Directional drilling laying cable conductor temperature evaluation method, device, equipment and product

Abstract

The invention relates to a directional drilling laying cable conductor temperature evaluation method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring space grid data and a parameter data set of a plurality of cables; the parameter data set at least comprises a material heat conductivity coefficient and geometric parameters; the spatial network data comprises distance parameters of a plurality of infinitesimal segments in the cables and relative position relations of the cables; respectively calculating the soil thermal resistance of each infinitesimal section relative to the earth surface boundary and the deep soil boundary according to the material heat conductivity coefficient, the geometric parameters and the space grid data to obtain thermal resistance parameter data; according to the relative position relation, mutual heat influence data between the cables is calculated; and calculating the conductor temperature of each infinitesimal section according to the thermal resistance parameter data and the mutual heat influence data to form infinitesimal temperature distribution data, and determining a conductor temperature distribution result according to the infinitesimal temperature distribution data. The method can improve the temperature evaluation accuracy of the laid cable conductor.

Description

Technical Field

[0001] This application relates to the field of cable laying technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for assessing the conductor temperature of cables laid by directional drilling. Background Technology

[0002] In recent years, with rapid economic development, urban loads have become increasingly dense and large-capacity, leading to a growing demand for power cable laying. Directional drilling, as an emerging cable laying technology, offers advantages such as minimal ground interference and flexible laying paths, and has been widely used in urban power grid construction. However, due to its unique laying method, directional drilling cables present challenges such as deep burial, long laying distances, and small cable spacing, resulting in numerous technical difficulties in assessing cable conductor temperature.

[0003] Traditional methods for calculating cable current carrying capacity can simplify the external soil medium into a semi-infinite domain, causing temperature calculation results to deviate from reality. In densely laid scenarios with small cable spacing, the mutual heating effect between cables will significantly affect the temperature distribution of each cable. Traditional methods cannot accurately capture this interaction, which often leads to large deviations when assessing the conductor temperature of directional drilling cables, and cannot provide accurate temperature predictions for cable design and operation. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for directional drilling cable conductor temperature assessment that can improve the accuracy of cable conductor temperature assessment during cable laying, in response to the above-mentioned technical problems.

[0005] In a first aspect, this application provides a method for evaluating the conductor temperature of cables laid using directional drilling, including:

[0006] Acquire spatial grid data and parameter datasets for multiple cables; the parameter datasets include at least the material thermal conductivity and geometric parameters; the spatial network data includes distance parameters of multiple micro-segments in the cables and the relative positional relationships of each cable;

[0007] Based on the thermal conductivity of the material, the geometric parameters, and the spatial grid data, the soil thermal resistance of each micro-segment relative to the surface boundary and the deep soil boundary is calculated to obtain thermal resistance parameter data.

[0008] Based on the relative positional relationship, calculate the mutual heat impact data between the cables;

[0009] Based on the thermal resistance parameter data and the mutual heat influence data, the conductor temperature of each micro-segment is calculated to form micro-segment temperature distribution data, and the conductor temperature distribution result is determined based on the micro-segment temperature distribution data.

[0010] In one embodiment, the parameter dataset further includes load current values, cable resistance parameters, cable voltage, and operating power; the method further includes:

[0011] Calculate the conductor loss power based on the load current value and the cable resistance parameters;

[0012] Calculate the insulation loss power based on the cable voltage and the operating frequency;

[0013] Calculate the total power loss based on the conductor loss power and the insulation loss power;

[0014] Based on the total power loss and the volume data of each micro-segment, the heat source power density per unit volume of each micro-segment is calculated to obtain the micro-element heat source power data of each micro-segment.

[0015] In one embodiment, the step of calculating the conductor temperature of each micro-segment based on the thermal resistance parameter data and the mutual heat influence data to form micro-element temperature distribution data includes:

[0016] Based on the upper and lower boundary soil thermal resistances of each micro-segment in the thermal resistance parameter data, the thermal conductivity data of each micro-segment is calculated.

[0017] Based on the upper boundary mirror mutual heat coefficient, lower boundary mirror mutual heat coefficient and corresponding thermal conductivity data of each micro-segment in the mutual heat influence data, calculate the mutual heat temperature rise contribution data;

[0018] Based on the micro-element heat source power data, the thermal conductivity data, and the mutual heat temperature rise contribution data, the conductor temperature of each micro-element segment is calculated to form micro-element temperature distribution data.

[0019] In one embodiment, determining the conductor temperature distribution result based on the micro-element temperature distribution data includes:

[0020] Based on the conductor temperature difference and axial thermal resistance of adjacent micro-element segments in the micro-element temperature distribution data, calculate the axial heat flow between each micro-element segment;

[0021] Based on the axial heat flow, the power data of the micro-element heat source is corrected, and the conductor temperature of each micro-element segment is recalculated to update the micro-element temperature distribution data;

[0022] The iteration stops when the relative rate of change of conductor temperature in all micro-segments is less than a preset convergence threshold, and the conductor temperature distribution result is output, by comparing the temperature distribution data of the micro-element obtained in two consecutive iterations.

[0023] In one embodiment, the parameter dataset further includes the soil thermal conductivity; the calculation of the mutual heat influence data between the cables based on the relative positional relationship includes:

[0024] Based on the position information of each micro-segment of each cable in the spatial grid data, calculate the position data of the surface mirror point of each micro-segment relative to the surface boundary and the position data of the deep mirror point relative to the deep soil boundary.

[0025] Calculate the spatial distance between the surface mirror point location data and each of the micro-segments to obtain the upper boundary mirror distance data, and calculate the spatial distance between the deep mirror point location data and each of the micro-segments to obtain the lower boundary mirror distance data;

[0026] Based on the soil thermal conductivity, the micro-element heat source power data, the upper boundary mirror distance data, and the lower boundary mirror distance data, the upper boundary mirror mutual heat coefficient and the lower boundary mirror mutual heat coefficient are calculated to obtain mutual heat influence data.

[0027] In one embodiment, acquiring the spatial grid data of the target cable includes:

[0028] Obtain the axial length of the target cable;

[0029] Based on the axial length of the cable, the target cable is divided into multiple micro-segments along the axial direction;

[0030] Calculate the distance parameters of each micro-element relative to the surface boundary and the deep soil boundary;

[0031] Spatial grid data is generated based on the distance parameters.

[0032] Secondly, this application also provides a device for assessing the temperature of cable conductors during directional drilling, comprising:

[0033] The data acquisition module is used to acquire spatial grid data and parameter datasets of multiple cables; the parameter dataset includes at least the thermal conductivity and geometric parameters of the materials; the spatial network data includes the distance parameters of multiple micro-segments in the cables and the relative positional relationships of each cable;

[0034] The first calculation module is used to calculate the soil thermal resistance of each micro-segment relative to the surface boundary and the deep soil boundary based on the thermal conductivity of the material, the geometric parameters and the spatial grid data, and obtain thermal resistance parameter data.

[0035] The second calculation module is used to calculate the mutual heat influence data between the cables based on the relative positional relationship.

[0036] The data processing module is used to calculate the conductor temperature of each micro-segment based on the thermal resistance parameter data and the mutual heat influence data, form micro-segment temperature distribution data, and determine the conductor temperature distribution result based on the micro-segment temperature distribution data.

[0037] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps described in the first aspect.

[0038] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps described in the first aspect.

[0039] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps described in the first aspect.

[0040] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for assessing the conductor temperature of directional drilling cables acquire spatial grid data and parameter datasets of multiple cables. The parameter datasets include at least the material's thermal conductivity and geometric parameters. The spatial network data includes distance parameters of multiple micro-segments within the cable and the relative positional relationships of each cable. Based on the material's thermal conductivity, geometric parameters, and spatial grid data, the soil thermal resistance of each micro-segment relative to the surface boundary and deep soil boundary is calculated, yielding thermal resistance parameter data. By explicitly considering the influence of the surface boundary and deep soil boundary, this accurately reflects the temperature of the directional drilling cable. The actual heat dissipation boundary conditions of the cable significantly improve the accuracy of soil thermal resistance calculation. Based on the relative positional relationship, the mutual heat influence data between each cable is calculated. By using the spatial relative positional relationship of the cables, the thermal interaction between the cables is quantitatively calculated, accurately capturing the thermal coupling effect between cables under dense laying conditions. This avoids the temperature assessment deviation caused by treating each cable as an independent heat source in traditional methods. Based on the thermal resistance parameter data and mutual heat influence data, the conductor temperature of each micro-segment is calculated, forming micro-segment temperature distribution data. Based on the micro-segment temperature distribution data, the conductor temperature distribution result is determined, improving the accuracy of conductor temperature assessment for directional drilling cables. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a diagram illustrating the application environment of a method for assessing the conductor temperature of directional drilling cable laying in one embodiment.

[0043] Figure 2 This is a flowchart illustrating a method for assessing the conductor temperature during directional drilling in one embodiment.

[0044] Figure 3 This is a schematic diagram of a quasi-three-dimensional heat transfer model of a cable in one embodiment.

[0045] Figure 4 This is a schematic diagram of a shape factor calculation model in one embodiment.

[0046] Figure 5 This is a schematic diagram of a model representing the thermal influence of cables in one embodiment.

[0047] Figure 6 This is a flowchart illustrating step S206 of the directional drilling cable conductor temperature assessment method in one embodiment.

[0048] Figure 7 This is a structural block diagram of a directional drilling cable conductor temperature assessment device in one embodiment.

[0049] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0051] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0052] The method for assessing the conductor temperature of directional drilling cables provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located on the cloud or other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0053] In one exemplary embodiment, such as Figure 2 As shown, a method for evaluating the conductor temperature of cables laid using directional drilling is provided, which can be applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps S202 to S208. Wherein:

[0054] Step S202: Obtain spatial grid data and parameter datasets for multiple cables; the parameter datasets shall include at least the material thermal conductivity and geometric parameters.

[0055] The parameter dataset can be a collection of data containing cable structural characteristics, material properties, and operating conditions, including material thermal conductivity, geometric parameters, electrical performance parameters, and environmental boundary parameters. Material thermal conductivity can include parameters related to the heat transfer capacity of each layer of the medium, such as conductor thermal conductivity, insulation thermal conductivity, buffer layer thermal conductivity, outer sheath thermal conductivity, duct thermal conductivity, and soil thermal conductivity. Geometric parameters can include spatial dimensional information such as cable conductor radius, insulation layer thickness, buffer layer thickness, outer sheath thickness, duct radius, cable axial length, and cable spacing.

[0056] The spatial network data includes distance parameters for multiple micro-segments within the cable and the relative positional relationships of each cable segment. Spatial grid data can refer to a data structure containing micro-segment location information and relative positional relationships, formed by axial discretization of the cable. A micro-segment is a small segment formed by equally dividing the cable along its axial direction, and each micro-segment has independent thermal properties and spatial location attributes. Distance parameters can refer to the spatial distances of each micro-segment relative to the surface boundary and the deep soil boundary.

[0057] For example, server 104 can obtain the axial length of the target cable; divide the target cable into multiple micro-segments along the axial direction based on the axial length; calculate the distance parameters of each micro-segment relative to the surface boundary and deep soil boundary; and generate spatial grid data based on the distance parameters. Server 104 can read the basic parameter information of the target cable from a preset database or configuration file to obtain a structured parameter dataset. It can use an axial discretization method to transform the continuous cable structure into a discretized model that is easy for numerical calculation. Server 104 determines an appropriate discretization density based on the calculation accuracy requirements and cable length, and can divide the cable into dozens to hundreds of micro-segments. The length of each micro-segment is determined by dividing the total axial length by the number of micro-segments. In this process, server 104 can establish an independent coordinate system for each micro-segment, record its spatial position in the overall cable system, and calculate the relative positional relationship between the micro-segment and other micro-segments and environmental boundaries.

[0058] For example, server 104 can obtain the axial length L of the cable for directional drilling, which can be determined based on the actual measurement results of the laying path. Subsequently, server 104 can divide the axial length L into n micro-segments according to preset accuracy requirements, each micro-segment having a length of... The path of heat transfer from each micro-element segment to the surface boundary and deep soil boundary along the radial path is characterized by thermal resistance, and the axial heat transfer path of the cable conductors between each micro-element segment is characterized by axial thermal resistance, such as... Figure 3 As shown, T1 is the insulation thermal resistance, T2 is the buffer layer thermal resistance, T3 is the outer sheath thermal resistance, and T4 is the pipe thermal resistance.

[0059] The parameter dataset may also include load current values, cable resistance parameters, cable voltage, and operating power. For example, server 104 may also calculate conductor loss power based on load current values ​​and cable resistance parameters; calculate insulation loss power based on cable voltage and operating frequency; calculate total loss power based on conductor loss power and insulation loss power; and calculate the heat source power density per unit volume of each micro-segment based on the total loss power and the volume data of each micro-segment, thus obtaining the micro-element heat source power data for each micro-segment.

[0060] The load current value refers to the magnitude of the current carried by the cable during actual operation, which can be determined based on the load demand of the power system and the current-carrying capacity of the cable. Cable resistance parameters can include conductor DC resistance, conductor AC resistance, and temperature coefficient of resistance, used to describe the characteristics of resistive losses generated when current passes through the conductor. Cable voltage refers to the rated voltage or actual operating voltage of the cable, used to calculate the electric field strength and dielectric loss in the insulation medium. Operating frequency refers to the frequency of the alternating current, which can be the power frequency of 50 Hz or 60 Hz. Server 104 can comprehensively consider various loss types generated during cable operation, including conductor loss, insulation loss, and other additional losses. Conductor loss is the Joule heat generated by current passing through the conductor resistance and is the main source of cable heating. Insulation loss is the dielectric loss generated by the alternating electric field acting on the insulation medium; although it can be less than conductor loss, it still has a significant impact in high-voltage cables.

[0061] For example, server 104 can calculate conductor loss power based on the load current value and cable resistance parameters in the parameter dataset. In this process, server 104 can consider the temperature dependence of the resistance, applying a temperature correction to the resistance value based on the current conductor temperature. The relationship between conductor resistance and temperature can be described using a linear temperature coefficient model, and server 104 can calculate the resistance value at the actual operating temperature based on the standard resistance value at 20 degrees Celsius and the temperature coefficient. Subsequently, server 104 can multiply the corrected resistance value by the square of the load current value to obtain the conductor loss power. The conductor loss Q of the i-th cable in each infinitesimal element cmi for:

[0062]

[0063] Furthermore, the first Insulation loss of the i-th cable in a infinitesimal element :

[0064]

[0065] in, For the first The conductor temperature of the i-th cable in a given infinitesimal element. For AC resistance, Where is the conductor radius, It represents electric current. U0 represents the power supply frequency, in rad / s; U0 represents the voltage to ground (phase voltage), in V. This indicates the insulation loss factor under power system and operating temperature conditions. The dielectric constant of the insulation material is taken as 2.5; Di represents the outer diameter of the single-phase insulation layer in mm; dc represents the conductor diameter in mm. After obtaining the conductor loss power and insulation loss power, server 104 adds them together to obtain the total loss power. For certain special types of cables, server 104 can also consider other types of losses, such as circulating current loss in the metal sheath and eddy current loss in the armor layer. When calculating the heat source power density per unit volume of each micro-segment, server 104 needs to process the total loss power and the volume data of each micro-segment. The volume of each micro-segment is determined by its axial length and conductor cross-sectional area. The axial length is equal to the total cable length divided by the number of micro-segments, and the conductor cross-sectional area is calculated based on the conductor radius. Server 104 can divide the total loss power by the volume of the micro-segment to obtain the heat source power density per unit volume, which represents the amount of heat generated per unit volume.

[0066] In the process of generating micro-element heat source power data, server 104 can also consider the characteristics of heat source power variation with time and operating conditions. Under dynamic operating conditions, the load current may change over time, and the conductor temperature will also change accordingly, thus affecting the resistance value and the magnitude of power loss. By establishing a coupling relationship between heat source power and temperature and current, server 104 can achieve dynamic updates of heat source power and improve the dynamic response capability of temperature calculation.

[0067] Through the above steps, server 104 achieves a precise conversion from cable electrical operating parameters to heat source distribution. This heat source calculation method, which comprehensively considers multiple loss types, is more accurate than a simplified heat source model and can effectively reflect the heating characteristics of the cable under actual operating conditions. In particular, the consideration of the temperature dependence of conductor resistance effectively couples heat source calculation with temperature calculation, improving the accuracy and physical realism of the overall calculation results.

[0068] Furthermore, during the calculation of distance parameters, server 104 can determine the location parameters of the surface boundary and the deep soil boundary. The surface boundary can be set as a horizontal plane with a vertical coordinate of zero, and the depth of the deep soil boundary can be determined based on local geological conditions and temperature distribution characteristics. For the i-th phase cable in the m-th micro-element segment, server 104 calculates its distance relative to the upper boundary, and so on. Distance between each infinitesimal element and the upper boundary :

[0069]

[0070] Calculate its distance relative to the lower boundary, the first... Distance between each infinitesimal element and the lower boundary :

[0071]

[0072] When generating spatial grid data, server 104 can also construct a relative positional relationship matrix between cables, which describes the spatial distance and angular relationship between different cable micro-segments. For a three-phase cable system, server 104 can record the planar coordinates of the three cables in each micro-segment and calculate the spatial distance between any two cables.

[0073] Step S204: Based on the material's thermal conductivity, geometric parameters, and spatial grid data, calculate the soil thermal resistance of each micro-segment relative to the surface boundary and the deep soil boundary to obtain thermal resistance parameter data.

[0074] Soil thermal resistance refers to the thermal resistance encountered when heat is transferred from the cable surface through the soil medium to the environmental boundary. Thermal resistance parameter data is a dataset containing information on the thermal resistance of each micro-segment of the cable, which can include different types of thermal resistance parameters such as radial thermal resistance, axial thermal resistance, and boundary thermal resistance. Radial thermal resistance covers the thermal resistance of each layer inside the cable, including the insulation layer thermal resistance, buffer layer thermal resistance, outer sheath thermal resistance, and conduit thermal resistance. Boundary thermal resistance refers to the soil thermal resistance from the cable's outer surface to the environmental boundary, including the upper and lower boundary soil thermal resistances. Axial thermal resistance refers to the thermal resistance of the cable conductor when heat is transferred along the axial direction, used to describe the heat exchange characteristics between adjacent micro-segments.

[0075] For example, server 104 can establish the theoretical basis for thermal resistance calculation based on Fourier's law of heat transfer, which describes the relationship between heat transfer and temperature gradient. During the calculation process, server 104 can comprehensively consider the complexity of the cable structure and the non-uniformity of the soil medium, employing a layered calculation method to handle different types of thermal resistance separately. For the internal thermal resistance of the cable, server 104 can calculate it using a cylindrical wall heat transfer model based on the geometric dimensions and thermophysical parameters of each layer of medium. For soil thermal resistance, server 104 can consider the influence of surface and deep boundaries, using the mirror method to handle the correction effect of boundary conditions on the heat transfer process.

[0076] In the specific calculation implementation, server 104 can handle the radial thermal resistance calculation of each layer inside the cable. Server 104 can obtain the cable conductor radius, the thickness of each insulation layer, and the corresponding material thermal conductivity based on the geometric parameters in the parameter dataset, and calculate the thermal resistance of each layer according to the theoretical model of steady-state heat transfer in a cylindrical wall. The calculation of the insulation layer thermal resistance considers the radial heat transfer path from the conductor surface to the outer surface of the insulation layer. The calculations of the buffer layer thermal resistance and the outer sheath thermal resistance correspond to the heat transfer process of the respective layers. The cable body insulation layer, shielding layer, buffer layer, and outer sheath are considered as a cylindrical wall structure, and their thermal resistance... The calculation formula is as follows:

[0077]

[0078] in, Let be the thermal conductivity of each layer, and D and d be the outer and inner diameters of the cylindrical wall structure, respectively. The thermal resistance parameters of the filling material inside the pipe can be calculated using the shape factor, such as... Figure 4 As shown:

[0079]

[0080] in, The outer radius of the cable. The inner diameter of the pipe. The thermal conductivity is that of a high thermal conductivity material.

[0081] For calculating the thermal resistance of the soil boundary, server 104 calculates the distance parameters of each micro-element relative to the surface boundary and the deep soil boundary based on the position information of each micro-element in the spatial grid data. Based on this, server 104 can establish an equivalent heat transfer model using the mirror method. The surface boundary can be considered an isothermal boundary, with its temperature remaining relatively stable, while the deep soil boundary is considered an isothermal boundary, with its temperature unaffected by cable heating. For example, a cable-boundary coordinate system is established with the deep geothermal boundary as the abscissa and the vertical ground as the ordinate, and the relative positional relationship between the cables is calculated. The positions of the three cable elements in the individual elements are as follows: Let the ordinate of the upper boundary be... The ordinate of the lower boundary is Because the three cables are buried at different depths, the soil thermal resistance of the cable micro-elements is different. Server 104 can determine the distance between the i-th cable micro-element in the m-th micro-element segment and the upper boundary based on the spatial grid data. Subsequently, based on the theoretical calculation of the image method, the soil thermal resistance was determined. The soil thermal resistance can be expressed as... :

[0082]

[0083] For calculating the thermal resistance of the soil at the lower boundary, server 104 can use the distance between the i-th cable element in the m-th micro-element segment and the lower boundary. Then calculate the soil thermal resistance, which can be expressed as: :

[0084]

[0085] in, It is the soil thermal resistivity.

[0086] Step S206: Calculate the mutual heat influence data between each cable based on their relative positions.

[0087] Mutual heat influence refers to the effect of heat generated by one cable on the temperature of other cables when multiple cables are running simultaneously. A model for characterizing mutual heat influence of cables is as follows: Figure 5 As shown in the figure, T iu Let T be the soil thermal resistance from node i to the upper boundary. iu Let be the soil thermal resistance from node i to the lower boundary. When calculating the mutual heat influence data, server 104 uses the principle of temperature rise superposition, demonstrating that under linear heat transfer conditions, the temperature rise effect generated by multiple heat sources can be obtained through simple mathematical superposition to obtain the total temperature rise distribution. Server 104 can calculate the spatial distance and relative positional relationship between any two cable micro-segments based on the spatial coordinates of each cable micro-segment recorded in the spatial grid data.

[0088] For example, server 104 can classify cables into two categories: target cables and thermally related cables. The thermal impact of thermally related cables on target cables is calculated using the mirror method. For a three-phase cable system laid using directional drilling, the target cable refers to the cable whose temperature distribution needs to be calculated, while the thermally related cable refers to other cables that affect the temperature of the target cable. In a three-phase cable system, each cable can serve as both a target cable and a thermally related cable, and server 104 can establish a mutual thermal influence matrix to describe this interaction.

[0089] For example, server 104 can calculate the basic mutual heat influence between cables using a Green's function-based method. For a linear heat source in an infinitely large homogeneous soil medium, the temperature rise generated at any point in space can be obtained through the analytical solution of the heat conduction equation. Server 104 can calculate the temperature rise contribution generated at the target cable location based on the heat source intensity and spatial location of the heat-related cable. Due to the limitations of boundary conditions in the actual soil environment, server 104 can use the mirror method to correct the basic solution to consider the correction effect of boundary conditions on mutual heat influence. Further, when calculating the mirror mutual heat influence at the upper boundary, the coordinates of the mirror point of each cable micro-element relative to the upper boundary can be determined based on the position coordinates of the three cable micro-elements in the m-th micro-element segment and the position of the upper boundary. The calculation of the mirror point follows the reflection principle in geometric optics, that is, the distance of the mirror point relative to the boundary is equal to the distance of the origin relative to the boundary, but it is located on the other side of the boundary. Server 104 can calculate the distance between the mirror point of the j-th micro-element relative to the upper boundary and the i-th micro-element, and then calculate the upper boundary mirror mutual heat influence coefficient based on this distance and the actual distance between the cables. :

[0090]

[0091] in, Describing a microelement The symmetrical point and infinitesimal element relative to the upper isothermal interface The distance between them. Describing a microelement With micro elements The distance between them It is the soil thermal resistivity.

[0092] For calculating the mutual heating effect of the lower boundary mirror image, server 104 can use a similar method. Server 104 calculates the distance between the mirror point of the j-th micro-element relative to the lower boundary and the i-th micro-element, and then calculates the mutual heating effect coefficient of the lower boundary mirror image. :

[0093]

[0094] in, Describing a microelement The symmetry point and infinitesimal element relative to the lower isothermal interface The distance between them.

[0095] Through the above calculation process, the complex thermal interaction relationships between cables are transformed into a quantitatively calculable mathematical model. The mutual thermal influence data accurately reflects the thermal coupling characteristics of multiple cables operating simultaneously, especially the correction effect of boundary conditions on the mutual thermal influence. This mutual thermal influence calculation method based on the mirror method is more accurate than the traditional method that ignores the interaction between cables, and can effectively capture the thermal coupling effect between cables under dense laying conditions, significantly improving the accuracy of temperature calculation.

[0096] Step S208: Calculate the conductor temperature of each micro-segment based on the thermal resistance parameter data and mutual heat influence data, form micro-segment temperature distribution data, and determine the conductor temperature distribution result based on the micro-segment temperature distribution data.

[0097] For example, server 104 can calculate the thermal conductivity data of each micro-segment based on the upper and lower boundary soil thermal resistances of each micro-segment in the thermal resistance parameter data; calculate the mutual heat rise contribution data based on the upper and lower boundary mirror mutual heat coefficients and corresponding thermal conductivity data of each micro-segment in the mutual heat influence data; and calculate the conductor temperature of each micro-segment based on the micro-element heat source power data, thermal conductivity data, and mutual heat rise contribution data to form micro-element temperature distribution data. For example, server 104 can establish a set of heat balance equations to describe the heat transfer process of the cable system, solve these heat balance equations using finite difference or finite element numerical methods, and obtain a converged temperature distribution solution through iterative calculation. When establishing the heat balance equations, an independent heat balance relationship can be established for each micro-segment. For the m-th micro-segment of the i-th cable, its heat balance includes the self-heating of the micro-segment, heat dissipation to the environment through the soil, mutual heat influence from other cables, and axial heat transfer with adjacent micro-segments. Server 104 can represent these heat flux terms using corresponding temperature differences and thermal resistances, forming a system of linear or nonlinear equations with the temperature of each infinitesimal segment as unknowns. The coefficient matrix of the equation system is determined by the thermal resistance parameter, while the constant term vector contains heat source terms and boundary condition terms. Considering the temperature change characteristics of the AC resistance of the cable conductor, an iterative calculation matrix for the cable surface temperature, taking into account mutual heating effects, is constructed:

[0098]

[0099] in, The upper boundary temperature. The lower boundary temperature is used. Server 104 can calculate the thermal conductivity data of each micro-segment based on the upper and lower boundary soil thermal resistances of each micro-segment in the thermal resistance parameter data. The thermal conductivity is equal to the reciprocal of the thermal resistance. For the m-th micro-segment of the i-th cable, the thermal conductivity calculation includes both the upper and lower boundary thermal conductivity. When calculating the mutual heat rise contribution data, for the mutual heat rise contribution at the upper boundary, server 104 can multiply the upper boundary mirror mutual heat coefficient in the mutual heat influence data with the corresponding upper boundary thermal conductivity, and then sum over all heat-related cables. The mutual heat rise contribution at the lower boundary is calculated using a similar method. Server 104 can combine these mutual heat rise contributions with the boundary temperature term to form mutual heat rise contribution data, which reflects the degree of influence of the cable-to-cable interaction on the temperature of each micro-segment.

[0100] Furthermore, server 104 can calculate the axial heat flow between each micro-element segment based on the conductor temperature difference and axial thermal resistance of adjacent micro-element segments in the micro-element temperature distribution data; based on the axial heat flow, correct the micro-element heat source power data, recalculate the conductor temperature of each micro-element segment, and update the micro-element temperature distribution data; compare the micro-element temperature distribution data obtained from two consecutive iterations, and stop the iteration when the relative rate of change of conductor temperature in all micro-element segments is less than the preset convergence threshold, and output the conductor temperature distribution result.

[0101] When establishing a system of linear equations to solve for the cable surface temperature, Server 104 can construct a coefficient matrix and a vector of constant terms. The coefficient matrix is ​​in diagonal form, where the diagonal elements are equal to the sum of the thermal conductivity coefficients of the upper and lower boundaries of the corresponding infinitesimal segments. When solving for the cable surface temperature, the following definition applies:

[0102]

[0103] Cable surface temperature matrix ,have:

[0104]

[0105] After obtaining the cable surface temperature, server 104 can further calculate the conductor temperature. The conductor temperature equals the cable surface temperature plus the temperature rise caused by the conductor heating up and passing through the internal thermal resistance of the cable. Server 104 can calculate the temperature rise value based on the micro-element heat source power data and the total internal thermal resistance of the cable. The formula for calculating the conductor temperature of each micro-element is as follows:

[0106]

[0107] Furthermore, server 104 can correct the micro-element heat source power data based on the calculated axial heat flow. For micro-element segments with axial heat inflow, the incoming heat needs to be added to the original heat source power; for micro-element segments with axial heat outflow, the outflowing heat needs to be subtracted. The corrected heat source power more accurately reflects the actual net heat generation of each micro-element segment after considering the axial heat transfer effect. Server 104 can use the corrected heat source power data to recalculate the conductor temperature of each micro-element segment, thereby updating the micro-element temperature distribution data. (Define conductor temperature matrix) Cable surface temperature matrix calorific value matrix constant matrix .

[0108] .

[0109] have:

[0110] In the iterative calculation of axial heat transfer, server 104 can establish a coupled iterative process of axial heat flow and temperature distribution. Server 104 can set an initial temperature distribution, assuming that each micro-segment has the same initial temperature. Then, the axial heat flow is calculated, the heat source power is corrected, and the temperature distribution is recalculated. Server 104 repeats this process until the temperature distribution converges. In each iteration, server 104 recalculates the heat generation power based on the updated conductor temperature. Specifically, in the first iteration, the temperature matrix is ​​initialized.

[0111] ,calculate ,renew , and mutual heating and Solve for the surface temperature of the cable. The conductor temperature is updated and calculated after one iteration. This process continues until the condition is met. At that time, the output matrix As a result of radial iteration of the cross-sectional micro-element, the conductor temperature matrix of each micro-element is calculated sequentially following the above steps. Considering the cable cross-sectional area, the distance between adjacent micro-elements, and the thermal conductivity, the axial thermal resistance of the conductors between micro-elements is calculated:

[0112]

[0113] in, The thermal conductivity of a conductor, Let be the radius of the cable conductor. Based on the axial thermal resistance of each infinitesimal element obtained through radial iteration, the temperature at the boundary is taken as a constant temperature; the cable infinitesimal elements at the boundary do not participate in the iteration. The remaining infinitesimal elements participate in the iteration. In the nth iteration, for the th The first infinitesimal Cable micro-element, updating heat dissipation :

[0114]

[0115]

[0116] in, For the first During the nth iteration, the 1st The first cross-sectional element at the... The heat generated by the cable micro-element For the first The heat generation calculated in each iteration. The first The result of the iteration calculation is the first The first in the cross-sectional infinitesimal element The conductor temperature of a single cable element. This represents the thermal resistance between adjacent cross-sectional micro-elements.

[0117] Furthermore, based on the axial heat flow and combined with the soil thermal resistance and mutual heat influence parameters calculated during the radial iteration process, the conductor temperature is updated. :

[0118]

[0119] During the iterative convergence determination, server 104 can compare the micro-element temperature distribution data obtained from two consecutive iterations and calculate the relative rate of change of conductor temperature in each micro-element segment. When the relative rate of change of temperature in all micro-element segments is less than the preset convergence threshold, server 104 can stop the iteration process and output the final conductor temperature distribution result.

[0120] In the aforementioned method for assessing the conductor temperature of directional drilling cables, spatial grid data and parameter datasets of multiple cables are acquired. The parameter datasets include at least the material's thermal conductivity and geometric parameters. The spatial grid data includes the distance parameters of multiple micro-segments within the cable and the relative positional relationships of each cable. Based on the material's thermal conductivity, geometric parameters, and spatial grid data, the soil thermal resistance of each micro-segment relative to the surface boundary and deep soil boundary is calculated, yielding thermal resistance parameter data. By explicitly considering the influence of the surface boundary and deep soil boundary, the actual heat dissipation boundary conditions of the directional drilling cable are accurately reflected, significantly improving the accuracy of soil thermal resistance calculation. Based on the relative positional relationships, the mutual heat influence data between the cables is calculated. By quantitatively calculating the thermal interaction between cables using their spatial relative positional relationships, the thermal coupling effect between cables under dense laying conditions is accurately captured, thus avoiding the temperature assessment bias caused by treating each cable as an independent heat source in traditional methods. Based on the thermal resistance parameter data and mutual heat influence data, the conductor temperature of each micro-segment is calculated, forming micro-segment temperature distribution data. The conductor temperature distribution result is determined based on the micro-segment temperature distribution data, improving the accuracy of conductor temperature assessment for directional drilling cables.

[0121] In one exemplary embodiment, such as Figure 6 As shown, the parameter dataset also includes the soil thermal conductivity; step S206 includes steps S302 to S306. Wherein:

[0122] Step S302: Based on the position information of each micro-segment of each cable in the spatial grid data, calculate the position data of the surface mirror point of each micro-segment relative to the surface boundary and the position data of the deep mirror point relative to the deep soil boundary.

[0123] For example, for the i-th cable element in the m-th micro-segment, server 104 can set its spatial coordinates as (xi, yi, zi), where yi is the depth coordinate of the micro-element relative to the ground surface. Server 104 can set the ground surface boundary as a horizontal plane with y=0, then the coordinates of the mirror point of the micro-element relative to the ground surface boundary are (xi, -yi, zi). The x and z coordinates of the mirror point remain unchanged, while the y coordinate becomes the opposite of the original coordinate. Server 104 can repeat this calculation process for all micro-segments to form a complete dataset of ground surface mirror point locations.

[0124] For calculating the deep mirror point, server 104 can assume that the deep soil boundary lies on the horizontal plane of y=H, where H is the distance from the surface to the deep boundary. For a micro-element with coordinates (xi, yi, zi), its mirror point coordinates relative to the deep boundary are (xi, 2H-yi, zi), ensuring that the mirror point and the origin are symmetrically distributed about the deep boundary.

[0125] Step S304: Calculate the spatial distance between the surface mirror point location data and each micro-segment to obtain the upper boundary mirror distance data, and calculate the spatial distance between the deep mirror point location data and each micro-segment to obtain the lower boundary mirror distance data.

[0126] For example, server 104 can establish a distance matrix to describe the spatial relationship between each mirror point and the micro-segment. For a system containing n micro-segments, the mirror distance matrix of each boundary is an n×n square matrix, where the element in the i-th row and j-th column represents the distance between the mirror point of the i-th micro-segment and the j-th micro-segment. The diagonal elements of the matrix may not participate in subsequent calculations, because the distance between the cable micro-segment and its own mirror point is mainly used for calculating its own temperature rise, rather than the calculation of mutual heat effects. Server 104 can construct the upper boundary mirror distance matrix and the lower boundary mirror distance matrix separately. Server 104 can use the formula for calculating the distance between two points in three-dimensional space. For the coordinates of the surface mirror point of the i-th micro-segment being (xi, -yi, zi), and the coordinates of the j-th micro-segment being (xj, yj, zj), the distance between the two points is calculated using the three-dimensional Euclidean distance formula. Server 104 can calculate for all possible combinations of i and j, where i and j can be the same or different. When i equals j, the distance between the infinitesimal segment and its own mirror point is calculated; when i does not equal j, the mirror distance between different infinitesimal segments is calculated. For the calculation of deep mirror distance, the coordinates of the deep mirror point of the i-th infinitesimal segment are (xi, 2H-yi, zi), and the distance between it and the j-th infinitesimal segment is also calculated using the three-dimensional Euclidean distance formula.

[0127] When storing mirror distance data, since the distance matrix can have sparsity (the impact of long-distance mirroring is small and can be ignored), server 104 can adopt a sparse matrix storage method, storing only distance values ​​exceeding a certain threshold. Server 104 can also utilize the symmetry of the distance matrix, storing only the upper or lower triangular part of the matrix, further reducing storage requirements.

[0128] Step S306: Based on the soil thermal conductivity, micro-element heat source power data, upper boundary mirror distance data, and lower boundary mirror distance data, calculate the upper boundary mirror mutual heat coefficient and the lower boundary mirror mutual heat coefficient to obtain mutual heat influence data.

[0129] The mirror heat transfer coefficient refers to the temperature rise influence coefficient of one cable segment on another segment through its mirror heat source, after considering the influence of boundary conditions. The upper boundary mirror heat transfer coefficient represents the intensity of heat transfer influence generated by the mirror effect of the ground surface boundary, and the lower boundary mirror heat transfer coefficient represents the intensity of heat transfer influence generated by the mirror effect of the deep boundary.

[0130] For example, for an infinitely long linear heat source in a homogeneous medium, the temperature rise at a point at a distance r is directly proportional to the heat source intensity, inversely proportional to the natural logarithm of the distance, and inversely proportional to the thermal conductivity of the medium. Server 104 can calculate the temperature rise contribution of each mirrored heat source at the target micro-element position based on this fundamental relationship and the superposition principle of the image method. When calculating the upper boundary mirror mutual heating coefficient, for the upper boundary mirror mutual heating influence of the i-th cable on the j-th cable in the m-th micro-element, server 104 can obtain the micro-element heat source power of the j-th cable and then perform calculations using the soil thermal conductivity and relevant geometric parameters. The calculation of the upper boundary mirror mutual heating coefficient requires the ratio of the upper boundary mirror distance to the actual distance between the cables. The calculation of the lower boundary mirror mutual heating coefficient can also consider the mirror effect of deep boundaries, using the ratio of the lower boundary mirror distance to the actual distance.

[0131] When establishing the mutual heat influence data structure, server 104 can construct a complete mutual heat influence matrix for each micro-segment. For a system containing three cables, server 104 can establish three 3x3 mutual heat influence matrices for each micro-segment, corresponding to direct mutual heat influence, upper boundary mirror mutual heat influence, and lower boundary mirror mutual heat influence, respectively. The element in the i-th row and j-th column of the matrix represents the mutual heat influence coefficient of the j-th cable on the i-th cable. When i equals j, this element is zero, and the cable does not produce mutual heat influence on itself.

[0132] When handling the mirror effect of boundary conditions, server 104 can also consider the correction for the mutual heat effect of boundary temperature. When the boundary temperature is not zero, the intensity of the mirror heat source needs to be adjusted accordingly. Server 104 can correct the mirror mutual heat coefficient according to the boundary temperature and boundary condition type to ensure that the calculation results can correctly reflect the actual boundary condition effects.

[0133] Through the above calculation process, the complex influence of boundary conditions is transformed into a quantitatively calculable mutual heat influence coefficient. The mutual heat influence data accurately reflects the intensity of thermal interaction between cables after considering the boundary conditions, which significantly improves the accuracy of mutual heat influence assessment compared with the calculation method that ignores the boundary conditions.

[0134] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0135] Based on the same inventive concept, this application also provides a directional drilling cable conductor temperature assessment device for implementing the above-described method for assessing the temperature of directional drilling cable conductors. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the directional drilling cable conductor temperature assessment device provided below can be found in the limitations of the directional drilling cable conductor temperature assessment method described above, and will not be repeated here.

[0136] In one exemplary embodiment, such as Figure 7 As shown, a device for assessing the conductor temperature of directional drilling-laid cables is provided, comprising: a data acquisition module 702, a first calculation module 704, a second calculation module 706, and a data processing module 708, wherein:

[0137] The data acquisition module 702 is used to acquire spatial grid data and parameter datasets of multiple cables; the parameter dataset includes at least the thermal conductivity and geometric parameters of the materials; the spatial network data includes the distance parameters of multiple micro-segments in the cable and the relative positional relationship of each cable;

[0138] The first calculation module 704 is used to calculate the soil thermal resistance of each micro-segment relative to the surface boundary and the deep soil boundary based on the material thermal conductivity, geometric parameters and spatial grid data, and obtain thermal resistance parameter data.

[0139] The second calculation module 706 is used to calculate the mutual heat influence data between the cables based on their relative positional relationships.

[0140] The data processing module 708 is used to calculate the conductor temperature of each micro-segment based on the thermal resistance parameter data and mutual heat influence data, form micro-segment temperature distribution data, and determine the conductor temperature distribution result based on the micro-segment temperature distribution data.

[0141] In one embodiment, the parameter dataset further includes load current value, cable resistance parameter, cable voltage, and operating power; the device further includes: a third calculation module, used to calculate conductor loss power based on load current value and cable resistance parameter; calculate insulation loss power based on cable voltage and operating frequency; calculate total loss power based on conductor loss power and insulation loss power; and calculate heat source power density per unit volume of each micro-segment based on total loss power and volume data of each micro-segment, thereby obtaining micro-element heat source power data for each micro-segment.

[0142] In one embodiment, the data processing module 708 is specifically used to calculate the thermal conductivity data of each micro-element segment based on the upper boundary soil thermal resistance and lower boundary soil thermal resistance of each micro-element segment in the thermal resistance parameter data; calculate the mutual heat rise contribution data based on the upper boundary mirror mutual heat coefficient, lower boundary mirror mutual heat coefficient and corresponding thermal conductivity data of each micro-element segment in the mutual heat influence data; and calculate the conductor temperature of each micro-element segment based on the micro-element heat source power data, thermal conductivity data and mutual heat rise contribution data to form micro-element temperature distribution data.

[0143] In one embodiment, the data processing module 708 is specifically used to calculate the axial heat flow between each micro-element segment based on the conductor temperature difference and axial thermal resistance of adjacent micro-element segments in the micro-element temperature distribution data; correct the micro-element heat source power data based on the axial heat flow, recalculate the conductor temperature of each micro-element segment to update the micro-element temperature distribution data; compare the micro-element temperature distribution data obtained from two consecutive iterations, stop the iteration when the relative change rate of conductor temperature in all micro-element segments is less than a preset convergence threshold, and output the conductor temperature distribution result.

[0144] In one embodiment, the parameter dataset also includes the soil thermal conductivity; the second calculation module 706 is specifically used to: calculate the position data of the surface mirror point of each micro-segment relative to the surface boundary and the position data of the deep mirror point relative to the deep soil boundary based on the position information of each micro-segment of each cable in the spatial grid data; calculate the spatial distance between the surface mirror point position data and each micro-segment to obtain the upper boundary mirror distance data, and calculate the spatial distance between the deep mirror point position data and each micro-segment to obtain the lower boundary mirror distance data; calculate the upper boundary mirror mutual heat coefficient and the lower boundary mirror mutual heat coefficient based on the soil thermal conductivity, micro-element heat source power data, upper boundary mirror distance data and lower boundary mirror distance data to obtain mutual heat influence data.

[0145] In one embodiment, the data acquisition module 702 is specifically used to acquire the axial length of the target cable; divide the target cable into multiple micro-segments along the axial direction according to the axial length; calculate the distance parameters of each micro-segment relative to the surface boundary and the deep soil boundary; and generate spatial grid data based on the distance parameters.

[0146] Each module in the aforementioned directional drilling cable conductor temperature assessment device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0147] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for evaluating the conductor temperature of directional drilling cable laying.

[0148] Those skilled in the art will understand that Figure 8The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0149] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0150] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0151] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0152] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0153] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0154] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0155] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for evaluating the conductor temperature of cables laid using directional drilling, characterized in that, The method includes: Acquire spatial grid data and parameter datasets for multiple cables; the parameter datasets include at least the material thermal conductivity and geometric parameters; the spatial network data includes distance parameters of multiple micro-segments in the cables and the relative positional relationships of each cable; Based on the thermal conductivity of the material, the geometric parameters, and the spatial grid data, the soil thermal resistance of each micro-segment relative to the surface boundary and the deep soil boundary is calculated to obtain thermal resistance parameter data. Based on the relative positional relationship, calculate the mutual heat impact data between the cables; Based on the thermal resistance parameter data and the mutual heat influence data, the conductor temperature of each micro-segment is calculated to form micro-segment temperature distribution data, and the conductor temperature distribution result is determined based on the micro-segment temperature distribution data.

2. The method according to claim 1, characterized in that, The parameter dataset also includes load current values, cable resistance parameters, cable voltage, and operating power; the method further includes: Calculate the conductor loss power based on the load current value and the cable resistance parameters; Calculate the insulation loss power based on the cable voltage and the operating frequency; Calculate the total power loss based on the conductor loss power and the insulation loss power; Based on the total power loss and the volume data of each micro-segment, the heat source power density per unit volume of each micro-segment is calculated to obtain the micro-element heat source power data of each micro-segment.

3. The method according to claim 2, characterized in that, The step of calculating the conductor temperature of each micro-segment based on the thermal resistance parameter data and the mutual heat influence data to form micro-element temperature distribution data includes: Based on the upper and lower boundary soil thermal resistances of each micro-segment in the thermal resistance parameter data, the thermal conductivity data of each micro-segment is calculated. Based on the upper boundary mirror mutual heat coefficient, lower boundary mirror mutual heat coefficient and corresponding thermal conductivity data of each micro-segment in the mutual heat influence data, calculate the mutual heat temperature rise contribution data; Based on the micro-element heat source power data, the thermal conductivity data, and the mutual heat temperature rise contribution data, the conductor temperature of each micro-element segment is calculated to form micro-element temperature distribution data.

4. The method according to claim 3, characterized in that, The step of determining the conductor temperature distribution result based on the micro-element temperature distribution data includes: Based on the conductor temperature difference and axial thermal resistance of adjacent micro-element segments in the micro-element temperature distribution data, calculate the axial heat flow between each micro-element segment; Based on the axial heat flow, the power data of the micro-element heat source is corrected, and the conductor temperature of each micro-element segment is recalculated to update the micro-element temperature distribution data; The iteration stops when the relative rate of change of conductor temperature in all micro-element segments is less than a preset convergence threshold, and the conductor temperature distribution result is output, by comparing the temperature distribution data of the micro-element obtained in two consecutive iterations.

5. The method according to claim 2, characterized in that, The parameter dataset also includes the soil thermal conductivity; the calculation of the mutual heat influence data between the cables based on the relative positional relationship includes: Based on the position information of each micro-segment of each cable in the spatial grid data, calculate the position data of the surface mirror point of each micro-segment relative to the surface boundary and the position data of the deep mirror point relative to the deep soil boundary. Calculate the spatial distance between the surface mirror point location data and each of the micro-segments to obtain the upper boundary mirror distance data, and calculate the spatial distance between the deep mirror point location data and each of the micro-segments to obtain the lower boundary mirror distance data; Based on the soil thermal conductivity, the micro-element heat source power data, the upper boundary mirror distance data, and the lower boundary mirror distance data, the upper boundary mirror mutual heat coefficient and the lower boundary mirror mutual heat coefficient are calculated to obtain mutual heat influence data.

6. The method according to any one of claims 1 to 5, characterized in that, The acquisition of spatial grid data of the target cable includes: Obtain the axial length of the target cable; Based on the axial length of the cable, the target cable is divided into multiple micro-segments along the axial direction; Calculate the distance parameters of each micro-element relative to the surface boundary and the deep soil boundary; Spatial grid data is generated based on the distance parameters.

7. A device for assessing the temperature of cable conductors during directional drilling, characterized in that, The device includes: The data acquisition module is used to acquire spatial grid data and parameter datasets of multiple cables; the parameter dataset includes at least the thermal conductivity and geometric parameters of the materials; the spatial network data includes the distance parameters of multiple micro-segments in the cables and the relative positional relationships of each cable; The first calculation module is used to calculate the soil thermal resistance of each micro-segment relative to the surface boundary and the deep soil boundary based on the thermal conductivity of the material, the geometric parameters and the spatial grid data, and obtain thermal resistance parameter data. The second calculation module is used to calculate the mutual heat influence data between the cables based on the relative positional relationship. The data processing module is used to calculate the conductor temperature of each micro-segment based on the thermal resistance parameter data and the mutual heat influence data, form micro-segment temperature distribution data, and determine the conductor temperature distribution result based on the micro-segment temperature distribution data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images