An automatic calibration system for a float-type water level meter

Abstract

The application discloses a kind of float type water level gauge automatic calibration system, it is related to automatic calibration technical field, and the system includes man-machine interaction module, power drive and control module, signal interaction module, mechanical execution and transmission module, power guarantee module, environmental perception and data acquisition module, intelligent maintenance and fault prediction module and core control module;Real-time receive water level value, adjust the speed, steering and position of servo motor, to adjust water level gauge, complete the calibration process of water level gauge, real-time acquisition water level, ambient temperature, atmospheric pressure and water quality parameter, carry out fault prediction analysis, build component performance degradation model and judge potential fault, when component performance is normal, water level gauge is dynamically adjusted, the application accurately adjusts servo motor by power drive and control module, enhances the automation degree of water level gauge adjustment, predicts the remaining life of execution component in advance, so that maintenance personnel can formulate maintenance plan in advance, avoid the influence of fault on water level monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic calibration, and particularly relates to a float type water level gauge automatic calibration system. BACKGROUND

[0002] In the fields of water conservancy and hydrological monitoring, accurate measurement of water level is crucial. The float type water level gauge, as a commonly used water level measurement device, is widely used due to its simple structure and stable performance. However, the traditional float type water level gauge has many problems to be solved in actual use. The calibration of the water level gauge has long relied on manual operation, which is inefficient and prone to errors. Manual calibration not only requires a lot of manpower and time cost, but also cannot guarantee the accuracy and consistency of calibration. At the same time, due to the influence of environmental factors, personnel skill level and other factors, the calibration work is more difficult and risky in complex and harsh environments. 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 intelligent level of the water level gauge. The traditional water level gauge cannot real-time perceive environmental changes, such as changes in environmental temperature, atmospheric pressure and water quality parameters, which may affect the accuracy of water level measurement. However, the traditional device is difficult to effectively compensate and correct these factors. The timely discovery and maintenance of device failure is also a big problem. The traditional water level gauge lacks effective fault prediction and diagnosis mechanism, and often is not discovered until the device has serious failure, which affects the continuity and accuracy of water level monitoring. SUMMARY

[0003] The present application relates to the technical field of automatic calibration, and particularly relates to a float type water level gauge automatic calibration system.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a float type water level gauge automatic calibration system, the system comprises a man-machine interaction module, a power drive and control module, a signal interaction module, a mechanical execution and transmission module, a power guarantee module, an environment perception and data acquisition module, an intelligent maintenance and fault prediction module and a core control module.

[0005] The man-machine interaction module is used for real-time receiving of water level value, real-time display of system running state, calibration progress and fault early warning information generated by the intelligent maintenance module.

[0006] The power drive and control module adjusts the speed, direction and position of the servo motor, so as to adjust the water level gauge.

[0007] The signal interaction module is used for receiving instructions from the touch screen and transmitting to the execution component, and simultaneously real-time acquisition of feedback signals of the water level gauge and the sensor group.

[0008] The mechanical execution and transmission module is composed of an electromagnetic clamp, a water level gauge shaft and a water level gauge steel wire rope, and is used for completing a calibration process of the water level gauge;

[0009] The power guarantee module is used for supplying power, and has perfect overvoltage protection, overcurrent protection and short circuit protection functions, so that the stable operation of each component of the system can be ensured under different power environments, and the damage of equipment caused by power problems can be avoided;

[0010] The environment sensing and data acquisition module includes a sensor group, which is used for real-time acquisition of water level, environmental temperature, atmospheric pressure and water quality parameters;

[0011] The intelligent maintenance and fault prediction module is used for fault prediction analysis of the execution components of the system, and a component performance degradation model is constructed to predict potential faults;

[0012] The core control module monitors the component performance in real time, and dynamically adjusts the water level gauge when the component performance is normal;

[0013] Further, the man-machine interaction module is connected with the camera through a Bluetooth network, and the water level value collected by the camera is obtained in real time, and data interaction is performed with the core processor through a communication protocol;

[0014] Further, the signal interaction module is used for receiving instructions from the touch screen and transmitting to the execution components, and simultaneously collecting feedback signals of the water level gauge and the sensor group in real time, and the execution components include the electromagnetic clamp and the servo driver;

[0015] Further, the environment sensing 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;

[0016] Further, the intelligent maintenance and fault prediction module predicts a potential fault occurrence time, and the potential fault occurrence time includes a motor fault occurrence time length and a clamp fault warning opening and closing times;

[0017] The servo motor is analyzed, and a wear degree analysis model about the wear degree W m is established:

[0018] ;

[0019] Wherein u m is the rotating speed of the servo motor, u0 is the rated rotating 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 length of the servo motor, and t0 is the maximum service life of the servo motor, The historical load fluctuation of the servo motor is updated in real time by marking the historical load, and the calculation method of the historical load fluctuation is, for example, the data in the database is arranged according to time, and the data is marked in sequence every interval of a certain time period, and the torque at time point P q is V q , and 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 fluctuation at q time points, and statistical analysis is performed again.

[0020] Wherein k1 is a coefficient parameter related to u m , T m and t1, c is an exponential parameter related to t1, k2 is a coefficient parameter related to , and d is an exponential parameter related to , and the acquisition method of k1, k2, c and d is, for example, by collecting a large amount of historical operation data of the servo motor, including the rotating speed u m , torque u0, running time t and historical load fluctuation data at different times, and combining the actual wear state data of the motor at the corresponding time (obtained by disassembling and checking the motor, performance test or based on experience), these data are input into the long short-term memory network (LSTM) algorithm for training, and finally the optimized model parameters k1, k2, c and d are obtained.

[0021] And then calculate the motor fault duration t f1 :

[0022] ;

[0023] Wherein 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 database in the last monitoring period of wear), the intelligent maintenance and fault prediction module is used to analyze the servo motor and other executing components in depth, and the fault occurrence time is predicted in advance. In the past, under the traditional water level meter maintenance mode, due to the lack of effective fault prediction means, only regular maintenance strategy can be adopted, whether the equipment really needs maintenance or not, maintenance and maintenance are carried out according to fixed period, which leads to a large amount of unnecessary maintenance cost, after introducing the system, according to the predicted fault occurrence time, the maintenance personnel can reasonably arrange the maintenance plan before the fault occurs, and replace or repair the components that will fail, reduce the potential economic loss caused by the interruption of water level monitoring due to equipment failure, and effectively improve the operation efficiency and economic benefit of water conservancy facilities.

[0024] Further, the electromagnetic clamp is analyzed, and a performance degradation analysis model about the performance degradation degree D e is established:

[0025] ;

[0026] wherein n is the percentage of the used time length of the electromagnetic clamp to the maximum use time length, is the attenuation percentage of the current clamping force of the electromagnetic clamp compared with the initial clamping force, t2 represents the number of times of opening and closing of the electromagnetic clamp, and t represents the maximum number of times of opening and closing of the electromagnetic clamp. The maximum number of times of opening and closing of the electromagnetic clamp is obtained by, for example, collecting the historical number of times of opening and closing of the electromagnetic clamp when the electromagnetic clamp fails, and taking the average value of the number of times of opening and closing when the electromagnetic clamp fails as the maximum number of times of opening and closing of the electromagnetic clamp;

[0027] wherein k3 is a coefficient parameter related to n, and t, s is an exponential parameter related to , and g represents an exponential parameter related to t2. The obtaining method of k3, s and g is, for example, collecting the historical use data of the electromagnetic clamp, including the number of times of opening and closing n, the clamping force change and the use time t2, and combining the corresponding performance degradation degree (obtained by disassembling and checking the electromagnetic clamp, performance testing or experience-based judgment, etc.), taking these data as training samples, and using the long short-term memory network (LSTM) algorithm to obtain the corresponding model parameters k3, s and g, and then calculating the clamp failure warning opening and closing times t f2 :

[0028] ;

[0029] wherein D e_critThe performance degradation analysis model and the fault time prediction function can accurately grasp the performance state of the electromagnetic clamp for the critical attenuation value of the electromagnetic clamp (calculated by the average value of the historical performance degradation degree of the same type of electromagnetic clamp in the database). In the traditional case, the maintenance of the electromagnetic clamp is often based on experience or fixed time period, which may lead to excessive maintenance or untimely maintenance. The system can realize targeted maintenance of the electromagnetic clamp according to the calculated performance degradation degree and fault occurrence time, and through performance degradation analysis and fault prediction of the electromagnetic clamp, the potential failure can be predicted in advance, and the abnormal operation of the water level gauge system caused by the failure of the electromagnetic clamp can be avoided. The electromagnetic clamp 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 using this system, maintenance preparation is made in advance to ensure the stable operation of the water level gauge system, and the system analyzes the historical use data of the electromagnetic clamp in detail to obtain the performance degradation analysis model parameters, which can guide resource allocation. The management personnel can reasonably allocate spare parts inventory and maintenance resources according to the performance degradation trend of the electromagnetic clamp. At the same time, the cost of emergency maintenance caused by sudden failure of the electromagnetic clamp is avoided, and the maintenance cost is effectively controlled.

[0030] Further, when the intelligent maintenance and fault prediction module predicts that the motor failure occurrence time is less than the system set motor failure occurrence time threshold, the motor failure warning is triggered; when the clamp failure warning opening and closing times are less than the system set clamp failure warning opening and closing times threshold, the clamp failure warning is triggered, and the intelligent maintenance and fault prediction module pushes the maintenance demand notification to the user through the touch screen. The notification content includes the component name of the warning failure, the motor failure occurrence time and the clamp failure warning opening and closing times, the historical data are combined with the LSTM algorithm to train the model parameters, and the intelligent upgrading of the electromagnetic clamp management is realized. The management personnel can arrange maintenance work at the right time according to the information predicted by the system, instead of relying on artificial experience judgment. This intelligent management method makes the equipment management more scientific and efficient, promotes the intelligent development of the whole water level gauge system, and improves the management level in the field of water conservancy monitoring;

[0031] Further, the core control module judges whether the system can be calibrated when the intelligent maintenance and fault prediction module does not trigger the motor failure warning and the clamp failure warning, otherwise, the system cannot be calibrated;

[0032] Further, when the system can be calibrated, if the system monitors the difference between the displayed water level of the water level gauge and the actual water level is greater than the difference tolerance set by the system, the calibration process is entered, which includes: when starting calibration, the suspension cable is clamped by the electromagnetic clamp, the servo motor is rotated 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, the motor is in coasting state, and the core control module takes the early warning information of the intelligent maintenance and fault prediction module as the basis to allow calibration only when the motor and the clamp have no hidden troubles, thereby greatly ensuring the stability of the calibration process. The faulty equipment may cause calibration action execution deviation and affect the calibration result.

[0033] Compared with the prior art, the beneficial effects of the present application are: on the one hand, through the cooperation of the power driving and control module, the mechanical execution and transmission module and the core control module, automatic calibration of the water level gauge is realized. The power driving and control module accurately adjusts the servo motor, the mechanical execution and transmission module quickly responds to complete the calibration action, and the problems of low efficiency and errors caused by manual operation are avoided; on the other hand, the environmental perception and data acquisition module collects the water level, environmental temperature, atmospheric pressure and water quality parameters in real time, and the core control module dynamically adjusts the water level gauge according to these data. Under different environmental conditions, the system can automatically compensate the influence of environmental factors on water level measurement, and ensure that the measurement result is stable and reliable; on the other hand, the intelligent maintenance and fault prediction module uses machine learning algorithm to construct component performance degradation model, and predicts the remaining life and potential fault occurrence time of the servo motor, the electromagnetic clamp and other execution components in advance. When the predicted fault occurrence time is lower than the set threshold, an early warning is triggered in time and a maintenance notice is pushed through the touch screen. This function enables maintenance personnel to make maintenance plans in advance, prepare for maintenance spare parts, and avoid the influence of equipment sudden failure on water level monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 FIG. 1 is a structural schematic diagram of a float type water level gauge automatic calibration system according to the present application. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0036] As Figure 1As shown, the present application provides a technical solution, a float type water level gauge automatic calibration system, which comprises a man-machine interaction module, a power drive and control module, a signal interaction module, a mechanical execution and transmission module, a power guarantee module, an environment perception and data acquisition module, an intelligent maintenance and fault prediction module and a core control module;

[0037] The man-machine interaction module is used for real-time receiving of water level values, real-time display of system running states, calibration progress and fault warning information generated by the intelligent maintenance module;

[0038] The power drive and control module adjusts the rotation speed, rotation direction and position of the servo motor, so as to adjust the water level gauge;

[0039] The signal interaction module is used for receiving instructions from the touch screen and transmitting to the execution components, and simultaneously collecting feedback signals of the water level gauge and the sensor group in real time;

[0040] The mechanical execution and transmission module is composed of an electromagnetic clamp, a water level gauge shaft and a water level gauge steel wire rope, and is used for completing the calibration process of the water level gauge;

[0041] The power guarantee module is used for supplying power, and the module has perfect overvoltage protection, overcurrent protection and short circuit protection functions, can ensure stable operation of each component of the system under different power environments, and avoid equipment damage caused by power problems;

[0042] The environment perception and data acquisition module comprises a sensor group, and is used for real-time collection of water level, environmental temperature, atmospheric pressure and water quality parameters;

[0043] The intelligent maintenance and fault prediction module is used for fault prediction analysis on the execution components of the system, and constructs a component performance degradation model to predict potential faults;

[0044] The core control module monitors the component performance in real time, and dynamically adjusts the water level gauge when the component performance is normal.

[0045] The man-machine interaction module is connected with the camera through a Bluetooth network, real-time acquires water level values collected by the camera, and exchanges data with the core processor through a communication protocol;

[0046] The signal interaction module is used for receiving instructions from the touch screen and transmitting to the execution components, and simultaneously collecting feedback signals of the water level gauge and the sensor group in real time, and the execution components comprise an electromagnetic clamp and a servo driver;

[0047] The environment perception and data acquisition module comprises a sensor group, and the sensor group comprises a water level sensor, a temperature sensor, a pressure sensor and a water quality sensor;

[0048] Further, the intelligent maintenance and fault prediction module predicts the potential fault occurrence time, which includes the motor fault occurrence time length and the clamp fault warning opening and closing times.

[0049] The servo motor is analyzed, and a wear degree analysis model about the wear degree W m is established:

[0050] ;

[0051] wherein 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 (the historical load fluctuation is updated in real time by marking the historical load of the motor), and the calculation method of the historical load fluctuation is, for example: the data in the database is arranged according to time, and the data is marked in sequence every interval of a certain time period, and it is assumed that the value of the torque at time point P q is 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 statistical analysis is performed again.

[0052] wherein k1 is a coefficient parameter related to u m , T m and t1, c is an exponential parameter related to t1, k2 is a coefficient parameter related to , and d is an exponential parameter related to The method for obtaining k1, k2, c and d is, for example: a large amount of historical operation data of the servo motor is collected, including the speed u m , torque u0, running time t and historical load fluctuation data at different times, and the actual wear state data of the motor at the corresponding time (obtained by disassembling and checking the motor, performance testing or based on experience) is combined, these data are input into the long short-term memory network (LSTM) algorithm for training, and finally the optimized model parameters k1, k2, c and d are obtained.

[0053] The application steps of the specific (LSTM) algorithm are as follows: obtaining the model parameters of the servo motor and the electromagnetic clamp by using the LSTM algorithm. First, relevant historical operation data such as the speed and torque of the servo motor, the opening and closing times of the electromagnetic clamp, etc. are collected and normalized, and the actual wear or performance degradation data is obtained as the target value. Then, the LSTM network is built, the number of input, hidden and output layer neurons is set, the training set is trained, the validation set is evaluated to prevent overfitting, and the test set is tested for generalization ability. Finally, the trained model calculates the optimized model parameters k1, k2, c and d through the output layer weights and biases;

[0054] Then, the duration t of the motor fault is calculated f1 :

[0055] ;

[0056] Where W m_crit is the critical wear value of the motor (derived by calculating the average wear degree of the same type of motor in the database in the previous monitoring period of wear), the intelligent maintenance and fault prediction module is used to analyze the servo motor and other components in depth, and the fault occurrence time is 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 periodic maintenance strategy can be adopted, whether the equipment needs maintenance or not, maintenance and maintenance are carried out according to fixed period, which leads to a large amount of unnecessary maintenance cost, after introducing the system, according to the predicted fault occurrence time, the maintenance personnel can reasonably arrange the maintenance plan before the fault occurs, and replace or repair the components that will fail, reduce the potential economic loss caused by the interruption of water level monitoring due to equipment failure, and effectively improve the operation efficiency and economic benefit of water conservancy facilities.

[0057] Further, the electromagnetic clamp is analyzed, and a performance degradation analysis model about the performance degradation degree D e is established:

[0058] ;

[0059] Where n is the percentage of the used duration of the electromagnetic clamp to the maximum use duration, is the attenuation percentage of the current clamping force of the electromagnetic clamp compared with the initial clamping force, t2 represents the number of opening and closing times of the electromagnetic clamp, and t represents the maximum number of opening and closing times of the electromagnetic clamp;

[0060] Where k3 is a coefficient parameter related to n, and t, and s is a coefficient parameter related to t2 and t. The related index parameters g represents the index parameters related to t2, and the acquisition method of k3, s and g is, for example: when acquiring the electromagnetic clamp performance degradation analysis model parameters, first collect its historical use data, including the opening and closing times n, the clamping force change (reflected as the decay percentage of the current clamping force compared with the initial clamping force), the use time t2, and the corresponding performance degradation degree is obtained through disassembly inspection, performance test, experience judgment. Then normalize these data and input them into the LSTM network as training samples. The input layer dimension of the network is 3, the hidden layer is composed of multiple LSTM units, which can capture long-term dependence of data. After training, the LSTM network outputs the parameters k3, s and g related to n, , t2.

[0061] Further calculate the clamp fault warning opening and closing times t f2 :

[0062] ;

[0063] Where D e_crit is the critical decay value of the electromagnetic clamp (calculated by the average value of the historical performance degradation degree of the same type of electromagnetic clamp in the database), and the performance degradation analysis model and the fault time prediction function can accurately master the performance state of the electromagnetic clamp. In the traditional case, the maintenance of the electromagnetic clamp is often based on experience or fixed time period, which may lead to excessive maintenance or untimely maintenance. The system can realize targeted maintenance of the electromagnetic clamp according to the calculated performance degradation degree and fault occurrence time, and through performance degradation analysis and fault prediction of the electromagnetic clamp, the potential failure can be predicted in advance, and the abnormal operation of the water level meter system caused by electromagnetic clamp failure can be avoided. The electromagnetic clamp plays a key role in the calibration process of the water level meter, and its failure may cause the water level meter to malfunction. After using this system, maintenance preparation is made in advance to ensure the stable operation of the water level meter system. The system analyzes the historical use data of the electromagnetic clamp in detail, and the performance degradation analysis model parameters obtained can guide resource allocation. Management personnel can reasonably allocate spare parts inventory and maintenance resources according to the performance degradation trend of the electromagnetic clamp. At the same time, the cost of emergency maintenance caused by sudden failure of the electromagnetic clamp is avoided, and the maintenance cost is effectively controlled;

[0064] The intelligent maintenance and fault prediction module triggers motor fault early warning when predicting that the motor fault occurrence duration is lower than the motor fault occurrence duration threshold set by the system, and triggers clamp fault early warning when predicting that the clamp fault early warning opening and closing times are lower than the clamp fault early warning opening and closing times threshold set by the system. The intelligent maintenance and fault prediction module pushes maintenance demand notification to the user through the touch screen, and the notification content includes the component name of the early warning fault, the motor fault occurrence duration and the clamp fault early warning opening and closing times. The historical data are combined with the LSTM algorithm to train the model parameters, so that the intelligent upgrade of the electromagnetic clamp management is realized. The management personnel can arrange maintenance work at the appropriate time according to the information predicted by the system, instead of relying on artificial experience judgment. This intelligent management mode makes the equipment management more scientific and efficient, promotes the intelligent development of the entire water level gauge system, and improves the management level in the water conservancy monitoring field.

[0065] When the intelligent maintenance and fault prediction module does not trigger motor fault early warning and clamp fault early warning, the core control module judges that the system can be calibrated, otherwise, the system cannot be calibrated.

[0066] When the system can be calibrated, if the system monitors 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, the calibration process is entered. The calibration process includes: when starting calibration, the electromagnetic clamp clamps the suspension cable, the servo motor rotates to drive the water level wheel to rotate, and when the calibration is completed, the electromagnetic clamp is released, the servo motor enable end is reset, and the motor is in coasting state. The core control module allows calibration only when there is no fault hidden danger in the motor and the clamp based on the early warning information of the intelligent maintenance and fault prediction module, which greatly guarantees the stability of the calibration process. Faulty equipment may cause calibration action execution deviation and affect the calibration result.

[0067] Embodiment 1: Reservoirs play a key role in flood control, irrigation, and resident water supply as important water conservancy hubs in the region. In order to change the situation that the traditional water level monitoring system has high maintenance cost and frequent faults, the management department introduces an intelligent water level monitoring system.

[0068] After the system is put into use, the man-machine interaction module and the high-definition camera establish a stable connection through 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 environment perception and data acquisition module start to work cooperatively. The water level sensor has an accuracy of ±0.01 meters, ensuring accurate collection of water level data.

[0069] After the system runs for a period of time, the intelligent maintenance and fault prediction module plays an important role. Taking a servo motor as an example, the rated speed is 1000 rpm, the rated torque is 30 N·m, and the maximum service life is set to 6000 hours. In a certain monitoring, the actual speed of the motor is 900 rpm, the torque is 25 N·m, the running time has reached 3500 hours, and the historical load fluctuation is monitored as 0.08.

[0070] The wear degree analysis model obtained by training a large amount of historical data in the early stage has parameters k1=0.15, k2=0.25, c=0.03, and d=1. According to the wear degree analysis model, the wear degree of the current servo motor can be calculated:

[0071]

[0072] The critical wear value of the motor of this type is W m_crit =0.1, and the motor failure prediction formula is used to predict that the motor failure may occur after about 1800 hours. The system quickly triggers the motor failure warning and pushes the warning information and the predicted failure time to the maintenance personnel through the touch screen.

[0073] When calibrating the water level meter, the system monitors the electromagnetic clamp. The cumulative opening and closing times of the electromagnetic clamp are 3000 times, n=0.4, and the current clamping force attenuation percentage compared with the initial clamping force is 0.15. According to the performance degradation analysis model, the performance degradation degree is calculated to be D e =2.2. Knowing that the critical attenuation value of the electromagnetic clamp is D e_crit =2.5, it is predicted that the clamp failure may occur after 1000 opening and closing times, and the system timely issues a clamp failure warning.

[0074] The maintenance personnel prepare for maintenance in advance according to the warning information, and complete the replacement of the servo motor and the electromagnetic clamp before the failure occurs. This measure effectively guarantees the stable operation of the water level monitoring, reduces the maintenance cost, and significantly improves the operation efficiency of the reservoir.

[0075] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, and the scope of the present application is defined by the appended claims rather than the above description, and therefore all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present application. 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 by: The system comprises a human-computer interaction module, a power driving and control module, a signal interaction module, a mechanical execution and transmission module, a power 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 configured to receive water level values in real time, display system operation states, calibration progress, and fault warning information generated by the intelligent maintenance module in real time. The power driving 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 configured to receive instructions from the touch screen and transmit them to the execution components, while collecting feedback signals of the water level gauge and the sensor group in real time. The mechanical execution and transmission module comprises an electromagnetic clamp, a water level gauge shaft, and a water level gauge steel wire rope, and is configured to complete the calibration process of the water level gauge. The power guarantee module is configured to supply power. The environment perception and data acquisition module is configured to collect water level, environmental temperature, atmospheric pressure, and water quality parameters in real time. The intelligent maintenance and fault prediction module is configured to perform fault prediction analysis on the execution components of the system, and to construct a component performance degradation model to predict potential faults. The core control module is configured to monitor the performance of the components in real time, and to dynamically adjust the water level gauge when the performance of the components is normal.

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

3. A float type water level gauge automatic calibration system according to claim 2, characterized in that: The signal interaction module is configured to receive instructions from the touch screen and transmit them to the execution components, while collecting feedback signals of the water level gauge and the sensor group in real time.

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

5. A float type water level gauge automatic calibration system according to claim 4, characterized in that: The intelligent maintenance and fault prediction module predicts the occurrence time of potential faults, which comprises motor fault occurrence duration and clamp fault warning opening and closing times. The servo motor is analyzed, and a wear degree analysis model about the wear degree W m is established. ； wherein u m is the rotation speed of the servo motor, u0 is the rated rotation 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; wherein k1 is a coefficient parameter related to u m , T m , and t1, c is an index parameter related to t1, k2 is a coefficient parameter related to , d is an index parameter related to , and t f1 is the duration of the motor fault occurrence. ； where W m_crit is the critical wear value for the motor.

6. A float type water level gauge automatic calibration system according to claim 5, characterized in that: An electromagnetic clamp is analyzed, and a performance degradation analysis model is established with respect to a performance degradation degree D e of the electromagnetic clamp. ； where n is the percentage of the maximum usage time length of the electromagnetic clamp that has been used, is the percentage of the decay of the initial clamping force for the current clamping force of the electromagnetic clamp, 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 can be opened and closed. where k3 is a coefficient parameter related to n, and t, s is an index parameter related to t2, and g represents an index parameter related to t2, and then calculates the number of opening and closing times t f2 of the jig failure warning ； where D e_crit is the critical damping value of the electromagnetic clamp.

7. A float type water level gauge automatic calibration system according to claim 6, characterized in that: When the intelligent maintenance and fault prediction module predicts that the motor fault occurrence duration is lower than a system-set motor fault occurrence duration threshold, a motor fault warning is triggered; when the intelligent maintenance and fault prediction module predicts that the clamp fault warning opening and closing times are lower than a system-set clamp fault warning opening and closing times threshold, a clamp fault warning is triggered. The intelligent maintenance and fault prediction module pushes maintenance requirement notifications to the user through the touch screen, and the notification content comprises the names of the components that have issued the warnings, the motor fault occurrence duration, and the clamp fault warning opening and closing times.

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

9. A float type water level gauge automatic calibration system according to claim 8, characterized in that: When the system can be calibrated, if the system monitors that the difference between the displayed water level and the actual water level of the water level gauge is greater than a system-set difference tolerance, the calibration process is entered, which comprises the following steps: when the calibration starts, the electromagnetic clamp clamps the suspension cable, 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 coasting state.

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