Method for predicting dynamic ampacity of directly buried cable and related device

Abstract

The embodiment of the application discloses a dynamic current-carrying capacity prediction method of a direct-buried cable and related devices, and the method comprises the following steps: obtaining historical weather data and historical cable load current of a direct-buried cable in a historical time period in a preset area; the historical weather data comprises historical ground temperature and historical precipitation; determining soil dynamic characteristic parameters based on the historical ground temperature and the historical precipitation; determining the historical dynamic current-carrying capacity of the direct-buried cable based on the soil dynamic characteristic parameters and the historical cable load current; obtaining future weather data and future cable load current of the preset area in a future time period; determining a target quantile point; determining a regression coefficient vector corresponding to the target quantile point based on the target quantile point, the historical weather data, the soil dynamic characteristic parameters, the historical dynamic current-carrying capacity and the historical cable load current; and determining the future dynamic current-carrying capacity of the direct-buried cable corresponding to the target quantile point in the future time period based on the future weather data, the future cable load current and the regression coefficient vector.

Description

Technical Field

[0001] The present invention relates to the technical field of current carrying capacity prediction, and in particular to a dynamic current carrying capacity prediction method and related devices for direct buried cables. Background Art

[0002] In the operation of the power system, the current carrying capacity of the direct buried cable is an important parameter for power dispatching and resource optimization. The current carrying capacity refers to the maximum current that the conductor can continuously carry under specified conditions without causing its stable temperature to exceed the specified value. Determining the current carrying capacity of the direct buried cable can prevent overload and equipment damage in power dispatching, and can improve equipment utilization in terms of resource optimization.

[0003] However, the traditional current-carrying capacity prediction method has low prediction accuracy under different environmental conditions and different cable loads.

[0004] Therefore, there is an urgent need for a dynamic current-carrying capacity prediction method for direct buried cables to improve the prediction accuracy when predicting the current-carrying capacity of cables. Summary of the Invention

[0005] In order to solve the above problems, an embodiment of the present invention provides a method and related apparatus for predicting the dynamic current carrying capacity of a direct buried cable, which can improve the prediction accuracy when predicting the current carrying capacity of the cable.

[0006] In a first aspect, an embodiment of the present invention provides a method for predicting the dynamic current carrying capacity of a direct buried cable, comprising:

[0007] Acquiring historical weather data and historical cable load current related to direct buried cables in a preset area during a historical time period; the historical weather data includes historical surface temperature and historical precipitation in the preset area during the historical time period;

[0008] determining soil dynamic characteristic parameters based on the historical surface temperature and the historical precipitation;

[0009] Determining the historical dynamic current carrying capacity of the direct buried cable based on the soil dynamic characteristic parameters and the historical cable load current;

[0010] Obtaining future weather data and future cable load current of the preset area in a future time period;

[0011] determining a target quantile based on the historical weather data, the historical cable load current, the future weather data, and the future cable load current;

[0012] Determine a regression coefficient vector corresponding to the target quantile based on the target quantile, the historical weather data, the soil dynamic characteristic parameter, the historical dynamic current carrying capacity, and the historical cable load current;

[0013] The future dynamic current carrying capacity of the direct buried cable corresponding to the target quantile in the future time period is determined based on the future weather data, the future cable load current and the regression coefficient vector.

[0014] In a second aspect, an embodiment of the present invention provides a dynamic current carrying capacity prediction device for a direct buried cable, the device comprising an acquisition unit and a processing unit;

[0015] The acquisition unit is used to acquire historical weather data and historical cable load current related to the direct buried cables in a preset area in a historical time period; the historical weather data includes the historical surface temperature and historical precipitation of the preset area in the historical time period;

[0016] The processing unit is configured to determine soil dynamic characteristic parameters based on the historical surface temperature and the historical precipitation;

[0017] Determining the historical dynamic current carrying capacity of the direct buried cable based on the soil dynamic characteristic parameters and the historical cable load current;

[0018] Obtaining future weather data and future cable load current of the preset area in a future time period;

[0019] determining a target quantile based on the historical weather data, the historical cable load current, the future weather data, and the future cable load current;

[0020] Determine a regression coefficient vector corresponding to the target quantile based on the target quantile, the historical weather data, the soil dynamic characteristic parameter, the historical dynamic current carrying capacity, and the historical cable load current;

[0021] The future dynamic current carrying capacity of the direct buried cable corresponding to the target quantile in the future time period is determined based on the future weather data, the future cable load current and the regression coefficient vector.

[0022] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor and a memory, wherein the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory so that the electronic device performs the method described in the first aspect.

[0023] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method as described in the first aspect.

[0024] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer is operable to enable the computer to execute the method described in the first aspect.

[0025] The implementation of the embodiments of the present application has the following beneficial effects:

[0026] In an embodiment of the present application, historical weather data and historical cable load current related to the direct buried cables in a preset area in a historical time period are first obtained, wherein the historical weather data include the historical surface temperature and historical precipitation of the preset area in the historical time period. Then, the soil dynamic characteristic parameters are determined based on the historical surface temperature and historical precipitation, and the historical dynamic current-carrying capacity of the direct buried cable is determined based on the soil dynamic characteristic parameters and the historical cable load current. Next, future weather data and future cable load current of the preset area in a future time period are obtained. Then, a target quantile is determined based on the historical weather data, the historical cable load current, the future weather data and the future cable load current. A regression coefficient vector corresponding to the target quantile is determined based on the target quantile, the historical weather data, the soil dynamic characteristic parameters, the historical dynamic current-carrying capacity and the historical cable load current. Finally, the future dynamic current-carrying capacity of the direct buried cable corresponding to the target quantile in the future time period is determined based on the future weather data, the future cable load current and the regression coefficient vector. Therefore, by determining the soil dynamic characteristic parameters, historical dynamic current carrying capacity and target quantile, and determining the regression coefficient vector corresponding to the target quantile, and finally determining the future dynamic current carrying capacity of the direct buried cable corresponding to the target quantile in the future time period based on the regression coefficient vector, the prediction accuracy can be improved when predicting the current carrying capacity of the cable. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 This is a flow chart of a method for predicting the dynamic current carrying capacity of a direct buried cable provided in an embodiment of the present application;

[0029] Figure 2 This is a flow chart of a dynamic current carrying capacity prediction model training for a direct buried cable provided in an embodiment of the present application;

[0030] Figure 3 This is a flow chart of dynamic current carrying capacity prediction based on a quantitative regression model provided in an embodiment of the present application;

[0031] Figure 4 This is a schematic structural diagram of a dynamic current-carrying capacity prediction device for a direct-buried cable provided in an embodiment of the present application;

[0032] Figure 5 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0034] The terms "first," "second," "third," and "fourth," etc., in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, rather than to describe a particular order. In addition, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to the process, method, product, or apparatus.

[0035] References herein to "embodiments" mean that a particular feature, result, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0036] In a possible embodiment, for real-time current-carrying capacity calculation, cable temperature detection can be performed, for example, optical fiber temperature measurement, thermocouples on the cable skin, infrared imaging, etc., to obtain the temperature of the metal sheath or cable skin. Combined with a thermal circuit model or a finite element model, the loss is obtained by load current calculation, and the cable core temperature is inferred in reverse. The cable core temperature is used to determine whether the cable load can be increased or whether the cable load is overloaded, thereby determining the real-time dynamic current-carrying capacity of the cable.

[0037] In a possible embodiment, for the prediction of the dynamic ampacity of the cable, a data-driven method such as random forest regression or neural network is applied to the prediction of the dynamic ampacity of the cable, and the two methods can effectively process high-dimensional input variables and have certain anti-noise ability. However, the random forest regression is centered on the mean value, and cannot provide reliable prediction for a specific quantile, such as the risk in the worst case or the typical behavior of the median, which may lead to insufficient prediction of extreme environmental conditions, and the neural network needs a large amount of historical data for training, and insufficient training set may lead to decreased prediction accuracy, in addition, the neural network is sensitive to errors in the prediction process, and the predicted value may be biased, which is not conducive to ensuring the safety of transmission.

[0038] The dynamic ampacity prediction method of the direct-buried cable provided by the embodiment of the present application focuses on the prediction of the target quantile through quantitative regression, which can more robustly reflect the dynamic ampacity of the cable under typical operating conditions and reduce the influence of extreme values on the prediction. Moreover, by adjusting the target quantile, the dynamic ampacity under the worst case or other risk conditions can be predicted, significantly enhancing the adaptability of the prediction. In addition, the calculation complexity of the quantitative regression is lower than that of the neural network, and in the case of insufficient data quality or large noise, the quantitative regression performs more stably and reliably. The quantitative regression model explicitly provides the relationship between the input features and the prediction results, which is convenient for power dispatchers to understand and use, and improves the trustworthiness in actual application. The quantile is used to describe the position of data in the distribution, and for a given probability distribution, the quantile is a numerical point that divides the distribution into several parts. The target quantile in the embodiment of the present application takes the median, i.e., the 50% quantile, as an example for illustration.

[0039] Referring to Figure 1 , Figure 1 is a flowchart of a dynamic ampacity prediction method of a direct-buried cable provided by an embodiment of the present application, as Figure 1 shown, the dynamic ampacity prediction method of the direct-buried cable provided by the embodiment of the present application includes but is not limited to the following steps:

[0040] Step S101: Obtain historical weather data and historical cable load current of a direct-buried cable in a historical time period in a preset region;

[0041] The historical weather data includes historical ground temperature and historical precipitation of the preset region in the historical time period;

[0042] Step S102: Determine the soil dynamic characteristic parameter based on the historical ground temperature and the historical precipitation;

[0043] Step S103: Determine the historical dynamic ampacity of the direct-buried cable based on the soil dynamic characteristic parameter and the historical cable load current;

[0044] Step S104: obtaining future weather data and future cable load current of a preset area in a future time period;

[0045] Step S105: determining a target quantile based on historical weather data, historical cable load current, future weather data, and future cable load current;

[0046] Step S106: determining a regression coefficient vector corresponding to the target quantile based on the target quantile, historical weather data, soil dynamic characteristic parameters, historical dynamic current carrying capacity, and historical cable load current;

[0047] Step S107: Determine the future dynamic current carrying capacity of the direct buried cable corresponding to the target quantile in the future time period based on the future weather data, the future cable load current and the regression coefficient vector.

[0048] In one possible embodiment, dynamic current carrying capacity refers to the current value that a cable can safely carry at different times. It varies with factors such as the cable's operating environment and load changes. There are many ways to lay cables, such as direct burial, overhead, and in cable trenches. Unlike overhead cables, whose heat dissipation, and thus current carrying capacity, is primarily affected by factors such as ambient temperature and wind speed, direct burial cables rely primarily on soil for heat dissipation. The thermal resistance of the soil affects the cable's current carrying capacity. Surface temperature and precipitation contribute primarily to the calculation of soil dynamic characteristic parameters by affecting soil moisture content. Precipitation directly changes soil moisture content, while surface temperature indirectly affects soil moisture content by affecting the dynamic balance between evaporation and soil moisture, thereby affecting parameters such as soil thermal diffusivity, thermal resistivity, and temperature.

[0049] In one possible embodiment, when determining the future dynamic current carrying capacity of the direct buried cable corresponding to the target quantile in the future time period based on future weather data, future cable load current and regression coefficient vector, the calculation is based on the 50% quantile quantitative regression model. When constructing the quantitative regression model, refer to Figure 2 , Figure 2 This is a flow chart of a dynamic current carrying capacity prediction model training for a direct buried cable provided in an embodiment of the present application. Figure 2 As shown, first input the historical surface temperature and precipitation, and based on this solve the soil thermal diffusivity, soil thermal resistivity and soil temperature, then calculate the dynamic current carrying capacity through the thermal circuit model to obtain the historical dynamic current carrying capacity. Next, based on the historical dynamic current carrying capacity, historical soil temperature, precipitation and cable load, build a training data set and a validation data set, and input the training data set into the 50% quantile quantitative regression model for model training. Combined with the validation data set to verify the model, save the model when the model reaches the ideal accuracy. After determining the quantitative regression model, refer to Figure 3 , Figure 3is a dynamic load current prediction flowchart based on a quantile regression model provided by an embodiment of the present application, as shown Figure 3 For the 50% quantile regression model, the soil temperature, rainfall and cable load current in the prediction period can be input to obtain the dynamic load current of the cable in the prediction period.

[0050] In a possible embodiment, the target quantile is adjusted to predict the dynamic load current in the worst case or other risk conditions, and when determining the target quantile based on historical weather data, historical cable load current, future weather data and future cable load current, a first weather feature of the historical weather data is determined, which is a representative feature extracted from the historical weather data. For historical precipitation, the average, variance, maximum, minimum, seasonal fluctuation characteristics of precipitation, etc. can be calculated, and for historical soil temperature, the average soil temperature, daily variation amplitude of soil temperature, seasonal variation characteristics of soil temperature, etc. can be calculated. Then, a second weather feature of the future weather data is determined, for future precipitation, the predicted precipitation trend, probability distribution characteristics of precipitation, etc. are analyzed, for future soil temperature, the predicted change trend of soil temperature, possible extreme temperature conditions, etc. are considered, and a first adjustment amount is determined based on the first weather feature and the second weather feature, which reflects the influence degree of the historical weather data on the initial quantile. Next, the historical load feature and the future load feature are determined according to the historical cable load current and the future cable load current respectively, and the second adjustment amount is determined based on the historical load feature and the future load feature, and finally, the target quantile is determined by combining the first adjustment amount, the second adjustment amount and the initial quantile, for example, the target quantile is the sum of the initial quantile, the first adjustment amount and the second adjustment amount. For example, the initial quantile is 50%, the preset weather feature is the weather feature under extreme weather conditions, and the preset load feature is the load feature under extreme load conditions, when the first weather feature and the second weather feature are consistent with the preset weather feature, the first adjustment amount can be determined according to the weather extreme level of the first weather feature and the second weather feature, for example, the first adjustment amount can be determined to be 30%, so the target quantile is 80%, or when the historical load feature and the future load feature are consistent with the preset load feature, the second adjustment amount can be determined according to the load level of the load in the historical load feature and the future load feature, for example, the second adjustment amount can be determined to be 20%, so the target quantile is 70%.

[0051] In the embodiments of the present application, in order to address the problem of insufficient ability to predict the dynamic current-carrying capacity of direct-buried cables, predictions are made by optimizing the 50% quantile, that is, the median, thereby significantly improving the robustness and accuracy of the predictions. In particular, under changing environmental conditions, the utilization efficiency of existing cable resources is improved, while ensuring the safe operation of the power system, thereby achieving a more efficient and robust prediction of the dynamic current-carrying capacity of direct-buried cables, significantly improving the ability to predict the dynamic current-carrying capacity of direct-buried cables, and providing important support for the efficient utilization and safe operation of cable resources.

[0052] Optionally, the soil dynamic characteristic parameters include soil thermal diffusivity, soil thermal resistivity, and historical soil temperature. Step S102, determining the soil dynamic characteristic parameters based on historical surface temperature and historical precipitation, may include the following steps:

[0053] Step S201: obtaining soil composition, dry soil density, and groundwater reference temperature of a preset region; the soil composition is used to reflect the ratio of sand and clay particles in the preset region;

[0054] Step S202: determining soil moisture based on historical precipitation and a preset water diffusion equation;

[0055] Step S203: determining soil thermal diffusivity based on soil composition and soil moisture content;

[0056] Step S204: determining soil thermal resistivity based on dry soil density, soil moisture content, soil thermal diffusivity, and soil composition;

[0057] Step S205: determining an initial temperature range based on historical surface temperatures and groundwater layer reference temperatures;

[0058] Step S206: Determine the historical soil temperature based on the soil thermal diffusivity and the initial temperature range.

[0059] In a possible embodiment, the soil thermal diffusivity, soil thermal resistivity and historical soil temperature of the entire historical time period are calculated by using the surface temperature and precipitation of the historical time period. The historical time period can be 1 to 36 hours from the past to the present. The soil thermal diffusivity, soil thermal resistivity and historical soil temperature are respectively calculated by δ T (t,h),ρ T (t,h) and T S (t,h), where t is time and h is the depth from the ground surface. The soil thermal diffusivity δ can be expressed by formula (1): T (t,h) is calculated:

[0060] δ T (t,h)=-14.8+0.209N+4.79θ(t,h)…………(1)

[0061] Where N is the soil composition and θ(t,h) is the soil moisture content, which is the volume fraction of water per unit volume. The dynamic change of soil moisture θ(t,h) over time can be expressed by formula (2), which is the water diffusion equation:

[0062]

[0063] Where z is the vertical direction related to depth, and the partial derivative of z is used to determine the change in the vertical direction. The left side of the equation is the change of soil moisture θ(t,h) with time t, δ θ (t,h) is the water diffusivity, k θ (t,h) is the water conductivity, and the calculation formulas for the two can be expressed by formula (3) and formula (4) respectively:

[0064]

[0065] Among them, k sat is the saturation conductivity, α and m are the Van Genuchten coefficients, θ sat is the saturated water content, θ res is the residual water content.

[0066] For soil thermal resistivity ρ T (t,h) can be calculated using formula (5):

[0067]

[0068] Among them, σ s is the dry soil density.

[0069] For the historical soil temperature T S (t,h) can be calculated using formula (6):

[0070]

[0071] Where z is the vertical direction related to depth, and the partial derivative of z is used to determine the change in the vertical direction. The left side of the equation is the historical soil temperature T S (t,h) changes with time t.

[0072] In one possible embodiment, to solve the aforementioned differential equations in a numerical simulation, a finite difference method is used for temporal and spatial discretization. The time step can be set to minutes, for example, 5 minutes. The upper boundary of the initial soil temperature range is the soil temperature derived from historical surface temperatures, and the lower boundary is the reference temperature of the groundwater layer. The initial soil temperature is obtained between the upper and lower boundaries through linear interpolation, and the initial soil moisture content is calculated using historical precipitation and the water diffusion equation.

[0073] Optionally, step S103, determining the historical dynamic current carrying capacity of the direct buried cable based on the soil dynamic characteristic parameters and the historical cable load current, may include the following steps:

[0074] Step S301: determining a plurality of time steps of a historical time period based on soil dynamic characteristic parameters and historical cable load current;

[0075] Step S302: determining a plurality of initial current carrying capacity reference values ​​corresponding to a plurality of time steps based on historical cable load current;

[0076] Step S303: determining the conductor temperature of the direct buried cable corresponding to each time step in a plurality of time steps based on a plurality of initial current-carrying capacity reference values, to obtain a plurality of conductor temperatures;

[0077] Step S304: obtaining the target operating temperature of the direct buried cable;

[0078] Step S305: When the difference between the target conductor temperature and the target operating temperature is less than or equal to a preset error value, the target initial current-carrying capacity reference value is increased based on the difference to obtain a target historical current-carrying capacity; the target conductor temperature is any conductor temperature among the multiple conductor temperatures; and the target initial current-carrying capacity reference value is an initial current-carrying capacity reference value corresponding to the target conductor temperature among the multiple initial current-carrying capacity reference values;

[0079] Step S306: Integrate multiple target historical current carrying capacities to obtain historical dynamic current carrying capacities.

[0080] In a possible embodiment, the soil dynamic characteristic parameter, namely the soil thermal diffusivity δ T (t,h), soil thermal resistivity ρ T (t,h) and historical soil temperature T S (t,h) Input the International Electrotechnical Commission (IEC) standard thermal circuit model to calculate the current carrying capacity, and the historical dynamic current carrying capacity can be obtained.

[0081] In one possible embodiment, in determining the historical dynamic load current, an iterative calculation method is adopted. In the first step, the conductor temperature T(t) is set as the target working temperature, and the initial value of the load current I(t) of the direct-buried cable is set as the actual load current of the cable history, that is, the historical cable load current is taken as the initial load current reference value. In the second step, the conductor loss W c (t) is calculated o (t) and the conductor internal temperature rise AT i (t) at the time step t is obtained c (t) is calculated o (t) and the conductor internal temperature rise AT i (t) at the time step t is obtained (1) , where the superscript indicates the first iteration value. In the fourth step, it is judged whether T(t) (1) ≤ ε, where ε is a preset acceptable error value. If the judgment condition is not met, the value of I(t) is increased, and the first step to the third step are repeatedly executed until the iteration is stopped when the judgment condition is met, and the historical dynamic load current I(t) is obtained (n) . In the fifth step, t is set as t+Δt, and it is judged whether T(t) If the condition is not met, the first step to the fourth step are repeatedly executed until the calculation is stopped when the condition is met. Through the above five steps, the historical dynamic load current data of the entire historical time period can be solved.

[0082] Optionally, in step S303, the conductor temperature of the direct-buried cable corresponding to each time step in the plurality of time steps is determined based on the plurality of initial load current reference values, and a plurality of conductor temperatures are obtained, which can include the following steps.

[0083] In step S401, the number of conductor cores, the insulation coefficient and the cable diameter of the direct-buried cable are obtained. The insulation coefficient is used to reflect the heat capacity and thermal resistance of the insulation part of the direct-buried cable.

[0084] In step S402, the conductor loss corresponding to each time step in the plurality of time steps is determined based on the number of conductor cores and the plurality of initial load current reference values, and a plurality of conductor losses are obtained.

[0085] In step S403, the conductor internal temperature rise corresponding to each time step in the plurality of time steps is determined based on the plurality of conductor losses and the insulation coefficient, and a plurality of conductor internal temperature rises are obtained.

[0086] In step S404, the conductor external temperature rise corresponding to each time step in the plurality of time steps is determined based on the plurality of conductor losses, the soil thermal resistance, the plurality of soil thermal diffusivity and the cable diameter, and a plurality of conductor external temperature rises are obtained.

[0087] Step S405: determining multiple historical soil temperatures corresponding to multiple time steps;

[0088] Step S406: determining a conductor temperature corresponding to each time step in a plurality of time steps based on the plurality of historical soil temperatures, the plurality of conductor internal temperature rises, and the plurality of conductor external temperature rises, to obtain a plurality of conductor temperatures.

[0089] In one possible embodiment, the conductor loss W c (t) can be calculated using formula (7):

[0090] W c (t) = n c R c,20 ℃[1+α T (T(t)-20)]I 2 (t)…………(7)

[0091] Among them, n c is the number of conductor cores of direct buried cable, R c,20℃ is the conductor resistance at 20°C, α T is the temperature correction coefficient, T(t) is the conductor temperature, and I(t) is the load current of the direct buried cable. The conductor temperature T(t) can be calculated using formula (8):

[0092] T(t)=T S (t,h)+α(t)ΔT o (t)+ΔT i (t)…………(8)

[0093] Among them, the conductor temperature T(t) is composed of the superposition of multiple temperatures, R S (t,h) is the historical soil temperature at depth h, α(t) is the correction factor for thermal interaction between the cable and the outside world, ΔT o (t) is the external temperature rise of the conductor, that is, the temperature rise of the outer surface of the direct buried cable, ΔT i (t) is the temperature rise inside the conductor.

[0094] Conductor internal temperature rise ΔT i (t) due to conductor loss W c (t) is caused and can be calculated by formula (9):

[0095]

[0096] Where A, B, a, and b are coefficients related to the thermal capacitance and thermal resistance of the insulation and sheath, i.e., the insulation coefficient, ΔW c (t n ) is the loss change of one time step, that is, W c (t n )-Wc (t n-1 ), k F The total number of steps is k hours with F time steps per hour.

[0097] Conductor external temperature rise ΔT o (t) can be calculated using formula (10):

[0098]

[0099] Among them, Ei(·) is the exponential integral function, D e is the cable diameter.

[0100] Optionally, step S306, integrating multiple target historical current carrying capacities to obtain historical dynamic current carrying capacities, may include the following steps:

[0101] Step S501: determining a load current value corresponding to each time step in a plurality of time steps based on historical cable load currents, to obtain a plurality of load current values;

[0102] Step S502: determining a time difference between each time step in a plurality of time steps based on adjacent load current values ​​in a plurality of load current values, to obtain a plurality of time differences;

[0103] Step S503: determining a reference dynamic ampacity for each of the multiple time differences based on the multiple target historical ampacities, to obtain multiple reference dynamic ampacities;

[0104] Step S504: Based on multiple time steps and multiple time differences, multiple target historical current carrying capacities and multiple reference dynamic current carrying capacities are integrated to obtain a historical dynamic current carrying capacity.

[0105] In a possible embodiment, the load current value corresponding to each time step in multiple time steps is inconsistent, and each load current value corresponds to a target historical current carrying capacity. Since there is a difference between adjacent load current values, there is a time difference when the target historical current carrying capacity corresponds to the load current value. When integrating the multiple target historical current carrying capacities, a reference dynamic current carrying capacity for each time difference in the multiple time differences is first determined based on the multiple target historical current carrying capacities to obtain multiple reference dynamic current carrying capacities. Then, based on the multiple time steps and the multiple time differences, the multiple target historical current carrying capacities and the multiple reference dynamic current carrying capacities are integrated to obtain the historical dynamic current carrying capacity. As a result, the integrated historical dynamic current carrying capacity is more consistent and practical.

[0106] Optionally, step S106, determining a regression coefficient vector corresponding to the target quantile based on the target quantile, historical weather data, soil dynamic characteristic parameters, historical dynamic current carrying capacity, and historical cable load current, may include the following steps:

[0107] Step S601: Based on historical precipitation, historical soil temperature, and historical cable load current, multiple feature matrices are constructed according to multiple time steps and preset time windows; each time step corresponds to a feature matrix;

[0108] Step S602: determining the current carrying capacity of the historical dynamic current carrying capacity corresponding to each characteristic matrix in the plurality of characteristic matrices at the target quantile, and obtaining a plurality of current carrying capacities;

[0109] Step S603: Determine multiple reference vectors corresponding to multiple current carrying capacities and multiple characteristic matrices based on a preset equation relationship; the equation relationship is used to reflect the corresponding relationship between the multiple current carrying capacities, the multiple characteristic matrices, and the regression coefficient vector;

[0110] Step S604: constructing a deviation loss function for the target quantile;

[0111] Step S605: Determine a regression coefficient vector from multiple reference vectors based on the deviation loss function.

[0112] In a possible embodiment, a quantitative regression model is trained based on historical dynamic current carrying capacity, historical precipitation in historical weather data, historical soil temperature in soil dynamic characteristic parameters, and historical cable load current. After training, a regression coefficient vector β of the model can be obtained.

[0113] For a quantitative regression model with a target quantile of 50%, it can be expressed by formula (11):

[0114] Q 0.5 (I DLR_t |X t )=X t β…………(11)

[0115] Among them, Q 0.5 (I DLR_t |X t ) is the feature matrix X at time t t Under the condition, dynamic current carrying capacity I DLR_t The median of the feature matrix X t Including the time series of historical soil temperature, historical precipitation and historical cable load current, that is, T S is the historical soil temperature, r is the historical precipitation, I is the historical cable load current, and w is the time window. The time window is used to determine the time span of the data in the feature matrix. The time window can be adjusted according to the time range of the historical time period.

[0116] In the training phase, the goal is to minimize the deviation loss function of the 50% quantile to optimize the regression coefficient vector β. The corresponding objective function can be expressed by formula (12):

[0117]

[0118] wherein I DLR_i is the target dynamic ampacity of the i-th time period, X i is the feature matrix vector of the i-th time period, p 0.5 (u) is the quantile loss function, which can be expressed by formula (13):

[0119]

[0120] Thus, the optimized regression coefficient vector can be obtained.

[0121] Optionally, the future weather data comprises future soil temperature and future precipitation of the future time period; and step S107 of determining the future dynamic ampacity corresponding to the target quantile point of the direct-buried cable in the future time period based on the future weather data, the future cable load current and the regression coefficient vector can comprise the following steps:

[0122] Step S701 of constructing a prediction matrix based on the future soil temperature, the future precipitation and the future cable load current;

[0123] Step S702 of determining the future dynamic ampacity corresponding to the target quantile point based on the regression coefficient vector and the prediction matrix.

[0124] In a possible embodiment, the future weather data is consistent with the data type of the historical weather data, when the historical weather data comprises historical precipitation and historical soil temperature, correspondingly, the future weather data comprises future precipitation and future soil temperature.

[0125] In a possible embodiment, the future weather data can be determined by a prediction method of the future weather data, the future cable load current can be determined by a prediction method of the future cable load current, and a prediction matrix X f is constructed according to the future soil temperature and the future precipitation of the future weather data and the future cable load current, and the regression coefficient vector β can be combined to obtain the future dynamic ampacity prediction value I DLR_f when the target quantile point is 50%, and the calculation method can be expressed by formula (14):

[0126] I DLR_f = X f β … … (14)

[0127] Thus, the dynamic current carrying capacity prediction value in the short time range in the future can be calculated, which can provide a basis for real-time cable scheduling. It can also be used to predict the current carrying capacity trend in the long term in the future by adjusting the data in the prediction matrix, which can provide support for scheduling optimization.

[0128] To summarize, in an embodiment of the present application, historical weather data and historical cable load current related to the direct buried cables in a preset area in a historical time period are first obtained, wherein the historical weather data include the historical surface temperature and historical precipitation of the preset area in the historical time period. Then, the soil dynamic characteristic parameters are determined based on the historical surface temperature and historical precipitation, and the historical dynamic current-carrying capacity of the direct buried cable is determined based on the soil dynamic characteristic parameters and the historical cable load current. Next, future weather data and future cable load current of the preset area in a future time period are obtained. Then, the target quantile is determined based on the historical weather data, the historical cable load current, the future weather data and the future cable load current. The regression coefficient vector corresponding to the target quantile is determined based on the target quantile, the historical weather data, the soil dynamic characteristic parameters, the historical dynamic current-carrying capacity and the historical cable load current. Finally, the future dynamic current-carrying capacity of the direct buried cable corresponding to the target quantile in the future time period is determined based on the future weather data, the future cable load current and the regression coefficient vector. Therefore, by determining the soil dynamic characteristic parameters, historical dynamic current carrying capacity and target quantile, and determining the regression coefficient vector corresponding to the target quantile, and finally determining the future dynamic current carrying capacity of the direct buried cable corresponding to the target quantile in the future time period based on the regression coefficient vector, the prediction accuracy can be improved when predicting the current carrying capacity of the cable.

[0129] The above describes in detail the method according to the embodiment of the present invention. The following provides an apparatus according to the embodiment of the present invention.

[0130] See Figure 4 , Figure 4 Schematic diagram of a dynamic current-carrying capacity prediction device for a direct-buried cable provided in an embodiment of the present application. The dynamic current-carrying capacity prediction device 800 for a direct-buried cable includes an acquisition unit 801 and a processing unit 802;

[0131] An acquisition unit 801 is configured to acquire historical weather data and historical cable load current related to direct buried cables in a preset area in a historical time period; the historical weather data includes historical surface temperature and historical precipitation in the preset area in the historical time period;

[0132] A processing unit 802 is configured to determine soil dynamic characteristic parameters based on historical surface temperature and historical precipitation;

[0133] Determine the historical dynamic current carrying capacity of direct buried cables based on soil dynamic characteristic parameters and historical cable load current;

[0134] Obtain future weather data and future cable load current for a preset area in a future time period;

[0135] Determine the target quantile based on historical weather data, historical cable load current, future weather data, and future cable load current;

[0136] Determine the regression coefficient vector corresponding to the target quantile based on the target quantile, historical weather data, soil dynamic characteristic parameters, historical dynamic current carrying capacity, and historical cable load current;

[0137] Based on future weather data, future cable load current and regression coefficient vector, the future dynamic current carrying capacity of the direct buried cable corresponding to the target quantile in the future time period is determined.

[0138] In some possible embodiments, the soil dynamic characteristic parameters include soil thermal diffusivity, soil thermal resistivity, and historical soil temperature. In determining the soil dynamic characteristic parameters based on historical surface temperature and historical precipitation, the processing unit 802 is specifically configured to:

[0139] Obtain soil composition, dry soil density and groundwater layer reference temperature of a preset area; soil composition is used to reflect the ratio of sand and clay particles in the preset area;

[0140] Determine soil moisture content based on historical precipitation and a preset water diffusion equation;

[0141] Determine soil thermal diffusivity based on soil composition and soil moisture content;

[0142] Determine soil thermal resistivity based on dry soil density, soil moisture content, soil thermal diffusivity, and soil composition;

[0143] Determine the initial temperature range based on historical surface temperatures and groundwater level reference temperatures;

[0144] Determine historical soil temperature based on soil thermal diffusivity and initial temperature range.

[0145] In some possible embodiments, in determining the historical dynamic current carrying capacity of the direct buried cable based on the soil dynamic characteristic parameters and the historical cable load current, the processing unit 802 is specifically configured to:

[0146] Determine multiple time steps of the historical time period based on soil dynamic characteristic parameters and historical cable load current;

[0147] Determine multiple initial current carrying capacity reference values ​​corresponding to multiple time steps based on historical cable load current;

[0148] determining a conductor temperature of the direct buried cable corresponding to each time step in a plurality of time steps based on a plurality of initial current carrying capacity reference values, to obtain a plurality of conductor temperatures;

[0149] Obtain the target operating temperature of the direct buried cable;

[0150] When the difference between the target conductor temperature and the target operating temperature is less than or equal to a preset error value, the target initial current-carrying capacity reference value is increased based on the difference to obtain a target historical current-carrying capacity; the target conductor temperature is any conductor temperature among multiple conductor temperatures; and the target initial current-carrying capacity reference value is an initial current-carrying capacity reference value corresponding to the target conductor temperature among the multiple initial current-carrying capacity reference values;

[0151] Integrate multiple target historical current carrying capacities to obtain historical dynamic current carrying capacity.

[0152] In some possible embodiments, in determining the conductor temperature of the direct buried cable corresponding to each time step in a plurality of time steps based on a plurality of initial ampacity reference values ​​to obtain a plurality of conductor temperatures, the processing unit 802 is specifically configured to:

[0153] Obtain the number of conductor cores, insulation coefficient, and cable diameter of the direct-buried cable; the insulation coefficient is used to reflect the thermal capacity and thermal resistance of the insulation part of the direct-buried cable;

[0154] determining a conductor loss corresponding to each time step in a plurality of time steps based on the number of conductor cores and a plurality of initial current carrying capacity reference values, thereby obtaining a plurality of conductor losses;

[0155] determining a conductor internal temperature rise corresponding to each time step in a plurality of time steps based on a plurality of conductor losses and insulation coefficients, thereby obtaining a plurality of conductor internal temperature rises;

[0156] determining a conductor external temperature rise corresponding to each time step in a plurality of time steps based on a plurality of conductor losses, a plurality of soil thermal resistivities, a plurality of soil thermal diffusivities, and a cable diameter, to obtain a plurality of conductor external temperature rises;

[0157] determining multiple historical soil temperatures corresponding to multiple time steps;

[0158] A conductor temperature corresponding to each time step in a plurality of time steps is determined based on a plurality of historical soil temperatures, a plurality of conductor internal temperature rises, and a plurality of conductor external temperature rises to obtain a plurality of conductor temperatures.

[0159] In some possible embodiments, in terms of integrating multiple target historical ampacities to obtain historical dynamic ampacities, the processing unit 802 is specifically configured to:

[0160] determining a load current value corresponding to each time step in a plurality of time steps based on the historical cable load current, and obtaining a plurality of load current values;

[0161] determining a time difference of each time step in a plurality of time steps based on adjacent load current values ​​in a plurality of load current values, to obtain a plurality of time differences;

[0162] Determine a reference dynamic ampacity for each of a plurality of time differences based on a plurality of target historical ampacities, and obtain a plurality of reference dynamic ampacities;

[0163] Based on multiple time steps and multiple time differences, multiple target historical ampacities and multiple reference dynamic ampacities are integrated to obtain the historical dynamic ampacity.

[0164] In some possible embodiments, in determining the regression coefficient vector corresponding to the target quantile based on the target quantile, historical weather data, soil dynamic characteristic parameters, historical dynamic current carrying capacity, and historical cable load current, the processing unit 802 is specifically configured to:

[0165] Based on historical precipitation, historical soil temperature, and historical cable load current, multiple feature matrices are constructed according to multiple time steps and preset time windows; each time step corresponds to a feature matrix;

[0166] Determine the current carrying capacity of the historical dynamic current carrying capacity corresponding to each characteristic matrix in the plurality of characteristic matrices at the target quantile, and obtain a plurality of current carrying capacities;

[0167] Determining multiple reference vectors corresponding to multiple current carrying capacities and multiple characteristic matrices based on a preset equation relationship; the equation relationship is used to reflect the corresponding relationship between the multiple current carrying capacities, the multiple characteristic matrices, and the regression coefficient vector;

[0168] Construct the deviation loss function of the target quantile;

[0169] A regression coefficient vector is determined from multiple reference vectors based on a deviation loss function.

[0170] In some possible embodiments, the future weather data includes a future soil temperature and a future precipitation in a future time period. In determining the future dynamic ampacity corresponding to a target quantile of the direct buried cable in the future time period based on the future weather data, the future cable load current, and the regression coefficient vector, the processing unit 802 is specifically configured to:

[0171] Construct a prediction matrix based on future soil temperature, future precipitation, and future cable load current;

[0172] The future dynamic ampacity corresponding to the target quantile is determined based on the regression coefficient vector and the prediction matrix.

[0173] See Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 5As shown, electronic device 900 includes a transceiver 901, a processor 902, and a memory 903, which are connected via a bus 904. Memory 903 is used to store computer programs and data, and can transmit data stored in memory 903 to processor 902. The electronic device can be the aforementioned dynamic current-carrying capacity prediction device for a direct-buried cable, and processor 902 can be the aforementioned acquisition unit 801 and processing unit 802.

[0174] The processor 902 is configured to read the computer program in the memory 903 and perform the following operations:

[0175] Obtain historical weather data and historical cable load current related to direct buried cables in a preset area within a historical time period; the historical weather data includes historical surface temperature and historical precipitation in the preset area within a historical time period;

[0176] Determine soil dynamic characteristic parameters based on historical surface temperature and historical precipitation;

[0177] Determine the historical dynamic current carrying capacity of direct buried cables based on soil dynamic characteristic parameters and historical cable load current;

[0178] Obtain future weather data and future cable load current for a preset area in a future time period;

[0179] Determine the target quantile based on historical weather data, historical cable load current, future weather data, and future cable load current;

[0180] Determine the regression coefficient vector corresponding to the target quantile based on the target quantile, historical weather data, soil dynamic characteristic parameters, historical dynamic current carrying capacity, and historical cable load current;

[0181] Based on future weather data, future cable load current and regression coefficient vector, the future dynamic current carrying capacity of the direct buried cable corresponding to the target quantile in the future time period is determined.

[0182] In some possible embodiments, the soil dynamic characteristic parameters include soil thermal diffusivity, soil thermal resistivity, and historical soil temperature. In determining the soil dynamic characteristic parameters based on historical surface temperature and historical precipitation, the processor 902 is specifically configured to perform the following operations:

[0183] Obtain soil composition, dry soil density and groundwater layer reference temperature of a preset area; soil composition is used to reflect the ratio of sand and clay particles in the preset area;

[0184] Determine soil moisture content based on historical precipitation and a preset water diffusion equation;

[0185] Determine soil thermal diffusivity based on soil composition and soil moisture content;

[0186] Determine soil thermal resistivity based on dry soil density, soil moisture content, soil thermal diffusivity, and soil composition;

[0187] Determine the initial temperature range based on historical surface temperatures and groundwater level reference temperatures;

[0188] Determine historical soil temperature based on soil thermal diffusivity and initial temperature range.

[0189] In some possible embodiments, in determining the historical dynamic current carrying capacity of the direct buried cable based on the soil dynamic characteristic parameters and the historical cable load current, the processor 902 is specifically configured to perform the following operations:

[0190] Determine multiple time steps of the historical time period based on soil dynamic characteristic parameters and historical cable load current;

[0191] Determine multiple initial current carrying capacity reference values ​​corresponding to multiple time steps based on historical cable load current;

[0192] determining a conductor temperature of the direct buried cable corresponding to each time step in a plurality of time steps based on a plurality of initial current carrying capacity reference values, to obtain a plurality of conductor temperatures;

[0193] Obtain the target operating temperature of the direct buried cable;

[0194] When the difference between the target conductor temperature and the target operating temperature is less than or equal to a preset error value, the target initial current-carrying capacity reference value is increased based on the difference to obtain a target historical current-carrying capacity; the target conductor temperature is any conductor temperature among multiple conductor temperatures; and the target initial current-carrying capacity reference value is an initial current-carrying capacity reference value corresponding to the target conductor temperature among the multiple initial current-carrying capacity reference values;

[0195] Integrate multiple target historical current carrying capacities to obtain historical dynamic current carrying capacity.

[0196] In some possible embodiments, in determining the conductor temperature of the direct buried cable corresponding to each of the multiple time steps based on the multiple initial ampacity reference values ​​to obtain the multiple conductor temperatures, the processor 902 is specifically configured to perform the following operations:

[0197] Obtain the number of conductor cores, insulation coefficient, and cable diameter of the direct-buried cable; the insulation coefficient is used to reflect the thermal capacity and thermal resistance of the insulation part of the direct-buried cable;

[0198] determining a conductor loss corresponding to each time step in a plurality of time steps based on the number of conductor cores and a plurality of initial current carrying capacity reference values, thereby obtaining a plurality of conductor losses;

[0199] determining a conductor internal temperature rise corresponding to each time step in a plurality of time steps based on a plurality of conductor losses and insulation coefficients, thereby obtaining a plurality of conductor internal temperature rises;

[0200] determining a conductor external temperature rise corresponding to each time step in a plurality of time steps based on a plurality of conductor losses, a plurality of soil thermal resistivities, a plurality of soil thermal diffusivities, and a cable diameter, to obtain a plurality of conductor external temperature rises;

[0201] determining multiple historical soil temperatures corresponding to multiple time steps;

[0202] A conductor temperature corresponding to each time step in a plurality of time steps is determined based on a plurality of historical soil temperatures, a plurality of conductor internal temperature rises, and a plurality of conductor external temperature rises to obtain a plurality of conductor temperatures.

[0203] In some possible embodiments, in terms of integrating multiple target historical ampacities to obtain historical dynamic ampacities, the processor 902 is specifically configured to perform the following operations:

[0204] determining a load current value corresponding to each time step in a plurality of time steps based on the historical cable load current, and obtaining a plurality of load current values;

[0205] determining a time difference of each time step in a plurality of time steps based on adjacent load current values ​​in a plurality of load current values, to obtain a plurality of time differences;

[0206] Determine a reference dynamic ampacity for each of a plurality of time differences based on a plurality of target historical ampacities, and obtain a plurality of reference dynamic ampacities;

[0207] Based on multiple time steps and multiple time differences, multiple target historical ampacities and multiple reference dynamic ampacities are integrated to obtain the historical dynamic ampacity.

[0208] In some possible embodiments, in determining the regression coefficient vector corresponding to the target quantile based on the target quantile, historical weather data, soil dynamic characteristic parameters, historical dynamic current carrying capacity, and historical cable load current, the processor 902 is specifically configured to perform the following operations:

[0209] Based on historical precipitation, historical soil temperature, and historical cable load current, multiple feature matrices are constructed according to multiple time steps and preset time windows; each time step corresponds to a feature matrix;

[0210] Determine the current carrying capacity of the historical dynamic current carrying capacity corresponding to each characteristic matrix in the plurality of characteristic matrices at the target quantile, and obtain a plurality of current carrying capacities;

[0211] Determining multiple reference vectors corresponding to multiple current carrying capacities and multiple characteristic matrices based on a preset equation relationship; the equation relationship is used to reflect the corresponding relationship between the multiple current carrying capacities, the multiple characteristic matrices, and the regression coefficient vector;

[0212] Construct the deviation loss function of the target quantile;

[0213] A regression coefficient vector is determined from multiple reference vectors based on a deviation loss function.

[0214] In some possible embodiments, the future weather data includes a future soil temperature and a future precipitation in a future time period. In determining the future dynamic ampacity corresponding to a target quantile of the direct buried cable in the future time period based on the future weather data, the future cable load current, and the regression coefficient vector, the processor 902 is specifically configured to perform the following operations:

[0215] Construct a prediction matrix based on future soil temperature, future precipitation, and future cable load current;

[0216] The future dynamic ampacity corresponding to the target quantile is determined based on the regression coefficient vector and the prediction matrix.

[0217] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement part or all of the steps of any method for dynamic current carrying capacity prediction of a direct buried cable as described in the above method embodiments.

[0218] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute part or all of the steps of any of the dynamic current-carrying capacity prediction methods for direct buried cables as described in the above method embodiments.

[0219] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions and modules involved are not necessarily required by this application.

[0220] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0221] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or modules can be electrical or other forms.

[0222] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of the present embodiment according to actual needs.

[0223] In addition, the functional modules in the various embodiments of the present application may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The above-mentioned integrated modules may be implemented in the form of hardware or software program modules.

[0224] If the integrated module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0225] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for predicting the dynamic current carrying capacity of a direct buried cable, characterized in that: include: Acquiring historical weather data and historical cable load current related to direct buried cables in a preset area during a historical time period; the historical weather data includes historical surface temperature and historical precipitation in the preset area during the historical time period; determining soil dynamic characteristic parameters based on the historical surface temperature and the historical precipitation; Determining the historical dynamic current carrying capacity of the direct buried cable based on the soil dynamic characteristic parameters and the historical cable load current; Obtaining future weather data and future cable load current of the preset area in a future time period; determining a target quantile based on the historical weather data, the historical cable load current, the future weather data, and the future cable load current; Determine a regression coefficient vector corresponding to the target quantile based on the target quantile, the historical weather data, the soil dynamic characteristic parameter, the historical dynamic current carrying capacity, and the historical cable load current; The future dynamic current carrying capacity of the direct buried cable corresponding to the target quantile in the future time period is determined based on the future weather data, the future cable load current and the regression coefficient vector.

2. The method according to claim 1, wherein The soil dynamic characteristic parameters include soil thermal diffusivity, soil thermal resistivity, and historical soil temperature; and determining the soil dynamic characteristic parameters based on the historical surface temperature and the historical precipitation includes: Obtaining soil composition, dry soil density, and groundwater level reference temperature of the preset area; the soil composition is used to reflect the ratio of sand and clay particles in the preset area; Determining the soil moisture content based on the historical precipitation and a preset water diffusion equation; determining the soil thermal diffusivity based on the soil composition and the soil moisture content; determining the soil thermal resistivity based on the dry soil density, the soil moisture content, the soil thermal diffusivity, and the soil composition; determining an initial temperature range based on the historical surface temperature and the groundwater layer reference temperature; The historical soil temperature is determined based on the soil thermal diffusivity and the initial temperature range.

3. The method according to claim 2, wherein Determining the historical dynamic current carrying capacity of the direct buried cable based on the soil dynamic characteristic parameters and the historical cable load current includes: determining a plurality of time steps of the historical time period based on the soil dynamic characteristic parameter and the historical cable load current; Determining a plurality of initial current carrying capacity reference values ​​corresponding to the plurality of time steps based on the historical cable load current; Determining a conductor temperature of the direct buried cable corresponding to each time step in a plurality of time steps based on the plurality of initial current-carrying capacity reference values ​​to obtain a plurality of conductor temperatures; Obtaining a target operating temperature of the direct buried cable; When the difference between the target conductor temperature and the target operating temperature is less than or equal to a preset error value, the target initial current-carrying capacity reference value is increased based on the difference to obtain a target historical current-carrying capacity; the target conductor temperature is any conductor temperature among the multiple conductor temperatures; the target initial current-carrying capacity reference value is an initial current-carrying capacity reference value corresponding to the target conductor temperature among the multiple initial current-carrying capacity reference values; A plurality of target historical ampacities are integrated to obtain the historical dynamic ampacity.

4. The method according to claim 3, wherein The step of determining the conductor temperature of the direct buried cable corresponding to each time step in a plurality of time steps based on the plurality of initial current-carrying capacity reference values ​​to obtain a plurality of conductor temperatures includes: Obtaining the number of conductor cores, insulation coefficient, and cable diameter of the direct-buried cable; the insulation coefficient is used to reflect the thermal capacity and thermal resistance of the insulation part of the direct-buried cable; Determine the conductor loss corresponding to each time step in a plurality of time steps based on the number of conductor cores and the plurality of initial current carrying capacity reference values ​​to obtain a plurality of conductor losses; determining a conductor internal temperature rise corresponding to each time step in the plurality of time steps based on the plurality of conductor losses and the insulation coefficient, to obtain a plurality of conductor internal temperature rises; determining a conductor external temperature rise corresponding to each time step in the plurality of time steps based on the plurality of conductor losses, the soil thermal resistivity, the plurality of soil thermal diffusivities, and the cable diameter, to obtain a plurality of conductor external temperature rises; determining a plurality of historical soil temperatures corresponding to the plurality of time steps; A conductor temperature corresponding to each time step in a plurality of time steps is determined based on the plurality of historical soil temperatures, the plurality of conductor internal temperature rises, and the plurality of conductor external temperature rises to obtain the plurality of conductor temperatures.

5. The method according to claim 3 or 4, wherein: The integrating multiple target historical ampacities to obtain the historical dynamic ampacity includes: Determine a load current value corresponding to each time step in the multiple time steps based on the historical cable load current to obtain multiple load current values; determining a time difference between each time step in the plurality of time steps based on adjacent load current values ​​in the plurality of load current values ​​to obtain a plurality of time differences; Determine a reference dynamic ampacity of each of the multiple time differences based on the multiple target historical ampacities to obtain multiple reference dynamic ampacities; Based on the multiple time steps and the multiple time differences, the multiple target historical ampacities and the multiple reference dynamic ampacities are integrated to obtain the historical dynamic ampacity.

6. The method according to claim 2, wherein The determining of the regression coefficient vector corresponding to the target quantile based on the target quantile, the historical weather data, the soil dynamic characteristic parameter, the historical dynamic current carrying capacity, and the historical cable load current includes: Based on the historical precipitation, the historical soil temperature, and the historical cable load current, constructing multiple feature matrices according to the multiple time steps and the preset time windows; each time step corresponds to a feature matrix; Determine the current carrying capacity of the historical dynamic current carrying capacity corresponding to each characteristic matrix in the multiple characteristic matrices at the target quantile, and obtain multiple current carrying capacities; Determining a plurality of reference vectors corresponding to the plurality of current carrying capacities and the plurality of characteristic matrices based on a preset equation relationship; the equation relationship is used to reflect the corresponding relationship between the plurality of current carrying capacities, the plurality of characteristic matrices, and the regression coefficient vector; Constructing a deviation loss function for the target quantile; The regression coefficient vector is determined from the plurality of reference vectors based on the deviation loss function.

7. The method according to claim 6, wherein The future weather data includes the future soil temperature and future precipitation in the future time period; and determining the future dynamic current carrying capacity of the direct buried cable corresponding to the target quantile in the future time period based on the future weather data, the future cable load current, and the regression coefficient vector includes: constructing a prediction matrix based on the future soil temperature, the future precipitation, and the future cable load current; The future dynamic current carrying capacity corresponding to the target quantile is determined based on the regression coefficient vector and the prediction matrix.

8. A dynamic current-carrying capacity prediction device for a direct buried cable, characterized in that: The device includes an acquisition unit and a processing unit; The acquisition unit is used to acquire historical weather data and historical cable load current related to the direct buried cables in a preset area in a historical time period; the historical weather data includes the historical surface temperature and historical precipitation of the preset area in the historical time period; The processing unit is configured to determine soil dynamic characteristic parameters based on the historical surface temperature and the historical precipitation; Determining the historical dynamic current carrying capacity of the direct buried cable based on the soil dynamic characteristic parameters and the historical cable load current; Obtaining future weather data and future cable load current of the preset area in a future time period; determining a target quantile based on the historical weather data, the historical cable load current, the future weather data, and the future cable load current; Determine a regression coefficient vector corresponding to the target quantile based on the target quantile, the historical weather data, the soil dynamic characteristic parameter, the historical dynamic current carrying capacity, and the historical cable load current; The future dynamic current carrying capacity of the direct buried cable corresponding to the target quantile in the future time period is determined based on the future weather data, the future cable load current and the regression coefficient vector.

9. An electronic device, characterized in that: include: A processor and a memory, the processor being connected to the memory, the memory being used to store a computer program, and the processor being used to execute the computer program stored in the memory, so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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