System for predicting service life of filter element of vehicle-mounted air conditioner

Abstract

The invention belongs to the technical field of automobile parts, and provides a vehicle-mounted air conditioner filter element service life prediction system which comprises the steps that performance index vectors of a vehicle-mounted air conditioner filter element are collected, whether the vehicle-mounted air conditioner filter element is in a stable attenuation period or not is judged, if yes, the stable attenuation period is marked, vehicle-mounted air conditioner filter element service life prediction is triggered, and if not, the vehicle-mounted air conditioner filter element service life prediction is finished. Working information of the vehicle-mounted air conditioner in a historical time period is collected, working condition types are divided for moments in the historical time period to construct a time sequence working condition sequence, the time sequence working condition sequence is periodically analyzed, if periodicity exists, the working condition period is calculated, whether dynamic working condition attenuation adaptation is triggered or not is judged, and if not, average attenuation analysis is triggered; and an average performance attenuation model is constructed based on the performance index vectors at all moments, if dynamic working condition attenuation adaptation is triggered, a working condition performance attenuation rate change model is constructed, and the average performance attenuation model or the working condition performance attenuation rate change model is adopted to predict the remaining life of the vehicle-mounted air conditioner filter element.

Description

Technical Field

[0001] The invention belongs to the technical field of automobile accessories, and in particular is a system for predicting the life of a vehicle-mounted air-conditioning filter element. Background Art

[0002] As a core component to ensure the air quality in the car, the car air-conditioning filter's main function is to filter out particulate matter, pollen, odors and some harmful gases in the air entering the car, which directly affects the respiratory health and car-using experience of the people in the car. As time goes by, the filter will gradually degrade due to reasons such as particulate matter adhesion and fiber aging, and the filtration efficiency will decrease and the air resistance will increase. When the performance decays to a critical value, it needs to be replaced in time, otherwise it may lead to insufficient air volume of the air conditioner and deterioration of the air quality in the car.

[0003] However, there are many technical pain points in the existing life management methods of vehicle air-conditioning filters. The discovery of these problems is closely related to industry development and the upgrading of user needs. In the early days, the industry generally adopted a fixed-cycle replacement mode, but in practice, it was found that the actual attenuation rate of the filter element was significantly affected by factors such as the external environment and usage habits. For example, vehicles that have been driving in areas with severe haze for a long time have a filter element attenuation rate much faster than vehicles used in a clean environment. Frequently turning on the high-volume mode will also accelerate the clogging of the filter element. The fixed cycle cannot adapt to individual differences, which often leads to excessive replacement or delayed replacement.

[0004] As users pay more attention to in-car health, some technologies attempt to judge the lifespan through subjective feelings or a single parameter. For example, they rely on the user's perception of reduced air volume and obvious odor, or judge the degree of blockage only by monitoring the pressure difference on both sides of the filter element. However, the former has serious lag and is often discovered only after the filter element fails. The latter is one-sided. The increase in resistance only reflects the blockage status of the filter element and cannot be directly correlated with the filtration efficiency, resulting in a high risk of misjudgment.

[0005] Further research shows that the performance degradation of the filter element is a process of coordinated changes in multiple parameters, and is strongly correlated with the operating conditions of the vehicle air conditioner. In the existing technology, there is a lack of real-time monitoring and quantitative analysis of core performance indicators such as filtration efficiency and resistance, and no dynamic correlation model between operating conditions and degradation rate has been established, resulting in low accuracy in life prediction. In addition, the existing prediction methods are mostly static estimates, and cannot dynamically adjust the prediction results according to the real-time performance status and operating conditions of the filter element. It is difficult to meet users' needs for accurate life warnings. There is a lack of dynamic adjustment mechanism and the life prediction cannot be updated according to the real-time status, which can easily cause users to miss the best time for replacement.

[0006] In response to the above problems, the present invention proposes a system for predicting the life of a vehicle air-conditioning filter element. Summary of the Invention

[0007] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0008] The technical solution adopted by the present invention to solve the technical problem is: a prediction system for the life of a vehicle air-conditioning filter element, comprising: Performance Analysis Module: This module collects the performance indicator vectors of the vehicle air conditioning filter and determines in real time whether the filter is in a stable attenuation period. If so, it marks the stable attenuation period and triggers the life prediction of the filter. Working condition analysis module: If the vehicle air conditioning filter life prediction is triggered, the vehicle air conditioning operating information within the historical period is collected, the working condition type is divided into time series, and a time series working condition sequence is constructed. The time series working condition sequence is analyzed for periodicity. If periodicity exists, the working condition period is calculated, and it is determined whether to trigger dynamic working condition attenuation adaptation. If not, the average attenuation analysis is triggered. Model construction module: Based on the performance indicator vector at each moment, if the average attenuation analysis is triggered, an average performance attenuation model is constructed. If the dynamic working condition attenuation adaptation is triggered, the working condition cycle is combined with the time-series working condition sequence group within the stable attenuation period, and the working condition performance attenuation rate change model is constructed based on the time-series working condition sequence group; Prediction and deduction module: Use the obtained average performance attenuation model or operating performance attenuation rate change model to predict the remaining life of the vehicle air conditioning filter element; The method for determining whether the vehicle air conditioning filter is in a stable attenuation period is as follows: Obtain the result of determining whether the air conditioning filter is in a stable period. If the air conditioning filter is not in a stable period, calculate the Euclidean distance between the performance index vector of the air conditioning filter at the current moment and the previous moment, marking it as the adjacent attenuation distance. Calculate the Euclidean distance between the performance index vector of the air conditioning filter and the preset performance index vector bottom line, marking it as the attenuation end distance. If the adjacent attenuation distance at the current moment is less than or equal to the preset instantaneous change standard, and the attenuation end point distance at the current moment is greater than or equal to the preset distance standard, it is determined that the vehicle air conditioning filter is in a stable attenuation period; The method for determining whether the vehicle air conditioning filter is in a stable period is as follows: Obtain the performance index vector of the vehicle air conditioning filter at the starting time of its use, mark it as the initial performance index vector, and calculate the Euclidean distance between the performance index vector of the vehicle air conditioning filter and the initial performance index vector. If the Euclidean distance is less than a preset distance standard, it is determined that the vehicle air conditioning filter is in a stable period. The method of dividing the working condition types at each moment is as follows: Obtain the operating information of the vehicle air conditioner during the historical period. The operating information includes the air volume level and external environmental parameters. Normalize and integrate the operating information during the historical period to obtain a normalized operating information vector at each moment. The moments when the air volume level is 0 are divided into the same working condition type. The K-means clustering algorithm is used to divide the working condition type for the moments when the air volume level is not 0. The elbow method is used to determine the number of clusters for the moments when the air volume level is not 0 in the historical period and clustering is performed iteratively until convergence is determined. The number of temporary clusters is obtained, and each temporary cluster corresponds to a type of working condition. The moments corresponding to the normalized working information vector belonging to the same temporary cluster are divided into the same working condition type. The construction method of the time sequence operating condition sequence is as follows: Assign a unique working condition digital code to each working condition type. Take the current moment as the end point and intercept a sliding analysis window of constant length. Obtain the working condition digital code corresponding to the working condition type at each moment within the sliding analysis window. Integrate them according to the time sequence to obtain the time sequence working condition sequence. The method for determining whether to trigger dynamic operating condition attenuation adaptation is as follows: The autocorrelation function is used to analyze the regularity of the time series working condition sequence to analyze whether the time series working condition sequence has periodicity. If the time series working condition sequence has significant periodicity, the working condition cycle is obtained according to the autocorrelation function. If there are at least two complete working condition cycles in the stable attenuation period, it is determined that the dynamic working condition attenuation adaptation is triggered; The average performance attenuation model is constructed as follows: The performance index vector at the starting point of the stable attenuation period and the performance index vector at the current moment are obtained respectively. The performance index vector includes filtration efficiency and resistance ratio. The average attenuation rate of filtration efficiency and the average growth rate of resistance ratio during the stable attenuation period are calculated. Combined with the performance index vector at the current moment, a model of the change of filtration efficiency and resistance ratio over time is constructed, and the average performance attenuation model is obtained by integration. The method for obtaining the time period operating condition sequence group is as follows: Consecutive moments of the same operating condition type are combined into one operating condition period. The same operating condition type is the operating condition type of the operating condition period. The performance index vectors of the starting and ending points of each operating condition period within the stable attenuation period are obtained. The average attenuation rate of filtration efficiency and the average growth rate of resistance ratio within the operating condition period are calculated. Obtain all working condition periods within a working condition cycle and integrate them according to the time sequence and mark the sequence numbers in sequence to obtain a time period working condition sequence. Integrate all time period working condition sequences within the stable attenuation period according to the time sequence and mark the sequence numbers in sequence to obtain a time period working condition sequence group. Both the cycle number and the sequence number are integer counted starting from 1. The construction method of the operating performance attenuation rate change model is as follows: In the time-series cycle operating condition sequence group, all operating condition periods with the same sequence number in the cycle are integrated according to the time sequence to obtain the time-series operating condition period sequence with the sequence number in the corresponding cycle; In each time-series operating condition period, the sequence number corresponding to the operating condition period is used as the independent variable, and the average attenuation rate of filtration efficiency and the average growth rate of resistance ratio in the operating condition period are used as the dependent variables. The linear relationship is fitted by the least squares method to obtain the filtration efficiency attenuation rate change model and the resistance ratio growth rate change model of the sequence number in each cycle, and then the results are summarized and integrated to obtain the operating condition performance attenuation rate change model. The method for predicting the remaining life of the vehicle air conditioning filter element using the operating performance attenuation rate change model is as follows: Set a deduction period with the current moment as the starting point. If the current moment is not the end of the operating cycle, predict and complete the current operating cycle within the deduction period. Use the operating performance attenuation rate change model to sequentially calculate the Euclidean distance between the performance index vector at the end of each operating period within the deduction period and the preset performance index vector baseline, marking it as the predicted life end distance. If the predicted life end distance is less than the preset distance standard, the deduction stops. If the predicted life end distance at the end of the current operating cycle is still greater than or equal to the preset distance standard, the next operating cycle will continue to be deduced within the deduction period until a working period is found that stops the deduction. This is marked as the end operating period. The predicted life end distances of the moments within the end operating period are calculated in sequence according to the time sequence. If the predicted life end distance at any moment is less than the preset distance standard, the moment is determined to be the end of the life of the vehicle air conditioning filter element, and subsequent moments are no longer calculated. Calculate the time interval between the current moment and the end of the lifespan to obtain the remaining lifespan of the vehicle air conditioning filter.

[0009] The beneficial effects of the present invention are as follows: 1. By accurately collecting vehicle air conditioning filter performance indicator vectors, this invention can promptly determine whether a filter has entered a stable decay period and mark this period, providing an accurate basis for subsequent analysis. By collecting historical operating information, classifying operating conditions, constructing a time-series operating condition sequence, and performing periodic analysis, this method can comprehensively understand the filter's operating environment characteristics. Based on different trigger conditions, it can construct average or operating condition performance decay models that are tailored to actual usage, effectively improving the accuracy of vehicle air conditioning filter remaining life predictions and providing a scientific basis for filter replacement.

[0010] 2. After determining that the vehicle has entered the stable attenuation period, the present invention carefully analyzes historical operating conditions. Whether it is triggering average attenuation analysis to construct an average performance attenuation model, or triggering dynamic operating condition attenuation adaptation to construct an operating condition performance attenuation rate change model, it can fully consider various usage scenarios. This flexible and accurate modeling method makes the prediction results more in line with the actual situation, helps users plan filter element replacement in advance, reduces the impact of poor filter element performance on the vehicle's air quality and air-conditioning system, and improves the driving experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The present invention will be further described below with reference to the accompanying drawings.

[0012] Figure 1 This is a module architecture diagram of a vehicle air conditioning filter life prediction system according to an embodiment of the present invention; Figure 2 This is a flow chart of the steps for predicting the remaining life of a vehicle air-conditioning filter element in a vehicle air-conditioning filter element life prediction system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0013] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0014] Example 1 See also Figure 1 As shown, a system for predicting the life of a vehicle air-conditioning filter element according to an embodiment of the present invention includes the following steps: Performance analysis module: This module collects the performance index vector of the vehicle air conditioning filter element and determines whether the vehicle air conditioning filter element is in a stable attenuation period. If so, it marks the stable attenuation period and triggers the vehicle air conditioning filter element life prediction; A sensor group consisting of a particle counter and a pressure sensor is installed in the vehicle air conditioner. The particle counter is installed at the air inlet and outlet of the vehicle air conditioner filter, and the pressure sensor is installed on both sides of the vehicle air conditioner filter. The installed sensor group collects key performance indicators of the vehicle air conditioner filter in real time, including filtration efficiency and resistance ratio. The key performance indicators are integrated into a performance indicator vector at each moment. The filtration efficiency η reflects the filter element's ability to filter particulate matter in the air. It is calculated by using a particle counter to simultaneously detect the particle concentration at the air inlet and outlet of the vehicle air conditioning filter element. The formula is: η=(particle concentration at the air inlet - particle concentration at the air outlet) / particle concentration at the air inlet × 100%; Among them, the resistance ratio Reflects the difficulty of air passing through the filter element. It is calculated by detecting the air pressure values ​​on both sides of the vehicle air conditioning filter element through the pressure sensor. The air pressure values ​​on both sides of the vehicle air conditioning filter element include the air pressure value on the air inlet side and the air pressure value on the air outlet side. The formula is: = (air inlet pressure value - air outlet pressure value) / air inlet pressure value × 100%; Obtain the performance index vector of the vehicle air conditioning filter at the starting time when the vehicle air conditioning filter is put into use, mark it as the initial performance index vector, and calculate the Euclidean distance between the performance index vector of the vehicle air conditioning filter at the current time and the initial performance index vector. If the calculated Euclidean distance is less than a preset distance standard, it is determined that the vehicle air conditioning filter has not experienced significant attenuation at the current time and is currently in a stable period of the vehicle air conditioning filter. If the current moment is not in the stable period of the vehicle air conditioning filter, calculate the Euclidean distance between the performance index vector of the vehicle air conditioning filter at the current moment and the previous moment, and mark it as the adjacent attenuation distance at the current moment. Calculate the Euclidean distance between the performance index vector of the vehicle air conditioning filter at the current moment and the preset performance index vector bottom line, and mark it as the attenuation end distance at the current moment. If the adjacent attenuation distance at the current moment is greater than the preset instantaneous change standard, or the attenuation end distance at the current moment is less than the preset distance standard, it is judged that the vehicle air conditioning filter element has experienced cliff-like attenuation, and the vehicle air conditioning filter element has reached the end of its life at the current moment. Otherwise, it is judged that the current moment is in a stable attenuation period, and the stable attenuation period will be marked from the moment of the first stable attenuation period to the current moment as the end point, triggering the vehicle air conditioning filter element life prediction; If the vehicle air conditioning filter reaches the end of its life, a filter replacement reminder will be generated and sent to the user terminal; It should be noted that the function of this module is to collect the filtration efficiency and resistance ratio of the vehicle air-conditioning filter element in real time through the sensor group, construct a performance index vector, quantify the performance change based on the Euclidean distance, and judge whether the vehicle air-conditioning filter element is in the stable period, stable attenuation period or the end of its life. It also triggers the life prediction or directly generates a replacement reminder, realizing real-time monitoring of the performance of the vehicle air-conditioning filter element and accurate status classification, avoiding the waste of cost due to premature replacement or the impact of replacement on air conditioning effect and health due to late replacement, improving the efficiency of filter element use, introducing the difference of Euclidean distance quantification performance index vector, breaking through the limitation of traditional reliance on a single indicator to judge life, and realizing dynamic identification of multi-dimensional performance attenuation; Working condition analysis module: If the vehicle air conditioning filter life prediction is triggered, the vehicle air conditioning operating information within the historical period is collected, the working condition type is divided for each moment in the historical period, a time series working condition sequence is constructed based on the working condition type, and a periodic analysis is performed on the time series working condition sequence. If periodicity exists, the working condition period is calculated, and it is determined whether dynamic working condition attenuation adaptation is triggered. If not, average attenuation analysis is triggered. If the vehicle air conditioning filter life prediction is triggered, the starting time of the vehicle air conditioning filter is obtained, and the period between the starting time and the current time is marked as a historical period. The vehicle system comprehensively records and collects the historical working information of the vehicle air conditioning. The historical working information includes the working information of the vehicle air conditioning at each time during the historical period, including the air volume level and external environmental parameters; The air volume level is obtained through the electronic operation record of the vehicle air conditioning control panel. If the vehicle air conditioning is not turned on, the air volume level is 0. The external environmental parameters include the air inlet particle concentration, ambient temperature and ambient humidity. The air inlet particle concentration is collected by a particle counter installed at the air inlet of the vehicle air conditioning filter element, the ambient temperature is collected by a temperature sensor installed outside the vehicle, and the ambient humidity is collected by the vehicle humidity sensor. Each type of work information is normalized in the historical period, and the normalized work information is integrated at each moment to obtain the normalized work information vector at each moment; For the moment when the air volume level is not 0, the K-means clustering algorithm is used to divide the working condition type for each moment; Specifically, the elbow method is used to determine the number of clusters for the moments when the air volume level is not 0 in the historical period, and the normalized working information vectors at the cluster number moments are selected as cluster centers. The Euclidean distance between the normalized working information vector at each moment and the cluster center is calculated, and each normalized working information vector is assigned to the cluster where the cluster center with the closest Euclidean distance is located to form the cluster number temporary clusters. For each temporary cluster, the average value of each component of all normalized working information vectors in the temporary cluster is calculated as the new cluster center, and the difference between the new cluster center and the previous cluster center is compared. If the change in all cluster centers is less than the set convergence threshold, it is determined that convergence has occurred and the clustering is terminated. Otherwise, each normalized working information vector is reallocated to the cluster where the cluster center with the closest Euclidean distance is located and the cluster center is updated until convergence is achieved. After clustering, temporary clusters of the number of clusters are obtained. Each temporary cluster corresponds to a type of working condition. The moments corresponding to the normalized working information vectors belonging to the same temporary cluster are divided into the same working condition type. The moments when the air volume gear is 0 are also divided into the same working condition type. A unique working condition digital code is assigned to each type of working condition. Taking the current moment as the end point, a sliding analysis window of constant length is intercepted. The sliding analysis window slides with the change of the current moment. The digital codes of the working conditions corresponding to each moment in the sliding analysis window are integrated according to the time sequence to obtain the time sequence working condition sequence; The autocorrelation function is used to analyze the regularity of the time series operating condition sequence and analyze whether there is periodicity in each operating condition type within the sliding analysis window. The formula of the autocorrelation function is: ; in, represents the autocorrelation coefficient, represents the hysteresis value, Indicates the digital code of the working condition corresponding to the tth moment in the time series working condition sequence, represents the mean of the time series operating condition sequence, and n represents the length of the time series operating condition sequence; Calculate different lag values The autocorrelation coefficient , where the hysteresis value ranges from 1 to n / 2. If there is a hysteresis value , making is greater than the preset autocorrelation coefficient standard and is within the lag value range, If the autocorrelation coefficients at the integer multiple lag values ​​are all greater than the preset autocorrelation coefficient bottom line, it is determined that the time series operating condition sequence has significant periodicity, and the operating condition cycle ,in, Indicates the duration between adjacent moments; If there is significant periodicity in the time sequence of operating conditions, and there are at least two complete operating condition cycles within the stable attenuation period, dynamic operating condition attenuation adaptation is triggered, otherwise average attenuation analysis is triggered; It should be noted that the function of this module is to collect historical operating information of the vehicle's on-board air conditioner, classify the operating condition types through a clustering algorithm, analyze the periodicity of the operating conditions using the autocorrelation function, and trigger two prediction modes: average attenuation analysis or dynamic operating condition attenuation adaptation based on the periodic characteristics. This module considers the impact of actual operating conditions on filter element attenuation, avoids prediction deviations caused by differences in operating conditions, and lays the foundation for subsequent accurate predictions. It converts complex operating conditions into discrete coding sequences to facilitate time series analysis. It combines the clustering algorithm with the autocorrelation function, first classifying the operating conditions and then analyzing their periodicity, so that the prediction model adapts to the actual usage pattern, breaking through the traditional prediction approach that ignores differences in operating conditions. Model construction module: Based on the performance indicator vector at each moment, if the average attenuation analysis is triggered, an average performance attenuation model is constructed. If the dynamic working condition attenuation adaptation is triggered, the working condition period is marked according to the working condition type at the moment. The time-series working condition sequence group is integrated within the stable attenuation period according to the working condition cycle, and the working condition performance attenuation rate change model is constructed based on the time-series working condition sequence group. If the average decay analysis is triggered, the performance index vector at the starting point of the stable decay period and the performance index vector at the current moment are obtained respectively; For the performance index vector, calculate the average decay rate of the filtration efficiency included in the performance index vector during the stable decay period , and the average growth rate of the drag ratio including the performance index vector , the formula is: ; ; in, represents the duration of the stable decay period, and The performance index vectors representing the starting point of the stable attenuation period include filtration efficiency and resistance ratio, and Respectively represent the performance index vector at the current moment, including the filtration efficiency and resistance ratio; Based on the performance index vector at the current moment, combined with the average attenuation rate of filtration efficiency and the average growth rate of resistance ratio during the stable attenuation period, a model of filtration efficiency and resistance ratio changing with time is constructed, and the average performance attenuation model is obtained by integration. If dynamic working condition attenuation adaptation is triggered, consecutive moments of the same working condition type are merged into one working condition period, and the same working condition type is the working condition type of the working condition period; Based on the performance index vectors at the start and end points of each operating period within the stable attenuation period, the average attenuation rate of filtration efficiency and the average growth rate of resistance ratio within the operating period are calculated; Obtain all working condition periods within a working condition cycle and integrate them according to the time sequence and sequentially mark the sequence number i within the cycle to obtain a time period working condition sequence. Integrate all time period working condition sequences within the stable attenuation period according to the time sequence and sequentially mark the sequence number j to obtain a time period working condition sequence group. The sequence number i within the cycle and the sequence number j are both integer counted starting from 1. In the time-series cycle operating condition sequence group, all operating condition periods with the same sequence number in the cycle are integrated according to the time sequence to obtain the time-series operating condition period sequence with the sequence number in the corresponding cycle; For any time-series operating condition period sequence, within the time-series operating condition period sequence, the sequence number corresponding to the operating condition period is used as the independent variable, and the average attenuation rate of filtration efficiency and the average growth rate of resistance ratio within the operating condition period are used as the dependent variables. The linear relationship is fitted by the least squares method to construct the filtration efficiency attenuation rate change model and the resistance ratio growth rate change model for the sequence number in the corresponding period; The filtration efficiency attenuation rate change model and the resistance ratio growth rate change model of the serial numbers in all cycles are summarized and integrated to obtain the working condition performance attenuation rate change model; It should be noted that the function of this module is to construct a corresponding attenuation model based on the results of the working condition analysis. If the average attenuation analysis is triggered, the average attenuation rate of the filtration efficiency and the average growth rate of the resistance ratio in the stable attenuation period are calculated to construct an average performance attenuation model. If the dynamic working condition attenuation adaptation is triggered, the attenuation rate change model in different cycles is fitted according to the working condition period to form a working condition performance attenuation rate change model. The average performance attenuation model is suitable for working conditions without significant cycles, and the working condition performance attenuation rate change model is suitable for periodic working conditions, which is highly consistent with the actual attenuation law and improves the accuracy of subsequent predictions. Under dynamic working conditions, different working condition periods in the cycle are modeled separately, and the linear relationship between the attenuation rate and the periodic sequence is fitted by the least squares method to capture the dynamic impact of working condition changes on the attenuation rate, thereby overcoming the defect that the traditional average model cannot reflect working condition fluctuations. Prediction and deduction module: uses the average performance attenuation model or the operating performance attenuation rate change model to predict the remaining life of the vehicle air conditioning filter element; Specifically, if an average performance decay model is obtained, a performance index vector at the current moment is obtained, and a preset performance index vector bottom line of the vehicle air conditioning filter element is obtained, where the performance index vector includes filtration efficiency and resistance ratio, and the performance index vector bottom line includes a filtration efficiency threshold and a resistance ratio threshold. Using the average performance decay model, the required time for the filtration efficiency and resistance ratio of the vehicle air conditioning filter element to reach the filtration efficiency threshold and the resistance ratio threshold is calculated, respectively. The two calculated required time periods are compared, and the minimum required time period is selected as the remaining life of the vehicle air conditioning filter element. If the operating performance attenuation rate change model is obtained, a deduction period is set with the current moment as the starting point. If the current moment is the end of a working condition cycle, the current working condition cycle is judged to be complete; otherwise, the current working condition cycle is judged to be incomplete. If the current operating cycle is incomplete, the current operating cycle is predicted and completed within the deduction period. The operating performance attenuation rate change model is used. Based on the sequence number corresponding to the current operating cycle, the average attenuation rate of the filtration efficiency and the average growth rate of the resistance ratio of each operating period within the current operating cycle are predicted and calculated. Combined with the performance index vector at the current moment, the performance index vector of the end point of each operating period within the deduction period is calculated in sequence, and the Euclidean distance between the performance index vector and the bottom line of the performance index vector is calculated and marked as the predicted life end distance. If the predicted life end distance is less than the preset distance standard, the deduction stops. If the predicted life end distance of the end point of the current operating cycle is still greater than or equal to the preset distance standard, the end point of the current operating cycle is used as the starting point, and the next operating cycle is continued to be deduced within the deduction period, marked as the deduced operating cycle, and the time-series cycle operating sequence of the deduced operating cycle is sorted and incorporated into the time-series cycle operating sequence group, and the sequence number is marked as N+1, where N is the sequence number of the time-series cycle operating sequence of the current operating cycle; Similarly, the average attenuation rate of the filtration efficiency and the average growth rate of the resistance ratio in each working period within the deduced working cycle are calculated based on the sequence number prediction. Combined with the performance index vector at the end of the current working cycle, the performance index vector at the end of each working period within the deduced working cycle is calculated in sequence, and the predicted life end distance at the end of the working period is calculated. If the predicted life end distance is less than the preset distance standard, the deduction stops. If the predicted life end distance at the end of the deduced working cycle is still greater than or equal to the preset distance standard, the deduction operation is repeated until the deduction stops. When the simulation stops, the operating period that caused the simulation to stop is determined and marked as the end operating period. Based on the performance index vector of the operating period before the end operating period, combined with the average attenuation rate of filtration efficiency and the average growth rate of resistance ratio in the end operating period, the performance index vectors of the moments in the end operating period are predicted and calculated in sequence according to the time sequence, and the predicted life end distance of each moment is calculated. If the predicted life end distance at any moment is less than the preset distance standard, the moment is determined to be the life end of the vehicle air conditioning filter element, and subsequent moments are no longer calculated; Calculate the time interval between the current moment and the end of the life span to obtain the remaining life span of the vehicle air conditioning filter element; Based on the remaining life of the vehicle air conditioning filter, a predicted replacement time for the vehicle air conditioning filter is generated and sent to the user terminal; It should be noted that the function of this module is to use the average performance decay model or the operating performance decay rate change model to calculate the remaining life. The average performance decay model predicts the life by calculating the shortest time for the performance indicator to reach the threshold. The operating performance decay rate change model completes incomplete cycles and continuously deduces subsequent cycles to accurately determine the end of life at a specific moment. Finally, the replacement time is generated and pushed to the user, providing users with accurate remaining life and replacement time, facilitating advance maintenance planning, and ensuring the filtering effect of the vehicle air conditioner and the air quality in the car. The cycle completion and continuous deduction mechanism based on the operating performance decay rate change model can accurately predict the end of life at a specific moment within the operating period. Compared with the traditional method of estimating the approximate duration, it achieves more accurate time-series prediction and is more in line with actual usage scenarios. The technical solution of an embodiment of the present invention is: collecting the performance index vector of the vehicle air-conditioning filter element, judging whether the vehicle air-conditioning filter element is in a stable attenuation period, and if so, marking the stable attenuation period and triggering the vehicle air-conditioning filter element life prediction; if the vehicle air-conditioning filter element life prediction is triggered, collecting the working information of the vehicle air-conditioning in the historical period, dividing the working condition type for the moments in the historical period, constructing a time series working condition sequence based on the working condition type, performing periodic analysis on the time series working condition sequence, if there is periodicity, calculating the working condition cycle, and judging whether to trigger dynamic working condition attenuation adaptation; if not triggered, triggering the average attenuation analysis; based on the performance index vector at each moment, if the average attenuation analysis is triggered, constructing an average performance attenuation model; if the dynamic working condition attenuation adaptation is triggered, marking the working condition period according to the working condition type at the moment, integrating the time series period working condition sequence group in the stable attenuation period in combination with the working condition cycle, constructing a working condition performance attenuation rate change model based on the time series period working condition sequence group, and using the average performance attenuation model or the working condition performance attenuation rate change model to predict the remaining life of the vehicle air-conditioning filter element.

[0015] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A system for predicting the life of a vehicle air conditioning filter, characterized by: include: Performance Analysis Module: This module collects the performance indicator vectors of the vehicle air conditioning filter and determines in real time whether the filter is in a stable attenuation period. If so, it marks the stable attenuation period and triggers the life prediction of the filter. Working condition analysis module: If the vehicle air conditioning filter life prediction is triggered, the vehicle air conditioning operating information within the historical period is collected, the working condition type is divided into time series, and a time series working condition sequence is constructed. The time series working condition sequence is analyzed for periodicity. If periodicity exists, the working condition period is calculated, and it is determined whether to trigger dynamic working condition attenuation adaptation. If not, the average attenuation analysis is triggered. Model construction module: Based on the performance indicator vector at each moment, if the average attenuation analysis is triggered, an average performance attenuation model is constructed. If the dynamic working condition attenuation adaptation is triggered, the working condition cycle is combined with the time-series working condition sequence group within the stable attenuation period, and the working condition performance attenuation rate change model is constructed based on the time-series working condition sequence group; Prediction and deduction module: Use the obtained average performance attenuation model or operating performance attenuation rate change model to predict the remaining life of the vehicle air-conditioning filter element.

2. The system for predicting the life of a vehicle air conditioning filter according to claim 1, characterized in that: The method for judging whether the vehicle air conditioning filter is in the stable attenuation period is as follows: Obtain the result of determining whether the air conditioning filter is in a stable period. If the air conditioning filter is not in a stable period, calculate the Euclidean distance between the performance index vector of the air conditioning filter at the current moment and the previous moment, marking it as the adjacent attenuation distance. Calculate the Euclidean distance between the performance index vector of the air conditioning filter and the preset performance index vector bottom line, marking it as the attenuation end distance. If the adjacent attenuation distance at the current moment is less than or equal to the preset instantaneous change standard, and the attenuation end point distance at the current moment is greater than or equal to the preset distance standard, it is determined that the vehicle air-conditioning filter is in a stable attenuation period.

3. The vehicle air conditioning filter life prediction system according to claim 2, characterized in that: The method for judging whether the vehicle air conditioning filter is in the stable period is as follows: Obtain the performance index vector of the vehicle air conditioning filter at the starting point of its use, mark it as the initial performance index vector, and calculate the Euclidean distance between the performance index vector of the vehicle air conditioning filter and the initial performance index vector. If the Euclidean distance is less than a preset distance standard, determine that the vehicle air conditioning filter is in a stable period.

4. The vehicle air conditioning filter life prediction system according to claim 1, characterized in that: The method of dividing the working condition types at each moment is as follows: Obtain the operating information of the vehicle air conditioner during the historical period. The operating information includes the air volume level and external environmental parameters. Normalize and integrate the operating information during the historical period to obtain a normalized operating information vector at each moment. The moments when the air volume gear is 0 are divided into the same working condition type. The K-means clustering algorithm is used to divide the working condition type for the moments when the air volume gear is not 0. The elbow method is used to determine the number of clusters for the moments when the air volume gear is not 0 in the historical period, and iterative clustering is performed until convergence is determined. The number of temporary clusters is obtained, and each temporary cluster corresponds to a type of working condition. The moments corresponding to the normalized working information vector belonging to the same temporary cluster are divided into the same working condition type.

5. The vehicle air conditioning filter life prediction system according to claim 1, characterized in that: The construction method of the time sequence operating condition sequence is: Assign a unique working condition digital code to each type of working condition. Take the current moment as the end point and intercept a sliding analysis window of constant length. Obtain the working condition digital code corresponding to the working condition type at each moment within the sliding analysis window, and integrate them according to the time sequence to obtain the time sequence working condition sequence.

6. The vehicle air conditioning filter life prediction system according to claim 1, characterized in that: The method for determining whether to trigger dynamic working condition attenuation adaptation is as follows: The autocorrelation function is used to analyze the regularity of the time series operating condition sequence to analyze whether the time series operating condition sequence has periodicity. If the time series operating condition sequence has significant periodicity, the operating condition cycle is obtained according to the autocorrelation function. If there are at least two complete operating condition cycles in the stable attenuation period, it is determined that the dynamic operating condition attenuation adaptation is triggered.

7. The vehicle air conditioning filter life prediction system according to claim 1, characterized in that: The average performance decay model is constructed as follows: The performance index vector at the starting point of the stable attenuation period and the performance index vector at the current moment are obtained respectively. The performance index vector includes filtration efficiency and resistance ratio. The average attenuation rate of filtration efficiency and the average growth rate of resistance ratio during the stable attenuation period are calculated. Combined with the performance index vector at the current moment, a model of the change of filtration efficiency and resistance ratio over time is constructed, and the average performance attenuation model is obtained by integration.

8. The vehicle air conditioning filter life prediction system according to claim 1, characterized in that: The method for obtaining the timing period working condition sequence group is as follows: Consecutive moments of the same operating condition type are combined into one operating condition period. The same operating condition type is the operating condition type of the operating condition period. The performance index vectors of the starting and ending points of each operating condition period within the stable attenuation period are obtained. The average attenuation rate of filtration efficiency and the average growth rate of resistance ratio within the operating condition period are calculated. Obtain all working condition periods within a working condition cycle and integrate them according to the time sequence and mark the sequence numbers in sequence to obtain a time period working condition sequence. All time period working condition sequences within the stable attenuation period are integrated according to the time sequence and marked with sequence numbers in sequence to obtain a time period working condition sequence group. Both the cycle sequence number and the sequence number are integer counted starting from 1.

9. The vehicle air conditioning filter life prediction system according to claim 8, characterized in that: The construction method of the working condition performance attenuation rate change model is as follows: In the time-series cycle operating condition sequence group, all operating condition periods with the same sequence number in the cycle are integrated according to the time sequence to obtain the time-series operating condition period sequence with the sequence number in the corresponding cycle; In each time-series operating condition period sequence, the sequence number corresponding to the operating condition period is taken as the independent variable, and the average attenuation rate of filtration efficiency and the average growth rate of resistance ratio in the operating condition period are taken as dependent variables. The linear relationship is fitted by the least squares method to obtain the filtration efficiency attenuation rate change model and the resistance ratio growth rate change model of the sequence number in each cycle, and the results are summarized and integrated to obtain the operating condition performance attenuation rate change model.

10. The vehicle air conditioning filter life prediction system according to claim 9, characterized in that: The remaining life of the vehicle air conditioning filter element is predicted using the operating performance attenuation rate change model as follows: Set a deduction period with the current moment as the starting point. If the current moment is not the end of the operating cycle, predict and complete the current operating cycle within the deduction period. Use the operating performance attenuation rate change model to sequentially calculate the Euclidean distance between the performance index vector at the end of each operating period within the deduction period and the preset performance index vector baseline, marking it as the predicted life end distance. If the predicted life end distance is less than the preset distance standard, the deduction stops. If the predicted life end distance at the end of the current operating cycle is still greater than or equal to the preset distance standard, the next operating cycle will continue to be deduced within the deduction period until a working period is found that stops the deduction. This is marked as the end operating period. The predicted life end distances of the moments within the end operating period are calculated in sequence according to the time sequence. If the predicted life end distance at any moment is less than the preset distance standard, the moment is determined to be the end of the life of the vehicle air conditioning filter element, and subsequent moments are no longer calculated. Calculate the time interval between the current moment and the end of the lifespan to obtain the remaining lifespan of the vehicle air conditioning filter.

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