Cable safety laying control and quality detection method and system

Abstract

The invention discloses a cable safety laying control and quality detection method and system, and relates to the technical field of industrial control, and the method comprises a data collection step, a laying quality evaluation step, a laying quality analysis step, a strategy adjustment step and a strategy updating step. The system comprises a data acquisition module, a quality detection module, a quality analysis module, a strategy adjustment module and a data updating module. According to the invention, the dynamic adjustment and strategy optimization of the equipment can be carried out in time according to the actual situation of the site, and the safety and quality control level of the laying process are systematically improved, so that the accuracy of cable laying control is improved.

Description

Technical Field

[0001] This invention relates to the field of industrial control technology, specifically to a method and system for controlling and inspecting the safe laying of cables and for quality inspection. Background Technology

[0002] With the rapid development of modern power engineering technology towards large-scale, complex, and intelligent construction, the quality of cable laying, as the core carrier of power transmission and information communication, directly impacts the long-term operational safety and reliability of the power system. During laying, cables undergo multiple processes such as traction, bending, and conduit insertion. Improper mechanical operations, such as overloaded traction tension, excessive lateral pressure, or insufficient bending radius, can cause immediate or cumulative damage to the cable insulation, shielding, and even the conductor, potentially leading to severe power outages and safety accidents after long-term operation. Therefore, achieving intelligent control and real-time quality monitoring of the cable laying process is a crucial step in improving the lifecycle safety of cable systems.

[0003] Traditional cable laying quality control methods heavily rely on the personal experience of construction workers and scattered mechanical instruments. Workers determine the path based on design drawings and experience, intermittently checking key parameters such as traction force and bending angle using mechanical tension gauges and handheld inclinometers. Adjusting cable posture, releasing stress, and identifying abnormal conditions depend heavily on manual observation and operation. This method is inherently subjective and time-consuming, making it difficult to achieve continuous and precise control of the laying process, and highly susceptible to cable damage due to human negligence or misjudgment.

[0004] Currently, various improvement schemes utilizing sensors and automated equipment have emerged in existing technologies. One type of scheme focuses on process monitoring of a single parameter. For example, Chinese patent CN120724560A uses distributed fiber optic sensors to continuously measure the strain and bending radius of the cable and compares it with the BIM design model to detect construction deviations. Another type of scheme predicts the bending radius of submarine cables by establishing a catenary model and adjusts the speed of the cable-laying vessel accordingly. Another type of scheme emphasizes post-laying status monitoring and fault early warning. For example, Chinese patent CN120801966A predicts the probability of faults in laid cables by fusing partial discharge signals with hyperspectral remote sensing images. In addition, there are schemes dedicated to improving mechanical devices for specific construction stages, such as the specialized tools for pipe guidance or residual stress release in Chinese patent CN121011949A.

[0005] Regarding the above-mentioned technical solutions, existing technologies only focus on monitoring and early warning of laying parameters, or only make predictive adjustments to a single device. They cannot analyze cable damage in a timely manner, and it is difficult to make dynamic adjustments to the equipment and optimize strategies according to the actual situation on site, thereby reducing the accuracy of cable laying control. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for controlling the safe laying and quality inspection of cables, so as to solve the problems mentioned in the background art.

[0007] In a first aspect, the present invention provides a method for controlling the safe laying and quality inspection of cables, which achieves the purpose of the invention by adopting the following technical solution:

[0008] A method for controlling the safe laying and quality inspection of cables includes the following steps:

[0009] Data acquisition steps: Real-time synchronous acquisition of multi-source data and historical time-series data of the current cable laying section. Multi-source data includes laying mechanical data and cable spatial attitude data obtained based on multi-source sensors integrated into the cable. Laying mechanical data includes cable traction tension, lateral pressure and bending radius. Cable spatial attitude data includes three-dimensional tilt angle, angular velocity and heading angle.

[0010] Laying quality assessment steps: Based on multi-source data and historical time series data, the real-time health index of the current laying section is calculated through a preset health assessment model;

[0011] Laying quality analysis steps: Based on the comparison results between the real-time health index and the preset threshold, generate the first type of adjustment instruction for protective control of the laid cable;

[0012] Strategy adjustment steps: The generated adjustment instructions are sent to the laying actuator to dynamically adjust the traction force, laying speed, and guiding device.

[0013] By adopting the above technical solution, and through synchronous, real-time, and continuous acquisition of multi-source data and historical time-series data, especially the simultaneous acquisition of laying mechanical data such as traction tension, lateral pressure, and bending radius, as well as spatial attitude data such as tilt angle, angular velocity, and heading angle, the actual stress and deformation state of the cable during the laying process can be fully and accurately restored. Compared with traditional methods that rely on manual experience or single-parameter monitoring, this method significantly improves the comprehensiveness and accuracy of perception of the complex stress and spatial morphology of the cable, breaking through the limitations of discrete and lagging data acquisition in traditional laying, and providing a real-time, continuous, and multi-dimensional data foundation for decision-making. Secondly, by introducing a preset health assessment model to process multi-source data and historical time-series data, a comprehensive real-time health index can be calculated, which can integrate multiple previously scattered and complex physical quantities into an intuitive and quantifiable assessment index. This transforms the judgment of the laying status from a qualitative description based on experience to a quantitative assessment based on a model, greatly improving the objectivity of the judgment. The method improves consistency and reduces the difficulty for operators to make comprehensive judgments on complex data. Finally, it can generate adjustment instructions based on the health index and send them to the laying execution mechanism. This dynamic adjustment, direct drive of traction and guidance mechanisms, etc., constitutes a dynamic closed-loop control loop. This mechanism shortens the time delay from risk identification to protective measures, which helps to intervene in the early stages of cable damage. This reduces the probability of construction damage such as cable overload and excessive bending caused by delayed human response or improper operation. It provides a more automated and intelligent technical means to maintain the safety of the laying process. The system can not only detect risks in time, but also automatically adjust the laying strategy to correct deviations. It can adjust key parameters such as traction force and speed in real time, and can dynamically adjust the equipment and optimize the strategy in a timely manner according to the actual situation on site. This systematically improves the safety and quality control level of the laying process, thereby improving the accuracy of cable laying control.

[0014] Optionally, in the laying quality assessment step, the calculation model for the real-time health index is as follows: , This is a real-time over-limit risk index. The calculation model is as follows: Where Sat(x) is the saturation normalization exponent, Sat(x)=0 when x≤1; when x>1, Sat(x)=min(1,(x-1) / η), and η is the preset excess risk gain coefficient. Let t be the real-time traction tension at time t. To achieve the maximum safe traction tension, The real-time side pressure at time t. For maximum safe side pressure, This represents the real-time bending radius at time t, calculated based on the cable's spatial attitude data. Minimum allowable bending radius;

[0015] In the laying quality analysis step, the health index threshold is set as follows: ,when At that time, the first type of adjustment instruction is generated, which is the stop protection adjustment instruction.

[0016] By adopting the above technical solution, the specific calculation model of the real-time health index based on the saturated normalization function is further defined, and the health assessment model is concretized and optimized. The real-time over-limit risk index normalizes and saturates the three core safety parameters—traction tension, lateral pressure, and bending radius—achieving standardized and quantitative assessment of instantaneous over-limit risks. The saturation function is designed so that it is 0 when x≤1 and increases proportionally when x>1, allowing the real-time over-limit risk index to clearly distinguish between a safe state (no risk contribution) and an over-limit state (risk increases proportionally with the over-limit). The output of the real-time health index directly characterizes the immediate severity of the parameter's over-limit. Only when parameters such as tension, lateral pressure, and bending actually exceed the safety threshold will the index produce a non-zero value. This effectively avoids false alarms caused by measurement noise or normal fluctuations, improves the intuitiveness of risk assessment and the clarity of decision-making basis, and enhances system stability. The application of the max function improves the rigor of the assessment; the system always monitors the most dangerous parameters, and if any of the tension, lateral pressure, or bending radius exceeds the standard, the system will... It can immediately detect and capture complex risks in a timely manner. By introducing an excess risk gain coefficient η, it allows for calibration of the sensitivity to risk increases. For different models or more vulnerable cables, the system can be made more sensitive to minor exceedances by reducing the value of η, thus improving the practicality and reliability of the assessment strategy. Finally, the real-time health index output by the model is a clearly defined and physically meaningful indicator. When it exceeds the health index threshold, it will trigger the first type of adjustment command for the stop-pull protection. This clear mapping relationship enhances the system's ability to respond to major and immediate risks, enabling automatic execution of protective actions when key safety parameters exceed the limits. This helps to suppress the risk of acute mechanical damage that may be caused by continuous overload, providing a procedural guarantee for cable safety under extreme working conditions. It provides a clear automated execution basis for dealing with sudden severe overloads, enhances the rapid response capability in critical situations, and enables timely dynamic adjustment of equipment and strategy optimization based on actual site conditions. It systematically improves the safety and quality control level of the laying process, thereby improving the accuracy of cable laying control.

[0017] Optionally, in the laying quality assessment step, the cumulative fatigue damage index of the current laying section is also calculated. Where k is the cable fatigue cumulative rate coefficient, and H(x) is a linear weighting function. When x < 1, the output is close to 0; when x ≥ 1, the output is positively correlated with x. As the first weighting coefficient, This is the second weighting coefficient. This is the third weighting coefficient. , The traction tension fatigue threshold, The lateral pressure fatigue threshold, The real-time bending angular velocity at time t is calculated based on the cable's spatial attitude data. The fatigue threshold is the rate of change of bending angle. , , This indicates that the time is integrated over time from the start time 0 of the laying to the current time t;

[0018] In the laying quality analysis step, the system also generates a first type of adjustment instruction for protective control of the laid cable or a second type of adjustment instruction for adjusting the laid cable, based on the different level ranges of the cumulative fatigue damage index.

[0019] By adopting the above technical solution, the direction of laying quality assessment is expanded to include both instantaneous safety and long-term lifespan. The cumulative fatigue damage index quantifies the load history of the cable during the entire laying process, which is below the instantaneous failure threshold but has a cumulative effect, by integrating the time history. By setting a fatigue threshold below the absolute safety upper limit and using a weighting function H(x) to accumulate loads exceeding the fatigue threshold, this model can effectively characterize the gradual process of material fatigue damage caused by repeated bending, continuous low-intensity over-tension, and other working conditions. Most existing technologies only focus on whether instantaneous parameters exceed the limit. By introducing the cumulative fatigue damage index, the system can record those slight overloads or continuous loads that do not immediately cause failure but will accelerate material aging, effectively assessing the potential loss to the long-term service life of the cable during the laying process. Secondly, this model comprehensively considers static forces such as tension and lateral pressure, and dynamic effects such as bending angular velocity. Bending angular velocity reflects the dynamic changes in cable bending. The intensity of repeated bending at high frequencies can also lead to fatigue. Incorporating this into the model makes the assessment of cumulative damage more comprehensive and scientific. Finally, the system can generate differentiated adjustment instructions based on the different levels of the cumulative fatigue damage index, enabling the system to implement preventive maintenance. For example, when the system detects that the cumulative damage index is rising but has not reached a dangerous level, it can issue an early warning or take mild measures such as slowing down to prevent further damage. This allows for intervention before an accident occurs. This mechanism enhances the preventive maintenance capability of the laying process and helps to make adjustments before latent damage accumulates to a certain extent. This has a positive supporting role in extending the overall service life of cables and optimizing reliability-centered laying strategies. It is of profound significance for ensuring the decades-long service life of cables. It can make timely dynamic adjustments to equipment and optimize strategies based on actual site conditions, systematically improving the safety and quality control level of the laying process, thereby improving the accuracy of cable laying control.

[0020] Optionally, the laying quality analysis step is further updated to: based on the comparison result of the real-time health index and the preset threshold and the different level ranges of the cumulative fatigue damage index, generate a first type of adjustment instruction for protective control of the laid cable or a second type of adjustment instruction for adjusting the laid cable, and set the health index threshold as follows: The first cumulative threshold is The second cumulative threshold is ,when or When, the first type of adjustment instruction is generated, the first type of adjustment instruction is the stop protection adjustment instruction, when and When determining the safety of cable laying, and At that time, a second type of adjustment instruction is generated, which is a laying speed reduction adjustment instruction.

[0021] By adopting the above technical solution, a dual-threshold logic for collaborative decision-making was established, constructing a multi-level, refined risk system based on dual-dimensional assessment. This significantly improves the intelligence and adaptability of system decision-making while ensuring safety, optimizing construction efficiency. Three decision-making scenarios are clearly defined: For the most urgent instantaneous severe over-limit or extremely high cumulative damage, the system takes the most stringent stop-laying measures to ensure the safety baseline. When the system determines it is safe, normal laying is allowed, reducing unnecessary intervention and maintaining basic construction efficiency. For situations where the situation is not urgent but the cumulative damage has entered the warning range, the system generates a second type of instruction to reduce laying speed. This speed reduction directly lowers traction tension, lateral pressure, and dynamic bending frequency, thereby slowing the rate of damage accumulation and providing the system with a recovery window. The system adopts a relatively mild intervention approach of slowing down the laying process. This approach mitigates the rate of damage accumulation while avoiding the impact of an overly conservative complete shutdown on construction efficiency, thus balancing construction continuity. Clearly defined threshold ranges provide operators with a clear basis for status assessment and decision-making, enhancing the transparency and controllability of human-machine interaction. This improves the precision of risk management and the rationality of engineering practice. This tiered strategy is significantly superior to the crude control of a single threshold shutdown, enabling the system to differentiate between the urgency and severity of risks and make reasonable responses. It maximizes construction continuity and economy, and can dynamically adjust equipment and optimize strategies in a timely manner based on actual site conditions. This systematically improves the safety and quality control level of the laying process, thereby enhancing the accuracy of cable laying control.

[0022] Optionally, in the laying quality assessment step, the real-time health index is... The calculation model is revised, and the revised calculation model is updated as follows: Where α is the dynamic fusion weight, 0 < α < 1;

[0023] The strategy adjustment step is followed by a strategy update step: when a second type of adjustment instruction is detected, the execution of the laying quality assessment step is returned, and the values ​​of the real-time bending radius and real-time bending angular velocity in the laying quality assessment step are updated.

[0024] By adopting the above technical solutions, the system's adaptability and the dynamic accuracy of the decision-making model are significantly improved. The health index model is modified into a dynamic weighted fusion of instantaneous and cumulative indices, and a strategy update step based on instruction feedback is introduced, further enhancing the system's adaptability, assessment accuracy, and the integrity of the control loop. The system can dynamically adjust the weight ratio of instantaneous risk and historical cumulative risk in the final decision based on the laying stage, environmental conditions, or cable type. Instead of mechanically considering the two indices in parallel and making separate decisions, the system can flexibly balance the proportion of current instantaneous risk and historical cumulative loss in the final decision by adjusting the fusion weight α according to the actual situation. For example, in complex curved areas or later stages of laying, α can be increased to focus more on instantaneous geometric risk; in the initial stage of laying or long-distance straight traction sections, α can be decreased to increase the weight of cumulative damage, focusing more on long-term tension accumulation, thus reflecting an emphasis on long-term health status. This improves the adaptability of the health assessment model to different working conditions, making the comprehensive assessment results more scenario-based. Furthermore, the weighted fusion model itself integrates information from both instantaneous and long-term dimensions, and its output provides real-time... The health index, theoretically, reflects the current overall condition of the cable more comprehensively than a single index, providing a more balanced and accurate basis for analysis and decision-making. Finally, by introducing a strategy update step, an important feedback optimization closed loop is formed. When the system issues a speed reduction command due to high accumulated damage, this step can proactively trigger data updates and restart the evaluation process. This allows the system to verify the effectiveness of its control actions and iteratively evaluate and make decisions based on the new state. After speed reduction, an increase in bending radius and a decrease in bending angular velocity should be observed, leading to a decrease in the newly calculated real-time health index. If the evaluation results improve, the correctness of the adjustment command is confirmed; if not, it may indicate other problems requiring further investigation. This gives the entire system a continuous optimization cycle function, enabling self-verification and adjustment based on execution results. This enhances its robustness and reliability in handling complex and uncertain laying environments, allowing for timely dynamic adjustments and strategy optimization based on actual site conditions. It systematically improves the safety and quality control level of the laying process, thereby increasing the accuracy of cable laying control.

[0025] Secondly, this invention provides a cable safety laying control and quality inspection system, which achieves its objective using the following technical solution:

[0026] A cable safety laying control and quality inspection system includes the following modules:

[0027] Data acquisition module: used to collect multi-source data and historical time-series data of the current cable laying section in real time. The multi-source data includes laying mechanical data and cable spatial attitude data obtained based on multi-source sensors integrated into the cable. The laying mechanical data includes the cable's traction tension, lateral pressure and bending radius. The cable spatial attitude data includes three-dimensional tilt angle, angular velocity and heading angle.

[0028] Quality inspection module: used to calculate the cumulative fatigue damage index of the current laying section based on multi-source data and historical time series data. The calculation of the cumulative fatigue damage index is based on the integration of the historical time series data of the real-time traction tension, real-time lateral pressure and real-time bending angular velocity calculated from the cable spatial attitude data.

[0029] Quality Analysis Module: Used to generate either a first-class adjustment command for protective control of the laid cable or a second-class adjustment command for adjusting the laid cable based on the different level ranges of the cumulative fatigue damage index.

[0030] Strategy adjustment module: Used to send the generated adjustment instructions to the laying actuator to dynamically adjust the traction force, laying speed and guiding device.

[0031] By adopting the above technical solution, the quantitative assessment of cumulative fatigue damage is combined with active control. The data acquisition module synchronously, in real-time, and continuously collects multi-source data and historical time-series data, particularly simultaneously acquiring laying mechanical data such as traction tension, lateral pressure, and bending radius, as well as spatial attitude data such as tilt angle, angular velocity, and heading angle. This allows for a comprehensive and high-precision reconstruction of the cable's actual stress and deformation state during laying. Compared to traditional methods relying on manual experience or single-parameter monitoring, this significantly improves the comprehensiveness and accuracy of perceiving the complex stress and spatial morphology of the cable. It breaks through the limitations of discrete and lagging data acquisition in traditional laying methods, providing a real-time, continuous, and multi-dimensional data foundation for decision-making. Secondly, the quality inspection module processes multi-source data and historical time-series data to calculate the cumulative fatigue damage index based on the integration of historical time-series data. This helps the system identify and quantify progressive damage processes that may not immediately cause failures but could affect the long-term lifespan of the cable. It integrates multiple previously scattered and complex physical quantities into an intuitive and quantifiable evaluation index, enabling the judgment of the laying status to move beyond qualitative descriptions based on experience. The system transforms from a descriptive approach to a model-based quantitative assessment, significantly improving the objectivity and consistency of judgments and reducing the difficulty for operators to make comprehensive judgments on complex data. Finally, the quality analysis module can generate adjustment instructions based on the cumulative fatigue damage index, and the strategy adjustment module sends the adjustment instructions to the laying execution mechanism. This dynamic closed-loop control circuit is formed by dynamically adjusting, directly driving traction and guiding execution mechanisms. This mechanism shortens the time delay from risk identification to taking protective measures, which helps to intervene in a timely manner in the early stages of cable damage occurrence or expansion. This reduces the probability of construction damage such as cable overload and excessive bending caused by delayed human response or improper operation. It provides a more automated and intelligent technical means to maintain the safety of the laying process. The system can not only detect risks in a timely manner, but also automatically adjust the laying strategy execution to correct deviations. It can adjust key parameters such as traction force and speed in real time, and can dynamically adjust equipment and optimize strategies in a timely manner according to the actual situation on site. This systematically improves the safety and quality control level of the laying process, thereby improving the accuracy of cable laying control.

[0032] Optionally, in the quality inspection module, the calculation model for the cumulative fatigue damage index is as follows: Where k is the cable fatigue cumulative rate coefficient, and H(x) is a linear weighting function. When x < 1, the output is close to 0; when x ≥ 1, the output is positively correlated with x. As the first weighting coefficient, This is the second weighting coefficient. This is the third weighting coefficient. , The traction tension fatigue threshold, The lateral pressure fatigue threshold, The real-time bending angular velocity at time t is calculated based on the cable's spatial attitude data. The fatigue threshold is the rate of change of bending angle. , , This indicates that the time interval from the start time 0 of the laying to the current time t is used for integration.

[0033] By adopting the above technical solution, the specific calculation model of the cumulative fatigue damage index based on the weighting function H(x) is further defined, enabling the system's assessment of fatigue damage to be implemented from the conceptual level to an operable quantitative algorithm, thereby bringing about a more definite improvement in technical effect. The cumulative fatigue damage index quantifies the load history that the cable bears during the entire laying process, which is below the instantaneous failure threshold but has a cumulative effect, by integrating over the time history. By setting a fatigue threshold below the absolute safety upper limit and using the weighting function H(x) to accumulate loads exceeding the fatigue threshold, this model can effectively characterize the gradual process of material fatigue damage caused by repeated bending, continuous low-intensity over-tension, and other working conditions. Most existing technologies only focus on whether instantaneous parameters exceed the limit. By introducing the cumulative fatigue damage index, the system can record those slight overloads or continuous loads that, although not immediately causing failure, will accelerate material aging, and can effectively assess the potential loss of the cable's long-term service life during the laying process. Secondly, this model comprehensively considers static forces such as tension, lateral pressure, and dynamic forces. Dynamic effects, such as bending angular velocity, reflect the dynamic severity of cable bending. High-frequency repeated bending can also lead to fatigue. Incorporating this into the model makes the assessment of cumulative damage more comprehensive and scientific. Finally, the system can generate differentiated adjustment instructions based on different levels of the cumulative fatigue damage index, enabling the system to implement preventive maintenance. For example, when the system detects an increase in the cumulative damage index but not reaching a dangerous level, it can issue an early warning or take mild measures such as speed reduction to prevent further damage. This allows for intervention before an accident occurs. This mechanism enhances the preventive maintenance capability of the laying process, helping to adjust before latent damage accumulates to a certain extent. This plays a positive supporting role in extending the overall service life of cables and optimizing reliability-centered laying strategies. It has profound significance for ensuring the decades-long service life of cables, enabling timely dynamic adjustments and strategy optimization based on actual site conditions. This systematically improves the safety and quality control level of the laying process, thereby enhancing the accuracy of cable laying control.

[0034] Optionally, the quality detection module is further used to calculate the real-time health index. ,in, This is a real-time over-limit risk index. The calculation model is as follows: Where Sat(x) is the saturation normalization exponent, Sat(x)=0 when x≤1; when x>1, Sat(x)=min(1,(x-1) / η), and η is the preset excess risk gain coefficient. Let t be the real-time traction tension at time t. To achieve the maximum safe traction tension, The real-time side pressure at time t. For maximum safe side pressure, This represents the real-time bending radius at time t, calculated based on the cable's spatial attitude data. Minimum allowable bending radius;

[0035] The quality analysis module also generates a first type of adjustment instruction for protective control of the laid cable based on the comparison result between the real-time health index and the preset threshold.

[0036] By adopting the above technical solution, the direction of laying quality assessment is expanded to include both instantaneous safety and long-term lifespan. A specific calculation model for the real-time health index based on a saturated normalization function is defined, and the health assessment model is concretized and optimized. The real-time over-limit risk index normalizes and saturates the three core safety parameters—traction tension, lateral pressure, and bending radius—achieving standardized and quantitative assessment of instantaneous over-limit risks. The saturation function is designed so that it is 0 when x≤1 and increases proportionally when x>1, allowing the real-time over-limit risk index to clearly distinguish between a safe state (no risk contribution) and an over-limit state (risk increases proportionally with the over-limit). The output of the real-time health index directly characterizes the immediate severity of parameter over-limits. Only when parameters such as tension, lateral pressure, and bending actually exceed the safety threshold will the index produce a non-zero value. This effectively avoids false alarms caused by measurement noise or normal fluctuations, improves the intuitiveness of risk assessment and the clarity of decision-making basis, and enhances system stability. The application of the max function improves the rigor of the assessment; the system always monitors the most dangerous parameters. As long as tension, lateral pressure, or bending radius exceeds the safety threshold, the index will generate a non-zero value. When an indicator exceeds the standard, the system will immediately detect it, thus enabling timely capture of complex risks. By introducing an excess risk gain coefficient η, the sensitivity to risk escalation can be calibrated. For different types or more vulnerable cables, the system can be made more sensitive to minor exceedances by reducing the value of η, improving the practicality and reliability of the assessment strategy. Ultimately, the real-time health index output by the model is a clearly defined and physically meaningful indicator. When it exceeds the health index threshold, it will trigger the first type of adjustment command for the cable stop protection. This clear mapping relationship enhances the system's response capability to major and immediate risks, enabling automatic execution of protective actions when critical safety parameters exceed the standard. This helps to suppress the risk of acute mechanical damage that may be caused by continuous overload, providing procedural protection for cable safety under extreme working conditions. It provides a clear automated execution basis for dealing with sudden severe overloads, enhances the rapid response capability in critical situations, and enables timely dynamic adjustment of equipment and strategy optimization based on actual site conditions. This systematically improves the safety and quality control level of the laying process, thereby improving the accuracy of cable laying control.

[0037] Optionally, the quality analysis module is further updated to: generate a first type of adjustment command for protective control of the laid cable or a second type of adjustment command for adjusting the laid cable based on the comparison result of the real-time health index and the preset threshold and the different level ranges of the cumulative fatigue damage index, and set the health index threshold as follows: The first cumulative threshold is The second cumulative threshold is ,when or When, the first type of adjustment instruction is generated, the first type of adjustment instruction is the stop protection adjustment instruction, when and When determining the safety of cable laying, and At that time, a second type of adjustment instruction is generated, which is a laying speed reduction adjustment instruction.

[0038] By adopting the above technical solution, a dual-threshold logic for collaborative decision-making was established, constructing a multi-level, refined risk system based on dual-dimensional assessment. This significantly improves the intelligence and adaptability of system decision-making while ensuring safety, optimizing construction efficiency. Three decision-making scenarios are clearly defined: For the most urgent instantaneous severe over-limit or extremely high cumulative damage, the system takes the most stringent stop-laying measures to ensure the safety baseline. When the system determines it is safe, normal laying is allowed, reducing unnecessary intervention and maintaining basic construction efficiency. For situations where the situation is not urgent but the cumulative damage has entered the warning range, the system generates a second type of instruction to reduce laying speed. This speed reduction directly lowers traction tension, lateral pressure, and dynamic bending frequency, thereby slowing the rate of damage accumulation and providing the system with a recovery window. The system adopts a relatively mild intervention approach of slowing down the laying process. This approach mitigates the rate of damage accumulation while avoiding the impact of an overly conservative complete shutdown on construction efficiency, thus balancing construction continuity. Clearly defined threshold ranges provide operators with a clear basis for status assessment and decision-making, enhancing the transparency and controllability of human-machine interaction. This improves the precision of risk management and the rationality of engineering practice. This tiered strategy is significantly superior to the crude control of a single threshold shutdown, enabling the system to differentiate between the urgency and severity of risks and make reasonable responses. It maximizes construction continuity and economy, and can dynamically adjust equipment and optimize strategies in a timely manner based on actual site conditions. This systematically improves the safety and quality control level of the laying process, thereby enhancing the accuracy of cable laying control.

[0039] Optionally, the quality detection module includes a real-time health index. The calculation model is revised, and the revised calculation model is updated as follows: Where α is the dynamic fusion weight, 0 < α < 1;

[0040] It also includes a data update module: when the quality analysis module generates a second type of adjustment instruction, it returns to execute the quality detection module and updates the values ​​of real-time bending radius and real-time bending angular velocity in the quality detection module.

[0041] By adopting the above technical solutions, the system's adaptability and the dynamic accuracy of the decision-making model are significantly improved. The health index model is modified into a dynamic weighted fusion of instantaneous and cumulative indices, and a strategy update step based on instruction feedback is introduced, further enhancing the system's adaptability, assessment accuracy, and the integrity of the control loop. The system can dynamically adjust the weight ratio of instantaneous risk and historical cumulative risk in the final decision based on the laying stage, environmental conditions, or cable type. Instead of mechanically considering the two indices in parallel and making separate decisions, the system can flexibly balance the proportion of current instantaneous risk and historical cumulative loss in the final decision by adjusting the fusion weight α according to the actual situation. For example, in complex curved areas or later stages of laying, α can be increased to focus more on instantaneous geometric risk; in the initial stage of laying or long-distance straight traction sections, α can be decreased to increase the weight of cumulative damage, focusing more on long-term tension accumulation, thus reflecting an emphasis on long-term health status. This improves the adaptability of the health assessment model to different working conditions, making the comprehensive assessment results more scenario-based. Furthermore, the weighted fusion model itself integrates information from both instantaneous and long-term dimensions, and its output provides real-time... The health index, theoretically, reflects the current overall condition of the cable more comprehensively than a single index, providing a more balanced and accurate basis for analysis and decision-making. Finally, by introducing a strategy update step, an important feedback optimization closed loop is formed. When the system issues a speed reduction command due to high accumulated damage, this step can proactively trigger data updates and restart the evaluation process. This allows the system to verify the effectiveness of its control actions and iteratively evaluate and make decisions based on the new state. After speed reduction, an increase in bending radius and a decrease in bending angular velocity should be observed, leading to a decrease in the newly calculated real-time health index. If the evaluation results improve, the correctness of the adjustment command is confirmed; if not, it may indicate other problems requiring further investigation. This gives the entire system a continuous optimization cycle function, enabling self-verification and adjustment based on execution results. This enhances its robustness and reliability in handling complex and uncertain laying environments, allowing for timely dynamic adjustments and strategy optimization based on actual site conditions. It systematically improves the safety and quality control level of the laying process, thereby increasing the accuracy of cable laying control.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] 1. By synchronously, in real-time, and continuously collecting multi-source data and historical time-series data, the actual stress and deformation state of cables during the laying process can be comprehensively and accurately reconstructed. Compared with traditional methods that rely on manual experience or single-parameter monitoring, this significantly improves the comprehensiveness and accuracy of the perception of the complex stress and spatial morphology of cables, providing a real-time, continuous, and multi-dimensional data foundation for decision-making. Secondly, by introducing a pre-set health assessment model to process multi-source data and historical time-series data, a comprehensive real-time health index can be calculated. This integrates multiple previously scattered and complex physical quantities into an intuitive and quantifiable assessment indicator, enabling a more accurate assessment of the laying status. The assessment of the cable's condition has shifted from qualitative descriptions based on experience to quantitative evaluation based on models. Finally, this method can generate adjustment instructions based on the health index and send them to the laying actuators to dynamically adjust, directly drive traction and guidance actuators, shortening the time delay from risk identification to taking protective measures. This helps to intervene in the early stages of cable damage or its spread, and allows for real-time adjustment of key parameters such as traction force and speed. It can also dynamically adjust equipment and optimize strategies according to the actual situation on site, systematically improving the safety and quality control level of the laying process, thereby improving the accuracy of cable laying control.

[0044] 2. By normalizing and saturating the three core safety parameters—traction tension, lateral pressure, and bending radius—through a real-time over-limit risk index, a standardized and quantitative assessment of instantaneous over-limit risk is achieved. The design of the saturation function allows the real-time over-limit risk index to clearly distinguish between a safe state (no risk contribution) and an over-limit state (risk increases with the over-limit ratio). The output of the real-time health index directly characterizes the immediate severity of the parameter's over-limit, improving the intuitiveness of risk assessment and the clarity of decision-making basis. By introducing an over-limit risk gain coefficient η, the sensitivity to risk escalation can be calibrated. For different cable models or more vulnerable cables, the system can be made more sensitive to minor over-limits by reducing the η value, improving the practicality and reliability of the assessment strategy. Finally, when the health index threshold is exceeded, the first type of adjustment command for the stop-traction protection will be triggered, enabling automatic execution of protection actions when critical safety parameters exceed the limits. This helps to suppress the risk of acute mechanical damage that may be caused by continuous overload and enhances the rapid response capability in critical situations.

[0045] 3. The assessment of cable laying quality is expanded to include both instantaneous safety and long-term lifespan. The cumulative fatigue damage index quantifies the cumulative load history of the cable during the entire laying process by integrating the time history. This load, below the instantaneous failure threshold, has a cumulative effect. By setting a fatigue threshold below the absolute safety upper limit and using a weighting function to accumulate loads exceeding the fatigue threshold, the model can effectively characterize the progressive process of material fatigue damage caused by repeated bending, continuous low-intensity over-tension, and other working conditions. This allows the system to effectively assess the potential losses to the long-term service life of the cable during the laying process. Different adjustment instructions can be generated based on the different levels of the cumulative fatigue damage index, enabling the system to implement preventive maintenance and intervene before accidents occur. This enhances the preventive maintenance capability of the laying process and helps to adjust before latent damage accumulates to a certain extent. This has profound significance for ensuring the decades-long service life of the cable. It can also dynamically adjust the equipment and optimize strategies in a timely manner according to the actual site conditions, systematically improving the safety and quality control level of the laying process.

[0046] 4. A dual-threshold logic for collaborative decision-making was established, significantly improving the intelligence and adaptability of the system's decision-making, optimizing construction efficiency, and clearly defining three decision scenarios: For the most urgent instantaneous severe over-limit or extremely high cumulative damage, the system takes the most stringent stop-laying measures to ensure the safety baseline. When the system determines it is safe, normal laying is allowed, reducing unnecessary intervention and maintaining basic construction efficiency. For situations where the situation has not reached an urgent level but the cumulative damage has entered the warning range, the system generates a second type of instruction to reduce the laying speed. The speed reduction can directly reduce traction tension, lateral pressure, and dynamic bending frequency, thereby slowing down the rate of damage accumulation, giving the system a recovery window, taking into account the continuity of construction, improving the refinement of risk management and the rationality of engineering practice, enabling the system to distinguish between the urgency and severity of risks, make reasonable responses, maximize construction continuity and economy, and make timely dynamic adjustments to equipment and strategy optimizations based on actual site conditions. This systematically improves the safety and quality control level of the laying process, thereby improving the accuracy of cable laying control.

[0047] 5. Significantly improved system adaptability and dynamic accuracy of decision-making models. The health index model was revised into a dynamic weighted fusion of instantaneous and cumulative indices. The system can dynamically adjust the weight ratio of instantaneous risk and historical cumulative risk in the final decision based on the laying stage, environmental conditions, or cable type, improving the adaptability of the health assessment model to different working conditions. Secondly, the weighted fusion model itself integrates information from both instantaneous and long-term dimensions, reflecting the current comprehensive status of the cable more comprehensively than a single index, providing a more balanced and accurate basis for analysis and decision-making steps. Finally, by introducing a strategy update step, an important feedback optimization closed loop is formed. When the system issues a speed reduction command due to high cumulative damage, this step can proactively trigger data updates and restart the evaluation process, allowing the system to verify the effectiveness of its control actions. This gives the entire system a continuous optimization cycle function, enabling it to self-verify and adjust based on execution results, enhancing its robustness and reliability in handling complex and uncertain laying environments. Attached Figure Description

[0048] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative effort. In the drawings:

[0049] Figure 1 This is a block diagram of a cable safety laying control and quality inspection method according to an embodiment of the present invention;

[0050] Figure 2 This is a flowchart of a cable safety laying control and quality inspection system according to an embodiment of the present invention. Detailed Implementation

[0051] The following will be based on embodiments of the present invention. Figure 1 and Figure 2 The technical solutions in the embodiments of the present invention are clearly and completely described herein. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1: This example discloses a method for controlling the safe laying and quality inspection of cables, referring to... Figure 1 It includes data acquisition steps, laying quality assessment steps, laying quality analysis steps, strategy adjustment steps, and strategy update steps.

[0053] Data acquisition steps: Real-time synchronous acquisition of multi-source data and historical time-series data of the current cable laying section. Multi-source data includes laying mechanical data and cable spatial attitude data obtained based on multi-source sensors integrated into the cable. Laying mechanical data includes cable traction tension, lateral pressure and bending radius. Cable spatial attitude data includes three-dimensional tilt angle, angular velocity and heading angle.

[0054] Laying quality assessment steps: Based on multi-source data and historical time-series data, the real-time health index of the current laying section is calculated using a preset health assessment model. Where α is the dynamic fusion weight, 0 < α < 1;

[0055] This is a real-time over-limit risk index. The calculation model is as follows: Where Sat(x) is the saturation normalization exponent, Sat(x)=0 when x≤1; when x>1, Sat(x)=min(1,(x-1) / η), and η is the preset excess risk gain coefficient. Let t be the real-time traction tension at time t. To achieve the maximum safe traction tension, The real-time side pressure at time t. For maximum safe side pressure, This represents the real-time bending radius at time t, calculated based on the cable's spatial attitude data. Minimum allowable bending radius;

[0056] To accumulate fatigue damage index, The computational model is Where k is the cable fatigue cumulative rate coefficient, and H(x) is a linear weighting function. When x < 1, the output is close to 0; when x ≥ 1, the output is positively correlated with x. As the first weighting coefficient, This is the second weighting coefficient. This is the third weighting coefficient. , The traction tension fatigue threshold, The lateral pressure fatigue threshold, The real-time bending angular velocity at time t is calculated based on the cable's spatial attitude data. The fatigue threshold is the rate of change of bending angle. , , This indicates that the time interval from the start time 0 of the laying to the current time t is used for integration.

[0057] In this method, the preset health assessment model can also be a machine learning classification / regression model, fuzzy logic, or autoencoder, etc., as an alternative or enhanced algorithm, to calculate the real-time health index or the real-time over-limit risk index.

[0058] For example, when using machine learning classification models, , , and And their short-term statistics, such as mean and variance, or even frequency domain features, are used as input features. The model is trained with risk levels or expert scores marked in historical data as labels, and the probability or value of the real-time health index or the real-time over-limit risk index is directly output through the model.

[0059] Cable laying quality analysis steps: Based on the comparison results between the real-time health index and the preset threshold, and the different level ranges of the cumulative fatigue damage index, generate either a first-type adjustment command for protective control of the laid cable or a second-type adjustment command for adjusting the laid cable. Set the health index threshold as follows: The first cumulative threshold is The second cumulative threshold is ,when or At that time, the first type of adjustment instruction is generated, which is the stop protection adjustment instruction;

[0060] when and At that time, determine the safety of cable laying;

[0061] when and At that time, a second type of adjustment instruction is generated, which is a laying speed reduction adjustment instruction.

[0062] Strategy adjustment steps: The generated adjustment instructions are sent to the laying actuator to dynamically adjust the traction force, laying speed, and guiding device.

[0063] Strategy update steps: After detecting the generation of a second type of adjustment instruction, return to the execution of the laying quality assessment step and update the values ​​of real-time bending radius and real-time bending angular velocity in the laying quality assessment step.

[0064] The implementation principle of the cable safety laying control and quality inspection method in this embodiment is as follows:

[0065] By synchronously, in real-time, and continuously collecting multi-source data and historical time-series data, especially by simultaneously collecting laying mechanical data such as traction tension, lateral pressure, and bending radius, as well as spatial attitude data such as tilt angle, angular velocity, and heading angle, this method can comprehensively and accurately reconstruct the actual stress and deformation state of the cable during the laying process. Compared with traditional methods that rely on human experience or single-parameter monitoring, this method significantly improves the comprehensiveness and accuracy of the perception of the complex stress and spatial morphology of the cable, breaks through the limitations of discrete and lagging data acquisition in traditional laying, and provides a real-time, continuous, and multi-dimensional data foundation for decision-making.

[0066] By introducing a pre-defined health assessment model to process multi-source data and historical time-series data, a comprehensive real-time health index is calculated. This integrates multiple physical quantities that were originally scattered and complex into an intuitive and quantifiable assessment indicator. This transforms the judgment of the laying status from a qualitative description based on experience to a quantitative assessment based on a model, greatly improving the objectivity and consistency of the judgment and reducing the difficulty for operators to make comprehensive judgments on complex data.

[0067] The health index model was modified into a dynamic weighted fusion of instantaneous and cumulative indices, which further enhanced the system's adaptability, assessment accuracy, and the integrity of the control loop. The system can dynamically adjust the weight ratio of instantaneous risk and historical cumulative risk in the final decision based on the laying stage, environmental conditions, or cable type. Instead of mechanically looking at the two indices in parallel and making decisions separately, the system can flexibly balance the proportion of current instantaneous risk and historical cumulative loss in the final decision by adjusting the fusion weight α according to the actual situation.

[0068] For example, in complex curved areas or later stages of laying, α can be increased to give more weight to instantaneous geometric risks; in the initial stage of laying or long straight traction sections, α can be decreased to increase the weight of cumulative damage and give more attention to long-term tension accumulation, thereby reflecting the emphasis on long-term health status, improving the adaptability of the health assessment model to different working conditions, and making the comprehensive assessment results more scenario-based.

[0069] The weighted fusion model integrates information from both instantaneous and long-term dimensions. Theoretically, its output real-time health index value can more comprehensively reflect the current overall status of the cable than a single index, and can provide a more balanced and accurate basis for analysis and decision-making.

[0070] By defining a specific calculation model for the real-time health index based on a saturated normalization function, the health assessment model is concretized and optimized. The real-time over-limit risk index is used to normalize and saturate the three core safety parameters of traction tension, lateral pressure, and bending radius, thereby achieving a standardized and quantitative assessment of instantaneous over-limit risks.

[0071] The saturation function is designed to be 0 when x≤1 and grows proportionally when x>1. This allows the real-time over-limit risk index to clearly distinguish between the safe state (no risk contribution) and the over-limit state (risk increases proportionally with the over-limit). The output of the real-time health index directly characterizes the immediate severity of the parameter's over-limit. The index only produces a non-zero value when parameters such as tension, lateral pressure, and bending actually exceed the safety threshold. This effectively avoids false alarms caused by measurement noise or normal fluctuations, improves the intuitiveness of risk assessment and the clarity of decision-making basis, and enhances the stability of the system.

[0072] The application of the max function enhances the rigor of the assessment. The system constantly monitors the most critical parameters, and immediately detects any exceedance of tension, lateral pressure, or bending radius, thus enabling timely capture of complex risks. By introducing an exceedance risk gain coefficient η, the sensitivity to risk escalation can be calibrated. For different cable models or more vulnerable cables, adjusting the η value makes the system more sensitive to minor exceedances, improving the practicality and reliability of the assessment strategy.

[0073] The real-time health index output by this model is a clearly defined and physically meaningful indicator. When it exceeds the health index threshold, it will trigger the first type of adjustment command for the cable stop protection. This clear mapping relationship enhances the system's ability to respond to major and immediate risks, enabling automatic execution of protective actions when critical safety parameters exceed the limits. This helps to suppress the risk of acute mechanical damage that may be caused by continuous overload, providing a programmed guarantee for cable safety under extreme working conditions. It also provides a clear automated execution basis for dealing with sudden severe overloads, enhances the ability to respond quickly in critical situations, and enables timely dynamic adjustment of equipment and strategy optimization based on actual site conditions. This systematically improves the safety and quality control level of the laying process, thereby improving the accuracy of cable laying control.

[0074] The scope of laying quality assessment is expanded to include both instantaneous safety and long-term lifespan. The cumulative fatigue damage index quantifies the load history of the cable during the entire laying process, which is below the instantaneous failure threshold but has a cumulative effect, by integrating the time history. By setting a fatigue threshold below the absolute safety upper limit and using a weighting function H(x) to accumulate loads exceeding the fatigue threshold, this model can effectively characterize the gradual process of material fatigue damage caused by repeated bending, continuous low-intensity over-tension and other working conditions.

[0075] Most existing technologies only focus on whether instantaneous parameters exceed the limit. By introducing the cumulative fatigue damage index, the system can record minor overloads or continuous loads that do not immediately cause failures but accelerate material aging, and can effectively assess the potential losses that the laying process will cause to the long-term service life of the cable.

[0076] This model comprehensively considers static forces such as tension and lateral pressure, as well as dynamic effects such as bending angular velocity. Bending angular velocity reflects the dynamic severity of cable bending; high-frequency repeated bending can also lead to fatigue. Incorporating this into the model makes the assessment of cumulative damage more comprehensive and scientific. The system can generate differentiated adjustment instructions based on different levels of the cumulative fatigue damage index, enabling the system to implement preventative maintenance.

[0077] For example, when the system detects an increase in the cumulative damage index but it has not reached a dangerous level, it can issue an early warning or take mild measures such as slowing down to prevent further damage. This allows for intervention before an accident occurs, enhancing the preventative maintenance capabilities of the laying process. It helps to make adjustments before hidden damage accumulates to a certain extent, thus playing a positive supporting role in extending the overall service life of cables and optimizing reliability-centered laying strategies. It has profound significance for ensuring the decades-long service life of cables, enabling timely dynamic adjustments to equipment and optimization of strategies based on actual site conditions. This systematically improves the safety and quality control level of the laying process, thereby enhancing the accuracy of cable laying control.

[0078] By setting a dual-threshold logic for collaborative decision-making, a multi-level and refined risk system based on dual-dimensional assessment was constructed, thereby significantly improving the intelligence and adaptability of system decision-making and optimizing construction efficiency while ensuring safety.

[0079] Three decision-making scenarios are clearly defined: For the most urgent instantaneous severe over-limit or extremely high cumulative damage, the system takes the most stringent stop-layout measures to ensure the safety baseline. When the system determines that it is safe, normal laying is allowed, reducing unnecessary intervention and maintaining the efficiency of basic construction. For the state where the emergency level has not been reached, but the cumulative damage has entered the warning range, the system generates a second type of instruction to reduce the laying speed. The speed reduction can directly reduce the traction tension, lateral pressure and dynamic bending frequency, thereby slowing down the rate of damage accumulation and giving the system a recovery window.

[0080] The system adopts a relatively mild intervention method of slowing down the laying speed, which not only takes mitigation measures to slow down the rate of damage accumulation, but also avoids the impact of overly conservative complete shutdown on construction efficiency, thus taking into account the continuity of construction. The clearly defined threshold range provides operators with clear status judgment and decision-making basis, enhances the transparency and controllability of human-machine interaction, and improves the level of precision in risk management and the rationality of engineering practice.

[0081] This tiered strategy is significantly superior to the crude control of single-threshold shutdown. It enables the system to distinguish between the urgency and severity of risks, make reasonable responses, maximize construction continuity and economy, and make timely dynamic adjustments to equipment and optimization of strategies based on actual site conditions. This systematically improves the safety and quality control level of the laying process, thereby enhancing the accuracy of cable laying control.

[0082] Finally, this method can generate adjustment instructions based on the cumulative fatigue damage index and health index, and send them to the laying actuators to dynamically adjust, directly drive traction and guidance actuators, forming a dynamic closed-loop control loop. This mechanism shortens the time delay from risk identification to taking protective measures, which helps to intervene in a timely manner in the early stages of cable damage occurrence or expansion. This reduces the probability of construction damage such as cable overload and excessive bending caused by delayed human response or improper operation to a certain extent, and provides a more automated and intelligent technical means to maintain the safety of the laying process.

[0083] The system can not only detect risks in a timely manner, but also automatically adjust the laying strategy to correct deviations. It can adjust key parameters such as traction force and speed in real time, and make dynamic adjustments to equipment and optimize strategies in a timely manner according to the actual situation on site. This systematically improves the safety and quality control level of the laying process, thereby improving the accuracy of cable laying control.

[0084] By introducing a strategy update step, an important feedback optimization closed loop is formed. When the system issues a deceleration command due to high accumulated damage, this step can proactively trigger data updates and restart the evaluation process, enabling the system to verify the effectiveness of its control actions and to iteratively evaluate and make decisions based on the new state.

[0085] After the speed reduction, an increase in the bending radius and a decrease in the bending angular velocity should be observed, leading to a decrease in the newly calculated real-time health index. If the evaluation results improve, it confirms the correctness of the adjustment command; if not, it may indicate other problems requiring further investigation. This gives the entire system a continuous optimization cycle function, enabling it to self-verify and adjust based on execution results. This enhances its robustness and reliability in handling complex and uncertain laying environments, allowing for timely dynamic adjustments and strategy optimization based on actual site conditions. It systematically improves the safety and quality control level of the laying process, thereby increasing the accuracy of cable laying control.

[0086] Example 2: This example discloses a cable safety laying control and quality inspection system, referring to... Figure 2 It includes a data acquisition module, a quality inspection module, a quality analysis module, a strategy adjustment module, and a data update module.

[0087] Data acquisition module: used to collect multi-source data and historical time-series data of the current cable laying section in real time. The multi-source data includes laying mechanical data and cable spatial attitude data obtained based on multi-source sensors integrated into the cable. The laying mechanical data includes the cable's traction tension, lateral pressure and bending radius. The cable spatial attitude data includes three-dimensional tilt angle, angular velocity and heading angle.

[0088] The data acquisition module includes a set of mechanical sensors deployed at the cable traction machine and key bends to acquire the laying mechanical data, and a miniature inertial measurement unit encapsulated in the cable's waterproof housing and fixed near the cable traction head to acquire the cable's spatial attitude data.

[0089] In the laying force sensor group, the traction tension sensor adopts a spoke-type or S-type tension sensor, which is installed on the tension wheel axle at the traction machine outlet or on a dedicated force-measuring roller. The output is a millivolt-level analog signal or a 4-20mA current signal / RS-485 digital signal converted by a transmitter. The side pressure sensor adopts a miniature thin-film pressure sensor array or a piezoelectric force sensor, which is encapsulated in a wear-resistant rubber pad and embedded in the inner side of the guide roller bracket or at key points of the bend in the protective wall where the cable may come into contact. The output is an analog voltage signal.

[0090] An auxiliary geometric measurement unit is set up at key bends, and a laser rangefinder or binocular vision camera is installed to assist in the calculation of the bending radius by measuring the positional relationship between the cable and the reference surface and combining the algorithm. This serves as a verification and supplement to the calculation results of the inertial measurement unit.

[0091] The miniature inertial measurement unit (IMU) is an industrial-grade MEMS-IMU, integrating a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. It is encapsulated in a cylindrical, waterproof, and shockproof metal housing, which is tightly fixed to the cable sheath behind the cable puller using mechanical clamps or high-strength adhesive. Moving synchronously with the cable, it directly measures three-dimensional tilt angles, angular velocities, and heading angles. The IMU performs data fusion via a built-in microprocessor and outputs the calculated attitude Euler angles and angular velocity data via serial port or CAN bus.

[0092] The specific hardware implementation of the data acquisition module is as follows: Industrial data acquisition boxes are deployed near each sensor cluster, containing analog input modules, digital input modules, and protocol conversion gateways. Each box is equipped with an industrial microprocessor and a high-precision synchronous clock module for local preprocessing, such as filtering and scaling transformation, to synchronize the timestamps of all sensor data. Each data acquisition box acts as a field slave, connected to a central edge computing server at the installation site via industrial Ethernet or high-speed fieldbus, forming a real-time data network.

[0093] The quality inspection module is used to calculate the cumulative fatigue damage index of the current laying section based on multi-source data and historical time-series data. The calculation of the cumulative fatigue damage index is based on the integration of the historical time-series data of the real-time traction tension, real-time lateral pressure, and real-time bending angular velocity calculated from the cable spatial attitude data. The calculation model of the cumulative fatigue damage index is as follows: Where k is the cable fatigue cumulative rate coefficient, and H(x) is a linear weighting function. When x < 1, the output is close to 0; when x ≥ 1, the output is positively correlated with x. As the first weighting coefficient, This is the second weighting coefficient. This is the third weighting coefficient. , The traction tension fatigue threshold, The lateral pressure fatigue threshold, The real-time bending angular velocity at time t is calculated based on the cable's spatial attitude data. The fatigue threshold is the rate of change of bending angle. , , This indicates that the time is integrated over time from the start time 0 of the laying to the current time t;

[0094] Calculate the real-time health index as follows Where α is the dynamic fusion weight, 0 < α < 1. This is a real-time over-limit risk index. The calculation model is as follows: Where Sat(x) is the saturation normalization exponent, Sat(x)=0 when x≤1; when x>1, Sat(x)=min(1,(x-1) / η), and η is the preset excess risk gain coefficient. Let t be the real-time traction tension at time t. To achieve the maximum safe traction tension, The real-time side pressure at time t. For maximum safe side pressure, This represents the real-time bending radius at time t, calculated based on the cable's spatial attitude data. This is the minimum allowable bending radius.

[0095] In the quality inspection module of this system, the calculation model for the cumulative fatigue damage index can also be an alternative or enhanced algorithm such as LSTM / GRU-based sequence modeling, physical information-based neural networks, or hidden Markov models, to calculate the cumulative fatigue damage index.

[0096] For example, when using LSTM / GRU-based sequence modeling, , and The system takes a sliding time window sequence with equal parameters as input and cumulative damage quantities calibrated by experiments or simulations, such as microcrack propagation length and material stiffness reduction factor, as training targets. It captures long-term dependencies through an LSTM / GRU network, automatically learns the differential contribution of different load sequences and combinations to damage, and directly outputs the cumulative fatigue damage index through the trained network.

[0097] The specific hardware implementation of the quality inspection module is as follows: an industrial edge computing server or a high-performance embedded industrial control computer is used as the hardware carrier, equipped with a GPU accelerator card to provide parallel computing capabilities, and connected to the field data network through an industrial Ethernet switch. It subscribes to real-time data streams from the data acquisition module. The module runs dedicated computing engine software, which reads data from the real-time database and performs calculations in a fixed control cycle, such as 10-100 milliseconds. The data is then pushed to the quality analysis module through memory mapping or a real-time data publishing service.

[0098] Quality Analysis Module: Based on the comparison between the real-time health index and the preset threshold, and the different level ranges of the cumulative fatigue damage index, it generates either a first-type adjustment command for protective control of the laid cable or a second-type adjustment command for adjusting the laid cable. The health index threshold is set as follows: The first cumulative threshold is The second cumulative threshold is ,when or At that time, the first type of adjustment instruction is generated, which is the stop protection adjustment instruction;

[0099] when and At that time, determine the safety of cable laying;

[0100] when and At that time, a second type of adjustment instruction is generated, which is a laying speed reduction adjustment instruction.

[0101] The quality analysis module typically runs as a software component on the same edge computing server as the quality inspection module to minimize communication latency and enable rapid decision-making cycles. In large systems, it can also be deployed on a higher-level monitoring server, where its decision logic is implemented as a configurable rule engine or embedded decision code. The first or second type of adjustment instructions generated by the decision are sent to the industrial controller of the strategy adjustment module via the server's network interface using defined industrial protocol messages.

[0102] Strategy adjustment module: Used to send the generated adjustment instructions to the laying actuator to dynamically adjust the traction force, laying speed and guiding device.

[0103] The specific hardware implementation of the strategy adjustment module is as follows: the industrial controller is used as the core, and a programmable logic controller or motion controller is selected. The Ethernet port of the industrial controller is connected to the edge computing server and communicates with the quality analysis module. It receives adjustment instructions and converts them into executable commands for the device, and feeds them back to the upper-level monitoring system via Ethernet.

[0104] The laying actuator includes multiple drive units, controlled by the industrial controller, which drive the traction machine frequency converter, the conveyor motor, and the servo mechanism of the electric guide roller.

[0105] Data update module: When the quality analysis module generates a second type of adjustment instruction, it returns to execute the quality detection module and updates the values ​​of real-time bending radius and real-time bending angular velocity in the quality detection module.

[0106] The data update module is typically implemented in software integrated into the edge computing server where the quality inspection module resides. When the quality analysis module generates a second type of adjustment instruction, the event triggers the data update module through an internal message or flag. The module then executes the following: resetting or adjusting the relevant algorithm parameters or filter states used for calculation in the quality inspection module, and instructing the data acquisition module to perform a new round of data acquisition under the new operating conditions.

[0107] The overall system connection architecture is as follows: the sensor layer aggregates to the data acquisition box via fieldbus / analog lines, the acquisition box sends the synchronized data to the edge computing server via industrial Ethernet, the server runs the quality detection module, quality analysis module, and data update module, the server issues decision commands to the industrial controller via Ethernet, the controller controls the drive unit to execute via high-speed real-time Ethernet, and the execution status is fed back to the upper network via the controller. All hardware connections use connectors and cables with protection levels suitable for industrial field environments, enabling the system to operate stably in environments with vibration, oil, and electromagnetic interference.

[0108] The implementation principle of the cable safety laying control and quality inspection system in this embodiment is as follows:

[0109] By combining quantitative assessment of fatigue cumulative damage with active control, the data acquisition module synchronously, in real time, and continuously collects multi-source data and historical time-series data. In particular, it simultaneously collects laying mechanical data such as traction tension, lateral pressure, and bending radius, as well as spatial attitude data such as tilt angle, angular velocity, and heading angle. This enables a comprehensive and high-precision reconstruction of the actual stress and deformation state of the cable during the laying process.

[0110] Compared to traditional methods that rely on human experience or single-parameter monitoring, this method significantly improves the comprehensiveness and accuracy of perception of the complex stress and spatial morphology of cables, breaking the limitations of discrete and lagging data acquisition in traditional laying methods, and providing a real-time, continuous and multi-dimensional data foundation for decision-making.

[0111] The quality inspection module processes multi-source data and historical time-series data to calculate the cumulative fatigue damage index based on the integration of historical time-series data. This helps the system identify and quantify progressive damage processes that may not immediately cause failures but could affect the long-term lifespan of cables. It can integrate multiple physical quantities that were originally scattered and complex into an intuitive and quantifiable evaluation index, transforming the judgment of the laying status from a qualitative description based on experience to a quantitative assessment based on a model. This significantly improves the objectivity and consistency of the judgment and reduces the difficulty for operators to make comprehensive judgments on complex data.

[0112] The health index model was modified into a dynamic weighted fusion of instantaneous and cumulative indices, which further enhanced the system's adaptability, assessment accuracy, and the integrity of the control loop. The system can dynamically adjust the weight ratio of instantaneous risk and historical cumulative risk in the final decision based on the laying stage, environmental conditions, or cable type. Instead of mechanically looking at the two indices in parallel and making decisions separately, the system can flexibly balance the proportion of current instantaneous risk and historical cumulative loss in the final decision by adjusting the fusion weight α according to the actual situation.

[0113] For example, in complex curved areas or later stages of laying, α can be increased to give more weight to instantaneous geometric risks; in the initial stage of laying or long straight traction sections, α can be decreased to increase the weight of cumulative damage and give more attention to long-term tension accumulation, thereby reflecting the emphasis on long-term health status, improving the adaptability of the health assessment model to different working conditions, and making the comprehensive assessment results more scenario-based.

[0114] The weighted fusion model integrates information from both instantaneous and long-term dimensions. Theoretically, its output real-time health index value can more comprehensively reflect the current overall status of the cable than a single index, and can provide a more balanced and accurate basis for analysis and decision-making.

[0115] By defining a specific calculation model for the cumulative fatigue damage index based on the weighted function H(x), the system's assessment of fatigue damage is transformed from a conceptual level to an operable quantitative algorithm, resulting in a clearer improvement in technical effectiveness. The cumulative fatigue damage index quantifies the load history that the cable experiences during the entire laying process, which is below the instantaneous failure threshold but has a cumulative effect, by integrating over time.

[0116] By setting a fatigue threshold below the absolute safety limit and using a weighting function H(x) to accumulate loads exceeding the fatigue threshold, this model can effectively characterize the progressive process of material fatigue damage caused by conditions such as repeated bending and continuous low-intensity over-tension. Most existing technologies only focus on whether instantaneous parameters exceed limits. By introducing a cumulative fatigue damage index, the system can record minor overloads or continuous loads that, while not immediately causing failure, accelerate material aging, thus effectively assessing the potential losses to the long-term service life of cables during the laying process.

[0117] This model comprehensively considers static forces such as tension and lateral pressure, as well as dynamic effects such as bending angular velocity. Bending angular velocity reflects the dynamic severity of cable bending. High-frequency repeated bending can also lead to fatigue. Incorporating it into the model makes the assessment of cumulative damage more comprehensive and scientific.

[0118] The scope of laying quality assessment has been expanded to include both instantaneous safety and long-term lifespan. A specific calculation model for the real-time health index based on a saturated normalization function has been defined. The health assessment model has been specified and optimized. By normalizing and saturating the three core safety parameters of traction tension, lateral pressure, and bending radius through the real-time over-limit risk index, the standardized and quantitative assessment of instantaneous over-limit risk has been achieved.

[0119] The saturation function is designed to be 0 when x≤1 and grows proportionally when x>1. This allows the real-time over-limit risk index to clearly distinguish between the safe state (no risk contribution) and the over-limit state (risk increases proportionally with the over-limit). The output of the real-time health index directly characterizes the immediate severity of the parameter's over-limit. The index only produces a non-zero value when parameters such as tension, lateral pressure, and bending actually exceed the safety threshold. This effectively avoids false alarms caused by measurement noise or normal fluctuations, improves the intuitiveness of risk assessment and the clarity of decision-making basis, and enhances the stability of the system.

[0120] The application of the max function enhances the rigor of the assessment. The system constantly monitors the most dangerous parameters. If any of the indicators such as tension, lateral pressure, or bending radius exceeds the standard, the system will immediately detect it, thereby achieving timely capture of complex risks. By introducing the excess risk gain coefficient η, the sensitivity to risk escalation can be calibrated. For different models or more vulnerable cables, the system can be made more sensitive to slight exceedances by reducing the value of η, thereby improving the practicality and reliability of the assessment strategy.

[0121] Ultimately, the real-time health index output by the model is a clearly defined and physically meaningful indicator. When it exceeds the health index threshold, it will trigger the first type of adjustment command for the stop protection. This clear mapping relationship enhances the system's ability to respond to major and immediate risks, enabling automatic execution of protective actions when critical safety parameters exceed the limits, thereby helping to suppress the risk of acute mechanical damage that may be caused by continuous overload.

[0122] This setup provides a programmed guarantee for cable safety under extreme operating conditions, offers clear automated execution guidelines for responding to sudden severe overloads, enhances rapid response capabilities in emergency situations, enables timely dynamic adjustments to equipment and strategy optimization based on actual site conditions, systematically improves the safety and quality control level of the laying process, and thus enhances the accuracy of cable laying control.

[0123] The quality analysis module generates adjustment instructions based on the cumulative fatigue damage index and health index. The strategy adjustment module sends these instructions to the laying actuators, which dynamically adjust, directly drive traction and guidance mechanisms, forming a dynamic closed-loop control circuit. This mechanism shortens the time delay from risk identification to protective measures, facilitating timely intervention in the early stages of cable damage. This reduces the probability of construction damage such as cable overload and excessive bending caused by delayed human response or improper operation, providing a more automated and intelligent technical means to maintain the safety of the laying process.

[0124] The system can not only detect risks in a timely manner, but also automatically adjust the laying strategy to correct deviations. It can adjust key parameters such as traction force and speed in real time, and make dynamic adjustments to equipment and optimize strategies in a timely manner according to the actual situation on site. This systematically improves the safety and quality control level of the laying process, thereby improving the accuracy of cable laying control.

[0125] The system can generate differentiated adjustment instructions based on the different levels of the cumulative fatigue damage index, enabling it to implement preventative maintenance. For example, when the system detects an increase in the cumulative damage index but it has not reached a dangerous level, it can issue an early warning or take gentle measures such as reducing speed to prevent further damage and thus intervene before an accident occurs.

[0126] This mechanism enhances the preventative maintenance capabilities of the laying process, helping to make adjustments before latent damage accumulates to a certain extent. This provides positive support for extending the overall service life of cables and optimizing reliability-centered laying strategies. It has profound significance for ensuring the decades-long service life of cables, enabling timely dynamic adjustments to equipment and optimization of strategies based on actual site conditions. This systematically improves the safety and quality control level of the laying process, thereby enhancing the accuracy of cable laying control.

[0127] By setting a dual-threshold logic for collaborative decision-making, a multi-level and refined risk system based on dual-dimensional assessment was constructed, thereby significantly improving the intelligence and adaptability of system decision-making and optimizing construction efficiency while ensuring safety.

[0128] Three decision-making scenarios are clearly defined: For the most urgent instantaneous severe over-limit or extremely high cumulative damage, the system takes the most stringent stop-layout measures to ensure the safety baseline. When the system determines that it is safe, normal laying is allowed, reducing unnecessary intervention and maintaining the efficiency of basic construction. For the state where the emergency level has not been reached, but the cumulative damage has entered the warning range, the system generates a second type of instruction to reduce the laying speed. The speed reduction can directly reduce the traction tension, lateral pressure and dynamic bending frequency, thereby slowing down the rate of damage accumulation and giving the system a recovery window.

[0129] The system adopts a relatively mild intervention method of slowing down the laying speed, which not only takes mitigation measures to slow down the rate of damage accumulation, but also avoids the impact of overly conservative complete shutdown on construction efficiency, thus taking into account the continuity of construction. The clearly defined threshold range provides operators with clear status judgment and decision-making basis, enhances the transparency and controllability of human-machine interaction, and improves the level of precision in risk management and the rationality of engineering practice.

[0130] This tiered strategy is significantly superior to the crude control of single-threshold shutdown. It enables the system to distinguish between the urgency and severity of risks, make reasonable responses, maximize construction continuity and economy, and make timely dynamic adjustments to equipment and optimization of strategies based on actual site conditions. This systematically improves the safety and quality control level of the laying process, thereby enhancing the accuracy of cable laying control.

[0131] By introducing a strategy update step, an important feedback optimization closed loop is formed. When the system issues a deceleration command due to high accumulated damage, this step can proactively trigger data updates and restart the evaluation process, enabling the system to verify the effectiveness of its control actions and to iteratively evaluate and make decisions based on the new state.

[0132] After the speed is reduced, an increase in the bending radius and a decrease in the bending angular velocity should be observed, which in turn leads to a decrease in the real-time health index calculated in the new round. If the assessment results improve, it confirms the correctness of the adjustment command. If there is no improvement, it may indicate that there are other problems that require further investigation.

[0133] This enables the entire system to have a continuous optimization cycle function, allowing it to self-verify and adjust based on the execution results. This enhances its robustness and reliability in handling complex and uncertain laying environments. It also allows for timely dynamic adjustments to equipment and strategy optimization based on actual site conditions, systematically improving the safety and quality control level of the laying process, thereby increasing the accuracy of cable laying control.

[0134] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for cable safety laying control and quality detection, characterized in that, The method comprises the following steps: a data acquisition step: synchronously acquiring multi-source data and historical time series data of the current laying section of the cable in real time, the multi-source data comprising laying mechanical data and cable spatial attitude data obtained based on multi-source sensors integrated in the cable, the laying mechanical data comprising traction tension, side pressure and bending radius of the cable, and the cable spatial attitude data comprising three-dimensional inclination, angular velocity and heading angle; a laying quality evaluation step: based on the multi-source data and the historical time series data, a real-time health index of the current laying section is calculated through a preset health degree evaluation model; a laying quality analysis step: according to the comparison result of the real-time health index and a preset threshold, a first type of adjustment instruction for protective control of the laying cable is generated; a strategy adjustment step: the generated adjustment instruction is sent to a laying execution mechanism to dynamically adjust the traction force, laying speed and guiding device.

2. The method of claim 1, wherein: In the laying quality evaluation step, the calculation model of the real-time health index is: , is a real-time overrun risk index, The calculation model is: , wherein Sat(x) is a saturation normalization index, Sat(x)=0 when x≤1; Sat(x)=min(1,(x-1) / η) when x>1, η is a preset overrun risk gain coefficient, is a real-time tension at time t, is a maximum safe tension, is a real-time lateral pressure at time t, is a maximum safe lateral pressure, is a real-time bending radius at time t calculated according to cable spatial posture data, is a minimum allowable bending radius; In the laying quality analysis step, a health index threshold is set as When a first type of adjustment instruction is generated, and the first type of adjustment instruction is a stop-tension-protection adjustment instruction.

3. A method of cable safety laying control and quality detection according to claim 2, characterized in that: In the laying quality evaluation step, the cumulative fatigue damage index of the current laying section is also calculated as where k is the cable fatigue accumulation rate coefficient, H(x) is a linear weighting function, and when x<1, the output is close to 0, and when x≥1, the output is positively correlated with x, is a first weight coefficient, is a second weight coefficient, is a third weight coefficient, , is a traction tension fatigue threshold, is a side pressure fatigue threshold, is a real-time bending angular velocity at time t calculated according to the cable spatial posture data, is a bending angle change rate fatigue threshold, , , indicates that the time is integrated from the laying start time 0 to the current time t; In the laying quality analysis step, a first type of adjustment instruction for protective control of the laying cable or a second type of adjustment instruction for adjustment of the laying cable is also generated according to different grade intervals of the cumulative fatigue damage index.

4. The method of claim 3, wherein: The laying quality analysis step is further updated as follows: according to the comparison result of the real-time health index and the preset threshold value and the different grade intervals of the cumulative fatigue damage index, a first type of adjustment instruction for protective control of the laid cable or a second type of adjustment instruction for adjustment of the laid cable is generated, the health index threshold value is set as , the first cumulative threshold value is , the second cumulative threshold value is , when or , the first type of adjustment instruction is generated, the first type of adjustment instruction is a stop traction protection adjustment instruction, when and , the cable laying safety is judged, when and , the second type of adjustment instruction is generated, and the second type of adjustment instruction is a laying speed reduction adjustment instruction.

5. The method of claim 3, wherein: In the laying quality evaluation step, the calculation model of the real-time health index is corrected, and the corrected calculation model is updated as: wherein, a is a dynamic fusion weight, 0 ​ The strategy adjustment step is followed by a strategy updating step: when the second type of adjustment instruction is detected, the laying quality evaluation step is executed again, and the values of the real-time bending radius and real-time bending angular velocity in the laying quality evaluation step are updated.

6. A cable safety laying control and quality detection system characterized by, The method comprises the following modules: a data acquisition module: used for synchronously acquiring multi-source data and historical time series data of the current laying section of the cable in real time, the multi-source data comprising laying mechanical data and cable spatial attitude data obtained based on multi-source sensors integrated in the cable, the laying mechanical data comprising traction tension, side pressure and bending radius of the cable, and the cable spatial attitude data comprising three-dimensional inclination, angular velocity and heading angle; a quality detection module: used for calculating a cumulative fatigue damage index of the current laying section based on the multi-source data and the historical time series data, the calculation of the cumulative fatigue damage index being based on the integration of historical time series data of the real-time traction tension, real-time side pressure and real-time bending angular velocity calculated from the cable spatial attitude data; a quality analysis module: used for generating a first type of adjustment instruction for protective control of the laying cable or a second type of adjustment instruction for adjustment of the laying cable according to different grade intervals of the cumulative fatigue damage index; a strategy adjustment module: used for sending the generated adjustment instruction to a laying execution mechanism to dynamically adjust the traction force, laying speed and guiding device.

7. A cable safety laying control and quality detection system according to claim 6, characterized in that: The calculation model of the cumulative fatigue damage index in the quality detection module is wherein k is a cable fatigue accumulation rate coefficient, H(x) is a linear weighting function, and when x<1, the output is close to 0, and when x≥1, the output is positively correlated with x, is a first weight coefficient, is a second weight coefficient, is a third weight coefficient, , is a traction tension fatigue threshold, is a side pressure fatigue threshold, is a real-time bending angular velocity at time t calculated according to cable spatial posture data, is a bending angle change rate fatigue threshold, , , indicates integration with respect to time from the laying start time 0 to the current time t.

8. A cable safety laying control and quality detection system according to claim 7, characterized in that: The mass detection module is further configured to calculate a real-time health index as wherein, is a real-time overrun risk index, The calculation model is: wherein, Sat(x) is a saturation normalization index, Sat(x)=0 when x≤1; Sat(x)=min(1,(x-1) / η) when x>1, and η is a preset overrun risk gain coefficient, is a real-time traction tension at time t, is a maximum safe traction tension, is a real-time side pressure at time t, is a maximum safe side pressure, is a real-time bending radius at time t calculated according to cable spatial posture data, is a minimum allowable bending radius; In the quality analysis module, a first type of adjustment instruction for protective control of the laying cable is also generated according to the comparison result of the real-time health index and a preset threshold.

9. A cable safety laying control and quality detection system according to claim 8, characterized in that: The quality analysis module is further updated to generate a first type of adjustment instruction for protective control of the laid cable or a second type of adjustment instruction for adjustment of the laid cable according to a comparison result of the real-time health index and the preset threshold value and different grade intervals of the accumulated fatigue damage index, set the health index threshold value as , the first accumulated threshold value as , and the second accumulated threshold value as , generate the first type of adjustment instruction when or , the first type of adjustment instruction being a stop-pulling protection adjustment instruction, judge the cable laying safety when and , and generate the second type of adjustment instruction when and , the second type of adjustment instruction being a laying speed reduction adjustment instruction.

10. The cable safety laying control and quality detection system according to claim 8, characterized in that: The quality detection module corrects a calculation model of the real-time health index The corrected calculation model is updated as: Wherein, a is a dynamic fusion weight, 0 < a < 1. The method further comprises a data updating module: when the second type of adjustment instruction is generated by the quality analysis module, the quality detection module is executed again, and the values of the real-time bending radius and real-time bending angular velocity in the quality detection module are updated.

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