Float-type water level gauge automatic calibration system

Abstract

The invention discloses an automatic calibration system for a float-type water level gauge, and relates to the technical field of automatic calibration. The system comprises a man-machine interaction module, a power drive and control module, a signal interaction module, a mechanical execution and transmission module, an electric power guarantee module, an environment perception and data acquisition module, an intelligent maintenance and fault prediction module and a core control module. The method comprises the following steps: receiving a water level value in real time, and adjusting the rotating speed, rotating direction and position of a servo motor, thereby adjusting a water level gauge, completing the calibration process of the water level gauge, collecting water level, environment temperature, atmospheric pressure and water quality parameters in real time, performing fault prediction analysis, constructing a component performance degradation model to pre-judge potential faults, and dynamically adjusting the water level gauge when the component performance is normal. The servo motor is accurately adjusted through the power drive and control module, the automation degree of water level gauge adjustment is enhanced, the residual life of an execution component is predicted in advance, maintenance personnel can make a maintenance plan in advance, and the situation that water level monitoring is affected by faults is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic calibration, and specifically to an automatic calibration system for a float-type water level gauge. Background Art

[0002] In the fields of water conservancy, hydrological monitoring, etc., the accurate measurement of water level is crucial. As a commonly used water level measurement device, the float-type water level gauge has been widely used due to its simple structure, stable performance, etc. However, there are many problems to be solved in the actual use of traditional float-type water level gauges. The calibration work of water level gauges has long relied on manual operation, with low efficiency and prone to errors. Manual calibration not only requires a large amount of human and time costs, but also is difficult to ensure the accuracy and consistency of calibration. At the same time, due to the great influence of manual operation by environmental factors, personnel skill levels, etc., in complex and harsh environments, the difficulty and risk of calibration work are further increased. With the continuous development of water conservancy facilities and the increasing requirements of hydrological monitoring, higher requirements are put forward for the real-time performance, reliability, and intelligence level of water level gauges. Traditional water level gauges cannot perceive environmental changes in real time. For example, changes in environmental temperature, atmospheric pressure, and water quality parameters may all affect the water level measurement accuracy, but traditional devices are difficult to effectively compensate and correct for these factors. The timely discovery and maintenance of equipment failures are also a major problem. Traditional water level gauges lack an effective fault prediction and diagnosis mechanism, and are often discovered only after serious equipment failures, affecting the continuity and accuracy of water level monitoring. Summary of the Invention

[0003] The purpose of the present invention is to provide an automatic calibration system for a float-type water level gauge to solve the problems raised in the prior art.

[0004] To achieve the above purpose, the present invention provides the following technical solution: An automatic calibration system for a float-type water level gauge, which system includes a human-machine interaction module, a power drive and control module, a signal interaction module, a mechanical execution and transmission module, a power supply guarantee module, an environmental perception and data acquisition module, an intelligent maintenance and fault prediction module, and a core control module; The human-machine interaction module is used to receive the water level value in real time, and display the system operation status, calibration progress, and fault warning information generated by the intelligent maintenance module in real time; The power drive and control module adjusts the rotation speed, steering, and position of the servo motor, thereby adjusting the water level gauge; The signal interaction module is used to receive instructions from the touch screen and transmit them to the execution components, and at the same time collect the feedback signals of the water level gauge and the sensor group in real time; The mechanical execution and transmission module consists of an electromagnetic fixture, a water level gauge shaft, and a water level gauge steel wire rope, and is used to complete the calibration process of the water level gauge; The power supply module is used to supply power. This module has perfect overvoltage protection, overcurrent protection and short-circuit protection functions, and can ensure the stable operation of each component of the system in different power environments, avoiding equipment damage caused by power problems; The environmental perception and data acquisition module includes a sensor group, which is used to collect water level, environmental temperature, atmospheric pressure and water quality parameters in real time; The intelligent maintenance and fault prediction module is used to conduct fault prediction and analysis on the execution components of the system, and build a component performance degradation model to predict potential faults; The core control module monitors the component performance in real time. When the component performance is normal, it dynamically adjusts the water level gauge; Furthermore, the human-machine interaction module is connected to the camera through a Bluetooth network, obtains the water level value collected by the camera in real time, and exchanges data with the core processor through a communication protocol; Furthermore, the signal interaction module is used to receive instructions from the touch screen and transmit them to the execution components. At the same time, it collects the feedback signals of the water level gauge and the sensor group in real time. The execution components include electromagnetic clamps and servo drivers; Furthermore, the environmental perception and data acquisition module includes a sensor group, and the sensor group includes a water level sensor, a temperature sensor, a pressure sensor and a water quality sensor; Furthermore, the intelligent maintenance and fault prediction module predicts the occurrence time of potential faults. The occurrence time of potential faults includes the occurrence duration of motor faults and the number of opening and closing times of fixture fault warnings; Analyze the servo motor and establish a wear degree analysis model for the wear degree W m : ; where u m is the rotation speed of the servo motor, u 0 is the rated rotation speed of the servo motor, T m is the torque of the servo motor, T 0 is the rated torque of the servo motor, t 1 is the running duration of the servo motor, t 0 is the maximum life of the servo motor, is the historical load fluctuation of the servo motor (by marking the historical load of the motor and updating the historical load fluctuation in real time). The calculation method of the historical load fluctuation is as follows: Arrange the data in the database according to time, and perform data marking at intervals of a fixed time period. Let the value of the torque at time point P q be V q , then the load fluctuation at time point P q is V q -V q-1, the historical load fluctuation is the average value of the load fluctuations at q time points, and then statistical analysis is performed on it; where k 1 is the coefficient parameter related to u m , T m and t 1 ; c is the exponential parameter related to t 1 ; k 2 is the coefficient parameter related to ; d is the exponential parameter related to ; k 1 , k 2 , c and d can be obtained in the following way: by collecting a large amount of historical operation data of servo motors, including the rotational speed u m , torque u 0 , running time t and historical load fluctuation data , and combining the actual wear state data of the motor at the corresponding moment (obtained by disassembling and inspecting the motor, performing performance tests or based on experience judgment, etc.), these data are used as training samples and input into the long short-term memory network (LSTM) algorithm for training, and finally the optimized model parameters k 1 , k 2 , c and d are obtained.

[0005] Furthermore, calculate the motor fault occurrence duration t f1 : ; where W m_crit is the critical wear value of the motor (obtained by calculating the average value of the wear degree of the same type of motor in the previous monitoring cycle in the database). Through in-depth analysis of the servo motor and other actuators by the intelligent maintenance and fault prediction module, the fault occurrence time can be accurately predicted in advance. In the past, under the traditional water level gauge maintenance mode, due to the lack of effective fault prediction means, only a regular maintenance strategy could be adopted. Regardless of whether the equipment really needed maintenance, it was overhauled and maintained according to a fixed cycle, which led to a large amount of unnecessary maintenance cost expenditure. After introducing this system, according to the predicted fault occurrence time, maintenance personnel can reasonably arrange the maintenance plan before the fault occurs, and specifically replace or repair the components that are about to fail, reducing the potential economic losses caused by the interruption of water level monitoring due to equipment failures, and effectively improving the operation efficiency and economic benefits of water conservancy facilities.

[0006] Furthermore, analyze the electromagnetic fixture and establish a performance degradation analysis model for the performance degradation degree D e : ; where n is the percentage of the used duration of the electromagnetic fixture in the maximum used duration, The decay percentage of the current clamping force of the electromagnetic fixture compared to the initial clamping force, t 2 represents the number of times the electromagnetic fixture has been opened and closed, t represents the maximum number of times the electromagnetic fixture can be opened and closed. The method for obtaining the maximum number of times the electromagnetic fixture can be opened and closed is, for example: collecting the number of times the electromagnetic fixture has malfunctioned in history, and taking the average value of the number of times of malfunction as the maximum number of times the electromagnetic fixture can be opened and closed; where k 3 is a coefficient parameter related to n, and t, s is an exponential parameter related to , and g represents an exponential parameter related to t 2 , k 3 , s, and g are obtained, for example: by collecting the historical usage data of the electromagnetic fixture, including the number of times of opening and closing n, the change in clamping force and the usage time t 2 , and at the same time combining the corresponding degree of performance degradation (obtained by disassembling and inspecting the electromagnetic fixture, performance testing, or based on experience judgment, etc.), taking these data as training samples, and using the long short-term memory network (LSTM) algorithm to train the corresponding model parameters k 3 , s, and g, and then calculating the number of times of opening and closing t f2 for fixture failure warning: ; where D e_crit is the critical decay value of the electromagnetic fixture (calculated from the average value of the historical performance degradation degree of the same type of electromagnetic fixture in the database). Using the performance degradation analysis model and the fault time prediction function, the performance state of the electromagnetic fixture can be accurately grasped. In the traditional case, the maintenance of the electromagnetic fixture is often based on experience or a fixed time period, which may lead to over-maintenance or untimely maintenance. And this system can achieve targeted maintenance of the electromagnetic fixture according to the calculated degree of performance degradation and the time of fault occurrence. Through the performance degradation analysis and fault prediction of the electromagnetic fixture, its potential faults can be predicted in advance, avoiding abnormal operation of the water level gauge system caused by the failure of the electromagnetic fixture. The electromagnetic fixture plays a key role in the calibration process of the water level gauge, and its failure may cause the water level gauge to malfunction. After adopting this system, make preparations for maintenance in advance to ensure the stable operation of the water level gauge system. The system conducts a detailed analysis of the historical usage data of the electromagnetic fixture, and the obtained performance degradation analysis model parameters can guide resource allocation. Managers can reasonably allocate spare parts inventory and maintenance resources according to the performance degradation trend of the electromagnetic fixture. At the same time, it avoids the emergency maintenance cost caused by the sudden failure of the electromagnetic fixture, effectively controlling the maintenance cost; Further, when the intelligent maintenance and fault prediction module predicts that the motor fault occurrence duration is lower than the motor fault occurrence duration threshold set by the system, it triggers a motor fault warning; when it predicts that the number of opening and closing times of the fixture fault warning is lower than the fixture fault warning opening and closing times threshold set by the system, it triggers a fixture fault warning. The intelligent maintenance and fault prediction module pushes a maintenance requirement notice to the user through the touch screen. The notice content includes the name of the component with the warning fault, the motor fault occurrence duration, and the number of opening and closing times of the fixture fault warning. By combining historical data with the LSTM algorithm to train the model parameters, the intelligent upgrade of electromagnetic fixture management is realized. Managers can arrange maintenance work at the appropriate time according to the information predicted by the system, rather than relying on manual experience judgment. This intelligent management method makes equipment management more scientific and efficient, promotes the intelligent development of the entire water level gauge system, and improves the management level in the field of water conservancy monitoring; Further, when the intelligent maintenance and fault prediction module does not trigger a motor fault warning and a fixture fault warning, the core control module determines that the system can be calibrated; otherwise, it determines that the system cannot be calibrated; Further, when the system can be calibrated, if the difference between the displayed water level and the actual water level of the water level gauge monitored by the system is greater than the difference tolerance set by the system, it enters the calibration process. The calibration process includes: when starting calibration, the electromagnetic fixture clamps the suspension cable, and the servo motor rotates to drive the water level wheel to rotate. When calibration is completed, the electromagnetic fixture is released, the servo motor enable terminal is reset, and the motor is in a coasting state. The core control module is based on the warning information of the intelligent maintenance and fault prediction module and only allows calibration when there are no potential faults in the motor and the fixture, which greatly guarantees the stability of the calibration process. Faulty equipment may cause deviations in the execution of calibration actions and affect the calibration results.

[0007] Compared with the prior art, the beneficial effects of the present invention are as follows: On the one hand, through the coordinated work of the power drive and control module, the mechanical execution and transmission module, and the core control module, the automatic calibration of the water level gauge is realized. The power drive and control module precisely adjusts the servo motor, and the mechanical execution and transmission module quickly responds to complete the calibration action, avoiding the problems of low efficiency and errors caused by manual operation; on the one hand, the environment perception and data acquisition module collects water level, ambient temperature, atmospheric pressure, and water quality parameters in real time, and the core control module dynamically adjusts the water level gauge based on these data. Under different environmental conditions, the system can automatically compensate for the influence of environmental factors on water level measurement to ensure stable and reliable measurement results; on the other hand, the intelligent maintenance and fault prediction module uses machine learning algorithms to construct a component performance degradation model, and predicts in advance the remaining life and potential fault occurrence time of execution components such as servo motors and electromagnetic fixtures. When the predicted fault occurrence time is lower than the set threshold, a warning is triggered in time and a maintenance notice is pushed through the touch screen. This function enables maintenance personnel to formulate maintenance plans in advance, prepare repair parts, and avoid sudden equipment failures affecting water level monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a schematic structural diagram of an automatic calibration system for a float-type water level gauge according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0009] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0010] As Figure 1 shown, the present invention provides a technical solution, an automatic calibration system for a float-type water level gauge, which includes a human-computer interaction module, a power drive and control module, a signal interaction module, a mechanical execution and transmission module, a power supply guarantee module, an environment perception and data acquisition module, an intelligent maintenance and fault prediction module, and a core control module; The human-computer interaction module is used to receive the water level value in real time, and display the system operation status, calibration progress, and fault warning information generated by the intelligent maintenance module in real time; The power drive and control module adjusts the rotation speed, rotation direction, and position of the servo motor, thereby adjusting the water level gauge; The signal interaction module is used to receive instructions from the touch screen and transmit them to the execution components, and at the same time collect the feedback signals of the water level gauge and the sensor group in real time; The mechanical execution and transmission module consists of an electromagnetic fixture, a water level gauge shaft, and a water level gauge wire rope, and is used to complete the calibration process of the water level gauge; The power supply module is used to supply power. This module has perfect overvoltage protection, overcurrent protection, and short-circuit protection functions, and can ensure the stable operation of each component of the system in different power environments, avoiding equipment damage caused by power problems; The environmental perception and data acquisition module includes a sensor group, which is used to collect water level, environmental temperature, atmospheric pressure, and water quality parameters in real time; The intelligent maintenance and fault prediction module is used to conduct fault prediction analysis on the execution components of the system, and build a component performance degradation model to predict potential faults; The core control module monitors the component performance in real time. When the component performance is normal, it dynamically adjusts the water level gauge.

[0011] The human-machine interaction module is connected to the camera through a Bluetooth network, obtains the water level value collected by the camera in real time, and exchanges data with the core processor through a communication protocol; The signal interaction module is used to receive instructions from the touch screen and transmit them to the execution components. At the same time, it collects the feedback signals of the water level gauge and the sensor group in real time. The execution components include an electromagnetic fixture and a servo driver; The environmental perception and data acquisition module includes a sensor group, and the sensor group includes a water level sensor, a temperature sensor, a pressure sensor, and a water quality sensor; Furthermore, the intelligent maintenance and fault prediction module predicts the occurrence time of potential faults, and the occurrence time of potential faults includes the occurrence duration of motor faults and the number of warning opening and closing times of the fixture faults; Analyze the servo motor and establish a wear degree analysis model for the wear degree W m : ; where u m is the rotation speed of the servo motor, u 0 is the rated rotation speed of the servo motor, T m is the torque of the servo motor, T 0 is the rated torque of the servo motor, t 1 is the running duration of the servo motor, t 0 is the maximum life of the servo motor, is the historical load fluctuation of the servo motor (by marking the historical load of the motor and updating the historical load fluctuation in real time). The calculation method of the historical load fluctuation is as follows: Arrange the data in the database according to time, and perform data marking at intervals of a fixed time period. Let the value of the torque at time point P q be V q , then at time point Pq The load fluctuation is V q -V q-1 , and the historical load fluctuation is the average value of the load fluctuations at q time points, and then statistical analysis is performed on it; where k 1 is a coefficient parameter related to u m , T m and t 1 , c is an exponential parameter related to t 1 , k 2 is a coefficient parameter related to , d is an exponential parameter related to , k 1 , k 2 , c and d can be obtained as follows: by collecting a large amount of historical operation data of servo motors, including the rotational speed u m , torque u 0 , operation time t and historical load fluctuation data , and combining the actual wear state data of the motor at the corresponding moment (obtained by disassembling and inspecting the motor, performance testing or empirical judgment, etc.), these data are used as training samples and input into the long short-term memory network (LSTM) algorithm for training, and finally the optimized model parameters k 1 , k 2 , c and d are obtained.

[0012] The application steps of the specific (LSTM) algorithm are as follows: Obtain the model parameters of the servo motor and the electromagnetic fixture using the LSTM algorithm. First, collect relevant historical operation data, such as the rotational speed and torque of the servo motor, the number of opening and closing times of the electromagnetic fixture, etc., and perform normalization processing. At the same time, obtain the actual wear or performance degradation data as the target value. Then build an LSTM network, set the number of neurons in the input, hidden and output layers, train with the training set, evaluate with the validation set to prevent overfitting, and test the generalization ability with the test set. Finally, the optimized model parameters k 1 , k 2 , c and d are obtained through the weights and biases of the output layer; Furthermore, calculate the motor fault occurrence duration t f1 : ; where W m_critis the critical wear value of the motor (obtained by calculating the average wear degree of motors of the same type in the database in the previous monitoring cycle before wear). Through the intelligent maintenance and fault prediction module, in-depth analysis is carried out on the servo motor and other execution components to accurately predict the fault occurrence time in advance. In the past, under the traditional water level gauge maintenance mode, due to the lack of effective fault prediction means, only a regular maintenance strategy could be adopted. Regardless of whether the equipment really needed maintenance, it was overhauled and maintained according to a fixed cycle, which led to a large amount of unnecessary maintenance cost expenditure. After introducing this system, according to the predicted fault occurrence time, maintenance personnel can reasonably arrange the maintenance plan before the fault occurs, and specifically replace or repair the components that are about to fail, reducing the potential economic losses caused by the interruption of water level monitoring due to equipment failure, and effectively improving the operation efficiency and economic benefits of water conservancy facilities.

[0013] Furthermore, analyze the electromagnetic fixture and establish a performance degradation analysis model for the performance degradation degree D e : ; where n is the percentage of the used duration of the electromagnetic fixture in the maximum used duration, is the attenuation percentage of the current clamping force of the electromagnetic fixture compared to the initial clamping force, and t 2 represents the number of times the electromagnetic fixture has been opened and closed, and t represents the maximum number of times the electromagnetic fixture can be opened and closed; where k 3 is a coefficient parameter related to n, and t, s is an exponential parameter related to , and g represents an exponential parameter related to t 2 , and the acquisition methods of k 3 , s and g are as follows: When obtaining the parameters of the electromagnetic fixture performance degradation analysis model, first collect its historical usage data, including the number of times of opening and closing n, the change in clamping force (reflected as the attenuation percentage of the current clamping force compared to the initial clamping force), and the usage time t 2 , and obtain the corresponding performance degradation degree through disassembly inspection, performance testing, and experience judgment. Then normalize these data and use them as training samples to input into the LSTM network. The input layer dimension of this network is 3, and the hidden layer is composed of multiple LSTM units, which can capture the long-term dependence relationship of the data. After training, the LSTM network outputs the parameters k , s and g related to n, 2 , t 3 ; Furthermore, calculate the number of times of opening and closing t f2 for the fixture fault warning: ; where D e_critis the critical attenuation value of the electromagnetic fixture (calculated from the average of the historical performance degradation degrees of electromagnetic fixtures of the same type in the database). By using the performance degradation analysis model and the fault time prediction function, the performance state of the electromagnetic fixture can be accurately grasped. Under traditional circumstances, the maintenance of electromagnetic fixtures is often based on experience or fixed time periods, which may lead to over-maintenance or untimely maintenance. However, this system can achieve targeted maintenance of electromagnetic fixtures according to the calculated performance degradation degree and the fault occurrence time. Through the performance degradation analysis and fault prediction of electromagnetic fixtures, potential faults can be predicted in advance, avoiding abnormal operation of the water level gauge system caused by electromagnetic fixture failures. The electromagnetic fixture plays a key role in the calibration process of the water level gauge, and its failure may cause the water level gauge to malfunction. After adopting this system, maintenance preparations can be made in advance to ensure the stable operation of the water level gauge system. The system conducts a detailed analysis of the historical usage data of the electromagnetic fixture, and the obtained performance degradation analysis model parameters can guide resource allocation. Managers can reasonably allocate spare parts inventory and maintenance resources according to the performance degradation trend of the electromagnetic fixture. At the same time, the emergency repair costs caused by sudden failures of electromagnetic fixtures are avoided, effectively controlling the maintenance costs; When the intelligent maintenance and fault prediction module predicts that the motor fault occurrence duration is lower than the motor fault occurrence duration threshold set by the system, it triggers a motor fault warning; when it predicts that the fixture fault warning opening and closing times are lower than the fixture fault warning opening and closing times threshold set by the system, it triggers a fixture fault warning. The intelligent maintenance and fault prediction module pushes a maintenance requirement notice to the user through the touch screen. The notice content includes the name of the component with the warning fault, the motor fault occurrence duration, and the fixture fault warning opening and closing times. By combining historical data with the LSTM algorithm training model parameters, the intelligent upgrade of electromagnetic fixture management is realized. Managers can arrange maintenance work at the appropriate time according to the information predicted by the system, rather than relying on manual experience judgment. This intelligent management method makes equipment management more scientific and efficient, promotes the intelligent development of the entire water level gauge system, and improves the management level in the field of water conservancy monitoring; When the intelligent maintenance and fault prediction module does not trigger a motor fault warning and a fixture fault warning, the core control module determines that the system can be calibrated; otherwise, it determines that the system cannot be calibrated; When the system can be calibrated, if the system detects that the difference between the displayed water level and the actual water level of the water level gauge is greater than the difference tolerance set by the system, it enters the calibration process. The calibration process includes: when starting calibration, the electromagnetic fixture clamps the suspension cable, and the servo motor rotates to drive the water level wheel to rotate. When calibration is completed, the electromagnetic fixture is released, the enable terminal of the servo motor is reset, and the motor is in a coasting state. The core control module is based on the warning information of the intelligent maintenance and fault prediction module, and only allows calibration when there are no potential faults in the motor and the fixture, which greatly guarantees the stability of the calibration process. Faulty equipment may cause deviations in the execution of calibration actions and affect the calibration results; Example 1: As an important regional water conservancy hub, the reservoir plays a key role in flood control, irrigation, and water supply to residents. In order to change the situation of high maintenance costs and frequent failures of traditional water level monitoring systems, the management department introduced an intelligent water level monitoring system.

[0014] After the system is put into use, the human-machine interaction module and the high-definition camera establish a stable connection via Bluetooth. The camera collects water level data every 5 seconds, and these data can be quickly transmitted to the core processor. At the same time, the sensors in the environmental perception and data collection module start to work together. The accuracy of the water level sensor is ±0.01 meters, ensuring the accurate collection of water level data.

[0015] After the system has been running for some time, the intelligent maintenance and fault prediction module plays an important role. Taking a servo motor as an example, its rated speed is 1000 revolutions per minute, the rated torque is 30 N·m, and the maximum life is set at 6000 hours. During a certain monitoring, the actual speed of the motor is 900 revolutions per minute, the torque is 25 N·m, the operating time has reached 3500 hours, and the historical load fluctuation is monitored to be 0.08.

[0016] Through the wear degree analysis model trained with a large amount of historical data in the early stage, its parameters are k 1 = 0.15, k 2 = 0.25, c = 0.03, d = 1. According to the wear degree analysis model, the wear degree of the current servo motor can be calculated: ; Given that the critical wear value of this type of motor is W m_crit = 0.1, through the motor fault occurrence duration calculation formula, it is predicted that the motor fault may occur approximately 1800 hours later. The system quickly triggers a motor fault warning and pushes the warning information and the estimated fault time to the maintenance personnel through the touch screen.

[0017] During the calibration of the water level gauge, the system monitors the electromagnetic fixture. The cumulative number of openings and closings of the electromagnetic fixture is 3000 times, n = 0.4, and the percentage of attenuation of the current clamping force compared to the initial clamping force is 0.15. According to the performance degradation analysis model, the degree of performance degradation is calculated to be approximately D e = 2.2. Given that the critical attenuation value of the electromagnetic fixture is D e_crit = 2.5, it is predicted that the possible time of fixture failure will occur after 1000 openings and closings, and the system promptly issues a fixture failure warning.

[0018] Based on the warning information, the maintenance personnel prepared the maintenance work in advance and completed the replacement of the servo motor and the electromagnetic fixture before the failure occurred. This measure effectively ensured the stable operation of water level monitoring, reduced the maintenance cost, and significantly improved the operation efficiency of the reservoir.

[0019] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. A float type water level gauge automatic calibration system, characterized in that: The system includes a human-machine interaction module, a power drive and control module, a signal interaction module, a mechanical execution and transmission module, a power guarantee module, an environmental perception and data acquisition module, an intelligent maintenance and fault prediction module, and a core control module; The human-computer interaction module is used to receive the water level value in real time, and to display the system operation status, calibration progress, and fault warning information generated by the intelligent maintenance module in real time; The power drive and control module adjusts the speed, direction and position of the servo motor, thereby adjusting the water level gauge; The signal interaction module is used to receive instructions from the touch screen and transmit them to the execution component, while collecting feedback signals from the water level meter and the sensor group in real time; The mechanical execution and transmission module is composed of an electromagnetic fixture, a water level gauge shaft and a water level gauge wire rope, and is used to complete the calibration process of the water level gauge; The power guarantee module is used to supply power; The environmental sensing and data acquisition module is used to collect water level, environmental temperature, atmospheric pressure and water quality parameters in real time; The intelligent maintenance and fault prediction module is used to perform fault prediction analysis on the execution components of the system and to build a component performance degradation model to predict potential faults; The core control module monitors component performance in real time, and dynamically adjusts the water level gauge when the component performance is normal.

2. The automatic calibration system for a float type water level gauge according to claim 1, characterized in that: The human-computer interaction module is connected to the camera via a Bluetooth network, obtains the water level value collected by the camera in real time, and interacts with the core processor data through a communication protocol.

3. The automatic calibration system for a float type water level gauge according to claim 2 is characterized in that: The signal interaction module is used to receive instructions from the touch screen and transmit them to the execution component, while collecting feedback signals from the water level meter and the sensor group in real time. The execution component includes an electromagnetic clamp and a servo driver.

4. The automatic calibration system for a float type water level gauge according to claim 3 is characterized in that: The environment perception and data acquisition module includes a sensor group, which includes a water level sensor, a temperature sensor, a pressure sensor and a water quality sensor.

5. The automatic calibration system for a float type water level gauge according to claim 4 is characterized in that: The intelligent maintenance and fault prediction module predicts the time of potential fault occurrence, and the potential fault occurrence time includes the duration of motor fault occurrence and the number of times the fixture fault warning is opened and closed; Analyze the servo motor and establish the wear degree W m Wear degree analysis model: ； where u m is the speed of the servo motor, u0 is the rated speed of the servo motor, T m is the torque of the servo motor, T0 is the rated torque of the servo motor, t1 is the running time of the servo motor, t0 is the maximum life of the servo motor, is the historical load fluctuation of the servo motor; Where k1 is the same as u m 、T m The coefficient parameter related to t1, c is the exponential parameter related to t1, and k2 is The coefficient parameter d is related to Related index parameters, and then calculate the motor fault occurrence time t f1 : ； Where W m_crit is the critical wear value of the motor.

6. The automatic calibration system for a float type water level gauge according to claim 5, characterized in that: Analyze the electromagnetic fixture and establish the degree of performance degradation D e Performance degradation analysis model: ； Where n is the percentage of the electromagnetic clamp's usage time to its maximum usage time. is the attenuation percentage of the current clamping force of the electromagnetic clamp compared to the initial clamping force, t2 represents the number of times the electromagnetic clamp has been opened and closed, and t represents the maximum number of times the electromagnetic clamp has been opened and closed; Where k3 is the same as n, The coefficient parameter related to t, s is g represents the index parameter related to t2, and then the number of opening and closing times t of the fixture fault warning is calculated. f2 : ； Where D e_crit is the critical attenuation value of the electromagnetic clamp.

7. The automatic calibration system for a float type water level gauge according to claim 6, characterized in that: When the intelligent maintenance and fault prediction module predicts that the duration of the motor fault is lower than the motor fault duration threshold set by the system, the motor fault warning is triggered; when it is predicted that the number of opening and closing times of the fixture fault warning is lower than the number of opening and closing times of the fixture fault warning set by the system, the fixture fault warning is triggered, and the intelligent maintenance and fault prediction module pushes a maintenance requirement notification to the user through the touch screen. The notification content includes the name of the component of the warning fault, the duration of the motor fault and the number of opening and closing times of the fixture fault warning.

8. The automatic calibration system for a float type water level gauge according to claim 7, characterized in that: The core control module determines that the system can be calibrated when the intelligent maintenance and fault prediction module does not trigger the motor fault warning and the fixture fault warning, otherwise, the core control module determines that the system cannot be calibrated.

9. The automatic calibration system for a float type water level gauge according to claim 8, characterized in that: When the system can be calibrated, if the system detects that the difference between the displayed water level and the actual water level of the water level meter is greater than the difference tolerance set by the system, the calibration process begins. The calibration process includes: when the calibration starts, the suspension cable is clamped by the electromagnetic clamp, and the servo motor rotates to drive the water level wheel to rotate. When the calibration is completed, the electromagnetic clamp is released, the servo motor enable end is reset, and the motor is in a gliding state.

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