Cable joint detection method and system

Abstract

The invention discloses a cable joint detection method and system, and relates to the technical field of cable joint detection, and the method comprises the steps: selecting X-rays with different voltages to detect a cable joint; carrying out secondary detection on the detected cable joint by adopting a multi-angle transillumination arrangement technology, and constructing a defect data feature library of the cable joint; and optimizing X-ray parameters based on the defect feature image of the cable joint, and detecting the multi-angle variable-voltage X-ray parameters. According to the invention, the detection precision and efficiency are improved, the defects of the cable joint are comprehensively covered, the problem of single detection means in the prior art is overcome, the parameter setting of X-rays is optimized, the stability and reliability of the detection result are improved, the high efficiency and accuracy of the detection process are ensured, and the detection cost is reduced. The problems of long time consumption and low efficiency in a traditional detection method are solved, so that the comprehensive performance of cable joint defect detection is greatly improved, and a powerful technical guarantee is provided for safe and stable operation of a power system.

Description

Technical Field

[0001] The present invention relates to the technical field of cable joint detection, and in particular to a cable joint detection method and system. Background Art

[0002] In today's society, with the rapid development of power systems, cable joints, as important components of power transmission, have a quality and safety that is directly related to the stable operation of the power system. However, due to the defects that may occur in the manufacturing and use of cable joints, such as cracks, voids and foreign matter, the safe operation of the power system has brought great hidden dangers. Therefore, how to efficiently and accurately detect the defects of cable joints to ensure the safe operation of the power system has become an important issue that needs to be solved urgently.

[0003] The prior art has various technical defects in cable joint detection, mainly including low detection accuracy, difficulty in identifying minor defects, single detection means, inability to comprehensively cover various defect types, complex detection process, long time consumption, low efficiency, susceptibility to interference and poor stability. The present invention solves the problems of low detection accuracy, incomplete coverage, low efficiency and poor stability in the cable joint detection process of the prior art by adopting technical means such as multi-angle transformer X-ray detection and cable joint material density difference analysis, multi-objective optimization model construction and intelligent defect detection model. Summary of the invention

[0004] In view of the problems existing in the existing multi-source power grid information fusion and system based on the Internet of Things, the present invention is proposed.

[0005] Therefore, the purpose of the present invention is to provide a cable joint detection method and system. Aiming at the defects of low detection accuracy, incomplete coverage, low efficiency and poor stability in the prior art of cable joint detection, the present invention adopts technical means such as multi-angle transformer X-ray detection, cable joint material density difference analysis, multi-objective optimization model construction and intelligent defect detection model to solve the problems.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a cable joint detection method, which includes selecting X-rays of different voltages to detect the cable joint according to the density difference of the cable joint material;

[0008] The multi-angle transillumination arrangement technology is used to perform secondary inspection on the cable joints after inspection. Based on the existing cable joint defects, the multi-angle variable voltage X-ray is used to image the cable joints after secondary inspection to build a defect data feature library of the cable joints.

[0009] The defect data feature library is used to extract the defect feature images of the cable joint after imaging. The X-ray parameters are optimized based on the defect feature images of the cable joint. An intelligent detection model for cable joint defects is constructed to detect the X-ray parameters of multi-angle transformers.

[0010] As a preferred embodiment of the cable joint detection method of the present invention, the density difference comprises calculating the density difference of the metal or non-metal material of the cable joint material, selecting X-rays with different voltages according to the density difference, pre-detecting the cable joint based on the selected X-rays with different voltages, screening out the cable joints of the same material, calculating the total volume and the total mass of the cable joint, and calculating the overall density of the cable joint according to the total volume and the total mass of the cable joint;

[0011] The calculation formula for the total volume of the cable connector is:

[0012]

[0013] Among them, V total represents the total volume of the cable joint, n represents the number of types of materials, V i represents the volume of the i-th material;

[0014] The calculation formula for the total mass of the cable connector is:

[0015]

[0016] Among them, m total Indicates the total mass of the cable connector, n indicates the number of types of materials, m i Represents the mass of the i-th material.

[0017] As a preferred solution of the cable joint detection method of the present invention, the calculation formula for calculating the overall density of the cable joint is:

[0018]

[0019] Among them, ρ total Indicates the overall density of cable joints, m total Indicates the total mass of the cable connector, V total Indicates the total volume of the cable connector, m i represents the mass of the ith material, V i represents the volume of the i-th material;

[0020] Based on the i and j of any two materials, the density difference is calculated. The specific calculation formula is:

[0021] Δρ ij =|ρ i -ρ j |

[0022] When the cable connector has two or more materials, the average density difference is calculated as the basis for selecting the X-ray voltage. The specific formula for calculating the average density difference is:

[0023]

[0024] Among them, Δ ρavg represents the average density difference, n represents the number of material types, |ρ i -ρ j | represents the absolute value of the density difference between the i-th material and the j-th material.

[0025] As a preferred solution of the cable joint detection method of the present invention, the detection of the cable joint includes calculating the voltage range according to the average density difference, and the specific calculation formula of the voltage range is:

[0026]

[0027] Among them, k1, k2, α1, α2, b1 and b2 are constants fitted by experimental data, U min Indicates the minimum range of voltage, U max Indicates the maximum range of voltage, Δ ρavg represents the average density difference;

[0028] The voltage range is optimized by using the matching rule between the cable joint material characteristics and the voltage adaptability of the X-ray, and a mapping table M between the cable joint material characteristics and the X-ray is established. The specific formula of the mapping table M is:

[0029] M[i, j] = f(ρ i , U j )

[0030] Where M[i, j] represents the adaptability score of the i-th material at the j-th voltage level, f represents the multivariate function, and ρ i represents the density of the i-th material, U j represents the jth voltage level;

[0031] Combining the density difference, cable joint material properties and X-ray mapping table, a multi-objective optimization model is established to select the optimal voltage. The establishment formula of the multi-objective optimization model is:

[0032]

[0033] Among them, ω j represents the weight of the jth voltage level, n represents the number of material types, and m represents the number of voltage levels;

[0034] According to the established multi-objective optimization model, the average adaptability score of the i-th material at the j-th voltage level is calculated. The specific calculation formula is:

[0035]

[0036] Among them, M avg represents the average adaptability score, M[i, j] represents the adaptability score of the i-th material at the j-th voltage level, n represents the number of material types, and m represents the number of voltage levels;

[0037] The calculated average adaptability score is combined with the adaptability score value to calculate the standard value of the average density difference of the cable joint, and the threshold is set according to the average density difference and the standard value;

[0038] The specific formula for calculating the standard value of the average density difference of the cable joint is:

[0039]

[0040] Among them, σ Δρ It indicates the standard value of the average density difference of the cable joint, n indicates the number of material types, m indicates the number of voltage levels, M avg represents the average adaptability score, M[i, j] represents the adaptability score of the i-th material at the j-th voltage level;

[0041] The specific calculation formula for setting the threshold is:

[0042] Δ ρthreshold =Δ ραvg +k·σ Δρ

[0043] M threshold =M avg -k·σ Δρ

[0044] Among them, Δ ρavg represents the average density difference, σ Δρ It represents the standard value of the average density difference of cable joints, and k represents a constant.

[0045] As a preferred solution of the cable joint detection method of the present invention, the secondary detection of the detected cable joint comprises the following steps:

[0046] If the calculated average density difference Δ ρavg Less than the set threshold Δ ρthreshold When the voltage range is initially selected [U min , U max ] min The value is used as a benchmark to test low density difference materials;

[0047] Otherwise, select U max The voltage range is used to detect high density difference materials;

[0048] When the density of the material ρ i When it continues to increase, use high voltage U j Otherwise, use low voltage U j Conduct testing;

[0049] If the adaptability score M[i, j] of the i-th material at the j-th voltage level is greater than the set threshold M threshold When , the weight of voltage level ω is increased j ; Otherwise, reduce the weight of the voltage level ω j .

[0050] As a preferred solution of the cable joint detection method of the present invention, wherein: the imaging of the cable joint after the secondary detection includes determining the imaging according to the multi-factor optimized voltage selection, and the specific steps are as follows:

[0051] If the initially selected voltage level U j When the adaptability scores of all materials are met, they are all above the set threshold M threshold , then select the voltage level for detection;

[0052] Otherwise, the voltage level is adjusted and the fitness score is recalculated;

[0053] When the detected voltage level satisfies the detected image quality Q, the voltage level is continued to be used for detection; otherwise, the voltage parameter is dynamically adjusted according to the image quality Q.

[0054] As a preferred solution of the cable joint detection method of the present invention, the detection of the X-ray parameters of the multi-angle transformer includes imaging determined based on multi-factor optimization voltage selection, and after multiple iterations of optimization, the image quality Q reaches a normal detection level. The specific steps are as follows:

[0055] After multiple iterations of optimization, the image quality Q reaches the normal level detected, and the final voltage level U is determined. opt ;

[0056] Otherwise, continue to adjust the voltage level, re-evaluate and adjust the parameters of the optimization model, and perform a new round of detection until the image quality Q index reaches normal.

[0057] In a second aspect, an embodiment of the present invention provides a cable joint detection system, which includes: a selection module, which selects X-rays of different voltages to detect the cable joint according to the density difference of the cable joint material;

[0058] A construction module, which uses a multi-angle transillumination arrangement technology to perform secondary inspection on the inspected cable joints and construct a defect data feature library of the cable joints;

[0059] The detection module uses the defect data feature library to extract the cable joint defect feature image after imaging, builds the cable joint defect intelligent detection model, and detects the X-ray parameters of multi-angle transformer.

[0060] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, any step of the above-mentioned cable connector detection method is implemented.

[0061] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned cable joint detection method is implemented.

[0062] The beneficial effects of the present invention are as follows: the present invention improves the detection accuracy and efficiency by selecting X-rays in the optimal voltage range for detection based on the analysis of the density differences of cable joint materials, and adopts multi-angle transillumination arrangement technology and multi-angle transformer X-ray detection technology to achieve comprehensive coverage of cable joint defects, overcoming the problem of single detection means in the prior art, and optimizing the parameter settings of X-rays by constructing a cable joint defect data feature library and an intelligent detection model, thereby improving the stability and reliability of the detection results. The application of a multi-objective optimization model ensures the efficiency and accuracy of the detection process, and solves the problems of long time consumption and low efficiency in traditional detection methods, thereby greatly improving the comprehensive performance of cable joint defect detection and providing a strong technical guarantee for the safe and stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0064] Figure 1 A flow chart of a cable joint detection method and system provided by an embodiment of the present invention.

[0065] Figure 2 A specific flow chart of a cable joint detection method and a system defect detection method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0066] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0067] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0068] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0069] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0070] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships 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 direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0071] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected 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.

[0072] Example 1

[0073] Reference Figure 1 and Figure 2, which is the first embodiment of the present invention, provides a cable joint detection method, comprising:

[0074] S1: According to the density difference of cable joint materials, select X-rays with different voltages to detect the cable joints.

[0075] The density difference includes calculating the density difference of metal or non-metal materials of the cable joint material, selecting X-rays with different voltages according to the density difference, pre-detecting the cable joint based on the selected X-rays with different voltages, screening out the cable joints of the same material, calculating the total volume and the total mass of the cable joint, and calculating the overall density of the cable joint according to the total volume and the total mass of the cable joint;

[0076] The total volume of the cable connector is calculated as:

[0077]

[0078] Among them, V total represents the total volume of the cable joint, n represents the number of types of materials, V i represents the volume of the i-th material;

[0079] The total mass of the cable connector is calculated as follows:

[0080]

[0081] Among them, m total Indicates the total mass of the cable connector, n indicates the number of types of materials, m i Represents the mass of the i-th material.

[0082] S1.1: The formula for calculating the overall density of cable joints is:

[0083]

[0084] Among them, ρ total Indicates the overall density of cable joints, m total Indicates the total mass of the cable connector, V total Indicates the total volume of the cable connector, m i represents the mass of the ith material, V i represents the volume of the i-th material;

[0085] Based on the i and j of any two materials, the density difference is calculated. The specific calculation formula is:

[0086] Δρ ij =|ρ i -ρ j |

[0087] When the cable joint has two or more materials, the average density difference is calculated as the basis for selecting the X-ray voltage. The specific formula for calculating the average density difference is:

[0088]

[0089] Among them, Δ ρavg represents the average density difference, n represents the number of material types, |ρ i -ρ j | represents the absolute value of the density difference between the i-th material and the j-th material.

[0090] Furthermore, in the cable joint detection method of the present invention, according to the density difference of the cable joint material, X-rays of different voltages are selected to detect the cable joint, and the density difference of the metal or non-metal material in the cable joint is calculated. For example, if the cable joint is composed of copper and PVC, the density of copper is 8.96g / cm 3 , the density of PVC is 1.38g / cm 3 , the density difference between the two is calculated to be 7.58 g / cm 3 Based on this density difference, different voltages of X-rays are selected. Usually, for high-density materials such as copper, high voltage X-rays are selected for detection, while for low-density materials such as PVC, low voltage X-rays are selected for detection. During the detection process, cable joints of the same material are screened out, and the total volume and total mass of the cable joints are calculated. For example, assuming that the cable joint is composed of 70% copper and 30% PVC, the total volume of the cable joint is 10cm 3 , then the volume of copper is 7 cm 3 , the volume of PVC is 3cm 3 Calculate the total mass of the cable connector. Assuming that the mass of copper is 62.72g and the mass of PVC is 4.14g, the total mass of the cable connector is 66.86g. Based on the total volume and total mass of the cable connector, calculate its overall density, and the result is 6.686g / cm 3 Based on these data, the density difference between the two materials is calculated, and the average density difference is further calculated. Assuming that there are three materials in the cable joint, the average density difference is 4.35g / cm 3 , based on these calculation results, the appropriate X-ray voltage is selected for more accurate detection.

[0091] S1.2: Testing of cable joints includes calculating the voltage range based on the average density difference. The specific calculation formula for the voltage range is:

[0092]

[0093] Among them, k1, k2, α1, α2, b1 and b2 are constants fitted by experimental data, Umin Indicates the minimum range of voltage, U max Indicates the maximum range of voltage, Δ ρavg represents the average density difference;

[0094] The voltage range is optimized by using the matching rule between the cable joint material characteristics and the voltage adaptability of the X-ray, and a mapping table M between the cable joint material characteristics and the X-ray is established. The specific formula of the mapping table M is:

[0095] M[i, j] = f(ρ i , U j )

[0096] Where M[i, j] represents the adaptability score of the i-th material at the j-th voltage level, f represents the multivariate function, and ρ i represents the density of the i-th material, U j represents the jth voltage level;

[0097] Combining the density difference, cable joint material properties and X-ray mapping table, a multi-objective optimization model is established to select the optimal voltage. The formula for establishing the multi-objective optimization model is:

[0098]

[0099] Among them, ω j represents the weight of the jth voltage level, n represents the number of material types, and m represents the number of voltage levels;

[0100] According to the established multi-objective optimization model, the average adaptability score of the i-th material at the j-th voltage level is calculated. The specific calculation formula is:

[0101]

[0102] Among them, M avg represents the average adaptability score, M[i, j] represents the adaptability score of the i-th material at the j-th voltage level, n represents the number of material types, and m represents the number of voltage levels;

[0103] The calculated average adaptability score is combined with the adaptability score value to calculate the standard value of the average density difference of the cable joint, and the threshold is set according to the average density difference and the standard value;

[0104] The specific formula for calculating the standard value of the average density difference of the cable joint is:

[0105]

[0106] Among them, σ ΔρIt indicates the standard value of the average density difference of the cable joint, n indicates the number of material types, m indicates the number of voltage levels, M avg represents the average adaptability score, M[i, j] represents the adaptability score of the i-th material at the j-th voltage level;

[0107] The specific calculation formula for setting the threshold is:

[0108] Δ ρthreshold =Δ ραvg +k·σ Δρ

[0109] M threshold =M avg -k·σ Δρ

[0110] Among them, Δ ρavg represents the average density difference, σ Δρ It represents the standard value of the average density difference of cable joints, and k represents a constant.

[0111] Furthermore, in the cable joint detection method of the present invention, determining the appropriate X-ray voltage range based on the density difference of the cable joint material is one of the key steps. By calculating the average density difference, the system can derive the most suitable voltage range to ensure effective pre-detection and detailed imaging of cable joints of different materials. The optimized voltage selection depends on the characteristics of various materials and their adaptability at different voltage levels, thereby improving the accuracy and efficiency of detection and ensuring that possible minor defects in the cable joints can be captured.

[0112] S2: Use multi-angle transillumination arrangement technology to perform secondary inspection on the cable joints after inspection. Based on the existing cable joint defects, use multi-angle variable voltage X-rays to image the cable joints after secondary inspection to build a defect data feature library of the cable joints.

[0113] Among them, the secondary inspection of the detected cable joint includes the following steps:

[0114] If the calculated average density difference Δ ρavg Less than the set threshold Δ ρthreshold When the voltage range is initially selected [U min , U max ] min The value is used as a benchmark to test low density difference materials;

[0115] Otherwise, select U max The voltage range is used to detect high density difference materials;

[0116] When the density of the material ρ i When it continues to increase, use high voltage Uj Otherwise, use low voltage U j Conduct testing;

[0117] If the adaptability score M[i, j] of the i-th material at the j-th voltage level is greater than the set threshold M threshold When , the weight of voltage level ω is increased j ; Otherwise, reduce the weight of the voltage level ω j .

[0118] Preferably, in the cable joint detection method of the present invention, a multi-angle transillumination arrangement technology is used to perform secondary detection on the cable joint. For example, when the calculated average density difference is 0.03, the system selects a reference voltage range of 70-90kV, mainly for low-density difference materials for detection, and when the average density difference is 0.08, the voltage range is selected as 90-120kV, focusing on the detection of high-density difference materials. In addition, for the adaptability score setting of materials at different voltage levels, if the adaptability score of a certain material at 100kV is higher than the set threshold of 0.7, the system will increase the weight of the voltage level to improve the detection accuracy of the material, otherwise, the weight of the voltage level will be reduced to reduce energy consumption and ensure the effectiveness of the detection. The combination of these data and strategies effectively improves the accuracy and efficiency of cable joint detection, builds a comprehensive defect data feature library, and supports subsequent analysis and maintenance work. The comparison of cable joint detection parameters is shown in Table 1 below:

[0119] Table 1 Cable joint detection parameter comparison table

[0120] Average density difference Voltage range(kV) Material adaptability score impact 0.01 50-70 No special adjustments 0.02 60-80 No special adjustments 0.03 70-90 No special adjustments 0.04 75-95 No special adjustments 0.05 80-100 Increase the weight of the adaptability score at 100kV 0.06 85-110 Increase the weight of the adaptability score at 100kV 0.07 88-115 Increase the weight of the adaptability score at 100kV 0.08 90-120 Increase the weight of the adaptability score at 100kV

[0121] The above table shows the voltage range selected under different average density difference conditions and its influence. As the average density difference increases, the voltage range selected by the system changes from low to high to adapt to the material characteristics of cable joints with different density differences. Especially when the average density difference is large, the system tends to select a higher voltage range and optimize the adaptability score of the material at the 100kV voltage level under this range to improve the detection accuracy and effectiveness.

[0122] S2.1: Imaging the cable joint after secondary inspection includes determining the imaging based on multi-factor optimization voltage selection, and the specific steps are as follows:

[0123] If the initially selected voltage level U j When the adaptability scores of all materials are met, they are all above the set threshold M threshold , then select the voltage level for detection;

[0124] Otherwise, the voltage level is adjusted and the fitness score is recalculated;

[0125] When the detected voltage level satisfies the detected image quality Q, the voltage level is continued to be used for detection; otherwise, the voltage parameter is dynamically adjusted according to the image quality Q.

[0126] When the system initially selects a voltage level of 100kV for detection, it evaluates the adaptability score of each material at 100kV. If the scores of all materials reach or exceed the preset threshold of 0.7, the system will confirm that the 100kV voltage is suitable for detection. However, if the scores of some materials are below the threshold, the system will try to increase the voltage to 110kV or higher and re-evaluate the adaptability scores of each material. In addition, the system will adjust the voltage parameters based on real-time image quality feedback to ensure that the final imaging quality meets the requirements.

[0127] S3: Use the defect data feature library to extract the cable joint defect feature image after imaging, optimize the X-ray parameters based on the cable joint defect feature image, build a cable joint defect intelligent detection model, and detect the X-ray parameters of multi-angle transformer.

[0128] Among them, the detection of X-ray parameters of multi-angle voltage transformation includes imaging determined based on multi-factor optimization voltage selection. After multiple iterations of optimization, the image quality Q reaches a normal detection level. The specific steps are as follows:

[0129] After multiple iterations of optimization, the image quality Q reaches the normal level detected, and the final voltage level U is determined. opt ;

[0130] Otherwise, continue to adjust the voltage level, re-evaluate and adjust the parameters of the optimization model, and perform a new round of detection until the image quality Q index reaches normal.

[0131] Furthermore, during the intelligent detection of cable joint defects, the system uses a pre-built defect data feature library to extract the characteristic images of cable joint defects after imaging, and adjusts the X-ray parameters through multi-factor optimization. The system continuously adjusts the voltage level and X-ray projection angle through multiple iterative optimizations to ensure that the generated image quality reaches the standard detection level. This process continues until the system can generate high-quality images that meet the requirements, thereby effectively improving the accuracy and reliability of cable joint defect detection.

[0132] In a preferred embodiment, a cable joint detection system includes a selection module, which selects X-rays of different voltages to detect the cable joint according to the density difference of the cable joint material;

[0133] A construction module, which uses a multi-angle transillumination arrangement technology to perform secondary inspection on the inspected cable joints and construct a defect data feature library of the cable joints;

[0134] The detection module uses the defect data feature library to extract the cable joint defect feature image after imaging, builds the cable joint defect intelligent detection model, and detects the X-ray parameters of multi-angle transformer.

[0135] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0136] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0137] In summary, the present invention improves the detection accuracy and efficiency by selecting X-rays in the optimal voltage range for detection based on the analysis of the density differences of cable joint materials. It adopts multi-angle transillumination arrangement technology and multi-angle transformer X-ray detection technology to achieve comprehensive coverage of cable joint defects and overcome the problem of single detection means in the prior art. By constructing a cable joint defect data feature library and an intelligent detection model, the parameter setting of the X-ray is optimized, and the stability and reliability of the detection results are improved. The application of the multi-objective optimization model ensures the efficiency and accuracy of the detection process, solves the problems of long time consumption and low efficiency in traditional detection methods, thereby greatly improving the comprehensive performance of cable joint defect detection and providing a strong technical guarantee for the safe and stable operation of the power system.

[0138] Example 2

[0139] Reference Figure 1 and Figure 2 , which is the second embodiment of the present invention, and this embodiment provides a cable joint detection method. In order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through simulation experiments.

[0140] During the experiment, detailed research and testing were carried out on the intelligent detection of cable joint defects. First, a series of defect feature images were extracted from the imaged cable joints using the defect data feature library. These feature images cover cable joint defects of different types and degrees, providing important data support for the subsequent construction of intelligent detection models.

[0141] The cable joints were imaged using multi-angle voltage-changing X-ray technology, and the X-ray parameters were optimized. An initial voltage level of 80 kV was selected for testing, and the imaging effects of various defects at this voltage were evaluated. The results showed that the clarity of some defect images did not meet the expected level. Therefore, the voltage was adjusted to 90 kV based on experimental feedback, and the X-ray projection angle was reset.

[0142] After multiple iterations of optimization and adjustment, the voltage level of 100kV was finally confirmed as the optimal choice. At this voltage, high-quality cable joint defect feature images were successfully generated. The images not only met the detection standards, but also provided an important experimental basis for subsequent data analysis and algorithm optimization. The comparison of the impact of cable joint voltage level on image clarity is shown in Table 2 below:

[0143] Table 2 Comparison of the impact of cable connector voltage level on image clarity

[0144] Voltage Level Defect image clarity assessment 80 Not as clear as expected 85 Slightly improved 90 Further improvement 95 Meeting expectations 100 high quality

[0145] The above table shows the evaluation results of cable joint defect image clarity at different voltage levels. As the voltage gradually increases from 80kV to 100kV, a trend of gradually improving image clarity is observed. Specifically, from 80kV to 100kV, the image clarity gradually improves from unsatisfactory to high quality, and finally a high-quality defect feature image that meets the detection standard is successfully generated at a voltage of 100kV. The comparison between the technical solution of the present invention and the prior art is shown in Table 3 below:

[0146] Table 3 Comparison between the technical solution of the present invention and the prior art

[0147]

[0148] The technology of the present invention shows significant advantages over the existing technology. By optimizing voltage selection and parameter adjustment, the present invention has achieved a significant improvement in detection accuracy. By building an intelligent detection model, it realizes automated optimization and avoids the limitations of manual adjustment of voltage and projection angle in traditional methods. In addition, the data processing and analysis capabilities based on the defect data feature library improve the efficiency and accuracy of subsequent data processing. In terms of energy efficiency and resource utilization, the optimized voltage selection helps to reduce energy consumption and improve resource utilization efficiency. Compared with traditional methods, the energy efficiency is more superior. The application of multi-angle transformer technology further improves the detection efficiency. Compared with the existing technology of single angle and voltage setting, a more efficient cable joint defect detection process is achieved.

[0149] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A cable joint detection method, characterized in that: include, According to the density difference of cable joint materials, select X-rays with different voltages to detect cable joints; The multi-angle transillumination arrangement technology is used to perform secondary inspection on the cable joints after inspection. Based on the existing cable joint defects, the multi-angle variable-voltage X-ray is used to image the cable joints after secondary inspection to build a defect data feature library of the cable joints. The defect data feature library is used to extract the defect feature images of the cable joint after imaging. The X-ray parameters are optimized based on the defect feature images of the cable joint. An intelligent detection model for cable joint defects is constructed to detect the X-ray parameters of multi-angle transformers.

2. The cable joint detection method according to claim 1, characterized in that: The density difference includes calculating the density difference of the metal or non-metal material of the cable joint material, selecting X-rays with different voltages according to the density difference, pre-detecting the cable joint based on the selected X-rays with different voltages, screening out the cable joints of the same material, calculating the total volume and the total mass of the cable joint, and calculating the overall density of the cable joint according to the total volume and the total mass of the cable joint; The calculation formula for the total volume of the cable connector is: Among them, V total represents the total volume of the cable joint, n represents the number of types of materials, V i represents the volume of the i-th material; The calculation formula for the total mass of the cable connector is: Among them, m total Indicates the total mass of the cable connector, n indicates the number of types of materials, m i represents the mass of the i-th material.

3. The cable joint detection method according to claim 2, characterized in that: The calculation formula for calculating the overall density of the cable connector is: Among them, ρ total Indicates the overall density of cable joints, m total Indicates the total mass of the cable connector, V total Indicates the total volume of the cable connector, m i represents the mass of the i-th material, V i represents the volume of the i-th material; Based on the i and j of any two materials, the density difference is calculated. The specific calculation formula is: Dr. ij =|ρ i -r j | When the cable connector has two or more materials, the average density difference is calculated as the basis for selecting the X-ray voltage. The specific formula for calculating the average density difference is: Among them, Δ ρavg represents the average density difference, n represents the number of material types, |ρ i -ρ j | represents the absolute value of the density difference between the i-th material and the j-th material.

4. The cable joint detection method according to claim 3, characterized in that: The detection of the cable joint includes calculating the voltage range according to the average density difference. The specific calculation formula of the voltage range is: Among them, k1, k2, α1, α2, b1 and b2 are constants fitted by experimental data, U min Indicates the minimum range of voltage, U max Indicates the maximum range of voltage, Δ ρavg represents the average density difference; The voltage range is optimized by using the matching rule between the cable joint material characteristics and the voltage adaptability of the X-ray, and a mapping table M between the cable joint material characteristics and the X-ray is established. The specific formula of the mapping table M is: M[i,j]=f(ρ i ,U j ) Where M[i, j] represents the adaptability score of the i-th material at the j-th voltage level, f represents the multivariate function, and ρ i represents the density of the i-th material, U j represents the jth voltage level; Combining the density difference, cable joint material properties and X-ray mapping table, a multi-objective optimization model is established to select the optimal voltage. The establishment formula of the multi-objective optimization model is: Among them, ω j represents the weight of the jth voltage level, n represents the number of material types, and m represents the number of voltage levels; According to the established multi-objective optimization model, the average adaptability score of the i-th material at the j-th voltage level is calculated. The specific calculation formula is: Among them, M avg represents the average adaptability score, M[i, j] represents the adaptability score of the i-th material at the j-th voltage level, n represents the number of material types, and m represents the number of voltage levels; The calculated average adaptability score is combined with the adaptability score value to calculate the standard value of the average density difference of the cable joint, and the threshold is set according to the average density difference and the standard value; The specific formula for calculating the standard value of the average density difference of the cable joint is: Among them, σ Δρ It indicates the standard value of the average density difference of the cable joint, n indicates the number of material types, m indicates the number of voltage levels, M avg represents the average adaptability score, M[i, j] represents the adaptability score of the i-th material at the j-th voltage level; The specific calculation formula for setting the threshold is: D ρthreshold =D ραvg +k·s Δρ M threshold =M avg -k·s Δρ Among them, Δ ρavg represents the average density difference, σ Δρ It represents the standard value of the average density difference of cable joints, and k represents a constant.

5. The cable joint detection method according to claim 4, characterized in that: The secondary inspection of the detected cable connector comprises the following steps: If the calculated average density difference Δ ρavg Less than the set threshold Δ ρthreshold When the voltage range [U min , U max ] min The value is used as a benchmark to test low density difference materials; Otherwise, select U max The voltage range is used to detect high density difference materials; When the density of the material ρ i When it continues to increase, use high voltage U j Otherwise, use low voltage U j Conduct testing; If the adaptability score M[i, j] of the i-th material at the j-th voltage level is greater than the set threshold M threshold When , the weight of voltage level ω is increased j ; Otherwise, reduce the weight of the voltage level ω j .

6. The cable joint detection method according to claim 5, characterized in that: The imaging of the cable joint after the secondary detection includes determining the imaging according to the multi-factor optimized voltage selection, and the specific steps are as follows: If the initially selected voltage level U j When the adaptability scores of all materials are met, they are all above the set threshold M threshold , then select the voltage level for detection; Otherwise, the voltage level is adjusted and the fitness score is recalculated; When the detected voltage level satisfies the detected image quality Q, the voltage level is continued to be used for detection; otherwise, the voltage parameter is dynamically adjusted according to the image quality Q.

7. The cable joint detection method according to claim 6, characterized in that: The detection of the X-ray parameters of the multi-angle voltage transformation includes imaging determined based on multi-factor optimization voltage selection, and after multiple iterations of optimization, the image quality Q reaches a normal detection level. The specific steps are as follows: After multiple iterations of optimization, the image quality Q reaches the normal level detected, and the final voltage level U is determined. opt ; Otherwise, continue to adjust the voltage level, re-evaluate and adjust the parameters of the optimization model, and perform a new round of detection until the image quality Q index reaches normal.

8. A cable joint detection system, based on the cable joint detection method according to any one of claims 1 to 7, characterized in that: include, A selection module, which selects X-rays of different voltages to detect the cable joints according to the density difference of the cable joint materials; A construction module, which uses a multi-angle transillumination arrangement technology to perform secondary inspection on the inspected cable joints and construct a defect data feature library of the cable joints; The detection module uses the defect data feature library to extract the cable joint defect feature image after imaging, builds the cable joint defect intelligent detection model, and detects the X-ray parameters of multi-angle transformer.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the cable joint detection method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the cable joint detection method according to any one of claims 1 to 7 are implemented.

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