Water conservancy facility fault prediction system and method
By setting abnormal deviation thresholds in water conservancy facilities and adjusting them based on maintenance feedback, the problems of low efficiency and high error rate of traditional detection methods are solved, achieving more efficient and accurate fault prediction and improving the stability of water conservancy facilities.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- YUNNAN WATER INVESTMENT INFORMATION TECHNOLOGY CO LTD
- Filing Date
- 2025-12-26
- Publication Date
- 2026-05-12
AI Technical Summary
Traditional fault detection of water conservancy facilities relies on manual inspections and regular maintenance, which is inefficient and untimely. Digital twin prediction methods have a high error rate due to changes in the environment.
Abnormal deviation thresholds are set based on equipment and operating parameters. Detection parameters are obtained through digital twins, and the thresholds are dynamically adjusted in conjunction with maintenance feedback to improve prediction accuracy.
It reduced the error rate of fault prediction for water conservancy equipment, improved the timeliness and accuracy of detection, and enhanced the stability of water conservancy facilities.
Smart Images
Figure CN122022007A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of water conservancy facilities technology, specifically to a water conservancy facility fault prediction system and method. Background Technology
[0002] Water conservancy facilities are a crucial foundation for ensuring the rational use of water resources and for flood control and disaster reduction. However, during long-term operation, these facilities are susceptible to various malfunctions due to factors such as the natural environment and equipment aging. These malfunctions not only affect the normal operation of the facilities but can also lead to serious safety accidents and economic losses. Traditional methods for detecting malfunctions in water conservancy facilities mainly rely on manual inspections and periodic maintenance. This approach suffers from low efficiency and untimely detection, making it difficult to meet the demands of efficient and safe operation of modern water conservancy facilities.
[0003] Currently, there is a method of building digital twins based on equipment to predict whether equipment will malfunction. However, this method has the following drawbacks: as the equipment's service life increases and the on-site operating environment changes, the detection parameters output by the digital twin differ too much from the actual detection parameters output by the water conservancy equipment, which leads to frequent errors in equipment fault prediction. Summary of the Invention
[0004] The present application provides a water conservancy facility fault prediction system and method that can reduce the error rate of equipment faults.
[0005] The specific technical solution of this embodiment is as follows:
[0006] On the one hand, embodiments of this application provide a method for predicting the failure of water conservancy facilities, including:
[0007] S10. Construct a digital twin based on device parameters;
[0008] S20. Based on the operating parameters, obtain the first detection parameters output by the digital twin; and based on the equipment parameters and operating parameters, set the abnormal deviation threshold;
[0009] S30. Obtain the second detection parameter output by the device;
[0010] S40. Determine whether the difference between the first detection parameter and the second detection parameter exceeds the abnormal deviation threshold.
[0011] S50. When it is determined that the difference between the first detection parameter and the second detection parameter does not exceed the abnormal deviation threshold, the process ends.
[0012] S60. When it is determined that the difference between the first detection parameter and the second detection parameter exceeds the abnormal deviation threshold, the maintenance result of the equipment is obtained, and based on the maintenance result of the equipment, it is determined whether the equipment is abnormal.
[0013] S70. When it is determined that the equipment is not abnormal, the abnormal deviation threshold is corrected based on the difference between the first detection parameter and the second detection parameter.
[0014] In some embodiments, after determining whether the device has malfunctioned, the following steps are also included:
[0015] S80. When it is determined that the equipment is abnormal, the abnormal deviation threshold is reduced according to the first preset gradient value.
[0016] S90. In the subsequent repetition of steps S20-S70, when it is determined again that the equipment is not abnormal, the previous abnormal deviation threshold is selected as the short-term minimum abnormal deviation threshold.
[0017] In some embodiments, after selecting the previous abnormal deviation threshold as the short-term lowest abnormal deviation threshold, the following steps are also included:
[0018] S100. If the device is determined to be abnormal again within the preset time period, the process ends.
[0019] S110. After a preset time period, if the device is determined to be abnormal again, repeat steps S80-S90 to correct the short-term minimum abnormal deviation threshold.
[0020] In some embodiments, correcting the abnormal deviation threshold includes increasing the abnormal deviation threshold according to a second preset gradient value.
[0021] In some embodiments, correcting the abnormal deviation threshold includes replacing the abnormal deviation threshold based on the difference between the first detection parameter and the second detection parameter.
[0022] In some embodiments, setting the abnormal deviation threshold in step S20 includes: setting the abnormal deviation threshold based on the historical parameter records and operating parameter records of the previous equipment, and based on the fluctuation value of the equipment failure.
[0023] In some embodiments, based on historical parameter records and operating parameter records of the previous equipment, the smallest fluctuation value that indicates a fault in the equipment is selected as the abnormal deviation threshold.
[0024] On the other hand, embodiments of this application provide a water conservancy equipment fault prediction system, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the water conservancy facility fault prediction method of any of the above embodiments.
[0025] Compared with the prior art, the embodiments of this application have the following beneficial effects:
[0026] The water conservancy equipment fault prediction method provided in this application, based on equipment parameters and operating parameters, pre-sets anomaly deviation thresholds and predicts whether the water conservancy equipment will malfunction based on these thresholds. This solves the problems of low efficiency and untimely detection caused by current manual inspections and periodic maintenance. Furthermore, by selectively correcting the anomaly deviation thresholds through maintenance feedback, the accuracy of the prediction is improved, allowing the prediction results to continuously reflect real-time conditions and reducing the error rate of equipment fault prediction, thereby improving the stability of the water conservancy equipment. Attached Figure Description
[0027] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0028] Figure 1 This is a flowchart illustrating a method for predicting water conservancy facility failures provided in some embodiments of this application;
[0029] Figure 2 This is a flowchart illustrating a method for predicting water conservancy facility failures provided in other embodiments of this application. Detailed Implementation
[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0031] In the description of this application, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.
[0032] The use of "applies to" or "configured to" in this application implies open and inclusive language, which does not exclude the applicability to or configuration to devices performing additional tasks or steps. Additionally, the use of "based on" implies openness and inclusivity, because processes, steps, calculations, or other actions "based on" one or more of the stated conditions or values may in practice be based on additional conditions or values beyond those stated.
[0033] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.
[0034] On the one hand, please refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for predicting water conservancy facility failures according to some embodiments of this application. Embodiments of this application provide a method for predicting water conservancy equipment failures, including the following steps:
[0035] S10. Construct a digital twin based on device parameters.
[0036] In S10, you can choose to acquire all parameters of the device or select only some parameters, and then construct a digital twin based on the acquired parameters. For example, when you want to predict faults in all functions of the device, you acquire all parameters of the device; when you want to predict faults in only some functions of the device, you acquire the corresponding parameters.
[0037] S20. Based on the operating parameters, obtain the first detection parameters output by the digital twin; and based on the equipment parameters and operating parameters, set the abnormal deviation threshold.
[0038] In S20, the operating parameters include environmental parameters corresponding to specific detection functions. In a specific example, the operating parameters include fixed environmental parameters, such as height and dimensions, as well as real-time environmental parameters, such as temperature and water flow rate. The first detection parameter is the detection value of at least one function output by the digital twin simulating the hydraulic equipment, based on the corresponding parameters. The abnormal deviation threshold refers to the maximum allowable error between the second detection parameter subsequently output by the hydraulic equipment and the first detection parameter output by the digital twin.
[0039] S30. Obtain the second detection parameter output by the device.
[0040] In S30, the second detection parameter is the same type of detection value as the first detection parameter, such as the pressure value exerted on the equipment.
[0041] S40. Determine whether the difference between the first detection parameter and the second detection parameter exceeds the abnormal deviation threshold.
[0042] In S40, after setting the abnormal deviation threshold in advance, when the difference between the first detection parameter and the second detection parameter exceeds the abnormal deviation threshold, it can be preliminarily determined that the equipment is abnormal; when the difference between the second detection parameter and the second detection parameter does not exceed the abnormal deviation threshold, it can be preliminarily determined that the equipment is not abnormal.
[0043] S50. When it is determined that the difference between the first detection parameter and the second detection parameter does not exceed the abnormal deviation threshold, the process ends.
[0044] In S50, after initially determining that no abnormality has occurred in the equipment, the current step of predicting the fault of the water conservancy equipment ends, and the process awaits the next fault prediction step.
[0045] S60. When it is determined that the difference between the first detection parameter and the second detection parameter exceeds the abnormal deviation threshold, the maintenance result of the equipment is obtained, and based on the maintenance result of the equipment, it is determined whether the equipment is abnormal.
[0046] In S60, when an initial assessment indicates an equipment malfunction, other methods are employed, such as equipment self-inspection or manual inspection, to further determine if the malfunction has occurred and obtain the assessment results. If an equipment malfunction is detected, it indicates that the prediction for the water conservancy equipment was correct, and the current prediction process can be terminated, awaiting the next fault prediction step.
[0047] S70. When it is determined that the equipment is not abnormal, the abnormal deviation threshold is corrected based on the difference between the first detection parameter and the second detection parameter.
[0048] In S70, if it is determined that the equipment is not abnormal, it indicates that the prediction of the water conservancy equipment is biased.
[0049] In the above embodiments, based on equipment parameters and operating parameters, an abnormal deviation threshold is pre-set, and based on this threshold, the possibility of malfunction in the hydraulic equipment is predicted. This solves the problems of low efficiency and untimely detection caused by current manual inspections and periodic maintenance. Furthermore, by selectively correcting the abnormal deviation threshold through maintenance feedback, the accuracy of the prediction is improved, allowing the prediction results to continuously align with real-time conditions and reducing the error rate of equipment malfunction prediction, thereby improving the stability of the hydraulic equipment.
[0050] In some of these embodiments, please refer to Figure 2 , Figure 2 This is a flowchart illustrating a water conservancy facility fault prediction method provided in other embodiments of this application. In step S60, after determining whether the equipment has malfunctioned, the following steps are also included:
[0051] S80. When it is determined that the equipment is abnormal, the abnormal deviation threshold is reduced according to the first preset gradient value.
[0052] In S80, when an abnormality is detected in the equipment, it indicates that the prediction of the water conservancy equipment is correct. To further improve the accuracy of the prediction, the abnormal deviation threshold can be lowered to reduce the number of cases where water conservancy equipment abnormalities are not detected. The first preset gradient value can be a fixed value, that is, the abnormal deviation threshold is lowered according to a fixed value; or it can be multiple different values, for example, the abnormal deviation threshold is lowered according to a fixed value the first time, and the abnormal deviation threshold is lowered according to another fixed value the second time.
[0053] S90. In the subsequent repetition of steps S20-S70, when it is determined again that the equipment is not abnormal, the previous abnormal deviation threshold is selected as the short-term minimum abnormal deviation threshold.
[0054] In S90, each repetition of steps S20-S70 can be considered as completing one prediction step. Adjacent prediction steps can be performed consecutively or after a certain interval. When it is determined again that the equipment is not abnormal, that is, in the next one or more prediction steps, the difference between the first and second detection parameters exceeds the abnormal deviation threshold again, but based on the equipment maintenance results, it is determined that the equipment is not abnormal. In this case, the abnormal deviation threshold at which the difference between the first and second detection parameters exceeded the abnormal deviation threshold in the previous instance, and the abnormal deviation threshold at which the equipment was determined to be abnormal based on the equipment maintenance results, is selected as the short-term minimum abnormal deviation threshold, and the next prediction step is performed.
[0055] In the above embodiments, by continuously reducing the abnormal deviation threshold, the prediction of equipment failure can be made more accurate, and the number of equipment failures that are not predicted can be reduced.
[0056] In some embodiments, after selecting the previous abnormal deviation threshold as the short-term lowest abnormal deviation threshold in step S90, the following steps are also included:
[0057] S100. If the device is determined to be abnormal again within the preset time period, the process ends.
[0058] In S100, the preset time period can be set based on actual conditions, such as the safety level and service life of the water conservancy equipment. That is, after determining the short-term minimum abnormal deviation threshold, in the subsequent S60 steps, when it is determined that the equipment has an abnormality again, it indicates that the prediction is correct. At this time, the current prediction step ends and the abnormal deviation threshold is no longer adjusted.
[0059] S110. After a preset time period, if the equipment is found to be abnormal again, repeat steps S80-S90 to continuously correct the short-term minimum abnormal deviation.
[0060] In the above embodiments, after obtaining the short-term minimum abnormal deviation threshold, the water conservancy equipment is predicted based on the short-term minimum abnormal deviation threshold within a preset time period. When the preset time period is exceeded, the short-term minimum abnormal deviation threshold, which is used as the abnormal deviation threshold, is selectively adjusted again to achieve periodic correction of the water conservancy equipment fault prediction method.
[0061] In some embodiments, correcting the abnormal deviation threshold includes increasing the abnormal deviation threshold according to a second preset gradient value.
[0062] The second preset gradient value can be a fixed value, that is, the abnormal deviation threshold is reduced by a fixed value; or it can be multiple different values, such as increasing the abnormal deviation threshold by a fixed value the first time, and increasing the abnormal deviation threshold by another fixed value the second time.
[0063] The second preset gradient value can be the same as the first preset gradient value, or the second preset gradient value can be different from the first preset gradient value.
[0064] In the above embodiments, increasing the abnormal deviation threshold based on the second preset gradient value can reduce the prediction errors caused by abnormal fluctuations in equipment or environment, resulting in a large increase in the abnormal deviation threshold, thereby making the prediction results of water conservancy facilities more stable.
[0065] In some other embodiments, correcting the abnormal deviation threshold includes replacing the abnormal deviation threshold based on the difference between the first detection parameter and the second detection parameter.
[0066] In the above embodiments, by replacing the abnormal deviation threshold and increasing it, the prediction error rate can be reduced. Based on a second preset gradient value to increase the abnormal deviation threshold, this embodiment is more suitable for scenarios where equipment operation and environmental parameters are relatively stable.
[0067] In some embodiments, setting the abnormal deviation threshold in step S20 includes: setting the abnormal deviation threshold based on the historical parameter records and operating parameter records of the previous equipment, and based on the fluctuation value of the equipment failure.
[0068] In the above embodiments, the historical parameter records and operating parameter records of the equipment are used, including the results of manual inspections and regular maintenance. The fluctuation value of equipment failure refers to multiple fluctuation values when a manual inspection determines that the equipment has failed based on abnormal deviations in the detected values, but the equipment is ultimately found not to be faulty.
[0069] In some examples, the minimum fluctuation value, the maximum fluctuation value, or the fluctuation value with the highest frequency can be selected as the abnormal deviation threshold. In specific application scenarios, when the safety level of the equipment detection requirements is high, the minimum fluctuation value can be selected as the abnormal deviation threshold; when the safety level of the equipment detection requirements is low, or when manual inspections and periodic maintenance are frequent, or when the equipment has a short service life, the maximum fluctuation value can be selected as the abnormal deviation threshold; when there are many fluctuation values to be detected, the fluctuation value with the highest frequency can be selected as the abnormal deviation threshold.
[0070] In some embodiments, in step S20, based on the historical parameter records and operating parameter records of the previous equipment, the minimum fluctuation value that indicates a fault in the equipment is selected as the abnormal deviation threshold.
[0071] In the above embodiments, historical parameter records and operating parameter records of the equipment are used, including the results of manual inspections and regular maintenance. The minimum fluctuation value at which equipment failure occurs refers to the fluctuation value when a manual inspection based on an abnormal deviation in the detected value indicates a failure, but the equipment is ultimately found not to be faulty. These fluctuation value data are collected, and the smallest fluctuation value is selected as the abnormal deviation threshold, so that the pre-set abnormal deviation threshold can better predict equipment failures.
[0072] On the other hand, this application provides a water conservancy equipment fault prediction system, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the water conservancy facility fault prediction method described in any of the above embodiments.
[0073] This application also provides a computer storage medium storing a computer program that is executed to implement the water conservancy facility fault prediction method described in any of the above embodiments.
[0074] This application provides a method for predicting the failure of water conservancy facilities, mainly applied to the failure prediction of on-site testing equipment, such as flow monitoring equipment and sediment content monitoring equipment. Specifically, the method for predicting the failure of water conservancy facilities provided in this application first constructs a digital twin based on equipment parameters. Based on operating parameters, it obtains a first detection parameter output by the digital twin and a second detection parameter of the same type output by the equipment. Then, based on historical parameter records and operating parameter records of the equipment, as well as the fluctuation values of equipment failures, it sets an initial abnormal deviation threshold.
[0075] When predicting equipment failure, the difference between the first detection parameter and the second detection parameter is obtained. If the difference between the first detection parameter and the second parameter exceeds the initial abnormal deviation threshold, the equipment is manually inspected or automatically inspected. Based on the inspection results, it is determined whether the equipment is actually abnormal. If the equipment is determined not to be abnormal, the initial abnormal deviation threshold is increased according to the second preset gradient value. If the equipment is determined to be abnormal, the initial abnormal deviation threshold is decreased according to the first preset gradient value.
[0076] The present application provides a method for predicting water conservancy facility failures. By selectively correcting abnormal deviation thresholds through maintenance feedback, the accuracy of predictions can be improved, thereby enabling the prediction results to continuously match the real-time situation and reducing the error rate of equipment failure predictions, thus improving the stability of water conservancy equipment.
[0077] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for predicting the failure of water conservancy facilities, characterized in that, include: S10. Construct a digital twin based on device parameters; S20. Based on the operating parameters, obtain the first detection parameters output by the digital twin; And based on equipment parameters and operating parameters, set abnormal deviation thresholds; S30. Obtain the second detection parameter output by the device; S40. Determine whether the difference between the first detection parameter and the second detection parameter exceeds the abnormal deviation threshold. S50. When it is determined that the difference between the first detection parameter and the second detection parameter does not exceed the abnormal deviation threshold, the process ends. S60. When it is determined that the difference between the first detection parameter and the second detection parameter exceeds the abnormal deviation threshold, the maintenance result of the equipment is obtained, and based on the maintenance result of the equipment, it is determined whether the equipment is abnormal. S70. When it is determined that the equipment is not abnormal, the abnormal deviation threshold is corrected based on the difference between the first detection parameter and the second detection parameter.
2. The method for predicting water conservancy facility failures as described in claim 1, characterized in that, After determining whether the device is malfunctioning, the following steps are also included: S80. When it is determined that the equipment is abnormal, the abnormal deviation threshold is reduced according to the first preset gradient value. S90. In the subsequent repetition of steps S20-S70, when it is determined again that the equipment is not abnormal, the previous abnormal deviation threshold is selected as the short-term minimum abnormal deviation threshold.
3. The method for predicting water conservancy facility failures as described in claim 2, characterized in that, After selecting the previous outlier threshold as the short-term lowest outlier threshold, the following steps are also included: S100. If the device is determined to be abnormal again within the preset time period, the process ends. S110. After a preset time period, if the device is determined to be abnormal again, repeat steps S80-S90 to correct the short-term minimum abnormal deviation threshold.
4. The method for predicting water conservancy facility failures as described in claim 1, characterized in that, Correcting the abnormal deviation threshold includes increasing the abnormal deviation threshold according to the second preset gradient value.
5. The method for predicting water conservancy facility failures as described in claim 1, characterized in that, Correcting the abnormal deviation threshold includes replacing the abnormal deviation threshold based on the difference between the first detection parameter and the second detection parameter.
6. The method for predicting water conservancy facility failures as described in claim 1, characterized in that, In step S20, setting the abnormal deviation threshold includes: based on the historical parameter records and operating parameter records of the equipment, and based on the fluctuation value of the equipment failure, setting the abnormal deviation threshold.
7. The method for predicting water conservancy facility failures as described in claim 6, characterized in that, Based on the historical and operational parameter records of the equipment, the minimum fluctuation value that indicates a fault in the equipment is selected as the abnormal deviation threshold.
8. A fault prediction system for water conservancy equipment, characterized in that, The device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the water conservancy facility failure prediction method according to any one of claims 1-7.