Cable joint construction early warning model construction method and system

Abstract

The present invention discloses a method and system for constructing a cable joint construction early warning model, which relates to the field of model construction technology. The method obtains the change data of the cable joint and the cable line in real time through a sensing device, analyzes the change data of the cable joint and the cable line using a linear regression analysis model, and predicts the change trend of the cable joint and the cable line. While predicting the change trend, the classification model and the linear regression analysis model are integrated to construct an early warning model. When the early warning model predicts that the pressure change needs to be adjusted, the output pressure of the crimping equipment is dynamically regulated and an early warning signal is generated. By constructing the early warning model, the construction system can analyze the instantaneous change of pressure in real time during the crimping process of the cable joint, so that the pressure can be effectively adjusted before the pressure changes instantaneously, thereby ensuring the uniformity of the pressure and improving the crimping quality of the cable joint.

Description

Technical Field

[0001] The present invention relates to the technical field of model construction, and in particular to a method and system for constructing a cable joint construction early warning model. Background Art

[0002] The cable joint construction early warning model construction system aims to timely discover possible problems during the construction process through real-time monitoring and data analysis of the cable joint construction process, avoid the occurrence of failures, and ensure the stable operation of the power system. This system mainly focuses on various types of failures, accidents or quality problems that may occur during cable joint construction, and through prediction and early warning mechanisms, provides a basis for construction site managers and technicians to intervene in advance.

[0003] When crimping cable connectors, it is important to ensure that the tools used (such as crimping machines and dies) are suitable for the specifications of the cables used. The pressure should be uniform, avoiding being too tight or too loose. Excessive pressure can damage the cable structure, while insufficient pressure can cause poor contact. However, existing control systems typically crimp cable connectors based on a manually pre-entered constant pressure. In practice, the pressure may be too high or too low, depending on the cable connector itself, the cable, and the crimping equipment. This can lead to the following problems:

[0004] 1. If the pressure is too high, the internal structure of the cable may be damaged, especially the insulation layer and metal core of the conductor. Excessive compression may cause the mechanical strength of the conductor to decrease, or even cause breakage or local damage, thereby affecting the electrical performance of the cable.

[0005] 2. If the pressure is too small, insufficient crimping may cause the joint to loosen. As the cable is used or the external environment changes (such as vibration, temperature changes, etc.), the loosening of the joint may cause the cable to break or fall off, seriously affecting the stability of the power system.

[0006] Based on this, the present invention proposes a method and system for constructing a cable joint construction early warning model. By constructing the early warning model, the instantaneous changes in pressure can be analyzed in real time during the crimping process of the cable joint, so that the pressure can be effectively adjusted before the instantaneous change occurs, thereby ensuring the uniformity of the pressure and improving the crimping quality of the cable joint. Summary of the Invention

[0007] The purpose of the present invention is to provide a method and system for constructing a cable joint construction early warning model to address the shortcomings of the background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a method for constructing a cable joint construction early warning model, the method comprising the following steps:

[0009] Before crimping cable connectors, the system acquires the operating data of the crimping equipment and substitutes the operating data into the classification model. The classification model classifies the current operating status of the crimping equipment and determines whether the crimping equipment is suitable for crimping based on the classification results.

[0010] If supported, during the crimping process, the sensor equipment will be used to obtain the change data of the cable joints and cable lines in real time. After analyzing the change data of the cable joints and cable lines using a linear regression analysis model, the change trends of the cable joints and cable lines will be predicted.

[0011] While predicting the changing trend, the classification model is integrated with the linear regression analysis model to construct an early warning model. When the early warning model predicts the need to adjust the pressure change, the output pressure of the crimping equipment is dynamically regulated and an early warning signal is generated.

[0012] In a preferred embodiment, while predicting the change trend, the classification model is integrated with the linear regression analysis model to construct an early warning model, which includes the following steps:

[0013] Obtain the abnormal index of the crimping equipment in the classification model, and obtain the cable joint influence coefficient and cable line influence coefficient in the regression analysis model;

[0014] The adjustment index is obtained by comprehensively calculating the abnormal index, cable joint influence coefficient and cable line influence coefficient. The expression is: , where To adjust the index, is the cable joint influence coefficient, is the cable influence coefficient, is the abnormality index, 、 、 are the weights of the cable joint influence coefficient, cable line influence coefficient and abnormality index respectively, and ;

[0015] After obtaining the adjustment index, the adjustment index is compared with the first adjustment threshold and the second adjustment threshold to complete the construction of the early warning model.

[0016] In a preferred embodiment, the construction system obtains the operating data of the crimping equipment before crimping the cable connector, including the following steps:

[0017] Obtain the indenter breakage index and output power fluctuation of the crimping equipment;

[0018] The logic for obtaining the indenter damage index is as follows: the number of damage points and the maximum damage depth on the indenter damage index are obtained using ultrasonic equipment. The number of damage points and the maximum damage depth are normalized so that their value range is mapped to the range [0, 1]. The normalized value of the number of damage points and the normalized value of the maximum damage depth are obtained. The normalized value of the number of damage points and the normalized value of the maximum damage depth are summed to obtain the indenter damage index.

[0019] The calculation logic of the output power fluctuation is as follows: obtain the voltage values ​​at multiple time points during the operation of the crimping equipment, calculate the voltage standard deviation based on the voltage values ​​at multiple time points, and use the voltage standard deviation as the output power fluctuation.

[0020] In a preferred embodiment, the operation data is substituted into the classification model, and after the current operation state of the crimping device is classified by the classification model, whether the crimping device supports crimping is determined based on the classification result, including the following steps:

[0021] Substitute the obtained indenter breakage index and output power fluctuation into the classification model. The classification model first calculates the abnormality index of the crimping equipment through the indenter breakage index and output power fluctuation. The expression is:

[0022] , where is the abnormality index, is the indenter breakage index, is the output power fluctuation, 、 is the adjustment coefficient, and both are greater than 0;

[0023] Comparing the acquired abnormality index with the abnormality threshold, which is used to classify the operating state of the crimping equipment; if the abnormality index is less than or equal to the abnormality threshold, the operating state of the crimping equipment is classified as normal; if the abnormality index is greater than the abnormality threshold, the operating state of the crimping equipment is classified as abnormal;

[0024] When the crimping device is in a normal state, it is determined that the crimping device supports crimping. When the crimping device is in an abnormal state, it is determined that the crimping device does not support crimping.

[0025] In a preferred embodiment, during the crimping process, the changing data of the cable joint and the cable line are obtained in real time by a sensing device, including the following steps:

[0026] Obtaining cable joint variation data, including insulation thickness deviation, surface oxide layer area, joint length deviation, and joint eccentricity;

[0027] Obtain the cable line change data, which includes the outer sheath thickness deviation, conductor diameter deviation and conductor twist amplitude.

[0028] In a preferred embodiment, after analyzing the change data of the cable joint and the cable line using a linear regression analysis model, predicting the change trend of the cable joint and the cable line includes the following steps:

[0029] The model expression of the linear regression analysis model is: , where is the influence coefficient, is a variable, is the regression coefficient of each variable;

[0030] The cable joint influence coefficient is compared with the cable joint influence coefficient threshold. If the cable joint influence coefficient is greater than or equal to the cable joint influence coefficient threshold, it is predicted that the subsequent overall pressure required for the cable joint needs to be increased. If the cable joint influence coefficient is less than the cable joint influence coefficient threshold, it is predicted that the subsequent overall pressure required for the cable joint needs to be reduced.

[0031] The cable influence coefficient is compared with the cable influence coefficient threshold. If the cable influence coefficient is greater than or equal to the cable influence coefficient threshold, it is predicted that the subsequent overall pressure required for the cable needs to be increased. If the cable influence coefficient is less than the cable influence coefficient threshold, it is predicted that the subsequent overall pressure required for the cable needs to be reduced.

[0032] In a preferred embodiment, when the early warning model predicts that a pressure change needs to be adjusted, the output pressure of the crimping device is dynamically regulated and an early warning signal is generated, including the following steps:

[0033] If the adjustment index is greater than or equal to the first adjustment threshold, and the adjustment index is less than or equal to the second adjustment threshold, it is predicted that no adjustment pressure change is required;

[0034] If the adjustment index is less than the first adjustment threshold, it is predicted that the subsequent pressure needs to be reduced. If the adjustment index is greater than the second adjustment threshold, it is predicted that the subsequent pressure needs to be increased.

[0035] The adjusted pressure is compared with the standard pressure range required for crimping the current cable connector. If the adjusted pressure is within the standard pressure range, no warning signal is generated. If the adjusted pressure is not within the standard pressure range, a warning signal is generated and sent to the administrator.

[0036] In a preferred embodiment, if the adjustment index is less than the first adjustment threshold, it is predicted that the subsequent pressure needs to be reduced, and the adjustment algorithm is: , where is the adjusted pressure, is the pressure before adjustment, To adjust the index;

[0037] If the adjustment index is greater than the second adjustment threshold, it is predicted that the subsequent pressure needs to be increased. The adjustment algorithm is: , where is the adjusted pressure, is the pressure before adjustment, To adjust the index.

[0038] In a preferred embodiment, the change data of the cable joint includes the insulation layer thickness deviation, the surface oxide layer area, the joint length deviation and the joint eccentricity. In the regression analysis model of the cable joint, n is 4, and the updated model expression is: , where is the cable joint influence coefficient, They are the insulation layer thickness deviation, the surface oxide layer area, the joint length deviation and the joint eccentricity distance, are the regression coefficients of insulation layer thickness deviation, surface oxide layer area, joint length deviation and joint eccentricity distance, respectively, and are greater than 0, Less than 0.

[0039] Cable joint construction early warning model construction system, including equipment classification module, trend prediction module, and dynamic control module;

[0040] Equipment classification module: Before crimping the cable connector, the operating data of the crimping equipment is obtained and substituted into the classification model. After the classification model classifies the current operating status of the crimping equipment, it is determined whether the crimping equipment supports crimping based on the classification results. The judgment result is sent to the trend prediction module, and the classification model is sent to the dynamic control module;

[0041] Trend prediction module: If supported, during the crimping process, the sensor equipment will acquire the change data of the cable joints and cable lines in real time. After analyzing the change data of the cable joints and cable lines using the linear regression analysis model, the change trend of the cable joints and cable lines will be predicted, and the linear regression analysis model will be sent to the dynamic control module;

[0042] Dynamic control module: While predicting the changing trend, the classification model and the linear regression analysis model are integrated to build an early warning model. When the early warning model predicts the need to adjust the pressure change, the output pressure of the crimping equipment is dynamically controlled and an early warning signal is generated.

[0043] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0044] 1. The present invention uses a sensing device to obtain real-time data on changes in cable connectors and cable lines. After analyzing the data using a linear regression analysis model, the present invention predicts the trend of changes in the connectors and cable lines. While predicting the trend of changes, the classification model is integrated with the linear regression analysis model to construct an early warning model. When the early warning model predicts the need to adjust the pressure change, the output pressure of the crimping equipment is dynamically regulated and an early warning signal is generated. By constructing an early warning model, the system can analyze the instantaneous changes in pressure in real time during the crimping process of the cable connector, thereby effectively adjusting the pressure before the pressure changes, ensuring pressure uniformity, and improving the crimping quality of the cable connector.

[0045] 2. The present invention obtains the operating data of the crimping equipment before crimping the cable connector, substitutes the operating data into the classification model, classifies the current operating status of the crimping equipment through the classification model, and judges whether the crimping equipment supports crimping based on the classification result. In this way, before performing the crimping operation, it can effectively analyze whether the crimping equipment supports operation, and further ensure the stability of the crimping operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0047] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0049] Example 1: Please refer to Figure 1 As shown, the cable joint construction early warning model construction method of this embodiment includes the following steps:

[0050] Before crimping the cable joint, the system obtains the operating data of the crimping equipment and substitutes the operating data into the classification model. After classifying the current operating status of the crimping equipment through the classification model, it determines whether the crimping equipment supports crimping based on the classification results. If it supports, during the crimping process, the change data of the cable joint and the cable line are obtained in real time through the sensing device. After analyzing the change data of the cable joint and the cable line using the linear regression analysis model, the change trend of the cable joint and the cable line is predicted. While predicting the change trend, the classification model and the linear regression analysis model are integrated to construct an early warning model. When the early warning model predicts the need to adjust the pressure change, the output pressure of the crimping equipment is dynamically adjusted and an early warning signal is generated.

[0051] This application uses sensing equipment to obtain the change data of cable connectors and cable lines in real time. After analyzing the change data of cable connectors and cable lines using a linear regression analysis model, the change trend of cable connectors and cable lines is predicted. While predicting the change trend, the classification model is integrated with the linear regression analysis model to construct an early warning model. When the early warning model predicts the need to adjust the pressure change, the output pressure of the crimping equipment is dynamically regulated and an early warning signal is generated. By constructing an early warning model, this construction system can analyze the instantaneous change of pressure in real time during the crimping process of the cable connector, so that the pressure can be effectively adjusted before the pressure changes instantaneously, ensuring the uniformity of the pressure and improving the crimping quality of the cable connector.

[0052] This application obtains the operating data of the crimping equipment before crimping the cable connector, substitutes the operating data into the classification model, classifies the current operating status of the crimping equipment through the classification model, and judges whether the crimping equipment supports crimping based on the classification results. In this way, before performing the crimping operation, it is possible to effectively analyze whether the crimping equipment supports operation, thereby further ensuring the stability of the crimping operation.

[0053] Example 2: Constructing a system to obtain operating data of a crimping device before crimping a cable connector, including the following steps:

[0054] Obtain the indenter breakage index and output power fluctuation of the crimping equipment;

[0055] The logic for obtaining the indenter damage index is as follows: the number of damaged points and the maximum damage depth on the indenter damage index are obtained using ultrasonic equipment. The number of damaged points and the maximum damage depth are normalized so that their value ranges are mapped to [0, 1]. The normalized value of the number of damaged points and the normalized value of the maximum damage depth are obtained. The sum of the normalized values ​​of the number of damaged points and the normalized value of the maximum damage depth is used to obtain the indenter damage index. The larger the indenter damage index, the worse the overall stability of the indenter of the crimping equipment, which is less conducive to the crimping process.

[0056] The indenter breakage index evaluates the stability of the equipment by analyzing the damage of the indenter. The relationship between its value and the stability of the equipment operation is as follows:

[0057] A low indenter damage index (close to 0): A low indenter damage index indicates fewer damaged points or shallower damage, indicating good overall indenter health. This indicates greater stability in the crimping equipment, and the crimping action can evenly transmit pressure, ensuring consistent joint quality.

[0058] A high indenter damage index (close to 1): A high indenter damage index indicates a high number of damage points on the indenter, and these damage points are also deep, indicating that the indenter has suffered significant damage or wear. This directly impacts the equipment's crimping accuracy and effectiveness. Because the contact force and crimping quality of the damaged parts are uneven, uneven or unstable pressure may occur during the crimping process, further impacting the quality of the cable connector. Therefore, a higher indenter damage index indicates less stable crimping equipment and less ideal crimping results.

[0059] Conclusion: The larger the indenter breakage index is, the worse the stability of the crimping equipment is, and the more obvious its adverse effect on the crimping quality is.

[0060] The calculation logic of the output power fluctuation is: obtain the voltage values ​​at multiple time points during the operation of the crimping equipment, calculate the voltage standard deviation based on the voltage values ​​at multiple time points, and use the voltage standard deviation as the output power fluctuation. The larger the output power fluctuation value, the worse the circuit stability of the crimping equipment, and the less conducive it is to the crimping action.

[0061] Output power fluctuation reflects the voltage stability of the crimping equipment during operation. The relationship between its value and the equipment's operating stability is as follows:

[0062] Output power fluctuations are minimal (close to 0): When output power fluctuations are minimal, the equipment's circuitry is stable and the power supply system is capable of providing a stable voltage, which is crucial for the proper operation of the crimping equipment. Stable voltage ensures precise control of the crimping machine's drive system, sensors, and other electronic components, preventing unstable equipment performance or control errors caused by output power fluctuations. Therefore, when output power fluctuations are minimal, the crimping equipment's circuitry is more stable, resulting in more precise and stable crimping results.

[0063] Large output power fluctuations (close to 1): Large output power fluctuations indicate instability in the crimping equipment's power supply system or circuitry. Large output power fluctuations can reduce the accuracy of the equipment's control system, potentially causing fluctuations or failures in the pressure sensor or other control components during the crimping process, affecting crimping quality. Furthermore, large output power fluctuations can cause equipment overheating, damage, or other safety hazards, impacting long-term stability and efficiency.

[0064] Conclusion: The greater the output power fluctuation, the worse the circuit stability of the equipment, and the greater the impact on the stability of the crimping equipment and the crimping quality.

[0065] Substitute the operating data into the classification model, classify the current operating status of the crimping equipment through the classification model, and judge whether the crimping equipment supports crimping based on the classification result, including the following steps:

[0066] Substitute the obtained indenter breakage index and output power fluctuation into the classification model. The classification model first calculates the abnormality index of the crimping equipment through the indenter breakage index and output power fluctuation. The expression is: , where is the abnormality index, is the indenter breakage index, is the output power fluctuation, 、 is the adjustment coefficient, and both are greater than 0;

[0067] After obtaining the abnormality index, the larger the abnormality index is, the more unfavorable the crimping equipment is for crimping operations. The obtained abnormality index is compared with the abnormality threshold. The abnormality threshold is used to classify the operating state of the crimping equipment. If the abnormality index is less than or equal to the abnormality threshold, the operating state of the crimping equipment is classified as a normal state. If the abnormality index is greater than the abnormality threshold, the operating state of the crimping equipment is classified as an abnormal state.

[0068] When the crimping device is in a normal state, it is determined that the crimping device supports crimping. When the crimping device is in an abnormal state, it is determined that the crimping device does not support crimping.

[0069] If supported, during the crimping process, the sensor device will acquire the cable connector and cable line change data in real time, including the following steps:

[0070] Obtaining cable joint variation data, including insulation thickness deviation, surface oxide layer area, joint length deviation, and joint eccentricity;

[0071] 1) Online acquisition methods for insulation layer thickness deviation include:

[0072] Ultrasonic testing equipment: Ultrasonic testing equipment can be used to scan the insulation layer of cable connectors online. Ultrasonic sensors measure the thickness of the insulation layer by emitting ultrasonic signals and analyzing the echoes. By comparing the thickness values ​​at different locations, the thickness deviation of the insulation layer can be determined.

[0073] Laser measurement equipment: Laser measurement technology allows for precise thickness measurement of the insulation layer of cable connectors. The laser equipment uses the principle of reflection to obtain the insulation thickness in real time and calculate the deviation.

[0074] Digital imaging technology: Through digital image analysis technology, a camera or scanner can capture images of the cable surface and obtain the thickness information of the insulation layer through image processing algorithms;

[0075] Then, the absolute value of the difference between the actual insulation thickness and the standard insulation thickness is taken as the insulation thickness deviation.

[0076] 2) Online acquisition methods for the area occupied by the surface oxide layer include:

[0077] Infrared imaging technology: Infrared cameras can detect temperature changes caused by oxidation on the cable connector surface and calculate the area occupied by the oxidation layer through infrared image analysis. The oxidation layer emits different thermal radiation than the unoxidized area, and infrared imaging can capture the distribution of the oxidized area.

[0078] Spectroscopic analysis: Using a spectrometer (such as X-ray spectroscopy or laser spectroscopy) can analyze the chemical composition of oxides on the surface of cable connectors. By measuring the characteristic spectrum of the oxides, the thickness and proportion of the oxide layer can be calculated.

[0079] Image processing technology: A high-resolution camera is used to capture the surface of the cable connector, and then an image processing algorithm is used to analyze the oxidized area. By comparing the images, the percentage of the oxide layer can be accurately estimated.

[0080] 3) Online acquisition methods for joint length deviation include:

[0081] Laser scanner: Laser scanners can be used to accurately measure the actual length of cable connectors. The point cloud data obtained by scanning can be used for geometric modeling and calculation of the connector length.

[0082] Optical sensor: Use an optical sensor (such as a linear fiber optic sensor) to perform non-contact measurement of cable joints and obtain the length deviation of the joint in real time.

[0083] Barcode scanning and visual recognition: Use high-precision visual sensors to scan the logo or barcode on the cable connector to automatically identify the connector position and calculate the length deviation of the connector based on the known standard length;

[0084] Then the absolute value of the difference between the actual joint length and the standard joint length is taken as the joint length deviation.

[0085] 4) Online acquisition methods for joint eccentricity include:

[0086] Laser displacement sensor: The laser displacement sensor can measure the distance between the center of the cable connector and the standard position, determine whether the connector is eccentric, and then obtain the eccentric distance.

[0087] 3D vision system: Using a 3D vision system, multiple cameras are used to scan the cable connector from different angles in real time. This can accurately determine whether the connector is eccentric and calculate the eccentricity distance.

[0088] Image analysis and edge detection algorithm: Through image capture technology, obtain the image of the cable connector, apply edge detection algorithm (such as Canny edge detection), locate the geometric center of the connector and the actual connector position in the image, and then calculate the eccentricity distance.

[0089] Obtain cable line variation data, including outer sheath thickness deviation, conductor diameter deviation, and conductor torsion amplitude;

[0090] 1) The online acquisition method of outer sheath thickness deviation includes:

[0091] Laser Distance Sensors: Laser distance sensors accurately measure the cable's outer sheath, providing real-time thickness information. Data from multiple measurement points can be used to calculate thickness deviations. The laser sensor's non-contact measurement avoids physical damage to the cable while providing highly accurate data, making it suitable for dynamic monitoring.

[0092] Ultrasonic measurement technology: Ultrasonic equipment measures the thickness of a cable's outer sheath by emitting ultrasonic waves and receiving echo signals. By analyzing the echo time, the outer sheath thickness can be determined and compared with a standard thickness to determine the thickness deviation. This method is suitable for real-time monitoring of outer sheath thickness changes during the cable production process.

[0093] Coating Thickness Gauges: Electromagnetic coating thickness gauges can measure the thickness of the outer sheath without damaging the cable. These devices provide highly accurate outer sheath thickness data and enable online, real-time monitoring.

[0094] Capacitive measuring instrument: Capacitive sensors can also detect the thickness of the outer sheath online. Based on the capacitance principle, they measure the thickness change of the cable's external material and provide deviation data in real time.

[0095] 2) Online acquisition methods for conductor diameter deviation include:

[0096] Laser measurement systems are commonly used non-contact measurement tools that monitor cable conductor diameter in real time. By placing laser sensors at various locations along the cable, the system accurately measures the conductor diameter and calculates deviations from the standard value. By scanning multiple points, the system captures changes in the conductor's diameter and provides real-time feedback on deviations.

[0097] Optical image processing technology: A high-precision camera is used to capture images of cable conductors, and image processing software is used to analyze their diameter. By comparing the pixel ratio of the cable conductor in the image, the conductor's diameter is calculated and compared with the standard value to detect deviations in real time. This method can adapt to complex conductor shapes and accurately identify changes in conductor diameter.

[0098] Inductive sensors: Inductive sensors measure diameter changes by sensing the electromagnetic signature of a conductor. This method is suitable for cables with relatively uniform conductor material and provides continuous diameter monitoring.

[0099] Eddy current sensors: Eddy current sensors can determine the diameter of a cable conductor by measuring the changes in eddy currents around the conductor. This non-contact, high-precision method is suitable for monitoring changes in conductor diameter in real time.

[0100] 3) Online acquisition methods of conductor distortion amplitude include:

[0101] Visual sensing system: Equipped with a high-precision camera and light source, the system captures cable conductors in real time. Image analysis algorithms are used to identify cable conductor distortion. Commonly used techniques include edge detection and morphological operations, which can accurately detect conductor distortion. By comparing successive images, the magnitude of conductor distortion is calculated, capturing real-time changes.

[0102] Laser 3D Scanners: Laser 3D scanners generate real-time 3D images of cable conductors by scanning the conductor's surface profile. If the conductor is twisted, the scan will reveal an irregular geometry. By comparing the actual profile with the reference profile, the extent of the conductor's twist can be determined. This method enables dynamic monitoring on the production line and timely adjustment of process parameters.

[0103] Strain gauges and sensors: Miniature strain gauges are installed on conductors. When the cable conductor is twisted, the strain gauges detect minute changes in strain. By calculating the change in strain, the magnitude of the conductor twist can be inferred. This method is suitable for applications requiring precise measurement of conductor deformation.

[0104] Capacitive or magnetic sensors: These sensors can measure the deformation of cable conductors. If the conductor is twisted, the sensor's measured value changes, reflecting the degree of deviation. This method allows for real-time monitoring of conductor deformation and provides feedback on the magnitude of the twist.

[0105] The change data of the cable joint includes the insulation layer thickness deviation, the area occupied by the surface oxide layer, the joint length deviation and the joint eccentricity. The greater the insulation layer thickness deviation, the greater the subsequent pressure required for the cable joint needs to be. The larger the area occupied by the surface oxide layer, the greater the subsequent pressure required for the cable joint needs to be increased. The greater the joint length deviation, the greater the subsequent pressure required for the cable joint needs to be increased. The greater the joint eccentricity, the more the subsequent pressure required for the cable joint needs to be reduced. The specific reasons are as follows:

[0106] 1. The primary function of the insulation layer is to protect the conductors from external interference and maintain the electrical insulation properties of the cable. Excessive variations in insulation thickness indicate that the insulation is too thick or too thin at the connector, affecting the contact surface during crimping. Thicker insulation requires greater pressure from the crimping tool to ensure adequate contact between the conductors, preventing poor contact or excessive contact resistance. Increasing crimping pressure helps consolidate excessively thick insulation, ensuring an effective connection between the conductors and improving the mechanical strength and electrical performance of the connector.

[0107] 2. The oxide layer on the surface of cable conductors increases the contact resistance of the connector and affects electrical performance. This is especially true during the crimping process. If the oxide layer is not completely removed or compacted, it will cause significant heat and resistance when current passes through, reducing the reliability of the connector. Increasing pressure can help break down the oxide layer, enhancing contact between the conductors, reducing resistance, and ensuring a good electrical connection between the conductor and the connector. Therefore, when the oxide layer is large, increasing the crimping pressure is necessary to effectively remove the oxide layer and enhance the electrical and mechanical performance of the connector.

[0108] 3. If the cable connector length varies significantly, the connector may be longer or shorter than the standard design. Longer connectors can lead to uneven pressure distribution, which can easily cause poor contact or loose connections. Increasing the crimping pressure can help fill gaps in the connector, ensuring a tight connection between the cable conductor and the connector, thereby enhancing the connector's stability and reliability. For connectors that are too long, increasing the pressure appropriately can compensate for the connector's size, ensuring close contact between the connector and the cable conductor, and improving overall mechanical strength.

[0109] 4. Eccentricity refers to the difference in symmetry between the two sides of a cable connector. If the connector is eccentric, one side may experience greater pressure during crimping, while the other side experiences less pressure. This can lead to uneven deformation of the connector and compromise its mechanical strength. Increasing pressure can exacerbate eccentricity, causing uneven pressure distribution, resulting in excessive force on one side and insufficient force on the other, ultimately impacting the quality and stability of the cable connector. Therefore, in cases of significant eccentricity, appropriately reducing pressure can help avoid excessive force on the connector, maintain even pressure distribution, and improve the overall stability of the connector.

[0110] Obtain the cable change data, which includes the outer sheath thickness deviation, conductor diameter deviation, and conductor torsion amplitude. The greater the outer sheath thickness deviation, the greater the subsequent pressure required for the cable. The greater the conductor diameter deviation, the greater the subsequent pressure required for the cable. The greater the conductor torsion amplitude, the greater the subsequent pressure required for the cable. The specific reasons are as follows:

[0111] 1. The outer sheath protects the cable's internal conductor from external physical damage while ensuring the cable's electrical insulation. Excessive sheath thickness deviation (either too thick or too thin) will affect the contact between the conductor and the crimped connector during crimping. Excessively thick outer sheaths can hinder effective contact between the conductor and connector, necessitating increased pressure to ensure effective contact and avoid poor contact or electrical instability caused by excessive sheath thickness. Excessively thin outer sheaths can increase the impact of the external environment on the cable conductor. Increasing crimping pressure helps compensate for this deviation and ensure a tight fit between the cable sheath and connector. In general, when the outer sheath thickness varies significantly, increased pressure is required to ensure a tight connection between the conductor and connector.

[0112] 2. Conductor diameter deviation affects the physical contact of the connector. If the diameter of the conductor is too large or too small, it will affect the contact surface between the connector and the conductor during crimping, resulting in poor contact or uneven pressure distribution. Diameter is too large: During crimping, it is necessary to increase the pressure to ensure that the conductor is in full contact with the connector, to avoid insufficient crimping on one side of the connector due to the conductor being too large, resulting in electrical instability or loose connectors. Diameter is too small: Increasing the pressure can help fill the gap between the conductor and the connector, ensuring a stable connection between the conductor and the connector. Therefore, regardless of whether the increase in conductor diameter deviation is due to being too large or too small, it is usually necessary to increase the crimping pressure to ensure the crimping quality.

[0113] 3. Conductor distortion refers to changes in the geometric shape of the cable conductor, which may cause the conductor to be deformed or irregular. If the conductor distortion is large, uneven force distribution may occur during crimping. Excessive distortion means that part of the conductor may be subjected to excessive pressure, while other parts are subjected to lower pressure, resulting in uneven crimping of the joint, affecting electrical and mechanical performance. To avoid this situation, the crimping pressure should be appropriately reduced to avoid further deformation of the conductor. Conductors with large distortion may cause increased friction in the joint part, thereby affecting the quality of the crimping and the conductive performance. Reducing the pressure can help reduce this effect. Therefore, in the case of large conductor distortion, appropriately reducing the crimping pressure can reduce the risk of deformation and poor contact and ensure the quality of the joint.

[0114] After analyzing the change data of cable joints and cable lines using a linear regression analysis model, the change trends of cable joints and cable lines are predicted, including the following steps:

[0115] The model expression of the linear regression analysis model is: , where is the influence coefficient, is the variable (the number of changing data), is the regression coefficient of each variable. The positive or negative value of the regression coefficient depends on the positive and negative relationship between the change data and the influence coefficient;

[0116] Substitute the cable joint change data into the linear regression analysis model to calculate the cable joint influence coefficient. The cable joint change data includes the insulation layer thickness deviation, the area occupied by the surface oxide layer, the joint length deviation, and the joint eccentricity. If the cable joint influence coefficient is large, it is predicted that the subsequent overall pressure required for the cable joint will be large. If the cable joint influence coefficient is small, it is predicted that the subsequent overall pressure required for the cable joint will be small.

[0117] For example, the cable joint influence coefficient is compared with the cable joint influence coefficient threshold. If the cable joint influence coefficient is greater than or equal to the cable joint influence coefficient threshold, it is predicted that the subsequent overall pressure required for the cable joint needs to be increased. If the cable joint influence coefficient is less than the cable joint influence coefficient threshold, it is predicted that the subsequent overall pressure required for the cable joint needs to be reduced.

[0118] Substitute the cable line's change data into the linear regression analysis model to calculate the cable line's influence coefficient. The cable line's change data includes the outer sheath thickness deviation, the conductor diameter deviation, and the conductor twist amplitude. If the cable line's influence coefficient is large, it is predicted that the subsequent overall pressure required for the cable line will be large. If the cable line's influence coefficient is small, it is predicted that the subsequent overall pressure required for the cable line will be small.

[0119] Compare the cable influence coefficient with the cable influence coefficient threshold. If the cable influence coefficient is greater than or equal to the cable influence coefficient threshold, it is predicted that the subsequent overall pressure required for the cable needs to be increased. If the cable influence coefficient is less than the cable influence coefficient threshold, it is predicted that the subsequent overall pressure required for the cable needs to be reduced.

[0120] In this application:

[0121] The variation data of the cable joint includes the insulation layer thickness deviation, the area occupied by the surface oxide layer, the joint length deviation, and the joint eccentricity. Therefore, in the regression analysis model of the cable joint, n is set to 4, and the updated model expression is: , where is the cable joint influence coefficient, They are the insulation layer thickness deviation, the surface oxide layer area, the joint length deviation and the joint eccentricity distance, are the regression coefficients of insulation layer thickness deviation, surface oxide layer area, joint length deviation and joint eccentricity distance, respectively, and are greater than 0, Less than 0, this is because the eccentricity of the connector is inversely proportional to the influence coefficient of the cable connector;

[0122] Obtain the cable change data, which includes the outer sheath thickness deviation, conductor diameter deviation, and conductor twist amplitude. The calculation method of the cable joint influence coefficient is the same as above and will not be repeated in this application;

[0123] The logical factors of the influence coefficient when used in the present invention are as follows: taking the influence of changing data on pressure as an example, the first is the index, that is, the factor that causes the pressure change (the present invention refers to the influence of changing data on pressure); the second is the weight of these indicators, that is, the proportion of each type of changing data when it is generated; the third is the operation equation, that is, what kind of mathematical operation process is used to obtain the result, and the influence coefficient is obtained by calculating the indicators with their respective weights through the operation equation.

[0124] The main influencing data obtained from the sample were converted and processed into a data language that can be recognized by computer software. Secondly, these evaluation factors were analyzed by logistic regression using SPSS software to screen out factors and their weights that are significantly correlated with the results. Thirdly, the evaluation factors and weights were substituted into the logistic regression equation for calculation to obtain the results, which are as follows:

[0125] First, ensure the integrity of the main effect data, handle missing values ​​and outliers, and convert the data into a format that SPSS software can recognize. Usually, the data is stored in csv, xlsx and other formats, and then imported into SPSS. Open SPSS software, import the processed data file, and transform the variables as needed. For example, for continuous variables, standardize or normalize them. Select the "Analyze" menu, and then select the "Binary Logistic" option under "Regression". In the dialog box, add the dependent variable (outcome) and independent variable (main effect data) to the corresponding boxes. SPSS will fit the logistic regression based on the selected variables. Model, in the output results, you will see the model's coefficients, standard errors, p-values ​​and other information. Check the coefficients and p-values ​​in the output results to determine which variables have a significant correlation with the results. Usually, a p-value less than 0.05 is considered significant. While fitting the model, use variable selection methods, such as stepwise regression, to help screen the most relevant factors. According to the coefficients of the logistic regression model, the size of the coefficient reflects the degree of influence of each factor on the result, and the positive and negative signs of the coefficients indicate the direction of the influence. After obtaining the significant factors and their coefficients, the logistic regression equation is obtained, which is used to calculate the probability of each sample and then predict the results.

[0126] While predicting the changing trend, the classification model is integrated with the linear regression analysis model to build an early warning model, which includes the following steps:

[0127] Obtain the abnormal index of the crimping equipment in the classification model, and obtain the cable joint influence coefficient and cable line influence coefficient in the regression analysis model. When the abnormal index is less than or equal to the abnormal threshold, or when the abnormal index is close to the abnormal threshold, it indicates that the operating status of the crimping equipment has declined. In this case, the output pressure needs to be reduced to ensure the stable operation of the crimping equipment. The cable joint influence coefficient and the cable line influence coefficient respectively indicate the changes in the pressure requirements of the cable joint and the cable line when they are under pressure;

[0128] The adjustment index is obtained by comprehensively calculating the abnormal index, cable joint influence coefficient and cable line influence coefficient. The expression is: , where To adjust the index, is the cable joint influence coefficient, is the cable influence coefficient, is the abnormality index, 、 、 are the weights of the cable joint influence coefficient, cable line influence coefficient and abnormality index respectively, and ;

[0129] After obtaining the adjustment index, the adjustment index is compared with the first adjustment threshold and the second adjustment threshold to complete the construction of the early warning model.

[0130] When the early warning model predicts that pressure changes need to be adjusted, the output pressure of the crimping equipment is dynamically adjusted and an early warning signal is generated, including the following steps:

[0131] If the adjustment index is greater than or equal to the first adjustment threshold, and the adjustment index is less than or equal to the second adjustment threshold, it is predicted that no adjustment pressure change is required;

[0132] If the adjustment index is less than the first adjustment threshold, it is predicted that the subsequent pressure needs to be reduced. The adjustment algorithm is: , where is the adjusted pressure, is the pressure before adjustment, To adjust the index;

[0133] If the adjustment index is greater than the second adjustment threshold, it is predicted that the subsequent pressure needs to be increased. The adjustment algorithm is: , where is the adjusted pressure, is the pressure before adjustment, To adjust the index;

[0134] The adjusted pressure is compared with the standard pressure range required for crimping the current cable connector. If the adjusted pressure is within the standard pressure range, no warning signal is generated. If the adjusted pressure is not within the standard pressure range, a warning signal is generated and sent to the administrator.

[0135] Example 3: The cable joint construction early warning model construction system described in this embodiment includes an equipment classification module, a trend prediction module, and a dynamic control module;

[0136] Equipment classification module: Before crimping the cable connector, the operating data of the crimping equipment is obtained and substituted into the classification model. After the classification model classifies the current operating status of the crimping equipment, it is determined whether the crimping equipment supports crimping based on the classification results. The judgment result is sent to the trend prediction module, and the classification model is sent to the dynamic control module;

[0137] Trend prediction module: If supported, during the crimping process, the sensor equipment will acquire the change data of the cable joints and cable lines in real time. After analyzing the change data of the cable joints and cable lines using the linear regression analysis model, the change trend of the cable joints and cable lines will be predicted, and the linear regression analysis model will be sent to the dynamic control module;

[0138] Dynamic control module: While predicting the changing trend, the classification model and the linear regression analysis model are integrated to build an early warning model. When the early warning model predicts the need to adjust the pressure change, the output pressure of the crimping equipment is dynamically controlled and an early warning signal is generated.

[0139] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0140] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0141] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0142] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application. Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0143] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for constructing a cable joint construction early warning model, characterized by: The construction method comprises the following steps: Before crimping the cable connector, the operating data of the crimping equipment is obtained and substituted into the classification model. The classification model is used to classify the current operating status of the crimping equipment, and then the classification result is used to determine whether the crimping equipment is suitable for crimping. If supported, during the crimping process, the sensor equipment will be used to obtain the change data of the cable joints and cable lines in real time. After analyzing the change data of the cable joints and cable lines using a linear regression analysis model, the change trends of the cable joints and cable lines will be predicted. While predicting the change trend, the classification model and the linear regression analysis model are integrated to build an early warning model. The abnormal index of the crimping equipment is obtained in the classification model, and the cable joint influence coefficient and the cable line influence coefficient are obtained in the regression analysis model. The abnormal index, the cable joint influence coefficient and the cable line influence coefficient are comprehensively calculated to obtain the adjustment index. The expression is: , where To adjust the index, is the cable joint influence coefficient, is the cable influence coefficient, is the abnormality index, 、 、 are the weights of the cable joint influence coefficient, cable line influence coefficient and abnormality index respectively, and ; After obtaining the adjustment index, the adjustment index is compared with the first adjustment threshold and the second adjustment threshold to complete the construction of the early warning model. When the early warning model predicts the need to adjust the pressure change, the output pressure of the crimping equipment is dynamically adjusted and a warning signal is generated.

2. The cable joint construction early warning model construction method according to claim 1, characterized in that: Before crimping the cable connector, obtain the operating data of the crimping equipment, including the following steps: Obtain the indenter breakage index and output power fluctuation of the crimping equipment; The logic for obtaining the indenter damage index is as follows: the number of damage points and the maximum damage depth on the indenter damage index are obtained using ultrasonic equipment. The number of damage points and the maximum damage depth are normalized so that their value range is mapped to the range [0, 1]. The normalized value of the number of damage points and the normalized value of the maximum damage depth are obtained. The normalized value of the number of damage points and the normalized value of the maximum damage depth are summed to obtain the indenter damage index. The calculation logic of the output power fluctuation is as follows: obtain the voltage values ​​at multiple time points during the operation of the crimping equipment, calculate the voltage standard deviation based on the voltage values ​​at multiple time points, and use the voltage standard deviation as the output power fluctuation.

3. The cable joint construction early warning model construction method according to claim 2, characterized in that: Substitute the operating data into the classification model, classify the current operating status of the crimping equipment through the classification model, and judge whether the crimping equipment supports crimping based on the classification result, including the following steps: Substitute the obtained indenter breakage index and output power fluctuation into the classification model. The classification model first calculates the abnormality index of the crimping equipment through the indenter breakage index and output power fluctuation. The expression is: , where is the abnormality index, is the indenter breakage index, is the output power fluctuation, 、 is the adjustment coefficient, and both are greater than 0; Comparing the acquired abnormality index with the abnormality threshold, which is used to classify the operating state of the crimping equipment; if the abnormality index is less than or equal to the abnormality threshold, the operating state of the crimping equipment is classified as normal; if the abnormality index is greater than the abnormality threshold, the operating state of the crimping equipment is classified as abnormal; When the crimping device is in a normal state, it is determined that the crimping device supports crimping. When the crimping device is in an abnormal state, it is determined that the crimping device does not support crimping.

4. The cable joint construction early warning model construction method according to claim 3 is characterized by: During the crimping process, the sensor equipment is used to obtain the change data of the cable joint and the cable line in real time, including the following steps: Obtaining cable joint variation data, including insulation thickness deviation, surface oxide layer area, joint length deviation, and joint eccentricity; Obtain the cable line change data, which includes the outer sheath thickness deviation, conductor diameter deviation and conductor twist amplitude.

5. The cable joint construction early warning model construction method according to claim 4 is characterized in that: After analyzing the change data of cable joints and cable lines using a linear regression analysis model, the change trends of cable joints and cable lines are predicted, including the following steps: The model expression of the linear regression analysis model is: , where is the influence coefficient, is a variable, is the regression coefficient of each variable; The cable joint influence coefficient is compared with the cable joint influence coefficient threshold. If the cable joint influence coefficient is greater than or equal to the cable joint influence coefficient threshold, it is predicted that the subsequent overall pressure required for the cable joint needs to be increased. If the cable joint influence coefficient is less than the cable joint influence coefficient threshold, it is predicted that the subsequent overall pressure required for the cable joint needs to be reduced. The cable influence coefficient is compared with the cable influence coefficient threshold. If the cable influence coefficient is greater than or equal to the cable influence coefficient threshold, it is predicted that the subsequent overall pressure required for the cable needs to be increased. If the cable influence coefficient is less than the cable influence coefficient threshold, it is predicted that the subsequent overall pressure required for the cable needs to be reduced.

6. The cable joint construction early warning model construction method according to claim 5, characterized in that: When the early warning model predicts that pressure changes need to be adjusted, the output pressure of the crimping equipment is dynamically adjusted and an early warning signal is generated, including the following steps: If the adjustment index is greater than or equal to the first adjustment threshold, and the adjustment index is less than or equal to the second adjustment threshold, it is predicted that no adjustment pressure change is required; If the adjustment index is less than the first adjustment threshold, it is predicted that the subsequent pressure needs to be reduced. If the adjustment index is greater than the second adjustment threshold, it is predicted that the subsequent pressure needs to be increased. The adjusted pressure is compared with the standard pressure range required for crimping the current cable connector. If the adjusted pressure is within the standard pressure range, no warning signal is generated. If the adjusted pressure is not within the standard pressure range, a warning signal is generated and sent to the administrator.

7. The cable joint construction early warning model construction method according to claim 6, characterized in that: If the adjustment index is less than the first adjustment threshold, it is predicted that the subsequent pressure needs to be reduced. The adjustment algorithm is: , where is the adjusted pressure, is the pressure before adjustment, To adjust the index; If the adjustment index is greater than the second adjustment threshold, it is predicted that the subsequent pressure needs to be increased. The adjustment algorithm is: , where is the adjusted pressure, is the pressure before adjustment, To adjust the index.

8. The cable joint construction early warning model construction method according to claim 1, characterized in that: The change data of the cable joint includes the insulation layer thickness deviation, the area occupied by the surface oxide layer, the joint length deviation and the joint eccentricity. In the regression analysis model of the cable joint, n is 4, and the updated model expression is: , where is the cable joint influence coefficient, They are the insulation layer thickness deviation, the surface oxide layer area, the joint length deviation and the joint eccentricity distance, are the regression coefficients of insulation layer thickness deviation, surface oxide layer area, joint length deviation and joint eccentricity distance, respectively, and are greater than 0, Less than 0.

9. A cable joint construction early warning model construction system, used to implement the construction method according to any one of claims 1 to 8, characterized in that: Including equipment classification module, trend prediction module, and dynamic control module; Equipment classification module: Before crimping the cable connector, the operating data of the crimping equipment is obtained and substituted into the classification model. After the classification model classifies the current operating status of the crimping equipment, it is determined whether the crimping equipment supports crimping based on the classification results. The judgment result is sent to the trend prediction module, and the classification model is sent to the dynamic control module; Trend prediction module: If supported, during the crimping process, the sensor equipment will acquire the change data of the cable joints and cable lines in real time. After analyzing the change data of the cable joints and cable lines using the linear regression analysis model, the change trend of the cable joints and cable lines will be predicted, and the linear regression analysis model will be sent to the dynamic control module; Dynamic control module: While predicting the change trend, the classification model and the linear regression analysis model are integrated to build an early warning model. The abnormality index of the crimping equipment is obtained in the classification model, and the cable joint influence coefficient and the cable line influence coefficient are obtained in the regression analysis model. The abnormality index, the cable joint influence coefficient, and the cable line influence coefficient are comprehensively calculated to obtain the adjustment index. The expression is: , where To adjust the index, is the cable joint influence coefficient, is the cable influence coefficient, is the abnormality index, 、 、 are the weights of the cable joint influence coefficient, cable line influence coefficient and abnormality index respectively, and ; After obtaining the adjustment index, the adjustment index is compared with the first adjustment threshold and the second adjustment threshold to complete the construction of the early warning model. When the early warning model predicts the need to adjust the pressure change, the output pressure of the crimping equipment is dynamically adjusted and a warning signal is generated.

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