Air conditioning system efficient operation fault early warning maintenance system and method

Abstract

The invention discloses an air-conditioning system efficient operation fault early warning maintenance system and method, and relates to the technical field of air-conditioning systems, and the method comprises the steps: collecting environment parameters and equipment state parameters in the operation process of an air-conditioning system, and obtaining original monitoring data; calculating an energy efficiency deviation index according to the actually measured energy consumption and refrigerating capacity of the air conditioning system in the original monitoring data; generating a fault confidence index by using the energy efficiency deviation index and combining the compressor current characteristics, the power factor and the heat exchanger inlet and outlet temperature information; based on the physical coupling relation between the core components of the air conditioning system, a fault propagation map is constructed, the fault confidence index is input into the fault propagation map, and fault root causes and propagation paths thereof are inferred through the fault propagation map; calculating a maintenance urgency degree index according to the fault root cause in combination with a caused energy efficiency degradation trend, an equipment operation safety boundary and a maintenance resource demand; and performing priority ranking on all the identified faults according to the maintenance urgency index, and generating a maintenance work order.

Description

Technical Field

[0001] This invention relates to the field of air conditioning system technology, and in particular to an efficient air conditioning system fault early warning and maintenance system and method. Background Technology

[0002] With the increasing demand for building energy conservation and intelligent operation and maintenance, efficient operation and maintenance technologies for HVAC systems have become a research hotspot in the field of facility management. In recent years, fault detection and diagnosis methods based on the Internet of Things and big data analysis have gradually replaced the traditional periodic maintenance mode and are widely used in central air conditioning and multi-split systems. Existing technologies mainly rely on single energy efficiency indicators or threshold alarm mechanisms to monitor equipment status, and combine machine learning models to offline train historical operating data to identify typical fault modes.

[0003] However, current mainstream FDD technology still has significant limitations. Most systems only focus on instantaneous energy efficiency deviations and lack dynamic modeling of energy efficiency degradation rates. It is difficult to distinguish between short-term operating disturbances and long-term performance degradation, resulting in a high false alarm rate. Fault diagnosis is mostly based on black box models or isolated parameter thresholds, without fully considering the physical coupling relationship between core components such as compressors, condensers, expansion valves and evaporators in the refrigeration cycle. As a result, the diagnostic results often misjudge secondary abnormalities as root causes, and the interpretability and engineering guidance are insufficient. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an efficient operation fault early warning and maintenance system and method for air conditioning systems to address the following issues: However, current mainstream FDD technology still has significant limitations. Most systems only focus on instantaneous energy efficiency deviations and lack dynamic modeling of energy efficiency degradation rates. This makes it difficult to distinguish between short-term operating disturbances and long-term performance degradation, resulting in a high false alarm rate. Fault diagnosis is mostly based on black-box models or isolated parameter thresholds, without fully considering the physical coupling relationship between core components such as compressors, condensers, expansion valves, and evaporators in the refrigeration cycle. Consequently, the diagnostic results often misjudge secondary anomalies as root causes, resulting in insufficient interpretability and engineering guidance.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for efficient fault early warning and maintenance of an air conditioning system, comprising: Collect environmental parameters and equipment status parameters during the operation of the air conditioning system to obtain raw monitoring data; Based on the measured energy consumption and cooling capacity of the air conditioning system in the original monitoring data, calculate the energy efficiency deviation index; By utilizing the energy efficiency deviation index, combined with compressor current characteristics, power factor, and heat exchanger inlet and outlet temperature information, a fault confidence index is generated. Based on the physical coupling relationship between the core components of the air conditioning system, a fault propagation map is constructed, and the fault confidence index is input into the fault propagation map. The root cause of the fault and its propagation path are inferred through the fault propagation map. Based on the root cause of the failure, combined with the resulting trend of energy efficiency degradation, the safety boundary of equipment operation, and the demand for maintenance resources, the maintenance urgency index is calculated. All identified faults are prioritized based on the maintenance urgency index, and maintenance work orders are generated.

[0007] As a preferred embodiment of the efficient operation fault early warning and maintenance method for the air conditioning system described in this invention, the specific steps for collecting environmental parameters and equipment status parameters during the operation of the air conditioning system to obtain raw monitoring data are as follows: Outdoor dry-bulb temperature is obtained using a temperature sensor. and indoor return air temperature ; The current signal in the compressor power supply circuit is collected by a current transformer and denoted as follows: The voltage of the corresponding phase is acquired by a voltage transmitter and recorded as follows: Based on the synchronously acquired voltage and current signals, the AC active power calculation formula is used, and the expression is: ; in, Active power The power factor between voltage and current; Temperature sensors are installed on the refrigerant inlet and outlet pipes of the condenser in the air conditioning system to obtain the condenser inlet temperature. With outlet temperature Temperature sensors are installed on the refrigerant inlet and outlet pipes of the evaporator to obtain the evaporator inlet temperature. With outlet temperature ; Will , , , , , , and The data is recorded synchronously with a sampling period of 1 second and aligned with a unified timestamp to form the original monitoring dataset.

[0008] As a preferred embodiment of the efficient operation fault early warning and maintenance method for the air conditioning system described in this invention, the step of calculating the energy efficiency deviation index based on the measured energy consumption and cooling capacity of the air conditioning system from the original monitoring data includes the following steps: Based on the fan's factory air volume calibration value Compared with the specific heat constant of air at constant pressure Combined with the evaporator inlet and outlet temperatures from the original monitoring data and The actual cooling capacity of the system is calculated using the following expression: ; in, The actual measured cooling capacity of the system; The active power of the compressor Assuming current operating energy consumption, the measured coefficient of performance is calculated as follows: ; in, The coefficient of performance is the measured value. Based on the COP values ​​at standard test points under the rated operating conditions on the equipment's nameplate, and using a bilinear interpolation model constructed from the two-dimensional performance table provided by the standard, the inherent coefficients of multiple devices were obtained. This is used to construct a benchmark for dynamic theoretical performance coefficients, and its expression is: ; in, For inherent coefficients, As a benchmark for dynamic theoretical performance coefficients, Outdoor dry-bulb temperature, The indoor return air temperature, This is the product of indoor and outdoor temperatures; Will and Substituting into the energy efficiency deviation index formula, the expression is: ; in, It is a preset small positive constant used to avoid the denominator being zero; EEDI is the energy efficiency deviation index, used to represent the degree of performance degradation of the current system relative to the ideal healthy state system.

[0009] As a preferred embodiment of the efficient operation fault early warning and maintenance method for the air conditioning system described in this invention, the step of generating a fault confidence index by utilizing the energy efficiency deviation index, combined with compressor current characteristics, power factor, and heat exchanger inlet and outlet temperature information, includes the following specific steps: Extract compressor current from raw monitoring data And based on the average current during historical normal operation under the same operating conditions. The relative deviation is calculated using the following expression: ; in, This is the compressor current value extracted from the raw monitoring data at the current operating moment. To represent the average compressor current during the system's historical normal operation under the same operating conditions, This is the current deviation value; Using the voltage Current With active power The current power factor is calculated using the following expression: ; in, This is the power factor, and its value typically ranges from 0 to 1. The actual temperature rise is calculated using the inlet and outlet temperatures of the condenser. and the nominal condensing temperature rise given during the equipment design phase. Compare the results to obtain the temperature rise deviation. EEDI, relative current deviation, Temperature rise deviation is used as a four-dimensional input and substituted into the multi-dimensional fault confidence index formula, the expression is: ; in The normalized weight coefficients obtained through offline historical fault sample training satisfy... MFCI is a fault confidence index. This indicates the probability of a fault existence based on a comprehensive assessment of multi-dimensional operational characteristics. The fault confidence index is used to characterize the degree of abnormality in the current operating state of the air conditioning system, which deviates from the normal operating conditions.

[0010] As a preferred embodiment of the efficient operation fault early warning and maintenance method for air conditioning systems described in this invention, the following steps are taken: Based on the physical coupling relationship between the core components of the air conditioning system, a fault propagation graph is constructed, and a fault confidence index is input into the fault propagation graph. The root cause of the fault and its propagation path are then inferred from the fault propagation graph. Define the set of nodes for compressor, condenser, expansion valve, and evaporator. The four core components, namely the compressor, condenser, expansion valve, and evaporator, are respectively represented by a directed edge set based on the refrigerant flow direction in the refrigeration cycle. The expression is: ; in, Let be a set of directed edges. For compressors, For condenser, For expansion valve, For evaporators; Constructing a fault propagation map ; The MFCI is mapped to the corresponding node based on the anomalous variable. If the component's operating parameters are within the normal range, the initial confidence level of the node is set to zero or below the preset threshold. If the condenser temperature rise is abnormal, the relevant confidence level is assigned to... To form the initial abnormal distribution vector Each of them Represents a node The initial anomaly intensity; The overall failure probability of each node is iteratively updated using a causal decay propagation mechanism. The expression for the update rule is as follows: ; in, For nodes In the Fault confidence at the next iteration for A certain predecessor node, For nodes The initial anomaly intensity, For all predecessor nodes Sum of current confidence levels, Indicates all pointers The set of predecessor nodes, The preset causal decay factor has a value range of 0.5 to 0.8, and the iteration continues until the probability change between two consecutive pairs is less than the preset convergence threshold. The component corresponding to the highest probability of the converged node is taken as the root cause of the fault, and the fault propagation path is obtained by tracing back along the path of decreasing probability.

[0011] As a preferred embodiment of the efficient operation fault early warning and maintenance method for the air conditioning system described in this invention, the step of calculating the maintenance urgency index based on the root cause of the fault, combined with the resulting energy efficiency degradation trend, equipment operation safety boundary, and maintenance resource requirements, includes the following specific steps: The energy efficiency degradation rate Used to quantify the dynamic trend of performance degradation of air conditioning systems during long-term operation, and to calculate the theoretical coefficient of performance benchmark under current operating conditions. And combined with the active power of the compressor By combining the estimated cooling capacity, the actual coefficient of performance (COP) can be obtained. ; Then, the energy efficiency deviation index is calculated, and the expression is: ; in, Energy efficiency deviation index; Based on historical EEDI data within indoor temperature ranges, a time series was constructed, and the least squares method was used to perform linear regression fitting on the time series to obtain the energy efficiency degradation rate. The expression is: ; in, For the rate of energy efficiency degradation, Let EEDI be the instantaneous rate of change with respect to time t. Summing for all sample items; For the first The timestamp of each sampling moment; The sample covariance of time versus EEDI; This represents the sample variance of the time series. Linear regression was performed on the Energy Efficiency Deviation Index (EEDI) series calculated continuously over the past 24 hours to determine the rate of energy efficiency degradation. This is used to reflect the severity of a continued decline in performance; Based on condenser outlet temperature And the corresponding pressure, estimate the compressor discharge temperature and the safe upper temperature limit specified by the equipment manufacturer. In comparison, when Then calculate the over-temperature risk item. ,otherwise 0; Based on the component type corresponding to the root cause of the failure, query the maintenance knowledge base to obtain the average manpower and spare parts costs for the failure. and the system's annual maintenance budget cap Perform normalization; Rate of energy efficiency degradation Overheating risk items Standardized maintenance Substituting into the maintenance urgency index formula, the expression is: ; in, For the preset prediction time window, , , To satisfy the business weight coefficients set according to the operation and maintenance strategy. The Maintenance Urgency Index (RUI) is obtained.

[0012] As a preferred embodiment of the efficient operation fault early warning and maintenance method for the air conditioning system described in this invention, the specific steps of prioritizing all identified faults based on the maintenance urgency index and generating maintenance work orders are as follows: All fault events whose root causes have been identified through the fault propagation map within the current cycle are sorted in descending order according to the RUI value corresponding to the maintenance work order; Assign fault events their root cause component identifier, fault type label, recommended maintenance measures, required standard spare parts code list, estimated downtime, and affected end areas; The fault list is encapsulated into a maintenance work order according to the following structured mapping rules, expressed as: ; in The fault number is... This is a globally unique number for the maintenance work order. The root cause of the failure As a maintenance urgency index, To recommend repair operations, List of required spare parts To estimate downtime, The area where the faulty device is located; The maintenance work order is pushed to the central operation and maintenance management platform via the communication module.

[0013] Secondly, this invention provides an efficient fault early warning and maintenance system for air conditioning systems, comprising: The system includes a monitoring data module, an energy efficiency deviation module, a fault confidence module, a fault propagation graph module, a maintenance urgency module, and a maintenance work order module. The monitoring data module is used to collect environmental parameters and equipment status parameters during the operation of the air conditioning system to obtain raw monitoring data; The energy efficiency deviation module calculates the energy efficiency deviation index based on the measured energy consumption and cooling capacity of the air conditioning system in the original monitoring data. The fault confidence module uses the energy efficiency deviation index, combined with compressor current characteristics, power factor and heat exchanger inlet and outlet temperature information, to generate a fault confidence index. The fault propagation graph module constructs a fault propagation graph based on the physical coupling relationship between the core components of the air conditioning system, and inputs the fault confidence index into the fault propagation graph to infer the root cause of the fault and its propagation path through the fault propagation graph. The maintenance urgency module calculates the maintenance urgency index based on the root cause of the fault, combined with the resulting energy efficiency degradation trend, equipment operation safety boundary, and maintenance resource requirements. The maintenance work order module prioritizes all identified faults based on the maintenance urgency index and generates maintenance work orders.

[0014] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the air conditioning system efficient operation fault early warning and maintenance method as described in the first aspect of the present invention.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the air conditioning system efficient operation fault early warning and maintenance method as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: By calculating the energy efficiency deviation index from the measured energy consumption and cooling capacity of the air conditioning system in the original monitoring data, a dimensionless and adaptive quantitative assessment of the system's current operating efficiency relative to the theoretical healthy benchmark is achieved. The complex problem of thermal performance degradation is transformed into a comparable and traceable single indicator. By integrating the energy efficiency deviation index, compressor current relative deviation, power factor, and heat exchanger temperature rise deviation, a multi-dimensional fault confidence index is constructed. Current reflects mechanical and electrical loads, power factor represents power quality, and temperature rise reflects heat exchange efficiency, forming a complementary evidence chain with the energy efficiency deviation index. Its purpose is to generate a comprehensive fault probability output with strong robustness, effectively distinguishing between real faults and external disturbances, and significantly improving the accuracy and anti-interference ability of initial fault screening. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart for a fault early warning and maintenance method for efficient operation of an air conditioning system.

[0019] Figure 2 A schematic diagram of a fault early warning and maintenance system for efficient operation of an air conditioning system. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] Reference Figures 1-2 This embodiment of the invention provides a method for efficient operation fault early warning and maintenance of an air conditioning system, comprising the following steps: S1. Collect environmental parameters and equipment status parameters during the operation of the air conditioning system to obtain raw monitoring data.

[0024] Furthermore, the outdoor dry-bulb temperature is obtained through a temperature sensor. and indoor return air temperature ; The current signal in the compressor power supply circuit is collected by a current transformer and denoted as follows: The voltage of the corresponding phase is acquired by a voltage transmitter and recorded as follows: Based on the synchronously acquired voltage and current signals, the AC active power calculation formula is used, and the expression is: ; in, Active power The power factor between voltage and current; Temperature sensors are installed on the refrigerant inlet and outlet pipes of the condenser in the air conditioning system to obtain the condenser inlet temperature. With outlet temperature Temperature sensors are installed on the refrigerant inlet and outlet pipes of the evaporator to obtain the evaporator inlet temperature. With outlet temperature ; Will , , , , , , and The data is recorded synchronously with a sampling period of 1 second and aligned with a unified timestamp to form the original monitoring dataset.

[0025] It should be noted that by performing high-frequency synchronous acquisition and unified timestamp alignment of multi-source sensor signals, data distortion caused by asynchronous sampling is effectively eliminated, providing a high-fidelity raw data foundation for subsequent accurate energy efficiency assessment and fault diagnosis.

[0026] S2. Calculate the energy efficiency deviation index based on the measured energy consumption and cooling capacity of the air conditioning system in the original monitoring data.

[0027] Furthermore, based on the fan's factory-set airflow rating... Compared with the specific heat constant of air at constant pressure Combined with the evaporator inlet and outlet temperatures from the original monitoring data and The actual cooling capacity of the system is calculated using the following expression: ; in, The actual measured cooling capacity of the system; The active power of the compressor Assuming current operating energy consumption, the measured coefficient of performance is calculated as follows: ; in, The coefficient of performance is the measured value. Based on the COP values ​​at standard test points under the rated operating conditions on the equipment's nameplate, and using a bilinear interpolation model constructed from the two-dimensional performance table provided by the standard, the inherent coefficients of multiple devices were obtained. This is used to construct a benchmark for dynamic theoretical performance coefficients, and its expression is: ; in, For inherent coefficients, As a benchmark for dynamic theoretical performance coefficients, Outdoor dry-bulb temperature, The indoor return air temperature, This is the product of indoor and outdoor temperatures; Will and Substituting into the energy efficiency deviation index formula, the expression is: ; in, It is a preset small positive constant used to avoid the denominator being zero; EEDI is the energy efficiency deviation index, used to represent the degree of performance degradation of the current system relative to the ideal healthy state system.

[0028] It should be noted that the use of a dynamic theoretical COP benchmark based on standard performance tables avoids the problem of poor adaptability to operating conditions caused by relying on fixed rated values, and significantly improves the accuracy and comparability of the Energy Efficiency Deviation Index (EEDI) under varying operating conditions.

[0029] S3. Using the energy efficiency deviation index, combined with the compressor current characteristics, power factor, and heat exchanger inlet and outlet temperature information, a fault confidence index is generated.

[0030] Furthermore, compressor current is extracted from the raw monitoring data. And based on the average current during historical normal operation under the same operating conditions. The relative deviation is calculated using the following expression: ; in, This is the compressor current value extracted from the raw monitoring data at the current operating moment. To represent the average compressor current during the system's historical normal operation under the same operating conditions, This is the current deviation value; Using the voltage Current With active power The current power factor is calculated using the following expression: ; in, This is the power factor, and its value typically ranges from 0 to 1. The actual temperature rise is calculated using the inlet and outlet temperatures of the condenser. and the nominal condensing temperature rise given during the equipment design phase. Compare the results to obtain the temperature rise deviation. EEDI, relative current deviation, Temperature rise deviation is used as a four-dimensional input and substituted into the multi-dimensional fault confidence index formula, the expression is: ; in The normalized weight coefficients obtained through offline historical fault sample training satisfy... MFCI is a fault confidence index. This indicates the probability of a fault existence based on a comprehensive assessment of multi-dimensional operational characteristics. The fault confidence index is used to characterize the degree of abnormality in the current operating state of the air conditioning system, which deviates from the normal operating conditions.

[0031] It should be noted that the MFCI is constructed by integrating four-dimensional anomaly features of energy efficiency, current, power factor and heat exchange temperature rise, which overcomes the defect of high false alarm rate of single parameter and achieves more robust and sensitive early identification of potential faults.

[0032] S4. Based on the physical coupling relationship between the core components of the air conditioning system, construct a fault propagation map and input the fault confidence index into the fault propagation map. Infer the root cause of the fault and its propagation path through the fault propagation map.

[0033] Furthermore, define the set of nodes for compressor, condenser, expansion valve, and evaporator. The four core components, namely the compressor, condenser, expansion valve, and evaporator, are respectively represented by a directed edge set based on the refrigerant flow direction in the refrigeration cycle. The expression is: ; in, Let be a set of directed edges. For compressors, For condenser, For expansion valve, For evaporators; Constructing a fault propagation map ; The MFCI is mapped to the corresponding node based on the anomalous variable. If the component's operating parameters are within the normal range, the initial confidence level of the node is set to zero or below the preset threshold. If the condenser temperature rise is abnormal, the relevant confidence level is assigned to... To form the initial abnormal distribution vector Each of them Represents a node The initial anomaly intensity; The overall failure probability of each node is iteratively updated using a causal decay propagation mechanism. The expression for the update rule is as follows: ; in, For nodes In the Fault confidence at the next iteration for A certain predecessor node, For nodes The initial anomaly intensity, For all predecessor nodes Sum of current confidence levels, Indicates all pointers The set of predecessor nodes, The preset causal decay factor has a value range of 0.5 to 0.8, and the iteration continues until the probability change between two consecutive pairs is less than the preset convergence threshold. The component corresponding to the highest probability of the converged node is taken as the root cause of the fault, and the fault propagation path is obtained by tracing back along the path of decreasing probability.

[0034] It should be noted that the fault propagation map constructed based on the physical topology of the refrigeration cycle enables the anomaly confidence level to be reasonably transmitted along the energy flow direction, thereby effectively distinguishing between direct causes and secondary phenomena and significantly improving the accuracy of fault location.

[0035] S5. Based on the root cause of the failure, combined with the resulting trend of energy efficiency degradation, the safety boundary of equipment operation, and the demand for maintenance resources, calculate the maintenance urgency index.

[0036] Furthermore, the energy efficiency degradation rate Used to quantify the dynamic trend of performance degradation of air conditioning systems during long-term operation, and to calculate the theoretical coefficient of performance benchmark under current operating conditions. And combined with the active power of the compressor By combining the estimated cooling capacity, the actual coefficient of performance (COP) can be obtained. ; Then, the energy efficiency deviation index is calculated, and the expression is: ; in, Energy efficiency deviation index; Based on historical EEDI data within indoor temperature ranges, a time series was constructed, and the least squares method was used to perform linear regression fitting on the time series to obtain the energy efficiency degradation rate. The expression is: ; in, For the rate of energy efficiency degradation, Let EEDI be the instantaneous rate of change with respect to time t. Summing for all sample items; For the first The timestamp of each sampling moment; The sample covariance of time versus EEDI; This represents the sample variance of the time series. Linear regression was performed on the Energy Efficiency Deviation Index (EEDI) series calculated continuously over the past 24 hours to determine the rate of energy efficiency degradation. This is used to reflect the severity of a continued decline in performance; Based on condenser outlet temperature And the corresponding pressure, estimate the compressor discharge temperature and the safe upper temperature limit specified by the equipment manufacturer. In comparison, when Then calculate the over-temperature risk item. ,otherwise 0; Based on the component type corresponding to the root cause of the failure, query the maintenance knowledge base to obtain the average manpower and spare parts costs for the failure. and the system's annual maintenance budget cap Perform normalization;

[0037] Rate of energy efficiency degradation Overheating risk items Standardized maintenance Substituting into the maintenance urgency index formula, the expression is: ; in, For the preset prediction time window, , , To satisfy the business weight coefficients set according to the operation and maintenance strategy. The Maintenance Urgency Index (RUI) is obtained.

[0038] It should be noted that by integrating the three dimensions of energy efficiency degradation trend, equipment safety boundary and maintenance cost into the RUI calculation, the maintenance priority ranking not only reflects the severity of the fault, but also takes into account the economics of operation and maintenance and risk control, thus supporting scientific decision-making.

[0039] S6. Prioritize all identified faults based on the maintenance urgency index and generate maintenance work orders.

[0040] Furthermore, all fault events whose root causes have been confirmed through the fault propagation map within the current cycle are sorted in descending order according to the RUI value corresponding to the maintenance work order; Assign fault events their root cause component identifier, fault type label, recommended maintenance measures, required standard spare parts code list, estimated downtime, and affected end areas; The fault list is encapsulated into a maintenance work order according to the following structured mapping rules, expressed as: ; in The fault number is... This is a globally unique number for the maintenance work order. The root cause of the failure As a maintenance urgency index, To recommend repair operations, List of required spare parts To estimate downtime, The area where the faulty device is located; The maintenance work order is pushed to the central operation and maintenance management platform via the communication module.

[0041] It should be noted that the structured maintenance work order is automatically generated and... Dynamic sorting enables a seamless transition from "diagnostic results" to "executable tasks," significantly shortening response time, reducing manual intervention costs, and improving the efficiency of the operation and maintenance closed loop.

[0042] This embodiment also provides an efficient air conditioning system fault early warning and maintenance system, including: The system includes a monitoring data module, an energy efficiency deviation module, a fault confidence module, a fault propagation graph module, a maintenance urgency module, and a maintenance work order module. The monitoring data module is used to collect environmental parameters and equipment status parameters during the operation of the air conditioning system to obtain raw monitoring data; The energy efficiency deviation module calculates the energy efficiency deviation index based on the measured energy consumption and cooling capacity of the air conditioning system in the original monitoring data. The fault confidence module uses the energy efficiency deviation index, combined with compressor current characteristics, power factor and heat exchanger inlet and outlet temperature information, to generate a fault confidence index. The fault propagation graph module constructs a fault propagation graph based on the physical coupling relationship between the core components of the air conditioning system, and inputs the fault confidence index into the fault propagation graph to infer the root cause of the fault and its propagation path through the fault propagation graph. The maintenance urgency module calculates the maintenance urgency index based on the root cause of the fault, combined with the resulting energy efficiency degradation trend, equipment operation safety boundary, and maintenance resource requirements. The maintenance work order module prioritizes all identified faults based on the maintenance urgency index and generates maintenance work orders.

[0043] This embodiment also provides a computer device applicable to the method for early warning and maintenance of faults in the efficient operation of an air conditioning system, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the method for early warning and maintenance of faults in the efficient operation of an air conditioning system as proposed in the above embodiment.

[0044] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0045] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the method for efficient operation, fault warning, and maintenance of an air conditioning system as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0046] In summary, this invention achieves a dimensionless, adaptive quantitative assessment of the system's current operating efficiency relative to a theoretical healthy baseline by calculating the energy efficiency deviation index from the measured energy consumption and cooling capacity of the air conditioning system in the original monitoring data. It transforms the complex problem of thermal performance degradation into a comparable and traceable single indicator. By integrating the energy efficiency deviation index, compressor current relative deviation, power factor, and heat exchanger temperature rise deviation, a multi-dimensional fault confidence index is constructed. Current reflects mechanical and electrical loads, power factor characterizes power quality, and temperature rise reflects heat exchange efficiency, forming a complementary evidence chain with the energy efficiency deviation index. The purpose is to generate a robust comprehensive fault probability output, effectively distinguishing between real faults and external disturbances, and significantly improving the accuracy and anti-interference capability of initial fault screening.

[0047] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for efficient fault early warning and maintenance of an air conditioning system, characterized in that: include: Collect environmental parameters and equipment status parameters during the operation of the air conditioning system to obtain raw monitoring data; Based on the measured energy consumption and cooling capacity of the air conditioning system in the original monitoring data, calculate the energy efficiency deviation index; By utilizing the energy efficiency deviation index, combined with compressor current characteristics, power factor, and heat exchanger inlet and outlet temperature information, a fault confidence index is generated. Based on the physical coupling relationship between the core components of the air conditioning system, a fault propagation map is constructed, and the fault confidence index is input into the fault propagation map. The root cause of the fault and its propagation path are inferred through the fault propagation map. Based on the root cause of the failure, combined with the resulting trend of energy efficiency degradation, the safety boundary of equipment operation, and the demand for maintenance resources, the maintenance urgency index is calculated. All identified faults are prioritized based on the maintenance urgency index, and maintenance work orders are generated.

2. The method for efficient operation fault early warning and maintenance of an air conditioning system as described in claim 1, characterized in that: The process of collecting environmental parameters and equipment status parameters during the operation of the air conditioning system to obtain raw monitoring data involves the following steps: Outdoor dry-bulb temperature is obtained using a temperature sensor. and indoor return air temperature ; The current signal in the compressor power supply circuit is collected by a current transformer and denoted as follows: The voltage of the corresponding phase is acquired by a voltage transmitter and recorded as follows: Based on the synchronously acquired voltage and current signals, the AC active power calculation formula is used, and the expression is: ; in, Active power The power factor between voltage and current; Temperature sensors are installed on the refrigerant inlet and outlet pipes of the condenser in the air conditioning system to obtain the condenser inlet temperature. With outlet temperature Temperature sensors are installed on the refrigerant inlet and outlet pipes of the evaporator to obtain the evaporator inlet temperature. With outlet temperature ; Will , , , , , , and The data is recorded synchronously with a sampling period of 1 second and aligned with a unified timestamp to form the original monitoring dataset.

3. The method for efficient operation fault early warning and maintenance of an air conditioning system as described in claim 2, characterized in that: The specific steps for calculating the energy efficiency deviation index based on the measured energy consumption and cooling capacity of the air conditioning system from the original monitoring data are as follows: Based on the fan's factory air volume calibration value Compared with the specific heat constant of air at constant pressure Combined with the evaporator inlet and outlet temperatures from the original monitoring data and The actual cooling capacity of the system is calculated using the following expression: ； in, The actual measured cooling capacity of the system; The active power of the compressor Assuming current operating energy consumption, the measured coefficient of performance is calculated as follows: ； in, The coefficient of performance is the measured value. Based on the COP values ​​at standard test points under the rated operating conditions on the equipment's nameplate, and using a bilinear interpolation model constructed from the two-dimensional performance table provided by the standard, the inherent coefficients of multiple devices were obtained. It is used to construct the benchmark for dynamic theoretical performance coefficients, and its expression is: ； in, For inherent coefficients, As a benchmark for dynamic theoretical performance coefficients, Outdoor dry-bulb temperature, Indoor return air temperature, This is the product of indoor and outdoor temperatures; Will and Substituting into the energy efficiency deviation index formula, the expression is: ; in, It is a preset small positive constant used to avoid the denominator being zero; EEDI is the energy efficiency deviation index, used to represent the degree of performance degradation of the current system relative to the ideal healthy state system.

4. The method for efficient operation fault early warning and maintenance of an air conditioning system as described in claim 3, characterized in that: The process of generating a fault confidence index by utilizing the energy efficiency deviation index, combined with compressor current characteristics, power factor, and heat exchanger inlet and outlet temperature information, involves the following steps: Extract compressor current from raw monitoring data And based on the average current during historical normal operation under the same operating conditions. The relative deviation is calculated using the following expression: ; in, This is the compressor current value extracted from the raw monitoring data at the current operating moment. To represent the average compressor current during the system's historical normal operation under the same operating conditions, This is the current deviation value; Using the voltage Current With active power The current power factor is calculated using the following expression: ； in, This is the power factor, and its value typically ranges from 0 to 1. The actual temperature rise is calculated using the inlet and outlet temperatures of the condenser. and the nominal condensing temperature rise given during the equipment design phase. Compare the results to obtain the temperature rise deviation. EEDI, relative current deviation, Temperature rise deviation is used as a four-dimensional input and substituted into the multi-dimensional fault confidence index formula, the expression is: ; in The normalized weight coefficients obtained through offline historical fault sample training satisfy... MFCI is a fault confidence index. This indicates the probability of a fault existence based on a comprehensive assessment of multi-dimensional operational characteristics. The fault confidence index is used to characterize the degree of abnormality in the current operating state of the air conditioning system, which deviates from the normal operating conditions.

5. The method for efficient operation fault early warning and maintenance of an air conditioning system as described in claim 4, characterized in that: Based on the physical coupling relationship between the core components of the air conditioning system, a fault propagation map is constructed, and a fault confidence index is input into the fault propagation map. The root cause of the fault and its propagation path are inferred from the fault propagation map. The specific steps are as follows: Define the set of nodes for compressor, condenser, expansion valve, and evaporator. The four core components, namely the compressor, condenser, expansion valve, and evaporator, are respectively represented by a directed edge set based on the refrigerant flow direction in the refrigeration cycle. The expression is: ; in, Let be a set of directed edges. For compressors, For condenser, For expansion valve, For evaporators; Constructing a fault propagation map ; The MFCI is mapped to the corresponding node based on the anomalous variable. If the component's operating parameters are within the normal range, the initial confidence level of the node is set to zero or below the preset threshold. If the condenser temperature rise is abnormal, the relevant confidence level is assigned to... To form the initial abnormal distribution vector Each of them Represents a node The initial anomaly intensity; The overall failure probability of each node is iteratively updated using a causal decay propagation mechanism. The expression for the update rule is as follows: ; in, For nodes In the Fault confidence at the next iteration for A certain predecessor node, For nodes The initial anomaly intensity, For all predecessor nodes Sum of current confidence levels, Indicates all pointers The set of predecessor nodes, The preset causal decay factor has a value range of 0.5 to 0.8, and the iteration continues until the probability change between two consecutive pairs is less than the preset convergence threshold. The component corresponding to the highest probability of the converged node is taken as the root cause of the fault, and the fault propagation path is obtained by tracing back along the path of decreasing probability.

6. The method for efficient operation fault early warning and maintenance of an air conditioning system as described in claim 5, characterized in that: The maintenance urgency index is calculated based on the root cause of the failure, combined with the resulting energy efficiency degradation trend, equipment operating safety boundaries, and maintenance resource requirements. The specific steps are as follows: The energy efficiency degradation rate Used to quantify the dynamic trend of performance degradation of air conditioning systems during long-term operation, and to calculate the theoretical coefficient of performance benchmark under current operating conditions. And combined with the active power of the compressor By combining the estimated cooling capacity, the actual coefficient of performance (COP) can be obtained. ; Then, the energy efficiency deviation index is calculated, and the expression is: ; in, Energy efficiency deviation index; Based on historical EEDI data within indoor temperature ranges, a time series was constructed, and the least squares method was used to perform linear regression fitting on the time series to obtain the energy efficiency degradation rate. The expression is: ; in, For the rate of energy efficiency degradation, Let EEDI be the instantaneous rate of change with respect to time t. Summing for all sample items; For the first The timestamp of each sampling moment; The sample covariance of time versus EEDI; This represents the sample variance of the time series. Linear regression was performed on the Energy Efficiency Deviation Index (EEDI) series calculated continuously over the past 24 hours to determine the rate of energy efficiency degradation. This is used to reflect the severity of a continued performance decline; Based on condenser outlet temperature And the corresponding pressure, estimate the compressor discharge temperature and the safe upper temperature limit specified by the equipment manufacturer. In comparison, when Then calculate the over-temperature risk item. ,otherwise 0; Based on the component type corresponding to the root cause of the failure, query the maintenance knowledge base to obtain the average manpower and spare parts costs for the failure. and the system's annual maintenance budget cap Perform normalization; Rate of energy efficiency degradation Overheating risk items Standardized maintenance Substituting into the maintenance urgency index formula, the expression is: ; in, For the preset prediction time window, , , To satisfy the business weight coefficients set according to the operation and maintenance strategy. The Maintenance Urgency Index (RUI) is obtained.

7. The method for efficient operation fault early warning and maintenance of an air conditioning system as described in claim 6, characterized in that: The steps for prioritizing all identified faults based on the maintenance urgency index and generating maintenance work orders are as follows: All fault events whose root causes have been identified through the fault propagation map within the current cycle are sorted in descending order according to the RUI value corresponding to the maintenance work order; Assign fault events their root cause component identifier, fault type label, recommended maintenance measures, required standard spare parts code list, estimated downtime, and affected end areas; The fault list is encapsulated into a maintenance work order according to the following structured mapping rules, expressed as: ; in The fault number is... This is a globally unique number for the maintenance work order. The root cause of the failure As a maintenance urgency index, To recommend repair operations, List of required spare parts To estimate downtime, The area where the faulty device is located; The maintenance work order is pushed to the central operation and maintenance management platform via the communication module.

8. A fault early warning and maintenance system for efficient operation of an air conditioning system, based on the fault early warning and maintenance method for efficient operation of an air conditioning system according to any one of claims 1 to 7, characterized in that: include: The system includes a monitoring data module, an energy efficiency deviation module, a fault confidence module, a fault propagation graph module, a maintenance urgency module, and a maintenance work order module. The monitoring data module is used to collect environmental parameters and equipment status parameters during the operation of the air conditioning system to obtain raw monitoring data; The energy efficiency deviation module calculates the energy efficiency deviation index based on the measured energy consumption and cooling capacity of the air conditioning system in the original monitoring data. The fault confidence module uses the energy efficiency deviation index, combined with compressor current characteristics, power factor and heat exchanger inlet and outlet temperature information, to generate a fault confidence index. The fault propagation graph module constructs a fault propagation graph based on the physical coupling relationship between the core components of the air conditioning system, and inputs the fault confidence index into the fault propagation graph to infer the root cause of the fault and its propagation path through the fault propagation graph. The maintenance urgency module calculates the maintenance urgency index based on the root cause of the fault, combined with the resulting energy efficiency degradation trend, equipment operation safety boundary, and maintenance resource requirements. The maintenance work order module prioritizes all identified faults based on the maintenance urgency index and generates maintenance work orders.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the air conditioning system efficient operation fault early warning and maintenance method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the air conditioning system efficient operation fault early warning and maintenance method according to any one of claims 1 to 7.

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