Method and system for fusing multi-source satellite intelligence data with three-dimensional simulation space data

Abstract

The invention discloses a method and system for fusing multi-source satellite intelligence data with three-dimensional simulation space data. The method comprises: obtaining satellite image data of multiple satellites as satellite intelligence data, extracting coordinate information in the satellite image data, and obtaining coordinate information of three-dimensional simulation space data; setting a satellite image registration model, using the coordinate information of the three-dimensional simulation space data as a reference, registering the coordinate information in the satellite image data, generating satellite image data after coordinate unification, and extracting feature vectors of the satellite image data and feature vectors of the three-dimensional simulation space data; setting a fusion model for satellite intelligence data and three-dimensional simulation data, fusing the feature vectors of the satellite image data and the feature vectors of the three-dimensional simulation space data, and generating a fused feature vector.

Description

Technical Field

[0001] The present invention belongs to the technical field of data fusion, and more specifically, relates to a method and system for fusing multi-source satellite intelligence data with three-dimensional simulation space data. Background Art

[0002] Data fusion technology has become an important direction in the field of information technology. Data fusion refers to the integration of data from different sources, types and formats in order to better understand, analyze and use the data. The following are some of the main status and trends of data fusion technology:

[0003] Multi-source data integration: The development of data fusion technology makes it possible to integrate data from multiple sources, including sensor data, social media data, business data, etc. This integration can provide a more comprehensive view and promote deeper analysis and decision-making.

[0004] Big data and distributed computing: The development of big data technologies (such as Hadoop, Spark, etc.) enables efficient processing of large-scale data and data fusion through distributed computing. Such technologies can process and analyze data in a distributed environment and improve efficiency.

[0005] However, there is no technical solution in the prior art that can accurately fuse multi-source satellite intelligence data with three-dimensional simulation space data. Summary of the invention

[0006] In order to solve the above technical problems, the present invention proposes a method for fusing multi-source satellite intelligence data with three-dimensional simulation space data, comprising:

[0007] Acquire satellite image data of multiple satellites as satellite intelligence data, extract coordinate information from the satellite image data, and acquire coordinate information of three-dimensional simulation space data;

[0008] A satellite image registration model is set, and the coordinate information in the satellite image data is registered with reference to the coordinate information of the three-dimensional simulation space data, so as to generate satellite image data after the coordinates are unified, and a feature vector of the satellite image data and a feature vector of the three-dimensional simulation space data are extracted;

[0009] A fusion model of satellite intelligence data and three-dimensional simulation data is set up to fuse the feature vectors of satellite image data and the feature vectors of three-dimensional simulation space data to generate a fused feature vector.

[0010] Furthermore, the satellite image registration model includes:

[0011]

[0012] Among them, x′2 is the horizontal coordinate of the satellite image after registration, y′2 is the vertical coordinate of the satellite image after registration, a is the scaling factor on the horizontal axis during translation, b is the scaling factor on the vertical axis during translation, c is the shearing transformation parameter on the horizontal axis during translation, d is the shearing transformation parameter on the vertical axis during translation, s x is the scaling factor on the horizontal axis during rotation, x2 is the horizontal coordinate of the satellite image before registration, θ is the rotation angle during registration, and s y is the scaling factor on the vertical axis during rotation, y2 is the vertical coordinate of the satellite image before registration, Δx is the translation of the horizontal coordinate, and Δy is the translation of the vertical coordinate.

[0013] Furthermore, the satellite intelligence data and three-dimensional simulation data fusion model includes:

[0014]

[0015] Among them, F f is the fused feature vector set, β is the data fusion adjustment factor, g1(F1) is the mapping function of the feature vector set F1 of satellite intelligence data, and g2(F2) is the mapping function of the feature vector set F2 of three-dimensional simulation space data.

[0016] Furthermore, the mapping function g1(F1) of the feature vector set F1 of the satellite intelligence data and the mapping function g2(F2) of the feature vector set F2 of the three-dimensional simulation space data respectively include:

[0017]

[0018] g2(F2)=tanh(F2).

[0019] Furthermore, the feature vector set F1 of satellite intelligence data, the feature vector set F2 of three-dimensional simulation space data and the fused feature vector set F f They include:

[0020]

[0021]

[0022]

[0023] Among them, A i is the i-th feature vector of the target position in the feature vector set F1, B i is the i-th eigenvector of the target shape in the eigenvector set F1, C i is the i-th eigenvector of the target motion trajectory in the eigenvector set F1, A′ i is the i-th feature vector of the target position in the feature vector set F2, B′i is the i-th eigenvector of the target shape in the eigenvector set F2, C′ i is the i-th eigenvector of the target motion trajectory in the eigenvector set F2, A″ i is the feature vector set F f The i-th feature vector of the target position in B″ i is the feature vector set F f The i-th eigenvector of the target shape in C″ i is the feature vector set F f The i-th eigenvector of the target motion trajectory in .

[0024] The present invention also proposes a multi-source satellite intelligence data and three-dimensional simulation space data fusion system, comprising:

[0025] A coordinate information acquisition module is used to acquire satellite image data of multiple satellites as satellite intelligence data, extract coordinate information from the satellite image data, and acquire coordinate information of three-dimensional simulation space data;

[0026] A registration module is used to set a satellite image registration model, register the coordinate information in the satellite image data with reference to the coordinate information of the three-dimensional simulation space data, generate satellite image data after coordinate unification, and extract the feature vector of the satellite image data and the feature vector of the three-dimensional simulation space data;

[0027] The fusion module is used to set up a fusion model of satellite intelligence data and three-dimensional simulation data, fuse the feature vector of satellite image data and the feature vector of three-dimensional simulation space data, and generate a fused feature vector.

[0028] Furthermore, the satellite image registration model includes:

[0029]

[0030] Among them, x′2 is the horizontal coordinate of the satellite image after registration, y′2 is the vertical coordinate of the satellite image after registration, a is the scaling factor on the horizontal axis during translation, b is the scaling factor on the vertical axis during translation, c is the shearing transformation parameter on the horizontal axis during translation, d is the shearing transformation parameter on the vertical axis during translation, s x is the scaling factor on the horizontal axis during rotation, x2 is the horizontal coordinate of the satellite image before registration, θ is the rotation angle during registration, and s y is the scaling factor on the vertical axis during rotation, y2 is the vertical coordinate of the satellite image before registration, Δx is the translation of the horizontal coordinate, and Δy is the translation of the vertical coordinate.

[0031] Furthermore, the satellite intelligence data and three-dimensional simulation data fusion model includes:

[0032]

[0033] Among them, F f is the fused feature vector set, β is the data fusion adjustment factor, g1(F1) is the mapping function of the feature vector set F1 of satellite intelligence data, and g2(F2) is the mapping function of the feature vector set F2 of three-dimensional simulation space data.

[0034] Furthermore, the mapping function g1(F1) of the feature vector set F1 of the satellite intelligence data and the mapping function g2(F2) of the feature vector set F2 of the three-dimensional simulation space data respectively include:

[0035]

[0036] g2(F2)=tanh(F2).

[0037] Furthermore, the feature vector set F1 of satellite intelligence data, the feature vector set F2 of three-dimensional simulation space data and the fused feature vector set F f They include:

[0038]

[0039]

[0040]

[0041] Among them, A i is the i-th feature vector of the target position in the feature vector set F1, B i is the i-th eigenvector of the target shape in the eigenvector set F1, C i is the i-th eigenvector of the target motion trajectory in the eigenvector set F1, A′ i is the i-th feature vector of the target position in the feature vector set F2, B′ i is the i-th eigenvector of the target shape in the eigenvector set F2, C′ i is the i-th eigenvector of the target motion trajectory in the eigenvector set F2, A″ i is the feature vector set F f The i-th feature vector of the target position in B″ i is the feature vector set F f The i-th eigenvector of the target shape in C″ i is the feature vector set F f The i-th eigenvector of the target motion trajectory in .

[0042] Compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:

[0043] The present invention obtains satellite image data of multiple satellites as satellite intelligence data, extracts coordinate information in the satellite image data, and obtains coordinate information of three-dimensional simulation space data; sets a satellite image registration model, uses the coordinate information of the three-dimensional simulation space data as a reference, registers the coordinate information in the satellite image data, generates satellite image data after coordinate unification, and extracts feature vectors of the satellite image data and feature vectors of the three-dimensional simulation space data; sets a fusion model of satellite intelligence data and three-dimensional simulation data, fuses the feature vectors of the satellite image data and the feature vectors of the three-dimensional simulation space data, and generates a fused feature vector. Through the above technical scheme, the present invention can accurately fuse multi-source satellite intelligence data and three-dimensional simulation data, thereby improving the efficiency of subsequent data use. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flow chart of the method of embodiment 1 of the present invention;

[0045] Figure 2 It is a system structure diagram of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0046] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0047] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0048] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0049] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0050] The display screen is used to display the user interface of each application.

[0051] All subscripts in the formulas of the present invention are only used to distinguish parameters and have no actual meaning.

[0052] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0053] Example 1

[0054] like Figure 1 As shown, an embodiment of the present invention provides a method for fusing multi-source satellite intelligence data with three-dimensional simulation space data, comprising:

[0055] Step 101, obtaining satellite image data of multiple satellites as satellite intelligence data, extracting coordinate information from the satellite image data, and obtaining coordinate information of three-dimensional simulation space data;

[0056] Step 102, setting a satellite image registration model, using the coordinate information of the three-dimensional simulation space data as a reference, registering the coordinate information in the satellite image data, generating satellite image data after coordinate unification, and extracting feature vectors of the satellite image data and feature vectors of the three-dimensional simulation space data;

[0057] Specifically, the satellite image registration model includes:

[0058]

[0059] Among them, x′2 is the horizontal coordinate of the satellite image after registration, y′2 is the vertical coordinate of the satellite image after registration, a is the scaling factor on the horizontal axis during translation, b is the scaling factor on the vertical axis during translation, c is the shearing transformation parameter on the horizontal axis during translation, d is the shearing transformation parameter on the vertical axis during translation, s x is the scaling factor on the horizontal axis during rotation, x2 is the horizontal coordinate of the satellite image before registration, θ is the rotation angle during registration, and s y is the scaling factor on the vertical axis during rotation, y2 is the vertical coordinate of the satellite image before registration, Δx is the translation of the horizontal coordinate, and Δy is the translation of the vertical coordinate.

[0060] Step 103, setting a fusion model of satellite intelligence data and three-dimensional simulation data, fusing the feature vector of satellite image data and the feature vector of three-dimensional simulation space data to generate a fused feature vector.

[0061] Specifically, the feature vector set F1 of satellite intelligence data, the feature vector set F2 of three-dimensional simulation space data and the fused feature vector set F f They include:

[0062]

[0063]

[0064]

[0065] Among them, A i is the i-th feature vector of the target position in the feature vector set F1, B i is the i-th eigenvector of the target shape in the eigenvector set F1, C i is the i-th eigenvector of the target motion trajectory in the eigenvector set F1, A′ i is the i-th feature vector of the target position in the feature vector set F2, B′ i is the i-th eigenvector of the target shape in the eigenvector set F2, C′ i is the i-th eigenvector of the target motion trajectory in the eigenvector set F2, A″ i is the feature vector set F f The i-th feature vector of the target position in B″ i is the feature vector set F f The i-th eigenvector of the target shape in C″ i is the feature vector set F f The i-th eigenvector of the target motion trajectory in .

[0066] Specifically, the satellite intelligence data and three-dimensional simulation data fusion model includes:

[0067]

[0068] Among them, F f is the fused feature vector set, β is the data fusion adjustment factor, g1(F1) is the mapping function of the feature vector set F1 of satellite intelligence data, and g2(F2) is the mapping function of the feature vector set F2 of three-dimensional simulation space data.

[0069] Specifically, the mapping function g1(F1) of the feature vector set F1 of the satellite intelligence data and the mapping function g2(F2) of the feature vector set F2 of the three-dimensional simulation space data respectively include:

[0070]

[0071] g2(F2)=tanh(F2).

[0072] Example 2

[0073] like Figure 2 As shown, the embodiment of the present invention also proposes a multi-source satellite intelligence data and three-dimensional simulation space data fusion system, including:

[0074] A coordinate information acquisition module is used to acquire satellite image data of multiple satellites as satellite intelligence data, extract coordinate information from the satellite image data, and acquire coordinate information of three-dimensional simulation space data;

[0075] A registration module is used to set a satellite image registration model, register the coordinate information in the satellite image data with reference to the coordinate information of the three-dimensional simulation space data, generate satellite image data after coordinate unification, and extract the feature vector of the satellite image data and the feature vector of the three-dimensional simulation space data;

[0076] Specifically, the satellite image registration model includes:

[0077]

[0078] Among them, x′2 is the horizontal coordinate of the satellite image after registration, y′2 is the vertical coordinate of the satellite image after registration, a is the scaling factor on the horizontal axis during translation, b is the scaling factor on the vertical axis during translation, c is the shearing transformation parameter on the horizontal axis during translation, d is the shearing transformation parameter on the vertical axis during translation, s x is the scaling factor on the horizontal axis during rotation, x2 is the horizontal coordinate of the satellite image before registration, θ is the rotation angle during registration, and s y is the scaling factor on the vertical axis during rotation, y2 is the vertical coordinate of the satellite image before registration, Δx is the translation of the horizontal coordinate, and Δy is the translation of the vertical coordinate.

[0079] The fusion module is used to set up a fusion model of satellite intelligence data and three-dimensional simulation data, fuse the feature vector of satellite image data and the feature vector of three-dimensional simulation space data, and generate a fused feature vector.

[0080] Specifically, the feature vector set F1 of satellite intelligence data, the feature vector set F2 of three-dimensional simulation space data and the fused feature vector set F f They include:

[0081]

[0082]

[0083]

[0084] Among them, A i is the i-th feature vector of the target position in the feature vector set F1, B i is the i-th eigenvector of the target shape in the eigenvector set F1, C i is the i-th eigenvector of the target motion trajectory in the eigenvector set F1, A′ iis the i-th feature vector of the target position in the feature vector set F2, B′ i is the i-th eigenvector of the target shape in the eigenvector set F2, C′ i is the i-th eigenvector of the target motion trajectory in the eigenvector set F2, A″ i is the feature vector set F f The i-th feature vector of the target position in B″ i is the feature vector set F f The i-th eigenvector of the target shape in C″ i is the feature vector set F f The i-th eigenvector of the target motion trajectory in .

[0085] Specifically, the satellite intelligence data and three-dimensional simulation data fusion model includes:

[0086]

[0087] Among them, F f is the fused feature vector set, β is the data fusion adjustment factor, g1(F1) is the mapping function of the feature vector set F1 of satellite intelligence data, and g2(F2) is the mapping function of the feature vector set F2 of three-dimensional simulation space data.

[0088] Specifically, the mapping function g1(F1) of the feature vector set F1 of the satellite intelligence data and the mapping function g2(F2) of the feature vector set F2 of the three-dimensional simulation space data respectively include:

[0089]

[0090] g2(F2)=tanh(F2).

[0091] Example 3

[0092] The embodiment of the present invention further proposes a storage medium storing a plurality of instructions, wherein the instructions are used to implement the method for fusing multi-source satellite intelligence data with three-dimensional simulation space data.

[0093] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0094] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, obtaining satellite image data of a plurality of satellites as satellite intelligence data, extracting coordinate information from the satellite image data, and obtaining coordinate information of the three-dimensional simulation space data;

[0095] Step 102, setting a satellite image registration model, using the coordinate information of the three-dimensional simulation space data as a reference, registering the coordinate information in the satellite image data, generating satellite image data after coordinate unification, and extracting feature vectors of the satellite image data and feature vectors of the three-dimensional simulation space data;

[0096] Specifically, the satellite image registration model includes:

[0097]

[0098] Among them, x′2 is the horizontal coordinate of the satellite image after registration, y′2 is the vertical coordinate of the satellite image after registration, a is the scaling factor on the horizontal axis during translation, b is the scaling factor on the vertical axis during translation, c is the shearing transformation parameter on the horizontal axis during translation, d is the shearing transformation parameter on the vertical axis during translation, s x is the scaling factor on the horizontal axis during rotation, x2 is the horizontal coordinate of the satellite image before registration, θ is the rotation angle during registration, and s y is the scaling factor on the vertical axis during rotation, y2 is the vertical coordinate of the satellite image before registration, Δx is the translation of the horizontal coordinate, and Δy is the translation of the vertical coordinate.

[0099] Step 103, setting a fusion model of satellite intelligence data and three-dimensional simulation data, fusing the feature vector of satellite image data and the feature vector of three-dimensional simulation space data to generate a fused feature vector.

[0100] Specifically, the feature vector set F1 of satellite intelligence data, the feature vector set F2 of three-dimensional simulation space data and the fused feature vector set F f They include:

[0101]

[0102]

[0103]

[0104] Among them, A i is the i-th feature vector of the target position in the feature vector set F1, B i is the i-th eigenvector of the target shape in the eigenvector set F1, C i is the i-th eigenvector of the target motion trajectory in the eigenvector set F1, A′ i is the i-th feature vector of the target position in the feature vector set F2, B′ i is the i-th eigenvector of the target shape in the eigenvector set F2, C′ i is the i-th eigenvector of the target motion trajectory in the eigenvector set F2, A″ iis the feature vector set F f The i-th feature vector of the target position in B″ i is the feature vector set F f The i-th eigenvector of the target shape in C″ i is the feature vector set F f The i-th eigenvector of the target motion trajectory in .

[0105] Specifically, the satellite intelligence data and three-dimensional simulation data fusion model includes:

[0106]

[0107] Among them, F f is the fused feature vector set, β is the data fusion adjustment factor, g1(F1) is the mapping function of the feature vector set F1 of satellite intelligence data, and g2(F2) is the mapping function of the feature vector set F2 of three-dimensional simulation space data.

[0108] Specifically, the mapping function g1(F1) of the feature vector set F1 of the satellite intelligence data and the mapping function g2(F2) of the feature vector set F2 of the three-dimensional simulation space data respectively include:

[0109]

[0110] g2(F2)=tanh(F2).

[0111] Example 4

[0112] An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores multiple instructions, and the instructions can be loaded and executed by the processor to enable the processor to execute a method for fusing multi-source satellite intelligence data and three-dimensional simulation space data.

[0113] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

[0114] Among them, the storage medium can be used to store software programs and modules, such as a method for fusing multi-source satellite intelligence data and three-dimensional simulated space data in an embodiment of the present invention, and corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned method for fusing multi-source satellite intelligence data and three-dimensional simulated space data. The storage medium may include a high-speed random storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include a storage medium remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0115] The processor may call the information and application program stored in the storage medium through the transmission system to execute the steps: Step 101, obtaining satellite image data of multiple satellites as satellite intelligence data, extracting coordinate information from the satellite image data, and obtaining coordinate information of the three-dimensional simulation space data;

[0116] Step 102, setting a satellite image registration model, using the coordinate information of the three-dimensional simulation space data as a reference, registering the coordinate information in the satellite image data, generating satellite image data after coordinate unification, and extracting feature vectors of the satellite image data and feature vectors of the three-dimensional simulation space data;

[0117] Specifically, the satellite image registration model includes:

[0118]

[0119] Among them, x′2 is the horizontal coordinate of the satellite image after registration, y′2 is the vertical coordinate of the satellite image after registration, a is the scaling factor on the horizontal axis during translation, b is the scaling factor on the vertical axis during translation, c is the shearing transformation parameter on the horizontal axis during translation, d is the shearing transformation parameter on the vertical axis during translation, s x is the scaling factor on the horizontal axis during rotation, x2 is the horizontal coordinate of the satellite image before registration, θ is the rotation angle during registration, and s y is the scaling factor on the vertical axis during rotation, y2 is the vertical coordinate of the satellite image before registration, Δx is the translation of the horizontal coordinate, and Δy is the translation of the vertical coordinate.

[0120] Step 103, setting a fusion model of satellite intelligence data and three-dimensional simulation data, fusing the feature vector of satellite image data and the feature vector of three-dimensional simulation space data to generate a fused feature vector.

[0121] Specifically, the feature vector set F1 of satellite intelligence data, the feature vector set F2 of three-dimensional simulation space data and the fused feature vector set F f They include:

[0122]

[0123]

[0124]

[0125] Among them, A i is the i-th feature vector of the target position in the feature vector set F1, B i is the i-th eigenvector of the target shape in the eigenvector set F1, C i is the i-th eigenvector of the target motion trajectory in the eigenvector set F1, A′ i is the i-th feature vector of the target position in the feature vector set F2, B′ i is the i-th eigenvector of the target shape in the eigenvector set F2, C′ i is the i-th eigenvector of the target motion trajectory in the eigenvector set F2, A″ i is the feature vector set F f The i-th feature vector of the target position in B″ i is the feature vector set F f The i-th eigenvector of the target shape in C″ i is the feature vector set F f The i-th eigenvector of the target motion trajectory in .

[0126] Specifically, the satellite intelligence data and three-dimensional simulation data fusion model includes:

[0127]

[0128] Among them, F f is the fused feature vector set, β is the data fusion adjustment factor, g1(F1) is the mapping function of the feature vector set F1 of satellite intelligence data, and g2(F2) is the mapping function of the feature vector set F2 of three-dimensional simulation space data.

[0129] Specifically, the mapping function g1(F1) of the feature vector set F1 of the satellite intelligence data and the mapping function g2(F2) of the feature vector set F2 of the three-dimensional simulation space data respectively include:

[0130]

[0131] g2(F2)=tanh(F2).

[0132] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0133] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0134] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0135] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0137] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it 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 all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of 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 method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0138] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A method for fusing multi-source satellite intelligence data with three-dimensional simulation space data, characterized in that: include: Acquire satellite image data of multiple satellites as satellite intelligence data, extract coordinate information from the satellite image data, and acquire coordinate information of three-dimensional simulation space data; A satellite image registration model is set, and the coordinate information in the satellite image data is registered with reference to the coordinate information of the three-dimensional simulation space data, so as to generate satellite image data after the coordinates are unified, and a feature vector of the satellite image data and a feature vector of the three-dimensional simulation space data are extracted; The satellite image registration model includes: Among them, x′2 is the horizontal coordinate of the satellite image after registration, y′2 is the vertical coordinate of the satellite image after registration, a is the scaling factor on the horizontal axis during translation, b is the scaling factor on the vertical axis during translation, c is the shearing transformation parameter on the horizontal axis during translation, d is the shearing transformation parameter on the vertical axis during translation, s x is the scaling factor on the horizontal axis during rotation, x2 is the horizontal coordinate of the satellite image before registration, θ is the rotation angle during registration, and s y is the scaling factor on the vertical axis during rotation, y2 is the vertical coordinate of the satellite image before registration, Δx is the translation of the horizontal coordinate, and Δy is the translation of the vertical coordinate; A fusion model of satellite intelligence data and three-dimensional simulation data is set up to fuse the feature vector of satellite image data and the feature vector of three-dimensional simulation space data to generate a fused feature vector; The satellite intelligence data and three-dimensional simulation data fusion model includes: Among them, F f is the fused feature vector set, β is the data fusion adjustment factor, g1(F1) is the mapping function of the feature vector set F1 of satellite intelligence data, and g2(F2) is the mapping function of the feature vector set F2 of three-dimensional simulation space data; The mapping function g1(F1) of the feature vector set F1 of the satellite intelligence data and the mapping function g2(F2) of the feature vector set F2 of the three-dimensional simulation space data respectively include: g2(F2)=tanh(F2); The feature vector set F1 of satellite intelligence data, the feature vector set F2 of 3D simulation space data and the fused feature vector set F f They include: Among them, A i is the i-th feature vector of the target position in the feature vector set F1, B i is the i-th eigenvector of the target shape in the eigenvector set F1, C i is the i-th eigenvector of the target motion trajectory in the eigenvector set F1, A′ i is the i-th feature vector of the target position in the feature vector set F2, B′ i is the i-th eigenvector of the target shape in the eigenvector set F2, C′ i is the i-th eigenvector of the target motion trajectory in the eigenvector set F2, A″ i is the feature vector set F f The i-th feature vector of the target position in B″ i is the feature vector set F f The i-th eigenvector of the target shape in C″ i is the feature vector set F f The i-th eigenvector of the target motion trajectory in .

2. A multi-source satellite intelligence data and three-dimensional simulation space data fusion system, characterized in that: include: A coordinate information acquisition module is used to acquire satellite image data of multiple satellites as satellite intelligence data, extract coordinate information from the satellite image data, and acquire coordinate information of three-dimensional simulation space data; A registration module is used to set a satellite image registration model, register the coordinate information in the satellite image data with reference to the coordinate information of the three-dimensional simulation space data, generate satellite image data after coordinate unification, and extract the feature vector of the satellite image data and the feature vector of the three-dimensional simulation space data; The satellite image registration model includes: Among them, x′2 is the horizontal coordinate of the satellite image after registration, y′2 is the vertical coordinate of the satellite image after registration, a is the scaling factor on the horizontal axis during translation, b is the scaling factor on the vertical axis during translation, c is the shearing transformation parameter on the horizontal axis during translation, d is the shearing transformation parameter on the vertical axis during translation, s x is the scaling factor on the horizontal axis during rotation, x2 is the horizontal coordinate of the satellite image before registration, θ is the rotation angle during registration, and s y is the scaling factor on the vertical axis during rotation, y2 is the vertical coordinate of the satellite image before registration, Δx is the translation of the horizontal coordinate, and Δy is the translation of the vertical coordinate; A fusion module is used to set a fusion model of satellite intelligence data and three-dimensional simulation data, fuse the feature vector of satellite image data and the feature vector of three-dimensional simulation space data, and generate a fused feature vector; The satellite intelligence data and three-dimensional simulation data fusion model includes: Among them, F f is the fused feature vector set, β is the data fusion adjustment factor, g1(F1) is the mapping function of the feature vector set F1 of satellite intelligence data, and g2(F2) is the mapping function of the feature vector set F2 of three-dimensional simulation space data; The mapping function g1(F1) of the feature vector set F1 of the satellite intelligence data and the mapping function g2(F2) of the feature vector set F2 of the three-dimensional simulation space data respectively include: g2(F2)=tanh(F2); The feature vector set F1 of satellite intelligence data, the feature vector set F2 of 3D simulation space data and the fused feature vector set F f They include: Among them, A i is the i-th feature vector of the target position in the feature vector set F1, B i is the i-th eigenvector of the target shape in the eigenvector set F1, C i is the i-th eigenvector of the target motion trajectory in the eigenvector set F1, A′ i is the i-th feature vector of the target position in the feature vector set F2, B′ i is the i-th eigenvector of the target shape in the eigenvector set F2, C′ i is the i-th eigenvector of the target motion trajectory in the eigenvector set F2, A″ i is the feature vector set F f The i-th feature vector of the target position in B″ i is the feature vector set F f The i-th eigenvector of the target shape in C″ i is the feature vector set F f The i-th eigenvector of the target motion trajectory in .

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