Energy-saving intelligent control system for central air conditioner

Abstract

The application relates to the technical field of central air conditioner energy-saving control, and aims to provide a central air conditioner energy-saving intelligent control system, which realizes intelligent dynamic energy-saving control of a central air conditioner system through integration of a high-precision sensor module, a self-adaptive control module, a precise execution module and a dual-mode communication module; the system adopts a multi-host cooperative control strategy and an energy efficiency coefficient calculation model based on environmental data, combines with a deep learning user behavior sensing technology, dynamically adjusts air conditioner operation parameters, improves energy utilization efficiency, reduces operation cost, supports remote monitoring and energy-saving effect quantitative evaluation, has the advantages of high control precision, strong adaptability, high intelligentization degree and the like, and is suitable for scenes such as commercial buildings and industrial workshops.

Description

Technical Field

[0001] This invention relates to the field of energy-saving control technology for central air conditioning, and particularly to an intelligent energy-saving control system for central air conditioning. Background Technology

[0002] In existing technologies, central air conditioning systems generally suffer from problems such as high energy consumption, low control precision, and insufficient intelligence. For example, patent CN119826303A discloses a central air conditioning energy-saving automatic control and intelligent monitoring system, which achieves energy saving by dynamically adjusting the cooling load of each area's air outlet. However, it suffers from a single control strategy and poor adaptability, making it difficult to cope with complex environmental changes. Patent CN119022452A discloses a multi-host collaborative energy-saving control method and system for central air conditioning systems, which optimizes the start-up and shutdown timing of the hosts through a PID algorithm. However, it does not fully consider the impact of environmental data and user behavior on the energy-saving effect, resulting in insufficient control precision.

[0003] In addition, the existing system lacks user behavior perception capabilities and cannot dynamically adjust the air conditioning operation mode according to people's activities, resulting in energy waste. Summary of the Invention

[0004] The purpose of this invention is to provide a central air conditioning energy-saving intelligent control system to solve the problems mentioned in the background art and facilitate its promotion.

[0005] A central air conditioning energy-saving intelligent control system includes: Sensor module: It adopts a high-precision digital temperature and humidity sensor array, infrared CO2 sensor and human infrared pyroelectric sensor array to collect environmental parameters and equipment operating status of each air-conditioned area in real time. The sensor data is transmitted to the control module through a dual redundant CAN bus. The sampling frequency is adjustable from 1Hz to 10Hz and supports dynamic range calibration function. Control module: Built on ARM Cortex-M7 core processor, equipped with real-time operating system (RTOS), it executes dynamic energy-saving control algorithm including multi-host cooperative control strategy. The algorithm adopts adaptive PID iterative learning control model and combines fuzzy logic inference engine to achieve self-tuning of control parameters. The execution module includes a variable frequency fan array, an electric regulating valve assembly, and an intelligent flow controller. The variable frequency fan is driven by a brushless DC motor and supports 0-10V / 4-20mA dual-mode control signals with a speed control accuracy of ±0.1rpm. The electric regulating valve is equipped with a position feedback sensor to achieve closed-loop opening control with an opening accuracy of ±0.5%. Communication module: It integrates a 5G NR and Wi-Fi 6 dual-mode communication unit, supports MQTT protocol and HTTP / 2 protocol stack, has a built-in security encryption chip to achieve end-to-end encryption, supports remote firmware upgrade function, and ensures system scalability.

[0006] Furthermore, the multi-host collaborative control strategy specifically includes: Main unit start / stop decision unit: It determines start / stop conditions through event triggering strategy, calculates error value using three-stage PID algorithm, starts standby main unit when error exceeds threshold, and selects optimal main unit combination through start / stop algorithm; Load balancing algorithm: Calculates load coefficient based on real-time host parameters, achieves load balancing through dynamic weight allocation, supports prediction of host failure probability based on historical data, and provides early maintenance warnings; Iterative learning control unit: Error compensation is performed by daily iteration cycle, and the control effect is optimized by adaptive adjustment of the learning rate. The system records daily temperature curve data to form a historical database for strategy optimization.

[0007] Furthermore, the sensor module is specifically configured as follows: The temperature sensor uses a PT1000 platinum resistance thermometer, with a measurement range of -20℃ to 80℃ and an accuracy of ±0.1℃. The humidity sensor uses a capacitive principle, with a measurement range of 0-100%RH and an accuracy of ±2%RH. The CO2 sensor uses NDIR (non-dispersive infrared) technology, with a measurement range of 0-5000ppm and an accuracy of ±50ppm; the human infrared sensor uses a pyroelectric detector, with a detection distance of 5-10 meters and a viewing angle of 120 degrees. The module supports self-diagnostic functions, automatically switches to backup sensors in case of failure, and sends fault alarm information through the communication module.

[0008] Furthermore, the energy efficiency coefficient calculation model of the control module specifically includes: Energy Efficiency Coefficient Calculation Unit: The energy efficiency coefficient is calculated based on real-time regional temperature and humidity, CO2 data and building thermal parameters. A weighted average algorithm is used, and the weighting coefficients are dynamically adjusted seasonally. Air supply priority decision unit: Determines air supply priority based on energy efficiency coefficient, with higher priority areas receiving larger air volume, and supports manual priority adjustment function; Thermal insulation effect evaluation unit: The thermal insulation effect is evaluated by generating a change curve through continuous periodic characteristic temperature and using the least squares method to fit the curve slope. The results are used to optimize the air supply strategy.

[0009] Furthermore, the variable frequency fan control of the execution module specifically includes: The variable frequency fan uses a vector control algorithm to achieve precise speed control with a speed accuracy of ±0.1 rpm, and supports soft start function to reduce current surge. The electric regulating valve uses a proportional-integral control algorithm to achieve precise opening control with an opening accuracy of ±0.5%, and is equipped with a position sensor to achieve closed-loop position control. The intelligent flow controller monitors pipeline flow in real time based on the principle of differential pressure flow meter and supports automatic calibration to ensure measurement accuracy.

[0010] Furthermore, it also includes a user behavior perception module, which specifically includes: Camera image recognition unit: It uses deep learning algorithms to detect the number and distribution of people with an accuracy of over 95%, and supports face recognition to achieve personalized temperature adjustment; Behavioral Pattern Analysis Unit: Analyzes personnel behavioral patterns using clustering algorithms, identifies permanent and temporary areas, and adjusts air conditioning operation modes based on pattern prediction results; Dynamic adjustment strategy unit: Automatically adjusts the air supply volume according to the density of people. When the density exceeds the threshold, the emergency cooling mode is activated. It supports the function of setting custom thresholds.

[0011] Furthermore, the remote monitoring function of the communication module specifically includes: The remote monitoring platform supports real-time data visualization, displays the system's operating status through a web interface, and supports historical data query functions, allowing users to trace back data at any point in time. The fault diagnosis unit uses expert system technology to automatically diagnose the cause based on the fault code and supports remote fault handling to repair common faults. The energy-saving effect assessment unit quantifies the energy-saving effect by comparing historical energy consumption data and generates an energy-saving effect report. The report supports exporting to PDF format for easy archiving and sharing.

[0012] Furthermore, it also includes a system modular design module, which specifically includes: Modular hardware design: Each functional module adopts a standardized interface design, supports hot-swapping, and supports independent upgrades and maintenance without affecting the overall system operation; Modular software design: The control algorithm adopts a modular architecture design, supports plug-in expansion, and the system supports third-party algorithm integration interfaces to facilitate functional expansion; Extended functional modules: The system supports extended air quality monitoring modules and intelligent lighting control modules to realize integrated management of building intelligent systems. The extended modules support plug-and-play functionality.

[0013] Furthermore, it also includes an energy-saving effect evaluation module, which specifically includes: Energy-saving effect quantification unit: The energy-saving rate is calculated by comparing energy consumption data before and after the implementation of the system. The energy-saving rate calculation adopts a weighted average algorithm, taking into account seasonal changes and differences in usage patterns. Closed-loop optimization mechanism: The system automatically adjusts the control strategy based on the energy-saving effect evaluation results to form a closed-loop optimization. The optimization process is recorded in the historical database for subsequent strategy optimization. Report generation unit: The system regularly generates energy-saving effect reports, which include energy consumption comparison charts, energy saving rate statistics, and optimization suggestions. It also supports custom templates to meet the needs of different users.

[0014] As an improvement, the beneficial effects of the present invention are as follows: The present invention discloses a central air conditioning energy-saving intelligent control system, which integrates a sensor module, a control module, an execution module and a communication module to realize intelligent dynamic energy-saving control of the central air conditioning system. The system adopts a multi-host collaborative control strategy and an energy efficiency coefficient calculation model based on environmental data, combined with user behavior perception technology, to dynamically adjust the air conditioning operating parameters, improve energy utilization efficiency and reduce operating costs. It has the advantages of high control precision, strong adaptability and high degree of intelligence. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the overall architecture of a central air conditioning energy-saving intelligent control system according to the present invention; Figure 2 This is a schematic diagram of the multi-host collaborative control strategy architecture of the present invention; Figure 3 This is a schematic diagram of the energy efficiency coefficient calculation model architecture of the present invention; Figure 4 This is a schematic diagram of the user behavior perception module architecture of the present invention; Figure 5 This is a schematic diagram of the modular design module architecture of the system of the present invention; Figure 6 This is a schematic diagram of the energy-saving effect evaluation module architecture of the present invention; Detailed Implementation

[0016] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. Identical components are indicated by the same reference numerals. It should be noted that the terms "front," "rear," "left," "right," "up," and "down" used in the following description refer to directions in the accompanying drawings, while the terms "inner" and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0017] To make the content of this invention easier to understand, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Identical components are indicated by the same reference numerals.

[0018] Example 1: Detailed Design of System Hardware Architecture This system adopts a layered modular hardware architecture, including a sensor layer, a control layer, an execution layer, and a communication layer. The sensor layer is equipped with a high-precision digital temperature and humidity sensor array, with four temperature sensors and two humidity sensors deployed in each air-conditioned area, forming a redundant measurement system. Sensor data is transmitted to the control module via a dual-redundant CAN bus at a transmission rate of 1Mbps, supporting CRC checksums to ensure data integrity. The control layer is built on an ARM Cortex-M7 processor and runs a real-time operating system (RTOS) to ensure real-time response to control commands. The control module has a built-in 128MB NAND flash memory to store historical operating data and control algorithm parameters. The execution layer includes a variable frequency fan array and an electric regulating valve assembly. The variable frequency fans are driven by brushless DC motors, supporting 0-10V / 4-20mA dual-mode control signals with a speed control accuracy of ±0.1rpm. The electric regulating valves are equipped with position feedback sensors to achieve closed-loop position control with an opening control accuracy of ±0.5%. The communication layer integrates a 5G NR and Wi-Fi 6 dual-mode communication unit, supporting MQTT and HTTP / 2 protocol stacks. The communication module incorporates a built-in security encryption chip to achieve end-to-end encryption of data transmission, ensuring system security. The system supports remote firmware upgrades, enabling online updates of the control algorithm via OTA technology, thus improving system maintainability.

[0019] Example 2: Detailed Implementation of Multi-Host Cooperative Control Strategy This embodiment details the implementation of the multi-host collaborative control strategy. The host start / stop decision unit employs a three-stage PID control algorithm, using an event-triggered strategy to determine whether the host start / stop conditions are met. When the system detects that the temperature in a certain area deviates from the set value by more than a threshold, the backup host is activated for load compensation. The host selection algorithm calculates the load coefficient based on the real-time operating parameters of each host and selects the optimal host combination. In summer cooling mode, the system predicts daily load demand based on historical data and adjusts the main unit's operating parameters in advance to achieve energy-saving operation. The load balancing algorithm achieves load balancing among multiple main units through dynamic weight allocation and supports predicting the probability of main unit failure based on historical operating data, providing early maintenance warnings. The iterative learning control unit uses a daily iteration cycle for error compensation and optimizes control performance through an adaptive learning rate adjustment mechanism. The system records daily temperature curve data, forming a historical database for control strategy optimization. For example, the system optimizes PID control parameters by analyzing historical temperature curve data, improving control accuracy.

[0020] Example 3: Detailed Application of User Behavior Awareness Technology This embodiment details the specific application of user behavior perception technology. The camera image recognition unit uses deep learning algorithms to detect the number and distribution of people, achieving an accuracy of over 95%. The system supports facial recognition and enables personalized temperature adjustment, improving the user experience. The behavior pattern analysis unit analyzes personnel behavior patterns through clustering algorithms to identify permanent and temporary areas.

[0021] The system analyzes people's behavior patterns to identify meeting rooms as permanent areas and offices as temporary areas. Based on these behavioral pattern predictions, it adjusts the air conditioning operation mode to improve comfort. The dynamic adjustment strategy unit automatically adjusts the airflow based on people density, activating an emergency cooling mode when the density exceeds a threshold to ensure comfort. The system supports custom threshold settings to meet the needs of different scenarios. For example, in a meeting room scenario, the system automatically sets a higher people density threshold to ensure comfort during meetings.

[0022] Example 4: Detailed Method for Evaluating Energy Saving Effect This embodiment details the specific method for evaluating energy-saving effects. The energy-saving effect quantification unit calculates the energy-saving rate by comparing energy consumption data before and after system implementation. The energy-saving rate calculation uses a weighted average algorithm, taking into account seasonal variations and differences in usage patterns.

[0023] The system calculates energy-saving rates and evaluates the system's energy-saving performance in summer by comparing energy consumption data under summer cooling mode. A closed-loop optimization mechanism automatically adjusts the control strategy based on the energy-saving performance evaluation results, forming a closed-loop optimization. The optimization process is recorded in a historical database for subsequent strategy optimization. The report generation unit periodically generates energy-saving performance reports, which include energy consumption comparison charts, energy-saving rate statistics, optimization suggestions, etc. The reports support custom templates to meet different user needs. For example, the system can generate monthly energy-saving reports, providing a detailed analysis of the month's energy-saving performance and offering optimization suggestions.

[0024] Example 5: Detailed Functions of Remote System Monitoring This embodiment describes in detail the specific functions of the system's remote monitoring. The remote monitoring platform supports real-time data visualization, displaying the system's operating status through a web interface. The platform also supports historical data querying, allowing users to review operational data at any point in time.

[0025] Users can view the system's operational status at any point in time and analyze system trends through a web interface. The fault diagnosis unit employs expert system technology to automatically diagnose the cause of faults based on fault codes. The system supports remote fault handling, enabling the repair of common faults via remote control.

[0026] When the system detects a host failure, the communication module automatically sends an alarm message to the user terminal, reminding the user to handle the issue promptly. The energy-saving effect evaluation unit quantifies energy-saving performance by comparing historical energy consumption data and generates an energy-saving effect report. The report can be exported as a PDF for easy archiving and sharing. The remote monitoring platform supports multi-user access control to ensure system security.

[0027] The above are merely preferred embodiments of the present invention and are 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 scope of protection of the present invention.

Claims

1. A central air conditioning energy-saving intelligent control system, characterized in that, include: Sensor module: It adopts a high-precision digital temperature and humidity sensor array, infrared CO2 sensor and human infrared pyroelectric sensor array to collect environmental parameters and equipment operating status of each air-conditioned area in real time. The sensor data is transmitted to the control module through a dual redundant CAN bus. The sampling frequency is adjustable from 1Hz to 10Hz and supports dynamic range calibration function. Control module: Built on ARM Cortex-M7 core processor, equipped with real-time operating system (RTOS), it executes dynamic energy-saving control algorithm including multi-host cooperative control strategy. The algorithm adopts adaptive PID iterative learning control model and combines fuzzy logic inference engine to achieve self-tuning of control parameters. The execution module includes a variable frequency fan array, an electric regulating valve assembly, and an intelligent flow controller. The variable frequency fan is driven by a brushless DC motor and supports 0-10V / 4-20mA dual-mode control signals with a speed control accuracy of ±0.1rpm. The electric regulating valve is equipped with a position feedback sensor to achieve closed-loop opening control with an opening accuracy of ±0.5%. Communication module: It integrates a 5G NR and Wi-Fi 6 dual-mode communication unit, supports MQTT protocol and HTTP / 2 protocol stack, has a built-in security encryption chip to achieve end-to-end encryption, supports remote firmware upgrade function, and ensures system scalability.

2. The central air conditioning energy-saving intelligent control system according to claim 1, characterized in that, The multi-host collaborative control strategy specifically includes: Main unit start / stop decision unit: It determines start / stop conditions through event triggering strategy, calculates error value using three-stage PID algorithm, starts standby main unit when error exceeds threshold, and selects optimal main unit combination through start / stop algorithm; Load balancing algorithm: Calculates load coefficient based on real-time host parameters, achieves load balancing through dynamic weight allocation, supports prediction of host failure probability based on historical data, and provides early maintenance warnings; Iterative learning control unit: Error compensation is performed by daily iteration cycle, and the control effect is optimized by adaptive adjustment of the learning rate. The system records daily temperature curve data to form a historical database for strategy optimization.

3. The central air conditioning energy-saving intelligent control system according to claim 1, characterized in that, The specific configuration of the sensor module is as follows: The temperature sensor uses a PT1000 platinum resistance thermometer, with a measurement range of -20℃ to 80℃ and an accuracy of ±0.1℃. The humidity sensor uses a capacitive principle, with a measurement range of 0-100%RH and an accuracy of ±2%RH. The CO2 sensor uses NDIR (non-dispersive infrared) technology, with a measurement range of 0-5000ppm and an accuracy of ±50ppm; the human infrared sensor uses a pyroelectric detector, with a detection distance of 5-10 meters and a viewing angle of 120 degrees. The module supports self-diagnostic functions, automatically switches to backup sensors in case of failure, and sends fault alarm information through the communication module.

4. The central air conditioning energy-saving intelligent control system according to claim 1, characterized in that, The energy efficiency coefficient calculation model of the control module specifically includes: Energy Efficiency Coefficient Calculation Unit: The energy efficiency coefficient is calculated based on real-time regional temperature and humidity, CO2 data and building thermal parameters. A weighted average algorithm is used, and the weighting coefficients are dynamically adjusted seasonally. Air supply priority decision unit: Determines air supply priority based on energy efficiency coefficient, with higher priority areas receiving larger air volume, and supports manual priority adjustment function; Thermal insulation effect evaluation unit: The thermal insulation effect is evaluated by generating a change curve through continuous periodic characteristic temperature and using the least squares method to fit the curve slope. The results are used to optimize the air supply strategy.

5. The central air conditioning energy-saving intelligent control system according to claim 1, characterized in that, The variable frequency fan control of the execution module specifically includes: The variable frequency fan uses a vector control algorithm to achieve precise speed control with a speed accuracy of ±0.1 rpm, and supports soft start function to reduce current surge. The electric regulating valve uses a proportional-integral control algorithm to achieve precise opening control with an opening accuracy of ±0.5%, and is equipped with a position sensor to achieve closed-loop position control. The intelligent flow controller monitors pipeline flow in real time based on the principle of differential pressure flow meter and supports automatic calibration to ensure measurement accuracy.

6. The central air conditioning energy-saving intelligent control system according to claim 1, characterized in that, It also includes a user behavior awareness module, which specifically includes: Camera image recognition unit: It uses deep learning algorithms to detect the number and distribution of people with an accuracy of over 95%, and supports face recognition to achieve personalized temperature adjustment; Behavioral Pattern Analysis Unit: Analyzes personnel behavioral patterns using clustering algorithms, identifies permanent and temporary areas, and adjusts air conditioning operation modes based on pattern prediction results; Dynamic adjustment strategy unit: Automatically adjusts the air supply volume according to the density of people. When the density exceeds the threshold, the emergency cooling mode is activated. It supports the function of setting custom thresholds.

7. The central air conditioning energy-saving intelligent control system according to claim 1, characterized in that, The remote monitoring function of the communication module specifically includes: The remote monitoring platform supports real-time data visualization, displays the system's operating status through a web interface, and supports historical data query functions, allowing users to trace back data at any point in time. The fault diagnosis unit uses expert system technology to automatically diagnose the cause based on the fault code and supports remote fault handling to repair common faults. The energy-saving effect assessment unit quantifies the energy-saving effect by comparing historical energy consumption data and generates an energy-saving effect report. The report supports exporting to PDF format for easy archiving and sharing.

8. The central air conditioning energy-saving intelligent control system according to claim 1, characterized in that, It also includes a system modular design module, which specifically includes: Modular hardware design: Each functional module adopts a standardized interface design, supports hot-swapping, and supports independent upgrades and maintenance without affecting the overall system operation; Modular software design: The control algorithm adopts a modular architecture design, supports plug-in expansion, and the system supports third-party algorithm integration interfaces to facilitate functional expansion; Extended functional modules: The system supports extended air quality monitoring modules and intelligent lighting control modules to realize integrated management of building intelligent systems. The extended modules support plug-and-play functionality.

9. A central air conditioning energy-saving intelligent control system according to claim 1, characterized in that, It also includes an energy-saving effect evaluation module, which specifically includes: Energy-saving effect quantification unit: The energy-saving rate is calculated by comparing energy consumption data before and after the implementation of the system. The energy-saving rate calculation adopts a weighted average algorithm, taking into account seasonal changes and differences in usage patterns. Closed-loop optimization mechanism: The system automatically adjusts the control strategy based on the energy-saving effect evaluation results to form a closed-loop optimization. The optimization process is recorded in the historical database for subsequent strategy optimization. Report generation unit: The system regularly generates energy-saving effect reports, which include energy consumption comparison charts, energy saving rate statistics, and optimization suggestions. It also supports custom templates to meet the needs of different users.

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