Automatic detection and fault early warning system and method for self-service car washer

Abstract

The invention relates to an automatic detection and fault early warning system for a self-service car washer and a method thereof. The automatic detection and fault early warning system comprises a data acquisition module, a diagnosis circuit module, a water quality characteristic acquisition module, a correlation analysis module and an alarm module, according to the method, the operation state and the water quality condition of the equipment are comprehensively known by collecting various parameters in real time, potential problems are accurately recognized and an alarm is given in advance through correlation analysis and a dynamic early warning mechanism, the situations of false alarm and missing alarm are reduced, the early warning accuracy is improved, and the early warning efficiency is improved through correlation analysis and the dynamic early warning mechanism. The method adapts to the dynamic change of the equipment operation state and the water quality condition, predicts and intervenes potential problems in advance, and has the effects of improving the comprehensiveness of fault monitoring of the self-service car washer, enhancing the early warning accuracy and improving the adaptability and reliability of the system.

Description

Technical Field

[0001] The present application relates to the technical field of fault warning, and in particular to an automatic detection and fault warning system and method for a self-service car washing machine. Background Art

[0002] As the number of cars continues to increase, self-service car wash machines have become more and more popular among car owners as a convenient and efficient way to wash cars. However, during the long-term operation of self-service car wash machines, due to the influence of various factors such as complex working environment, equipment aging, and water quality changes, various execution devices (such as water pumps, motors, nozzles, etc.) and water treatment systems in their working systems are often prone to various failures, resulting in reduced car washing effects and even equipment damage or safety accidents.

[0003] In the existing technology, fault detection and early warning for self-service car wash machines mainly rely on manual inspection and simple sensor monitoring. Although manual inspection can intuitively find some problems, it has defects such as low efficiency and difficulty in real-time monitoring. Simple sensor monitoring can often only monitor a single parameter, which cannot fully reflect the operating status of the equipment and water quality, and cannot provide early warning of faults.

[0004] In particular, during the operation of a self-service car wash machine, there is a complex correlation between the operating parameters (such as motor speed, water pump pressure, nozzle spray angle, etc.) and the water quality. These correlations not only affect the car wash effect, but are also important precursors to failures. However, existing technologies often ignore the analysis and use of these correlations, resulting in a significant reduction in the accuracy and timeliness of fault warnings. Summary of the invention

[0005] In order to solve the above-mentioned defects, the present application provides an automatic detection and fault warning system and method for a self-service car washing machine.

[0006] The above-mentioned invention objective of the present application is achieved through the following technical solutions:

[0007] An automatic detection and fault warning system for a self-service car washing machine, comprising:

[0008] The data acquisition module is used to collect the operating parameters of each execution device in the working system of the self-service car washing machine and the water quality parameters in the water treatment system in real time;

[0009] A diagnostic circuit module, used to analyze operating parameters to diagnose the status of each execution device, and output operating parameter characteristics or issue an alarm signal based on the status analysis results;

[0010] A water quality feature acquisition module is used to extract and analyze the collected water quality parameters to obtain water quality features;

[0011] A correlation analysis module is used to perform correlation analysis on operating parameter characteristics and water quality characteristics to obtain trend characteristics and correlation characteristics, extract abnormal characteristics based on trend characteristics and correlation characteristics, and output abnormal signals;

[0012] The alarm module is used to issue fault prompt information when receiving an alarm signal or an abnormal signal.

[0013] By adopting the above technical solution, the data acquisition module is responsible for collecting the operating parameters of each executive device in the self-service car wash machine working system (such as speed, pressure, temperature, etc.) and the water quality parameters in the water treatment system (such as pH value, turbidity, residual chlorine, etc.) in real time; the diagnostic circuit module analyzes the collected operating parameters to determine whether the working status of each executive device is normal and outputs the operating parameter characteristics, and directly triggers the alarm signal when an abnormality is detected; the water quality feature acquisition module focuses on the processing and analysis of water quality parameters, and extracts key water quality features from the original data through feature extraction, providing an important basis for correlation analysis; the correlation analysis module conducts in-depth correlation analysis on the operating parameter characteristics and water quality characteristics to reveal the potential connection and trend between the two, and identify Identify abnormal features, output abnormal signals, and provide support for fault warning; when the alarm module receives an alarm signal or an abnormal signal, it will immediately issue a fault prompt message so that timely measures can be taken to prevent the fault from further deteriorating; this application collects multiple parameters in real time to fully understand the operating status and water quality of the equipment, and through correlation analysis and dynamic early warning mechanisms, accurately identify potential problems and issue alarms in advance, reduce false alarms and missed alarms, and improve the accuracy of early warnings. Through correlation analysis and dynamic early warning mechanisms, it adapts to the dynamic changes in the operating status of the equipment and the water quality, predicts potential problems and intervenes in advance, which has the effect of improving the comprehensiveness of self-service car wash machine fault monitoring, enhancing the accuracy of early warnings, and improving the adaptability and reliability of the system.

[0014] In a preferred example, the present application can be further configured as follows: the data acquisition module includes:

[0015] A physical sensor unit, wherein the physical sensor unit includes a temperature sensor, a pressure sensor, and a flow sensor;

[0016] A chemical sensor unit, wherein the chemical sensor includes a pH sensor, a conductivity sensor, a dissolved oxygen sensor, and an ammonia nitrogen sensor;

[0017] The self-calibration unit is used for periodically calibrating the physical sensor unit and the chemical sensor unit.

[0018] By adopting the above technical solution, the physical sensor unit includes a temperature sensor, a pressure sensor and a flow sensor, which are respectively used to monitor in real time the operating temperature and changes of each executive device of the self-service car washing machine during the car washing process, the working pressure of executive devices such as water pumps and nozzles during the car washing process, and the flow rate of water during the car washing process; the chemical sensor includes a pH sensor, a conductivity sensor, a dissolved oxygen sensor and an ammonia nitrogen sensor, wherein the pH sensor is used to measure the pH value of the water quality to ensure that the water used for car washing is within an appropriate pH range to protect the vehicle surface and the car washing equipment, the conductivity sensor reflects the electrical conductivity of water and helps to evaluate the purity of the water quality, the dissolved oxygen sensor monitors the dissolved oxygen content in the water, which is essential for maintaining the normal operation of the biological treatment system (such as the activated sludge method), and the ammonia nitrogen sensor is used to detect the ammonia nitrogen content in the water, which is one of the important indicators for evaluating the degree of water pollution; the self-calibration unit is used to regularly calibrate the physical sensor unit and the chemical sensor unit to ensure the accuracy and reliability of the sensor data and eliminate measurement errors caused by factors such as sensor aging and environmental changes.

[0019] In a preferred example, the present application can be further configured as follows: the diagnostic circuit module includes:

[0020] A predictive maintenance unit, used to predict the future operating state of the equipment and output operating parameter characteristics based on historical data and operating parameters input in real time by the data acquisition module;

[0021] A fault tree analysis unit, used to perform fault analysis on operating parameter characteristics and match corresponding fault paths based on the analysis results;

[0022] The microprocessor is used to receive the operating parameters input by the data acquisition module in real time and store historical data, and to send out an alarm signal when receiving fault path information and output operating parameter characteristics when receiving operating parameter characteristics.

[0023] By adopting the above technical solution, the predictive maintenance unit uses historical data and operating parameters input in real time by the data acquisition module to build a prediction model to explore the potential rules in the data, predict the future operating state of the equipment and output the operating parameter characteristics; the fault tree analysis unit analyzes the operating parameter characteristics output by the predictive maintenance unit: by building a fault tree model to analyze the possible causes and paths of the fault, and matching the corresponding fault path based on the analysis results; the microprocessor is responsible for receiving the operating parameters input in real time by the data acquisition module, and storing historical data for use by the predictive maintenance unit. At the same time, when the microprocessor receives the fault path information output by the fault tree analysis unit , an alarm signal will be issued immediately, and when the operating parameter characteristics output by the predictive maintenance unit are received, the microprocessor will output the operating parameter characteristics; through the above design, the present application realizes comprehensive monitoring and accurate early warning of the working system of the self-service car wash machine. The use of the predictive maintenance unit enables the system to detect potential faults in advance, avoids equipment downtime and service interruption caused by faults, and improves equipment availability and customer satisfaction. The introduction of the fault tree analysis unit simplifies the troubleshooting process and reduces the difficulty and cost of maintenance. The efficient processing capability and information transmission mechanism of the microprocessor ensure the coordinated work of various parts of the system and improves the overall efficiency.

[0024] In a preferred example, the present application can be further configured as follows: the water quality feature acquisition module includes:

[0025] A feature extraction unit is used to extract features from the collected water quality parameters, thereby obtaining several types of preliminary features;

[0026] A data analysis unit is used to pre-process and conduct in-depth analysis of preliminary features to obtain fluctuation range features;

[0027] The water quality status assessment unit is used to analyze the water quality status based on the fluctuation range characteristics and output the water quality characteristics based on the analysis results.

[0028] By adopting the above technical solution, the feature extraction unit performs preliminary processing on the water quality parameters (such as temperature, pH value, conductivity, dissolved oxygen, ammonia nitrogen, etc.) obtained by the data acquisition module, that is, extracts several types of preliminary features that can represent the water quality characteristics from the original data, including statistical quantities such as the mean, standard deviation, maximum value, minimum value of the parameters, or specific patterns identified by specific algorithms; the data analysis unit further preprocesses and deeply analyzes the preliminary features output by the feature extraction unit. The preprocessing includes data cleaning (removing outliers, filling missing values, etc.), normalization or standardization processing to ensure the accuracy and comparability of the data. In-depth analysis involves complex statistical methods, machine learning algorithms or data mining techniques to reveal the fluctuation range characteristics of water quality parameters, that is, the range of change and stability of water quality parameters under certain time or conditions; the water quality status assessment unit conducts a comprehensive assessment of the water quality status based on the fluctuation range characteristics output by the data analysis unit, and outputs the assessment results in the form of water quality characteristics; this application realizes efficient and accurate monitoring and assessment of the water quality of self-service car wash machines through the combination of feature extraction, data analysis and water quality status assessment, which has multiple effects such as improving water quality management efficiency, reducing water treatment costs, and ensuring car washing effects and equipment safety.

[0029] In a preferred example, the present application can be further configured as follows: the association analysis module includes:

[0030] A feature fusion unit is used to fuse the operating parameter features and the water quality features to generate comprehensive features;

[0031] A trend prediction unit, used for performing trend prediction on comprehensive features based on a preset time series analysis technology to obtain trend features;

[0032] An association mining unit, used for performing association mining on the comprehensive features based on a predefined data mining algorithm to obtain association features between the operating parameters and the water quality features;

[0033] The anomaly detection unit is used to dynamically set the anomaly threshold according to the correlation characteristics and trend characteristics, and perform anomaly detection on the comprehensive characteristics input in real time, and output an abnormal signal when an abnormal feature is detected.

[0034] By adopting the above technical solution, the feature fusion unit fuses the operating parameter features and water quality features provided by the data acquisition module and the water quality feature acquisition module respectively, integrates the two types of features in a unified space or framework, and generates comprehensive features that can fully reflect the working status and water quality status of the self-service car washing machine; the trend prediction unit uses the preset time series analysis technology to perform trend prediction on the fused comprehensive features; the association mining unit performs association mining on the comprehensive features based on the predefined data mining algorithm to reveal the correlation between the operating parameter features and the water quality features, such as how changes in certain operating parameters affect the water quality features, or how certain changes in water quality features reflect changes in the operating status; the anomaly detection unit dynamically sets the anomaly threshold according to the association features and trend features, and performs anomaly detection on the comprehensive features input in real time. When an abnormal feature is detected, the anomaly detection unit will immediately output an abnormal signal, which is helpful to timely discover and handle abnormal situations in the system and ensure the stable operation of the system; this application realizes comprehensive and in-depth analysis and intelligent early warning of the water treatment and monitoring system of the self-service car washing machine through the combination of feature fusion, trend prediction, association mining and anomaly detection, which has multiple effects such as improving system operation efficiency, reducing failure rate and optimizing water quality management.

[0035] In a preferred example, the present application may be further configured as follows: when generating comprehensive features, the feature fusion unit constructs time series data for the comprehensive features, and the trend prediction unit is used to:

[0036] Based on the preset time series clustering algorithm, the time series data in the comprehensive features are clustered and analyzed, and several different operation modes are identified;

[0037] Extract similar patterns from time series data and perform trend prediction through a preset dynamic time warping algorithm to obtain trend characteristics;

[0038] The association mining unit is used to:

[0039] Based on the results of cluster analysis, the association between operating parameter characteristics and water quality characteristics under different operating modes is mined to extract the associated features;

[0040] The extracted association features are combined with a preset data mining algorithm to perform association rule mining, thereby identifying the association rules corresponding to the association features.

[0041] By adopting the above technical solution, when generating comprehensive features, the feature fusion unit not only fuses the operating parameter features and the water quality features, but also constructs time series data for the comprehensive features, so that each comprehensive feature contains information on the time dimension; the trend prediction unit uses a preset time series clustering algorithm to perform clustering analysis on the time series data in the comprehensive features to classify features with similar change patterns into one category, thereby identifying several different operating modes, and extracts similar patterns from the time series data through a preset dynamic time warping algorithm and performs trend prediction. The dynamic time warping algorithm can handle nonlinear changes and time offset problems in time series data, thereby more accurately Predict future trends of features; the association mining unit mines the association between operating parameter features and water quality features under different operating modes based on the results of cluster analysis, and performs association rule mining on the extracted association features in combination with the pre-set data mining algorithm, and identifies the causal relationship or correlation between operating parameters and water quality features by mining the potential rules between the association features; this application combines feature fusion, time series construction, cluster analysis, dynamic time warping algorithm, and association rule mining to achieve comprehensive and in-depth analysis and intelligent early warning of the water treatment and monitoring system of self-service car wash machines, which not only improves the system operation efficiency and water quality management level, but also reduces the failure rate and maintenance cost.

[0042] The second object of the invention is achieved by the following technical solutions:

[0043] An automatic detection and fault warning method for a self-service car washing machine, comprising:

[0044] Collect the operating parameters of each execution device in the working system of the self-service car wash machine and the water quality parameters in the water treatment system in real time; analyze the operating parameters to diagnose the status of each execution device, and output the operating parameter characteristics or issue an alarm signal based on the status analysis results; extract and analyze the collected water quality parameters to obtain water quality characteristics; perform correlation analysis on the operating parameter characteristics and water quality characteristics to obtain trend characteristics and correlation characteristics, extract abnormal characteristics based on the trend characteristics and correlation characteristics and output abnormal signals; issue a fault prompt message when an alarm signal or abnormal signal is received.

[0045] By adopting the above technical scheme, the operating parameters of each execution device in the working system of the self-service car wash machine and the water quality parameters in the water treatment system are collected in real time; the operating parameters are analyzed to diagnose the status of each execution device, and the operating parameter characteristics are output or an alarm signal is issued based on the status analysis results; the collected water quality parameters are feature extracted and analyzed to obtain water quality characteristics; correlation analysis is performed on the operating parameter characteristics and water quality characteristics to obtain trend characteristics and correlation characteristics, and abnormal characteristics are extracted based on the trend characteristics and correlation characteristics and abnormal signals are output; and a fault prompt message is issued when an alarm signal or abnormal signal is received.

[0046] In a preferred example, the present application can be further configured as follows: analyzing the operating parameters to diagnose the status of each execution device, and outputting operating parameter characteristics or issuing an alarm signal based on the status analysis result, including:

[0047] Based on historical data and the operating parameters input by the data acquisition module in real time, the future operating state of the equipment is predicted and the operating parameter characteristics are output; fault analysis is performed on the operating parameter characteristics and the corresponding fault path is matched based on the analysis results; the operating parameters input by the data acquisition module in real time and the historical data are received, and an alarm signal is issued when the fault path information is received, and the operating parameter characteristics are output when the operating parameter characteristics are received.

[0048] By adopting the above technical solution, the operating parameters are analyzed to diagnose the status of each execution device, and the operating parameter characteristics are output or an alarm signal is issued based on the status analysis results, including predicting the future operating status of the equipment and outputting the operating parameter characteristics based on historical data and the operating parameters input in real time by the data acquisition module; performing fault analysis on the operating parameter characteristics and matching the corresponding fault path based on the analysis results; receiving the operating parameters input in real time by the data acquisition module and storing historical data, and issuing an alarm signal when receiving the fault path information, and outputting the operating parameter characteristics when receiving the operating parameter characteristics.

[0049] In a preferred example, the present application can be further configured as follows: extracting and analyzing the collected water quality parameters to obtain water quality characteristics includes:

[0050] The collected water quality parameters are feature extracted to obtain several types of preliminary features; the preliminary features are preprocessed and deeply analyzed to obtain the fluctuation range features; the water quality status is analyzed based on the fluctuation range features, and the water quality features are output based on the analysis results.

[0051] By adopting the above technical scheme, the collected water quality parameters are feature extracted and analyzed to obtain water quality characteristics, including feature extraction of the collected water quality parameters to obtain several types of preliminary features; preprocessing and in-depth analysis of the preliminary features to obtain fluctuation range features; analyzing the water quality state based on the fluctuation range features, and outputting the water quality characteristics based on the analysis results.

[0052] In summary, the present application includes at least one of the following beneficial technical effects:

[0053] 1. This application collects multiple parameters in real time to comprehensively understand the operating status of the equipment and the water quality conditions. Through correlation analysis and dynamic early warning mechanisms, it accurately identifies potential problems and issues alarms in advance, reduces false alarms and missed alarms, and improves the accuracy of early warnings. Through correlation analysis and dynamic early warning mechanisms, it adapts to the dynamic changes in the operating status of the equipment and the water quality conditions, predicts potential problems and intervenes in advance, which has the effect of improving the comprehensiveness of self-service car wash machine fault monitoring, enhancing the accuracy of early warnings, and improving the adaptability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0055] Figure 1 This is a block diagram of an automatic detection and fault warning system for a self-service car wash machine in the present application;

[0056] Figure 2 It is a block diagram of a data acquisition module in an embodiment of an automatic detection and fault warning system for a self-service car wash machine of the present application;

[0057] Figure 3 It is a block diagram of a diagnostic circuit module in an embodiment of an automatic detection and fault warning system for a self-service car wash machine of the present application;

[0058] Figure 4 It is a block diagram of a water quality characteristic acquisition module in an embodiment of an automatic detection and fault warning system for a self-service car washing machine of the present application;

[0059] Figure 5 It is a block diagram of a correlation analysis module in an embodiment of an automatic detection and fault warning system for a self-service car wash machine of the present application. DETAILED DESCRIPTION

[0060] The following is combined with Figure 1-5This application is described in further detail.

[0061] In one embodiment, if Figure 1 As shown, the present application discloses an automatic detection and fault warning system 100 for a self-service car washing machine, which specifically includes:

[0062] The data acquisition module 110 is used to collect the operating parameters of each execution device in the working system of the self-service car washing machine and the water quality parameters in the water treatment system in real time;

[0063] The diagnostic circuit module 120 is used to analyze the operating parameters to diagnose the status of each execution device, and output the operating parameter characteristics or issue an alarm signal based on the status analysis results;

[0064] The water quality characteristic acquisition module 130 is used to extract and analyze the collected water quality parameters to obtain water quality characteristics;

[0065] The correlation analysis module 140 is used to perform correlation analysis on the operating parameter characteristics and the water quality characteristics to obtain trend characteristics and correlation characteristics, extract abnormal characteristics based on the trend characteristics and the correlation characteristics, and output abnormal signals;

[0066] An alarm module 150 is used to issue a fault prompt message when receiving an alarm signal or an abnormal signal;

[0067] In this embodiment, the operating parameters refer to the specific values ​​or states of each executive device (such as motor speed, water pump pressure, nozzle spray angle, etc.) in the working system of the self-service car washing machine during the working process, which reflects the operating state and working performance of the equipment; water quality parameters refer to the physical and chemical properties of water in the water treatment system, such as pH value (acidity and alkalinity), turbidity (water clarity), conductivity (water conductivity), etc., which directly reflect the quality of water; state analysis is to determine whether the executive device is in a normal working state or whether there is a potential fault through the analysis of the operating parameters, which involves threshold judgment, trend analysis, pattern recognition, etc. of the parameters; operating parameter features are features extracted from the operating parameters that can represent the operating state of the equipment, such as average speed, pressure fluctuation range, etc.; the alarm signal is a signal issued when the diagnostic circuit module detects a device fault or abnormal state, which is used to trigger the alarm module to issue a fault prompt message; feature extraction is to extract features that can represent the water quality status from the water quality parameters, such as the stability of the pH value, the change trend of the turbidity, etc. Water quality characteristics are characteristic representations of water quality parameters obtained after feature extraction, which are used for subsequent water quality analysis and correlation analysis; correlation analysis is a data analysis method used to reveal the relationship between two or more variables. In the self-service car washing machine system, correlation analysis is used to reveal the intrinsic connection between operating parameter characteristics and water quality characteristics; trend characteristics are the changing trends of operating parameter characteristics or water quality characteristics over time obtained through time series analysis, such as gradual increase, decrease or fluctuation; correlation characteristics are characteristics that represent the correlation between operating parameter characteristics and water quality characteristics, such as the positive or negative correlation between the change of a certain operating parameter and a certain characteristic of water quality; abnormal characteristics are characteristics that are significantly different from the normal operating state or water quality condition, which may indicate equipment failure, water quality deterioration or other abnormal conditions; fault prompt information is the prompt information issued by the alarm module when the system detects equipment failure or water quality abnormality, which is used to inform users or maintenance personnel to take corresponding treatment measures, and can be presented in the form of sound, light, text or icons on the display screen;

[0068] Specifically, the data acquisition module is responsible for collecting the operating parameters of each executive device in the working system of the self-service car wash machine (such as speed, pressure, temperature, etc.) and the water quality parameters in the water treatment system (such as pH value, turbidity, residual chlorine, etc.) in real time; the diagnostic circuit module analyzes the collected operating parameters to determine whether the working status of each executive device is normal and outputs the operating parameter characteristics, and directly triggers the alarm signal when an abnormality is detected; the water quality feature acquisition module focuses on the processing and analysis of water quality parameters, and extracts key water quality features from the original data through feature extraction, providing an important basis for correlation analysis; the correlation analysis module conducts in-depth correlation analysis on the operating parameter characteristics and water quality characteristics to reveal the potential connection and trend between the two, and identifies abnormal characteristics, outputs abnormal signals, and provides support for fault warning; when the alarm module receives an alarm signal or an abnormal signal, it will immediately issue a fault prompt message so that timely measures can be taken to prevent the fault from further deteriorating.

[0069] In one embodiment, if Figure 2 As shown, the data acquisition module 110 includes:

[0070] A physical sensor unit 111, wherein the physical sensor unit includes a temperature sensor, a pressure sensor and a flow sensor;

[0071] A chemical sensor unit 112, wherein the chemical sensor includes a pH sensor, a conductivity sensor, a dissolved oxygen sensor, and an ammonia nitrogen sensor;

[0072] A self-calibration unit 113, used for periodically calibrating the physical sensor unit and the chemical sensor unit;

[0073] In this embodiment, the temperature sensor is used to measure the temperature of water in the working system and water treatment system of the self-service car washing machine; the pressure sensor is used to detect the working pressure of equipment such as water pumps and nozzles; the flow sensor is used to measure the flow rate of water, that is, the volume of water passing through a pipe or equipment per unit time; the pH sensor is used to measure the acidity and alkalinity (pH value) of water; the conductivity sensor is used to measure the conductivity of water, that is, the electrical conductivity of water; the dissolved oxygen sensor is used to measure the content of dissolved oxygen in water; the ammonia nitrogen sensor is used to detect the content of ammonia nitrogen in water; calibration is the process of correcting the measured value of the sensor to ensure that its accuracy is consistent with the standard value or reference value. Since the sensor may be affected by factors such as environment, temperature, humidity, etc. during use and produce deviations, it needs to be calibrated regularly;

[0074] Specifically, the physical sensor unit includes a temperature sensor, a pressure sensor and a flow sensor, which are used to monitor in real time the operating temperature and changes of each executive device of the self-service car wash machine during the car washing process, the working pressure of executive devices such as water pumps and nozzles during the car washing process, and the flow rate of water during the car washing process; the chemical sensor includes a pH sensor, a conductivity sensor, a dissolved oxygen sensor and an ammonia nitrogen sensor, among which the pH sensor is used to measure the pH value of the water to ensure that the water used for car washing is within an appropriate pH range to protect the vehicle surface and the car washing equipment. The conductivity sensor reflects the electrical conductivity of water and helps to evaluate the purity of the water quality. The dissolved oxygen sensor monitors the dissolved oxygen content in the water, which is crucial to maintaining the normal operation of the biological treatment system (such as the activated sludge method). The ammonia nitrogen sensor is used to detect the ammonia nitrogen content in the water, which is one of the important indicators for evaluating the degree of water pollution; the self-calibration unit is used to regularly calibrate the physical sensor unit and the chemical sensor unit to ensure the accuracy and reliability of the sensor data and eliminate measurement errors caused by factors such as sensor aging and environmental changes.

[0075] In one embodiment, if Figure 3 As shown, the diagnostic circuit module 120 includes:

[0076] A predictive maintenance unit 121, for predicting the future operating state of the equipment and outputting operating parameter characteristics based on historical data and operating parameters input in real time by the data acquisition module;

[0077] A fault tree analysis unit 122, used to perform fault analysis on the operating parameter characteristics and match corresponding fault paths based on the analysis results;

[0078] The microprocessor 123 is used to receive the operating parameters input by the data acquisition module in real time and store historical data, and to send out an alarm signal when receiving fault path information and output operating parameter characteristics when receiving operating parameter characteristics;

[0079] In this embodiment, predictive maintenance is the process of using historical data and real-time data to analyze the operating status of the equipment, predict possible future failures or performance degradation of the equipment, and formulate a maintenance plan based on this; historical data is the record data of the equipment operation in the past period of time, including operating parameters, failure records, maintenance records, etc.; operating parameter characteristics are the characteristics that can represent the operating status of the equipment extracted by the predictive maintenance unit through analyzing historical data and real-time input operating parameters, including the average value, standard deviation, change trend, etc. of the parameters, which are used to predict the future operating status of the equipment; fault tree analysis is a logical analysis method for identifying and analyzing various possible causes of equipment failure. It constructs a fault tree, takes the failure as the top event, and then decomposes the intermediate events and basic events that cause the failure layer by layer. The fault path is the path from the top event (fault) to the basic event (fault cause) in the fault tree analysis, and each fault path represents a combination of causes that may cause the fault to occur; the fault analysis is the process in which the fault tree analysis unit conducts an in-depth analysis of the operating parameter characteristics, identifies possible fault modes, and matches the corresponding fault path based on the analysis results; the alarm signal is the signal sent by the microprocessor when the fault tree analysis unit matches the fault path, and the alarm signal can trigger the alarm module to send out fault prompt information so that the user or maintenance personnel can take timely measures; the output operating parameter characteristics is the process in which the microprocessor outputs these characteristics to the association analysis module or other modules that need these data when receiving the operating parameter characteristics output by the predictive maintenance unit;

[0080] Specifically, the predictive maintenance unit uses historical data and operating parameters input in real time by the data acquisition module to build a prediction model to explore potential rules in the data, predict the future operating status of the equipment and output operating parameter characteristics; the fault tree analysis unit analyzes the operating parameter characteristics output by the predictive maintenance unit: by building a fault tree model to analyze the possible causes and paths of the fault, and matching the corresponding fault path based on the analysis results; the microprocessor is responsible for receiving the operating parameters input in real time by the data acquisition module, and storing historical data for use by the predictive maintenance unit. At the same time, when the microprocessor receives the fault path information output by the fault tree analysis unit, it will immediately issue an alarm signal, and when receiving the operating parameter characteristics output by the predictive maintenance unit, the microprocessor will output the operating parameter characteristics.

[0081] In one embodiment, if Figure 4 As shown, the water quality characteristic acquisition module 130 includes:

[0082] The feature extraction unit 131 is used to extract features from the collected water quality parameters, thereby obtaining several types of preliminary features;

[0083] A data analysis unit 132, used for preprocessing and further analyzing the preliminary features, so as to obtain the fluctuation range features;

[0084] A water quality status evaluation unit 133, for analyzing the water quality status based on the fluctuation range characteristics, and outputting water quality characteristics based on the analysis results;

[0085] In this embodiment, feature extraction is the process of extracting key information or indicators that can represent data characteristics from raw data. In water quality monitoring, the feature extraction unit 131 is used to extract preliminary features from the collected water quality parameters (such as pH value, conductivity, dissolved oxygen, etc.); preliminary features are basic features that can reflect certain aspects of water quality parameters after being processed by the feature extraction unit, including statistical quantities such as the mean, maximum, minimum, and standard deviation of the parameters, or characteristic values ​​calculated by a specific algorithm; preprocessing is the pre-processing of the preliminary features by the data analysis unit 132 before in-depth analysis, including data cleaning (removing outliers, filling missing values, etc.), data normalization (converting data of different dimensions to the same dimension), and other steps; in-depth analysis is the process of the data analysis unit performing a more in-depth analysis of the preliminary features on the basis of preprocessing, such as trend Analysis, correlation analysis, cluster analysis, etc., to reveal the inherent laws and abnormal changes of water quality parameters; the fluctuation range characteristics are obtained through in-depth analysis, which can reflect the fluctuation of water quality parameters, including the fluctuation amplitude, fluctuation frequency, fluctuation trend, etc. of the parameters; water quality state analysis is the process in which the water quality state assessment unit 133 analyzes the water quality state based on the fluctuation range characteristics to determine whether the water quality is in a normal state, whether there are abnormalities or potential risks; water quality characteristics are obtained after being processed by the water quality state assessment unit 133, which can comprehensively reflect the water quality status, including the quality grade of water quality, the type and concentration of pollutants, the stability of water quality, etc.; the output result is the process in which the water quality state assessment unit 133 outputs the water quality characteristics according to the analysis results, and this water quality characteristic can be presented to users or related systems in the form of reports, charts, alarms, etc., so that corresponding measures can be taken in time to ensure water quality safety;

[0086] Specifically, the feature extraction unit performs preliminary processing on the water quality parameters (such as temperature, pH value, conductivity, dissolved oxygen, ammonia nitrogen, etc.) obtained by the data acquisition module, that is, extracts several types of preliminary features that can represent the water quality characteristics from the original data, including statistical quantities such as the mean, standard deviation, maximum value, minimum value of the parameters, or specific patterns identified by specific algorithms; the data analysis unit further preprocesses and conducts in-depth analysis on the preliminary features output by the feature extraction unit. The preprocessing includes data cleaning (removing outliers, filling missing values, etc.), normalization or standardization processing to ensure the accuracy and comparability of the data. The in-depth analysis involves complex statistical methods, machine learning algorithms or data mining techniques to reveal the fluctuation range characteristics of water quality parameters, that is, the range of changes and stability of water quality parameters under certain time or conditions; the water quality status assessment unit conducts a comprehensive assessment of the water quality status based on the fluctuation range characteristics output by the data analysis unit, and outputs the assessment results in the form of water quality characteristics.

[0087] In one embodiment, if Figure 5 As shown, the association analysis module 140 includes:

[0088] A feature fusion unit 141 is used to fuse the operating parameter features and the water quality features to generate a comprehensive feature;

[0089] A trend prediction unit 142, configured to perform trend prediction on the comprehensive features based on a preset time series analysis technique to obtain trend features;

[0090] The association mining unit 143 is used to perform association mining on the comprehensive features based on a predefined data mining algorithm to obtain association features between the operating parameters and the water quality features;

[0091] The anomaly detection unit 144 is used to dynamically set an anomaly threshold according to the correlation characteristics and trend characteristics, and perform anomaly detection on the comprehensive characteristics input in real time, and output an anomaly signal when an abnormal feature is detected;

[0092] In this embodiment, feature fusion is to combine features from different sources or types (in this case, operating parameter features and water quality features) into a unified comprehensive feature; the comprehensive feature is obtained by feature fusion, and is a single representation that integrates the operating parameter features and water quality features; the time series analysis technology is a statistical technology used to analyze the changing trend of data over time. In the association analysis module 140, the trend prediction unit 142 uses the time series analysis technology to predict the future changes of the comprehensive feature; the trend feature is the trend information about the change of the comprehensive feature over time obtained by time series analysis; the data mining algorithm is an algorithm used to extract useful information and patterns from a large amount of data. In the association analysis module 140, the association mining unit 143 uses a predefined data mining algorithm to mine the operating parameter The association between the numerical features and the water quality features; the association features are the features obtained through association mining, which are about the relationship between the operating parameter features and the water quality features, which reveal the interaction and influence between different features; the abnormal threshold is a boundary value dynamically set according to the association features and trend features, and is used to determine whether the comprehensive features are abnormal; anomaly detection is the process of comparing the comprehensive features input in real time with the abnormal threshold to identify any features that deviate from the normal mode; the abnormal feature is the comprehensive feature identified as abnormal by the anomaly detection unit 144, which may indicate that the system has a fault, water quality deterioration or other abnormal conditions; the abnormal signal is the signal output by the anomaly detection unit 144 when the abnormal feature is detected, and the abnormal signal can trigger the alarm module or other response mechanism so that measures can be taken in time to deal with the abnormal situation;

[0093] Specifically, the feature fusion unit fuses the operating parameter features and water quality features provided by the data acquisition module and the water quality feature acquisition module respectively, integrates the two types of features in a unified space or framework, and generates comprehensive features that can comprehensively reflect the working status and water quality conditions of the self-service car wash machine; the trend prediction unit uses the preset time series analysis technology to perform trend prediction on the fused comprehensive features; the association mining unit performs association mining on the comprehensive features based on the predefined data mining algorithm to reveal the correlation between the operating parameter features and the water quality features, such as how changes in certain operating parameters affect the water quality characteristics, or how certain changes in water quality characteristics reflect changes in the operating status; the anomaly detection unit dynamically sets the anomaly threshold according to the association features and trend features, and performs anomaly detection on the comprehensive features input in real time. When an abnormal feature is detected, the anomaly detection unit will immediately output an abnormal signal, which helps to promptly discover and handle abnormal conditions in the system and ensure the stable operation of the system.

[0094] In one embodiment, when generating comprehensive features, the feature fusion unit 141 constructs time series data for the comprehensive features, and the trend prediction unit 142 is used to:

[0095] Based on the preset time series clustering algorithm, the time series data in the comprehensive features are clustered and analyzed, and several different operation modes are identified;

[0096] Extract similar patterns from time series data and perform trend prediction through a preset dynamic time warping algorithm to obtain trend characteristics;

[0097] The association mining unit 143 is used for:

[0098] Based on the results of cluster analysis, the association between operating parameter characteristics and water quality characteristics under different operating modes is mined to extract the associated features;

[0099] Combine the pre-set data mining algorithm to mine the association rules of the extracted association features, so as to identify the association rules corresponding to the association features;

[0100] In this embodiment, the time series data is a sequence of observation values ​​arranged in chronological order, which is constructed by the feature fusion unit 141 for the comprehensive features during the feature fusion process, and records the values ​​of a variable or a group of variables at different time points. In this embodiment, the time series data includes the changes of operating parameter characteristics and water quality characteristics over time; the time series clustering algorithm is an algorithm for grouping or clustering time series data, and the trend prediction unit 142 uses the time series clustering algorithm to perform cluster analysis on the time series data in the comprehensive features to identify several different operating modes, which may represent the working status of the system under different conditions or the water quality change mode; dynamic time warping The algorithm is an algorithm for measuring the similarity between two time series. The trend prediction unit 142 extracts similar patterns from the time series data through the DTW algorithm, and performs trend prediction based on the similar patterns to obtain trend features. The DTW algorithm can handle the nonlinear alignment problem of time series on the time axis, so it is suitable for identifying similar patterns with time delays or speed changes. Association rule mining is to discover the relationship or pattern between items in the data set. Association rules are rules about the relationship between associated features obtained through association rule mining. In this embodiment, the association rules describe the causal relationship or co-occurrence relationship between certain operating parameter features and water quality features under different operating modes.

[0101] Specifically, when generating comprehensive features, the feature fusion unit not only fuses the operating parameter features and water quality features, but also constructs time series data for the comprehensive features, so that each comprehensive feature contains information on the time dimension; the trend prediction unit uses a preset time series clustering algorithm to perform clustering analysis on the time series data in the comprehensive features to classify features with similar change patterns into one category, thereby identifying several different operating modes, and extracts similar patterns from the time series data through a preset dynamic time warping algorithm and performs trend prediction. The dynamic time warping algorithm can handle nonlinear changes and time offset problems in time series data, thereby more accurately predicting future trends of features; the association mining unit mines the association between operating parameter features and water quality features under different operating modes based on the results of cluster analysis, and combines the preset data mining algorithm to perform association rule mining on the extracted association features, and identifies the causal relationship or correlation between the operating parameters and water quality features by mining the potential rules between the association features.

[0102] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0103] In one embodiment, a method for automatic detection and fault warning for a self-service car washing machine is provided, and the method for automatic detection and fault warning for a self-service car washing machine corresponds one-to-one with the automatic detection and fault warning system for a self-service car washing machine in the above embodiment. The method for automatic detection and fault warning for a self-service car washing machine includes:

[0104] Collect the operating parameters of each execution device in the working system of the self-service car wash machine and the water quality parameters in the water treatment system in real time; analyze the operating parameters to diagnose the status of each execution device, and output the operating parameter characteristics or issue an alarm signal based on the status analysis results; extract and analyze the collected water quality parameters to obtain water quality characteristics; perform correlation analysis on the operating parameter characteristics and water quality characteristics to obtain trend characteristics and correlation characteristics, extract abnormal characteristics based on the trend characteristics and correlation characteristics, and output abnormal signals; issue fault prompt information when receiving an alarm signal or abnormal signal;

[0105] Optionally, analyzing the operating parameters to diagnose the status of each execution device, and outputting operating parameter characteristics or issuing an alarm signal based on the status analysis result, including:

[0106] Based on the historical data and the operating parameters input in real time by the data acquisition module, predict the future operating state of the equipment and output the operating parameter characteristics; perform fault analysis on the operating parameter characteristics and match the corresponding fault path based on the analysis results; receive the operating parameters input in real time by the data acquisition module and store the historical data, and issue an alarm signal when receiving the fault path information and output the operating parameter characteristics when receiving the operating parameter characteristics;

[0107] Optionally, feature extraction and analysis are performed on the collected water quality parameters to obtain water quality characteristics, including:

[0108] Extract features from the collected water quality parameters to obtain several types of preliminary features; pre-process and deeply analyze the preliminary features to obtain fluctuation range features; analyze the water quality status based on the fluctuation range features, and output water quality features based on the analysis results;

[0109] For the specific definition of an automatic detection and fault warning method for a self-service car wash machine, please refer to the above definition of an automatic detection and fault warning system for a self-service car wash machine, which will not be repeated here. Each module in the above-mentioned automatic detection and fault warning system for a self-service car wash machine can be implemented in whole or in part through software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0110] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An automatic detection and fault warning system for a self-service car washing machine, characterized in that: include: The data acquisition module is used to collect the operating parameters of each execution device in the working system of the self-service car washing machine and the water quality parameters in the water treatment system in real time; A diagnostic circuit module, used to analyze operating parameters to diagnose the status of each execution device, and output operating parameter characteristics or issue an alarm signal based on the status analysis results; A water quality feature acquisition module is used to extract and analyze the collected water quality parameters to obtain water quality features; A correlation analysis module is used to perform correlation analysis on operating parameter characteristics and water quality characteristics to obtain trend characteristics and correlation characteristics, extract abnormal characteristics based on trend characteristics and correlation characteristics, and output abnormal signals; The alarm module is used to issue fault prompt information when receiving an alarm signal or an abnormal signal.

2. The automatic detection and fault warning system for a self-service car washing machine according to claim 1, characterized in that: The data acquisition module comprises: A physical sensor unit, wherein the physical sensor unit includes a temperature sensor, a pressure sensor, and a flow sensor; A chemical sensor unit, wherein the chemical sensor includes a pH sensor, a conductivity sensor, a dissolved oxygen sensor, and an ammonia nitrogen sensor; The self-calibration unit is used for periodically calibrating the physical sensor unit and the chemical sensor unit.

3. The automatic detection and fault warning system for a self-service car washing machine according to claim 1, characterized in that: The diagnostic circuit module comprises: A predictive maintenance unit, used to predict the future operating state of the equipment and output operating parameter characteristics based on historical data and operating parameters input in real time by the data acquisition module; A fault tree analysis unit, used to perform fault analysis on operating parameter characteristics and match corresponding fault paths based on the analysis results; The microprocessor is used to receive the operating parameters input by the data acquisition module in real time and store historical data, and to send out an alarm signal when receiving fault path information and output operating parameter characteristics when receiving operating parameter characteristics.

4. The automatic detection and fault warning system for a self-service car washing machine according to claim 1, characterized in that: The water quality characteristic acquisition module comprises: A feature extraction unit is used to extract features from the collected water quality parameters, thereby obtaining several types of preliminary features; A data analysis unit is used to pre-process and conduct in-depth analysis of preliminary features to obtain fluctuation range features; The water quality status assessment unit is used to analyze the water quality status based on the fluctuation range characteristics and output the water quality characteristics based on the analysis results.

5. The automatic detection and fault warning system for a self-service car washing machine according to claim 1, characterized in that: The association analysis module comprises: A feature fusion unit is used to fuse the operating parameter features and the water quality features to generate comprehensive features; A trend prediction unit, used for performing trend prediction on comprehensive features based on a preset time series analysis technology to obtain trend features; An association mining unit, used for performing association mining on the comprehensive features based on a predefined data mining algorithm to obtain association features between the operating parameters and the water quality features; The anomaly detection unit is used to dynamically set the anomaly threshold according to the correlation characteristics and trend characteristics, and perform anomaly detection on the comprehensive characteristics input in real time, and output an abnormal signal when an abnormal feature is detected.

6. The automatic detection and fault warning system for a self-service car washing machine according to claim 5, characterized in that: When generating the comprehensive features, the feature fusion unit constructs time series data for the comprehensive features, and the trend prediction unit is used to: Based on the preset time series clustering algorithm, the time series data in the comprehensive features are clustered and analyzed, and several different operation modes are identified; Extract similar patterns from time series data and perform trend prediction through a preset dynamic time warping algorithm to obtain trend characteristics; The association mining unit is used to: Based on the results of cluster analysis, the association between operating parameter characteristics and water quality characteristics under different operating modes is mined to extract the associated features; The extracted association features are combined with a preset data mining algorithm to perform association rule mining, thereby identifying the association rules corresponding to the association features.

7. An automatic detection and fault warning method for a self-service car washing machine, characterized in that: include: Real-time collection of operating parameters of each executive device in the working system of the self-service car wash machine and water quality parameters in the water treatment system; Analyze the operating parameters to diagnose the status of each execution device, and output the operating parameter characteristics or issue an alarm signal based on the status analysis results; extract and analyze the collected water quality parameters to obtain water quality characteristics; Perform correlation analysis on the operating parameter characteristics and water quality characteristics to obtain trend characteristics and correlation characteristics, extract abnormal characteristics based on the trend characteristics and correlation characteristics, and output abnormal signals; issue fault prompt information when an alarm signal or abnormal signal is received.

8. The automatic detection and fault warning method for a self-service car washing machine according to claim 7, characterized in that: The analyzing the operating parameters to diagnose the status of each execution device and outputting the operating parameter characteristics or issuing an alarm signal based on the status analysis result includes: Based on historical data and the operating parameters input by the data acquisition module in real time, the future operating state of the equipment is predicted and the operating parameter characteristics are output; fault analysis is performed on the operating parameter characteristics and the corresponding fault path is matched based on the analysis results; the operating parameters input by the data acquisition module in real time and the historical data are received, and an alarm signal is issued when the fault path information is received, and the operating parameter characteristics are output when the operating parameter characteristics are received.

9. The automatic detection and fault warning method for a self-service car washing machine according to claim 7, characterized in that: The feature extraction and analysis of the collected water quality parameters to obtain water quality features includes: The collected water quality parameters are feature extracted to obtain several types of preliminary features; the preliminary features are preprocessed and deeply analyzed to obtain the fluctuation range features; the water quality status is analyzed based on the fluctuation range features, and the water quality features are output based on the analysis results.

Images