Opto-electric hybrid cable and process for its production

Abstract

The application belongs to the technical field of photoelectric hybrid cable production, and provides a photoelectric hybrid cable and a production process thereof, which comprises the following steps: combining historical production data of the photoelectric hybrid cable, extracting a correlation analysis data set through a clustering algorithm, analyzing the correlation between temperature difference and twisting interval performance values, and fitting modeling according to a correlation judgment result to obtain a temperature maximum fluctuation value. A correlation model of temperature difference and twisting quality is constructed through a data-driven method, a high-frequency temperature fluctuation scenario is screened by using clustering, dynamic prediction of temperature breakthrough time is realized by combining a learning model, and a closed-loop control system of "data modeling-real-time monitoring-intelligent adjustment" is formed. The system not only optimizes the problem of unstable twisting interval caused by temperature fluctuation, but also reduces the destructive influence of extreme temperature on production, reduces the rate of defective products, and improves the production efficiency and the automation level through temperature adjustment compensation and a double threshold protection mechanism.

Description

Technical Field

[0001] This invention belongs to the field of optoelectronic hybrid cable production technology, specifically an optoelectronic hybrid cable and its production process. Background Technology

[0002] The fiber-optic hybrid cable integrates optical fiber and copper wire into a single cable. It uses optical fiber to transmit data signals and copper wire to transmit power signals, combining the advantages of both. It can achieve high-speed data transmission and long-distance equipment power supply. By integrating optical fiber and copper wire into a single cable and through specific structural and protective layer designs, it ensures that optical signals and electrical signals do not interfere with each other during transmission. It is suitable for comprehensive cabling in various network systems, effectively reducing construction and network building costs and achieving the goal of multiple uses in one cable.

[0003] In the production of hybrid optical and electrical cables, there is a lack of quantitative analysis and dynamic compensation mechanism for the relationship between temperature difference and stranding pitch in response to the impact of temperature fluctuations on stranding quality. In real-time production, it is impossible to accurately predict when the temperature will exceed the critical value, which can easily lead to delayed or excessive temperature adjustment, resulting in uncontrolled stranding pitch and increased defect rate. In addition, traditional methods do not couple the stranding progress with the rate of temperature change and lack a linkage adjustment strategy based on production time and temperature fluctuations, which poses a risk of equipment damage or cable structure destruction.

[0004] Therefore, the present invention provides a hybrid optical-electric cable and its manufacturing process. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is: a manufacturing process for a hybrid optoelectronic cable, comprising the following steps:

[0007] By combining historical production data of hybrid optical and electrical cables, a clustering algorithm is used to extract correlation analysis datasets, analyze the correlation between temperature difference and stranding pitch performance values, and perform fitting modeling based on the correlation judgment results to obtain the maximum temperature fluctuation value;

[0008] Based on the obtained maximum temperature fluctuation value, the temperature data of the current stranding process is analyzed, and the time value at which the real-time temperature difference exceeds the maximum temperature fluctuation value is obtained as the temperature breakthrough time value.

[0009] Based on the current stranding speed and stranding progress, the actual production time required for the stranding process is output, and the actual production time required is compared and analyzed with the temperature breakthrough time value. If the actual production time required is greater than the temperature breakthrough time value, the temperature is adjusted based on the temperature change rate.

[0010] As a further aspect of the present invention: the process of extracting the correlation analysis dataset is as follows:

[0011] Obtain the time length values; perform cluster analysis on the time length value data, count the number of time length samples in each cluster, and extract the cluster with the largest number of time length samples as the correlation analysis dataset.

[0012] As a further aspect of the present invention: the process for obtaining the time length value data is as follows:

[0013] Obtain temperature data, stranding pitch, and production timestamp from historical production data of the hybrid optical and electrical cable;

[0014] Fit the historical temperature data change curve for each historical twisting process, calculate the absolute value of the difference between adjacent peaks and troughs in the historical temperature data change curve as the temperature difference value, and obtain the time length value corresponding to the temperature difference value.

[0015] As a further aspect of the present invention: the correlation analysis process is as follows:

[0016] The correlation between temperature difference and stranding pitch is calculated using the Pearson correlation coefficient.

[0017] If the correlation value is greater than or equal to the correlation limit, it indicates that the temperature difference and the twisting gap performance value are strongly positively correlated.

[0018] As a further aspect of the present invention: the process for obtaining the twisting pitch value is as follows:

[0019] Obtain the twisting gap value data within each time length sample;

[0020] The difference between each twisting gap value and the standard twisting gap value is calculated. The ratio of the absolute value of the difference to the standard twisting gap value is taken as the twisting gap deviation value. The average value of all twisting gap deviation values ​​is taken as the average deviation.

[0021] Calculate the mean and standard deviation of the twisting pitch data, and use the ratio of the standard deviation to the mean of the twisting pitch data as the uniformity.

[0022] The sum of the average deviation and the uniformity is used as the twisting pitch performance value.

[0023] As a further aspect of the present invention: the process of obtaining the maximum temperature fluctuation value is as follows:

[0024] If the temperature difference and the stranding pitch are strongly positively correlated, the linear correlation equation can be obtained by linearly fitting the temperature difference and the stranding pitch using the least squares method.

[0025] Inputting the maximum value of the preset twisting gap into the linear correlation equation, the corresponding maximum temperature difference output is the maximum temperature fluctuation value.

[0026] As a further aspect of the present invention: the temperature breakthrough time value is obtained through the following process:

[0027] Acquire real-time temperature data; fit a change curve based on the real-time temperature data, and calculate the absolute value of the difference between adjacent peaks and troughs in the real-time temperature data change curve as the temperature change value.

[0028] If the temperature change value is greater than or equal to the value close to the maximum temperature fluctuation value, then obtain the temperature change value data before the value close to the maximum temperature fluctuation value is greater than or equal to the value close to the maximum temperature fluctuation value;

[0029] To predict the trend of temperature change data and construct a temperature change prediction model;

[0030] The model outputs the temperature breakthrough time value based on the temperature change value prediction.

[0031] As a further aspect of the present invention: the actual production time value is obtained through the following process:

[0032] Obtain the length that the stranding equipment has completed so far, and combine it with the total design length of the optical-electric hybrid cable to calculate the change in stranding length per unit time, and obtain the stranding speed;

[0033] Assuming a constant twisting speed, calculate the difference between the total designed length and the currently completed length to obtain the remaining length. Then, calculate the ratio of the remaining length to the twisting speed to obtain the actual production time required.

[0034] As a further aspect of the present invention: the process of adjusting the temperature is as follows:

[0035] Obtain the current temperature change value, calculate the difference between the maximum temperature fluctuation value and the current temperature change value, and use the ratio of the difference to the temperature breakthrough time value as the temperature change rate.

[0036] The difference between the actual production time and the temperature breakthrough time is calculated to obtain the time difference. The product of the time difference and the rate of temperature change is used as the temperature adjustment amount.

[0037] If the current temperature fluctuation is in the rising phase, the difference between the real-time temperature value and the temperature adjustment amount will be used as the temperature adjustment value.

[0038] If the current temperature fluctuation is in a cooling phase, the sum of the real-time temperature value and the temperature adjustment amount will be used as the temperature adjustment value.

[0039] An optoelectronic hybrid cable is produced by an optoelectronic hybrid cable manufacturing process.

[0040] The beneficial effects of this invention are as follows:

[0041] By constructing a correlation model between temperature difference and stranding quality using a data-driven approach, clustering is used to screen high-frequency temperature fluctuation scenarios, and a learning model is combined to achieve dynamic prediction of temperature breakthrough time, forming a closed-loop control system of "data modeling - real-time monitoring - intelligent adjustment". This system not only optimizes the problem of unstable stranding pitch caused by temperature fluctuations, but also reduces the destructive impact of extreme temperatures on production through temperature adjustment compensation and dual threshold protection mechanisms, thereby reducing the defect rate and improving production efficiency and automation level. Attached Figure Description

[0042] The invention will now be further described with reference to the accompanying drawings.

[0043] Figure 1 This is a flowchart of the production process of a hybrid optoelectronic cable according to the present invention. Detailed Implementation

[0044] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0045] Example 1

[0046] The coefficient of thermal expansion of optical fiber is approximately 5 × 10⁻⁶. -7 At / ℃, the coefficient of thermal expansion of copper wire is relatively high, approximately 17×10. -6 / ℃, for every 10℃ increase in temperature, the length may increase by about 0.017%; the coefficients of thermal expansion of the two differ by about 30 times. Temperature fluctuations will cause the actual elongation of the optical fiber and copper wire to be inconsistent, which will disrupt the tension balance during stranding and thus change the stranding pitch. Therefore, when producing optical-electric hybrid cables, it is necessary to monitor and analyze the temperature difference and stranding progress, and to make temperature adjustment compensation in order to optimize production and improve production efficiency.

[0047] Please see Figure 1 As shown in the figure, the manufacturing process of a hybrid optoelectronic cable according to an embodiment of the present invention includes the following steps:

[0048] Step S10: Combining historical production data of the hybrid optical and electrical cable, the correlation analysis dataset is extracted through clustering algorithm to analyze the correlation between temperature difference and stranding gap, and the maximum temperature fluctuation value is obtained by fitting model based on the correlation.

[0049] In this embodiment, the process of extracting the correlation analysis dataset is as follows:

[0050] Obtain historical production data for hybrid optical and electrical cables, including but not limited to: temperature data, stranding pitch, and production timestamps;

[0051] For hybrid optical and electrical cables of the same specifications, acquire temperature data and stranding spacing data corresponding to each historical stranding process;

[0052] For each historical twisting process, fit the historical temperature data change curve, calculate the absolute value of the difference between adjacent peaks and troughs in the historical temperature data change curve as the temperature difference value; and obtain the time length value corresponding to the temperature difference value.

[0053] Cluster analysis is performed on time length data to obtain categories with similar time lengths and to extract the category containing the most samples;

[0054] The K-means clustering algorithm is selected, and the theoretical number of clusters K is set according to the range of time length and the preset time step. The specific implementation steps are as follows:

[0055] Obtain all time length values, extract the maximum and minimum values ​​of the time length values ​​as the range of time length, and preset the time step. The time step needs to be set by a person skilled in the art based on actual production experience and data characteristics.

[0056] Calculate the difference between the maximum and minimum values ​​of the time length, and use the ratio of the difference to the preset time step as the theoretical cluster number;

[0057] It is important to note that if the calculated theoretical number of clusters is not an integer, it should be rounded up to ensure coverage of all time lengths.

[0058] The center of each interval is obtained as the initial cluster center according to the preset time step;

[0059] Time-length sample allocation process: For each time-length sample, calculate its Euclidean distance to all initial cluster centers, and allocate the time-length sample to the nearest cluster;

[0060] The process of updating cluster centers involves calculating the mean time length of samples within each cluster and using it as the new cluster center.

[0061] Repeat the process of allocating samples and updating cluster centers over a longer period of time until the change in cluster centers between two adjacent iterations is less than a preset change threshold or the maximum number of iterations is reached.

[0062] The number of time length samples in each cluster was counted, and the cluster with the largest number of time length samples was extracted as the dataset for correlation analysis of temperature difference and twisting gap performance values.

[0063] It should be noted that the reason for extracting the correlation analysis dataset through the above clustering method is that the core purpose is to screen out the temperature fluctuation scenarios with the largest number of samples and similar time lengths in production: In the production process, the time length of temperature fluctuations may have multiple patterns (such as short-term disturbances, medium-term adjustment, and long-term drift), but the category with the largest number of samples corresponds to the most common temperature fluctuation cycle with approximately the same time length in production.

[0064] The process of obtaining the twisting pitch performance value is as follows: for each time length sample, obtain the twisting pitch value data within the corresponding time length.

[0065] The difference between each twisting gap value and the standard twisting gap value is calculated. The ratio of the absolute value of the difference to the standard twisting gap value is taken as the twisting gap deviation value. The average value of all twisting gap deviation values ​​is taken as the average deviation.

[0066] The standard value of the twisting gap is the average of the maximum and minimum values ​​of the preset twisting gap threshold range, and the twisting gap threshold is preset by those skilled in the art based on the production specifications and experience of the optoelectronic hybrid cable.

[0067] Calculate the mean and standard deviation of the twisting pitch data, and use the ratio of the standard deviation to the mean of the twisting pitch data as the uniformity.

[0068] The sum of the average deviation and the uniformity is used as the twisting pitch value;

[0069] The stranding pitch performance value is obtained by superimposing the average deviation and uniformity, both of which are dimensionless. The average deviation reflects the overall deviation of the stranding pitch from the design standard. The smaller the value, the closer the stranding pitch is to the ideal value and the more the cable structure meets the design expectations. The uniformity reflects the dispersion (i.e., stability) of the stranding pitch over time. The smaller the value, the smaller the pitch fluctuation. The reasonable physical meaning of the stranding pitch performance value is that during the stranding process, the pitch control meets both the design standard (low average deviation) and has high stability (low uniformity). This indicates that the current production process (especially within the time range corresponding to temperature fluctuations) can produce a reliable optical-electric hybrid cable with qualified performance.

[0070] In this embodiment, the correlation between temperature difference and stranding pitch is analyzed, and the maximum temperature fluctuation value is obtained by fitting and modeling based on the correlation. The specific implementation steps are as follows:

[0071] The correlation between temperature difference and stranding pitch is calculated using the Pearson correlation coefficient.

[0072] Before calculating the Pearson correlation coefficient, outliers in the correlation analysis dataset of temperature difference and twisting gap need to be removed. The Z-score method or IQR method can be used to detect and remove outliers in the temperature difference or performance values ​​to avoid interference from extreme data in the correlation analysis.

[0073] A correlation limit is set, which is a threshold set to determine whether the temperature difference and the twisting gap performance value are positively strongly correlated. It is generally set to 0.6 or 0.8, and is set by those skilled in the art based on experience.

[0074] If the correlation value is greater than or equal to the correlation limit, it indicates a strong positive correlation between the temperature difference and the stranding pitch performance value. The larger the temperature difference, the higher the stranding pitch performance value, which may be due to pitch instability caused by temperature fluctuations.

[0075] If the correlation value is less than the correlation limit, it indicates that the temperature difference and the stranding pitch are not strongly positively correlated. Temperature fluctuation may not be the main factor affecting the stranding pitch. Other parameters such as tension control and equipment stability may be dominant. Further analysis is needed to address these reasons.

[0076] Among them, the Pearson correlation coefficient is applicable to linear correlation scenarios. If the temperature difference value and the stranding pitch performance value are positively strongly correlated, it indicates that there is a linear relationship between the temperature difference value and the stranding pitch performance value.

[0077] The linear correlation equation was obtained by linearly fitting the temperature difference and the stranding pitch using the least squares method. The fitting formula is: bx=a*wc+b; where bx is the stranding pitch, a is the slope, wc is the temperature difference, and b is the intercept.

[0078] Input the maximum value of the preset twisting pitch into the linear correlation equation, and output the corresponding maximum temperature difference;

[0079] The maximum output temperature difference is the maximum temperature fluctuation value.

[0080] This step involves first extracting historical production data of hybrid optical and electrical cables containing temperature data, stranding spacing, and timestamps. For cables of the same specification, the peak-to-trough difference (temperature difference) and corresponding time length of the temperature change curve during each stranding process are calculated. The K-means clustering algorithm is used to filter the temperature fluctuation cycle category with the largest number of samples based on time length to form an analysis dataset. Then, the average deviation and uniformity of the stranding spacing from the standard value are calculated for each time length sample. The sum of the two is used as the stranding spacing performance value (the smaller the value, the better the quality). Next, the correlation between the temperature difference and the performance value is analyzed using the Pearson correlation coefficient. If a strong positive correlation is found after removing outliers, the maximum temperature fluctuation value is calculated by fitting a linear equation using the least squares method and inputting the preset maximum value of the stranding spacing performance.

[0081] Quantitative analysis is used to determine critical thresholds to reduce spacing deviations or fluctuations that exacerbate damage to the cable structure.

[0082] Cluster analysis improves analysis efficiency by focusing on scenarios with the largest number of samples and similar time lengths. Quantitative analysis determines the critical threshold (maximum temperature fluctuation value) to provide a basis for subsequent temperature adjustment, and early identification of risks reduces the defect rate and ensures structural reliability.

[0083] The average deviation and uniformity are used as dual indicators to comprehensively evaluate the twisting quality, and clustering is used to screen the scenarios with the largest number of samples and similar time lengths to fit the actual production situation.

[0084] Step S20: Based on the obtained maximum temperature fluctuation value, analyze the temperature data of the current twisting process, and obtain the time value when the real-time temperature difference exceeds the maximum temperature fluctuation value as the temperature breakthrough time value.

[0085] In this embodiment, the maximum temperature fluctuation value is obtained, and real-time temperature data is obtained for the real-time stranding process in the production of the optoelectronic hybrid cable.

[0086] The absolute value of the difference between adjacent peaks and troughs in the real-time temperature data change curve is calculated as the temperature change value based on the fitted change curve of the real-time temperature data.

[0087] The temperature change value is compared with the closest value to the maximum temperature fluctuation value. The value close to the maximum temperature fluctuation value is set to provide early warning and reserve time for analysis and prediction. It is set by those skilled in the art based on experience and historical temperature fluctuation characteristics.

[0088] If the temperature change is less than the nearest value of the maximum temperature fluctuation, then real-time comparison continues.

[0089] If the temperature change is greater than or equal to the value close to the maximum temperature fluctuation, then the predictive analysis process is executed.

[0090] In this embodiment, the specific steps for performing predictive analysis to obtain the temperature breakthrough time value are as follows:

[0091] Obtain temperature change data up to and including the value closest to the maximum temperature fluctuation, and construct a temperature change value dataset.

[0092] Perform trend prediction on the temperature change value dataset and construct a temperature change value prediction model;

[0093] Among them, the temperature change value prediction model can be a time series model, a machine learning model, or a deep learning model, which shall be selected by those skilled in the art based on the characteristics of the temperature change value dataset;

[0094] This embodiment uses the random forest model in machine learning to train a temperature change prediction model as an example.

[0095] Define input features and output labels;

[0096] Input features: Temperature change data, production timestamp; Output label: Remaining time from the current moment until the temperature change value first exceeds the maximum temperature fluctuation value;

[0097] Input features can be preprocessed by normalization, which can be achieved through standardization and normalization.

[0098] Set the parameters for the random forest model;

[0099] Random forest model parameters include: number of trees, maximum depth, subsampling rate, and splitting criterion;

[0100] For example, the default is 100 trees, and the optimal value is determined through subsequent optimization; the depth of the trees is limited to 5-10 layers to avoid overfitting; the subsampling rate is set to 0.8, that is, 80% of the samples and features of each tree are randomly selected for training to enhance the generalization ability of the model; mean squared error (MSE) is used as the node splitting standard (regression task).

[0101] The training and validation sets are divided in a 7:3 ratio, and time series cross-validation is used. For example, the first 70% of the data is used as the training set, and the last 30% is validated in segments in sequence.

[0102] Inputting the preprocessed feature matrix and labels, the random forest model generates multiple decision trees through bootstrap sampling. Each tree learns the nonlinear trend of temperature change (such as the pattern of continuous increase in temperature difference and increased fluctuation) in a random subspace.

[0103] The root mean square error (RMSE) is calculated using the validation set. If the RMSE exceeds a preset threshold (e.g., 10 minutes), the hyperparameters are adjusted.

[0104] The hyperparameters include: number of trees, maximum depth, etc.

[0105] When the real-time temperature change value first reaches a value close to the maximum temperature fluctuation value, extract the temperature change values ​​of the current time and the previous m (m is set by summarizing the total number of temperature change values ​​and experience) time points, convert them into feature vectors according to the preprocessing method during training, and use them as input;

[0106] The temperature change prediction model, obtained through training, outputs the temperature breakthrough time value.

[0107] The temperature breakout value is the remaining time before the temperature change value breaks through the maximum temperature fluctuation value.

[0108] This step targets the real-time stranding process of the hybrid optical and electrical cable. First, it fits the real-time temperature data change curve and calculates the difference between adjacent peaks and troughs (temperature change value). It compares this value with the closest value of the maximum temperature fluctuation. If it is less than the value, it continues to monitor. If it is greater than or equal to the value, it extracts the previous temperature change value data to build a dataset. It selects a model for trend prediction, using temperature change value and production timestamp as input features and remaining breakthrough time as output label. After normalization preprocessing, parameter setting, 7:3 ratio division of training and validation sets, and cross-validation optimization, when the real-time temperature change value first reaches the closest value, it inputs the feature vectors of the current time and the previous m time points. The trained model outputs the temperature breakthrough time value.

[0109] The problem of difficulty in responding in time when temperature fluctuations exceed the critical value in real-time production has been optimized. By dynamically predicting and determining the time when the temperature exceeds the standard in advance, the problem of loss of control of the stranding pitch caused by the temperature change value suddenly exceeding the maximum fluctuation value has been reduced.

[0110] Proactively predict temperature fluctuations to allow sufficient decision-making time for temperature adjustment and compensation, reduce the impact of sudden temperature changes on stranding quality, and improve the predictability and stability of the production process.

[0111] By introducing a proximity value as an early warning trigger condition, the lag response at the critical value is reduced. The proximity value is used for early warning and to reserve analysis time, while the temperature breakthrough time value provides a time basis for subsequent adjustments.

[0112] Step S30: Based on the current stranding speed and stranding progress, output the actual production time value required for the stranding process, and compare and analyze the actual production time value with the temperature breakthrough time value. If the actual production time value is less than or equal to the temperature breakthrough time value, no temperature adjustment is performed. If the actual production time value is greater than the temperature breakthrough time value, the temperature is adjusted based on the temperature change rate.

[0113] In this embodiment, the length that the stranding device has completed is obtained by an encoder or displacement sensor, and combined with the total design length of the optoelectronic hybrid cable, the stranding speed is obtained by continuously sampling the pulse signal of the encoder and calculating the change in stranding length per unit time.

[0114] Assuming a constant twisting speed, the actual production time is calculated as follows: the difference between the total designed length and the currently completed length is calculated to obtain the remaining length. The ratio of the remaining length to the twisting speed is then used to calculate the actual production time.

[0115] Compare the actual time required for production with the temperature breakthrough time value;

[0116] If the actual production time is less than or equal to the temperature breakthrough time, it means that the twisting process can be completed before the temperature change exceeds the temperature fluctuation value. In this case, normal production can continue without temperature adjustment compensation.

[0117] If the actual production time is greater than the temperature exceedance time, it means that the temperature change will exceed the temperature fluctuation value before the twisting process is completed, and the temperature adjustment compensation process will be executed.

[0118] In this embodiment, the specific implementation steps of the temperature adjustment compensation process are as follows:

[0119] Obtain the current temperature change value, calculate the difference between the maximum temperature fluctuation value and the current temperature change value, and use the ratio of the difference to the temperature breakthrough time value as the temperature change rate.

[0120] The difference between the actual production time and the temperature breakthrough time is calculated to obtain the time difference. The product of the time difference and the rate of temperature change is used as the temperature adjustment amount.

[0121] If the current temperature fluctuation is in the rising phase, the difference between the real-time temperature value and the temperature adjustment amount will be used as the temperature adjustment value.

[0122] If the current temperature fluctuation is in the cooling phase, the sum of the real-time temperature value and the temperature adjustment amount will be used as the temperature adjustment value.

[0123] It is worth noting that during the temperature adjustment and compensation process, upper and lower temperature thresholds and a temperature change rate threshold are set. When the temperature adjustment value is not within the range of the upper and lower temperature thresholds or the temperature change rate exceeds the temperature change rate threshold, the machine is directly triggered to stop and end the current production process.

[0124] This step involves obtaining the completed length of the stranding equipment, calculating the stranding speed based on the total design length, and calculating the ratio of the remaining length to the speed under the premise of constant speed to obtain the actual production time value. This value is then compared with the temperature breakthrough time value. If the actual production time value is less than or equal to the temperature breakthrough time value, the temperature is not adjusted. If the actual production time value is greater than the temperature breakthrough time value, the temperature adjustment compensation process is executed.

[0125] The temperature adjustment and compensation process is as follows: calculate the difference between the current temperature change value and the maximum temperature fluctuation value, divide it by the temperature breakthrough time value to obtain the temperature change rate, multiply the difference between the actual production time value and the temperature breakthrough time value by the rate to obtain the temperature adjustment amount, determine whether to reduce or increase the adjustment amount based on whether the current stage is heating or cooling to obtain the temperature adjustment value, and set the upper and lower temperature thresholds and the temperature change rate threshold. If the adjustment value exceeds the range or the rate exceeds the standard, the machine will be stopped.

[0126] The optimization addresses the issues of whether the stranding process can be completed before the temperature change value exceeds the critical value and how to accurately adjust the temperature according to the production schedule, reducing abnormal stranding spacing and cable structure damage caused by untimely or unreasonable temperature adjustment.

[0127] To achieve the linkage between production progress and temperature control, the temperature adjustment strategy is determined through quantitative calculation. This ensures that the stranding process is completed within the critical temperature range, while the shutdown threshold is set to prevent the destructive impact of extreme temperatures on production, thereby improving the safety of the production process and the stability of product quality.

[0128] The stranding progress is quantified into a time parameter and dynamically coupled with the temperature breakthrough time. Different adjustment logics are implemented for the heating and cooling stages. At the same time, a dual threshold is introduced to build a safety protection mechanism to reduce the temperature adjustment from exceeding the equipment or process tolerance range and the temperature change rate from exceeding the temperature change rate threshold, thereby reducing production accidents and making the adjustment process safe and controllable.

[0129] Example 2

[0130] Based on the aforementioned embodiment 1, this application provides a hybrid optical-electric cable, wherein the hybrid optical-electric cable includes: optical fiber, tight sleeve, reinforcing member, optical cable protective sleeve, conductor, insulation layer, wrapping tape, tear cord, and sheath;

[0131] The optoelectronic hybrid cable is produced by an optoelectronic hybrid cable manufacturing process described in Example 1 above.

[0132] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A manufacturing process for a hybrid optoelectronic cable, characterized in that: Includes the following steps: By combining historical production data of hybrid optical and electrical cables, a clustering algorithm is used to extract correlation analysis datasets, analyze the correlation between temperature difference and stranding pitch performance values, and perform fitting modeling based on the correlation judgment results to obtain the maximum temperature fluctuation value; Based on the obtained maximum temperature fluctuation value, the temperature data of the current stranding process is analyzed, and the remaining time value before the temperature change value breaks through the maximum temperature fluctuation value is obtained by the temperature change value prediction model as the temperature breakthrough time value. Based on the current stranding speed and stranding progress, the actual production time required for the stranding process is output, and the actual production time required is compared and analyzed with the temperature breakthrough time value. If the actual production time required is greater than the temperature breakthrough time value, the temperature is adjusted based on the temperature change rate.

2. The manufacturing process for a hybrid optoelectronic cable according to claim 1, characterized in that: The process of extracting the correlation analysis dataset is as follows: Obtain the time length values; perform cluster analysis on the time length value data, count the number of time length samples in each cluster, and extract the cluster with the largest number of time length samples as the correlation analysis dataset.

3. The manufacturing process for a hybrid optoelectronic cable according to claim 2, characterized in that: The process for obtaining the time length value data is as follows: Obtain temperature data, stranding pitch, and production timestamp from historical production data of the hybrid optical and electrical cable; Fit the historical temperature data change curve for each historical twisting process, calculate the absolute value of the difference between adjacent peaks and troughs in the historical temperature data change curve as the temperature difference value, and obtain the time length value corresponding to the temperature difference value.

4. The manufacturing process for a hybrid optoelectronic cable according to claim 1, characterized in that: The correlation analysis process is as follows: The correlation between temperature difference and stranding pitch is calculated using the Pearson correlation coefficient. If the correlation value is greater than or equal to the correlation limit, it indicates that the temperature difference and the twisting gap performance value are strongly positively correlated.

5. The manufacturing process for a hybrid optoelectronic cable according to claim 1, characterized in that: The process for obtaining the twisting pitch value is as follows: Obtain the twisting gap value data within each time length sample; The difference between each twisting gap value and the standard twisting gap value is calculated. The ratio of the absolute value of the difference to the standard twisting gap value is taken as the twisting gap deviation value. The average value of all twisting gap deviation values ​​is taken as the average deviation. Calculate the mean and standard deviation of the twisting pitch data, and use the ratio of the standard deviation to the mean of the twisting pitch data as the uniformity. The sum of the average deviation and the uniformity is used as the twisting pitch performance value.

6. The manufacturing process for a hybrid optoelectronic cable according to claim 4, characterized in that: The process for obtaining the maximum temperature fluctuation value is as follows: If the temperature difference and the stranding pitch are strongly positively correlated, the linear correlation equation can be obtained by linearly fitting the temperature difference and the stranding pitch using the least squares method. Inputting the maximum value of the preset twisting gap into the linear correlation equation, the corresponding maximum temperature difference output is the maximum temperature fluctuation value.

7. The manufacturing process for a hybrid optoelectronic cable according to claim 1, characterized in that: The temperature breakthrough time value is obtained through the following process: Acquire real-time temperature data; fit a change curve based on the real-time temperature data, and calculate the absolute value of the difference between adjacent peaks and troughs in the real-time temperature data change curve as the temperature change value. If the temperature change value is greater than or equal to the value close to the maximum temperature fluctuation value, then obtain the temperature change value data before the value close to the maximum temperature fluctuation value is greater than or equal to the value close to the maximum temperature fluctuation value; To predict the trend of temperature change data and construct a temperature change prediction model; The model outputs the temperature breakthrough time value based on the temperature change value prediction.

8. The manufacturing process for a hybrid optoelectronic cable according to claim 1, characterized in that: The process for obtaining the actual production time value is as follows: Obtain the length that the stranding equipment has completed so far, and combine it with the total design length of the optical-electric hybrid cable to calculate the change in stranding length per unit time, and obtain the stranding speed; Assuming a constant twisting speed, calculate the difference between the total designed length and the currently completed length to obtain the remaining length. Then, calculate the ratio of the remaining length to the twisting speed to obtain the actual production time required.

9. The manufacturing process for a hybrid optoelectronic cable according to claim 1, characterized in that: The process of adjusting the temperature is as follows: Obtain the current temperature change value, calculate the difference between the maximum temperature fluctuation value and the current temperature change value, and use the ratio of the difference to the temperature breakthrough time value as the temperature change rate. The difference between the actual production time and the temperature breakthrough time is calculated to obtain the time difference. The product of the time difference and the rate of temperature change is used as the temperature adjustment amount. If the current temperature fluctuation is in the rising phase, the difference between the real-time temperature value and the temperature adjustment amount will be used as the temperature adjustment value. If the current temperature fluctuation is in a cooling phase, the sum of the real-time temperature value and the temperature adjustment amount will be used as the temperature adjustment value.

10. A hybrid optical-electric cable, characterized in that, The optoelectronic hybrid cable is produced by the manufacturing process described in any one of claims 1-9.

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