CN112881818A - Electric field intensity measuring method, electric field intensity measuring device, computer equipment and storage medium - Google Patents
Electric field intensity measuring method, electric field intensity measuring device, computer equipment and storage medium Download PDFInfo
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- G—PHYSICS
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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
- G01R29/12—Measuring electrostatic fields or voltage-potential
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- G—PHYSICS
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- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
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Abstract
The application relates to an electric field strength measuring method, an electric field strength measuring device, a computer device and a storage medium. The method comprises the steps of obtaining a voltage signal of a target position in an electric field to be detected, which is a preset distance away from a power transmission line, obtaining the temperature and the humidity of the electric field to be detected, inputting the obtained voltage signal, the temperature and the humidity into a prediction model, and 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. The prediction model can be obtained by training through 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 by a mode based on nematic liquid crystal photonic crystal fiber penetration, the scheme obtains the electric field intensity of a target position by utilizing a voltage signal which is in a preset distance from a power transmission line in an electric field to be measured, considering factors such as the temperature and the humidity of the electric field to be measured and a prediction model, and improves the measurement precision of the electric field intensity.
Description
Technical Field
The present disclosure relates to the field of power monitoring technologies, and in particular, to a method and an apparatus for measuring electric field strength, a computer device, and a storage medium.
Background
The transmission line wire in the electric network is an important component in the electric power system, and the surface of the transmission line wire usually causes corona discharge due to abnormal field intensity, and the corona discharge can cause harm to workers and driving vehicles close to construction. Therefore, it is very important to calculate, measure and analyze the surface field intensity of the high-voltage ac transmission line. The current measurement method for the electric field intensity generally measures the electric field intensity through a mode based on nematic liquid crystal photonic crystal fiber penetration, however, the accuracy of the electric field intensity measured through the mode is not high due to the limitation of liquid crystal characteristics.
Therefore, the existing electric field strength measuring method has the defect of low measuring precision.
Disclosure of Invention
In view of the above, it is necessary to provide an electric field strength measurement method, apparatus, computer device, and storage medium capable of improving measurement accuracy.
A method of measuring electric field strength, the method comprising:
acquiring a voltage signal of a target position in an electric field to be detected; the target position is a position in the electric field to be detected, which is a preset distance away from the power transmission line;
acquiring the temperature and the humidity of the electric field to be detected;
inputting the voltage signal, the temperature and the humidity into a prediction model, and acquiring 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 measured; the prediction model is obtained by training through 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, which are sent by temperature and humidity sensing equipment; 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 acquiring the voltage signal of the target position in the electric field to be measured, the method further includes:
according to the voltage level and the wiring mode corresponding to the power transmission line, obtaining a simulation model corresponding to the power transmission line through finite element simulation calculation;
and dividing the simulation modeling into a plurality of grids, and determining a target position in the electric field to be detected, which is a preset distance away from the power transmission line, from the grids.
In one embodiment, the determining, from the plurality of grids, a target location in the electric field to be measured at a preset distance from a transmission line includes:
and establishing corresponding auxiliary lines in the target grid according to the preset step length corresponding to the target grid at the preset distance from the power transmission line to obtain the target position at 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:
and dividing the simulation modeling into a plurality of grids by an analog charge method.
In one embodiment, the method further comprises the following steps:
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 data amount 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 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 to obtain the prediction model.
In one embodiment, the method further comprises the following steps:
if the temperature is larger than a preset temperature threshold value, outputting temperature alarm information;
and/or the presence of a gas in the gas,
if the humidity is larger than a preset humidity threshold value, outputting humidity alarm information;
and/or the presence of a gas in the gas,
and if the electric field intensity is greater than a preset field intensity threshold value, outputting field intensity alarm information.
An electric field strength measuring 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; the target position is a position in the electric field to be detected, which is a preset distance away from the power transmission line;
the temperature and humidity acquisition module is used for acquiring the temperature and the humidity of the electric field to be measured;
the field intensity acquisition module is used for inputting the voltage signal, the temperature and the humidity into a prediction model, and acquiring 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 measured; the prediction model is obtained by training through 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 executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
According to the electric field intensity measuring method, the electric field intensity measuring device, the computer equipment and the storage medium, the voltage signal of the target position at the 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, then the obtained voltage signal, the temperature and the humidity are input into the prediction model, and 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 measured. The prediction model can be obtained by training through 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 by a mode based on nematic liquid crystal photonic crystal fiber penetration, the scheme obtains the electric field intensity of a target position by utilizing the voltage signal which is in the electric field to be measured and is away from the power transmission line by a preset distance, considering factors such as the temperature and the humidity of the electric field to be measured and a prediction model obtained based on the known temperature, the known humidity, the known voltage signal and a machine learning algorithm, and achieves the effect of improving the measurement precision of the electric field intensity.
Drawings
FIG. 1 is a diagram illustrating an exemplary embodiment of an electric field strength measurement method;
FIG. 2 is a schematic flow chart of an electric field strength measuring method according to an embodiment;
FIG. 3 is a schematic flow chart of an electric field strength measuring method according to another embodiment;
FIG. 4 is a block diagram showing the structure of an electric field strength measuring apparatus according to an embodiment;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The electric field intensity measuring method provided by the application can be applied to the application environment shown in fig. 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, wherein both 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 may obtain a voltage signal of a target position in the electric field to be measured, which is sent by the electric field magnetic field induction device 100, and the industrial control terminal 102 may also obtain the temperature and humidity of the electric field to be measured on the power grid site, which are sent by the temperature and humidity sensing device 104; the industrial control terminal 102 can also input the obtained voltage signal, temperature and humidity into the prediction model, and obtain the electric field intensity output by the prediction model, so that the industrial control terminal 102 can obtain the electric field intensity of the target position in the electric field to be measured. The industrial control terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, and tablet computers, and the electric field and 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, an electric field strength measuring method is provided, which is exemplified by the application of the method to the industrial control terminal in fig. 1, and includes the following steps:
step S202, acquiring a voltage signal of a target position in an electric field to be detected; the target position is a position away from the power transmission line in the electric field to be measured by a preset distance.
The electric field to be measured can be an electric field of which the electric field intensity needs 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 an electric field generated in a nearby range by a wire of the power transmission line. An electric field magnetic field induction device 100 may be disposed in the electric field to be measured, and the electric field magnetic field induction device 100 may sense and collect a voltage signal. The electric field and magnetic field sensing device 100 may be disposed at a target position in the electric field to be measured, which is a preset distance from the high voltage transmission line, the electric field and magnetic field sensing 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, the electric field and magnetic field sensing device 100 may transmit the voltage signal to the industrial control terminal 102, and the industrial control terminal 102 may obtain the voltage signal, so as to obtain a voltage signal of the target position in the electric field to be measured. The industrial control terminal 102 may include an electric field and magnetic field sensing main control computing module, and the industrial control terminal 102 may receive the voltage signal through the electric field and magnetic field sensing main control computing module; the target position may be determined from a region within a preset distance range based on the vicinity of the power transmission line, for example, the region within the preset distance range may be divided into a plurality of sub-regions, and the target position may be determined from the plurality of sub-regions.
And step S204, acquiring the temperature and the humidity of the electric field to be measured.
The electric field to be measured can be an electric field generated at the position of the power transmission line in the power grid field. Because both the temperature and the humidity can affect the field intensity generated by the power transmission line, the influence of the temperature and the humidity of the electric field to be measured on the electric field intensity needs to be considered when the electric field intensity of the electric field to be measured is measured. The industrial control terminal 102 can 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 the industrial control terminal 102 can acquire the temperature and humidity at the target position in order to correspond to the acquired target voltage of the electric field to be measured.
The temperature and the 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 configured to collect, detect, and collect the temperature and humidity in the electric field to be measured in a sensing and collecting manner. In some embodiments, the temperature and humidity sensing device 104 can transmit the collected temperature and humidity to the industrial control terminal 102. For example, after the temperature and humidity sensing device disposed at the target position collects the temperature, a first electrical signal corresponding to the temperature may be sent, after the temperature and humidity sensing device 104 collects the humidity, a second electrical signal corresponding to the humidity may be sent, 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, 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 form through a preset calculation manner and rule, and output and transmit the electrical signal form to the industrial control terminal 102, and the industrial control terminal 102 may receive the electrical signals corresponding to the temperature and the humidity respectively through the electric field and magnetic field sensing main control calculation module, and may restore the electrical signals corresponding to the temperature and the humidity into corresponding temperature data and humidity data, and may perform corresponding processing on the obtained temperature and humidity. In addition, the temperature and humidity sensing device 104 may be composed of a sensor capable of detecting temperature and humidity, or a sensor group composed of a temperature sensor and a humidity sensor.
Step S206, inputting the voltage signal, the temperature and the humidity into a prediction model, and acquiring 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 measured; the prediction model is obtained by training through 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 power transmission line to be detected by the electric field magnetic field sensing device 100, and the temperature and humidity may be a temperature and humidity corresponding to a target position detected by the temperature and humidity sensing device 104 in the electric field to be detected. The industrial control terminal 102 may input the obtained voltage signal, the temperature and the humidity into the prediction model, so as to obtain an electric field strength corresponding to a target position in the electric field to be measured output by the prediction model, and the electric field strength may be 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 strength measuring method, a voltage signal of a target position in an electric field to be measured, which is a preset distance away from a power transmission line, is obtained, the temperature and the humidity of the electric field to be measured are obtained, the obtained voltage signal, the temperature and the humidity are input into a prediction model, and the electric field strength output by the prediction model is obtained and used as the electric field strength corresponding to the target position in the electric field to be measured. The prediction model can be obtained by training through 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 by a mode based on nematic liquid crystal photonic crystal fiber penetration, the scheme obtains the electric field intensity of a target position by utilizing the voltage signal which is in the electric field to be measured and is away from the power transmission line by a preset distance, considering factors such as the temperature and the humidity of the electric field to be measured and a prediction model obtained based on the known temperature, the known humidity, the known voltage signal and a machine learning algorithm, and achieves the effect of improving the measurement precision of the electric field intensity.
In one embodiment, before acquiring the voltage signal of the target position in the electric field to be measured, the method further includes: according to the voltage grade and the wiring mode corresponding to the transmission line, carrying out finite element simulation calculation to obtain a simulation model corresponding to the transmission line; and dividing the simulation modeling into a plurality of grids, and determining a target position in the electric field to be detected, which is a preset distance away from the power transmission line, from the grids.
In this embodiment, the industrial control terminal 102 may obtain a target position at which the electric field strength needs to be measured by dividing a grid. The industrial control terminal 102 may obtain the simulation modeling corresponding to the transmission line through finite element simulation calculation according to the voltage level and the wiring manner of the transmission line in the electric field to be measured, for example, the industrial control terminal 102 may use finite element simulation software to model a simulation object, that is, the transmission line with different voltage levels and different wiring manners, to obtain a corresponding simulation model; and the industrial control terminal 102 can also divide the simulation modeling into a plurality of grids and determine a target position in the electric field to be measured away from the power transmission line by a preset distance from the plurality of grids. For example, the industrial control terminal 102 may use finite element simulation software to perform mesh division on the simulation model corresponding to the power transmission line to obtain a plurality of meshes corresponding to the power transmission line, and obtain a target position where electric field strength measurement needs to be performed from the plurality of meshes 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 to approximate the real system of infinite unknown quantity by using a limited number of unknown quantities by using simple and interactive units, and the finite element simulation calculation can adapt to various complex shapes and can effectively analyze the engineering.
Through the embodiment, the industrial control terminal 102 can obtain the simulation modeling corresponding to the power transmission line through finite element simulation calculation, and determine the target position from the multiple grids based on the simulation modeling, so that the electric field strength can be measured at the target position, and the effect of improving the measurement precision of the electric field strength is realized.
In one embodiment, determining a target location in an electric field under test at a predetermined distance from a transmission line from a plurality of grids comprises: and establishing corresponding auxiliary lines in the target grid according to the preset step length corresponding to the target grid at the preset distance from the power transmission line to obtain the target position at the preset distance from the power transmission line in the electric field to be detected.
In this embodiment, the industrial control terminal 102 can determine a target position where electric field strength measurement needs to be performed from a plurality of grids. The industrial control terminal 102 may obtain a solving step length of each grid, and according to a preset step length corresponding to a target grid at a preset distance from the power transmission line, establish a corresponding auxiliary line in the target grid, to obtain a target position at 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 accuracy requirements, so that a grid division manner needs to be set, the calculation time is shortened, and the industrial control terminal 102 can establish an auxiliary line at a specified position, for example, an auxiliary line at the target position in the simulation modeling after setting the solving step length, so that the electric field intensity distribution can be obtained in the target position, and the electric field intensity of the power grid field can be accurately detected.
In an embodiment, the meshing of the simulation modeling corresponding to the power transmission line in the electric field to be measured may be performed by using a preset algorithm, for example, the industrial control terminal 102 may perform meshing of the simulation modeling corresponding to the power transmission line by using an analog charge method, so as to divide the simulation modeling into a plurality of meshes. The analog charge method is one of the main methods for calculating the value of an electrostatic field, and is based on the uniqueness theorem of the electrostatic field, the analog charge method replaces the free charges continuously distributed on the surface of a conductor electrode with a set of discrete charges located inside the conductor, such as a set of point charges, line charges or ring charges, etc., where the discrete charges are called analog charges, and then calculates the electric field intensity of any point in a field area by using the analytic formula of the analog charges by using the superposition theorem, where the analog charges are determined according to the boundary conditions of the field area, and the analog charge method is key to find and determine the analog charges.
Through the embodiment, the industrial control terminal 102 can obtain a plurality of grids by using an analog charge method, and obtain a target position by using a solving step length and an auxiliary line, so that the effect of improving the measurement precision of the electric field intensity of the electric field to be measured can be realized.
In one embodiment, further comprising: 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 a plurality of known temperatures, a plurality of known humidities and a data amount and a data structure of a 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 a preset machine learning algorithm to obtain a prediction model.
In this embodiment, the industrial control terminal 102 may obtain the prediction model through machine learning training. The industrial control terminal 102 can obtain 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 can determine a preset machine learning algorithm which can be used for training the training data from a plurality of machine learning algorithms according to the data amount and data structure of the plurality of known temperatures, the plurality of known humidities, and the plurality of known voltage signals, for example, the plurality of machine learning algorithms can include algorithms in terms of decision trees, random forests, artificial neural networks, bayesian learning, deep learning, and the like, and the industrial control terminal 102 can determine a suitable machine learning algorithm according to the data amount and data structure of the plurality of known temperatures, the known humidities, and 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 strength corresponding to the known voltage signal can be obtained through training 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 strength through the preset machine learning algorithm to obtain a prediction model of the influence of the temperature and the humidity on the electric field strength.
By the embodiment, the industrial control terminal 102 can obtain a prediction model for predicting the electric field intensity by using a machine learning algorithm which is adaptive to the data quantity and the data structure of the temperature, the humidity and the voltage signals, so that the accuracy of measuring the electric field intensity is improved.
In one embodiment, further comprising: and if the temperature is greater than the preset temperature threshold value, outputting temperature alarm information.
In this embodiment, the industrial control terminal 102 may alarm the data that do not meet the requirements when the data of the temperature, the humidity, and the field strength exceed the corresponding threshold values. For example, the industrial control terminal 102 detects that the obtained temperature is greater than a preset temperature threshold, and the industrial control terminal 102 may output temperature alarm information. In one embodiment, if the industrial control terminal 102 detects that the obtained humidity is greater than the preset humidity threshold, the industrial control terminal 102 may output a humidity alarm message. In an 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 warning information to prompt the staff that the field intensity of the target position is abnormal, so that the staff can perform corresponding processing on the power transmission line.
In addition, the industrial control terminal 102 may also perform data presentation on the acquired data such as the temperature, the humidity, the electric field strength, and the like, for example, by using a display unit, such as a display screen, of the industrial control terminal 102.
Through the embodiment, the industrial control terminal 102 can give an alarm for illegal data, so that the safety of the power transmission line is improved, the 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 schematic flow chart of an electric field strength measuring method in another embodiment. The method comprises the following steps:
the industrial control terminal 102 may first use finite element simulation software to model the simulation object, i.e. the high-voltage wires with different voltage levels and different wiring modes, to obtain a corresponding simulation model for calculation.
Then, the industrial control terminal 102 can use simulation software to perform meshing on the simulation model by using an analog charge method, and different calculation regions have different calculation accuracy requirements, so that a meshing mode needs to be set, and the calculation time is shortened. And after the solving step length is set, establishing an auxiliary line of the designated position in the model to obtain the electric field intensity distribution at the designated position. The electric field intensity of the electric field to be detected on the power grid site is accurately detected.
The industrial control terminal 102 may be configured with an electric field magnetic field sensing device 100 and a temperature and humidity sensing device 104 in an electric field to be measured, in some embodiments, the industrial control terminal 102 may also be configured in the electric field to be measured, and 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, so as to collect corresponding data in a form of hardware or software module.
The electric field magnetic field sensing device 100 can collect electric field magnetic field information of a power grid field and output a voltage signal to the industrial control terminal 102, the electric field magnetic field sensing device 100 mainly detects a short distance between high-voltage transmission line conductors of an electric field to be detected in the power grid field, a strong electric field generated by the power grid field transmission line enables the electric field magnetic field sensing module to output a voltage signal proportional to the electric field magnetic field sensing module, and then the signal is transmitted to the electric field magnetic field sensing main control computing module in the industrial control terminal 102 to perform data training, computing, analyzing and preprocessing.
Temperature and humidity sensing equipment 104, such as a temperature and humidity sensor, can be further arranged in the electric field to be measured of the power grid site, the industrial control terminal 102 can measure the temperature and the humidity around the environment controlled and measured by the temperature and humidity sensing equipment 104 for the electric field magnetic field of the high-voltage power transmission line of the power grid, wherein the temperature and humidity sensing equipment 104 can convert the detected temperature or humidity data into an electric signal form according to the self calculation mode and rule, and send the electric signal form to an electric field magnetic field sensing main control calculation module in the industrial control terminal 102.
The industrial control terminal 102 can obtain the electric field intensity of the target position by using the prediction model by using the obtained voltage signal, temperature and humidity data. The industrial control terminal 102 may first train a prediction model, which may be used to calculate the influence of temperature and humidity on the field strength. For example, the industrial control terminal 102 can determine an appropriate machine learning algorithm based on the plurality of known temperatures, the plurality of known humidities, and the plurality of known voltage signals according to the magnitude of the data volume and the data structure of the plurality of known temperatures, the plurality of known humidities, and the plurality of known voltage signals, and the machine learning algorithm can include algorithms in terms 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 and magnetic field sensing main control calculation module, and use the above-mentioned corresponding machine learning algorithm to analyze the influence of temperature and humidity on the electric field strength, so as to obtain the corresponding relationship between the temperature and humidity and the electric field strength corresponding to the voltage signal, thereby obtaining a prediction model of the influence of temperature and humidity on the electric field strength. In actual measurement, the industrial control terminal 102 may input the acquired temperature, humidity, and voltage signals into the prediction model, and calculate the electric field strength through the prediction model by using the electric field and magnetic field sensing main control calculation module, so as to obtain a detection result of the electric field strength. The industrial control terminal 102 can also utilize the local data processing module to perform data display on 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 may also perform an alarm of corresponding information by using a local alarm unit after detecting that the temperature, humidity or field strength exceeds a set corresponding threshold.
In addition, the data communication mode between the electric field and magnetic field sensing main control computing module in the industrial control terminal 102 and the local data processing module in the industrial control terminal 102 may be a wired connection mode or a wireless communication mode. If the communication is performed in a wireless manner, the electric field and 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 receiving 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.
According to the embodiment, the industrial control terminal 102 obtains the electric field strength of the target position 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 strength.
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, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2 and 3 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 4, there is provided an electric field strength measuring apparatus including: 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 used for acquiring a voltage signal of a target position in an electric field to be detected; the target position is a position away from the power transmission line in the electric field to be measured by a preset distance.
And a temperature and humidity obtaining module 502 for obtaining the temperature and humidity of the electric field to be measured.
A field intensity obtaining module 504, configured to input the voltage signal, the temperature, and the humidity into the prediction model, and obtain an electric field intensity output by the prediction model, where the electric field intensity is used as an electric field intensity corresponding to a target position in an electric field to be measured; the prediction model is obtained by training through 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 an embodiment, the temperature and humidity obtaining module 502 is specifically configured to obtain a first electrical signal corresponding to temperature and a second electrical signal corresponding to humidity, which are sent by a temperature and humidity sensing device; the temperature and humidity sensing equipment is arranged in a target position; the temperature is obtained according to the first electrical signal, and the humidity is obtained according to the second electrical signal.
In one embodiment, the above apparatus further comprises: the dividing module is used for obtaining a simulation modeling corresponding to the transmission line through finite element simulation calculation according to the voltage grade and the wiring mode corresponding to the transmission line; and dividing the simulation modeling into a plurality of grids, and determining a target position in the electric field to be detected, which is a preset distance away from the power transmission line, from the grids.
In an embodiment, the dividing module is specifically configured to establish a corresponding auxiliary line in the 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 above apparatus further comprises: the training module is used 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 a plurality of known temperatures, a plurality of known humidities and a data amount and a data structure of a 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 a preset machine learning algorithm to obtain a prediction model.
In an embodiment, the dividing module is specifically configured to divide the simulation modeling into a plurality of grids by an analog charge method.
In one embodiment, the above apparatus further comprises: and the first warning module is used for outputting temperature warning information if the temperature is greater than a preset temperature threshold value.
In one embodiment, the above apparatus further comprises: and the second alarm module is used for outputting humidity alarm information if the humidity is greater than a preset humidity threshold value.
In one embodiment, the above apparatus further comprises: and the third alarm module is used for outputting field strength alarm information if the electric field strength is greater than a preset field strength threshold value.
For the specific definition of the electric field strength measuring device, reference may be made to the above definition of the electric field strength measuring method, which is not described herein again. The modules in the electric field strength measuring device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be an industrial control terminal, and its internal structure diagram 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 comprises a nonvolatile 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 an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication 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 measuring an electric field strength. 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, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory in which a computer program is stored and a processor which, when executing the computer program, implements the above-described electric field strength measurement method.
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.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An electric field strength measuring method, characterized in that the method comprises:
acquiring a voltage signal of a target position in an electric field to be detected; the target position is a position in the electric field to be detected, which is a preset distance away from the power transmission line;
acquiring the temperature and the humidity of the electric field to be detected;
inputting the voltage signal, the temperature and the humidity into a prediction model, and acquiring 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 measured; the prediction model is obtained by training through a preset machine learning algorithm based on a plurality of known temperatures, a plurality of known humidities and a plurality of known voltage signals.
2. The method of claim 1, wherein the obtaining 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, which are sent by temperature and humidity sensing equipment; 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 before obtaining the voltage signal of the target location in the electric field to be measured, the method further comprises:
according to the voltage level and the wiring mode corresponding to the power transmission line, obtaining a simulation model corresponding to the power transmission line through finite element simulation calculation;
and dividing the simulation modeling into a plurality of grids, and determining a target position in the electric field to be detected, which is a preset distance away from the power transmission line, from the grids.
4. The method of claim 3, wherein said determining a target location in said electric field under test a predetermined distance from a transmission line from said plurality of grids comprises:
and establishing corresponding auxiliary lines in the target grid according to the preset step length corresponding to the target grid at the preset distance from the power transmission line to obtain the target position at the preset distance from the power transmission line in the electric field to be detected.
5. The method of claim 3, wherein said partitioning the simulation modeling into a plurality of grids comprises:
and dividing the simulation modeling into a plurality of grids by an analog charge method.
6. The method of claim 1, further comprising:
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 data amount 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 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 to obtain the prediction model.
7. The method of any one of claims 1 to 6, further comprising:
if the temperature is larger than a preset temperature threshold value, outputting temperature alarm information;
and/or the presence of a gas in the gas,
if the humidity is larger than a preset humidity threshold value, outputting humidity alarm information;
and/or the presence of a gas in the gas,
and if the electric field intensity is greater than a preset field intensity threshold value, outputting field intensity alarm information.
8. An electric field strength measuring apparatus, characterized in that the apparatus comprises:
the signal acquisition module is used for acquiring a voltage signal of a target position in an electric field to be detected; the target position is a position in the electric field to be detected, which is a preset distance away from the power transmission line;
the temperature and humidity acquisition module is used for acquiring the temperature and the humidity of the electric field to be measured;
the field intensity acquisition module is used for inputting the voltage signal, the temperature and the humidity into a prediction model, and acquiring 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 measured; the prediction model is obtained by training through a preset machine learning algorithm based on a plurality of known temperatures, a plurality of known humidities and a plurality of known voltage signals.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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