CN112881818B - Electric field strength measurement method, device, computer equipment and storage medium - Google Patents
Electric field strength measurement method, device, computer equipment and storage medium Download PDFInfo
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R29/00—Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
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Abstract
本申请涉及一种电场强度测量方法、装置、计算机设备和存储介质。通过获取待测电场中距离输电线预设距离的目标位置的电压信号，并获取待测电场的温度和湿度，再讲上述获取到的电压信号、温度和湿度输入预测模型，并获取预测模型输出的电场强度，作为上述待测电场中目标位置对应的电场强度。其中，预测模型可以基于多个已知温度、多个已知湿度以及多个已知电压信号，通过预设机器学习算法训练得到。相较于传统的通过基于向列液晶光子晶体光纤渗透的方式测得电场强度的方式，本方案通过利用待测电场中距离输电线预设距离的电压信号，并考虑待测电场的温度和湿度等因素，通过预测模型，得到目标位置的电场强度，提高了电场强度测量精度。
The present application relates to a method, device, computer equipment and storage medium for measuring electric field strength. By obtaining a voltage signal at a target position at a preset distance from a transmission line in the electric field to be measured, and obtaining the temperature and humidity of the electric field to be measured, the voltage signal, temperature and humidity obtained are input into a prediction model, and the electric field strength output by the prediction model is obtained as the electric field strength corresponding to the target position in the electric field to be measured. Among them, the prediction model can be obtained by training a preset machine learning algorithm based on multiple known temperatures, multiple known humidity and multiple known voltage signals. Compared with the traditional method of measuring the electric field strength by a method based on nematic liquid crystal photonic crystal fiber penetration, this scheme uses the voltage signal at a preset distance from the transmission line in the electric field to be measured, and considers factors such as the temperature and humidity of the electric field to be measured, and obtains the electric field strength of the target position through a prediction model, thereby improving the measurement accuracy of the electric field strength.
Description
Technical Field
The present application relates to the field of power monitoring technology, and in particular, to a method and apparatus for measuring electric field strength, a computer device, and a storage medium.
Background
The transmission line wire in the electric wire netting is the important component in the electric power system, and transmission line wire surface can generally lead to corona discharge because field intensity is unusual, and corona discharge can produce harm to the staff who is close to the construction and driving the vehicle. Therefore, it is important to calculate, measure and analyze the surface field intensity of the high-voltage alternating current transmission line. The current method for measuring the electric field intensity generally measures the electric field intensity by a mode based on the penetration of a nematic liquid crystal photonic crystal fiber, however, the method is limited by the liquid crystal characteristics, and the accuracy of measuring the electric field intensity by the mode is not high.
Therefore, the current electric field intensity measuring method has the defect of low measuring precision.
Disclosure of Invention
In view of the foregoing, it is desirable to provide an electric field intensity measuring method, apparatus, computer device, and storage medium capable of improving measurement accuracy.
A method of electric field strength measurement, the method comprising:
acquiring a voltage signal of a target position in an electric field to be detected, wherein the target position is a position which is a preset distance away from a power transmission line in the electric field to be detected;
Acquiring the temperature and humidity of the electric field to be measured;
And inputting the voltage signals, the temperature and the humidity into a prediction model, obtaining the electric field intensity output by the prediction model as the electric field intensity corresponding to the target position in the electric field to be detected, wherein the prediction model is trained by a preset machine learning algorithm based on a plurality of known temperatures, a plurality of known humidities and a plurality of known voltage signals.
In one embodiment, the acquiring the temperature and the humidity of the electric field to be measured includes:
Acquiring a first electric signal corresponding to temperature and a second electric signal corresponding to humidity sent by temperature and humidity sensing equipment, wherein the temperature and humidity sensing equipment is arranged in the target position;
And obtaining the temperature according to the first electric signal and obtaining the humidity according to the second electric signal.
In one embodiment, before the step of obtaining the voltage signal of the target position in the electric field to be measured, the method further includes:
according to the voltage class and the wiring mode corresponding to the power transmission line, obtaining simulation modeling corresponding to the power transmission line through finite element simulation calculation;
dividing the simulation modeling into a plurality of grids, and determining a target position which is away from the power transmission line by a preset distance in the electric field to be detected from the grids.
In one embodiment, the determining, from the multiple grids, the target position of the electric field to be measured, which is a preset distance from the power transmission line, includes:
And establishing a corresponding auxiliary line in the target grid according to a preset step length corresponding to the target grid with the preset distance from the power transmission line, so as to obtain a target position with the preset distance from the power transmission line in the electric field to be detected.
In one embodiment, the dividing the simulation modeling into a plurality of grids includes:
The simulation modeling is divided into a plurality of grids by a simulated charge method.
In one embodiment, the method further comprises:
Acquiring a plurality of known temperatures, a plurality of known humidities and a plurality of known voltage signals;
determining the preset machine learning algorithm from a plurality of machine learning algorithms according to the plurality of known temperatures, the plurality of known humidities and the data volume and data structure of the plurality of known voltage signals;
And training to obtain the corresponding relation between the known temperature and the known humidity and the known electric field intensity corresponding to the known voltage signal according to the preset machine learning algorithm, so as to obtain the prediction model.
In one embodiment, the method further comprises:
if the temperature is greater than a preset temperature threshold, outputting temperature alarm information;
and/or the number of the groups of groups,
If the humidity is greater than a preset humidity threshold, outputting humidity alarm information;
and/or the number of the groups of groups,
And if the electric field intensity is larger than a preset field intensity threshold value, outputting field intensity alarm information.
An electric field strength measurement apparatus, the apparatus comprising:
The signal acquisition module is used for acquiring a voltage signal of a target position in an electric field to be detected, wherein the target position is a position which is a preset distance away from a power transmission line in the electric field to be detected;
the temperature and humidity acquisition module is used for acquiring the temperature and humidity of the electric field to be detected;
The field intensity obtaining module is used for inputting the voltage signal, the temperature and the humidity into a prediction model to obtain the electric field intensity output by the prediction model as the electric field intensity corresponding to the target position in the electric field to be detected, and the prediction model is obtained through training of a preset machine learning algorithm based on a plurality of known temperatures, a plurality of known humidities and a plurality of known voltage signals.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method described above when the processor executes the computer program.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method described above.
The electric field intensity measuring method, the electric field intensity measuring device, the computer equipment and the storage medium are characterized in that the voltage signal of the target position, which is a preset distance away from the transmission line, in the electric field to be measured is obtained, the temperature and the humidity of the electric field to be measured are obtained, the obtained voltage signal, the obtained temperature and the obtained humidity are input into the prediction model, and the electric field intensity output by the prediction model is obtained and is used as the electric field intensity corresponding to the target position in the electric field to be measured. The prediction model may be trained by a preset machine learning algorithm based on a plurality of known temperatures, a plurality of known humidities, and a plurality of known voltage signals. Compared with the traditional mode of measuring the electric field intensity based on the mode of penetrating the nematic liquid crystal photonic crystal fiber, the electric field intensity of the target position is obtained by utilizing the voltage signal of the electric field to be measured, which is a preset distance from the power transmission line, and taking the factors such as the temperature and the humidity of the electric field to be measured into consideration, and the like, and the effect of improving the measuring precision of the electric field intensity is achieved by obtaining the electric field intensity of the target position based on the prediction model obtained by the known temperature, the known humidity, the known voltage signal and the machine learning algorithm.
Drawings
FIG. 1 is a diagram of an application environment of a method for measuring electric field strength in one embodiment;
FIG. 2 is a flow chart of a method for measuring electric field strength according to an embodiment;
FIG. 3 is a flow chart of another embodiment of a method for measuring electric field strength;
FIG. 4 is a block diagram showing an electric field strength measuring apparatus according to an embodiment;
Fig. 5 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The electric field intensity measuring method provided by the application can be applied to an application environment shown in figure 1. The industrial control terminal 102 may communicate with the electric field magnetic field sensing device 100 and the temperature and humidity sensing device 104, respectively, where the electric field magnetic field sensing device 100 and the temperature and humidity sensing device 104 may be disposed in an electric field to be measured on a power grid site. The industrial control terminal 102 can acquire the voltage signal of the target position in the electric field to be measured sent by the electric field magnetic field sensing device 100, the industrial control terminal 102 can also acquire the temperature and humidity of the electric field to be measured in the power grid site sent by the temperature and humidity sensing device 104, the industrial control terminal 102 can also input the acquired voltage signal, temperature and humidity into the prediction model, and acquire the electric field intensity output by the prediction model, so that the industrial control terminal 102 can acquire the electric field intensity of the target position in the electric field to be measured. The industrial control terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, and tablet computers, and the electric field magnetic field sensing device 100 and the temperature and humidity sensing device 104 may be implemented by hardware devices or devices embedded with software modules.
In one embodiment, as shown in fig. 2, there is provided an electric field intensity measuring method, which is described by taking an example that the method is applied to the industrial control terminal in fig. 1, and includes the following steps:
Step S202, obtaining a voltage signal of a target position in the electric field to be detected, wherein the target position is a position which is a preset distance away from the power transmission line in the electric field to be detected.
The electric field to be measured can be an electric field with the intensity of the electric field to be measured, a power transmission line can be arranged in the electric field, and the field intensity generated by the electric field to be measured can be the field intensity of the electric field generated by a wire of the power transmission line in a nearby range. The electric field to be measured can be provided with an electric field magnetic field induction device 100, and the electric field magnetic field induction device 100 can sense and collect voltage signals. The electric field magnetic field induction device 100 may be disposed at a target position with a preset distance from the high voltage transmission line in the electric field to be measured, the electric field magnetic field induction device 100 may detect a strong electric field generated by the high voltage transmission line and output a voltage signal proportional to the electric field generated by the high voltage transmission line, and the electric field magnetic field induction device 100 may send the voltage signal to the industrial control terminal 102, so that the industrial control terminal 102 may obtain the voltage signal of the target position in the electric field to be measured. The industrial control terminal 102 may include an electric field magnetic field sensing main control computing module, the industrial control terminal 102 may receive the voltage signal through the electric field magnetic field sensing main control computing module, and the target position may be determined from an area within a preset distance range around the power transmission line, for example, the area within the preset range around the power transmission line may be divided into a plurality of sub-areas, and the target position may be determined from the plurality of sub-areas.
Step S204, the temperature and humidity of the electric field to be measured are obtained.
The electric field to be measured can be an electric field generated at the position of the power transmission line in the power grid site. Since temperature and humidity both affect the field strength generated by the power transmission line, the influence of the temperature and humidity of the electric field to be measured on the electric field strength needs to be considered when the electric field strength of the electric field to be measured is measured. The industrial control terminal 102 may also acquire the temperature and humidity of the electric field to be measured, for example, the temperature and humidity near the power transmission line in the electric field to be measured, and in order to correspond to the acquired target voltage of the electric field to be measured, the industrial control terminal 102 may acquire the temperature and humidity at the target position.
The temperature and humidity may be collected by the temperature and humidity sensing device 104, and the temperature and humidity sensing device 104 may be disposed in the electric field to be measured, for example, in a target position of the electric field to be measured. The temperature and humidity sensing device 104 may be used to detect and collect the temperature and humidity in the electric field to be measured by a sensing collection manner. In some embodiments, the temperature and humidity sensing device 104 may transmit the acquired temperature and humidity to the industrial control terminal 102. For example, the temperature and humidity sensing device disposed at the target location may send a first electrical signal corresponding to the temperature after the temperature is collected, the temperature and humidity sensing device 104 may send a second electrical signal corresponding to the humidity after the humidity is collected, and the industrial control terminal 102 may receive the first electrical signal and the second electrical signal sent by the temperature and humidity sensing device 104, and obtain the temperature corresponding to the electric field to be measured according to the first electrical signal, and obtain the humidity corresponding to the electric field to be measured according to the second electrical signal. The temperature and humidity sensing device 104 may convert the collected temperature and humidity data into an electrical signal through a preset calculation manner and rule, and output and transmit the electrical signal to the industrial control terminal 102, where the industrial control terminal 102 may receive the electrical signals corresponding to the temperature and the humidity respectively through an electric field magnetic field sensing main control calculation module, and may restore the electrical signals corresponding to the temperature and the electrical signals corresponding to the humidity into corresponding temperature data and humidity data, and may perform corresponding processing on the obtained temperature and humidity. The temperature and humidity sensing device 104 may be a single sensor that can detect temperature and humidity, or a sensor group that includes a temperature sensor and a humidity sensor.
Step S206, inputting the voltage signals, the temperature and the humidity into a prediction model, obtaining the electric field intensity output by the prediction model as the electric field intensity corresponding to the target position in the electric field to be detected, wherein the prediction model is obtained through training by a preset machine learning algorithm based on a plurality of known temperatures, a plurality of known humidities and a plurality of known voltage signals.
The voltage signal may be a voltage signal corresponding to the electric field transmission line to be measured detected by the electric field magnetic field sensing device 100, and the temperature and the humidity may be a temperature and a humidity corresponding to the target position detected by the temperature and humidity sensing device 104 in the electric field to be measured. The industrial control terminal 102 may input the obtained voltage signal, temperature and humidity into the prediction model, so as to obtain the electric field strength corresponding to the target position in the electric field to be measured output by the prediction model, where the electric field strength may be the electric field strength obtained by considering the influence of the temperature and the humidity on the electric field strength. The prediction model may be a model trained by a preset machine learning algorithm based on a plurality of known temperatures, a plurality of known humidities, and a plurality of known voltage signals. The preset machine learning algorithm may be at least one machine learning algorithm determined from a plurality of preset machine learning algorithms.
In the electric field intensity measuring method, the voltage signal of the target position, which is at a preset distance from the power transmission line, in the electric field to be measured is obtained, the temperature and the humidity of the electric field to be measured are obtained, the obtained voltage signal, temperature and humidity are input into the prediction model, and the electric field intensity output by the prediction model is obtained and is used as the electric field intensity corresponding to the target position in the electric field to be measured. The prediction model may be trained by a preset machine learning algorithm based on a plurality of known temperatures, a plurality of known humidities, and a plurality of known voltage signals. Compared with the traditional mode of measuring the electric field intensity based on the mode of penetrating the nematic liquid crystal photonic crystal fiber, the electric field intensity of the target position is obtained by utilizing the voltage signal of the electric field to be measured, which is a preset distance from the power transmission line, and taking the factors such as the temperature and the humidity of the electric field to be measured into consideration, and the like, and the effect of improving the measuring precision of the electric field intensity is achieved by obtaining the electric field intensity of the target position based on the prediction model obtained by the known temperature, the known humidity, the known voltage signal and the machine learning algorithm.
In one embodiment, before the voltage signal of the target position in the electric field to be measured is obtained, the method further comprises the steps of obtaining simulation modeling corresponding to the power transmission line through finite element simulation calculation according to the voltage grade corresponding to the power transmission line and a wiring mode, dividing the simulation modeling into a plurality of grids, and determining the target position, which is away from the power transmission line by a preset distance, in the electric field to be measured from the grids.
In this embodiment, the industrial control terminal 102 may obtain the target position where the electric field intensity needs to be measured by dividing a grid. The industrial control terminal 102 can obtain simulation modeling corresponding to the power transmission line through finite element simulation calculation according to the voltage class and the wiring mode of the power transmission line in the electric field to be tested, for example, the industrial control terminal 102 can use finite element simulation software to model the power transmission line of different voltage classes and different wiring modes of a simulation object to obtain a corresponding simulation model, and the industrial control terminal 102 can divide the simulation modeling into a plurality of grids and determine target positions of preset distances from the power transmission line in the electric field to be tested from the grids. For example, the industrial control terminal 102 may use finite element simulation software to grid the simulation model corresponding to the power line, so as to obtain multiple grids corresponding to the power line, and obtain the target position required to measure the electric field strength from the multiple grids in a specific manner. The finite element simulation is to simulate a real physical system, such as the transmission line, by using a mathematical approximation method, and a finite number of unknown quantities can be used to approximate an infinite unknown quantity real system by using simple and interactive units, so that finite element simulation calculation can adapt to various complex shapes, and engineering can be effectively analyzed.
According to the embodiment, the industrial control terminal 102 can obtain simulation modeling corresponding to the power transmission line through finite element simulation calculation, and determine the target position from a plurality of grids based on the simulation modeling, so that electric field intensity can be measured at the target position, and the effect of improving electric field intensity measurement accuracy is achieved.
In one embodiment, determining the target position of the preset distance from the power transmission line in the electric field to be measured from a plurality of grids comprises establishing corresponding auxiliary lines in the target grids according to the preset step length corresponding to the target grids of the preset distance from the power transmission line, so as to obtain the target position of the preset distance from the power transmission line in the electric field to be measured.
In this embodiment, the industrial control terminal 102 can determine the target location from a plurality of grids where the electric field strength measurement is required. The industrial control terminal 102 may obtain the solving step length of each grid, and establish a corresponding auxiliary line in the target grid according to the preset step length corresponding to the target grid with the preset distance from the power transmission line, so as to obtain the target position with the preset distance from the power transmission line in the electric field to be measured. For example, each grid can be used as a calculation area, and different calculation areas have different calculation precision requirements, so that a grid division mode needs to be set, calculation time is shortened, and after setting a solution step length, the industrial control terminal 102 can establish an auxiliary line of a designated position, for example, an auxiliary line of the target position in the simulation modeling, so that electric field intensity distribution can be obtained in the target position, and electric field magnetic field intensity of a power grid site can be accurately detected.
In one embodiment, the meshing of the simulation modeling corresponding to the power line in the electric field to be tested may be performed by a preset algorithm, for example, the industrial control terminal 102 may perform meshing of the simulation modeling corresponding to the power line into a plurality of grids by using a simulated charge method. The analog charge method is one of main methods for calculating electrostatic field values, and based on the uniqueness theorem of electrostatic field, the analog charge method replaces free charges continuously distributed on the surface of a conductor electrode with a group of discrete charges positioned inside the conductor, for example, a group of point charges, line charges or ring charges are arranged inside the conductor, the discrete charges are called analog charges, then the superposition theorem is utilized to calculate the electric field intensity of any point in a field by using an analytic formula of the analog charges, the analog charges are determined according to the boundary conditions of the field, and the analog charge method is critical in finding and determining the analog charges.
Through the above embodiment, the industrial control terminal 102 may obtain a plurality of grids by using the analog charge method, and obtain the target position by using the solving step length and the auxiliary line, so as to achieve the effect of improving the measurement accuracy of the electric field strength of the electric field to be measured.
In one embodiment, the method further comprises the steps of acquiring a plurality of known temperatures, a plurality of known humidities and a plurality of known voltage signals, determining a preset machine learning algorithm from a plurality of machine learning algorithms according to the data quantity and the data structure of the plurality of known temperatures, the plurality of known humidities and the plurality of known voltage signals, and training to obtain a corresponding relation between the known temperatures, the known humidities and the known electric field intensities corresponding to the known voltage signals according to the preset machine learning algorithm to obtain a prediction model.
In this embodiment, the industrial control terminal 102 may obtain the above-mentioned prediction model through machine learning training. The industrial control terminal 102 may acquire a plurality of known temperatures, a plurality of known humidities, and a plurality of known voltage signals as training data, the industrial control terminal 102 may determine a preset machine learning algorithm that may be used to train the training data from a plurality of machine learning algorithms, for example, the plurality of machine learning algorithms may include algorithms in terms of decision trees, random forests, artificial neural networks, bayesian learning, deep learning, etc., from the plurality of machine learning algorithms based on the plurality of known temperatures, the plurality of known humidities, and the data amount and data structure of the plurality of known voltage signals, and the industrial control terminal 102 may determine a suitable machine learning algorithm based on the plurality of known temperatures, the plurality of known humidities, and the data amount and data structure of the known voltage signals. After the industrial control terminal 102 determines a suitable preset machine learning algorithm, the corresponding relationship between the known temperature, the known humidity and the known electric field intensity corresponding to the known voltage signal can be trained and obtained according to the preset machine learning algorithm, so that the industrial control terminal 102 can analyze the influence of the temperature and the humidity on the electric field intensity through the preset machine learning algorithm to obtain a prediction model of the influence of the temperature and the humidity on the electric field intensity.
Through the embodiment, the industrial control terminal 102 can obtain a prediction model for predicting the electric field strength by using a machine learning algorithm which is adaptive to the data quantity and the data structure of the temperature, humidity and voltage signals, so that the accuracy of electric field strength measurement is improved.
In one embodiment, the method further comprises outputting temperature warning information if the temperature is greater than a preset temperature threshold.
In this embodiment, the industrial control terminal 102 may alarm the data that does not meet the requirements when the data of the temperature, the humidity and the field intensity exceeds the corresponding threshold. For example, the industrial control terminal 102 may output a temperature alarm message when the industrial control terminal 102 detects that the acquired temperature is greater than a preset temperature threshold. In one embodiment, if the industrial control terminal 102 detects that the acquired humidity is greater than the preset humidity threshold, the industrial control terminal 102 may output a humidity alert message. In one embodiment, if the industrial control terminal 102 detects that the obtained electric field intensity is greater than the preset field intensity threshold, the industrial control terminal 102 may output field intensity alarm information to prompt the staff that the field intensity at the target position is abnormal, so that the staff may perform corresponding processing on the power transmission line.
In addition, the industrial control terminal 102 may also perform data display on the obtained data such as the temperature, the humidity, and the electric field intensity, for example, through a display unit, such as a display screen, of the industrial control terminal 102.
Through the embodiment, the industrial control terminal 102 can alarm illegal data, so that the safety of the power transmission line is improved, acquired data can be displayed, and the intuitiveness of data acquisition is improved.
In one embodiment, as shown in fig. 3, fig. 3 is a flow chart of a method for measuring electric field strength in another embodiment. The method comprises the following steps:
the industrial control terminal 102 may first use finite element simulation software to model high-voltage wires with different voltage classes and different wiring modes as simulation objects to obtain corresponding simulation models for calculation.
Then, the industrial control terminal 102 can use simulation software to perform grid division on the simulation model by using a simulated charge method, and different calculation precision requirements are met for different calculation areas, so that a grid division mode is required to be set, and the calculation time is shortened. After setting the solving step length, establishing an auxiliary line at a designated position in the model to obtain electric field intensity distribution at the designated position. So as to accurately detect the electric field strength of the electric field to be detected on the power grid site.
The industrial control terminal 102 may set an electric field magnetic field sensing device 100 and a temperature and humidity sensing device 104 in an electric field to be measured, and in some embodiments, the industrial control terminal 102 may also be set in the electric field to be measured, where the electric field magnetic field sensing device 100 and the temperature and humidity sensing device 104 may be directly integrated in the industrial control terminal 102, and the corresponding data may be collected in a form of hardware or software modules.
The electric field magnetic field sensing device 100 can collect electric field magnetic field information of a power grid site and output voltage signals to the industrial control terminal 102, the electric field magnetic field sensing device 100 mainly detects the distance between high-voltage transmission lines of an electric field to be detected in the power grid site, a strong electric field generated by the power grid site transmission lines enables the electric field magnetic field sensing module to output voltage signals proportional to the voltage signals, and then the signals are transmitted to an electric field magnetic field sensing main control calculation module in the industrial control terminal 102 for data training, calculation, analysis and preprocessing.
The temperature and humidity sensing device 104, such as a temperature and humidity sensor, can be further arranged in the electric field to be measured on the power grid site, the industrial control terminal 102 can measure the temperature and humidity around the environment where the electric field of the high-voltage power transmission line of the power grid is controlled by using the temperature and humidity sensing device 104, wherein the temperature and humidity sensing device 104 can convert the detected temperature or humidity data into an electric signal according to the calculation mode and rule of the temperature and humidity sensing device 104 and send the electric signal to an electric field magnetic field sensing main control calculation module in the industrial control terminal 102.
The industrial control terminal 102 may obtain the electric field strength of the target location using the prediction model using the obtained voltage signal, temperature and humidity data. The industrial control terminal 102 may first train a predictive model, which may be used to calculate the effect of temperature, humidity on field strength. For example, the industrial control terminal 102 can determine a suitable machine learning algorithm based on a plurality of known temperatures, a plurality of known humidities, and a plurality of known voltage signals, based on a size and a data structure of a data volume of the plurality of known temperatures, the plurality of known humidities, and the plurality of known voltage signals, the machine learning algorithm can include algorithms in the form of decision trees, random forests, artificial neural networks, bayesian learning, deep learning, and the like. The industrial control terminal 102 may utilize the electric field magnetic field sensing master control calculation module to analyze the influence of temperature and humidity on the electric field intensity by using the corresponding machine learning algorithm, so as to obtain a corresponding relationship between the temperature and humidity and the electric field intensity corresponding to the voltage signal, thereby obtaining a prediction model of the influence of the temperature and the humidity on the electric field intensity. In actual measurement, the industrial control terminal 102 may input the obtained temperature, humidity and voltage signals into the prediction model, and calculate the electric field strength by using the electric field magnetic field sensing main control calculation module through the prediction model, so as to obtain a detection result of the electric field strength. The industrial control terminal 102 may also utilize a local data processing module to display the obtained electric field strength, state, temperature and humidity through a local display unit, for example, through a display screen of the industrial control terminal 102. The industrial control terminal 102 can also utilize the local alarm unit to alarm corresponding information after detecting that the temperature and humidity or the field intensity exceeds the set corresponding threshold.
In addition, the data communication manner between the electric field and magnetic field sensing master control calculation module in the industrial control terminal 102 and the local data processing module in the industrial control terminal 102 may be performed by a wired connection manner or may be performed by a wireless communication manner. If the communication is performed in a wireless manner, the electric field magnetic field sensing main control computing module can send the data to the wireless communication receiving unit of the local data processing module for data reception in a wireless communication protocol transmission manner of the wireless communication transmitting module, so that the industrial control terminal 102 can process the data by using the local data processing module.
With the present embodiment, the industrial control terminal 102 obtains the electric field intensity of the target location based on the known temperature, the known humidity, the known voltage signal and the prediction model obtained by the machine learning algorithm, and achieves the effect of improving the measurement accuracy of the electric field intensity.
It should be understood that, although the steps in the flowcharts of fig. 2 and 3 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2 and 3 may include a plurality of steps or stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily sequential, but may be performed in rotation or alternatively with at least some of the other steps or stages.
In one embodiment, as shown in FIG. 4, an electric field strength measuring apparatus is provided, comprising a signal acquisition module 500, a temperature and humidity acquisition module 502, and a field strength acquisition module 504, wherein:
the signal acquisition module 500 is configured to acquire a voltage signal of a target position in the electric field to be measured, where the target position is a position of a preset distance from the power transmission line in the electric field to be measured.
The temperature and humidity acquisition module 502 is configured to acquire temperature and humidity of an electric field to be measured.
The field strength obtaining module 504 is configured to input the voltage signal, the temperature and the humidity into a prediction model, obtain an electric field strength output by the prediction model as an electric field strength corresponding to a target position in the electric field to be measured, and obtain the prediction model by training a preset machine learning algorithm based on a plurality of known temperatures, a plurality of known humidities and a plurality of known voltage signals.
In one embodiment, the temperature and humidity acquiring module 502 is specifically configured to acquire a first electrical signal corresponding to temperature and a second electrical signal corresponding to humidity sent by a temperature and humidity sensing device, where the temperature and humidity sensing device is disposed in a target location, obtain the temperature according to the first electrical signal, and obtain the humidity according to the second electrical signal.
In one embodiment, the device further comprises a dividing module, wherein the dividing module is used for obtaining simulation modeling corresponding to the power transmission line through finite element simulation calculation according to the voltage class corresponding to the power transmission line and the wiring mode, dividing the simulation modeling into a plurality of grids, and determining target positions, which are preset distances from the power transmission line, in the electric field to be detected from the grids.
In one embodiment, the dividing module is specifically configured to establish a corresponding auxiliary line in a target grid according to a preset step length corresponding to the target grid at a preset distance from the power transmission line, so as to obtain a target position at the preset distance from the power transmission line in the electric field to be measured.
In one embodiment, the device further comprises a training module for acquiring a plurality of known temperatures, a plurality of known humidities and a plurality of known voltage signals, determining a preset machine learning algorithm from a plurality of machine learning algorithms according to the data quantity and the data structure of the plurality of known temperatures, the plurality of known humidities and the plurality of known voltage signals, and training to obtain a corresponding relation between the known temperatures, the known humidities and the known electric field intensities corresponding to the known voltage signals according to the preset machine learning algorithm to obtain a prediction model.
In one embodiment, the dividing module is specifically configured to divide the simulation modeling into a plurality of grids by using a simulated charge method.
In one embodiment, the device further comprises a first alarm module, wherein the first alarm module is used for outputting temperature alarm information if the temperature is greater than a preset temperature threshold.
In one embodiment, the device further comprises a second alarm module, configured to output humidity alarm information if the humidity is greater than a preset humidity threshold.
In one embodiment, the device further comprises a third alarm module, wherein the third alarm module is used for outputting field intensity alarm information if the electric field intensity is greater than a preset field intensity threshold value.
For specific limitations of the electric field intensity measuring device, reference may be made to the above limitations of the electric field intensity measuring method, and no further description is given here. The above-described respective modules in the electric field intensity measuring apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be an industrial control terminal, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a method of electric field strength measurement. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in FIG. 5 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory having a computer program stored therein and a processor that implements the above-described electric field strength measurement method when executing the computer program.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the above-described electric field strength measurement method.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A method of measuring electric field strength, the method comprising:
The method comprises the steps of obtaining voltage signals of target positions in an electric field to be measured, wherein the target positions are positions with preset distances from a power transmission line in the electric field to be measured, determining the target positions comprises the steps of obtaining simulation modeling corresponding to the power transmission line through finite element simulation calculation according to voltage grades corresponding to the power transmission line and a wiring mode, dividing the simulation modeling into a plurality of grids, determining the target positions with the preset distances from the power transmission line in the electric field to be measured from the grids, wherein each grid is a calculation area, different calculation areas have different calculation precision requirements, and determining the target positions with the preset distances from the power transmission line in the electric field to be measured from the grids, wherein the target positions with the preset distances from the power transmission line in the electric field to be measured are obtained by establishing corresponding auxiliary lines in the target grids according to preset step sizes corresponding to the target grids with the preset distances from the power transmission line;
Acquiring the temperature and humidity of the electric field to be measured;
The voltage signals, the temperature and the humidity are input into a prediction model, the electric field intensity output by the prediction model is obtained and used as the electric field intensity corresponding to the target position in the electric field to be detected, the training step of the prediction model comprises the steps of obtaining a plurality of known temperatures, a plurality of known humidities and a plurality of known voltage signals, determining a preset machine learning algorithm from a plurality of machine learning algorithms according to the data quantity and the data structure of the known temperatures, the known humidities and the known voltage signals, and training according to the preset machine learning algorithm to obtain the corresponding relation between the known temperatures, the known humidities and the known electric field intensity corresponding to the known voltage signals, so that the prediction model is obtained.
2. The method of claim 1, wherein the acquiring the temperature and humidity of the electric field to be measured comprises:
Acquiring a first electric signal corresponding to temperature and a second electric signal corresponding to humidity sent by temperature and humidity sensing equipment, wherein the temperature and humidity sensing equipment is arranged in the target position;
And obtaining the temperature according to the first electric signal and obtaining the humidity according to the second electric signal.
3. The method of claim 1, wherein the partitioning the simulation modeling into a plurality of grids comprises:
The simulation modeling is divided into a plurality of grids by a simulated charge method.
4. A method according to any one of claims 1 to 3, further comprising:
And if the temperature is greater than a preset temperature threshold, outputting temperature alarm information.
5. A method according to any one of claims 1 to 3, further comprising:
And if the humidity is greater than a preset humidity threshold, outputting humidity alarm information.
6. A method according to any one of claims 1 to 3, further comprising:
and if the electric field intensity is larger than a preset field intensity threshold value, outputting field intensity alarm information.
7. An electric field strength measuring apparatus, the apparatus comprising:
The system comprises a signal acquisition module, a dividing module, a simulation modeling module, a calculation module and a calculation module, wherein the signal acquisition module is used for acquiring a voltage signal of a target position in an electric field to be measured, the target position is a position which is at a preset distance from a power transmission line in the electric field to be measured, the dividing module is used for obtaining simulation modeling corresponding to the power transmission line through finite element simulation calculation according to a voltage grade corresponding to the power transmission line and a wiring mode, the simulation modeling is divided into a plurality of grids, the target position which is at the preset distance from the power transmission line in the electric field to be measured is determined from the grids, each grid is a calculation area, different calculation areas have different calculation precision requirements, and the calculation module is specifically used for establishing corresponding auxiliary lines in the target grids according to a preset step length corresponding to a target grid at the preset distance from the power transmission line to be measured;
the temperature and humidity acquisition module is used for acquiring the temperature and humidity of the electric field to be detected;
The field intensity obtaining module is used for inputting the voltage signals, the temperature and the humidity into the prediction model to obtain the electric field intensity output by the prediction model as the electric field intensity corresponding to the target position in the electric field to be detected, the prediction model is obtained through training of a preset machine learning algorithm based on a plurality of known temperatures, a plurality of known humidities and a plurality of known voltage signals, the field intensity obtaining module is used for obtaining a plurality of known temperatures, a plurality of known humidities and a plurality of known voltage signals, the preset machine learning algorithm is determined from a plurality of machine learning algorithms according to the data quantity and the data structure of the known temperatures, the known humidities and the known voltage signals, and the prediction model is obtained through training according to the preset machine learning algorithm.
8. The apparatus of claim 7, wherein the temperature and humidity acquisition module is configured to:
Acquiring a first electric signal corresponding to temperature and a second electric signal corresponding to humidity sent by temperature and humidity sensing equipment, wherein the temperature and humidity sensing equipment is arranged in the target position;
And obtaining the temperature according to the first electric signal and obtaining the humidity according to the second electric signal.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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| CN115808578B (en) * | 2022-12-02 | 2023-12-12 | 南方电网数字电网研究院有限公司 | Method, device, equipment and storage medium for acquiring voltage of power equipment |
| CN116184045A (en) * | 2023-04-10 | 2023-05-30 | 广东电网有限责任公司茂名供电局 | Electric field detection method, system, medium and computer for complex power grid environment |
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