Fan coil unit driven by direct current

Abstract

The invention provides a fan coil unit driven by direct current, which belongs to the technical field of fan coil units and comprises a unit shell, an air supply grille, a surface air cooler, a control circuit board, a direct current power supply motor, a fan, an air return box, an air return grille and an intelligent wireless temperature controller. An air supply grille, a surface air cooler, a fan and an air return box are sequentially arranged in the unit shell from top to bottom. The air supply grille is connected with an air outlet of the unit shell and used for swinging up and down and left and right to adjust the air supply direction. The surface air cooler is arranged below the air supply grille and used for cooling or heating flowing air. The fan is driven by a direct-current power supply motor and provides power for air flow; the air return box is located below the draught fan and connected with the air return grille. The control circuit board is installed in the unit shell and used for receiving signals of the intelligent wireless temperature controller and controlling operation of all parts of the unit. The problems that an existing fan coil is not high in system integration degree and poor in self-adaptive optimization operation capacity are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of fan coil units, and in particular, relates to a fan coil unit driven by direct current. Background Art

[0002] Since the 1990s, central air-conditioning systems and equipment have been widely used in public buildings in China. At present, the first batch of central air-conditioning systems in buildings are facing the requirement of upgrading. With the introduction of various national urban renewal policies, the upgrading of building air-conditioning equipment has gradually been put on the agenda. In the early days, the air-conditioning system was designed to meet only the basic heating and cooling needs. The air-conditioning equipment, especially the fan coil unit, had limited effect on air quality, and the requirements of low-carbon energy-saving and sustainable development were rarely considered. The installation space of the equipment was generally limited. Therefore, the transformation of the fan coil unit faces great difficulties and challenges: it must meet the requirements of performance improvement and function increase, while considering the cost of transformation, and cannot make major adjustments to the entire building structure and the air-conditioning system as a whole. In order to meet the needs of upgrading air-conditioning equipment, it is necessary to develop a new type of multifunctional integrated intelligent control fan coil unit. The structure of the fan coil unit equipment is already very mature, mainly composed of motors, fans, coils, water trays and shells. The principle is to let the air flow through the coil through the operation of the fan to achieve heat exchange, and the treated air enters the room to change the temperature and humidity of the indoor air, so as to achieve the purpose of improving the thermal comfort of the human body. Fan coil units are the simplest air handling equipment. Existing fan coil units can only handle the temperature and humidity of the air. They have problems such as low system integration and poor adaptive optimization operation capabilities, and lack important functions such as purification and fresh air. Summary of the invention

[0003] In view of this, the present invention provides a fan coil unit driven by direct current, which can solve the problems of low system integration and poor adaptive optimization operation capability of existing fan coil units.

[0004] The present invention is achieved in that:

[0005] The present invention provides a fan coil unit driven by direct current, which comprises: a unit casing, an air supply grille, a surface cooler, a control circuit board, a direct current power supply motor, a fan, a return air box, a return air grille and an intelligent wireless thermostat; the inside of the unit casing is provided with an air supply grille, a surface cooler, a fan and a return air box in sequence from top to bottom; the air supply grille is connected to the air outlet of the unit casing and is used to swing up and down and left and right to adjust the air supply direction; the surface cooler is arranged below the air supply grille and is used to cool or heat the air flowing through; the fan is driven by a direct current power supply motor to provide power for air flow; the return air box is located below the fan and is connected to the return air grille and is used to mix fresh air and return air; the control circuit board is installed inside the unit casing and is used to receive signals from the intelligent wireless thermostat and control the operation of various components of the unit.

[0006] On the basis of the above technical solution, the fan coil unit driven by direct current of the present invention can also be improved as follows:

[0007] Among them, it also includes a supply air muffler and a return air muffler; the supply air muffler is arranged between the supply air grille and the surface cooler, and is used to make the supply air flow more stable and reduce the supply air noise; the return air muffler is arranged between the return air box and the return air grille, and is used to reduce the return air noise.

[0008] Furthermore, it also includes an electric water valve; the electric water valve is connected to the surface cooler and is used to adjust the water flow entering the surface cooler according to the instructions of the control circuit board, thereby controlling the air supply temperature of the air conditioner.

[0009] Furthermore, it also includes a personnel activity sensor; the personnel activity sensor is installed inside the unit casing, used to monitor the indoor personnel activities and feed back the signal to the control circuit board.

[0010] Furthermore, a medium-efficiency filter is provided on the return air grille; the medium-efficiency filter is used to remove more than 80% of particulate matter.

[0011] Furthermore, it also includes a fresh air inlet; the fresh air inlet is arranged on the return air box, and is used to introduce processed outdoor fresh air.

[0012] Furthermore, it also includes a water receiving pan; the water receiving pan is arranged below the surface cooler and is used to receive condensed water generated during air conditioning cooling.

[0013] Furthermore, it also includes a lighting lamp and a negative ion generator; the lighting lamp is installed on the unit casing to illuminate the indoor space and is powered by a control circuit board; the negative ion generator is installed inside the unit to generate a large amount of negative air ions.

[0014] Furthermore, it also includes a fire smoke sprinkler head; the fire smoke sprinkler head is installed on the unit casing, is used to sense indoor smoke conditions, and is connected to the fire pipe.

[0015] Furthermore, the control circuit board is provided with an adaptive control module for dynamically adjusting the operating parameters of the fan coil unit according to indoor environmental changes and personnel activities, thereby improving energy efficiency and optimizing user experience; the following steps are included:

[0016] S10, setting operating parameters, including temperature, humidity, wind speed, and determining the initial sampling time interval;

[0017] S20, collecting current environmental parameters and user adjustment data in real time, including temperature collected by a temperature sensor and crowd distribution data collected by a personnel activity sensor, wherein the user adjustment data is the user's adjustment data for the intelligent wireless thermostat;

[0018] S30, establishing a user adjustment equation group and an automatic adjustment equation group considering environmental parameters, wherein the user adjustment equation group is used to map the user's adjustment data according to the current environmental parameters; and the automatic adjustment equation group is used to automatically adjust the unit operation parameters according to the personnel activity status and the indoor temperature change trend;

[0019] S40, fitting the user adjustment equation group using the current environmental parameters continuously collected over a period of time and the user's adjustment data; fitting the automatic adjustment equation group using the historical operation data and the preset comfort index;

[0020] S50, forming a fusion equation group by weighting the user adjustment equation group and the automatic adjustment equation group, and dynamically adjusting the weight coefficient according to the frequency of user adjustment;

[0021] S60, calculating optimal operating parameters based on the fusion equation group, including target temperature, wind speed and air supply direction, and generating corresponding control instructions;

[0022] S70: Send the control instruction to the corresponding execution component to adjust the operating state of the fan coil unit.

[0023] Specifically, in step S10, the initial operating parameters of the fan coil unit need to be set first, including indoor temperature, humidity and wind speed. These parameters are the target values ​​used by the control circuit board to adjust the operating state of the unit. Then, the sampling time interval needs to be determined, that is, how often the environmental parameter data and user adjustment data are collected.

[0024] In step S20, the current indoor temperature and humidity data are collected in real time through the temperature and humidity sensor. At the same time, the personnel activity sensor is used to monitor the distribution of indoor personnel, and the activity index A is calculated based on the frequency of personnel movement. In addition, the temperature, wind speed and air supply direction adjustment data performed by the user through the smart wireless thermostat must be recorded. These data provide a basis for the subsequent establishment of the user adjustment equation group and the automatic adjustment equation group.

[0025] In step S30, a user adjustment equation group considering environmental parameters is first established. This equation group describes the mathematical relationship between the user's subjective feelings and adjustment behaviors for factors such as temperature, humidity, personnel activities and temperature change rate. At the same time, it is also necessary to establish an automatic adjustment equation group, which automatically adjusts the unit operating parameters according to factors such as personnel activities and indoor temperature change trends to achieve the best energy efficiency and user experience. These two types of equation groups provide a basic model for subsequent parameter optimization.

[0026] In step S40, sufficient sample data is used to fit the user adjustment equation group and the automatic adjustment equation group. For the user adjustment equation group, the least square method is used to solve the linear equation group to obtain the optimal value of each parameter. For the automatic adjustment equation group, it is necessary to consider the preset comfort index, such as the optimal comfort temperature, etc., and the least square method is also used for fitting. These fitting results provide the necessary mathematical basis for subsequent parameter fusion.

[0027] In step S50, the user adjustment equation group and the automatic adjustment equation group are fused into a comprehensive equation group in a weighted manner. The frequency of user adjustment needs to be considered during fusion, and the weight coefficients of the two need to be dynamically adjusted. The purpose of this is to ensure user experience while taking into account the needs of energy efficiency optimization.

[0028] In step S60, the optimal temperature, wind speed and air supply direction parameters are calculated based on the fused equations. These parameter values ​​are obtained based on comprehensive consideration of user needs and automatic optimization rules. Next, the control circuit board will generate corresponding control instructions to prepare for the execution of subsequent steps.

[0029] In step S70, the control circuit board sends the optimized parameter instructions to the corresponding execution components, such as the electric water valve, fan, etc., so as to adjust the operating state of the fan coil unit. This step ensures that the unit can actually operate according to the calculated optimal parameters.

[0030] Among them, the user adjustment equation group is specifically expressed as follows:

[0031]

[0032] Where, T u The target temperature (℃) adjusted by the user; V u Wind speed adjusted by the user (m / s); D u Air supply direction adjusted by the user (°); T c is the current indoor temperature (℃); H is the current indoor humidity (%); A is the personnel activity index (dimensionless, range 0-10); is the indoor temperature change rate (℃ / h); t is time (h); ω is the daily cycle frequency (2π / 24rad / h); a1, a2, …, c5 are unknown coefficients; ε T ,ε V ,ε D are their respective error terms.

[0033] The parameter acquisition method is:

[0034] T c, H is directly measured by temperature and humidity sensors. A is calculated from the data collected by the personnel activity sensor. The specific steps are as follows:

[0035] Step 1: Count the number of personnel movements per unit time N;

[0036] Step 2: Calculate the activity index Where N max The preset maximum number of moves.

[0037] The calculation steps are as follows:

[0038] Step 1: Measure the initial temperature T1 and the final temperature T2 within the time interval Δt;

[0039] Step 2: Calculate the temperature change rate

[0040] The automatic adjustment equations are specifically expressed as follows:

[0041]

[0042] Where, T a is the target temperature for automatic adjustment (℃); V a is the automatically adjusted wind speed (m / s); D a is the automatically adjusted air supply direction (°); T opt is the optimal comfortable temperature (℃, the default value is 24℃); k1, k2, …, n5 are unknown coefficients; δ T ,δ V ,δ D are their respective error terms.

[0043] Parameter T opt The method to obtain is:

[0044] Step 1: Collect user satisfaction rating data at different temperatures;

[0045] Step 2: Use a quadratic function to fit the relationship between satisfaction rating and temperature: S = p(TT opt ) 2 +q, where S is the satisfaction score;

[0046] Step 3: Find the temperature corresponding to the maximum satisfaction value, which is T opt .

[0047] The method for obtaining the unknown coefficients (a1,…,c5) and (k1,…,n5) is:

[0048] Step 1: Collect enough sample data, including environmental parameters and corresponding adjustment results;

[0049] Step 2: Use the least squares method to solve the linear equation system: Xβ=y, where X is the independent variable matrix, y is the dependent variable vector, and β is the coefficient vector to be determined;

[0050] Step 3: Calculate the optimal solution: β = (X T X) -1 X T y.

[0051] Specifically, the control circuit board is provided with an adaptive control module for dynamically adjusting the operating parameters of the fan coil unit according to indoor environmental changes and personnel activities, thereby improving energy efficiency and optimizing user experience; the adaptive control module specifically includes the following steps:

[0052] Step S10, set initial operating parameters and sampling time interval. First, the control circuit board sets the initial temperature, humidity, wind speed and other operating parameters of the fan coil unit. At the same time, determine how often to collect environmental parameter data and user adjustment data. These parameters provide a benchmark for subsequent dynamic adjustments.

[0053] Step S20, real-time collection of current environmental parameters and user adjustment data. Real-time collection of indoor temperature and humidity data through temperature and humidity sensors. Use personnel activity sensors to monitor indoor personnel activities and calculate personnel activity indexes. In addition, record the temperature, wind speed and air supply direction adjustment data performed by users through the smart wireless thermostat. These data provide a basis for establishing user adjustment models and automatic adjustment models.

[0054] Step S30, establish a user adjustment equation group and an automatic adjustment equation group. The user adjustment equation group describes the mathematical relationship between the user's subjective feelings and adjustment behaviors for factors such as temperature, humidity, personnel activities and temperature change rate. The automatic adjustment equation group automatically adjusts the unit operating parameters according to factors such as personnel activities and indoor temperature change trends. The two types of models provide a basis for subsequent parameter optimization.

[0055] Step S40, fitting the user adjustment equation group and the automatic adjustment equation group. For the user adjustment equation group, the least square method is used to solve the linear equation group to determine the optimal value of each parameter. For the automatic adjustment equation group, it is necessary to consider the preset comfort index, such as the optimal comfort temperature, etc., and the least square method is also used for fitting. These fitting results provide a mathematical basis for parameter fusion.

[0056] Step S50, merging the user adjustment equation group and the automatic adjustment equation group. The two types of equation groups are fused into a comprehensive equation group in a weighted manner. When merging, the frequency of user adjustment needs to be considered, and the weight coefficients of the two need to be dynamically adjusted. This is done to ensure user experience while taking into account energy efficiency optimization.

[0057] Step S60, calculate the optimal operating parameters based on the fusion equations. The control circuit board calculates the optimal temperature, wind speed and air supply direction parameters based on the fusion equations, and generates corresponding control instructions. These parameter values ​​are obtained based on comprehensive consideration of user needs and automatic optimization rules.

[0058] Step S70, execute the optimized control instructions. The control circuit board sends the optimized parameter instructions to the corresponding execution components, such as electric water valves, fans, etc., to adjust the operating state of the fan coil unit. This step ensures that the unit can actually operate according to the calculated optimal parameters.

[0059] Compared with the prior art, the fan coil unit driven by direct current provided by the present invention integrates multiple detection elements such as temperature and humidity sensors and personnel activity sensors in the unit, combines with intelligent wireless thermostat, establishes an adaptive control algorithm that takes user preferences into consideration, and realizes dynamic optimization and adjustment of the unit's operating parameters. This technical solution can significantly improve the intelligence level and energy utilization efficiency of fan coil units, and significantly improve the user experience.

[0060] Specifically, the technical effects of the present invention are mainly reflected in the following aspects:

[0061] 1. Accurately control air supply parameters. The DC motor drives the fan, and with the intelligent control algorithm, it can achieve precise adjustment of parameters such as temperature, wind speed and air supply direction to meet personalized air conditioning services under different environmental conditions and user needs.

[0062] 2. Adaptive optimization of operating status. The control circuit board has a built-in adaptive control module that can dynamically analyze environmental changes and user preferences, continuously optimize the unit's temperature, humidity and air volume target values, and improve energy efficiency.

[0063] 3. Intelligent perception of environmental factors. The unit integrates temperature and humidity sensors, personnel activity sensors, etc., which can fully perceive the indoor environmental conditions, provide a basis for adaptive control, and meet diverse usage needs.

[0064] 4. Improve user experience. Modeling and integration optimization are carried out according to the user's temperature, wind speed and air supply direction adjustment needs, which not only respects the user's personalized preferences, but also can automatically adjust to the best state, greatly improving user satisfaction.

[0065] 5. Enhance energy-saving and environmental protection performance. The intelligent optimization algorithm can dynamically adjust the unit operating parameters to minimize energy consumption while meeting user needs, and cooperate with the fresh air system to improve indoor air quality and achieve a more environmentally friendly air conditioning service.

[0066] In summary, the present invention solves the problems of low system integration and poor automation capability in existing fan coil units. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic diagram of the structure of a fan coil unit driven by direct current provided by the present invention;

[0068] Figure 2 a flow chart of steps performed by an adaptive control module;

[0069] Figure 3 The temperature, humidity, personnel activities and energy consumption changes of the unit of user A in Example 2 within one week;

[0070] Figure 4 The temperature, humidity, personnel activities and energy consumption changes of the unit of user B in Example 3 within one week;

[0071] In the attached drawings, the list of components represented by each reference numeral is as follows: 1. Air supply grille; 2. Personnel activity sensor; 3. Air supply muffler; 4. Surface cooler; 5. Control circuit board; 6. Electric water valve; 7. Fan; 8. DC power supply motor; 9. Return air muffler; 10. Return air box; 11. Fresh air inlet; 12. Return air grille and medium-efficiency filter; 13. Lighting; 14. Water tray; 15. Smart wireless thermostat; 16. Fire smoke shower head; 17. Negative ion generator. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0073] like Figure 1 As shown, it is a structural schematic diagram of a fan coil unit driven by direct current provided by the present invention, comprising: a unit casing, an air supply grille 1, a surface cooler 4, a control circuit board 5, a DC power supply motor 8, a fan 7, a return air box 10, a return air grille 12 and an intelligent wireless thermostat 15; the inside of the unit casing is provided with an air supply grille, a surface cooler, a fan and a return air box from top to bottom in sequence; the air supply grille is connected to the air outlet of the unit casing and is used to swing up and down and left and right to adjust the air supply direction; the surface cooler is arranged under the air supply grille and is used to cool or heat the air flowing through; the fan is driven by a DC power supply motor to provide power for air flow; the return air box is located under the fan and is connected to the return air grille to mix fresh air and return air; the control circuit board is installed inside the unit casing and is used to receive signals from the intelligent wireless thermostat and control the operation of various components of the unit.

[0074] Furthermore, it also includes a supply air silencer 3 and a return air silencer 9; the supply air silencer is arranged between the supply air grille and the surface cooler to make the supply air flow more stable and reduce the supply air noise; the return air silencer is arranged between the return air box 10 and the return air grille 12 to reduce the return air noise.

[0075] Furthermore, it also includes an electric water valve 6; the electric water valve is connected to the surface cooler and is used to adjust the water flow entering the surface cooler according to the instructions of the control circuit board, thereby controlling the air supply temperature of the air conditioner.

[0076] Furthermore, it also includes a human activity sensor 2; the human activity sensor is installed inside the unit casing, and is used to monitor the indoor human activity and feed back the signal to the control circuit board.

[0077] Furthermore, a medium-efficiency filter is provided on the return air grille; the medium-efficiency filter is used to remove more than 75% of particulate matter.

[0078] Furthermore, it also includes a fresh air inlet 11; the fresh air inlet is arranged on the return air box, and is used to introduce processed outdoor fresh air.

[0079] Furthermore, it also includes a water receiving pan 14; the water receiving pan is arranged below the surface cooler and is used to receive condensed water generated during air conditioning cooling.

[0080] Furthermore, it also includes a lighting lamp 13 and a negative ion generator; the lighting lamp is installed on the unit casing to illuminate the indoor space and is powered by a control circuit board; the negative ion generator is installed inside the unit to generate a large amount of negative air ions.

[0081] Furthermore, it also includes a fire smoke sprinkler head 16; the fire smoke sprinkler head is installed on the unit casing, is used to sense the indoor smoke situation, and is connected to the fire pipe.

[0082] Furthermore, the control circuit board is provided with an adaptive control module for dynamically adjusting the operating parameters of the fan coil unit according to indoor environmental changes and personnel activities, thereby improving energy efficiency and optimizing user experience; the following steps are included:

[0083] S10, setting operating parameters, including temperature, humidity, wind speed, and determining the initial sampling time interval;

[0084] S20, collecting current environmental parameters and user adjustment data in real time, including temperature collected by a temperature sensor and crowd distribution data collected by a personnel activity sensor, wherein the user adjustment data is the user's adjustment data for the intelligent wireless thermostat;

[0085] S30, establishing a user adjustment equation group and an automatic adjustment equation group considering environmental parameters, wherein the user adjustment equation group is used to map the user's adjustment data according to the current environmental parameters; and the automatic adjustment equation group is used to automatically adjust the unit operation parameters according to the personnel activity status and the indoor temperature change trend;

[0086] S40, fitting the user adjustment equation group using the current environmental parameters continuously collected over a period of time and the user's adjustment data; fitting the automatic adjustment equation group using the historical operation data and the preset comfort index;

[0087] S50, forming a fusion equation group by weighting the user adjustment equation group and the automatic adjustment equation group, and dynamically adjusting the weight coefficient according to the frequency of user adjustment;

[0088] S60, calculating optimal operating parameters based on the fusion equation group, including target temperature, wind speed and air supply direction, and generating corresponding control instructions;

[0089] S70: Send the control instruction to the corresponding execution component to adjust the operating state of the fan coil unit.

[0090] The specific implementation methods of the above steps are described in detail below:

[0091] Wherein, the step S10 specifically includes the following sub-steps:

[0092] Step 101, setting the initial operating parameters of the fan coil unit. The control circuit board sets the initial indoor temperature, humidity and wind speed target values ​​as the reference parameters for the unit operation.

[0093] Step 102, determine the environmental parameter sampling time interval. The control circuit board determines how often to collect environmental parameter data such as temperature, humidity, and personnel activities. The length of this time interval affects the timeliness and accuracy of the sampled data.

[0094] Step 103: Transmit the set initial operating parameters and sampling time interval to each execution component. The control circuit board sends the initial parameters and sampling period to the fan, electric water valve and other actuators through digital signals to ensure that they can operate and collect data as required.

[0095] Through the above three sub-steps, the control circuit board completes the initial setting of the fan coil unit, laying the foundation for the subsequent dynamic adjustment. The reasonable setting of the initial parameters and the determination of the sampling interval directly affect the performance of the entire adaptive control system.

[0096] Wherein, the step S20 specifically includes the following sub-steps:

[0097] Step 201: Collect indoor temperature and humidity data in real time through temperature and humidity sensors. The control circuit board uses the temperature and humidity sensors installed inside the unit to obtain the current indoor temperature and humidity parameters in real time with a fixed sampling period. These parameters are the basis for dynamic adjustment.

[0098] Step 202: Calculate the personnel activity index. The control circuit board calculates a dimensionless index A reflecting the intensity of personnel activity according to a certain algorithm based on the number of indoor personnel movements collected by the personnel activity sensor. This index is closely related to user experience and automatic adjustment strategy.

[0099] Step 203, record the adjustment data of the user through the intelligent wireless thermostat. The control circuit board records the user's adjustment of temperature, wind speed and air supply direction in real time, providing a basis for establishing a user adjustment model. These adjustment data reflect the user's subjective feelings about the current environment.

[0100] Through the above three sub-steps, the control circuit board completes the real-time collection of current environmental parameters and user adjustment behaviors, providing necessary input data for subsequent parameter optimization. The accuracy and timeliness of obtaining these data directly affect the effect of adaptive control.

[0101] Wherein, the step S30 specifically includes the following sub-steps:

[0102] Step 301, establish a user adjustment equation group considering environmental parameters. The control circuit board establishes a mathematical model describing the relationship between the user's subjective feelings and adjustment behavior based on the temperature, humidity, personnel activity index, and temperature change rate data collected in step S20. This model provides a basis for the user's demand level for subsequent parameter fusion.

[0103] Step 302: Establish a set of equations for automatically adjusting operating parameters. The control circuit board establishes a mathematical model that can automatically adjust the temperature, wind speed and air supply direction based on factors such as personnel activity status and indoor temperature change trends. The model reflects the system's automatic adjustment strategy for achieving energy efficiency optimization and user experience.

[0104] Step 303: Ensure the fusion of the two types of equations. The control circuit board needs to ensure that the input and output variables of the user-adjusted equations and the automatic-adjusted equations are consistent to facilitate subsequent parameter fusion. This consistency ensures the comparability and compatibility of the two types of models.

[0105] Through the above three sub-steps, the control circuit board establishes two types of mathematical models to describe the user's subjective needs and the system's automatic optimization. This provides a basis for subsequent parameter fusion and optimal adjustment. The quality of model construction directly affects the effect of adaptive control.

[0106] Wherein, the step S40 specifically includes the following sub-steps:

[0107] Step 401, collect training sample data of the user adjustment equation group. The control circuit board needs to obtain enough environmental parameters such as indoor temperature, humidity, personnel activities, temperature change rate, and corresponding user temperature, wind speed and air supply direction adjustment data as input for fitting the user adjustment equation group.

[0108] Step 402: Fit the user adjustment equations using the least squares method. The control circuit board substitutes the sample data collected in step 401 into the linear equations, and uses the least squares method to solve the optimal value of each parameter to obtain a mathematical model that describes the user adjustment behavior.

[0109] Step 403, collect training sample data for the automatic adjustment equation group. The control circuit board needs to obtain historical operation data, including environmental parameters, unit operation parameters, and user satisfaction scores, etc., as input for fitting the automatic adjustment equation group.

[0110] Step 404: Fit the automatic adjustment equation group in combination with the preset comfort index. The control circuit board substitutes the sample data of step 403 and the indexes such as the optimal comfort temperature into the equation group, and fits it using the least square method to obtain a mathematical model describing the automatic adjustment strategy.

[0111] Through the above four sub-steps, the control circuit board completes the fitting of the user adjustment equation group and the automatic adjustment equation group. This provides the necessary mathematical basis for the subsequent parameter fusion. The fitting accuracy directly affects the performance of adaptive control.

[0112] Wherein, the step S50 specifically includes the following sub-steps:

[0113] Step 501: Analyze the characteristics of user adjustment behavior. The control circuit board statistically analyzes the frequency of users adjusting temperature, wind speed and air supply direction through the intelligent wireless thermostat to understand the changing characteristics of user preferences.

[0114] Step 502, dynamically adjust the weights of the user adjustment equation group and the automatic adjustment equation group. According to the analysis results of step 501, the control circuit board dynamically adjusts the weight coefficients of the two types of models in the fusion equation group. When the user adjustment frequency is high, the weight of the user adjustment equation group is appropriately increased; otherwise, it is appropriately reduced.

[0115] Step 503: Form a comprehensive parameter optimization equation group. The control circuit board merges the weighted user adjustment equation group and the automatic adjustment equation group to obtain a comprehensive parameter optimization model. This model takes into account both user needs and system energy efficiency, and provides a basis for subsequent optimal parameter calculations.

[0116] Through the above three sub-steps, the control circuit board dynamically adjusts the weights of the user adjustment model and the automatic adjustment model in the comprehensive equation group, making the fusion of the two types of models closer to actual needs. This adaptive weight adjustment improves the flexibility and effectiveness of the entire control strategy.

[0117] Wherein, the step S60 specifically includes the following sub-steps:

[0118] Step 601, calculate the optimal operating parameters based on the fusion equation group. The control circuit board substitutes the comprehensive parameter optimization model formed in step S50 into the actual environmental parameters to calculate the optimal temperature, wind speed and air supply direction values. These parameter values ​​are obtained under the premise of taking into account user needs and system energy efficiency.

[0119] Step 602: Generate corresponding control instructions. The control circuit board generates specific digital control instructions, such as opening instructions, speed instructions, etc., based on the optimal parameters calculated in step 601, to prepare for the subsequent adjustment of the execution components.

[0120] Step 603, ensure the continuity and stability of the control command. The control circuit board needs to consider the stability of the unit's operating state and properly smooth the calculated optimal parameters to avoid system oscillation caused by sudden changes in the command.

[0121] Through the above three sub-steps, the control circuit board calculates the optimal operating parameters according to the fused parameter optimization model and generates corresponding smooth and continuous digital control instructions. These instructions provide a scientific basis for the subsequent adjustment of the execution components and help to achieve the optimal control of the unit operation.

[0122] Wherein, the step S70 specifically includes the following sub-steps:

[0123] Step 701, sending the optimized control instructions to the execution components. The control circuit board transmits the optimal temperature, wind speed, air supply direction and other parameter instructions calculated in step S60 to the electric water valve, fan and other execution components through digital signals.

[0124] Step 702: Monitor the response of the execution components. The control circuit board monitors the response of each execution component to the instruction in real time to ensure that they can be accurately adjusted according to the instruction requirements. If an abnormality is found, timely feedback and remedial measures need to be taken.

[0125] Step 703, record the adjustment result to provide a basis for the next optimization. The control circuit board records the actual operating parameters after the adjustment as the historical data for the next round of adaptive control to provide a basis for further optimization.

[0126] Through the above three sub-steps, the control circuit board completes the issuance of optimization parameter instructions, monitoring of execution status, and recording of adjustment results. This ensures that the fan coil unit can actually operate according to the calculated optimal parameters and accumulate valuable data for the next round of adaptive control.

[0127] Specifically, the principle of the present invention is to establish an adaptive intelligent control algorithm that can dynamically adjust the fan operating parameters to meet user needs while improving energy efficiency. The design principle of the control algorithm is as follows:

[0128] First, the control circuit board needs to collect real-time environmental parameters such as indoor temperature and humidity, personnel activities, and record the temperature, wind speed and air supply direction adjustments made by users through the intelligent wireless thermostat. These data provide a basis for subsequent parameter optimization.

[0129] Secondly, the control circuit board established two types of mathematical models: one is the user adjustment equation group that describes the user's subjective adjustment habits, and the other is the automatic adjustment equation group based on the system automatic optimization rules. Both types of models take into account the impact of environmental parameters on the adjustment behavior.

[0130] Then, the control circuit board fits these two types of equations to determine the optimal values ​​of each parameter. On this basis, the user adjustment model and the automatic adjustment model are integrated into a comprehensive parameter optimization model. The fusion model can dynamically adjust the weights of the two according to the user adjustment frequency to achieve a balance between user preferences and system energy efficiency.

[0131] Finally, the control circuit board calculates the optimal temperature, wind speed and air supply direction target values ​​based on the fused optimization model, converts them into corresponding execution instructions, and sends them to the electric water valve, DC fan and other actuators to adjust the actual operating status of the unit. The entire closed-loop control process ensures that energy efficiency is maximized while meeting user needs.

[0132] The reason why this adaptive intelligent control solution can solve the problems existing in the existing technology is mainly due to the following points:

[0133] 1. A parameter optimization model that takes user preferences into consideration was established. The integration of user adjustment behavior and system automatic optimization strategy can take into account both personalized needs and overall energy efficiency, which is a significant improvement over existing solutions.

[0134] 2. Adopt a dynamic weight adjustment mechanism. The weights of the two types of models are dynamically adjusted according to the user's adjustment frequency, so that the control strategy can flexibly adapt to environmental changes and the dynamic evolution of user habits.

[0135] 3. Integrates a variety of intelligent sensing components. Temperature and humidity sensors, personnel activity sensors, etc., can fully perceive the indoor environment and provide more abundant input data for adaptive optimization.

[0136] 4. Use DC motor to drive the fan. DC motor has the advantages of strong controllability and low energy consumption, providing a good hardware foundation for fine parameter adjustment and energy-saving control.

[0137] To sum up, the adaptive intelligent control technology proposed in the present invention fully considers user needs and system energy efficiency, and realizes precise optimization of the operating parameters of the fan coil unit by constructing a fusion model and dynamically adjusting weights, thereby greatly improving energy utilization efficiency while satisfying personalized user experience.

[0138] In order to better understand and implement the present invention, a specific embodiment 1 of the adaptive control module in the present invention is provided below. The relevant steps of this embodiment 1 are described in detail as follows: In step S10, the control circuit board first needs to set the initial operating parameters and sampling time interval of the fan coil unit. Specifically, the following mathematical processing is included:

[0139] Assume that the initial indoor temperature target value is T0, the initial humidity target value is H0, and the initial wind speed target value is V0. These parameters are the initial target values ​​used by the control circuit board to adjust the operating status of the unit.

[0140] At the same time, determine the sampling time interval Δt of environmental parameter data and user adjustment data. The selection of this time interval needs to balance the timeliness and stability of data collection. Too short sampling interval may cause data jitter, while too long interval cannot capture the dynamic changes of the system. Usually Δt = 10min can be taken as the initial value.

[0141] In summary, step S10 can be described by a mathematical formula as follows:

[0142]

[0143] In step S20, the control circuit board needs to collect the current indoor environment parameters and user adjustment data in real time to provide a basis for subsequent parameter optimization. Specifically, the following mathematical processing is included:

[0144] 1. Temperature and humidity collection:

[0145] The temperature and humidity sensor installed inside the unit is used to control the circuit board to collect the indoor temperature T in real time with a sampling period Δt. c (t) and humidity H(t).

[0146] 2. Personnel activity monitoring:

[0147] Using the personnel activity sensor, the control circuit board counts the number of personnel movements per unit time N(t). Based on this, the personnel activity index A(t) can be calculated:

[0148]

[0149] Among them, N max The preset maximum number of moves.

[0150] 3. User adjustment records:

[0151] The control circuit board records the temperature adjustment made by the user through the intelligent wireless thermostat in real time. u (t), wind speed adjustment V u (t) and air supply direction adjustment D u (t).

[0152] In summary, step S20 can be described by a mathematical formula as follows:

[0153]

[0154] In step S30, the control circuit board needs to establish a mathematical model that describes the user's adjustment behavior and automatic adjustment strategy to provide a basis for subsequent parameter optimization.

[0155] 1. User-adjusted equations:

[0156] Considering the user's subjective feelings about factors such as temperature, humidity, human activity and temperature change rate, the control circuit board establishes the following user adjustment equations:

[0157]

[0158] Among them, a i ,b i and c i (i=1,2,…,5) are the unknown coefficients, ω=2π / 24rad / h is the daily cycle frequency, ε T ,ε V and ε D are their respective error terms.

[0159] 2. Automatic adjustment of equations:

[0160] Considering factors such as personnel activities and indoor temperature change trends, the control circuit board establishes the following automatic adjustment equations:

[0161]

[0162] Among them, k i ,m i and n i (i=1,2,…,5) are the unknown coefficients, Topt is the optimal comfortable temperature (default 24℃),δ T ,δ V and δ D are their respective error terms.

[0163] In summary, step S30 can be described by a mathematical formula as follows:

[0164] User-adjusted set of equations:

[0165]

[0166] Automatically adjust the equations:

[0167]

[0168] In step S40, the control circuit board needs to fit the user adjustment equation group and the automatic adjustment equation group to determine the optimal value of each parameter.

[0169] 1. User-adjusted equation fitting:

[0170] The control circuit board collects enough sample data Establish the following linear equations:

[0171]

[0172] The least squares method is used to solve the linear equations and the optimal values ​​of each unknown coefficient are obtained.

[0173] 2. Automatically adjust the equation group fitting:

[0174] The control circuit board collects historical operation data Establish the following linear equations:

[0175]

[0176] The least square method is used to solve the linear equations, considering the preset optimal comfort temperature T opt On this basis, the optimal values ​​of each unknown coefficient are obtained.

[0177] In summary, step S40 can be described by a mathematical formula as follows:

[0178] User-adjusted equations fitting:

[0179]

[0180] Automatically adjust the system of equations to fit:

[0181]

[0182] In step S50, the control circuit board needs to merge the user adjustment equation group and the automatic adjustment equation group to form a comprehensive parameter optimization model. Specifically, the following mathematical processing is included:

[0183] 1. Analyze user adjustment frequency:

[0184] The control circuit board statistically analyzes the number of times users adjust the temperature, wind speed and air supply direction through the intelligent wireless thermostat, recorded as N T ,N V and N D From these data, the relative frequency of user adjustments can be calculated:

[0185]

[0186] 2. Dynamically adjust the weight coefficient:

[0187] The control circuit board dynamically adjusts the weight coefficients of the user adjustment equation group and the automatic adjustment equation group in the fusion model according to the above user adjustment frequency:

[0188] ω T =k T α T ;

[0189] ω V =k V α V ;

[0190] ω D =k D α D ;

[0191] Among them, k T ,k V and k D is a preset constant weight, and its value range is [0,1].

[0192] 3. Form a comprehensive optimization equation:

[0193] The control circuit board combines the weighted user adjustment equations and the automatic adjustment equations into a comprehensive parameter optimization model:

[0194]

[0195] Among them, T o ,V o and D o It is the final optimized target value of temperature, wind speed and air supply direction.

[0196] In summary, step S50 can be described by a mathematical formula as follows:

[0197] User-adjusted frequency calculation:

[0198]

[0199] Dynamic adjustment of weight coefficient:

[0200]

[0201] Comprehensive optimization equation:

[0202]

[0203] In step S60, the control circuit board needs to calculate the optimal temperature, wind speed and air supply direction parameters based on the aforementioned fusion equation group and generate corresponding control instructions. Specifically, the following mathematical processing is included:

[0204] 1. Calculate the optimal operating parameters:

[0205] The control circuit board substitutes the comprehensive optimization equation obtained in step S50 into the actual environmental parameters Calculate the optimal temperature T o , wind speed V o and air supply direction D o .

[0206] 2. Generate control instructions:

[0207] The control circuit board generates corresponding digital control instructions, such as the opening instruction u, according to the calculated optimal parameter value. v , speed command u ω Etc. These instructions prepare for the subsequent execution component adjustments.

[0208] To ensure the smoothness of the control instructions, the control circuit board can perform appropriate filtering on the calculated optimal parameters, such as using a low-pass filter:

[0209]

[0210] Among them, τ is the filtering time constant, and its value range is 10 to 60s.

[0211] In summary, step S60 can be described by a mathematical formula as follows:

[0212] Calculation of optimal operating parameters:

[0213]

[0214] Control command generation:

[0215]

[0216] In step S70, the control circuit board needs to send the optimized control instructions to the corresponding execution components to adjust the operating state of the fan coil unit. Specifically, the following mathematical processing is included:

[0217] 1. Send control instructions:

[0218] The control circuit board calculates the opening instruction u obtained in step S60 v and speed command u ω Digital signals are sent to electric water valves, fans and other actuators, instructing them to adjust according to the optimized parameters.

[0219] 2. Monitor execution status:

[0220] The control circuit board monitors the response of each executive component to the command in real time, such as the actual opening v of the electric water valve, the actual speed ω of the fan, etc. If a deviation is found between the actual value and the command value, feedback and compensation are required in a timely manner.

[0221] 3. Record the results of this adjustment:

[0222] The control circuit board records the actual operating parameters after this adjustment. As historical data for the next round of adaptive control.

[0223] In summary, step S70 can be described by a mathematical formula as follows:

[0224] Control command sending:

[0225]

[0226] Execution status monitoring:

[0227]

[0228] Adjustment result record:

[0229]

[0230] In summary, through the mathematical modeling and algorithm optimization of the above steps S10 to S70, the control circuit board realizes the adaptive control of the fan coil unit. The core idea is to dynamically adjust the operating state of the unit in combination with user needs and environmental parameters to achieve the goal of energy efficiency optimization and user experience improvement. In the specific implementation, a variety of mathematical techniques such as solving linear equations and least squares fitting are involved to ensure the scientificity and effectiveness of the control strategy. Through continuous learning and adaptation, the fan coil unit can provide users with more comfortable and energy-saving air-conditioning services.

[0231] In order to further better understand and implement the present invention, the following provides a specific application scenario of the present invention, Example 2: When a residential area is being renovated, a DC-driven fan coil unit proposed by the present invention is installed. The unit can adaptively adjust operating parameters to meet the personalized air conditioning needs of different families. The following is a specific device implementation:

[0232] The shell size of the unit is 1.2m long, 0.8m wide and 0.6m high. It adopts a white appearance, which is consistent with the decoration style of the room. The upper end of the shell is equipped with an air supply grille that can swing up and down, left and right. Users can adjust the air supply direction according to actual needs. The shell is equipped with an air supply muffler, a surface cooler, a fan and a return air box from top to bottom. The surface cooler adopts a copper tube and aluminum sheet structure, which can quickly cool or heat. The fan is driven by a brushless DC motor, and the speed is adjusted by controlling the circuit board to achieve refined air volume control. The return air box is equipped with a medium-efficiency filter, which can remove more than 75% of particulate matter to ensure clean indoor air.

[0233] There is a fresh air inlet on the rear side of the unit housing to introduce fresh outside air that has been preliminarily treated. The fresh air volume is adjusted by the control circuit board to maintain the balance of indoor and outdoor air. There is a water receiving tray under the return air grille to collect condensed water generated during the refrigeration process. In addition, there is a lighting lamp on the top of the unit, which can be powered by the control circuit board to provide lighting for the indoor space. A negative ion generator is also integrated inside, which can continuously release a large amount of negative ions into the room to improve the air quality.

[0234] The front side of the unit is equipped with an intelligent wireless thermostat, through which users can check and adjust the temperature, wind speed and air supply direction at any time. The thermostat is connected to the control circuit board through wireless communication, and the user's adjustment instructions are uploaded in real time. At the same time, the control circuit board also integrates temperature and humidity sensors and personnel activity sensors, which can fully perceive the indoor environment conditions.

[0235] In addition, a fire smoke sprinkler head is installed above the unit casing, which is linked to the building fire protection system to detect fire hazards in time and protect the lives and property of users. The entire unit adopts a modular design, which is very convenient to install and disassemble, and maintenance is also relatively simple.

[0236] The following is an example of the actual use of a user's home to introduce the adaptive intelligent control process of the fan coil unit of the present invention:

[0237] User A's home is a 3-bedroom apartment with an area of ​​about 100 square meters. User A is an office worker, working from 8:00 to 18:00 every day from Monday to Friday. When they are at home, Mr. and Mrs. A and their two children around 10 years old spend time in the living room and master bedroom.

[0238] After installation, the control circuit board first sets the initial operating parameters: target temperature 24°C, target humidity 55%, target wind speed 3m / s. At the same time, the sampling period of environmental parameters is determined to be 10 minutes.

[0239] During the trial run before the official launch, the control circuit board recorded the following data in real time:

[0240] Table 1 Data records during trial operation

[0241]

[0242]

[0243] According to the data in Table 1, the control circuit board establishes a set of equations that describe the user adjustment behavior:

[0244]

[0245] At the same time, based on the optimal comfort temperature T opt =24℃, the automatic adjustment equations were established:

[0246]

[0247] Afterwards, the control circuit board merges the above two types of equations to form a comprehensive parameter optimization model. During fusion, the frequency N is adjusted according to the user's temperature, wind speed and air supply direction. T ,N V and N D , dynamically adjust their respective weight coefficients:

[0248] ω T =0.6α T ;

[0249] ω V =0.5α V ;

[0250] ω D =0.4α D ;

[0251] in,

[0252] Based on the fused optimization model, the control circuit board will calculate the optimal temperature, wind speed and air supply direction target values ​​within each 10-minute sampling period, and convert them into smooth execution instructions through filtering, which are sent to electric water valves, DC fans and other actuators to adjust the actual operating status of the unit.

[0253] After a week of adaptive learning and control, the operating status of user A's unit is as follows: Figure 3 As shown, Figure 3The temperature, humidity, personnel activities and energy consumption changes of the unit in user A's home within a week are shown. It can be seen that the unit can automatically adjust the temperature, humidity and wind speed according to the time changes and user habits, so that the indoor environment is always kept within a comfortable range. At the same time, compared with the traditional fixed parameter control, the unit's energy consumption fluctuations are small, and the overall trend is relatively stable, reflecting good energy-saving performance.

[0254] User A is very satisfied with this adaptive intelligent air conditioning system. They said that compared with the traditional fan coil units they used before, the new unit can not only automatically adjust to the best state, but also be fine-tuned according to their own preferences, making it more convenient and quick to use. In addition, the temperature and humidity of the indoor environment are stable, and the air is fresher, which greatly improves the quality of life.

[0255] In summary, the DC-driven fan coil unit proposed in the present invention can fully perceive environmental factors and user preferences by integrating intelligent sensing elements and adaptive control algorithms, dynamically optimize operating parameters, and significantly improve energy efficiency while meeting personalized needs. This technical solution points the way for the intelligent development of future building air conditioning systems.

[0256] Embodiment 3: Embodiment of a specific application scenario of the adaptive adjustment module

[0257] The control circuit board of a DC-driven fan coil unit integrates an adaptive adjustment module, which can dynamically optimize the unit's temperature, wind speed, and air supply direction target values ​​according to environmental changes and user needs, thereby improving user experience and energy efficiency. The following is an example of the specific working process of this module:

[0258] The work of the adaptive adjustment module is divided into the following steps:

[0259] Step 1: Set initial parameters

[0260] The control circuit board first sets the initial operating parameters of the unit, including target temperature T0 = 24 °C, target humidity H0 = 55, and target wind speed V0 = 3 m / s. At the same time, the sampling time interval of environmental parameters Δt = 10 min is determined. These parameters lay the foundation for subsequent dynamic adjustments.

[0261] Step 2: Collect environmental data in real time

[0262] The control circuit board uses the temperature and humidity sensor installed inside the unit to obtain the current indoor temperature T in real time with a sampling period of 10 minutes. c (t) and humidity H(t). At the same time, the dimensionless index A(t) reflecting the intensity of personnel activities is calculated through the personnel activity sensor:

[0263]

[0264] Among them, N(t) is the number of personnel movements per unit time, N max The preset maximum number of moves.

[0265] In addition, the control circuit board also records the temperature adjustment T performed by the user through the intelligent wireless thermostat u (t), wind speed adjustment V u (t) and air supply direction adjustment D u (t). These data provide a basis for subsequent parameter optimization.

[0266] Step 3: Build an optimization model

[0267] Based on the environmental parameters and user adjustment data collected in step 2, the control circuit board establishes two types of mathematical models:

[0268] User-adjusted set of equations:

[0269]

[0270] Automatically adjust the equations:

[0271]

[0272] Among them, ω=2π / 24rad / h is the daily cycle frequency, T opt =24℃ is the preset optimal comfortable temperature.

[0273] Step 4: Parameter fitting and fusion

[0274] The control circuit board uses the least squares method to fit the user adjustment equation group and the automatic adjustment equation group to determine the optimal value of each parameter.

[0275] After that, the control circuit board adjusts the frequency N according to the user's temperature, wind speed and air supply direction. T ,N V and N D , dynamically adjust the weight coefficients of the two types of models in the fusion equation:

[0276] ω T =0.6α T ;

[0277] ω V =0.5α V ;

[0278] ω D =0.4α D ;

[0279] in,

[0280] Finally, the control circuit board combines the weighted user adjustment equations and the automatic adjustment equations into a comprehensive parameter optimization model:

[0281]

[0282] Step 5 Parameter optimization and execution

[0283] Based on the comprehensive optimization model obtained in step 4, the control circuit board will calculate the optimal temperature T in each 10-minute sampling period. o , wind speed V o and air supply direction D o To ensure smooth control commands, these target values ​​are filtered by the first-order inertia link:

[0284]

[0285] Among them, ω=30s is the filtering time constant.

[0286] Finally, the control circuit board sends the optimized opening instruction u v and speed command u ω The digital signal is sent to the electric water valve, DC fan and other actuators to adjust the actual operating status of the unit. At the same time, the actual parameters after this adjustment will also be recorded. As historical data for the next round of adaptive control.

[0287] Through the above five steps, the adaptive adjustment module of the control circuit board can dynamically perceive environmental conditions and user needs, and calculate the optimal operating parameters based on the fused optimization model to accurately adjust the actual state of the unit. Compared with traditional fixed parameter control, this adaptive solution can continuously provide more comfortable and energy-saving air-conditioning services, greatly improving user experience.

[0288] The following takes a set of typical data as an example to illustrate the specific effect of the adaptive adjustment module:

[0289] Figure 4 The operation of the unit in a user's home B in a week is shown. It can be seen that the unit can automatically adjust the temperature, humidity and wind speed according to time changes and user habits, so that the indoor environment is always kept within a comfortable range. Compared with traditional fixed parameter control, the unit's energy consumption fluctuates less and presents a relatively stable trend overall, reflecting good energy-saving performance.

[0290] Through the intelligent control of the adaptive adjustment module, users can not only adjust the temperature, wind speed and air supply direction to the ideal state at any time, but the unit can also automatically detect environmental changes, continuously optimize operating parameters, and provide more comfortable and energy-saving air conditioning services. Compared with traditional fixed parameter control, this adaptive solution greatly improves the user experience.

[0291] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A fan coil unit driven by direct current, characterized in that: include: Unit casing, air supply grille, surface cooler, control circuit board, DC power supply motor, fan, return air box, return air grille and intelligent wireless thermostat; the inside of the unit casing is provided with air supply grille, surface cooler, fan and return air box from top to bottom in sequence; the air supply grille is connected to the air outlet of the unit casing and is used to swing up and down and left and right to adjust the air supply direction; the surface cooler is arranged under the air supply grille and is used to cool or heat the air flowing through; the fan is driven by a DC power supply motor to provide power for air flow; the return air box is located under the fan and is connected to the return air grille to mix fresh air and return air; the control circuit board is installed inside the unit casing and is used to receive signals from the intelligent wireless thermostat and control the operation of various components of the unit.

2. A fan coil unit driven by direct current according to claim 1, characterized in that: It also includes a supply air muffler and a return air muffler; the supply air muffler is arranged between the supply air grille and the surface cooler to make the supply air flow more stable and reduce the supply air noise; the return air muffler is arranged between the return air box and the return air grille to reduce the return air noise.

3. A fan coil unit driven by direct current according to claim 2, characterized in that: It also includes an electric water valve; the electric water valve is connected to the surface cooler and is used to adjust the water flow entering the surface cooler according to the instructions of the control circuit board, thereby controlling the air supply temperature of the air conditioner.

4. A fan coil unit driven by direct current according to claim 3, characterized in that: It also includes a personnel activity sensor; the personnel activity sensor is installed inside the unit casing, used to monitor the indoor personnel activities and feed back the signal to the control circuit board.

5. A fan coil unit driven by direct current according to claim 4, characterized in that: The return air grille is provided with a medium-efficiency filter; the medium-efficiency filter is used to remove more than 80% of particulate matter.

6. A fan coil unit driven by direct current according to claim 5, characterized in that: It also includes a fresh air inlet; the fresh air inlet is arranged on the return air box and is used to introduce processed outdoor fresh air.

7. A fan coil unit driven by direct current according to claim 6, characterized in that: It also includes a water receiving pan; the water receiving pan is arranged below the surface cooler and is used to receive condensed water generated during air conditioning cooling.

8. A fan coil unit driven by direct current according to claim 7, characterized in that: It also includes a lighting lamp and a negative ion generator; the lighting lamp is installed on the unit casing to illuminate the indoor space and is powered by a control circuit board; the negative ion generator is installed inside the unit to generate a large number of negative air ions.

9. A fan coil unit driven by direct current according to claim 8, characterized in that: It also includes a fire smoke sprinkler head; the fire smoke sprinkler head is installed on the unit casing, is used to sense indoor smoke conditions, and is connected to the fire pipe.

10. A fan coil unit driven by direct current according to claim 9, characterized in that: The control circuit board is provided with an adaptive control module for dynamically adjusting the operating parameters of the fan coil unit according to indoor environmental changes and personnel activities, thereby improving energy efficiency and optimizing user experience; the steps include: S10, setting operating parameters, including temperature, humidity, wind speed, and determining the initial sampling time interval; S20, collecting current environmental parameters and user adjustment data in real time, including temperature collected by a temperature sensor and crowd distribution data collected by a personnel activity sensor, wherein the user adjustment data is the user's adjustment data for the intelligent wireless thermostat; S30, establishing a user adjustment equation group and an automatic adjustment equation group considering environmental parameters, wherein the user adjustment equation group is used to map the user's adjustment data according to the current environmental parameters; and the automatic adjustment equation group is used to automatically adjust the unit operation parameters according to the personnel activity status and the indoor temperature change trend; S40, fitting the user adjustment equation group using the current environmental parameters continuously collected over a period of time and the user's adjustment data; fitting the automatic adjustment equation group using the historical operation data and the preset comfort index; S50, forming a fusion equation group by weighting the user adjustment equation group and the automatic adjustment equation group, and dynamically adjusting the weight coefficient according to the frequency of user adjustment; S60, calculating optimal operating parameters based on the fusion equation group, including target temperature, wind speed and air supply direction, and generating corresponding control instructions; S70: Send the control instruction to the corresponding execution component to adjust the operating state of the fan coil unit.

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