A temperature control system for air conditioner of motor train unit

Abstract

The application discloses a high-speed train air conditioner intelligent temperature control system, which comprises an environment parameter acquisition module, a passenger density detection module, a main controller, a temperature prediction unit, a fuzzy control unit, a variable frequency control unit, a air supply control unit, an energy consumption evaluation unit, a database, a display unit and an alarm unit. The variable frequency control unit controls the compressor motor speed through the power drive circuit, and the air supply control unit controls the air supply damper opening degree through the damper drive motor. The energy consumption evaluation unit collects the working current and voltage signals of the variable frequency control unit in real time, and calculates the real-time power and cumulative energy consumption of the air conditioner system. Through the multi-sensor fusion, predictive control and fuzzy control strategy, the application effectively solves the problem of temperature regulation response lag, improves the temperature control precision, and realizes the energy-saving operation of the air conditioner system.

Description

Technical Field

[0001] This invention relates to the field of railway transportation equipment technology, and in particular to an intelligent temperature control system for air conditioning in high-speed trains. Background Technology

[0002] With the rapid development of high-speed rail in my country, bullet trains have become an important means of transportation for daily travel. As a key device ensuring passenger comfort, the air conditioning system of bullet trains directly impacts the passenger experience. Currently, most bullet train air conditioning temperature control uses a combination of preset temperature values ​​and simple feedback adjustment. The control system adjusts based on the difference between the set target temperature and the actual temperature inside the carriage. However, this control method has limitations. Due to factors such as changes in passenger load, differences in ambient temperature inside and outside the carriage, and variations in solar radiation intensity during train operation, the temperature adjustment response is lagging, making precise control difficult and resulting in energy waste. Therefore, a bullet train air conditioning temperature control system capable of intelligently adjusting according to actual environmental changes is needed to improve temperature control accuracy and energy efficiency. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent temperature control system for air conditioning in high-speed trains, which solves the problems mentioned in the background art.

[0004] The present invention is implemented as follows: an intelligent temperature control system for air conditioning in high-speed trains, comprising an environmental parameter acquisition module, a passenger density detection module, a main controller, a temperature prediction unit, a fuzzy control unit, a frequency conversion control unit, an air supply control unit, an energy consumption assessment unit, a database, a display unit, and an alarm unit; The environmental parameter acquisition module includes a passenger compartment temperature sensor, an outdoor temperature sensor, a sunlight sensor, and a supply air temperature sensor. The passenger compartment temperature sensor is located at the top of the passenger compartment, the outdoor temperature sensor is located at the front of the passenger compartment, the sunlight sensor is located at the top of the passenger compartment, and the supply air temperature sensor is located at the air conditioning outlet. The passenger density detection module is located at the entrance of the carriage and includes an infrared array sensor and a pressure sensor. The infrared array sensor has at least eight detection points evenly arranged along the width of the carriage entrance, and the pressure sensor is embedded in the carriage floor. The environmental parameter acquisition module and the passenger density detection module are respectively connected to the input terminal of the main controller via signal transmission lines; The main controller integrates a temperature prediction unit and a fuzzy control unit. The temperature prediction unit uses a radial basis function neural network algorithm to construct a prediction model, and the fuzzy control unit uses a dual-input, three-output fuzzy inference rule. The output terminals of the main controller are respectively connected to the frequency conversion control unit and the air supply control unit; The frequency conversion control unit is connected to the air conditioner compressor motor and the condenser fan motor through a power drive circuit. The air supply control unit is connected to the air supply damper via a damper drive motor; The database stores historical operating data, environmental parameter threshold ranges, and passenger comfort mapping tables. The display unit is located at the top of both ends of the carriage and includes a temperature display screen and a mode indicator light; The alarm unit is located in the middle of the carriage and includes an audible and visual alarm. The environmental parameter acquisition module collects the temperature signals inside the carriage, the temperature signals outside the carriage, the solar radiation intensity signal, and the air supply temperature signal in real time, and then converts the collected parameter signals into digital signals through an A / D converter and transmits them to the main controller. The infrared array sensor in the passenger density detection module continuously scans the carriage entrance area and outputs a passenger count signal. The pressure sensor detects the pressure change signal on the floor when passengers stand up. The two signals are fused and processed to generate passenger density data and are transmitted to the main controller. After receiving various signals from the environmental parameter acquisition module and the passenger density detection module, the main controller first inputs the interior temperature signal, outdoor temperature signal, solar radiation intensity signal and air supply temperature signal into the temperature prediction unit. The temperature prediction unit calculates based on the environmental parameters at the current moment and the environmental parameters at the previous moment stored in the database, and outputs the predicted temperature value of the carriage for the next thirty minutes. The predicted value output by the temperature prediction unit is compared with the target temperature setpoint in the fuzzy control unit to generate a temperature deviation signal and a temperature deviation change rate signal. The fuzzy control unit performs fuzzy inference calculations according to preset fuzzy inference rules based on the temperature deviation signal and the temperature deviation rate of change signal, and outputs compressor frequency control signal and air volume control signal. The compressor frequency control signal is converted into a variable frequency AC signal by the frequency conversion control unit. This signal is output to the frequency input terminal of the compressor motor driver to control the compressor motor to run at the set frequency. The air supply volume control signal is output to the drive motor of the air supply damper to control the opening angle of the air supply damper. The energy consumption assessment unit collects the operating current and voltage signals of the frequency converter control unit in real time, calculates the real-time power of the air conditioning system, calculates the cumulative energy consumption data based on the running time and power, and transmits the energy consumption data to the main controller for storage and display. The main controller simultaneously transmits the real-time data collected by the environmental parameter acquisition module, the predicted data of the temperature prediction unit, the output parameters of the fuzzy control unit, and the energy consumption data of the energy consumption assessment unit to the database for storage. The display unit obtains various data information from the main controller, displays the current cabin temperature, set temperature, outdoor temperature and predicted temperature through the temperature display screen, and displays the current air conditioning operation mode through the mode indicator light; The alarm unit is connected to the main controller. When the temperature inside the carriage exceeds the preset upper temperature threshold or falls below the preset lower temperature threshold, or when the passenger density exceeds 90% of the carriage's rated capacity, the main controller sends an alarm trigger signal to the alarm unit, and the alarm unit activates the audible and visual alarm.

[0005] Furthermore, the cabin temperature sensor is a platinum resistance temperature sensor with a measurement range of -40 degrees Celsius to 80 degrees Celsius and a measurement accuracy of ±0.1 degree Celsius. The sensor output is connected to a signal amplification circuit, and the output of the amplification circuit is connected to an A / D converter.

[0006] Furthermore, the outdoor temperature sensor is a waterproof thermistor temperature sensor, which is installed inside a protective cover at the front of the vehicle body. The protective cover is made of stainless steel and has rainproof and dustproof functions.

[0007] Furthermore, the solar sensor uses a silicon photovoltaic cell as the photosensitive element, with the photosensitive surface facing upwards and mounted on the top of the vehicle. The sensor output is connected to a light intensity transmission circuit, which converts the photocurrent signal into a standard signal output of four to twenty milliamps.

[0008] Furthermore, the air supply temperature sensor is located at the air outlet of the air conditioner and is a patch-type temperature sensor. The sensor probe directly contacts the air outlet to monitor the air outlet temperature of the air conditioner in real time.

[0009] Furthermore, the infrared array sensor in the passenger density detection module adopts an eight-by-four infrared matrix arrangement to cover the detection area in the width and height directions of the carriage entrance. The sensor driving circuit adopts a pulse driving method with a driving frequency of 40 kHz.

[0010] Furthermore, the pressure sensor is a piezoresistive pressure sensor, with the sensor's sensitive element embedded in the interlayer of the vehicle floor. The sensor's range is 0 to 500 kPa, and its sensitivity is 1.5 millivolts per kPa.

[0011] Furthermore, the main controller uses a 32-bit ARM Cortex-M4 core microprocessor chip with a main frequency of 168 MHz. It integrates a floating-point arithmetic unit and a digital signal processing instruction set, and is externally expanded with UART interface, SPI interface and CAN bus interface.

[0012] Furthermore, the radial basis function neural network in the temperature prediction unit adopts a three-layer network structure. The input layer has four nodes, corresponding to the interior temperature of the carriage, the outdoor temperature, the solar radiation intensity, and the air supply temperature, respectively. The hidden layer has twenty nodes, and the activation function of the hidden layer is a Gaussian function. The output layer has three nodes, which output the predicted temperature values ​​for the next ten, twenty, and thirty minutes.

[0013] Furthermore, the fuzzy inference rules in the fuzzy control unit adopt the Mamdani-type inference method. The fuzzy subset of the temperature deviation variable is set to five levels: negative large, negative small, zero, positive small, and positive large. The fuzzy subset of the temperature deviation change rate variable is set to five levels: negative large, negative small, zero, positive small, and positive large. The fuzzy subset of the output variable compressor frequency is set to five levels: very low, low, medium, high, and very high.

[0014] Furthermore, the frequency conversion control unit uses an intelligent power module as the core control device. The power module integrates six IGBT drive circuits and overcurrent protection circuits. The module input is connected to the DC bus, and the DC bus voltage is 540 volts to 750 volts. The module output is connected to a three-phase asynchronous compressor motor.

[0015] Furthermore, the air supply control unit uses a DC brushless motor to drive the air supply damper. The motor has a rated voltage of 24 volts DC and a rated speed of 3,000 revolutions per minute. The motor driver adopts a closed-loop control method and adjusts the speed through a PWM signal.

[0016] Furthermore, the database uses non-volatile flash memory as the storage medium, with a storage capacity of eight megabytes. The environmental parameter threshold ranges stored in the database include an upper temperature threshold of 30 degrees Celsius, a lower temperature threshold of 18 degrees Celsius, an upper humidity threshold of 70%, and a lower humidity threshold of 30%.

[0017] Furthermore, the temperature display screen in the display unit is a seven-inch TFT LCD screen with a resolution of 800 x 480 pixels. The display screen is connected to the main controller via an SPI interface, and the display screen driver chip is model ILI9488.

[0018] Furthermore, the audible and visual alarm in the alarm unit adopts a combination of a buzzer and a red LED indicator. The buzzer volume is greater than 85 decibels, and the LED indicator has a diameter of 10 millimeters and uses a high-brightness red LED chip.

[0019] Furthermore, the main controller is also equipped with a communication interface, which is connected to the train control center through the train communication network to realize remote monitoring and data transmission functions.

[0020] Furthermore, the environmental parameter acquisition module is also equipped with a sensor self-test function. The main controller sends self-test signals to each sensor at regular intervals, and each sensor returns a standard signal value. The main controller judges the working status of the sensor based on the returned signal. When a sensor fault is detected, a fault prompt message is displayed on the display unit.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides an intelligent temperature control system for high-speed train air conditioning. It collects multi-dimensional environmental parameters such as interior and exterior temperatures, solar radiation intensity, and supply air temperature through an environmental parameter acquisition module, providing a comprehensive data foundation for temperature control. A passenger density detection module monitors the number of passengers in the carriage in real time, providing a basis for adjusting the temperature control strategy. An integrated temperature prediction unit within the main controller uses a radial basis function neural network algorithm to predict the carriage temperature at future times, enabling the control system to make adjustments in advance and effectively solving the problem of lag in temperature regulation response in previous technologies. A fuzzy control unit uses fuzzy inference rules for temperature control decisions, achieving adaptive adjustment based on environmental changes and improving temperature control accuracy. A variable frequency control unit continuously adjusts the compressor motor speed, allowing the air conditioning system to adjust its cooling or heating capacity according to actual needs, avoiding energy waste. An energy consumption assessment unit monitors the energy consumption of the air conditioning system in real time, providing data support for optimizing the control strategy. A display unit and an alarm unit allow passengers and staff to intuitively understand the operating status of the air conditioning system, promptly detect and handle abnormalities. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the intelligent temperature control system for high-speed train air conditioning according to the present invention; Figure 2 This is a flowchart illustrating the intelligent temperature control process of the intelligent temperature control system for high-speed train air conditioning according to the present invention.

[0023] Explanation of reference numerals in the attached figures: 1. Environmental parameter acquisition module; 2. Passenger density detection module; 3. Main controller; 4. Temperature prediction unit; 5. Fuzzy control unit; 6. Variable frequency control unit; 7. Air supply control unit; 8. Energy consumption assessment unit; 9. Display unit. Detailed Implementation

[0024] The specific embodiments of the present invention will now be described in further detail with reference to the accompanying drawings.

[0025] like Figure 1 The intelligent temperature control system for air conditioning in high-speed trains shown in this invention includes an environmental parameter acquisition module 1, a passenger density detection module 2, a main controller 3, a temperature prediction unit 4, a fuzzy control unit 5, a frequency conversion control unit 6, an air supply control unit 7, an energy consumption assessment unit 8, and a display unit 9. The modules are connected through signal transmission lines and power drive lines to form a complete closed-loop control system.

[0026] The environmental parameter acquisition module 1 is installed at corresponding locations within the train carriages to collect various environmental parameters both inside and outside the carriages. This module comprises four parts: a carriage temperature sensor, an outdoor temperature sensor, a sunlight sensor, and a supply air temperature sensor. The carriage temperature sensor uses a platinum resistance temperature sensor with a PT100 type platinum resistance element. The sensor probe is fixedly installed at the top of the carriage interior. The sensor housing is made of stainless steel. The sensor leads are connected to a signal amplification circuit via shielded cables. The signal amplification circuit uses an instrumentation amplifier architecture with a magnification of ten times. The output of the amplification circuit is connected to the input channel of an A / D converter via a multiplex analog switch. The outdoor temperature sensor is a waterproof thermistor temperature sensor with an NTC thermistor as the sensing element. The resistance is 10 kiloohms and the B value is 3950 Kelvin. The sensor is located inside a protective cover at the front of the vehicle's exterior. The cover is made of stainless steel and has a rain cap on top and ventilation holes on the sides. The cover is fixed to the exterior wall of the vehicle by a bracket. The sunlight sensor uses a silicon photovoltaic cell (model 2CU101) as the photosensitive element, with the photosensitive side facing upwards on the top of the vehicle. The sensor mounting bracket is made of aluminum alloy. The sensor output is connected to a light intensity transmitter circuit, which uses an I / V conversion circuit to convert the photocurrent signal into a standard current signal output of 4-20 mA. The air supply temperature sensor is located at the air conditioning vent and is a surface-mount temperature sensor. The sensing element is a DS18B20 digital temperature sensor. The sensor probe directly contacts the air supply air through thermal grease, and the sensor signal line uses a three-wire connection.

[0027] Each sensor in the environmental parameter acquisition module 1 is connected to the input terminal of the main controller 3 via signal transmission lines. The output signal of the passenger compartment temperature sensor is amplified by an amplifier circuit and then converted to digital by a first-channel A / D converter with a conversion accuracy of 12 bits and a sampling frequency of 10 Hz. The output signal of the outdoor temperature sensor is divided by a voltage divider circuit and then converted to digital by a second-channel A / D converter. The output signal of the sunlight sensor is converted to digital by an I / V converter circuit and then converted to digital by a third-channel A / D converter. The output signal of the air supply temperature sensor is a digital signal and is transmitted to the GPIO port of the main controller 3 via a single-bus interface.

[0028] The passenger density detection module 2 is located at the entrance of the carriage and consists of two parts: an infrared array sensor and a pressure sensor. The infrared array sensor uses an 8x4 infrared matrix arrangement, with a total of 32 infrared photodiodes. Eight detection points are evenly distributed along the width of the carriage entrance, and four detection points are distributed along the height. The sensor drive circuit uses a pulse-driven method with a drive frequency of 40 kHz and a drive pulse width of 10 microseconds. The output signal from the infrared receiver is processed by a comparator circuit to output a digital signal. There are eight signal lines, which are connected in parallel to the GPIO port of the main controller 3. The pressure sensor is a piezoresistive pressure sensor. The sensor's sensitive element is embedded inside the carriage floor interlayer. The sensitive element uses a diffused silicon piezoresistive chip. The sensor range is 0 to 500 kPa, and the sensitivity is 1.5 millivolts per kPa. The sensor output signal is amplified by an instrumentation amplifier and then converted from analog to digital by a fourth-channel A / D converter.

[0029] The infrared array sensor and pressure sensor in passenger density detection module 2 are connected to the input of main controller 3 via signal transmission lines. After receiving the output signal from the infrared array sensor, main controller 3 scans and detects eight columns of signals. When the infrared light in a certain column is blocked, it determines that a passenger has passed through that location and calculates the number of passengers based on the blocking sequence and time interval. After receiving the output signal from the pressure sensor, main controller 3 estimates the number of standing passengers based on the integral value of the pressure change curve. The two detection signals are weighted and fused internally by main controller 3. The fusion algorithm uses an adaptive weighted fusion method, with a weighting coefficient of 0.7 for the infrared array sensor and 0.3 for the pressure sensor. The fused data generates passenger density data.

[0030] The main controller 3 uses a 32-bit ARM Cortex-M4 core microprocessor chip, model STM32F407VG, with a main frequency of 168 MHz. It integrates a floating-point unit and a digital signal processing instruction set, with 1MB of on-chip Flash storage and 192KB of on-chip SRAM storage. The main controller 3 has peripheral expansion interfaces including UART, SPI, and CAN bus. The UART interface is used to connect to display unit 9, the SPI interface is used to connect to a database, and the CAN bus interface is used to connect to the train communication network. The main controller 3 has multiple A / D converters at its input terminals, each with 12-bit resolution, a sampling rate of 2MSPS, and eight input channels.

[0031] The main controller 3 integrates two functional modules: a temperature prediction unit 4 and a fuzzy control unit 5. The temperature prediction unit 4 uses a radial basis function neural network (RBF) algorithm to construct a prediction model. The training data for the neural network is stored in a database, including historical data on the interior temperature of the carriage, outdoor temperature, solar radiation intensity, and air supply temperature, as well as the corresponding temperature values ​​at subsequent times. The RDF neural network employs a three-layer structure: four input layer nodes corresponding to the four input parameters (interior temperature, outdoor temperature, solar radiation intensity, and air supply temperature); twenty hidden layer nodes using a Gaussian activation function with a standard deviation of 0.5; and three output layer nodes, outputting the predicted temperature values ​​for the next ten, twenty, and thirty minutes. The weight and bias parameters of the neural network are loaded from the database during system initialization, after which the neural network enters online prediction mode.

[0032] The fuzzy control unit 5 employs a dual-input, three-output fuzzy inference rule. The input variables are temperature deviation and the rate of change of temperature deviation, while the output variables are the compressor frequency control value, the airflow control value, and the target temperature correction value. The universe of discourse for the temperature deviation variable is set to -5°C to +5°C, with five fuzzy subsets: negative large, negative small, zero, positive small, and positive large. The membership function for each level is a trigonometric function. The universe of discourse for the rate of change of temperature deviation variable is set to -2°C to +2°C per minute, with five fuzzy subsets: negative large, negative small, zero, positive small, and positive large. The membership function for each level is a trigonometric function. The universe of discourse for the output variable, compressor frequency, is set to 20 to 120 Hz, with five fuzzy subsets: very low, low, medium, high, and very high. The fuzzy inference rules adopt the Mamdani-type inference method, and the rule base contains a total of twenty-five fuzzy rules. For example, when the temperature deviation is large and the rate of change of the temperature deviation is large, the compressor frequency output is very high; when the temperature deviation is zero and the rate of change of the temperature deviation is zero, the compressor frequency output is medium. Defuzzification adopts the centroid method, and the precise control quantity value is calculated based on the membership degree of each output fuzzy subset.

[0033] The output terminals of the main controller 3 are connected to the frequency converter control unit 6 and the air supply control unit 7, respectively. The PWM output channel of the main controller 3 is connected to the frequency setpoint input terminal of the frequency converter control unit 6. The frequency of the PWM signal is 10 kHz, and the duty cycle corresponds to the frequency setpoint. The digital output channel of the main controller 3 is connected to the direction control input terminal and the enable control input terminal of the air supply control unit 7.

[0034] The frequency converter control unit 6 uses an intelligent power module as its core controller. The intelligent power module, model FSB50250AB, integrates six IGBT drive circuits and overcurrent protection circuits. The module's input is connected to the DC bus, which has a voltage of 540V to 750V. This DC bus voltage is provided by the train's auxiliary power supply system, which outputs 600V DC. After passing through a filter capacitor and a current-limiting resistor, the output is connected to the DC input of the frequency converter control unit 6. The module's output is connected to a three-phase asynchronous compressor motor via a three-phase power line. The compressor motor is a two-pole motor with a rated power of 15 kW, a rated voltage of 380V, and a rated frequency of 50 Hz. The PWM input terminal of the frequency converter control unit 6 receives the frequency control signal output from the main controller 3. Internally, the module generates six PWM drive signals based on the frequency control signal. These six drive signals are staggered by 60 electrical degrees to drive the IGBT bridge arms to switch according to the set frequency and modulation ratio. The output terminal outputs a variable frequency three-phase AC signal with a frequency adjustment range of 20 to 120 Hz and a voltage adjustment range of 200 to 380 volts. The module also includes an overcurrent protection circuit and a protection action threshold. When the output current exceeds 150% of the rated current, the protection circuit immediately blocks all drive signals and sends a fault signal to the main controller 3.

[0035] The air supply control unit 7 uses a brushless DC motor to drive the air supply damper. The brushless DC motor is model EC45, with a rated voltage of 24V DC, a rated speed of 3000 rpm, and a rated power of 50 watts. The motor driver uses a closed-loop control method, and the driver model is BLDC3022. The driver input is connected to the PWM output port of the main controller 3. The PWM signal frequency is 20 kHz, and the duty cycle corresponds to the target speed value. The driver output is connected to the three-phase winding of the brushless DC motor. The air supply damper is an electrically adjustable damper. The damper blades are made of aluminum alloy, with a blade width of 100 mm and four blades. The damper shaft is connected to the output shaft of the brushless DC motor via a coupling. The motor driver adjusts the motor speed according to the air supply control signal output by the main controller 3, thereby adjusting the opening angle of the damper. The opening angle adjustment range is 0 to 90 degrees.

[0036] The energy consumption assessment unit 8 collects the operating current and voltage signals of the frequency converter control unit 6 in real time. The current signal is collected through a current transformer (model CT-0.66) with a current ratio of 100:5. The current signal is converted into a voltage signal by a sampling resistor and then sent to the A / D converter. The voltage signal is collected through a voltage divider circuit, using a series connection of a 100 kΩ and a 10 kΩ resistor with a voltage division factor of one-tenth. The energy consumption assessment unit 8 uses the remaining A / D channels of the main controller 3 to collect the current and voltage signals at a sampling frequency of 100 Hz. The sampled data is digitally filtered and then used for power calculation. The power calculation uses an instantaneous power algorithm, i.e., P(t) = u(t) × i(t). The instantaneous power is integrated to obtain the cumulative energy consumption data, which is stored in kilowatt-hours.

[0037] The database uses non-volatile flash memory as its storage medium, with a W25Q64 chip and a storage capacity of eight megabytes. The database stores historical operating data, environmental parameter threshold ranges, and a passenger comfort mapping table. Historical operating data records the timestamp, environmental parameters, control parameters, and energy consumption data for each temperature adjustment. This data is stored cyclically, with the most recent data overwriting the oldest. Environmental parameter threshold ranges include an upper temperature threshold of 30 degrees Celsius, a lower temperature threshold of 18 degrees Celsius, an upper humidity threshold of 70%, and a lower humidity threshold of 30%. The passenger comfort mapping table records comfort scores under different passenger densities and ambient temperatures, ranging from zero to one hundred. This mapping table data is used for target temperature correction in the fuzzy control unit 5.

[0038] Display units 9 are located at the top of both ends of the carriage and include temperature displays and mode indicator lights. The temperature displays are 7-inch TFT LCD screens with a resolution of 800 x 480 pixels. The screens are connected to the main controller 3 via an SPI interface, using an ILI9488 driver chip. The main controller 3 sends display data to the screens via the SPI interface, and the screen refresh rate is 60 Hz. The temperature displays show the current carriage temperature, set temperature, outdoor temperature, and predicted temperature. The screen interface is divided into four display areas, each showing one temperature parameter, using 16-point boldface font. The mode indicator lights use an LED indicator array, including three types: operating mode indicator, fault indicator, and communication status indicator. Each type contains three indicators, each indicating a different operating status.

[0039] The alarm unit, located in the middle of the carriage, includes an audible and visual alarm. The alarm uses a combination of a buzzer and a red LED indicator. The buzzer is an SMB-12 model with a rated voltage of 12 volts DC and a volume greater than 85 decibels. The LED indicator is 10 millimeters in diameter, uses a high-brightness red LED chip, and has an operating current of 20 milliamps. The alarm unit is connected to the digital output port of the main controller 3. When the main controller 3 detects that the interior temperature of the carriage exceeds the preset upper temperature threshold or falls below the preset lower temperature threshold, or when the passenger density exceeds 90% of the carriage's rated capacity, the main controller 3 outputs an alarm trigger signal to the alarm unit. Upon receiving the trigger signal, the alarm unit emits an intermittent alarm sound, and the LED indicator flashes at a frequency of 1 Hz.

[0040] After the system powers on, the main controller 3 first performs initialization operations, including configuring the functions of each GPIO port, initializing the A / D converter, loading parameter configurations from the database, and starting the running programs of the temperature prediction unit 4 and the fuzzy control unit 5. After initialization, the main controller 3 sends data acquisition commands to the environmental parameter acquisition module 1 and the passenger density detection module 2. Each sensor begins acquiring data and transmits the acquired data to the main controller 3 via the signal transmission line. The main controller 3 preprocesses the received data, including digital filtering, outlier removal, and dimension conversion.

[0041] The main controller 3 inputs four pre-processed parameters—the interior temperature of the carriage, the outdoor temperature, the solar radiation intensity, and the supply air temperature—to the temperature prediction unit 4. The temperature prediction unit 4 calculates the predicted carriage temperature for the next thirty minutes based on the current environmental parameters and the environmental parameters stored in the database. This prediction is achieved using a forward propagation algorithm of a radial basis function neural network. After the predicted value is output, the main controller 3 compares it with the target temperature setpoint in the fuzzy control unit 5, calculating two parameters: temperature deviation and the rate of change of temperature deviation.

[0042] The fuzzy control unit 5 performs fuzzy inference calculations based on two input parameters: temperature deviation and the rate of change of temperature deviation, according to preset fuzzy inference rules. The fuzzy inference process first fuzzifies the two input parameters, calculates the membership degree of each fuzzy subset based on the membership function, then performs inference calculations based on twenty-five fuzzy rules in the rule base to obtain the membership degree of each fuzzy subset of the output variable, and finally converts the fuzzy subset membership degrees into precise control quantity values ​​through defuzzification. The control quantities output by the fuzzy control unit 5 include two parameters: the compressor frequency control signal and the air volume control signal.

[0043] The main controller 3 sends the compressor frequency control signal output by the fuzzy control unit 5 to the frequency converter control unit 6 through the PWM output channel. After receiving the frequency control signal, the frequency converter control unit 6 generates six IGBT drive signals through the PWM modulation circuit inside the intelligent power module. The drive signals are isolated by the isolation transformer and then sent to the IGBT gate. The IGBT switches according to the drive signals and outputs a variable frequency three-phase AC signal to the compressor motor. The compressor motor runs at the set frequency under the drive of the frequency converter power supply. The compressor speed is proportional to the power supply frequency, and the cooling capacity is proportional to the compressor speed.

[0044] The main controller 3 sends the air volume control signal output by the fuzzy control unit 5 to the air supply control unit 7 via a digital output channel. After receiving the air volume control signal, the driver of the air supply control unit 7 adjusts the speed of the brushless DC motor according to the control signal. The motor speed is transmitted to the shaft of the air supply damper through a coupling, driving the damper blades to rotate, changing the opening angle of the damper, and thus adjusting the air volume. When the air volume control signal corresponds to a higher air volume, the driver outputs a higher speed, and the damper opening increases; when the air volume control signal corresponds to a lower air volume, the driver outputs a lower speed, and the damper opening decreases.

[0045] The energy consumption assessment unit 8 collects the operating current and voltage signals of the frequency converter control unit 6 in real time. The collected signals are converted from digital signals to digital signals and then sent to the main controller 3. The main controller 3 calculates the real-time power of the air conditioning system based on the collected current and voltage signals. The power calculation uses a discrete integral algorithm with a sampling interval of ten milliseconds. The real-time power is obtained by applying a moving average filter to the instantaneous power. Cumulative energy consumption data is obtained through integration, with the integration time unit being hours. The energy consumption data is stored in a database and simultaneously displayed through the display unit 9.

[0046] The main controller 3 simultaneously transmits the real-time data collected by the environmental parameter acquisition module 1, the predicted data from the temperature prediction unit 4, the output parameters from the fuzzy control unit 5, and the energy consumption data from the energy consumption assessment unit 8 to the database for storage. The database communicates with the main controller 3 via an SPI interface, and data is written in a page-based manner, with each write operation consisting of 256 bytes and a write cycle of once per second.

[0047] Display unit 9 acquires various data information from main controller 3 and displays the current cabin temperature, set temperature, outdoor temperature, and predicted temperature on the temperature display screen. The display screen uses a segmented display method, with each parameter occupying an independent display area. The font color is white, and the background color is black. The mode indicator light shows the current air conditioning operation mode, which includes four modes: cooling mode, heating mode, ventilation mode, and standby mode. Different modes correspond to different colored indicator lights.

[0048] The main controller 3 is equipped with a communication interface, which connects to the train's communication network via a CAN bus with a transmission rate of 500 kilobits per second. The main controller 3 periodically sends air conditioning system operating status data, including current temperature, set temperature, operating mode, compressor frequency, and cumulative energy consumption, to the train control center through the communication interface. The train control center can also send control commands to the main controller 3 via the communication interface, including commands to modify the set temperature, switch operating modes, and power on / off commands.

[0049] The environmental parameter acquisition module 1 is also equipped with a sensor self-test function. The main controller 3 sends a self-test signal to each sensor every hour. After receiving the self-test signal, each sensor returns a standard signal value to the main controller 3. The main controller 3 compares the returned standard signal value with a preset standard value range. When the sensor returns a value that exceeds the standard value range, it determines that the sensor is faulty or its performance is drifting. The main controller 3 displays a fault message on the display unit 9 and simultaneously reports the fault information to the train control center through the communication interface.

[0050] Assuming the high-speed train is operating during the hot summer months, with a high passenger density in the carriages, the air conditioning system needs to activate the cooling mode to lower the interior temperature of the carriages to the set temperature value. This specific application scenario describes the complete process of the system from startup to stable operation.

[0051] Step 1: Initialization and Data Acquisition: After the system is powered on, the main controller 3 first performs initialization operations, configuring the functions of each GPIO port, initializing the A / D converter, loading the parameter configuration from the database, and starting the running programs of the temperature prediction unit 4 and the fuzzy control unit 5. After initialization, the main controller 3 sends data acquisition commands to the environmental parameter acquisition module 1 and the passenger density detection module 2. The various sensors in the environmental parameter acquisition module 1 begin to work: the carriage temperature sensor collects the internal temperature data of the carriage, the outdoor temperature sensor collects the external temperature data of the carriage, the sunlight sensor collects the sunlight intensity data, and the air supply temperature sensor collects the air conditioning outlet temperature data. The analog signals collected by each sensor are converted into digital signals by the A / D converter after passing through the corresponding signal conditioning circuits, and then transmitted to the input terminal of the main controller 3 via the signal transmission line. Simultaneously, the infrared array sensor in the passenger density detection module 2 continuously scans the carriage entrance area. The infrared receiver-emitter coupler emits infrared light at a pulse frequency of 40 kHz. When a passenger passes through the entrance, they block the infrared light, and the signal output from the receiver tube is processed by the comparator circuit to generate a digital signal, which is then transmitted to the main controller 3. The pressure sensor detects the pressure change on the floor when the passenger stands. The pressure signal is amplified by the instrument amplifier and then converted into a digital signal by the A / D converter and transmitted to the main controller 3.

[0052] Step 2: Passenger Density Fusion Calculation: After receiving the output signal from the infrared array sensor, the main controller 3 scans and detects the eight columns of signals, calculating the number of passengers passing through based on the occlusion sequence and time interval. Simultaneously, after receiving the output signal from the pressure sensor, the main controller 3 estimates the number of standing passengers based on the integral value of the pressure change curve. The two detection signals are weighted and fused internally by the main controller 3 using an adaptive weighted fusion method. The weighting coefficient for the infrared array sensor is 0.7, and the weighting coefficient for the pressure sensor is 0.3. The fused data generates passenger density data. In this step, the passenger density data is used for target temperature correction in the subsequent fuzzy control unit 5. When the passenger density is high, the fuzzy control unit 5 appropriately lowers the target temperature setpoint to enhance the cooling effect.

[0053] Step 3: Temperature Prediction Calculation: The main controller 3 inputs four parameters—preprocessed interior temperature, outdoor temperature, solar radiation intensity, and air supply temperature—to the temperature prediction unit 4. The temperature prediction unit 4 calculates the temperature based on the current environmental parameters and the environmental parameters stored in the database from previous times. The temperature prediction unit 4 uses a radial basis function neural network algorithm for prediction. The input layer of the neural network receives the four input parameters, which are then activated by a Gaussian function with twenty hidden layer nodes. The output layer outputs the predicted temperature values ​​for the next ten, twenty, and thirty minutes. After the predicted values ​​are output, the main controller 3 compares them with the target temperature setpoint in the fuzzy control unit 5, calculating two parameters: temperature deviation and the rate of change of temperature deviation. In this step, the predicted values ​​output by the temperature prediction unit 4 reflect the future trend of the interior temperature, enabling the control system to make adjustments in advance and avoiding lag in temperature regulation response.

[0054] Step 4: Fuzzy Inference Control: The fuzzy control unit 5 receives two input parameters: temperature deviation and temperature deviation change rate. It performs fuzzy inference calculations according to preset fuzzy inference rules. First, the two input parameters are fuzzified. The membership degree of each fuzzy subset is calculated based on the membership function. After calculation using the triangular membership function, the temperature deviation variable yields five fuzzy subsets: negative large, negative small, zero, positive small, and positive large. Similarly, the temperature deviation change rate variable is calculated to obtain the membership degrees of five fuzzy subsets. Then, inference calculations are performed according to twenty-five Mamdani-type fuzzy rules in the rule base. For example, when the temperature deviation is positive large and the temperature deviation change rate is positive large, the membership degree of the output compressor frequency is very high, with a value of 0.9; when the temperature deviation is zero and the temperature deviation change rate is zero, the membership degree of the output compressor frequency is 1. Finally, through defuzzification, the precise compressor frequency control signal and air volume control signal values ​​are calculated using the centroid method based on the membership degrees of each output fuzzy subset. In this step, the control quantity output by the fuzzy control unit 5 directly determines the actions of the subsequent frequency converter control unit 6 and air supply control unit 7.

[0055] Step 5: Compressor Frequency Control: The main controller 3 sends the compressor frequency control signal output from the fuzzy control unit 5 to the frequency converter 6 via the PWM output channel. The PWM signal frequency is 10 kHz, and the duty cycle corresponds to the set frequency. After receiving the frequency control signal, the intelligent power module of the frequency converter 6 generates six IGBT drive signals through its internal PWM modulation circuit. These six drive signals are staggered by 60 electrical degrees, driving the IGBT bridge arms to switch according to the set frequency and modulation ratio. The module outputs a variable frequency three-phase AC signal to the compressor motor, with a frequency adjustment range of 20 to 120 Hz and a voltage adjustment range of 200 to 380 volts. Driven by the frequency converter power supply, the compressor motor operates at the set frequency. The compressor speed is directly proportional to the power supply frequency, and the cooling capacity is directly proportional to the compressor speed. In this step, the frequency converter 6 adjusts the compressor speed according to the output signal of the fuzzy control unit 5, achieving continuous adjustment of the cooling capacity and avoiding energy waste and temperature fluctuations caused by frequent compressor start-stop.

[0056] Step Six: Air Supply Damper Adjustment: The main controller 3 sends the air supply volume control signal output by the fuzzy control unit 5 to the air supply control unit 7 via the digital output channel. The driver of the air supply control unit 7 receives the air supply volume control signal and adjusts the speed of the brushless DC motor according to the control signal. The PWM signal frequency at the driver input is 20 kHz, and the duty cycle corresponds to the target speed value. The driver output is connected to the three-phase winding of the brushless DC motor. The motor speed is transmitted to the shaft of the air supply damper through a coupling, driving the damper blades to rotate and changing the damper opening angle. The opening angle adjustment range is 0 to 90 degrees. When the air supply volume control signal corresponds to a higher air supply volume, the driver outputs a higher speed, the damper opening increases, and the air supply volume increases; when the air supply volume control signal corresponds to a lower air supply volume, the driver outputs a lower speed, the damper opening decreases, and the air supply volume decreases. In this step, the air supply control unit 7 and the frequency converter control unit 6 work together to achieve precise adjustment of the air conditioning system's cooling capacity.

[0057] Step 7: Energy Consumption Calculation and Display: The energy consumption assessment unit 8 collects the operating current and voltage signals of the frequency converter control unit 6 in real time. The current signal is collected through a current transformer CT-0.66 with a current ratio of 100:5. The current signal is converted into a voltage signal by a sampling resistor and then sent to the A / D converter. The voltage signal is collected through a voltage divider circuit, with a 100 kΩ and a 10 kΩ resistor connected in series, and a voltage division factor of one-tenth. The sampling frequency is 100 Hz. The sampled data is digitally filtered and then used for power calculation. The power calculation uses the instantaneous power algorithm P(t)=u(t)×i(t), and the instantaneous power is integrated to obtain the cumulative energy consumption data. The main controller 3 simultaneously transmits the real-time data collected by the environmental parameter acquisition module 1, the predicted data of the temperature prediction unit 4, the output parameters of the fuzzy control unit 5, and the energy consumption data of the energy consumption assessment unit 8 to the database for storage. The database communicates with the main controller 3 through an SPI interface, and data is written in a page-based manner, with each write being 256 bytes. Display unit 9 acquires various data information from main controller 3 and displays the current cabin temperature, set temperature, outdoor temperature, and predicted temperature on the temperature display screen. The display screen interface is divided into four display areas, with each parameter occupying an independent display area. The mode indicator light shows the current air conditioning operating mode, which includes four modes: cooling mode, heating mode, ventilation mode, and standby mode. Different modes correspond to different colored indicator lights.

[0058] Step 8, Communication and Monitoring: The main controller 3 connects to the train communication network via a CAN bus interface, with a CAN bus transmission rate of 500 kilobits per second. The main controller 3 periodically sends air conditioning system operating status data to the train control center via the communication interface, including current temperature, set temperature, operating mode, compressor frequency, and cumulative energy consumption. The train control center can also send control commands to the main controller 3 via the communication interface, including set temperature modification commands, operating mode switching commands, and power on / off commands. The environmental parameter acquisition module 1 also has a sensor self-test function. The main controller 3 sends a self-test signal to each sensor every hour. After receiving the self-test signal, each sensor returns a standard signal value to the main controller 3. The main controller 3 compares the returned standard signal value with a preset standard value range. When a sensor's return value exceeds the standard value range, it determines that the sensor is faulty or experiencing performance drift. The main controller 3 displays a fault message on the display unit 9 and simultaneously reports the fault information to the train control center via the communication interface.

[0059] This specific application scenario details the complete workflow of the intelligent temperature control system for high-speed train air conditioning in summer cooling operation mode. From system initialization, data acquisition, passenger density calculation, temperature prediction, fuzzy control, frequency conversion regulation, air supply regulation to energy consumption display, each link works in concert to achieve adaptive temperature control based on changes in the internal and external environment of the carriage and changes in passenger density. This effectively solves the technical problems of temperature regulation response lag, difficulty in precise control, and energy waste mentioned in the background technology.

[0060] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart temperature control system for air conditioning in high-speed trains, characterized in that, include: The system comprises an environmental parameter acquisition module (1), a passenger density detection module (2), a main controller (3), a temperature prediction unit (4), a fuzzy control unit (5), a frequency conversion control unit (6), an air supply control unit (7), an energy consumption assessment unit (8), and a display unit (9). The environmental parameter acquisition module (1) includes a car body temperature sensor, an outdoor temperature sensor, a sunlight sensor, and an air supply temperature sensor. The car body temperature sensor is located at the top of the car body, the outdoor temperature sensor is located at the front of the car body, the sunlight sensor is located at the top of the car body, and the air supply temperature sensor is located at the air conditioning outlet. The passenger density detection module (2) includes an infrared array sensor and a pressure sensor. The infrared array sensor has at least eight sensors evenly arranged along the width of the car body entrance. The pressure sensor is embedded inside the carriage floor; the environmental parameter acquisition module (1) and the passenger density detection module (2) are respectively connected to the input end of the main controller (3) through signal transmission lines; the main controller (3) integrates a temperature prediction unit (4) and a fuzzy control unit (5). The temperature prediction unit (4) uses a radial basis function neural network algorithm to construct a prediction model, and the fuzzy control unit (5) uses a dual-input three-output fuzzy inference rule; the output end of the main controller (3) is respectively connected to the frequency converter control unit (6) and the air supply control unit (7); the frequency converter control unit (6) is connected to the air conditioning compressor motor through a power drive circuit; the air supply control unit (7) is connected to the air supply damper through a damper drive motor; the display unit (9) is set at the top of both ends of the carriage.

2. The intelligent temperature control system for EMU air conditioning according to claim 1, characterized in that: The temperature sensor in the carriage is a platinum resistance temperature sensor with a measurement range of -40 degrees Celsius to 80 degrees Celsius and a measurement accuracy of ±0.1 degree Celsius. The sensor output is connected to a signal amplification circuit, and the output of the amplification circuit is connected to an A / D converter.

3. The intelligent temperature control system for EMU air conditioning according to claim 1, characterized in that: The infrared array sensor in the passenger density detection module (2) adopts an eight-by-four infrared matrix arrangement, and the sensor driving circuit adopts a pulse driving mode with a driving frequency of 40 kHz. The pressure sensor adopts a piezoresistive pressure sensor with a range of 0 to 500 kPa and a sensitivity of 1.5 millivolts per kPa.

4. The intelligent temperature control system for EMU air conditioning according to claim 1, characterized in that: The main controller (3) uses a 32-bit ARM Cortex-M4 core microprocessor chip with a main frequency of 168 MHz. It integrates a floating-point arithmetic unit and a digital signal processing instruction set, and is externally extended with UART interface, SPI interface and CAN bus interface.

5. The intelligent temperature control system for EMU air conditioning according to claim 1, characterized in that: The radial basis function neural network in the temperature prediction unit (4) adopts a three-layer network structure. The number of input layer nodes is four, which correspond to the temperature inside the carriage, the outdoor temperature, the solar radiation intensity and the air supply temperature, respectively. The number of hidden layer nodes is twenty, and the activation function of the hidden layer is a Gaussian function. The number of output layer nodes is three, which output the predicted temperature values ​​after ten, twenty and thirty minutes.

6. The intelligent temperature control system for EMU air conditioning according to claim 1, characterized in that: The frequency conversion control unit (6) uses an intelligent power module as the core control device. The power module integrates six IGBT drive circuits and overcurrent protection circuits. The module input is connected to the DC bus, and the DC bus voltage is 540 to 750 volts. The module output is connected to a three-phase asynchronous compressor motor. The air supply control unit (7) uses a DC brushless motor to drive the air supply damper. The motor driver adopts a closed-loop control method and adjusts the speed through PWM signals.

7. The intelligent temperature control system for EMU air conditioning according to claim 1, characterized in that: It also includes an alarm unit, which is located in the middle of the carriage and includes an audible and visual alarm. The alarm unit is connected to the main controller (3). When the temperature inside the carriage exceeds the preset upper temperature threshold or falls below the preset lower temperature threshold, or when the passenger density exceeds 90% of the carriage's rated capacity, the main controller (3) sends an alarm trigger signal to the alarm unit, and the alarm unit activates the audible and visual alarm.

Images