Walker constellation orbit parameter optimization method and device, equipment and medium

Abstract

The invention provides a Walker constellation orbit parameter optimization method and device, equipment and a medium, and relates to the technical field of spaceflight, and the method comprises the steps: obtaining a plurality of first alternative parameter values of a to-be-optimized first orbit parameter item of a Walker constellation; and for any first alternative parameter value of the first orbit parameter item, optimizing a second alternative parameter value of a to-be-optimized second orbit parameter item of the Walker constellation, and continuously calling the orbit simulation model in the optimization process to output an evaluation result corresponding to the second alternative parameter value until the second alternative parameter value corresponding to the optimal evaluation result is determined, obtaining a parameter value combination; and screening out a first target parameter value and a second target parameter value from the plurality of parameter value combinations according to the evaluation result. According to the method, the workload of designers is effectively reduced, the problem of incomplete optimization caused by individual differences of the designers is solved, and the design efficiency and the design quality in the constellation argumentation stage are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of aerospace technology, and in particular to a method, device, equipment and medium for optimizing orbital parameters of a Walker constellation. Background Art

[0002] Currently, constellation design is carried out through professional software modeling, and parameters such as single-star orbit parameters and constellation configuration parameters are configured during modeling. In order to achieve a better constellation design effect, the parameters configured during modeling need to be optimized. However, the existing optimization process has low optimization efficiency and high manual participation, which poses a challenge to the work efficiency of constellation design. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a Walker constellation orbit parameter optimization method, device, equipment and medium, which effectively reduces the workload of designers, solves the problem of incomplete optimization caused by individual differences of designers, and effectively improves the design efficiency and design quality in the constellation demonstration stage.

[0004] In a first aspect, the present invention provides a Walker constellation orbit parameter optimization method, comprising:

[0005] Acquire multiple first candidate parameter values ​​of a first orbital parameter item to be optimized of the Walker constellation; wherein the first orbital parameter item is a discrete parameter item;

[0006] For any first candidate parameter value of the first orbital parameter item, optimize the second candidate parameter value of the second orbital parameter item to be optimized of the Walker constellation, and continuously call the orbital simulation model to output the evaluation result corresponding to the second candidate parameter value during the optimization process, until the second candidate parameter value corresponding to the optimal evaluation result is determined, so as to obtain a parameter value combination; wherein the second orbital parameter item is a continuous parameter item, and the parameter value combination includes the first candidate parameter value and the second candidate parameter value corresponding to the optimal evaluation result;

[0007] According to the evaluation result, a first target parameter value of the first track parameter item and a second target parameter value of the second track parameter item are screened out from the plurality of parameter value combinations.

[0008] In one embodiment, the first orbit parameter item includes a phase factor parameter item, and the first candidate parameter value is the phase factor parameter value; the second orbit parameter item includes an ascending node right ascension distribution range parameter item, and the second candidate parameter value is the ascending node right ascension distribution range;

[0009] For any first candidate parameter value of the first orbital parameter item, optimizing the second candidate parameter value of the second orbital parameter item to be optimized of the Walker constellation, and continuously calling the orbital simulation model to output the evaluation result corresponding to the second candidate parameter value during the optimization process, until the second candidate parameter value corresponding to the optimal evaluation result is determined, including:

[0010] For any phase factor parameter value, the ascending node right ascension distribution range of the Walker constellation is optimized, and during the optimization process, the orbit simulation model is continuously called to output the evaluation results corresponding to the ascending node right ascension distribution range, until the ascending node right ascension distribution range corresponding to the optimal evaluation result is determined, thereby realizing the one-dimensional optimization design of the ascending node right ascension distribution range.

[0011] In one implementation, for any phase factor parameter value, optimizing the right ascension distribution range of the ascending node of the Walker constellation, and continuously calling the orbit simulation model to output the evaluation result corresponding to the right ascension distribution range of the ascending node during the optimization process, until the right ascension distribution range of the ascending node corresponding to the optimal evaluation result is determined, including:

[0012] Obtain an initial interval of a second parameter value of the ascending node right ascension distribution range parameter item;

[0013] Calling the orbit simulation model to determine the distribution range of the right ascension of the ascending node from the initial interval of the second parameter value based on the phase factor parameter value;

[0014] Determine the compliance of the distribution range of the ascending node right ascension;

[0015] If the right ascension distribution range of the ascending node fails to pass the compliance judgment, the initial interval of the second parameter value is expanded, and a new right ascension distribution range of the ascending node is determined from the expanded initial interval of the second parameter value, until the new right ascension distribution range of the ascending node passes the compliance judgment, and the new right ascension distribution range of the ascending node is the right ascension distribution range of the ascending node corresponding to the optimal evaluation result.

[0016] In one embodiment, calling the orbit simulation model to determine the distribution range of the right ascension of the ascending node from the initial interval of the second parameter value based on the phase factor parameter value includes:

[0017] Determining a second parameter value at a split point from within an initial interval of second parameter values ​​based on a preset split coefficient;

[0018] Calling the orbit simulation model, based on the phase factor parameter value and the second parameter value at the split point, simulates and calculates the Walker constellation, and uses the simulation calculation result to evaluate the second parameter value at the split point, and the evaluation result is used to determine the second parameter value search interval;

[0019] Based on the preset split coefficient, a new second parameter value at the split point is determined from the second parameter value search interval until the length of the updated second parameter value search interval is less than the preset tolerance, and the updated second parameter value is used as the ascending node right ascension distribution range.

[0020] In one implementation, compliance determination of the distribution range of the ascending node right ascension includes:

[0021] Determine whether the distribution range of the right ascension of the ascending node is outside the initial interval of the second parameter value, or is on the boundary of the initial interval of the second parameter value;

[0022] If yes, it is determined that the distribution range of the right ascension of the ascending node fails to pass the compliance judgment; if no, it is determined that the distribution range of the right ascension of the ascending node passes the compliance judgment.

[0023] In one embodiment, the first orbit parameter item includes a phase factor parameter item, the first candidate parameter value is the phase factor parameter value, the second orbit parameter item includes an ascending node right ascension distribution range parameter item and an orbit inclination parameter item, and the second candidate parameter value is the orbit inclination value;

[0024] For any first candidate parameter value of the first orbital parameter item, optimizing the second candidate parameter value of the second orbital parameter item to be optimized of the Walker constellation, and continuously calling the orbital simulation model to output the evaluation result corresponding to the second candidate parameter value during the optimization process until the second candidate parameter value corresponding to the optimal evaluation result is determined, further comprising:

[0025] For any phase factor parameter value, the ascending node right ascension distribution range and orbital inclination value of the Walker constellation are optimized, and during the optimization process, the orbit simulation model is continuously called to output the evaluation results corresponding to the ascending node right ascension distribution range and orbital inclination value, until the ascending node right ascension distribution range and orbital inclination value corresponding to the optimal evaluation result are determined, thereby realizing the two-dimensional optimization design of the ascending node right ascension distribution range and orbital inclination value.

[0026] In one embodiment, the result of the simulation calculation of the Walker constellation includes a maximum revisit time, and the maximum revisit time and the distribution range of the right ascension of the ascending node show a first-order differential monotonic change trend;

[0027] According to the evaluation result, a first target parameter value of the first track parameter item and a second target parameter value of the second track parameter item are screened out from the plurality of parameter value combinations, including:

[0028] The maximum revisit time corresponding to each parameter value combination is compared, and the first candidate parameter value and the second candidate parameter value in the parameter value combination corresponding to the maximum revisit time with the smallest value are used as the first target parameter value of the first orbit parameter item and the second target parameter value of the second orbit parameter item, respectively.

[0029] In a second aspect, the present invention further provides a Walker constellation orbit parameter optimization device, comprising:

[0030] A parameter acquisition module, used to acquire a plurality of first candidate parameter values ​​of a first orbital parameter item to be optimized of the Walker constellation; wherein the first orbital parameter item is a discrete parameter item;

[0031] A parameter optimization module is used to optimize the second candidate parameter value of the second orbital parameter item to be optimized of the Walker constellation for any first candidate parameter value of the first orbital parameter item, and continuously call the orbital simulation model to output the evaluation result corresponding to the second candidate parameter value during the optimization process, until the second candidate parameter value corresponding to the optimal evaluation result is determined, so as to obtain a parameter value combination; wherein the second orbital parameter item is a continuous parameter item, and the parameter value combination includes the first candidate parameter value and the second candidate parameter value corresponding to the optimal evaluation result;

[0032] The parameter determination module is used to select a first target parameter value of the first track parameter item and a second target parameter value of the second track parameter item from multiple parameter value combinations according to the evaluation result.

[0033] In a third aspect, the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement any one of the methods provided in the first aspect.

[0034] In a fourth aspect, the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement any one of the methods provided in the first aspect.

[0035] The Walker constellation orbit parameter optimization method, device, equipment and medium provided by the present invention first obtain multiple first candidate parameter values ​​of the first orbit parameter item to be optimized by the Walker constellation; wherein the first orbit parameter item is a discrete parameter item; then, for any first candidate parameter value of the first orbit parameter item, optimize the second candidate parameter value of the second orbit parameter item to be optimized by the Walker constellation, and continuously call the orbit simulation model to output the evaluation result corresponding to the second candidate parameter value during the optimization process, until the second candidate parameter value corresponding to the optimal evaluation result is determined, so as to obtain a parameter value combination; wherein the second orbit parameter item is a discrete parameter item; then, for any first candidate parameter value of the first orbit parameter item, optimize the second candidate parameter value of the second orbit parameter item to be optimized by the Walker constellation, and continuously call the orbit simulation model to output the evaluation result corresponding to the second candidate parameter value during the optimization process, until the second candidate parameter value corresponding to the optimal evaluation result is determined, so as to obtain a parameter value combination; wherein the second orbit The parameter items are continuous parameter items, and the parameter value combinations include the first candidate parameter value and the second candidate parameter value corresponding to the optimal evaluation result; finally, according to the evaluation result, the first target parameter value of the first orbital parameter item and the second target parameter value of the second orbital parameter item are screened out from multiple parameter value combinations, thereby realizing the fully automatic evaluation of the Walker constellation orbital parameters. Moreover, in the constellation demonstration and design stage, repeated practical attempts and trial and error improvements can be made, which effectively reduces the workload of designers. At the same time, it solves the problem of incomplete optimization caused by individual differences among designers, and effectively improves the design efficiency and quality in the constellation demonstration stage.

[0036] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0037] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A schematic diagram of a flow chart of a Walker constellation orbit parameter optimization method provided by an embodiment of the present invention;

[0040] Figure 2 A schematic diagram of a flow chart of an optimization process provided by an embodiment of the present invention;

[0041] Figure 3 An example of a track simulation software scenario provided by an embodiment of the present invention;

[0042] Figure 4 A schematic diagram of a ground elevation angle constraint provided by an embodiment of the present invention;

[0043] Figure 5 An example of a Walker constellation orbit parameter optimization process provided by an embodiment of the present invention;

[0044] Figure 6 A schematic diagram of the structure of a Walker constellation orbit parameter optimization device provided by an embodiment of the present invention;

[0045] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described in combination with the embodiments below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] At present, there are problems of low optimization efficiency and much manual participation in constellation design. Based on this, the present invention provides a Walker constellation orbit parameter optimization method, device, equipment and medium, which effectively reduces the workload of designers, solves the problem of incomplete optimization caused by individual differences of designers, and effectively improves the design efficiency and quality in the constellation demonstration stage.

[0048] To facilitate understanding of this embodiment, a Walker constellation orbit parameter optimization method disclosed in an embodiment of the present invention is first described in detail. Figure 1 A schematic flow chart of a Walker constellation orbit parameter optimization method is shown, and the method mainly includes the following steps S102 to S106:

[0049] Step S102, obtaining a plurality of first candidate parameter values ​​of a first orbital parameter item to be optimized of the Walker constellation, wherein the first orbital parameter item is a discrete parameter item, the first orbital parameter item includes a phase factor parameter item, and the first candidate parameter value is a phase factor parameter value.

[0050] Step S104: For any first candidate parameter value of the first orbital parameter item, optimize the second candidate parameter value of the second orbital parameter item to be optimized of the Walker constellation, and continuously call the orbit simulation model to output the evaluation result corresponding to the second candidate parameter value during the optimization process, until the second candidate parameter value corresponding to the optimal evaluation result is determined to obtain a parameter value combination.

[0051] Among them, the second track parameter item is a continuous parameter item, and the parameter value combination includes the first candidate parameter value and the second candidate parameter value corresponding to the optimal evaluation result.

[0052] In one example, the second orbit parameter item may include the ascending node right ascension distribution range parameter item, and the second candidate parameter value is also the ascending node right ascension distribution range. For any phase factor parameter value, the ascending node right ascension distribution range can be optimized, and the orbit simulation model is continuously called during the optimization process to evaluate the ascending node right ascension distribution range until the ascending node right ascension distribution range corresponding to the optimal evaluation result is determined, thereby realizing one-dimensional optimization design (such as the golden section line search method, etc.). In another example, the second orbit parameter item may include the ascending node right ascension distribution range parameter item and the orbit inclination parameter item, and the second candidate parameter value is also the ascending node right ascension distribution range and the orbit inclination value. For any phase factor parameter value, the ascending node right ascension distribution range and the orbit inclination value can be optimized, and the orbit simulation model is continuously called during the optimization process to evaluate the ascending node right ascension distribution range and the orbit inclination value until the ascending node right ascension distribution range and the orbit inclination value corresponding to the optimal evaluation result are determined, thereby realizing two-dimensional optimization design (such as the Newton iteration algorithm, etc.).

[0053] The process of calling the orbit simulation model for evaluation is as follows: the candidate parameter values ​​of the above parameter items are input into the orbit simulation model to use the orbit simulation model to simulate the Walker constellation, and the result of the simulation calculation is the maximum revisit time, and the maximum revisit time and the distribution range of the right ascension of the ascending node show a first-order differential monotonic change trend. In one example, the maximum revisit time is used to evaluate the distribution range of the right ascension of the ascending node in an embodiment of the present invention.

[0054] Step S106: According to the evaluation result, a first target parameter value of the first track parameter item and a second target parameter value of the second track parameter item are screened out from the plurality of parameter value combinations.

[0055] In one example, taking the minimum value of the maximum revisit time as a constraint, a target parameter combination is determined from multiple parameter value combinations, and the first alternative parameter value and the second alternative parameter value in the target parameter combination are respectively determined as the first target parameter value of the first orbital parameter item and the second target parameter value of the second orbital parameter item.

[0056] The Walker constellation orbit parameter optimization method provided by the embodiment of the present invention runs fully automatically. The second candidate parameter value of the second orbit parameter item corresponding to each first candidate parameter value of the first orbit parameter item is determined through optimization design, and the orbit simulation model is continuously called in the optimization process to evaluate the candidate parameter values ​​of the two parameter items to obtain multiple parameter value combinations and their corresponding evaluation results. Finally, the target parameter values ​​of the two parameter items are screened out from the multiple parameter value combinations according to the evaluation results. The embodiment of the present invention repeatedly practices and tries in the constellation demonstration design stage, and makes trial and error improvements, which effectively reduces the workload of designers, and solves the problem of incomplete optimization caused by individual differences of designers, and effectively improves the design efficiency and design quality in the constellation demonstration stage.

[0057] In one implementation, the embodiment of the present invention is aimed at the constellation orbit design evaluation optimization problem for large field of view payloads. Based on the system's underlying transmission protocol, the inter-plane phase factor and the ascending node right ascension distribution range of the Walker constellation are designed under the given number of satellites, orbit altitude and payload visible range, number of planes, and number of satellites. Surveys show that for the above-mentioned target problem, under the given configuration of constellation orbit parameters, the objective function value (i.e., the maximum revisit time) and the parameter to be optimized (i.e., the ascending node right ascension distribution range) show a first-order differential monotonic change trend, and the basic convex optimization idea can be used for optimization. The optimal value is obtained after a limited number of evaluations. For other inter-plane phase factors, the same idea is used. After repeated execution, the optimal constellation orbit parameters under the specified configuration can be statistically obtained.

[0058] For ease of understanding, an embodiment of the present invention provides a specific implementation of a Walker constellation orbit parameter optimization method.

[0059] The aforementioned step S104 is specifically divided into the following two situations:

[0060] In case 1, the first orbit parameter item includes a phase factor parameter item, and the second orbit parameter item includes an ascending node right ascension distribution range parameter item. On this basis, an embodiment of the present invention provides an implementation method of an optimization design, which optimizes the ascending node right ascension distribution range of the Walker constellation for any phase factor parameter value, and continuously calls the orbit simulation model to output the evaluation result corresponding to the ascending node right ascension distribution range during the optimization process, until the ascending node right ascension distribution range corresponding to the optimal evaluation result is determined, thereby realizing a one-dimensional optimization design of the ascending node right ascension distribution range.

[0061] Case 2: The first orbital parameter item includes a phase factor parameter item, and the second orbital parameter item includes an ascending node right ascension distribution range parameter item and an orbital inclination parameter item. On this basis, an embodiment of the present invention provides an implementation method for optimizing the design, which optimizes the ascending node right ascension distribution range and orbital inclination value of the Walker constellation for any phase factor parameter value, and continuously calls the orbital simulation model to output the evaluation results corresponding to the ascending node right ascension distribution range and orbital inclination value during the optimization process, until the ascending node right ascension distribution range and orbital inclination value corresponding to the optimal evaluation result are determined, thereby realizing a two-dimensional optimization design of the ascending node right ascension distribution range and orbital inclination value.

[0062] The embodiment of the present invention reserves the extensibility of the design dimension at the architectural level, that is, the increase of the second orbit parameter item to be optimized. For example, the present architecture is expected to be further expanded to add the design of orbital inclination to the underlying optimization logic to form a 2D optimization problem, and the expanded problem can still be solved according to the convex optimization problem.

[0063] In a specific implementation, the aforementioned step S104 is explained by taking case 1 as an example, that is, the case of optimizing the phase factor parameter item InterPlanePhase and the ascending node right ascension distribution range parameter item RAANSpacing between the design surfaces. The first candidate parameter value of the phase factor parameter item InterPlanePhase (that is, the phase factor parameter value) is recorded as the candidate InterPlanePhase value, and the second candidate parameter value of the ascending node right ascension distribution range parameter item RAANSpacing (that is, the ascending node right ascension distribution range) is recorded as the candidate RAANSpacing value. For the discrete variable phase factor parameter item InterPlanePhase, a simple loop is used to make the one-dimensional optimization problem of the ascending node right ascension distribution range parameter item RAANSpacing formed in each loop. The evaluation function provided by the orbit simulation software is combined with the optimizer to obtain the optimal candidate RAANSpacing value corresponding to the candidate RAANSpacing value.

[0064] The embodiment of the present invention uses two layers of logic to process the optimization problem of two orbital parameter items to be optimized. Figure 2 The flowchart of an optimization process shown in FIG. 1 is a flowchart of an optimization process, in which the discrete phase factor parameter item InterPlanePhase is examined in the upper logic, and each possible value, which is also the first candidate parameter value, is evaluated in a cyclic manner. In the lower logic, the one-dimensional optimization problem of the continuous ascending node right ascension distribution range parameter item RAANSpacing is performed.

[0065] The two orbital parameter items to be optimized and the two-layer optimization logic mentioned in the embodiment of the present invention are determined through prior investigation and research. The investigation found that in the large-field-of-view payload constellation orbit performance evaluation optimization problem faced by the embodiment of the present invention, if the constellation orbit parameters and payload semi-cone angle configuration are given, the maximum revisit time and the ascending node right ascension distribution range show a first-order differential monotonic change trend, and the basic convex optimization idea can be used for optimization.

[0066] Optionally, the one-dimensional optimization design algorithm is performed using the Golden Section Search method. The application of this algorithm in the embodiment of the present invention is determined through prior investigation and research. Compared with the Newton-like convex optimization algorithm that requires gradient information, this algorithm does not require gradient information, avoiding the potential problem of convergence failure or low convergence efficiency caused by unreliable gradient information caused by the differential sensitivity of the problem. Specifically, for each specified alternative InterPlanePhase value, the alternative RAANSpacing value is repeatedly tried using the Golden Section Search method, and is input into the orbit simulation software to obtain its corresponding maximum revisit time. Finally, the corresponding optimal maximum revisit time and its corresponding alternative RAANSpacing value are found, which are regarded as the optimal solution under the alternative InterPlanePhase value.

[0067] Furthermore, the embodiment of the present invention provides a specific implementation method for determining the optimal candidate RAANSpacing value for each candidate InterPlanePhase value using the golden section search method, including the following (1) to (4):

[0068] (1) Obtaining an initial interval of the second parameter value of the ascending node right ascension distribution range parameter item. In one example, the initial interval of the second parameter value is also the initial interval of the ascending node right ascension distribution range, and a second parameter value initial interval [a, b] containing an extreme point is selected.

[0069] (2) Calling the orbit simulation model to determine the distribution range of the right ascension of the ascending node from the initial interval of the second parameter value based on the phase factor parameter value. For details, see (2.1) to (2.3) below:

[0070] (2.1) Determine the second parameter value at the split point within the initial interval of the second parameter value based on the preset split coefficient. The split point is also the golden section point, and the second parameter value is also the distribution range of the right ascension of the ascending node. In one example, two internal points (also the golden section points) c and d are calculated according to the following formula so that the interval is divided into the golden ratio:

[0071] c = br·(ba);

[0072] d = a + r·(ba);

[0073] where r is the golden ratio coefficient, equal to

[0074] (2.2) Invoke the orbit simulation model, and based on the phase factor parameter value and the second parameter value at the segmentation point, perform simulation calculations on the Walker constellation. Use the results of the simulation calculations to evaluate the second parameter value at the segmentation point, and the evaluation results are used to determine the search interval of the second parameter value. The search interval of the second parameter value is also the search interval of the right ascension of the ascending node distribution range.

[0075] In one example, input the alternative InterPlanePhase value and the RAANSpacing value at the internal point c into the orbit simulation model, so that it outputs the corresponding maximum revisit time, denoted as f(c); similarly, input the alternative InterPlanePhase value and the RAANSpacing value at the internal point d into the orbit simulation model, so that it outputs the corresponding maximum revisit time, denoted as f(d). Among them, f(c) and f(d) are also the results of the simulation calculations.

[0076] In one example, compare the values of f(c) and f(d), and narrow the search interval of the second parameter value according to the following rules: If f(c) < f(d), then the minimum value is in the interval [a, d], and update b = d; if f(c) > f(d), then the minimum value is in the interval [c, b], and update a = c.

[0077] (2.3) Determine a new second parameter value at the segmentation point from the search interval of the second parameter value based on a preset segmentation coefficient, until the length of the updated search interval of the second parameter value is less than the preset tolerance, and use the updated second parameter value as the right ascension of the ascending node distribution range.

[0078] In one example, for the narrowed search interval of the second parameter value, repeat steps (2.1) to (2.2) until the length of the updated search interval of the second parameter value is less than the preset tolerance. At this time, the RAANSpacing value of the alternative InterPlanePhase value can be obtained.

[0079] (3) Perform compliance determination on the right ascension of the ascending node distribution range.

[0080] In one example, determine whether the right ascension of the ascending node distribution range is outside the initial interval of the second parameter value or on the boundary of the initial interval of the second parameter value; if so, determine that the right ascension of the ascending node distribution range fails the compliance determination; if not, determine that the right ascension of the ascending node distribution range passes the compliance determination.

[0081] (4) If the distribution range of the right ascension of the ascending node fails to pass the compliance judgment, the initial interval of the second parameter value is expanded, and a new distribution range of the right ascension of the ascending node is determined from the expanded initial interval of the second parameter value, until the new distribution range of the right ascension of the ascending node passes the compliance judgment. The new distribution range of the right ascension of the ascending node is the distribution range of the right ascension of the ascending node corresponding to the optimal evaluation result.

[0082] Determine whether the RAANSpacing value falls outside the initial interval of the second parameter value or is on the boundary of the initial interval of the second parameter value. If the determination is yes, adjust to a new initial interval of the second parameter value and re-execute (2) and (3) until the RAANSpacing value falls within the current expanded initial interval of the second parameter value, and use the RAANSpacing value at this time as the candidate RAANSpacing value corresponding to the candidate InterPlanePhase value.

[0083] The optimal value compliance determination process provided by the embodiment of the present invention is configured with an automatic recognition mechanism for the case where the optimal value falls outside the initial interval of the second parameter value. After recognition, the initial interval of the second parameter value is automatically expanded and the search is restarted to ensure that the initial interval of the second parameter value can cover the optimal value landing point, thereby ensuring the convergence stability and reliability of the method.

[0084] Furthermore, with respect to the aforementioned (2.2), an embodiment of the present invention provides a specific implementation method for evaluating the right ascension distribution range of the ascending node by calling an orbit simulation model, including two steps: setting calculation conditions and professional software modeling and calculation.

[0085] (I) Setting calculation conditions: Input calculation conditions, including orbital altitude, orbital inclination, number of constellation faces, number of satellites per face, elevation angle constraint and other information required for scene setting. Other information includes scene start / end time, orbital epochs of each satellite, epoch orbital parameters other than orbital altitude and inclination of each satellite, latitude and longitude range of simulation area, finite element mesh size and other parameter settings necessary for simulation.

[0086] (II) Professional software modeling and calculation:

[0087] The evaluation function is repeatedly called by step S104. Based on the parameters set in (a), the track simulation software STK is driven by the system underlying transmission protocol to establish the track scene and perform the maximum revisit time (Maximum Revisit Time) finite element calculation. Wait until the calculation is completed. The track simulation software returns the calculation result of the maximum revisit time.

[0088] The automatic execution process in the orbit simulation software is driven by the underlying transmission protocol of the Windows system. The scene modeling is done through code rather than manual operation. The automatically established scene model includes sample satellite model, load model, Walker constellation satellite / load model, finite element coverage calculation model, simulation calculation report, coverage data geographical distribution drawing and other functions, such as Figure 3 An example of a track simulation software scenario is shown.

[0089] Specific:

[0090] (a) Through the underlying transport protocol, the track simulation software sets the start / end time of the scene.

[0091] (b) Through the underlying transport protocol, the orbit simulation software sets the sample satellite orbit epoch and epoch orbit (according to the specified alternative InterPlanePhase value). Based on the sample satellite, it is extended to the Walker constellation with the specified number of orbital planes, number of satellites, and alternative RAANSpacing values.

[0092] (c) Through the underlying transmission protocol, let the track simulation software set the load and its semi-cone angle. The calculation method of the load semi-cone angle C is as follows:

[0093]

[0094] Where E is the ground elevation constraint, such as Figure 4 A schematic diagram of ground elevation angle constraint is shown in FIG. 1 , where h is the orbit height, R e is the radius of the Earth.

[0095] (d) Through the underlying transmission protocol, the track simulation software sets the latitude and longitude range of the simulation area and the finite element grid size.

[0096] (e) Through the underlying transmission protocol, the track simulation software is instructed to perform simulation calculations and output the maximum revisit time. A geographical distribution map of the maximum revisit time is drawn and displayed when necessary.

[0097] In one embodiment, the result of the simulation calculation of the Walker constellation includes the maximum revisit time, and the maximum revisit time and the distribution range of the right ascension of the ascending node show a first-order differential monotonic change trend. Based on this, the embodiment of the present invention provides a specific implementation of the aforementioned step S106, including: comparing the maximum revisit time corresponding to each parameter value combination, and using the first candidate parameter value and the second candidate parameter value in the parameter value combination corresponding to the maximum revisit time with the smallest value as the first target parameter value of the first orbital parameter item and the second target parameter value of the second orbital parameter item, respectively. In the specific implementation, under each first candidate parameter value, according to the optimal maximum revisit time obtained by the optimized design, the candidate InterPlanePhase value and the candidate RAANSpacing value corresponding to the maximum revisit time with the smallest value are used as the target InterPlanePhase value and the target RAANSpacing value.

[0098] For example, see Figure 5 An example of Walker constellation orbit parameter optimization process is shown, where P×S is the number of planes×the number of stars per plane, F is the candidate InterPlanePhase value, the horizontal axis is the candidate RAANSpacing value corresponding to the candidate InterPlanePhase value, and the vertical axis is the maximum revisit time MaxRevisit corresponding to the candidate InterPlanePhase value. In one example, for each candidate InterPlanePhase value and its corresponding candidate RAANSpacing value, when input into the orbit simulation software, the corresponding maximum revisit time can be output, and the minimum value of the maximum revisit time is constrained, that is, the candidate InterPlanePhase value and the candidate RAANSpacing value corresponding to the maximum revisit time with the minimum value are used as the target InterPlanePhase value and the target RAANSpacing value.

[0099] In summary, the method is a set of automated optimization solutions for the design of the longitude range of the ascending node of the constellation, including: setting calculation conditions, professional software modeling and calculation, design optimization, optimal value compliance judgment, comparison and output. Aiming at the orbital performance evaluation optimization problem of large field-of-view payloads, an optimization design solution for the core orbital parameters of the constellation is provided under the premise of a given number of satellites, orbital altitude, payload visible range, number of faces, and number of satellites. The embodiment of the invention runs fully automatically without manual intervention. On the one hand, it effectively reduces the workload of designers, and the easy and efficient evaluation method helps to interpret the relationship between design variables and design goals and clarifies the design logic; on the other hand, the systematic design method avoids the incomplete optimization caused by individual differences of designers in manual optimization design, and effectively improves the design efficiency and quality in the constellation demonstration stage.

[0100] Based on the above embodiments, the present invention provides a Walker constellation orbit parameter optimization device. Figure 6 A schematic diagram of the structure of a Walker constellation orbit parameter optimization device is shown, and the device mainly includes the following parts:

[0101] The parameter acquisition module 602 is used to acquire a plurality of first candidate parameter values ​​of a first orbital parameter item to be optimized of the Walker constellation; wherein the first orbital parameter item is a discrete parameter item;

[0102] The parameter optimization module 604 is used to optimize the second candidate parameter value of the second orbital parameter item to be optimized of the Walker constellation for any first candidate parameter value of the first orbital parameter item, and continuously call the orbital simulation model to output the evaluation result corresponding to the second candidate parameter value during the optimization process, until the second candidate parameter value corresponding to the optimal evaluation result is determined, so as to obtain a parameter value combination; wherein the second orbital parameter item is a continuous parameter item, and the parameter value combination includes the first candidate parameter value and the second candidate parameter value corresponding to the optimal evaluation result;

[0103] The parameter determination module 606 is used to select a first target parameter value of the first track parameter item and a second target parameter value of the second track parameter item from multiple parameter value combinations according to the evaluation result.

[0104] The Walker constellation orbit parameter optimization device provided by the embodiment of the present invention operates fully automatically, and determines the second candidate parameter value of the second orbit parameter item corresponding to each first candidate parameter value of the first orbit parameter item through optimization design, thereby calling the orbit simulation model to evaluate the candidate parameter values ​​of the two parameter items, and then determining the target parameter values ​​of the two parameter items. The embodiment of the present invention repeatedly practices and tries, and makes improvements through trial and error in the constellation demonstration design stage, which effectively reduces the workload of designers, and solves the problem of incomplete optimization caused by individual differences of designers, and effectively improves the design efficiency and design quality in the constellation demonstration stage.

[0105] In one implementation, the first orbit parameter item includes a phase factor parameter item, and the first candidate parameter value is the phase factor parameter value; the second orbit parameter item includes an ascending node right ascension distribution range parameter item, and the second candidate parameter value is an ascending node right ascension distribution range; the parameter optimization module 604 is specifically used to:

[0106] For any phase factor parameter value, the ascending node right ascension distribution range of the Walker constellation is optimized, and during the optimization process, the orbit simulation model is continuously called to output the evaluation results corresponding to the ascending node right ascension distribution range, until the ascending node right ascension distribution range corresponding to the optimal evaluation result is determined, thereby realizing the one-dimensional optimization design of the ascending node right ascension distribution range.

[0107] In one implementation, the parameter optimization module 604 is specifically used to:

[0108] Obtain an initial interval of a second parameter value of the ascending node right ascension distribution range parameter item;

[0109] Calling the orbit simulation model to determine the distribution range of the right ascension of the ascending node from the initial interval of the second parameter value based on the phase factor parameter value;

[0110] Determine the compliance of the distribution range of the ascending node right ascension;

[0111] If the right ascension distribution range of the ascending node fails to pass the compliance judgment, the initial interval of the second parameter value is expanded, and a new right ascension distribution range of the ascending node is determined from the expanded initial interval of the second parameter value, until the new right ascension distribution range of the ascending node passes the compliance judgment, and the new right ascension distribution range of the ascending node is the right ascension distribution range of the ascending node corresponding to the optimal evaluation result.

[0112] In one implementation, the parameter optimization module 604 is specifically used to:

[0113] Determining a second parameter value at a split point from within an initial interval of second parameter values ​​based on a preset split coefficient;

[0114] Calling the orbit simulation model, based on the phase factor parameter value and the second parameter value at the split point, simulates and calculates the Walker constellation, and uses the simulation calculation result to evaluate the second parameter value at the split point, and the evaluation result is used to determine the second parameter value search interval;

[0115] Based on the preset split coefficient, a new second parameter value at the split point is determined from the second parameter value search interval until the length of the updated second parameter value search interval is less than the preset tolerance, and the updated second parameter value is used as the ascending node right ascension distribution range.

[0116] In one implementation, the parameter optimization module 604 is specifically used to:

[0117] Determine whether the distribution range of the right ascension of the ascending node is outside the initial interval of the second parameter value, or is on the boundary of the initial interval of the second parameter value;

[0118] If yes, it is determined that the distribution range of the right ascension of the ascending node fails to pass the compliance judgment; if no, it is determined that the distribution range of the right ascension of the ascending node passes the compliance judgment.

[0119] In one implementation, the first orbit parameter item includes a phase factor parameter item, the first candidate parameter value is the phase factor parameter value, the second orbit parameter item includes an ascending node right ascension distribution range parameter item and an orbit inclination parameter item, and the second candidate parameter value is the orbit inclination value; the parameter optimization module 604 is further specifically used for:

[0120] For any phase factor parameter value, the ascending node right ascension distribution range and orbital inclination value of the Walker constellation are optimized, and during the optimization process, the orbit simulation model is continuously called to output the evaluation results corresponding to the ascending node right ascension distribution range and orbital inclination value, until the ascending node right ascension distribution range and orbital inclination value corresponding to the optimal evaluation result are determined, thereby realizing the two-dimensional optimization design of the ascending node right ascension distribution range and orbital inclination value.

[0121] In one implementation, the result of the simulation calculation of the Walker constellation includes the maximum revisit time, and the maximum revisit time and the distribution range of the right ascension of the ascending node show a first-order differential monotonic change trend. The parameter determination module 606 is specifically used for:

[0122] The maximum revisit time corresponding to each parameter value combination is compared, and the first candidate parameter value and the second candidate parameter value in the parameter value combination corresponding to the maximum revisit time with the smallest value are used as the first target parameter value of the first orbit parameter item and the second target parameter value of the second orbit parameter item, respectively.

[0123] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0124] An embodiment of the present invention provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned implementation methods.

[0125] Figure 7 A structural diagram of an electronic device provided in an embodiment of the present invention, the electronic device 100 includes: a processor 70, a memory 71, a bus 72 and a communication interface 73, wherein the processor 70, the communication interface 73 and the memory 71 are connected via the bus 72; the processor 70 is used to execute an executable module stored in the memory 71, such as a computer program.

[0126] The memory 71 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 73 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0127] The bus 72 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0128] Among them, the memory 71 is used to store programs, and the processor 70 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 70 or implemented by the processor 70.

[0129] The processor 70 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 70. The above processor 70 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to be executed, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 71, and the processor 70 reads the information in the memory 71 and completes the steps of the above method in combination with its hardware.

[0130] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0131] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0132] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A Walker constellation orbit parameter optimization method, characterized in that: include: Acquire multiple first candidate parameter values ​​of a first orbital parameter item to be optimized of the Walker constellation; wherein the first orbital parameter item is a discrete parameter item; For any of the first candidate parameter values ​​of the first orbital parameter item, optimize the second candidate parameter value of the second orbital parameter item to be optimized of the Walker constellation, and continuously call the orbital simulation model to output the evaluation result corresponding to the second candidate parameter value during the optimization process, until the second candidate parameter value corresponding to the optimal evaluation result is determined, so as to obtain a parameter value combination; wherein the second orbital parameter item is a continuous parameter item, and the parameter value combination includes the first candidate parameter value and the second candidate parameter value corresponding to the optimal evaluation result; According to the evaluation result, a first target parameter value of the first orbit parameter item and a second target parameter value of the second orbit parameter item are screened out from the plurality of parameter value combinations.

2. The Walker constellation orbit parameter optimization method according to claim 1, characterized in that: The first orbit parameter item includes a phase factor parameter item, and the first candidate parameter value is the phase factor parameter value; the second orbit parameter item includes an ascending node right ascension distribution range parameter item, and the second candidate parameter value is an ascending node right ascension distribution range; For any of the first candidate parameter values ​​of the first orbital parameter item, optimizing a second candidate parameter value of a second orbital parameter item to be optimized of the Walker constellation, and continuously calling the orbital simulation model to output an evaluation result corresponding to the second candidate parameter value during the optimization process until an optimal second candidate parameter value corresponding to the evaluation result is determined, including: For any of the phase factor parameter values, the ascending node right ascension distribution range of the Walker constellation is optimized, and during the optimization process, the orbit simulation model is continuously called to output the evaluation result corresponding to the ascending node right ascension distribution range, until the optimal ascending node right ascension distribution range corresponding to the evaluation result is determined, thereby realizing the one-dimensional optimization design of the ascending node right ascension distribution range.

3. The Walker constellation orbit parameter optimization method according to claim 2, characterized in that: For any of the phase factor parameter values, optimizing the right ascension distribution range of the ascending node of the Walker constellation, and continuously calling the orbit simulation model to output the evaluation result corresponding to the right ascension distribution range of the ascending node during the optimization process, until the right ascension distribution range of the ascending node corresponding to the optimal evaluation result is determined, including: Obtaining an initial interval of a second parameter value of the ascending node right ascension distribution range parameter item; Calling the orbit simulation model to determine the distribution range of the right ascension of the ascending node from the initial interval of the second parameter value based on the phase factor parameter value; Conducting compliance assessment on the distribution range of the ascending node right ascension; If the ascending node right ascension distribution range fails to pass the compliance judgment, the second parameter value initial interval is expanded, and a new ascending node right ascension distribution range is determined from the expanded second parameter value initial interval, until the new ascending node right ascension distribution range passes the compliance judgment, and the new ascending node right ascension distribution range is the ascending node right ascension distribution range corresponding to the optimal evaluation result.

4. The Walker constellation orbit parameter optimization method according to claim 3, characterized in that: Calling the orbit simulation model to determine the distribution range of the right ascension of the ascending node from the initial interval of the second parameter value based on the phase factor parameter value, including: Determining a second parameter value at a split point from within the initial interval of the second parameter value based on a preset split coefficient; calling an orbit simulation model, performing simulation calculation on the Walker constellation based on the phase factor parameter value and the second parameter value at the split point, evaluating the second parameter value at the split point using the simulation calculation result, and using the evaluation result to determine a second parameter value search interval; Based on the preset split coefficient, a new second parameter value at a split point is determined from the second parameter value search interval until the length of the updated second parameter value search interval is less than a preset tolerance, and the updated second parameter value is used as the ascending node right ascension distribution range.

5. The Walker constellation orbit parameter optimization method according to claim 3, characterized in that: The compliance of the distribution range of the ascending node right ascension is determined, including: Determining whether the distribution range of the right ascension of the ascending node is outside the initial interval of the second parameter value, or is on the boundary of the initial interval of the second parameter value; If yes, it is determined that the distribution range of the right ascension of the ascending node fails to pass the compliance judgment; if no, it is determined that the distribution range of the right ascension of the ascending node passes the compliance judgment.

6. The Walker constellation orbit parameter optimization method according to claim 1, characterized in that: The first orbit parameter item includes a phase factor parameter item, and the first candidate parameter value is a phase factor parameter value; the second orbit parameter item includes an ascending node right ascension distribution range parameter item and an orbit inclination parameter item, and the second candidate parameter value is an orbit inclination value; For any of the first candidate parameter values ​​of the first orbital parameter item, optimizing a second candidate parameter value of a second orbital parameter item to be optimized of the Walker constellation, and continuously calling an orbit simulation model to output an evaluation result corresponding to the second candidate parameter value during the optimization process until an optimal second candidate parameter value corresponding to the evaluation result is determined, further comprising: For any phase factor parameter value, the ascending node right ascension distribution range and the orbital inclination value of the Walker constellation are optimized, and during the optimization process, the orbital simulation model is continuously called to output the evaluation results corresponding to the ascending node right ascension distribution range and the orbital inclination value, until the optimal ascending node right ascension distribution range and the orbital inclination value corresponding to the evaluation results are determined, thereby realizing the two-dimensional optimization design of the ascending node right ascension distribution range and the orbital inclination value.

7. The Walker constellation orbit parameter optimization method according to claim 2 or 6, characterized in that: The result of the simulation calculation of the Walker constellation includes a maximum revisit time, and the maximum revisit time and the distribution range of the right ascension of the ascending node present a first-order differential monotonic change trend; According to the evaluation result, selecting a first target parameter value of the first track parameter item and a second target parameter value of the second track parameter item from the plurality of parameter value combinations includes: Compare the maximum revisit times corresponding to each parameter value combination, and use the first candidate parameter value and the second candidate parameter value in the parameter value combination corresponding to the maximum revisit time with the smallest numerical value as the first target parameter value of the first orbital parameter item and the second target parameter value of the second orbital parameter item, respectively.

8. A Walker constellation orbit parameter optimization device, characterized in that: include: A parameter acquisition module, used to acquire multiple first candidate parameter values ​​of a first orbital parameter item to be optimized of the Walker constellation; wherein the first orbital parameter item is a discrete parameter item; a parameter optimization module, for optimizing a second candidate parameter value of a second orbital parameter item to be optimized of the Walker constellation for any of the first candidate parameter values ​​of the first orbital parameter item, and continuously calling the orbital simulation model to output an evaluation result corresponding to the second candidate parameter value during the optimization process, until the second candidate parameter value corresponding to the optimal evaluation result is determined, so as to obtain a parameter value combination; wherein the second orbital parameter item is a continuous parameter item, and the parameter value combination includes the first candidate parameter value and the second candidate parameter value corresponding to the optimal evaluation result; A parameter determination module is used to screen out a first target parameter value of the first track parameter item and a second target parameter value of the second track parameter item from a plurality of parameter value combinations according to the evaluation result.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement 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 computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 7.

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