Cable ampacity dynamic optimization method and system combined with arrangement parameters of calandria

Abstract

This invention discloses a method and system for dynamically optimizing cable current carrying capacity by combining duct layout parameters, relating to the field of cable heat conduction and heat dissipation technology. The method includes: constructing a dynamic identification module for a pre-managed duct area, based on a digital thermal path and a thermal-electric equivalent network; using a fiber optic temperature measurement array deployed within the pre-managed duct area to perform real-time temperature acquisition, driving the dynamic identification module embedded in the edge port to update the digital thermal path, and determining the dynamic safe current carrying capacity through optimization and inverse transformation under thermal-electric analogy conversion; and connecting the dynamic identification module to the power grid dispatching system to perform optimized allocation management based on the dynamic safe current carrying capacity. This invention solves the technical problems of fixed cable current carrying capacity in traditional duct layouts, which cannot adapt to dynamic environments and heat dissipation changes, achieving the technical effect of dynamically and accurately determining cable current carrying capacity in accordance with actual operating conditions, ensuring operational safety, and fully tapping the current carrying potential.

Description

Technical Field

[0001] This invention relates to the field of cable heat conduction and heat dissipation technology, and in particular to a method and system for dynamic optimization of cable current carrying capacity by combining duct layout parameters. Background Technology

[0002] Conduit laying is a common installation method for high-voltage cables. Their current-carrying capacity directly affects the safe and stable operation of the power grid and power supply efficiency; therefore, accurately determining the current-carrying capacity is crucial for power system optimization. In existing technologies, cable current-carrying capacity calculations are mostly based on fixed design parameters such as conduit structure, ambient temperature, and material properties. While this method has played a role in stable operating scenarios, it reveals significant limitations when applied to actual conduit installation scenarios as grid loads increase and operational requirements rise. Conduit layouts vary physically, and soil thermal properties and environmental loads are dynamically changing. Traditional fixed calculation methods cannot adapt to these variables, resulting in current-carrying capacity data that is either overly conservative and redundant or exceeds safety thresholds, making it difficult to meet the needs of accurate cable current-carrying capacity optimization and efficient power grid dispatch. Summary of the Invention

[0003] This application provides a method and system for dynamic optimization of cable current carrying capacity by combining duct layout parameters, which solves the technical problems of fixed cable current carrying capacity in traditional ducts and inability to adapt to dynamic environment and heat dissipation changes.

[0004] The first aspect of this application provides a method for dynamically optimizing cable current carrying capacity by combining duct layout parameters. The method includes: constructing a dynamic identification module for a pre-managed duct range, wherein the dynamic identification module is constructed based on a digital thermal circuit and a thermal-electric equivalent network; using a fiber optic temperature measurement array deployed within the pre-managed duct range to perform real-time temperature acquisition, driving the dynamic identification module embedded in the edge port to perform digital thermal circuit updates, and determining the dynamic safe current carrying capacity through optimization solution and inverse conversion under thermal-electric analogy conversion; and connecting the dynamic identification module to the power grid dispatching system to perform optimized allocation management based on the dynamic safe current carrying capacity.

[0005] A second aspect of this application provides a dynamic optimization system for cable current carrying capacity based on duct layout parameters. The system includes: a dynamic identification module construction module for constructing a dynamic identification module for a pre-managed duct area, wherein the dynamic identification module is constructed based on a digital thermal path and a thermal-electric equivalent network; a dynamic safe current carrying capacity acquisition module for performing real-time temperature acquisition using a fiber optic temperature measurement array deployed within the pre-managed duct area, driving the dynamic identification module embedded in the edge-side port to update the digital thermal path, and determining the dynamic safe current carrying capacity through optimization solution and inverse conversion under thermal-electric analogy conversion; and an optimized allocation management execution module for performing optimized allocation management based on the dynamic safe current carrying capacity by connecting the dynamic identification module to the power grid dispatching system.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] This application constructs a specific identification module for duct laying scenarios, uses deployed temperature measurement equipment to collect real-time temperature data, drives the module to update relevant thermal models, and determines the current carrying capacity through equivalent transformation and iterative optimization. Combined with the power grid dispatch system, load allocation is adjusted, thereby dynamically adapting to changes in duct laying conditions. This makes the determination of cable current carrying capacity more in line with actual operating needs, ensuring safety while fully tapping potential, and adapting to the power grid's optimized dispatch requirements. It achieves the technical effect of dynamically and accurately determining cable current carrying capacity in line with actual operating conditions, ensuring operational safety, and fully tapping current carrying capacity. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0009] Figure 1 This is a flowchart illustrating the dynamic optimization method for cable current carrying capacity based on duct layout parameters provided in this application embodiment.

[0010] Figure 2 This is a schematic diagram of the structure of the dynamic optimization system for cable current carrying capacity based on duct layout parameters provided in this application embodiment.

[0011] Figure labeling: Dynamic identification module construction module 1, dynamic security traffic acquisition module 2, and optimized allocation management execution module 3. Detailed Implementation

[0012] This application provides a method and system for dynamic optimization of cable current carrying capacity by combining duct layout parameters, which solves the technical problems of fixed cable current carrying capacity in traditional ducts and inability to adapt to dynamic environment and heat dissipation changes.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0015] Example 1, as Figure 1 As shown, a dynamic optimization method for cable current carrying capacity based on duct layout parameters is described, wherein the method includes:

[0016] For the pre-managed pipeline area, a dynamic identification module is constructed, which is based on the digital thermal circuit and the thermal-electric equivalent network.

[0017] In this embodiment, the pre-managed cable duct range is a pre-defined, controlled area within the power grid that includes the cable duct to be monitored and optimized for current carrying capacity, as well as its surrounding soil, filling materials, nearby heat sources, and other associated environmental factors. The digital thermal path is a digital thermal simulation model reflecting the heat transfer nodes, paths, and initial thermal resistance of this area. The thermal-electric equivalent network, through thermal-electric analogy rules, maps the heat generation power, temperature, and other thermal quantities of the cable duct cooling system to electrical quantities, thus forming an equivalent model suitable for circuit analysis.

[0018] Specifically, within the pre-managed pipe layout area, the construction process of the digital thermal path is as follows: First, the physical layout of the pipe arrangement, the spatial distribution of the filling material, the thermal properties of the soil layers, and the nearby heat sources are parameterized to form pipe arrangement parameters. Next, the material properties of the filling material, which include at least thermal conductivity, are obtained. Finally, the thermal nodes and paths are determined by combining the above pipe arrangement parameters and material properties, thus forming a digital thermal path marked with initial thermal resistance data.

[0019] Next, a dynamic identification module is constructed. Specifically, the cable conduit cooling system is first transformed into a thermo-electric network through a thermo-electric analogy. The cable's heating power corresponds to a controlled current source proportional to the square of the operating current, while temperature, heat flow, thermal resistance, and thermal capacity are analogous to voltage, current, resistance, and capacitance, respectively. Then, the constructed digital thermal circuit is combined with this thermo-electric equivalent network to complete the construction of the dynamic identification module.

[0020] A fiber optic temperature measurement array deployed within a pre-managed pipe network is used to perform real-time temperature acquisition, drive the dynamic identification module embedded in the edge port, update the digital thermal path, and determine the dynamic safe current carrying capacity through optimization and inverse conversion under thermal-electric analogy conversion.

[0021] Optionally, the temperature of the cable duct is first collected using a distributed fiber optic temperature measurement array to obtain a continuous temperature spatial distribution. Then, based on this continuous temperature spatial distribution, a real-time update or short-term prediction operation is performed on the digital thermal circuit to complete the update. Finally, the updated digital thermal circuit is mapped to a thermal-electric equivalent network, and a dynamic safe current-carrying capacity is output by performing circuit analysis under the safe operating domain solution.

[0022] By connecting the dynamic identification module to the power grid dispatching system, optimized allocation management based on the dynamic safe carrying capacity is performed.

[0023] In this embodiment, the power grid dispatching system is a core control system that coordinates power grid operation, receives data such as dynamic safe carrying capacity and synchronizes with power grid topology information, performs load optimization allocation based on dynamic thermal stability limits, and ensures the safe and efficient operation of the power grid.

[0024] In one embodiment of this application, a standardized communication protocol is first used to connect the dynamic safe carrying capacity to the power grid dispatching system and synchronize it with the power grid topology information. Then, this dynamic safe carrying capacity is used as the dynamic thermal stability limit for the corresponding power grid line to perform load optimization allocation management.

[0025] Furthermore, the method provided in this application embodiment includes:

[0026] The physical layout of the pipe arrangement, the spatial distribution of the filling material, the thermal properties of the soil layers, and the nearby heat sources are parameterized as the pipe arrangement parameters. The material properties of the filling material are obtained, wherein the material properties include at least thermal conductivity, which includes the trend curve of thermal conductivity with temperature and pressure and flow-solidification characteristics. Based on the pipe arrangement parameters and the thermal conductivity, thermal nodes and paths are determined to form the digital thermal path, wherein the digital thermal path is identified with initial thermal resistance data.

[0027] Specifically, before constructing the dynamic identification module, it is necessary to parameterize the key parameters related to the pipe layout. For the physical layout of the pipes, on-site surveys combined with CAD modeling are used. Data such as the arrangement of pipes, pipe diameter, spacing between pipes, and burial depth are measured using a laser rangefinder, and these physical characteristics are converted into quantifiable coordinate values ​​and dimensional parameters. For the spatial distribution of the filling material, ultrasonic detection technology is used to scan the gap area between the pipes and the cable body to determine the filling range and thickness distribution of the filling material. Then, the distribution is converted into quantitative data through grid division. For the thermal properties of the soil layers, ground-penetrating radar is used to detect the layered structure of the soil, delineate the boundaries of different soil layers, and then soil samples are collected from each soil layer. The thermal conductivity, specific heat capacity, and other thermal property parameters of each sample are measured using the hot wire method to form a soil layer thermal property dataset. For nearby heat sources, infrared thermometers are used to comprehensively detect the area around the pipes, identify heat pipes, other operating cables, and other heat sources, and measure data such as the distance between the heat source and the pipes and the stable heating power of the heat source. This information is then incorporated into the pipe layout parameter system.

[0028] Subsequently, the material properties of the filler were obtained, with a focus on thermal conductivity. For the trend curve of thermal conductivity as a function of temperature and pressure, a high and low temperature humidity test chamber was used to simulate different temperature and pressure environments. The temperature range covered the range of -20℃ to 80℃ that cable operation might involve, and the pressure range was set from standard atmospheric pressure to 0.3MPa. Under each temperature and pressure combination, the thermal conductivity of the filler was measured using a flat-plate thermal conductivity meter. All measurement data were fitted using the least squares method to obtain the trend curve of thermal conductivity as a function of temperature and pressure. For the flow-curing characteristics, a rotational rheometer was used to test the filler. Within the temperature range from room temperature to the completion of curing, the viscosity value of the material was measured every 5 minutes, recording the viscosity change pattern during the transition from a flowable state to a solid state. Simultaneously, by periodically sampling and observing the morphological changes of the material, key characteristic parameters such as flow initiation temperature, curing initiation time, and complete curing time were determined.

[0029] After completing the parameterization of the duct layout and obtaining material properties, the construction of the digital thermal path began. Based on the obtained duct layout parameters and the thermal conductivity of the filling material, a node division method was used to determine the thermal nodes: the center of the cable conductor, the outer surface of the cable insulation layer, the inner wall of the duct, the outer wall of the duct, the interface between the filling material and the soil, and the interfaces of each soil layer were designated as key nodes. Surfaces near heat sources were also designated as special nodes. Next, based on the basic laws of heat conduction, the heat transfer paths between each node were identified, clarifying all possible paths for heat to diffuse outward from the cable conductor through the insulation layer, filling material, duct, and soil, as well as the transfer of heat from nearby heat sources to the duct. Then, using existing thermal resistance calculation methods, combined with data such as the material's thermal conductivity and the dimensions of each structural segment, the initial thermal resistance of each heat transfer path was calculated. Finally, according to the construction rules of the thermal path network, all thermal nodes and paths were integrated into a network structure to form a digital thermal path, and the initial thermal resistance data corresponding to each path was clearly marked in the thermal path.

[0030] By combining on-site surveys, experimental tests, and modeling analysis, the system completed the quantification of pipe layout parameters, acquisition of filling material properties, and construction of digital thermal circuits, providing an accurate and realistic thermal model foundation for the subsequent construction of the dynamic identification module.

[0031] Furthermore, the method provided in this application embodiment includes:

[0032] Through thermal-electric analogy conversion, the cable duct heat dissipation system is likened to a thermal-electric network, where the cable heating power is likened to a controlled current source proportional to the square of the operating current, temperature is likened to voltage, heat flow to current, thermal resistance to resistor, and thermal capacity to capacitor; a dynamic identification module is constructed based on the digital thermal circuit and the thermal-electric equivalent network.

[0033] Optionally, a thermo-electric analogy conversion is first performed to transform the cable duct cooling system into a thermo-electric network. For the cable's heating power, a power meter is used to collect real-time data on the actual heating power during cable operation, while a current sensor records the corresponding operating current value. A data fitting tool is then used to establish a proportional relationship between the heating power and the square of the operating current. Based on this, the parameter setting rules for the controlled current source in the thermo-electric network are determined, ensuring that the output of the controlled current source accurately reflects the actual heating situation of the cable.

[0034] Based on the analogy of temperature and voltage, typical locations such as key heating areas of cables, inner walls of ducts, and soil surfaces were selected. Calibrated temperature sensors were used to measure the real-time temperature at each location, while a voltage detector was used to record the reference voltage values ​​of the corresponding nodes in the thermo-electric network. Through multiple experiments, a linear conversion relationship between temperature and voltage was established to ensure that temperature changes can be accurately mapped to voltage fluctuations.

[0035] To make an analogy between heat flow and current, a heat flow meter is used to measure the heat transfer rate at different locations, and an ammeter is used to record the current value of the corresponding branch of the heat-electric network. By comparing the data, the conversion coefficient between heat flow and current is determined, so as to achieve accurate conversion from heat flow magnitude to current value.

[0036] In the analogy between thermal resistance and electrical resistance, thermal resistance testing equipment is used to measure the thermal resistance data of various parts such as pipes, filling materials, and soil. Then, resistance values ​​of different specifications of resistors are selected through a resistance tester to establish a one-to-one correspondence between thermal resistance and electrical resistance, so that changes in thermal resistance can be reflected through resistance parameters.

[0037] Based on the analogy between heat capacity and capacitance, a specific heat capacity meter is used to acquire the heat capacity data of each material. Combined with the measurement results of a capacitance meter, the conversion relationship between heat capacity and capacitance is determined through experimental calibration, and the corresponding settings of each parameter of the thermo-electric network are completed. Finally, all the above analogy relationships are integrated to construct a thermo-electric analogy rule library and pre-stored in the dynamic identification module for easy subsequent use.

[0038] Next, after completing the construction of the thermo-electric equivalent network, a dynamic identification module is built by combining the digital thermal circuit with this network. First, professional network modeling software is used to import the thermal nodes, heat transfer paths, and initial thermal resistance data of the constructed digital thermal circuit into the software. Then, the controlled current sources, voltages, currents, resistances, and capacitances of the thermo-electric equivalent network are associated with the corresponding thermal nodes and paths of the digital thermal circuit. Through the software's interface development function, a data synchronization channel is set up so that when parameters such as thermal resistance and thermal capacitance in the digital thermal circuit change, the corresponding resistance and capacitance parameters in the thermo-electric equivalent network can be updated synchronously in real time. Simultaneously, a data processing unit is embedded in the module. This unit uses signal processing algorithms to receive dynamic data from the digital thermal circuit and analytical data from the thermo-electric equivalent network, enabling collaborative processing of both data. Ultimately, this forms a dynamic identification module based on the digital thermal circuit and with the thermo-electric equivalent network as the core of the analysis.

[0039] Through measurement, fitting, modeling, and interface development, the thermal-electric analogy conversion and dynamic identification module was constructed, transforming the complex thermal analysis of cable duct heat dissipation systems into an easy-to-operate circuit analysis, providing efficient and reliable technical support for the subsequent optimization of dynamic safe current carrying capacity.

[0040] Furthermore, the method provided in this application embodiment includes:

[0041] Temperature data of cable ducts is collected using a distributed fiber optic temperature measurement array to determine the continuous temperature spatial distribution. Based on this distribution, the digital thermal path is updated, whereby real-time updates or short-term predictions are performed. The updated digital thermal path is then mapped to the thermal-electric equivalent network, and circuit analysis is performed under the safe operating domain solution to output the dynamic safe current carrying capacity.

[0042] In this embodiment, the safe operating domain is the allowable range of circuit parameters in the thermo-electric equivalent network, bounded by the voltage safety threshold corresponding to the cable's safe temperature, ensuring that the cable does not exceed its temperature limit and operates stably. The edge-side port is a data processing and instruction execution terminal used to receive temperature measurement data and drive digital thermal circuit updates.

[0043] Specifically, the first step is to collect temperature data from the cable ducts to determine the continuous spatial temperature distribution. Within the pre-managed duct area, a distributed fiber optic temperature measurement array is deployed. Fiber optic sensors are laid close to the outer wall of the duct along its length, with increased fiber optic density at key sections such as the middle section and near heat sources to ensure comprehensive temperature coverage. The fiber optic temperature measurement system is then activated, employing Raman scattering thermometry at a frequency of once per minute to continuously collect temperature data from various locations within the cable duct. After data collection, the discrete temperature data is processed using data processing software. Cubic spline interpolation is used to fill in the data gaps, generating a continuous spatial temperature distribution map covering the entire pre-managed duct area, clearly showing the temperature differences and distribution patterns in different regions.

[0044] Next, based on the continuous temperature spatial distribution, the dynamic identification module embedded in the edge port is driven to update the digital thermal path. The continuous temperature spatial distribution data collected by the distributed fiber optic temperature measurement array is transmitted in real-time to the dynamic identification module embedded in the edge port via industrial Ethernet communication. After receiving the data, the module initiates its built-in data verification program, using the 3σ criterion to remove outliers and filter out interference data in the temperature measurement data, ensuring the accuracy of the input data. For scenarios requiring real-time updates, the module directly extracts the verified temperature data, replaces the original temperature parameters of the corresponding nodes in the digital thermal path, and recalculates the thermal resistance of each heat transfer path based on the linear correlation formula between thermal resistance and temperature, completing the real-time update of the digital thermal path.

[0045] For scenarios requiring short-term prediction, the dynamic identification module first filters historical real-time temperature measurement data for the past 7 days within the pre-managed pipe area, integrates it with the current continuous temperature spatial distribution data to form a complete dataset, uses the 3σ criterion to remove outliers, and fills in missing values ​​using linear interpolation to complete preprocessing. The preprocessed dataset is then divided into training and testing sets in an 8:2 ratio. Based on the training set data, the optimal parameters (p, d, q) of the ARIMA model are determined using the AIC criterion. After training the model using the training set, the model's prediction accuracy is verified using the testing set, ensuring the prediction error is controlled within ±0.5℃. Next, the trained ARIMA model is used to predict the temperature in segments for the next hour, outputting a predicted temperature value every 10 minutes to generate a continuous temperature change trend curve. Finally, based on the predicted temperature at each time point in the trend curve, and combined with the linear correlation formula between thermal resistance and temperature, the change in thermal resistance of the corresponding heat transfer path is calculated one by one. The thermal parameters of each relevant node in the digital thermal circuit are updated synchronously, allowing the digital thermal circuit to adapt to the thermal state of future operating conditions in advance, providing forward-looking data support for the accurate solution of subsequent dynamic safe current carrying capacity.

[0046] Next, the updated digital thermal path is mapped to a thermal-electric equivalent network. The pre-stored thermal-electric analogy rule library in the dynamic identification module is invoked, containing the thermal-electric analogy conversion relationships established in the preceding steps. Then, the thermal parameters in the updated digital thermal path are converted one by one into the electrical parameters in the thermal-electric equivalent network. Specifically, the real-time temperature of each node in the digital thermal path is mapped to the voltage parameters of the corresponding node in the thermal-electric equivalent network according to a preset conversion formula; the real-time thermal resistance of each heat transfer path is mapped to the resistance parameters of the corresponding branch; the heating power of the cable is mapped to the parameters of a controlled current source proportional to the square of the operating current; and the heat capacity is mapped to the corresponding capacitance parameters. Through parameter-by-parameter mapping and network structure matching, the complete mapping of the updated digital thermal path to the thermal-electric equivalent network is completed, ensuring that the thermal-electric equivalent network accurately reflects the current thermal state of the cable duct.

[0047] Finally, circuit analysis is performed under the safe operating domain solution. For the thermo-electric equivalent network, voltage safety thresholds are set for nodes at critical locations of the corresponding cable. Then, the core variable is defined as the output value of the controlled current source. Based on the circuit solver, with the optimization objective of maximizing the output of the core variable, target optimization is performed by combining the voltage safety thresholds and the core variable to determine the dynamic safe current carrying capacity. This step will be explained in detail later.

[0048] By deploying a coherent process of fiber optic temperature measurement, data transmission processing, thermal path updating, and network mapping, the real-time perception and digital transformation of the thermal state of cable ducts were realized, providing an accurate and dynamic model foundation for subsequent circuit analysis under the safe operation domain solution.

[0049] Furthermore, the method provided in this application embodiment includes:

[0050] For the aforementioned thermo-electric equivalent network, a voltage safety threshold is set for nodes corresponding to critical locations in the cable. This voltage safety threshold is obtained by converting the real-time temperature upper limit of the digital thermal circuit through thermo-electric analogy physical relationships. A core variable is defined, where the core variable is the output value of the controlled current source. Based on the voltage safety threshold and the core variable, a target optimization process based on a circuit solver is performed to determine the dynamic safe current carrying capacity, where maximizing the output of the core variable is the optimization objective.

[0051] Specifically, the first step is to set voltage safety thresholds for nodes at critical locations in the thermal-electric equivalent network corresponding to the cable. Based on cable operation safety specifications and design standards, critical locations are identified, selecting nodes prone to temperature exceeding limits, such as the center of the cable conductor, the outer surface of the insulation layer, the inner wall of the duct, and the outer wall of the duct near nearby heat sources. The temperature status of these nodes directly determines whether the cable can operate safely. By querying the maximum allowable operating temperature data of the cable material stored in the digital thermal circuit, combined with the ambient temperature correction value under real-time operating conditions, the real-time temperature upper limit for each critical location is determined. For example, the maximum allowable conductor temperature for cross-linked polyethylene insulated cables is typically set at 90℃, serving as a temperature safety benchmark. A linear analogy conversion method is used to establish the thermo-electric analogy physical relationship. First, matching data of different temperature values ​​and corresponding electrical voltage values ​​are collected through multiple experiments. The least squares method is used to fit the temperature-voltage conversion formula. Then, the real-time temperature upper limit for each critical location is substituted into this formula to calculate the corresponding voltage value, which is set as the voltage safety threshold for the corresponding node in the thermal-electric equivalent network. This ensures that the threshold accurately maps to the temperature safety upper limit, achieving indirect control of cable temperature.

[0052] Subsequently, the core variables were defined and their relationships established. Considering the difficulty of directly solving the nonlinear heat conduction problem in complex pipe systems, a heat-electric analogy was used to map it to a circuit network problem. This problem was then solved using principles such as power flow analysis in the power field. Therefore, the core variable was defined as the output value of the controlled current source in the heat-electric equivalent network. Current sensors were used to collect different operating current values ​​of the cable in real time, while a power meter simultaneously recorded the corresponding cable heating power. Multiple sets of operating current and heating power data were imported into data processing software. The least squares method was used to fit and establish a proportional relationship between the heating power and the square of the operating current, determining the proportionality coefficient. Based on this proportional relationship, the output value of the controlled current source was directly correlated with the cable heating power, ensuring that the output intensity of the controlled current source accurately reflects the magnitude of the cable heating power. This lays the foundation for subsequently maximizing the cable's allowable heating power indirectly by maximizing the intensity of the controlled current source, and thus inversely determining the operating current.

[0053] Finally, a target optimization process based on a circuit solver is performed. The circuit solver sets the first core variable value and solves for the first steady-state voltage distribution of all nodes in the thermo-electric equivalent network. The first steady-state voltage distribution is then evaluated to determine if it violates a voltage safety threshold. An optimization generator updates the search value of the first core variable as the second core variable value. The steady-state voltage distribution solution and voltage safety threshold judgment are then repeated for the second core variable value, iterating until convergence is met. The largest core variable value that satisfies the voltage safety threshold is selected, and the dynamic safe current carrying capacity is determined. This step will be explained in detail later.

[0054] By transforming the nonlinear heat conduction problem into a constrained circuit optimization problem, and by using thermoelectric analogy conversion, threshold setting, and variable definition methods, the transformation of cable temperature safety constraints into circuit voltage constraints and the correlation between core variables and heat generation power were realized. This provides clear and feasible optimization objectives and constraints for maximizing cable operating current and accurately solving dynamic safe current carrying capacity while ensuring that the temperature does not exceed the limit.

[0055] Furthermore, the method provided in this application embodiment includes:

[0056] According to the circuit solver, by setting the first core variable value, the first steady-state voltage distribution of all nodes in the thermal-electric equivalent network is solved; for the first steady-state voltage distribution, it is determined whether it violates the voltage safety threshold, and the search value of the first core variable value is updated by the optimization generator as the second core variable value; for the second core variable value, the steady-state voltage distribution is solved and the voltage safety threshold is judged, and the process is iterated until convergence is satisfied, the largest core variable value that satisfies the voltage safety threshold is selected, and the dynamic safe current carrying capacity is determined.

[0057] In this embodiment, the circuit solver is a dedicated software tool or device that has conventional solving algorithms such as the nodal voltage method, can calculate steady-state electrical parameters of the thermal-electric equivalent network, and provides data support for dynamic safety current carrying capacity optimization.

[0058] Specifically, firstly, based on the cable's rated heating power, historical operating data, and the parameter range of the thermo-electric equivalent network, the initial search interval for the core variables is determined. Typically, 50% of the controlled current source output value corresponding to the cable's rated heating power is taken as the initial value of the first core variable. A circuit solver, such as MATLAB's SimscapeElectrical module or PSpice software, is selected to import the parameters of the thermo-electric equivalent network, including resistance, capacitance, and controlled current source. The nodal voltage method is used as the steady-state solution method: a reference node in the thermo-electric equivalent network is selected, and KCL equations are written for each of the remaining nodes, forming a set of nodal voltage equations. This set of equations is then solved using the solver's built-in Gaussian elimination method to obtain the first steady-state voltage distribution data for all nodes in the thermo-electric equivalent network, and the voltage values ​​for each node are output in tabular form.

[0059] Next, threshold judgment and core variable value update are performed for the steady-state voltage distribution. The voltage value of each node in the first steady-state voltage distribution obtained by solution is compared with the preset voltage safety threshold one by one. If the voltage of all nodes is less than or equal to the corresponding threshold, it means that the current core variable value is within the safe operating range; if the voltage of any node exceeds the corresponding threshold, it means that the current core variable value exceeds the safety boundary. The optimization generator is enabled, and a binary search strategy is used to update the core variable value: first, the search lower limit of the core variable is set to 0, and the search upper limit is the output value of the controlled current source corresponding to the maximum allowable heat power of the cable; if the current core variable value is safe, the search lower limit is updated to this value; if it is not safe, the search upper limit is updated to this value. Then, the median value of the new search interval is calculated and used as the second core variable value to ensure that the search range is narrowed with each update and the optimization efficiency is improved.

[0060] Finally, the maximum core variable value that meets the requirements is selected through iterative convergence. The second core variable value is then substituted into the circuit solver, and the steps of solving the steady-state voltage distribution and judging the voltage safety threshold are repeated: if all the newly solved node voltages meet the voltage safety threshold requirements, the search lower limit is adjusted upwards using the bisection method; if not, the search upper limit is adjusted downwards, new core variable values ​​are generated, and the solution and judgment are repeated. The iterative convergence condition is set as the output value of the controlled current source corresponding to a difference of less than 0.01A between two consecutive core variable values, or the iteration is stopped when the number of iterations reaches 50. After the iteration is completed, the maximum value among all core variable values ​​that meet the voltage safety threshold is selected. This value is the optimal core variable value under the maximization objective, and the dynamic safe current carrying capacity is determined accordingly.

[0061] By combining circuit solving, threshold judgment, and bisection iterative search, the maximum core variable value is efficiently selected while ensuring that the voltage of all nodes in the thermal-electric equivalent network does not exceed the safety threshold. This provides a practical and optimized solution for accurately determining the dynamic safe current carrying capacity.

[0062] Furthermore, the method provided in this application embodiment includes:

[0063] Based on the thermoelectric analogy physical relationship and cable resistance parameters, the maximum core variable value is converted into the maximum allowable operating current of the cable conductor, which is used as the dynamic safe current carrying capacity.

[0064] In one embodiment, the key parameters required for the conversion are first obtained. A DC resistance tester is used to measure the DC resistance of the cable conductor. During measurement, the two probes of the tester are connected to both ends of the cable conductor respectively, ensuring good contact, and the measured resistance parameters are recorded. At the same time, the thermoelectric analogy physical relationship established when constructing the thermo-electric equivalent network in the previous steps is retrieved to clarify the correspondence between the core variable (the output value of the controlled current source) and the cable heating power. This correspondence has been determined by fitting previous experimental data and stored in the dynamic identification module.

[0065] Next, the core formula upon which the conversion is based is clarified. According to fundamental principles of electricity and the thermoelectric analogy, the relationship between the cable's heating power and the operating current satisfies... Where P is the cable's heating power, i.e., the maximum core variable value, I is the operating current of the cable conductor, and R is the resistance parameter of the cable conductor. Combining the thermoelectric analogy, the maximum core variable value corresponds to the cable's maximum allowable heating power under the current heat dissipation condition, so it can be calculated backwards based on this formula.

[0066] Then, specific conversion calculations are performed. The obtained maximum core variable value (maximum allowable heat output) and cable conductor resistance parameter are substituted into the above formula, and the operating current is obtained through basic algebraic operations. ,in This is the maximum allowable operating current of the cable conductor. After calculation, the result is verified by comparing it with the rated current range of the cable material to ensure that the calculation result is within a reasonable range and to eliminate outliers caused by parameter errors.

[0067] Finally, the dynamic safe current carrying capacity is determined. After verification, the calculated maximum allowable operating current of the cable conductor is used. It is directly used as dynamic safe carrying capacity, completing the conversion from the maximum core variable value to the actual engineering parameter, and providing accurate carrying capacity data support for the subsequent optimized allocation and management of the power grid dispatching system.

[0068] Furthermore, the method provided in this application embodiment includes:

[0069] Temperature data of the cable duct is continuously collected, and the dynamic safe current carrying capacity is updated in real time through real-time updates of the digital thermal circuit and equivalent analysis based on the thermal-electric equivalent network; wherein, the dynamic safe current carrying capacity is sent to the power grid dispatching system in real time.

[0070] Optionally, continuous temperature monitoring of the cable ducts is performed first. The distributed fiber optic temperature array deployed within the pre-managed duct area is kept running continuously. A fixed acquisition frequency is set using Raman scattering thermometry, typically acquiring full-range temperature data every 30 seconds to ensure timely capture of temperature changes. The acquired temperature data is transmitted in real-time to the edge port via industrial Ethernet, with CRC checksums used to ensure data integrity during transmission. After receiving the data, the edge port initiates a built-in real-time data processing program. It uses the 3σ criterion to quickly eliminate abnormal temperature values ​​caused by environmental interference, and then uses linear interpolation to fill in any missing data points, generating continuous and accurate real-time temperature spatial distribution data to provide reliable input for subsequent thermal path updates.

[0071] Next, the digital thermal circuit is updated in real time based on real-time temperature data. The dynamic identification module embedded in the edge port continuously receives the processed real-time temperature spatial distribution data and compares it node by node with the currently stored temperature parameters in the digital thermal circuit. For nodes with temperature changes exceeding 0.5℃, the module automatically extracts the new temperature data to replace the original parameters and recalculates the thermal resistance value of the associated heat transfer path of that node according to the linear correlation formula between thermal resistance and temperature, synchronously updating the corresponding initial thermal resistance data in the digital thermal circuit. The entire update process is triggered synchronously according to the acquisition frequency, ensuring that the digital thermal circuit always accurately reflects the current actual thermal state of the cable duct.

[0072] Subsequently, the dynamic safe current carrying capacity is updated in real time through equivalent analysis of the thermo-electric equivalent network. The updated digital thermal circuit is quickly mapped to the thermo-electric equivalent network according to preset thermo-electric analogy rules using a data mapping algorithm, updating the corresponding voltage, resistance, and other electrical parameters in the network. Reusing the circuit analysis process established in the preceding steps, the circuit solver is invoked to solve the safe operating domain of the updated thermo-electric equivalent network. Through steps such as setting initial values ​​for core variables, solving for steady-state voltage distribution, threshold judgment, and bisection iterative convergence, the maximum core variable value that meets the voltage safety threshold is quickly selected. Then, based on the thermo-electric analogy physical relationship and pre-measured and stored cable resistance parameters, the dynamic safe current carrying capacity is updated in real time through... Algebraic operations are used to inversely convert the maximum core variable value into the maximum allowable operating current of the cable conductor under the current operating conditions, thus completing the real-time update of the dynamic safe current carrying capacity.

[0073] Finally, the updated dynamic safe carrying capacity is sent to the power grid dispatching system in real time. The IEC 61850 communication protocol, standardized by the power industry, is used to establish a stable real-time data transmission channel between the edge port and the power grid dispatching system. Whenever the dynamic safe carrying capacity is updated, the system automatically triggers a data transmission command, packaging and transmitting the carrying capacity data along with the collection timestamp, pre-managed pipeline range identifier, and other related information to ensure the standardization and traceability of data transmission. After receiving the data, the power grid dispatching system automatically synchronizes and integrates it with existing power grid data, providing real-time data support for subsequent load optimization and allocation.

[0074] Through a coherent process of continuous temperature measurement and data acquisition, real-time thermal circuit updates, dynamic equivalent analysis, and standardized data transmission, the system achieves real-time updates of dynamic safe load capacity and synchronous sharing with the power grid dispatching system, providing accurate and timely decision-making basis for the optimal allocation of power grid load.

[0075] Furthermore, the method provided in this application embodiment includes:

[0076] A standardized communication protocol is adopted to connect the dynamic safe carrying capacity to the power grid dispatching system and synchronize it with the power grid topology information; the dynamic safe carrying capacity is used as the dynamic thermal stability limit of the corresponding power grid line to perform load optimization allocation management.

[0077] In one embodiment, the dynamic safe carrying capacity is first integrated with the power grid dispatching system, and topology information is synchronized. The IEC 61850 communication protocol, a standard used in the power industry, is selected. A compatible communication interface module is configured between the dynamic safe carrying capacity output terminal and the power grid dispatching system to complete protocol parameter matching and communication link establishment. The data access program is initiated, encapsulating the real-time updated dynamic safe carrying capacity data according to the protocol's specified format, including key information such as carrying capacity value, corresponding line identifier, and data generation timestamp. This data is continuously transmitted to the power grid dispatching system through the established communication link. Simultaneously, the power grid dispatching system's built-in topology information synchronization interface is invoked to obtain current power grid node connection relationships, line parameters, equipment configurations, and other topology data. A data association algorithm is used to bind the dynamic safe carrying capacity data one-to-one with the topology information of the corresponding power grid lines, ensuring that the dynamic carrying capacity data of each line accurately matches its topology location and operating parameters, achieving real-time synchronization of data and topology information.

[0078] Subsequently, load optimization allocation management based on dynamic thermal stability limits is implemented. The power grid dispatching system directly sets the synchronized dynamic safe carrying capacity as the dynamic thermal stability limit of the corresponding power grid line, replacing the original fixed thermal stability limit parameters. A linear programming load optimization algorithm is employed, aiming to maximize the overall operating efficiency of the power grid. The dynamic thermal stability limit of each line is used as a constraint, while also incorporating operational constraints such as total load demand, power output capacity, and line transmission losses to construct a load optimization allocation model. The model is calculated using the algorithm solver built into the power grid dispatching system to obtain the optimal load allocation scheme for each line, clarifying the maximum load capacity that each line can carry and the direction of load adjustment. Finally, based on the optimized allocation scheme, the power grid dispatching system issues load adjustment commands to each power supply terminal and load node, adjusting the load distribution of each line in real time to ensure that the operating load of all lines does not exceed the corresponding dynamic thermal stability limit, while achieving a balanced and efficient distribution of the power grid load.

[0079] In summary, the cable current carrying capacity dynamic optimization method combining duct layout parameters provided in this application has the following technical effects:

[0080] This application addresses the pre-managed cable ducting area by constructing a dynamic identification module containing a digital thermal path and a thermal-electric equivalent network. Data is collected in real time via a deployed fiber optic temperature measurement array, driving the module to update the thermal path. Dynamic safe current carrying capacity is determined through thermal-electric analogy optimization and inverse conversion, and then connected to the power grid dispatching system for optimized allocation. This achieves precise dynamic optimization of cable current carrying capacity, resulting in accurate determination of cable current carrying capacity in accordance with actual operating conditions, ensuring operational safety, and fully tapping into current carrying potential.

[0081] Example 2, as Figure 2 As shown, based on the same inventive concept as in Embodiment 1 above, this application provides a dynamic optimization system for cable current carrying capacity that combines duct layout parameters. The system includes:

[0082] Dynamic identification module construction module 1 is used to construct a dynamic identification module for the pre-managed pipe arrangement range, wherein the dynamic identification module is constructed based on the digital thermal circuit and the thermal-electric equivalent network.

[0083] The dynamic safe current carrying capacity acquisition module 2 is used to perform real-time temperature acquisition using a fiber optic temperature measurement array deployed within a pre-managed pipe network, drive the dynamic identification module embedded in the edge port to update the digital thermal path, and determine the dynamic safe current carrying capacity through optimization and inverse conversion under thermal-electric analogy conversion.

[0084] The optimized allocation management execution module 3 is used to perform optimized allocation management based on the dynamic safe carrying capacity by connecting the dynamic identification module to the power grid dispatching system.

[0085] Furthermore, the dynamic identification module construction module 1 is used to perform the following steps:

[0086] The physical layout of the pipe arrangement, the spatial distribution of the filling material, the thermal properties of the soil layers, and the nearby heat sources are parameterized as the pipe arrangement parameters. The material properties of the filling material are obtained, wherein the material properties include at least thermal conductivity, which includes the trend curve of thermal conductivity with temperature and pressure and flow-solidification characteristics. Based on the pipe arrangement parameters and the thermal conductivity, thermal nodes and paths are determined to form the digital thermal path, wherein the digital thermal path is identified with initial thermal resistance data.

[0087] Furthermore, the dynamic identification module construction module 1 is used to perform the following steps:

[0088] Through thermal-electric analogy conversion, the cable duct heat dissipation system is likened to a thermal-electric network, where the cable heating power is likened to a controlled current source proportional to the square of the operating current, temperature is likened to voltage, heat flow to current, thermal resistance to resistor, and thermal capacity to capacitor; a dynamic identification module is constructed based on the digital thermal circuit and the thermal-electric equivalent network.

[0089] Furthermore, the dynamic security carrying capacity acquisition module 2 is used to perform the following steps:

[0090] Temperature data of cable ducts is collected using a distributed fiber optic temperature measurement array to determine the continuous temperature spatial distribution. Based on this distribution, the digital thermal path is updated, whereby real-time updates or short-term predictions are performed. The updated digital thermal path is then mapped to the thermal-electric equivalent network, and circuit analysis is performed under the safe operating domain solution to output the dynamic safe current carrying capacity.

[0091] Furthermore, the dynamic security carrying capacity acquisition module 2 is used to perform the following steps:

[0092] For the aforementioned thermo-electric equivalent network, a voltage safety threshold is set for nodes corresponding to critical locations in the cable. This voltage safety threshold is obtained by converting the real-time temperature upper limit of the digital thermal circuit through thermo-electric analogy physical relationships. A core variable is defined, where the core variable is the output value of the controlled current source. Based on the voltage safety threshold and the core variable, a target optimization process based on a circuit solver is performed to determine the dynamic safe current carrying capacity, where maximizing the output of the core variable is the optimization objective.

[0093] Furthermore, the dynamic security carrying capacity acquisition module 2 is used to perform the following steps:

[0094] According to the circuit solver, by setting the first core variable value, the first steady-state voltage distribution of all nodes in the thermal-electric equivalent network is solved; for the first steady-state voltage distribution, it is determined whether it violates the voltage safety threshold, and the search value of the first core variable value is updated by the optimization generator as the second core variable value; for the second core variable value, the steady-state voltage distribution is solved and the voltage safety threshold is judged, and the process is iterated until convergence is satisfied, the largest core variable value that satisfies the voltage safety threshold is selected, and the dynamic safe current carrying capacity is determined.

[0095] Furthermore, the dynamic security carrying capacity acquisition module 2 is used to perform the following steps:

[0096] Based on the thermoelectric analogy physical relationship and cable resistance parameters, the maximum core variable value is converted into the maximum allowable operating current of the cable conductor, which is used as the dynamic safe current carrying capacity.

[0097] Furthermore, the dynamic security carrying capacity acquisition module 2 is used to perform the following steps:

[0098] Temperature data of the cable duct is continuously collected, and the dynamic safe current carrying capacity is updated in real time through real-time updates of the digital thermal circuit and equivalent analysis based on the thermal-electric equivalent network; wherein, the dynamic safe current carrying capacity is sent to the power grid dispatching system in real time.

[0099] Furthermore, the optimized allocation management execution module 3 is used to perform the following steps:

[0100] A standardized communication protocol is adopted to connect the dynamic safe carrying capacity to the power grid dispatching system and synchronize it with the power grid topology information; the dynamic safe carrying capacity is used as the dynamic thermal stability limit of the corresponding power grid line to perform load optimization allocation management.

[0101] The cable current carrying capacity dynamic optimization system based on duct layout parameters provided in this embodiment of the invention can execute the cable current carrying capacity dynamic optimization method based on duct layout parameters provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0102] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0103] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for dynamic optimization of cable current carrying capacity based on duct layout parameters, characterized in that, The method includes: For the pre-managed pipe laying range, a dynamic identification module is constructed, wherein the dynamic identification module is constructed based on the digital thermal circuit and the thermal-electric equivalent network; A fiber optic temperature measurement array deployed within a pre-managed pipe layout is used to perform real-time temperature acquisition, drive the dynamic identification module embedded in the edge port, perform digital thermal circuit updates, and determine the dynamic safe current carrying capacity through optimization and inverse conversion under thermal-electric analogy conversion. By connecting the dynamic identification module to the power grid dispatching system, optimized allocation management based on the dynamic safe carrying capacity is performed.

2. The method for dynamic optimization of cable current carrying capacity based on duct layout parameters as described in claim 1, characterized in that, Before constructing the dynamic identification module, the construction of the digital thermal path includes: The physical layout of the pipe arrangement, the spatial distribution of the filling material, the thermal properties of the soil stratification, and the nearby heat source are parameterized and used as the pipe arrangement parameters. Obtain the material properties of the filler material, wherein the material properties include at least thermal conductivity, and the thermal conductivity includes the trend curve of thermal conductivity with temperature and pressure, and flow curing characteristics; Based on the pipe arrangement parameters and the thermal conductivity, thermal nodes and paths are determined to form the digital thermal circuit, wherein the digital thermal circuit is identified with initial thermal resistance data.

3. The method for dynamic optimization of cable current carrying capacity based on duct layout parameters as described in claim 2, characterized in that, The dynamic identification module is constructed, including: Through the thermal-electric analogy conversion, the cable duct heat dissipation system is likened to a thermal-electric network. In this network, the cable heating power is likened to a controlled current source that is proportional to the square of the operating current, the temperature is likened to voltage, the heat flow is likened to current, the thermal resistance is likened to resistance, and the thermal capacity is likened to capacitance. A dynamic identification module is constructed based on the digital thermal circuit and the thermo-electric equivalent network.

4. The method for dynamic optimization of cable current carrying capacity based on duct layout parameters as described in claim 1, characterized in that, Determine the dynamic safe carrying capacity, including: Temperature data is collected from cable ducts using a distributed fiber optic temperature measurement array to determine the continuous spatial temperature distribution. The digital thermal path is updated based on the continuous temperature spatial distribution, wherein the digital thermal path performs real-time updates or short-term predictions. Based on the updated digital thermal path, it is mapped to the thermal-electric equivalent network, and circuit analysis under the safe operating domain solution is performed to output the dynamic safe current carrying capacity.

5. The method for dynamic optimization of cable current carrying capacity based on duct layout parameters as described in claim 4, characterized in that, Perform circuit analysis under the safe operating domain solution and output dynamic safe current carrying capacity, including: For the aforementioned thermo-electric equivalent network, a voltage safety threshold is set for nodes corresponding to critical locations of the cable. The voltage safety threshold is obtained by converting the real-time temperature upper limit of the digital thermal circuit through thermo-electric analogy physical relationships. Define a core variable, where the core variable is the output value of the controlled current source; Based on the voltage safety threshold and the core variable, a target optimization process based on a circuit solver is performed to determine the dynamic safe current carrying capacity, wherein maximizing the output of the core variable is the optimization objective.

6. The method for dynamic optimization of cable current carrying capacity based on duct layout parameters as described in claim 5, characterized in that, Performing objective optimization processing based on a circuit solver to determine the dynamic safe current carrying capacity includes: According to the circuit solver, by setting the first core variable value, the first steady-state voltage distribution of all nodes in the thermo-electric equivalent network is solved; For the first steady-state voltage distribution, determine whether it violates the voltage safety threshold, and use an optimization generator to update the search value of the first core variable value as the second core variable value; For the second core variable value, the steady-state voltage distribution is solved and the voltage safety threshold is judged. The process is iterated until convergence is achieved. The maximum core variable value that meets the voltage safety threshold is selected, and the dynamic safe current carrying capacity is determined.

7. The method for dynamic optimization of cable current carrying capacity based on duct layout parameters as described in claim 6, characterized in that, Based on the thermoelectric analogy physical relationship and cable resistance parameters, the maximum core variable value is converted into the maximum allowable operating current of the cable conductor, which is used as the dynamic safe current carrying capacity.

8. The method for dynamic optimization of cable current carrying capacity based on duct layout parameters as described in claim 7, characterized in that, Temperature data of the cable duct is continuously collected, and the dynamic safe current carrying capacity is updated in real time through real-time updates of the digital thermal circuit and equivalent analysis based on the thermal-electric equivalent network. The dynamic safety carrying capacity is sent to the power grid dispatching system in real time.

9. The method for dynamic optimization of cable current carrying capacity based on duct layout parameters as described in claim 1, characterized in that, Perform optimized allocation management based on the aforementioned dynamic security traffic, including: A standardized communication protocol is used to connect the dynamic safety carrying capacity to the power grid dispatching system and synchronize it with the power grid topology information; The dynamic safe current carrying capacity is used as the dynamic thermal stability limit for the corresponding power grid line to perform load optimization and allocation management.

10. A dynamic optimization system for cable current carrying capacity based on duct layout parameters, characterized in that, For implementing the dynamic optimization method for cable current carrying capacity based on duct layout parameters as described in any one of claims 1-9, the system comprises: A dynamic identification module construction module is used to construct a dynamic identification module for the pre-managed pipe laying range, wherein the dynamic identification module is constructed based on the digital thermal circuit and the thermal-electric equivalent network; The dynamic safe current carrying capacity acquisition module is used to perform real-time temperature acquisition for fiber optic temperature measurement arrays deployed in pre-managed pipe arrays, drive the dynamic identification module embedded in the edge side port to perform digital thermal circuit updates, and determine the dynamic safe current carrying capacity through optimization solution and inverse conversion under thermal-electric analog conversion. The optimized allocation management execution module is used to perform optimized allocation management based on the dynamic safe carrying capacity by connecting the dynamic identification module to the power grid dispatching system.

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