Working condition adjusting device for central air conditioner charging

Abstract

The invention discloses a working condition adjusting device for central air conditioner charging, which comprises a temperature uniformizing module and a humidity adjusting part, a base, a vertical rotating shaft, a bent angle support arm, a horizontal rotating shaft, a pitching support with two sections movably connected with the support arm through bolts and a telescopic support rod which are connected in sequence are arranged in the temperature uniformizing module, and a temperature uniformizing fan is arranged at the tail end of the temperature uniformizing module; a tail end air supply track curve is planned based on the azimuth characteristics of a room, the movement angle of each joint is calculated, the movement speed is planned synchronously according to the temperature of a track point, air is supplied according to the planned track and speed, and the temperature gradient in the room is reduced. Therefore, the thermal load deviation is controlled when two cold sources of the central air conditioning terminal unit and the reference air conditioning unit work. According to the method, the generalization ability of the cooling capacity metering model is improved by controlling the consistency of the working conditions, fair charging according to the effective cooling capacity is achieved by accurately metering the actual cooling capacity of the central air conditioner terminal unit under different working conditions, and the air conditioner can be saved and utilized.

Description

[0001] This application is a divisional application of application No. 202211358529.1, titled "Central air conditioning billing device and system", with a filing date of November 1, 2022. TECHNICAL FIELD

[0002] The present application relates to the field of central air conditioning billing, in particular to a working condition adjusting device for central air conditioning billing. BACKGROUND

[0003] A central air conditioning system is composed of a cold and heat source system and a cold and heat transfer and air conditioning system. In summer, a refrigeration system provides the required cold energy for the air conditioning system to offset the heat load of the indoor environment; in winter, a heating system provides cold energy for the air conditioning system to offset the heat load of the indoor environment. Central air conditioning supplies cold to each terminal unit through a refrigerant pipeline, and the refrigerant can be any of water, air or refrigerant. Taking a water-cooled central air conditioning system as an example, it is an air conditioner that supplies cold energy to different rooms through a main machine connected to multiple fan coil terminal units to achieve the purpose of indoor air conditioning.

[0004] The most prominent feature of central air conditioning is to provide a comfortable working and living environment. With the rapid development of air conditioning demand in domestic large public buildings, more and more office buildings, shopping malls, hotel apartments and other buildings begin to install central air conditioning systems.

[0005] In the new century, mankind is facing two major problems of energy and environment, namely energy shortage and environmental degradation. The construction industry, industrial production and transportation have become China's three major energy consumption industries, accounting for about 30% of the country's energy consumption. The energy consumption of air conditioning and heating systems accounts for 50% to 60% of the 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, the billing method of central air conditioning in many places still uses the traditional and simple area allocation method; this method is simple and convenient, but under this billing method, most people do not consider whether the air conditioning use is energy-saving, resulting in the phenomenon of air conditioning being turned on even if no one is present, leading to energy waste.

[0006] If the energy metering method is used, pay as much as you use, then people's energy-saving awareness can be awakened. Adopting a reasonable billing method can change consumers' energy use habits, therefore, it is of great significance to choose and adopt a central air conditioning billing method reasonably.

[0007] The initial charging method of central air conditioning is area allocation, so there is a waste of air conditioning. Therefore, in recent years, a new household billing system based on central air conditioning has been proposed, hoping to reasonably allocate costs to each room user, achieve reasonable billing, and reduce the phenomenon of serious electricity waste and high building energy consumption.

[0008] In the new technology of central air conditioning cooling capacity metering and apportioning, chilled water metering method appears. Chilled water metering method is divided into two kinds. One is to install water meter at the outlet of fan coil, and to meter the chilled water flow in fan coil through water meter. This method simply solves the problem of different cost for different use, but does not consider the chilled water inlet and outlet temperature. The other method is just the opposite, considering that chilled water flow is constant, only the chilled water inlet and outlet temperature difference is metered, and only when the air conditioning is turned on, the cost is charged. These methods, including the subsequent improved energy meter method, detect several fixed parameters at the end, and cannot reflect the influence of overall working condition change of central air conditioning system on end cooling capacity supply, so it is difficult to reflect the real cooling capacity consumption of user.

[0009] Taking water-cooled central air conditioning as an example, another problem that needs attention in traditional cooling capacity metering is that the supply and return water temperature difference is much smaller than that of heating, so the measurement accuracy of temperature sensor needs to be higher. Undoubtedly, using high-precision sensor at each end will greatly increase the cost of user; and using ordinary sensor generally has the problem of large sampling fluctuation and inaccurate metering.

[0010] High-cost energy meter is difficult to promote. At present, according to the investigation, the new apportioning charging system of various central air conditioning mainly uses indirect charging or equivalent charging project, and among them, time type charging is the most. The cooling capacity equivalent or cumulative consumption of time type metering is the calculated amount under the rated test condition. The basic principle of CN 100504338C is to calculate the cumulative time of each fan coil speed. Time type charging starts from the proportionality of refrigeration capacity, water temperature difference of fan coil and water volume, and considers that the larger the fan coil speed, the larger the air volume flow, and the larger the cooling capacity equivalent. For the fan coil with high, medium and low speed V H , V M , V L , the cooling capacity equivalent is:

[0011] Q=K H t H +K M t M +K L t L ,

[0012] Wherein, t H , t M , t L are the opening time (s) of two-way valve under high, medium and low speed, K H , K M , K LThe proportional coefficient (kJ / s) under high, medium and low wind speed. The two-way valve itself is a switch component, by detecting the opening and closing of each two-way valve, so that the cumulative time of two-way valve opening in a certain time and the different gears of wind speed can be obtained. It can be seen that in the time type measurement method, the cooling capacity of each terminal fan of the central air conditioning system is calculated by using the cumulative value of the opening time of the two-way valve under different wind speeds as the basis. The key of this method is how to obtain the K H , K M , K L coefficient? The current method is to use the estimated empirical or theoretical value, or to use the coefficient value measured by the fan coil manufacturer based on the rated conditions such as dry bulb 27℃, wet bulb 19.5℃, chilled water inlet temperature 7℃, and the cooling capacity under each wind speed.

[0013] This method of calculating cooling capacity under dynamic conditions with fixed coefficient can only be used for estimation and cannot be accurately measured.

[0014] The cooling capacity measurement of central air conditioning system is the basis for cooling capacity billing, but many factors need to be considered from measurement to billing. For general buildings, the cooling cost cannot be simply calculated by multiplying the air conditioning cooling capacity by the cooling capacity unit price, and the influence of different positions of air conditioning rooms and inter-room heat transfer also needs to be considered.

[0015] Because the room heat load is related to environmental parameters, solar radiation, maintenance structure heat transfer characteristics and other factors, the heat load of rooms with different orientations such as south and north, and different heights such as top, middle and bottom layers may differ greatly. Therefore, the central cooling system should consider the cooling capacity or unit price of rooms in different positions and orientations. In addition, inter-room heat transfer is also an important factor. In the case of low or vacant use of adjacent rooms, the cooling load of air conditioning rooms will increase. If only the cooling capacity is charged, the cooling cost of rooms with the same area will differ greatly, which is unfair.

[0016] However, due to the coupling of sunlight, structure and adjacent room and other factors, it is difficult to independently distinguish the influence degree of each factor, and the empirical coefficient method is often used in existing billing methods. For example, Tian Yuchen of Tianjin University in the degree thesis "Research on Related Problems of Heat Metering" combined with the reference specification to calculate the correction coefficient of each household by using statistical method for different building structures, different room types and different orientations. The correction coefficient of rooms in different positions of non-energy-saving residential buildings is calculated, and the correction coefficient of 9 types of rooms is given as 0.55-1.00. This example method can only give a rough guide and cannot accurately determine the cooling consumption relationship of rooms in different directions of specific buildings.

[0017] Therefore, the industry urgently needs a set of devices and systems that can accurately measure the cooling equivalent of the central air conditioning terminal under actual working conditions, correct the cooling consumption of different orientation rooms, and thus reasonably allocate the billing based on cooling consumption and quantitatively correct the environmental factors, so as to achieve the goal of paying more for more use and paying less for less use, thereby ultimately improving the energy-saving awareness of the majority of users and achieving the purpose of energy saving and environmental protection. In order to make reasonable corrections, high consistency of working conditions needs to be provided for the cooling supply / cold consumption samples of the room. SUMMARY

[0018] Therefore, the purpose of the present application is to provide a working condition adjusting device for central air conditioning billing, which provides comparable working condition basis for accurate measurement of the cooling capacity of the terminal unit in the central air conditioning system, so that the central air conditioning billing system can reasonably and effectively bill according to the actual cooling consumption after quantitatively correcting the cooling consumption of each room.

[0019] The technical solution of the present application is to provide a working condition adjusting device for central air conditioning billing, which comprises a uniform temperature module and a humidity adjusting part; the uniform temperature module further comprises a base, a vertical rotating shaft, a bent arm, a horizontal rotating shaft, a pitchable bracket having two sections of arms connected by bolts, and a telescopic support between the outer ends of the two sections of arms, which is connected to the horizontal rotating shaft at an acute angle, and a uniform temperature fan carried at the end of the pitchable bracket,

[0020] The uniform temperature module is configured to make the shaft of the uniform temperature fan move in a spatial spiral line during the sample data collection process of the neural network by the central air conditioning billing system, so as to deliver the cold air blown by the central air conditioning terminal unit and / or the reference air conditioning unit to each orientation of the room until the temperature difference of the multiple temperature detection modules in the room is less than a temperature difference threshold,

[0021] The humidity adjusting part is configured to operate based on the monitoring of the humidity measuring points in the room when the reference air conditioning unit is working during the sample data collection process of the first neural network in the neural network by the central air conditioning billing system, so as to keep the humidity of the room at a preset target humidity.

[0022] As a preferred, the uniform temperature fan has a fan cover at the back. As a preferred, the multiple temperature detection modules located at the same height in the room are respectively located on two perpendicular diagonal lines in the room; the multiple temperature detection modules are 3-6, and the setting height is about 2 meters.

[0023] As preferred, in the central air conditioning billing system, the room where the terminal unit of the central air conditioning is located is taken as the heat load, the first neural network takes the working condition of the central air conditioning system as the input quantity, and takes the equivalent cooling capacity of the terminal unit per unit time as the output quantity, and the working condition adjusting device is further configured to: when collecting the first data sample for training the first neural network, set the target temperature according to the initial temperature of the room, control the terminal damper fan driver to operate to adjust the temperature of the room to the target temperature and obtain the current humidity as the target humidity; after the room is kept at the target temperature for a period of time, the reference air conditioning unit and the terminal damper are respectively taken as the cold source to work for a certain time to collect samples, and the working condition adjusting device is operated to keep the room temperature at the target temperature when the two kinds of cold sources work respectively, wherein the terminal damper works in PWM mode; based on the working characteristics and working conditions of the reference air conditioning unit, the cooling capacity consumption power is calculated and divided by the average PWM duty cycle of the terminal damper, and the obtained value is taken as the equivalent cooling capacity per unit time when the terminal damper is in the current open state value F.

[0024] As preferred, the neural network in the central air conditioning billing system further includes a second neural network, the second neural network takes the current room temperature and outdoor temperature as two scalar inputs, and takes the vector composed of the indoor temperature values of the current room in six directions of front, back, left, right, up and down as the input quantity, and takes the equivalent cooling capacity per unit time of the current room as the output quantity, and the working condition adjusting device is further configured to: adaptively control the terminal unit in the current room, operate the working condition adjusting device, and keep the room temperature unchanged in stages, and when the working condition is approximately stable, collect the second data sample for training the second neural network under different input conditions; wherein the output quantity in the sample is obtained by the first neural network.

[0025] As preferred, the working condition adjusting device is further configured to: the uniform temperature module is periodically operated, when the temperature difference between the highest temperature and the lowest temperature in the plurality of temperature detection modules is less than a temperature difference threshold, the operation is stopped and the data sample can be collected; when it is monitored that the temperature difference exceeds the threshold, the operation is started again so that the overall temperature of the room is dynamically balanced at the target temperature. Wherein, the temperature difference threshold can be a value between 0.1℃ and 0.3℃.

[0026] As preferred, the bottom of the corner arm is provided with an image acquisition module, the vertical rotating shaft is rotated to acquire a global image of the room, and the working condition adjusting device is further configured to: based on the room image acquired by the image acquisition module, the image processing module analyzes the room orientation features and extracts two mutually perpendicular diagonal lines; the uniform temperature module is planned to operate along a trajectory, so that the axis of the uniform temperature fan, i.e. the end thereof, moves along a spatial spiral line to deliver the cold air blown by the central air conditioner terminal unit and / or the reference air conditioning unit to each area of the room until the temperature difference of the multiple temperature detection modules in the room is less than a temperature difference threshold.

[0027] As preferred, the working condition adjusting device is further configured to: the orientation features include the distribution and length of the room structure ridge lines, and the directions and distances of the cold source air outlet, the return air inlet and the uniform temperature module relative to the corners of the room; based on the diagonal lines, the wind direction of the uniform temperature fan in the uniform temperature module is planned to move along a spiral trajectory, and the uniform temperature module is controlled to blow air along the trajectory at each time period.

[0028] As preferred, the trajectory is planned based on the obtained room diagonal lines, taking the line connecting the cold air outlet position to the farthest position of the room that can be reached by the cold air outlet or the position opposite to the side of the room as one of the main diagonal lines, and taking another straight line perpendicular to the main diagonal line as the other main diagonal line; when the air outlet is located at a corner, a curve moving along the inner wall surface of a cone in a spiral manner is planned as the target trajectory, with the main diagonal line as the center line of the cone; when the air outlet is located at the middle of a side wall, a curve moving along the inner wall surface of a cylinder in a spiral manner is planned as the target trajectory, with the main diagonal line as the center line of the cylinder. As preferred, the planned target trajectory avoids the return air inlet to avoid cold loss.

[0029] As preferred, the working condition adjusting device is further configured to: when the uniform temperature module is planned to operate along the trajectory, the output angles of the joints in the uniform temperature module, including the vertical rotating shaft, the horizontal rotating shaft and the telescopic support rod, are analyzed based on inverse kinematics.

[0030] As preferred, the joint angle data sequence corresponding to the trajectory can also be stored through on-site demonstration, and the joints are controlled in sequence according to the sequence.

[0031] As preferred, the working condition adjusting device is further configured to: based on the planned trajectory, at each time period of sample collection, first, the uniform temperature module operates at a uniform speed to generally lower the temperature; then, according to the temperature features of the multiple temperature detection modules in the room, the linear speed at which the uniform temperature fan moves along the trajectory is changed, so that the linear speed is inversely proportional to the temperature difference between the temperature at the corresponding trajectory point and the target temperature, wherein the temperature at each trajectory point can be obtained by interpolation calculation based on the temperature values of the multiple temperature detection points.

[0032] As preferred, the first neural network is trained by the following method:

[0033] After the room is kept at the target temperature for a period of time, the driver is turned off and the 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, and the working condition adjusting device is controlled to keep the room humidity at the target humidity. The refrigeration power q(t) is calculated based on the working characteristics of the reference air conditioning unit and the working condition, and the refrigeration amount during the first period is accumulated.

[0034] The reference air conditioning unit is turned off and the timer is started again. After setting the open state value F of the fan damper, the driver is controlled to work in PWM mode and maintain the room temperature at the target temperature from t=0 to t=T2. The equivalent time is calculated. Where Δ(t) is the PWM value,

[0035] The driver is turned off again and the timer is started again. The reference air conditioning unit is controlled to maintain the room temperature at the target temperature from t=0 to t=T1, and the working condition adjusting device is controlled to keep the room humidity at the target humidity. The refrigeration amount during the third period is calculated again.

[0036] The unit time refrigeration amount equivalent of the fan damper under the current working condition is calculated:

[0037] As preferred, the central air conditioner is a water-cooled central air conditioner, the terminal unit is a terminal fan damper, and the first neural network takes the four scalars of the chilled water supply flow of the central air conditioner host, the supply and return water temperature difference, the current temperature and humidity of the room, and the vector composed of the open state values of all terminal fan dampers as input quantities. Alternatively, the central air conditioner is a full-air central air conditioner, the terminal unit is a terminal fan damper, and the first neural network takes the four scalars of the supply air flow of the central air conditioner host, the supply and return air temperature difference, the current temperature and humidity of the room, and the vector composed of the open state values of all terminal fan dampers as input quantities. Alternatively, the central air conditioner is a variable refrigerant flow central air conditioner, the terminal unit is an indoor unit, and the first neural network takes the four scalars of the power of the central air conditioner host, the power of the indoor unit, the current temperature and humidity of the room, and the vector composed of the open state values of all terminal units as input quantities.

[0038] As preferred, the first neural network takes the central air conditioner host power, indoor unit power, current temperature and humidity of the room, and a vector composed of the on-off state values of all terminal units as input, and takes the equivalent cooling capacity of the terminal unit of the room per unit time, i.e. the equivalent refrigerating capacity, as output.

[0039] As preferred, based on the first neural network and the second neural network established offline, the central air conditioner billing system allocates the cost of the central air conditioner by time period,

[0040] First, the correction coefficient of the current room condition is calculated In the formula, i is the current room number, the room number i = 1 ~ N is any integer, N is the total number of rooms, p i , p i0 are the mapping outputs of the second neural network when the input vector is , respectively, is the current condition of room i, the current room temperature and outdoor temperature are the same as , and the room indoor temperature values in the six directions of the current room are the current room temperature;

[0041] The cost of the i-th room in the d-th time period is calculated as:

[0042] In the formula, PF i (t) is the equivalent cooling capacity of the terminal unit in the current room based on the first neural network prediction, Q jd is the current period cooling capacity of room j, k j3 is the coefficient corresponding to room j, C d is the total cost of the central air conditioner to be allocated in the current period.

[0043] As preferred, based on the first neural network and the second neural network established offline, the central air conditioner billing system allocates the cost of the central air conditioner by time period,

[0044] First, the conversion coefficient of the current room condition is calculated In the formula, i is the current room number, s i , s j are the areas of the i-th and j-th rooms, respectively, the room number i, j = 1 ~ N is any integer, N is the total number of rooms, p i , p j are the mapping outputs of the second neural network when the input vector is , respectively, is the current condition of room i,

[0045] and calculate the comfort coefficient wherein p ic is the room i The mapping output of the second neural network when the input vector is The difference between and is that the current room temperature value is a preset comfort temperature, such as 25 degrees Celsius;

[0046] The cost of the i-th room in the d-th time period is calculated as:

[0047] wherein k j1 , k j2 is the corresponding coefficient of the j-th room, and C d is the total cost of the central air conditioner to be allocated in the current time period.

[0048] As a preferred embodiment, the comfort temperature can be, for example, 25 degrees Celsius in cooling and 18 degrees Celsius in heating; the room number corresponding to the max() function can be obtained offline; in the calculation of the correction coefficient in the d-th time period, the input vector of the second neural network The values of the parameters in the input vector are the average values in the time period.

[0049] As a preferred embodiment, a solar radiation intensity parameter is added to the input of the second neural network.

[0050] As a preferred embodiment, when there are multiple terminal units in the room, the cooling consumption of the current room in the current time period is wherein F i is the set of all air dampers in the current room.

[0051] As a preferred embodiment, the cost allocation of the central air conditioner also includes an area allocation part, and the cost of the i-th room in the d-th time period is:

[0052]

[0053] wherein C d0 is the basic cost of the central air conditioner.

[0054] As a preferred embodiment, when collecting data samples for the second neural network, the thermal load conditions and cooling consumption parameters of the central air conditioner cooling rooms are periodically and continuously collected, and a period in which the input of the second neural network changes within a preset fluctuation threshold is searched out, and the data of the selected period is stored in the data sample set of the second neural network. The preset fluctuation threshold is ±5%.

[0055] As preferred, the preset fluctuation threshold comprises a first fluctuation threshold and a second fluctuation threshold, when the change of the second neural network input quantity in a period exceeds the first fluctuation threshold and is less than the second fluctuation threshold, the sample value of the input quantity in the period is taken as the time average value of the input quantity in the period.

[0056] As preferred, the outdoor temperature t w For example, the average outdoor temperature in a period T, i.e. the time average value thereof, is As preferred, the first fluctuation threshold and the second fluctuation threshold are ±2% and ±5% respectively.

[0057] As preferred, the humidity is sensed by a humidity sensing module arranged in the middle of the room return air duct in the sensing detection unit; corresponding to the low, medium and high three gears of the fan speed, the opening state values of the air damper can be respectively taken as the three fan power normalized values corresponding to the three gears.

[0058] As preferred, when the driver works in the PWM mode, the equivalent time t wherein k(t) is the normalized speed, e.g. the highest speed is 1, and the speed value at other speeds is taken as the ratio of the speed value to the highest speed.

[0059] As preferred, the temperature difference between the target temperature and the initial temperature is ≥5℃; in the same central air conditioning system, a first neural network is established for each type of terminal unit, and the training sample is collected respectively.

[0060] As preferred, the rated cooling power of the reference air conditioning unit is 0.85-1.15 times the maximum air cooling capacity of the terminal fan.

[0061] As preferred, the reference air conditioning unit is provided with dry bulb temperature detection module, wet bulb temperature detection module and supply air volume detection module, the sensible heat capacity thereof under different working conditions is obtained according to the standard test, and the parameters are recorded as the working characteristic table or curve, based on the dry bulb temperature and wet bulb temperature of the current inlet and outlet air and the supply air volume, the cooling capacity of the reference air conditioning unit in the current room is calculated by querying and interpolating the working characteristics.

[0062] As preferred, taking the water-cooled central air conditioner as an example, the first neural network adopts the BP neural network, and the model thereof is:

[0063] The output of the jth node in the hidden layer is

[0064] The output of the output layer is

[0065] Wherein, x1-x4 are four scalars of chilled water supply flow of central air conditioner host, supply and return water temperature difference, current temperature and humidity of the room, x5-xn are all end unit opening state values; f() function takes sigmoid function, w ij and v j are connection weights from input layer to hidden layer and from hidden layer to output layer respectively, θ j and θ are hidden layer and output layer thresholds respectively, n and k are input layer and hidden layer node numbers respectively, and gradient descent method is used for network training.

[0066] As preferred, the driver is built in the end unit to drive the fan motor, and the control unit controls the end unit to work in PWM mode through the driver interface.

[0067] Compared with the prior art, the device and the central air conditioner billing system have the following advantages: the present application takes the rooms where the central air conditioner terminal units are distributed in different positions as the heat load, takes the movable reference air conditioning unit which is calibrated in advance with high precision as the reference, and calibrates the cold equivalent of the central air conditioner terminal unit; and the terminal unit after calibration is used to measure the unit area cold consumption power of different orientation rooms under the condition that the sunlight and the influence of adjacent rooms are similar, and the correction coefficient of each room is obtained accordingly, so that the billing purpose of the central air conditioner according to the actual cold consumption of the indoor basic heat load and the comfort level is realized. The present application takes the four scalars such as the main machine chilled water supply flow, the supply and return water temperature difference, the current temperature and humidity of the room, and the vector composed of the opening state values of all terminal units as the input, takes the unit time refrigerating capacity of the current air damper of the room, i.e. the cold equivalent, as the output, establishes a first neural network as a terminal unit metering mapping model; the model can reflect the influence of the actual working condition change of the central air conditioner on the refrigerating capacity of the terminal unit, and can dynamically reflect the change of the refrigerating capacity of the terminal unit, and overcomes the defects of the prior art that the fixed coefficient is used to estimate the refrigerating capacity of the air damper with actual change. At the same time, the measuring points of the supply water temperature difference and the flow are only arranged at the inlet and outlet of the central air conditioner main machine chilled water supply main pipe to replace the multi-point arrangement of each terminal, and the detection of the large flow relative to the small flow of the terminal can reduce the relative error, and the significant reduction of the measuring points can further reduce the metering error by using high-precision temperature difference detection. In addition, the present application classifies the fan terminal according to the model, the horizontal distance and the vertical distance from the inlet of the central air conditioner main machine chilled water supply main pipe, takes the opening state values of all terminal units as the input in the model, so as to realize the decoupling of the mutual restriction between the terminal units of each air damper. In the cost allocation, the room cooling model represented by the second neural network takes the air temperature and the radiation factor as the input, compensates for the difference of the sunlight on different orientation rooms, takes the room temperature of the six direction adjacent rooms as the input, and reflects the influence of the adjacent room heat transfer on the cold consumption, and finally the difference of the envelope structure is also eliminated through the correction coefficient.

[0068] The present application can accurately meter the actual cold of the central air conditioner terminal unit under different working conditions, and realizes the billing according to the effective cold consumption of the indoor heat load, which is helpful for the energy saving and utilization of the air conditioner.

[0069] And, the provided working condition adjusting device for central air conditioner billing, when the central air conditioner terminal unit and the reference air conditioning unit respectively supply cold, according to the indoor orientation characteristics of the room and based on the geometric equation, the output angle of the internal joints including the vertical rotating shaft, the horizontal rotating shaft and the telescopic support rod is analyzed, so that the operation of the uniform temperature module is controlled based on the planned trajectory, so that the temperature difference between the highest temperature and the lowest temperature in the multiple temperature measuring points in the room is less than the temperature difference threshold, the temperature gradient in the room is greatly reduced, so that the heat load deviation when the two cold sources work is controlled, and the basis for accurately predicting the cold consumption equivalent of the room by the cold supply equivalent of the first neural network is provided. In addition, the speed planning of the trajectory moving speed is also carried out according to the temperature of the trajectory point during trajectory planning. Through the speed planning, the uniformity of the room temperature of each space point can be improved, so as to ensure the working condition consistency and improve the generalization ability of the metering model. Further, through the present application, the room temperature is kept dynamic stable during sample collection, the balance between supply and demand of cold quantity is realized, so that the output of the first neural network can be used as the sample parameter of the second neural network, and then the basis for calculating the correction coefficient between rooms under different orientations and comfort levels is provided. BRIEF DESCRIPTION OF DRAWINGS

[0070] Figure 1A 、 Figure 1B It is two structural schematic diagrams of the central air conditioner billing device and system of the present application.

[0071] Figure 2 It is a structural schematic diagram of the control unit of the present application.

[0072] Figure 3A It is a structural schematic diagram of the water-cooled central air conditioner, Figure 3B It is a structural schematic diagram of the variable refrigerant flow central air conditioner.

[0073] Figure 4A It is a schematic diagram of the reference air conditioning unit for cooling, Figure 4B It is a schematic diagram of the terminal fan unit for cooling.

[0074] Figure 5A 、 Figure 5B It is a schematic diagram of the temperature detection module distribution and uniform temperature treatment.

[0075] Figure 6 It is a structural schematic diagram of the uniform temperature module of the present application.

[0076] Figure 7A It is a schematic diagram of the cold quantity metering mapping principle of the present application. Figure 7B It is a schematic diagram of the room cooling conversion principle.

[0077] Figure 8A It is a structural schematic diagram of the mapping module in the present application, Figure 8BA first neural network structure schematic diagram;

[0078] Figure 9 A terminal unit room temperature adjustment principle schematic diagram.

[0079] In the figure: 1000 central air conditioning billing system, 100 central air conditioning billing device, 200 server, 300 terminal unit / terminal air dam, 400 driver, 500 chilled water pipeline; 600 reference air conditioning unit;

[0080] 120 sensing detection unit, 130 working condition adjustment device, 140 user interface unit, 150 control unit;

[0081] 121 temperature detection module, 122 flow detection module, 123 image acquisition module, 124 electric metering module;

[0082] 131 uniform temperature module, 132 vertical rotating shaft, 133 curved corner support arm, 134 horizontal rotating shaft, 135 telescopic support rod, 136 pitchable support, 137 fan cover, 138 uniform temperature fan, 139 base;

[0083] 151 input module, 152 main processing module, 153 image processing module, 154 working condition processing module, 155 fan processing module, 156 output module, 157 storage module, 158 mapping module;

[0084] 1521 sample extraction part, 1522 correction coefficient calculation part, 1523 billing processing part;

[0085] 1541 uniform temperature processing part, 1542 humidity adjustment part;

[0086] 1581 first neural network, 1582 first connection array, 1583 iterative learning part, 1584 second connection array;

[0087] 310 fan, 320 fan coil, 330 return air inlet; 340 indoor unit;

[0088] 610 outdoor unit module, 620 indoor unit module. DETAILED DESCRIPTION

[0089] The preferred embodiments of the present application are described in detail below with reference to the accompanying drawings, but the present application is not limited to only these embodiments. The present application covers any alternatives, modifications, equivalent methods and solutions made within the spirit and scope of the present application.

[0090] In order for the public to have a thorough understanding of the present application, specific details are described in detail in the following preferred embodiments of the present application, and the present application can also be completely understood without the description of these details by those skilled in the art.

[0091] The application will be described in greater detail in the following paragraphs with reference to the attached drawings. It should be noted that the drawings are in simplified form and are not drawn to precise scale. They are merely used to facilitate an easy and clear understanding of the embodiments of the present application.

[0092] Embodiment 1:

[0093] Central air conditioning is a kind of air conditioning which uses one main machine or unit to supply refrigerant to different rooms through air pipe, water pipe or refrigerant pipe to achieve the purpose of indoor air conditioning. Referring to Figure 3A As shown, water-cooled central air conditioning uses water as refrigerant, while all-air central air conditioning is a VAV variable air volume air conditioning system which controls and adjusts the temperature of an air conditioning area by changing the air supply volume in the air pipe instead of the air supply temperature, so as to adapt to the change of the load of the air conditioning area. Referring to Figure 3B As shown, variable refrigerant flow central air conditioning is a VRV or VRF air conditioning system which controls the flow of refrigerant and realizes refrigeration or heating through direct evaporation or direct condensation of refrigerant. Compared with the twice heat exchange of all-air and water-cooled central air conditioning, VRV or VRF central air conditioning system only needs once heat exchange, so it is more efficient, but its single main machine power is limited, so it is suitable for small-scale central cooling / warming occasions such as villas and local floors of office buildings.

[0094] Without loss of generality, the present embodiment first describes the apportionment billing of water-cooled central air conditioning system. In the water-cooled central air conditioning system, the terminal device is a terminal unit, a terminal fan unit or a terminal damper.

[0095] As shown in FIG. 1, the central air conditioning billing device 100 of the present application comprises a control unit 150, and a sensing detection unit 120, a user interface unit 140 and a working condition adjusting device 130 connected with the control unit 150. Among them, the present application takes the room where the water-cooled central air conditioning terminal unit 300 is located as the thermal load, and the reference air conditioning unit 600 is used as the reference for changing the cooling capacity under different working conditions of the room, the sensing detection unit 120 detects the operating conditions of the water-cooled central air conditioning and the reference air conditioning unit 600, and the cooling conditions of the room, the user interface unit 140 is used for entering parameters and initiating operations, including display during human-computer interaction, such as displaying the electricity bill of each household.

[0096] The water-cooled central air conditioning system is composed of one or more cold and heat source systems and multiple terminal air conditioning systems. The process of operation of the central air conditioning system is essentially a heat transfer process, referring to Figure 3A , Figure 4BAs shown, the cold / heat source is the host computer, and in the host computer, the refrigeration is carried out by a compressor, and the circulating water is cooled to chilled water after passing through the heat exchanger, and is delivered to each end air conditioning system, i.e. the fan coil 320 in the figure through the chilled water pipeline 500; after the chilled water is supplied to each user room through the fan coil, the water temperature rises, and after circulating back to the heat exchanger, the chilled water is cooled by the refrigeration working medium evaporated by the compressor to take away heat, so as to continuously take away heat from the room; at the same time, the refrigeration working medium is sucked into the compressor and compressed into high-pressure steam, and then discharged to the condenser, and the outdoor fan or cooling water system discharges the heat of the condenser, i.e. secondary heat exchange is carried out on the condenser, and the hot gas taking away the heat emitted by the condenser is discharged to the outdoor environment.

[0097] Reference Figure 4B As shown, the fan coil 320 is widely used in hotels, shopping malls, office buildings, hospitals, office buildings and other places, and is a working unit for one-time heat exchange between indoor air and chilled water. Its working principle is that the fan makes indoor air or outdoor mixed air flow under the action of the fan 310, cools and is sent into the indoor room after flowing through the surface cooler, i.e. the curved pipeline through which the chilled water flows, so as to reduce the indoor air temperature to meet the comfort requirements of people; the blown cold air is heated by the personnel, equipment and surrounding walls in the room, and then passes through the return air inlet 330 and circulates to the fan coil 320 to be reheated.

[0098] In the heat transfer process of the central air conditioner, the host computer delivers cold energy to each end unit. Reference Figure 7B As shown, in order to fairly charge the cold energy consumed by each room, two problems need to be solved: how much cold energy is consumed by each room?

[0099] And how much difference is there in the comfort level of each room after consuming the same amount of cold energy due to factors such as sunlight, surrounding structure and adjacent room, and how to correct this difference? At the same time, how to distinguish the charging of different temperatures for different users who set different target temperatures?

[0100] Among them, how much cold energy is consumed by each room through the end unit? This is the first question that must be answered for energy consumption charging.

[0101] Currently, the calculation of the refrigeration capacity of the end unit is based on the monitoring of the state of the fan three-speed switch, and the weighted sum of the working time of the high, medium and low speed positions is obtained, and the weight value, i.e. the coefficient, of each position can only be the data marked by the manufacturer under the rated working condition.

[0102] The limitation of the fixed weight coefficient metering method is obvious. First, due to the influence of installation conditions such as the distance from the host, the actual air volume of different fans may differ from the nominal value of each air volume; second, and more importantly, the working condition of the water-cooled central air conditioning system including each terminal unit is dynamically changing, and using a fixed quantity to calculate an actual changing quantity is obviously unreasonable.

[0103] In the cold rooms, not only will there be a difference between the air volume and the nominal value, but also the total cooling capacity of the host is changing, and the distribution of the total cooling capacity among the terminal units is not a simple linear proportional relationship but a mutual restraint among each other, that is, there is a nonlinear coupling relationship between each air volume.

[0104] Therefore, the water-cooled central air conditioning system including each terminal unit is regarded as a whole, the distribution of the cooling capacity among the terminal units is regarded as a black box, and based on the nonlinear modeling theory, the mapping relationship between the key working condition of the system and the refrigeration equivalent of the terminal unit is modeled.

[0105] In order to identify the model, a data set for identification is needed. Among them, the cooling equivalent under different working conditions needs to be obtained. How to obtain the cooling equivalent? At present, the enthalpy difference method of heat transfer medium is mostly used.

[0106] This method is first used in the central air conditioning billing system to construct a heat meter to measure the heating heat. The heat meter is composed of a hot water flow meter, a pair of temperature sensors and an integrator, and its working principle is that the hot water provided by the heat source flows into the heat exchange system at a certain high temperature and flows out at a lower temperature, and in this process, the heat is provided to the user. In a certain period of time, the heat obtained by the user can be calculated by the following equation:

[0107] E = ∫K (Ts-Tr) dV,

[0108] Where E is the heat output by the heat exchange system, K is the correction coefficient of the specific heat and specific gravity of hot water, Ts and Tr are the supply and return water temperatures, and V is the hot water flow through the heating system in a period of time.

[0109] The main error sources of the enthalpy difference method come from the measurement of the working fluid flow and the determination of the enthalpy value, especially the measurement error of small flow is larger. Similarly, there is also a method of calculating the cooling capacity by detecting the return air of the air side. Compared with the water side measurement, the air side measurement reduces the precision requirement of the temperature measurement equipment and instrument because the air supply temperature difference is much larger than the cold water supply and return water temperature difference, but no matter the air side or the water side measurement, at present, the measurement points are mainly set on the supply and return lines of the working fluid at the end, and due to the small end flow and the fluctuation of the temperature and flow parameters being much larger than the main machine end, there is a contradiction between the sensor precision and the instrument equipment cost.

[0110] Based on the above research, in order to improve the model generalization ability and prediction accuracy, the present application takes the chilled water supply flow of the water-cooled central air conditioner host, the supply and return water temperature difference, the current temperature and humidity of the room, and the vector composed of the opening state value of all terminal units as input, and takes the equivalent cooling capacity of the current air damper of the room per unit time as output, and establishes a first neural network in the control unit as a terminal unit metering mapping model. Among them, the two key factors of flow and temperature difference only need one measurement point, and more importantly, the detected is the flow on the main pipeline, which is much larger than the end flow, which can effectively improve the measurement accuracy.

[0111] The present application takes the room where the terminal unit of the water-cooled central air conditioner is located as the heat load, and takes the reference air conditioning unit as the reference object of the cooling capacity change under different working conditions of the room to obtain the cooling equivalent value in the data sample required for system identification.

[0112] Specifically, as shown in Figure 7A , the reference air conditioning unit, i.e. the reference machine, selects an integrated movable air conditioner, first obtains the sensible cooling capacity under different working conditions according to the standard test and records the parameters as a working characteristic table or curve, establishes a second mapping of the working condition of the reference machine to the cooling capacity, and provides a basis for sample cooling capacity calculation in the working room at the air damper end.

[0113] Then, under the condition of each input combination, the training sample of the established first neural network is obtained offline. According to the characteristics of the cooling capacity transmission from the host to the terminal unit of the water-cooled central air conditioning system, in order to solve the influence of system time lag and large inertia, combined with Figure 4A 、 Figure 4B , the present application configures the control unit to collect sample data of the terminal unit metering mapping model in the following way:

[0114] The control unit drives the fan corresponding to the water-cooled central air conditioner terminal unit through the driver;

[0115] The room where the water-cooled central air conditioner terminal unit is located is taken as the heat load, and the target temperature is set according to the initial temperature of the room; the temperature difference between the multiple temperature detection modules in the sensing detection unit located at different positions in the room is less than the temperature difference threshold through the working condition adjusting device; the drive is controlled to adjust the room temperature to the target temperature by the central air conditioner terminal fan, and the current humidity is taken as the target humidity,

[0116] After the room is kept at the target temperature for a period of time, the drive is turned off and the timing is started, the reference air conditioning unit is controlled to maintain the room temperature at the target temperature from t=0 to t=T1, and the working condition adjusting device is controlled to keep the room humidity at the target humidity, the refrigeration power q(t) is calculated based on the working characteristics and working condition of the reference air conditioning unit, and the refrigeration amount in the first period is accumulated

[0117] The reference air conditioning unit is turned off and the timing is started again, the opening state value F of the air damper of the fan is set, the drive is controlled to work in the PWM mode and maintain the room temperature at the target temperature from t=0 to t=T2, and the equivalent time is calculated Where Δ(t) is the PWM value,

[0118] The drive is turned off again and the timing is started again, the reference air conditioning unit is controlled to maintain the room temperature at the target temperature from t=0 to t=T1, and the working condition adjusting device is controlled to keep the room humidity at the target humidity, and the refrigeration amount in the third period is calculated again

[0119] The equivalent refrigeration amount per unit time of the air damper under the current working condition is calculated:

[0120] As a preferred, the length of the third period can be different from the length of the first period, such as the difference between the two is within 20%. As a preferred, the opening state value F is three gears of low speed, medium speed and high speed respectively.

[0121] During the continuous sampling of the sample set, only the working condition of the water-cooled central air conditioning system can be changed and the data in the second period can be collected, and the first and third periods can be collected again every few samples. The first sample collection after changing the target temperature is performed in three periods.

[0122] In combination Figure 7AAs shown in the figures, the present application establishes a first neural network in the control unit as a first mapping of the end unit working condition state of the central air conditioning system to the refrigeration capacity / cold equivalent, in order to avoid errors caused by small flow of the end, and the measured point parameters at the large flow of the main pipeline are taken as the input quantity of the mapping; and in the working room, the end unit and the reference air conditioning unit are alternately refrigerated, and the end unit to be metered is calculated based on equivalent refrigeration, that is, the cold obtained by the reference air conditioning unit through the second mapping under the same working condition is taken as the cold equivalent of the end unit. During this period, the heat load conditions of the two cold sources, that is, the room inflow heat flux density under cooling, are made the same through sensing and control of the working condition, so as to ensure the reliability of the equivalent calculation.

[0123] Without loss of generality, when the water-cooled central air conditioning end unit is stably working online, the humidity of the room will basically remain stable after the main machine is set, mainly affected by the seasonal climate. Therefore, the current humidity of the room during cooling of the central air conditioner is taken as the target humidity, and the humidity of the room is maintained at the target humidity during cooling of the reference air conditioning unit.

[0124] For the room temperature, the target temperature needs to be set according to the initial temperature of the room under natural conditions without cooling. As preferred, the temperature difference between the target temperature and the initial temperature is ≥5℃, and the load rate of the end unit during sample collection is greater than a set value, such as the power reaching 0.5-1 times of the rated power.

[0125] During cooling, the temperature gradient will appear in the room due to the proportion of the cold air inlet to the room surface area, which may cause deviation of the heat load during working of the two cold sources. In order to reduce the deviation of the heat load, refer to Figure 5A 、 Figure 5B , and in combination with Figure 6 , the present application sets multiple temperature detection modules 121 at different positions in the room, and makes the temperature difference of these temperature detection modules less than a temperature difference threshold through the uniform temperature module 131 in the working condition adjusting device.

[0126] Specifically, as shown in Figure 6 , the working condition adjusting device is provided with a uniform temperature module 131, which includes a base 139, a vertical rotating shaft 132, a bent arm 133, a horizontal rotating shaft 134, a pitchable support 136 with two sections of support arms connected by bolts, and a telescopic support rod 135 connected between the outer ends of the two sections of support arms and forming an acute angle with the axis of the horizontal rotating shaft 134, and a uniform temperature fan 138 is carried at the end of the pitchable support 136. As preferred, the back of the uniform temperature fan 138 is provided with a fan cover 137. An image acquisition module 123 is arranged in the sensing and detecting unit, which can be arranged at the bottom of the bent arm 133, so as to obtain the global image of the room through rotation of the vertical rotating shaft 132.

[0127] Referring to Figure 2 As shown in the figure, preferably, the control unit 150 comprises an input module 151, a main processing module 152, an image processing module 153, a working condition processing module 154, a fan processing module 155, a mapping module 158 and an output module 157, wherein the working condition processing module 154 further comprises a uniform temperature planning part 1541 and a humidity adjusting part 1542. The control unit is further configured to:

[0128] The main processing module responds to events and schedules other modules;

[0129] Based on the room image obtained by the image acquisition module, the image processing module 153 analyzes the room orientation features and extracts two mutually perpendicular diagonal lines. The orientation features include the distribution and length of the room structure edge lines, as well as the directions and distances of the cold source air outlet, the return air inlet and the uniform temperature module 131 in the room relative to the corners of the room.

[0130] In combination Figure 5A , Figure 5B As shown in the figure, the uniform temperature planning part 1541 in the working condition processing module 154 plans the operation trajectory for the uniform temperature module 131 based on the above-mentioned orientation features, so that the axis of the uniform temperature fan 138, i.e. the end of its pointing direction, moves in a spatial spiral line to deliver the cold air blown out by the central air conditioning terminal unit and / or the reference air conditioning unit to each area of the room, until the temperature difference of the multiple temperature detection modules in the room is less than the temperature difference threshold.

[0131] The planning of the trajectory can be based on the obtained room diagonal lines, taking the line connecting the cold air outlet position to the farthest place in the room that it can reach or its opposite side as one of the main diagonal lines, and taking a straight line perpendicular to it as the other diagonal line; then, planning a spiral trajectory with the main diagonal line as the axis. In Figure 5A , the black dot is the air outlet located in the corner, and a curve spirally moving around the inner wall surface of the cone is planned as the target trajectory with the diagonal line where the air outlet is located as the center, wherein the main diagonal line is the center line of the cone. In Figure 5B , the air outlet is located in the middle of the side wall, and a curve spirally moving around the inner wall surface of the cylinder is planned as the target trajectory with the side wall as the ground of the cylinder, wherein the main diagonal line is the center line of the cylinder. The planned target trajectory should avoid the return air inlet to avoid cold loss.

[0132] As a preferred embodiment, typical indoor orientation features can be summarized and classified, the end trajectory curve is planned for each orientation category based on geometric equations, and the output angles of each joint in the uniform temperature module, including the vertical rotation axis, the horizontal rotation axis and the telescopic support rod, are analyzed based on inverse kinematics.

[0133] As preferred, the joint angle data sequence corresponding to the trajectory can also be stored by on-site demonstration, and the joint control is performed according to the sequence.

[0134] The movement according to the planned trajectory can make the temperature of each area of the room be reduced as soon as possible, and reduce the temperature gradient between the areas. The uniform temperature planning unit controls the uniform temperature module according to the planned trajectory, and in each time period of sample collection, firstly, the uniform temperature fan is operated at a constant speed to reduce the temperature generally; in order to further reduce the temperature difference between the areas, as preferred, then, according to the temperature characteristics of the multiple temperature detection modules in the room, the linear speed of the uniform temperature fan moving according to the trajectory is changed, and the linear speed is inversely proportional to the size of the temperature difference between the temperature at the trajectory point and the target temperature. The temperature at each trajectory point can be obtained by interpolation calculation according to the temperature values of the multiple temperature detection points. Through the speed planning, the uniformity of each space point of the room temperature can be improved, so as to ensure the consistency of the working condition and improve the generalization ability of the measurement model.

[0135] The uniform temperature module is periodically operated, and when the temperature difference between the highest temperature and the lowest temperature of the multiple temperature detection modules is less than a temperature difference threshold, the operation is stopped and the data sample can be collected; when the temperature difference is monitored to be greater than the threshold, the operation is started again so that the overall temperature of the room is dynamically balanced at the target temperature. As preferred, the temperature difference threshold is a value between 0.1°C and 0.3°C.

[0136] In a second time period during sample collection, the fan processing module in the control unit adjusts the PWM duty cycle value of the driver according to the average of the multiple temperatures at different positions in the room, i.e. the overall temperature of the room. Figure 9 As shown, according to the error value e(t) between the target temperature and the current overall temperature of the room, the fan processing module in the host unit calculates the PWM value connected to the fan driver based on the PID control law, changes the driving power pulse width of the driver to change the operation speed of the fan, so that the error value e(t) dynamically approaches 0.

[0137] The working condition processing module also has a humidity adjusting unit, which controls the operation of the humidity adjusting module in the working condition adjusting device based on the monitoring of the humidity detection point in the room, so that the humidity of the room is kept at the target humidity.

[0138] Combining Figure 3A , Figure 5A , Figure 5B As shown, the sensing detection unit is provided with a humidity detection module in the middle of the return air duct of the room; a flow detection module 122 and a water temperature detection module 123 are arranged at the inlet of the chilled water supply main pipe of the central air conditioner host; a water temperature detection module is arranged at the outlet end of the return water main pipe connected to the host; multiple temperature detection modules are arranged at different positions in the room, which can be arranged at the same height and respectively located on two perpendicular diagonal lines.

[0139] As preferred, the plurality of temperature detection modules is 3-6, and the setting height is about 2 meters; the temperature values of the plurality of temperature detection modules can be averaged to obtain the current temperature of the room.

[0140] During sample collection, the central air conditioner host can be operated to adjust the power to keep the chilled water supply flow and the supply and return water temperature difference constant. As preferred, the two parameters can be taken as the average values during the sample collection period based on the proportional relationship between the cooling capacity and the temperature difference and flow.

[0141] As preferred, the plurality of temperature detection modules set for controlling the consistency of the heat loads of the two cold sources are only used for sample collection, so one of the temperature detection modules, such as the module near the return air inlet temperature measurement point, can be selected as the current temperature of the room in the first neural network input quantity in online application, thereby simplifying the system structure and facilitating actual operation.

[0142] For each terminal unit of the water-cooled central air conditioner, the wind deflector opening state value can be taken as the normalized value of the fan power corresponding to the low, medium and high three gears of the fan speed, for example, the highest power value is taken as 1, and the power of the other two gears is scaled proportionally.

[0143] The cooling system of the water-cooled central air conditioner is a nonlinear lag system, so changes in operating parameters need a period of time to feedback their effects. Therefore, the present application sets sampling conditions when collecting training samples, and allows the system to enter a steady state before sampling, and allows the room conditions to remain for a period of time when the reference air conditioning unit and the terminal unit are cooled, so that the sampling eliminates the randomness of short-term sampling. At the same time, by sampling the reference air conditioning unit before and after the terminal unit is cooled, the influence of slow fluctuations in working conditions on the sampling data is eliminated, and the prediction accuracy of the network model is improved.

[0144] The first neural network established by training the collected sample set is used to predict the equivalent cooling capacity of the current wind deflector per unit time in the field environment, and the prediction value is output through the output module. This value can be used as the basis for charging the water-cooled central air conditioner for each terminal.

[0145] As shown in Figure 7A , the working room is the heat load, and the cooling capacity equivalent provided by the terminal unit under the same working condition is obtained through the second mapping of the reference air conditioning unit. Therefore, the working characteristics of the movable reference air conditioning unit need to be obtained through high-precision calibration in advance.

[0146] Referring to Figure 4AAs shown, the reference air conditioning unit 600 comprises an outdoor unit module 610 and an indoor unit module 620. In the reference air conditioning unit, a dry-bulb temperature detection module and a supply air volume detection module can be provided; a test device for establishing a room air enthalpy method is established according to a room air conditioner standard, and sensible heat release thereof under different working conditions is tested and recorded as a working characteristic table or curve as the working characteristic of the reference air conditioning unit. Then, when a sample is collected, based on the dry-bulb temperature and the wet-bulb temperature of the current inlet and outlet air and the supply air volume, the refrigerating capacity of the reference air conditioning unit under the current working condition is calculated through querying and interpolating the working characteristic.

[0147] In the air enthalpy method, the calculation formula of the sensible heat release is:

[0148] φ sc = q m · c pa · (t a1 -t a2 ) / V n · (1+W n ),

[0149] wherein φ sc is the sensible heat release (W), q m is the supply air volume (m 3 / s) of the measuring point, V n is the specific volume of the wet air (m 3 / kg) at the measuring point, W n is the air humidity at the measuring point, t a1 and t a2 are the return air temperature and the supply air temperature (℃) respectively, and the constant-pressure specific heat capacity c pa = 1005 + 1846W n (J / (kg·K)).

[0150] In the reference air conditioning unit, the dry-bulb temperature and the wet-bulb temperature are detected by sensors placed in the supply air inlet and the return air outlet in a heat preservation section. The dry-bulb and the wet-bulb are used to detect the supply air temperature, the return air temperature and the humidity, and these parameters can also be obtained by using a temperature sensor and a relative humidity sensor. During the collection of the training samples, the reference air conditioning unit adjusts the operating frequency under the instruction of the control unit to maintain the temperature of the room at the target temperature.

[0151] Preferably, the working characteristic data of the reference air conditioning unit is recorded in the form of a table, and the sensible heat release under the current working condition is calculated based on the working characteristic table lookup and multi-dimensional interpolation.

[0152] Preferably, the rated refrigerating capacity of the reference air conditioning unit is 0.85-1.15 times the maximum air volume refrigerating capacity of the terminal fan.

[0153] In combinationFigure 8A 、 Figure 8B As shown in FIG. 8, the control unit establishes a first neural network 1581 as an end unit metering mapping model in the mapping module 158, the input layer of the first neural network receives input quantities from the main processing module 152, and the output quantities of the output layer are transmitted to the iterative learning part 1583 and the main processing module 152 through the first connection matrix 1582 and the second connection matrix 1584, respectively; when training the first neural network offline, the iterative learning part 1583 adjusts the connection weight of the first neural network 1581 until the learning is completed according to the wind deflector unit time refrigeration quantity equivalent actual value and the network output value input by the main processing module 152 and the first neural network 1581 through the first connection matrix 1582, respectively; when metering online, the first connection matrix 1582 is disconnected, the first neural network 1581 predicts the wind deflector unit time refrigeration quantity equivalent and outputs it to the main processing module 152 through the second connection matrix 1584, and the main processing module 152 processes and analyzes it and then outputs it through the output module 156. In combination with Figure 1A 、 Figure 2 As shown in FIG. 8, the output module can transmit the metering result to the user interface unit 140 for display or store it in the server 200, wherein the server 200 can realize communication with one or more systems of the application through a cloud platform. In the main processing module 152, the sample extraction part 1521 controls the collection and screening of training samples, the correction coefficient calculation part 1522 calculates various coefficients for apportioning charges, and the charge processing part 1523 apportions the central air conditioning charges based on the working conditions of each end unit in a distributed processing manner.

[0154] As a preferred, the first neural network adopts a BP first neural network, and the model thereof is as follows:

[0155] The output of the jth node of the hidden layer is

[0156] The output of the output layer is

[0157] Wherein, x1-x4 are four scalars of the central air conditioning host refrigerated water supply flow, supply and return water temperature difference, current temperature and humidity of the room, x5-xn are all end unit opening state values; the function f() is a sigmoid function, w ij and v j are the connection weights from the input layer to the hidden layer and the connection weights from the hidden layer to the output layer, respectively, θ j and θ are the threshold values of the hidden layer and the output layer, respectively, n and k are the node numbers of the input layer and the hidden layer, respectively, and the gradient descent method is used for network training.

[0158] As a preferred, other redundant factors such as the end unit supply air temperature can also be added to the network input quantity.

[0159] Because the chilled water supply pipe is shared, the water supply pressure will decrease step by step with the distribution of the chilled water. The actual cooling capacity of the water-cooled central air conditioning fan coil end unit is not only related to the model and the air damper position, but also related to the distance from the main unit. Therefore, for the same model end unit, the horizontal distance and the vertical distance from the end air damper to the chilled water supply main pipe inlet of the central air conditioning main unit can be used to further subdivide the end unit into small categories. A first neural network is established for each small category, and the training samples are collected respectively, so that the prediction of the cooling capacity equivalent is more accurate.

[0160] As preferred, for each category, one end unit is selected for sample acquisition and training. For the first neural network of each category end unit, a room with slow and small peripheral temperature change is selected as the heat load during the collection of training samples, so that the sample collection can be continuously performed, and the sampling time of the overall sample set is shortened. For this purpose, the rooms inside the building can be used as the collection environment. As preferred, for the rooms located at the corners of the building, the sample collection is performed at night or during the time without direct sunlight during the day.

[0161] Cooling capacity measurement is the basis for charging the central air conditioning system according to the cooling capacity. However, in addition to the measurement, many factors need to be considered for charging. For example, Figure 7B As shown, the cooling capacity required for cooling per unit area is very different for rooms in different positions in the building. For example, the first floor lobby has less envelope structure, and more cold air is delivered to the environment. The rooms at the edges and the top are obviously more affected by solar radiation than the rooms in the middle, and therefore require more cooling capacity. Obviously, if the actual cooling capacity consumption is simply used for charging, it is unfair to the rooms that consume more cooling capacity due to environmental factors.

[0162] Therefore, the cooling characteristics of rooms in different positions in the building must be analyzed first, and the cooling capacity of rooms in different positions must be compensated and converted accordingly. First of all, how to describe the cooling characteristics of rooms in different positions needs to be studied, such as whether a model can be established and how to establish the model. Such a model should reflect the main and quantifiable factors that cause the change in cooling capacity consumption in different positions, and these factors should be able to be converted to correct or compensate different rooms. Then, even if a model can be established, how to correct it based on the model? After a lot of analysis and research, we found that such correction should reflect the influence of environmental factors, and the current temperature set value or actual value of each room should be excluded from the body of such correction, otherwise it will return to the charging mode of using more or less the same.

[0163] For this purpose, see Figure 7BAs shown, the present application takes the current room temperature, outdoor temperature, and the vector composed of the room indoor temperature values in the six directions of front, back, left, right, up and down of the current room as the input quantity, and takes the refrigerating capacity equivalent of the unit time of the air damper of the current room as the output quantity, and establishes a second neural network in the control unit according to the room, which is used as the cooling model of each room. Then, based on the model, the unit time cooling consumption / usage equivalent of each room under the approximate working condition is predicted, and the correction coefficient is calculated accordingly; finally, according to the correction coefficient, the actual cooling consumption of the room predicted based on the first neural network is corrected and the total water-cooled central air conditioner cost to be allocated is allocated.

[0164] Specifically, referring to the modeling process of the first neural network, Figure 8A 、 Figure 8B and the second neural network, the second neural network can also use a BP network, in which the nine input quantities are x1-x2, which are the current room temperature, outdoor temperature, and solar radiation intensity, and x3-x8, which are the room indoor temperature values in the six directions of front, back, left, right, up and down; and the output quantity y(t) is the unit time cooling consumption equivalent of the current room. In order to enable the first neural network to predict and calculate the cooling consumption equivalent, i.e. the cooling demand equivalent, the present application uses a method of maintaining the dynamic stability of the room temperature during sample collection to realize the balance between supply and demand of cooling capacity, and therefore the output quantity of the second neural network is the unit time refrigerating / cooling capacity equivalent of the air damper of the current room. When the terminal unit supplies cooling, the room humidity is mainly affected by the seasonal climate; however, as an option, an indoor humidity can also be included as an input quantity in the second neural network; when the adjacent room is empty, the temperature in that direction, such as the outdoor temperature, is used as the room indoor temperature. As an option, when the water-cooled central air conditioning system as a whole enters a stable working state, the six-direction room temperature values in the second neural network can be replaced by the six-direction room terminal unit fan on / off state values. As an option, a solar radiation intensity parameter is also added to the input quantity of the second neural network.

[0165] Under the approximate stable working condition, the data samples of the second neural network under different input conditions are collected, in which the output quantity is predicted by the corresponding first neural network of the terminal unit, and the trained second neural network is used to predict the unit time cooling consumption equivalent of each room under the current working condition.

[0166] In order to realize the automatic collection of the second neural network data sample, the application is provided with a sample extraction module in the main processing module of the control unit, which is used to collect the thermal load working condition and the cooling consumption parameter of the water-cooled central air conditioning cooling room periodically and continuously, and search for the period in which the variation range of each input variable of the second neural network is within the preset fluctuation threshold, and store the data of the selected period into the data sample set of the second neural network. Through the automatic extraction of the sample, the sample can be obtained during the working process of the water-cooled central air conditioning, and it is not necessary to specially start up for a long time for debugging, so that the data collection cost is greatly saved.

[0167] As preferred, the preset fluctuation threshold is ±5%, and the value of each input sample parameter is the arithmetic mean or the median value in the period.

[0168] As preferred, the preset fluctuation threshold can also include two sets of threshold ranges of the first fluctuation threshold and the second fluctuation threshold, and when the variation of the second neural network input variable in the period exceeds the first fluctuation threshold and is less than the second fluctuation threshold, the sample value of the input variable in the period is taken as the time average value of the input variable in the period. For example, the average outdoor temperature in the period T is w the time average value of the outdoor temperature in the period T is

[0169] As preferred, the first fluctuation threshold and the second fluctuation threshold are ±2% and ±5% respectively.

[0170] When the second neural network is established, a model can be established for each room respectively; as preferred, the same network can be used to represent the same type of rooms in the same orientation feature, such as the middle rooms. Through the sharing of the model, the sample amount can be further reduced, and the calculation and modeling time can be saved.

[0171] After the above two modeling is completed offline, the water-cooled central air conditioning is cost allocated in different time periods in online application. Different from the existing method, when the cooling consumption of the rooms in different orientations is corrected, the application does not use the fixed, statistical empirical value as the coefficient, but real-time conversion is carried out according to the actual working condition of each room.

[0172] Specifically, the correction coefficient of the current room working condition is calculated according to the cooling consumption model In the formula, i is the current room number, the room number i is any integer in 1 to N, N is the total number of rooms, p i , p i0 is the mapping output of the second neural network when is taken as the input vector, is the current working condition of the room i, the current temperature of the room and the outdoor temperature and The room indoor temperature value in the six directions of the same and current room is the current room temperature;

[0173] The i room cost in the d time period is calculated again as:

[0174] Wherein, PF is the predicted end unit cooling equivalent per unit time in the current room based on the first neural network i (t) is the accumulated current period cooling capacity, Q jd Q is the current period cooling capacity of room j, k j3 C is the corresponding coefficient of room j d is the total central air conditioning cost to be allocated in the current period.

[0175] The correction coefficient is necessarily related to the cooling characteristics and cooling equivalent of other rooms, and other rooms have their own room temperature at the same time due to the differences in the starting time, environment, and set temperature. Therefore, when the correction coefficient of a room is obtained, is the cooling equivalent obtained based on the working condition of each room?

[0176] Through in-depth research, the application uses the generalization characteristics of the room cooling model to uniformly predict the cooling consumption of the same room in the working condition without additional heat load of adjacent rooms. The predicted value is actually the cooling demand of the internal basic heat load of the room, and the ratio of the cooling demand corresponding to the internal basic heat load of the room to the actual cooling demand is taken as the correction coefficient k i3 ; in the central air conditioning cost allocation calculation formula, the actual cooling capacity of the room is multiplied by the correction coefficient k i3 , so as to realize the allocation of the cooling demand corresponding to the internal basic heat load of each room in the billing, and reflect the fairness of the billing. It can be understood that according to the cost allocation calculation formula, the correction coefficient k i3 of the room near the edge of the building, the top and bottom floors, and the like will be smaller than that of the central room of the building, so that the coefficient can be obtained to compensate for the billing.

[0177] At the same time, in different periods, if the set temperature of the same room is low, the actual cooling capacity will be large, so more costs will be allocated. Therefore, the device also reflects the billing according to the comfort degree, which helps to guide the reasonable consumption of cooling and achieves the energy-saving effect.

[0178] As a preferred embodiment, in the correction coefficient calculation of the d time period, the input vector of the second neural network is the average value in the period; and the current period cooling capacity Q id can be obtained in the form of short period accumulation in a discrete period system. As a preferred embodiment, The outdoor temperature can be taken as the current room temperature. The total cost to be allocated can be calculated by the consumption of electricity, water, etc.

[0179] When there are multiple terminal units in the room, the current room cooling load in the current period Where F i is the set of all air dampers in the current room.

[0180] It can be understood that, compared with water-cooled central air conditioning, all-air central air conditioning uses air instead of cooling water as refrigerant. Therefore, the water-cooled central air conditioning billing device solution in the embodiment is also applicable to all-air central air conditioning. Due to the difference in refrigeration process details, in the control unit of the billing device, the first neural network is modified to use the supply air flow rate of the all-air central air conditioning host, the supply and return air temperature difference, the current temperature and humidity of the room, and the vector composed of the opening state values of all terminal air dampers as input quantities.

[0181] Embodiment 2:

[0182] In the process of collecting the first neural network training samples, if the target temperature is initially set too high or too low, or the power of the terminal unit does not match the heat load of the room, it will cause the terminal fan working condition to be limited to low power or high power state when collecting samples.

[0183] Therefore, in order to make the samples cover different load rates of the terminal fan from low to high, in the embodiment, when the control unit controls the driver to work in PWM mode, during the collection of a sample, the terminal unit is switched to work in different gears, and the equivalent time is calculated by multiplying the normalized speed of the fan motor at different gear speeds by the integral of the on-duty ratio Δ(t) in the period Where k(t) is the normalized speed, and when the highest speed is 1, the value of other speeds is the ratio of the speed value to the highest speed.

[0184] Based on the same reason, the embodiment can also use the following method to obtain the training samples. The reference air conditioning unit uses a cold and hot air conditioner; if the ratio of the equivalent time dT to the length of the second period T2 is less than the duty cycle threshold Δs, during the collection of the sample, the reference air conditioning unit is also controlled to work in heating mode from τ=0 to τ=T3 in the second period, and the equivalent heat of heating is recorded Correspondingly, the equivalent refrigeration capacity per unit time of the air damper in the current working condition is calculated as follows:

[0185]

[0186] As preferred, an electric heating module can also be provided in the reference air conditioning unit, and the electric heating module is controlled to heat from t=0 to t=T3, 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.

[0187] As preferred, in the time range of T1, T2, the heating module can be turned on with known power and heat calculation is performed, so as to expand the working condition coverage of the reference air conditioning unit and the water-cooled central air conditioning terminal unit.

[0188] The working characteristics of the reference air conditioning unit is the mapping between the working condition and the sensible cooling capacity. In this embodiment, the mapping can also be represented by a third neural network, and the input of the network can be selected as the dry and wet bulb temperatures of the inlet and outlet air and the supply air volume; or can be preferably the electric power, the outdoor condenser temperature, the indoor evaporator temperature and humidity; or can be preferably the compressor operating frequency, the supply air fan power, the supply and return air temperature and humidity.

[0189] Considering the central air conditioning equipment and installation costs, and the fact that there are some public rooms in the building, etc., the existing charges are composed of fixed charges and operating charges. The costs of the air conditioning system mainly include the following aspects: a. The electricity cost of the air conditioning system operation (including the electricity cost of the chiller, cooling tower, water pump and other equipment); b. The consumption of water supplement of the air conditioning system; c. The operation and management cost of the air conditioning system; d. The depreciation cost of the air conditioning system; e. The maintenance and repair cost of the air conditioning system; f. Other additional costs and property management costs. Among the above types of costs, the first three are the operating costs of the central air conditioning system, and the last three are basically a fixed value during the operation of the air conditioning system, which is called basic fee.

[0190] Different from embodiment 1, according to the combined charging method, the total cost to be allocated of the water-cooled central air conditioning is divided into two parts, in addition to the actual cooling capacity allocation, there is also an area allocation part, then the cost of the i room in the d time period is:

[0191]

[0192] Wherein, C d1 and C d0 are the operating cost and basic cost of the central air conditioning respectively.

[0193] Embodiment 3:

[0194] This embodiment provides another allocation and charging calculation method, and takes a variable refrigerant flow multi-connected air conditioner as the central air conditioner as an example.

[0195] Variable refrigerant flow central air conditioning, also known as multi-connected central air conditioning system, is composed of a single refrigeration / heat cycle air conditioning system, also known as VRV or VRF, which is connected to a plurality of different or same type and capacity direct expansion indoor units from a single outdoor unit.

[0196] Referring to Figure 3B , VRF is intended to be directly expanded, which directly transmits the low-temperature liquid refrigerant compressed and expanded by the outdoor unit to the indoor unit as the end unit through a thin copper pipe without rough air pipe and water pipe, and then enters the evaporator through the electronic expansion valve to become gas and take away heat at the same time.

[0197] In the VRF system, the refrigerant flow is variable, which is determined according to the indoor load. When the indoor load is high, a large amount of refrigerant is transported to the indoor unit to provide more cooling / heat; otherwise. The compressor of the main unit adjusts the refrigerant flow required by the whole system, and the refrigerant flow of the single indoor unit is adjusted by the electronic expansion valve. In the indoor unit, the indoor air supply volume is also adjusted by the fan.

[0198] Different from example 1, referring to Figure 1B , in the VRF system, the diameter of the refrigerant conveying pipe is much smaller than that of the water pipe and the air pipe, and the flow is smaller. Therefore, an electric metering module 124 is connected in the power supply circuit of each indoor unit 340 as an end unit.

[0199] Correspondingly, when metering and modeling the cooling equivalent of the end unit, the first neural network takes the central air conditioner main unit power, indoor unit power, current temperature and humidity of the room, and a vector composed of the on-off state values of all end unit fans as input, and takes the cooling equivalent of the end unit per unit time, i.e. the refrigeration equivalent, as output.

[0200] After completing the two modeling of the first and second neural networks for the end unit cooling and room cooling, in the online application, another time-sharing allocation method is used to charge the water-cooled central air conditioner.

[0201] Specifically, the conversion coefficient of the current room working condition is calculated , wherein i is the current room number, s i , s j are the areas of the i-th and j-th rooms, respectively, the room numbers i, j = 1 ~ N, N is the total number of rooms, p i , p j are the conversion coefficients of the i-th and j-th rooms, respectively, when the input vector is taken as the input vector , the respective mapping outputs of the second neural network are taken as the input vector for the current working condition of room i,

[0202] and calculate the comfort coefficient wherein p ic is the current working condition of room i with as the input vector and its mapping output corresponding to the second neural network, the working condition is different from only in that its current room temperature is a preset comfortable temperature such as 25 degrees Celsius;

[0203] The cost of the ith room in the dth time period is calculated as follows:

[0204] wherein k j1 , k j2 is the corresponding coefficient of room j, C d is the total cost of the central air conditioner to be allocated in the current period.

[0205] In calculating the conversion coefficient k i1 , the room with the maximum unit area cooling consumption is found by the max() function. Since the input quantity when the mapping obtains the cooling consumption is the same, i.e., the working condition is the same, the denominator corresponds to the room with the same comfort level, i.e., the maximum unit area cooling consumption in the same room, under the premise of equivalent external heat load; that is, the denominator corresponds to the room with the maximum internal heat load. Therefore, the meaning of the conversion coefficient k i1 is that the ratio of the unit area internal heat load of the current room i to the room with the maximum internal heat load.

[0206] Since more cooling capacity is needed to achieve a lower indoor temperature, the comfort coefficient is introduced in the present embodiment, i.e., the same room under the same other working condition, in order to achieve the current indoor temperature, relative to a preset comfortable temperature, the proportion coefficient k i2 that needs to consume more or less cooling capacity. For example, the comfortable temperature can be taken as 25 degrees Celsius in refrigeration and 18 degrees Celsius in heating.

[0207] The present embodiment converts the current working condition of each room to the room with the maximum internal load based on the conversion coefficient k i1 , and cleverly avoids the influence of the heat load caused by different orientations or directions through proportional calculation; and based on the generalization of the cooling model, the comfort coefficient k i2The cooling consumption of different indoor temperatures is compensated by the calculation. Thus, the final billing can overcome the influence of room orientation and temperature setting difference. The model itself takes air temperature, six-direction adjacent room temperature and other factors as input, which reflects the difference of sunlight on different orientation rooms and the influence of adjacent rooms on cooling consumption. Through the normalization correction of the cooling equivalent of each room per unit area under the same working condition of different rooms, and the proportional correction of the cooling consumption under different target room temperatures of the same room, the differences of orientation and envelope structure, and the actual temperature enjoyed are compensated.

[0208] Embodiment 4:

[0209] In combination with Figure 1A , Figure 4A , Figure 4B and Figure 7A , Figure 7B , the embodiment provides a central air conditioning billing system 1000, which comprises:

[0210] a user interface unit 140 for typing parameters, initiating operations and human-computer interaction;

[0211] a driver 400 for driving the fan corresponding to the central air conditioning terminal unit;

[0212] a reference air conditioning unit 600 for pre-acquiring working characteristics as a reference for cooling capacity;

[0213] a sensing detection unit 120 for detecting the working condition and environment of the central air conditioning and the reference air conditioning unit;

[0214] a working condition adjusting device 130 for adjusting the running working condition of the central air conditioning terminal unit and the reference air conditioning unit;

[0215] a control unit 150 connected with the driver 400, the reference air conditioning unit 600, the sensing detection unit 120, the user interface unit 140 and the working condition adjusting device 130;

[0216] The control unit 150 is configured to:

[0217] First, the cooling equivalent of the terminal unit is modeled,

[0218] the room where the terminal unit is located is taken as the heat load, the working condition of the central air conditioning system is taken as the input quantity, and the cooling capacity equivalent of the terminal unit per unit time is taken as the output quantity. A first neural network is established in the control unit,

[0219] the cooling capacity of the reference air conditioning unit in the system under the same heat load condition is taken as the cooling capacity equivalent of the terminal unit, and the first data sample is collected under different working conditions and the first neural network is trained.

[0220] Secondly, the central air conditioning cooling rooms are classified by orientation type, and a cooling consumption model is established for each type of orientation room,

[0221] The current room temperature and outdoor temperature, and a vector composed of the indoor temperature values in the six directions of the current room are taken as input quantities, and the cooling consumption equivalent per unit time of the current room is taken as an output quantity. A second neural network is established in the control unit according to the room,

[0222] The terminal unit in the current room is adaptively controlled to keep the room temperature unchanged in stages. When the working condition is approximately stable, the second data sample of the second neural network under different input conditions is collected, the output quantity in the sample is predicted by the first neural network, and training is performed based on the collected data sample;

[0223] Based on the offline established cooling and cooling consumption models, the central air conditioner is charged by time period when applied online,

[0224] First, the correction coefficient of the current room is calculated according to the cooling consumption model under the condition of the current room working condition In the formula, i is the room number, the room number i = 1 ~ N is any integer, N is the total number of rooms, p i , p i0 is the mapping output of the second neural network when is taken as the input vector, is the current working condition of room i, the current room temperature and outdoor temperature are the same as , and the indoor temperature values of the current room in the six directions are the current room temperature;

[0225] The cost of the i-th room in the d-th time period is calculated as:

[0226] Wherein, PF i (t) is the cumulative cooling capacity of the current period based on the predicted cooling capacity per unit time of the terminal unit in the current room by the first neural network, Q jd is the cooling capacity of room j in the current period, k j3 is the corresponding coefficient of room j, C d is the total cost of the central air conditioner to be allocated in the current period.

[0227] Referring to Figure 1B , when the central air conditioner is a VRF air conditioner, the driver is built into the terminal unit of the central air conditioner to drive the fan motor, and the control unit controls the terminal unit to work in PWM mode through the driver interface.

[0228] As a preference, the cost sharing calculation can also be replaced by:

[0229] First, the current room condition is used to calculate the conversion coefficient In the formula, i is the current room number, s i , s j are the areas of the i and j rooms, respectively, the room number i, j = 1 ~ N any integer, N is the total number of rooms, p i , p j are the room i and j, respectively, when is used as the input vector , their respective mapping outputs of the second neural network, is the current condition of room i,

[0230] and the comfort coefficient is calculated In the formula, p ic is the mapping output of the second neural network when room i uses as the input vector , the difference between the conditions and is only that the current room temperature is a preset comfortable temperature such as 25 degrees Celsius;

[0231] The cost of the i room in the d time period is calculated as:

[0232] Where k j1 , k j2 are the coefficients corresponding to room j, C d is the total cost of the central air conditioner to be shared in this period.

[0233] In order to realize fair charging according to the cooling capacity, the present application firstly models the cooling capacity characteristics of the terminal unit and the cooling consumption characteristics of the rooms in different orientations, and then predicts the cooling capacity of the terminal unit and the cooling consumption of the rooms based on the two nonlinear models under actual working conditions; the actual cooling consumption is corrected based on the cooling consumption characteristics of the rooms in different orientations to overcome the difference in environmental influence in different orientations. In the first modeling, in order to avoid the error caused by the detection of the small air volume of the terminal unit in online application, the present application takes the main pipe of the chilled water supply of the central air conditioner as the measuring point, detects the flow and the supply and return water temperature difference as the input of the air damper metering mapping model; and through the consistency control of the thermal load working condition, the reference air conditioning unit is used as the calculation reference of the cooling capacity of the terminal unit, the nonlinear mapping model is trained by the sample set, and the cooling capacity of the terminal unit of the central air conditioner is predicted and calculated by the model obtained by training in online application. In the second modeling, the influence of sunlight, maintenance structure and adjacent room is effectively compensated through the design of the model parameter structure and the correction coefficient formula. The present application can ensure the metering accuracy without increasing the cost, and realizes the decoupling of the mutual restriction between the terminal units in different air dampers, and can accurately meter the dynamically changing cooling capacity; and then through the introduction of the compensation of the actual working condition in the cooling consumption correction calculation of the rooms in different orientations, the fairness and reasonableness of the charging are ensured.

[0234] It can be understood that after the refrigeration and heating working conditions are interchanged in the present application, the present application is also applicable to the apportionment charging of the terminal unit of the central air conditioner in the heating season.

[0235] The above describes several embodiments of the present application, but these embodiments are presented as examples and do not limit the scope of the application. These embodiments can be implemented in other various ways, and various omissions, substitutions, combinations, changes can be made within the scope of the main idea of the application. These embodiments or their modifications are included in the scope or main idea of the application, and are also included in the application and its equivalent scope recorded in the claims.

Claims

1. A working condition adjusting device for central air conditioning billing, comprising a uniform temperature module and a humidity adjusting part, the uniform temperature module further comprises a base, a vertical rotating shaft, a bent arm, a horizontal rotating shaft, a pitchable support with two sections of support arms connected by bolts, an extensible support rod connected between the outer ends of the two sections of support arms at an acute angle with the axis of the horizontal rotating shaft, and a uniform temperature fan carried at the end of the pitchable support, the uniform temperature module is configured to, in the process of sample data collection of the neural network by the central air conditioning billing system, make the shaft of the uniform temperature fan move in a spatial spiral line to deliver the cold air blown by the terminal unit of the central air conditioning and / or the reference air conditioning unit to all directions of the room until the temperature difference of the multiple temperature detection modules in the room is less than a temperature difference threshold, the humidity adjusting part is configured to, in the process of sample data collection of a first neural network in the neural network by the central air conditioning billing system, based on the monitoring of the humidity measuring points in the room, make the reference air conditioning unit work to keep the humidity of the room at a preset target humidity.

2. The operating condition adjusting device for billing of a central air conditioner according to claim 1, wherein in the central air conditioning billing system, the room where the terminal unit of the central air conditioning is located is the thermal load, the first neural network takes the working condition of the central air conditioning system as the input quantity and the equivalent cooling capacity per unit time of the terminal unit in the room as the output quantity, and the working condition adjusting device is further configured to: when collecting the first data sample for training the first neural network, set the target temperature according to the initial temperature of the room, control the terminal damper fan driver to operate to adjust the temperature of the room to the target temperature and obtain the current humidity as the target humidity; after the room is kept at the target temperature for a period of time, the reference air conditioning unit and the terminal damper are respectively used as the cold source to work for a certain time to collect samples, and the working condition adjusting device is operated to keep the temperature of the room at the target temperature when the two kinds of cold sources are working respectively, wherein the terminal damper works in PWM mode, based on the working characteristics and working conditions of the reference air conditioning unit, the cooling capacity consumption power is calculated and divided by the average PWM duty cycle of the terminal damper, and the obtained value is taken as the equivalent cooling capacity per unit time when the current opening state value F of the terminal damper.

3. The operating condition adjusting device for billing of a central air conditioner according to claim 1, wherein the neural network in the central air conditioning billing system further comprises a second neural network, the second neural network takes the current temperature of the room when cooling, the outdoor temperature, and a vector composed of the indoor temperature values of the room in six directions of front, back, left, right, up and down as input quantities, and takes the equivalent cooling capacity per unit time of the room as output quantity, and the working condition adjusting device is further configured to: adaptively control the terminal unit in the current room, operate the working condition adjusting device, and keep the temperature of the room unchanged in stages, and when the working condition is approximately stable, collect the second data sample for training the second neural network under different input conditions; wherein the output quantity in the sample is obtained by prediction of the first neural network.

4. The operating condition adjusting device for billing of a central air conditioner according to claim 1, wherein The working condition adjusting device is further configured to: periodically operate the uniform temperature module, stop operation and collect data samples when the temperature difference between the highest temperature and the lowest temperature in the multiple temperature detection modules is less than a temperature difference threshold, and operate again to dynamically balance the overall temperature of the room at the target temperature when the temperature difference exceeds the threshold.

5. The operating condition adjusting device for billing of a central air conditioner according to claim 1, wherein The bottom of the bent corner support arm is provided with an image acquisition module, the vertical rotating shaft is rotated to obtain a global image of the room, and the working condition adjusting device is further configured to: based on the room image obtained by the image acquisition module, the image processing module analyzes the room orientation features and extracts two mutually perpendicular diagonal lines; the uniform temperature module is planned to operate along a trajectory, so that the axis of the uniform temperature fan, i.e. the end thereof, moves in a spatial spiral line to deliver the cold air blown by the central air conditioner terminal unit and / or the reference air conditioning unit to each area of the room until the temperature difference between the multiple temperature detection modules in the room is less than the temperature difference threshold.

6. The operating condition adjusting device for billing of a central air conditioner according to claim 1, wherein The working condition adjusting device is further configured to: the orientation features include the distribution and length of the room structure edge lines, and the directions and distances of the cold source air outlet, the return air inlet and the uniform temperature module relative to the corners of the room; based on the diagonal lines, the wind direction of the uniform temperature fan in the uniform temperature module is planned to move along a spiral trajectory, and the uniform temperature module is controlled to blow air along the trajectory at each time period.

7. The operating condition adjusting device for billing of a central air conditioner according to claim 1, wherein The planning of the trajectory is based on the obtained room diagonal lines, and a line connecting the position of the cold air outlet to the farthest position of the room that can be reached by the cold air outlet or the position opposite to the farthest position is taken as one of the main diagonal lines, and another straight line perpendicular to the main diagonal line is taken as the other main diagonal line; When the air outlet is located at a corner, a curve moving spirally along the inner wall surface of a cone is planned as the target trajectory with the diagonal line as the center, wherein the main diagonal line is the center line of the cone, When the air outlet is located at the middle of a side wall, a curve moving spirally along the inner wall surface of a cylinder is planned as the target trajectory with the side wall as the ground of the cylinder, wherein the main diagonal line is the center line of the cylinder.

8. The operating condition adjusting device for billing of a central air conditioner according to claim 1, wherein The working condition adjusting device is further configured to: when the uniform temperature module is planned to operate along the trajectory, the output angles of each joint in the uniform temperature module including the vertical rotating shaft, the horizontal rotating shaft and the telescopic support rod are analyzed based on inverse kinematics; Based on the planned trajectory, in each time period of sample collection, first, the uniform temperature module is uniformly operated to generally reduce the temperature; then, according to the temperature characteristics of the multiple temperature detection modules in the room, the linear velocity of the uniform temperature fan moving along the trajectory is changed, so that the linear velocity is inversely proportional to the temperature difference between the temperature at the corresponding trajectory point and the target temperature; wherein the temperature at each trajectory point can be obtained by interpolation calculation based on the temperature values of the multiple temperature measurement points.

9. The operating condition adjusting device for billing of a central air conditioner according to claim 1, wherein The central air conditioner is a water-cooled central air conditioner, the terminal unit is a terminal air damper, the first neural network takes the four scalars of the chilled water supply flow of the central air conditioner host, the supply and return water temperature difference, the current temperature and humidity of the room, and the vector composed of the opening state values of all terminal air dampers as input quantities, Or, the central air conditioner is a full-air central air conditioner, the terminal unit is a terminal air baffle, the first neural network takes the supply air flow of the central air conditioner host air supply port, the supply and return air temperature difference, the current temperature and humidity of the room, and a vector composed of the opening state values of all terminal air baffles as input quantities, Or, the central air conditioner is a variable refrigerant flow central air conditioner, the terminal unit is an indoor unit, the first neural network takes the central air conditioner host power, the indoor unit power, the current temperature and humidity of the room, and a vector composed of the opening state values of all terminal unit fans as input quantities.

10. The operating condition adjusting device for billing of a central air conditioner according to claim 1, wherein Based on the first neural network and the second neural network established offline, the central air conditioner billing system apportions the cost of the central air conditioner by time period, First, the current room condition is taken as a condition to calculate the correction coefficient In the formula, i is the current room number, the room number i = 1 ~ N is any integer, N is the total number of rooms, p i , p i0 is the mapping output of the second neural network when the input vector is , is the current condition of room i, The current room temperature and outdoor temperature are the same, and the current room temperature is taken as the value of the current room temperature in the six directions of the room. ​ The fee of the i-th room in the d-th time period is recalculated as: wherein, PF is the predicted cooling capacity of the end unit per unit time in the current room based on the first neural network i (t) the cumulative cooling capacity of the current period, Q jd Qk is the cooling capacity of room j in the current period, k j3 Cj is the corresponding coefficient of room j d is the total cost of the central air conditioner to be allocated in the current period.

Images