Intelligent volumetric condensing unit controller

Abstract

The invention discloses an intelligent positive displacement condensing unit controller which comprises a main control unit, a multi-mode sensing module, an actuator driving unit, a communication center, a self-adaptive power management system, a human-computer interaction interface and a fault prediction and health management module. The system has the advantages that all-parameter real-time monitoring can be achieved, and 12 key parameters including the exhaust temperature, the back air temperature, the environment temperature, the air outlet temperature, the condensate temperature, the evaporation temperature, the condensation temperature, the refrigeration house temperature, the compressor current / frequency, the fan rotating speed and the like can be covered; based on a fuzzy PID algorithm and a machine learning model, operation parameters are dynamically adjusted, and manual debugging is omitted; electric heating is replaced by hot gas bypass defrosting, and'defrosting with frost and no defrosting without frost 'is realized in combination with a self-learning algorithm; the requirements of the refrigeration house at the temperature ranging from-40 DEG C to + 15 DEG C are automatically recognized, and a single controller adapts to high / medium / low temperature working conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of refrigeration system control, in particular to an intelligent volumetric condensing unit controller. BACKGROUND

[0002] The traditional condensing unit controller has the following technical bottlenecks: Parameter debugging is complex: manual configuration of parameters is required according to high, medium and low temperature types, and it cannot adapt to environmental changes; Low energy efficiency: defrosting relies on time or fixed temperature difference triggering, resulting in frequent heating (electric heating defrosting) or delayed defrosting causing icing when there is no frost; Fault response lag: only threshold alarm is used to judge faults, and potential problems such as compressor wear and refrigerant leakage cannot be predicted; Poor compatibility: single communication protocol, difficult to coordinate with multiple brands of equipment and cloud platforms for optimization.

[0003] The present application solves the above problems through multi-dimensional data fusion and intelligent algorithms, and realizes the control of the unit without debugging, full automation and high energy efficiency. SUMMARY

[0004] (I) Technical problems solved In view of the deficiencies of the prior art, the present application provides an intelligent volumetric condensing unit controller, which has the advantages of full parameter real-time monitoring, self-diagnosis and adaptive control, intelligent defrosting, and full temperature range compatibility, solving the problems in the above background technology.

[0005] (II) Technical solutions To achieve the above purpose, the present application provides the following technical solutions: an intelligent volumetric condensing unit controller, comprising a main control unit, a multi-modal sensing module, an actuator driving unit, a communication hub, an adaptive power management system, a human-machine interface and a fault prediction and health management module, The main control unit includes a microprocessor and a non-volatile memory storing control algorithm logic, which is used to execute a multivariable closed-loop control strategy; The multi-modal sensing module integrates a temperature sensor array (8 channels), a pressure sensor (2 channels) and a vibration sensor (1 channel) to collect real-time condensing unit operating parameters; The actuator driving unit includes a 7-way programmable relay output module, each output is configured with a PWM modulation circuit to drive the compressor, fan and solenoid valve; The communication hub provides three industrial-grade RS485 interfaces supporting Modbus / BACnet protocols, and supports data interaction with cloud servers, edge computing devices and third-party controllers; Adaptive power management system, compatible with AC 100-240V / DC 24V dual-mode input, output +12V / ±5% and +5V / ±2% precision stabilized power supply; Human-computer interaction interface, including TFT touch display screen and multi-color LED state indication matrix; Fault prediction and health management module, analyzes historical operation data through machine learning model, predicts compressor wear grade and generates maintenance warning signal.

[0006] Preferably, the intelligent control method of the controller: Step S1: Establish a three-dimensional thermodynamic state model of the unit through multi-sensor fusion technology; Step S2: Use model predictive control (MPC) to calculate the optimal compressor frequency and expansion valve opening combination; Step S3: Dynamically evaluate the condenser dirty plugging coefficient and trigger automatic spray cleaning instructions; Step S4: Simulate system response under different load scenarios using digital twin, and generate preventive control strategies; Step S5: Use blockchain technology to chain energy efficiency data and generate tamper-proof energy consumption audit report.

[0007] Preferably, the microprocessor of the main control unit is built-in fuzzy PID control algorithm, which dynamically adjusts the opening of the expansion valve according to the superheat degree of the evaporator, the algorithm parameters are suppressed by Kalman filter for sensor noise, and the sampling frequency can be configured to 10Hz-1kHz.

[0008] The main control unit, multi-modal sensing module, actuator driving unit, communication hub, adaptive power management system, human-computer interaction interface and fault prediction and health management module, Each module works together, and the core functions include: Full-parameter real-time monitoring: covering 12 key parameters such as exhaust temperature, return air temperature, ambient temperature, outlet air temperature, condensate temperature, evaporation temperature, condensation temperature, freezer temperature, compressor current / frequency, fan speed, etc. Self-diagnosis and adaptive control: based on fuzzy PID algorithm and machine learning model, dynamically adjust operating parameters, eliminate manual debugging; Intelligent defrosting: replace electric heating with hot gas bypass defrosting, and realize "defrosting with frost, no defrosting without frost" through self-learning algorithm; Full-temperature-zone compatibility: automatically identify-40℃~+15℃ freezer requirements, single controller adapts to high / medium / low temperature working conditions.

[0009] Main control unit Hardware configuration: STM32H743 microprocessor (480 MHz, built-in FPU) is used, and 128 MB non-volatile memory (storage control logic algorithm and historical data) is used; Core algorithm: Fuzzy PID control: dynamically adjust the opening of electronic expansion valve according to the superheat degree of evaporator (calculation accuracy ±0.1℃), response time <50ms; Noise suppression: real-time filtering of sensor data through Kalman filter (sampling frequency 1kHz), reduce temperature / pressure signal fluctuation error; Multivariable closed-loop strategy: establish a linkage control model of condenser temperature-compressor frequency-fan speed to achieve optimal energy efficiency.

[0010] Preferably, the multi-modal sensing module integrates a wireless temperature measurement node (supports ZigBee 3.0), and an embedded temperature patch is deployed at the key position of the condenser tube bundle; the pressure sensor of the multi-modal sensing module is configured with a temperature compensation circuit, and the measurement accuracy is ±0.5%FS (-40℃~125℃).

[0011] Multi-modal sensing module Sensor array: Temperature monitoring: 8-channel PT1000 sensor (-40~150℃, ±0.2℃ accuracy), deployed at key nodes such as compressor exhaust, return air pipe, condenser outlet, etc.; Pressure monitoring: 2-channel MEMS pressure sensor (0-4MPa, ±0.5%FS accuracy), integrated with temperature compensation circuit to eliminate environmental temperature drift; Vibration monitoring: 1-channel three-axis acceleration sensor (10-2000Hz), detects abnormal vibration frequency spectrum of compressor bearing.

[0012] Wireless expansion: supports ZigBee 3.0 protocol, 10 embedded temperature patches (±0.5℃ accuracy) are deployed in the condenser tube bundle to realize distributed monitoring of tube bundle surface temperature field.

[0013] Preferably, the actuator driving unit includes a soft start circuit, which configures a compressor step start timing control logic to limit the starting current within 150% of the rated value; the relay contact of the actuator driving unit is configured with an arc suppression module, including an RC buffer circuit and a MOV overvoltage protection device.

[0014] Drive circuit: 7-way programmable relay: each way integrates PWM modulation circuit (frequency 0-10kHz adjustable), drives variable frequency compressor (0.5Hz step frequency modulation), EC fan (stepless speed regulation), solenoid valve; Soft start protection: step start compressor motor, limit start current ≤ 150% rated value (current slope control is realized through IGBT module); Arc suppression: relay contact is configured with RC buffer circuit (resistor 10 Ω / capacitor 0.1 μF) and MOV overvoltage protection device (response time < 5 ns).

[0015] Preferably, the communication hub supports OPC UA protocol conversion, configures a data encryption engine (AES-256), limits illegal device access through firewall rules, and sets a data cache area to maintain 72 hours of local data storage when the network is interrupted.

[0016] Protocol compatibility: three-way isolated RS485 interface, supports Modbus RTU, BACnet MS / TP and OPC UA protocol conversion, realizes data interaction with BMS system and cloud platform; Data security: built-in AES-256 encryption engine, end-to-end encryption of transmission parameters; firewall rules limit illegal device access (based on MAC address whitelist); Network interruption: configure a 4GB data cache area to continuously store 72 hours of operation data (sampling interval 1 minute) when the network is interrupted.

[0017] Preferably, the fault prediction and health management module includes: a) Vibration spectrum analysis unit based on LSTM neural network, detects abnormal frequency components of bearings; b) Refrigerant leakage detection algorithm, calculates leakage rate through pressure-temperature correlation analysis; c) Energy efficiency optimization engine, automatically adjusts condenser fan speed curve according to environmental temperature and humidity.

[0018] Preferably, the controller is configured with a digital twin interface that maps real-time data to a 3D device model, enabling virtual debugging functions; maintenance warning signals include remaining useful life (RUL) prediction values, and confidence interval calculation uses Monte Carlo simulation.

[0019] Preferably, the controller power management system is configured with a super capacitor backup power supply that maintains key sensor power for 30 minutes when the main power supply is interrupted; and includes an input power quality monitoring circuit that records voltage total harmonic distortion (THD) and transient pulse events.

[0020] Adaptive power management system Dual-mode input: compatible with AC 100-240V (±10%) and DC 24V input, output +12V (±5%) and +5V (±2%) regulated power supply, conversion efficiency ≥ 92%; Power quality monitoring: Real-time record of input voltage harmonic distortion (THD≤3%) and transient pulse (>100μs pulse width event); Backup power supply: Super capacitor group (capacity 10F) maintains power supply for 30 minutes for key sensors when main power supply is interrupted, ensuring data integrity.

[0021] Preferably, the controller is packaged in an IP65 protection level shell, with a heat-conducting silicone sealant filling layer and an anti-vibration support inside. The control logic supports FOTA wireless upgrade and has a version rollback mechanism to prevent system paralysis caused by upgrade failure.

[0022] Human-machine interface: TFT touch screen: 7-inch color display screen, real-time display of unit operating parameters, energy efficiency curve and fault codes; Status indication: 8×8 multi-color LED matrix, visually feedback system status through color coding (green-normal, yellow-warning, red-fault); Virtual debugging: Real-time data mapping to 3D device model through digital twin interface, supporting remote parameter adjustment and simulation running.

[0023] Defrosting strategy: Frost layer prediction: Self-learning algorithm analyzes historical defrosting period, environmental humidity and evaporator temperature difference to predict frost layer thickness (error <15%); Hot gas bypass defrosting: Utilize compressor exhaust heat (80-120℃) to melt frost layer, more than 60% energy saving than traditional electric heating; Dynamic triggering: Start defrosting when condenser outlet air temperature difference >5℃ and tube bundle temperature distribution standard deviation >2℃, avoiding ineffective defrosting.

[0024] (Three) beneficial effects Compared with the prior art, the present application provides an intelligent volumetric condensing unit controller with the following beneficial effects: The intelligent volumetric condensing unit controller can realize real-time monitoring of all parameters: covering 12 key parameters such as discharge temperature, return air temperature, ambient temperature, outlet air temperature, condensate temperature, evaporating temperature, condensing temperature, freezer temperature, compressor current / frequency, and fan speed; With self-diagnosis and adaptive control features: based on fuzzy PID algorithm and machine learning model, dynamically adjust operating parameters, eliminating the need for manual debugging; Intelligent defrosting can be realized: replace electric heating with hot gas bypass defrosting, combined with self-learning algorithm to realize "defrosting with frost, no defrosting without frost"; Full temperature range compatibility: automatically identify -40℃~+15℃ freezer requirements, a single controller adapts to high / medium / low temperature conditions. BRIEF DESCRIPTION OF DRAWINGS

[0025] Fig. 1 Structure diagram of the controller of the present application; Fig. 2 Circuit diagram of the controller of the present application; Fig. 3 Principle diagram of the controller of the present application in actual implementation. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0027] Please refer to Figs. 1-3 , the intelligent volumetric condensing unit controller includes a main control unit, a multi-modal sensing module, an actuator driving unit, a communication hub, an adaptive power management system, a human-machine interaction interface, and a fault prediction and health management module, The main control unit includes a microprocessor and a non-volatile memory storing controller logic algorithms, and is used to execute a multivariable closed-loop control strategy. The multi-modal sensing module integrates a temperature sensor array (8 channels), a pressure sensor (2 channels), and a vibration sensor (1 channel), and collects real-time condensing unit operating parameters. The actuator driving unit includes a 7-way programmable relay output module, each output of which is configured with a PWM modulation circuit to drive the compressor, fan, and electromagnetic valve. The communication hub provides three industrial-grade RS485 interfaces supporting Modbus / BACnet protocols, and supports data interaction with cloud servers, edge computing devices, and third-party controllers. The adaptive power management system is compatible with AC 100-240V / DC 24V dual-mode input, and outputs +12V / ±5% and +5V / ±2% precision regulated power supplies. The human-machine interaction interface includes a TFT touch display screen and a multi-color LED status indication matrix. The fault prediction and health management module analyzes historical operating data through a machine learning model, predicts the wear grade of the compressor, and generates a maintenance warning signal.

[0028] Intelligent control method of the controller: Step S1: Establish a three-dimensional thermodynamic state model of the unit through multi-sensor fusion technology; Step S2: Calculate the optimal compressor frequency and expansion valve opening combination using model predictive control (MPC). Step S3: Dynamically evaluate the condenser dirty coefficient, trigger automatic spray cleaning instruction; Step S4: Simulate system response under different load scenarios using digital twin, generate preventive control strategy; Step S5: Chain energy efficiency data through blockchain technology, generate tamper-proof energy consumption audit report.

[0029] In use, the microprocessor of the main control unit has a built-in fuzzy PID control algorithm that dynamically adjusts the opening degree of the expansion valve according to the evaporator superheat degree. The algorithm parameters suppress sensor noise through a Kalman filter, and the sampling frequency can be configured from 10Hz to 1kHz.

[0030] The main control unit, multi-modal sensing module, actuator driving unit, communication hub, adaptive power management system, human-machine interface, and fault prediction and health management module, Each module works together, and the core functions include: Full-parameter real-time monitoring: Covers 12 key parameters such as exhaust temperature, return air temperature, ambient temperature, outlet air temperature, condensate temperature, evaporation temperature, condensation temperature, freezer temperature, compressor current / frequency, and fan speed; Self-diagnosis and adaptive control: Based on fuzzy PID algorithm and machine learning model, dynamically adjust operating parameters, eliminating the need for manual debugging; Intelligent defrosting: Replace electric heating with hot gas bypass defrosting, and combine with self-learning algorithm to achieve "defrosting with frost, no defrosting without frost"; Full-temperature-range compatibility: Automatically identifies -40℃~+15℃ freezer requirements, and a single controller adapts to high / medium / low temperature conditions.

[0031] Main control unit Hardware configuration: STM32H743 microprocessor (480MHz main frequency, built-in FPU) is used, combined with 128MB non-volatile memory (stores control logic algorithm and historical data); Core algorithm: Fuzzy PID control: dynamically adjusts the opening degree of the electronic expansion valve according to the evaporator superheat degree (calculation accuracy ±0.1℃), with a response time of <50ms; Noise suppression: Real-time filtering of sensor data through Kalman filter (sampling frequency 1kHz), reduces temperature / pressure signal fluctuation error; Multivariable closed-loop strategy: Establishes a linkage control model of condensation temperature-compressor frequency-fan speed to achieve optimal energy efficiency.

[0032] Wireless temperature measurement nodes (support ZigBee 3.0) are integrated in the multi-modal sensing module, and embedded temperature patches are deployed at key positions of the condenser tube bundle; the pressure sensor of the multi-modal sensing module is configured with a temperature compensation circuit, and the measurement accuracy is ±0.5% FS (-40℃~125℃).

[0033] Multi-modal sensing module Sensor array: Temperature monitoring: 8-channel PT1000 sensor (-40~150℃, ±0.2℃ accuracy), deployed at key nodes such as compressor exhaust, return air pipe, condenser outlet, etc. Pressure monitoring: 2-channel MEMS pressure sensor (0-4MPa, ±0.5% FS accuracy), integrated with temperature compensation circuit to eliminate environmental temperature drift; Vibration monitoring: 1-channel three-axis acceleration sensor (10-2000Hz), detects abnormal vibration frequency spectrum of compressor bearings.

[0034] Wireless extension: supports ZigBee 3.0 protocol, 10 embedded temperature patches are deployed in the condenser tube bundle (±0.5℃ accuracy), realizing distributed monitoring of tube bundle surface temperature field.

[0035] Actuator drive unit contains soft start circuit, configured with compressor step start timing control logic, limiting the starting current within 150% of the rated value; the relay contact of the actuator drive unit is configured with an arc suppression module, including an RC buffer circuit and an MOV overvoltage protection device.

[0036] Drive circuit: 7-way programmable relay: each channel integrates PWM modulation circuit (frequency 0-10kHz adjustable), drives variable frequency compressor (0.5Hz step frequency modulation), EC fan (stepless speed regulation), solenoid valve; Soft start protection: step start compressor motor, limit starting current ≤150% rated value (achieved by IGBT module current slope control); Arc suppression: relay contact is configured with RC buffer circuit (resistor 10Ω / capacitor 0.1μF) and MOV overvoltage protection device (response time <5ns).

[0037] Communication hub supports OPC UA protocol conversion, configured with data encryption engine (AES-256), restricts illegal device access through firewall rules, and sets data buffer area to maintain 72 hours of local data storage when network is interrupted.

[0038] Protocol compatibility: three-way isolated RS485 interface, supports Modbus RTU, BACnet MS / TP and OPC UA protocol conversion, realizes data interaction with BMS system and cloud platform; Data security: built-in AES-256 encryption engine, end-to-end encryption of transmission parameters; firewall rules limit illegal device access (based on MAC address whitelist); Offline data transmission: configure 4GB data buffer area, continuously store 72 hours of running data (sampling interval 1 minute) when the network is interrupted.

[0039] Fault prediction and health management module includes: a) Vibration spectrum analysis unit based on LSTM neural network, detects abnormal frequency components of bearings; b) Refrigerant leakage detection algorithm, calculates leakage rate through pressure-temperature correlation analysis; c) Energy efficiency optimization engine, automatically adjusts condenser fan speed curve according to ambient temperature and humidity.

[0040] Preferably, the controller is configured with a digital twin interface, which maps real-time data to a 3D device model, enabling virtual debugging functions; maintenance warning signals include remaining useful life (RUL) prediction values, and confidence interval calculation uses Monte Carlo simulation.

[0041] Preferably, the controller power management system is configured with a super capacitor backup power supply that maintains key sensors for 30 minutes when the main power supply is interrupted; and includes an input power quality monitoring circuit that records voltage total harmonic distortion (THD) and transient pulse events.

[0042] Adaptive power management system Dual-mode input: compatible with AC 100-240V (±10%) and DC 24V input, output +12V (±5%) and +5V (±2%) regulated power supply, conversion efficiency ≥92%; Power quality monitoring: real-time recording of input voltage total harmonic distortion (THD ≤3%) and transient pulse (>100μs pulse width event); Backup power supply: super capacitor group (capacity 10F) maintains key sensors for 30 minutes when the main power supply is interrupted, ensuring data integrity.

[0043] Preferably, the controller is packaged in an IP65 protection level shell, with a heat-conducting silicone sealant layer and an anti-vibration bracket inside, and the control logic supports FOTA wireless upgrade with version rollback mechanism to prevent system failure due to upgrade failure.

[0044] Human-machine interaction interface: TFT touch screen: 7-inch color display, real-time display of unit running parameters, energy efficiency curve and fault codes; Status indication: 8x8 multicolor LED matrix, visually feedback system status through color coding (green-normal, yellow-warning, red-fault); Virtual commissioning: Real-time data is mapped to 3D device model through digital twin interface, supporting remote parameter adjustment and simulation running.

[0045] Defrost strategy: Frost prediction: Self-learning algorithm analyzes historical defrost cycle, environmental humidity, and evaporator temperature difference to predict frost thickness (error <15%); Hot gas bypass defrosting: Utilize compressor exhaust heat (80-120℃) to melt frost, more than 60% energy saving than traditional electric heating; Dynamic triggering: Start defrosting when condenser outlet temperature difference >5℃ and tube bundle temperature distribution standard deviation >2℃, to avoid ineffective defrosting.

[0046] Example 1: Cold storage adaptive control Initialization: After power-on, the controller automatically scans the sensor network, calibrates temperature / pressure signals (time consumption <1 minute); Pattern recognition: According to the cold storage set temperature (e.g. -25℃), call the pre-stored three-dimensional control mapping table (condensing temperature-evaporating temperature-compressor frequency), switch to low-temperature freezing mode; Dynamic adjustment: Update control parameters every 30 minutes, for example: When the ambient temperature rises from 25℃ to 35℃, automatically increase the condenser fan speed to 85%, maintain the condensing pressure at 1.8MPa; When the compressor current harmonic distortion rate >8% is detected, trigger winding imbalance warning; Defrost execution: High-temperature exhaust gas is introduced into the evaporator through the hot gas bypass valve, defrosting is completed within 10 minutes, and the cold storage temperature fluctuation is ≤±0.5℃.

[0047] Example 2: Fault prediction and maintenance Bearing wear warning: LSTM model detects that the amplitude of 200Hz component in the vibration spectrum rises by 20%, predicts the remaining life of 600 hours (confidence 90%); Refrigerant leakage treatment: Pressure-temperature correlation analysis shows that the low-pressure side pressure abnormally drops by 0.2MPa, triggering leakage alarm and locating the leakage point; Energy efficiency optimization: When the ambient temperature is 18℃ at night, automatically reduce the condenser fan speed to 50%, and the system COP increases to 3.8.

[0048] Example 3: Use case, as shown in Fig. 3 The refrigerant gas is sucked into the compressor, and after compression, it becomes high-temperature and high-pressure gas, which enters the oil separator.

[0049] Oil separation and condensation Lubricant oil is separated and returned to the compressor, and the remaining refrigerant gas enters the condenser and is condensed into high-pressure liquid through fan or water cooling.

[0050] Throttling and supercooling High-pressure liquid refrigerant is throttled by an electronic expansion valve, and the economizer may be cooled twice to increase the supercooling degree.

[0051] Evaporation heat absorption: Low-temperature and low-pressure refrigerant enters the evaporator and absorbs ambient heat (such as indoor heat) to evaporate into gas.

[0052] Return and circulation: Evaporated gas returns to the compressor to complete a refrigeration cycle.

[0053] Adjust the compressor frequency according to the ambient temperature to avoid frequent start-stop, improve comfort and save energy.

[0054] Electronic expansion valve and sensor cooperate to realize real-time optimization of refrigerant flow and supercooling degree.

[0055] Four-way reversing valve supports refrigeration / heat switching to meet different seasonal needs.

[0056] Industrial applicability: This controller has passed 2,000 hours of continuous operation test, and realized: Temperature control accuracy: ±0.5℃ (traditional controller ±2℃); Energy saving effect: Integrated energy efficiency ratio (COP) increased by 28%, defrosting energy consumption decreased by 62%; Maintenance cost: PHM module warns of failure 300 hours in advance, reduces unplanned downtime by 90%.

[0057] Suitable for 5-100HP volumetric refrigeration units, compatible with common refrigerants such as R404A, R507A, etc.

[0058] In summary, the intelligent volumetric condensing unit controller, It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended to exclude myriad other embodiments of the present application that other inventors can develop based on the same general inventive concepts embodied by the described embodiments. That is, although the present application is described in terms of particular embodiments and implementations, it is to be understood that the terminology used is for the purpose of descriptive clarity and that it is intended to be limited only by the words recited in the appended claims. It is to be understood that the terms "including", "comprising", "consisting" and variations thereof do not preclude the addition of further integers to the combinations of integers specified in the claims. It is to be understood that the terms "including", "comprising", "consisting" and variations thereof encompass the various features of the embodiments described herein. It is not intended that the application be limited to the implementation that is described in detail and / or shown in the drawings. It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended to exclude myriad other embodiments of the present application that other inventors can develop based on the same general inventive concepts embodied by the described embodiments. That is, although the present application is described in terms of particular embodiments and implementations, it is to be understood that the terminology used is for the purpose of descriptive clarity and that it is intended to be limited only by the words recited in the appended claims.

[0059] While the embodiments of the application have been shown and described herein, it is to be understood that the application is not limited to the embodiments described, and it is not intended to exclude myriad other embodiments of the present application that other inventors can develop based on the same general inventive concepts embodied by the described embodiments. That is, although the present application is described in terms of particular embodiments and implementations, it is to be understood that the terminology used is for the purpose of descriptive clarity and that it is intended to be limited only by the words recited in the appended claims.

Claims

1. Intelligent positive displacement condensing unit controller, characterized by: It includes a main control unit, a multimodal perception module, an actuator drive unit, a communication hub, an adaptive power management system, a human-computer interaction interface, and a fault prediction and health management module. The main control unit includes a microprocessor and non-volatile memory that stores the controller logic algorithm for executing the multivariable closed-loop control strategy; Multimodal sensing module, integrating temperature sensor array (8 channels), pressure sensor (2 channels) and vibration sensor (1 channel), to collect condensing unit operating parameters in real time; Actuator drive unit, including 7-channel programmable relay output modules, each output is equipped with PWM modulation circuit to drive the compressor, fan and solenoid valve; The communication hub provides three industrial-grade RS485 interfaces supporting Modbus / BACnet protocols, enabling data interaction with cloud servers, edge computing devices, and third-party controllers. Adaptive power management system, compatible with AC 100-240V / DC 24V dual-mode input, output +12V / ±5% and +5V / ±2% precision regulated power supply; Human-computer interaction interface, including TFT touch display and multi-color LED status indicator matrix; The fault prediction and health management module uses machine learning models to analyze historical operating data, predict the compressor wear level and generate maintenance warning signals.

2. The intelligent positive displacement condensing unit controller according to claim 1, characterized in that: The microprocessor of the main control unit has a built-in fuzzy PID control algorithm, which dynamically adjusts the expansion valve opening according to the evaporator superheat. The algorithm parameters suppress sensor noise through the Kalman filter, and the sampling frequency can be configured to 10Hz-1kHz.

3. The intelligent positive displacement condensing unit controller according to claim 1, characterized in that: The multimodal sensing module integrates a wireless temperature measurement node (supporting ZigBee 3.0) and deploys embedded temperature patches at key locations on the condenser tube bundle. The pressure sensor of the multimodal sensing module is equipped with a temperature compensation circuit, with a measurement accuracy of ±0.5% FS (-40°C to 125°C).

4. The intelligent positive displacement condensing unit controller according to claim 1, characterized in that: The actuator drive unit includes a soft start circuit, configured with compressor staged start timing control logic, and limits the starting current to within 150% of the rated value; the relay contacts of the actuator drive unit are configured with an arc suppression module, including an RC buffer circuit and an MOV overvoltage protection device.

5. The intelligent positive displacement condensing unit controller according to claim 1, characterized in that: The communication hub supports OPC UA protocol conversion, is configured with a data encryption engine (AES-256), and restricts access by illegal devices through firewall rules. The communication hub sets up a data cache area to maintain 72 hours of local data storage in the event of a network interruption.

6. The intelligent positive displacement condensing unit controller according to claim 1, characterized in that: The fault prediction and health management module includes: a) A vibration spectrum analysis unit based on an LSTM neural network to detect abnormal frequency components of bearings; b) Refrigerant leak detection algorithm, which calculates the leak rate through pressure-temperature correlation analysis; c) Energy efficiency optimization engine, automatically adjusting the condensing fan speed curve according to the ambient temperature and humidity.

7. The intelligent positive displacement condensing unit controller according to claim 1, characterized in that: The controller is equipped with a digital twin interface to map real-time data to a 3D equipment model, enabling virtual commissioning. The maintenance warning signal includes a predicted remaining useful life (RUL), and Monte Carlo simulation is used to calculate the confidence interval.

8. The intelligent positive displacement condensing unit controller according to claim 1, characterized in that: The controller power management system is equipped with a supercapacitor backup power supply to maintain power to key sensors for 30 minutes when the main power is interrupted; and includes an input power quality monitoring circuit to record voltage harmonic distortion (THD) and transient pulse events.

9. The intelligent positive displacement condensing unit controller according to any one of claims 1 to 9, characterized in that: The controller is encapsulated in an IP65 protection grade housing, with a thermal conductive silicone potting layer and an anti-vibration bracket inside. The control logic supports FOTA wireless upgrades and has a version rollback mechanism to prevent system paralysis due to upgrade failure.

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