Cable laying construction method and system based on digital twin technology

Abstract

The present invention relates to the technical field of cable laying construction using digital twin technology, and specifically discloses a cable laying construction method and system based on digital twin technology. The tension data in the cable laying process is collected in real time through a tension sensor arranged at the construction site, and compared with the historical tension data in the digital twin system to judge the accuracy of the real-time data, calculate the stress accumulation index and the tension fluctuation index based on the accurate data, and calculate the tension anomaly coefficient through comprehensive analysis to judge whether the tension data in the construction process is normal; establish a risk assessment model, use normal data to train the model, and input abnormal data into the model for risk assessment, divide the cable laying construction process into different risk levels, and take corresponding early warning and response measures accordingly; the present invention effectively improves the safety and reliability of cable laying construction by introducing digital twin technology and intelligent analysis means.

Description

Technical Field

[0001] The present invention relates to the technical field of cable laying construction based on digital twin technology, and in particular to a cable laying construction method and system based on digital twin technology. Background Art

[0002] With the acceleration of urbanization and the continuous growth of electricity demand, the importance of cable laying projects has become increasingly prominent; cable laying is not only related to the stable operation of the power system, but also directly affects the safety and reliability of urban infrastructure; in the process of cable laying, a series of technical difficulties and safety risks are often faced.

[0003] In recent years, digital twin technology, as an emerging information technology means, has gradually been applied to various fields, including manufacturing, construction, and power systems. The core of digital twin technology is to achieve real-time monitoring, predictive analysis, and optimization control of physical entities by building virtual models of physical entities. In the field of cable laying, digital twin technology also has broad application prospects.

[0004] However, existing technologies are lacking: existing cable laying construction technologies have many problems such as inaccurate tension control, insufficient data utilization, inaccurate risk assessment and backward construction management methods; these problems may lead to cable damage, construction delays and even safety hazards, especially in the laying of long-distance and large-diameter cables; existing construction technologies mostly rely on manual experience and simple mechanical devices, which make it difficult to achieve real-time precise control and effective use of data, resulting in inaccurate risk assessment and inefficient management. Summary of the invention

[0005] The purpose of the present invention is to provide a cable laying construction method and system based on digital twin technology to solve the problems in the above background.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] The cable laying construction method based on digital twin technology includes the following steps:

[0008] S1: Monitor the real-time tension data of the cable during the cable laying construction process, compare the monitored real-time tension data with the historical tension data in the digital twin system, and determine whether the real-time data is accurate;

[0009] S2: marking the real-time tension data according to the judgment result, and dividing the real-time tension data into accurate data and inaccurate data;

[0010] S3: Based on the accurate data, the tension data is processed to obtain the stress accumulation index and tension fluctuation index of the tension data;

[0011] The process of obtaining the stress accumulation index of the tension data is as follows:

[0012] Based on accurate data, the tension data is acquired in time series, the time is discretized using the trapezoidal integration method, and the stress accumulation index is calculated;

[0013] Wherein, the calculation expression of the stress accumulation index is: ;

[0014] In the formula, Represents the stress accumulation index of tension data, i represents the time point of acquisition, represents the i-th acquisition time point, represents the i+1th acquisition time point, Represents the time interval between two consecutive time points. represents the tension value at the i-th time point, represents the tension value at the i+1th time point, and n represents the total number of tension data points;

[0015] The process of obtaining the tension fluctuation index is as follows:

[0016] Based on accurate data, the tension data is acquired in time series, and the tension data is fast Fourier transformed to convert it into a frequency domain signal;

[0017] Perform discrete Fourier transform on the tension data to obtain the frequency component. The calculation expression is:

[0018] ;

[0019] Where n represents the total number of tension data points, represents the i-th acquisition time point, k represents the frequency index, and the value of k is n-1. represents the frequency at k, Indicates tension data at frequency , i represents the time point of collection, and e represents the natural constant;

[0020] Calculate the power spectral density and analyze the energy distribution of tension fluctuations;

[0021] The power spectrum density is calculated as follows:

[0022] ;

[0023] In the formula, Indicates frequency The power spectral density at ;

[0024] Based on frequency The power spectral density at Calculate the tension fluctuation index;

[0025] Wherein, the calculation expression of the tension fluctuation index is: ;

[0026] In the formula, Indicates a frequency greater than a preset frequency threshold, represents the tension fluctuation index;

[0027] S4: Comprehensively analyze the stress accumulation index and tension fluctuation index of the tension data to determine whether the tension data during the cable laying construction process is normal;

[0028] S5: Based on the judgment results, the tension data is divided into normal data and abnormal data, a risk assessment model is established, the normal data is used to train the risk assessment model, the abnormal data is input into the trained risk assessment model, and the risk assessment of the cable laying construction process is performed;

[0029] The step of establishing a risk assessment model and training the risk assessment model using normal data specifically includes:

[0030] The abnormal coefficient of normal data is analyzed by decision tree model , stress accumulation index and tension fluctuation index Conduct analysis;

[0031] Collecting abnormal coefficients of normal data , stress accumulation index and tension fluctuation index , and collect the corresponding cable laying construction process risk level data as training data;

[0032] Preprocess the training data and select the decision tree algorithm to build a classification model;

[0033] The abnormal coefficient of normal data , stress accumulation index and tension fluctuation index As input features of the model;

[0034] The risk level of the cable laying construction process is used as the output of the model;

[0035] Divide the dataset into training and testing sets to ensure the generalization ability of the model;

[0036] Train the decision tree model using the training set data.

[0037] As a further solution of the present invention: comparing the monitored real-time tension data with the historical tension data in the digital twin system to determine whether the real-time data is accurate specifically includes:

[0038] The real-time collected tension data Historical tension data in the digital twin system and the standard deviation of historical tension data Processing is performed to obtain the accuracy coefficient of real-time tension data;

[0039] Determine whether the accuracy coefficient is greater than or equal to the preset accuracy coefficient threshold. If so, record it as accurate data; if not, record it as inaccurate data.

[0040] As a further solution of the present invention: the comprehensive analysis of the stress accumulation index and the tension fluctuation index of the tension data specifically includes:

[0041] The stress accumulation index and tension fluctuation index of the tension data were normalized and the tension anomaly coefficient was calculated.

[0042] As a further solution of the present invention: the division of tension data into normal data and abnormal data based on the judgment result specifically includes:

[0043] Determine whether the tension anomaly coefficient is greater than or equal to the preset tension anomaly coefficient threshold. If so, record it as abnormal data; if not, record it as inaccurate data.

[0044] As a further solution of the present invention: the step of inputting abnormal data into a trained risk assessment model to conduct risk assessment on the cable installation process specifically includes:

[0045] Anomaly coefficient for input abnormal data , stress accumulation index and tension fluctuation index ;

[0046] The cable installation process is classified into different risk levels, including high risk, medium risk and low risk, through prediction by the trained decision tree model.

[0047] Prioritize early warning of high-risk cable installation construction processes, stop the construction process in time, and conduct abnormality inspections;

[0048] Increase the monitoring frequency for medium-risk cable installation construction processes. If medium-risk assessment results continue to appear, issue an early warning, stop the construction process in time, and conduct abnormal investigations. If the risk level decreases, reduce the monitoring frequency.

[0049] The low-risk cable installation construction process should be monitored normally. If the risk increases, the monitoring frequency should be increased.

[0050] The cable laying construction system based on digital twin technology includes:

[0051] A tension data acquisition module, the data acquisition module is used to monitor the real-time tension data of the cable laying during the cable laying construction process;

[0052] A tension data judgment module, which is used to compare the monitored real-time tension data with the historical tension data in the digital twin system to determine whether the real-time data is accurate;

[0053] A tension data processing module, which processes the tension data based on accurate data to obtain a stress accumulation index and a tension fluctuation index of the tension data;

[0054] A comprehensive analysis module, which is used to comprehensively analyze the stress accumulation index and tension fluctuation index of the tension data to determine whether the tension data during the cable laying construction process is normal;

[0055] The risk assessment module divides the tension data into normal data and abnormal data based on the judgment result, establishes a risk assessment model, uses normal data to train the risk assessment model, inputs the abnormal data into the trained risk assessment model, and performs risk assessment on the cable laying construction process.

[0056] The cable laying construction system based on digital twin technology, the tension data processing module at least includes:

[0057] A stress accumulation index calculation unit, wherein the stress accumulation index calculation unit is used to process the tension data and calculate the stress accumulation index of the tension data;

[0058] A tension fluctuation index calculation unit is used to process the tension data and calculate the tension fluctuation index of the tension data.

[0059] Beneficial effects of the present invention:

[0060] (1) The present invention can timely detect any tension changes beyond the normal range during the construction process by real-time monitoring of the tension data during the cable laying process and comparing and analyzing it with the historical data in the digital twin system. This real-time monitoring mechanism enables the construction team to promptly learn about the problems that may occur during the cable laying process, such as the risk of cable damage or breakage caused by excessive tension. By calculating the stress accumulation index and the tension fluctuation index, and further calculating the tension anomaly coefficient, the system can accurately evaluate whether the actual load borne by the cable during the laying process is reasonable. When abnormal data is detected, the system will automatically mark it and further analyze it through the risk assessment model to divide the cable laying construction process into different risk levels. For high-risk situations, the system will immediately issue an early warning to prompt the construction personnel to stop the operation in time and conduct abnormality inspections, thereby avoiding cable damage or other safety accidents caused by excessive tension. For medium-risk situations, the system will increase the monitoring frequency to capture any possible deterioration trend in time. For low-risk situations, the normal monitoring rhythm can be maintained. In this way, not only the downtime caused by cable damage is reduced, but also the safety hazards in the construction process are greatly reduced, ensuring the smooth progress of the construction project.

[0061] (2) By using the risk assessment model trained with normal data, the present invention can also help the construction party to better understand the changing patterns of various parameters in the cable laying process, so as to formulate a more reasonable construction plan; this intelligent decision support based on data analysis not only improves the safety of construction, but also enhances the overall reliability and sustainability of the construction project; in practical applications, this means that resources can be allocated more accurately, the construction process can be optimized, and ultimately the purpose of reducing costs and improving efficiency can be achieved. By integrating advanced sensing technology and data analysis methods, the present invention provides a comprehensive intelligent management platform for cable laying construction, which can monitor and evaluate various risks in the construction process in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The present invention will be further described below in conjunction with the accompanying drawings.

[0063] Figure 1 It is a flowchart of the specific steps of the cable laying construction method based on digital twin technology of the present invention;

[0064] Figure 2 It is a flow chart of the cable laying construction system based on digital twin technology in the present invention;

[0065] Figure 3 It is a flowchart of risk assessment of cable installation process using tension data in the present invention. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] See also Figure 1 As shown, the present invention is a cable laying construction method based on digital twin technology, comprising the following steps:

[0068] S1: Monitor the real-time tension data of the cable during the cable laying construction process, compare the monitored real-time tension data with the historical tension data in the digital twin system, and determine whether the real-time data is accurate;

[0069] S2: marking the real-time tension data according to the judgment result, and dividing the real-time tension data into accurate data and inaccurate data;

[0070] S3: Based on the accurate data, the tension data is processed to obtain the stress accumulation index and tension fluctuation index of the tension data;

[0071] S4: Comprehensively analyze the stress accumulation index and tension fluctuation index of the tension data to determine whether the tension data during the cable laying construction process is normal;

[0072] S5: Based on the judgment results, the tension data is divided into normal data and abnormal data, and a risk assessment model is established. The normal data is used to train the risk assessment model, and the abnormal data is input into the trained risk assessment model to conduct a risk assessment on the cable laying construction process.

[0073] In S1, the real-time tension data of the cable laying during the cable laying construction process is monitored, and the monitored real-time tension data is compared with the historical tension data in the digital twin system to determine whether the real-time data is accurate. Specifically, the following are included:

[0074] Real-time tension data during cable laying is collected through tension sensors arranged at the construction site. , and extract historical tension data in the digital twin system and the standard deviation of historical tension data ;

[0075] The real-time collected tension data Historical tension data in the digital twin system and the standard deviation of historical tension data Processing is performed to obtain the accuracy coefficient of real-time tension data;

[0076] Wherein, d represents the number of real-time tension data, d is a positive integer greater than 0, s represents the number of historical tension data, s is a positive integer greater than 0;

[0077] The calculation expression of the accuracy coefficient is: ;

[0078] Where W represents the accuracy coefficient of tension data;

[0079] It should be noted that the accuracy coefficient is calculated to determine the degree of deviation between the historical data and the current real-time data in the digital twin system, ensuring that the collected tension data is accurate, so that subsequent tension data analysis is based on accurate data. The larger the accuracy coefficient, the more accurate the corresponding tension data.

[0080] In S2, according to the judgment result, the real-time tension data is marked and divided into accurate data and inaccurate data;

[0081] Compare the accuracy coefficient W with a preset accuracy coefficient threshold;

[0082] If the accuracy coefficient W is greater than or equal to the preset accuracy coefficient threshold, it means that the corresponding tension data is accurate and is recorded as accurate data;

[0083] If the accuracy coefficient W is less than the preset accuracy coefficient threshold, it means that the corresponding tension data is inaccurate and is recorded as inaccurate data;

[0084] It should be noted that the accuracy coefficient of tension data is used to determine whether the tension data is different from the historical tension data in the digital twin system. If it exceeds the difference range, it means that the corresponding tension data is unstable. The larger the accuracy coefficient of the tension data, the higher the fit between the corresponding tension data and the historical data, and the more accurate the corresponding tension data.

[0085] In S3, based on accurate data, the tension data is processed to obtain the stress accumulation index and tension fluctuation index of the tension data, which specifically include:

[0086] Based on accurate data, the tension data is acquired in time series, the time is discretized using the trapezoidal integration method, and the stress accumulation index is calculated;

[0087] Wherein, the calculation expression of the stress accumulation index is: ;

[0088] In the formula, Represents the stress accumulation index of tension data, i represents the time point of acquisition, represents the i-th acquisition time point, represents the i+1th acquisition time point, Represents the time interval between two consecutive time points. represents the tension value at the i-th time point, represents the tension value at the i+1th time point, and n represents the total number of tension data points;

[0089] It should be noted that the stress accumulation index reflects the cumulative tension that the cable is subjected to during the laying process, and the larger the stress accumulation index, the higher the cumulative tension that the cable is subjected to during the laying process. Being in a high stress state for a long time may have an adverse effect on the cable itself and the entire construction process. The larger the stress accumulation index, the higher the long-term cumulative load that the cable is subjected to, which will significantly increase the risk of fatigue damage to the cable, damage to the insulation layer, and construction safety hazards. Therefore, during the cable laying construction process, the tension should be controlled within a reasonable range as much as possible to ensure that the cable is laid under a safe stress state to extend the service life of the cable and reduce construction risks.

[0090] Based on accurate data, the tension data is acquired in time series, and the tension data is transformed into frequency domain signals by fast Fourier transform, including:

[0091] Perform discrete Fourier transform on the tension data to obtain the frequency component. The calculation expression is: ;

[0092] Where n represents the total number of tension data points, represents the i-th acquisition time point, n is a positive integer greater than 0, k represents the frequency index, and the value of k is n-1, represents the frequency at k, Indicates tension data at frequency , i represents the time point of acquisition, i is a positive integer greater than 0, and e represents a natural constant;

[0093] By calculating the power spectrum density, the energy distribution of tension fluctuation is analyzed, and the calculation expression is: ;

[0094] In the formula, Indicates frequency The power spectral density at ;

[0095] Based on frequency The power spectral density at Calculate the tension fluctuation index, the calculation expression is: ;

[0096] In the formula, Indicates a frequency greater than a preset frequency threshold, represents the tension fluctuation index;

[0097] It should be noted that the tension fluctuation index is an important indicator for evaluating the tension stability during cable laying. The larger the tension fluctuation index, the more severe the tension fluctuation the cable is subjected to, which may lead to problems such as cable fatigue, insulation damage and construction safety hazards. Therefore, during the cable laying construction process, the tension fluctuation index should be controlled within a reasonable range as much as possible to ensure the safety of construction and the long-term reliability of the cable.

[0098] In S4, the stress accumulation index and tension fluctuation index of the tension data are comprehensively analyzed to determine whether the tension data during the cable laying construction process is normal, including:

[0099] The stress accumulation index of the tension data and tension fluctuation index Perform normalization processing and calculate the tension anomaly coefficient;

[0100] Wherein, the calculation expression of the tension anomaly coefficient is:

[0101] ;

[0102] In the formula, represents the tension anomaly coefficient, and is the preset scale factor, and and All are greater than 0;

[0103] It should be noted that the stress accumulation index of tension data and tension fluctuation index Tension Anomaly Coefficient is proportional to, and when the stress accumulation index and tension fluctuation index The larger the corresponding tension abnormality coefficient The larger it is, the higher the abnormality of the corresponding tension data.

[0104] See also Figure 3 As shown, in S5, a risk assessment model is established, the risk assessment model is trained using normal data, and the abnormal data is input into the trained risk assessment model to perform risk assessment on the cable laying construction process:

[0105] The tension anomaly coefficient Compare with the preset tension abnormality coefficient threshold;

[0106] If the tension abnormality coefficient If it is greater than or equal to the preset tension abnormality coefficient threshold, it means that the corresponding data is abnormal and recorded as abnormal data;

[0107] If the tension abnormality coefficient If it is less than the preset tension abnormality coefficient threshold, it means that the corresponding data is normal and recorded as normal data;

[0108] Establish a risk assessment model and use the decision tree model to analyze the abnormal coefficient of normal data. , stress accumulation index and tension fluctuation index After analysis, the cable laying construction process is divided into different risk levels, including:

[0109] Collecting abnormal coefficients of normal data , stress accumulation index and tension fluctuation index , and collect the corresponding cable laying construction process risk level data as training data;

[0110] Preprocess the training data and select the decision tree algorithm to build a classification model;

[0111] The abnormal coefficient of normal data , stress accumulation index and tension fluctuation index As input features of the model;

[0112] The risk level of the cable laying construction process (such as low risk, medium risk, high risk) is used as the output of the model;

[0113] Divide the dataset into training and testing sets to ensure the generalization ability of the model;

[0114] Train the decision tree model using the training set data;

[0115] Construct decision trees through methods such as feature selection, information gain or Gini coefficient;

[0116] Use the test set data to verify the model performance, calculate indicators such as accuracy, recall, and F1 score, and further evaluate the stability and generalization ability of the model through cross-validation;

[0117] Anomaly coefficient for input abnormal data , stress accumulation index and tension fluctuation index ;

[0118] The cable installation process is classified into different risk levels, including high risk, medium risk and low risk, through prediction by the trained decision tree model.

[0119] Prioritize early warning of high-risk cable installation construction processes, stop the construction process in time, and conduct abnormality inspections;

[0120] Increase the monitoring frequency for medium-risk cable installation construction processes. If medium-risk assessment results continue to appear, issue an early warning, stop the construction process in time, and conduct abnormal investigations. If the risk level decreases, reduce the monitoring frequency.

[0121] Normal monitoring of low-risk cable installation construction processes, and increasing the frequency of monitoring if the risk increases;

[0122] It should be noted that by establishing a risk assessment model, using a decision tree model to train normal data, and inputting abnormal data into the risk assessment model to achieve risk prediction of abnormal data, the risk level of the cable laying construction process can be accurately identified, so that corresponding processing can be made.

[0123] See also Figure 2 As shown in the figure, the cable laying construction system based on digital twin technology includes:

[0124] A tension data acquisition module, the data acquisition module is used to monitor the real-time tension data of the cable laying during the cable laying construction process;

[0125] A tension data judgment module, which is used to compare the monitored real-time tension data with the historical tension data in the digital twin system to determine whether the real-time data is accurate;

[0126] A tension data processing module, which processes the tension data based on accurate data to obtain a stress accumulation index and a tension fluctuation index of the tension data;

[0127] A comprehensive analysis module, which is used to comprehensively analyze the stress accumulation index and tension fluctuation index of the tension data to determine whether the tension data during the cable laying construction process is normal;

[0128] The risk assessment module divides the tension data into normal data and abnormal data based on the judgment result, establishes a risk assessment model, uses normal data to train the risk assessment model, inputs the abnormal data into the trained risk assessment model, and performs risk assessment on the cable laying construction process.

[0129] The working principle of the present invention is to improve the safety and efficiency of cable laying projects through real-time monitoring and intelligent analysis. The method monitors the tension data during the cable laying process at the construction site in real time, and compares and analyzes it with the historical data stored in the digital twin system to determine the accuracy of the current tension data. This process is achieved by calculating the difference coefficient between the real-time tension data and the historical data. The system only further processes the data marked as accurate, calculates the stress accumulation index using the trapezoidal integration method, converts the tension data to the frequency domain through fast Fourier transform, and calculates the tension fluctuation index, so as to evaluate the stability and cumulative load of the tension exerted on the cable during the laying process, normalizes the obtained stress accumulation index and tension fluctuation index, and calculates a comprehensive tension anomaly coefficient. In this way, it is judged whether the tension data during the construction process is normal; according to the comparison result of the tension anomaly coefficient with the preset threshold, the data is divided into normal data and abnormal data, and a risk assessment system of a decision tree model is trained based on the normal data; the system can evaluate the risk level of abnormal data, divide the cable laying construction process into different risk levels, such as low risk, medium risk and high risk, and take corresponding early warning and response measures accordingly. For high-risk situations, construction is immediately stopped and abnormalities are checked, medium-risk situations are monitored more closely and emergency responses are prepared, while low-risk situations are monitored routinely; the entire method not only improves the safety and reliability of cable laying construction, but also realizes the intelligent management of the construction process by introducing digital twin technology, thereby effectively preventing potential safety hazards and improving construction efficiency and quality.

[0130] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0131] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0132] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0133] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0134] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A cable laying construction method based on digital twin technology, characterized in that: The following steps are involved: S1: Monitor the real-time tension data of the cable during the cable laying construction process, compare the monitored real-time tension data with the historical tension data in the digital twin system, and determine whether the real-time data is accurate; S2: marking the real-time tension data according to the judgment result, and dividing the real-time tension data into accurate data and inaccurate data; S3: Based on the accurate data, the tension data is processed to obtain the stress accumulation index and tension fluctuation index of the tension data; The process of obtaining the stress accumulation index of the tension data is as follows: Based on accurate data, the tension data is acquired in time series, the time is discretized using the trapezoidal integration method, and the stress accumulation index is calculated; Wherein, the calculation expression of the stress accumulation index is: ; In the formula, Represents the stress accumulation index of tension data, i represents the time point of acquisition, represents the i-th acquisition time point, represents the i+1th acquisition time point, Represents the time interval between two consecutive time points. represents the tension value at the i-th time point, represents the tension value at the i+1th time point, and n represents the total number of tension data points; The process of obtaining the tension fluctuation index is as follows: Based on accurate data, the tension data is acquired in time series, and the tension data is fast Fourier transformed to convert it into a frequency domain signal; Perform discrete Fourier transform on the tension data to obtain the frequency component. The calculation expression is: ; Where n represents the total number of tension data points, represents the i-th acquisition time point, k represents the frequency index, and the value of k is n-1. represents the frequency at k, Indicates tension data at frequency , i represents the time point of collection, and e represents the natural constant; Calculate the power spectral density and analyze the energy distribution of tension fluctuations; The power spectrum density is calculated as follows: ； In the formula, Indicates frequency The power spectral density at ; Based on frequency The power spectral density at Calculate the tension fluctuation index; Wherein, the calculation expression of the tension fluctuation index is: ； In the formula, Indicates a frequency greater than a preset frequency threshold, represents the tension fluctuation index; S4: Comprehensively analyze the stress accumulation index and tension fluctuation index of the tension data to determine whether the tension data during the cable laying construction process is normal; S5: Based on the judgment results, the tension data is divided into normal data and abnormal data, a risk assessment model is established, the normal data is used to train the risk assessment model, the abnormal data is input into the trained risk assessment model, and the risk assessment of the cable laying construction process is performed; The step of establishing a risk assessment model and training the risk assessment model using normal data specifically includes: The abnormal coefficient of normal data is analyzed by decision tree model , stress accumulation index and tension fluctuation index Conduct analysis; Collecting abnormal coefficients of normal data , stress accumulation index and tension fluctuation index , and collect the corresponding cable laying construction process risk level data as training data; Preprocess the training data and select the decision tree algorithm to build a classification model; The abnormal coefficient of normal data , stress accumulation index and tension fluctuation index As input features of the model; The risk level of the cable laying construction process is used as the output of the model; Divide the dataset into training and testing sets to ensure the generalization ability of the model; Train the decision tree model using the training set data.

2. The cable laying construction method based on digital twin technology according to claim 1 is characterized in that: The process of comparing the monitored real-time tension data with the historical tension data in the digital twin system to determine whether the real-time data is accurate specifically includes: The real-time collected tension data Historical tension data in the digital twin system and the standard deviation of historical tension data Processing is performed to obtain the accuracy coefficient of real-time tension data; Determine whether the accuracy coefficient is greater than or equal to the preset accuracy coefficient threshold. If so, record it as accurate data; if not, record it as inaccurate data.

3. The cable laying construction method based on digital twin technology according to claim 1 is characterized in that: The comprehensive analysis of the stress accumulation index and the tension fluctuation index of the tension data specifically includes: The stress accumulation index and tension fluctuation index of the tension data were normalized and the tension anomaly coefficient was calculated.

4. The cable laying construction method based on digital twin technology according to claim 1 is characterized in that: The method of dividing the tension data into normal data and abnormal data based on the judgment result specifically includes: Determine whether the tension anomaly coefficient is greater than or equal to the preset tension anomaly coefficient threshold. If so, record it as abnormal data; if not, record it as inaccurate data.

5. The cable laying construction method based on digital twin technology according to claim 1 is characterized in that: The inputting of abnormal data into the trained risk assessment model to conduct risk assessment on the cable installation process specifically includes: Anomaly coefficient for input abnormal data , stress accumulation index and tension fluctuation index ; The cable installation process is classified into different risk levels, including high risk, medium risk and low risk, through prediction by the trained decision tree model. Prioritize early warning of high-risk cable installation construction processes, stop the construction process in time, and conduct abnormality inspections; Increase the monitoring frequency for medium-risk cable installation construction processes. If medium-risk assessment results continue to appear, issue an early warning, stop the construction process in time, and conduct abnormal investigations. If the risk level decreases, reduce the monitoring frequency. The low-risk cable installation construction process should be monitored normally. If the risk increases, the monitoring frequency should be increased.

6. The cable laying construction system based on digital twin technology is characterized by: A cable laying construction method based on digital twin technology as claimed in any one of claims 1 to 5, comprising: A tension data acquisition module, the data acquisition module is used to monitor the real-time tension data of the cable laying during the cable laying construction process; A tension data judgment module, which is used to compare the monitored real-time tension data with the historical tension data in the digital twin system to determine whether the real-time data is accurate; A tension data processing module, which processes the tension data based on accurate data to obtain a stress accumulation index and a tension fluctuation index of the tension data; A comprehensive analysis module, which is used to comprehensively analyze the stress accumulation index and tension fluctuation index of the tension data to determine whether the tension data during the cable laying construction process is normal; The risk assessment module divides the tension data into normal data and abnormal data based on the judgment result, establishes a risk assessment model, uses normal data to train the risk assessment model, inputs the abnormal data into the trained risk assessment model, and performs risk assessment on the cable laying construction process.

7. The cable laying construction system based on digital twin technology according to claim 6 is characterized in that: The tension data processing module at least includes: A stress accumulation index calculation unit, wherein the stress accumulation index calculation unit is used to process the tension data and calculate the stress accumulation index of the tension data; A tension fluctuation index calculation unit is used to process the tension data and calculate the tension fluctuation index of the tension data.

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