Non-contact residual current detection method and device, terminal equipment and storage medium

Abstract

The invention provides a non-contact residual current detection method and device, terminal equipment and a storage medium. The non-contact residual current detection method comprises the following steps: acquiring a magnetic induction intensity x component test value based on a magnetoresistive sensing array, and inputting the magnetic induction intensity x component test value and an established mathematical model into a differential evolution algorithm; generating an initial population with a plurality of individuals and calculating the fitness value of each individual; selecting the individual corresponding to the minimum fitness value from all the fitness values for variation and crossover to obtain a new individual, calculating the fitness value, performing multiple times of variation and crossover on the new individual with the fitness value smaller than the minimum fitness value, and taking the individual with the variation frequency and the crossover frequency reaching the preset frequency as the optimal individual, the residual current and the wire position of the target dual-core wire are calculated through the dimension combination of the optimal individual; under the condition that the specific position of a measurement target is unknown, full-non-contact detection of residual current and position calibration of a double-core wire can be carried out without damaging a wall body or penetrating through the wire, and the detection safety and the detection precision are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power measurement, and in particular to a contactless residual current detection method, device, terminal equipment and storage medium. Background Art

[0002] With the vigorous development of the national power industry, the level of electrification and electricity consumption of urban and rural residents has been significantly improved, but the incidence of electric shock accidents and electrical fire accidents caused by electrical equipment failures has also increased significantly. According to incomplete statistics, from January to October 2023, a total of 745,000 fires were reported nationwide, killing 1,381 people, injuring 2,063 people, and the direct property losses of 6.15 billion yuan have been verified. Compared with the same period in 2022, the number of fires and the number of injured people increased by 2.5% and 6.5% respectively, and the number of deaths and losses decreased by 13.2% and 9.7% respectively. Among them, there were 217,000 fires caused by electricity, resulting in 418 deaths, 590 injuries, and direct property losses of 2.63 billion yuan, accounting for 29.1%, 30.3%, 28.6% and 42.8% of the total, respectively. The main cause of electric shock accidents and electrical fires is leakage current caused by aging of the insulation of distribution lines. The main cause of leakage current is the non-standard design of user circuits. The concealed wires in households are directly buried in the wall without casing. When the insulation of the concealed wires ages, leakage points are easily generated at the damaged insulation. This leakage point is located inside the wall and cannot be determined using a multimeter. The only way to determine its location is to break the wall and check from the end of the line step by step. This not only wastes the time of operation and maintenance personnel, but also greatly increases the cost of leakage detection.

[0003] At present, residual current protectors are usually managed using a hierarchical leakage protection system, which achieves an ideal balance between power supply reliability and power supply safety for users through hierarchical management. According to the product specifications, action time, technical parameters, etc. of the residual current protector used, the leakage protection system is divided into three levels, namely, general protection, medium protection and home protection. The three-level leakage protection system effectively improves the defects of malfunction of the protector, improves the continuity, commissioning rate and safety of power supply, but the safe use of low-voltage power grids in some towns and rural areas is still not optimistic. The reasons are not only the lack of residents' awareness of safe use of electricity, but also the defects of electrical products. On the one hand, some residents lack awareness and common sense of safe use of electricity during the use of electricity, the safe use of electricity system they follow is not perfect, and the safety measures are not in place. Data show that electric shock accidents caused by violations or improper operation account for more than 90% of the total electric shock accidents. On the other hand, the electrical products used by some residents are of poor quality, with large contact resistance at the electrical connection parts, loose connection of mechanical parts, and low insulation level, which leads to frequent action of home protection. Therefore, some users privately dismantle or cross-connect home protection, further increasing the risk of electricity use. Summary of the invention

[0004] The present invention provides a non-contact residual current detection method, device, terminal equipment and storage medium, the purpose of which is to achieve full non-contact measurement without destroying walls or penetrating wires without affecting the detection accuracy.

[0005] In order to achieve the above object, the present invention provides a non-contact residual current detection method, comprising:

[0006] Step 1, constructing a three-dimensional rectangular coordinate system on a target wall on which a target double-core conductor is laid, wherein the z-axis of the three-dimensional rectangular coordinate system is perpendicular to the target wall and faces outward, and the x-axis of the three-dimensional rectangular coordinate system is used as a main sensitive axis, and five magnetoresistive sensors are evenly arranged on the main sensitive axis to obtain a magnetoresistive sensor array;

[0007] Step 2, based on the voltage output by each magnetoresistive sensor in the magnetoresistive sensor array, obtaining the magnetic induction intensity of the target double-core wire at each magnetoresistive sensor as a magnetic induction intensity x component test value, where the target double-core wire consists of a live wire and a neutral wire;

[0008] Step 3, establish a mathematical model between the magnetic induction intensity and the live wire current, neutral wire current, concealed depth, y-axis deflection, and horizontal offset of the target twin-core conductor, where the y-axis deflection is the angle between the target twin-core conductor and the y-axis of the three-dimensional rectangular coordinate system, and the horizontal offset is the distance between the intersection of the target twin-core conductor and the x-axis in the three-dimensional rectangular coordinate system to the origin of the three-dimensional rectangular coordinate system, and input the mathematical model and the magnetic induction intensity x-component test value into the differential evolution algorithm;

[0009] Step 4: determine the population size and the upper and lower boundaries of each dimension according to the range of the conductor position and the upper and lower limits of the current in the actual working condition, generate an initial candidate set including the live wire current, the neutral wire current, the concealed installation depth, the y-axis deflection angle, and the horizontal offset within the population size and the upper and lower boundaries of each dimension as the initial population, and each individual in the initial population is composed of a dimensional combination, and the dimensional combination includes the live wire current, the neutral wire current, the concealed installation depth, the y-axis deflection angle, and the horizontal offset;

[0010] Step 5, in the differential evolution algorithm, based on each individual in the population, the magnetic induction intensity component of each individual is calculated according to the mathematical model as the magnetic induction intensity component calculation value, and the sum of the squares of the differences between each magnetic induction intensity calculation value and each magnetic induction intensity component test value is calculated;

[0011] Step 6: construct an objective function in the differential evolution algorithm, take the upper and lower boundaries of each dimension as inequality constraints, and calculate the fitness value of each individual by the objective function and the sum of the squares of the difference;

[0012] Step 7, select the minimum fitness value from all fitness values, mutate the individual corresponding to the minimum fitness value through the perturbation vector to obtain the mutated individual, and cross the mutated individual through the crossover factor to obtain a new individual;

[0013] Step 8, calculating the fitness value of the new individual through the objective function, and judging whether the fitness value of the new individual is less than the minimum fitness value;

[0014] If the fitness value of the new individual is greater than the minimum fitness value, the disturbance vector and the crossover factor are updated, and the updated disturbance vector and the crossover factor are used as the disturbance vector and the crossover factor in step 7 and the process returns to step 7;

[0015] If the fitness value of the new individual is less than the minimum fitness value, the new individual is mutated and crossovered multiple times, and the individual when the number of mutations and crossovers reaches the preset number of times is taken as the optimal individual, and the residual current and conductor position of the target double-core conductor are calculated by combining the dimensions of the optimal individual.

[0016] Specifically, the five magnetoresistive sensors are all TMR linear magnetic field sensors, the distance between each two adjacent magnetoresistive sensors is 15 mm, and the third magnetoresistive sensor is located at the origin of the three-dimensional rectangular coordinate system.

[0017] Furthermore, the magnetic induction intensity B x The expression is:

[0018]

[0019] Where, μ0 represents the magnetic permeability in vacuum, I1 represents the live wire current, I2 represents the neutral wire current, r1 represents the distance between the magnetoresistive sensor array and the live wire, r2 represents the distance between the magnetoresistive sensor array and the neutral wire, α i1 It represents the angle between the perpendicular line from the ith magnetoresistive sensor in the magnetoresistive sensor array to the live line I1 and the height from the perpendicular point to the plane where the ith magnetoresistive sensor is located, α i2 represents the angle between the perpendicular line from the i-th magnetoresistive sensor to the zero line I2 in the magnetoresistive sensor array and the height of the perpendicular point to the plane where the i-th magnetoresistive sensor is located, β represents the angle between the energized conductor and the y-axis, and δ represents the angle between the magnetoresistive sensor plane and the energized conductor.

[0020] Specifically, the distance between the magnetoresistive sensor array and the live wire includes the distance between each magnetoresistive sensor and the live wire. The distance calculation expression is as follows:

[0021]

[0022]

[0023] The distance between the magnetoresistive sensor array and the zero line includes the distance between each magnetoresistive sensor and the zero line. The distance calculation expression is as follows:

[0024]

[0025] Among them, r 11 Represents the distance from the first magnetoresistive sensor to the live wire I1, r 21 Represents the distance from the second magnetoresistive sensor to the live wire I1, r 31 Represents the distance from the third magnetoresistive sensor to the live wire I1, r 41 Represents the distance from the fourth magnetoresistive sensor to the live wire I1, r 51 Represents the distance from the fifth magnetoresistive sensor to the live wire I1, r 12 Represents the distance from the first magnetoresistive sensor to the zero line I2, r 22 Represents the distance from the second magnetoresistive sensor to the zero line I2, r 32 Represents the distance from the third magnetoresistive sensor to the zero line I2, r 42 Represents the distance from the fourth magnetoresistive sensor to the zero line I2, r 52 It represents the distance from the 5th magnetoresistive sensor to the zero line I2, h represents the dark depth, and D represents the horizontal offset.

[0026] More specifically, step 3 includes:

[0027] Taking the x-component test value of the magnetic induction intensity as the adjustment index, each magnetoresistive sensor in the magnetoresistive sensor array is rotated clockwise or counterclockwise with the origin of the three-dimensional rectangular coordinate system as the center;

[0028] When B x =0, sinβ=0, β=0, and the magnetoresistive sensor array is located at the optimal test angle;

[0029] A mathematical model is established between the magnetic induction intensity and the live current, neutral current, concealed depth, y-axis deflection, and horizontal offset of the target twin-core conductor. The y-axis deflection is the angle between the target twin-core conductor and the y-axis of the three-dimensional rectangular coordinate system. The horizontal offset is the distance between the intersection of the target twin-core conductor and the x-axis in the three-dimensional rectangular coordinate system and the origin of the three-dimensional rectangular coordinate system.

[0030] The mathematical model and the test value of the x component of magnetic induction intensity are input into the differential evolution algorithm.

[0031] Specifically, the mathematical model is:

[0032]

[0033]

[0034] Among them, I1, I2, h, β, and D represent the live wire current, neutral wire current, concealed depth, y-axis deflection angle, and horizontal offset of the target double-core conductor, respectively.

[0035] The present invention also provides a non-contact residual current detection device, comprising:

[0036] A construction module is provided, wherein a three-dimensional rectangular coordinate system is constructed on a target wall on which a target double-core conductor is laid, wherein a z-axis of the three-dimensional rectangular coordinate system is perpendicular to the target wall and faces outward, and an x-axis of the three-dimensional rectangular coordinate system is used as a main sensitive axis, and five magnetoresistive sensors are arranged at equal intervals on the main sensitive axis to obtain a magnetoresistive sensor array;

[0037] An acquisition module is used to acquire the magnetic induction intensity of the target double-core wire at each magnetoresistive sensor as a magnetic induction intensity x-component test value based on the voltage output by each magnetoresistive sensor in the magnetoresistive sensor array, where the target double-core wire consists of a live wire and a neutral wire;

[0038] Establish a module for establishing a mathematical model between the magnetic induction intensity and the live wire current, neutral wire current, concealed depth, y-axis deflection, and horizontal offset of the target twin-core conductor, where the y-axis deflection is the angle between the target twin-core conductor and the y-axis of the three-dimensional rectangular coordinate system, and the horizontal offset is the distance between the intersection of the target twin-core conductor and the x-axis in the three-dimensional rectangular coordinate system to the origin of the three-dimensional rectangular coordinate system, and input the mathematical model and the test value of the x-component of the magnetic induction intensity into the differential evolution algorithm;

[0039] A generation module is used to determine the population size and the upper and lower boundaries of each dimension according to the range of the conductor position and the upper and lower limits of the current in the actual working condition, and generate an initial candidate set including the live wire current, the neutral wire current, the concealed installation depth, the y-axis deflection angle, and the horizontal offset as the initial population within the population size and the upper and lower boundaries of each dimension, and each individual in the initial population is composed of a dimensional combination, and the dimensional combination includes the live wire current, the neutral wire current, the concealed installation depth, the y-axis deflection angle, and the horizontal offset;

[0040] A calculation module is used to calculate the magnetic induction intensity component of each individual in the population according to a mathematical model as a magnetic induction intensity component calculation value in a differential evolution algorithm, and calculate the sum of squares of differences between each magnetic induction intensity calculation value and each magnetic induction intensity component test value;

[0041] The construction module is used to construct the objective function in the differential evolution algorithm, taking the upper and lower boundaries of each dimension as inequality constraints, and calculating the fitness value of each individual by the objective function and the sum of the squares of the difference;

[0042] The mutation crossover module is used to select the minimum fitness value among all fitness values, mutate the individual corresponding to the minimum fitness value through the perturbation vector to obtain the mutated individual, and cross the mutated individual through the crossover factor to obtain a new individual;

[0043] A judgment module is used to calculate the fitness value of the new individual through the objective function and judge whether the fitness value of the new individual is less than the minimum fitness value;

[0044] If the fitness value of the new individual is greater than the minimum fitness value, the perturbation vector and crossover factor are updated, and the updated perturbation vector and crossover factor are used as the perturbation vector and crossover factor of the mutation crossover module and returned to perform mutation and crossover;

[0045] If the fitness value of the new individual is less than the minimum fitness value, the new individual is mutated and crossovered multiple times, and the individual when the number of mutations and crossovers reaches the preset number of times is taken as the optimal individual, and the residual current and conductor position of the target double-core conductor are calculated by combining the dimensions of the optimal individual.

[0046] 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 implements a non-contact residual current detection method when executing the computer program.

[0047] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, a non-contact residual current detection method is implemented.

[0048] The above scheme of the present invention has the following beneficial effects:

[0049] Compared with the prior art, the present invention constructs a three-dimensional rectangular coordinate system on a target wall on which a target double-core conductor is laid, takes the x-axis of the three-dimensional rectangular coordinate system as the main sensitive axis, and arranges five magnetoresistive sensors equidistantly on the main sensitive axis to obtain a magnetoresistive sensor array; based on the magnetoresistive sensor array, the magnetic induction intensity of the target double-core conductor at each magnetoresistive sensor is obtained as a test value of the x-component of the magnetic induction intensity; a mathematical model is established and input into a differential evolution algorithm together with the test value of the x-component of the magnetic induction intensity; an initial population is generated within a determined population size and upper and lower boundaries of each dimension, and each individual in the initial population is composed of a dimensional combination including live current, neutral current, concealed depth, y-axis deflection, and horizontal offset; the magnetic induction intensity component of each individual is calculated as a calculated value of the magnetic induction intensity component according to the mathematical model, and the sum of the squares of the differences between each calculated value of the magnetic induction intensity and each test value of the magnetic induction intensity component is calculated; an objective function is constructed, and the upper and lower boundaries of each dimension are used as inequality constraints, and the value of each individual is calculated by the objective function and the sum of the squares of the differences. The fitness value of each individual is obtained; the minimum fitness value is selected from all fitness values, and the individual corresponding to the minimum fitness value is mutated by the perturbation vector to obtain the mutated individual, and the mutated individual is crossed by the crossover factor to obtain a new individual; the fitness value of the new individual is calculated by the objective function, and if the fitness value of the new individual is greater than the minimum fitness value, the perturbation vector and the crossover factor are updated, and the updated perturbation vector and the crossover factor are used as the perturbation vector and the crossover factor in step 7 and the mutation and crossover are returned to execute; if the fitness value of the new individual is less than the minimum fitness value, the new individual is mutated and crossed for multiple times, and the individual when the number of mutations and the number of crossovers reach the preset number of times is taken as the optimal individual, and the residual current of the target double-core conductor and the conductor position are calculated by the dimensional combination of the optimal individual; in the case of unknown specific position of the measurement target, full non-contact detection of residual current and position calibration of the double-core conductor are realized without destroying the wall or penetrating the conductor, thereby improving the safety and accuracy of detection.

[0050] Other beneficial effects of the present invention will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of a flow chart of an embodiment of the present invention;

[0052] Figure 2 is a front view of a magnetoresistive sensor array according to an embodiment of the present invention;

[0053] Figure 3 is a top view of a magnetoresistive sensor array according to an embodiment of the present invention;

[0054] Figure 4 Schematic diagram of the distance from the TMR to the dual-core conductor in an embodiment of the present invention;

[0055] Figure 5 Schematic diagram of a non-contact residual current detection device in an embodiment of the present invention;

[0056] Figure 6 Schematic diagram of a terminal device in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0059] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installation", "connection" and "connection" should be understood in a broad sense, for example, it can be a locking connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0060] In addition, 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.

[0061] In view of the existing problems, the present invention provides a contactless residual current detection method, device, terminal equipment and storage medium.

[0062] like Figure 1 As shown, an embodiment of the present invention provides a non-contact residual current detection method, comprising:

[0063] Step 1, constructing a three-dimensional rectangular coordinate system on a target wall on which a target double-core conductor is laid, wherein the z-axis of the three-dimensional rectangular coordinate system is perpendicular to the target wall and faces outward, and the x-axis of the three-dimensional rectangular coordinate system is used as a main sensitive axis, and five magnetoresistive sensors are evenly arranged on the main sensitive axis to obtain a magnetoresistive sensor array;

[0064] Step 2, based on the voltage output by each magnetoresistive sensor in the magnetoresistive sensor array, obtaining the magnetic induction intensity of the target double-core wire at each magnetoresistive sensor as a magnetic induction intensity x component test value, where the target double-core wire consists of a live wire and a neutral wire;

[0065] Step 3, establish a mathematical model between the magnetic induction intensity and the live wire current, neutral wire current, concealed depth, y-axis deflection, and horizontal offset of the target twin-core conductor, where the y-axis deflection is the angle between the target twin-core conductor and the y-axis of the three-dimensional rectangular coordinate system, and the horizontal offset is the distance between the intersection of the target twin-core conductor and the x-axis in the three-dimensional rectangular coordinate system to the origin of the three-dimensional rectangular coordinate system, and input the mathematical model and the magnetic induction intensity x-component test value into the differential evolution algorithm;

[0066] Step 4: determine the population size and the upper and lower boundaries of each dimension according to the range of the conductor position and the upper and lower limits of the current in the actual working condition, generate an initial candidate set including the live wire current, the neutral wire current, the concealed installation depth, the y-axis deflection angle, and the horizontal offset within the population size and the upper and lower boundaries of each dimension as the initial population, and each individual in the initial population is composed of a dimensional combination, and the dimensional combination includes the live wire current, the neutral wire current, the concealed installation depth, the y-axis deflection angle, and the horizontal offset;

[0067] Step 5, in the differential evolution algorithm, based on each individual in the population, the magnetic induction intensity component of each individual is calculated according to the mathematical model as the magnetic induction intensity component calculation value, and the sum of the squares of the differences between each magnetic induction intensity calculation value and each magnetic induction intensity component test value is calculated;

[0068] Step 6: construct an objective function in the differential evolution algorithm, take the upper and lower boundaries of each dimension as inequality constraints, and calculate the fitness value of each individual by the objective function and the sum of the squares of the difference;

[0069] Step 7, select the minimum fitness value from all fitness values, mutate the individual corresponding to the minimum fitness value through the perturbation vector to obtain the mutated individual, and cross the mutated individual through the crossover factor to obtain a new individual;

[0070] Step 8, calculating the fitness value of the new individual through the objective function, and judging whether the fitness value of the new individual is less than the minimum fitness value;

[0071] If the fitness value of the new individual is greater than the minimum fitness value, the disturbance vector and the crossover factor are updated, and the updated disturbance vector and the crossover factor are used as the disturbance vector and the crossover factor in step 7 and the process returns to step 7;

[0072] If the fitness value of the new individual is less than the minimum fitness value, the new individual is mutated and crossovered multiple times, and the individual when the number of mutations and crossovers reaches the preset number of times is taken as the optimal individual, and the residual current and conductor position of the target double-core conductor are calculated by combining the dimensions of the optimal individual.

[0073] Most preferably, all five magnetoresistive sensors are TMR linear magnetic field sensors, the distance between each two adjacent magnetoresistive sensors is 15 mm, and the third magnetoresistive sensor is located at the origin of a three-dimensional rectangular coordinate system, the three-dimensional rectangular coordinate system is constructed on a target wall where a target double-core conductor is laid, and the z-axis of the three-dimensional rectangular coordinate system is perpendicular to the target wall and points outward.

[0074] In the embodiment of the present invention, Figure 2-Figure 4 As shown, the five magnetoresistive sensors are Sensor1, Sensor2, Sensor3, Sensor4 and Sensor5. Figure 2-Figure 4 S1, S2, S3, S4, and S5 are five single-axis TMR linear magnetic field sensors with the y-axis as the sensitive axis, and are equidistantly arranged on the main sensitive axis of the array. The xoy plane in the three-dimensional rectangular coordinate system is the plane where the magnetoresistive sensor array is located. Figure 2-Figure 4 I1, I2, h, β, and D correspond to the live wire current, neutral wire current, concealed wiring depth, y-axis deflection angle, and horizontal offset, respectively.

[0075] In an embodiment of the present invention, the target twin-core conductor consists of a live wire and a neutral wire, so the residual current measurement under the twin-core conductor working condition includes five unknowns, namely the live wire current, the neutral wire current, the concealed depth, the y-axis deflection angle, and the horizontal offset. The concealed depth is the straight-line distance from the target twin-core conductor to the wall surface, the y-axis deflection angle is the angle between the target twin-core conductor and the y-axis of the three-dimensional rectangular coordinate system, and the horizontal offset is the distance between the intersection of the target twin-core conductor and the x-axis in the three-dimensional rectangular coordinate system to the origin of the three-dimensional rectangular coordinate system. The residual current is the difference between the live wire current and the neutral wire current. The concealed depth, the y-axis deflection angle, and the horizontal offset are used to calibrate the position of the twin-core conductor.

[0076] According to the Biot-Savart law and the superposition law of magnetic induction intensity of double-core conductors, considering that the length of the long straight conductor is much greater than the concealed depth, it can be regarded as an infinitely long straight conductor. The x-axis component of the magnetic induction intensity generated by the double-core conductor at TMR is equal to the magnetic induction intensity B' generated by the live wire current I1 at it. i1 The component on the x-axis is the magnetic induction intensity B'' generated by the zero-line current I2 at that locationi2 The vector sum of the components on the x-axis is expressed as:

[0077]

[0078] Similarly, the y-axis component of the magnetic induction intensity generated by the double-core conductor at TMR is equal to the magnetic induction intensity B' generated by the live wire current I1 at it. i1 The component on the y-axis is the same as the magnetic induction intensity B'' generated by the zero-line current I2 at that location i2 The vector sum of the components on the y-axis is expressed as:

[0079]

[0080] Similarly, the z-axis component of the magnetic induction intensity generated by the twin-core conductor at TMR is equal to the magnetic induction intensity B' generated by the live wire current I1 at it. i1 The component on the z-axis and the magnetic induction intensity B'' generated by the zero-line current I2 i2 The vector sum of the components on the z-axis is expressed as:

[0081]

[0082] Where μ0 represents the magnetic permeability in vacuum, I1 represents the live wire current, I2 represents the neutral wire current, r1 represents the distance between the magnetoresistive sensor and the live wire, r2 represents the distance between the magnetoresistive sensor and the neutral wire, α i1 represents the angle between the perpendicular line from the ith magnetoresistive sensor to the live line I1 and the height from the perpendicular point to the plane where the ith magnetoresistive sensor is located, α i2 represents the angle between the perpendicular line from the ith magnetoresistive sensor to the zero line I2 and the height of the perpendicular point to the plane where the ith magnetoresistive sensor is located, β represents the angle between the energized conductor and the y-axis, and δ represents the angle between the magnetoresistive sensor plane and the energized conductor.

[0083] In the embodiment of the present invention, the formula for the distance r from a point in space to a straight line is:

[0084]

[0085] in, It means that the line connecting any two points A and B on the double-core conductor is from A to B. It represents the line from point A to the selected TMR, with the direction from A to TMR. Figure 4 shown.

[0086] Most preferably, the distance between each magnetoresistive sensor and the target dual-core conductor is calculated as:

[0087]

[0088]

[0089] Among them, r 11 Represents the distance from the first magnetoresistive sensor to the live wire I1, r 21 Represents the distance from the second magnetoresistive sensor to the live wire I1, r 31 Represents the distance from the third magnetoresistive sensor to the live wire I1, r 41 Represents the distance from the fourth magnetoresistive sensor to the live wire I1, r 51 Represents the distance from the fifth magnetoresistive sensor to the live wire I1, r 12 Represents the distance from the first magnetoresistive sensor to the zero line I2, r 22 Represents the distance from the second magnetoresistive sensor to the zero line I2, r 32 Represents the distance from the third magnetoresistive sensor to the zero line I2, r 42 Represents the distance from the fourth magnetoresistive sensor to the zero line I2, r 52 It represents the distance from the 5th magnetoresistive sensor to the zero line I2, h represents the dark depth, and D represents the horizontal offset.

[0090] Most preferably, step 3 comprises:

[0091] Taking the x-component test value of the magnetic induction intensity as the adjustment index, each magnetoresistive sensor in the magnetoresistive sensor array is rotated clockwise or counterclockwise with the origin of the three-dimensional rectangular coordinate system as the center;

[0092] When B x =0, sinβ=0, β=0, and the magnetoresistive sensor array is located at the optimal test angle;

[0093] Establish a mathematical model between the magnetic induction intensity and the live wire current, neutral wire current, concealed laying depth, y-axis deflection angle, and horizontal offset of the target double-core conductor;

[0094] The mathematical model and the test value of the x component of magnetic induction intensity are input into the differential evolution algorithm.

[0095] Most preferably, the mathematical model is:

[0096]

[0097]

[0098] Substituting the distance from each TMR to the target twin-core conductor into the mathematical model, we obtain:

[0099]

[0100] Among them, I1, I2, h, β, and D represent the live wire current, neutral wire current, concealed depth, y-axis deflection angle, and horizontal offset of the target double-core conductor, respectively.

[0101] The differential evolution algorithm is a global optimization algorithm used to solve complex mathematical optimization problems. In the embodiment of the present invention, the principle of the differential evolution algorithm is as follows:

[0102] Define the dimension of the mathematical optimization problem, that is, define the number of quantities to be determined. In the embodiment of the present invention, the number of quantities to be determined is set to 5, namely, the live wire current I1, the neutral wire current I2, the concealed depth h, the y-axis deflection angle β, and the horizontal offset D;

[0103] Set the upper and lower boundaries of 5 quantities to be determined, define lb as the lower boundary of the quantity to be determined, define the lower limit value for each quantity to be determined, define ub as the upper boundary of the quantity to be determined, and define the upper limit value for each quantity to be determined. In the embodiment of the present invention, the lower limit value of the live wire current I1 and the neutral wire current I2 is defined as -10A, and the upper limit value is 10A, the lower limit value of the concealed depth h is -20mm, and the upper limit value is 20mm, the lower limit value of the y-axis deflection angle β is -90°, and the upper limit value is 90°, the lower limit value of the horizontal offset D is -50mm, and the upper limit value is 50mm;

[0104] Set the population size, that is, the number of individuals in the population in the differential evolution algorithm. In this embodiment of the present invention, the population size is set to 1000;

[0105] Set the differential weight (Scaling Factor) in the differential evolution algorithm. The differential weight is a parameter that controls the mutation operation and is used to balance the degree of mutation. In the embodiment of the present invention, the differential weight is set to 0.4;

[0106] Set the crossover probability, which is used to control the degree to which individual vectors exchange information with each other during the crossover operation. In the embodiment of the present invention, the crossover probability is set to 0.7;

[0107] Set the maximum number of iterations, that is, the maximum number of iterations executed by the differential evolution algorithm. In the embodiment of the present invention, the maximum number of iterations is set to 10,000;

[0108] Initialize the population. First, calculate the difference between the upper limit and the lower limit, and multiply the difference by the NP×D random matrix generated by rand(NP, D) element by element to obtain the initialized population. The initialized population includes multiple individuals, each of which includes a dimension combination. Each dimension combination includes 5 dimensions, namely, the live current I1, the neutral current I2, the concealed depth h, the y-axis deflection angle β, and the horizontal offset D. The value of each dimension is randomly initialized within the upper and lower boundaries.

[0109] Calculate the fitness value of each individual in the initialized population, initialize the fitness value to a matrix of length NP, and initialize each element in the matrix to infinity, then traverse each individual in the population, construct an objective function to calculate the fitness value of each individual, and store the fitness value. In the embodiment of the present invention, the objective function is the objfun function;

[0110] Initialize the current evolution iteration number to 1, use a while loop to make the iteration number reach the maximum number of iterations. During the iteration process, for each individual, update it according to the mutation, crossover and selection strategies of the differential evolution algorithm;

[0111] Select the minimum fitness value among all fitness values, and obtain the individual corresponding to the minimum fitness value;

[0112] Randomly select 3 different indexes r from the index vector after removing the i-th individual, calculate a disturbance vector v according to the mutation strategy of the differential evolution algorithm, and the disturbance vector v does not exceed the boundary constraint range [lb, ub], and mutate the individual corresponding to the minimum fitness value according to the disturbance vector to obtain the mutated individual;

[0113] According to the crossover strategy of the differential evolution algorithm, a crossover factor is randomly selected as the parameter that must be modified in the crossover operation. For each dimension, whether to introduce new genes from the perturbation vector is determined according to the preset crossover probability. If the random number is less than the crossover probability or the dimension is equal to the crossover factor, the mutated individual is updated to a new individual.

[0114] Calculate the fitness value of the new individual. If the fitness value of the new individual is less than the fitness value of the individual output in the previous iteration, use the new individual to update the individual obtained in the previous iteration, and update the stored fitness value at the same time;

[0115] Until the number of iterations reaches the maximum number of iterations, the new individual generated by the last iteration is output, and this iteration ends.

[0116] In an embodiment of the present invention, according to the live wire current I1, neutral wire current I2, concealed installation depth h, y-axis deflection angle β, and horizontal offset D in the dimensional combination corresponding to the new individual generated by the last iteration, the residual current and the position calibration of the target double-core conductor can be obtained. The residual current is the difference between the live wire current and the neutral wire current. The position of the target double-core conductor is calibrated by the concealed installation depth h, y-axis deflection angle β, and horizontal offset D.

[0117] In order to verify the correctness of the non-contact residual current detection method provided by the embodiment of the present invention, experimental values ​​of 5 quantities to be determined are given under actual working conditions, as shown in the following Table 1:

[0118] Table 1

[0119] Calculation example <![CDATA[Given I1 (A)]]> <![CDATA[Given I2(A)]]> <![CDATA[Given I P (A)]]> Given β(°) Given D(mm) 1 0 -0.704 0.704 0 0 2 0 -0.845 0.845 0 0 3 0 -0.988 0.988 0 0 4 0 -1.255 1.255 0 0 5 0 -1.416 1.416 0 0 6 0 -1.571 1.571 0 0 7 0 -0.988 0.988 0 -10 8 0 -1.250 1.250 0 -10 9 0 -1.412 1.412 0 -17.5 10 -0.441 -0.367 -0.074 0 -2.75 11 0.446 0.708 -0.262 0 1.5 12 1.189 1.597 -0.408 30 -16.5 13 0.965 1.498 -0.533 0 1.25 14 1.575 2.115 -0.540 30 -17 15 1.558 2.223 -0.665 20 0 16 1.960 2.640 -0.680 30 -17 17 1.860 2.680 -0.820 20 0 18 2.366 3.176 -0.810 30 -16.5 19 2.330 3.268 -0.938 20 0 20 2.710 3.657 -0.947 30 -16.5 21 -1.413 -0.460 -0.953 16 0 22 3.397 4.491 -1.094 30 -11.50 23 3.708 5.077 -1.369 30 -11.50

[0120] Among them, Example 1-9 measures the situation when different currents are passed through a single conductor, and Example 10-23 measures the situation when different currents are passed through two conductors.

[0121] The embodiment of the present invention uses MATLAB to solve the current back-propagation problem, and uses a predefined solution to check whether the algorithm converges to the correct result. As shown in Table 2 below, the values ​​of five unknown quantities back-propagated by the differential evolution algorithm for the magnetic induction intensity component measured by the magnetic sensor array are shown. A total of 23 independent examples are selected to evaluate the performance of the algorithm. For each example, the population size is set to 1000.

[0122] Table 2

[0123]

[0124]

[0125] Table 3 below shows the live line current I1, neutral line current I2, and residual current I obtained by independent iteration under 23 sets of examples. P , y-axis deflection angle β, horizontal offset D error data:

[0126] Table 3

[0127] Calculation example <![CDATA[I1 Error (%)]]> <![CDATA[I2 Error (%)]]> IP error (%) β error (%) D error (%) 1 0 0.8523 0.8523 0 0 2 0 1.0651 1.0651 0 0 3 0 1.1133 1.1133 0 0 4 0 -1.1952 -1.1952 0 0 5 0 1.2712 1.2712 0 0 6 0 -1.4004 -1.4004 0 0 7 0 -1.3158 -1.3158 0 0 8 0 -1.2800 -1.2800 0 0 9 0 -0.7790 -0.7790 0 0 10 0.3265 0.0518 1.6892 0 0.5491 11 -0.6323 -0.4534 -0.1489 0 1.2467 12 0.2691 0.2567 0.2206 0.1633 -0.8606 13 -0.2953 0.1869 1.0600 0 -0.2160 14 0.1714 -0.2364 -1.4444 1.767 -0.5941 15 0.1797 -0.1709 0.9925 1.735 0 16 -1.0867 -0.9470 -0.5441 1.6833 -0.9590 17 -0.1935 0.3582 1.6098 1.83 -0.9590 18 0.2874 -0.1322 -1.3641 0.7367 -1.60 19 0.3047 0.0031 -0.7463 -0.6350 0 20 -1.0867 -0.9470 -1.0876 1.2267 -0.9590 21 -0.7360 -1.0957 -0.5624 -1.7438 0 22 0.2914 0.3073 0.3565 0.9267 -0.3652 23 -0.2131 0.2068 1.3440 1.52 1.1913

[0128] In the case of only a single conductor, the residual current I P The minimum error is only -0.7790%, and the maximum error reaches -1.4004%, which meets the accuracy requirements of residual current detection.

[0129] Compared with the prior art, the embodiment of the present invention constructs a three-dimensional rectangular coordinate system on a target wall on which a target double-core conductor is laid, takes the x-axis of the three-dimensional rectangular coordinate system as the main sensitive axis, and arranges five magnetoresistive sensors equidistantly on the main sensitive axis to obtain a magnetoresistive sensor array; based on the magnetoresistive sensor array, the magnetic induction intensity of the target double-core conductor at each magnetoresistive sensor is obtained as a test value of the x-component of the magnetic induction intensity; a mathematical model is established and input into a differential evolution algorithm together with the test value of the x-component of the magnetic induction intensity; an initial population is generated within a determined population size and upper and lower boundaries of each dimension, and each individual in the initial population is composed of a dimensional combination including live current, neutral current, concealed depth, y-axis deflection, and horizontal offset; the magnetic induction intensity component of each individual is calculated as a calculated value of the magnetic induction intensity component according to the mathematical model, and the sum of the squares of the differences between each calculated value of the magnetic induction intensity and each test value of the magnetic induction intensity component is calculated; an objective function is constructed, and the upper and lower boundaries of each dimension are used as inequality constraints, and the objective function and the sum of the squares of the differences are calculated. Calculate the fitness value of each individual; select the minimum fitness value among all fitness values, mutate the individual corresponding to the minimum fitness value through the perturbation vector to obtain the mutated individual, and cross the mutated individual through the crossover factor to obtain a new individual; calculate the fitness value of the new individual through the objective function, if the fitness value of the new individual is greater than the minimum fitness value, update the perturbation vector and the crossover factor, use the updated perturbation vector and the crossover factor as the perturbation vector and the crossover factor in step 7 and return to perform mutation and crossover; if the fitness value of the new individual is less than the minimum fitness value, perform multiple mutations and crossovers on the new individual, and use the individual when the number of mutations and the number of crossovers reach the preset number as the optimal individual, and calculate the residual current and the wire position of the target double-core wire through the dimensional combination of the optimal individual; in the case of unknown specific position of the measurement target, it is realized that the residual current can be fully non-contact detected and the position calibration of the double-core wire can be performed without destroying the wall or penetrating the wire, thereby improving the safety and accuracy of the detection.

[0130] The embodiment of the present invention also provides a non-contact residual current detection device, such as Figure 5 As shown, the non-contact residual current detection device 100 includes:

[0131] A construction module 101 is used to construct a three-dimensional rectangular coordinate system on a target wall on which a target double-core conductor is laid, wherein the z-axis of the three-dimensional rectangular coordinate system is perpendicular to the target wall and faces outward, and the x-axis of the three-dimensional rectangular coordinate system is used as a main sensitive axis, and five magnetoresistive sensors are arranged at equal intervals on the main sensitive axis to obtain a magnetoresistive sensor array;

[0132] An acquisition module 102 is used to acquire the magnetic induction intensity of the target double-core wire at each magnetoresistive sensor as a magnetic induction intensity x component test value based on the voltage output by each magnetoresistive sensor in the magnetoresistive sensor array, where the target double-core wire consists of a live wire and a neutral wire;

[0133] Establishing module 103, used to establish a mathematical model between magnetic induction intensity and live wire current, neutral wire current, concealed depth, y-axis deflection, and horizontal offset of the target twin-core conductor, where the y-axis deflection is the angle between the target twin-core conductor and the y-axis of the three-dimensional rectangular coordinate system, and the horizontal offset is the distance between the intersection of the target twin-core conductor and the x-axis in the three-dimensional rectangular coordinate system to the origin of the three-dimensional rectangular coordinate system, and input the mathematical model and the test value of the x-component of magnetic induction intensity into the differential evolution algorithm;

[0134] The generation module 104 is used to determine the population size and the upper and lower boundaries of each dimension according to the range of the conductor position and the upper and lower limits of the current in the actual working condition, and generate an initial candidate set including the live wire current, the neutral wire current, the concealed installation depth, the y-axis deflection angle, and the horizontal offset as the initial population within the population size and the upper and lower boundaries of each dimension, and each individual in the initial population is composed of a dimensional combination, and the dimensional combination includes the live wire current, the neutral wire current, the concealed installation depth, the y-axis deflection angle, and the horizontal offset;

[0135] The calculation module 105 is used to calculate the magnetic induction intensity component of each individual in the population according to the mathematical model as the magnetic induction intensity component calculation value in the differential evolution algorithm, and calculate the sum of squares of the differences between each magnetic induction intensity calculation value and each magnetic induction intensity component test value;

[0136] A construction module 106 is used to construct an objective function in the differential evolution algorithm, taking the upper and lower boundaries of each dimension as inequality constraints, and calculating the fitness value of each individual by the objective function and the sum of the squares of the difference;

[0137] A mutation crossover module 107 is used to select the minimum fitness value among all fitness values, mutate the individual corresponding to the minimum fitness value through the perturbation vector to obtain a mutated individual, and cross the mutated individual through the crossover factor to obtain a new individual;

[0138] A judgment module 108 is used to calculate the fitness value of the new individual through the objective function, and to judge whether the fitness value of the new individual is less than the minimum fitness value;

[0139] If the fitness value of the new individual is greater than the minimum fitness value, the perturbation vector and crossover factor are updated, and the updated perturbation vector and crossover factor are used as the perturbation vector and crossover factor of the mutation crossover module and returned to perform mutation and crossover;

[0140] If the fitness value of the new individual is less than the minimum fitness value, the new individual is mutated and crossovered multiple times, and the individual when the number of mutations and crossovers reaches the preset number of times is taken as the optimal individual, and the residual current and conductor position of the target double-core conductor are calculated by combining the dimensions of the optimal individual.

[0141] The establishment module 103 is specifically used to use the magnetic induction intensity x component test value as the adjustment index, and to rotate each magnetoresistive sensor in the magnetoresistive sensor array clockwise or counterclockwise with the origin of the three-dimensional rectangular coordinate system as the center; when B x =0, sinβ=0, β=0, and the magnetoresistive sensor array is located at the optimal test angle; a mathematical model is established between the magnetic induction intensity and the live wire current, neutral wire current, concealed depth, y-axis deflection, and horizontal offset of the target double-core conductor. The y-axis deflection is the angle between the target double-core conductor and the y-axis of the three-dimensional rectangular coordinate system, and the horizontal offset is the distance between the intersection of the target double-core conductor and the x-axis in the three-dimensional rectangular coordinate system to the origin of the three-dimensional rectangular coordinate system; the mathematical model and the test value of the x-component of the magnetic induction intensity are input into the differential evolution algorithm.

[0142] The establishment module 103 includes:

[0143] The rotation submodule is used to rotate each magnetoresistive sensor in the magnetoresistive sensor array clockwise or counterclockwise with the origin of the three-dimensional rectangular coordinate system as the center, using the test value of the x component of the magnetic induction intensity as the adjustment index;

[0144] When B x =0, sinβ=0, β=0, and the magnetoresistive sensor array is located at the optimal test angle;

[0145] A submodule is established to establish a mathematical model between the magnetic induction intensity and the live wire current, neutral wire current, concealed depth, y-axis deflection, and horizontal offset of the target twin-core conductor. The y-axis deflection is the angle between the target twin-core conductor and the y-axis of the three-dimensional rectangular coordinate system. The horizontal offset is the distance between the intersection of the target twin-core conductor and the x-axis in the three-dimensional rectangular coordinate system and the origin of the three-dimensional rectangular coordinate system.

[0146] The input submodule is used to input the mathematical model and the test value of the x component of the magnetic induction intensity into the differential evolution algorithm.

[0147] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0148] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by 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 embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0149] 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 implements a non-contact residual current detection method when executing the computer program.

[0150] like Figure 6 As shown, the terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 6 Only one processor is shown in the figure), a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, wherein the processor D100 implements the steps in any of the above-mentioned method embodiments when executing the computer program D102. Alternatively, the processor D100 implements the functions of each module / unit in the above-mentioned device embodiments when executing the computer program D102.

[0151] The terminal device D10 may be a computing device such as a desktop computer, a notebook, a PDA, a server, a server cluster, a cloud server, etc. The terminal device may include, but is not limited to, a processor D100 and a memory D101. Those skilled in the art will appreciate that Figure 6 This is only an example of the terminal device D10 and does not constitute a limitation on the terminal device D10. The terminal device D10 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0152] The processor D100 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0153] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may also be an external storage device of the terminal device D10, such as a plug-in hard disk, a smart memory card (SMC, SmartMedia Card), a secure digital (SD, Secure Digital) card, a flash card (Flash Card), etc. equipped on the terminal device D10. Further, the memory D101 may also include both an internal storage unit of the terminal device D10 and an external storage device. The memory D101 is used to store an operating system, an application program, a boot loader (BootLoader), data and other programs, such as the program code of the computer program, etc. The memory D101 may also be used to temporarily store data that has been output or is to be output.

[0154] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0155] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by 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 embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0156] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, a non-contact residual current detection method is implemented.

[0157] If the integrated unit is implemented in the form of 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, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the construction device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, U disk, mobile hard disk, disk or optical disk, etc. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0158] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A non-contact residual current detection method, characterized in that: include: Step 1, constructing a three-dimensional rectangular coordinate system on a target wall on which a target double-core conductor is laid, wherein the z-axis of the three-dimensional rectangular coordinate system is perpendicular to the target wall and faces outward, and the x-axis of the three-dimensional rectangular coordinate system is used as a main sensitive axis, and five magnetoresistive sensors are evenly arranged on the main sensitive axis to obtain a magnetoresistive sensor array; Step 2, based on the voltage output by each magnetoresistive sensor in the magnetoresistive sensor array, obtaining the magnetic induction intensity of the target double-core wire at each magnetoresistive sensor as a magnetic induction intensity x component test value, wherein the target double-core wire consists of a live wire and a neutral wire; Step 3, establishing a mathematical model between the magnetic induction intensity and the live wire current, neutral wire current, concealed depth, y-axis deflection, and horizontal offset of the target twin-core conductor, wherein the y-axis deflection is the angle between the target twin-core conductor and the y-axis of the three-dimensional rectangular coordinate system, and the horizontal offset is the distance between the intersection of the target twin-core conductor and the x-axis in the three-dimensional rectangular coordinate system and the origin of the three-dimensional rectangular coordinate system, and inputting the mathematical model and the magnetic induction intensity x-component test value into the differential evolution algorithm; Step 4, determining the population size and the upper and lower boundaries of each dimension according to the range of the conductor position and the upper and lower limits of the current in the actual working condition, generating an initial candidate set including the live wire current, the neutral wire current, the concealed installation depth, the y-axis deflection angle, and the horizontal offset as the initial population within the population size and the upper and lower boundaries of each dimension, and each individual in the initial population is composed of a dimensional combination, and the dimensional combination includes the live wire current, the neutral wire current, the concealed installation depth, the y-axis deflection angle, and the horizontal offset; Step 5, in the differential evolution algorithm, based on each individual in the population, the magnetic induction intensity component of each individual is calculated according to the mathematical model as a magnetic induction intensity component calculation value, and the sum of the squares of the differences between each magnetic induction intensity calculation value and each magnetic induction intensity component test value is calculated; Step 6, constructing an objective function in the differential evolution algorithm, taking the upper and lower boundaries of each dimension as inequality constraints, and calculating the fitness value of each individual by the objective function and the sum of the squares of the difference; Step 7, selecting the minimum fitness value from all fitness values, mutating the individual corresponding to the minimum fitness value by using the perturbation vector to obtain a mutated individual, and crossovering the mutated individual by using the crossover factor to obtain a new individual; Step 8, calculating the fitness value of the new individual through the objective function, and determining whether the fitness value of the new individual is less than the minimum fitness value; If the fitness value of the new individual is greater than the minimum fitness value, the disturbance vector and the cross factor are updated, and the updated disturbance vector and the cross factor are used as the disturbance vector and the cross factor in step 7 and the process returns to step 7; If the fitness value of the new individual is less than the minimum fitness value, the new individual is mutated and crossovered multiple times, and the individual when the number of mutations and crossovers reaches a preset number of times is taken as the optimal individual, and the residual current and the conductor position of the target double-core conductor are calculated by combining the dimensions of the optimal individual.

2. The non-contact residual current detection method according to claim 1, characterized in that: The five magnetoresistive sensors are all TMR linear magnetic field sensors, the distance between every two adjacent magnetoresistive sensors is 15 mm, and the third magnetoresistive sensor is located at the origin of the three-dimensional rectangular coordinate system.

3. The non-contact residual current detection method according to claim 2, characterized in that: The magnetic induction intensity B x The expression is: Where, μ0 represents the magnetic permeability in vacuum, I1 represents the live wire current, I2 represents the neutral wire current, r1 represents the distance between the magnetoresistive sensor array and the live wire, r2 represents the distance between the magnetoresistive sensor array and the neutral wire, α i1 It represents the angle between the perpendicular line from the ith magnetoresistive sensor in the magnetoresistive sensor array to the live line I1 and the height from the perpendicular point to the plane where the ith magnetoresistive sensor is located, α i2 represents the angle between the perpendicular line from the i-th magnetoresistive sensor to the zero line I2 in the magnetoresistive sensor array and the height of the perpendicular point to the plane where the i-th magnetoresistive sensor is located, β represents the angle between the energized conductor and the y-axis, and δ represents the angle between the magnetoresistive sensor plane and the energized conductor.

4. The non-contact residual current detection method according to claim 3, characterized in that: The distance between the magnetoresistive sensor array and the live wire includes the distance between each magnetoresistive sensor and the live wire, and the distance calculation expression is as follows: The distance between the magnetoresistive sensor array and the zero line includes the distance between each magnetoresistive sensor and the zero line. The distance calculation expression is as follows: Among them, r 11 Represents the distance from the first magnetoresistive sensor to the live wire I1, r 21 Represents the distance from the second magnetoresistive sensor to the live wire I1, r 31 Represents the distance from the third magnetoresistive sensor to the live wire I1, r 41 Represents the distance from the fourth magnetoresistive sensor to the live wire I1, r 51 Represents the distance from the fifth magnetoresistive sensor to the live wire I1, r 12 Represents the distance from the first magnetoresistive sensor to the zero line I2, r 22 Represents the distance from the second magnetoresistive sensor to the zero line I2, r 32 Represents the distance from the third magnetoresistive sensor to the zero line I2, r 42 Represents the distance from the fourth magnetoresistive sensor to the zero line I2, r 52 It represents the distance from the 5th magnetoresistive sensor to the zero line I2, h represents the dark depth, and D represents the horizontal offset.

5. The non-contact residual current detection method according to claim 4, characterized in that: The step 3 comprises: Taking the magnetic induction intensity x component test value as the adjustment index and taking the origin of the three-dimensional rectangular coordinate system as the center, each magnetoresistive sensor in the magnetoresistive sensor array is rotated clockwise or counterclockwise; When B x =0, sinβ=0, β=0, and the magnetoresistive sensor array is located at the optimal test angle; Establish a mathematical model between the magnetic induction intensity and the live current, neutral current, concealed depth, y-axis deflection, and horizontal offset of the target twin-core conductor, wherein the y-axis deflection is the angle between the target twin-core conductor and the y-axis of the three-dimensional rectangular coordinate system, and the horizontal offset is the distance between the intersection of the target twin-core conductor and the x-axis in the three-dimensional rectangular coordinate system and the origin of the three-dimensional rectangular coordinate system; The mathematical model and the magnetic induction intensity x component test value are input into the differential evolution algorithm.

6. The non-contact residual current detection method according to claim 5, characterized in that: The mathematical model is: Among them, I1, I2, h, β, and D represent the live wire current, neutral wire current, concealed depth, y-axis deflection angle, and horizontal offset of the target double-core conductor, respectively.

7. A non-contact residual current detection device, characterized in that: include: A construction module is provided, wherein a three-dimensional rectangular coordinate system is constructed on a target wall on which a target double-core conductor is laid, wherein a z-axis of the three-dimensional rectangular coordinate system is perpendicular to the target wall and faces outward, and an x-axis of the three-dimensional rectangular coordinate system is used as a main sensitive axis, and five magnetoresistive sensors are arranged at equal intervals on the main sensitive axis to obtain a magnetoresistive sensor array; An acquisition module, configured to acquire the magnetic induction intensity of a target double-core conductor at each of the magnetoresistive sensors as a magnetic induction intensity x-component test value based on the voltage output by each of the magnetoresistive sensors in the magnetoresistive sensor array, wherein the target double-core conductor consists of a live wire and a neutral wire; Establishing a module, used to establish a mathematical model between the magnetic induction intensity and the live wire current, neutral wire current, concealed depth, y-axis deflection, and horizontal offset of the target twin-core conductor, wherein the y-axis deflection is the angle between the target twin-core conductor and the y-axis of the three-dimensional rectangular coordinate system, and the horizontal offset is the distance between the intersection of the target twin-core conductor and the x-axis in the three-dimensional rectangular coordinate system and the origin of the three-dimensional rectangular coordinate system, and input the mathematical model and the magnetic induction intensity x-component test value into a differential evolution algorithm; A generation module is used to determine the population size and the upper and lower boundaries of each dimension according to the range of the conductor position and the upper and lower limits of the current in the actual working condition, generate an initial candidate set including the live wire current, the neutral wire current, the concealed installation depth, the y-axis deflection angle, and the horizontal offset as the initial population within the population size and the upper and lower boundaries of each dimension, and each individual in the initial population is composed of a dimensional combination, and the dimensional combination includes the live wire current, the neutral wire current, the concealed installation depth, the y-axis deflection angle, and the horizontal offset; A calculation module, used for calculating the magnetic induction intensity component of each individual in the population according to the mathematical model in the differential evolution algorithm as a magnetic induction intensity component calculation value, and calculating the sum of squares of differences between each magnetic induction intensity calculation value and each magnetic induction intensity component test value; A construction module, used to construct an objective function in the differential evolution algorithm, taking the upper and lower boundaries of each dimension as inequality constraints, and calculating the fitness value of each individual by the objective function and the sum of the squares of the difference; A mutation crossover module is used to select the minimum fitness value among all fitness values, mutate the individual corresponding to the minimum fitness value through the perturbation vector to obtain the mutated individual, and cross the mutated individual through the crossover factor to obtain a new individual; A judgment module, used for calculating the fitness value of the new individual through the objective function, and judging whether the fitness value of the new individual is less than the minimum fitness value; If the fitness value of the new individual is greater than the minimum fitness value, the disturbance vector and the crossover factor are updated, and the updated disturbance vector and the crossover factor are used as the disturbance vector and the crossover factor of the mutation and crossover module, and the mutation and crossover are returned to be executed; If the fitness value of the new individual is less than the minimum fitness value, the new individual is mutated and crossovered multiple times, and the individual when the number of mutations and crossovers reaches a preset number of times is taken as the optimal individual, and the residual current and the conductor position of the target double-core conductor are calculated by combining the dimensions of the optimal individual.

8. 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, the non-contact residual current detection method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the non-contact residual current detection method according to any one of claims 1 to 6 is implemented.

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