High hydraulic valve health diagnosis system and method

Abstract

The invention relates to the technical field of high-hydraulic valve control, and discloses a high-hydraulic valve health diagnosis system which comprises a data module used for collecting parameter data of a high-hydraulic valve and preprocessing the collected parameter data, a valve monitoring module used for receiving the preprocessed parameter data and establishing a valve monitoring model, and a data processing module used for processing the valve monitoring model. The valve health assessment model is used for receiving predicted valve parameter data output by the valve monitoring model, and a health assessment module is used for visualizing the parameter data of the high-hydraulic valve and a health condition visualization module, so that the health degree of the high-hydraulic valve is assessed by integrating health assessment. The effects of parameter monitoring and prediction and health diagnosis of the high-hydraulic valve are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of high hydraulic valve control and discloses a high hydraulic valve health diagnosis system. Background Art

[0002] Hydraulic valve health management systems play an important role in modern industry, but there are still some development deficiencies, including the possibility of missing data or outliers in actual operations, which requires effective data preprocessing and repair methods. Machine learning models may perform well on training data, but lack generalization capabilities in actual applications, resulting in inaccurate predictions of new data. Sometimes suggestions rely on the experience and judgment of operators, and suggestions automatically generated by the system may not be reliable enough. Most of the inspections are done manually, and machine learning is unstable. Manual health inspections of hydraulic valves are highly subjective and prone to omissions, resulting in subjective and inaccurate health management. Developing and deploying advanced hydraulic valve health management systems may require a high initial investment, and companies need to evaluate the return on investment.

[0003] For example, the existing Chinese patent number is CN109630503A. The present invention discloses a hydraulic pump diagnostic system and a diagnostic method thereof, including a communication module, an oil temperature detection circuit, and an oil circuit blockage detection module controller module for data communication with the controller of the hydraulic system to be diagnosed; the oil temperature detection circuit is used to measure the oil temperature, and the oil circuit blockage detection module is used to detect the oil circuit blockage. The oil temperature detection circuit, the oil circuit blockage detection module, and the communication module all transmit data with the controller module, and the communication module obtains the speed and output pressure data of the hydraulic pump in the controller of the hydraulic system to be diagnosed, and the controller performs data analysis based on the obtained data.

[0004] However, the above patent has the following problems: the conventional data processing method is still used, resulting in low fault diagnosis accuracy, especially for some complex and hidden fault modes; secondly, the real-time monitoring capability is insufficient, which makes it difficult to meet the needs of industrial production for timely detection of the health status of high hydraulic valves and taking measures; finally, the intelligence level is insufficient. The health diagnosis method of the above patent is based on expert experience and rule-making, and lacks the ability to conduct deep learning and analysis of large amounts of data, which limits the accuracy and universality of the model. Summary of the invention

[0005] 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.

[0006] In order to solve the above technical problems, the main purpose of the present invention is to provide a high hydraulic valve health diagnosis system, comprising:

[0007] A data module, used for collecting parameter data of the high hydraulic valve and preprocessing the collected parameter data;

[0008] A valve monitoring module, used to receive pre-processed parameter data and establish a valve monitoring model;

[0009] A health assessment module, used for receiving the valve monitoring model output predicted valve parameter data, and passing through the valve health assessment model;

[0010] Visualization module, including functions for visualizing parameter data and health of high hydraulic valves.

[0011] As a preferred solution of a high hydraulic valve health diagnosis system of the present invention, wherein:

[0012] Establishing the valve monitoring model based on the pre-processed collected high hydraulic valve parameter data;

[0013] The valve monitoring model includes the pre-processed collected high hydraulic valve parameter data, characteristic values, and deviation values, extracts the characteristic values ​​of the pre-processed collected high hydraulic valve parameter data through feature engineering, calculates the prediction results through forward propagation, and optimizes and updates the valve monitoring model through feedback propagation of the predicted deviation normal distribution;

[0014] The predicted deviation normal distribution establishes a normal distribution graph based on the valve parameter data predicted by the valve monitoring model, and performs deviation calculation on the valve parameter data predicted by the valve monitoring model by fitting the shape of the normal distribution graph.

[0015] As a preferred solution of a high hydraulic valve health diagnosis system of the present invention, wherein:

[0016] The calculation expression of the valve monitoring model is as follows:

[0017]

[0018] Among them, p i is the i-th prediction data of the valve to be predicted by the valve monitoring model, λ i is the eigenvalue extracted corresponding to the i-th parameter data, is the real-time data of the i-th valve to be predicted, is the deviation value;

[0019] All parameter data are reflected by the normal distribution of prediction deviation, and the prediction deviation calculation expression is as follows:

[0020]

[0021] Among them, f(p i ) is the deviation coefficient of the predicted value output by the valve monitoring model, γ is the variance between the valve predicted valve parameter data and the actual valve working data, i is the number of valve working parameter data groups, and i is a positive integer.

[0022] As a preferred solution of a high hydraulic valve health diagnosis system of the present invention, wherein:

[0023] The optimal control of the high hydraulic valve parameter solution is composed of the ratio of the high hydraulic valve parameter data to the parameter data under rated operation at the tth moment and the high hydraulic valve parameter control entropy value;

[0024] The product of the high hydraulic valve parameter control entropy value and the parameter data change rate under the rated operation of the high hydraulic valve at the t moment is calculated, and the high hydraulic valve control working parameter changes are mapped out through the high hydraulic valve parameter control entropy value, and the optimal path of the high hydraulic valve control parameter solution is sought to optimize the high hydraulic valve parameters.

[0025] As a preferred solution of a high hydraulic valve health diagnosis system of the present invention, wherein:

[0026] The optimal control calculation expression of the high hydraulic valve parameter solution is as follows:

[0027]

[0028] Among them, Q(t) is the optimization parameter of the high hydraulic valve parameter solution at the tth moment, P(t) is the high hydraulic valve parameter data at the tth moment, P * (t) is the parameter data of the high hydraulic valve under rated operation at the tth moment, r t is the high hydraulic valve parameter control adjustment coefficient at the tth moment, is the parameter control entropy value of the high hydraulic valve, and Δq is the parameter compensation coefficient.

[0029] As a preferred solution of a high hydraulic valve health diagnosis system of the present invention, wherein:

[0030] Evaluate and diagnose the health of high hydraulic valves through valve health assessment model;

[0031] If the optimization parameter of the high hydraulic valve parameter solution at the tth moment is greater than the difference between the optimization parameter after the high hydraulic valve adjustment control optimization at the tth moment and the rated optimization parameter of the high hydraulic valve at the tth moment, and is less than the sum of the optimization parameter difference and the maximum optimization parameter allowed for the adjustment control of the high hydraulic valve, it means that the health of the high hydraulic valve is low;

[0032] If the optimization parameter of the high hydraulic valve parameter solution at time t is greater than the maximum optimization parameter allowed for adjustment and control of the high hydraulic valve, the high hydraulic valve is unhealthy and a warning is issued;

[0033] If the optimization parameters of the high hydraulic valve parameter solution at the tth moment are greater than or equal to the difference between the optimization parameters after the high hydraulic valve adjustment control optimization at the tth moment and the rated optimization parameters of the high hydraulic valve at the tth moment, and are less than or equal to the sum of the optimization parameter difference and the maximum inverse optimization parameter allowed for the adjustment control of the high hydraulic valve, it means that the high hydraulic valve has a high health.

[0034] As a preferred solution of a high hydraulic valve health diagnosis system of the present invention, wherein:

[0035] The calculation expression of the valve health assessment model is as follows:

[0036]

[0037] Where L(t) is the valve health value of the valve health assessment at the tth moment, Q(t) is the optimization parameter of the high hydraulic valve parameter solution at the tth moment, N r (t) is the optimal parameter after the high hydraulic valve adjustment control optimization at the tth moment, N l (t) is the rated optimization parameter of the high hydraulic valve at the tth moment, Γ(t) is the optimization parameter compensation coefficient, and max{Q(t)} is the maximum optimization parameter at the tth moment.

[0038] As a preferred solution of a high hydraulic valve health diagnosis system of the present invention, wherein:

[0039] The parameter data include pressure, temperature and flow rate of high hydraulic valve;

[0040] The preprocessing includes data cleaning and feature extraction of the parameter data;

[0041] The data cleaning includes processing missing values, outliers and noise;

[0042] The feature extraction is used to select the features of the valve state and perform standardization processing on the features.

[0043] As a preferred solution of a high hydraulic valve health diagnosis system of the present invention, wherein:

[0044] The visualization module includes a health status indicator and a dashboard;

[0045] The health status indicator displays the health status of the valve through color coding;

[0046] Dashboards are used to display real-time data such as pressure, flow, and temperature.

[0047] A high hydraulic valve health diagnosis method, characterized by:

[0048] S1. Collect high hydraulic valve parameter data, perform data cleaning on the parameter data, and establish a parameter data label quantity set;

[0049] S2. Based on the data set of high hydraulic valve parameter data labels, a valve monitoring model is established to monitor and predict multiple parameters of high hydraulic valves in real time;

[0050] S3, analyzing and evaluating the health of high hydraulic valves through multiple parameters of high hydraulic valves predicted by valve monitoring models;

[0051] S4, receiving the health assessment result through the high hydraulic valve control unit to control the parameters of the high hydraulic valve;

[0052] S5. The parameter data, prediction data and health status analysis of high hydraulic valves are displayed through visualization.

[0053] Beneficial effects of the present invention:

[0054] The high-hydraulic valve health diagnosis system and method provided in the present application can significantly improve the accuracy and reliability of the model through systematic data preprocessing and feature engineering; secondly, through the neural network architecture and optimization algorithm, it can be optimized for different problems to ensure that the model has good generalization capabilities and can monitor and predict the parameters of the hydraulic valves in real time. It is highly intelligent and automated, and because computer control is highly objective, it avoids the subjectivity and errors of expert experience, enhances the accuracy and universality of the model, and finally integrates health assessment to evaluate the health of high-hydraulic valves. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] 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:

[0056] Figure 1 A system composition diagram of a high hydraulic valve health diagnosis system of the present invention;

[0057] Figure 2 The present invention is a flowchart of a high hydraulic valve health diagnosis method. DETAILED DESCRIPTION

[0058] 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.

[0059] 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.

[0060] 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.

[0061] Embodiment 1:

[0062] like Figure 1 As shown, a high hydraulic valve health diagnostic system includes:

[0063] A data module, used for collecting parameter data of the high hydraulic valve and preprocessing the collected parameter data;

[0064] Wherein, the parameter data includes the pressure, temperature and flow rate of the high hydraulic valve;

[0065] The preprocessing includes data cleaning and feature extraction of the parameter data;

[0066] The data cleaning includes processing missing values, outliers and noise;

[0067] The feature extraction is used to select the features of the valve state and standardize the features. The valve monitoring module is used to receive the pre-processed parameter data to establish a valve monitoring model;

[0068] Wherein, the valve monitoring model includes the pre-processed collected high hydraulic valve parameter data, characteristic values, and deviation values, extracts the characteristic values ​​of the pre-processed collected high hydraulic valve parameter data through feature engineering, calculates the prediction results through forward propagation, and optimizes and updates the valve monitoring model through feedback propagation of the predicted deviation normal distribution;

[0069] The predicted deviation normal distribution establishes a normal distribution graph based on the valve parameter data predicted by the valve monitoring model, and performs deviation calculation on the valve parameter data predicted by the valve monitoring model by fitting the shape of the normal distribution graph.

[0070] The calculation expression of the valve monitoring model is as follows:

[0071]

[0072] Among them, pi is the i-th prediction data of the valve to be predicted by the valve monitoring model, λ i is the eigenvalue extracted corresponding to the i-th parameter data, is the real-time data of the i-th valve to be predicted, is the deviation value;

[0073] By preprocessing the collected parameter data and extracting eigenvalues, noise and interference can be reduced, data quality and model accuracy can be improved. Extracting effective eigenvalues ​​helps to capture important information and simplify model complexity, thereby improving prediction performance. Forward propagation allows the model to quickly predict the state of the valve, which helps to monitor and adjust operating strategies in real time. Feedback propagation through the normal distribution of predicted deviations can accurately identify and correct model errors, thereby improving model stability and accuracy.

[0074] All parameter data are reflected by the normal distribution of prediction deviation, and the prediction deviation calculation expression is as follows:

[0075]

[0076] Among them, f(p i ) is the deviation coefficient of the predicted value output by the valve monitoring model, γ is the variance between the valve predicted valve parameter data and the actual valve working data, i is the number of valve working parameter data groups, and i is a positive integer.

[0077] The inventor of the present application creatively combines the control entropy value and the compensation coefficient, so that the valve control strategy can be adjusted more accurately and the overall performance can be improved. Specifically, in some embodiments, the optimization control of the high hydraulic valve parameter solution is composed of the ratio of the high hydraulic valve parameter data at the tth moment to the parameter data under rated operation and the high hydraulic valve parameter control entropy value;

[0078] The product of the high hydraulic valve parameter control entropy value and the parameter data change rate under the rated operation of the high hydraulic valve at the t moment is calculated, and the high hydraulic valve control working parameter changes are mapped out through the high hydraulic valve parameter control entropy value, and the optimal path of the high hydraulic valve control parameter solution is sought to optimize the high hydraulic valve parameters.

[0079] The ratio of the parameter data at the tth moment to the rated working parameter data can reflect the operating status of the valve in real time and dynamically adjust the control strategy.

[0080] Using control entropy to quantify the complexity and uncertainty of the system helps to understand the state changes of the system and its impact on control, so as to adjust the control strategy more accurately.

[0081] The product of entropy value and parameter change rate is calculated to accurately capture the changes in system status and optimize the control path, thereby improving the accuracy and stability of valve operation.

[0082] Seek the best path to optimize parameters, achieve more efficient system regulation, reduce energy consumption and improve valve performance.

[0083] The optimal control calculation expression of the high hydraulic valve parameter solution is as follows:

[0084]

[0085] Among them, Q(t) is the optimization parameter of the high hydraulic valve parameter solution at the tth moment, P(t) is the high hydraulic valve parameter data at the tth moment, P * (t) is the parameter data of the high hydraulic valve under rated operation at the tth moment, r t is the high hydraulic valve parameter control adjustment coefficient at the tth moment, is the high hydraulic valve parameter control entropy value, Δq is the parameter compensation coefficient;

[0086] If the high hydraulic valve parameter data at the tth moment is the rated working parameter of the high hydraulic valve, the coefficient multiplied by the high hydraulic valve parameter control growth entropy value is 0. Therefore, the parameter compensation coefficient is used to prevent the high hydraulic valve parameter solution from entering an infinite loop when searching for the optimal solution, thereby ensuring the real-time prediction of the dynamic parameters of the high hydraulic valve.

[0087] If the parameter data at the tth moment meets the rated working conditions, the high hydraulic valve parameter control growth entropy value multiplication coefficient is 0, avoiding excessive adjustment under normal working conditions, thereby ensuring system stability. Through the parameter compensation coefficient, even under rated working conditions, it can still prevent the optimization algorithm from falling into an infinite loop, thereby ensuring that the model finds the optimal solution. By using real-time prediction of dynamic parameters, the model can respond quickly to changes, make necessary control adjustments, and improve the adaptability and efficiency of the system.

[0088] A health assessment module, used for receiving the valve monitoring model output predicted valve parameter data, and passing through the valve health assessment model;

[0089] Evaluate and diagnose the health of high hydraulic valves through valve health assessment model;

[0090] The calculation expression of the valve health assessment model is as follows:

[0091]

[0092] Where L(t) is the valve health value of the valve health assessment at the tth moment, Q(t) is the optimization parameter of the high hydraulic valve parameter solution at the tth moment, N r (t) is the optimal parameter after the high hydraulic valve adjustment control optimization at the tth moment, Nl (t) is the rated optimization parameter of the high hydraulic valve at the tth moment, Γ(t) is the optimization parameter compensation coefficient, and max{Q(t)} is the maximum optimization parameter at the tth moment.

[0093] If the optimization parameter of the high hydraulic valve parameter solution at the tth moment is greater than the difference between the optimization parameter after the high hydraulic valve adjustment control optimization at the tth moment and the rated optimization parameter of the high hydraulic valve at the tth moment, and is less than the sum of the optimization parameter difference and the maximum optimization parameter allowed for the adjustment control of the high hydraulic valve, it means that the health of the high hydraulic valve is low;

[0094] If the optimization parameter of the high hydraulic valve parameter solution at time t is greater than the maximum optimization parameter allowed for adjustment and control of the high hydraulic valve, the high hydraulic valve is unhealthy and a warning is issued;

[0095] If the optimization parameters of the high hydraulic valve parameter solution at the tth moment are greater than or equal to the difference between the optimization parameters after the high hydraulic valve adjustment control optimization at the tth moment and the rated optimization parameters of the high hydraulic valve at the tth moment, and are less than or equal to the sum of the optimization parameter difference and the maximum inverse optimization parameter allowed for the adjustment control of the high hydraulic valve, it means that the high hydraulic valve has a high health.

[0096] Visualization module, including for visualizing parameter data and health status of high hydraulic valves;

[0097] The visualization module includes a health status indicator and a dashboard;

[0098] The health status indicator uses color coding (e.g., green, yellow, red) to visually display the health status of the valve, helping to quickly identify normal, warning, or fault conditions.

[0099] The dashboard displays real-time data such as pressure, flow and temperature, through charts or numerical displays, allowing operators to monitor the operating status and performance of the valve in real time to ensure timely adjustments.

[0100] Embodiment 2:

[0101] like Figure 2 As shown, a high hydraulic valve health diagnosis system method includes:

[0102] S1. Collect high hydraulic valve parameter data, perform data cleaning on the parameter data, and establish a parameter data label quantity set;

[0103] S2. Based on the data set of high hydraulic valve parameter data labels, a valve monitoring model is established to monitor and predict multiple parameters of high hydraulic valves in real time;

[0104] S3, analyzing and evaluating the health of high hydraulic valves through multiple parameters of high hydraulic valves predicted by valve monitoring models;

[0105] S4, receiving the health assessment result through the high hydraulic valve control unit to control the parameters of the high hydraulic valve;

[0106] S5. The parameter data, prediction data and health status analysis of high hydraulic valves are displayed through visualization.

[0107] 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 high hydraulic valve health diagnostic system, comprising: A data module, used for collecting parameter data of the high hydraulic valve and preprocessing the collected parameter data; A valve monitoring module, used to receive pre-processed parameter data and establish a valve monitoring model; A health assessment module, used for receiving the valve monitoring model output predicted valve parameter data, and passing through the valve health assessment model; Visualization module, including functions for visualizing parameter data and health of high hydraulic valves.

2. A high hydraulic valve health diagnosis system according to claim 1, characterized in that: Establishing the valve monitoring model based on the pre-processed collected high hydraulic valve parameter data; The valve monitoring model includes the pre-processed collected high hydraulic valve parameter data, characteristic values, and deviation values, extracts the characteristic values ​​of the pre-processed collected high hydraulic valve parameter data through feature engineering, calculates the prediction results through forward propagation, and optimizes and updates the valve monitoring model through feedback propagation of the predicted deviation normal distribution; The predicted deviation normal distribution establishes a normal distribution graph based on the valve parameter data predicted by the valve monitoring model, and performs deviation calculation on the valve parameter data predicted by the valve monitoring model by fitting the shape of the normal distribution graph.

3. A high hydraulic valve health diagnosis system according to claim 1, characterized in that: The calculation expression of the valve monitoring model is as follows: Among them, p i is the i-th prediction data of the valve to be predicted by the valve monitoring model, λ i is the eigenvalue extracted corresponding to the i-th parameter data, is the real-time data of the i-th valve to be predicted, is the deviation value; All parameter data are reflected by the normal distribution of prediction deviation, and the prediction deviation calculation expression is as follows: Among them, f(p i ) is the deviation coefficient of the predicted value output by the valve monitoring model, γ is the variance between the valve predicted valve parameter data and the actual valve working data, i is the number of valve working parameter data groups, and i is a positive integer.

4. A high hydraulic valve health diagnosis system according to claim 3, characterized in that: The optimal control of the high hydraulic valve parameter solution is composed of the ratio of the high hydraulic valve parameter data to the parameter data under rated operation at the tth moment and the high hydraulic valve parameter control entropy value; The product of the high hydraulic valve parameter control entropy value and the parameter data change rate under the rated operation of the high hydraulic valve at the t moment is calculated, and the high hydraulic valve control working parameter changes are mapped out through the high hydraulic valve parameter control entropy value, and the optimal path of the high hydraulic valve control parameter solution is sought to optimize the high hydraulic valve parameters.

5. A high hydraulic valve health diagnosis system according to claim 4, characterized in that: The optimal control calculation expression of the high hydraulic valve parameter solution is as follows: Among them, Q(t) is the optimization parameter of the high hydraulic valve parameter solution at the tth moment, P(t) is the high hydraulic valve parameter data at the tth moment, P * (t) is the parameter data of the high hydraulic valve under rated operation at the tth moment, r t is the high hydraulic valve parameter control adjustment coefficient at the tth moment, is the parameter control entropy value of the high hydraulic valve, and Δq is the parameter compensation coefficient.

6. A high hydraulic valve health diagnosis system according to claim 5, characterized in that: Evaluate and diagnose the health of high hydraulic valves through valve health assessment model; If the optimization parameter of the high hydraulic valve parameter solution at the tth moment is greater than the difference between the optimization parameter after the high hydraulic valve adjustment control optimization at the tth moment and the rated optimization parameter of the high hydraulic valve at the tth moment, and is less than the sum of the optimization parameter difference and the maximum optimization parameter allowed for the adjustment control of the high hydraulic valve, it means that the health of the high hydraulic valve is low; If the optimization parameter of the high hydraulic valve parameter solution at time t is greater than the maximum optimization parameter allowed for adjustment and control of the high hydraulic valve, the high hydraulic valve is unhealthy and a warning is issued; If the optimization parameters of the high hydraulic valve parameter solution at the tth moment are greater than or equal to the difference between the optimization parameters after the high hydraulic valve adjustment control optimization at the tth moment and the rated optimization parameters of the high hydraulic valve at the tth moment, and are less than or equal to the sum of the optimization parameter difference and the maximum inverse optimization parameter allowed for the adjustment control of the high hydraulic valve, it means that the high hydraulic valve has a high health.

7. A high hydraulic valve health diagnosis system according to claim 6, characterized in that: The calculation expression of the valve health assessment model is as follows: Where L(t) is the valve health value of the valve health assessment at the tth moment, Q(t) is the optimization parameter of the high hydraulic valve parameter solution at the tth moment, N r (t) is the optimal parameter after the high hydraulic valve adjustment control optimization at the tth moment, N l (t) is the rated optimization parameter of the high hydraulic valve at the tth moment, Γ(t) is the optimization parameter compensation coefficient, and max{Q(t)} is the maximum optimization parameter at the tth moment.

8. A high hydraulic valve health diagnosis system according to claim 7, characterized in that: The parameter data include pressure, temperature and flow rate of high hydraulic valve; The preprocessing includes data cleaning and feature extraction of the parameter data; The data cleaning includes processing missing values, outliers and noise; The feature extraction is used to select the features of the valve state and perform standardization processing on the features.

9. A high hydraulic valve health diagnosis system according to claim 8, characterized in that: The visualization module includes a health status indicator and a dashboard; The health status indicator displays the health status of the valve through color coding; Dashboards are used to display real-time data such as pressure, flow, and temperature.

10. A high hydraulic valve health diagnosis method, which is implemented based on a high hydraulic valve health diagnosis system according to any one of claims 1 to 9, characterized in that: The method comprises the following specific steps: S1. Collect high hydraulic valve parameter data, perform data cleaning on the parameter data, and establish a parameter data label quantity set; S2. Based on the data set of high hydraulic valve parameter data labels, a valve monitoring model is established to monitor and predict multiple parameters of high hydraulic valves in real time; S3, analyzing and evaluating the health of high hydraulic valves through multiple parameters of high hydraulic valves predicted by valve monitoring models; S4, receiving the health assessment result through the high hydraulic valve control unit to control the parameters of the high hydraulic valve; S5. The parameter data, prediction data and health status analysis of high hydraulic valves are displayed through visualization.

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