A non-contact residual current detection method and related equipment

Abstract

This invention provides a non-contact residual current detection method and related equipment, comprising: deploying a magnetic sensor array on a target wall to acquire measured values ​​of magnetic induction intensity components and inputting them into a particle swarm optimization algorithm; establishing a mathematical model between the magnetic induction intensity components and live wire current, neutral wire current, concealed installation depth, and horizontal offset, and constructing a dataset based on the upper and lower limits of conductor positions and currents in actual working conditions, all of which are input into the particle swarm optimization algorithm; calculating the calculated value of the magnetic induction intensity component at each particle position based on the mathematical model, and the sum of squares of the differences between each calculated value of the magnetic induction intensity component and the measured value of the magnetic induction intensity component; using a constructed penalty function to superimpose the out-of-bounds amount as a penalty term onto the sum of squares of the differences, obtaining the optimal position of the particle swarm after iteration termination as the optimal solution; obtaining the residual current detection result based on the optimal solution; this invention achieves non-contact detection of residual current in concealed wiring without damaging the wall or inserting conductors.

Description

Technical Field

[0001] This invention relates to the field of power testing technology, and in particular to a non-contact residual current detection method and related equipment. Background Technology

[0002] With the rapid advancement of a new round of electrification in my country, the proportion of electricity in final energy demand continues to increase, and the total demand for electricity continues to grow. As an indispensable system in the power distribution process, problems with the low-voltage power distribution system directly affect the reliability and safety of people's electricity use.

[0003] Households are the end users of low-voltage power distribution systems and the basic units that make up these systems. In households, leakage current is the most common and frequent problem, easily disrupting people's normal lives and work. Leakage current, also known as residual current, refers to the current in the live and neutral wires where the vector sum is not zero. Aging wiring, mechanical damage, and deterioration of insulation can all cause leakage current in power lines and electrical equipment, which is a major factor leading to electrical fires and electric shocks.

[0004] Household wiring is mostly concealed within walls, making the exact location of the lines unknown. Leakage points are highly hidden, and residual current values ​​are relatively small, making it difficult for users to locate and eliminate leakage faults in a timely manner. If traditional access-type or through-type current measurement methods are used, the wall structure must be damaged, which greatly increases the cost and technical difficulty of residual current detection. Therefore, there is an urgent need for a detection method that can measure and locate residual current without contacting the conductor itself. Summary of the Invention

[0005] This invention provides a non-contact residual current detection method and related equipment, the purpose of which is to achieve non-contact detection of residual current in concealed wiring without damaging the wall.

[0006] To achieve the above objectives, the present invention provides a non-contact residual current detection method, comprising:

[0007] Step 1: Construct a rectangular coordinate system on the target wall, take the horizontal axis of the rectangular coordinate system as the main sensing axis, take the vertical axis of the rectangular coordinate system as the secondary sensing axis, deploy multiple sensors on the main sensing axis, and deploy one sensor on the secondary sensing axis to obtain a magnetic sensor array.

[0008] Step 2: Based on the magnetic sensor array, obtain the measured values ​​of the magnetic induction intensity components at all sensors. The measured values ​​of the magnetic induction intensity components include the measured values ​​of the magnetic induction intensity x component and the magnetic induction intensity y component. Establish a mathematical model between the magnetic induction intensity components and the live wire current, neutral wire current, dark caulking depth, and horizontal offset. Input all the measured values ​​of the magnetic induction intensity x component and the mathematical model into the particle swarm optimization algorithm.

[0009] Step 3: Based on the range of the conductor position and the upper and lower limits of the current in the actual working conditions, set the particle flight speed range and particle position range. Construct a dataset containing live wire current, neutral wire current, concealed installation depth and horizontal offset within the particle flight speed range and particle position range. Input the data into the particle swarm optimization algorithm. In the particle swarm optimization algorithm, the dataset is used as the particle swarm, and the live wire current, neutral wire current, concealed installation depth and horizontal offset in the dataset are used as the dimension combination of the position of a single particle in the particle swarm.

[0010] Step 4: In the particle swarm optimization algorithm, based on the dimensional combination of each particle position in the particle swarm, the magnetic induction intensity component of each particle position is calculated according to the mathematical model to obtain multiple calculated values ​​of magnetic induction intensity components, and the sum of squares of the differences between each calculated value of magnetic induction intensity component and the test value of magnetic induction intensity component is calculated.

[0011] Step 5: Construct a penalty function in the particle swarm optimization algorithm. The penalty function uses inequality constraints to superimpose the out-of-bounds quantities in the calculation process as penalty terms onto the sum of squared differences to obtain the fitness value of each particle in the particle swarm. Then, the current optimal position of the individual particle and the current optimal position of the particle swarm are determined by the fitness value of each particle.

[0012] Step 6: In the particle swarm optimization algorithm, the velocity and position of each particle are iterated based on the current individual optimal position of the particle and the current optimal position of the particle swarm. The optimal position of the particle swarm after the iteration is terminated is obtained and the optimal position of the particle swarm after the iteration is terminated is taken as the optimal solution.

[0013] Step 7: Calculate the residual current of the conductor under test based on the dimensional combination of the individual particle positions corresponding to the optimal solution, and obtain the residual current detection result.

[0014] Furthermore, step 2 includes:

[0015] Based on the magnetic sensor array, the magnetic induction intensity component measurement values ​​at all sensors are obtained. The magnetic induction intensity component test values ​​include the magnetic induction intensity x component test value and the magnetic induction intensity y component test value.

[0016] The value of B is measured by the y-component of magnetic induction. y To adjust the parameters, the magnetic sensor array is rotated clockwise or counterclockwise with the origin of the rectangular coordinate system as the center.

[0017] When B y When β = 0, sinβ = 0, β = 0, and the magnetic sensor array is located at the optimal measurement angle;

[0018] Establish a mathematical model relating magnetic induction intensity components to live wire current, neutral wire current, concealed depth, and horizontal offset;

[0019] All the measured values ​​of the magnetic flux density x-components and the mathematical model are input into the particle swarm optimization algorithm.

[0020] Furthermore, the measured value of the y-component of magnetic induction intensity, B y for:

[0021]

[0022] Where μ0 represents the permeability in vacuum, μ0 = 4π × 10⁻⁶ -7 T·m / A, I1 represents the live wire current, D represents the concealed installation depth, I2 represents the neutral wire current, X represents the horizontal offset, β represents the angle between the y-axis of the magnetic sensor array and the conductor under test, and v represents the spacing between the conductors.

[0023] Furthermore, the mathematical model relating the magnetic flux density component to the live wire current, neutral wire current, caulking depth, and horizontal offset is as follows:

[0024]

[0025]

[0026]

[0027]

[0028] Where B1, B2, B3, and B4 represent the x-axis components of magnetic flux density, and μ0 represents the permeability in vacuum, μ0 = 4π × 10⁻⁶. - 7 T·m / A, I1 represents the live wire current, D represents the concealed depth, I2 represents the neutral wire current, d represents the distance between the two sensors on the main sensing axis, X represents the horizontal offset, β represents the angle between the y-axis of the magnetic sensor array and the conductor under test, and V represents the spacing between the conductors.

[0029] Furthermore, the range of particle flight speeds is:

[0030] v Imin <v I1 v I2 <v Imax

[0031] v Dmin <v D <v Dmax

[0032] v xmin <v x <v xmax

[0033] The particle position range is:

[0034] I min <I1, I2 <I max

[0035] D min <D<D max

[0036] X min <X<X max

[0037] Among them, I min I represents the minimum current defined according to actual operating conditions. max D represents the maximum current defined according to actual operating conditions. min D represents the minimum concealed installation depth of the conductor within the target wall. max X represents the maximum concealed depth of the conductor within the target wall. min X represents the minimum horizontal offset that the magnetic sensor array may experience during the measurement process. max This indicates the maximum horizontal offset that the magnetic sensor array may experience during the measurement process.

[0038] Furthermore, the penalty function F is:

[0039] F = f + k * (q1 + q2 + q3 + q4)

[0040] q1 = max(max(I1, I max )-I max I min -min(I1,I min ))

[0041] q2 = max(max(I2, I) max )-I max I min -min(I2,I min ))

[0042] q3 = max(max(D, D) max )-D max D min -min(D,D min ))

[0043] q4 = max(max(X, X) max )-X max X min -min(X, X min ))

[0044] Where F represents the fitness value in the particle swarm optimization algorithm, f represents the original objective function, and f = (Bx -B c ) 2 k represents the penalty factor, q1 represents the penalty term for live wire current, q2 represents the penalty term for neutral wire current, q3 represents the penalty term for concealed installation depth, and q4 represents the penalty term for horizontal offset.

[0045] Furthermore, the residual current ΔI of the conductor under test is:

[0046] ΔI=I2-I1

[0047] Where I2 represents the neutral current in the dimension combination corresponding to the optimal solution, and I1 represents the live current in the dimension combination corresponding to the optimal solution.

[0048] The present invention also provides a non-contact residual current detection device, comprising:

[0049] The module is used to construct a rectangular coordinate system on the target wall. The horizontal axis of the rectangular coordinate system is used as the main sensing axis, and the vertical axis of the rectangular coordinate system is used as the secondary sensing axis. Multiple sensors are deployed on the main sensing axis and one sensor is deployed on the secondary sensing axis to obtain a magnetic sensor array.

[0050] The acquisition module is used to acquire the measured values ​​of magnetic induction intensity components at all sensors based on the magnetic sensor array. The measured values ​​of magnetic induction intensity components include the measured values ​​of magnetic induction intensity x component and magnetic induction intensity y component. A mathematical model is established between the magnetic induction intensity components and the live wire current, neutral wire current, dark caulking depth, and horizontal offset. All the measured values ​​of magnetic induction intensity x component and the mathematical model are input into the particle swarm optimization algorithm.

[0051] The construction module is used to set the particle flight speed range and particle position range according to the range of the conductor position and the upper and lower limits of the current in the actual working conditions. Within the particle flight speed range and particle position range, a dataset containing live wire current, neutral wire current, concealed laying depth and horizontal offset is constructed. The data is input into the particle swarm optimization algorithm. In the particle swarm optimization algorithm, the dataset is used as a particle swarm, and the live wire current, neutral wire current, concealed laying depth and horizontal offset in the dataset are used as the dimension combination of the position of individual particles in the particle swarm.

[0052] The first calculation module is used in the particle swarm optimization algorithm to calculate the magnetic induction intensity component of each particle position based on the dimension combination of each particle position in the particle swarm and according to the mathematical model, to obtain multiple calculated values ​​of magnetic induction intensity components, and to calculate the sum of squares of the differences between each calculated value of magnetic induction intensity component and the test value of magnetic induction intensity component.

[0053] The second calculation module is used to construct a penalty function in the particle swarm optimization algorithm. The penalty function uses inequality constraints to superimpose the out-of-bounds quantities in the calculation process as penalty terms onto the sum of squares of the differences, thereby obtaining the fitness value of each particle in the particle swarm. The fitness value of each particle is used to determine the current optimal position of the individual particle and the current optimal position of the particle swarm.

[0054] The iteration module is used in the particle swarm optimization algorithm to iterate the velocity and position of each particle based on the current individual optimal position and the current optimal position of the particle swarm, to obtain the optimal position of the particle swarm after the iteration terminates, and to take the optimal position of the particle swarm after the iteration terminates as the optimal solution.

[0055] The third calculation module is used to calculate the residual current of the conductor under test based on the dimensional combination of the individual particle positions corresponding to the optimal solution, and obtain the residual current detection result.

[0056] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a non-contact residual current detection method.

[0057] The present invention also provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a non-contact residual current detection method.

[0058] The above-described solution of the present invention has the following beneficial effects:

[0059] This invention obtains a magnetic sensor array by deploying multiple sensors on the target wall. Deploying multiple sensors on the wall is low-cost and easy to install. Based on the magnetic sensor array, the measured values ​​of the magnetic induction intensity components at all sensor locations are obtained. The magnetic sensor array can collect magnetic induction intensity components without damaging the wall. A mathematical model is established between the magnetic induction intensity components and the live wire current, neutral wire current, concealed installation depth, and horizontal offset. All the measured values ​​of the magnetic induction intensity x-components and the mathematical model are input into a particle swarm optimization algorithm. Based on the range of the conductor position and the upper and lower limits of the current under actual working conditions, a packet is constructed within the range of particle flight velocity and particle position. A dataset containing live wire current, neutral wire current, concealed installation depth, and horizontal offset is used as a particle swarm optimization (PSO) algorithm. The live wire current, neutral wire current, concealed installation depth, and horizontal offset are considered as the dimension combinations of individual particle positions within the PSO. In the PSO optimization algorithm, based on the dimension combination of each particle position, the magnetic flux density component at each particle position is calculated according to a mathematical model, resulting in multiple calculated values ​​for the magnetic flux density components. The sum of squares of the differences between each calculated value and the measured value of the magnetic flux density component is then calculated. A penalty function is constructed within the PSO algorithm, and this penalty function uses inequality constraints to mitigate the impact of calculations. The out-of-bounds values ​​are added as a penalty term to the sum of squared differences to obtain the fitness value of each particle in the particle swarm. The fitness value of each particle is used to determine the current optimal position of the individual particle and the current optimal position of the particle swarm. In the particle swarm optimization algorithm, the velocity and position of each particle are iterated based on the current optimal position of the individual particle and the current optimal position of the particle swarm to obtain the optimal position of the particle swarm after the iteration terminates. This optimal position is then taken as the optimal solution. Based on the dimension combination of the individual particle positions corresponding to the optimal solution, the residual current of the conductor under test is calculated to obtain the residual current detection result. Compared with existing technologies, this… The invention collects the magnetic induction intensity test values ​​of wires inside a wall using a magnetic sensor array, and establishes a mathematical model based on the relationship between magnetic induction intensity and concealed wires. The mathematical model is used to calculate the calculated value of magnetic induction intensity, and the sum of squares of the differences between the calculated value and the measured value of magnetic induction intensity is calculated. A constructed penalty function is used to add the out-of-bounds amount as a penalty term to the sum of squares of the differences. The optimal position of the particle swarm after the iteration terminates is obtained as the optimal solution. The residual current is calculated through the optimal solution. This invention overcomes the problem of measuring the residual current of concealed wiring in enclosed walls and achieves non-contact detection of the residual current of concealed wiring without damaging the wall or inserting wires.

[0060] Other beneficial effects of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0061] Figure 1 This is a flowchart illustrating an embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram showing the placement of the magnetic sensor array in an embodiment of the present invention. Detailed Implementation

[0063] To make the technical problems, solutions, and advantages of this invention clearer, a detailed description will be provided below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0064] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0065] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a locking connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0066] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0067] This invention addresses existing problems by providing a non-contact residual current detection method and related equipment.

[0068] like Figure 1 As shown, an embodiment of the present invention provides a non-contact residual current detection method, comprising:

[0069] Step 1: Construct a rectangular coordinate system on the target wall, take the horizontal axis of the rectangular coordinate system as the main sensing axis, take the vertical axis of the rectangular coordinate system as the secondary sensing axis, deploy multiple sensors on the main sensing axis, and deploy one sensor on the secondary sensing axis to obtain a magnetic sensor array.

[0070] Step 2: Based on the magnetic sensor array, obtain the measured values ​​of the magnetic induction intensity components at all sensors. The measured values ​​of the magnetic induction intensity components include the measured values ​​of the magnetic induction intensity x component and the magnetic induction intensity y component. Establish a mathematical model between the magnetic induction intensity components and the live wire current, neutral wire current, dark caulking depth, and horizontal offset. Input all the measured values ​​of the magnetic induction intensity x component and the mathematical model into the particle swarm optimization algorithm.

[0071] Based on the range of conductor positions and the upper and lower limits of current in actual working conditions, the particle flight speed range and particle position range are set. Within the particle flight speed range and particle position range, a dataset containing live wire current, neutral wire current, concealed installation depth, and horizontal offset is constructed. The data is input into the particle swarm optimization algorithm. In the particle swarm optimization algorithm, the dataset is used as a particle swarm, and the live wire current, neutral wire current, concealed installation depth, and horizontal offset in the dataset are used as the dimension combination of the position of a single particle in the particle swarm.

[0072] Step 4: In the particle swarm optimization algorithm, based on the dimensional combination of each particle position in the particle swarm, the magnetic induction intensity component of each particle position is calculated according to the mathematical model to obtain multiple calculated values ​​of magnetic induction intensity components, and the sum of squares of the differences between each calculated value of magnetic induction intensity component and the test value of magnetic induction intensity component is calculated.

[0073] Step 5: Construct a penalty function in the particle swarm optimization algorithm. The penalty function uses inequality constraints to superimpose the out-of-bounds quantities in the calculation process as penalty terms onto the sum of squared differences to obtain the fitness value of each particle in the particle swarm. Then, the current optimal position of the individual particle and the current optimal position of the particle swarm are determined by the fitness value of each particle.

[0074] Step 6: In the particle swarm optimization algorithm, the velocity and position of each particle are iterated based on the current individual optimal position of the particle and the current optimal position of the particle swarm. The optimal position of the particle swarm after the iteration is terminated is obtained and the optimal position of the particle swarm after the iteration is terminated is taken as the optimal solution.

[0075] Step 7: Calculate the residual current of the conductor under test based on the dimensional combination of the individual particle positions corresponding to the optimal solution, and obtain the residual current detection result.

[0076] Specifically, in step 1, a rectangular coordinate system is constructed on the target wall. The X-axis of the rectangular coordinate system is parallel to the ground, and the Y-axis is perpendicular to the ground. The X-axis is the primary sensing axis, and the Y-axis is the secondary sensing axis. The deployed sensors are single-axis (Tunnel Magnetoresistance, TMR) tunnel magnetoresistive sensors, such as... Figure 2As shown, four single-axis TMR sensors, labeled TMR1, TMR2, TMR3, and TMR4, are arranged on the main sensing axis to measure the residual current and determine the relative position of the magnetic sensor array and the conductor under test. Using the midpoint of the line connecting TMR2 and TMR3 as the origin, a single-axis TMR sensor, labeled TMR5, is arranged on the secondary sensing axis to adjust the angle between the y-axis of the magnetic sensor array and the conductor under test. TMR sensors represent the fourth generation of magnetic sensor technology. TMR sensors offer advantages such as high accuracy, high sensitivity, low power consumption, small size, good temperature stability, and a wide operating temperature range. The TMR sensor integrates two orthogonally symmetrical push-pull Wheatstone bridges, primarily used to sense the magnetic field components along the X and Y axes of the applied magnetic field.

[0077] Specifically, step 2 includes:

[0078] Based on the magnetic sensor array, the magnetic induction intensity component measurement values ​​at all sensors are obtained. The magnetic induction intensity component test values ​​include the magnetic induction intensity x component test value and the magnetic induction intensity y component test value.

[0079] The value of B is measured by the y-component of magnetic induction. y To adjust the parameters, the magnetic sensor array is rotated clockwise or counterclockwise with the origin of the rectangular coordinate system as the center.

[0080] When B y When β = 0, sinβ = 0, β = 0, and the magnetic sensor array is located at the optimal measurement angle;

[0081] A mathematical model is established between the magnetic induction intensity components and the live wire current, neutral wire current, caulking depth, and horizontal offset. All the measured values ​​of the magnetic induction intensity components and the mathematical model are then input into the particle swarm optimization algorithm.

[0082] Specifically, the measured value of the y-component of magnetic induction intensity B v for:

[0083]

[0084] Where μ0 represents the permeability in vacuum, μ0 = 4π × 10⁻⁶ -7 T·m / A, I1 represents the live wire current, D represents the concealed installation depth, I2 represents the neutral wire current, X represents the horizontal offset, β represents the angle between the y-axis of the magnetic sensor array and the conductor under test, and v represents the spacing between the conductors.

[0085] Measuring residual current in a two-core conductor requires four unknowns: live wire current, neutral wire current, concealed installation depth, and horizontal offset. The difference between the live wire current and the neutral wire current is the residual current. According to the Biot-Savart law: the current element I in a current-carrying conductor... dl The magnetic induction d at a point P in a vacuum B The magnitude and current element I dl The magnitude is directly proportional to the current element I. dl The magnetic field strength B is proportional to the sine of the angle θ between the current element and the position vector r at point P, and inversely proportional to the square of the magnitude of the position vector r. Considering that the length of the conductor is much greater than the burial depth, the magnetic induction intensity B generated at any point in free space is approximately given by the following formula:

[0086]

[0087] Where I is the current flowing through the conductor, μ0 is the permeability of vacuum, and μ0 = 4π × 10⁻⁶. -7 T·m / A, where D is the depth of the camouflage layer.

[0088] According to the Biot-Savart law, the superposition property and Figure 1 The geometric relationship between the conductor and the magnetic sensor array is determined, and the x-axis component of the magnetic flux density at each single-axis TMR sensor is calculated. Taking the third single-axis TMR sensor, TMR3, as an example, the x-axis component of the magnetic flux density generated by the dual-core conductor at TMR3 is equal to the vector sum of the x-axis component of the magnetic flux density generated by the live wire current at TMR3 and the x-axis component of the magnetic flux density generated by the neutral wire current at TMR3. The formula is as follows:

[0089] B 3x =B′ 3x +B″ 3x

[0090] Among them, B′ 3x B″ represents the x-axis component of the magnetic flux density generated by the live wire current at TMR3. 3x This represents the x-axis component of the magnetic flux density generated by the zero-line current at TMR3.

[0091] Specifically, the mathematical model relating the magnetic flux density component to the live wire current, neutral wire current, concealed depth, and horizontal offset is as follows:

[0092]

[0093]

[0094]

[0095]

[0096] Where B1, B2, B3, and B4 represent the x-axis components of magnetic flux density, and μ0 represents the permeability in vacuum, μ0 = 4π × 10⁻⁶. - 7 T·m / A, I1 represents the live wire current, D represents the concealed depth, I2 represents the neutral wire current, d represents the distance between the two sensors on the main sensing axis, X represents the horizontal offset, β represents the angle between the y-axis of the magnetic sensor array and the conductor under test, and V represents the spacing between the conductors.

[0097] In this embodiment of the invention, the vector sum of the x-axis components of the magnetic induction intensity generated by the dual-core wire at TMR1, TMR2, TMR3, and TMR4 are respectively B. 1x B 2x B 3x B 4x And the vector sum B that produces the y-axis component of the magnetic induction intensity at TMR5 5y The horizontal offset between the magnetic sensor array and the dual-core wire is defined as X. When the midpoint of TMR2 and TMR3 is directly above the midpoint of the live wire current and the neutral wire current, X = 0. In the process of building the mathematical model, the magnetic sensor array is taken to be positive in the positive x-axis direction and negative in the opposite direction.

[0098] In this embodiment of the invention, the angle between the y-axis of the magnetic sensor array and the conductor under test is defined as β. When the current-carrying conductor is parallel to the y-axis of the sensing coordinate system, β = 0 and cosβ = 0. At this time, the complexity of solving for the vector sum of the x-axis components of the magnetic induction intensity generated by the dual-core conductor at TMR1, TMR2, TMR3, and TMR4 is the lowest. Therefore, β = 0 is the optimal measurement angle for the sensor array. Thus, in this embodiment of the invention, the measured value B of the TMR5 sensor is used. 5y To adjust the parameters, rotate the magnetic sensor array clockwise or counterclockwise around the origin of the coordinate system. When B 5y When = 0, sinβ = 0, β = 0, and at this time the magnetic sensor array is located at the optimal measurement angle.

[0099] This invention, based on the idea of ​​nonlinear least squares, sets particle flight velocity and particle position ranges according to the range of conductor positions and the upper and lower limits of current in actual working conditions. Within these ranges, a dataset containing live wire current, neutral wire current, concealed depth, and horizontal offset is constructed. This dataset is used as a particle swarm, and the live wire current, neutral wire current, concealed depth, and horizontal offset are combined as the dimensions of individual particle positions to minimize the difference between the calculated and measured values ​​of the magnetic flux density component. This process finds the optimal fit for the measured magnetic flux density component value B from the magnetic sensor array. 1xB 2x B 3x B 4x The dimensions are a combination of live wire current I1, neutral wire current I2, caching depth D, and horizontal offset X. For any given combination of dimensions, the calculated value B of the magnetic flux density component can be obtained by substituting it into the mathematical model. 1c B 2c B 3c B 4c .

[0100] The square of the difference between the measured and calculated magnetic flux density components at each single-axis TMR sensor can be considered as the error value, and these error values ​​should be as close to zero as possible. Based on the above idea, a basic objective function f is established:

[0101] f = (B 1x -B 1c ) 2 +(B 2x -B 2c ) 2 +(B 3x -B 3c ) 2 +(B 4x -B 4c ) 2

[0102] In addition to the equality constraints mentioned above, there are also necessary inequality constraints:

[0103] For example, the particle position range is:

[0104] I min <I1, I2 <I max

[0105] D min <D<D max

[0106] X min <X<X max

[0107] Among them, I min I represents the minimum current defined according to actual operating conditions. max D represents the maximum current defined according to actual operating conditions. min D represents the minimum concealed depth of the conductor within the target area. max X represents the maximum concealed depth of the conductor within the target area. min X represents the minimum horizontal offset that the magnetic sensor array may experience during the measurement process. max This indicates the maximum horizontal offset that the magnetic sensor array may experience during the measurement process.

[0108] Based on the concept of a penalty function, the total amount of inequality constraints exceeding the limits during calculation is multiplied by a penalty factor and added as a penalty term to the basic objective function, thus constructing a new penalty function F. The penalty function is as follows:

[0109] F = f + k * (q1 + q2 + q3 + q4)

[0110] q1 = max(max(I1, I max )-I max I min -min(I1,I min ))

[0111] q2 = max(max(I2, I) max )-I max I min -min(I2,I min ))

[0112] q3 = max(max(D, D) max )-D max D min -min(D,D min ))

[0113] q4 = max(max(X, X) max )-X max X min -min(X, X min ))

[0114] Where F represents the fitness value in the particle swarm optimization algorithm, f represents the original objective function, and f = (B x -B c ) 2 k represents the penalty factor, q1 represents the penalty term for live wire current, q2 represents the penalty term for neutral wire current, q3 represents the penalty term for concealed installation depth, and q4 represents the penalty term for horizontal offset.

[0115] Based on the above formula, the method of dynamically adjusting the penalty function transforms the non-intrusive residual current detection problem into a problem of finding the minimum value of the penalty function F, where F is the fitness value in the particle swarm optimization algorithm. This embodiment of the invention improves the particle swarm optimization algorithm by dynamically adjusting the penalty function. By applying dynamic penalties to particles that exceed the limits in the objective function, the position of each particle in the algorithm is effectively restricted, thereby improving the convergence ability and solution accuracy of the algorithm.

[0116] The process of detecting residual current using the particle swarm optimization algorithm in this embodiment of the invention is as follows:

[0117] 1) Algorithm initialization, input the test value B of the magnetic induction intensity x component.1x B 2x B 3x B 4x Set the basic parameters of the particle swarm optimization algorithm, including the learning factors c1 and c2, the inertia weight ω, the particle swarm size N, the iteration convergence accuracy eps, the maximum number of iterations iter, the particle flight velocity range, and the particle position range. The particle flight velocity range is as follows:

[0118] v Imin <v I1 v I2 <v Imax

[0119] v Dmin <v D <v Dmax

[0120] v xmin <v x <v xmax

[0121] The particle position range is:

[0122] I min <I1, I2 <I max

[0123] D min <D<D max

[0124] X min <X<X max

[0125] 2) Determine the live wire current I1, neutral wire current I2, dark depth D, and horizontal offset X as the four dimensions of a single particle's pop position. The four dimensions of each particle's pop position are combined as a single dimension, as follows:

[0126]

[0127] Construct a dataset, assuming it is an N×4 multidimensional array, with the positions of N particles as row vectors and the combination of dimensions as column vectors. Define the size of the particle swarm as N, and the dataset... Treating the dataset as a particle swarm, for each particle in the swarm, a dimension combination is randomly selected within the particle's position range as the particle's initial position; a v is randomly selected within the particle's velocity range. I1 v I2 v D v xAs the initial velocity, a fitness value is calculated for each particle position in the particle swarm. The specific process is as follows: The magnetic induction intensity component B is calculated based on I1, I2, D, and X at each particle position in the particle swarm. 1c B 2c B 3c B 4c The four magnetic induction intensity components were calculated as B. 1c B 2c B 3c B 4c and the measured value of the x component of magnetic induction intensity B 1x B 2x B 3x B 4x Substituting the values ​​into the objective function and then adding the penalty term q yields the particle's fitness value.

[0128] 3) Evaluate the fitness of particles in the particle swarm. The minimum fitness of each particle is fitnesspBest, and the position of the particle at this fitness value is the individual optimal position pBest; the minimum fitness of all particles in the particle swarm is fitnessgBest, and the position of the particle at this fitness value is the optimal position gBest of the particle swarm.

[0129] The velocity v of each particle is updated according to the following formula, and the velocities of particles that exceed the range of particle flight speeds are constrained to the boundaries of the upper and lower limits.

[0130]

[0131] Where i is the particle number and t is the iteration number. Let f be the velocity of the i-th particle in the (t+1)-th iteration, ω represent the inertia weight, and f rand A random number between 0 and 1 Let gBest be the optimal position of the i-th particle in the t-th iteration. t Let be the optimal position of the particle swarm in the t-th iteration. Know Let represent the position and velocity of the i-th particle in the t-th iteration.

[0132] The position of each particle is calculated using the following formula:

[0133]

[0134] If a particle's position exceeds its range constraints in a certain dimension, then the particle's position is restricted to the boundaries of the upper and lower limits, and a penalty term q is calculated.

[0135] 4) Calculate and evaluate the fitness value of each particle in the particle swarm after changing its position. If a particle's fitness value (fitness) is less than its own optimal fitness value, update the particle's fitness values ​​(pBest and pBest). If a particle's fitness value (fitness) is less than the swarm's optimal fitness value, update the swarm's fitness values ​​(gBest and gBest). The update iteration count is t = t + 1.

[0136] If the optimal fitness value (fitnessgBest) of the particle swarm is less than the iterative convergence accuracy (eps) or the algorithm reaches the maximum number of iterations (iter), then the gBest value after the last iteration is output. This final output gBest is the optimal solution, which is a combination of particle positions in terms of dimensions, including I1, I2, D, and X. The residual current of the conductor under test is calculated using I1, I2, D, and X, as shown in the following formula:

[0137] ΔI=I2-I1

[0138] Where I2 represents the neutral current in the dimension combination corresponding to the optimal solution, I1 represents the live current in the dimension combination corresponding to the optimal solution, and ΔI represents the magnitude of the remaining current.

[0139] If the stopping criterion of the algorithm is not met, continue to update the velocity v and position pop of each particle, and constrain the velocity and position of particles that exceed the range of particle flight velocity and position to the upper and lower limits. Calculate the fitness value of each particle in the particle swarm after changing position, and update the individual optimal position of the particle and the optimal position of the particle swarm until the stopping iteration criterion is met.

[0140] To verify the correctness of the detection method provided in this embodiment of the invention, this embodiment utilizes the MATLAB platform to solve the computational problem of the particle swarm optimization algorithm, and uses a preset experimental group to check whether the algorithm converges to the correct result. In the preset experimental group, different live wire currents I1, neutral wire currents I2, caulking depths D, and horizontal offsets X are set respectively. The magnetic induction intensity under each experimental group is measured, and the results are input into the particle swarm optimization algorithm to evaluate the magnitude of the residual current calculation error for each experimental group. Specifically, the residual current error formula is as follows:

[0141] error=(ΔI-ΔI c ) / ΔI×100%

[0142] Where error is the percentage of error in the residual current calculation, and ΔI is the given residual current magnitude in the preset experimental group. c This is the magnitude of the residual current obtained through the particle swarm optimization algorithm.

[0143] Eight independent experimental groups were selected to evaluate the algorithm's performance. For each group, the particle swarm size and maximum number of iterations were 200 and 500, respectively. Due to the randomness of the particle swarm optimization algorithm, it cannot be guaranteed that a correct solution will be found in every run. Therefore, 14 independent runs were performed for each experimental group to ensure that the algorithm has sufficient convergence ability and solution accuracy. The residual current calculation error (%) data obtained from running the particle swarm algorithm are shown in Table 1 below:

[0144] Table 1

[0145]

[0146] The table presents the minimum, maximum, and average residual current errors obtained from 14 independent iterations across 8 cases. In 112 cases, the minimum error reached 0.094%, and the maximum error was only 2.783%, meeting the accuracy requirements for residual current detection. Meanwhile, the average error of the 8 cases in each iteration is listed in the last column of Table 1, with the maximum value not exceeding 1%. To clearly demonstrate and evaluate the algorithm's performance, the minimum, maximum, and average values ​​from the 14 iterations across the 8 cases were averaged, yielding results of 0.661%, 0.951%, and 0.824%, respectively. This confirms the superior performance of the non-contact residual current detection method proposed in this embodiment.

[0147] This invention provides a magnetic sensor array obtained by deploying multiple uniaxial TMR sensors on a target wall. By deploying multiple sensors on the wall, no iron core or windings are required, the array position can be arbitrarily changed, resulting in low cost and easy installation. Based on the magnetic sensor array, the magnetic induction intensity component measurements at all uniaxial TMR sensors are obtained. The magnetic induction array allows for the acquisition of magnetic induction intensity components without damaging the wall. A mathematical model is established between the magnetic induction intensity components and the live wire current, neutral wire current, concealed installation depth, and horizontal offset. All magnetic induction intensity x-component measurements and the mathematical model are input into a particle swarm optimization algorithm. The algorithm is then optimized based on the actual operating conditions, considering the range of the conductor position and the current. Upper and lower limits are defined. A dataset containing live wire current, neutral wire current, burial depth, and horizontal offset is constructed within the particle flight velocity and position range. This dataset is used as a particle swarm, and the live wire current, neutral wire current, burial depth, and horizontal offset in the dataset are used as the dimension combination of individual particle positions within the swarm. In the particle swarm optimization algorithm, based on the dimension combination of each particle position in the swarm, the magnetic flux density component of each particle position is calculated according to a mathematical model, resulting in multiple calculated values ​​of the magnetic flux density components. The sum of squares of the differences between each calculated value and the tested value of the magnetic flux density component is then calculated. A penalty function is constructed in the particle swarm optimization algorithm to penalize... The penalty function, through inequality constraints, superimposes out-of-bounds quantities during the calculation process as penalty terms onto the sum of squared differences, obtaining the fitness value of each particle in the particle swarm. The fitness value of each particle is then used to determine the current optimal position of both the individual particle and the overall optimal position of the particle swarm. In the particle swarm optimization algorithm, the velocity and position of each particle are iterated based on their current optimal positions, yielding the optimal position of the particle swarm after iteration termination. This optimal position is then taken as the optimal solution. Based on the dimensional combination of the individual particle positions corresponding to the optimal solution, the residual current of the conductor under test is calculated, resulting in the residual current detection result. Compared with existing technologies, this invention collects the magnetic induction intensity test values ​​of wires inside walls using a magnetic sensor array, establishes a mathematical model based on the relationship between magnetic induction intensity and concealed wires, calculates the calculated magnetic induction intensity value using the mathematical model, calculates the sum of squares of the differences between the calculated magnetic induction intensity value and the measured magnetic induction intensity value, and uses a constructed penalty function to superimpose the out-of-bounds amount as a penalty term onto the sum of squares of the differences, obtaining the optimal position of the particle swarm after the iteration terminates as the optimal solution, and calculates the residual current through the optimal solution. This overcomes the problem of measuring the residual current of concealed wiring in enclosed walls, and achieves non-contact detection of the residual current of concealed wiring without damaging the wall or inserting wires.

[0148] This invention also provides a non-contact residual current detection device, comprising:

[0149] The module is used to construct a rectangular coordinate system on the target wall. The horizontal axis of the rectangular coordinate system is used as the main sensing axis, and the vertical axis of the rectangular coordinate system is used as the secondary sensing axis. Multiple sensors are deployed on the main sensing axis and one sensor is deployed on the secondary sensing axis to obtain a magnetic sensor array.

[0150] The acquisition module is used to acquire the measured values ​​of magnetic induction intensity components at all sensors based on the magnetic sensor array. The measured values ​​of magnetic induction intensity components include the measured values ​​of magnetic induction intensity x component and magnetic induction intensity y component. A mathematical model is established between the magnetic induction intensity components and the live wire current, neutral wire current, dark caulking depth, and horizontal offset. All the measured values ​​of magnetic induction intensity components and the mathematical model are input into the particle swarm optimization algorithm.

[0151] The construction module is used to set the particle flight speed range and particle position range according to the range of the conductor position and the upper and lower limits of the current in the actual working conditions. Within the particle flight speed range and particle position range, a dataset containing live wire current, neutral wire current, concealed laying depth and horizontal offset is constructed. The data is input into the particle swarm optimization algorithm. In the particle swarm optimization algorithm, the dataset is used as a particle swarm, and the live wire current, neutral wire current, concealed laying depth and horizontal offset in the dataset are used as the dimension combination of the position of individual particles in the particle swarm.

[0152] The first calculation module is used in the particle swarm optimization algorithm to calculate the magnetic induction intensity component of each particle position based on the dimension combination of each particle position in the particle swarm and according to the mathematical model, to obtain multiple calculated values ​​of magnetic induction intensity components, and to calculate the sum of squares of the differences between each calculated value of magnetic induction intensity component and the test value of magnetic induction intensity component.

[0153] The second calculation module is used to construct a penalty function in the particle swarm optimization algorithm. The penalty function uses inequality constraints to superimpose the out-of-bounds quantities in the calculation process as penalty terms onto the sum of squares of the differences, thereby obtaining the fitness value of each particle in the particle swarm. The fitness value of each particle is used to determine the current optimal position of the individual particle and the current optimal position of the particle swarm.

[0154] The iteration module is used in the particle swarm optimization algorithm to iterate the velocity and position of each particle based on the current individual optimal position and the current optimal position of the particle swarm, to obtain the optimal position of the particle swarm after the iteration terminates, and to take the optimal position of the particle swarm after the iteration terminates as the optimal solution.

[0155] The third calculation module is used to calculate the residual current of the conductor under test based on the dimensional combination of the individual particle positions corresponding to the optimal solution, and obtain the residual current detection result.

[0156] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0157] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of the embodiments of the present invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0158] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a non-contact residual current detection method.

[0159] If an integrated module is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above method embodiments of the present invention can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a building device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0160] This invention also provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a non-contact residual current detection method.

[0161] The terminal device can be a desktop computer, laptop, handheld computer, server, server cluster, or cloud server, etc. This terminal device may include, but is not limited to, a processor and memory.

[0162] The processor referred to can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0163] In some embodiments, the memory may be an internal storage unit of the terminal device, such as a hard drive or RAM. In other embodiments, the memory may be an external storage device of the terminal device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital Card (SD), or Flash Card. Furthermore, the memory may include both internal and external storage units of the terminal device. The memory is used to store the operating system, applications, boot loader, data, and other programs, such as the program code of the computer program. The memory can also be used to temporarily store data that has been output or will be output.

[0164] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0165] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of the embodiments of the present invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0166] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A non-contact residual current detection method, characterized by, include: Step 1: Construct a rectangular coordinate system on the target wall, take the horizontal axis of the rectangular coordinate system as the main sensing axis, take the vertical axis of the rectangular coordinate system as the secondary sensing axis, arrange multiple sensors on the main sensing axis, and arrange one sensor on the secondary sensing axis to obtain a magnetic sensor array; Step 2: Based on the magnetic sensor array, obtain the test values ​​of the magnetic induction intensity components at all sensors. The test values ​​of the magnetic induction intensity components include the test values ​​of the magnetic induction intensity x component and the magnetic induction intensity y component. Establish a mathematical model between the magnetic induction intensity components and the live wire current, neutral wire current, caulking depth and horizontal offset. Input all the magnetic induction intensity x component measurements and the mathematical model into the particle swarm optimization algorithm. Step 3: Based on the range of the conductor position and the upper and lower limits of the current in the actual working conditions, set the particle flight speed range and particle position range. Construct a dataset containing live wire current, neutral wire current, concealed depth and horizontal offset within the particle flight speed range and particle position range. Input the dataset into the particle swarm optimization algorithm. In the particle swarm optimization algorithm, the dataset is used as the particle swarm, and the live wire current, neutral wire current, concealed depth and horizontal offset in the dataset are used as the dimension combination of the position of a single particle in the particle swarm. Step 4: In the particle swarm optimization algorithm, based on the dimension combination of each particle position in the particle swarm, the magnetic induction intensity component of each particle position is calculated according to the mathematical model to obtain multiple calculated values ​​of magnetic induction intensity components, and the sum of squares of the differences between each calculated value of magnetic induction intensity component and the test value of magnetic induction intensity component is calculated. Step 5: Construct a penalty function in the particle swarm optimization algorithm. The penalty function uses inequality constraints to superimpose the out-of-bounds quantities in the calculation process as penalty terms onto the sum of squares of the differences to obtain the fitness value of each particle in the particle swarm. Then, determine the current optimal position of the individual particle and the current optimal position of the particle swarm based on the fitness value of each particle. Step 6: In the particle swarm optimization algorithm, the velocity and position of each particle are iterated based on the current individual optimal position of the particle and the current optimal position of the particle swarm to obtain the optimal position of the particle swarm after the iteration terminates, and the optimal position of the particle swarm after the iteration terminates is taken as the optimal solution. Step 7: Calculate the residual current of the conductor under test based on the dimensional combination of the individual particle positions corresponding to the optimal solution, and obtain the residual current detection result.

2. The non-contact residual current detection method according to claim 1, characterized in that, Step 2 includes: Based on the magnetic sensor array, the test values ​​of magnetic induction intensity components at all sensors are obtained. The test values ​​of magnetic induction intensity components include the test values ​​of the x component of magnetic induction intensity and the test values ​​of the y component of magnetic induction intensity. with the magnetic induction intensity y component test value for adjusting the index, rotating the magnetic sensor array clockwise or counterclockwise with the origin of the rectangular coordinate system as the center; When , , , the magnetic sensor array is located at the optimal measurement angle, represents the angle between the y-axis of the magnetic sensor array and the wire to be measured. Establish a mathematical model relating magnetic induction intensity components to live wire current, neutral wire current, concealed depth, and horizontal offset; All the measured values ​​of the magnetic flux density x-components are input into the particle swarm optimization algorithm along with the mathematical model.

3. The non-contact residual current detection method according to claim 2, characterized in that, The measured value of the y-component of the magnetic induction intensity for: in, Indicates the permeability in vacuum. , Indicates the live wire current. Indicates the depth of the dark coating. Indicates the neutral wire current. Indicates the horizontal offset. This represents the angle between the y-axis of the magnetic sensor array and the conductor being measured. This indicates the spacing between wires.

4. The non-contact residual current detection method according to claim 2, characterized in that, The mathematical model relating the magnetic induction intensity component to the live wire current, neutral wire current, concealment depth, and horizontal offset is as follows: ； ； ； ； in, , , , Both represent the x-axis component of magnetic flux density. Indicates the permeability in vacuum. , Indicates the live wire current. Indicates the depth of the dark coating. Indicates the neutral wire current. This indicates the distance between the two sensors on the main sensing axis. Indicates the horizontal offset. This represents the angle between the y-axis of the magnetic sensor array and the conductor being measured. This indicates the spacing between wires.

5. The non-contact residual current detection method according to claim 1, characterized in that, The particle flight speed range is: The particle position range is: in, This represents the minimum current defined based on actual operating conditions. Indicates the live wire current. Indicates the neutral wire current. This represents the maximum current defined based on actual operating conditions. This indicates the minimum concealed installation depth of the conductor within the target wall. Indicates the depth of the dark coating. This indicates the maximum concealed depth of the conductor within the target wall. This represents the minimum horizontal offset that the magnetic sensor array may experience during the measurement process. Indicates the horizontal offset. This indicates the maximum horizontal offset that the magnetic sensor array may experience during the measurement process.

6. The non-contact residual current detection method according to claim 5, characterized in that, The penalty function for: in, The value represents the fitness value in the particle swarm optimization algorithm. fitness , Represents the original objective function. , This represents the measured value of the magnetic flux density component. This represents the calculated value of the magnetic flux density component. Indicates the penalty factor. This indicates a penalty term for live wire current. The penalty term represents the neutral wire current. The penalty item indicating the depth of the shadow application. This represents the penalty for horizontal offset.

7. The non-contact residual current detection method according to claim 6, characterized in that, The residual current of the conductor under test for: in, This represents the zero-line current in the dimension combination corresponding to the optimal solution. This represents the live current in the dimension combination corresponding to the optimal solution.

8. A non-contact residual current detection device, characterized in that, include: A construction module is used to construct a rectangular coordinate system on the target wall, with the horizontal axis of the rectangular coordinate system as the main sensing axis and the vertical axis of the rectangular coordinate system as the secondary sensing axis. Multiple sensors are deployed on the main sensing axis and one sensor is deployed on the secondary sensing axis to obtain a magnetic sensor array. The acquisition module is used to acquire the magnetic induction intensity component test values ​​at all sensors based on the magnetic sensor array. The magnetic induction intensity component test values ​​include the magnetic induction intensity x component test value and the magnetic induction intensity y component test value. It also establishes a mathematical model between the magnetic induction intensity components and the live wire current, neutral wire current, dark caulking depth and horizontal offset, and inputs all the magnetic induction intensity x component test values ​​and the mathematical model into the particle swarm optimization algorithm. The construction module is used to set the particle flight speed range and particle position range according to the range of the conductor position and the upper and lower limits of the current in the actual working conditions. Within the particle flight speed range and the particle position range, a dataset containing live wire current, neutral wire current, concealed laying depth and horizontal offset is constructed. The dataset is input into the particle swarm optimization algorithm. In the particle swarm optimization algorithm, the dataset is used as a particle swarm, and the live wire current, neutral wire current, concealed laying depth and horizontal offset in the dataset are used as the dimension combination of the position of individual particles in the particle swarm. The first calculation module is used in the particle swarm optimization algorithm to calculate the magnetic induction intensity component of each particle position based on the dimension combination of each particle position in the particle swarm and according to the mathematical model, to obtain multiple calculated values ​​of magnetic induction intensity components, and to calculate the sum of squares of the differences between each calculated value of magnetic induction intensity component and the test value of magnetic induction intensity component. The second calculation module is used to construct a penalty function in the particle swarm optimization algorithm. The penalty function uses inequality constraints to superimpose the out-of-bounds amount in the calculation process as a penalty term onto the sum of squares of the differences to obtain the fitness value of each particle in the particle swarm. The fitness value of each particle is used to determine the current optimal position of the individual particle and the current optimal position of the particle swarm. An iteration module is used in the particle swarm optimization algorithm to iterate the velocity and position of each particle based on the current individual optimal position of the particle and the current optimal position of the particle swarm, to obtain the optimal position of the particle swarm after the iteration terminates, and to take the optimal position of the particle swarm after the iteration terminates as the optimal solution. The third calculation module is used to calculate the residual current of the conductor under test based on the dimensional combination of the individual particle positions corresponding to the optimal solution, and obtain the residual current detection result.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the non-contact residual current detection method as described in any one of claims 1 to 7.

10. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the non-contact residual current detection method as described in any one of claims 1 to 7.

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