. If the absolute value of the temperature gradient in all directions of this region is not greater than 0.6℃ / m, then it is determined that there is no risk of condensation in this region. Since the region at coordinates (1,2) satisfies both the condition that its surface temperature is lower than the indoor air temperature and its temperature gradient exceeds the baseline value, the region represented by coordinates (1,2) is selected. This process iterates through all 9 measurement point regions, and finally integrates all regions that simultaneously meet both conditions, such as the regions with coordinates (1,2), (2,1), and (2,2), to obtain a set of potential condensation regions.

[0029] The trend area marking submodule, based on the set of potential condensation areas, traverses the position information of all divided areas on the surface of the cold radiation panel, assigns a condensation trend status label to areas belonging to the set of potential condensation areas, and assigns a normal status label to other areas, thus generating a preliminary condensation trend judgment area. Using a set of potential condensation regions {(1,2), (2,1), (2,2)}, a 3×3 state matrix consistent with the sensor layout is established to mark the condensation trend status of all divided areas on the surface of the cold radiation panel. All position information in the state matrix is ​​traversed from (0,0) to (2,2). During the traversal, it is determined whether the currently traversed coordinate exists within the set of potential condensation regions. If the currently traversed coordinate, for example (1,2), exists in the set of potential condensation regions, a condensation trend status identifier is assigned to the corresponding position in the state matrix, specifically with a value of "1". If the currently traversed coordinate, for example (0,0), does not exist in the set of potential condensation regions, a normal status identifier is assigned to the corresponding position in the state matrix, specifically with a value of "0". After traversing and assigning values ​​to all 9 positions, a complete state matrix is ​​obtained, for example, [,,]. This matrix with status identifiers is the initial condensation trend judgment area.

[0030] Please see Figure 2 and Figure 5 The air volume reference generation module includes: The regional feature extraction submodule analyzes the coordinate position and area boundary information of the initial condensation trend judgment area on the surface of each cold radiation panel, and calls the temperature and humidity gradient dataset of the cold radiation panel surface to match and obtain the temperature and humidity gradient corresponding to each initial condensation trend judgment area, and establishes the gradient intensity index of the initial judgment area. First, the state matrix of the initial condensation trend assessment region [,,] was analyzed. Using connected component analysis in image processing, all regions marked "1" were identified as forming an independent connected region, containing coordinates (1,2), (2,1), and (2,2). The area boundary information of the connected region was recorded as covering the three measurement points in the lower right corner of the panel. Next, the temperature and humidity gradient dataset of the cold radiation panel surface was retrieved, and the temperature and humidity gradient values ​​in all directions at coordinates (1,2), (2,1), and (2,2) were extracted based on the timestamp and coordinate position. For example, the matching yielded the following results: the maximum absolute value of the temperature gradient at coordinate (1,2) was 0.7℃ / m, and the maximum absolute value of the humidity gradient was 1.5%RH / m; the maximum absolute value of the temperature gradient at coordinate (2,1) was 0.8℃ / m, and the maximum absolute value of the humidity gradient was 1.3%RH / m; and the maximum absolute value of the temperature gradient at coordinate (2,2) was 0.9℃ / m, and the maximum absolute value of the humidity gradient was 1.8%RH / m. To establish the gradient intensity index for this preliminary assessment area, the maximum values ​​of all gradients at all points in the area are selected as representatives, namely, the temperature gradient is 0.9℃ / m and the humidity gradient is 1.8%RH / m.

[0031] The risk level classification submodule compares the gradient intensity index of the initially judged area with the preset risk level classification threshold, assigns a corresponding risk level to each initially judged area, and obtains the regional risk level classification result. The process of comparing the gradient intensity index of the initially assessed region with the preset risk level classification threshold and assigning a corresponding risk level to each initially assessed region is as follows: Preset the temperature gradient weighting coefficient and humidity gradient weighting coefficient respectively for the temperature gradient and humidity gradient in the temperature and humidity gradient; The temperature gradient of each region where the condensation trend is initially determined is multiplied by the temperature gradient weighting coefficient to obtain the weighted temperature gradient value, and the humidity gradient is multiplied by the humidity gradient weighting coefficient to obtain the weighted humidity gradient value. The weighted temperature gradient value and the weighted humidity gradient value are summed to generate the initial gradient intensity index of the region. Multiple risk level division intervals are set, and each risk level division interval has independent upper and lower limit thresholds and corresponds to a unique risk level; The generated initial regional gradient strength index is numerically compared with the upper and lower thresholds of multiple risk level division intervals. Based on the risk level division interval where the initial assessment regional gradient intensity index is located, the risk level corresponding to the risk level division interval is assigned to the initial assessment region of condensation trend. The initial gradient intensity index of the assessed area {temperature gradient: 0.9℃ / m, humidity gradient: 1.8%RH / m} is compared with the preset risk level classification threshold. First, weighting coefficients are preset for the temperature and humidity gradients. These weighting coefficients are based on fluid dynamics and thermodynamic analysis, combined with the results of multiple regression analysis of condensation experiment data. The analysis shows that, near saturation, the contribution of humidity gradient changes to the condensation rate is approximately 1.4 times that of temperature gradient changes. Therefore, the weighting coefficient for the temperature gradient is set to 1.0, and the weighting coefficient for the humidity gradient is set to 1.4. Next, the weighted gradient values ​​are calculated: weighted temperature gradient = 0.9℃ / m × 1.0 = 0.9; weighted humidity gradient = 1.8%RH / m × 1.4 = 2.52. Then, the weighted temperature gradient and weighted humidity gradient values ​​are summed to generate the initial gradient intensity index of the assessed area = 0.9 + 2.52 = 3.42. The risk level classification intervals were also based on experimental data, establishing a correlation between the calculated results of a large number of gradient intensity indices and the actual condensation time. Experiments showed that when the index value was below 1.5, there was no risk of condensation within 30 minutes; when the index value was between 1.5 and 3.0, condensation might occur within 15 minutes; and when the index value was above 3.0, the risk of condensation was extremely high within 5 minutes. Based on this, three risk level classification intervals are set: Level 1 (low risk) corresponds to the indicator value interval [0, 1.5]; Level 2 (medium risk) corresponds to the indicator value interval (1.5, 3.0]; and Level 3 (high risk) corresponds to the indicator value interval (3.0, +∞). The calculated preliminary judgment area gradient strength index 3.42 is compared with these intervals. If the index value is in the interval [0, 1.5], the risk level is assigned as "Level 1"; if the index value is in the interval (1.5, 3.0], the risk level is assigned as "Level 2"; and if the index value is in the interval (3.0, +∞), the risk level is assigned as "Level 3". In this example, 3.42 falls within the interval (3.0, +∞), therefore, the risk level corresponding to this interval, "Level 3", is assigned to the preliminary judgment area of ​​condensation trend, resulting in the regional risk level classification.

[0032] The air volume level mapping submodule matches each risk level with the preset adjustable air volume level of the micro fan based on the regional risk level classification results, assigns the corresponding air volume output level to the risk level area and establishes the corresponding relationship, and generates an air volume adjustment reference list. Based on the regional risk level classification (the risk level of region {(1,2), (2,1), (2,2)} is "Level 3"), each risk level is matched with the preset adjustable airflow level of the micro-fan. The micro-fan has four adjustable airflow levels: 0 (off), 1 (low wind speed, 0.1 m / s), 2 (medium wind speed, 0.25 m / s), and 3 (high wind speed, 0.4 m / s). The matching rule is preset as follows: if the regional risk level is "Level 1", the matched airflow level is "Level 1"; if the regional risk level is "Level 2", the matched airflow level is "Level 2"; if the regional risk level is "Level 3", the matched airflow level is "Level 3"; for normal areas not identified as risky, the matched airflow level is "Level 0". According to this matching rule, the region with a risk level of "Level 3" is assigned the corresponding airflow output level "Level 3". Finally, a correspondence is established, explicitly indicating that the coordinate region {(1,2), (2,1), (2,2)} requires intervention at wind speed level 3, while other coordinate regions require level 0. These correspondences are then integrated to generate a reference list for airflow adjustment.

[0033] Please see Figure 2 and Figure 6 The load analysis module includes: The indoor status monitoring submodule uses infrared sensors to monitor the changes in the frequency of movement of people in the room in real time within the same period, and uses carbon dioxide sensors to monitor the rate of change of indoor gas concentration in real time, and establishes a feature set of indoor people and gas changes. The process of using infrared sensors to monitor changes in the frequency of movement of people indoors in real time is as follows: The continuous monitoring time is divided into multiple equal monitoring cycles; Infrared sensors are used to count the number of events triggered by human movement within each monitoring cycle, and the number of events is defined as the movement frequency of the current cycle. Obtain the historical movement frequency of the previous monitoring period; Calculate the difference between the current cycle's movement frequency and the historical movement frequency to obtain the change in personnel movement frequency; During the same period as the panel data acquisition, monitoring was conducted using a wide-angle infrared sensor (PIR sensor) installed on the ceiling and a carbon dioxide sensor (NDIR type) on the wall. First, the continuous monitoring time was divided into multiple equal monitoring cycles, each lasting 60 seconds. Within the current monitoring cycle T_current, the infrared sensor counted the number of events triggered by people moving or turning around in the room. For example, if the sensor triggered 8 times in this cycle, the movement frequency for the current cycle was defined as 8 times / minute. Simultaneously, the historical movement frequency from the previous monitoring cycle T_previous was obtained, with a historical movement frequency of 5 times / minute. The difference between the current cycle's movement frequency and the historical movement frequency was calculated, resulting in a change in the person's movement frequency: 8 times / minute - 5 times / minute = 3 times / minute. At the same time, the carbon dioxide sensor monitored changes in indoor gas concentration. At the start of the current 60-second monitoring cycle, the carbon dioxide concentration was measured at 650 ppm, and at the end of the cycle, the concentration was measured at 675 ppm. Based on this, the indoor gas concentration change rate was calculated to be (675 ppm - 650 ppm) / 1 minute = 25 ppm / minute. Finally, the frequency of personnel movement (3 times / minute) and the gas concentration change rate (25 ppm / minute) were integrated to establish a feature set of indoor personnel and gas changes.

[0034] The load characteristic matching submodule calls the movement frequency change and gas concentration change rate of indoor personnel and personnel in the gas change characteristic set, and matches them with the corresponding relationship in the air volume adjustment reference list to obtain the regional load characteristic matching result. The system retrieves specific values ​​from the indoor occupant and gas change characteristic set: occupant movement frequency changes 3 times / minute, and gas concentration change rate is 25 ppm / minute. Simultaneously, it retrieves the airflow adjustment reference list, which indicates that areas {(1,2), (2,1), (2,2)} require intervention at fan speed level 3. The matching process correlates the indoor dynamic load with the intervention needs of condensation risk areas. Both occupant movement frequency changes and gas concentration change rates reflect the presence of active occupant loads indoors. If the occupant movement frequency change or gas concentration change rate is positive, it indicates that the indoor occupant load is increasing or in an active state, which will continuously dissipate heat and moisture, exacerbating the risk of localized condensation. In this case, the active load characteristics are matched with areas {(1,2), (2,1), (2,2)} requiring the highest level of airflow intervention to confirm that the intervention measures for high-risk areas are appropriate for the current indoor load status. If the frequency of personnel movement changes to zero or negative, and the rate of change of gas concentration is also zero or negative, it indicates that the indoor personnel load is stable or decreasing. However, as long as the risk of condensation exists, the need for intervention in high-risk areas remains valid. The matching result will record the current low-load state, but the intervention level will still be determined by the condensation risk. The final result is the regional load characteristic matching result.

[0035] The load dataset creation submodule formats and processes the regional load characteristic matching results, and generates a cold radiant panel load regulation dataset by combining the operating parameters of the cold radiant panel with the regional location information. The regional load characteristic matching results are formatted. The matched load information (personnel movement frequency change: 3 times / minute, gas concentration change rate: 25 ppm / minute) is structurally combined with the corresponding condensation risk area information (coordinates: {(1,2), (2,1), (2,2)}, risk level: Level 3, required airflow level: Level 3). Furthermore, the operating parameters of the radiant cooling panel itself must be considered, such as the current average surface temperature of the panel being 22.4℃ and the water supply temperature being 18.0℃. All this information—regional location information, risk level, load characteristics, required intervention measures, and panel operating parameters—is integrated into a complete data record. Finally, multiple such data records (corresponding to analysis results at different time points or in different regions) are aggregated to generate a comprehensive radiant cooling panel load regulation dataset.

[0036] Please see Figure 2 and Figure 7 The linkage control module includes: The linkage target analysis submodule, based on the cold radiation panel load adjustment dataset and the air volume adjustment reference list, confirms the target air volume output level and refrigerant flow range for each area in the room, and establishes a linkage control target instruction set; The data was analyzed based on the cold radiant panel load adjustment dataset and airflow adjustment reference list. Key information was extracted from the dataset: the high-risk area is located at coordinates {(1,2), (2,1), (2,2)}, and the required target airflow output level is 3. Simultaneously, the high-risk area corresponds to the refrigerant flow range requiring enhanced treatment, meaning the refrigerant supply needs to focus on covering the lower right corner of the panel. Based on this information, the target airflow output level for each area in the room was determined: for area {(1,2), (2,1), (2,2)}, the target airflow is 3; for other areas, the target airflow is 0. Furthermore, the adjustment requirement for the refrigerant flow range was confirmed as "enhancing cooling in the lower right corner area." These specific execution objectives were integrated to establish a set of linked control target instructions.

[0037] The fan air volume linkage execution submodule calls the target air volume output level corresponding to each area in the room in the linkage control target instruction set, generates an air volume adjustment instruction and sends it to the micro fan, drives the fan to switch to the target air volume output level, obtains the fan's operating status parameters after switching, and obtains the fan's real-time operating status information. The system invokes the linkage control target instruction set. It extracts the target airflow output level for each indoor area from this set. Specifically, the instruction is to drive the micro-fan unit responsible for area {(1,2), (2,1), (2,2)} to switch to level 3 (high speed), and drive the fan units responsible for the remaining areas to remain at level 0 (off). Based on these instructions, specific airflow adjustment instructions are generated (e.g., sending the target speed or level setting value to the fan controller via the Modbus protocol) and transmitted to the corresponding micro-fan. Upon receiving the instruction, the fan executes the switching action. The fan's built-in Hall sensor feeds back the actual operating speed after the switch (e.g., 1500 RPM, corresponding to level 3 speed) as a status parameter. The control device receives this feedback, obtains the fan's operating status parameters after the switch, and records them as real-time fan operating status information, confirming that the instruction has been accurately executed.

[0038] The panel refrigerant linkage execution submodule adjusts the cooling boundary of the cold radiation panel according to the refrigerant flow range adjustment requirements in the linkage control target instruction set and with reference to the real-time operating status information of the fan, and generates refrigeration control results. The process of adjusting the cooling boundary of the cold radiant panel in conjunction with other functions is as follows: Multiple refrigerant parameter adjustment combinations are preset to meet the refrigerant flow range adjustment requirements, and each refrigerant parameter adjustment combination includes a refrigerant inlet water temperature target value and a refrigerant flow rate target value. Based on the refrigerant flow range adjustment requirements in the linkage control target instruction set, match and select the corresponding refrigerant parameter adjustment combination; Adjust the inlet water temperature of the refrigerant supply system according to the target value of the refrigerant inlet water temperature in the selected refrigerant parameter adjustment combination; Adjust the flow rate of the refrigerant supply system according to the target value of the refrigerant flow rate in the selected refrigerant parameter adjustment combination. Based on the refrigerant flow range adjustment requirements in the linkage control target instruction set, namely "enhancing cooling in the lower right corner area," and referring to the real-time fan operation status information confirming that the fan is already running at a high speed, the cooling boundary of the cold radiation panel is adjusted accordingly. First, several corresponding refrigerant parameter adjustment combinations are preset for different refrigerant flow range adjustment requirements. These combinations are obtained through experimental calibration. If the adjustment requirement is "enhancing cooling in the lower right corner area," the matched refrigerant parameter adjustment combination is {target refrigerant inlet water temperature: 17.5℃, target refrigerant flow rate: 0.25 m / s}. If the requirement is "comprehensively enhanced cooling," the matched combination might be {17.0℃, 0.3 m / s}. If the requirement is "maintaining the status quo," the combination is {18.0℃, 0.2 m / s}. The basis for setting this combination is: while keeping the cooling in other areas basically unchanged, slightly reducing the overall inlet water temperature and increasing the flow rate to enhance heat exchange efficiency can most effectively suppress temperature rise in high-risk areas. Based on the instruction set's requirement to "enhance cooling in the lower right corner area," the corresponding parameter combination was matched and selected. Subsequently, an instruction was sent to the controller adjusting the refrigerant supply: the inlet water temperature of the refrigerant supply was adjusted from the current 18.0℃ to 17.5℃; simultaneously, the flow rate of the refrigerant supply was increased to 0.25 m / s by adjusting the frequency of the circulating water pump. After the adjustment was completed, the overall cooling capacity of the panel was improved, particularly enhancing the cooling effect in the lower right corner area, generating the refrigeration control result.

[0039] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A radiant cooling panel system with a micro-fan, characterized in that, The system includes: The temperature and humidity gradient extraction module acquires temperature and humidity data from multiple measuring points on the surface of the cold radiation panel within a specified period, calculates the temperature and humidity gradients of adjacent measuring points, and forms a dataset of temperature and humidity gradients on the surface of the cold radiation panel. The condensation trend recognition module acquires indoor air state parameters within the same period, identifies the condensation trend status of each area on the surface of the cold radiation panel, and marks the area where the condensation trend is initially judged. The air volume reference generation module extracts the initial condensation trend judgment area on the surface of each cold radiation panel, divides the risk level of all initial judgment areas by combining the temperature and humidity gradient dataset of the cold radiation panel surface, establishes a correspondence with the adjustable air volume level of the micro fan, and outputs an air volume adjustment reference list. The load analysis module obtains the frequency of indoor human activity and the characteristics of gas concentration changes, and establishes a cold radiation panel load adjustment dataset by referring to the corresponding relationship in the air volume adjustment reference list. The linkage control module, based on the load adjustment dataset of the cold radiant panel and the air volume adjustment reference list, performs linkage to adjust the air volume output of the micro fan and the refrigerant flow range of the cold radiant panel, thereby obtaining the refrigeration control result.

2. The radiant cooling panel system with a micro-fan according to claim 1, characterized in that: The cold radiation panel surface temperature and humidity gradient dataset includes the measurement point spacing, temperature gradient value, humidity gradient value, and spatial distribution direction. The condensation trend preliminary judgment area specifically includes the area number, spatial boundary position, and trend identifier type. The air volume adjustment reference list includes the area risk level, air volume level correspondence, and air volume output level. The cold radiation panel load adjustment dataset includes personnel activity frequency parameters, gas concentration change, and heat load classification label. The refrigeration control results specifically refer to the refrigerant flow range, fan air volume output level, and cold radiation panel cooling capacity effective range.

3. The radiant cooling panel system with a micro-fan according to claim 1, characterized in that: The temperature and humidity gradient extraction module includes: The data space construction submodule acquires temperature and humidity data from multiple measuring points on the surface of the cold radiation panel within a specified period using temperature and humidity sensors. It establishes a two-dimensional coordinate space according to the location of the temperature and humidity sensors and matches the real-time temperature and humidity values ​​of each temperature and humidity sensor to the corresponding positions in the coordinate space to generate the spatiotemporal temperature and humidity measurement results of the cold radiation panel surface. The gradient difference calculation submodule aligns the temperature and humidity values ​​at the same time point for all adjacent measuring points in the two-dimensional coordinate space of the spatiotemporal temperature and humidity measurement results of the cold radiation panel surface, and quantifies the temperature gradient and humidity gradient in different directions between adjacent measuring points to obtain discrete temperature and humidity gradient values. The gradient data integration submodule classifies the discrete temperature and humidity gradient values ​​in all directions into a unified dataset of temperature and humidity gradients on the surface of the cold radiation panel.

4. The radiant cooling panel system with a micro-fan according to claim 1, characterized in that: The condensation trend identification module includes: The indoor parameter acquisition submodule acquires the average indoor air temperature and average air humidity within the same period and establishes a set of indoor air state parameters. The trend state comparison submodule compares the temperature gradient of each area of ​​the cold radiation panel with the preset temperature gradient enhancement benchmark value based on the indoor air state parameter set of the indoor air temperature and average air humidity. It also determines whether the surface temperature of the cold radiation panel is lower than the indoor air temperature, filters out areas that meet both conditions, and obtains a set of potential condensation areas. The trend area marking submodule, based on the set of potential condensation areas, traverses the position information of all divided areas on the surface of the cold radiation panel, assigns a condensation trend status mark to areas belonging to the set of potential condensation areas, and assigns a normal status mark to other areas, thereby generating a preliminary condensation trend judgment area.

5. The radiant cooling panel system with a micro-fan according to claim 1, characterized in that: The air volume reference generation module includes: The regional feature extraction submodule parses the coordinate position and area boundary information of the initial condensation trend judgment area on the surface of each cold radiation panel, and calls the temperature and humidity gradient dataset of the surface of the cold radiation panel to match and obtain the temperature and humidity gradient corresponding to each initial condensation trend judgment area, and establishes the gradient intensity index of the initial judgment area. The risk level classification submodule compares the gradient intensity index of the preliminary judgment area with the preset risk level classification threshold, assigns a corresponding risk level to each preliminary judgment area, and obtains the regional risk level classification result. The airflow level mapping submodule matches each risk level with the preset adjustable airflow level of the micro fan based on the regional risk level classification results, assigns a corresponding airflow output level to the risk level area and establishes a corresponding relationship, and generates an airflow adjustment reference list.

6. The radiant cooling panel system with a micro-fan according to claim 5, characterized in that: The process of comparing the gradient intensity index of the initially assessed region with the preset risk level classification threshold and assigning a corresponding risk level to each initially assessed region is as follows: Preset a temperature gradient weighting coefficient and a humidity gradient weighting coefficient for the temperature gradient and humidity gradient in the temperature and humidity gradient, respectively; The temperature gradient of each of the condensation trend preliminary judgment areas is multiplied by the temperature gradient weighting coefficient to obtain the weighted temperature gradient value, and the humidity gradient is multiplied by the humidity gradient weighting coefficient to obtain the weighted humidity gradient value. The weighted temperature gradient value and the weighted humidity gradient value are summed to generate the gradient intensity index of the preliminary judgment area; Multiple risk level division intervals are set, and each risk level division interval has independent upper and lower limit thresholds and corresponds to a unique risk level; The generated gradient intensity index of the preliminary judgment area is numerically compared with the upper and lower thresholds of the multiple risk level division intervals. Based on the risk level division interval where the initial judgment area gradient intensity index is located, the risk level corresponding to the risk level division interval is assigned to the initial judgment area of ​​the condensation trend.

7. The radiant cooling panel system with a micro-fan according to claim 1, characterized in that: The load analysis module includes: The indoor status monitoring submodule uses infrared sensors to monitor the changes in the frequency of movement of people in the room in real time within the same period, and uses carbon dioxide sensors to monitor the rate of change of indoor gas concentration in real time, and establishes a feature set of indoor people and gas changes. The load characteristic matching submodule calls the movement frequency change and gas concentration change rate of the indoor personnel and the personnel in the gas change characteristic set, and matches them with the corresponding relationship in the air volume adjustment reference list to obtain the regional load characteristic matching result. The load dataset creation submodule formats and processes the regional load characteristic matching results, and generates a cold radiant panel load regulation dataset by combining the operating parameters of the cold radiant panel with the regional location information.

8. The radiant cooling panel cooling system with a micro-fan according to claim 7, characterized in that: The process of using infrared sensors to monitor changes in the movement frequency of people indoors in real time is as follows: The continuous monitoring time is divided into multiple equal monitoring cycles; The infrared sensor is used to count the number of events triggered by human movement within each monitoring cycle, and the number of events is defined as the movement frequency of the current cycle; Obtain the historical movement frequency of the previous monitoring period; The difference between the current movement frequency and the historical movement frequency is calculated to obtain the change in the movement frequency of the personnel.

9. The radiant cooling panel cooling system with a micro-fan according to claim 1, characterized in that: The linkage control module includes: The linkage target analysis submodule, based on the cold radiation panel load adjustment dataset and the air volume adjustment reference list, confirms the target air volume output level and refrigerant flow range for each area in the room, and establishes a linkage control target instruction set; The fan air volume linkage execution submodule calls the target air volume output level corresponding to each area in the room in the linkage control target instruction set, generates an air volume adjustment command and transmits it to the micro fan, drives the fan to switch to the target air volume output level, obtains the fan's operating status parameters after switching, and obtains the fan's real-time operating status information. The panel refrigerant linkage execution submodule adjusts the cooling boundary of the cold radiation panel according to the refrigerant flow range adjustment requirements in the linkage control target instruction set and with reference to the real-time operating status information of the fan, and generates refrigeration control results.

10. The radiant cooling panel system with a micro-fan according to claim 9, characterized in that: The process of adjusting the cooling boundary of the cold radiation panel in a coordinated manner is as follows: Multiple refrigerant parameter adjustment combinations are preset for the refrigerant flow range adjustment requirements, and each refrigerant parameter adjustment combination includes a refrigerant inlet water temperature target value and a refrigerant flow rate target value. Based on the refrigerant flow range adjustment requirements in the linkage control target instruction set, match and select the corresponding refrigerant parameter adjustment combination; Adjust the inlet water temperature of the refrigerant supply system according to the target value of the refrigerant inlet water temperature in the selected refrigerant parameter adjustment combination; The flow rate of the refrigerant supply system is adjusted according to the target value of the refrigerant flow rate in the selected refrigerant parameter adjustment combination.

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