Air conditioner temperature sensing bag fault diagnosis method and device, air conditioner and storage medium

Abstract

The application discloses a kind of air conditioner temperature sensing bag fault diagnosis method, device, air conditioner and storage medium, and the application relates to air conditioner fault diagnosis technical field, it includes according to historical operation data, sub-model library is established in working condition mapping, wherein, mapping sub-model library includes multiple mapping sub-models, each mapping sub-model is used to describe the mapping relationship between the temperature of line controller and the reference temperature of temperature sensing bag;In each sampling period, the measured temperature of temperature sensing bag and the temperature of line controller are acquired, and the corresponding mapping sub-model is matched according to current operating condition parameters;Reference temperature is calculated according to the temperature of line controller and the matched mapping sub-model;According to reference temperature and measured temperature, instantaneous temperature deviation is calculated and time sequence characteristic quantity is calculated in sliding time window;Fault diagnosis is carried out on temperature sensing bag based on instantaneous temperature deviation and time sequence characteristic quantity, to determine the fault type of temperature sensing bag.The application can carry out fault diagnosis on temperature sensing bag, and identify fault type.

Description

Technical Field

[0001] This invention relates to the field of air conditioner fault diagnosis technology, and in particular to a method, device, air conditioner, and storage medium for diagnosing faults in an air conditioner temperature sensor. Background Technology

[0002] In air conditioners, the indoor unit's temperature sensor is the core temperature detection element of the air conditioning control system. Its measurement results directly affect several key functions, such as compressor start-up and shutdown, electronic expansion valve adjustment, and defrosting judgment. Because the temperature sensor is usually installed inside the indoor unit's air intake duct, it is constantly exposed to a circulating environment of alternating hot and cold temperatures and is easily affected by condensate and airflow fluctuations. Its material properties are prone to degradation during long-term operation, leading to a shift in the resistance-temperature characteristics and consequently causing inaccurate temperature measurement malfunctions.

[0003] Existing temperature sensor fault detection technologies primarily rely on determining the absolute temperature threshold at a single moment or simple comparison of fixed temperature differences between dual sensors. When the temperature sensor reading deviates, it typically manifests as the system's controlled temperature deviating from expectations, triggering a standard temperature sensor fault alarm or directly causing air conditioning control malfunction. However, this detection method, based on a single static dimension or fixed mapping, can only determine whether the temperature sensor exceeds its tolerance limit; it cannot accurately detect and distinguish the specific fault type when the temperature sensor reading deviates. Summary of the Invention

[0004] This invention provides a method, device, air conditioner, and storage medium for diagnosing air conditioner temperature sensor faults, aiming to solve the technical problem of currently being unable to identify the fault type of the temperature sensor.

[0005] In a first aspect, embodiments of the present invention provide a method for diagnosing faults in an air conditioner temperature sensor, the method comprising: Based on historical operating data, a mapping sub-model library is established for different operating conditions. The mapping sub-model library includes multiple mapping sub-models, each of which describes the mapping relationship between the wired controller temperature and the reference temperature of the temperature sensor under the corresponding operating condition. Within each sampling period, the measured temperature of the temperature sensor and the temperature of the wired controller are obtained, and the corresponding mapping sub-model is matched according to the current operating condition parameters; The reference temperature is calculated based on the controller temperature and the matched mapping sub-model; The instantaneous temperature deviation is calculated based on the reference temperature and the measured temperature, and the time-series characteristic quantity is calculated within the sliding time window; The fault diagnosis of the temperature sensing element is performed based on the instantaneous temperature deviation and the time-series characteristic quantity to determine the fault type of the temperature sensing element.

[0006] Secondly, embodiments of the present invention also provide an air conditioner temperature sensor fault diagnosis device, the device comprising: The first establishment unit is used to establish a mapping sub-model library according to historical operating data and different operating conditions. The mapping sub-model library includes multiple mapping sub-models, and each mapping sub-model is used to describe the mapping relationship between the wired controller temperature and the reference temperature of the temperature sensor under the corresponding operating condition. The first acquisition unit is used to acquire the measured temperature of the temperature sensing bag and the temperature of the wired controller in each sampling period, and match the corresponding mapping sub-model according to the current operating condition parameters. The first calculation unit is used to calculate the reference temperature based on the temperature of the wire controller and the matched mapping sub-model; The second calculation unit is used to calculate the instantaneous temperature deviation based on the reference temperature and the measured temperature, and to calculate the time series characteristic quantity within the sliding time window; The first diagnostic unit is used to perform fault diagnosis on the temperature sensing element based on the instantaneous temperature deviation and the time-series characteristic quantity, so as to determine the fault type of the temperature sensing element.

[0007] Thirdly, embodiments of the present invention also provide an air conditioner, which includes a memory and a processor connected to the memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0008] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the above-described method.

[0009] This invention provides a method, apparatus, air conditioner, and storage medium for diagnosing faults in an air conditioner temperature sensor. The method includes: establishing a mapping sub-model library based on historical operating data, categorized by operating condition; wherein the mapping sub-model library includes multiple mapping sub-models, each describing the mapping relationship between the controller temperature and the reference temperature of the temperature sensor under the corresponding operating condition; acquiring the measured temperature of the temperature sensor and the controller temperature in each sampling period, and matching the corresponding mapping sub-model according to the current operating condition parameters; calculating the reference temperature based on the controller temperature and the matched mapping sub-model; calculating the instantaneous temperature deviation based on the reference temperature and the measured temperature, and calculating a time-series characteristic quantity within a sliding time window; and performing fault diagnosis on the temperature sensor based on the instantaneous temperature deviation and the time-series characteristic quantity to determine the fault type of the temperature sensor. In this embodiment of the invention, a mapping sub-model library can be established according to historical operating data and different operating conditions. Then, the corresponding mapping sub-model is matched with the real-time acquired controller temperature and the current operating condition parameters to calculate the reference temperature. This allows the construction of a dynamic health baseline that adapts to the current operating condition. The instantaneous temperature deviation between the reference temperature and the measured temperature, as well as the time-series characteristic quantities within the sliding time window, are then calculated. Based on the instantaneous temperature deviation and the time-series characteristic quantities, a comprehensive fault diagnosis is performed on the temperature sensing element, which can accurately identify the fault type of the temperature sensing element. Attached Figure Description

[0010] 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating the air conditioner temperature sensor fault diagnosis method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the first sub-process of the air conditioner temperature sensor fault diagnosis method provided in the embodiment of the present invention; Figure 3 This is a schematic diagram of the second sub-process of the air conditioner temperature sensor fault diagnosis method provided in the embodiment of the present invention; Figure 4 This is a schematic diagram of the third sub-process of the air conditioner temperature sensor fault diagnosis method provided in the embodiment of the present invention; Figure 5 This is a schematic diagram of the fourth sub-process of the air conditioner temperature sensor fault diagnosis method provided in this embodiment of the invention; Figure 6 This is a schematic diagram of the fifth sub-process of the air conditioner temperature sensor fault diagnosis method provided in the embodiment of the present invention; Figure 7 This is a schematic block diagram of an air conditioner temperature sensor fault diagnosis device provided in an embodiment of the present invention; Figure 8 This is a schematic block diagram of an air conditioner provided in an embodiment of the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] It should be understood that, when used in this specification and the appended claims, terms include and encompass the presence of the described features, integrals, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, operations, elements, components and / or collections thereof.

[0014] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms "one," "an," and "this" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms used in this specification and the appended claims refer to any combination of one or more of the associated listed items and all possible combinations, and include such combinations.

[0015] Please see Figure 1 , Figure 1 This is a flowchart illustrating the air conditioner temperature sensor fault diagnosis method provided in this embodiment of the invention. The air conditioner temperature sensor fault diagnosis method of this embodiment can be applied to air conditioners and can accurately identify the fault type of the temperature sensor. Figure 1 As shown, the method includes steps S100 to S140.

[0016] S100, Based on historical operating data, establish a mapping sub-model library for different operating conditions. The mapping sub-model library includes multiple mapping sub-models, each of which describes the mapping relationship between the wired controller temperature and the reference temperature of the temperature sensor under the corresponding operating condition.

[0017] In this embodiment of the invention, the air conditioner controller acquires the measured temperature of the temperature sensor and the temperature of the wired controller in real time during each sampling cycle, and matches the corresponding mapping sub-model from the established mapping sub-model library according to the current operating condition parameters. The core purpose of this step is to connect the previously constructed static model library with the real-time dynamic operating state of the system, so as to prepare the required measured data, reference input data, and calculation tools for subsequent calculation of instantaneous temperature deviation and time-series characteristic quantities, and ensure that the fault diagnosis logic can operate continuously, stably, and accurately based on the latest system state.

[0018] In the above steps, the sampling period refers to the time interval (Δt) between two consecutive temperature data acquisitions by the controller. In this invention, it is recommended that the sampling period be set between 30 seconds and 2 minutes, with the specific value determined based on the communication refresh frequency of the actual control system. This parameter range aims to achieve a reasonable balance between filtering high-frequency noise and maintaining detection response speed: if the sampling period is too short (e.g., on the order of seconds), it will introduce a large number of noise samples caused by short-term fluctuations such as air conditioning start-up and shutdown, and airflow disturbances, interfering with the subsequent calculation of timing characteristics; if the sampling period is too long (e.g., more than 10 minutes), it will reduce the system's response speed to abrupt faults, resulting in sudden faults such as poor contact not being detected in a timely manner.

[0019] The measured temperature of the temperature sensor refers to the actual temperature value (T1) measured by the indoor unit's temperature sensor (usually an NTC thermistor) at the current sampling moment. The wired controller temperature refers to the temperature value (T2) measured by the built-in temperature sensor of the wired controller at the current sampling moment, serving as a physical reference independent of the temperature sensor. Current operating condition parameters refer to parameters characterizing the current thermodynamic and control states of the air conditioner, mainly including the real-time operating frequency of the compressor, the current speed setting of the indoor fan, and the current operating mode of the system (cooling or heating). Matching refers to the process by which the controller maps the real-time acquired operating condition parameters to a preset classification dimension (such as mapping continuous frequency values ​​to low, medium, and high frequency bands), combines them to generate a unique operating condition category index, and calls the corresponding mapping sub-model parameters from non-volatile memory accordingly. In practical engineering applications, to avoid reference jumps caused by frequent model switching due to parameter fluctuations near boundary values, the matching process can also introduce hysteresis intervals or time filtering mechanisms to ensure the stability of model calls.

[0020] In some embodiments, such as in embodiments of the present invention, as Figure 2 As shown, step S100 includes steps S101-S102.

[0021] S101, respectively obtain the compressor operating frequency range, indoor fan speed level and operating mode of the air conditioner; S102, the operating condition type of the air conditioner is determined according to the compressor operating frequency range, the indoor fan speed setting and the operating mode, and the mapping sub-model is established according to the operating condition type to form the mapping sub-model library.

[0022] In this embodiment of the invention, the air conditioner controller first executes step S101 to obtain the compressor operating frequency range, indoor fan speed setting, and operating mode of the air conditioner; then, it executes step S102 to determine the air conditioner's operating condition type based on the obtained parameters of these three dimensions, and establishes a corresponding mapping sub-model based on the operating condition type, finally summarizing them to form a mapping sub-model library. The core purpose of these two steps is to discretize the continuous and complex air conditioner operating state into several standard operating condition categories, and to tailor a model for each category that can accurately describe the mathematical relationship between the wired controller temperature and the reference temperature of the temperature sensor, thereby laying a solid model foundation for the accurate calculation of the reference temperature under different operating environments.

[0023] In step S101, the compressor operating frequency range refers to the discrete frequency bands into which the real-time operating frequency of the compressor is divided according to a preset threshold. Specifically, it includes the low-frequency band (≤30Hz), the medium-frequency band (30Hz~60Hz), and the high-frequency band (>60Hz). This dimension mainly reflects the core influence of the air conditioner's cooling or heating capacity on the indoor temperature field distribution. The indoor fan speed level refers to the current operating speed level of the indoor unit's fan, specifically including low, medium, and high speed. This dimension mainly reflects the degree of direct interference of airflow organization on the temperature sensing environment. The operating mode refers to the current macroscopic working logic of the air conditioner, mainly including cooling mode and heating mode. Since the roles of the evaporator and condenser are interchanged and the heat source direction is completely opposite in the two modes, their influence mechanisms on the indoor temperature distribution are also different.

[0024] In step S102, the operating condition type (i.e., operating condition category k) refers to a unique operating state classification formed by combining the three dimensions of compressor operating frequency range, indoor fan speed setting, and operating mode. The mapping sub-model is a function used to describe the mathematical relationship between the controller temperature and the sensor reference temperature under a specific operating condition type; its specific form can be a linear regression model. T-REF=a_k·T2+b_k Where T-REF is the reference temperature, and a_k and b_k are parameters.

[0025] The mapping sub-model library is a collection of mapping sub-models corresponding to all operating conditions. When establishing the mapping sub-model, the system uses historical operating data of the temperature of the sensing bulb and the temperature of the wire controller, which are independently collected under the corresponding operating condition, to continuously fit and update the parameters a_k and b_k in the linear regression model using the least squares method.

[0026] In a specific application scenario, assume the air conditioner is currently in summer cooling mode. In step S101, the controller reads that the current compressor operating frequency is 65Hz, the indoor fan is at high speed, and the system is in cooling mode. The controller maps 65Hz to a high-frequency band (>60Hz), and combining this with the high fan speed and cooling mode, confirms in step S102 that the current operating condition is high frequency + high fan speed + cooling (assuming the operating condition category is k_1). Subsequently, the controller calls up the historical operating data accumulated under this operating condition type and uses the least squares method to fit the corresponding linear regression model parameters (e.g., calculating a_k1=0.95, b_k1=-2.5), thus establishing a mapping sub-model for this operating condition type: T-REF=0.95×T2-2.5. By traversing all possible dimension combinations (i.e., 3 frequency bands × 3 fan speeds × 2 operating modes = 18 operating condition types), the system finally establishes a complete mapping sub-model library containing 18 mapping sub-models for subsequent real-time diagnostics.

[0027] S110, in each sampling period, acquire the measured temperature of the temperature sensor and the temperature of the wired controller, and match the corresponding mapping sub-model according to the current operating condition parameters.

[0028] In this embodiment of the invention, the air conditioner controller acquires the measured temperature of the temperature sensor and the temperature of the wired controller in real time during each sampling cycle, and matches the corresponding mapping sub-model from the established mapping sub-model library according to the current operating condition parameters. The core purpose of this step is to connect the previously constructed static model library with the real-time dynamic operating state of the system, so as to prepare the required measured data, reference input data, and calculation tools for subsequent calculation of instantaneous temperature deviation and time-series characteristic quantities, and ensure that the fault diagnosis logic can operate continuously, stably, and accurately based on the latest system state.

[0029] In the above steps, the sampling period refers to the time interval (Δt) between two consecutive temperature data acquisitions by the controller. In this invention, it is recommended that the sampling period be set between 30 seconds and 2 minutes, with the specific value determined based on the communication refresh frequency of the actual control system. This parameter range aims to achieve a reasonable balance between filtering high-frequency noise and maintaining detection response speed: if the sampling period is too short (e.g., on the order of seconds), it will introduce a large number of noise samples caused by short-term fluctuations such as air conditioning start-up and shutdown, and airflow disturbances, interfering with the subsequent calculation of timing characteristics; if the sampling period is too long (e.g., more than 10 minutes), it will reduce the system's response speed to abrupt faults, resulting in sudden faults such as poor contact not being detected in a timely manner.

[0030] In practical engineering applications, to avoid frequent model switching and benchmark jumps caused by parameter fluctuations near boundary values, the matching process can also introduce hysteresis intervals or time filtering mechanisms to ensure the stability of model calls.

[0031] In a specific application scenario, assume the system's sampling period is set to 1 minute. At a certain sampling moment, the controller obtains the measured temperature of the indoor unit's temperature sensor as 24.5℃ via the internal communication bus, and simultaneously obtains the wired controller temperature as 26.0℃. Simultaneously, the controller reads that the current compressor operating frequency is 45Hz, the indoor fan speed is set to medium, and the system is in cooling mode. The controller maps 45Hz to the mid-frequency band (30Hz~60Hz), and combines this with the medium fan speed and cooling mode to generate an operating condition category index (e.g., operating condition category k_2). Subsequently, based on this index, the controller accurately retrieves the linear regression model parameters corresponding to operating condition category k_2 from the mapping sub-model library in the EEPROM (assuming a_k2=0.98, b_k2=-1.2). At this point, the controller has prepared all the necessary elements for the next calculation step: the measured temperature (24.5℃), the wired controller temperature (26.0℃), and the mapping sub-model corresponding to the current operating condition (T-REF=0.98×T2-1.2).

[0032] S120, calculate the reference temperature based on the controller temperature and the matched mapping sub-model.

[0033] In this embodiment of the invention, the air conditioner controller calculates the reference temperature of the temperature sensor under the current operating conditions based on the acquired wired controller temperature and the mapping sub-model matched in the previous steps. The core purpose of this step is to combine the physical reference input (wired controller temperature) independent of the indoor unit with the mathematical model (mapping sub-model) that adapts to the current operating environment, and to deduce in real time the theoretically appropriate temperature value that the temperature sensor should present at its location under ideal conditions where it is functioning perfectly normally. Through this calculation process, the system can construct a healthy baseline that dynamically follows changes in system load and operating conditions, thereby completely abandoning the rigid logic of traditional technologies that rely on a single fixed temperature threshold or fixed temperature difference for judgment, and providing reliable benchmark data for subsequent accurate calculation of instantaneous temperature deviations.

[0034] In the above calculation process, the reference temperature (i.e., T-REF) is a calculated virtual theoretical value, representing the standard temperature that the indoor unit's air inlet should reach under the current specific operating conditions, assuming that the temperature sensor has not experienced any material aging or hardware failure. It effectively eliminates the systematic temperature difference between the wired controller and the temperature sensor caused by different spatial installation positions, allowing subsequent deviation calculations to be performed under the same physical benchmark.

[0035] In a specific application scenario, continuing the aforementioned summer cooling operation scenario, assuming the current system is in high-frequency cooling and high-fan operation mode, the controller has matched the corresponding mapping sub-model in step S110 as T-REF=0.95×T2-2.5. Within the current sampling period, the controller obtains the wired controller temperature measured by the built-in sensor as 26.0℃. Subsequently, the controller substitutes this wired controller temperature into the matched mapping sub-model for calculation, obtaining a reference temperature of 22.2℃ (i.e., 0.95×26.0-2.5) for the temperature sensor under the current operating conditions. The physical meaning of this calculation result is that under the specific operating conditions of high-frequency cooling and high-fan operation, the indoor unit evaporator absorbs a large amount of heat, causing the temperature at the air inlet to be approximately 3.8℃ lower than the temperature at the center wall of the room. Therefore, 22.2℃ is the healthy standard value that the temperature sensor should measure under the current operating conditions. If the actual temperature measured by the temperature sensor deviates significantly from 22.2℃, the system can accurately detect this anomaly without misjudging the overall temperature drop caused by the air conditioner being in a strong cooling state.

[0036] S130, calculate the instantaneous temperature deviation based on the reference temperature and the measured temperature, and calculate the timing characteristic quantity within the sliding time window.

[0037] In this embodiment of the invention, the air conditioner controller calculates the instantaneous temperature deviation based on the reference temperature calculated in the preceding steps and the measured temperature, and calculates the temporal characteristic quantity within a sliding time window. The core purpose of this step is to transform the absolute physical quantity collected by the sensor into a relative deviation quantity reflecting the health status of the temperature sensor, and further extract the evolution trend of the deviation from the time dimension. Through this three-dimensional characterization method that combines the degree of spatial deviation (instantaneous deviation) and the temporal change trend (temporal characteristic quantity), the system can effectively isolate external interference factors such as ambient temperature fluctuations and changes in the overall system load, providing standardized and interference-resistant core input features for subsequent accurate differentiation of the fault type of the temperature sensor (such as aging drift and poor contact).

[0038] In the above calculation process, the instantaneous temperature deviation (ΔT) refers to the absolute value of the difference between the reference temperature and the measured temperature at the current sampling moment, calculated as ΔT = |T - REF - T1|. The reason for using the absolute value is that the direction of temperature sensor aging drift can be positive (measured temperature too high) or negative (measured temperature too low). Both deviations physically represent inaccurate temperature measurement, and their impact on the control system needs to be addressed. Therefore, using the absolute value to measure the deviation magnitude simplifies subsequent judgment logic and avoids missed judgments due to sign differences. The reference temperature (T - REF) is the dynamic health benchmark established in the previous steps, and the measured temperature (T1) is the current actual output of the temperature sensor. The instantaneous temperature deviation, from a spatial perspective, measures the degree to which the temperature sensor's measurement capability deviates from the health standard under the current steady-state or quasi-steady-state conditions.

[0039] The sliding time window (W) refers to a continuous monitoring period that slides forward over time, with a recommended length of 30 minutes to 2 hours. This window length is designed to filter short-term disturbances caused by changes in operating conditions while maintaining sufficient detection sensitivity for aging drift. The time-series characteristic is a dynamic indicator used to characterize the evolution of instantaneous temperature deviation over time, specifically including the deviation change rate (dΔT / dt) and the cumulative deviation integral (ΣΔT). The deviation change rate is defined as the ratio of the change in instantaneous temperature deviation between two adjacent sampling moments to the sampling period, i.e., dΔT / dt = (ΔT_t - ΔT{t - Δt}) / Δt. This characteristic reflects how quickly the temperature deviation changes over time and is a core criterion for distinguishing between abrupt and drift-type faults. The cumulative deviation integral is defined as the sum of the instantaneous temperature deviations at all sampling moments within the sliding time window, i.e., ΣΔT = ΣΔT i (i=1,2,3,…,n, where n is the number of sampling points within the window). This feature reflects the cumulative degree of temperature deviation over a period of time and is a key indicator for identifying drift-type faults.

[0040] In some embodiments, such as in embodiments of the present invention, as Figure 3 As shown, step S130 includes steps S131-S133.

[0041] S131, obtain the instantaneous temperature deviation at the current sampling time and the instantaneous temperature deviation at the previous sampling time; S132, calculate the rate of change of the deviation based on the instantaneous temperature deviation at the current sampling time and the instantaneous temperature deviation at the previous sampling time; S133, sum the instantaneous temperature deviations sampled at all sampling times within the sliding time window to obtain the cumulative deviation integral.

[0042] In this embodiment of the invention, the air conditioner controller sequentially executes steps S131, S132, and S133 to accurately calculate the temporal characteristic quantities that characterize the evolution of the health status of the temperature sensor. The core purpose of these three steps is to transform discrete instantaneous temperature deviation data into dynamic indicators with time-dimensional characteristics, thereby providing a solid mathematical criterion for accurately distinguishing the fault mechanisms of the temperature sensor (such as drift-type faults caused by aging and abrupt faults caused by poor contact).

[0043] In step S131, the controller acquires the instantaneous temperature deviation at the current sampling time and the instantaneous temperature deviation at the previous sampling time, providing a basic data pair for calculating the rate of change. In step S132, the controller calculates the rate of change of the deviation based on the instantaneous temperature deviations at these two times. The calculation formula is as follows: dΔT / dt=(ΔTt-ΔT{t-Δt}) / Δt Where ΔT_t is the instantaneous temperature deviation at the current sampling moment, ΔT_{t-Δt} is the instantaneous temperature deviation at the previous sampling moment, and Δt is the sampling period. This characteristic reflects the rate of change of temperature deviation over time and is the core criterion for distinguishing between abrupt and drifting faults. Its physical meaning is as follows: When the temperature sensor experiences abrupt faults such as poor contact or hardware damage, the sensor output jumps within a very short time, and the absolute value of dΔT / dt will show a significant peak within one or a very few sampling periods, exhibiting a steep change characteristic; while when the temperature sensor experiences characteristic drift due to aging, the deviation accumulates slowly over time, and the absolute value of dΔT / dt remains at a low level, exhibiting a stable characteristic. The threshold for judging abrupt changes is recommended to be set according to the normal temperature measurement response rate in the sensor specifications, with a typical reference value of 0.1℃ / min~0.3℃ / min (i.e., the maximum natural rate of change of temperature of the temperature sensor under normal airflow changes). When the absolute value exceeds this threshold, the possibility of natural temperature change can be ruled out, and it is considered an abnormal jump.

[0044] In step S133, the controller sums the instantaneous temperature deviations acquired at all sampling moments within the sliding time window to obtain the cumulative deviation integral. The calculation formula is as follows: ΣΔT=ΣΔT i (i=1,2,...,n, where n is the number of sampling points within the window) This characteristic reflects the cumulative degree of temperature deviation over a period of time and is a key indicator for identifying drift-type faults. A typical characteristic of drift-type faults is that the ΔT value at a single moment is small (not yet exceeding the fault threshold), but if left unchecked, ΣΔT will monotonically increase over time, showing a continuous accumulation trend. By monitoring the changing trend of ΣΔT (i.e., whether it monotonically increases within the window rather than fluctuating randomly), aging drift trends can be identified in advance before the deviation reaches the fault alarm threshold, enabling preventative calibration. The recommended sliding time window length is 30 minutes to 2 hours. A window that is too short (e.g., 5 minutes) may easily misidentify short-term temperature differences caused by normal operating condition switching as drift trends; a window that is too long (e.g., more than 8 hours) will reduce the sensitivity to gradual drift. A setting of 30 minutes to 2 hours can effectively filter short-term disturbances during operating condition switching while maintaining sufficient detection sensitivity to aging drift.

[0045] In a specific application scenario, continuing the aforementioned high-frequency cooling operation in summer, assuming the sampling period Δt is set to 1 minute and the sliding time window is set to 1 hour (containing 60 sampling points), at the current sampling time (t), the controller obtains the current instantaneous temperature deviation ΔT_t as 2.3℃ in step S131, and the instantaneous temperature deviation ΔT_{t-Δt} at the previous sampling time (t-1) is 2.1℃. In step S132, the controller calculates the deviation change rate: dΔT / dt=(2.3-2.1) / 1=0.2℃ / min. Since 0.2℃ / min is within the normal natural rate of change range (not exceeding the sudden change threshold of 0.3℃ / min), the system initially eliminates the possibility of a sudden fault. Subsequently, in step S133, the controller includes the current 2.3℃ in the 1-hour sliding window and sums it with the deviation values ​​of the first 59 sampling points within the window, assuming the cumulative deviation integral ΣΔT obtained by the summation is 120℃. The controller continuously monitored the integral value and found that it showed a clear monotonically increasing trend (rather than random fluctuations) over the past hour. This combination of time-series characteristics—a small rate of deviation change (0.2℃ / min) but a continuous monotonically increasing cumulative deviation integral—perfectly matches the typical physical behavior of a temperature sensor aging drift fault, providing crucial data support for triggering accurate fault diagnosis and calibration procedures.

[0046] S140, perform fault diagnosis on the temperature sensing element based on the instantaneous temperature deviation and the time-series characteristic quantity to determine the fault type of the temperature sensing element.

[0047] In this embodiment of the invention, the air conditioner controller performs a comprehensive fault diagnosis on the temperature sensor based on the instantaneous temperature deviation and temporal characteristic quantities calculated in the preceding steps, in order to determine the specific fault type of the temperature sensor. The core purpose of this step is to combine the spatial deviation at a single moment with the temporal evolution trend within a sliding time window to construct a multi-dimensional progressive judgment logic, thereby accurately distinguishing different fault mechanisms of the temperature sensor (such as drift caused by material aging and sudden changes caused by poor contact), effectively overcoming the problems of misjudgment, missed judgment, and inappropriate handling strategies caused by the single judgment dimension in the prior art.

[0048] In the above fault diagnosis process, the system uses a three-layer progressive logic to determine the fault type, specifically including normal state, sudden change fault, and drift fault. The first layer is the normal state judgment: if the instantaneous temperature deviation is less than or equal to the normal tolerance threshold (typically 1℃), the temperature sensor is determined to be in a normal state. The 1℃ threshold is set to cover the systemic temperature difference (0.5℃~1℃) caused by different installation positions of the wired controller and the temperature sensor, as well as the manufacturing error of the sensor itself (±0.5℃), thus filtering out normal errors and preventing the system from frequently triggering unnecessary calibration actions. The second layer is the sudden change fault judgment: if the instantaneous temperature deviation is greater than or equal to the sudden change amplitude threshold (typically 5℃), and the absolute value of the deviation change rate is greater than or equal to the preset sudden change rate threshold (typically 0.1℃ / min~0.3℃ / min), the temperature sensor is determined to be in a sudden change fault. The 5℃ amplitude exceeds the interpretation range of normal operating condition fluctuations. Combined with the abrupt change characteristic of the deviation change rate, faults such as poor contact or hardware damage can be identified with high confidence, thereby promptly alarming and preventing erroneous automatic calibration. The third layer is for drift-type fault detection: if the instantaneous temperature deviation is greater than the normal tolerance threshold but less than the sudden change amplitude threshold, and the absolute value of the deviation change rate is consistently less than or equal to the stability threshold, and the cumulative deviation integral shows a monotonically increasing trend within the sliding time window, then the temperature sensor is determined to be a drift-type fault. This layer of logic accurately captures the core physical characteristics of aging drift deviation: moderate amplitude, slow change, but continuous accumulation. Monotonicity judgment eliminates short-term interference caused by operating condition fluctuations, thus triggering preventative maintenance.

[0049] In some embodiments, such as in embodiments of the present invention, as Figure 4 As shown, step S140 includes steps S141-S144.

[0050] S141, obtain the normal tolerance threshold, mutation amplitude threshold, preset mutation rate threshold and stability threshold; S142, if the instantaneous temperature deviation is less than or equal to the normal tolerance threshold, then the temperature sensing bulb is determined to be in a normal state; S143, if the instantaneous temperature deviation is greater than or equal to the sudden change amplitude threshold, and the absolute value of the deviation change rate is greater than or equal to the preset sudden change rate threshold, then the temperature sensor is determined to be a sudden change type fault. S144, if the instantaneous temperature deviation is greater than the normal tolerance threshold and less than the sudden change amplitude threshold, and the absolute value of the deviation change rate is continuously less than or equal to the stability threshold, and the cumulative deviation integral shows a monotonically increasing trend within the sliding time window, then the temperature sensor is determined to be a drift-type fault.

[0051] In this embodiment of the invention, the air conditioner controller sequentially executes steps S141 to S144, performing a three-layer progressive fault judgment logic based on the acquired thresholds, instantaneous temperature deviation, and temporal characteristic quantities to accurately determine the fault type of the temperature sensor. The core purpose of these four steps is to combine the spatial deviation at a single moment with the temporal evolution trend within a sliding time window to construct a multi-dimensional progressive judgment mechanism. This completely solves the core problem in existing technologies where binary judgment (only judging normal or fault) cannot distinguish the fault mechanism, enabling refined identification and classification of the temperature sensor's state.

[0052] In step S141, the controller acquires four key judgment thresholds: normal tolerance threshold, sudden change amplitude threshold, preset sudden change rate threshold, and stability threshold. The normal tolerance threshold is recommended to be set to 1℃, based on the fact that it covers the systematic temperature difference (0.5℃~1℃) between the wired controller and the temperature sensor due to different installation positions, as well as the manufacturing error of the NTC sensor itself (±0.5℃), used to filter out normal errors. The sudden change amplitude threshold is recommended to be set to 5℃, based on the fact that during normal operating condition switching, the residual after mapping model correction usually does not exceed 2℃~3℃, and 5℃ exceeds the reasonable explanation range for operating condition fluctuations and sensor errors. The preset sudden change rate threshold and stability threshold are recommended to be set to 0.1℃ / min~0.3℃ / min, representing the maximum natural rate of change of the temperature sensor temperature under normal airflow changes, used to distinguish between natural temperature changes and abnormal jumps.

[0053] In step S142, the controller performs the first-level normal state judgment: if the instantaneous temperature deviation is less than or equal to the normal tolerance threshold (e.g., 1℃), the temperature sensor is determined to be in a normal state. In this state, the system maintains its current operating state, does not perform any calibration actions, and continues to accumulate time-series samples. The problem and effect of this judgment layer is: effectively filtering out minor deviations caused by inherent sensor errors or differences in normal measurement positions, preventing the system from frequently entering unnecessary calibration processes, reducing the controller's computational load, and ensuring the normal and stable operation of the air conditioner.

[0054] In step S143, the controller performs a second-level abrupt fault judgment: if the instantaneous temperature deviation is greater than or equal to the abrupt amplitude threshold (e.g., 5°C), and the absolute value of the deviation change rate is greater than or equal to the preset abrupt rate threshold, then the temperature sensor is determined to be an abrupt fault. The physical meaning is that when the temperature sensor experiences poor contact or hardware damage, the sensor output jumps within a very short time, exhibiting abrupt changes with extremely large deviation amplitude and a very steep rate of change. When an abrupt fault is determined, the system will report the temperature sensor fault code to the main controller, exit the automatic calibration process, and notify the user to contact maintenance via alarm output. This judgment effectively avoids misjudging hardware faults as calibrable offsets and performing incorrect calibration, protecting the security of stored data.

[0055] In step S144, the controller performs a third-level drift-type fault judgment: if the instantaneous temperature deviation is greater than the normal tolerance threshold and less than the sudden change amplitude threshold (i.e., within the recognition window of 1℃ to 5℃), and the absolute value of the deviation change rate is continuously less than or equal to the stability threshold, and the cumulative deviation integral shows a monotonically increasing trend within the sliding time window, then the temperature sensor is determined to be a drift-type fault. Its physical meaning is that the typical characteristics of aging drift-type faults are moderate deviation amplitude, slow change, but continuous accumulation. The determination of the monotonically increasing trend can be achieved by performing a first-order linear fit on the sequence of changes in the cumulative deviation integral over time within the window. If the fitting slope is greater than zero and the goodness of fit (R0) is high, the sensor is considered to be drift-type faulty. 2 If the deviation exceeds a preset threshold (e.g., 0.8), it is confirmed as a monotonically increasing deviation, thus eliminating the possibility of random integral jitter caused by fluctuations in operating conditions. This judgment layer enables the system to identify aging drift trends in advance and trigger an incremental offset compensation calibration process before the deviation accumulates to a level that significantly affects control accuracy, thereby achieving preventative maintenance.

[0056] In a specific application scenario, assuming a multi-split air conditioning system is in summer cooling mode, the system simultaneously monitors the temperature sensor status of three indoor units (Indoor Unit A, Indoor Unit B, and Indoor Unit C). For indoor unit A, which is operating normally, the controller calculates its instantaneous temperature deviation to be 0.8℃. In step S142, since 0.8℃ is less than the normal tolerance threshold of 1℃, the system determines that the temperature sensor of indoor unit A is in a normal state and continues to operate stably. For indoor unit B, its temperature sensor experiences poor contact due to a loose connector. The controller calculates that its instantaneous temperature deviation suddenly increases to 6.5℃, and the deviation change rate reaches 1.5℃ / min. In step S143, since 6.5℃ is greater than the sudden change amplitude threshold of 5℃, and 1.5℃ / min is much greater than the preset sudden change rate threshold of 0.3℃ / min, the system accurately determines that indoor unit B has experienced a sudden fault, immediately reports a hardware fault code, and disables automatic calibration. For indoor unit C, its temperature sensor has undergone material aging due to long-term operation. The controller calculates its instantaneous temperature deviation to be 2.5℃, with a deviation change rate of 0.15℃ / min. Furthermore, the slope of the cumulative deviation integral fitting within the 1-hour sliding window is greater than zero, and R... 2 =0.85; In step S144, since 2.5℃ is between 1℃ and 5℃, 0.15℃ / min is less than the stability threshold of 0.3℃ / min, and the cumulative deviation integral shows a monotonically increasing trend, the system accurately determines that the indoor unit C has a drift-type fault and successfully triggers the subsequent incremental offset compensation calibration process. Through this progressive judgment process, the system simultaneously makes the most accurate classification and handling of the three different temperature sensing bulbs in the most consistent with their physical mechanisms.

[0057] In some embodiments, such as in embodiments of the present invention, as Figure 5As shown, the method further includes steps S150-S153.

[0058] S150, if the temperature sensor is a drift-type fault, then enter the incremental offset compensation calibration mode. S151, in the incremental offset compensation calibration mode, the original correspondence table and the independent offset compensation table are obtained respectively. S152, read the original temperature from the original correspondence table and read the offset compensation value from the independent offset compensation table; S153, calculate the calibration temperature based on the original temperature and the offset compensation value, and output the calibration temperature.

[0059] In this embodiment of the invention, when the controller determines that the temperature sensor has a drift-type fault in the preceding steps, it sequentially executes steps S150 to S153 to enter the incremental offset compensation calibration mode, and calculates and outputs the calibration temperature based on an independent dual-meter architecture. The core purpose of these steps is to correct the output temperature in real time through a software-level offset compensation mechanism when the temperature sensor experiences aging and drift, leading to inaccurate temperature measurement. This maintains the continuity and accuracy of the air conditioner's control logic. Simultaneously, by keeping the original calibration data unchanged, it completely solves the technical problem of irreversible and untraceable miscalibration caused by directly overwriting the original data in existing calibration schemes.

[0060] In step S150, the incremental offset compensation calibration mode refers to a special operating state of the system triggered by drift-type faults. In this mode, the system no longer directly relies on the measured temperature with deviations from the temperature sensor for control, but instead activates a compensation mechanism. To ensure that the air conditioning cooling or heating control does not fluctuate drastically during calibration and does not affect the user experience, the system switches the indoor ambient temperature reference value used by the air conditioning control logic from the measured temperature of the temperature sensor to the reference temperature (T-REF) calculated in the previous steps, and continuously accumulates samples in the background to update the compensation parameters.

[0061] In steps S151 and S152, the original correspondence table (i.e., the original temperature sensor sample value-temperature correspondence table) refers to the NTC characteristic curve lookup table, which records the inherent mapping relationship between the sampling level (or resistance value) and temperature when the temperature sensor was calibrated at the factory, and is the foundation of the entire temperature measurement link. The independent offset compensation table is a separate data table maintained by the system to store calibration increments. The original temperature refers to the base temperature value (i.e., T_table(V)) obtained by looking up the current sampling level output by the temperature sensor from the original correspondence table. The offset compensation value refers to the temperature correction amount (i.e., Offset(V)) read from the independent offset compensation table corresponding to the same sampling level range. The reason for adopting this independent dual-table architecture is that if the original correspondence table is directly modified, the system will not be able to restore to the factory state if miscalibration occurs due to abnormal samples; however, by decoupling the calibration increment from the base data, the independent offset compensation table can be independently cleared at any time (i.e., all offset compensation values ​​are set to zero), realizing one-click restoration to the uncalibrated state, and supporting the storage of multiple historical calibration records for traceability analysis.

[0062] In step S153, the controller calculates and outputs the calibration temperature (T_output) based on the read raw temperature and offset compensation value. The calculation formula is as follows: T_output = T_table(V) + Offset(V) The physical meaning of this formula is that when the system outputs temperature, it uses the factory-calibrated original temperature as a reference, and adds an offset compensation value generated after rigorous quality inspection to obtain the corrected true ambient temperature. This incremental output mechanism corrects the drift error caused by the aging of the temperature sensor, while ensuring the absolute security of the underlying basic data.

[0063] In a specific application scenario, assume the system has determined that the temperature sensor of indoor unit C has experienced a drift-type fault and entered incremental offset compensation calibration mode. At a certain sampling moment, the temperature sensor of indoor unit C outputs a sampling level V. In step S151, the controller retrieves the original correspondence table and the independent offset compensation table stored in the EEPROM. In step S152, the controller looks up the table according to the sampling level V, and reads the corresponding original temperature of 24.0℃ from the original correspondence table (i.e., T_table(V) = 24.0℃); at the same time, it reads the offset compensation value corresponding to this sampling level range from the independent offset compensation table as -1.5℃ (i.e., Offset(V) = -1.5℃, indicating that the current temperature measured by the temperature sensor is 1.5℃ higher). In step S153, the controller calculates the calibration temperature: T_output = 24.0 + (-1.5) = 22.5℃, and outputs 22.5℃ as the calibration temperature to the main control logic of the air conditioner. Through this process, the air conditioner uses the corrected accurate temperature (22.5℃) to adjust the compressor frequency and electronic expansion valve, avoiding overcooling caused by the aging of the temperature sensor. At the same time, the original 24.0℃ data in the correspondence table remains unchanged. If a new temperature sensor is replaced later, the maintenance personnel only need to clear the independent offset compensation table, and the system can directly use the original correspondence table output without recalibrating the basic data.

[0064] In some embodiments, such as in embodiments of the present invention, as Figure 6 As shown, the method further includes steps S160-S164.

[0065] S160, calculate the offset candidate value based on the reference temperature and the original temperature; S161, The offset candidate value is added to the sample queue of the corresponding sampling level interval to update the sample queue; S162, Perform a two-condition confidence test on the updated sample queue, wherein the two-condition test includes a quantity condition test and a quality condition test; S163, when both the quantity condition test and the quality condition test pass, the offset compensation value for the corresponding sampling level interval is generated and updated based on the current sample mean. S164, if the offset compensation value of all the sampling level intervals has been updated at least once, then exit the incremental offset compensation calibration mode.

[0066] In this embodiment of the invention, when the system enters the incremental offset compensation calibration mode, the controller sequentially executes steps S160 to S164. This involves calculating candidate offset values, maintaining a sample queue, performing a two-condition confidence test, and finally generating and updating the offset compensation value. The system exits the calibration mode after updating all intervals. The core purpose of these steps is to rigorously control the calibration data from two independent dimensions: sample quantity and sample quality. This effectively prevents erroneous calibration caused by abnormal samples or operational disturbances, ensuring that the generated offset compensation value possesses extremely high statistical reliability and robustness, thereby achieving a safe and traceable automatic calibration closed loop.

[0067] In step S160, the offset candidate value refers to the single instantaneous deviation between the actual output temperature of the temperature sensor and the theoretical standard value at the current sampling level V. Its calculation formula is as follows: Offset_candidate(V)=T-REF-T_table(V) Wherein, T-REF is the reference temperature calculated in the previous step, and T_table(V) is the original temperature read from the original correspondence table. The reason for directly using T-REF instead of the measured temperature T1 of the temperature sensor or the wired controller temperature T2 in the calculation is that T-REF is a reference temperature corrected by a multi-condition mapping model, which eliminates the systematic positional differences between the wired controller and the temperature sensor. It is closer to the true ambient temperature of the temperature sensor's location than directly using the original reading, significantly improving the accuracy of the offset candidate value calculation. In step S161, the controller incorporates the acquired offset candidate value into the sample queue of the corresponding sampling level interval to update the statistics. Because the NTC characteristic curve of the temperature sensor is nonlinear across different temperature ranges, a single offset compensation value cannot accurately describe the deviation distribution across the entire measurement range. Therefore, the system divides the complete sampling level range of the temperature sensor into several sub-intervals (typically 10–20 intervals, each corresponding to approximately 2°C–3°C). Each interval independently maintains a sample queue managed according to the FIFO (First-In, First-Out) principle, and synchronously updates the sample counter N(V), sample mean (V), and sample variance σ for that interval. 2 (V).

[0068] In step S162, the two-condition confidence test is the core defense against miscalibration, including a quantitative condition test and a qualitative condition test. The quantitative condition requires N(V) ≥ N_min, where N_min is the minimum sample size threshold (recommended to be set to 10–30 samples). The qualitative condition requires σ... 2 (V)≤σ 2 _max, where σ 2 _max is the maximum variance threshold (recommended setting: 0.3℃). 2 ~(0.5℃) 2 ).

[0069] Only when both conditions are met simultaneously will the system generate and update the offset compensation value in step S163 using the current sample mean: Offset(V) = mean{Offset_candidate_i(V)}, and reset the sample counter and variance for that interval to continue accumulating new samples and cope with the continuous aging of the sensor. In step S164, after the offset compensation value for all sampling level intervals has been updated at least once, the system determines that the calibration is complete, exits the incremental offset compensation calibration mode, switches the control reference value back to the calibration temperature T_output, and records the calibration event in the system log.

[0070] In a specific application scenario, continuing the previous scenario of aging drift of the indoor unit C temperature sensor, after the system enters calibration mode, at a certain sampling moment, the indoor unit C temperature sensor outputs a sampling level V (falling into sampling level interval 5). In step S160, the controller calculates the reference temperature T-REF as 22.2℃, looks up the original temperature T_table(V) as 23.5℃, and calculates the candidate offset value as -1.3℃. In step S161, the controller adds -1.3℃ to the sample queue of interval 5. At this time, the queue has accumulated 15 samples (N(V)=15), the calculated sample mean is -1.4℃, and the sample variance is (0.2℃). 2 In step S162, the controller performs a dual-condition check: the quantity condition 15 ≥ N_min (assuming N_min = 10), which is satisfied; the quality condition (0.2℃) is also satisfied. 2 ≤σ 2 _max (assuming σ) 2 _max=(0.3℃) 2 The conditions are met. Since both conditions are passed, in step S163, the controller updates the offset compensation value of interval 5 to -1.4℃ and resets the counter. As the air conditioner continues to operate, other sampling level intervals also successively pass the dual-condition test and update their compensation values. Finally, in step S164, when all 15 sampling level intervals have completed at least one valid update, the controller determines that the calibration is complete, exits the calibration mode, switches the control reference value back to T_output (i.e., the offset compensation value after superimposing the original temperature), and writes the complete offset compensation table with timestamps to the EEPROM. Through this rigorous process, the system not only safely and accurately corrects the full-range nonlinear drift of the temperature sensor, but also avoids the risk of miscalibration caused by occasional interference.

[0071] The air conditioner temperature sensor fault diagnosis method disclosed in this invention can establish a mapping sub-model library based on historical operating data and different operating conditions. Then, it combines the real-time acquired controller temperature and the current operating condition parameters to match the corresponding mapping sub-model to calculate the reference temperature. This allows the construction of a dynamic health baseline that adapts to the current operating condition. The instantaneous temperature deviation between the reference temperature and the measured temperature, as well as the time-series characteristic quantities within the sliding time window, are then calculated. Based on the instantaneous temperature deviation and the time-series characteristic quantities, a comprehensive fault diagnosis is performed on the temperature sensor, which can accurately identify the fault type of the temperature sensor.

[0072] Figure 7 This is a schematic block diagram of an air conditioner temperature sensor fault diagnosis device 200 provided in an embodiment of the present invention. Figure 7 As shown, corresponding to the above-described air conditioner temperature sensor fault diagnosis method, the present invention also provides an air conditioner temperature sensor fault diagnosis device 200. This air conditioner temperature sensor fault diagnosis device 200 includes a unit for performing the above-described air conditioner temperature sensor fault diagnosis method. Specifically, please refer to... Figure 7 The air conditioner temperature sensor fault diagnosis device includes a first establishment unit, a first acquisition unit, a first calculation unit, a second calculation unit, and a first diagnosis unit.

[0073] The first establishment unit is used to establish a mapping sub-model library according to historical operating data and different operating conditions. The mapping sub-model library includes multiple mapping sub-models, and each mapping sub-model is used to describe the mapping relationship between the wire controller temperature and the reference temperature of the temperature sensor under the corresponding operating condition. The first acquisition unit is used to acquire the measured temperature of the temperature sensing bag and the temperature of the wired controller in each sampling period, and match the corresponding mapping sub-model according to the current operating condition parameters. The first calculation unit is used to calculate the reference temperature based on the temperature of the wire controller and the matched mapping sub-model; The second calculation unit is used to calculate the instantaneous temperature deviation based on the reference temperature and the measured temperature, and to calculate the time series characteristic quantity within the sliding time window; The first diagnostic unit is used to perform fault diagnosis on the temperature sensing element based on the instantaneous temperature deviation and the time-series characteristic quantity, so as to determine the fault type of the temperature sensing element.

[0074] In some embodiments, such as this embodiment, the first establishment unit includes a second acquisition unit, a first confirmation unit, and a second establishment unit.

[0075] The second acquisition unit is used to acquire the compressor operating frequency range, indoor fan speed level, and operating mode of the air conditioner, respectively. The first confirmation unit is used to confirm the operating condition type of the air conditioner based on the compressor operating frequency range, the indoor fan speed setting, and the operating mode. The second establishment unit is used to establish the mapping sub-model according to the working condition type to form the mapping sub-model library.

[0076] In some embodiments, such as this embodiment, the second calculation unit includes a third acquisition unit, a third calculation unit, and a fourth calculation unit.

[0077] The third acquisition unit is used to acquire the instantaneous temperature deviation at the current sampling time and the instantaneous temperature deviation at the previous sampling time. The third calculation unit is used to calculate the rate of change of the deviation based on the instantaneous temperature deviation at the current sampling time and the instantaneous temperature deviation at the previous sampling time; The fourth calculation unit is used to sum the instantaneous temperature deviations obtained at all sampling times within the sliding time window to obtain the cumulative deviation integral.

[0078] In some embodiments, such as this embodiment, the first diagnostic unit includes a fourth acquisition unit, a first judgment unit, a second judgment unit, and a third judgment unit.

[0079] The fourth acquisition unit is used to acquire the normal tolerance threshold, the mutation amplitude threshold, the preset mutation rate threshold, and the stability threshold. The first judgment unit is used to determine that the temperature sensing bag is in a normal state if the instantaneous temperature deviation is less than or equal to the normal tolerance threshold. The second judgment unit is used to determine that the temperature sensing bag is a sudden fault if the instantaneous temperature deviation is greater than or equal to the sudden change amplitude threshold and the absolute value of the deviation change rate is greater than or equal to the preset sudden change rate threshold. The third judgment unit is used to determine that the temperature sensor is a drift-type fault if the instantaneous temperature deviation is greater than the normal tolerance threshold and less than the sudden change amplitude threshold, the absolute value of the deviation change rate is continuously less than or equal to the stability threshold, and the cumulative deviation integral shows a monotonically increasing trend within the sliding time window.

[0080] In some embodiments, such as this one, the air conditioning temperature sensor fault diagnosis device further includes a first triggering unit, a fifth calculation unit, a first update unit, a first verification unit, and a second update unit.

[0081] The first triggering unit is used to enter the incremental offset compensation calibration mode if the temperature sensing bag is a drift-type fault. The fifth calculation unit is used to calculate the candidate value of the offset based on the reference temperature and the original temperature corresponding to the current sampling level in the incremental offset compensation calibration mode. The first update unit is used to include the offset candidate value into the sample queue of the corresponding sampling level interval to update the sample queue; The first testing unit is used to perform a two-condition confidence test on the updated sample queue, wherein the two-condition test includes a quantity condition test and a quality condition test. The second update unit is used to generate and update the offset compensation value of the corresponding sampling level interval based on the current sample mean when both the quantity condition test and the quality condition test are passed.

[0082] In some embodiments, such as this one, the air conditioning temperature sensor fault diagnosis device further includes a fifth acquisition unit, a first reading unit, and a sixth calculation unit.

[0083] The fifth acquisition unit is used to acquire the original sample value-temperature correspondence table and the independent offset compensation table respectively in the incremental offset compensation calibration mode. The first reading unit is used to read the original temperature from the original sampled value-temperature correspondence table, and to read the updated offset compensation value from the independent offset compensation table. The sixth calculation unit is used to calculate the calibration temperature based on the original temperature and the updated offset compensation value, and output the calibration temperature.

[0084] In some embodiments, such as this one, the air conditioning temperature sensor fault diagnosis device further includes a second triggering unit.

[0085] The second triggering unit is used to exit the incremental offset compensation calibration mode if the offset compensation value of all the sampling level intervals has been updated at least once.

[0086] The aforementioned air conditioner temperature sensor fault diagnosis device can be implemented as a computer program, which can, for example, Figure 8 The air conditioner shown is running.

[0087] Please see Figure 8 , Figure 8 This is a schematic block diagram of an air conditioner provided in an embodiment of this application. It can be a terminal or a server. The terminal can be an electronic device with communication functions, such as a smartphone, tablet, laptop, desktop computer, personal digital assistant, or wearable device. The server can be a standalone server or a server cluster composed of multiple servers.

[0088] See Figure 8The air conditioner 300 includes a processor 302, a memory, and an interface 307 connected via a system bus 301. The memory may include a non-volatile storage medium 303 and internal memory 304.

[0089] The non-volatile storage medium 303 can store an operating system 3031 and a computer program 3032. When the computer program 3032 is executed, it causes the processor 302 to execute a method for diagnosing a fault in an air conditioning temperature sensor.

[0090] The processor 302 is used to provide computing and control capabilities to support the operation of the entire air conditioner 300.

[0091] The internal memory 304 provides an environment for the operation of the computer program 3032 in the non-volatile storage medium 303. When the computer program 3032 is executed by the processor 302, the processor 302 can execute a method for diagnosing faults in an air conditioning temperature sensor.

[0092] This interface 305 is used for communication with other devices. Those skilled in the art will understand that... Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the air conditioner 300 to which the present application is applied. The specific air conditioner 300 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0093] It should be understood that in the embodiments of this application, the processor 302 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0094] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program may be stored in a storage medium, which is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0095] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program. When executed by a processor, the computer program implements any embodiment of the above-described air conditioning temperature sensor fault diagnosis method.

[0096] The storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0097] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0098] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0099] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the device of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0100] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an air conditioner to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0101] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0102] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Since these modifications and variations fall within the scope of the claims and their equivalents, this invention also intends to include these modifications and variations.

[0103] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for diagnosing faults in an air conditioner temperature sensor, characterized in that, The method includes: Based on historical operating data, a mapping sub-model library is established for different operating conditions. The mapping sub-model library includes multiple mapping sub-models, each of which describes the mapping relationship between the wired controller temperature and the reference temperature of the temperature sensor under the corresponding operating condition. Within each sampling period, the measured temperature of the temperature sensor and the temperature of the wired controller are obtained, and the corresponding mapping sub-model is matched according to the current operating condition parameters; The reference temperature is calculated based on the controller temperature and the matched mapping sub-model; The instantaneous temperature deviation is calculated based on the reference temperature and the measured temperature, and the time-series characteristic quantity is calculated within the sliding time window; The fault diagnosis of the temperature sensing element is performed based on the instantaneous temperature deviation and the time-series characteristic quantity to determine the fault type of the temperature sensing element.

2. The method according to claim 1, characterized in that, The steps for establishing a mapping sub-model library for each work condition include: The compressor operating frequency range, indoor fan speed setting, and operating mode of the air conditioner are obtained respectively. The operating condition type of the air conditioner is determined based on the compressor operating frequency range, the indoor fan speed setting, and the operating mode, and the mapping sub-model is established based on the operating condition type to form the mapping sub-model library.

3. The method according to claim 1, characterized in that, The time-series characteristic quantities include the rate of change of deviation and the cumulative deviation integral. The step of calculating the time-series characteristic quantities within the sliding time window includes: Obtain the instantaneous temperature deviation at the current sampling time and the instantaneous temperature deviation at the previous sampling time; The rate of change of the deviation is calculated based on the instantaneous temperature deviation at the current sampling time and the instantaneous temperature deviation at the previous sampling time; The cumulative deviation integral is obtained by summing the instantaneous temperature deviations sampled at all sampling times within the sliding time window.

4. The method according to claim 3, characterized in that, The step of diagnosing the fault of the temperature sensor based on the instantaneous temperature deviation and the time-series characteristic quantity to determine the fault type of the temperature sensor includes: Obtain the normal tolerance threshold, mutation amplitude threshold, preset mutation rate threshold, and stability threshold; If the instantaneous temperature deviation is less than or equal to the normal tolerance threshold, the temperature sensor is determined to be in a normal state. If the instantaneous temperature deviation is greater than or equal to the sudden change amplitude threshold, and the absolute value of the deviation change rate is greater than or equal to the preset sudden change rate threshold, then the temperature sensor is determined to be a sudden change type fault. If the instantaneous temperature deviation is greater than the normal tolerance threshold and less than the sudden change amplitude threshold, and the absolute value of the deviation change rate is continuously less than or equal to the stability threshold, and the cumulative deviation integral shows a monotonically increasing trend within the sliding time window, then the temperature sensor is determined to be a drift-type fault.

5. The method according to claim 4, characterized in that, The method further includes: If the temperature sensor is experiencing a drift-type fault, then the incremental offset compensation calibration mode is entered. In the incremental offset compensation calibration mode, the original correspondence table and the independent offset compensation table are obtained respectively. Read the original temperature from the original correspondence table and the offset compensation value from the independent offset compensation table; The calibration temperature is calculated based on the original temperature and the offset compensation value, and then the calibration temperature is output.

6. The method according to claim 5, characterized in that, The method further includes: Calculate candidate offset values ​​based on the reference temperature and the original temperature; The candidate offset values ​​are added to the sample queue of the corresponding sampling level range to update the sample queue; Perform a two-condition confidence test on the updated sample queue, wherein the two-condition test includes a quantity condition test and a quality condition test; When both the quantity condition test and the quality condition test pass, the offset compensation value for the corresponding sampling level interval is generated and updated based on the current sample mean.

7. The method according to claim 6, characterized in that, The method further includes: If the offset compensation value of all the sampling level intervals has been updated at least once, the incremental offset compensation calibration mode will be exited.

8. A fault diagnosis device for an air conditioner temperature sensor, characterized in that, The device includes: The first establishment unit is used to establish a mapping sub-model library according to historical operating data and different operating conditions. The mapping sub-model library includes multiple mapping sub-models, and each mapping sub-model is used to describe the mapping relationship between the wired controller temperature and the reference temperature of the temperature sensor under the corresponding operating condition. The first acquisition unit is used to acquire the measured temperature of the temperature sensing bag and the temperature of the wired controller in each sampling period, and match the corresponding mapping sub-model according to the current operating condition parameters. The first calculation unit is used to calculate the reference temperature based on the temperature of the wire controller and the matched mapping sub-model; The second calculation unit is used to calculate the instantaneous temperature deviation based on the reference temperature and the measured temperature, and to calculate the time series characteristic quantity within the sliding time window; The first diagnostic unit is used to perform fault diagnosis on the temperature sensing element based on the instantaneous temperature deviation and the time-series characteristic quantity, so as to determine the fault type of the temperature sensing element.

9. An air conditioner, characterized in that, The air conditioner includes a memory and a processor connected to the memory; the memory is used to store a computer program; the processor is used to run the computer program stored in the memory to perform the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, can implement the steps of the method as described in any one of claims 1-7.

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