Water-cooled central air conditioning terminal windshield metering method

Abstract

This invention discloses a method for metering the cooling load of terminal air deflectors in a water-cooled central air conditioning system. The method uses a vector composed of four scalars—chilled water supply flow rate, supply and return water temperature difference, current room temperature and humidity—and the open / closed status values ​​of all terminal air deflectors as input, and the cooling load per unit time of each terminal air deflector as output, to establish a neural network. Using the room as the heat load, a reference air conditioning unit and the terminal air deflectors are cooled under the same operating conditions. A calibrated reference air conditioning unit is used as a reference for collecting cooling load samples from the terminal air deflectors. The trained network is then used to predict the cooling load of the terminal air deflectors under the current operating conditions. Compared to methods that only detect the terminal air deflectors, this invention ensures metering accuracy without increasing costs. Furthermore, it eliminates the impact of slow fluctuations in operating conditions on the sampled data through alternating cooling supply and decouples the mutual constraints between the various air deflector terminals, enabling accurate measurement of dynamically changing cooling load.

Description

Technical Field

[0001] This invention relates to the field of metering for water-cooled central air conditioning systems, and more specifically to a metering method for the terminal windshields of water-cooled central air conditioning systems. Background Technology

[0002] A central air conditioning system consists of a cold / heat source system and a cold / heat transfer and air conditioning system. In summer, the cooling system provides the necessary cooling capacity to the air conditioning system to offset the heat load of the indoor environment; in winter, the heating system provides the same cooling capacity. A water-cooled central air conditioning system uses a single main unit connected to multiple fan coil units via chilled water pipes to deliver cooling capacity to different rooms to achieve indoor air conditioning.

[0003] The most prominent feature of central air conditioning is that it creates a comfortable working and living environment. With the rapid development of air conditioning demand in large public buildings in China, more and more office buildings, shopping malls, serviced apartments and other buildings are beginning to install central air conditioning systems.

[0004] In the new century, humanity faces two major challenges: energy shortages and environmental degradation. The construction industry, along with industrial production and transportation, are the three largest energy-consuming sectors in my country, accounting for approximately 30% of the nation's total energy consumption. Air conditioning and heating systems alone account for 50% to 60% of total building energy consumption. Energy-saving measures for central air conditioning in public buildings can effectively reduce building energy consumption and achieve sustainable development. However, many places still use the traditional, simple area-based billing method for central air conditioning. While this method is convenient, most people do not consider whether their air conditioning usage is energy-efficient, leading to situations where air conditioning is used even when no one is present, resulting in energy waste.

[0005] If energy metering is adopted, charging only for what is used can raise people's awareness of energy conservation. Reasonable billing methods can change consumers' energy consumption habits; therefore, choosing a reasonable central air conditioning billing method is of great significance for energy conservation.

[0006] The initial billing method for central air conditioning was based on area allocation, which resulted in wasted electricity. To address this, in recent years, a new individual billing system based on central air conditioning has been proposed. This system aims to achieve reasonable billing by allocating costs to each user in a given room, thereby reducing significant electricity waste and high building energy consumption.

[0007] Among the new technologies for cooling capacity metering and allocation billing in central air conditioning systems, chilled water metering methods have emerged. These methods are further divided into two types. One method involves installing a water meter at the outlet of the fan coil unit to measure the chilled water flow rate within the unit. This method simply solves the problem of inconsistent usage, but it doesn't consider the inlet and outlet temperatures of the chilled water. The other method, conversely, assumes a constant chilled water flow rate and only measures the temperature difference between the inlet and outlet, billing as long as the air conditioning is on. These methods, including the later improved energy metering method, all measure a few fixed parameters at the terminal, failing to reflect the impact of overall changes in the central air conditioning system's operating conditions on the terminal cooling capacity supply, thus making it difficult to accurately reflect the user's true cooling capacity consumption.

[0008] For water-cooled central air conditioning, another issue that needs to be considered in traditional cooling capacity metering is that the temperature difference between the supply and return water is much smaller than that for heating. Therefore, this requires higher measurement accuracy of the temperature sensor. Undoubtedly, using high-precision sensors at each terminal will greatly increase the user's cost. On the other hand, using ordinary sensors generally results in large sampling fluctuations and inaccurate measurement.

[0009] High-cost energy meters face difficulties in widespread adoption. Currently, according to surveys, most newly built central air conditioning cooling capacity allocation and billing systems employ indirect or equivalent billing methods, with time-based billing being the most common. Time-based metering calculates the equivalent or cumulative cooling capacity under rated test conditions, as illustrated by Chinese patent CN 100504338C, which calculates the cumulative operating time of each fan speed setting at the terminal unit. Time-based billing assumes a direct proportionality between cooling capacity and the inlet and outlet water temperature difference of the fan coil unit, as well as the water volume. It considers fan speed as a factor influencing temperature difference, assuming that higher fan speeds result in higher air volumetric flow rates and consequently, higher cooling capacity equivalents. For systems with high, medium, and low fan speed settings... H V M V L For a constant fan coil unit, the equivalent cooling capacity is:

[0010] Q = K H t H +K M t M +K L t L ,

[0011] Among them, t H t M t L K represents the opening time (s) of the two-way valve under high, medium, and low wind speeds. H K M K LThis represents the proportionality coefficient (kJ / s) for high, medium, and low wind speeds. The two-way valve itself is a switching component. By detecting the opening and closing of each two-way valve, the cumulative opening time of the two-way valve within a certain period, as well as the different wind speed settings, can be obtained. Therefore, in the time-based metering method, the cooling capacity delivered by each fan coil unit of a water-cooled central air conditioning system is calculated based on the cumulative time of opening and closing the two-way valve at different wind speeds. The key to this method lies in how to obtain K. H K M K L The coefficient? The current method is to use estimated empirical or theoretical values, or to use the coefficient value calculated by the fan coil unit manufacturer based on rated conditions such as dry bulb temperature of 27°C, wet bulb temperature of 19.5°C, and chilled water inlet temperature of 7°C, and the cooling capacity at various fan speeds.

[0012] This method of calculating cooling capacity under dynamic operating conditions using a fixed coefficient is clearly only suitable for estimation and not for accurate measurement. Furthermore, the labeling method of calculating the cooling capacity of the test object using a specially designed test setup and the air enthalpy method is also unsuitable for the diverse and changing operating environments and conditions of central air conditioning terminal units.

[0013] Therefore, the industry urgently needs a method to accurately measure the equivalent cooling capacity of the windshield end under actual operating conditions, providing a metering basis for billing based on the principle of "more usage, more payment; less usage, less payment," thereby ultimately raising energy-saving awareness among users and achieving the goals of energy conservation and environmental protection. Summary of the Invention

[0014] In view of this, the purpose of this invention is to provide a method for accurately measuring the cooling capacity of the terminal air deflectors in a water-cooled central air conditioning system, thereby providing a basis for effective billing based on actual cooling capacity.

[0015] The technical solution of the present invention is to provide a method for metering the air deflector at the terminal of a water-cooled central air conditioning unit, comprising the following steps:

[0016] S1. Based on the cooling capacity of the terminal windshield of the water-cooled central air conditioning system, a reference air conditioning unit is selected as the comparison of cooling capacity. The sensible cooling capacity of the reference air conditioning unit under different operating conditions is obtained according to standard tests, and the parameters are recorded as operating characteristics.

[0017] S2. A neural network is established using a vector composed of four scalars: chilled water supply flow rate of the water-cooled central air conditioning unit, supply and return water temperature difference, current temperature and humidity of the room, and the opening status values ​​of all terminal windshields of the water-cooled central air conditioning unit. The equivalent cooling capacity per unit time of the current windshield of the room is used as the output.

[0018] S3. Pre-cooling and initialization operating condition parameter settings: The room where the terminal air deflector is located is taken as the heat load, and the target temperature is set according to the natural temperature of the room; the room temperature is adjusted to the target temperature based on the cooling of the central air conditioning terminal air deflector, and the current humidity is obtained as the target humidity;

[0019] S4. Determine whether this is the first sample collection at the current target temperature. If so, maintain the room at the target temperature for a preset time and obtain the current humidity as the target humidity. Otherwise, proceed to S5.

[0020] S5. Collect the neural network training samples: Control the terminal fan and the reference air conditioning unit to cool independently in stages and alternately. In each stage, maintain the room temperature at the target temperature. The operating condition adjustment unit ensures that the temperature difference between multiple temperature detection modules located at different positions in the room is less than the temperature difference threshold.

[0021] The cooling capacity per unit time calculated by the reference air conditioning unit under the same operating conditions is used as the equivalent cooling capacity of the terminal windshield.

[0022] S6. Determine whether the sampling end condition is met. If yes, proceed to S7. Otherwise, change the operating conditions of the water-cooled central air conditioning unit and the terminal windshield, and proceed to step S4 to collect samples again.

[0023] S7. Train the neural network based on the training sample set and adjust the connection weights of the neural network;

[0024] S8. In the field environment, based on the current operating conditions, a trained neural network is used to predict the equivalent cooling capacity per unit time of the end windshield, and the predicted value is output.

[0025] Preferably, the process of cooling independently in stages and in an alternating manner described in step S5 is as follows:

[0026] During three time periods, the reference air conditioning unit, the terminal windshield, and the reference air conditioning unit are used as cold sources individually, and operate for durations T1, T2, and T1 respectively.

[0027] During each time period, air circulation within the room is used to ensure that the temperature difference between the temperature detection modules at multiple measurement points is less than the temperature difference threshold. In the first and third time periods, room humidity is also adjusted and maintained at the target humidity. In the second time period, the room temperature is maintained at the target temperature by controlling the driver of the terminal fan to operate in PWM mode. The equivalent cooling capacity per unit time of the fan when the fan is currently open (value F) is calculated. Among them, the cooling capacity in the first period Cooling capacity in the third period Equivalent time of the second period In the formula, the cooling power q(t) is calculated based on the working characteristics and operating conditions, and Δ(t) is the PWM value.

[0028] Preferably, step S5 includes:

[0029] S51. Close the end fan in this room and start timing. Control the reference air conditioning unit to maintain the room temperature at the target temperature from t=0 to t=T1 during the first time period. At the same time, control the operating condition adjustment unit to maintain the room humidity at the target humidity. Calculate the cooling power q(t) based on the operating characteristics and operating conditions, and accumulate the cooling capacity for the first time period.

[0030] S52. Turn off the reference air conditioning unit and start timing again. After setting the opening state value F of the terminal windshield, control its fan driver to work in PWM mode and maintain the room temperature at the target temperature from the second time period from t=0 to t=T2. Calculate its equivalent time. Where Δ(t) is the PWM value,

[0031] S53. Close the terminal fan again and restart the timing. Control the reference air conditioning unit to maintain the room temperature at the target temperature during the third time period from t=0 to t=T1. At the same time, control the operating condition adjustment unit to maintain the room humidity at the target humidity. Calculate the cooling capacity for the third time period again.

[0032] S54. Calculate the equivalent cooling capacity per unit time of the windshield under the current operating conditions:

[0033] Preferably, the chilled water supply flow rate and the supply-return water temperature difference in the input quantities of the neural network are obtained through measuring points set at the adjacent ends of the chilled water supply and return water main pipes and the central air conditioning unit, respectively.

[0034] The temperature detection modules can be set at the same height and located on two vertical diagonals respectively. The temperature difference threshold can be a value between 0.1℃ and 0.5℃. The current temperature of this room is the average of the temperatures of multiple measurement points or the temperature value of the return air vent.

[0035] The humidity is sensed by a humidity detection module installed in the middle of the room's return air duct.

[0036] Preferably, the air circulation in step S5 is achieved through a temperature uniformity module, which further includes:

[0037] Acquire room images, and extract orientation features and two mutually perpendicular diagonals through image processing. The orientation features include the distribution and length of the room's structural edges, as well as the direction and distance of the cold source air outlet, return air outlet, and temperature uniform module relative to each corner of the room.

[0038] Take a line connecting the location of the cold air outlet to the furthest point in the room it can reach or the position directly opposite it as one of the main diagonals, and take another line perpendicular to it as a secondary diagonal; then, plan a spiral trajectory with the main diagonal as the axis.

[0039] The fan axis at the end of the temperature uniform module moves along the planned trajectory to deliver the cold air blown out by the central air conditioning terminal windshield and / or the reference air conditioning unit to various areas of the room until the temperature difference between multiple temperature detection modules in the room is less than the temperature difference threshold.

[0040] Preferably, the movement along the planned trajectory specifically includes: first, running at a constant speed during each time period of sample collection; then, according to the temperature distribution characteristics of multiple temperature measuring points in the room, changing the linear velocity of the terminal fan moving along the trajectory so that the linear velocity is inversely proportional to the temperature difference between the temperature at the corresponding trajectory point and the target temperature.

[0041] Preferably, the opening state value of the end windshield can be taken as the normalized value of the three fan power corresponding to the low, medium and high speed of the fan. For example, the highest power value is taken as 1, and the power of the other two speeds is proportionally converted.

[0042] Preferably, the operating characteristics of the reference air conditioning unit are obtained based on a room-type air enthalpy test device. The sensible cooling capacity of the reference air conditioning unit under different operating conditions is tested, and the operating parameters and the sensible cooling capacity are recorded as a table or curve of operating characteristics. When collecting samples, the cooling capacity of the reference air conditioning unit under the current operating conditions is calculated by querying and interpolating the table or curve based on the current operating parameter values.

[0043] Preferably, all terminal air deflectors are categorized according to their specific models and their horizontal and vertical distances from the inlet of the chilled water supply main pipe of the central air conditioning unit. A neural network is established for each sub-category, and training samples are collected for each sub-category.

[0044] Preferably, when the driver in step S5 operates in PWM mode, its equivalent time is calculated by multiplying the normalized speed of the fan motor at different wind speed settings by the integral of the duty cycle Δ(t) within the cycle. Where k(t) is the normalized rotational speed, which is 1 for the highest speed and the ratio of the rotational speed to the highest speed for other speeds.

[0045] Preferably, the neural network is a BP neural network, and its model is as follows:

[0046] The output of the j-th node in the hidden layer is

[0047] The output of the output layer is

[0048] Where x1~x4 are four scalars: chilled water supply flow rate of the central air conditioning unit, supply and return water temperature difference, current temperature and humidity of the room; x5~xn are the opening status values ​​of all terminal fan dampers; the f() function is taken as the sigmoid function, w ij and v j These are the connection weights from the input layer to the hidden layer and the connection weights from the hidden layer to the output layer, θ. j θ and θ are the thresholds for the hidden and output layers, respectively, and n and k are the number of nodes in the input and hidden layers, respectively. Gradient descent is used for network training.

[0049] As a preferred option, the windshield opening status value of each terminal is obtained through communication with the server in a distributed system composed of multiple terminals.

[0050] Preferably, there are 3 to 6 temperature detection modules, and the height is approximately 2 meters.

[0051] Preferably, the temperature difference between the target temperature and the initial temperature is ≥5℃.

[0052] Preferably, the reference air conditioning unit is a heating and cooling air conditioner; if the ratio of the equivalent time dT to the duration T2 of the second time period is less than the duty cycle threshold Δs, when collecting the sample, the reference air conditioning unit is also controlled to operate in heating mode from τ=0 to τ=T3 within the second time period, and the heating equivalent is recorded. Accordingly, the equivalent cooling capacity per unit time of the windshield under the current operating conditions is calculated:

[0053]

[0054] Preferably, an electric heating module can also be set in the reference air conditioning unit, and the electric heating module can be controlled to heat from τ=0 to τ=T3 in time range, and the heat equivalent Q3=pr·T3 is recorded, where pr is the heating power of the electric heating module (kW i.e. kJ / s), and PF is calculated similarly.

[0055] Preferably, within the time ranges T1 and T2, the heating module can be turned on with known power and heat calculation can be performed.

[0056] Preferably, the rated cooling power of the reference air conditioning unit is 0.85 to 1.15 times the maximum cooling capacity of the terminal fan.

[0057] Preferably, the temperature equalization module includes a base, a vertical rotating shaft, a curved support arm, a horizontal rotating shaft, and a tiltable bracket with two sections of support arms movably connected by bolts. A telescopic support rod at an acute angle to the axis of the horizontal rotating shaft is connected between the outer ends of the two sections of support arms. A temperature equalization fan is supported at the end of the tiltable bracket, and a fan cover is provided on the back of the fan.

[0058] Preferably, image acquisition is performed through an image acquisition module in the sensing and detection unit, and training sample acquisition is performed through a control unit; the control unit includes an input module, a main processing module, an image processing module, a wind turbine processing module, a mapping module, and an output module, and the control unit is configured as follows:

[0059] The image processing module analyzes the room's orientation features based on the room images acquired by the image acquisition module and extracts two mutually perpendicular diagonals;

[0060] The main processing module responds to events and schedules other modules.

[0061] The fan processing module adjusts the duty cycle of the driver's PWM wave based on the average of multiple temperatures at different locations in the room.

[0062] The neural network is established in the mapping module. The input layer of the neural network receives the input from the main processing module, and the output of the output layer is transmitted to the iterative learning unit and the main processing module through the first connection array and the second connection array, respectively. When training the neural network offline, the iterative learning unit adjusts the connection weights of the neural network according to the actual value of the windshield cooling equivalent per unit time input by the main processing module and the neural network through the first connection array and the network output value, respectively. When measuring online, the first connection array is disconnected, the neural network predicts the windshield cooling equivalent per unit time and outputs it to the main processing module through the second connection array. The main processing module processes and analyzes the data and outputs it through the output module.

[0063] Preferably, the reference air conditioning unit is equipped with a dry-bulb and wet-bulb temperature detection module and an air supply volume detection module. The sensible cooling capacity under different operating conditions is obtained according to standard tests, and the parameters are recorded as a working characteristic table or curve. Based on the current dry-bulb and wet-bulb temperatures and air supply volume of the inlet and outlet air, the cooling capacity of the reference air conditioning unit in the current room is calculated by querying and interpolating the working characteristics.

[0064] Compared with the prior art, the method of this invention has the following advantages: This invention uses the rooms where the water-cooled central air conditioning terminal windshields are located in different positions as the heat load, and a pre-calibrated movable reference air conditioning unit as a reference to calculate the equivalent cooling capacity of the water-cooled central air conditioning terminal windshields; it uses a vector composed of four scalars—chilled water supply flow rate of the central air conditioning unit, supply and return water temperature difference, current temperature and humidity of the room, and the opening status values ​​of all terminal windshields—as input, and the equivalent cooling capacity per unit time of the current windshield in the room as output, to establish a neural network as a terminal windshield metering mapping model. This model can reflect the impact of actual operating condition changes of the water-cooled central air conditioning on the terminal cooling capacity, and can dynamically reflect the changes in the cooling capacity of the terminal windshields, overcoming the defect of the prior art in estimating the actual changing cooling capacity of the windshields with fixed coefficients. Meanwhile, the measuring points for water supply temperature difference and flow rate are only set at the inlet and outlet of the main chilled water supply pipeline of the central air conditioning unit, replacing the multi-point arrangement at each terminal. The detection of large flow rates relative to small flow rates at the terminals reduces relative error, and the significant reduction in measuring points allows for further reduction of measurement error by employing high-precision temperature difference detection. Furthermore, this invention categorizes the terminal units based on their model and their horizontal and vertical distances from the inlet of the main chilled water supply pipeline of the central air conditioning unit. The model uses the open / closed state values ​​of all terminal unit windshields as input, thereby decoupling the mutual constraints between the various windshield terminals. This invention can accurately measure the actual cooling capacity of water-cooled central air conditioning terminal windshields under different operating conditions, providing a basis for billing based on cooling capacity and contributing to the economical use of air conditioning. Attached Figure Description

[0065] Figure 1 This is a flowchart of the method of the present invention;

[0066] Figure 2A A schematic diagram of the structure of a water-cooled central air conditioning terminal windshield metering device and system;

[0067] Figure 2B This is a schematic diagram of the control unit.

[0068] Figure 3 This is a schematic diagram of a water-cooled central air conditioning system.

[0069] Figure 4A For reference, see the air conditioning unit cooling diagram. Figure 4B Schematic diagram of cooling supply for terminal fan units;

[0070] Figure 5A , Figure 5B This is a schematic diagram showing the distribution of the temperature detection module and the temperature uniformization process.

[0071] Figure 6 This is a schematic diagram of the temperature uniformity module;

[0072] Figure 7 This is a schematic diagram of the cooling capacity metering mapping principle of the present invention;

[0073] Figure 8A This is a schematic diagram of the mapping module. Figure 8B This is a schematic diagram of a neural network structure;

[0074] Figure 9 A schematic diagram illustrating the principle of temperature regulation by the end-of-line windshield.

[0075] In the diagram: 1000 water-cooled central air conditioning terminal air damper metering system, 100 water-cooled central air conditioning terminal air damper metering device, 200 server, 300 terminal fan unit, 400 driver, 500 chilled water pipe.

[0076] 110 Reference air conditioning unit, 120 Sensing and detection unit, 130 Operating condition adjustment unit, 140 User interface unit, 150 Control unit;

[0077] 111 Outdoor unit module, 112 Indoor unit module;

[0078] 121 Temperature detection module, 122 Flow detection module, 123 Image acquisition module;

[0079] 131 Temperature uniform module, 132 Vertical rotating shaft, 133 Bent-angle support arm, 134 Horizontal rotating shaft, 135 Telescopic support rod, 136 Tilting bracket, 137 Fan cover, 138 Temperature uniform fan, 139 Base.

[0080] 151 Input module, 152 Main processing module, 153 Image processing module, 154 Operating condition processing module, 155 Fan processing module, 156 Output module, 157 Storage module, 158 Mapping module;

[0081] 1541 Temperature Uniformation Processing Unit, 1542 Humidity Control Unit;

[0082] 1581 Neural Network, 1582 First Connection Matrix, 1583 Iterative Learning Unit, 1584 Second Connection Matrix;

[0083] 310 fan, 320 fan coil unit, 330 return air outlet. Detailed Implementation

[0084] The preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings, but the present invention is not limited to these embodiments. The present invention covers any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of the present invention.

[0085] To provide the public with a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the invention, but those skilled in the art can fully understand the invention without these details.

[0086] The invention is described in more detail below by way of example with reference to the accompanying drawings. It should be noted that the drawings are in a simplified form and use non-precise proportions, and are only used to facilitate and clarify the illustration of the embodiments of the invention.

[0087] Example 1:

[0088] Combination Figure 1 , Figure 7 As shown, the water-cooled central air conditioning terminal windshield metering method of the present invention includes the following steps:

[0089] S1. Based on the cooling capacity of the terminal windshield of the water-cooled central air conditioning system, a reference air conditioning unit is selected as the comparison of cooling capacity. The sensible cooling capacity of the reference air conditioning unit under different operating conditions is obtained according to standard tests, and the parameters are recorded as operating characteristics.

[0090] S2. A neural network is established using a vector composed of four scalars: chilled water supply flow rate of the water-cooled central air conditioning unit, supply and return water temperature difference, current temperature and humidity of the room, and the opening status values ​​of all terminal windshields of the water-cooled central air conditioning unit. The equivalent cooling capacity per unit time of the current windshield of the room is used as the output.

[0091] S3. Pre-cooling and initialization operating condition parameter settings: The room where the terminal air deflector is located is taken as the heat load, and the target temperature is set according to the natural temperature of the room; the room temperature is adjusted to the target temperature based on the cooling of the central air conditioning terminal air deflector, and the current humidity is obtained as the target humidity;

[0092] S4. Determine whether this is the first sample collection at the current target temperature. If so, maintain the room at the target temperature for a preset time and obtain the current humidity as the target humidity. Otherwise, proceed to S5.

[0093] S5. Collect the neural network training samples: Control the terminal fan and the reference air conditioning unit to cool independently in stages and alternately. In each stage, maintain the room temperature at the target temperature. The operating condition adjustment unit ensures that the temperature difference between multiple temperature detection modules located at different positions in the room is less than the temperature difference threshold.

[0094] The cooling capacity per unit time calculated by the reference air conditioning unit under the same operating conditions is used as the equivalent cooling capacity of the terminal windshield.

[0095] S6. Determine whether the sampling end condition is met. If yes, proceed to S7. Otherwise, change the operating conditions of the water-cooled central air conditioning unit and the terminal windshield, and proceed to step S4 to collect samples again.

[0096] S7. Train the neural network based on the training sample set and adjust the connection weights of the neural network;

[0097] S8. In the field environment, based on the current operating conditions, a trained neural network is used to predict the equivalent cooling capacity per unit time of the end windshield, and the predicted value is output.

[0098] Combination Figure 2A , Figure 4A , Figure 4B and Figure 7 As shown, the water-cooled central air conditioning terminal windshield metering system 1000 using the method of the present invention includes a water-cooled central air conditioning terminal windshield metering device 100, a server 200 for data communication and storage, and a driver 400 for driving the fan in the terminal fan unit 300.

[0099] like Figure 2A As shown, the water-cooled central air conditioning terminal fan baffle metering device 100 includes: a control unit 150, and a reference air conditioning unit 110, a sensing and detection unit 120, a user interface unit 140, and an operating condition adjustment unit 130 connected to the control unit 150. In this invention, the room where the water-cooled central air conditioning terminal fan baffle unit 300 is located is taken as the heat load. The reference air conditioning unit 110 serves as a reference for the varying cooling capacity of the room under different operating conditions. The sensing and detection unit 120 detects parameters of the operating conditions of the water-cooled central air conditioning unit and the reference air conditioning unit 110. The user interface unit 140 is used for inputting parameters and initiating operations, including display during human-machine interaction.

[0100] A water-cooled central air conditioning system consists of one or more cold / heat source systems and multiple terminal air conditioning systems. The operation of a central air conditioning system is essentially a heat transfer process. Figure 3 , Figure 4B As shown, the cold / heat source is the main unit. Taking refrigeration as an example, the main unit uses a compressor for refrigeration. After passing through a heat exchanger, the circulating water is cooled into chilled water, which is then transported to each terminal air conditioning system, i.e., the fan coil unit 320 in the figure, through the chilled water pipe 500. After the chilled water is supplied to each user's room through the fan coil unit, the water temperature rises. It then circulates back to the heat exchanger and is evaporated by the compressor refrigerant to remove heat and cool down to chilled water, thus continuously removing heat from the room. At the same time, the compressor refrigerant is drawn into the compressor and compressed into high-pressure vapor before being discharged to the condenser. The outdoor fan or cooling water system removes the heat from the condenser, i.e., a secondary heat exchange occurs on the condenser, and the hot air that carries away the heat emitted by the condenser is discharged into the outdoor environment.

[0101] refer to Figure 4BAs shown, the fan coil unit 320 is widely used in hotels, office buildings, hospitals, and other places. It is a working unit for heat exchange between indoor air and chilled water. Its working principle is that the fan 310 draws indoor air or a mixture of indoor and outdoor air, which flows through the surface cooler (i.e., the curved pipe through which chilled water flows) and is cooled before being sent into the room, thus lowering the indoor temperature to meet people's comfort requirements. The cooled air blown in is heated by the heat from people, equipment, and the walls in the room, and then circulates back to the fan coil unit 320 through the return air vent 330 for reheating.

[0102] In the heat transfer process of a central air conditioning system, the main unit delivers cooling capacity to each terminal fan. How much cooling capacity is consumed by each room through the terminal fan? This is the first question that must be answered when paying based on energy consumption.

[0103] Currently, the calculation of the cooling capacity of the terminal windshield is based on monitoring the status of the three-speed switch of the fan. It is obtained by weighted summation of the working time of the high, medium and low wind speed settings. However, the weight value, or coefficient, of each setting can often only be obtained from the data calibrated by the manufacturer under rated operating conditions.

[0104] The limitations of this fixed-weight coefficient measurement method are obvious. First, due to the influence of installation conditions, including distance from the main unit, heat exchange efficiency varies, and the actual air volume of different fans may differ from the nominal value of each air volume setting. Second, and more importantly, the operating conditions of a water-cooled central air conditioning system, including each terminal air deflector, are dynamically changing. Using a fixed quantity to calculate an actually changing quantity is clearly unreasonable.

[0105] In each room where cooling is used, not only will there be a difference between the fan baffle and the nominal value, but the total cooling output of the main unit will also vary. Furthermore, the distribution of this total cooling output by each terminal fan baffle is not a simple linear proportional relationship, but rather there is a mutual constraint between them, that is, there is a non-linear coupling relationship between each fan baffle.

[0106] Therefore, this invention treats the water-cooled central air conditioning system, including each terminal windshield, as a whole, and considers the distribution of cooling capacity among each terminal windshield as a black box. Based on nonlinear modeling theory, it models the mapping relationship between the key operating conditions of the system and the cooling equivalent of the terminal windshield.

[0107] To identify the model, a dataset for identification is needed. This includes obtaining the equivalent cooling capacity under different operating conditions. How is the equivalent cooling capacity obtained? Currently, the enthalpy difference method of the heat transfer medium is commonly used.

[0108] This method was first used in central air conditioning billing systems to construct heat meters to measure the amount of heat used for heating. The heat meter consists of a hot water flow meter, a pair of temperature sensors, and an integrator. Its working principle is that hot water supplied by a heat source flows into the heat exchange system at a relatively high temperature and flows out at a lower temperature. During this process, heat is provided to the user through heat exchange. The amount of heat received by the user within a certain time period can be calculated using the following equation:

[0109] E=∫K(Ts-Tr)dV,

[0110] Where E is the heat output of the heat exchange system, K is the correction coefficient for the specific gravity and specific heat of hot water, Ts and Tr are the supply and return water temperatures, respectively, and V is the flow rate of hot water through the heating system over a period of time.

[0111] The main errors in the enthalpy difference method stem from the measurement of the working fluid flow rate and the determination of its enthalpy value, especially with smaller flow rates. Similarly, there are methods that calculate cooling capacity by detecting the supply and return air on the air side. Compared to water-side metering, air-side metering reduces the accuracy requirements of temperature measurement equipment and instruments because the supply air temperature difference is significantly larger than the supply and return water temperature difference. However, both air-side and water-side metering currently primarily involve setting measurement points on the supply and return lines of the working fluid at the end. Due to the smaller flow rate at the end and the much greater fluctuations in temperature and flow parameters compared to the main unit, a trade-off exists between sensor accuracy and instrument cost.

[0112] Based on the above research, to improve the model's generalization ability and prediction accuracy, this invention uses a vector composed of four scalars—chilled water supply flow rate of the central air conditioning unit, supply and return water temperature difference, current room temperature and humidity—and the opening status values ​​of all terminal air dampers as input quantities. The equivalent cooling capacity per unit time of the current air damper in the room is used as the output quantity. A neural network is established in the control unit as the terminal air damper metering mapping model. Notably, the two key influencing factors, flow rate and temperature difference, only require one measurement point. More importantly, the measured flow rate is on the main pipeline, which is much larger than the terminal flow rate, effectively improving measurement accuracy.

[0113] This invention uses the room where the water-cooled central air conditioning terminal windshield is located as the heat load, and uses a reference air conditioning unit as the reference object for the varying cooling capacity of the room under different operating conditions to obtain the equivalent value of cooling capacity in the data sample required for system identification.

[0114] Specifically, such as Figure 7 As shown, the reference air conditioning unit, i.e. the reference machine, is an integrated portable air conditioner. First, its sensible cooling capacity under different operating conditions is obtained according to standard tests, and the parameters are recorded as a working characteristic table or curve. A second mapping from the operating conditions of the reference machine to the cooling capacity is established, which provides a basis for calculating the sample cooling capacity in the room where it works at the end of the windshield.

[0115] Then, under various input combinations, training samples for the established neural network are acquired offline. Based on the characteristics of cooling capacity transfer from the main unit to the terminal fan baffles in a water-cooled central air conditioning system, to address the impact of system time delay and large inertia, [further details are needed]. Figure 4A , Figure 4B As shown, the present invention configures the control unit to collect sample data of the end-windshield metering mapping model by independently cooling the end-windshield and the reference air conditioning unit in stages and alternately. For example, the end-windshield can be cooled first and then the reference air conditioning unit, or the reference air conditioning unit can be cooled first and then the end-windshield; and the number of times the two are cooled independently can be different, such as alternating between two independent coolings of the end-windshield and one independent cooling of the reference air conditioning unit, and vice versa.

[0116] For example, sample data for the end-windshield metering mapping model can be collected in the following manner:

[0117] The control unit drives the fan corresponding to the terminal air deflector of the water-cooled central air conditioner via a driver;

[0118] Using the room where the water-cooled central air conditioning terminal fan is located as the heat load, a target temperature is set based on the room's initial temperature. The operating condition adjustment unit ensures that the temperature difference between multiple temperature detection modules located at different positions within the room is less than a temperature difference threshold. The control driver adjusts the room temperature to the target temperature using the central air conditioning terminal fan and obtains the current humidity as the target humidity.

[0119] After the room temperature is maintained at the target temperature for a period of time, the drive is turned off and a timer is started. The reference air conditioning unit is controlled to maintain the room temperature at the target temperature from t=0 to t=T1 in the first time period. At the same time, the operating condition adjustment unit is controlled to maintain the room humidity at the target humidity. The cooling power q(t) is calculated based on the operating characteristics and conditions of the reference air conditioning unit, and the cooling capacity in the first time period is accumulated.

[0120] After turning off the reference air conditioning unit and timing again, and setting the fan damper opening value F, the driver is controlled to operate in PWM mode to maintain the room temperature at the target temperature from the second time period from t=0 to t=T2, and its equivalent time is calculated. Where Δ(t) is the PWM value,

[0121] The drive is turned off again and the timing is restarted. The reference air conditioning unit is controlled to maintain the room temperature at the target temperature during the third time period from t=0 to t=T1. At the same time, the operating condition adjustment unit is controlled to maintain the room humidity at the target humidity. The cooling capacity for the third time period is calculated again.

[0122] Calculate the equivalent cooling capacity per unit time of the windshield under the current operating conditions:

[0123] Preferably, the duration of the third time period can differ from that of the first time period, such as the difference being within 20%. Preferably, the activation state value F represents three speeds: low, medium, and high.

[0124] During continuous sample collection, data can be collected in the second time period simply by changing the operating conditions of the water-cooled central air conditioning system, while the first and third time periods can be re-collected every few samples. The first sample collection after changing the target temperature should be carried out sequentially in three time periods.

[0125] Combination Figure 7 As shown, this invention establishes a neural network in the control unit as the first mapping from the operating state of the central air conditioning system's fan terminal damper to the equivalent cooling capacity. To avoid errors caused by low flow rates at the terminal, the parameters of the measuring point at the high flow rate section of the main duct are used as the input for the mapping. In the working room, the terminal damper and the reference air conditioning unit alternately cool, and the measurement of the terminal damper to be measured is based on equal cooling capacity. That is, the cooling capacity obtained by the reference air conditioning unit under the same operating conditions through the second mapping is used as the equivalent cooling capacity of the terminal damper. During this process, the heat load conditions of the two cold sources, i.e., the heat flux density flowing into the room under cooling conditions, are the same through sensing and control of the operating conditions to ensure the reliability of the equal-capacity measurement.

[0126] Without loss of generality, when the terminal fan of a water-cooled central air conditioning system is operating stably online, the room humidity will remain relatively stable, mainly due to seasonal climate constraints, once the main unit settings are determined. Therefore, the current room humidity during central air conditioning cooling is used as the target humidity, and the room humidity is maintained at this target humidity when the reference air conditioning unit is cooling.

[0127] For room temperature, the target temperature needs to be set based on the initial temperature of the room under natural conditions without cooling. Preferably, the temperature difference between the target temperature and the initial temperature is ≥5℃, and the load rate of the terminal windshield during sample collection is greater than the set value, such as allowing its power to reach 0.5 to 1 times the rated power.

[0128] When cooling is provided, the ratio of the cold air inlet area to the room surface area creates a temperature gradient within the room, which may cause a deviation in the heat load when the two cooling sources are operating. To reduce this heat load deviation, see [link to relevant documentation]. Figure 5A , Figure 5B and combined Figure 6 As shown, the present invention sets multiple temperature detection modules 121 at different locations in the room, and uses the temperature uniform module 131 in the operating condition adjustment unit to make the temperature difference between these temperature detection modules less than the temperature difference threshold.

[0129] Specifically, such as Figure 6 As shown, the operating condition adjustment unit includes a temperature equalization module 131, which comprises a base 139, a vertical rotation shaft 132, a curved support arm 133, a horizontal rotation shaft 134, and a tiltable bracket 136 with two sections of support arms movably connected by bolts. A telescopic support rod 135, forming an acute angle with the axis of the horizontal rotation shaft 134, is connected between the outer ends of the two support arms. A temperature equalization fan 138 is mounted at the end of the tiltable bracket 136. Preferably, the temperature equalization fan 138 has a fan cover 137 on its back. The sensing and detection unit includes an image acquisition module 123, which can be located at the bottom of the curved support arm 133, thereby acquiring a global image of the room through the rotation of the vertical rotation shaft 132.

[0130] See Figure 2B As shown, preferably, the control unit 150 includes an input module 151, a main processing module 152, an image processing module 153, an operating condition processing module 154, a fan processing module 155, a mapping module 158, and an output module 157. The operating condition processing module 154 further includes a temperature uniformity planning unit 1541 and a humidity adjustment unit 1542. The control unit is also configured to:

[0131] The main processing module responds to events and schedules other modules.

[0132] Based on the room images acquired by the image acquisition module, the image processing module 153 analyzes the room's orientation features and extracts two mutually perpendicular diagonals. The orientation features include the distribution and length of the room's structural edges, as well as the direction and distance of the cold source air outlet, return air outlet, and temperature equalization module 131 relative to the corners of the room.

[0133] Combination Figure 5A , Figure 5B As shown, the temperature uniformity planning unit 1541 in the operating condition processing module 154 plans the operating trajectory of the temperature uniformity module 131 based on the above-mentioned orientation features, so that the axis of the temperature uniformity fan 138, i.e. the end it points to, moves in a spatial spiral to deliver the cold air blown out by the central air conditioning terminal windshield and / or the reference air conditioning unit to each area of ​​the room until the temperature difference of multiple temperature detection modules in the room is less than the temperature difference threshold.

[0134] The trajectory planning can be based on the obtained room diagonals. A main diagonal is formed by connecting the location of the cold air outlet to the furthest point in the room it can reach, or to the opposite side. A secondary diagonal is formed by drawing a line perpendicular to this line. Then, a spiral trajectory is planned around the main diagonals. Figure 5AIn the diagram, the black dot represents the air outlet located in the corner. A spiral curve, centered on the diagonal line of the dot, is planned to move around the inner wall of the cone as the target trajectory. The main diagonal line is the center line of the cone. Figure 5B In this design, the air outlet is located in the middle of one side wall. Using this side wall as the base of a cylinder, a spiral trajectory is planned around the inner wall of the cylinder, with the main diagonal being the center line of the cylinder. The planned trajectory must avoid the return air vent to prevent heat loss.

[0135] As a preferred approach, typical indoor orientation features can be summarized and classified, and the terminal trajectory curve of each orientation category can be planned based on geometric equations. The output angles of each joint in the temperature equalization module, including the vertical rotation axis, the horizontal rotation axis, and the telescopic support rod, can be analyzed based on inverse kinematics.

[0136] Alternatively, on-site teaching can be used to store the joint angles corresponding to the trajectory as a data sequence, and then the joints can be controlled online according to this sequence. If the air outlet is located in the middle of the room, multiple trajectories can be planned similarly.

[0137] Moving along a planned trajectory allows for rapid cooling of all areas of the room, reducing temperature gradients between them. The temperature uniformity planning unit controls the temperature uniformity module based on this planned trajectory. During each time period of sample collection, it first operates at a constant speed to achieve general cooling. To further reduce regional temperature differences, and as a preferred method, it then adjusts the linear velocity of the temperature uniformity fan along the planned trajectory based on the temperature characteristics of multiple temperature detection modules in the room. This linear velocity is inversely proportional to the temperature difference between the temperature at the corresponding trajectory point and the target temperature. The temperature at each trajectory point can be calculated through interpolation based on the temperature values ​​from multiple temperature measurement points. This speed planning improves the uniformity of room temperature across different spatial points, thereby ensuring consistent operating conditions and enhancing the generalization ability of the metrology model.

[0138] The temperature equalization module operates periodically. When the temperature difference between the highest and lowest temperatures among the multiple temperature detection modules is less than a temperature difference threshold, it stops operating and can collect data samples. When the temperature difference is detected to exceed the threshold, it restarts, thereby dynamically balancing the overall room temperature at the target temperature. Preferably, the temperature difference threshold is a value between 0.1℃ and 0.3℃.

[0139] During the second time period of sample collection, the fan processing module in the control unit adjusts the PWM wave duty cycle of the driver based on the average of multiple temperatures at different locations in the room, i.e., the overall room temperature. (Reference) Figure 9As shown, based on the error value e(t) between the target temperature and the current overall room temperature, the fan processing module in the host unit calculates the PWM value connected to the fan driver based on the PID control law, and changes the fan speed by changing the drive power pulse width of the driver, so that the error value e(t) dynamically approaches 0.

[0140] The operating condition processing module is also equipped with a humidity control unit, which controls the operation of the humidity control module in the operating condition control unit based on the monitoring of humidity measurement points in the room, so that the humidity of the room is maintained at the target humidity.

[0141] Combination Figure 3 , Figure 5A , Figure 5B As shown, the sensing and detection unit has a humidity detection module in the middle of the room return air duct; a flow detection module 122 and a water temperature detection module 121 are installed at the inlet of the chilled water supply main pipe of the central air conditioning unit; a water temperature detection module is installed at the outlet of the return water main pipe connected to the unit; and multiple temperature detection modules are installed at different locations in the room, which can be installed at the same height and located on two vertical diagonals respectively.

[0142] Preferably, there are 3 to 6 temperature detection modules, set at a height of about 2 meters; the average value of the temperature values ​​from the multiple temperature detection modules can be used as the current temperature of the room.

[0143] During sample collection, the chilled water supply flow rate and supply-return water temperature difference of the central air conditioning unit can be kept constant by adjusting the operating power of the unit. Preferably, these two parameters can be taken as the average value during the sample collection period based on the ratio of cooling capacity to temperature difference and flow rate.

[0144] As a preferred option, the multiple temperature detection modules set up to control the heat load of the two cold sources are only used for sample collection. Therefore, one of the temperature detection modules, such as the module near the temperature measurement point of the return air vent, can be selected as the current room temperature in the neural network input when applying online, thereby simplifying the system structure and facilitating actual operation.

[0145] For each terminal fan baffle of a water-cooled central air conditioner, the fan baffle opening status value can be taken as the normalized value of the three fan power corresponding to the low, medium and high fan speeds. For example, the highest power value is taken as 1, and the power of the other two speeds is calculated proportionally.

[0146] The cooling system of a water-cooled central air conditioning system is a nonlinear hysteresis system. Therefore, changes in operating parameters require a period of time to reflect their impact. To address this, this invention sets sampling conditions when collecting training samples. Before sampling, the system is brought to a steady-state operating state. When the reference air conditioning unit and the terminal fan deflector are cooling, the room conditions are maintained for a period of time to eliminate the randomness of short-duration sampling. At the same time, by sampling the reference air conditioning unit before and after the terminal fan deflector is cooling, the influence of slow fluctuations in operating conditions on the sampled data is eliminated, thus improving the prediction accuracy of the network model.

[0147] The neural network is trained using the collected sample set. In the field environment, the trained neural network predicts the equivalent cooling capacity per unit time of the current windshield and outputs the predicted value through the output module. This value can be used as the basis for billing each terminal of the water-cooled central air conditioning system.

[0148] like Figure 7 As shown, with the working room as the heat load, the equivalent cooling capacity provided by the terminal air deflector under the same operating conditions is obtained through a second mapping using a reference air conditioning unit. Therefore, the operating characteristics of the portable reference air conditioning unit must be obtained beforehand through high-precision calibration.

[0149] See Figure 4A As shown, the reference air conditioning unit 110 includes an outdoor unit module 111 and an indoor unit module 112. This reference air conditioning unit can be equipped with dry-bulb and wet-bulb temperature detection modules and an airflow detection module. A test apparatus based on the room air enthalpy method is established according to room air conditioning standards to test its sensible cooling capacity under different operating conditions, and the parameters are recorded as a working characteristic table or curve, serving as the working characteristics of the reference air conditioning unit. Then, when collecting samples, based on the current dry-bulb and wet-bulb temperatures and airflow of the inlet and outlet air, the cooling capacity of the reference air conditioning unit under the current operating conditions is calculated by querying and interpolating this working characteristic.

[0150] In the air enthalpy method, the formula for calculating sensible cooling is:

[0151] φ sc =q m ·c pa ·(t a1 -t a2 ) / V n ·(1+W n ),

[0152] in, q represents the sensible cooling capacity (W). m The air supply volume (m³) at the measuring point 3 / s), V n The specific volume of moist air at the measuring point (m³) 3 / kg), W n t represents the air humidity at the measuring point. a1 and t a2 The return air and supply air temperatures (°C) and the specific heat capacity at constant pressure (c) are respectively. pa =1005 + 1846W n (J / (kg·K)).

[0153] In the reference air conditioning unit, dry-bulb and wet-bulb temperatures are detected by sensors placed in an insulated section at the supply and return air vents, respectively. The dry-bulb and wet-bulb sensors are used to detect supply air temperature, return air temperature, and humidity; these parameters can also be obtained using temperature and relative humidity sensors. During training sample collection, the reference air conditioning unit, under the command of the control unit, adjusts its operating frequency to maintain the room temperature at the target temperature.

[0154] Preferably, the operating characteristic data of the reference air conditioning unit is recorded in tabular form, and the sensible cooling capacity under the current operating condition is calculated based on the table lookup and multidimensional interpolation.

[0155] Preferably, the rated cooling power of the reference air conditioning unit is 0.85 to 1.15 times the maximum cooling capacity of the terminal fan.

[0156] Combination Figure 8A , Figure 8B As shown, the control unit establishes a neural network 1581 in the mapping module 158 as a terminal windshield metering mapping model. The input layer of this neural network receives input from the main processing module 152, and the output of the output layer is transmitted to the iterative learning unit 1583 and the main processing module 152 through the first connection array 1582 and the second connection array 1584, respectively. When training the neural network offline, the iterative learning unit 1583 adjusts the connection weights of the neural network 1581 until the learning ends, based on the actual value of the equivalent cooling capacity of the windshield per unit time input by the main processing module 152 and the network output value of the neural network 1581 through the first connection array 1582, respectively. During online metering, the first connection array 1582 is disconnected, and the neural network 1581 predicts the equivalent cooling capacity of the windshield per unit time and outputs it to the main processing module 152 through the second connection array 1584. The main processing module 152 processes and analyzes the data and outputs it through the output module 156. Figure 2A As shown, the output module can transmit the measurement results to the user interface unit 140 for display, or store them in the server 200.

[0157] Preferably, the neural network is a BP neural network, and its model is as follows:

[0158] The output of the j-th node in the hidden layer is

[0159] The output of the output layer is

[0160] Where x1~x4 are four scalars: chilled water supply flow rate of the central air conditioning unit, supply and return water temperature difference, current temperature and humidity of the room; x5~xn are the opening status values ​​of all terminal fan dampers; the f() function is taken as the sigmoid function, w ij and v j These are the connection weights from the input layer to the hidden layer and the connection weights from the hidden layer to the output layer, θ. j θ and θ are the thresholds for the hidden and output layers, respectively, and n and k are the number of nodes in the input and hidden layers, respectively. Gradient descent is used for network training.

[0161] As an alternative, other redundancy factors, such as the air supply temperature of the terminal windshield, can be added to the network input.

[0162] Because the chilled water supply pipeline is shared, and the pipeline water pressure decreases progressively with the distribution of chilled water, the actual cooling capacity of the terminal air dampers of water-cooled central air conditioning fan coil units depends not only on the model and damper setting, but also on their distance from the main unit. Therefore, for terminal air dampers of the same model, they can be further subdivided into smaller categories based on the horizontal and vertical distances of the terminal air damper from the inlet of the chilled water supply pipeline of the central air conditioning unit. A neural network is established for each subcategory, and training samples are collected separately, thereby making the prediction and measurement of cooling capacity equivalent more accurate.

[0163] Preferably, for each category, a single end windshield is selected for sample acquisition and training. For the neural network of each category's end windshield, when collecting training samples, a room with minimal external temperature variation is selected as the heat load, allowing for continuous sample collection and shortening the overall sample set sampling time. Therefore, rooms inside the building can be used as the collection environment. Preferably, for rooms located at the corners of the building, sample collection is conducted at night or during periods of daytime without direct sunlight.

[0164] Example 2:

[0165] If the target temperature is initially set too high or too low during the collection of training samples, or if the power of the terminal fan is not matched with the heat load of the room, the terminal fan will be limited to low power or high power operation when collecting samples.

[0166] Therefore, in order to ensure that the sample covers different load rates of the terminal fans from low to high, in this embodiment, when the control unit controls the driver to operate in PWM mode, it switches the terminal fan damper state to different levels during a sample acquisition period. The equivalent time is calculated by multiplying the normalized speed of the fan motor at different fan damper levels by the integral of the duty cycle Δ(t) within the period. Where k(t) is the normalized rotational speed, which is 1 for the highest speed and the ratio of the rotational speed to the highest speed for other speeds.

[0167] For the same reason, this embodiment can also use the following method to obtain training samples. The reference air conditioning unit is a heating and cooling air conditioner; if the ratio of the equivalent time dT to the duration T2 of the second time period is less than the duty cycle threshold Δs, when collecting the samples, within the second time period, the reference air conditioning unit is also controlled to operate in heating mode from τ=0 to τ=T3, and the heating equivalent is recorded. Accordingly, the equivalent cooling capacity per unit time of the windshield under the current operating conditions is calculated:

[0168]

[0169] Preferably, an electric heating module can also be set in the reference air conditioning unit, and the electric heating module is controlled to heat from τ=0 to τ=T3 in time range, and the heat equivalent Q3=pr·T3 is recorded, where pr is the heating power of the electric heating module (kW or kJ / s), and PF is calculated similarly.

[0170] Preferably, within the time ranges T1 and T2, the heating module can be turned on with a known power and heat calculation can be performed, thereby expanding the operating condition coverage of the reference air conditioning unit and the water-cooled central air conditioning terminal windshield.

[0171] The reference air conditioning unit's operating characteristics represent a mapping between its operating conditions and sensible cooling capacity. In this embodiment, this mapping can also be represented by a second neural network. The inputs to this network can be selected as the dry and wet bulb temperatures of the inlet and outlet air and the air volume; or preferably, electrical power, outdoor condenser temperature, indoor evaporator temperature and humidity; or preferably, compressor operating frequency, blower motor power, supply and return air temperature and humidity.

[0172] To avoid errors caused by detecting small airflow at the terminal units during online applications, this invention uses the main chilled water supply pipeline of the central air conditioning system as the measuring point, detecting its flow rate and the supply-return water temperature difference as inputs to the fan damper metering mapping model. Furthermore, through consistent control of the heat load conditions, a reference air conditioning unit is used as the calculation reference for the cooling capacity supplied by the terminal fan dampers. The nonlinear mapping model is trained using a sample set, and during online applications, the trained model is used to predict and calculate the cooling capacity of the water-cooled central air conditioning terminal fan dampers, providing a basis for central air conditioning billing. This invention ensures metering accuracy without increasing costs and decouples the mutual constraints between the various fan damper terminals, enabling accurate measurement of dynamically changing cooling capacity.

[0173] It is understood that by interchangeding the cooling and heating operating conditions in this invention, this invention is also applicable to the metering of the air deflectors at the terminal of a central air conditioning system during the heating season.

[0174] The foregoing has described several embodiments of the present invention, but these embodiments are merely illustrative examples and do not limit the scope of the invention. These embodiments can be implemented in various other ways, and various omissions, substitutions, combinations, and modifications can be made without departing from the spirit of the invention. These embodiments or their variations are included within the scope or spirit of the invention, and are similarly included within the scope of the invention as described in the claims and its equivalents.

Claims

1. A water-cooled central air conditioner terminal damper metering method, comprising the following steps: S1, selecting a reference air conditioning unit as a cooling capacity reference based on the cooling capacity of a water-cooled central air conditioner terminal damper, obtaining the sensible cooling capacity of the reference air conditioning unit under different working conditions according to standard tests, and recording the parameters as working characteristics; S2, establishing a neural network by taking the chilled water supply flow, the chilled water supply and return temperature difference, the current temperature and humidity of the room, and the opening state vector of all terminal dampers of the water-cooled central air conditioner as input quantities, and taking the cooling capacity equivalent of the current terminal damper per unit time as the output quantity; S3, pre-cooling and initialization working condition parameter setting: taking the room where the terminal damper is located as the heat load, setting the target temperature according to the natural temperature of the room, and adjusting the temperature of the room to the target temperature based on the cooling of the central air conditioner terminal damper and obtaining the current humidity as the target humidity; S4, determining whether it is the first sample collection at the current target temperature, if so, keeping the room at the target temperature for a predetermined time, and obtaining the current humidity as the target humidity, otherwise, turning to S5; S5, collecting the neural network training samples: controlling the terminal damper and the reference air conditioning unit to independently cool in stages in an alternating manner, maintaining the temperature of the room at the target temperature in each stage, and adjusting the working condition by the working condition adjusting unit to make the temperature difference between the multiple temperature detection modules at different positions in the room less than a temperature difference threshold, taking the cooling capacity equivalent of the terminal damper per unit time as the cooling capacity equivalent of the terminal damper under the same working condition; S6, determining whether the sampling end condition is met, if so, turning to S7, otherwise, changing the working condition of the water-cooled central air conditioner host and the terminal damper, and turning to step S4 to collect samples again; S7, training the neural network based on the training sample set and adjusting the connection weight of the neural network; S8, in the field environment, predicting the cooling capacity equivalent of the terminal damper per unit time based on the current working condition using the trained neural network, and outputting the prediction value.

2. The water-cooled central air conditioner terminal damper metering method according to claim 1, wherein The process of independently cooling in stages in an alternating manner in step S5 is as follows: the reference air conditioning unit, the terminal damper, and the reference air conditioning unit are sequentially used as the cooling source and work for T1, T2, and T1 time lengths respectively in three time periods, wherein, the temperature difference between the multiple temperature detection modules at different positions in the room is less than a temperature difference threshold in each time period; the humidity of the room is adjusted and maintained at the target humidity in the first and third time periods; the temperature of the room is maintained at the target temperature by controlling the driver of the terminal damper fan to work in PWM mode in the second time period, calculating the equivalent refrigerating capacity per unit time of the fan at the current open state value F of the damper: wherein the first period refrigeration capacity the third period refrigeration capacity the equivalent time of the second period wherein the refrigeration power q(t) is calculated based on the working characteristics and the working condition, and Δ(t) is the PWM value.

3. The water-cooled central air conditioner terminal air barrier metering method according to claim 2, characterized in that, the step S5 comprises: S51, close the end damper of the room and start timing, control the reference air conditioning unit to maintain the room temperature at the target temperature from t=0 to t=T1, and control the working condition air conditioning unit to maintain the room humidity at the target humidity, calculate the refrigeration power q(t) based on the working characteristics and the working condition, and accumulate the refrigeration amount of the first period S52, turn off the reference air conditioning unit and time again, set the opening state value F of the end dam, control the corresponding drive of the fan to work in PWM mode and maintain the room temperature at the target temperature from t=0 to t=T2 second period, calculate its equivalent time where Δ(t) is the PWM value, S53, again closing the end damper and restarting the timer, controlling the reference air conditioning unit to maintain the room temperature at the target temperature from t=0 to t=Tl, while controlling the working condition air conditioning unit to maintain the room humidity at the target humidity, again calculating the third period of time, the cooling capacity S54、calculating the equivalent refrigerating capacity per unit time of the damper under the current working condition:

4. The water-cooled central air conditioner terminal damper metering method of claim 1, wherein, the chilled water supply flow and the chilled water supply and return temperature difference in the input quantity of the neural network are obtained by measuring points arranged adjacent to the central air conditioner host at the chilled water supply and return main pipe, The temperature detecting modules can be arranged at the same height and on two vertical diagonal lines respectively, the temperature difference threshold can be a value between 0.1℃ and 0.5℃, and the current temperature of the room can be the average of the temperatures of the multiple measuring points or the temperature of the return air outlet; The humidity is sensed by the humidity detecting module arranged in the middle of the return air duct of the room.

5. The water-cooled central air conditioner terminal damper metering method according to claim 1, wherein The air circulation in step S5 is realized by a uniform temperature module, which further comprises: Collecting images of the room, extracting orientation features and two mutually perpendicular diagonal lines through image processing, the orientation features including the distribution and length of the structural edges of the room, and the direction and distance of the cold air outlet, the return air outlet and the uniform temperature module relative to the corners of the room; Taking the line connecting the position of the cold air outlet to the farthest position of the room reached by the cold air outlet or the opposite position of the side as one of the main diagonal lines, and taking a straight line perpendicular to the main diagonal line as the other main diagonal line; then, planning a spiral trajectory with the main diagonal line as the axis; Making the end fan axis of the uniform temperature module, i.e. the end direction, move according to the planned trajectory to deliver the cold air blown out by the end baffle of the central air conditioner and / or the reference air conditioning unit to each area of the room until the temperature difference of the multiple temperature detecting modules in the room is less than the temperature difference threshold.

6. The water-cooled central air conditioner terminal air baffle metering method according to claim 5, characterized in that, The movement according to the planned trajectory specifically includes: in each time period of sample collection, first, uniform speed operation; then, according to the temperature distribution characteristics of the multiple temperature measuring points in the room, changing the linear speed of the end fan moving according to the trajectory, so that the linear speed is inversely proportional to the temperature difference value between the temperature at the trajectory point corresponding to the linear speed and the target temperature.

7. The water-cooled central air conditioner terminal air barrier metering method according to claim 1, wherein The end baffle opening state value can be respectively taken as the normalized value of the fan power corresponding to the low, medium and high three gears of the fan speed, with the highest power value as 1, and the other two power values are proportionally converted.

8. The water-cooled central air conditioner terminal air barrier metering method of claim 1, wherein, The working characteristics of the reference air conditioning unit are obtained based on a room air enthalpy method test device, by testing the sensible heat of the reference air conditioning unit under different working conditions and recording the working condition parameters and the sensible heat as a working characteristic table or curve, when collecting samples, based on the current working condition parameter value, the refrigerating capacity of the reference air conditioning unit under the current working condition is calculated by querying and interpolating the table or curve.

9. The water-cooled central air conditioner terminal air barrier metering method of claim 1, wherein, According to the specific model of the end baffle, and the horizontal and vertical distances of the end baffle from the inlet of the chilled water supply main pipe of the central air conditioner host, all end baffles are classified, and a neural network is established for each classification and training samples are collected.

10. The water-cooled central air conditioner terminal air barrier metering method according to claim 3, characterized in that, When the driver in step S5 works in PWM mode, the equivalent time is calculated by multiplying the integral of the normalized speed of the fan motor at different wind speed of the wind deflector by the on-duty ratio Δ(t) in a period where k(t) is the normalized speed, for example, the highest speed is 1, and the speed value at other speeds is the ratio of the speed value to the highest speed.

11. The water-cooled central air conditioner terminal air baffle metering method according to claims 1-9, characterized in that, The neural network adopts a BP neural network, and the model is: The output of the jth node of the hidden layer is The output of the output layer is Wherein, x1-x4 are four scalar values of chilled water supply flow, supply and return water temperature difference, current temperature and humidity of the central air conditioner host, x5-xn are all end dam opening state values; f() function is sigmoid function, w ij and v j are connection weights from input layer to hidden layer and from hidden layer to output layer, θ j and θ are hidden layer and output layer thresholds, n and k are input layer and hidden layer node numbers, and gradient descent method is used for network training.

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