Air conditioning temperature control system and method based on ultra-long experimental tunnel

Abstract

This invention relates to an air conditioning temperature control system and method based on an ultra-long experimental tunnel. Working shafts are located at both ends of the experimental tunnel, each containing an air conditioning unit. The experimental tunnel is divided into a closed test area by two baffles. Multiple air outlets equipped with terminal air supply valves are installed on the main air supply duct extending into the closed test area. Multiple temperature sensors are installed in the main supply and return air ducts and within the closed test area. Cold coil water valves and hot coil water valves are installed in the main air supply duct, connected to cold and hot water pipes respectively. The system also includes a controller, a switch, a server, and a client. The server and client are connected to the controller via the switch. The controller controls the opening of the cold coil water valves, hot coil water valves, and terminal air supply valves based on the temperature sensor readings. This invention can meet full-load requirements and optimize the temperature control within the closed test area of ​​the experimental tunnel, achieving a constant temperature fluctuation of ±0.1℃.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology, and in particular to an air conditioning temperature control system and method based on an ultra-long experimental tunnel. Background Technology

[0002] An experimental tunnel is an engineering structure buried underground, representing a form of human utilization of underground space. It is a tunnel structure specifically designed for scientific experiments, technological verification, and engineering research. It is typically an underground passage constructed of robust materials such as concrete or rock, with a defined length and cross-sectional shape. Experimental tunnels are commonly used to study and test various engineering problems, such as airflow and water flow behavior, flood control and drainage systems, transportation and tunnel engineering, underground drainage, and geotechnical mechanics. They can also be used to test the performance of new materials, advanced instruments, and equipment, and to research and provide engineering solutions for practical problems. The design and use of experimental tunnels must strictly adhere to relevant safety standards and regulations.

[0003] Chinese patent CN218763758U discloses a zoned control air conditioning system for subway tunnel boring machines. This system delivers surface fresh air to the main fresh air supply duct via a fresh air system, and also supplies a portion of the surface fresh air to the general control area of ​​the tunnel boring machine's experimental tunnel via a first branch duct. This fresh air supply method handles the heat dissipation load of the equipment in the general control area, ensuring normal equipment operation. Simultaneously, a cooling-type duct dehumidifier cools and dehumidifies another portion of the surface fresh air before delivering it to multiple workstation air conditioning outlets in the strictly controlled area of ​​the tunnel boring machine's experimental tunnel. This workstation air conditioning system cools and dehumidifies the environment in the strictly controlled area to meet the thermal comfort needs of the staff. While this patent solves the temperature control problem in the experimental tunnel by using fresh air supply for cooling the general control area and workstation air conditioning for cooling and dehumidifying the strictly controlled area, the following issues remain in practical operation:

[0004] 1. The single air conditioning unit installed in the experimental tunnel cannot meet the full load requirements, making it difficult to achieve uniform and constant temperature in the experimental tunnel for cooling or heating.

[0005] 2. When collecting temperature data in the experimental tunnel, the real-time temperature of each area of ​​the tunnel was not accurately obtained, which led to unstable temperature control in the later stages.

[0006] 3. The performance of the temperature measuring device was not effectively acquired, which resulted in the inability to measure temperature after the temperature measuring device malfunctioned, thus making it impossible to control the temperature;

[0007] 4. When controlling the air supply temperature of the air conditioner, the temperature was not specifically controlled according to the actual temperature of the experimental tunnel, resulting in inaccurate temperature control and low steady-state control accuracy. Summary of the Invention

[0008] This invention provides an air conditioning temperature control system and method based on an ultra-long experimental tunnel to solve the above-mentioned technical problems.

[0009] To address the aforementioned technical problems, this invention provides an air conditioning temperature control system based on an ultra-long experimental tunnel. Working shafts are located at both ends of the experimental tunnel, and air conditioning units are installed in each of the two working shafts.

[0010] The experimental tunnel is equipped with two baffles, and the tunnel space between the two baffles is a closed test area. The main air supply pipe of the air conditioning unit extends into the closed test area. The main air supply pipe in the closed test area is equipped with multiple evenly distributed air outlets, and each air outlet is equipped with a corresponding terminal air supply valve. The return air pipe of the air conditioning unit is connected to the opening on the baffle. Multiple temperature sensors are installed in the main air supply pipe, the return air pipe, and the closed test area.

[0011] The main air supply duct is equipped with a cold coil water valve and a hot coil water valve, which are connected to the cold water pipe and the hot water pipe, respectively.

[0012] The system includes a controller, a switch, a server, and a client. The server and client are connected to the controller through the switch. The controller controls the opening degree of the cold coil water valve, the hot coil water valve, and the terminal air supply valve based on the detection results of the temperature sensor.

[0013] Preferably, the closed test area is further provided with multiple electric heating devices, and the multiple electric heating devices are respectively connected to the output terminal of the controller.

[0014] Preferably, the air conditioning unit adopts a constant air volume air conditioning system.

[0015] This invention also provides a method for controlling air conditioning temperature within an ultra-long experimental tunnel, applied to the air conditioning temperature control system described above, comprising the following steps:

[0016] Step 1: The temperature sensor collects the duct temperature data of the main supply air duct and the main return air duct, as well as the ambient temperature data in the closed test area;

[0017] Step 2: Based on the magnitude of the error, select either fuzzy control or cascade PID control to control the opening degree of the cold coil water valve and the hot coil water valve;

[0018] Step 3: Calculate the real-time ambient temperature gradient within the closed test area, compare it with the target temperature, and adjust the opening of the terminal air supply valve accordingly.

[0019] Preferably, step 1 further includes evaluating and analyzing the temperature data after collection to determine whether the temperature data is qualified data, including:

[0020] Step S111: Convert the real-time monitored temperature data into an analog temperature signal using an A / D converter.

[0021] Step S112: Select the corresponding projection frequency domain for the signal frequency of the temperature simulation signal, and obtain the high-precision estimation result corresponding to the temperature simulation signal;

[0022] Step S113: Confirm whether the high-precision estimation result conforms to the heating pattern of the temperature monitoring area in the experimental tunnel. If yes, confirm that the temperature simulation signal initially meets the standard; if no, confirm that the temperature simulation signal does not meet the standard.

[0023] Preferably, the process of determining whether the temperature data is qualified data further includes:

[0024] Step S114: Divide the temperature simulation signal that has been initially confirmed to be in compliance into multiple signal frames; perform singular spectrum decomposition on each signal frame to obtain the singular spectrum component corresponding to that signal frame; calculate the sample entropy of the singular spectrum component corresponding to each signal frame;

[0025] Step S115: Perform feature point detection on each signal frame based on the sample entropy of the singular spectral component corresponding to each signal frame, and obtain the detection results;

[0026] Step S116: Based on the detection results, confirm the temperature feature related parameters corresponding to each signal frame; arrange and combine the temperature feature related parameters corresponding to each signal frame to obtain the temperature feature related parameter set corresponding to the temperature simulation signal;

[0027] Step S117: Obtain the feature vectors corresponding to the temperature feature-related parameter set; use the feature vectors to construct a model and obtain a temperature parameter estimation model; use the temperature parameter estimation model to track the temperature parameters of the temperature simulation signal and obtain the tracking results;

[0028] Step S118: Confirm the correlation index of temperature parameters in the temperature simulation signal based on the tracking results; filter out the temperature parameter related signal values ​​in the temperature simulation signal based on the correlation index; determine the time series changes of the temperature parameter related signal values, and judge the stability of the temperature parameter related signal values ​​based on the time series changes;

[0029] Step S119: Confirm whether the stability is greater than or equal to the preset threshold. If yes, confirm that the temperature simulation signal further meets the standard; otherwise, confirm that the temperature simulation signal does not meet the standard, and match the temperature simulation signal that does not meet the standard with the temperature monitoring area, and mark the area as abnormal.

[0030] Preferably, methods for obtaining temperature data include:

[0031] Step S121: Define the area between the main air supply pipe and the main return air pipe as the temperature monitoring area, divide the temperature monitoring area into multiple sub-areas of equal area, and obtain the thickness and density parameters of the experimental tunnel corresponding to each sub-area;

[0032] Step S122: Based on the obtained thickness and density parameters of the experimental tunnel, confirm the heat release coefficient and heat absorption coefficient of the experimental tunnel in this sub-region;

[0033] Step S123: Determine the maximum heat absorption temperature in each sub-region based on the density parameter of each sub-region, calculate the maximum heat absorption temperature and the thickness of the experimental tunnel to obtain the spatial temperature inside the experimental tunnel in that sub-region.

[0034] Preferably, for each of the temperature sensors, the mounting position is adjusted based on its physical parameters, including:

[0035] Step S131: Obtain the physical parameters of each temperature sensor, and determine the sensitivity of the temperature sensor within each temperature threshold based on the physical parameters;

[0036] Step S132: Calculate the maximum radiant heat of each sub-region based on its thermal emissivity and maximum heat absorption temperature; the thermal emissivity is parameter data obtained during the construction of the experimental tunnel and is adjusted in real time according to changes in the working conditions within the experimental tunnel.

[0037] Step S133: Confirm the maximum radiant heat in the sub-region corresponding to each temperature sensor, and determine the target sensitivity of the temperature sensor in that sub-region based on the maximum radiant heat.

[0038] Step S134: Calculate the performance index of each temperature sensor in the sub-region based on the target sensitivity of the temperature sensor;

[0039] Step S135: Determine the installation location of each temperature sensor based on its volume and the area of ​​the sub-region where it is installed, and assign a unique number to each temperature sensor whose location has been confirmed.

[0040] Preferably, step 2 includes: fuzzy control is adopted in response to the error being within a preset large error range; and cascade PID control is adopted in response to the error being within a preset small error range.

[0041] Preferably, step 3 includes:

[0042] Step 311: Obtain the heat transfer coefficient of the inner surface of the experimental tunnel, the fluid temperature of the experimental tunnel, and the wall temperature of the experimental tunnel, and calculate the current actual temperature gradient of the experimental tunnel based on the heat transfer coefficient of the inner surface of the experimental tunnel, the fluid temperature of the experimental tunnel, and the wall temperature of the experimental tunnel.

[0043] Step 312: Obtain the required temperature gradient in the experimental tunnel, and evaluate the air supply volume of the air outlet in the experimental tunnel based on the difference between the required temperature gradient in the experimental tunnel and the current actual temperature gradient of the experimental tunnel, and obtain the target air supply volume.

[0044] Step 313: Obtain the preset air supply duration and determine the air supply rate when supplying air to the air outlet of the experimental tunnel according to the target air outlet air volume.

[0045] Step 314: Calculate the target thrust for supplying air to the entrance of the experimental tunnel based on the air supply rate;

[0046] Step 315: Generate control commands based on the target thrust, and the controller controls the air conditioning temperature control system to perform air supply operation according to the control commands.

[0047] Compared with existing technologies, the air conditioning temperature control system and method based on ultra-long experimental tunnels provided by this invention have the following advantages:

[0048] 1. The air conditioning temperature control method provided by the present invention uses two air conditioning boxes, left and right, which can meet the requirements of full load. By adjusting the air volume and air temperature of the air outlet, the constant temperature effect with fluctuation of ±0.1℃ in the closed test area of ​​the experimental tunnel can be optimized, and the temperature control accuracy can be further refined.

[0049] 2. This invention adds an electric heating device inside the air duct to help improve the stability of the ambient temperature in the experimental tunnel and reduce the impact of air volume fluctuations in the experimental tunnel.

[0050] 3. In this invention, the control of the air supply temperature of the air conditioner adopts a fuzzy-cascade PID control method. The average temperature of the experimental tunnel and the supply temperature are used as system feedback factors to regulate the water valves of the hot and cold coils. This makes the system's precise temperature control action more sensitive, the response speed faster, and the adjustment more timely, effectively improving the control quality.

[0051] 4. By calculating the current temperature gradient of the experimental tunnel, this invention can effectively assess the air supply volume of the air outlet, thereby determining the air supply rate when supplying air to the air outlet of the experimental tunnel, and thus effectively calculating the target thrust for supplying air to the entrance of the experimental tunnel. This effectively realizes intelligent control of the air conditioning equipment in the experimental tunnel, improving the intelligence and effectiveness of the air conditioning equipment in the experimental tunnel. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of the design of an air conditioning temperature control system based on an ultra-long experimental tunnel according to a specific embodiment of the present invention;

[0053] Figure 2 This is a control principle diagram of an air conditioning temperature control system based on an ultra-long experimental tunnel in a specific embodiment of the present invention.

[0054] Figure 3 This is a system block diagram of an air conditioning temperature control system based on an ultra-long experimental tunnel, according to a specific embodiment of the present invention.

[0055] Figure 4 This is a schematic diagram of the simulated experimental temperature distribution curve within a closed test area according to a specific embodiment of the present invention;

[0056] Figure 5 This is a schematic diagram of fuzzy-cascade PID composite control in a specific embodiment of the present invention;

[0057] Figure 6 This is a control flowchart of a cascaded PID controller in a specific embodiment of the present invention.

[0058] In the diagram: 01-Experimental tunnel; 10-Working shaft; 20-Air conditioning unit; 21-Main air supply pipe; 22-Main return air pipe; 23-Terminal air supply valve; 30-Baffle; 40-Temperature sensor; 50-Cold water pipe; 51-Cold coil water valve; 60-Hot water pipe; 61-Hot coil water valve; 70-Electric heating device; 81-Controller; 82-Switch; 83-Server; 84-Client. Detailed Implementation

[0059] To illustrate the technical solutions of the invention in more detail, specific embodiments are listed below to demonstrate the technical effects; it should be emphasized that these embodiments are used to illustrate the invention and not to limit the scope of the invention.

[0060] The present invention provides an air conditioning temperature control system based on an ultra-long experimental tunnel, such as... Figure 1 As shown, working shafts 10 are provided at both ends of the experimental tunnel 01, and air conditioning units 20 are provided in the two working shafts 10 respectively. The provision of two air conditioning units 20 on the left and right can meet the requirements of full load.

[0061] Please refer to this carefully. Figure 2The experimental tunnel 01 is equipped with two baffles 30, and the tunnel space between the two baffles 30 is a closed test area. The main air supply pipe 21 of the air conditioning unit 20 extends into the closed test area. The main air supply pipe 21 in the closed test area is equipped with multiple evenly distributed air outlets. In some embodiments, the air outlets may be unevenly distributed in the closed test area, and their specific installation positions can be determined based on the experimental tunnel model and simulation data analysis. Each air outlet is equipped with a corresponding terminal air supply valve 23 to adjust the air volume. The return air main pipe 22 of the air conditioning unit 20 is connected to the opening on the baffle 30 to form a wind circulation. Of course, an exhaust pipe can also be set as needed. In the closed test area, the air supply is uniformly supplied upwards, and the airflow is concentrated at both ends and returned sideways. Multiple temperature sensors 40 (such as high-precision temperature sensors) are respectively installed in the main air supply pipe 21, the return air main pipe 22, and in the closed test area.

[0062] The main air supply pipe 21 is equipped with a cold coil water valve 51 and a hot coil water valve 61. The cold coil water valve 51 and the hot coil water valve 61 are respectively connected to the cold water pipe 50 and the hot water pipe 60. The temperature of the cold and hot water can be controlled by adjusting the mixing valve on the cold water pipe 50 and the hot water pipe 60, and the flow rate of the cold and hot water can be controlled by the cold and hot coil water valves 51 and 61.

[0063] Please refer to this carefully. Figure 3 The system includes a controller 81, a switch 82, a server 83, and a client 84. The server 83 and the client 84 are connected to the controller 81 through the switch 82. The controller 81 controls the opening degree of the cold coil water valve 51, the hot coil water valve 61, and the terminal air supply valve 23 based on the detection result of the temperature sensor 40.

[0064] This invention can optimize the constant temperature effect with fluctuations of ±0.1℃ in the closed test area of ​​experimental tunnel 01 by adjusting the air supply volume and air supply temperature of the air outlet, and further refine the temperature control accuracy.

[0065] In some embodiments, please refer to the following: Figure 2The closed test area is also equipped with multiple electric heating devices 70, each of which is connected to the output terminal of the controller 81. This invention, by adding electric heating devices 70 inside the air duct, helps to improve the stability of the ambient temperature in the experimental tunnel and reduces the impact of airflow fluctuations within the experimental tunnel 01. In some embodiments, the electric heating devices 70 can be evenly distributed within the closed test area. A temperature loss threshold is set; when a single temperature sensor 40 detects a temperature decrease below the temperature loss threshold, the electric heating device 70 corresponding to that temperature sensor 40 is activated; when the temperature rises to the target temperature, the corresponding electric heating device 70 stops operating. Thus, heating at a specific location is achieved through the cooperation of the electric heating devices 70 and the temperature sensors 40.

[0066] In some embodiments, the air conditioning unit 20 can be a constant air volume air conditioning system, a combined air conditioning unit including a fresh air handling section, a fresh and return air mixing section, a chilled and hot water coil section, and a fan section. The temperature of the air supplied from the vents according to the temperature gradient requirement is transmitted to the control terminal via a signal from the temperature sensor 40. The control terminal determines the temperature; if the collected temperature threshold is higher than the terminal's set temperature threshold, the terminal controls multiple terminal air supply valves 23 to supply air. The air supply volume can be set to be strong or weak based on whether the collected temperature exceeds the set temperature threshold. If the collected temperature threshold is lower than the terminal's set temperature threshold, the terminal controls multiple terminal air supply valves 23 to reduce the air supply volume, thereby achieving precise temperature control within the baffle 30 area and further enhancing the accuracy of temperature control.

[0067] This invention also provides a method for controlling air conditioning temperature within an ultra-long experimental tunnel, applied to the air conditioning temperature control system described above, comprising the following steps:

[0068] Step 1: The temperature sensor 40 collects the duct temperature data of the main supply air duct 21 and the main return air duct 22, as well as the ambient temperature data within the closed test area. Specifically, the temperature sensor 40 installed in the main supply air duct 21 and the main return air duct 22 can be used to collect the duct temperature data of the supply and return air in real time; the temperature sensor 40 in the closed test area can be used to collect the real-time ambient temperature data of the closed test area.

[0069] Step 2: Based on the error magnitude, select either fuzzy control or cascade PID control to control the opening degree of the cold coil water valve 51 and the hot coil water valve 61. In some embodiments, such as Figure 5As shown, fuzzy control is used when the error magnitude is within a preset large error range; cascade PID control is used when the error magnitude is within a preset small error range. The real-time average temperature of the experimental tunnel and the real-time temperature of the air conditioning supply are compared and fused as two feedback factors for the control system. When the air conditioning supply temperature is not at the target value, the accuracy of the air conditioning supply temperature is adjusted by feedback control of the cold and hot water valves 51 and 61, as shown. Figure 6 As shown. In other words, this invention uses fuzzy-cascade PID composite control to "coarsely adjust" the ambient temperature inside experimental tunnel 01; it employs a single-variable two-dimensional fuzzy controller and a PID controller, with the input being the temperature deviation and the rate of change of the deviation, and the output being the control signals for the cold and hot water coil valves.

[0070] Step 3: Calculate the real-time ambient temperature gradient within the closed test area. In this embodiment, the simulated experimental temperature distribution curve within the closed test area is as follows: Figure 4 As shown, the opening of the terminal air supply valve 23 is adjusted by comparing the temperature with the target temperature to achieve a constant temperature effect for high-precision temperature control in the tunnel, which is to "fine-tune" the ambient temperature.

[0071] In this embodiment, air is uniformly supplied through various vents within the closed test tunnel model set up with a 270-meter baffle 30. By continuously optimizing and adjusting the distance and number of vents on the model, approximately 80% of the points can ultimately meet the temperature gradient requirement of 0.1℃ / 5m. Later, by adjusting the air supply volume of each vent, the constant temperature effect within the closed test area of ​​test tunnel 01 with fluctuations of ±0.1℃ is further optimized. Additionally, two air supply temperature fluctuations of ±0.5℃ and ±0.2℃ are selected and quantitatively supplied through the vents at both ends of the test tunnel model to analyze their impact on the ±0.1℃ temperature control of the test tunnel. When the air supply temperature fluctuation is ±0.5℃, the temperature fluctuation value in the constant temperature area of ​​test tunnel 01 is -0.36℃. The temperature range is between 1 and 0.414℃. When the supply air temperature fluctuates by ±0.2℃, except for the area near the middle (180-210m) which exceeds the accuracy requirement, the rest can meet the ±0.1℃ requirement. Through simulation experiments, it was found that the number and location of the air supply outlets in the constant temperature area of ​​the experimental tunnel are the core foundation for high-precision temperature control. In addition, besides the feedback of the return air temperature, the stability of the supply air temperature at both ends of the experimental tunnel 01 is very important for the ±0.1℃ accuracy control. Therefore, the control of the process air conditioning system is introduced, and the accuracy of the temperature sensor 40 must meet the required process requirements. Further reducing the supply air temperature difference or adjusting the air supply volume of the relevant air outlets can further refine the ±0.1℃ temperature accuracy control of the constant temperature area.

[0072] When the supply air temperature in the experimental tunnel is lower than the standard set value, the hot water coil is opened to increase the supply air temperature; as the supply air temperature gradually approaches the set value, the hot water coil is gradually closed until it is shut off; as the supply air temperature gradually exceeds the set temperature, the cold water coil is gradually opened until the temperature in the experimental tunnel stabilizes near the standard set value. The electric heating device 70 in experimental tunnel 01 can help to quickly increase the ambient temperature of experimental tunnel 01, reducing the impact of airflow fluctuations within experimental tunnel 01. The air conditioning system in experimental tunnel 01 mainly adopts a constant air volume control method. Based on the feedback between the average ambient temperature of the experimental tunnel and the supply air temperature, the opening of the cold and hot water coil valves (two-way valves) is rationally controlled. The accuracy of the supply air temperature is controlled through temperature changes, ensuring that the supply air temperature quickly and stably reaches the target value. Each air outlet in experimental tunnel 01 is equipped with an electric terminal air supply valve 23 for fine control of the airflow at each outlet, ensuring that the ambient temperature in each area of ​​the tunnel quickly and stably reaches the target value.

[0073] In some embodiments, step 1 further includes evaluating and analyzing the temperature data after acquisition to determine whether the temperature data is qualified data. After acquiring the temperature of the temperature monitoring area, the acquired temperature is evaluated and judged more accurately to ensure the stability of the acquired temperature. Specifically, this includes the following steps:

[0074] Step S111: Convert the real-time monitored temperature threshold data into an analog temperature signal using an A / D converter.

[0075] Step S112: Select the corresponding projection frequency domain for the signal frequency of the temperature simulation signal, and obtain the high-precision estimation result corresponding to the temperature simulation signal;

[0076] Step S113: Confirm whether the high-precision estimation result conforms to the heating pattern of the temperature monitoring area in experimental tunnel 01. If yes, confirm that the temperature simulation signal initially meets the standard; if no, confirm that the temperature simulation signal does not meet the standard.

[0077] In some embodiments, the temperature data can be further analyzed, specifically including:

[0078] Step S114: Divide the temperature simulation signal that has been initially confirmed to be in compliance into multiple signal frames; perform singular spectrum decomposition on each signal frame to obtain the singular spectrum component corresponding to that signal frame; calculate the sample entropy of the singular spectrum component corresponding to each signal frame;

[0079] Step S115: Perform feature point detection on each signal frame based on the sample entropy of the singular spectral component corresponding to each signal frame, and obtain the detection results;

[0080] Step S116: Based on the detection results, confirm the temperature feature related parameters corresponding to each signal frame; arrange and combine the temperature feature related parameters corresponding to each signal frame to obtain the temperature feature related parameter set corresponding to the temperature simulation signal;

[0081] Step S117: Obtain the feature vectors corresponding to the temperature feature-related parameter set; use the feature vectors to construct a model and obtain a temperature parameter estimation model; use the temperature parameter estimation model to track the temperature parameters of the temperature simulation signal and obtain the tracking results;

[0082] Step S118: Confirm the correlation index of temperature parameters in the temperature simulation signal based on the tracking results; filter out the temperature parameter related signal values ​​in the temperature simulation signal based on the correlation index; determine the time series changes of the temperature parameter related signal values, and judge the stability of the temperature parameter related signal values ​​based on the time series changes;

[0083] Step S119: Confirm whether the stability is greater than or equal to the preset threshold. If yes, confirm that the temperature simulation signal further meets the standard; otherwise, confirm that the temperature simulation signal does not meet the standard, and match the temperature simulation signal that does not meet the standard with the temperature monitoring area, and mark the area as abnormal.

[0084] Singular Spectrum Decomposition (SSD) is a mathematical method for decomposing time-series data sequences into multiple components. It achieves this decomposition by transforming the time-series data sequence into a combination of eigenvectors and eigenvalue pairs.

[0085] Specifically, for a time-series data sequence of length N, the steps of singular spectral decomposition are as follows:

[0086] 1. Construct the matrix: Treat the time series data sequence as a column and construct a matrix X. The dimension of matrix X is N×M, where M is the embedding dimension of the selected time series data, which can usually be selected based on experience or predetermined rules.

[0087] 2. Singular Value Decomposition: Perform singular value decomposition (SVD) on matrix X to obtain eigenvector matrix U, singular value matrix Σ, and eigenvector matrix V.

[0088] 3. Component Selection: Based on the singular value matrix Σ, components can be selected to retain. Generally, components with larger singular values ​​are chosen because they represent larger energies or variances.

[0089] 4. Reconstruction Components: Reconstruction calculations are performed based on the selected eigenvectors and singular values. First, a diagonal matrix Σ (where k is the number of selected components) is formed by selecting the first k eigenvector matrices U and the first k singular values, resulting in k projection matrices. Then, the projection matrices are multiplied by the transpose of the eigenvector matrix V to obtain the reconstruction matrix. Finally, the columns of the reconstruction matrix are summed to obtain the reconstructed time-series data sequence.

[0090] Singular spectral decomposition can be used for applications such as dimensionality reduction, noise removal, and trend extraction of time-series data. By selecting different numbers of components, the granularity of the decomposition and the accuracy of the reconstruction can be controlled.

[0091] Meanwhile, singular spectral decomposition (SSC) is a method for decomposing time series data into multiple singular spectral components (SSCs). These SSCs can be used to extract characteristic patterns and variation regularities from the time series.

[0092] In this application, the use of singular spectral decomposition can achieve the following effects:

[0093] 1. Extracting Feature Patterns from Temperature Data: Singular Spectrum Decomposition (SSD) can break down temperature data into multiple singular spectral components, each representing a feature pattern in the time series. Analyzing these feature patterns allows for a better understanding of the patterns and trends in temperature data variation.

[0094] 2. Identifying periodic variations in temperature data: Components in the singular spectrum can be used to describe the periodic variations in temperature data. By analyzing the singular spectrum components, the main period of the temperature data can be determined, and temperature control strategies can be designed and adjusted accordingly.

[0095] 3. Detecting Abnormal Changes in Temperature Data: By calculating singular spectral components and sample entropy, abnormal changes in temperature data can be detected. These abnormal changes may be caused by system malfunctions or other abnormal factors. Timely detection and identification of these abnormal changes helps to adjust temperature control strategies promptly, ensuring the stability and accuracy of temperature control.

[0096] Temperature characteristic parameter sets and feature vectors are obtained after analyzing and extracting features from temperature simulation signals. They can be used to construct temperature parameter estimation models and perform parameter tracking, further improving the accuracy and precision of temperature control. Feature vectors can contain multiple temperature characteristic parameters; through analysis and processing of feature vectors, useful information for temperature control can be obtained, guiding adjustments and optimizations. Therefore, temperature characteristic parameter sets and feature vectors play a crucial role in high-precision temperature control. Furthermore, after A / D conversion, they enable better digital control for signal transmission to devices in different subspaces and provide timely warnings of spaces with abnormal temperature changes, thus alerting the system to pay extra attention to those spaces.

[0097] Specifically, by confirming whether the high-precision estimation results conform to the heating pattern of the temperature monitoring area in experimental tunnel 01, the dual-standard judgment of the temperature simulation signal can accurately and objectively evaluate whether the temperature simulation signal meets the standard of the experimental tunnel temperature from multiple perspectives, including the data form and signal composition form of the temperature simulation signal. This improves the accuracy and stability of the experimental tunnel temperature acquisition. Based on the tracking results, the correlation index of the temperature parameters in the temperature simulation signal is confirmed. Based on the correlation index, the relevant signal values ​​of the temperature parameters in the temperature simulation signal are screened, which can enhance the stability of the relevant signal values ​​of the temperature parameters obtained in the temperature simulation signal. By judging whether the stability is greater than or equal to the preset threshold, the accuracy and objectivity of the experimental tunnel temperature determination are further improved.

[0098] In some embodiments, in order to accurately obtain the real-time temperature of each area of ​​experimental tunnel 01 and avoid the problem of unstable temperature control in the later stage, the present invention proposes the following steps for the method of obtaining temperature data:

[0099] Step S121: Define the area between the main air supply pipe 21 and the main return air pipe 22 as the temperature monitoring area, divide the temperature monitoring area into multiple sub-areas of equal area, and obtain the thickness and density parameters of the experimental tunnel 01 corresponding to each sub-area;

[0100] Step S122: Based on the obtained thickness and density parameters of the experimental tunnel 01, confirm the heat release coefficient and heat absorption coefficient of the experimental tunnel 01 in this sub-region;

[0101] Step S123: Determine the maximum heat absorption temperature in each sub-region based on the density parameter of each sub-region, calculate the maximum heat absorption temperature and the thickness of the experimental tunnel to obtain the spatial temperature inside the experimental tunnel in that sub-region.

[0102] Calculating the internal temperature of experimental tunnel 01 requires using the heat conduction equation from heat transfer theory, as shown in the following formula:

[0103] q = -k*A*(dT / dx)

[0104] Where: q represents the heat transferred from the inside of the experimental tunnel per unit time (heat absorption, unit is watts W), which is the heat that needs to be replenished per unit time; k represents the heat transfer coefficient of the experimental tunnel (unit is watts / meter-Kelvin W / (m·K)); A represents the cross-sectional area of ​​the experimental tunnel (unit is square meters m^2); dT / dx represents the temperature gradient inside the experimental tunnel, that is, the rate of change of temperature with spatial location (unit is Kelvin / meter-K / m).

[0105] Based on the above formula, the heat loss per unit area of ​​the experimental tunnel can be calculated using the known thickness, density parameters, and maximum heat absorption temperature. This allows us to obtain a value representing the amount of heat that needs to be replenished per unit area per unit time. This value can serve as the basis for temperature and airflow control, enabling the calculation of initial values ​​such as the most basic supply air temperature and fluid velocity.

[0106] Specifically, temperature sensors 40 are installed in multiple equal-area sub-regions of the temperature monitoring area, which can effectively collect the temperature in each sub-region, improving the accuracy of temperature acquisition. Based on the thickness and density parameters of the experimental tunnel 01, the heat release coefficient and heat absorption coefficient of the experimental tunnel 01 in the sub-region are determined, and the maximum heat absorption temperature is calculated with the thickness of the experimental tunnel to obtain the space temperature inside the experimental tunnel in the sub-region. This can improve the real-time temperature situation inside the experimental tunnel and in the temperature monitoring area, and enable more effective temperature control based on different temperatures, thus improving the accuracy of temperature control.

[0107] In some embodiments, in order to effectively obtain the performance of the temperature measuring device during experimental tunnel temperature measurement and avoid the problem of being unable to measure temperature after the temperature measuring device malfunctions, the present invention employs the following control method for each temperature sensor 40: adjusting its installation position based on its physical parameters, including:

[0108] Step S131: Obtain the physical parameters of each temperature sensor 40, and determine the sensitivity of the temperature sensor 40 within each temperature threshold based on the physical parameters;

[0109] Step S132: The maximum radiant heat of each sub-region is calculated based on its thermal emissivity and maximum heat absorption temperature, laying the foundation for subsequent installation area determination and improving practicality; the thermal emissivity is parameter data obtained during the construction of the experimental tunnel and is adjusted in real time according to the changes in working conditions within the experimental tunnel 01.

[0110] Step S133: Confirm the maximum radiant heat in the sub-region corresponding to each temperature sensor 40, and determine the target sensitivity of the temperature sensor 40 in that sub-region based on the maximum radiant heat.

[0111] Step S134: Calculate the working performance index of each temperature sensor 40 in the sub-region based on the target sensitivity of the temperature sensor 40. This can quickly evaluate the impact of each sub-region on the temperature measurement performance of each high-precision temperature sensor 40, thereby selecting the sub-region with the best performance of the temperature sensor 40.

[0112] Step S135: Based on the volume of each temperature sensor 40 and the area of ​​the sub-region where the temperature sensor 40 is installed, determine the installation position of the temperature sensor 40, and assign a unique number to the temperature sensor 40 whose position has been confirmed. When an abnormal temperature is detected, the specific area of ​​the temperature sensor 40 can be obtained as quickly as possible based on the unique number, thus improving the security of temperature acquisition.

[0113] In some embodiments, step 3 may include:

[0114] Step 311: Obtain the heat transfer coefficient of the inner surface of the experimental tunnel, the fluid temperature of the experimental tunnel, and the wall temperature of the experimental tunnel, and calculate the current actual temperature gradient of the experimental tunnel based on the heat transfer coefficient of the inner surface of the experimental tunnel, the fluid temperature of the experimental tunnel, and the wall temperature of the experimental tunnel.

[0115]

[0116] Where D represents the current temperature gradient of the experimental tunnel; Q1 represents the fluid temperature of the experimental tunnel; Q2 represents the wall temperature of the experimental tunnel; τ represents the heat transfer coefficient of the inner surface of the experimental tunnel; and A represents the area of ​​the experimental tunnel perpendicular to the direction of heat flow.

[0117] Step 312: Obtain the required temperature gradient in experimental tunnel 01, and based on the difference between the required temperature gradient in experimental tunnel 01 and the current actual temperature gradient of the experimental tunnel, evaluate the air supply volume of the air vents in the experimental tunnel to obtain the target air supply volume. The required temperature gradient can be pre-set according to the requirements of the experimental tunnel.

[0118] Target air supply volume = Current air supply volume + ΔQ

[0119] Where ΔQ represents the difference between the required temperature gradient and the current temperature gradient in the experimental tunnel. The specific calculation formula is as follows:

[0120] ΔQ=k*(ΔT / Δt)

[0121] Where ΔT represents the difference between the required temperature gradient and the current temperature gradient, Δt represents the air supply time interval, and k represents a constant that can be set according to actual conditions to adjust the adjustment speed of the air supply volume at the air outlet.

[0122] It should be noted that the constant k and the air supply time interval Δt in the specific calculation formula need to be set and adjusted according to the actual situation to meet the control requirements of the required temperature gradient.

[0123] Step 313: Obtain the preset air supply duration and determine the air supply rate when supplying air to the air outlet of the experimental tunnel based on the target air outlet air supply volume.

[0124] Step 314: Calculate the target thrust for supplying air to the entrance of the experimental tunnel based on the air supply rate.

[0125]

[0126] Where F represents the target thrust for supplying air to the entrance of the experimental tunnel; P represents the absolute static pressure of the experimental tunnel; ρ represents the average density of air in the experimental tunnel; v represents the air supply rate; g represents the gravitational acceleration; Z represents the vertical height of the experimental tunnel; and σ represents the error factor, with a value range of (0.01, 0.02).

[0127] Step 315: Generate control commands based on the target thrust, and the controller 81 controls the air conditioning temperature control system to perform air supply operation according to the control commands.

[0128] This invention calculates the current temperature gradient of the experimental tunnel, thereby effectively assessing the air supply volume of the air vents and determining the air supply rate when supplying air to the air vents of the experimental tunnel. This allows for the effective calculation of the target thrust for supplying air to the tunnel entrance, effectively realizing intelligent control of the air conditioning equipment in the experimental tunnel and improving the intelligence and effectiveness of the air conditioning equipment.

[0129] This invention ensures the accuracy of air supply temperature control by using feedback from tunnel temperature gradient or average temperature and air supply temperature; it further controls the terminal air supply valve 23 by using temperature feedback from each sub-area in the temperature monitoring area to precisely control the tunnel temperature; and it is further supplemented by an electric heating device 70 to quickly heat the tunnel when the temperature is too low, thereby ensuring the speed and accuracy of temperature control.

[0130] In summary, the air conditioning temperature control system and method based on ultra-long experimental tunnels provided by this invention have beneficial effects including, but not limited to:

[0131] 1. By adjusting the air supply volume at the air outlet, the constant temperature effect within the closed test area of ​​the experimental tunnel with fluctuations of ±0.1℃ can be further optimized, and the temperature accuracy control of the constant temperature area within ±0.1℃ can be further refined; the electric heating device 70 inside the air duct can help improve the stability of the ambient temperature in the experimental tunnel and reduce the impact of air volume fluctuations in the experimental tunnel.

[0132] 2. By confirming whether the high-precision estimation results conform to the heating pattern of the temperature monitoring area in the experimental tunnel, the dual-standard judgment of the temperature simulation signal can accurately and objectively evaluate whether the temperature simulation signal conforms to the standard of the experimental tunnel temperature from multiple perspectives, including the data form and signal composition form of the temperature simulation signal, thereby improving the accuracy and stability of the experimental tunnel temperature acquisition.

[0133] 3. The control of the air supply temperature adopts a fuzzy-cascade PID control method. The average temperature of the experimental tunnel and the supply temperature are used as system feedback factors to adjust the water valves 51 and 61 of the cold and hot coils. This makes the system's precise temperature control more sensitive, the response speed faster, and the adjustment more timely, effectively improving the control quality.

[0134] 4. By calculating the current temperature gradient of the experimental tunnel, the air supply volume of the air outlet can be effectively evaluated, and the air supply rate when supplying air to the air outlet of the experimental tunnel can be determined. This allows for the effective calculation of the target thrust for supplying air to the tunnel entrance, effectively realizing intelligent control of the air conditioning equipment in the experimental tunnel and improving the intelligence and effectiveness of the air conditioning equipment.

[0135] Obviously, those skilled in the art can make various modifications and variations to the invention without departing from the spirit and scope of the invention. Therefore, if these modifications and variations fall within the scope of the claims of the invention and their equivalents, the invention is also intended to include these modifications and variations.

Claims

1. A method for controlling air conditioning temperature within an ultra-long experimental tunnel, applied in an air conditioning temperature control system within an ultra-long experimental tunnel, wherein working shafts are respectively provided at both ends of the air conditioning temperature control system, and air conditioning units are respectively installed in the two working shafts. The experimental tunnel is equipped with two baffles, and the tunnel space between the two baffles is a closed test area. The main air supply pipe of the air conditioning unit extends into the closed test area. The main air supply pipe in the closed test area is equipped with multiple evenly distributed air outlets, and each air outlet is equipped with a corresponding terminal air supply valve. The return air pipe of the air conditioning unit is connected to the opening on the baffle. Multiple temperature sensors are installed in the main air supply pipe, the return air pipe, and the closed test area. The main air supply duct is equipped with a cold coil water valve and a hot coil water valve, which are connected to the cold water pipe and the hot water pipe, respectively. The system includes a controller, a switch, a server, and a client. The server and client are connected to the controller through the switch. The controller controls the opening degree of the cold coil water valve, the hot coil water valve, and the terminal air supply valve based on the detection results of the temperature sensor. Its features are, Includes the following steps: Step 1: The temperature sensor collects the duct temperature data of the main supply air duct and the main return air duct, as well as the ambient temperature data in the closed test area; Step 2: Based on the magnitude of the error, select either fuzzy control or cascade PID control to control the opening degree of the cold coil water valve and the hot coil water valve; Step 3: Calculate the real-time ambient temperature gradient within the closed test area, compare it with the target temperature, and adjust the opening of the terminal air supply valve accordingly. This step includes: Step 311: Obtain the heat transfer coefficient of the inner surface of the experimental tunnel, the fluid temperature of the experimental tunnel, and the wall temperature of the experimental tunnel, and calculate the current actual temperature gradient of the experimental tunnel based on the heat transfer coefficient of the inner surface of the experimental tunnel, the fluid temperature of the experimental tunnel, and the wall temperature of the experimental tunnel. Step 312: Obtain the required temperature gradient in the experimental tunnel, and evaluate the air supply volume of the air outlet in the experimental tunnel based on the difference between the required temperature gradient in the experimental tunnel and the current actual temperature gradient of the experimental tunnel, and obtain the target air supply volume. Step 313: Obtain the preset air supply duration and determine the air supply rate when supplying air to the air outlet of the experimental tunnel according to the target air outlet air volume. Step 314: Calculate the target thrust for supplying air to the entrance of the experimental tunnel based on the air supply rate; Step 315: Generate control commands based on the target thrust, and the controller controls the air conditioning temperature control system to perform air supply operation according to the control commands.

2. The air conditioning temperature control method based on an ultra-long experimental tunnel as described in claim 1, characterized in that, Step 1 also includes evaluating and analyzing the temperature data after collection to determine whether the temperature data is qualified, including: Step S111: Convert the real-time monitored temperature data into an analog temperature signal using an A / D converter. Step S112: Select the corresponding projection frequency domain for the signal frequency of the temperature simulation signal, and obtain the high-precision estimation result corresponding to the temperature simulation signal; Step S113: Confirm whether the high-precision estimation result conforms to the heating pattern of the temperature monitoring area in the experimental tunnel. If yes, confirm that the temperature simulation signal initially meets the standard; if no, confirm that the temperature simulation signal does not meet the standard.

3. The air conditioning temperature control method based on an ultra-long experimental tunnel as described in claim 2, characterized in that, The process of determining whether the temperature data is qualified data also includes: Step S114: Divide the temperature simulation signal that has been initially confirmed to be in compliance into multiple signal frames; perform singular spectrum decomposition on each signal frame to obtain the singular spectrum component corresponding to that signal frame; calculate the sample entropy of the singular spectrum component corresponding to each signal frame; Step S115: Perform feature point detection on each signal frame based on the sample entropy of the singular spectral component corresponding to each signal frame, and obtain the detection results; Step S116: Based on the detection results, confirm the temperature feature related parameters corresponding to each signal frame; arrange and combine the temperature feature related parameters corresponding to each signal frame to obtain the temperature feature related parameter set corresponding to the temperature simulation signal; Step S117: Obtain the feature vectors corresponding to the temperature feature-related parameter set; use the feature vectors to construct a model and obtain a temperature parameter estimation model; use the temperature parameter estimation model to track the temperature parameters of the temperature simulation signal and obtain the tracking results; Step S118: Confirm the correlation index of temperature parameters in the temperature simulation signal based on the tracking results; filter out the temperature parameter related signal values ​​in the temperature simulation signal based on the correlation index; determine the time series changes of the temperature parameter related signal values, and judge the stability of the temperature parameter related signal values ​​based on the time series changes; Step S119: Confirm whether the stability is greater than or equal to the preset threshold. If yes, confirm that the temperature simulation signal further meets the standard; otherwise, confirm that the temperature simulation signal does not meet the standard, and match the temperature simulation signal that does not meet the standard with the temperature monitoring area, and mark the area as abnormal.

4. The air conditioning temperature control method based on an ultra-long experimental tunnel as described in claim 1, characterized in that, Methods for obtaining temperature data include: Step S121: Define the area between the main air supply pipe and the main return air pipe as the temperature monitoring area, divide the temperature monitoring area into multiple sub-areas of equal area, and obtain the thickness and density parameters of the experimental tunnel corresponding to each sub-area; Step S122: Based on the obtained thickness and density parameters of the experimental tunnel, confirm the heat release coefficient and heat absorption coefficient of the experimental tunnel in this sub-region; Step S123: Determine the maximum heat absorption temperature in each sub-region based on the density parameter of each sub-region, calculate the maximum heat absorption temperature and the thickness of the experimental tunnel to obtain the spatial temperature inside the experimental tunnel in that sub-region.

5. The air conditioning temperature control method based on an ultra-long experimental tunnel as described in claim 4, characterized in that, For each of the temperature sensors, adjust its mounting position based on its physical parameters, including: Step S131: Obtain the physical parameters of each temperature sensor, and determine the sensitivity of the temperature sensor within each temperature threshold based on the physical parameters; Step S132: Calculate the maximum radiant heat of each sub-region based on its thermal emissivity and maximum heat absorption temperature; the thermal emissivity is parameter data obtained during the construction of the experimental tunnel and is adjusted in real time according to changes in the working conditions within the experimental tunnel. Step S133: Confirm the maximum radiant heat in the sub-region corresponding to each temperature sensor, and determine the target sensitivity of the temperature sensor in that sub-region based on the maximum radiant heat. Step S134: Calculate the performance index of each temperature sensor in the sub-region based on the target sensitivity of the temperature sensor; Step S135: Determine the installation location of each temperature sensor based on its volume and the area of ​​the sub-region where it is installed, and assign a unique number to each temperature sensor whose location has been confirmed.

6. The air conditioning temperature control method based on an ultra-long experimental tunnel as described in claim 1, characterized in that, Step 2 includes: fuzzy control is adopted when the error magnitude is within a preset large error range; cascade PID control is adopted when the error magnitude is within a preset small error range.

7. The air conditioning temperature control method based on an ultra-long experimental tunnel as described in claim 1, characterized in that, The closed test area is also equipped with multiple electric heating devices, each of which is connected to the output terminal of the controller.

8. The air conditioning temperature control method based on an ultra-long experimental tunnel as described in claim 1, characterized in that, The air conditioning unit adopts a constant air volume air conditioning system.

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