An intelligent cable with automatic induction monitoring and control method

Abstract

The application relates to the technical field of intelligent cables, in particular to an intelligent cable with automatic induction monitoring and a control method, which comprises the following steps: collecting temperature data of the cable, processing the temperature data in time and space based on temperature values of each position and each time in the temperature data of the cable, and constructing a temperature space-time matrix of the cable; predicting the temperature space-time matrix of the cable to obtain a temperature space-time matrix containing several time instants after the current time instant, which is used as a predicted temperature space-time matrix of the current time instant; determining a cooling control coefficient by using temperature changes and distribution in the predicted temperature space-time matrix of the current time instant; and controlling the temperature of the cable based on the cooling control coefficient. The application realizes the transformation from passive cooling to active prevention, effectively prevents the failure and fire risk caused by overheating, and optimizes the allocation and utilization efficiency of cooling resources.

Description

Technical Field

[0001] This invention relates to the field of intelligent cable technology, and specifically to an intelligent cable with automatic sensing and monitoring, and a control method thereof. Background Technology

[0002] Power transmission and signal communication place increasingly stringent demands on cable performance. In various infrastructure, industrial production, and daily life sectors, traditional cables face multiple technical challenges, making it difficult to meet the needs of modern complex applications. When cable temperatures are too high, ordinary cables lack effective automatic sensing and cooling mechanisms, making them highly susceptible to damage from high temperatures, leading to power transmission and signal communication interruptions and potentially more serious safety accidents. While some existing technologies incorporate flame-retardant materials and cooling mechanisms to address situations where cables experience excessively high temperatures due to high loads or environmental influences, these technologies have not addressed these issues.

[0003] Currently, cable temperature control lacks intelligent control, cannot proactively intervene in cable temperature development, and lacks the ability to automatically identify and take intelligent cooling measures. As a result, the temperature control effect of cables is not ideal, and it is impossible to reasonably adjust the degree of cooling when the cable temperature changes, ensure the balance between cooling effect and cable temperature cooling resources, and achieve intelligent management and control of cable temperature. Summary of the Invention

[0004] This invention provides an intelligent cable for automatic sensing and monitoring, and a control method thereof, to solve existing problems.

[0005] The present invention provides an intelligent cable for automatic sensing and monitoring, and a control method thereof, which adopts the following technical solution:

[0006] One embodiment of the present invention provides an intelligent cable control method for automatic sensing and monitoring, the method comprising the following steps:

[0007] Collect temperature data of the cable, which includes the temperature values ​​of the cable at different locations and at different times;

[0008] Based on the temperature values ​​at various locations and times in the cable's temperature data, the temperature data is processed in time and space to construct the cable's temperature spatiotemporal matrix; based on the cable's temperature spatiotemporal matrix, a temperature spatiotemporal matrix including several times after the current time is obtained, which is used as the predicted temperature spatiotemporal matrix for the current time.

[0009] The cooling regulation coefficient is determined by using the temperature change and distribution in the predicted temperature spatiotemporal matrix at the current moment;

[0010] Temperature regulation of cables is achieved based on cooling regulation coefficients.

[0011] Optionally, the method for processing the temperature data in time and space based on the temperature values ​​at various locations and times in the cable temperature data to construct the cable temperature spatiotemporal matrix includes the following specific methods:

[0012] For any given moment, obtain the sequence of temperature values ​​corresponding to all consecutive positions of the cable at that moment, denoted as the temperature space sequence of the cable at that moment. Based on the temperature value level in the temperature space sequence at the current moment, determine several points of interest in the temperature space sequence at the current moment. Use the points of interest to determine the time length, obtain the temperature space sequence within the range corresponding to the current moment and the time length before the current moment, and arrange the temperature space sequences at all moments within the range corresponding to the time length in chronological order from top to bottom to form a corresponding matrix, denoted as the temperature spatiotemporal matrix at the current moment.

[0013] Optionally, the specific method for obtaining several points of interest in the temperature spatial sequence at the current moment is as follows:

[0014] A preset temperature threshold is used to identify elements in the temperature space sequence whose temperature values ​​are greater than the threshold and are local maxima, as points of interest in the temperature space sequence at the current moment.

[0015] Optionally, the specific method for determining the time length using points of interest includes:

[0016] With a preset initial time length and time step, for any point of interest in the temperature spatial sequence at the current moment, the temperature spatial ratio of the point of interest at the current moment is calculated based on the change of the peak corresponding to the point of interest. The DTW algorithm is used to obtain the elements in the temperature spatial sequence at the current moment k that have a matching relationship with the point of interest at the time k-1, which are taken as the matching points of interest at the time k-1. Further, the elements in the temperature spatial sequence at the time k-1 that have a matching relationship with the matching points of interest at the time k-2 are obtained, which are taken as the matching points of interest at the time k-2, and so on, to obtain the matching points of interest corresponding to the point of interest at each moment before the current moment. The temperature spatial ratio of the matching points of interest at the corresponding moment is obtained using the method for obtaining the temperature spatial ratio. Based on the change of the temperature spatial ratios corresponding to the point of interest and the matching points of interest, the initial time length is iteratively increased by the time step based on the initial time length to obtain the time length at the current moment.

[0017] Optionally, the specific method for calculating the temperature spatial ratio of the point of interest at the current moment based on the change of the peak corresponding to the point of interest is as follows:

[0018] Obtain the troughs on both sides of the peak corresponding to the point of interest in the temperature spatial sequence at the current moment, and denote them as the first trough and the second trough, respectively. The distance between the first trough and the second trough is taken as the temperature spatial range value of the point of interest. Obtain the cumulative difference between the temperature value and the temperature threshold at all positions of the cable between the first trough and the second trough at the current moment, and denote it as the local temperature parameter of the point of interest at the current moment. Denote the difference between the point of interest and the temperature threshold as the temperature exceedance value of the point of interest. Calculate the temperature spatial ratio of the point of interest at the current moment based on the temperature spatial range value, the temperature exceedance value, and the local temperature parameter. The temperature spatial ratio is positively correlated with the local temperature parameter, while the temperature spatial range value and the temperature exceedance value are both negatively correlated with the temperature spatial ratio.

[0019] Optionally, the method for iteratively increasing the initial time length based on the time step, according to the changes in the temperature space ratio corresponding to the points of interest and the matching points of interest, to obtain the time length at the current moment, includes the following specific methods:

[0020] For any point of interest in the temperature spatial sequence at the current moment, obtain the temperature spatial ratios corresponding to the point of interest at the current moment and the corresponding matching points of interest at all moments within the range of the initial time length, and map them to a two-dimensional rectangular coordinate system. The horizontal axis of the two-dimensional rectangular coordinate system is time, and the vertical axis is temperature spatial ratio. Obtain the absolute value of the slope of the matching point of interest at the minimum moment in the two-dimensional rectangular coordinate system. Normalize the product of the absolute value of the slope and the temperature spatial ratio of the corresponding matching point of interest, and record it as the temperature spatiotemporal coefficient of the matching point of interest at the corresponding moment. When the temperature spatiotemporal coefficient is greater than the preset spatiotemporal coefficient threshold, iteratively increase the initial time length based on the time step until the temperature spatiotemporal coefficient of the matching point of interest at the minimum moment in the two-dimensional rectangular coordinate system is less than or equal to the spatiotemporal coefficient threshold. Stop iteratively increasing the initial time length to obtain the time length of the point of interest at the current moment. Take the maximum value of the time lengths of all points of interest at the current moment as the time length of the current moment.

[0021] Optionally, the specific method for obtaining the predicted temperature spatiotemporal matrix at the current moment includes:

[0022] All points of interest at the current moment are used as seed points for region growing of the temperature-space-time matrix of the cable at the current moment. The temperature-space-time matrix is ​​grown using a region growing algorithm to obtain several temperature-space-time regions. For each temperature-space-time region, its temperature change sequence in the time dimension is extracted. The temperature change sequence is trained and predicted using a long short-term memory network model to obtain the predicted temperature value of the region at future moments. The prediction results of all temperature-space-time regions are spliced ​​according to their spatial location to form an extended temperature-space-time matrix that includes several moments after the current moment, which is used as the predicted temperature-space-time matrix for the current moment.

[0023] Optionally, the method for determining the cooling control coefficient by utilizing the temperature changes and distribution in the predicted temperature spatiotemporal matrix at the current moment includes:

[0024] The next moment after the current moment is recorded as the future moment, and the temperature value of the future moment in the predicted temperature spatiotemporal matrix is ​​recorded as the predicted temperature value. For each position in the predicted temperature spatiotemporal matrix, the difference between the predicted temperature value of the position at the future moment and the temperature threshold, as well as the temperature change rate over time at the position, are obtained. The product of the difference and the temperature change rate is used as the temperature risk index of the position.

[0025] The temperature risk index of all locations is sorted, and the average temperature risk index of the top C high-risk locations is taken as the overall risk level index, where C is the preset first parameter;

[0026] The overall risk level is used as the input to the proportional-integral control algorithm. The output of the proportional-integral control algorithm is normalized, and the normalization result is used as the cooling control coefficient.

[0027] Optionally, the specific method for regulating the cable temperature based on the cooling regulation coefficient includes:

[0028] A duty cycle signal is generated based on the cooling control coefficient and pulse width modulation technology; the stepper motor of the flow valve is driven by the duty cycle signal to adjust the valve core opening; at the same time, the cooling medium circulation pump is started to make the cooling medium flow in the pipes of the cooling system to dissipate heat from the cable.

[0029] An intelligent cable with automatic sensing and monitoring includes a cable body, a memory, a processor, and a computer program stored in the memory and executable on the processor. The cable body contains an intelligent optical fiber, a cooling system, and a control system. The intelligent optical fiber is used to collect temperature data. When the processor executes the computer program, it implements the steps of any one of the automatic sensing and monitoring intelligent cable control methods described above and inputs the calculation results into the control system, which then controls the cooling system.

[0030] The beneficial effects of the technical solution of this invention are as follows: By real-time monitoring of the temperature of the entire cable and constructing a temperature spatiotemporal matrix based on spatiotemporal characteristics, the dynamic evolution law of local hot spots in the cable is effectively captured. Furthermore, temperature trend prediction is performed on the temperature spatiotemporal matrix, improving the prediction accuracy and local adaptability. Based on the prediction results, the temperature risk index at each location is calculated, and the overall risk level of the cable is comprehensively assessed. This drives the adaptive generation of the cooling control coefficient, achieving precise adjustment of the cooling system flow rate. This realizes the transformation from passive cooling to active prevention, effectively preventing the risk of failure and fire caused by overheating. At the same time, it optimizes the allocation and utilization efficiency of cooling resources, reducing energy consumption while ensuring stable system operation. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a flowchart illustrating the steps of an intelligent cable control method for automatic sensing and monitoring according to the present invention.

[0033] Figure 2 This is a schematic diagram of the internal structure of the cable body of an intelligent cable for automatic sensing and monitoring according to the present invention. Detailed Implementation

[0034] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an intelligent cable and control method for automatic sensing and monitoring according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0036] The following description, in conjunction with the accompanying drawings, details a specific scheme for an intelligent cable and control method for automatic sensing and monitoring provided by the present invention.

[0037] Please see Figure 1The diagram illustrates a flowchart of an intelligent cable control method for automatic sensing and monitoring according to an embodiment of the present invention. The method includes the following steps:

[0038] Step S001: Collect temperature data of the cable.

[0039] It should be noted that cable temperature is usually affected by the environment and the cable itself. Specifically, when the temperature of the environment in which the cable is located is high, the cable will continuously accumulate temperature, causing the cable temperature to rise continuously, which may lead to power accidents. In order to facilitate timely cooling of the cable, the intelligent optical fiber inside the cable needs to collect temperature data through distributed optical fiber sensing technology.

[0040] Specifically, in order to implement the intelligent cable control method with automatic sensing and monitoring proposed in this embodiment, it is first necessary to collect temperature data at various locations on the cable in real time. The specific process is as follows:

[0041] First, install the smart cable. The cable body consists of a cooling system, insulating support tube, braided conductor, insulation layer, smart optical fiber, optical fiber protective layer, inner sheath, shielding layer, outer sheath, multi-core signal conductor, multi-core signal insulation layer, multi-core signal protective layer, and steel wire.

[0042] like Figure 2 The diagram shows the internal structure of the cable body. In the diagram, number 1 corresponds to the cooling system; number 2 corresponds to the insulating support tube; number 3 corresponds to the braided conductor; number 4 corresponds to the insulation layer; number 5 corresponds to the intelligent optical fiber; number 6 corresponds to the optical fiber protective layer; number 7 corresponds to the inner sheath; number 8 corresponds to the shielding layer; number 9 corresponds to the outer sheath; number 10 corresponds to the multi-core signal wire conductor; number 11 corresponds to the multi-core signal wire insulation; number 12 corresponds to the multi-core signal wire protective layer; and number 13 corresponds to the steel wire.

[0043] It should be noted that, for the cable body, the cooling system includes cooling pipes and a cooling medium circulation pump. The cooling medium circulation pump circulates the cooling medium (such as deionized water or air) through the cooling pipes, achieving cooling and temperature reduction. The insulating support tube maintains the structural integrity of the conductor, preventing displacement or deformation during operation and ensuring reliable current transmission. The braided conductor is constructed by braiding the conductor material and placing it outside the cooling system for power transmission or signal communication. The insulation layer uses ethylene propylene rubber as the insulation material. The intelligent optical fiber uses fiber optic sensing technology to monitor ambient temperature and conductor current carrying capacity, transmitting signals to the control system. The control system then activates the cooling system to ensure the cable's ambient temperature remains within the normal range. The optical fiber protective layer uses antifreeze nylon tubing to protect the intelligent optical fiber from being crushed or pulled apart, ensuring its normal function. The inner sheath uses halogen-free, low-smoke insulation. Flame-retardant polyolefin sheathing wraps around the cable insulation layer to prevent moisture, mechanical damage, chemical corrosion, or photocorrosion, providing reliable primary protection for the cable's internal structure. The shielding layer, woven from fine metal filaments, tightly wraps the core, confining electromagnetic noise within the cable and preventing interference from external magnetic fields. The outer sheath, made of neoprene rubber, provides waterproofing, moisture resistance, and chemical corrosion protection, while also offering some electromagnetic shielding. Multi-core signal conductors are constructed from oxygen-free copper, providing transmission paths for different signal types and preventing interference. The multi-core signal insulation layer, wrapped with PVC insulation, enhances signal transmission stability. The multi-core signal protection layer, wrapped with aluminum foil, shields against external electromagnetic interference. The steel wire is galvanized high-carbon steel wire, ensuring the cable's mechanical strength and tensile properties.

[0044] Then, using fiber optic sensing technology in the smart optical fiber in the cable, the temperature values ​​at different locations in the cable are collected in real time and corresponding time-series data are generated and recorded as temperature data.

[0045] It should be noted that, since cables typically have a large spatial span, the environmental influences on cables vary at different spatial locations, resulting in different temperature performances at different locations. Therefore, the fiber optic sensing technology used in this embodiment of the invention employs distributed fiber optic sensing technology. This technology can acquire temperature data at different locations of the cable, thus the temperature data in this embodiment of the invention reflects the temperature conditions of the environment at different locations of the cable over a continuous period of time.

[0046] Finally, the collected temperature data were subjected to Gaussian filtering and standardization.

[0047] Thus, the temperature data of the cable was obtained using the above method.

[0048] Step S002: Based on the temperature values ​​at various locations and times in the cable temperature data, process the temperature data in time and space to construct the cable temperature spatiotemporal matrix; make predictions based on the cable temperature spatiotemporal matrix to obtain a temperature spatiotemporal matrix that includes several times after the current time, which is used as the predicted temperature spatiotemporal matrix for the current time.

[0049] It should be noted that the temperature change of cables usually has a certain trend, and the temperature change will continue to change under this trend. Therefore, in order to enable the control system to respond to environmental changes in a timely manner based on environmental conditions, and thus enable the control system to adaptively adjust the cooling system of the smart cable, this embodiment of the invention selects to perform predictive analysis on the collected time series data, so as to improve the control effect of cable temperature by using the analysis results in the future.

[0050] Specifically, in step S201, based on the temperature values ​​at various locations and times in the cable's temperature data, the temperature data is processed and analyzed in time and space to construct the cable's temperature spatiotemporal matrix.

[0051] It should be noted that cable temperature is affected by various factors and has inertia, making it difficult to accurately predict future trends based solely on current measurements. Furthermore, traditional methods only focus on the absolute temperature value at a single location (e.g., whether it exceeds a threshold), ignoring the spatial distribution characteristics and dynamic evolution trends of hotspots. This results in an inability to effectively predict cable temperature changes during cooling regulation, leading to low adaptability of the cooling process and an inability to respond promptly to sensed temperature changes, resulting in low utilization of cooling resources. Therefore, this invention constructs a temperature spatiotemporal matrix based on temperature changes sensed by intelligent optical fibers, facilitating subsequent temperature prediction and advance control of the cooling medium flow rate. This improves cooling efficiency while maximizing the utilization of cooling resources.

[0052] As a preferred embodiment, the method for constructing the temperature-space-time matrix is ​​as follows: For any given time, obtain the sequence of temperature values ​​corresponding to all consecutive positions of the cable at that time, denoted as the temperature-space sequence of the cable at that time; determine several points of interest in the temperature-space sequence at the current time based on the temperature value levels in the temperature-space sequence at the current time; determine the time length using the points of interest, obtain the temperature-space sequence within the range corresponding to the current time and the time length before the current time, and arrange the temperature-space sequences at all times within the range corresponding to the time length in a top-to-bottom order according to the time sequence to form a corresponding matrix, denoted as the temperature-space-time matrix at the current time.

[0053] As an optional embodiment, the method for determining the focus based on the temperature value level in the temperature spatial sequence at the current moment includes: setting a preset temperature threshold, and taking the elements in the temperature spatial sequence at the current moment whose temperature value is greater than the temperature threshold and are maximum points as the focus in the temperature spatial sequence at the current moment.

[0054] It should be noted that the preset temperature threshold is 100 degrees Celsius based on experience, but it can be adjusted according to the rated operating temperature of the cable and the manufacturing process. It is not specifically limited in this embodiment of the invention.

[0055] As an optional embodiment, the method for determining the time length using the point of interest includes: presetting an initial time length and a time step; for any point of interest in the temperature spatial sequence at the current moment, calculating the temperature spatial ratio of the point of interest at the current moment based on the change of the peak corresponding to the point of interest; using the DTW algorithm to obtain the elements in the temperature spatial sequence at the current moment k that have a matching relationship with the point of interest at the time k-1, as the matching point of interest at the time k-1; further obtaining the elements in the temperature spatial sequence at the time k-2 that have a matching relationship with the matching point of interest at the time k-1, as the matching point of interest at the time k-2, and so on, to obtain the matching points of interest corresponding to the point of interest at each moment before the current moment; using the method for obtaining the temperature spatial ratio to obtain the temperature spatial ratio of the matching point of interest at the corresponding moment; and based on the change of the temperature spatial ratios corresponding to the point of interest and the matching point of interest, iteratively increasing the initial time length based on the time step to obtain the time length at the current moment.

[0056] It should be noted that, based on experience, the initial time length is preset to 30s and the time step is 5s, which can be adjusted according to the actual situation. This embodiment of the invention does not impose specific limitations.

[0057] As an optional embodiment, the specific method for obtaining the temperature spatial ratio of the point of interest at the current moment is as follows: Obtain the troughs on both sides of the peak corresponding to the point of interest in the temperature spatial sequence at the current moment, denoted as the first trough and the second trough, respectively; use the distance between the first trough and the second trough as the temperature spatial range value of the point of interest; obtain the cumulative difference between the temperature value and the temperature threshold at all positions of the cable between the first trough and the second trough at the current moment, denoted as the local temperature parameter of the point of interest at the current moment; denot the difference between the point of interest and the temperature threshold as the temperature exceedance value of the point of interest; calculate the temperature spatial ratio of the point of interest at the current moment based on the temperature spatial range value, the temperature exceedance value, and the local temperature parameter, wherein the temperature spatial ratio is positively correlated with the local temperature parameter, while both the temperature spatial range value and the temperature exceedance value are negatively correlated with the temperature spatial ratio.

[0058] As an optional embodiment, the specific method for calculating the temperature space ratio of the point of interest at the current moment is as follows: Where β represents the temperature space ratio of the point of interest at the current moment; y represents the local temperature parameter of the point of interest at the current moment; w represents the temperature space range value of the point of interest at the current moment; and c represents the temperature excess value of the point of interest at the current moment.

[0059] It should be noted that the temperature space ratio describes the heat load density within a local area of ​​the point of interest. A larger temperature space ratio indicates a higher heat load density within that local area, resulting in a higher heat load on the cable and increasing the risk of cable burnout. Therefore, when cooling the cable through the cooling system, the flow rate of the cooling medium should be increased to reduce the heat load on the cable at the point of interest and ensure its safe and effective operation. In the method of obtaining the temperature space ratio, the local temperature parameter represents the total heat in the local area. A larger local temperature parameter indicates a higher heat concentration in the local area, which can cause greater damage to the cable. w×c represents the maximum total heat that can be reached within the local area corresponding to the temperature space range value, with the point of interest as the highest point. When the proportion of the total heat reflected by the local temperature parameter of the point of interest to the maximum total heat is larger, it will lead to a larger thermal gradient at both ends of that local area, resulting in more drastic local temperature changes, which can easily cause thermoelectric stress concentration and increase the risk of cable melting.

[0060] As an optional embodiment, the method of iteratively increasing the initial time length based on the time step, according to the changes in the temperature space ratios corresponding to the points of interest and the matched points of interest, to obtain the time length at the current moment, includes the following specific method: For any point of interest in the temperature space sequence at the current moment, obtain the temperature space ratios corresponding to the point of interest at the current moment and the matched points of interest at all moments within the range corresponding to the initial time length, and map them to a two-dimensional Cartesian coordinate system, where the horizontal axis of the two-dimensional Cartesian coordinate system represents time and the vertical axis represents the temperature space ratio, and obtain the minimum value in the two-dimensional Cartesian coordinate system. The absolute value of the slope corresponding to the matching point of interest at each time step is normalized by multiplying the absolute value of the slope by the temperature-space ratio of the corresponding matching point of interest. This product is recorded as the temperature-space-time coefficient of the matching point of interest at the corresponding time step. When the temperature-space-time coefficient is greater than a preset time-space coefficient threshold, the initial time length is iteratively increased based on the time step until the temperature-space-time coefficient of the matching point of interest at the minimum time step in the two-dimensional rectangular coordinate system is less than or equal to the time-space coefficient threshold. At this point, the iterative increase of the initial time length is stopped, and the time length of the point of interest at the current time is obtained. The maximum value of the time lengths of all points of interest at the current time is taken as the time length of the current time step.

[0061] It should be noted that, since the spatial span of cables is usually quite large, the cooling system needs a certain amount of time to deliver the cooling medium in the cable, resulting in a certain delay. Therefore, in this embodiment of the invention, the time window length of the temperature spatiotemporal matrix is ​​dynamically determined by the temperature space ratio to expand the initial time length of the window and capture the complete evolution process of the heat change of the point of concern with excessively high temperature at the current moment. This improves the accuracy of subsequent prediction of heat change trends, thereby enabling better prediction of the subsequent temperature environment evolution process of the cable. This allows the cooling system to respond to temperature changes in advance and cool the cable by adjusting the cooling system through the control system.

[0062] Step S202: Based on the temperature spatiotemporal matrix of the cable, a temperature spatiotemporal matrix including several times after the current time is obtained, which is used as the predicted temperature spatiotemporal matrix for the current time.

[0063] It should be noted that cable temperature changes have inertia, requiring a comprehensive assessment combining model predictions and real-time measurements. Predictive algorithms can provide more accurate temperature state estimates, enabling predictive control. In fire-risk scenarios, abnormal temperature trends can be identified in advance, and cooling can be initiated to prevent cable damage and fire spread due to temperature runaway. In normal operation scenarios, by optimizing cooling intensity, cooling resource consumption can be effectively reduced while ensuring the safe operation of the cable.

[0064] As a preferred embodiment, the specific method for obtaining the predicted temperature spatiotemporal matrix at the current moment is as follows: all points of interest at the current moment are used as seed points for region growing of the temperature spatiotemporal matrix of the cable at the current moment, and the temperature spatiotemporal matrix is ​​processed by region growing algorithm to obtain several temperature spatiotemporal regions. For each temperature spatiotemporal region, its temperature change sequence in the time dimension is extracted, and the temperature change sequence is trained and predicted using a Long Short-Term Memory (LSTM) network model to obtain the predicted temperature value of the region at future moments. The prediction results of all temperature spatiotemporal regions are spliced ​​according to spatial location to form an extended temperature spatiotemporal matrix containing several moments after the current moment, which is used as the predicted temperature spatiotemporal matrix at the current moment.

[0065] The training process of the LSTM model includes: using historical temperature spatiotemporal matrices as input data, optimizing model parameters through backpropagation algorithm, and minimizing the mean square error between predicted and actual temperatures; the prediction process includes: inputting the current temperature spatiotemporal matrix into the trained LSTM model, and outputting predicted temperature values ​​for the next N times, where N is a preset prediction duration.

[0066] It should be noted that, in the embodiments of the present invention, the predicted duration N is preset to a range of 5-10 minutes based on experience, and the specific value can be set to 6 minutes. In other embodiments, it can be adjusted according to the actual situation. The embodiments of the present invention do not impose specific limitations.

[0067] It should be noted that the region growing algorithm can effectively identify local regions with similar thermal behavior characteristics in the cable (such as hotspot clusters), avoiding the accuracy degradation problem caused by neglecting spatial heterogeneity in traditional global prediction methods. By independently modeling and predicting each temperature spatiotemporal region, the system can accurately capture the dynamic evolution trend of local hotspots (such as the rate of temperature rise and spatial diffusion direction), significantly improving the local adaptability of the prediction. Due to its strong modeling ability for long-term time series dependencies, the LSTM model can effectively handle the nonlinear and time-varying characteristics of cable temperature data (such as sudden environmental changes or load fluctuations), ensuring high reliability of the prediction results in both fire prevention and normal operation scenarios. The generation of the temperature spatiotemporal matrix after prediction provides a forward-looking basis for subsequent cooling control, enabling the system to proactively intervene before the temperature reaches the dangerous threshold, avoiding safety accidents caused by response lag.

[0068] Thus, the temperature-space-time matrix containing several moments after the current moment is obtained through the above method.

[0069] Step S003: Determine the cooling control coefficient by using the temperature change and distribution in the predicted temperature spatiotemporal matrix at the current moment.

[0070] It should be noted that the predicted temperature spatiotemporal matrix obtained by using the predictive analysis results effectively reflects the spatial and temporal trend of temperature changes in the environment where the cable is located. Therefore, the embodiments of the present invention can accurately understand the overall temperature change of the cable in the future by using the prediction results, thereby determining the corresponding control coefficient for the cooling system when cooling the cable in the future.

[0071] Specifically, based on the predicted temperature value of each spatial location in the predicted temperature spatiotemporal matrix at future times, the temperature risk index of each location is calculated; based on the distribution of temperature risk indices at all locations, the overall risk level of the cable is determined; and combined with the maximum flow capacity and response time characteristics of the cooling system, the cooling control coefficient is calculated.

[0072] First, calculate the temperature risk index to determine the overall risk level of the cable at future times.

[0073] As an optional embodiment, the specific method for obtaining the temperature risk index is as follows: the next moment after the current moment is recorded as the future moment, the temperature value of the future moment in the predicted temperature spatiotemporal matrix is ​​recorded as the predicted temperature value, for each position in the predicted temperature spatiotemporal matrix, the difference between the predicted temperature value of the position at the future moment and the temperature threshold, and the temperature change rate over time at the position are obtained; the product of the difference and the temperature change rate is used as the temperature risk index of the position.

[0074] As an optional embodiment, the specific method for obtaining the overall risk level is as follows: sort the temperature risk index of all locations, and take the average temperature risk index of the top C high-risk locations as the overall risk level index, where C is a preset first parameter.

[0075] It should be noted that the first parameter C is preset to 10% based on experience, and can be adjusted according to the actual situation. This embodiment of the invention does not impose specific limitations.

[0076] Then, the overall risk level is used as the input to the proportional-integral (PI) control algorithm, the output of the proportional-integral (PI) control algorithm is normalized, and the normalization result is used as the cooling control coefficient.

[0077] It should be noted that the temperature risk index comprehensively quantifies the degree and trend of temperature exceeding the standard, and can more comprehensively reflect local thermal risks, such as the tendency for thermoelectric stress concentration to occur in areas of sudden temperature changes. The determination of the overall risk level avoids over-regulation caused by misjudgment of a single location, ensuring that cooling resources are preferentially allocated to high-risk areas. The design of the cooling regulation coefficient realizes risk-driven adaptive adjustment, reducing the cooling intensity to save resources in low-risk situations and increasing the cooling intensity to ensure safety in high-risk situations, thereby achieving the optimal balance between cooling effect and resource efficiency. In addition, in this embodiment of the invention, the cooling regulation coefficient represents the adjustment ratio of the cooling medium flow rate relative to the reference flow rate. In order to facilitate the adjustment of the cooling medium flow rate, its value range is [0,1] when normalized in this invention, thereby providing a precise control basis for step S004 by directly associating this coefficient with the cooling system actuator.

[0078] Thus, the cooling regulation coefficient is obtained through the above method.

[0079] Step S004: Temperature regulation of the cable is performed based on the cooling regulation coefficient.

[0080] It should be noted that in order to cool the cable, the cooling control coefficient needs to be signal modulated, which can drive the stepper motor in the control system. The stepper motor can then adjust the valve core opening of the flow valve, thereby controlling the flow rate of the cooling medium.

[0081] Specifically, the cooling control coefficient is converted into a control command for the cooling system; the flow rate of the cooling medium is controlled by adjusting the opening of the cooling medium flow valve; the cable temperature data after control is collected in real time and compared with the predicted temperature spatiotemporal matrix; if the temperature deviation exceeds the preset tolerance threshold, the cooling control coefficient is dynamically corrected to form a closed-loop feedback control.

[0082] First, a duty cycle signal is generated based on the cooling regulation coefficient and combined with pulse width modulation (PWM) technology.

[0083] As an optional embodiment, the specific formula for calculating the duty cycle is: D = γ × Dmax; where Dmax is the preset maximum duty cycle and γ represents the cooling control coefficient.

[0084] It should be noted that, in the embodiments of the present invention, the maximum duty cycle is preset to a range of 80%-100% based on experience, which corresponds to the maximum flow rate of the cooling system. The specific value can be 95%. In other embodiments, it can be adjusted according to the actual situation. The embodiments of the present invention do not impose specific limitations.

[0085] Then, the stepper motor of the flow valve is driven by the duty cycle signal to adjust the valve core opening; at the same time, the cooling medium circulation pump is started to make the cooling medium flow in the pipes of the cooling system to dissipate heat from the cable.

[0086] It should be noted that PWM technology enables precise adjustment of the flow valve, avoiding system vibration or energy waste caused by sudden flow changes. The directional flow of the cooling medium targets high-risk areas for cooling, significantly improving resource utilization efficiency. This step represents a shift from passive response to proactive prevention, effectively reducing cooling energy consumption while ensuring the safe operation of the cable.

[0087] The above steps complete the intelligent cooling and temperature reduction process for the cable.

[0088] In other embodiments of the present invention, an intelligent cable with automatic sensing and monitoring is also provided, including a cable body, a memory, a processor, and a computer program stored in the memory and executable on the processor. The cable body contains an intelligent optical fiber, a cooling system, and a control system. The intelligent optical fiber is used to collect temperature data. When the processor executes the computer program, it implements the steps of any one of the automatic sensing and monitoring intelligent cable control methods described above, and inputs the calculation results into the control system, thereby controlling the cooling system through the control system.

[0089] The memory may be volatile or non-volatile, or may include both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which serves as an external cache. Many forms of RAM are available by way of example, but not limitation. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Sync Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM).

[0090] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting Advanced Reduced Instruction Set Machines (ARM) architecture.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart cable control method with automatic sensing and monitoring, characterized in that, The method includes the following steps: Collect temperature data of the cable, which includes the temperature values ​​of the cable at different locations and at different times; Based on the temperature values ​​at various locations and times in the cable's temperature data, the temperature data is processed in time and space to construct the cable's temperature spatiotemporal matrix; based on the cable's temperature spatiotemporal matrix, a temperature spatiotemporal matrix including several times after the current time is obtained, which is used as the predicted temperature spatiotemporal matrix for the current time. The cooling regulation coefficient is determined by using the temperature change and distribution in the predicted temperature spatiotemporal matrix at the current moment; Temperature control of the cable is based on the cooling regulation coefficient; The temperature data at various locations and times based on the cable's temperature data is processed in both time and space to construct a spatiotemporal temperature matrix for the cable. Specific methods include: For any given moment, obtain the sequence of temperature values ​​corresponding to all consecutive positions of the cable at that moment, denoted as the temperature space sequence of the cable at that moment. Based on the temperature value level in the temperature space sequence at the current moment, determine several points of interest in the temperature space sequence at the current moment. Use the points of interest to determine the time length, obtain the temperature space sequence within the range corresponding to the current moment and the time length before the current moment, and arrange the temperature space sequences at all moments within the range corresponding to the time length in chronological order from top to bottom to form a corresponding matrix, denoted as the temperature spatiotemporal matrix at the current moment. The specific method for obtaining the predicted temperature spatiotemporal matrix at the current moment is as follows: All points of interest at the current moment are used as seed points for region growing of the temperature-space-time matrix of the cable at the current moment. The temperature-space-time matrix is ​​grown using a region growing algorithm to obtain several temperature-space-time regions. For each temperature-space-time region, its temperature change sequence in the time dimension is extracted. The temperature change sequence is trained and predicted using a long short-term memory network model to obtain the predicted temperature value of the region at future moments. The prediction results of all temperature-space-time regions are spliced ​​according to their spatial location to form an extended temperature-space-time matrix that includes several moments after the current moment, which is used as the predicted temperature-space-time matrix for the current moment.

2. The intelligent cable control method for automatic sensing and monitoring according to claim 1, characterized in that, The specific method for obtaining several points of interest in the temperature spatial sequence at the current moment is as follows: A preset temperature threshold is used to identify elements in the temperature space sequence whose temperature values ​​are greater than the threshold and are local maxima, as points of interest in the temperature space sequence at the current moment.

3. The intelligent cable control method for automatic sensing and monitoring according to claim 1, characterized in that, The specific methods for determining the time length using points of interest are as follows: Given a preset initial time length and time step, for any point of interest in the temperature spatial sequence at the current moment, calculate the temperature spatial ratio of the point of interest at the current moment based on the changes in the corresponding peak, and use the DTW algorithm to obtain the current moment. The focus of attention in the temperature spatial sequence is at time Elements with matching relationships in the temperature spatial sequence are the focus of attention at time [time]. The following are the matching focus points; further obtain the time. The matching focus is on the moment Elements in the lower temperature space sequence that have a matching relationship are considered as the focus at time [time value missing]. The matching focus points are obtained by analogy, and the matching focus points corresponding to the focus points at each time point before the current time point are obtained. The temperature space ratio of the matching focus points at the corresponding time point is obtained by using the temperature space ratio acquisition method. Based on the changes in the temperature space ratios corresponding to the focus points and the matching focus points, the initial time length is iteratively increased by the time step based on the initial time length to obtain the time length at the current time point.

4. The intelligent cable control method for automatic sensing and monitoring according to claim 3, characterized in that, The specific method for calculating the temperature spatial ratio of the point of interest at the current moment based on the change of the corresponding peak is as follows: Obtain the troughs on both sides of the peak corresponding to the point of interest in the temperature spatial sequence at the current moment, and denote them as the first trough and the second trough, respectively. The distance between the first trough and the second trough is taken as the temperature spatial range value of the point of interest. Obtain the cumulative difference between the temperature value and the temperature threshold at all positions of the cable between the first trough and the second trough at the current moment, and denote it as the local temperature parameter of the point of interest at the current moment. Denote the difference between the point of interest and the temperature threshold as the temperature exceedance value of the point of interest. Calculate the temperature spatial ratio of the point of interest at the current moment based on the temperature spatial range value, the temperature exceedance value, and the local temperature parameter. The temperature spatial ratio is positively correlated with the local temperature parameter, while the temperature spatial range value and the temperature exceedance value are both negatively correlated with the temperature spatial ratio.

5. The intelligent cable control method for automatic sensing and monitoring according to claim 3, characterized in that, The method for iteratively increasing the initial time length based on the time step, according to the changes in the temperature space ratio corresponding to the points of interest and the matching points of interest, to obtain the time length at the current moment, includes the following specific methods: For any point of interest in the temperature spatial sequence at the current moment, obtain the temperature spatial ratios corresponding to the point of interest at the current moment and the corresponding matching points of interest at all moments within the range of the initial time length, and map them to a two-dimensional rectangular coordinate system. The horizontal axis of the two-dimensional rectangular coordinate system is time, and the vertical axis is temperature spatial ratio. Obtain the absolute value of the slope of the matching point of interest at the minimum moment in the two-dimensional rectangular coordinate system. Normalize the product of the absolute value of the slope and the temperature spatial ratio of the corresponding matching point of interest, and record it as the temperature spatiotemporal coefficient of the matching point of interest at the corresponding moment. When the temperature spatiotemporal coefficient is greater than the preset spatiotemporal coefficient threshold, iteratively increase the initial time length based on the time step until the temperature spatiotemporal coefficient of the matching point of interest at the minimum moment in the two-dimensional rectangular coordinate system is less than or equal to the spatiotemporal coefficient threshold. Stop iteratively increasing the initial time length to obtain the time length of the point of interest at the current moment. Take the maximum value of the time lengths of all points of interest at the current moment as the time length of the current moment.

6. The intelligent cable control method for automatic sensing and monitoring according to claim 2, characterized in that, The method for determining the cooling control coefficient by utilizing the temperature changes and distribution in the predicted temperature spatiotemporal matrix at the current moment includes the following specific methods: The next moment after the current moment is recorded as the future moment, and the temperature value of the future moment in the predicted temperature spatiotemporal matrix is ​​recorded as the predicted temperature value. For each position in the predicted temperature spatiotemporal matrix, the difference between the predicted temperature value of the position at the future moment and the temperature threshold, as well as the rate of temperature change over time at the position are obtained. The product of the difference and the rate of temperature change is used as the temperature risk index for the location. The temperature risk index of all locations was ranked, and the top-ranked locations were selected. The average temperature risk index of each high-risk location is used as the overall risk level indicator, where C is the preset first parameter; The overall risk level is used as the input to the proportional-integral control algorithm. The output of the proportional-integral control algorithm is normalized, and the normalization result is used as the cooling control coefficient.

7. The intelligent cable control method for automatic sensing and monitoring according to claim 1, characterized in that, The specific method for temperature control of the cable based on the cooling regulation coefficient is as follows: A duty cycle signal is generated based on the cooling control coefficient and pulse width modulation technology; the stepper motor of the flow valve is driven by the duty cycle signal to adjust the valve core opening; at the same time, the cooling medium circulation pump is started to make the cooling medium flow in the pipes of the cooling system to dissipate heat from the cable.

8. An intelligent cable for automatic sensing and monitoring, comprising a cable body, a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The cable body contains an intelligent optical fiber, a cooling system, and a control system. The intelligent optical fiber is used to collect temperature data. When the processor executes the computer program, it implements the steps of the intelligent cable control method with automatic sensing and monitoring as described in any one of claims 1 to 7, and inputs the calculation results into the control system, which then controls the cooling system.

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