Earth rotation parameter prediction method based on least square and long short-term memory network

Abstract

The present application relates to the technical field of geophysics, and particularly relates to a method for predicting earth rotation parameters based on least squares and long short-term memory network, comprising: collecting and preprocessing basic sequence data of earth rotation and effective angular momentum data; converting the preprocessed basic sequence data of earth rotation into geodetic angular momentum; fitting the effective angular momentum data and the geodetic angular momentum to obtain fitted sequences and residual sequences of the effective angular momentum and the geodetic angular momentum respectively; inputting the residual sequences of the effective angular momentum and the geodetic angular momentum into a long short-term memory network model to obtain error terms of the effective angular momentum and the geodetic angular momentum; and integrating the fitted sequences of the effective angular momentum and the fitted sequences, the error terms of the effective angular momentum and the geodetic angular momentum to obtain a prediction result of the earth rotation parameters. Thus, the problems that the traditional earth rotation parameter prediction method is difficult to capture the complex nonlinear characteristics in the change of the rotation parameters are solved.

Description

Technical Field

[0001] The present invention relates to the field of geophysics technology, and in particular to a method for predicting earth rotation parameters based on least squares and long short-term memory networks. Background Art

[0002] High-precision Earth rotation parameters are essential for establishing and maintaining the Earth's reference frame and are key for converting between the International Terrestrial Reference Frame (ITRF) and the International Celestial Reference Frame (ICRF). They play a vital role in applications such as deep space exploration, precise orbit determination of artificial satellites, navigation and positioning, and high-precision timing. Accurate predictions of Earth rotation parameters (ERPs) are crucial for modern geodesy, autonomous space positioning, and satellite navigation systems. The effective operation of these fields depends on long-term predictions of ERPs. Therefore, improving the accuracy of ERP predictions is crucial for improving satellite orbit determination and spacecraft positioning.

[0003] Although researchers have access to a wide range of observational data from various geodetic techniques, ERPs cannot be provided in real time due to the complexity of their processing. Traditional prediction methods typically rely on simple linear regression analysis of historical data, which struggles to capture the complex nonlinear characteristics of rotation parameter variations. Therefore, a new prediction method is urgently needed that can capture these complex nonlinear characteristics and thereby improve the accuracy of Earth rotation parameter predictions. Summary of the Invention

[0004] The present invention provides a method for predicting the Earth's rotation parameters based on least squares and long short-term memory networks to solve the problem that traditional Earth's rotation parameter prediction methods usually rely on simple linear regression analysis of historical data and are difficult to capture the complex nonlinear characteristics of rotation parameter changes.

[0005] A first embodiment of the present invention provides a method for predicting Earth rotation parameters based on least squares and long short-term memory network, comprising the following steps:

[0006] The earth rotation basic sequence data and the effective angular momentum data are collected; the earth rotation basic sequence data and the effective angular momentum data are preprocessed to obtain preprocessed earth rotation basic sequence data and preprocessed effective angular momentum data; the preprocessed earth rotation basic sequence data is converted into geodetic angular momentum; the effective angular momentum data and the geodetic angular momentum are fitted to obtain an effective angular momentum fitting sequence, an effective angular momentum residual sequence, a geodetic angular momentum fitting sequence and a geodetic angular momentum residual sequence; the effective angular momentum residual sequence and the geodetic angular momentum residual sequence are input into a long short-term memory network model constructed in advance to obtain an effective angular momentum error term and a geodetic angular momentum error term; the effective angular momentum fitting sequence, the geodetic angular momentum fitting sequence, the effective angular momentum error term and the geodetic angular momentum error term are integrated to obtain an earth total angular momentum prediction result, and the earth total angular momentum prediction result is converted into an earth rotation parameter prediction result.

[0007] Optionally, the earth rotation basic sequence data includes polar motion and earth rotation rate; and the effective angular momentum data includes atmospheric angular momentum, ocean angular momentum, land water angular momentum and sea level angular momentum.

[0008] Optionally, the preprocessing of the earth rotation basic sequence data and the effective angular momentum data to obtain preprocessed earth rotation basic sequence data and preprocessed effective angular momentum data comprises:

[0009] The effective angular momentum data is interpolated into the same interval and the same time as the earth rotation basic sequence data to obtain the preprocessed effective angular momentum data; and the earth rotation basic sequence data is subjected to leap second deduction and earth solid tide correction to obtain the preprocessed earth rotation basic sequence data.

[0010] Optionally, the conversion of the preprocessed earth rotation basic sequence data into geodetic angular momentum comprises:

[0011] The preprocessed earth rotation basic sequence data is converted from polar motion domain to excitation domain through a Liouville equation to obtain the geodetic angular momentum.

[0012] Optionally, the fitting of the effective angular momentum data and the geodetic angular momentum to obtain an effective angular momentum fitting sequence, an effective angular momentum residual sequence, a geodetic angular momentum fitting sequence and a geodetic angular momentum residual sequence comprises:

[0013] The effective angular momentum data and the geodetic angular momentum are fitted through a least square model to obtain the effective angular momentum fitting sequence and the geodetic angular momentum fitting sequence.

[0014] calculate the geodetic angular momentum residual sequence according to the geodetic angular momentum and the geodetic angular momentum fitting sequence.

[0015] Optionally, the converting the earth total angular momentum prediction result into the earth rotation parameter prediction result comprises:

[0016] converting the earth total angular momentum prediction result from an excitation domain to a polar motion domain to obtain the earth rotation parameter prediction result.

[0017] The second aspect of the present application provides an earth rotation parameter prediction device based on least square and long short-term memory network, comprising:

[0018] The acquisition module is configured to acquire earth rotation basic sequence data and effective angular momentum data; the preprocessing module is configured to preprocess the earth rotation basic sequence data and the effective angular momentum data to obtain preprocessed earth rotation basic sequence data and preprocessed effective angular momentum data; the conversion module is configured to convert the preprocessed earth rotation basic sequence data into geodetic angular momentum; the fitting module is configured to fit the effective angular momentum data and the geodetic angular momentum to obtain an effective angular momentum fitting sequence, an effective angular momentum residual sequence, a geodetic angular momentum fitting sequence and a geodetic angular momentum residual sequence; the training module is configured to train a long short-term memory network model constructed in advance by using the effective angular momentum residual sequence and the geodetic angular momentum residual sequence to obtain an effective angular momentum error term and a geodetic angular momentum error term; and the prediction module is configured to integrate the effective angular momentum fitting sequence, the geodetic angular momentum fitting sequence, the effective angular momentum error term and the geodetic angular momentum error term to obtain an earth total angular momentum prediction result, and convert the earth total angular momentum prediction result into an earth rotation parameter prediction result.

[0019] The third aspect of the present application provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the earth rotation parameter prediction method based on least square and long short-term memory network as described in the above embodiments.

[0020] The fourth aspect of the present application provides a computer program product, wherein the computer program / instruction is executed by a processor to implement the earth rotation parameter prediction method based on least square and long short-term memory network as described above.

[0021] The fifth aspect of the present invention provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned method for predicting the earth's rotation parameters based on least squares and long short-term memory network.

[0022] The Earth rotation parameter prediction method based on least squares and long short-term memory networks in this embodiment of the present invention utilizes a combined LS+LSTM model, leveraging both the interpretability of linear models and the powerful modeling capabilities of deep learning for complex time series data. This effectively overcomes the poor accuracy of traditional linear models in predicting the random portion of Earth rotation parameters. It also considers the impact of physical excitation factors on ERPs, providing higher prediction accuracy and stability than traditional methods. This method achieves high-precision medium- and long-term predictions of Earth parameters, providing a novel research approach for predicting Earth rotation parameters.

[0023] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0025] Figure 1 A flowchart of a method for predicting Earth rotation parameters based on least squares and long short-term memory networks provided by an embodiment of the present invention;

[0026] Figure 2 A schematic diagram of the basic sequence of Earth rotation parameters from EOP 20C04 provided in an embodiment of the present invention;

[0027] Figure 3 Schematic diagrams of interpolated AAM, OAM, HAM, and SLAM provided in an embodiment of the present invention, where (a) is AAM, (b) is OAM, (c) is HAM, and (d) is SLAM;

[0028] Figure 4 Schematic diagram of the combined equatorial components χ1, χ2 and axial component χ3 of the effective angular momentum provided by an embodiment of the present invention;

[0029] Figure 5 Schematic diagram of the UT1-UTC prediction process provided by an embodiment of the present invention;

[0030] Figure 6 Schematic diagram of error statistics for LSTM model parameter training based on PMX sequences provided in an embodiment of the present invention;

[0031] Figure 7 A block diagram of an earth rotation parameter prediction device based on least squares and long short-term memory network provided by an embodiment of the present invention;

[0032] Figure 8 This is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0034] The following describes, with reference to the accompanying drawings, a method for predicting Earth rotation parameters based on least squares and long short-term memory networks according to an embodiment of the present invention. In response to the problems mentioned in the above background technology center, this application takes into account that the LSTM model can establish implicit but unknown connections for different training windows and different features. At the same time, the LSTM model has a high degree of parameterization and a long time span, and can utilize more information to improve the prediction accuracy. Therefore, the LSTM model can be used to capture the nonlinear structure between different periods in the ERPs time series. In addition, since the atmosphere, ocean, terrestrial hydrosphere, etc. affect the Earth's rotation, it is necessary to consider these physical excitation factors when predicting ERPs. Therefore, for the ERPs prediction problem, a discretized Liouville equation is established, and the geodetic angular momentum is obtained by solving it. The geodetic angular momentum and the effective angular momentum are then comprehensively applied to the ERPs prediction. Therefore, this application provides a method for combining the physical excitation factors affecting the Earth's rotation with the LS+LSTM model to predict the Earth's rotation parameters, thereby improving the accuracy and stability of the Earth's rotation parameter prediction.

[0035] Specifically, Figure 1 A flowchart of a method for predicting Earth rotation parameters based on least squares and long short-term memory networks is provided in an embodiment of the present invention.

[0036] like Figure 1 As shown in FIG, the method for predicting the Earth's rotation parameters based on least squares and long short-term memory network includes the following steps:

[0037] In step S101 , the Earth rotation basic sequence data and effective angular momentum data are collected.

[0038] In the actual implementation process, Figure 2 and 3As shown in Figure 1, the physical excitation factors related to the Earth's rotation parameters are effective angular momentum data, which mainly include atmospheric angular momentum (AAM), oceanic angular momentum (OAM), hydrospheric angular momentum (HAM) and sea-level angular momentum (SLAM). At the same time, the basic sequence of Earth rotation parameters with a time interval of 1 day is downloaded from the official website of the International Earth Rotation and Reference Systems Service (IERS), including polar motion (PM) and Earth's rotation rate (Universal Time-Coordinated Universal Time, UT1-UTC).

[0039] In step S102, the Earth rotation basic sequence data and the effective angular momentum data are preprocessed to obtain preprocessed Earth rotation basic sequence data and preprocessed effective angular momentum data.

[0040] In some embodiments, preprocessing the Earth rotation basic sequence data and the effective angular momentum data to obtain preprocessed Earth rotation basic sequence data and preprocessed effective angular momentum data includes:

[0041] Interpolating the effective angular momentum data to the same interval and time as the Earth rotation basic sequence data to obtain pre-processed effective angular momentum data;

[0042] The Earth rotation basic sequence data are subjected to leap second deduction and Earth solid tide correction to obtain the preprocessed Earth rotation basic sequence data.

[0043] In the actual implementation process, Figure 4 and 5 As shown in Figure 1, the effective angular momentum data related to the polar motion are merged and interpolated. The interpolation time interval is consistent with the ERPs sequence provided by IERS, that is, the collected effective angular momentum data are interpolated to the same interval and time as the basic sequence of the Earth's rotation parameters, and the preprocessed Earth rotation basic sequence data and the preprocessed effective angular momentum data are obtained.

[0044] It should be noted that before preprocessing the UT1-UTC sequence, it is necessary to deduct the leap second and correct the Earth's solid tide. The leap second can be corrected by the value provided by the Earth's rotation center. The Earth's solid tide correction model is:

[0045]

[0046] Among them, δUT1 is the Earth solid tide correction term, B i ,G i They are given in the 7th and 8th columns of Table 8.1 of the 2010 IERS Convention Standard, α ij is the α of the ith tide in Table 8.1 j Integer multiples, from 1 to 5 columns, α j is the sun and moon nutation parameter.

[0047] In step S103, the pre-processed Earth rotation basic sequence data is converted into geodetic angular momentum.

[0048] In some embodiments, converting the pre-processed Earth rotation basic sequence data into geodetic angular momentum includes:

[0049] The pre-processed Earth rotation basic sequence data are converted from the polar motion domain to the excitation domain through the Liouville equation to obtain the geodetic angular momentum.

[0050] In the actual implementation process, the Earth's rotation parameters are transferred to the excitation domain to obtain the geodetic angular momentum. For the polar motion, it is converted from the polar motion domain to the excitation domain by the Liouville equation, and the formula is as follows:

[0051]

[0052] Where χ(t) represents the excitation function, σ CW represents the Chandler oscillation frequency, where σ CW =2π(1+i / 2Q) / T ch , Q and T ch denote the quality factor and the Chandler oscillation period, respectively. Δt is the sampling interval of the polar motion sequence. The polar motion is converted into an excitation function using the PM at times t-Δt, t, and t+Δt.

[0053] In step S104, the effective angular momentum data and the geodetic angular momentum are fitted to obtain an effective angular momentum fitting sequence, an effective angular momentum residual sequence, a geodetic angular momentum fitting sequence, and a geodetic angular momentum residual sequence.

[0054] In some embodiments, fitting the effective angular momentum data and the geodetic angular momentum to obtain an effective angular momentum fitting sequence, an effective angular momentum residual sequence, a geodetic angular momentum fitting sequence, and a geodetic angular momentum residual sequence includes:

[0055] The effective angular momentum data and the geodetic angular momentum are fitted by the least squares model to obtain the effective angular momentum fitting sequence and the geodetic angular momentum fitting sequence;

[0056] Calculate the effective angular momentum residual sequence according to the preprocessed effective angular momentum data and the effective angular momentum fitting sequence;

[0057] A geodetic angular momentum residual sequence is calculated based on the geodetic angular momentum and the geodetic angular momentum fitting sequence.

[0058] In the actual implementation process, the effective angular momentum data and the geodetic angular momentum are used to construct a linear LS model through least squares to obtain the effective angular momentum fitting sequence and the geodetic angular momentum fitting sequence. The linear LS model is constructed according to the following formula:

[0059]

[0060] Among them, a k represents a constant, a k+1 represents the linear term, k is [0,7], i is the segment number of LS fitting, C i and D i is the coefficient corresponding to the periodic term, R i represents the periodic term. The periodic terms considered in the fitting process include annual swing, semi-annual swing, 1 / 3 annual swing and 13.7 day swing. The values ​​are usually 1 year, 0.5 year, 1 / 3 year and 13.7 days. Please note that it is calculated in years. i represents zero-mean white noise, and t represents UTC time.

[0061] Furthermore, the preprocessed effective angular momentum is subtracted from the effective angular momentum fitting sequence to obtain the effective angular momentum residual. The geodetic angular momentum residual sequence is calculated based on the geodetic angular momentum and the geodetic angular momentum fitting sequence. Finally, the effective angular momentum residual sequence and the geodetic angular momentum residual sequence are used as part of the input of the pre-built long short-term memory network LSTM model.

[0062] In step S105 , the effective angular momentum residual sequence and the geodetic angular momentum residual sequence are input into a pre-built long short-term memory network model to obtain the effective angular momentum error term and the geodetic angular momentum error term.

[0063] In the actual implementation process, in the prediction of the Earth's rotation parameters, before model training, the Earth's rotation parameters are first converted to the excitation domain according to step S103 to obtain geodetic angular momentum. The least squares fitting method is used to obtain the geodetic angular momentum fitting sequence and fitting residual sequence. Then, the effective angular momentum residual sequence and the geodetic angular momentum residual sequence are used as input values ​​of the LSTM model for model parameter training. First, the output result c(t) of the LSTM model cell is calculated, and the error term of the LSTM cell is calculated. According to the specific error term, the weight gradient is solved, and the weight is re-determined using the gradient optimization algorithm.

[0064] In step S106, the effective angular momentum fitting sequence, the geodetic angular momentum fitting sequence, the effective angular momentum error term, and the geodetic angular momentum error term are integrated to obtain the Earth's total angular momentum prediction result, and the Earth's total angular momentum prediction result is converted into the Earth's rotation parameter prediction result.

[0065] In some embodiments, converting the Earth's total angular momentum prediction result into the Earth's rotation parameter prediction result includes:

[0066] The prediction results of the Earth's total angular momentum are converted from the excitation domain to the polar shift domain to obtain the prediction results of the Earth's rotation parameters.

[0067] In the actual implementation process, the effective angular momentum fitting sequence, the geodetic angular momentum fitting sequence, the effective angular momentum error sequence predicted by the LSTM model, and the geodetic angular momentum error sequence are added together to obtain the prediction sequence by the LS+LSTM model, that is, the total angular momentum prediction result of the Earth.

[0068] Furthermore, the prediction results of the total angular momentum of the Earth in the excitation domain are converted into the prediction results of the Earth's rotation parameters through the Liouville equation. It should be noted that for the UT1-UTC prediction sequence, the UT1-TAI sequence prediction value is obtained through the Liouville equation. On this basis, the influence of leap seconds and the Earth's solid tides needs to be added to obtain the final UT1-UTC prediction result.

[0069] The angular momentum can be converted from the excitation domain to the polar shift domain using the following formula:

[0070]

[0071] Among them, P(t) represents the polar shift, and the meanings of other parameters are the same as in the above formula.

[0072] For UT1-UTC,

[0073]

[0074] Where UT1 represents universal time, TAI represents atomic time, UT1-TAI is the result obtained by preprocessing UT1-UTC, Ψ is the geodetic angular momentum function, and Ω is the average angular velocity of the Earth, which is 7.292115×10 -5 rand s -1 .

[0075] It should be noted that the embodiment of the present invention also uses the mean absolute error (MAE) to perform accuracy statistics on the prediction results of the Earth rotation parameters, and compares them with the accuracy of the forecast sequence Bulletin A provided by the International Earth Rotation and Reference System Service. The accuracy evaluation standard of the Earth rotation parameters is based on the following formula:

[0076]

[0077] Among them, MAE j is the accuracy of the jth time series, n is the length of the time series, i is the length of the predicted time series, P i is the i-th forecast value, X i is the i-th true value.

[0078] Among them, when conducting MAE progress evaluation, the true value is selected as the IERS EOP 20C04 sequence. The prediction accuracy of the Earth's rotation parameters is compared with the prediction accuracy of Bulletin A, which is generally considered to have the highest ERPs prediction accuracy internationally, to further verify the effectiveness of the linear model and deep learning combination model used in this experiment.

[0079] The working process of the earth rotation parameter prediction method based on least squares and long short-term memory network proposed in an embodiment of the present invention is further explained below.

[0080] Step 1: Collect the basic sequence data of the Earth's rotation parameters and effective angular momentum data, such as Figure 2 and Figure 3 shown.

[0081] Step 2: Before building the model, the effective angular momentum data and UT1-UTC data are first preprocessed.

[0082] When processing effective angular momentum data, the equatorial and axial components of AAM, OAM, HAM, and SLAM are first interpolated. The interpolated moments are consistent with the IERS EOP 20C04 sequence. Then, the interpolated equatorial and axial components of AAM, OAM, HAM, and SLAM are merged together, as shown in the following example: Figure 4When predicting the UT1-UTC sequence, first deduct the leap second and the part affected by the earth's solid tide. The UT1-UTC preprocessed sequence is the UT1-TAI sequence, as shown in Figure 5 shown.

[0083] Step three, the Earth's rotation parameters are transferred to the excitation domain according to the Liouville equation. In this process, it should be noted that the discrete Liouville equation is used to convert the Earth's rotation parameters into geodetic angular momentum.

[0084] Step 4: LS models are constructed according to the effective angular momentum and geodetic angular momentum sequences, and then the fitting sequences and residual sequences of the effective angular momentum and geodetic angular momentum are obtained.

[0085] Step 5: The residual sequence of effective angular momentum and geodetic angular momentum obtained in step 4 is used as the input sequence for constructing the LSTM model. The two residual sequences are used as the basic sequences for model parameter training to obtain the optimal training parameters. Taking the X component of polar motion as an example, the error statistics after LSTM model parameter training are as follows: Figure 6 shown.

[0086] In step 6, the extrapolated results of the effective angular momentum and geodetic angular momentum fitting sequences obtained using the LS model are added to the predictions of the effective angular momentum and geodetic angular momentum residual sequences using the LSTM model to obtain the total angular momentum prediction. At this point, the Liouville equation is used to convert these into Earth rotation parameters, thereby obtaining the prediction results for the Earth rotation parameters. For the UT1-UTC sequence, the UT1R-TAI sequence is predicted, and the effects of the leap second and the Earth's solid tides must be accounted for to obtain the final prediction results.

[0087] In step seven, the prediction results of the Earth rotation parameters of LS+LSTM taking into account the physical excitation factors are statistically analyzed by MAE, and the prediction accuracy is compared with the prediction accuracy of Bulletin A, which further verifies the effectiveness and reliability of the accuracy improvement of the Earth rotation parameter prediction method proposed in this invention.

[0088] The method for predicting Earth rotation parameters based on least squares and long short-term memory network proposed in an embodiment of the present invention has the following beneficial effects:

[0089] (1) By adopting the LS+LSTM combined model, the advantages of both can be fully utilized, making full use of the interpretability of the linear model and introducing the powerful modeling ability of deep learning for complex time series data;

[0090] (2) The combined model can better capture seasonal variations and long-term trends in the Earth's rotation parameters, improving the accuracy of predictions;

[0091] (3) The method can be widely applied to the prediction of different types of Earth rotation parameters and has high versatility and flexibility;

[0092] (4) By utilizing the online learning capability of LSTM, rapid response and real-time update of new observation data can be achieved, providing timely support for relevant decision-making;

[0093] (5) The prediction results of the Earth's rotation parameters can provide a scientific basis for fields such as weather forecasting, navigation systems, and earthquake monitoring, and promote intelligent decision-making and applications in various fields.

[0094] Next, an earth rotation parameter prediction device based on least squares and long short-term memory network proposed in an embodiment of the present invention will be described with reference to the accompanying drawings.

[0095] Figure 7 It is a block diagram of an earth rotation parameter prediction device based on least squares and long short-term memory network according to an embodiment of the present invention.

[0096] like Figure 7 As shown, the earth rotation parameter prediction device 70 based on least squares and long short-term memory network includes: an acquisition module 701, a preprocessing module 702, a conversion module 703, a fitting module 704, a training module 705 and a prediction module 706.

[0097] The acquisition module 701 is used to acquire Earth rotation basic sequence data and effective angular momentum data. The preprocessing module 702 is used to preprocess the Earth rotation basic sequence data and effective angular momentum data to obtain preprocessed Earth rotation basic sequence data and preprocessed effective angular momentum data. The conversion module 703 is used to convert the preprocessed Earth rotation basic sequence data into geodetic angular momentum. The fitting module 704 is used to fit the effective angular momentum data and geodetic angular momentum to obtain an effective angular momentum fitting sequence, an effective angular momentum residual sequence, a geodetic angular momentum fitting sequence, and a geodetic angular momentum residual sequence. The training module 705 is used to train a pre-constructed long short-term memory network model using the effective angular momentum residual sequence and the geodetic angular momentum residual sequence to obtain an effective angular momentum error term and a geodetic angular momentum error term. The prediction module 706 is used to integrate the effective angular momentum fitting sequence, the geodetic angular momentum fitting sequence, the effective angular momentum error term and the geodetic angular momentum error term to obtain the Earth's total angular momentum prediction result, and convert the Earth's total angular momentum prediction result into the Earth's rotation parameter prediction result.

[0098] It should be noted that the above explanation of the embodiment of the method for predicting the earth rotation parameters based on least squares and long short-term memory network is also applicable to the earth rotation parameter prediction device based on least squares and long short-term memory network in this embodiment, and will not be repeated here.

[0099] The device for predicting earth rotation parameters based on least squares and long short-term memory network proposed in an embodiment of the present invention has the following beneficial effects:

[0100] (1) By adopting the LS+LSTM combined model, the advantages of both can be fully utilized, making full use of the interpretability of the linear model and introducing the powerful modeling ability of deep learning for complex time series data;

[0101] (2) The combined model can better capture seasonal variations and long-term trends in the Earth's rotation parameters, improving the accuracy of predictions;

[0102] (3) The method can be widely applied to the prediction of different types of Earth rotation parameters and has high versatility and flexibility;

[0103] (4) By utilizing the online learning capability of LSTM, rapid response and real-time update of new observation data can be achieved, providing timely support for relevant decision-making;

[0104] (5) The prediction results of the Earth's rotation parameters can provide a scientific basis for fields such as weather forecasting, navigation systems, and earthquake monitoring, and promote intelligent decision-making and applications in various fields.

[0105] Figure 8 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0106] A memory 801 , a processor 802 , and a computer program stored in the memory 801 and executable on the processor 802 .

[0107] When the processor 802 executes the program, the method for predicting the earth rotation parameters based on least squares and long short-term memory network provided in the above embodiment is implemented.

[0108] Furthermore, the electronic device further includes:

[0109] The communication interface 803 is used for communication between the memory 801 and the processor 802 .

[0110] The memory 801 is used to store computer programs that can be run on the processor 802.

[0111] The memory 801 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0112] If the memory 801, processor 802, and communication interface 803 are implemented independently, the communication interface 803, memory 801, and processor 802 can be connected to each other via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0113] Optionally, in a specific implementation, if the memory 801, the processor 802 and the communication interface 803 are integrated on a chip, the memory 801, the processor 802 and the communication interface 803 can communicate with each other through an internal interface.

[0114] The processor 802 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0115] An embodiment of the present invention further provides a computer program product, which, when executed by a processor, implements the above-mentioned method for predicting the earth rotation parameters based on least squares and long short-term memory network.

[0116] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for predicting the earth rotation parameters based on least squares and long short-term memory network.

[0117] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0118] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "N" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0119] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or N executable instructions for implementing a custom logical function or step of a process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0120] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program can be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing it in other suitable ways as necessary, and then storing it in a computer memory.

[0121] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0122] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0123] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0124] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and are not to be construed as limiting the present invention. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for predicting Earth rotation parameters based on least squares and long short-term memory network, characterized in that: include: Collect basic sequence data of Earth rotation and effective angular momentum data; Preprocessing the Earth rotation basic sequence data and the effective angular momentum data to obtain preprocessed Earth rotation basic sequence data and preprocessed effective angular momentum data; Converting the pre-processed Earth rotation basic sequence data into geodetic angular momentum; Fitting the effective angular momentum data and the geodetic angular momentum to obtain an effective angular momentum fitting sequence, an effective angular momentum residual sequence, a geodetic angular momentum fitting sequence, and a geodetic angular momentum residual sequence, specifically comprising: Performing data fitting on the effective angular momentum data and the geodetic angular momentum using a least squares model to obtain the effective angular momentum fitting sequence and the geodetic angular momentum fitting sequence; Calculating the effective angular momentum residual sequence according to the preprocessed effective angular momentum data and the effective angular momentum fitting sequence; Calculating the geodetic angular momentum residual sequence according to the geodetic angular momentum and the geodetic angular momentum fitting sequence; Inputting the effective angular momentum residual sequence and the geodetic angular momentum residual sequence into a pre-built long short-term memory network model to obtain an effective angular momentum error term and a geodetic angular momentum error term; The effective angular momentum fitting sequence, the geodetic angular momentum fitting sequence, the effective angular momentum error term, and the geodetic angular momentum error term are integrated to obtain a total angular momentum prediction result of the Earth, and the total angular momentum prediction result of the Earth is converted into a prediction result of the Earth rotation parameters.

2. The method for predicting earth rotation parameters based on least squares and long short-term memory network according to claim 1, characterized in that: The basic sequence data of the earth's rotation includes polar motion and the earth's rotation rate; the effective angular momentum data includes atmospheric angular momentum, ocean angular momentum, land water angular momentum and sea level angular momentum.

3. The method for predicting earth rotation parameters based on least squares and long short-term memory network according to claim 1, characterized in that: The preprocessing of the Earth rotation basic sequence data and the effective angular momentum data to obtain preprocessed Earth rotation basic sequence data and preprocessed effective angular momentum data includes: interpolating the effective angular momentum data to the same interval and time as the Earth rotation basic sequence data to obtain the pre-processed effective angular momentum data; The Earth rotation basic sequence data is subjected to leap second deduction and Earth solid tide correction to obtain the preprocessed Earth rotation basic sequence data.

4. The method for predicting earth rotation parameters based on least squares and long short-term memory network according to claim 1, characterized in that: The converting the pre-processed Earth rotation basic sequence data into geodetic angular momentum comprises: The pre-processed Earth rotation basic sequence data is converted from the polar shift domain to the excitation domain through the Liouville equation to obtain the geodetic angular momentum.

5. The method for predicting earth rotation parameters based on least squares and long short-term memory network according to claim 1, characterized in that: The converting of the Earth's total angular momentum prediction result into an Earth rotation parameter prediction result comprises: The Earth's total angular momentum prediction result is converted from the excitation domain to the polar shift domain to obtain the Earth's rotation parameter prediction result.

6. A device for predicting earth rotation parameters based on least squares and long short-term memory network, characterized in that: include: Acquisition module, used to collect basic sequence data of Earth rotation and effective angular momentum data; a preprocessing module, configured to preprocess the Earth rotation basic sequence data and the effective angular momentum data to obtain preprocessed Earth rotation basic sequence data and preprocessed effective angular momentum data; a conversion module, configured to convert the pre-processed Earth rotation basic sequence data into geodetic angular momentum; A fitting module is used to fit the effective angular momentum data and the geodetic angular momentum to obtain an effective angular momentum fitting sequence, an effective angular momentum residual sequence, a geodetic angular momentum fitting sequence, and a geodetic angular momentum residual sequence, specifically comprising: Performing data fitting on the effective angular momentum data and the geodetic angular momentum using a least squares model to obtain the effective angular momentum fitting sequence and the geodetic angular momentum fitting sequence; Calculating the effective angular momentum residual sequence according to the preprocessed effective angular momentum data and the effective angular momentum fitting sequence; Calculating the geodetic angular momentum residual sequence according to the geodetic angular momentum and the geodetic angular momentum fitting sequence; A training module, configured to train a pre-built long short-term memory network model using the effective angular momentum residual sequence and the geodetic angular momentum residual sequence to obtain an effective angular momentum error term and a geodetic angular momentum error term; A prediction module is used to integrate the effective angular momentum fitting sequence, the geodetic angular momentum fitting sequence, the effective angular momentum error term, and the geodetic angular momentum error term to obtain a total angular momentum prediction result of the Earth, and convert the total angular momentum prediction result of the Earth into a prediction result of the Earth rotation parameters.

7. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for predicting the earth rotation parameters based on least squares and long short-term memory network as described in any one of claims 1 to 5.

8. A computer program product, characterized in that When the computer program / instruction is executed by a processor, the method for predicting the earth rotation parameters based on least squares and long short-term memory network described in any one of claims 1 to 5 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for predicting earth rotation parameters based on least squares and long short-term memory network as described in any one of claims 1 to 5.

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