Cable joint construction early warning model construction method and system

Abstract

The invention discloses a cable joint construction early warning model construction method and system, and relates to the technical field of model construction, and the method comprises the steps: obtaining the change data of a cable joint and a cable in real time through a sensing device, and analyzing the change data of the cable joint and the cable through a linear regression analysis model; and predicting the change trend of the cable joint and the cable, fusing the classification model and the linear regression analysis model to construct an early warning model while predicting the change trend, and dynamically regulating and controlling the output pressure of the crimping equipment and generating an early warning signal when predicting that the pressure change needs to be adjusted through the early warning model. According to the construction system, by constructing the early warning model, the instantaneous change of the pressure can be analyzed in real time in the crimping process of the cable joint, so that the pressure can be effectively adjusted before the instantaneous change of the pressure, the uniformity of the pressure is guaranteed, and the crimping quality of the cable joint is improved.

Description

Technical Field

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

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

[0003] When crimping a cable joint, it is necessary to ensure that the tools used (such as a crimping machine, die) are suitable for the specifications of the cable being used, and the pressure should be uniform to avoid being too tight or too loose. Excessive pressure will damage the cable structure, and too little pressure will result in poor contact. However, existing control systems usually only crimp the cable joint according to a constant pressure pre-input manually. In actual situations, affected by the cable joint itself, the cable line, and the crimping equipment, it may lead to excessive or too little pressure, which may cause the following problems:

[0004] 1. If the pressure is too high, it may damage the internal structure of the cable, especially the insulation layer of the conductor, the metal wire core, etc. Excessive compression may cause a decrease in the mechanical strength of the conductor, and even fracture or local damage may occur, thus affecting the electrical performance of the cable.

[0005] 2. If the pressure is too low, insufficient crimping may cause the joint to loosen. With the use of the cable or changes in the external environment (such as vibration, temperature changes, etc.), the loosening of the joint part 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 warning model. By constructing a warning model, it is possible to analyze the instantaneous change of pressure in real time during the crimping process of the cable joint, so as to effectively adjust the pressure before the instantaneous change of pressure occurs, ensure the uniformity of the pressure, and improve the crimping quality of the cable joint. Summary of the Invention

[0007] The object of the present invention is to provide a method and system for constructing a cable joint construction warning model to solve the deficiencies in the background art.

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

[0009] Before the crimping of the cable joint, the construction system obtains the operation data of the crimping equipment, substitutes the operation data into the classification model, classifies the current operation state of the crimping equipment through the classification model, and then determines whether the crimping equipment supports crimping use according to the classification result;

[0010] If it is supported, during the crimping process, the construction system obtains the change data of the cable joint and the cable in real time through the sensing equipment, analyzes the change data of the cable joint and the cable by using the linear regression analysis model, and then predicts the change trend of the cable joint and the cable;

[0011] While predicting the change trend, the construction system fuses the classification model and the linear regression analysis model to construct an early warning model. When it is predicted by the early warning model 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.

[0012] In a preferred embodiment, while predicting the change trend, fusing the classification model and the linear regression analysis model to construct an early warning model 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 the cable influence coefficient in the regression analysis model;

[0014] Comprehensively calculate the adjustment index by combining the abnormal index, the cable joint influence coefficient, and the cable influence coefficient. The expression is: , where is the adjustment index, is the cable joint influence coefficient, is the cable influence coefficient, is the abnormal index, , , are the weights of the cable joint influence coefficient, the cable influence coefficient, and the abnormal index respectively, and ;

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

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

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

[0018] The acquisition logic of the indenter breakage index is as follows: obtain the number of breakage points and the maximum breakage depth on the indenter breakage index through an ultrasonic device, perform normalization processing on the number of breakage points and the maximum breakage depth, map the value ranges of the number of breakage points and the maximum breakage depth to between [0, 1], obtain the normalized value of the number of breakage points and the normalized value of the maximum breakage depth, and sum the normalized value of the number of breakage points and the normalized value of the maximum breakage depth to obtain the indenter breakage 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 device, 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, after substituting the operation data into the classification model and classifying the current operation state of the crimping device through the classification model, determine whether the crimping device supports crimping use according to 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 device 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, 、 are adjustment coefficients and are both greater than 0;

[0023] Compare the obtained abnormality index with the abnormality threshold. The abnormality threshold is used to classify the operation state of the crimping device. If the abnormality index is less than or equal to the abnormality threshold, classify the operation state of the crimping device as the normal state. If the abnormality index is greater than the abnormality threshold, classify the operation state of the crimping device as the abnormal state;

[0024] When the crimping device is in the normal state, determine that the crimping device supports crimping use. When the crimping device is in the abnormal state, determine that the crimping device does not support crimping use.

[0025] In a preferred embodiment, during the crimping process, obtain the change data of the cable joint and the cable in real time through a sensing device, including the following steps:

[0026] Obtain the change data of the cable joint. 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 distance;

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

[0028] In a preferred embodiment, after analyzing the change data of the cable joint and the cable using a linear regression analysis model, predict the change trends of the cable joint and the cable, including the following steps:

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

[0030] Compare the cable joint influence coefficient 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, predict that the overall pressure required for the cable joint in the future needs to increase. If the cable joint influence coefficient is less than the cable joint influence coefficient threshold, predict that the overall pressure required for the cable joint in the future needs to decrease;

[0031] 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, predict that the overall pressure required for the cable in the future needs to increase. If the cable influence coefficient is less than the cable influence coefficient threshold, predict that the overall pressure required for the cable in the future needs to decrease.

[0032] In a preferred embodiment, when predicting that the pressure change needs to be adjusted through the warning model, dynamically adjust the output pressure of the crimping device and generate a warning signal, including the following steps:

[0033] If the adjustment index is greater than or equal to the first adjustment threshold and less than or equal to the second adjustment threshold, predict that no pressure change needs to be adjusted;

[0034] If the adjustment index is less than the first adjustment threshold, predict that the subsequent pressure needs to be adjusted downward. If the adjustment index is greater than the second adjustment threshold, predict that the subsequent pressure needs to be adjusted upward;

[0035] Compare the adjusted pressure with the standard pressure range required for the current cable joint crimping. 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 and it is predicted that the subsequent pressure needs to be adjusted downward, the adjustment algorithm is: , where in the formula, is the adjusted pressure, is the pressure before adjustment, is the adjustment index;

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

[0038] In a preferred embodiment, 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 distance. In the regression analysis model of the cable joint, when n takes the value of 4, the updated model expression is: , where is the cable joint influence coefficient, are respectively the insulation layer thickness deviation, the area occupied by the surface oxide layer, the joint length deviation, and the joint eccentricity distance, are respectively the regression coefficients of the insulation layer thickness deviation, the area occupied by the surface oxide layer, the joint length deviation, and the joint eccentricity distance, and are all greater than 0, is less than 0.

[0039] The cable joint construction early warning model construction system includes an equipment classification module, a trend prediction module, and a dynamic regulation module;

[0040] Equipment classification module: Before the cable joint is crimped, obtain the operation data of the crimping equipment, substitute the operation data into the classification model, classify the current operation state of the crimping equipment through the classification model, and then judge whether the crimping equipment supports crimping use according to the classification result. The judgment result is sent to the trend prediction module, and the classification model is sent to the dynamic regulation module;

[0041] Trend prediction module: If supported, during the crimping process, obtain the change data of the cable joint and the cable in real time through the sensing equipment, analyze the change data of the cable joint and the cable by using the linear regression analysis model, and then predict the change trend of the cable joint and the cable. The linear regression analysis model is sent to the dynamic regulation module;

[0042] Dynamic regulation module: While predicting the change trend, fuse the classification model and the linear regression analysis model to construct an early warning model. When it is predicted by the early warning model that the pressure change needs to be adjusted, dynamically regulate the output pressure of the crimping equipment and generate an early warning signal.

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

[0044] 1. The present invention obtains the change data of cable joints and cable lines in real time through sensing devices. After analyzing the change data of cable joints and cable lines using a linear regression analysis model, it predicts the change trends of cable joints and cable lines. While predicting the change trends, it fuses a classification model with the linear regression analysis model to construct an early warning model. When the early warning model predicts that the pressure change needs to be adjusted, it dynamically controls the output pressure of the crimping device and generates an early warning signal. By constructing the early warning model, this construction system can analyze the instantaneous change of pressure in real time during the crimping process of cable joints, so as to effectively adjust the pressure before the instantaneous change of pressure occurs, ensure the uniformity of pressure, and improve the crimping quality of cable joints.

[0045] 2. Before crimping the cable joint, the present invention obtains the operation data of the crimping device, substitutes the operation data into the classification model, classifies the current operation state of the crimping device through the classification model, and then judges whether the crimping device supports crimping use according to the classification result. In this way, it can effectively analyze whether the crimping device supports operation before the crimping operation, and further ensure the stability of the crimping operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings.

[0047] Figure 1 It is the flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0049] Embodiment 1: Please refer to Figure 1 As shown, the method for constructing a cable joint construction early warning model in this embodiment includes the following steps:

[0050] Before the cable joint is crimped, the construction system obtains the operating data of the crimping device, substitutes the operating data into the classification model, classifies the current operating state of the crimping device through the classification model, and then determines whether the crimping device supports crimping according to the classification result. If it supports, during the crimping process, the construction system obtains the change data of the cable joint and the cable line in real time through the sensing device, analyzes the change data of the cable joint and the cable line by using the linear regression analysis model, predicts the change trend of the cable joint and the cable line, and while predicting the change trend, fuses the classification model and the linear regression analysis model to construct an early warning model. When it is predicted by the early warning model that the pressure change needs to be adjusted, the output pressure of the crimping device is dynamically regulated and an early warning signal is generated.

[0051] In this application, 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 by 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 fused to construct an early warning model. When it is predicted by the early warning model that the pressure change needs to be adjusted, the output pressure of the crimping device 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 as to effectively adjust the pressure before the instantaneous change of pressure occurs, ensure the uniformity of pressure, and improve the crimping quality of the cable joint.

[0052] Before the cable joint is crimped in this application, the operating data of the crimping device are obtained, and the operating data are substituted into the classification model. After classifying the current operating state of the crimping device through the classification model, it is determined whether the crimping device supports crimping according to the classification result. In this way, before the crimping operation, it can be effectively analyzed whether the crimping device supports operation, further ensuring the stability of the crimping operation.

[0053] Embodiment 2: Before the cable joint is crimped, the construction system obtains the operating data of the crimping device, including the following steps:

[0054] Obtain the head breakage index and output power fluctuation of the crimping device;

[0055] The acquisition logic of the head breakage index is as follows: Obtain the number of breakage points and the maximum breakage depth on the head breakage index through the ultrasonic device, normalize the number of breakage points and the maximum breakage depth, map the value ranges of the number of breakage points and the maximum breakage depth to between [0, 1], obtain the normalized value of the number of breakage points and the normalized value of the maximum breakage depth, and sum the normalized value of the number of breakage points and the normalized value of the maximum breakage depth to obtain the head breakage index. The larger the head breakage index, the worse the overall stability of the head of the crimping device, and the more unfavorable it is for the crimping operation.

[0056] The indenter breakage index is used to evaluate the stability of the equipment by analyzing the breakage condition of the indenter. The relationship between its value and the operating stability of the equipment is as follows:

[0057] The indenter breakage index is small (close to 0): When the indenter breakage index is small, it indicates that there are fewer breakage points on the indenter or the breakage depth is shallow, which means the overall health condition of the indenter is good. At this time, the stability of the crimping equipment is strong, and the crimping force can transmit pressure more evenly, ensuring the stability of the joint quality.

[0058] The indenter breakage index is large (close to 1): When the indenter breakage index is large, it indicates that there are many breakage points on the indenter and the depth of these breakage points is also large, meaning that the indenter has suffered relatively serious damage or wear. This will directly affect the crimping accuracy and effect of the equipment because the contact force and crimping quality of the damaged part are uneven, and uneven or unstable pressure may occur during the crimping process, thus affecting the quality of the cable joint. Therefore, the larger the indenter breakage index, the worse the stability of the crimping equipment and the more unsatisfactory the crimping effect.

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

[0060] 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. The larger the output power fluctuation value, the worse the circuit stability of the crimping equipment and the more unfavorable it is for the crimping operation.

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

[0062] The output power fluctuation is small (close to 0): When the output power fluctuation is small, it indicates that the circuit of the equipment is operating stably, and the power supply system can provide a stable voltage, which is very important for the normal operation of the crimping equipment. The stable voltage can ensure the precise control of electronic components such as the drive system and sensors of the crimper, and avoid equipment performance instability or control errors caused by output power fluctuations. Therefore, when the output power fluctuation is small, the circuit stability of the crimping equipment is good, and the crimping effect is also more accurate and stable.

[0063] The output power fluctuates greatly (close to 1): When the output power fluctuates greatly, it indicates that there is instability in the power supply system or circuit of the crimping device. A large fluctuation in the output power may lead to a decrease in the accuracy of the device control system, and fluctuations or failures may occur in the pressure sensor or other control parts during the crimping process, affecting the crimping quality. In addition, a large fluctuation in the output power may cause the device to overheat, be damaged or pose other safety hazards, thereby affecting the long-term stability and working efficiency of the device.

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

[0065] Substitute the operation data into the classification model. After classifying the current operation state of the crimping device through the classification model, judge whether the crimping device supports crimping use according to the classification result, including the following steps:

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

[0067] After obtaining the abnormality index, the larger the abnormality index, the more unfavorable the crimping device is for crimping operations. Compare the obtained abnormality index with the abnormality threshold. The abnormality threshold is used to classify the operation state of the crimping device. If the abnormality index is less than or equal to the abnormality threshold, classify the operation state of the crimping device as the normal state. If the abnormality index is greater than the abnormality threshold, classify the operation state of the crimping device as the abnormal state;

[0068] When the crimping device is in the normal state, judge that the crimping device supports crimping use. When the crimping device is in the abnormal state, judge that the crimping device does not support crimping use.

[0069] If it is supported, then during the crimping process, the change data of the cable joint and the cable are obtained in real time through the sensing device, including the following steps:

[0070] Obtain the change data of the cable joint. 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 distance;

[0071] 1) The on-line acquisition method of the insulation layer thickness deviation includes:

[0072] Ultrasonic detector: An ultrasonic detection instrument can be used to scan the insulation layer of the cable joint online. The ultrasonic sensor can measure the thickness of the insulation layer by emitting ultrasonic signals and analyzing the echoes. By comparing the thickness values at different positions, the thickness deviation of the insulation layer can be obtained.

[0073] Laser measurement equipment: Using laser measurement technology, precise thickness measurement of the insulation layer of the cable joint can be carried out. The laser equipment obtains the thickness of the insulation layer in real time through the reflection principle and calculates the deviation.

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

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

[0076] 2) The online acquisition method for the area occupied by the surface oxide layer includes:

[0077] Infrared imaging technology: An infrared camera can detect the temperature change on the surface of the cable joint caused by oxidation and calculate the area occupied by the oxide layer through infrared image analysis. There are differences in thermal radiation between the oxide layer and the non-oxidized part, and the distribution of the oxidized area can be captured through infrared imaging.

[0078] Spectral analysis method: A spectrometer (such as X-ray spectrum, laser spectrum, etc.) can be used to analyze the chemical composition of the oxide on the surface of the cable joint. By measuring the characteristic spectrum of the oxide, the thickness and proportion of the oxide layer can be deduced.

[0079] Image processing technology: Use a high-resolution camera to take pictures of the surface of the cable joint, and then use image processing algorithms to analyze the oxidized area. Through image comparison, the proportion of the oxide layer can be accurately estimated.

[0080] 3) The online acquisition method for the joint length deviation includes:

[0081] Laser scanner: A laser scanner can be used to accurately measure the actual length of the cable joint. Through the point cloud data obtained by scanning, geometric modeling can be carried out and the length of the joint can be calculated.

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

[0083] Barcode scanning and visual recognition: Through a high-precision visual sensor, scan the identification or barcode on the cable joint, automatically identify the position of the joint, and calculate the length deviation of the joint according to the known standard length;

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

[0085] 4) The online acquisition method of the joint eccentricity distance includes:

[0086] Laser displacement sensor: Through the laser displacement sensor, the distance between the center of the cable joint and the standard position can be measured to determine whether the joint is eccentric, and then the eccentric distance can be obtained.

[0087] Three-dimensional vision system: Using the three-dimensional vision system, multiple cameras are used to scan the cable joint in real time from different angles, which can accurately determine whether there is an eccentricity phenomenon in the joint and calculate its eccentric distance.

[0088] Image analysis and edge detection algorithm: Through image capture technology, an image of the cable joint is obtained, and the edge detection algorithm (such as Canny edge detection) is applied to locate the geometric center and the actual joint position in the image, so as to calculate the eccentric distance.

[0089] Obtain the change data of the cable. The change data of the cable includes the outer sheath thickness deviation, the conductor diameter deviation, and the conductor twist amplitude;

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

[0091] Laser ranging sensor: The laser ranging sensor can accurately measure the outer sheath of the cable and obtain the thickness of the outer sheath in real time. Through the data of multiple measurement points, the outer sheath thickness deviation can be calculated. The non-contact measurement of the laser sensor can avoid physical damage to the cable and provide high-precision data at the same time, which is suitable for dynamic monitoring.

[0092] Ultrasonic measurement technology: The ultrasonic device measures the thickness of the cable outer sheath by emitting ultrasonic waves and receiving the echo signal. By analyzing the echo time, the thickness of the outer sheath can be obtained and compared with the standard thickness to obtain the thickness deviation. This method is suitable for real-time monitoring of the change of the outer sheath thickness during the cable production process.

[0093] Coating thickness gauge: The electromagnetic coating thickness gauge can measure the thickness of the outer sheath without damaging the cable. These devices can provide high-precision outer sheath thickness data and can monitor in real time online.

[0094] Capacitance gauge: The capacitance sensor can also detect the thickness of the outer sheath online, measure the change of the thickness of the cable external material based on the capacitance principle, and provide deviation data in real time.

[0095] 2) The online acquisition method of the conductor diameter deviation includes:

[0096] Laser measurement system: The laser measurement system is a commonly used non-contact measurement tool that can monitor the diameter of cable conductors in real time. By setting laser sensors at different positions on the cable, the system can accurately measure the conductor diameter and calculate the deviation from the standard value. By scanning multi-point data, the diameter change of the conductor can be obtained and the deviation can be fed back in real time.

[0097] Optical image processing technology: Use a high-precision camera to capture images of cable conductors and perform diameter analysis through image processing software. By comparing the pixel ratio of the cable conductor in the image, the diameter of the conductor is calculated and compared with the standard value to obtain the deviation in real time. This method can adapt to relatively complex conductor shapes and can accurately identify the diameter change of the conductor.

[0098] Inductive sensor: The inductive sensor can measure the diameter change of a conductor by sensing the electromagnetic wave characteristics of the conductor. This method is applicable to cables with relatively uniform conductor materials and can provide continuous diameter monitoring.

[0099] Eddy current sensor: The eddy current sensor can judge the diameter of a cable conductor by measuring the eddy current change around the conductor. This method is non-contact and highly accurate, suitable for real-time monitoring of the diameter change of the conductor.

[0100] 3) The online acquisition method of the conductor twist amplitude includes:

[0101] Vision sensing system: By installing a high-precision camera and a light source, the vision sensing system is used to take real-time pictures of cable conductors. Through image analysis algorithms, the twisting condition of the cable conductor is identified. Commonly used technologies include edge detection, morphological operations, etc., which can accurately detect whether the conductor is twisted. By comparing consecutive images, the twist amplitude of the conductor is calculated to obtain its real-time change.

[0102] Laser 3D scanner: The laser 3D scanner can generate a 3D image of the cable conductor in real time by scanning the surface contour of the conductor. If the conductor is twisted, the scanning result will show an irregular geometric shape. By comparing the difference between the actual contour and the standard contour, the twist amplitude of the conductor can be obtained. This method can perform dynamic monitoring on the production line and adjust process parameters in a timely manner.

[0103] Strain gauge and sensor: Install a micro strain gauge on the conductor. When the cable conductor is twisted, the strain gauge will detect a small strain change. By calculating the change in the strain value, the twist amplitude of the conductor can be inferred. This method is applicable to occasions where accurate measurement of conductor deformation is required.

[0104] Capacitive or magnetic sensors: Capacitive or magnetic sensors can measure the deformation of cable conductors. If the conductor is twisted, the measured value of the capacitive or magnetic sensor will change, reflecting the degree of deviation of the conductor. This method can monitor the deformation of the conductor in real time and feedback the twisting amplitude.

[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 distance. The greater the insulation layer thickness deviation, the greater the pressure required for the cable joint in the future. The greater the area occupied by the surface oxide layer, the greater the pressure required for the cable joint in the future. The greater the joint length deviation, the greater the pressure required for the cable joint in the future. The greater the joint eccentricity distance, the smaller the pressure required for the cable joint in the future. The specific reasons are as follows:

[0106] 1. The main function of the insulation layer is to protect the conductor, avoid external interference, and maintain the electrical insulation performance of the cable. If the insulation layer thickness deviation is too large, it means that the insulation layer at the joint position is too thick or too thin, which will affect the contact surface during the crimping process. When the insulation layer is thick, the crimping tool needs to apply greater pressure to ensure sufficient contact between the conductors, avoid poor contact or excessive contact resistance. Increasing the crimping pressure helps to compact the too-thick insulation layer, ensure the effective connection of the electrical conductors, and improve the mechanical strength and electrical performance of the joint.

[0107] 2. The oxide layer on the surface of the cable conductor will increase the contact resistance of the joint and affect the electrical performance. Especially during the crimping process, if the oxide layer cannot be completely removed or compacted, it will cause large heat and resistance when the current passes through, reducing the reliability of the joint. Increasing the pressure can help to break the oxide layer, enhance the contact between the electrical conductors, reduce the resistance, and ensure good electrical connection between the conductor and the joint. Therefore, when the area of the oxide layer is large, it is necessary to increase the crimping pressure to effectively remove the oxide layer and enhance the electrical and mechanical performance of the joint.

[0108] 3. If the length deviation of the cable joint is large, the joint part may be longer or shorter than the standard design. A longer joint may lead to uneven pressure distribution, easy to cause poor contact or joint looseness. Increasing the crimping pressure can help to fill the gap in the joint part, ensure the tight connection between the cable conductor and the joint, and thus enhance the stability and reliability of the joint. For the case of a longer joint, appropriately increasing the pressure can make up for the deficiency in the joint size, ensure the tight contact between the joint and the cable conductor, and improve the overall mechanical strength.

[0109] 4. Eccentricity refers to the symmetry difference on both sides of the cable joint. If eccentricity occurs in the joint, one side may be under greater pressure during crimping while the other side is under less pressure, which may lead to non-uniform deformation of the joint and affect the mechanical strength of the joint. Increasing the pressure may exacerbate the eccentricity phenomenon, resulting in uneven pressure distribution, causing excessive stress on one side of the joint and insufficient stress on the other side, ultimately affecting the quality and stability of the cable joint. Therefore, in the case of large eccentricity, appropriately reducing the pressure helps avoid excessive stress on the joint, maintain uniform pressure distribution, and thus improve the overall stability of the joint.

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

[0111] 1. The function of the outer sheath is to protect the internal conductor of the cable from external physical damage and ensure the electrical insulation of the cable. If the deviation of the outer sheath thickness is large (either too thick or too thin), it will affect the contact between the conductor and the crimp joint during crimping. An overly thick outer sheath will hinder the effective contact between the conductor and the joint, so it is necessary to increase the pressure to ensure effective contact between the conductor and the joint and avoid poor contact or electrical instability caused by the overly thick outer sheath. An overly thin outer sheath may increase the influence of the external environment on the cable conductor. Increasing the crimping pressure helps compensate for this deviation and ensure the tightness between the cable outer sheath and the joint. Generally speaking, when the deviation of the outer sheath thickness is large, it is necessary to increase the pressure to ensure the tight connection between the conductor and the joint.

[0112] 2. The deviation of the conductor diameter affects the physical contact of the joint part. If the diameter of the conductor is larger or smaller, it will affect the contact surface between the joint and the conductor during crimping, resulting in poor contact or uneven pressure distribution. When the diameter is too large: It is necessary to increase the pressure during crimping to ensure that the conductor can be in full contact with the joint and avoid the situation where one side of the joint cannot be fully crimped due to the overly large conductor, causing electrical instability or joint loosening. When the diameter is too small: Increasing the pressure can help fill the gap between the conductor and the joint and ensure a stable connection between the conductor and the joint. Therefore, regardless of whether the increase in the deviation of the conductor diameter 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 twist refers to a change in the geometry of the cable conductor, which may cause the conductor to deform or become irregular. If the twist amplitude of the conductor is large, uneven stress distribution may occur during crimping. An excessive twist amplitude means that a part of the conductor may be under too much pressure while other parts are under less pressure, resulting in uneven crimping of the joint and affecting electrical and mechanical properties. To avoid this, the crimping pressure should be appropriately reduced to prevent further deformation of the conductor. A conductor with a large twist amplitude may increase the friction at the joint part, thus affecting the crimping quality and conductivity. Reducing the pressure helps to reduce this effect. Therefore, in the case of a large conductor twist amplitude, appropriately reducing the crimping pressure can reduce the risks of deformation and poor contact and ensure the quality of the joint.

[0114] After analyzing the change data of the cable joint and the cable using a linear regression analysis model, the change trends of the cable joint and the cable 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 change data), are the regression coefficients of each variable. The positive or negative value of the regression coefficient depends on the direct or inverse ratio relationship between the change data and the influence coefficient;

[0116] Substitute the change data of the cable joint into the linear regression analysis model to calculate the cable joint influence coefficient. 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 distance. If the cable joint influence coefficient has a large value, it is predicted that the overall subsequent pressure required for the cable joint is large; if the cable joint influence coefficient has a small value, it is predicted that the overall subsequent pressure required for the cable joint is small;

[0117] For example, compare the cable joint influence coefficient 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 overall subsequent 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 overall subsequent pressure required for the cable joint needs to be decreased.

[0118] Substitute the change data of the cable into the linear regression analysis model to calculate the cable influence coefficient. The change data of the cable includes the outer sheath thickness deviation, the conductor diameter deviation, and the conductor twist amplitude. If the cable influence coefficient has a large value, it is predicted that the overall subsequent pressure required for the cable is large; if the cable influence coefficient has a small value, it is predicted that the overall subsequent pressure required for the cable is small;

[0119] Compare the cable line influence coefficient with the cable line influence coefficient threshold. If the cable line influence coefficient is greater than or equal to the cable line influence coefficient threshold, it is predicted that the overall pressure required for the cable line in the future needs to increase. If the cable line influence coefficient is less than the cable line influence coefficient threshold, it is predicted that the overall pressure required for the cable line in the future needs to decrease;

[0120] In this application:

[0121] 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 distance. Therefore, in the regression analysis model of the cable joint, when n takes the value of 4, the updated model expression is: , where is the cable joint influence coefficient, are respectively the insulation layer thickness deviation, the area occupied by the surface oxide layer, the joint length deviation, and the joint eccentricity distance, are respectively the regression coefficients of the insulation layer thickness deviation, the area occupied by the surface oxide layer, the joint length deviation, and the joint eccentricity distance, and are all greater than 0, is less than 0, which is determined by the inverse relationship between the joint eccentricity distance and the cable joint influence coefficient;

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

[0123] The logical factors of the influence coefficient in the use of the present invention: Taking the influence of the change data on the pressure as an example, one is the index, that is, the factor that causes the pressure to change (in the present invention, it refers to the influence of the change data on the pressure); the second is the weight of these indexes, that is, the proportion occupied when each change data occurs; the third is the operation equation, that is, through what kind of mathematical operation process to obtain the result, and the influence coefficient obtained by operating the indexes with their respective weights through the operation equation.

[0124] Perform data conversion and processing on the main influence data obtained from the sample, and convert it into the data language recognized by computer software; secondly, use SPSS software to perform Logistic regression analysis on these evaluation factors, and screen out the factors and their weights that have important correlations with the results; thirdly, substitute the evaluation factors and weights into the Logistic regression equation for operation, so as to obtain the results. Specifically:

[0125] First, ensure the integrity of the main influencing data, handle missing values and outliers, and convert the data into a format that can be recognized by SPSS software. Usually, the data is stored in formats such as csv or xlsx, and then imported into SPSS. Open the SPSS software, import the processed data file, and transform the variables as needed. For example, for continuous variables, perform standardization or normalization. Select the "Analyze" menu, then select the "Binary Logistic" option under "Regression". In the dialog box, add the dependent variable (result) and independent variables (main influencing data) to the corresponding boxes. SPSS will fit a Logistic regression model based on the selected variables. In the output results, information such as the coefficients, standard errors, and p-values of the model will be seen. Check the coefficients and p-values in the output results to determine which variables have a significant correlation with the result. 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 magnitude of the coefficient reflects the degree of influence of each factor on the result, and the sign of the coefficient indicates the direction of the influence. After obtaining the significant factors and their coefficients, obtain the Logistic regression equation, which is used to calculate the probability of each sample and then predict the result.

[0126] While predicting the change trend, fuse the classification model and the linear regression analysis model to construct an early warning model, including the following steps:

[0127] Obtain the abnormal index of the crimping equipment in the classification model, and obtain the cable joint influence coefficient and the cable line influence coefficient in the regression analysis model. Among them, when the abnormal index is less than or equal to the abnormal threshold, when the abnormal index is close to the abnormal threshold, it indicates that the operating state of the crimping equipment is declining. At this time, it is necessary to reduce the output pressure to ensure the stable operation of the crimping equipment. The cable joint influence coefficient and the cable line influence coefficient respectively indicate the change in pressure demand of the cable joint and the cable line when being pressed.

[0128] Comprehensively calculate the abnormal index, the cable joint influence coefficient, and the cable line influence coefficient to obtain an adjustment index. The expression is: , where is the adjustment index, is the cable joint influence coefficient, is the cable line influence coefficient, is the abnormal index, , , are the weights of the cable joint influence coefficient, the cable line influence coefficient, and the abnormal index respectively, and ;

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

[0130] When predicting the need to adjust the pressure change through the warning model, dynamically regulate the output pressure of the crimping device and generate a warning signal, 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 there is no need to adjust the pressure change;

[0132] 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, is the adjustment index;

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

[0134] Compare the adjusted pressure with the standard pressure range required for the current cable joint crimping. 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] Embodiment 3: The cable joint construction warning model construction system described in this embodiment includes an equipment classification module, a trend prediction module, and a dynamic regulation module;

[0136] Equipment classification module: Before crimping the cable joint, obtain the operation data of the crimping device, substitute the operation data into the classification model, classify the current operation state of the crimping device through the classification model, and then judge whether the crimping device supports crimping use according to the classification result. The judgment result is sent to the trend prediction module, and the classification model is sent to the dynamic regulation module;

[0137] Trend prediction module: If it is supported, during the crimping process, obtain the change data of the cable joint and the cable line in real time through the sensing device, analyze the change data of the cable joint and the cable line using the linear regression analysis model, and then predict the change trend of the cable joint and the cable line. The linear regression analysis model is sent to the dynamic regulation module;

[0138] Dynamic regulation module: While predicting the change trend, a warning model is constructed by integrating a classification model and a linear regression analysis model. When it is predicted by the warning model that the pressure change needs to be adjusted, the output pressure of the crimping device is dynamically regulated and a warning signal is generated.

[0139] The above formulas are all dimensionless and only take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0140] It should be understood that the term "and / or" in this article is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Among them, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context before and after.

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

[0142] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but this implementation should not be considered to exceed the scope of the present application. Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0143] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claimed rights.

Claims

1. Method for constructing cable joint construction warning model, characterized by: The construction method includes the following steps: Before the cable joint is crimped, the construction system obtains the operation data of the crimping device, substitutes the operation data into the classification model, classifies the current operation state of the crimping device through the classification model, and then determines whether the crimping device supports crimping use according to the classification result; If it supports, during the crimping process, the construction system obtains the change data of the cable joint and the cable in real time through the sensing device, analyzes the change data of the cable joint and the cable by using the linear regression analysis model, and then predicts the change trend of the cable joint and the cable; While predicting the change trend, the construction system fuses the classification model and the linear regression analysis model to construct an early warning model. When the early warning model predicts that the pressure change needs to be adjusted, it dynamically regulates the output pressure of the crimping device and generates an early warning signal.

2. The method for constructing a cable joint construction warning model according to claim 1, wherein: While predicting the change trend, the construction system fuses the classification model and the linear regression analysis model to construct an early warning model, including the following steps: Obtain the abnormal index of the crimping device in the classification model, and obtain the cable joint influence coefficient and the cable influence coefficient in the regression analysis model; The adjusted index is obtained by comprehensively calculating the anomaly index, the cable joint influence coefficient, and the cable line influence coefficient. The expression is as follows: , where is the adjusted index, is the cable joint influence coefficient, is the cable line influence coefficient, is the anomaly index, , , are the weights of the cable joint influence coefficient, the cable line influence coefficient, and the anomaly index respectively, and ; After obtaining the adjustment index, compare the adjustment index with the first adjustment threshold and the second adjustment threshold to complete the construction of the early warning model.

3. The method for constructing a cable joint construction warning model according to claim 2, characterized in that: Before the cable joint is crimped, the construction system obtains the operation data of the crimping device, including the following steps: Obtain the crimping head breakage index and the output power fluctuation of the crimping device; The obtaining logic of the crimping head breakage index is as follows: Use the ultrasonic device to obtain the number of breakage points and the maximum breakage depth on the crimping head breakage index, perform normalization processing on the number of breakage points and the maximum breakage depth, map the value range of the number of breakage points and the maximum breakage depth to the range of [0, 1], obtain the normalized value of the number of breakage points and the normalized value of the maximum breakage depth, and sum the normalized value of the number of breakage points and the normalized value of the maximum breakage depth to obtain the crimping head breakage 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 device, 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.

4. The method for constructing a cable joint construction warning model according to claim 3, characterized in that: Substitute the operation data into the classification model, classify the current operation state of the crimping device through the classification model, and then determine whether the crimping device supports crimping use according to the classification result, including the following steps: Substitute the obtained crimping head breakage index and output power fluctuation into the classification model. The classification model first calculates the abnormal index of the crimping device through the crimping head breakage index and output power fluctuation. The expression is: , where is the anomaly index, is the indenter breakage index, is the output power fluctuation, , are adjustment coefficients, and both are greater than 0; Compare the obtained abnormal index with the abnormal threshold. The abnormal threshold is used to classify the operation state of the crimping device. If the abnormal index is less than or equal to the abnormal threshold, classify the operation state of the crimping device as the normal state. If the abnormal index is greater than the abnormal threshold, classify the operation state of the crimping device as the abnormal state; When the crimping device is in the normal state, determine that the crimping device supports crimping use. When the crimping device is in the abnormal state, determine that the crimping device does not support crimping use.

5. The method for constructing a cable joint construction warning model according to claim 4, wherein: During the crimping process, the construction system obtains the change data of the cable joint and the cable in real time through the sensing device, including the following steps: Obtain the change data of the cable joint. 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 distance; Obtain the change data of the cable. The change data of the cable includes the outer sheath thickness deviation, the conductor diameter deviation, and the conductor twist amplitude.

6. The method for constructing a cable joint construction warning model according to claim 5, characterized in that: After analyzing the change data of the cable joint and the cable using the linear regression analysis model, predict the change trends of the cable joint and the cable, including the following steps: The model expression of the linear regression analysis model is as follows: , where is the influence coefficient, is the variable, are the regression coefficients of each variable; Compare the cable joint influence coefficient 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, predict that the overall pressure required for the cable joint in the future needs to increase. If the cable joint influence coefficient is less than the cable joint influence coefficient threshold, predict that the overall pressure required for the cable joint in the future needs to decrease; 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, predict that the overall pressure required for the cable in the future needs to increase. If the cable influence coefficient is less than the cable influence coefficient threshold, predict that the overall pressure required for the cable in the future needs to decrease.

7. The method for constructing a cable joint construction warning model according to claim 6, characterized in that: When it is predicted that the pressure needs to be adjusted through the warning model, dynamically adjust the output pressure of the crimping device and generate a warning signal, including the following steps: If the adjustment index is greater than or equal to the first adjustment threshold and less than or equal to the second adjustment threshold, predict that there is no need to adjust the pressure change; If the adjustment index is less than the first adjustment threshold, predict that the subsequent pressure needs to be reduced. If the adjustment index is greater than the second adjustment threshold, predict that the subsequent pressure needs to be increased; Compare the adjusted pressure with the standard pressure range required for the current cable joint crimping. 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.

8. The method for constructing a cable joint construction warning model according to claim 7, wherein: 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 as follows: , where is the adjusted pressure, is the pressure before adjustment, is the adjustment index; If the adjustment index is greater than the second adjustment threshold, it is predicted that the subsequent pressure needs to be increased, and the adjustment algorithm is as follows: , where is the adjusted pressure, is the pressure before adjustment, is the adjustment index.

9. The method for constructing a cable joint construction warning model according to claim 1, wherein: 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 distance. In the regression analysis model of the cable joint, when n takes the value of 4, the updated model expression is: , where is the cable joint influence coefficient, are respectively the insulation layer thickness deviation, the area occupied by the surface oxide layer, the joint length deviation, and the joint eccentricity distance, are respectively the regression coefficients of the insulation layer thickness deviation, the area occupied by the surface oxide layer, the joint length deviation, and the joint eccentricity distance, and are all greater than 0, is less than 0.

10. A cable joint construction warning model building system for implementing the building method according to any one of claims 1-9, characterized in that: It includes an equipment classification module, a trend prediction module, and a dynamic regulation module; Equipment classification module: Before crimping the cable joint, obtain the operation data of the crimping device, substitute the operation data into the classification model, classify the current operation state of the crimping device through the classification model, and then judge whether the crimping device supports crimping use according to the classification result. The judgment result is sent to the trend prediction module, and the classification model is sent to the dynamic regulation module; Trend prediction module: If it supports, during the crimping process, obtain the change data of the cable joint and the cable in real time through the sensing device. After analyzing the change data of the cable joint and the cable using the linear regression analysis model, predict the change trends of the cable joint and the cable. The linear regression analysis model is sent to the dynamic regulation module; Dynamic regulation module: While predicting the change trend, fuse the classification model and the linear regression analysis model to construct a warning model. When it is predicted that the pressure needs to be adjusted through the warning model, dynamically adjust the output pressure of the crimping device and generate a warning signal.

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