A method and system for preloading multidimensional data of space targets

Abstract

The present invention discloses a method and system for preloading multidimensional data of a space target, and relates to the field of data processing. The method comprises: inputting a space target data set of a target to be analyzed, selecting and counting N dimensions of the space target data set, setting a first statistical interval parameter for each dimension, and preloading the space target data set according to the N dimensions and the first statistical interval parameter. Through the scheme of the present invention, it is possible to support multidimensional data analysis and dimension switching data analysis on the basis of loading data once.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method and system for preloading multidimensional data of space targets. Background Art

[0002] The multidimensional data of space targets include time, longitude, latitude, azimuth, pitch, speed, acceleration, country, code, type, reply code, modulation and demodulation data and other dimensions. The analysis of multidimensional data of space targets often requires switching between one or several dimensions during statistical analysis, or switching between different range intervals on a single dimension. Traditional methods need to reload data when switching statistical dimensions, which leads to slow response time, poor effect and strong constraints in massive data environments. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for preloading multidimensional data of space targets in view of the deficiencies of the prior art.

[0004] The technical solution of the present invention to solve the above technical problems is as follows:

[0005] A method for preloading multidimensional data of a space target, comprising:

[0006] S1, inputting a spatial target data set of a target to be analyzed, selecting and counting N dimensions of the spatial target data set;

[0007] S2, setting the first statistical interval parameter of each dimension;

[0008] S3, preloading the space target data set according to N dimensions and the first statistical interval parameter.

[0009] The beneficial effect of the present invention is that by setting the dimension of the space target data set and the first statistical interval parameter of each dimension, the space target data set is preloaded according to N dimensions and the first statistical interval parameter, thereby realizing the preloading of multidimensional data of the space target.

[0010] The multidimensional data of space targets include multiple dimensions such as time, longitude, latitude, azimuth, pitch, speed, acceleration, country, code, type, reply code, modulation and demodulation data, etc. The multidimensional data analysis of space targets often requires switching between one or several dimensions during statistical analysis, or switching between different range intervals on a single dimension. The traditional method needs to reload the data when switching the statistical dimension. In a massive data environment, the response time is slow, the effect is poor, and the constraints are strong. Through the scheme of the present invention, it is possible to support multidimensional data analysis and dimension switching data analysis on the basis of loading data once.

[0011] Furthermore, the preloading of the space target data set according to N dimensions and the first statistical interval parameter specifically includes:

[0012] The spatial target data set is preloaded using a watermark mechanism according to N dimensions and the first statistical interval parameter.

[0013] The beneficial effect of adopting the above further scheme is: this scheme provides a watermark mechanism for preloading data, and decides whether to perform data aggregation according to target characteristics, so as to avoid the problem of premature aggregation of target data and loss of details due to unreasonable setting of longitude and latitude statistical intervals.

[0014] Furthermore, it also includes: when performing multidimensional data retrieval on the preloaded spatial target data set, dimension selection, data query, dimension level change or data aggregation are performed on the preloaded spatial target data set through the MapReduce method.

[0015] The beneficial effect of adopting the above further solution is: this solution avoids redundant storage of multiple span data by adopting MapReduce method in different dimensions.

[0016] Furthermore, the S3 further includes:

[0017] According to the target modulation and demodulation code of the modulation and demodulation data, a preset target recognition feature library is retrieved to extract the motion feature threshold of the target and mark it as the first feature threshold;

[0018] When the first statistical interval parameter is greater than the first characteristic threshold, the target point trace data within the first statistical interval parameter is not subjected to aggregation calculation in the preloading process, and the target point trace data is retained;

[0019] When the first statistical interval parameter is not greater than the first characteristic threshold, performing aggregation calculation of the preloading process on the target point trace data within the first statistical interval parameter;

[0020] The space target data set includes: the modulation and demodulation data and longitude and latitude data.

[0021] The beneficial effects of adopting the above further scheme are: this scheme utilizes the motion characteristics of space targets to retain data details as much as possible during data preloading; uses the minimum span interval for data storage, and adopts data aggregation retrieval to achieve data switching when multidimensional data analysis is required under the condition of one data preloading. It can efficiently perform multidimensional analysis of space target data for visual display, and has obvious advantages under TB and PB level data conditions.

[0022] Furthermore, it also includes: if there is no corresponding data in the first statistical interval parameter of the preloaded space target data set, then null value filling is performed.

[0023] The beneficial effect of adopting the above further scheme is: this scheme ensures data alignment by filling null values ​​when there is no corresponding data in the minimum span interval of the data set, which is conducive to improving efficiency when MapReduce data aggregation is used in subsequent dimensional data retrieval.

[0024] Another technical solution of the present invention to solve the above technical problems is as follows:

[0025] A preloading system for multi-dimensional data of space targets, comprising: a dimension statistics module, an interval parameter setting module and a preloading module;

[0026] The dimension statistics module is used to input a spatial target data set of a target to be analyzed, and select and count N dimensions of the spatial target data set;

[0027] The interval parameter setting module is used to set the first statistical interval parameter of each dimension;

[0028] The preloading module is used to preload the space target data set according to N dimensions and the first statistical interval parameter.

[0029] The beneficial effect of the present invention is that by setting the dimension of the space target data set and the first statistical interval parameter of each dimension, the space target data set is preloaded according to N dimensions and the first statistical interval parameter, thereby realizing the preloading of multidimensional data of the space target.

[0030] The multidimensional data of space targets include multiple dimensions such as time, longitude, latitude, azimuth, pitch, speed, acceleration, country, code, type, reply code, modulation and demodulation data, etc. The multidimensional data analysis of space targets often requires switching between one or several dimensions during statistical analysis, or switching between different range intervals on a single dimension. The traditional method needs to reload the data when switching the statistical dimension. In a massive data environment, the response time is slow, the effect is poor, and the constraints are strong. Through the scheme of the present invention, it is possible to support multidimensional data analysis and dimension switching data analysis on the basis of loading data once.

[0031] Furthermore, the preloading module is specifically used to preload the spatial target dataset according to the N dimensions and the first statistical interval parameter using a watermark mechanism.

[0032] The beneficial effect of adopting the above further scheme is: this scheme provides a watermark mechanism for preloading data, and decides whether to perform data aggregation according to target characteristics, so as to avoid the problem of premature aggregation of target data and loss of details due to unreasonable setting of longitude and latitude statistical intervals.

[0033] Furthermore, it also includes: a retrieval module, which is used to perform dimension selection, data query, dimension level change or data aggregation on the preloaded spatial target data set through the MapReduce method when performing multidimensional data retrieval on the preloaded spatial target data set.

[0034] The beneficial effect of adopting the above further solution is that this solution avoids redundant storage of multiple span data by adopting the MapReduce method in different dimensions.

[0035] Further, the preloading module further includes: an aggregation module, which is used to retrieve a preset target recognition feature library according to the target modulation and demodulation code of the modulation and demodulation data, extract the motion feature threshold of the target, and mark it as the first feature threshold;

[0036] When the first statistical interval parameter is greater than the first characteristic threshold, the target point trace data within the first statistical interval parameter is not subjected to aggregation calculation in the preloading process, and the target point trace data is retained;

[0037] When the first statistical interval parameter is not greater than the first characteristic threshold, performing aggregation calculation of the preloading process on the target point trace data within the first statistical interval parameter;

[0038] The space target data set includes: the modulation and demodulation data and longitude and latitude data.

[0039] The beneficial effects of adopting the above further scheme are: this scheme utilizes the motion characteristics of space targets to retain data details as much as possible during data preloading; uses the minimum span interval for data storage, and adopts data aggregation retrieval to achieve data switching when multidimensional data analysis is required under the condition of one data preloading. It can efficiently perform multidimensional analysis of space target data for visual display, and has obvious advantages under TB and PB level data conditions.

[0040] Furthermore, it also includes: a parameter value supplement module, which is used to fill in null values ​​if there is no corresponding data in the first statistical interval parameter of the preloaded spatial target data set.

[0041] The beneficial effect of adopting the above further scheme is: this scheme ensures data alignment by filling null values ​​when there is no corresponding data in the minimum span interval of the data set, which is conducive to improving efficiency when MapReduce data aggregation is used in subsequent dimensional data retrieval.

[0042] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic flow chart of a method for preloading multidimensional data of a space target provided by an embodiment of the present invention;

[0044] Figure 2 A structural block diagram of a system for preloading multi-dimensional data of space targets provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention but not to limit the scope of the present invention.

[0046] like Figure 1 As shown, a method for preloading multidimensional data of a space target provided by an embodiment of the present invention includes:

[0047] S1, input the spatial target data set of the target to be analyzed, select and count N dimensions of the spatial target data set;

[0048] In a certain embodiment, a space target data set to be analyzed is input. In a specific data analysis, it is often not necessary to load all the data, so all dimensions to be analyzed are first selected. For example, three dimensions of time, longitude, and latitude are selected for the space target. Among them, the specific data may include: time, longitude, latitude, azimuth, pitch, frequency, pulse width, pulse interval or demodulation code, etc. The analysis dimensions for the space target are known in advance and may include time, longitude, and latitude.

[0049] S2, setting the first statistical interval parameter of each dimension;

[0050] In a certain embodiment, the first statistical interval parameter can be the minimum statistical interval of each dimension; the minimum statistical interval parameter of the dimension is set. For example, for the time dimension, the statistical analysis requires four span intervals of hours, days, months, and years. In this embodiment, the hour is set as the minimum statistical interval parameter of time. In the data model, the data is stored with the hour as the data span interval. The three span intervals of day, month, and year are aggregated using the MapReduce method, thereby avoiding redundant storage of multiple span data. In a certain embodiment, the MapReduce method may include: using the hour as the span interval means that the data is divided and stored by hour, and the stored index field contains yyyy-MM-dd HH (year-month-day-time). In this way, matching the data counted by day only needs to match the yyyy-MM-dd of the index field and aggregate it. The same is true for the statistics by month and year.

[0051] In one embodiment, multi-dimensional data analysis and dimension switching data analysis are supported on the basis of loading data once. The modulation and demodulation data is the target recognition data obtained by the radar sensor. When the spatial target multidimensional data set contains the modulation and demodulation data and the longitude and latitude data, the method provides a watermark mechanism for data preloading, and determines whether to perform data aggregation according to the target characteristics, which is used to solve the problem of premature aggregation of target data and loss of details caused by unreasonable setting of longitude and latitude statistical intervals.

[0052] S3, pre-loading the spatial target data set according to N dimensions and the first statistical interval parameter.

[0053] In a certain embodiment, the original data set is preloaded according to the selected dimensions and the minimum span interval parameters of each dimension, and null values ​​are filled in when there is no corresponding data in the minimum span interval of the data set to ensure data alignment, which is beneficial to improving efficiency when MapReduce data aggregation is used in subsequent dimensional data retrieval.

[0054] In one embodiment, the processing of the space target modulation and demodulation data may include:

[0055] The modulation and demodulation data of the target contains the identification feature information of the target, such as flight speed, acceleration, surface area, transponder code, nationality, etc. Through this information, the target can be identified. If it is a very fast aircraft, the longitude and latitude can be processed without aggregation. Aggregation means taking the average of the longitude and latitude of multiple points and merging them into one point. If it is a slow ship, aggregation of the longitude and latitude can effectively reduce the amount of data.

[0056] According to the target modulation and demodulation code, the preset target identification feature library is retrieved, the target motion feature threshold is extracted, and marked as a watermark. When the selected minimum span interval of longitude and latitude is greater than the watermark, the target point data within the minimum span interval is not aggregated and the details are retained. In a certain embodiment, the Watermark mechanism is a method of increasing data tolerance in data processing. For example, the system sets the minimum span of longitude to 0.1, but there is a type of target that is identified as an airplane. In order to retain the details of the flight trajectory, it is necessary to reduce the aggregation range or not aggregate. The threshold value obtained based on the target characteristics is 0.01. If it is aggregated according to 0.1, it is likely that the target data will lose its meaning after merging. At this time, the target should be aggregated according to 0.01 instead of 0.1. The value of 0.01 is called the watermark value.

[0057] In the analysis of spatial target data, the motion characteristics of different targets vary greatly. For example, the speeds of ships and airplanes vary greatly, which makes it difficult to select the minimum span interval of longitude and latitude. If it is set too large, all the point data of the aircraft target will be aggregated and the data details will be lost. If it is set too small, the point data of the ship target will be filled with a large number of null values, forming a sparse matrix and causing a waste of resources. The role of the watermark mechanism is to retain the target point data details as much as possible in the aggregation calculation of the data preloading process according to the motion characteristics of the target.

[0058] By setting the dimension of the space target data set and the first statistical interval parameter of each dimension, the space target data set is preloaded according to N dimensions and the first statistical interval parameter, thereby realizing the preloading of multidimensional data of the space target.

[0059] The multidimensional data of space targets include multiple dimensions such as time, longitude, latitude, azimuth, pitch, speed, acceleration, country, code, type, reply code, modulation and demodulation data, etc. The multidimensional data analysis of space targets often requires switching between one or several dimensions during statistical analysis, or switching between different range intervals on a single dimension. The traditional method needs to reload the data when switching the statistical dimension. In a massive data environment, the response time is slow, the effect is poor, and the constraints are strong. Through the scheme of the present invention, it is possible to support multidimensional data analysis and dimension switching data analysis on the basis of loading data once.

[0060] Preferably, in any of the above embodiments, preloading the space target dataset according to the N dimensions and the first statistical interval parameter specifically includes:

[0061] The spatial target dataset is preloaded with the watermark mechanism according to N dimensions and the first statistical interval parameter.

[0062] This solution provides a watermark mechanism for preloading data and decides whether to aggregate data based on target features, thereby avoiding the problem of premature aggregation of target data and loss of details due to unreasonable settings of longitude and latitude statistical intervals.

[0063] Preferably, in any of the above embodiments, it also includes: when performing multidimensional data retrieval on the preloaded spatial target dataset, dimension selection, data query, dimension level change or data aggregation are performed on the preloaded spatial target dataset through the MapReduce method.

[0064] In one embodiment, the preloaded data set supports four common algorithms: dimension selection, data query, dimension level change, and data aggregation. These algorithms are usually implemented using the MapReduce method. For example, in data aggregation, the input and output of the Mapper and Reducer are key-value pairs in a format defined by the user. <Key <t>,Value <t>> form, Mapper first Key <t>De-linearization, according to the Reducer output key information, complete the dimension span interval climbing, Reducer according to the user specified aggregation method Aggregation Value <t>Perform aggregation operations and construct the final result <Key <t>,Value <t>>Form output.

[0065] This solution avoids redundant storage of multiple span data by using MapReduce method in different dimensions.

[0066] Preferably, in any of the above embodiments, S3 further includes:

[0067] According to the target modulation and demodulation code of the modulation and demodulation data, a preset target recognition feature library is retrieved to extract the motion feature threshold of the target and mark it as the first feature threshold;

[0068] When the first statistical interval parameter is greater than the first characteristic threshold, the target point trace data within the first statistical interval parameter is not subjected to aggregation calculation in the preloading process, and the target point trace data is retained;

[0069] When the first statistical interval parameter is not greater than the first characteristic threshold, the target point trace data within the first statistical interval parameter is subjected to aggregation calculation of the preloading process;

[0070] The space target data set includes: modulation and demodulation data and longitude and latitude data.

[0071] This solution uses the motion characteristics of space targets to retain data details as much as possible during data preloading; it uses the minimum span interval for data storage, and adopts data aggregation retrieval to achieve data switching when multidimensional data analysis is required under the condition of one data preloading. It can efficiently perform multidimensional analysis of space target data for visual display, and has obvious advantages under TB and PB level data conditions.

[0072] Preferably, in any of the above embodiments, it further includes: if there is no corresponding data in the first statistical interval parameter of the preloaded spatial target data set, then null value filling is performed.

[0073] This solution ensures data alignment by filling in empty values ​​when there is no corresponding data in the minimum span interval of the data set, which is conducive to improving efficiency when MapReduce data aggregation is used in subsequent dimensional data retrieval.

[0074] In one embodiment, if Figure 2 As shown, a preloading system for multi-dimensional data of space targets includes: a dimension statistics module 1001, an interval parameter setting module 1002 and a preloading module 1003;

[0075] The dimension statistics module 1001 is used to input the spatial target data set of the target to be analyzed, select and count N dimensions of the spatial target data set;

[0076] The interval parameter setting module 1002 is used to set the first statistical interval parameter of each dimension;

[0077] The preloading module 1003 is used to preload the space object data set according to N dimensions and the first statistical interval parameter.

[0078] By setting the dimension of the space target data set and the first statistical interval parameter of each dimension, the space target data set is preloaded according to N dimensions and the first statistical interval parameter, thereby realizing the preloading of multidimensional data of the space target.

[0079] The multidimensional data of space targets include multiple dimensions such as time, longitude, latitude, azimuth, pitch, speed, acceleration, country, code, type, reply code, modulation and demodulation data, etc. The multidimensional data analysis of space targets often requires switching between one or several dimensions during statistical analysis, or switching between different range intervals on a single dimension. The traditional method needs to reload the data when switching the statistical dimension. In a massive data environment, the response time is slow, the effect is poor, and the constraints are strong. Through the scheme of the present invention, it is possible to support multidimensional data analysis and dimension switching data analysis on the basis of loading data once.

[0080] Preferably, in any of the above embodiments, the preloading module 1003 is specifically used to preload the spatial object dataset according to the N dimensions and the first statistical interval parameter using the watermark mechanism.

[0081] This solution provides a watermark mechanism for preloading data and decides whether to aggregate data based on target features, thereby avoiding the problem of premature aggregation of target data and loss of details due to unreasonable settings of longitude and latitude statistical intervals.

[0082] Preferably, in any of the above embodiments, it also includes: a retrieval module, which is used to perform dimension selection, data query, dimension level change or data aggregation on the preloaded spatial target data set through the MapReduce method when performing multidimensional data retrieval on the preloaded spatial target data set.

[0083] This solution avoids redundant storage of multiple span data by using MapReduce method in different dimensions.

[0084] Preferably, in any of the above embodiments, the preloading module further comprises: an aggregation module, configured to retrieve a preset target recognition feature library according to the target modulation and demodulation code of the modulation and demodulation data, extract a motion feature threshold of the target, and mark it as a first feature threshold;

[0085] When the first statistical interval parameter is greater than the first characteristic threshold, the target point trace data within the first statistical interval parameter is not subjected to aggregation calculation in the preloading process, and the target point trace data is retained;

[0086] When the first statistical interval parameter is not greater than the first characteristic threshold, the target point trace data within the first statistical interval parameter is subjected to aggregation calculation of the preloading process;

[0087] The space target data set includes: modulation and demodulation data and longitude and latitude data.

[0088] This solution uses the motion characteristics of space targets to retain data details as much as possible during data preloading; it uses the minimum span interval for data storage, and adopts data aggregation retrieval to achieve data switching when multidimensional data analysis is required under the condition of one data preloading. It can efficiently perform multidimensional analysis of space target data for visual display, and has obvious advantages under TB and PB level data conditions.

[0089] Preferably, in any of the above embodiments, it further comprises: a parameter value supplement module, which is used to fill in null values ​​if there is no corresponding data in the first statistical interval parameter of the preloaded spatial target data set.

[0090] This solution ensures data alignment by filling in empty values ​​when there is no corresponding data in the minimum span interval of the data set, which is conducive to improving efficiency when MapReduce data aggregation is used in subsequent dimensional data retrieval.

[0091] It can be understood that in some embodiments, some or all of the optional implementation modes in the above embodiments may be included.

[0092] It should be noted that the above embodiments are product embodiments corresponding to the previous method embodiments. For the description of the optional implementation methods in the product embodiments, reference can be made to the corresponding description in the above method embodiments, which will not be repeated here.

[0093] The reader should understand that 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 representation of the above terms does 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 more 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.

[0094] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0095] 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 embodiments of the present invention.

[0096] 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.

[0097] 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 is essentially 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 various embodiments 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 and other media that can store program codes.

[0098] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.< / t> < / t> < / t> < / t> < / t> < / t>

Claims

1. A method for preloading multidimensional data of space targets, characterized in that: include: S1, inputting a spatial target data set of a target to be analyzed, selecting and counting N dimensions of the spatial target data set; S2, setting a first statistical interval parameter of each dimension, where the first statistical interval parameter is the minimum statistical interval of each dimension; S3, preloading the space target data set according to N dimensions and the first statistical interval parameter; The preloading of the space target data set according to N dimensions and the first statistical interval parameter specifically includes: Preloading the spatial target data set with a watermark mechanism according to N dimensions and the first statistical interval parameter, wherein the watermark mechanism is a method for increasing data tolerance in data processing; The S3 further includes: According to the target modulation and demodulation code of the modulation and demodulation data, a preset target recognition feature library is retrieved to extract the motion feature threshold of the target and mark it as the first feature threshold; When the first statistical interval parameter is greater than the first characteristic threshold, the target point trace data within the first statistical interval parameter is not subjected to aggregation calculation in the preloading process, and the target point trace data is retained; When the first statistical interval parameter is not greater than the first characteristic threshold, performing aggregation calculation of the preloading process on the target point trace data within the first statistical interval parameter; The space target data set includes: the modulation and demodulation data and longitude and latitude data.

2. A method for preloading multidimensional data of space targets according to claim 1, characterized in that: Also includes: When multidimensional data retrieval is performed on the preloaded spatial target data set, dimension selection, data query, dimension level change or data aggregation is performed on the preloaded spatial target data set through the MapReduce method.

3. A method for preloading multidimensional data of space targets according to claim 1 or 2, characterized in that: Also includes: If there is no corresponding data in the first statistical interval parameter of the preloaded spatial target data set, null value filling is performed.

4. A preloading system for multidimensional data of space targets, characterized in that: include: Dimension statistics module, interval parameter setting module and preloading module; The dimension statistics module is used to input a spatial target data set of a target to be analyzed, and select and count N dimensions of the spatial target data set; The interval parameter setting module is used to set a first statistical interval parameter for each dimension, where the first statistical interval parameter is the minimum statistical interval of each dimension; The preloading module is used to preload the space target data set according to N dimensions and the first statistical interval parameter; The preloading of the space target data set according to N dimensions and the first statistical interval parameter specifically includes: Preloading the spatial target data set with a watermark mechanism according to N dimensions and the first statistical interval parameter, wherein the watermark mechanism is a method for increasing data tolerance in data processing; The preloading module is specifically used for: According to the target modulation and demodulation code of the modulation and demodulation data, a preset target recognition feature library is retrieved to extract the motion feature threshold of the target and mark it as the first feature threshold; When the first statistical interval parameter is greater than the first characteristic threshold, the target point trace data within the first statistical interval parameter is not subjected to aggregation calculation in the preloading process, and the target point trace data is retained; When the first statistical interval parameter is not greater than the first characteristic threshold, performing aggregation calculation of the preloading process on the target point trace data within the first statistical interval parameter; The space target data set includes: the modulation and demodulation data and longitude and latitude data.

5. A preloading system for multi-dimensional data of space targets according to claim 4, characterized in that: Also includes: The retrieval module is used to perform dimension selection, data query, dimension level change or data aggregation on the preloaded spatial target data set through the MapReduce method when performing multidimensional data retrieval on the preloaded spatial target data set.

6. A preloading system for multi-dimensional data of space targets according to claim 4 or 5, characterized in that: Also includes: The parameter value supplement module is used to fill in null values ​​if there is no corresponding data in the first statistical interval parameter of the preloaded spatial target data set.

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