Safety early warning device based on spraying water pump

Abstract

The invention discloses a safety early warning device based on a spraying water pump, which comprises at least one sensor unit used for monitoring operation parameters of the spraying water pump in real time, the sensor unit comprises a pressure sensor, a flow sensor and a temperature sensor, and the sensor unit has automatic calibration and fault diagnosis functions. Key operation parameters of the water pump are monitored in real time through the advanced sensor technology, deep analysis is conducted through a machine learning algorithm, and therefore rapid identification and early warning of potential faults are achieved. Due to a user-friendly interaction interface and a remote monitoring function, an operator can know the state of the water pump anytime and anywhere and respond in time, and the safety and reliability of the system are remarkably improved. Meanwhile, due to the modular design and the self-adaptive learning capability of the early warning device, the compatibility with an existing system and the long-term technical adaptability are ensured, the maintenance cost is reduced, and the use experience of a user is optimized.

Description

Technical Field

[0001] The invention relates to the technical field of fluid control and monitoring, and in particular to a safety early warning device based on a spray water pump. Background Art

[0002] Existing sprinkler pump systems are relatively mature in basic fluid control, but they still have deficiencies in real-time status monitoring, fault warning, and intelligent management; traditional monitoring methods usually rely on a single sensor and simple threshold judgment, lack comprehensive analysis and prediction capabilities, and fail to identify and respond to complex fault modes in a timely manner. In addition, the user interface is not intuitive enough, and the remote monitoring and control system is poorly integrated, which limits the flexibility of the system and the user's interactive experience. Therefore, it is necessary to develop a safety warning device that integrates advanced sensors, intelligent data analysis, and a user-friendly interface to improve the reliability and operational convenience of the sprinkler pump system. . Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the fact that the above-mentioned existing traditional monitoring methods usually rely on a single sensor and simple threshold judgment, lack comprehensive analysis and prediction capabilities, resulting in the inability to timely identify and respond to complex failure modes, the present invention is proposed.

[0005] Therefore, the object of the present invention is to provide a safety warning device based on a sprinkler water pump.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a safety warning device based on a sprinkler water pump, comprising at least one sensor unit for real-time monitoring of the operating parameters of the sprinkler water pump, the sensor unit comprising a pressure sensor, a flow sensor and a temperature sensor, and the sensor unit has automatic calibration and fault diagnosis functions.

[0007] As a preferred solution of the safety warning device based on the sprinkler water pump of the present invention, wherein: the sensor unit has the following characteristics;

[0008] Waterproof and corrosion-resistant, suitable for wet and chemically corrosive environments;

[0009] Quick-plug interface for quick replacement and maintenance on site.

[0010] As a preferred solution of the safety warning device based on the sprinkler water pump of the present invention, the data processing unit of the warning device has the following functions;

[0011] Real-time data processing and historical data storage functions;

[0012] User-configurable threshold settings for customizing anomaly detection criteria.

[0013] As a preferred solution of the safety warning device based on the sprinkler water pump described in the present invention, the data processing unit includes an intelligent algorithm based on machine learning, which is used to determine whether the operating status of the water pump is normal and can identify at least one abnormal operating mode; the intelligent algorithm can optimize its warning accuracy through continuous learning.

[0014] As a preferred solution of the safety warning device based on the sprinkler water pump described in the present invention, it includes at least one actuator unit for sending a warning signal when an abnormality is detected by the intelligent algorithm, and the actuator unit includes an alarm, an indicator light, an automatic shutdown device, and an optional remote control interface.

[0015] As a preferred solution of the safety warning device based on the sprinkler water pump described in the present invention, it also includes a user interaction interface, allowing the user to view the monitoring data and warning information, and the user interaction interface includes a touch screen, voice interaction function, and remote access capability.

[0016] As a preferred solution of the safety warning device based on the sprinkler water pump of the present invention, wherein: the user interaction interface can display real-time data charts, historical data records, and provide detailed logs of warning events, and support data export function for further analysis;

[0017] The early warning device can communicate with the remote monitoring system wirelessly or wired to achieve remote monitoring and control. The communication methods include Wi-Fi, Bluetooth, 4G / 5G network and Ethernet, and have data encryption and secure transmission functions;

[0018] It also includes a self-check function, a modular design, a power management unit, and compatibility with existing sprinkler pump systems. The self-check function can be automatically executed regularly to ensure the health of the system. The modular design allows for rapid upgrades and maintenance. The power management unit can ensure stable operation of the device when the power supply is unstable or interrupted.

[0019] As a preferred solution of the safety warning device based on the sprinkler water pump of the present invention, wherein: the intelligent algorithm includes:

[0020] A data preprocessing step is to normalize the input data using the following formula,

[0021]

[0022] Among them, X is the original data set, X norm is the normalized data set, X min and X max are the minimum and maximum values ​​in the data set, respectively;

[0023] A feature extraction step, using principal component analysis (PCA) to transform the data, the formula is as follows,

[0024] Z=W T X norm

[0025] Where Z is the eigenvector after PCA transformation and W is the eigenvector matrix of PCA.

[0026] As a preferred solution of the safety warning device based on the sprinkler water pump of the present invention, wherein: the intelligent algorithm also includes:

[0027] A model training step uses support vector machine (SVM) as the classifier, and its optimization problem can be expressed as:

[0028]

[0029] Where w is the weight vector, b is the bias term, ξ is the slack variable, C is the regularization parameter, Φ(x i ) is the mapping function, y i is the true label of the i-th sample.

[0030] As a preferred solution of the safety warning device based on the sprinkler water pump of the present invention, wherein: the intelligent algorithm also includes:

[0031] In a prediction step, given a new sample, x new , its predicted category

[0032] y pred =sign(w T Φ(x new )+b)

[0033] A self-learning capability that uses an online learning strategy to update model parameters. The formula is as follows:

[0034] w t+1 =w t +η(y pred -y true )Φ(x new )b t+1 =b t +η(y pred -ytrue ),

[0035] Among them, wt and bt are the model parameters of the tth step, η is the learning rate, and y true is the true label;

[0036] An anomaly detection threshold setting step uses the following formula to determine the warning threshold,

[0037] Threshold=μKσ

[0038] Among them, μ is the mean value of the model output under normal conditions, σ is the standard deviation, and k is the confidence level coefficient.

[0039] The beneficial effects of the present invention are as follows: by using advanced sensor technology to monitor the key operating parameters of the water pump in real time and using machine learning algorithms for in-depth analysis, rapid identification and early warning of potential faults can be achieved. Its user-friendly interactive interface and remote monitoring function enable operators to understand the status of the water pump anytime and anywhere and respond in a timely manner, significantly improving the safety and reliability of the system. At the same time, the modular design and adaptive learning capabilities of the early warning device ensure compatibility with existing systems and long-term technical adaptability, reduce maintenance costs, and optimize the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0041] Figure 1 The figure is a schematic diagram of the working process of the sprinkler water pump safety early warning device of the present invention. DETAILED DESCRIPTION

[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0043] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0044] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0045] Secondly, the present invention is described in detail with reference to the schematic diagram. When describing the embodiments of the present invention in detail, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0046] Example 1

[0047] Reference Figure 1 , provides a safety warning device based on a sprinkler water pump, including at least one sensor unit for real-time monitoring of the operating parameters of the sprinkler water pump, the sensor unit including a pressure sensor, a flow sensor and a temperature sensor, and the sensor unit has automatic calibration and fault diagnosis functions.

[0048] Specifically, the sensor unit has the following characteristics: waterproof and corrosion-resistant characteristics, suitable for humid and chemically corrosive environments; quick plug-in interface, convenient for rapid replacement and maintenance on site.

[0049] Furthermore, the data processing unit of the early warning device has the following functions: real-time data processing and historical data storage functions; user-configurable threshold settings for customizing abnormality detection criteria, and the data processing unit has the following functions: a real-time data receiving function that can continuously obtain the operating parameters of the water pump from the sensor unit; a data preprocessing function, including data cleaning, denoising and normalization processing to improve the accuracy of subsequent analysis; a feature extraction function that uses mathematical transformation or machine learning technology to identify features useful for water pump status assessment from the raw data;

[0050] An intelligent analysis function that uses advanced algorithms, such as support vector machines (SVM), to perform pattern recognition and state prediction on feature data; an anomaly detection function that determines whether the pump has abnormal operating status by comparing the analysis results with the preset safety threshold; a result output function that outputs the analysis results and anomaly detection status to the user interface or remote monitoring system in an easy-to-understand form; an adaptive learning capability that automatically adjusts the analysis model based on historical data and user feedback to improve the accuracy and responsiveness of the early warning system.

[0051] It should be noted that the data processing unit includes an intelligent algorithm based on machine learning, which is used to determine whether the operating status of the water pump is normal and can identify at least one abnormal operating mode; the intelligent algorithm can optimize its warning accuracy through continuous learning.

[0052] In particular, it includes at least one actuator unit for issuing a warning signal when an abnormality is detected by the intelligent algorithm. The actuator unit includes an alarm, an indicator light, an automatic shutdown device, and an optional remote control interface.

[0053] In addition, it also includes a user interaction interface that allows users to view monitoring data and warning information. The user interaction interface includes a touch screen, voice interaction function, and remote access capability.

[0054] Furthermore, the user interface can display real-time data charts, historical data records, and provide detailed logs of warning events, while supporting data export functions for further analysis;

[0055] The early warning device can communicate with the remote monitoring system wirelessly or wired to achieve remote monitoring and control. The communication methods include Wi-Fi, Bluetooth, 4G / 5G network and Ethernet, and have data encryption and secure transmission functions;

[0056] It also includes a self-check function, a modular design, a power management unit, and compatibility with existing sprinkler pump systems. The self-check function can be automatically executed regularly to ensure the health of the system. The modular design allows for rapid upgrades and maintenance. The power management unit can ensure stable operation of the device when the power supply is unstable or interrupted.

[0057] In addition, the intelligent algorithm includes a data preprocessing step, which uses the following formula to normalize the input data:

[0058]

[0059] Among them, X is the original data set, X norm is the normalized data set, X min and X max are the minimum and maximum values ​​in the data set, respectively;

[0060] A feature extraction step, using principal component analysis (PCA) to transform the data, the formula is as follows,

[0061] Z=W T X norm

[0062] Where Z is the eigenvector after PCA transformation, and W is the eigenvector matrix of PCA;

[0063] A model training step uses support vector machine (SVM) as the classifier, and its optimization problem can be expressed as:

[0064]

[0065] Where w is the weight vector, b is the bias term, ξ is the slack variable, C is the regularization parameter, Φ(x i ) is the mapping function, y i is the true label of the i-th sample;

[0066] In a prediction step, given a new sample, x new , its predicted category

[0067] y pred =sign(w T Φ(x new )+b)

[0068] A self-learning capability that uses an online learning strategy to update model parameters. The formula is as follows:

[0069] w t+1 =w t +η(y pred -y true )Φ(x new )b t+1 =b t +η(y pred -y true ),

[0070] Among them, wt and bt are the model parameters of the tth step, η is the learning rate, and y true is the true label;

[0071] An anomaly detection threshold setting step uses the following formula to determine the warning threshold,

[0072] Threshold=μKσ

[0073] Among them, μ is the mean value of the model output under normal conditions, σ is the standard deviation, and k is the confidence level coefficient.

[0074] Operation process: First, the system starts and initializes all sensor units to collect real-time pressure, flow and temperature data of the water pump. Subsequently, the data preprocessing module normalizes the collected raw data for subsequent analysis. Next, the feature extraction module uses principal component analysis technology to extract key features from the normalized data. The intelligent algorithm module receives these features and uses a pre-trained support vector machine model to perform real-time analysis and prediction of the operating status of the water pump. Once the model predicts a potential abnormal state, the system will determine whether an early warning is needed based on the set threshold. If the abnormality is confirmed, the actuator unit will automatically trigger an audible and visual alarm, and the user interface will display detailed early warning information. In addition, the system supports remote monitoring, allowing managers to receive real-time system status updates and early warning notifications through mobile devices or computers. Throughout the process, the intelligent algorithm will continuously learn and adjust based on the actual early warning results and user feedback to improve the accuracy of early warnings and system response performance.

[0075] Importantly, it should be noted that the construction and arrangement of the present application shown in a number of different exemplary embodiments are only exemplary. Although only a few embodiments are described in detail in this disclosure, it should be readily understood by those who refer to this disclosure that many modifications are possible, for example, the size, scale, structure, shape and proportion of various elements, and parameter values ​​such as temperature, pressure, etc., mounting arrangements, use of materials, color, directional changes, etc., without substantially departing from the novel teachings and advantages of the subject matter described in the application. For example, the element shown as integrally formed can be composed of multiple parts or elements, the position of the element can be inverted or otherwise changed, and the nature or number or position of the discrete element can be changed or changed. Therefore, all such modifications are intended to be included in the scope of the present invention. The order or sequence of any process or method steps can be changed or reordered according to alternative embodiments. In the claims, any "device plus function" clause is intended to cover the structure described herein that performs the function, and is not only structurally equivalent but also equivalent structure. Without departing from the scope of the present invention, other replacements, modifications, changes and omissions can be made in the design, operating conditions and arrangement of the exemplary embodiments. Therefore, the invention is not limited to a specific embodiment, but extends to numerous modifications still falling within the scope of the appended claims.

[0076] Furthermore, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment may not be described, i.e., those features that are not relevant to the best mode presently contemplated for carrying out the invention or those features that are not relevant to implementing the invention.

[0077] It will be appreciated that in the development of any actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but will be a routine task of design, fabrication, and production for those of ordinary skill having the benefit of this disclosure without undue experimentation.

[0078] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A safety warning device based on a sprinkler water pump, characterized in that: It comprises at least one sensor unit for real-time monitoring of the operating parameters of the sprinkler water pump, wherein the sensor unit comprises a pressure sensor, a flow sensor and a temperature sensor, and the sensor unit has automatic calibration and fault diagnosis functions.

2. The safety warning device based on the sprinkler water pump according to claim 1, characterized in that: The sensor unit has the following characteristics; Waterproof and corrosion-resistant, suitable for wet and chemically corrosive environments; Quick-plug interface for quick replacement and maintenance on site.

3. The safety warning device based on the sprinkler water pump according to claim 2, characterized in that: The data processing unit of the early warning device has the following functions: Real-time data processing and historical data storage functions; User-configurable threshold settings for customizing anomaly detection criteria.

4. The safety warning device based on the sprinkler water pump according to claim 3 is characterized in that: The data processing unit includes an intelligent algorithm based on machine learning, which is used to determine whether the operating status of the water pump is normal and can identify at least one abnormal operating mode; the intelligent algorithm can optimize its warning accuracy through continuous learning.

5. The safety warning device based on the sprinkler water pump according to claim 4, characterized in that: It includes at least one actuator unit for sending out a warning signal when the intelligent algorithm detects an abnormality. The actuator unit includes an alarm, an indicator light, an automatic shutdown device, and an optional remote control interface.

6. The safety warning device based on the sprinkler water pump according to claim 5, characterized in that: It also includes a user interface that allows users to view monitoring data and warning information. The user interface includes a touch screen, voice interaction function, and remote access capability.

7. The safety warning device based on the sprinkler water pump according to claim 6, characterized in that: The user interface can display real-time data charts, historical data records, and provide detailed logs of warning events, while supporting data export functions for further analysis; The early warning device can communicate with the remote monitoring system wirelessly or wired to achieve remote monitoring and control. The communication methods include Wi-Fi, Bluetooth, 4G / 5G network and Ethernet, and have data encryption and secure transmission functions; It also includes a self-check function, a modular design, a power management unit, and compatibility with existing sprinkler pump systems. The self-check function can be automatically executed regularly to ensure the health of the system. The modular design allows for rapid upgrades and maintenance. The power management unit can ensure stable operation of the device when the power supply is unstable or interrupted.

8. The safety warning device based on the sprinkler water pump according to claim 7, characterized in that: The intelligent algorithm includes: A data preprocessing step is to normalize the input data using the following formula, Among them, X is the original data set, X norm is the normalized data set, X min and X max are the minimum and maximum values ​​in the data set, respectively; A feature extraction step, using principal component analysis (PCA) to transform the data, the formula is as follows, Z=W T X norm Where Z is the eigenvector after PCA transformation and W is the eigenvector matrix of PCA.

9. The safety warning device based on the sprinkler water pump according to claim 8, characterized in that: The intelligent algorithm also includes: A model training step uses support vector machine (SVM) as the classifier, and its optimization problem can be expressed as: Where w is the weight vector, b is the bias term, ξ is the slack variable, C is the regularization parameter, Φ(x i ) is the mapping function, y i is the true label of the i-th sample.

10. The safety warning device based on a sprinkler water pump according to claim 9, characterized in that: The intelligent algorithm also includes: In a prediction step, given a new sample, x new , its predicted category y pred =sign(w T Φ(x new )+b) A self-learning capability that uses an online learning strategy to update model parameters. The formula is as follows: w t+1 =w t +η(y pred -y true )Φ(x new )b t+1 =b t +η(y pred -y true ), Among them, wt and bt are the model parameters of the tth step, η is the learning rate, and y true is the true label; An anomaly detection threshold setting step uses the following formula to determine the warning threshold, Threshold=μKσ Among them, μ is the mean value of the model output under normal conditions, σ is the standard deviation, and k is the confidence level coefficient.

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