Low-memory gravity database construction and indexing method based on sparse longitude and latitude

Abstract

The invention discloses a low-storage-capacity gravity database construction method based on longitude and latitude sparsity. The method comprises the following steps: step 1, generating a global gravity grid database by using a global high-precision gravity model; step 2, for grid points in the global gravity grid database generated in the step 1, adopting a sequential adjacent gravity data difference threshold comparison method along the longitude or dimension direction, storing and removing gravity data of later grid points meeting a threshold condition, and obtaining a sparse global gravity grid database; and step 3, generating a grid point state table, and setting different state values for the grid points in which the gravity data are stored and the grid points in which the gravity data are not stored. The method comprises a database generation part and a database indexing part, the thought of similar value omission is utilized in the database generation part, and data points needing to be stored by the gravity database are reduced. And the database index part explains how to read the gravity data value of the specified point by using the state table.

Description

Technical Field

[0001] The invention belongs to the technical field of inertial navigation, and in particular relates to a method for constructing and indexing a low-storage gravity database based on sparse longitude and latitude. Background Art

[0002] In the field of civil aviation, the inertial reference system is an important source of navigation information that provides attitude, position, and speed information for aircraft. In order to ensure that the navigation system can meet the operational requirements of civil aircraft, it is necessary to compensate for various errors in the navigation system. For the inertial reference system, gravity compensation error is an extremely critical error source.

[0003] Although the high-order spherical harmonics model can be used directly to calculate the gravity value at a certain point, this method requires the processor to have high computing power. For low-cost (processors with higher computing power cannot be used) or products that have been in service for many years (due to early design, processor computing power is limited and cannot be recalled and changed in a centralized manner), this method cannot be used directly. In order to ensure that the inertial reference system can obtain accurate gravity data and reduce gravity compensation errors, it is necessary to pre-store a high-precision gravity compensation database in the system to calculate the gravity value at a fixed position in real time. In actual engineering applications, considering the design and development costs of the system, a gravity database with a smaller storage capacity should be selected as the product implementation solution.

[0004] Traditional gravity databases are stored at equal intervals of longitude and latitude. Although this solution can reduce the number of storage points by increasing the longitude and latitude intervals, this method ignores the trend of global gravity changes and may lose characteristic information of areas with drastic gravity changes. Therefore, this method will greatly reduce the accuracy of the database. Summary of the invention

[0005] The purpose of the present invention is to provide a low-storage gravity database construction and indexing method based on latitude and longitude sparseness for an inertial navigation system. The method includes two parts: database generation and database indexing. The database generation part uses the idea of ​​omitting similar values ​​to reduce the data points required to be stored in the gravity database. The database indexing part will explain how to use the state table to read the gravity data value of a specified point.

[0006] Technical solution of the present invention: According to the first aspect of the present invention, a method for constructing a low-storage gravity database based on longitude and latitude sparsity is provided. Global high-precision gravity models are used to generate global gravity grid data; data at grid points are judged along the longitude or latitude. If there are multiple grid points with similar gravity values on the same longitude line or latitude line, only one value is stored in the database; meanwhile, a status table is established, which records whether the gravity data in the global gravity grid is stored in the database. If the grid point data is stored in the database, it is marked as 1 in the status table, and if the grid point data is not stored in the status table, it is marked as 0 in the status table; finally, when the navigation system needs the gravity value of a certain point, the gravity value of this point is indexed through the status table.

[0007] The global gravity grid is a grid of longitude and latitude that stores gravity data at a fixed longitude and latitude interval globally with high precision. The global gravity grid data represents the gravity data at the intersection of the longitude line and the latitude line on the global gravity grid globally. The grid point represents the intersection of the longitude line and the latitude line on the global gravity grid globally.

[0008] The construction method specifically includes the following steps:

[0009] Step 1: Use the global high-precision gravity model to generate a global gravity grid database;

[0010] Step 2: Along the longitude or latitude direction of the grid points in the global gravity grid database generated in Step 1, use the sequential adjacent gravity data difference threshold comparison method to store and eliminate the gravity data of the subsequent grid points that meet the threshold conditions, and obtain a globally sparse global gravity grid database;

[0011] Step 3: Generate a grid point status table, and set different status values for the grid points with stored gravity data and the grid points without stored gravity data.

[0012] In a possible embodiment, in Step 1, it specifically includes the following steps: Determine the longitude and latitude intervals, and use the global high-precision gravity model to calculate and record the gravity data of the grid points at the intersection of the longitude line and the latitude line.

[0013] Preferably, the global high-precision gravity model includes the EGM-2008 model and the EIGEN-6C4 model.

[0014] Preferably, the longitude and latitude intervals are taken as 5 or 10 arc minutes;

[0015] In a possible embodiment, in Step 2, the specific process of using the sequential adjacent gravity data difference threshold comparison method along the longitude direction includes:

[0016] Select a certain longitude line, and the grid points on this longitude line are denoted as PJ1 to PJn; the gravity values at the grid points PJ1 to PJN are denoted as [G_E1, G_N1, G_U1] to [G_En, G_Nn, G_Un], where E, N, and U represent the components of the gravity data vector in the east, north, and up directions respectively;

[0017] Set the difference threshold α, and successively perform adjacent difference comparisons along the longitude direction in the order of east, north, and up, and calculate |G_Ei - G_E(i+1)|. If |G_Ei - G_E(i+1)| ≤ α, where i = 1, 2, 3..., n; it means that G_Ei and G_E(i+1) are extremely close in value, and the database only stores the eastward gravity data G_Ei of the PJi grid point, and eliminates the eastward gravity data G_E(i+1) of the PJ(i+1) grid point; if |G_Ei - G_E(i+1)| > α, it means that G_E1 and G_E2 are not close in value, and the database needs to store both the eastward gravity data G_Ei of the PJi grid point and the eastward gravity data G_E(i+1) of the PJ(i+1) grid point; after the components of one direction of gravity on a meridian are stored, the same sparse operation is immediately carried out on the components of the remaining directions of gravity; traverse the remaining meridians to obtain the globally sparse gravity grid database.

[0018] In a possible embodiment, in step 3, the status value of the grid point storing gravity data is assigned 1, and the status value of the grid point not storing gravity data is set to 0.

[0019] According to the second aspect of the present invention, a low-storage gravity database indexing method based on longitude and latitude sparsity is proposed, which specifically includes the following steps:

[0020] Step 1: Index the grid point position of the point to be indexed according to the grid point status table in step 3;

[0021] Step 2: According to the status value of the grid point position indexed in step 4, if the status value corresponds to the grid point storing gravity data, the gravity data of the point to be indexed is obtained; if the status value corresponds to the grid point not storing gravity data, according to the database sparse record type, the status value of the first grid point storing gravity data is reversely searched in the status table to obtain the gravity data of the point to be indexed.

[0022] According to the third aspect of the present invention, an electronic device is proposed, including a memory and a processor, and the memory and the processor are coupled; the memory stores program instructions, and when the program instructions are executed by the processor, the electronic device executes a low-storage gravity database construction method based on longitude and latitude sparsity and a low-storage gravity database indexing method based on longitude and latitude sparsity as described above.

[0023] According to the third aspect of the present invention, a computer-readable storage medium is provided, including a computer program, which, when running on an electronic device, causes the electronic device to execute the above-mentioned method for constructing a low-storage-capacity gravity database based on longitude and latitude sparsity and a method for indexing a low-storage-capacity gravity database based on longitude and latitude sparsity.

[0024] According to the fourth aspect of the present invention, a computer program product containing instructions is provided, which, when running on a computer, causes the computer to execute the above-mentioned method for constructing a low-storage-capacity gravity database based on longitude and latitude sparsity and a method for indexing a low-storage-capacity gravity database based on longitude and latitude sparsity.

[0025] The advantages of the present invention are as follows: The present invention has the characteristics of high compensation accuracy, fast indexing speed, and low database storage capacity, and is applicable to high-precision inertial reference systems with memory limitations and processor running speed limitations. Since the database construction method involved in the present invention can flexibly cut and adjust the grid points of gravity data according to different application conditions, it has broad prospects in engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a schematic diagram of longitude sparsity of a preferred embodiment of the present invention;

[0027] Figure 2 is a flow chart of longitude sparsity of a preferred embodiment of the present invention;

[0028] Figure 3 is a schematic diagram of latitude sparsity of a preferred embodiment of the present invention;

[0029] Figure 4 is a flow chart of latitude sparsity of a preferred embodiment of the present invention;

[0030] Figure 5 is a schematic diagram of storage by longitude of a preferred embodiment of the present invention;

[0031] Figure 6 is a schematic diagram of storage by latitude of a preferred embodiment of the present invention;

[0032] Figure 7 is a state table of longitude sparsity of a preferred embodiment of the present invention;

[0033] Figure 8 is a flow chart of the calculation method of the state table of longitude sparsity of a preferred embodiment of the present invention;

[0034] Figure 9 is a state table of latitude sparsity of a preferred embodiment of the present invention;

[0035] Figure 10It is a flowchart of the calculation method for the latitude sparse state table in the preferred embodiment of the present invention;

[0036] Figure 11 It is a schematic diagram of the longitude sparse global database in the preferred embodiment of the present invention;

[0037] Figure 12 It is a schematic diagram of the latitude sparse global database in the preferred embodiment of the present invention;

[0038] Figure 13 It is a schematic diagram of the principle of the latitude sparse scheme in the preferred embodiment of the present invention;

[0039] Figure 14 It is a flowchart of the database compression generation process in the preferred embodiment of the present invention;

[0040] Figure 15 It is a schematic diagram of the generation process of the state table in the preferred embodiment of the present invention;

[0041] Figure 16 It is a schematic diagram of the storage method of the state table in the preferred embodiment of the present invention;

[0042] Figure 17 It is a schematic diagram of the storage method of the gravity database in the preferred embodiment of the present invention;

[0043] Figure 18 It is a schematic diagram of the interpolation point index scheme in the preferred embodiment of the present invention. Detailed implementation manners

[0044] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0045] The features and illustrative embodiments of all aspects of the present invention will be described in detail below. In the following detailed description, many specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is only intended to provide a better understanding of the present invention by showing examples of the present invention. The present invention is in no way limited to any specific settings and methods set forth below, but covers any improvements, substitutions, and modifications of structures, methods, and devices without departing from the spirit of the present invention. Well-known structures and technologies are not shown in the drawings and the following description to avoid unnecessarily obscuring the present invention.

[0046] It should be noted that, without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other, and each embodiment can be referred to and cited mutually. The present invention will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0047] A method for constructing a low-storage gravity database based on longitude and latitude sparsity specifically includes the following steps:

[0048] Step 101: Generate global gravity grid data using a global high-precision gravity model;

[0049] Select a grid of longitude and latitude with an interval of Δ. Using a global high-precision gravity model (such as the EGM-2008, EIGEN-6C4 models, etc.), calculate and record the gravity data at the intersections of the longitude lines and latitude lines.

[0050] Preferably, Δ is taken as 5 or 10 arc minutes;

[0051] Step 102: Sparsify the global gravity grid data along the longitude or latitude;

[0052] If sparsification along the longitude is selected, its schematic diagram is as Figure 1 shown. First, select a certain longitude line LON1, and the grid points on this longitude line are denoted as PJ1 to PJ7.

[0053] The gravity values at the grid points PJ1 to PJ7 are denoted as [G_E1, G_N1, G_U1] to [G_E7, G_N7, G_U7] (where E, N, and U represent the components of the gravity vector in the eastward, northward, and upward directions).

[0054] Next, the global gravity grid data will be thinned along the longitude in the order of eastward, northward, and upward. Taking the gravity values at grid points PJ1 to PJ7 as an example, starting from PJ1, calculate |G_E1 - G_E2|. If |G_E1 - G_E2| ≤ α, it means that G_E1 and G_E2 are extremely close in value, and only G_E1 needs to be stored in the database. If G_E2 needs to be retrieved from the database, G_E1 can be used instead. If |G_E1 - G_E2| > α, it means that G_E1 and G_E2 are not close in value, and both G_E1 and G_E2 need to be stored in the database. At the same time, starting from G_E2, calculate |G_E3 - G_E2|. If |G_E3 - G_E2| ≤ α, it means that G_E3 and G_E2 are extremely close in value, and only G_E2 needs to be stored in the database. If G_E3 needs to be retrieved from the database, G_E2 can be used instead. If |G_E3 - G_E2| > α, it means that G_E3 and G_E2 are not close in value, and both G_E3 and G_E2 need to be stored in the database. Then, starting from G_E3, calculate |G_E4 - G_E3|, and so on. After the components of the gravity in one direction on a certain longitude line are stored, the same thinning operation is immediately carried out on the components of the gravity in the remaining directions.

[0055] The process of the longitude thinning method can be referred to Figure 2 , where G_Fk represents the component of the gravity vector in the F direction at the kth grid point on the LON1 longitude line; G_F(i + 1) represents the component of the gravity vector in the F direction at the (i + 1)th grid point on the LON1 longitude line.

[0056] If thinning along the latitude is selected, its schematic diagram is as shown in Figure 3 . First, select a certain latitude line LAT1, and the grid points on this latitude line are denoted as PW1 to PW7. The gravity values at grid points PW1 to PW7 are denoted as [G_E1, G_N1, G_U1] to [G_E7, G_N7, G_U7] (where E, N, and U represent the components of the gravity vector in the eastward, northward, and upward directions).

[0057] Next, the global gravity grid data will be thinned along the latitude in the order of eastward, northward, and skyward. Taking the gravity values at grid points PW1 to PW7 as an example, starting from PW1, calculate |G_E1 - G_E2|. If |G_E1 - G_E2| ≤ α, it means that G_E1 and G_E2 are extremely close in value, and only G_E1 needs to be stored in the database. If G_E2 needs to be retrieved from the database, it can be replaced with G_E1. If |G_E1 - G_E2| > α, it means that G_E1 and G_E2 are not close in value, and both G_E1 and G_E2 need to be stored in the database. At the same time, starting from G_E2, calculate |G_E3 - G_E2|. If |G_E3 - G_E2| ≤ α, it means that G_E3 and G_E2 are extremely close in value, and only G_E2 needs to be stored in the database. If G_E3 needs to be retrieved from the database, it can be replaced with G_E2. If |G_E3 - G_E2| > α, it means that G_E3 and G_E2 are not close in value, and both G_E3 and G_E2 need to be stored in the database. At the same time, starting from G_E3, calculate |G_E4 - G_E3|, and so on. After the components in one direction of gravity on a latitude line are stored, the same thinning operation is immediately carried out on the components in the other directions of gravity.

[0058] The process of the longitude thinning method can be referred to Figure 4 , where G_Fk represents the component of the gravity vector in the F direction at the k-th grid point on the LAT1 latitude line; G_F(i + 1) represents the component of the gravity vector in the F direction at the (i + 1)-th grid point on the LAT1 latitude line.

[0059] Preferably, the value of α is 15 μg.

[0060] Step 103: Select a storage form to store the database in a fixed form;

[0061] The database storage methods are divided into two types, storing by longitude and storing by latitude. These two storage methods have nothing to do with the schemes of thinning by longitude or latitude. However, for indexing convenience, the preferred scheme is: store by longitude for longitude thinning; store by latitude for latitude thinning.

[0062] The stored values will be arranged in the form of a one-dimensional array. Since the gravity vector has components in three directions, finally, the gravity database of a certain fixed plane will be composed of 3 one-dimensional arrays.

[0063] The storage logic for storing by longitude is as Figure 5 shown. Starting from PJ1, store the gravity data required in step 102 along the meridian, from PJ1 to PJA, then from PJB to PJC, and finally from PJD to PJE.

[0064] The storage logic for storing by latitude is asFigure 6 As shown, starting from PW1, store the gravity data required in step 102 along the meridian, from PW1 to PWA, then from PWB to PWC, and finally from PWD to PWE.

[0065] Step 104: Establish a status table;

[0066] The storage status table indicates whether a grid point is stored in the gravity database. The purpose of establishing the status table is to facilitate the user to index the gravity data of a specified point. The form of the status table is related to the sparse scheme by longitude or latitude. The stored values will be arranged in the form of a one-dimensional array. Since the gravity vector has components in three directions, a gravity database for a certain fixed plane is composed of 3 one-dimensional arrays, and correspondingly, 3 status tables will be used to facilitate the retrieval of the values in the database.

[0067] If the sparse by longitude is selected, the form of its status table is as Figure 7 shown, Figure 7 In it, the gravity values of PJ1 in a certain direction are relatively close to those of PJ2, PJ3, PJ4, and PJ5 (the discrimination method is shown in step 102). Therefore, the gravity values of PJ2, PJ3, PJ4, and PJ5 in a certain direction can be omitted from storage. According to this condition, the status table 1000011 can be generated, corresponding to Figure 7 shown on the right.

[0068] The calculation process of the longitude sparse status table is as Figure 8 shown:

[0069] If the sparse by latitude is selected, the form of its status table is as Figure 8 shown, Figure 8 In it, the gravity values of PW1 in a certain direction are relatively close to those of PW2, PW3, PW4, and PW5 (the discrimination method is shown in step 102). Therefore, the gravity values of PW2, PW3, PW4, and PW5 in a certain direction can be omitted from storage. According to this condition, the status table 1000011 can be generated, corresponding to Figure 9 shown below.

[0070] The calculation process of the latitude sparse status table is as Figure 10 shown:

[0071] Step 105: Select a storage form to store the status table in a fixed form;

[0072] The storage methods of the status table are divided into two types: storage by longitude and storage by latitude (the same method as in step 103). The two storage methods have nothing to do with the sparse scheme by longitude or latitude. However, for the convenience of indexing, the preferred scheme is: the status table generated by longitude sparse is stored by longitude; the status table of latitude sparse is stored by latitude.

[0073] Step 106: Index the gravity value of a specified point in the gravity database according to the status table;

[0074] If indexing the gravity value of a specified point in the gravity database according to the longitude sparse method, first, the storage location of this point in the database needs to be obtained. If the database is selected to be stored in the form of longitude, the database can be simplified to Figure 11 as shown. This database stores a total of 3×N_LON×M_LON gravity values (considering the three directions of northeast and sky), and its storage form is as Figure 11 shown by the dotted line in. According to the storage method of the status table in step 105, the status table generated by longitude sparsity is generally stored in the form of longitude storage. If a gravity component in a certain direction is to be selected, first, the database and the status table corresponding to this direction need to be found. The one-dimensional array corresponding to this direction is called LON_DATA, and the status table is called Table01. If the value in the i-th row and j-th column of the database is to be indexed, the following calculations are required: 1) First, calculate (j - 1)×N_LON + i; 2) Then calculate This formula means to accumulate the values in Table01 from the first value in the array to the (j - 1)×N_LON + i-th value, and record the calculation result as Kp; 3) Index the Kp-th value in LON_DATA, which is the required value.

[0075] If indexing the gravity value of a specified point in the gravity database according to the latitude sparse method, first, the storage location of this point in the database needs to be obtained. If the database is selected to be stored in the form of latitude, the database can be simplified to Figure 12 as shown. This database stores a total of 3×N_LAT×M_LAT gravity values (considering the three directions of northeast and sky), and its storage form is as Figure 12 shown by the dotted line in. According to the storage method of the status table in step 105, the status table generated by latitude sparsity is generally stored in the form of latitude storage. If a gravity component in a certain direction is to be selected, first, the database and the status table corresponding to this direction need to be found. The one-dimensional array corresponding to this direction is called LAT_DATA, and the status table is called Table01.

[0076] If the value in the i-th row and j-th column of the database is to be indexed, the following calculations are required: 1) First, calculate (i - 1)×M_LON + j; 2) Then calculate This formula means to accumulate the values in Table01 from the first value in the array to the (i - 1)×M_LON + j-th value, and record the calculation result as Kp; 3) Index the Kp-th value in LAT_DATA, which is the required value.

[0077] Example 1

[0078] Step 1: Based on EGM2008, generate a database with a resolution of 10 arcminutes.

[0079] Step 2: Determine the data points to be stored. The basic principle of the latitude sparse scheme is to replace the points with relatively small differences in gravity values (judging the gravity value differences using a pre-set threshold Δ) between adjacent data points in the original database (the database with a resolution of 10 arcminutes) with a single gravity value, so as to reduce the storage volume. As Figure 13 shown, on the latitude line where the latitude is θ1, if the differences in the gravity components of points PD1 to PD5 in the east, north, and zenith directions are less than a certain specific value, then only the gravity value of PD1 needs to be stored.

[0080] Using the relationship between the database size and the threshold Δ (the larger the threshold is set, the smaller the database volume, but the cost is to reduce the database accuracy), the gravity database can be compressed to the specified range. Finally, the selected threshold Δ for this scheme is 15 μg, and the final storage capacity of the database is 4.6 megabytes. Figure 14 The flowchart in is the process of generating the compressed database, where 2161 represents how many interpolation nodes there are at intervals of 10 arcminutes on each latitude line.

[0081] Step 3: Store the data that needs to be stored in Step 2 according to longitude.

[0082] Step 4: Establish a status table.

[0083] Whether each interpolation node is stored can be determined. If the node is stored, it is represented as state 1, and if the node is not stored, it is represented as state 0. As Figure 15 shown, the corresponding storage status 11000011 can be obtained, and this data will also be stored to determine the status of the interpolation nodes.

[0084] Step 5: Store the status table.

[0085] In the actual engineering solution, the storage order of the status table is to store it from left to right in sequence according to latitude. The storage method is as Figure 16 shown, and the storage form of the gravity database is as Figure 17 shown.

[0086] Step 6: Index the gravity values.

[0087] The engineering interpolation point indexing scheme is as Figure 18 shown (since the original accumulation process will cause a large computational burden, it is necessary to pre-store the number of nodes with state 1 in each column according to longitude for simplification). Among them, the interpolation point indexing scheme is shown in the following figure. Among them, the value of Index_i (i = 1, 2, 3, 4) is taken as:

[0088]

Claims

1. A method for constructing a gravity database with low storage capacity based on the sparsity of longitude and latitude, characterized in that, The specific steps include: Step 1: Generate a global gravity grid database using a global high-precision gravity model; Step 2: for the grid points in the global gravity grid database generated in step 1, a threshold comparison method of difference between sequentially adjacent gravity data is used along the longitude or latitude direction to remove the gravity data storage of the subsequent grid points that meet the threshold conditions, so as to obtain a sparse global gravity grid database; Step 3: Generate a grid point status table, and set different status values ​​for the grid points that store gravity data and the grid points that do not store gravity data.

2. A method for constructing a low-storage gravity database based on sparse longitude and latitude, according to claim 1, characterized in that In the step 1, the following steps are specifically included: determining the longitude and latitude intervals, using a global high-precision gravity model, calculating and recording the gravity data of the grid points at the intersection of the longitude and latitude lines.

3. A method for constructing a low-storage gravity database based on sparse longitude and latitude, according to claim 2, wherein The global high-precision gravity models include the EGM-2008 model and the EIGEN-6C4 model.

4. A method for constructing a low-storage gravity database based on sparse longitude and latitude, as claimed in claim 2, wherein The intervals of longitude and latitude are 5 or 10 arc minutes.

5. According to the method for constructing a low-storage gravity database based on sparse longitude and latitude according to claim 1, in step 2, the specific process of using the sequential adjacent gravity data difference threshold comparison method along the longitude direction includes: Select a certain longitude line, and the grid points on the longitude line are recorded as PJ1~PJn; The gravity values ​​at grid points PJ1 to PJN are recorded as [G_E1, G_N1, G_U1] to [G_En, G_Nn, G_Un], where E, N, and U represent the components of the gravity data vector in the east, north, and sky directions; Set the difference threshold α, compare the adjacent differences along the longitude direction in the order of east, north, and celestial, and calculate |G_Ei-G_E(i+1)|. If |G_Ei-G_E(i+1)|≤α, i=1, 2, 3..., n; it means that G_Ei and G_E(i+1) are very close in value. The database only stores the eastward gravity data G_Ei of the PJi grid point, and removes the eastward gravity data G_Ei of the PJ(i+1) grid point. Gravity data G_E(i+1); if |G_Ei-G_E(i+1)|>α, it means that G_E1 and G_E2 are not close in value, and the database needs to store the eastward gravity data G_Ei of the PJi grid point and the eastward gravity data G_E(i+1) of the PJ(i+1) grid point at the same time; when the gravity component in one direction on a meridian is stored, the same sparse operation is performed on the gravity components in other directions; The remaining meridians are traversed to obtain the sparse global gravity grid database.

6. According to the method for constructing a low-storage gravity database based on sparse longitude and latitude as described in claim 1, in step 3, the state value of the grid point that stores gravity data is assigned 1, and the state value of the grid point that does not store gravity data is set to 0.

7. A gravity database indexing method with low storage based on longitude and latitude sparsity, characterized in that, The sparse global gravity grid database and grid point status table constructed by using the low-storage gravity database construction method based on latitude and longitude sparseness described in any one of claims 1 to 6 specifically include the following steps: Step 1: Index the grid point position of the index point according to the grid point status table; Step 2: According to the status value of the grid point position obtained by indexing in Step 1, if the status value corresponds to a grid point storing gravity data, the gravity data of the point to be indexed is obtained; if the status value corresponds to a grid point not storing gravity data, according to the sparse record type of the database, the status value of the first grid point storing gravity data is reversely searched in the status table to obtain the gravity data of the point to be indexed.

8. An electronic device, characterized in that, It includes a memory and a processor, and the memory is coupled to the processor; the memory stores program instructions, and when the program instructions are executed by the processor, the electronic device is caused to execute a method for constructing a low-storage gravity database based on longitude and latitude sparsity as described in any one of claims 1-6, and an indexing method for a low-storage gravity database based on longitude and latitude sparsity as described in claim 7.

9. A computer-readable storage medium, characterized in that, It includes a computer program, and when the computer program runs on an electronic device, the electronic device is caused to execute a method for constructing a low-storage gravity database based on longitude and latitude sparsity as described in any one of claims 1-6, and an indexing method for a low-storage gravity database based on longitude and latitude sparsity as described in claim 7.

10. A computer program product comprising instructions, characterized in that, When the computer program product runs on a computer, the computer is caused to execute a method for constructing a low-storage gravity database based on longitude and latitude sparsity as described in any one of claims 1-6, and an indexing method for a low-storage gravity database based on longitude and latitude sparsity as described in claim 7.

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