Electroscope operation intelligent control system based on intelligent sensing technology

Abstract

The invention provides an electroscope operation intelligent control system based on an intelligent sensing technology, and belongs to the field of electroscope control. Comprising a processor which is in communication connection with a maintenance analysis module, a fault monitoring module and a prediction analysis module. The maintenance analysis module is used for carrying out maintenance analysis on the electroscope; the fault monitoring module is used for monitoring the operation fault of the electroscope in real time, and marking the execution process corresponding to the electricity verification operation of the electroscope as a normal process or a fault process. And the prediction analysis module is used for performing prediction analysis on the fault probability of the normal process at the end moment of the normal process, and marking the corresponding normal process as a risk process when the normal process has a fault risk. According to the invention, the fault probability of the normal process can be predicted and analyzed at the end time of the normal process through the prediction and analysis module, so that the electroscope is actively maintained when the fault probability is abnormal, the fault frequency of the electroscope is reduced, and the actual operation risk of the electroscope is effectively suppressed.

Description

Technical Field

[0001] The invention belongs to the technical field of electroscope control, and in particular relates to an electroscope operation intelligent control system based on intelligent sensing technology. Background Art

[0002] An electroscope is an instrument that can detect whether an object is charged and roughly estimate the amount of charge. When the object being tested touches the conductor at the top of the electroscope, the charge it carries will be transferred to the foil in the glass bell jar. Since like charges repel each other, the foil will automatically separate and open at a certain angle. The amount of charge on the object can be estimated based on the angle between the two foils.

[0003] The intelligent control system for the operation of the electroscope in the prior art can only monitor the faults of the electroscope according to its operating parameters, and provide feedback and early warning when a fault occurs. However, when the electroscope is operating normally, its actual operating status cannot be analyzed according to the operating parameters, and thus it is impossible to predict risks and perform active maintenance when risks exist, resulting in the inability to curb the actual operating risks of the electroscope.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent control system for electroscope operation based on intelligent sensing technology, which is used to solve the problem that the intelligent control system for electroscope operation in the prior art cannot analyze its actual operating state according to operating parameters when the electroscope operates normally, and thus cannot perform risk prediction; The technical problem to be solved by the present invention is: how to provide an intelligent control system for the operation of an electroscope based on intelligent sensing technology, which can actively perform maintenance when there is a risk in the electroscope.

[0006] The purpose of the present invention can be achieved through the following technical solutions: An intelligent control system for the operation of an electroscope based on intelligent sensing technology includes a processor, wherein the processor is communicatively connected to a maintenance analysis module, a fault monitoring module, and a prediction analysis module; The maintenance analysis module is used to perform maintenance analysis on the electroscope: generate an operation cycle, divide the operation cycle into a number of maintenance periods, and perform operation maintenance on the electroscope at the end of the maintenance period; The fault monitoring module is used to monitor the operation fault of the electroscope in real time: when the electroscope performs the test operation during the maintenance period, the real-time coefficient SS of the electroscope is obtained in real time, and the execution process corresponding to the electroscope test operation is marked as a normal process or a fault process by the real-time coefficient SS; The prediction and analysis module is used to predict and analyze the failure probability of the normal process at the end of the normal process. When the normal process has a failure risk, the corresponding normal process is marked as a risky process, an active maintenance signal is generated and sent to the mobile phone terminal of the administrator.

[0007] Furthermore, the process of obtaining the real-time coefficient SS of the electroscope includes: obtaining the current influence data LY, the voltage influence data YY and the temperature influence data WY of the electroscope, and using the formula The real-time coefficient SS of the electroscope is obtained, wherein m1, m2 and m3 are preset proportional coefficients of the current influence data LY, the voltage influence data YY and the temperature influence data WY respectively, and m1>m2>m3>1.

[0008] Furthermore, the current influence data LY is the absolute value of the difference between the current value of the electroscope and the current standard value, the voltage influence data YY is the absolute value of the difference between the voltage value of the electroscope and the voltage standard value, and the temperature influence data WY is the absolute value of the difference between the temperature value of the electroscope and the temperature standard value.

[0009] Furthermore, the specific process of marking the execution process corresponding to the electric test operation of the electric tester as a normal process or a faulty process includes: calling the real-time threshold SSmax of the electric tester, and comparing the real-time coefficient SS of the electric tester with the real-time threshold SSmax: if the real-time coefficient SS is less than the real-time threshold SSmax, it is determined that the real-time operating status of the electric tester meets the requirements; if the real-time coefficient SS is greater than or equal to the real-time threshold SSmax, it is determined that the real-time operating status of the electric tester does not meet the requirements, the electric test operation is terminated, and the execution process of this electric test operation is marked as a faulty process; the complete execution process of the electric tester whose real-time operating status meets the requirements is marked as a normal process.

[0010] Furthermore, the specific process of the prediction and analysis module performing predictive analysis on the failure probability of the normal process at the end of the normal process includes: obtaining the prediction value and the execution value of the operation cycle, marking the ratio of the prediction value to the execution value as a prediction coefficient, and comparing the prediction coefficient with a preset prediction threshold: if the prediction coefficient is less than the prediction threshold, it is determined that the normal process does not have a failure risk; if the prediction coefficient is greater than or equal to the prediction threshold, it is determined that the normal process has a failure risk.

[0011] Furthermore, the process of obtaining the predicted value and execution value of the operation cycle includes: establishing a rectangular coordinate system with the execution time of the normal process as the X-axis and the real-time coefficient SS of the electroscope in the normal process as the Y-axis, making a curve corresponding to the real-time coefficient SS in the normal process in the rectangular coordinate system and marking it as the real-time curve, marking the point with the largest horizontal coordinate value and the point with the largest vertical coordinate value in the real-time curve as the horizontal marking point and the vertical marking point respectively, making a straight line perpendicular to the X-axis based on the horizontal marking point and marking it as the vertical cutting line, making a straight line perpendicular to the Y-axis based on the vertical marking point and marking it as the horizontal cutting line, marking the area value of the closed rectangular area composed of the X-axis, Y-axis, horizontal cutting line and vertical cutting line as the execution value, and marking the area value of the closed area composed of the X-axis, Y-axis, real-time curve and vertical cutting line as the predicted value.

[0012] The present invention has the following beneficial effects: The fault monitoring module can monitor the operation fault of the electroscope in real time. When the electroscope is performing the test, multiple operation parameters of the electroscope are obtained in real time and the real-time coefficient is obtained by comprehensive calculation. The operation status of the electroscope is fed back through the real-time coefficient, and an early warning is issued in time when there is an operation fault. The prediction analysis module can predict and analyze the failure probability of the normal process at the end of the normal process, calculate the prediction coefficient in the normal process by curve analysis, and provide feedback on the failure probability of the normal process through the prediction coefficient, so as to actively maintain the electroscope when the failure probability is abnormal, reduce the failure frequency of the electroscope, and effectively curb the actual operation risk of the electroscope; The overall operation risk of the electroscope in the operation cycle is analyzed through the risk analysis module. Specifically, the evaluation coefficient is obtained by calculating the number of risk processes and the number of fault processes in the operation cycle of the electroscope. The overall operation risk of the electroscope in the operation cycle is fed back through the evaluation coefficient, so as to perform distribution analysis on the electroscope when necessary. The distribution analysis module is used to analyze the distribution of risk processes and fault processes during the maintenance period. The maintenance optimization decision of the electroscope is determined based on the obtained occupancy coefficient. The electroscope is maintained and optimized using the most optimized processing method to improve its abnormality processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 A system block diagram of the present invention as a whole; Figure 2 is a system block diagram of Embodiment 1 of the present invention; Figure 3 is a system block diagram of Embodiment 2 of the present invention; Figure 4 This is a method flow chart of embodiment 3 of the present invention. DETAILED DESCRIPTION

[0015] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] like Figure 1 As shown, the intelligent control system for the operation of the electroscope based on the intelligent sensing technology includes a monitoring subsystem and a management subsystem. The monitoring subsystem includes a processor, and the processor is connected to the maintenance analysis module, the fault monitoring module and the prediction analysis module in communication. The management subsystem includes a server, and the server is connected to the processor in communication. The server is connected to the risk analysis module, the distribution analysis module and the database in communication. The processor and the server are mainly used for transferring data and signals between the modules. The embodiment of the present invention will not elaborate on this part. Embodiment 1

[0017] like Figure 2 As shown, the maintenance analysis module is used to perform maintenance analysis on the electroscope: generate an operation cycle, divide the operation cycle into several maintenance periods, and perform operation maintenance on the electroscope at the end of the maintenance period. The operation maintenance methods include line connection detection, component aging detection and replacement of aged components.

[0018] The fault monitoring module is used to monitor the operational faults of the tester in real time: when the tester performs the test operation during the maintenance period, the current influence data LY, voltage influence data YY and temperature influence data WY of the tester are obtained in real time. The current influence data LY is the absolute value of the difference between the current value of the tester and the current standard value, the voltage influence data YY is the absolute value of the difference between the voltage value of the tester and the voltage standard value, and the temperature influence data WY is the absolute value of the difference between the temperature value of the tester and the temperature standard value. The current value, voltage value and temperature value are collected by corresponding sensors to realize intelligent perception of the operating parameters. The current standard value, voltage standard value and temperature standard value are pre-processed from the normal current range, normal voltage range and normal temperature range when the tester is running, respectively. For example, the current standard value adopts the average value of the maximum and minimum values ​​of the normal current range, the voltage standard value adopts the average value of the maximum and minimum values ​​of the normal voltage range, and the temperature standard value adopts the average value of the maximum and minimum values ​​of the normal temperature range; through the formula The real-time coefficient SS of the electric tester is obtained, wherein m1, m2 and m3 are respectively the preset proportional coefficients of the current influence data LY, the voltage influence data YY and the temperature influence data WY, and m1>m2>m3>1; the real-time threshold SSmax of the electric tester is retrieved, and the real-time coefficient SS of the electric tester is compared with the real-time threshold SSmax: if the real-time coefficient SS is less than the real-time threshold SSmax, it is determined that the real-time operating state of the electric tester meets the requirements; if the real-time coefficient SS is greater than or equal to the real-time threshold SSmax, it is determined that the real-time operating state of the electric tester does not meet the requirements, the electric test operation is terminated, and the execution process of this electric test operation is marked as a fault process; the complete execution process of the electric tester whose real-time operating state meets the requirements is marked as a normal process; the embodiment of the present invention monitors the operating faults of the electric tester in real time, obtains multiple operating parameters of the electric tester in real time when the electric tester performs the electric test operation, and performs comprehensive calculation to obtain the real-time coefficient, and feeds back the operating state of the electric tester through the real-time coefficient, and issues a warning in time when an operating fault occurs.

[0019] The prediction analysis module is used to predict and analyze the failure probability of the normal process at the end of the normal process: a rectangular coordinate system is established with the execution time of the normal process as the X-axis and the real-time coefficient SS of the electroscope in the normal process as the Y-axis, a curve corresponding to the real-time coefficient SS in the normal process is made in the rectangular coordinate system and marked as the real-time curve, and the point with the largest horizontal coordinate value and the point with the largest vertical coordinate value in the real-time curve are marked as horizontal marking points and vertical marking points respectively, a straight line perpendicular to the X-axis is made based on the horizontal marking point and marked as the vertical cutting line, and a straight line perpendicular to the Y-axis is made based on the vertical marking point and marked as the horizontal cutting line, and the area value of the closed rectangular area formed by the X-axis, the Y-axis, the horizontal cutting line and the vertical cutting line is marked as the execution value. , mark the area value of the closed area formed by the X-axis, Y-axis, real-time curve and longitudinal cutting line as the predicted value, mark the ratio of the predicted value to the executed value as the predicted coefficient, and compare the predicted coefficient with the preset prediction threshold: if the predicted coefficient is less than the predicted threshold, it is determined that the normal process has no failure risk; if the predicted coefficient is greater than or equal to the predicted threshold, it is determined that the normal process has a failure risk, and the corresponding normal process is marked as a risky process, and an active maintenance signal is generated and sent to the mobile phone terminal of the administrator; the embodiment of the present invention calculates the prediction coefficient in the normal process by curve analysis, and feeds back the failure probability of the normal process through the prediction coefficient, so that the electroscope is actively maintained when the failure probability is abnormal, thereby reducing the failure frequency of the electroscope. Embodiment 2

[0020] like Figure 3 As shown, the fault monitoring module and the prediction analysis module send the complete monitoring data of the electroscope during the fault process, normal process and risk process to the database for storage.

[0021] The risk analysis module is used to analyze the overall operation risk of the electroscope during the operation cycle: the number of corresponding fault processes and risk processes of the electroscope during the operation cycle are marked as fault value GZ and risk value FX respectively, and the evaluation coefficient PG of the electroscope during the operation cycle is obtained by the formula PG=k1*GZ+k2*FX, wherein k1 and k2 are both proportional coefficients, and k1>k2>1; the evaluation coefficient PG of the electroscope during the operation cycle is compared with the preset evaluation threshold PGmax: if the evaluation coefficient PG is less than the evaluation threshold PGmax, it is determined that the overall operation risk of the electroscope during the operation cycle meets the requirements; if the evaluation coefficient PG is greater than or equal to the evaluation threshold PGmax, it is determined that the overall operation risk of the electroscope during the operation cycle does not meet the requirements, and a distribution analysis signal is generated and sent to the distribution analysis module; the embodiment of the present invention calculates the number of risk processes and the number of fault processes of the electroscope during the operation cycle to obtain an evaluation coefficient, and the overall operation risk of the electroscope during the operation cycle is fed back through the evaluation coefficient, so as to perform distribution analysis on the electroscope when necessary.

[0022] The distribution analysis module is used to analyze the distribution of risk processes and fault processes in the maintenance period: mark the duration of the maintenance period as SC, and obtain the interception value JQ through the formula JQ=t1*SC, where t1 is the proportional coefficient, 1.05≤t1≤1.15, and intercept a period with a duration of the interception value JQ in the maintenance period, and the end time of the interception period coincides with the end time of the maintenance period; determine whether there is a risk process or a fault process in the interception period, if so, mark the interception period as an occupied period; if not, mark the interception period as a normal period; mark the ratio of the number of occupied periods to the number of maintenance periods as the operation cycle The occupancy coefficient of the period is calculated, and the occupancy coefficient is compared with a preset occupancy threshold: if the occupancy coefficient is less than the occupancy threshold, a maintenance optimization signal is generated and sent to the mobile phone terminal of the administrator; if the occupancy coefficient is greater than or equal to the occupancy threshold, the optimization value YH is obtained through the formula YH=(1-t1)*SC, and the duration of the maintenance period in the next operation cycle is set to the optimization value YH; the embodiment of the present invention analyzes the distribution of risk processes and fault processes in the maintenance period, determines the maintenance optimization decision of the electroscope according to the obtained occupancy coefficient, adopts the most optimized processing means to perform maintenance optimization processing on the electroscope, and improves its abnormality processing efficiency. Embodiment 3

[0023] like Figure 4 As shown, the intelligent control method for the operation of the electroscope based on the intelligent sensing technology includes the following steps: Step 1: Perform maintenance analysis on the electroscope: generate an operation cycle, divide the operation cycle into several maintenance periods, and perform operation maintenance on the electroscope at the end of the maintenance period; Step 2: Monitor the operation failure of the electroscope: When the electroscope performs the electroscope operation during the maintenance period, the current influence data LY, voltage influence data YY and temperature influence data WY of the electroscope are obtained in real time and numerically calculated to obtain the real-time coefficient SS. The execution process corresponding to the electroscope operation is marked as a normal process or a fault process through the real-time coefficient SS; Step 3: At the end of the normal process, the failure probability of the normal process is predicted and analyzed to obtain the prediction coefficient of the normal process, and whether the normal process has a failure risk is determined by the prediction coefficient; Step 4: Analyze the overall operation risk of the electroscope during the operation cycle: mark the number of fault processes and risk processes corresponding to the electroscope during the operation cycle as fault value GZ and risk value FX respectively, perform numerical calculation on the fault value GZ and the risk value FX to obtain the evaluation coefficient PG, and use the evaluation coefficient PG to determine whether the overall operation risk of the electroscope during the operation cycle meets the requirements. If it does not meet the requirements, execute step 5; Step 5: Analyze the distribution of risk processes and fault processes in the maintenance period: mark the occupied periods in the maintenance period, and mark the ratio of the number of occupied periods to the number of maintenance periods as the occupancy coefficient of the operation cycle. Generate a maintenance optimization signal through the occupancy coefficient or set the duration of the maintenance period in the next operation cycle.

[0024] In summary, the intelligent control system for the operation of the electric tester based on intelligent sensing technology provided in the embodiment of the present invention generates an operation cycle when working, divides the operation cycle into several maintenance periods, and performs operation and maintenance on the electric tester at the end of the maintenance period; when the electric tester performs the electric test operation within the maintenance period, the current influence data LY, voltage influence data YY and temperature influence data WY of the electric tester are obtained in real time and numerical calculations are performed to obtain the real-time coefficient SS, and the execution process corresponding to the electric test operation of the electric tester is marked as a normal process or a fault process through the real-time coefficient SS; at the end of the normal process, the failure probability of the normal process is predicted and analyzed and the prediction coefficient of the normal process is obtained, and whether the normal process has a failure risk is determined by the prediction coefficient.

[0025] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0026] The above formulas are obtained by collecting a large amount of data and performing software simulation to select a formula close to the actual value. The coefficients in the formula are set by technicians in this field according to actual conditions; for example: Formula ; A technician in this field collects multiple groups of sample data and sets corresponding real-time coefficients for each group of sample data; Substitute the set real-time coefficients and the collected sample data into the formula, any three formulas constitute a three-variable linear equation system, screen the calculated coefficients and take the average, and obtain the values ​​of m1, m2 and m3 as 4.28, 2.23 and 2.06 respectively; The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the preliminary setting of the corresponding real-time coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value, such as the real-time coefficient is proportional to the value of the current affecting data.

[0027] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0028] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. Intelligent control system for electroscope operation based on intelligent sensing technology, characterized in that: It includes a processor, wherein the processor is communicatively connected to a maintenance analysis module, a fault monitoring module, and a prediction analysis module; The maintenance analysis module is used to perform maintenance analysis on the electroscope: generate an operation cycle, divide the operation cycle into a number of maintenance periods, and perform operation maintenance on the electroscope at the end of the maintenance period; The fault monitoring module is used to monitor the operation fault of the electroscope in real time: when the electroscope performs the test operation during the maintenance period, the real-time coefficient SS of the electroscope is obtained in real time, and the execution process corresponding to the electroscope test operation is marked as a normal process or a fault process by the real-time coefficient SS; The prediction and analysis module is used to predict and analyze the failure probability of the normal process at the end of the normal process. When the normal process has a failure risk, the corresponding normal process is marked as a risky process, an active maintenance signal is generated and sent to the mobile phone terminal of the administrator.

2. The intelligent control system for electroscope operation based on intelligent sensing technology according to claim 1 is characterized in that: The process of obtaining the real-time coefficient SS of the electroscope includes: obtaining the current influence data LY, voltage influence data YY and temperature influence data WY of the electroscope, and using the formula The real-time coefficient SS of the electroscope is obtained, wherein m1, m2 and m3 are preset proportional coefficients of the current influence data LY, the voltage influence data YY and the temperature influence data WY respectively, and m1>m2>m3>1.

3. The intelligent control system for electroscope operation based on intelligent sensing technology according to claim 2 is characterized in that: The current influence data LY is the absolute value of the difference between the current value of the electroscope and the current standard value, the voltage influence data YY is the absolute value of the difference between the voltage value of the electroscope and the voltage standard value, and the temperature influence data WY is the absolute value of the difference between the temperature value of the electroscope and the temperature standard value.

4. The intelligent control system for electroscope operation based on intelligent sensing technology according to claim 3 is characterized in that: The specific process of marking the execution process corresponding to the test operation of the tester as a normal process or a faulty process includes: calling the real-time threshold SSmax of the tester, and comparing the real-time coefficient SS of the tester with the real-time threshold SSmax: if the real-time coefficient SS is less than the real-time threshold SSmax, it is determined that the real-time operating status of the tester meets the requirements; if the real-time coefficient SS is greater than or equal to the real-time threshold SSmax, it is determined that the real-time operating status of the tester does not meet the requirements, the test operation is terminated, and the execution process of this test operation is marked as a faulty process; the complete execution process of the tester whose real-time operating status meets the requirements is marked as a normal process.

5. The intelligent control system for electroscope operation based on intelligent sensing technology according to claim 4 is characterized in that: The specific process of the prediction analysis module predicting and analyzing the failure probability of the normal process at the end of the normal process includes: obtaining the prediction value and execution value of the operation cycle, marking the ratio of the prediction value to the execution value as the prediction coefficient, and comparing the prediction coefficient with the preset prediction threshold: if the prediction coefficient is less than the prediction threshold, it is determined that the normal process does not have a failure risk; if the prediction coefficient is greater than or equal to the prediction threshold, it is determined that the normal process has a failure risk.

6. The intelligent control system for electroscope operation based on intelligent sensing technology according to claim 5 is characterized in that: The process of obtaining the predicted value and execution value of the operation cycle includes: establishing a rectangular coordinate system with the execution time of the normal process as the X-axis and the real-time coefficient SS of the electroscope in the normal process as the Y-axis, making a curve corresponding to the real-time coefficient SS in the normal process in the rectangular coordinate system and marking it as the real-time curve, marking the point with the largest horizontal coordinate value and the point with the largest vertical coordinate value in the real-time curve as the horizontal marking point and the vertical marking point respectively, making a straight line perpendicular to the X-axis based on the horizontal marking point and marking it as the vertical cutting line, making a straight line perpendicular to the Y-axis based on the vertical marking point and marking it as the horizontal cutting line, marking the area value of the closed rectangular area composed of the X-axis, Y-axis, horizontal cutting line and vertical cutting line as the execution value, and marking the area value of the closed area composed of the X-axis, Y-axis, real-time curve and vertical cutting line as the predicted value.

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