Current measurement method, electronic equipment and storage medium

Abstract

The invention discloses a current measurement method, electronic equipment and a storage medium, and belongs to the technical field of current test.The current measurement method comprises the steps that a long straight wire magnetic field model under the simulated eccentricity and inclination condition is established; acquiring sample data based on the long and straight wire magnetic field model under the eccentric and inclined conditions; processing the sample data and generating a training set and a test set; constructing, optimizing and training a neural network model based on the training set; based on the trained neural network model, predicting the offset distance of the electrified long straight wire, the rotation angle of the annular array around the Z axis and the rotation angle of the annular array around the X axis; a current is calculated based on the predicted data. According to the method, the error influence caused by eccentricity and inclination of the wire can be reduced, very high measurement precision can be realized, the neural network model is optimized, and the current measurement error can be controlled below 0.5%.

Description

Technical Field

[0001] The present invention relates to the technical field of current testing, and in particular to a current measuring method, electronic equipment and storage device. Background Art

[0002] In recent years, with the rapid development of smart grid and energy Internet, applications such as load control and energy efficiency monitoring have emerged, and measurement points have become distributed and ubiquitous, which has put forward higher requirements for electrical quantity measurement technology. As the core technology of smart grid and energy Internet, current measurement technology directly affects the stable operation and future development of smart grid and energy Internet.

[0003] In traditional power systems, current measurement usually relies on current transformers. Although these transformers have the advantages of high and low voltage isolation and stable output, they also have problems such as single function, large size, slow transient response and easy saturation of the core, and are gradually unable to meet the needs of smart grids.

[0004] However, the current measurement of current sensors based on magnetic field sensing chips faces the problem of measurement errors caused by wire eccentricity and tilt. In order to reduce or even eliminate these errors, many researchers have conducted a lot of research. However, most of the research only considers the case where the wire is perpendicular to the measurement plane, that is, only the wire eccentricity factor is considered, but the case of wire tilt is not fully considered.

[0005] In view of the challenges and problems in current current measurement, the present invention provides a new current measurement method. This method significantly reduces the measurement error caused by wire eccentricity and tilt by introducing deep learning technology and combining it with the magnetic field chip sensing signal, thereby improving the accuracy of current measurement. Summary of the invention

[0006] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0007] In view of the above problems and / or the problems existing in the prior art, the present invention is proposed.

[0008] Therefore, the present invention provides a current measurement method, an electronic device and a storage device, which can reduce the problem of measurement error caused by the eccentricity and inclination of the wire.

[0009] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0010] In a first aspect, the present invention provides a current measurement method, which includes establishing a long straight conductor magnetic field model simulating eccentricity and inclination; acquiring sample data based on the long straight conductor magnetic field model under eccentricity and inclination; processing the sample data and generating a training set and a test set; constructing and optimizing a neural network model based on the training set; predicting the offset distance of the energized long straight conductor, the rotation angle of the circular array around the Z axis, and the rotation angle of the circular array around the X axis based on the trained neural network model; and calculating the current based on the predicted data.

[0011] As a preferred solution of the current measurement method of the present invention, the long straight wire magnetic field model simulating eccentricity and tilt includes a long straight wire with power and a sensor, and multiple sensors are provided. The multiple sensors form a circular array, and the long straight wire with power passes through the circular array; the circular array is defined to be offset by a distance r along the positive direction of the X-axis, the circular array is rotated by an angle θ around the Z-axis, and the circular array is rotated by an angle φ around the X-axis, wherein the magnetic induction intensity values ​​sensed by the multiple sensors are respectively expressed as:

[0012]

[0013] Among them, the magnetic induction intensity values ​​sensed by multiple sensors are B 1 , B 2 , B 3 and B 4 , μ 0 is the magnetic permeability of vacuum, R is the radius of the annular array, and I is the current in the long straight wire.

[0014] As a preferred solution of the current measurement method of the present invention, the magnetic induction intensity values ​​B sensed by multiple sensors are obtained based on the offset distance r of the energized long straight wire, the angle θ of the annular array rotating around the Z axis, and the angle φ of the annular array rotating around the X axis taking different values. 1 , B 2 , B 3 and B 4 , and output multiple sets of sample data.

[0015] As a preferred solution of the current measurement method of the present invention, multiple groups of sample data are normalized and expressed as:

[0016]

[0017] Among them, B i * For B i The normalized result, i is the number of multiple sensors; θ j * is θ j The normalized result, φ j* is φ j The normalized result, r 0j * For r 0j The normalized result, j is the number of the j-th group of sample data; where:

[0018] B max =max(B 11 ,B 21 ,B 31 ,B 41 …B 1j ,B 2j ,B 3j ,B 4j …B 1M ,B 2M ,B 3M ,B 4M );

[0019] θ max =max(θ 1 …θ j …θ M );

[0020] φ max =max(φ 1 …φ j …φ M );

[0021] r max =max(r 01 …r 0j …r 0M );

[0022] Where M is the number of groups of sample data.

[0023] As a preferred solution of the current measurement method of the present invention, after the normalization process is completed, the sample data is divided into a training set and a test set using a cross-validation method, wherein:

[0024] OUT=(r,θ,φ)=f′(w m B m-1 +b m )

[0025] As output, where w m 、b m are the weight matrix and threshold matrix between the last hidden layer and the output layer respectively; f' is the activation function of the output layer;

[0026] IN=(B1 1 * ,B2 1 *,B3 1 * ,B4 1 * …B1 j * ,B2 j * ,B3 j * ,B4 j * …B1 N * ,B2 N * ,B3 N * ,B4 N * )

[0027] As input, construct a training set and a test set, where j is the number of the jth group of sample data and N is the number of groups of sample data.

[0028] As a preferred solution of the current measurement method of the present invention, the pre-trained model is fine-tuned by using transfer learning, the Adam algorithm is selected, the magnetic induction intensity of each group of sample data in the training set is used as input, the offset distance r of the energized long straight wire, the angle θ of the annular array rotating around the Z axis and the angle φ of the annular array rotating around the X axis are used as outputs, and the constructed neural network model is trained using the Adam algorithm to obtain a trained neural network model.

[0029] As a preferred solution of the current measurement method of the present invention, the offset distance r of the energized long straight conductor, the angle θ of the circular array rotating around the Z axis, and the angle φ of the circular array rotating around the X axis are obtained by using the trained neural network model and are denormalized to obtain the corresponding predicted value.

[0030] As a preferred solution of the current measurement method of the present invention, the current I passing through the long straight wire, the radius R of the annular array, the offset distance r of the energized long straight wire, the angle θ of the annular array rotating around the Z axis, the angle φ of the annular array rotating around the X axis, and the magnetic induction intensity value B sensed by multiple sensors are established. 1 , B 2 , B 3 and B 4 and inversely deduce the magnetic induction intensity value B sensed by multiple sensors 1 , B 2 , B 3 and B 4 The corresponding current value I 1 ,I 2 ,I 3 and I 4; For four current values ​​I 1 ,I 2 ,I 3 and I 4 Find the average current I avg , average current I avg This is the measurement of the current flowing through a long straight wire.

[0031] In a second aspect, the present invention provides an electronic device comprising a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions, which, when executed by the processor, implement the steps of the above-mentioned current measurement method.

[0032] In a third aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above-mentioned current measurement method.

[0033] The beneficial effects of the present invention are as follows: when only the offset distance r of the long straight conductor carrying electricity, the angle θ of the annular array rotating around the Z axis, and the angle φ of the annular array rotating around the X axis are introduced, any situation of conductor eccentricity and inclination can be simulated, thereby reducing the unknown parameters introduced into the model, simplifying the model, and making it more convenient for researchers to obtain data when the conductor is eccentric and inclined; in addition, the error influence caused by the eccentricity and inclination of the conductor can be reduced, and a very high measurement accuracy can be achieved. The neural network model is optimized so that the current measurement error can be controlled below 0.5%. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0035] Figure 1 Schematic diagram of the magnetic field model for the current measurement method when the current-carrying conductor is eccentric and tilted.

[0036] Figure 2 Schematic diagram of magnetic field test error caused by tilt in a circular array of a current measurement method.

[0037] Figure 3 Schematic diagram of error analysis of each current prediction value of the current measurement method. DETAILED DESCRIPTION

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0039] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0040] Secondly, the present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention in detail, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0041] Furthermore, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0042] Example 1

[0043] Reference Figure 1 to Figure 3 , which is the first embodiment of the present invention, provides a current measurement method, comprising:

[0044] S100: Establish a magnetic field model of a long straight wire under eccentricity and tilt conditions;

[0045] S200: acquiring sample data based on a long straight wire magnetic field model under eccentricity and tilt conditions;

[0046] S300: Process the sample data and generate a training set and a test set;

[0047] S400: Build and optimize the training neural network model based on the training set;

[0048] S500: Based on the trained neural network model, predict the offset distance of the energized long straight wire, the rotation angle of the circular array around the Z axis, and the rotation angle of the circular array around the X axis;

[0049] S600: Calculate current based on the predicted data.

[0050] Example 2

[0051] This embodiment is the second embodiment of the present invention.

[0052] We adopted the same basic steps of the current measurement method as in Example 1, but in acquiring the sample data, we adjusted the value range and step size of the offset distance r, the angle θ of the annular array rotating around the Z axis, and the angle φ of the annular array rotating around the X axis to adapt to the characteristics of the TMR sensor. For example, we adjusted the value range of r to 0-0.003m, with a step size of 0.0003m; the value range of θ to 0°-18°, with a step size of 0.5°; the value range of φ to 0°-30°, with a step size of 0.5°. Such adjustments make the sample data precise and can well capture subtle changes in the magnetic field.

[0053] Example 3

[0054] See also Figure 1 to Figure 3 , which is the third embodiment of the present invention, and this embodiment is based on embodiment 1.

[0055] Specifically, the long straight wire magnetic field model simulating eccentricity and tilt includes a long straight wire with power and a sensor. There are multiple sensors. In this embodiment, there are four sensors and single-axis Hall is used. The four sensors form a circular array. The four sensors are respectively located at the four points of the circular array. The direction of each single-axis Hall magnetic sensitive axis is tangent to the circular array in the same clockwise direction, and the long straight wire with power passes through the circular array.

[0056] Preferably, the annular array is defined as offset distance r along the positive direction of the X-axis, the annular array is rotated around the Z-axis by an angle θ, and the annular array is rotated around the X-axis by an angle φ, wherein the magnetic induction intensity values ​​sensed by the multiple sensors are respectively expressed as:

[0057]

[0058] Among them, the magnetic induction intensity values ​​sensed by multiple sensors are B 1 , B 2 , B 3 and B 4 , μ 0 is the vacuum magnetic permeability, R is the radius of the annular array, and I is the current in the long straight wire.

[0059] Preferably, based on the offset distance r of the energized long straight wire, the angle θ of the annular array rotating around the Z axis, and the angle φ of the annular array rotating around the X axis taking different values, the magnetic induction intensity values ​​B sensed by the multiple sensors are obtained. 1 , B 2 , B 3 and B 4 , and output multiple sets of sample data.

[0060] The specific method for obtaining sample data is as follows: the current I flowing through the long straight wire is preset to 500A; the radius of the annular array is preset to 0.05m; the value range of r is set to 0-0.0045m, with a step length of 0.0005m; the value range of θ is set to 0°-23°, with a step length of 1°; the value range of φ is set to 0°-45°, with a step length of 1°; using the established magnetic field model, the magnetic induction intensity B sensed by the four sensors under different values ​​of r, θ and φ is obtained. 1 , B 2 , B 3 and B 4 ; Take each set of r, θ and φ and its corresponding magnetic induction intensity B 1 , B 2 , B 3 and B 4 is a set of sample data, and a total of 9315 sets of sample data were obtained.

[0061] Preferably, multiple groups of sample data are normalized, which is expressed as:

[0062]

[0063] Among them, B i * For B i The normalized result, i is the number of multiple sensors; θ j * is θ j The normalized result, φ j * is φ j The normalized result, r 0j * For r 0j The normalized result, j is the number of the j-th group of sample data; where:

[0064] B max =max(B 11 ,B 21 ,B 31 ,B 41 …B 1j ,B 2j ,B 3j ,B 4j …B 1M ,B 2M ,B 3M ,B 4M );

[0065] θ max =max(θ 1 …θ j …θ M );

[0066] φ max =max(φ 1 …φ j …φ M );

[0067] r max =max(r 01 …r 0j …r 0M );

[0068] Where M is the number of groups of sample data.

[0069] Preferably, after normalization, the sample data is divided into a training set and a test set using a cross-validation method, wherein:

[0070] OUT=(r,θ,φ)=f′(w m B m-1 +b m )

[0071] As output, where w m 、b m are the weight matrix and threshold matrix between the last hidden layer and the output layer respectively; f' is the activation function of the output layer;

[0072] IB=(B1 1 * ,B2 1 * ,B3 1 * ,B4 1 * …B1 j * ,B2 j * ,B3 j * ,B4 j * …B1 N * ,B2 N * ,B3 N * ,B4 N * )

[0073] As input, construct a training set and a test set, where j is the number of the jth group of sample data and N is the number of groups of sample data.

[0074] Furthermore, transfer learning is used to fine-tune the pre-trained model. The Adam algorithm is selected to use the magnetic induction intensity of each group of sample data in the training set as input, and the offset distance r of the energized long straight wire, the angle θ of the circular array around the Z axis, and the angle φ of the circular array around the X axis are used as outputs. The constructed neural network model is trained using the Adam algorithm to obtain a trained neural network model.

[0075] Specifically, the Adam algorithm is an optimization algorithm used to replace random gradient descent in deep learning models. It combines the advantages of the AdaGrad algorithm and the RMSProp algorithm, introduces quadratic gradient correction, and can achieve efficient calculation. The prediction process of optimizing the neural network model based on the Adam algorithm uses a divided training set containing 7452 groups, takes the magnetic induction intensity B of each group of sample data in the training set as input, and r, θ and φ as output. The constructed neural network model is trained using the Adam algorithm to obtain a trained neural network model.

[0076] Furthermore, the trained neural network model is used to obtain the offset distance r of the energized long straight wire, the angle θ of the annular array rotating around the Z axis, and the angle φ of the annular array rotating around the X axis parameters and perform an inverse normalization transformation to obtain the corresponding prediction value.

[0077] Furthermore, the current I passing through the long straight wire, the radius R of the circular array, the offset distance r of the energized long straight wire, the angle θ of the circular array rotating around the Z axis, the angle φ of the circular array rotating around the X axis, and the magnetic induction intensity value B sensed by multiple sensors are established. 1 , B 2 , B 3 and B 4 and inversely deduce the magnetic induction intensity value B sensed by multiple sensors 1 , B 2 , B 3 and B 4 The corresponding current value I 1 ,I 2 ,I 3 and I 4 ; For four current values ​​I 1 ,I 2 ,I 3 and I 4 Find the average current I avg , average current I avg This is the measurement of the current flowing through a long straight wire.

[0078] Using the trained neural network model, 9315 sets of sample data are predicted and calculated to obtain the corresponding current prediction values ​​I 1 ,I 2,I 3 and I 4 And calculate its influence on the error. The current measurement error is expressed as a relative error, and the expression is as follows:

[0079]

[0080] Where i is the current number and is the actual current value applied.

[0081] Figure 3 Display the predicted values ​​of r, θ and φ for each current I 1 ,I 2 ,I 3 and I 4 It can be seen from the figure that in the case of eccentricity or tilt, the overall error of the first sensor and the fourth sensor is larger, and the error of the second sensor and the third sensor is relatively small. However, within the fluctuation range of r, θ and φ, the predicted current values ​​I 1 ,I 2 ,I 3 and I 4 The error is basically below 1%.

[0082] Furthermore, in the embodiment, when the eccentric distance r and the inclination angle θ fluctuate within the sample data range, the measurement error has been stable within 0.3%, but the change in the value of the inclination angle φ has a greater influence on the measurement error. The measurement error increases with the increase of the φ value. However, within the fluctuation range of the eccentric distance r, the inclination angle θ and φ, the error of the current prediction value is below 0.5%. This result shows the reliability and high precision of the current calculation method provided in this article.

[0083] This embodiment further provides a computing device suitable for data aggregation, integration and optimization, including:

[0084] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the data aggregation and integration optimization method proposed in the above embodiment.

[0085] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the data aggregation and integration optimization method proposed in the above embodiment is implemented.

[0086] The storage medium proposed in this embodiment and the method for implementing data aggregation, integration and optimization proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0087] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (Random Access Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0088] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A current measurement method, characterized in that: include: Establish a magnetic field model for a long straight wire simulating eccentricity and tilt; Acquire sample data based on the magnetic field model of a long straight wire under eccentricity and tilt; Process the sample data and generate training sets and test sets; Build and optimize the training neural network model based on the training set; Based on the trained neural network model, the offset distance of the energized long straight wire, the rotation angle of the circular array around the Z axis, and the rotation angle of the circular array around the X axis are predicted; The current is calculated based on the predicted data.

2. The current measurement method according to claim 1, characterized in that: The long straight wire magnetic field model simulating eccentricity and tilt includes a powered long straight wire and a sensor, wherein a plurality of sensors are provided, and the plurality of sensors form a circular array, and the powered long straight wire runs through the circular array; Define the annular array offset distance along the positive direction of the X axis as r, the annular array rotates around the Z axis by an angle θ, and the annular array rotates around the X axis by an angle φ, wherein the magnetic induction intensity values ​​sensed by the multiple sensors are expressed as: Among them, the magnetic induction intensity values ​​sensed by multiple sensors are B1, N2, B3 and B4 respectively, μ0 is the magnetic permeability of vacuum, R is the radius of the circular array, and I is the current of the long straight wire.

3. The current measurement method according to claim 2, characterized in that: Based on the different values ​​of the offset distance r of the energized long straight wire, the angle θ of the circular array rotating around the Z axis, and the angle φ of the circular array rotating around the X axis, the magnetic induction intensity values ​​B1, B2, B3 and N4 sensed by multiple sensors are obtained, and multiple groups of sample data are output.

4. The current measurement method according to claim 3, characterized in that: Normalize multiple groups of sample data, expressed as: Among them, B i * For B i The normalized result, i is the number of multiple sensors; θ j * is θ j The normalized result, φ j * is φ j The normalized result, r 0j * For r 0j The normalized result, j is the number of the j-th group of sample data; where: B max =max(B 11 ,B 21 ,B 31 ,B 41 …B 1j ,B 2j ,B 3j ,B 4j …B 1M ,B 2M ,B 3M ,B 4M ); i max =max(θ1…θ j …i M ); f max =max(φ1…φ j …f M ); r max =max(r 01 …r 0j …r 0M ); Where M is the number of groups of sample data.

5. The current measurement method according to claim 4, characterized in that: After completing the normalization process, the cross-validation method is used to divide the sample data into a training set and a test set, where: OUT=(r,θ,φ)=f′(w m B m-1 +b m ) As output, where w m , b m are the weight matrix and threshold matrix between the last hidden layer and the output layer respectively; f′ is the activation function of the output layer; <h2 style=";text-align:left;direction:ltr">IN=(B11<h2 style=";text-align:left;direction:ltr"> * <h2 style=";text-align:left;direction:ltr"> B21<h2 style=";text-align:left;direction:ltr"> * <h2 style=";text-align:left;direction:ltr"> B31<h2 style=";text-align:left;direction:ltr"> * <h2 style=";text-align:left;direction:ltr"> B41<h2 style=";text-align:left;direction:ltr"> * <h2 style=";text-align:left;direction:ltr"> …B1<h2 style=";text-align:left;direction:ltr"> j <h2 style=";text-align:left;direction:ltr"> * <h2 style=";text-align:left;direction:ltr"> B2<h2 style=";text-align:left;direction:ltr"> j <h2 style=";text-align:left;direction:ltr"> * <h2 style=";text-align:left;direction:ltr"> B3<h2 style=";text-align:left;direction:ltr"> j <h2 style=";text-align:left;direction:ltr"> * <h2 style=";text-align:left;direction:ltr"> B4<h2 style=";text-align:left;direction:ltr"> j <h2 style=";text-align:left;direction:ltr"> * <h2 style=";text-align:left;direction:ltr"> …B1<h2 style=";text-align:left;direction:ltr"> N <h2 style=";text-align:left;direction:ltr"> * <h2 style=";text-align:left;direction:ltr"> B2<h2 style=";text-align:left;direction:ltr"> N <h2 style=";text-align:left;direction:ltr"> * <h2 style=";text-align:left;direction:ltr"> B3<h2 style=";text-align:left;direction:ltr"> N <h2 style=";text-align:left;direction:ltr"> * <h2 style=";text-align:left;direction:ltr"> B4<h2 style=";text-align:left;direction:ltr"> N <h2 style=";text-align:left;direction:ltr"> * <h2 style=";text-align:left;direction:ltr"> ) As input, construct a training set and a test set, where j is the number of the jth group of sample data and N is the number of groups of sample data.

6. The current measurement method according to claim 5, characterized in that: Transfer learning is used to fine-tune the pre-trained model. The Adam algorithm is selected to use the magnetic induction intensity of each group of sample data in the training set as input, the offset distance r of the energized long straight wire, the angle θ of the circular array around the Z axis, and the angle φ of the circular array around the X axis as output. The constructed neural network model is trained using the Adam algorithm to obtain a trained neural network model.

7. The current measurement method according to claim 6, characterized in that: The offset distance r of the energized long straight wire, the angle θ of the annular array rotating around the Z axis, and the angle φ of the annular array rotating around the X axis are obtained by using the trained neural network model and are denormalized to obtain the corresponding prediction values.

8. The current measurement method according to claim 7, characterized in that: Establish a relationship between the current I passing through the long straight wire, the radius R of the circular array, the offset distance r of the energized long straight wire, the angle θ of the circular array rotating around the Z axis, the angle φ of the circular array rotating around the X axis, and the magnetic induction intensity values ​​B1, B2, B3 and B4 sensed by multiple sensors; And inversely deduce the current values ​​I1, I2, I3 and I4 corresponding to the magnetic induction intensity values ​​B1, B2, B3 and B4 sensed by multiple sensors; Calculate the average current I for the four current values ​​I1, I2, I3 and I4 avg , average current I avg This is the measurement of the current flowing through a long straight wire.

9. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the current measurement method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium, characterized in that: It stores computer executable instructions, which, when executed by a processor, implement the steps of the current measurement method described in any one of claims 1 to 8.

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