Water leakage detection method, device and equipment and storage medium

Abstract

The invention discloses a water leakage detection method, device and equipment and a storage medium, and relates to the technical field of intelligent household appliances. The method comprises the following steps: acquiring waterway characteristics and water consumption characteristics of the drinking equipment; inputting the waterway features into a pre-trained waterway anomaly detection model to obtain a first water leakage detection result; inputting the water consumption characteristics into a pre-trained water flow anomaly detection model to obtain a second water leakage detection result; and determining a water leakage detection result of the drinking equipment according to the first water leakage detection result and the second water leakage detection result. According to the technical scheme, the water leakage condition can be monitored in real time, and the water leakage detection efficiency and accuracy are improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of smart home appliances, and in particular, to a method, device, equipment, and storage medium for leak detection. Background Art

[0002] As an indispensable household appliance in modern life, the safety and stability of drinking water equipment are directly related to the drinking water health and usage experience of users. However, in actual applications, due to various factors such as equipment aging, component damage, improper installation, or operation errors, leakage problems occur from time to time. Leakage not only causes waste of water resources but may also damage the equipment itself and the surrounding environment, and even pose a safety hazard in severe cases. Therefore, it is particularly important to detect leaks in drinking water equipment.

[0003] Currently, traditional leak detection methods mainly rely on manual inspections or single physical sensors for detection. Although manual inspections can detect obvious leakage phenomena, they are inefficient and cannot monitor leakage in real time. And a single physical sensor can only conduct preliminary monitoring of leakage, but is easily affected by the environment, resulting in false alarms or missed alarms, thus affecting the accuracy of detection.

[0004] Therefore, there is an urgent need to propose a new method to solve the above problems. Summary of the Invention

[0005] The present invention provides a method, device, equipment, and storage medium for leak detection, which can not only monitor leakage in real time but also improve the efficiency and accuracy of leak detection.

[0006] In a first aspect, embodiments of the present invention provide a method for leak detection, the method comprising:

[0007] Obtain the water path characteristics and water usage characteristics of the drinking water equipment;

[0008] Input the water path characteristics into a pre-trained water path anomaly detection model to obtain a first leak detection result;

[0009] Input the water usage characteristics into a pre-trained water flow anomaly detection model to obtain a second leak detection result;

[0010] Determine the leak detection result of the drinking water equipment according to the first leak detection result and the second leak detection result.

[0011] An embodiment of the present invention provides a water leakage detection method, including: obtaining the water path characteristics and water usage characteristics of a drinking water device; inputting the water path characteristics into a pre-trained water path anomaly detection model to obtain a first water leakage detection result; inputting the water usage characteristics into a pre-trained water flow anomaly detection model to obtain a second water leakage detection result; and determining the water leakage detection result of the drinking water device according to the first water leakage detection result and the second water leakage detection result. In the above technical solution, the water path characteristics and water usage characteristics of the drinking water device are first obtained, providing a data basis for obtaining the first water leakage detection result and the second water leakage detection result later. Then, the water path characteristics are input into the pre-trained water path anomaly detection model to obtain the first water leakage detection result, and the water leakage condition of the drinking water device can be accurately determined based on the water path characteristics, significantly improving the efficiency and accuracy of water leakage detection, making the detection process faster and the result more reliable. At the same time, this automated process not only realizes real-time monitoring of water leakage but also reduces the labor burden. Inputting the water usage characteristics into the pre-trained water flow anomaly detection model to obtain the second water leakage detection result can determine the water leakage condition of the drinking water device by analyzing the water usage characteristics, greatly improving the efficiency and accuracy of water leakage detection, making the entire detection process faster, more accurate and efficient. Finally, determining the water leakage detection result of the drinking water device according to the first water leakage detection result and the second water leakage detection result can comprehensively evaluate the water leakage condition of the drinking water device. And compared with the traditional single-dimensional detection method, this multi-dimensional comprehensive judgment strategy fully considers the dynamic changes of different water path characteristics and water usage characteristics, reduces environmental interference, thus significantly improving the accuracy and reliability of water leakage detection, effectively reducing the probability of misjudgment and missed judgment, and then ensuring the safe operation and long-term stability of the drinking water device, providing a more solid and reliable water usage guarantee for users, and solving the problems of inability to real-time monitor water leakage, low efficiency and low accuracy in the prior art.

[0012] In a second aspect, an embodiment of the present invention further provides a water leakage detection device, which includes:

[0013] An acquisition module, configured to obtain the water path characteristics and water usage characteristics of a drinking water device;

[0014] A first detection module, configured to input the water path characteristics into a pre-trained water path anomaly detection model to obtain a first water leakage detection result;

[0015] A second detection module, configured to input the water usage characteristics into a pre-trained water flow anomaly detection model to obtain a second water leakage detection result;

[0016] A target detection module, configured to determine the water leakage detection result of the drinking water device according to the first water leakage detection result and the second water leakage detection result.

[0017] In a third aspect, an embodiment of the present invention further provides a drinking water device, which includes:

[0018] at least one processor; and a memory communicatively connected to the at least one processor;

[0019] wherein, the memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute any one of the leakage detection methods in the first aspect.

[0020] In a fourth aspect, an embodiment of the present invention further provides a storage medium containing computer-executable instructions, and when the computer-executable instructions are executed by a computer processor, any one of the leakage detection methods in the first aspect is implemented.

[0021] It should be noted that the above computer instructions may be stored in whole or in part on a computer-readable storage medium. Among them, the computer-readable storage medium may be packaged together with the processor of the leakage detection device, or may be packaged separately from the processor of the leakage detection device. This application does not make any limitation in this regard.

[0022] For the descriptions of the second aspect, the third aspect, and the fourth aspect in this application, reference may be made to the detailed description of the first aspect; and for the beneficial effects of the descriptions of the second aspect, the third aspect, and the fourth aspect, reference may be made to the analysis of the beneficial effects of the first aspect, which will not be elaborated here.

[0023] In this application, the names of the above-mentioned leakage detection devices do not constitute limitations on the devices or functional modules themselves. In actual implementation, these devices or functional modules may appear under other names. As long as the functions of each device or functional module are similar to those of this application and fall within the scope of the claims of this application and their equivalent technologies.

[0024] These aspects or other aspects of this application will be more clearly understood in the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0026] Figure 1 is a flowchart of a leakage detection method provided by an embodiment of the present invention;

[0027] Figure 2 is a flowchart of another leakage detection method provided by an embodiment of the present invention;

[0028] Figure 3 A structural schematic diagram of a water leakage detection device provided by an embodiment of the present invention;

[0029] Figure 4 A structural schematic diagram of a drinking water device provided by an embodiment of the present invention. Detailed implementation manners

[0030] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. Additionally, it should be noted that for the sake of description, only the parts related to the present invention rather than all the structures are shown in the drawings.

[0031] The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone.

[0032] Terms such as "first" and "second" in the description of this application and the accompanying drawings are used to distinguish different objects or different processes for the same object, rather than to describe the specific order of the objects.

[0033] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include other unlisted steps or units, or may optionally further include other steps or units inherent to these processes, methods, products or devices.

[0034] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operations are completed, but it may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc. In addition, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0035] It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0036] In the description of the present application, unless otherwise specified, the meaning of "a plurality" refers to two or more.

[0037] Figure 1 The figure is a flowchart of a water leakage detection method provided for an embodiment of the present invention. This embodiment is applicable to the situation where a drinking water device needs to perform water leakage detection. This method can be executed by a water leakage detection device, and the device can be implemented in a software and / or hardware manner. Exemplarily, the device can be a computer or a server. Refer to Figure 1 , the water leakage detection method of this embodiment specifically includes the following steps:

[0038] Step 110: Obtain the water path characteristics and water usage characteristics of the drinking water device.

[0039] Specifically, the drinking water device refers to a device that provides drinking water services. For example, the drinking water device can be a water dispenser or a tea bar machine. The water path characteristics refer to various parameters of the water path in the drinking water device. For example, the water path characteristics can be the water path temperature, the water path humidity, and the water path pressure. The water usage characteristics refer to various parameters related to the behavior of using water by the drinking water device. For example, the water usage characteristics can be at least one of the water flow rate, the water flow volume, and the user's water usage time.

[0040] In specific implementation, various sensors installed on the drinking water device can be used to obtain the water path characteristics and water usage characteristics of the drinking water device. For example, the water path temperature can be obtained through a temperature sensor on the water path, and the water flow volume can be obtained through a water flow sensor.

[0041] In this embodiment, by obtaining the water path characteristics and water usage characteristics of the drinking water device, a data basis is provided for obtaining the first water leakage detection result and the second water leakage detection result later.

[0042] Step 120: Input the water path characteristics into a pre-trained water path anomaly detection model to obtain a first water leakage detection result.

[0043] Specifically, in this embodiment, the pre-trained waterway anomaly detection model refers to a model trained based on the waterway characteristics of each drinking device and its corresponding leakage state, which can be used to determine the leakage situation of the drinking device according to the waterway characteristics of the drinking device, that is, the first leakage detection result. In this embodiment, the first leakage detection result refers to the leakage detection result obtained by the waterway anomaly detection model based on the waterway characteristics.

[0044] In specific implementation, after obtaining the waterway characteristics, the obtained waterway characteristics can be preprocessed first to remove noise and redundant data. Then, the preprocessed waterway characteristics can be input into the pre-trained waterway anomaly detection model to obtain the first leakage detection result.

[0045] In this embodiment, through the above steps, the leakage situation of the drinking device can be accurately determined based on the waterway characteristics, significantly improving the efficiency and accuracy of leakage detection, making the detection process faster and the result more reliable. At the same time, this automated process not only realizes real-time monitoring of the leakage situation but also reduces the labor burden.

[0046] Step 130: Input the water usage characteristics into the pre-trained water flow anomaly detection model to obtain the second leakage detection result.

[0047] Specifically, the pre-trained water flow anomaly detection model refers to a model trained based on the water usage characteristics of each drinking device and its corresponding leakage state, which can be used to determine the leakage situation of the drinking device according to the water usage characteristics of the drinking device, that is, the second leakage detection result. The second leakage detection result refers to the leakage detection result obtained by the water flow anomaly detection model based on the water usage characteristics.

[0048] In specific implementation, after obtaining the water usage characteristics, the obtained water usage characteristics can be preprocessed first to remove noise and redundant data. Then, the preprocessed water usage characteristics can be input into the pre-trained water flow anomaly detection model to obtain the second leakage detection result.

[0049] In this embodiment, through the above steps, the leakage situation of the drinking device can be determined by analyzing the water usage characteristics, greatly improving the efficiency and accuracy of leakage detection, making the entire detection process faster, more accurate and efficient.

[0050] Step 140: Determine the leakage detection result of the drinking device according to the first leakage detection result and the second leakage detection result.

[0051] Specifically, the leakage detection result refers to the final leakage detection result determined according to the first leakage detection result and the second leakage detection result.

[0052] In a specific implementation, after obtaining the first water leakage detection result and the second water leakage detection result, the first water leakage detection result and the second water leakage detection result can be compared first to obtain a comparison result, and then the water leakage detection result of the drinking water device can be determined according to the comparison result. Specifically, if the comparison result is inconsistent, it is determined that the water leakage detection result is a minor water leakage; if the comparison result is consistent, when both the first water leakage detection result and the second water leakage detection result indicate water leakage, it is determined that the water leakage detection result is a severe water leakage; when both the first water leakage detection result and the second water leakage detection result indicate no water leakage, it is determined that the water leakage detection result is no water leakage.

[0053] In this embodiment, through the above steps, a more comprehensive assessment of the water leakage condition of the drinking water device can be carried out. And compared with the traditional single-dimensional detection method, this multi-dimensional comprehensive judgment strategy fully considers the dynamic changes of different waterway characteristics and water usage characteristics, reduces environmental interference, thus significantly improving the accuracy and reliability of water leakage detection, effectively reducing the probability of misjudgment and missed judgment, and then ensuring the safe operation and long-term stability of the drinking water device, providing a more solid and reliable water usage guarantee for users.

[0054] In the embodiment of the present invention, first, the waterway characteristics and water usage characteristics of the drinking water device are obtained, providing a data basis for obtaining the first water leakage detection result and the second water leakage detection result later. Then, the waterway characteristics are input into a pre-trained waterway anomaly detection model to obtain the first water leakage detection result, which can accurately determine the water leakage condition of the drinking water device based on the waterway characteristics, significantly improving the efficiency and accuracy of water leakage detection, making the detection process faster and the result more reliable. At the same time, this automated process not only realizes real-time monitoring of water leakage but also reduces the human burden. The water usage characteristics are input into a pre-trained water flow anomaly detection model to obtain the second water leakage detection result, which can determine the water leakage condition of the drinking water device by analyzing the water usage characteristics, greatly improving the efficiency and accuracy of water leakage detection, making the entire detection process faster, more accurate and efficient. Finally, the water leakage detection result of the drinking water device is determined according to the first water leakage detection result and the second water leakage detection result, enabling a more comprehensive assessment of the water leakage condition of the drinking water device. And compared with the traditional single-dimensional detection method, this multi-dimensional comprehensive judgment strategy fully considers the dynamic changes of different waterway characteristics and water usage characteristics, reduces environmental interference, thus significantly improving the accuracy and reliability of water leakage detection, effectively reducing the probability of misjudgment and missed judgment, and then ensuring the safe operation and long-term stability of the drinking water device, providing a more solid and reliable water usage guarantee for users, and solving the problems of inability to monitor water leakage in real time, low efficiency and low accuracy in the prior art.

[0055] Figure 2The flowchart of another leak detection method provided by an embodiment of the present invention. This embodiment is a specific implementation based on the above embodiment. In this embodiment, the method may further include:

[0056] Optionally, the waterway features include direct waterway features and indirect waterway features.

[0057] Step 210: Obtain the direct waterway features of the drinking water device.

[0058] Specifically, the direct waterway features refer to the waterway parameters that can be directly measured and obtained by devices (such as sensors) installed on the drinking water device. For example, the direct waterway features may include waterway pressure, waterway temperature, and waterway humidity.

[0059] In a specific implementation, various sensors installed on the drinking water device can be used to obtain the direct waterway features of the drinking water device.

[0060] In this embodiment, by obtaining the direct waterway features of the drinking water device, a data basis is provided for calculating the indirect waterway features later.

[0061] Step 211: Calculate the indirect waterway features based on the direct waterway features.

[0062] Specifically, the indirect waterway features refer to the features obtained by calculating the direct waterway features. For example, the indirect waterway features include temperature change rate, humidity change rate, humidity difference, and pressure change rate.

[0063] In a specific implementation, after obtaining the direct waterway features, the direct waterway features can be input into the indirect waterway feature determination model to obtain the indirect waterway features.

[0064] It should be noted that the indirect waterway features can be determined in advance according to the actual situation or requirements. The indirect waterway feature determination model refers to a model obtained by training a deep learning model in advance based on the direct waterway features and their corresponding indirect waterway features of each drinking water device.

[0065] In this embodiment, calculating the indirect waterway features based on the direct waterway features can more comprehensively utilize the existing data to obtain more waterway state information, thereby improving the accuracy of the first leak detection result determined later.

[0066] Optionally, the direct waterway features include waterway pressure, at least two waterway temperatures, and at least two waterway humidities, and the indirect waterway features include temperature change rate, humidity change rate, humidity difference, and pressure change rate.

[0067] Further, calculate the indirect waterway features based on the direct waterway features, including: calculating the average waterway temperature based on at least two waterway temperatures, and calculating the average waterway humidity based on at least two waterway humidities; calculating the temperature change rate according to the average waterway temperature and the historical average waterway temperature, and calculating the humidity change rate according to the average waterway humidity and the historical average waterway humidity; calculating the difference between at least two waterway humidities to obtain the humidity difference; calculating the pressure change rate according to the waterway pressure and the historical waterway pressure.

[0068] Specifically, the waterway pressure refers to the pressure of the water flow in the pipeline of the drinking water device. The waterway temperature refers to the temperature of the water flow in the internal pipeline of the drinking water device. The waterway humidity refers to the humidity on the surface of the internal pipeline of the drinking water device or in the surrounding air. The temperature change rate refers to the rate of change of the waterway temperature over time. The humidity change rate refers to the rate of change of the waterway humidity over time. The humidity difference refers to the humidity difference at different positions in the waterway of the drinking water device. The pressure change rate refers to the rate of change of the waterway pressure over time.

[0069] Exemplarily, if the waterway temperatures are 25 degrees Celsius (°C), 26 °C, and 27 °C respectively, the waterway humidities are 50%, 52%, and 54% respectively, the historical average waterway temperature is 25 °C, the historical average waterway humidity is 50%, the waterway pressure is 2 megapascals, the historical pressure is 1.5 megapascals, and the time interval between the historical data and the current data is 1 minute, then the average waterway temperature is (25 + 26 + 27) / 3 = 26 °C, the average waterway humidity is (50 + 52 + 54) / 3 = 52%, the temperature change rate is (26 - 25) / 1 = 1 °C / minute, the humidity change rate is (52 - 50) / 1 = 2% / minute, the humidity differences are 2%, 4%, and 2%, and the pressure change rate is (2 - 1.5) / 1 = 0.5 megapascal / minute.

[0070] Exemplarily, if the waterway pressures include P1 and P2, the corresponding historical pressures for P1 are P3, the corresponding historical pressures for P2 are P4, and the time intervals between P1 and P3 and between P2 and P4 are both t1, then the pressure change rates include (P1 - P3) / t1 and (P2 - P4) / t1.

[0071] In this embodiment, through the above steps, the accuracy and efficiency of the determined indirect waterway features are improved.

[0072] Step 212, obtain the environmental features of the drinking water device.

[0073] Specifically, the environmental features refer to various characteristic parameters of the environment where the drinking water device is located. For example, the environmental features include the environmental temperature and the environmental humidity.

[0074] In a specific implementation, the environmental characteristics of the drinking water device can be obtained through sensors (such as temperature sensors and humidity sensors) installed on the surface of the drinking water device. Additionally, environmental parameters (such as the surface temperature of the device and at least two waterway temperatures) can be collected first through sensors installed on the surface and inside of the drinking water device, and then the collected data can be preprocessed (such as calculating the average value of the waterway temperatures), and then the preprocessed data can be input into a pre-trained environmental characteristic determination model to obtain the environmental characteristics of the drinking water device. Among them, the environmental characteristic determination model refers to a model obtained by training a machine learning model based on the environmental parameters of the drinking water device and their corresponding environmental characteristics.

[0075] In this embodiment, by obtaining the environmental characteristics of the drinking water device, a data basis is provided for obtaining the waterway detection threshold later.

[0076] Step 213: Input the environmental characteristics into a pre-trained detection parameter determination model to obtain the waterway detection threshold.

[0077] Specifically, the detection parameter determination model refers to a model obtained by training based on the environmental characteristics of the drinking water device and their corresponding waterway detection thresholds, and is used to determine the threshold required for waterway detection according to the environmental characteristics, that is, the waterway detection threshold. The waterway detection threshold refers to the threshold calculated by the detection parameter determination model according to the environmental characteristics.

[0078] In a specific implementation, after obtaining the environmental characteristics, the environmental characteristics can be input into a pre-trained detection parameter determination model to obtain the waterway detection threshold.

[0079] In one implementation, the training process of the detection parameter determination model is as follows: First, obtain the environmental characteristics of each drinking water device and their corresponding waterway detection thresholds, and clean and preprocess these data to ensure that they meet the input requirements of the model. Then, use the obtained environmental characteristics of each drinking water device and their corresponding waterway detection thresholds as training data to train a machine learning model. This process usually includes two steps: forward propagation and backward propagation. Forward propagation is to pass the input data through the model to obtain a prediction result, and then calculate the loss between the prediction result and the true target. Backward propagation is to update the parameters of the model according to the loss function to reduce the gap between the prediction result and the true target. Finally, the model can be optimized based on the backpropagation algorithm until the loss function converges, thereby obtaining the detection parameter determination model.

[0080] In this embodiment, through the above steps, the waterway detection requirements under different environmental conditions can be adapted. That is, when the environmental characteristics change, the detection parameter determination model can automatically adjust the detection threshold, thereby ensuring the accuracy and effectiveness of the detection result, and further improving the adaptability and flexibility of the leakage detection.

[0081] Step 214: Input the waterway detection threshold and the indirect waterway features into a pre-trained waterway anomaly detection model to obtain a first water leakage detection result.

[0082] Specifically, in this embodiment, the pre-trained waterway anomaly detection model refers to a model trained based on the indirect waterway features, waterway detection thresholds, and corresponding water leakage states of each drinking water device, and can be used to determine the water leakage situation of the drinking water device according to the indirect waterway features and waterway detection thresholds of the drinking water device, that is, the first water leakage detection result. In this embodiment, the first water leakage detection result refers to the water leakage detection result obtained by the waterway anomaly detection model based on the indirect waterway features and waterway detection thresholds.

[0083] In specific implementation, after obtaining the waterway detection threshold and the indirect waterway features, the waterway detection threshold and the indirect waterway features can be input into a pre-trained waterway anomaly detection model to obtain a first water leakage detection result.

[0084] In practical applications, the training process of the waterway anomaly detection model is similar to that of the detection parameter determination model, except that the training data they use is different. For the waterway anomaly detection model in this embodiment, its training data is the indirect waterway features, waterway detection thresholds, and corresponding water leakage states of each drinking water device. For example: the indirect waterway features are the temperature change rate, humidity change rate, humidity difference, and pressure change rate. The training data of the waterway anomaly detection model can be: the temperature change rate is 0.1 °C / minute, the humidity change rate is 0.5% / minute, the humidity difference is 8%, the pressure change rate is 0.02 MPa / minute, the waterway detection threshold is 1.2, and the water leakage state is water leakage; the temperature change rate is 0.03 °C / minute, the humidity change rate is 0.15% / minute, the humidity difference is 3%, the pressure change rate is 0.005 MPa / minute, the waterway detection threshold is 1.5, and the water leakage state is no water leakage; the temperature change rate is 0.15 °C / minute, the humidity change rate is 0.6% / minute, the humidity difference is 10%, the pressure change rate is 0.03 MPa / minute, the waterway detection threshold is 1.5, and the water leakage state is water leakage; the temperature change rate is 0.03 °C / minute, the humidity change rate is 0.1% / minute, the humidity difference is 3%, the pressure change rate is 0.004 MPa / minute, the waterway detection threshold is 1.5, and the water leakage state is no water leakage; the temperature change rate is 0.02 °C / minute, the humidity change rate is 0.05% / minute, the humidity difference is 1%, 1.2%, and 0.8%, the pressure change rate is 0.0005 MPa / minute, the waterway detection threshold is 0.7, and the water leakage state is no water leakage; the temperature change rate is 0.1 °C / minute, the humidity change rate is 0.3% / minute, the humidity difference is 3%, 4%, and 3.5%, the pressure change rate is 0.003 MPa / minute, the waterway detection threshold is 0.7, and the water leakage state is water leakage.

[0085] In this embodiment, through the above steps, the accuracy and real-time performance of the determined first water leakage detection result are improved.

[0086] Step 215: Input the water usage characteristics into a pre-trained water flow anomaly detection model to obtain a second water leakage detection result.

[0087] In practical applications, the training process of the water flow anomaly detection model is similar to that of the detection parameter determination model. The difference lies in the training data they use. For the water flow anomaly detection model in the present invention, its training data is the water usage characteristics of each drinking water device and their corresponding leakage states. For example: the water usage characteristics are water flow velocity and water flow rate, and the training data of the water path anomaly detection model can be: water flow velocity is 0.5 m / s, water flow rate is 2 L / min, and the leakage state is no leakage; water flow velocity is 1.5 m / s, water flow rate is 10 L / min, and the leakage state is leakage.

[0088] For example: the water usage characteristics are water flow velocity, water flow rate, and the user's water usage time. The training data of the water path anomaly detection model can be: water flow velocity is 0.5 m / s, water flow rate is 2 L / min, the user's water usage time is 20:00:01 - 20:00:21, and the leakage state is no leakage; water flow velocity is 0.5 m / s, water flow rate is 2 L / min, the user's water usage time is 2:00:01 - 2:00:21, and the leakage state is leakage.

[0089] Step 216: Determine the water leakage detection result of the drinking water device according to the first water leakage detection result and the second water leakage detection result.

[0090] Further, step 216 may specifically include: comparing the first water leakage detection result and the second water leakage detection result to obtain a comparison result; if the comparison result is inconsistent, determine that the water leakage detection result is a minor leak; if the comparison result is consistent, when the first water leakage detection result and the second water leakage detection result are both leakage, determine that the water leakage detection result is a severe leak; when the first water leakage detection result and the second water leakage detection result are both no leakage, determine that the water leakage detection result is no leakage.

[0091] Specifically, the comparison result refers to the result obtained by comparing the first water leakage detection result and the second water leakage detection result, and is used to determine the water leakage detection result of the drinking water device.

[0092] Exemplarily, if the first water leakage detection result is leakage and the second water leakage detection result is no leakage, then the water leakage detection result of the drinking water device is a minor leak. At this time, the warning light of the drinking water device can be lit to remind the user that the drinking water device has a minor leak.

[0093] Exemplarily, if the first water leakage detection result is no water leakage and the second water leakage detection result is no water leakage, then the water leakage detection result of the drinking water device is no water leakage.

[0094] Exemplarily, if the first water leakage detection result is water leakage and the second water leakage detection result is water leakage, then the water leakage detection result of the drinking water device is serious water leakage. At this time, the buzzer of the drinking water device can be activated to remind the user that the drinking water device has serious water leakage.

[0095] In this embodiment, through the above steps, the reliability and efficiency of water leakage detection are improved, and the degree of water leakage can be evaluated when water leakage occurs, providing more targeted maintenance guidance for subsequent staff, thereby improving the processing efficiency.

[0096] Further, after step 216, it further includes: when it is determined that the water leakage detection result is water leakage, checking the components of the drinking water device according to the preset component priority to obtain the water leakage component.

[0097] Specifically, the preset component priority refers to the importance degree of each component of the drinking water device set in advance according to the actual situation or requirements. The water leakage component refers to the specific component that causes water leakage determined by checking the components of the drinking water device according to the preset component priority.

[0098] In specific implementation, after it is determined that the water leakage detection result is water leakage, each component (such as the heating component) in the device can be checked one by one according to the preset component priority to obtain the water leakage component. Specifically, sensors installed within the preset range of the component to be detected can be used to obtain the sensor data of the component. If the data exceeds the preset sensor data range, it is determined that the component is a water leakage component.

[0099] In this embodiment, checking the components according to the priority can quickly locate the component most likely to have problems, avoiding blind comprehensive inspection, improving the maintenance efficiency, effectively preventing the expansion of the water leakage problem, avoiding damage to other components of the device due to water leakage, ensuring the continuous and stable operation of the drinking water device, and ensuring that users can stably obtain safe drinking water.

[0100] Further, after step 216, it further includes: generating a water leakage report according to the water leakage detection result; sending the water leakage report to the user terminal corresponding to the drinking water device.

[0101] Specifically, the water leakage report refers to a report generated according to the water leakage detection result. For example, the water leakage report includes the water leakage situation and recommended measures. The user terminal refers to the device used by the user associated with the drinking water device. For example, the user terminal can be a mobile phone, a tablet computer, a smart watch, etc.

[0102] In a specific implementation, after obtaining the water leakage detection result, a water leakage report can be generated according to the water leakage detection result and a preset report format, and the water leakage report can be sent to the user terminal corresponding to the drinking water device by means such as text message, email or dedicated software, so as to ensure that the user can receive the water leakage report in a timely manner and understand the water leakage situation of the drinking water device. After receiving the water leakage report, the user can take corresponding measures (such as power off or arranging maintenance) according to the water leakage report.

[0103] In this embodiment, through the above steps, it can help the user understand potential water leakage risks in advance and take corresponding preventive measures, which helps to avoid problems such as equipment damage and water quality pollution caused by water leakage, thereby reducing the user's losses.

[0104] The water leakage detection method provided by the embodiment of the present invention first obtains the direct water path characteristics of the drinking water device, providing a data basis for calculating the indirect water path characteristics later. Then, calculating the indirect water path characteristics based on the direct water path characteristics can more comprehensively utilize the existing data to obtain more water path state information, thereby improving the accuracy of the first water leakage detection result determined later. After that, obtaining the environmental characteristics of the drinking water device provides a data basis for obtaining the water path detection threshold later. Then, inputting the environmental characteristics into the pre-trained detection parameter determination model to obtain the water path detection threshold can adapt to the water path detection requirements under different environmental conditions, thereby ensuring the accuracy and effectiveness of the detection result, and further improving the adaptability and flexibility of water leakage detection. Then, inputting the water path detection threshold and the indirect water path characteristics into the pre-trained water path anomaly detection model to obtain the first water leakage detection result improves the accuracy and real-time performance of the determined first water leakage detection result. Inputting the water usage characteristics into the pre-trained water flow anomaly detection model to obtain the second water leakage detection result can determine the water leakage condition of the drinking water device by analyzing the water usage characteristics, greatly improving the efficiency and accuracy of water leakage detection, making the entire detection process faster, more accurate and efficient. Finally, determining the water leakage detection result of the drinking water device according to the first water leakage detection result and the second water leakage detection result can more comprehensively evaluate the water leakage condition of the drinking water device. And compared with the traditional single-dimensional detection method, this multi-dimensional comprehensive determination strategy fully considers the dynamic changes of different water path characteristics and water usage characteristics, reduces environmental interference, thereby significantly improving the accuracy and reliability of water leakage detection, effectively reducing the probability of false judgment and missed judgment, and further ensuring the safe operation and long-term stability of the drinking water device, providing a more solid and reliable water usage guarantee for users, and solving the problems of inability to monitor water leakage in real time, low efficiency and low accuracy in the prior art.

[0105] Figure 3The structural schematic diagram of a water leakage detection device provided by an embodiment of the present invention. This device and the water leakage detection methods of the above embodiments belong to the same inventive concept. For the details not described in detail in the embodiment of the water leakage detection device, reference can be made to the embodiments of the above water leakage detection methods.

[0106] As Figure 3 shown, the device includes:

[0107] An acquisition module 310, configured to acquire the water path characteristics and water usage characteristics of the drinking water device;

[0108] A first detection module 320, configured to input the water path characteristics into a pre-trained water path anomaly detection model to obtain a first water leakage detection result;

[0109] A second detection module 330, configured to input the water usage characteristics into a pre-trained water flow anomaly detection model to obtain a second water leakage detection result;

[0110] A target detection module 340, configured to determine the water leakage detection result of the drinking water device according to the first water leakage detection result and the second water leakage detection result.

[0111] Based on the above embodiments, the acquisition module 310 acquires the water path characteristics of the drinking water device, including:

[0112] Acquiring the direct water path characteristics of the drinking water device;

[0113] Calculating the indirect water path characteristics based on the direct water path characteristics.

[0114] Based on the above embodiments, the direct water path characteristics include water path pressure, at least two water path temperatures, and at least two water path humidities. The indirect water path characteristics include temperature change rate, humidity change rate, humidity difference, and pressure change rate. The acquisition module 310 calculates the indirect water path characteristics based on the direct water path characteristics, including:

[0115] Calculating the average water path temperature based on the at least two water path temperatures, and calculating the average water path humidity based on the at least two water path humidities;

[0116] Calculating the temperature change rate according to the average water path temperature and the historical average water path temperature, and calculating the humidity change rate according to the average water path humidity and the historical average water path humidity;

[0117] Calculating the difference between the at least two water path humidities to obtain the humidity difference;

[0118] Calculating the pressure change rate according to the water path pressure and the historical water path pressure.

[0119] Based on the above embodiments, the first detection module 320 is specifically configured to:

[0120] Obtain the environmental characteristics of the drinking water device;

[0121] Input the environmental characteristics into a pre-trained detection parameter determination model to obtain a waterway detection threshold;

[0122] Input the waterway detection threshold and the indirect waterway characteristics into a pre-trained waterway anomaly detection model to obtain the first water leakage detection result.

[0123] Based on the above embodiments, the target detection module 340 is specifically configured to:

[0124] Compare the first water leakage detection result and the second water leakage detection result to obtain a comparison result;

[0125] If the comparison result is inconsistent, determine that the water leakage detection result is a minor water leakage;

[0126] If the comparison result is consistent, when the first water leakage detection result and the second water leakage detection result are both water leakage, determine that the water leakage detection result is a serious water leakage; when the first water leakage detection result and the second water leakage detection result are both no water leakage, determine that the water leakage detection result is no water leakage.

[0127] Based on the above embodiments, the device further includes:

[0128] A re-inspection module, configured to, after determining the water leakage detection result of the drinking water device according to the first water leakage detection result and the second water leakage detection result, when determining that the water leakage detection result is water leakage, check the components of the drinking water device according to a preset component priority to obtain water leakage components.

[0129] Based on the above embodiments, the device further includes:

[0130] A sending module, configured to generate a water leakage report according to the water leakage detection result after determining the water leakage detection result of the drinking water device according to the first water leakage detection result and the second water leakage detection result; send the water leakage report to the user terminal corresponding to the drinking water device.

[0131] The water leakage detection device provided by the embodiments of the present invention can execute the water leakage detection method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0132] It should be noted that in the embodiments of the above-mentioned water leakage detection device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0133] Figure 4 FIG. is a schematic structural diagram of a drinking water device provided by an embodiment of the present invention. Figure 4 FIG. shows a block diagram of an exemplary drinking water device 4 suitable for implementing the embodiments of the present invention. Figure 4 The shown drinking water device 4 is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present invention.

[0134] As Figure 4 shown, the drinking water device 4 is presented in the form of a general-purpose computing electronic device. The components of the drinking water device 4 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing unit 16).

[0135] The bus 18 represents one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the multiple bus structures. For example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0136] The drinking water device 4 typically includes a variety of computer system-readable media. These media can be any available media accessible by the drinking water device 4, including volatile and non-volatile media, removable and non-removable media.

[0137] The system memory 28 may include computer system-readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The drinking water device 4 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, a storage system 34 may be used for reading and writing non-removable, non-volatile magnetic media ( Figure 4 not shown, commonly referred to as a "hard disk drive"). Although Figure 4Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (such as a "floppy disk") and an optical disk drive for reading and writing a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM, or other optical medium) can be provided. In these cases, each drive can be connected to the bus 18 through one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0138] A program / utility 40 having a set (at least one) of program modules 42 can be stored, for example, in the system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment. The program modules 42 generally perform the functions and / or methods in the embodiments described in the present invention.

[0139] The drinking water device 4 can also communicate with one or more external devices 14 (such as a keyboard, a pointing device, a display 24, etc.), and can also communicate with one or more devices that enable a user to interact with the drinking water device 4, and / or communicate with any device that enables the drinking water device 4 to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication can be carried out through the input / output (I / O) interface 22. Moreover, the drinking water device 4 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 20. As Figure 4 shown, the network adapter 20 communicates with other modules of the drinking water device 4 through the bus 18. It should be understood that although Figure 4 not shown in the figure, other hardware and / or software modules can be used in combination with the drinking water device 4, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0140] The processing unit 16 executes various functional applications and page displays by running the programs stored in the system memory 28, for example, implementing the water leakage detection method provided by the embodiments of the present invention. The method includes:

[0141] Obtaining the water path characteristics and water usage characteristics of the drinking water device;

[0142] Inputting the water path characteristics into a pre-trained water path anomaly detection model to obtain a first water leakage detection result;

[0143] Input the water usage characteristics into a pre-trained water flow anomaly detection model to obtain a second water leakage detection result;

[0144] Determine the water leakage detection result of the drinking water device according to the first water leakage detection result and the second water leakage detection result.

[0145] Of course, those skilled in the art can understand that the processor can also implement the technical solutions of the water leakage detection method provided in any embodiment of the present invention.

[0146] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements, for example, the water leakage detection method provided in the embodiment of the present invention. The method includes:

[0147] Obtain the water circuit characteristics and water usage characteristics of the drinking water device;

[0148] Input the water circuit characteristics into a pre-trained water circuit anomaly detection model to obtain a first water leakage detection result;

[0149] Input the water usage characteristics into a pre-trained water flow anomaly detection model to obtain a second water leakage detection result;

[0150] Determine the water leakage detection result of the drinking water device according to the first water leakage detection result and the second water leakage detection result.

[0151] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or combined with an instruction execution system, apparatus, or device.

[0152] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which computer-readable program code is carried. Such a propagated data signal may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0153] The program code contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical fiber cable, RF, and the like, or any suitable combination of the foregoing.

[0154] The computer program code for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0155] Those of ordinary skill in the art should understand that the various modules or steps of the present invention described above may be implemented using a general-purpose computing device. They may be concentrated on a single computing device or distributed across a network composed of multiple computing devices. Optionally, they may be implemented using program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them may be fabricated into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0156] In addition, in the technical solution of the present invention, the acquisition, storage, use, processing, etc. of data all comply with the relevant provisions of national laws and regulations.

[0157] Note that the above is only the preferred embodiment of the present invention and the technical principles applied. Those skilled in the art will understand that the present invention is not limited to the specific embodiments here, and various obvious changes, re-adjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments only. Without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A water leakage detection method, characterized in that: include: Obtain the waterway characteristics and water use characteristics of drinking water equipment; Inputting the water channel feature into a pre-trained water channel anomaly detection model to obtain a first water leakage detection result; Inputting the water use characteristics into a pre-trained water flow anomaly detection model to obtain a second water leakage detection result; The water leakage detection result of the drinking water equipment is determined according to the first water leakage detection result and the second water leakage detection result.

2. The water leakage detection method according to claim 1, characterized in that: The waterway characteristics include direct waterway characteristics and indirect waterway characteristics. Acquiring the waterway characteristics of the drinking water equipment includes: Obtaining direct water path characteristics of the drinking water equipment; The indirect waterway characteristics are calculated based on the direct waterway characteristics.

3. The water leakage detection method according to claim 2, characterized in that: The direct water channel characteristics include water channel pressure, at least two water channel temperatures and at least two water channel humidity, and the indirect water channel characteristics include temperature change rate, humidity change rate, humidity difference and pressure change rate. The indirect water channel characteristics are calculated based on the direct water channel characteristics, including: Calculating a mean water channel temperature based on the at least two water channel temperatures, and calculating a mean water channel humidity based on the at least two water channel humidity; Calculating the temperature change rate according to the water channel temperature average and the historical water channel temperature average, and calculating the humidity change rate according to the water channel humidity average and the historical water channel humidity average; Calculating the difference between the humidity of the at least two water channels to obtain the humidity difference; The pressure change rate is calculated based on the waterway pressure and historical waterway pressure.

4. The water leakage detection method according to claim 2, characterized in that: The water channel feature is input into a pre-trained water channel anomaly detection model to obtain a first water leakage detection result, including: Acquiring environmental characteristics of the drinking water equipment; Inputting the environmental features into a pre-trained detection parameter determination model to obtain a waterway detection threshold; The water channel detection threshold and the indirect water channel feature are input into a pre-trained water channel anomaly detection model to obtain the first water leakage detection result.

5. The water leakage detection method according to claim 1, characterized in that: Determining the water leakage detection result of the drinking water equipment according to the first water leakage detection result and the second water leakage detection result includes: Comparing the first water leakage detection result and the second water leakage detection result to obtain a comparison result; If the comparison result is inconsistent, determining that the water leakage detection result is a slight water leakage; If the comparison result is consistent, then when the first water leakage detection result and the second water leakage detection result are water leakage, the water leakage detection result is determined to be a serious water leakage; when the first water leakage detection result and the second water leakage detection result are no water leakage, the water leakage detection result is determined to be no water leakage.

6. The water leakage detection method according to claim 1, characterized in that: After determining the water leakage detection result of the drinking water equipment according to the first water leakage detection result and the second water leakage detection result, the method further includes: When it is determined that the water leakage detection result is a water leakage, the components of the drinking water equipment are checked according to preset component priorities to obtain the leaking components.

7. The water leakage detection method according to claim 1, characterized in that: After determining the water leakage detection result of the drinking water equipment according to the first water leakage detection result and the second water leakage detection result, the method further includes: generating a water leakage report according to the water leakage detection result; The water leakage report is sent to a user terminal corresponding to the drinking water device.

8. A water leakage detection device, characterized in that: include: An acquisition module, used to acquire water path characteristics and water use characteristics of drinking water equipment; A first detection module, used for inputting the water channel feature into a pre-trained water channel anomaly detection model to obtain a first water leakage detection result; A second detection module, used for inputting the water use characteristics into a pre-trained water flow anomaly detection model to obtain a second water leakage detection result; The target detection module is used to determine the water leakage detection result of the drinking water equipment according to the first water leakage detection result and the second water leakage detection result.

9. A drinking water device, characterized in that: The drinking water equipment comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the water leakage detection method described in any one of claims 1-7.

10. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to execute the water leakage detection method described in any one of claims 1-7 when executed by a computer processor.

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