Map tile retrieval method and system

Abstract

The invention discloses a map tile retrieval method and system, and the method specifically comprises the steps: S1, carrying out the initialization and dynamic extension of a search domain, and determining the search domain; s2, guessing the positions of the elements to be queried by adopting an interpolation retrieval algorithm according to the uniformity of element distribution; s3, optimizing a keyword positioning process by adopting a self-adaptive linear interpolation algorithm; s4, accurate position determination and sequence search optimization are carried out; and S5, dynamically adjusting a starting point and a range of sequential searching by adopting an optimization algorithm. The retrieval speed of the map during tile loading is improved, and the display speed of the tile map is increased.

Description

Technical Field

[0001] The present invention relates to the field of map tile data retrieval, and in particular to a map tile retrieval method and system. Background Art

[0002] In the field of map tile data retrieval, binary search is a widely used algorithm that can provide O(log(n)) time complexity in sorted arrays. However, when the array length is unknown or the search element is close to the beginning of the array, the efficiency of binary search is not ideal. In addition, the existing technology also faces performance bottlenecks when processing non-uniformly distributed data sets, such as map tile data. For example, the traditional binary search method in tile retrieval, due to the uneven distribution of data, leads to low retrieval efficiency and cannot meet the needs of fast response. Summary of the invention

[0003] In view of the above problems, the present invention is proposed to provide a map tile retrieval method and system that overcomes the above problems or at least partially solves the above problems.

[0004] According to one aspect of the present invention, a map tile retrieval method is provided, and the retrieval method specifically comprises:

[0005] Step S1: Initialize and dynamically expand the search domain and determine the search domain;

[0006] Step S2: according to the uniformity of element distribution, an interpolation search algorithm is used to guess the position of the element to be searched;

[0007] Step S3: using an adaptive linear interpolation algorithm to optimize the keyword positioning process;

[0008] Step S4: accurate location determination and sequential search optimization;

[0009] Step S5: Adopt an optimization algorithm to dynamically adjust the starting point and range size of the sequential search.

[0010] Optionally, the step S1: initializing and dynamically expanding the search domain, determining the search domain specifically includes:

[0011] Initialize the search domain, set the starting index of the initial search domain to 0, the first element of the data array; set the upper bound of the search domain to 1, indicating the end position of the current search range;

[0012] The fast retrieval strategy adopts the idea of ​​binary search and constantly adjusts the upper bound of the search domain;

[0013] Dynamically adjust the search domain, set a multiplication factor α (α>1), and multiply the upper bound of the search domain by α in each iteration; use the following formula to update the upper bound of the search domain: Unew =U old ×α. Among them, U new represents the new upper bound of the search domain, U old Indicates the current upper bound of the search domain.

[0014] Optionally, the fast search strategy adopts the idea of ​​binary search and continuously adjusts the upper bound of the search domain, specifically including:

[0015] Compare the size of the keyword to be searched with the middle position element of the current search domain;

[0016] If the value of the element at the middle position is less than the keyword to be searched, the starting index of the search domain is adjusted to after the middle position; conversely, if the value of the element at the middle position is greater than or equal to the keyword to be searched, the upper limit of the search domain is adjusted to the middle position;

[0017] According to the comparison result, the upper limit of the search domain is increased exponentially until it exceeds the maximum position where the keyword to be searched exists.

[0018] Optionally, the step S2: using an interpolation search algorithm to guess the position of the element to be searched according to the uniformity of element distribution specifically includes:

[0019] Calculate the distribution uniformity factor, and calculate the distribution uniformity factor λ for the elements in the determined search domain;

[0020]

[0021] Among them, N avg Represents the average value of the element interval in the search domain, N max Indicates the maximum interval value;

[0022] Interpolation algorithm positioning;

[0023] Adjust the search domain according to the interpolation predicted position P quess , readjust the search domain and update the starting index of the search domain to P quess , and continue to use binary search to accurately locate.

[0024] Optionally, the interpolation algorithm positioning specifically includes:

[0025] According to the distribution uniformity factor λ, the position of the keyword to be checked is interpolated and predicted;

[0026] If λ is close to 1, it indicates that the elements are evenly distributed and linear interpolation is used directly; if the λ value is small, it indicates that the elements are unevenly distributed and nonlinear interpolation is used;

[0027] Linear interpolation formula:

[0028] Among them, Pquess is the predicted keyword position, P start and P end are the starting and ending positions of the search domain, K target is the keyword to be searched, K start and K end are the starting and ending keywords of the search domain.

[0029] Optionally, the step S3: using an adaptive linear interpolation algorithm to optimize the keyword positioning process specifically includes:

[0030] Compare the predicted position with the keyword to be searched, and set the predicted position P quess Compare with the keyword to be checked to determine whether the predicted position is accurate; if the predicted position matches the keyword to be checked, directly output the result; otherwise, proceed to the next step;

[0031] Update the beginning and end of the search space, and adaptively adjust the search space according to the comparison result between the predicted position and the keyword to be checked; if the predicted position is higher than the keyword to be checked, adjust the end of the search space to the previous element of the predicted position; conversely, if the predicted position is lower than the keyword to be checked, adjust the beginning of the search space to the next element of the predicted position;

[0032] Use the following formula to update the start and end index of the search space:

[0033] If K target >P guess , then P start =P guess +1; otherwise P end =P guess -1

[0034] Linear interpolation calculates the middle position. In the updated search space, an improved linear interpolation algorithm is used to calculate the possible middle position of the keyword to be searched;

[0035] The improved linear interpolation algorithm takes into account the local characteristics of element distribution and calculates the intermediate position P through the formula mid :

[0036]

[0037] Among them, ∈ is a positive number and is adjusted according to the actual data distribution.

[0038] Optionally, the step S4: accurate location determination and sequential search optimization specifically includes:

[0039] Determine the accuracy of the predicted position and the position P of the interpolated prediction midWhether it matches the keyword to be searched; if it matches, continue to use the interpolation algorithm for retrieval;

[0040] Sequentially search for the start condition. If the predicted position is inaccurate, then search around the predicted position P. guess Set a search range W between 0 and 10, and adjust the range according to the actual situation; within the range W, start from the next element of the predicted position and search in order until the keyword to be searched is found or the entire range is traversed;

[0041] Sequential search process, starting from P guess +1, and compare each element one by one to see if it is the keyword to be searched, until a matching element is found or the end of the range is reached; if the keyword to be searched is found in the range, the retrieval process ends; otherwise, the range is expanded and the sequential search continues.

[0042] The present invention also provides a map tile retrieval system, which applies the above-mentioned map tile retrieval method, and the retrieval system specifically includes:

[0043] A search domain determination module, used for search domain initialization and dynamic expansion, and determining the search domain;

[0044] The interpolation retrieval algorithm module is used to guess the position of the element to be searched by using the interpolation retrieval algorithm according to the uniformity of element distribution;

[0045] A keyword optimization module is used to optimize the keyword positioning process by using an adaptive linear interpolation algorithm;

[0046] Search optimization module, used for accurate location determination and sequential search optimization;

[0047] The dynamic sequence adjustment module is used to dynamically adjust the starting point and range size of the sequential search using an optimization algorithm.

[0048] The present invention provides a map tile retrieval method and system, the retrieval method specifically includes: step S1: initializing and dynamically expanding the search domain to determine the search domain; step S2: using an interpolation retrieval algorithm to guess the position of the element to be searched according to the uniformity of element distribution; step S3: using an adaptive linear interpolation algorithm to optimize the keyword positioning process; step S4: accurate position determination and sequential search optimization; step S5: using an optimization algorithm to dynamically adjust the starting point and range size of the sequential search. The retrieval speed of the map when loading tiles is improved, and the display speed of the tile map is accelerated.

[0049] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically exemplified below. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0051] Figure 1 A flowchart of a map tile retrieval method provided for the implementation of the present invention;

[0052] Figure 2 A flowchart for initializing and dynamically expanding a search domain and determining a search domain provided for the implementation of the present invention;

[0053] Figure 3 A flowchart for the implementation of the present invention for guessing the position of an element to be searched using an interpolation search algorithm based on the uniformity of element distribution;

[0054] Figure 4 A flowchart of a process of optimizing keyword location using an adaptive linear interpolation algorithm provided for the implementation of the present invention;

[0055] Figure 5 A flow chart of precise location determination and sequential search optimization provided for the implementation of the present invention;

[0056] Figure 6 A flow chart for dynamically adjusting the starting point and range size of a sequential search using an optimization algorithm is provided for the implementation of the present invention. DETAILED DESCRIPTION

[0057] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0058] The terms "comprises" and "having" and any variations thereof in the description embodiments, claims and drawings of the present invention are intended to cover non-exclusive inclusions, for example, including a series of steps or units.

[0059] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0060] Example 1

[0061] like Figure 1 As shown, a map tile retrieval method, the retrieval method specifically includes:

[0062] Step S1: Initialize and dynamically expand the search domain and determine the search domain;

[0063] Step S2: according to the uniformity of element distribution, an interpolation search algorithm is used to guess the position of the element to be searched;

[0064] Step S3: using an adaptive linear interpolation algorithm to optimize the keyword positioning process;

[0065] Step S4: accurate location determination and sequential search optimization;

[0066] Step S5: Adopt an optimization algorithm to dynamically adjust the starting point and range size of the sequential search.

[0067] A map tile retrieval method, comprising:

[0068] Step 1: Search domain initialization and dynamic expansion, such as Figure 2 As shown:

[0069] Initialize the search domain: Set the starting index of the initial search domain to 0, which is the first element of the data array. Set the upper bound of the search domain to 1, which indicates the end position of the current search range.

[0070] Fast search strategy: Adopt the idea of ​​binary search and continuously adjust the upper bound of the search domain through the following steps: ① Compare the size of the key to be searched with the element in the middle position of the current search domain. ② If the value of the element in the middle position is less than the key to be searched, adjust the starting index of the search domain to after the middle position; conversely, if the value of the element in the middle position is greater than or equal to the key to be searched, adjust the upper bound of the search domain to the middle position. ③ Based on the comparison result, increase the upper bound of the search domain exponentially until it exceeds the maximum possible position of the key to be searched.

[0071] Dynamically adjust the search domain: ① Set a multiplication factor α (α>1), and multiply the upper bound of the search domain by α in each iteration. ② Use the following formula to update the upper bound of the search domain: U new =U old ×α. Among them, U new represents the new upper bound of the search domain, U old Indicates the current upper bound of the search domain.

[0072] Step 2: After determining the search domain, use the interpolation retrieval algorithm to guess the location of the element to be searched based on the uniformity of the element distribution, such as Figure 3 As shown:

[0073] After the search domain is determined in step 1, in order to further narrow the search scope and improve the accuracy of locating the tiles to be searched, the present invention introduces an interpolation search algorithm based on the uniformity of element distribution. The algorithm optimizes the traditional binary search through the following steps:

[0074] Calculate the distribution uniformity factor:

[0075] ① For the elements in the determined search domain, calculate the distribution uniformity factor λ, which reflects the size of the interval between elements. The more uniform the distribution, the closer the λ value is to 1.

[0076] ② Calculate λ using the following formula: Among them, N avg Represents the average value of the element interval in the search domain, N max Indicates the maximum interval value.

[0077] Interpolation algorithm positioning:

[0078] ① According to the distribution uniformity factor λ, the position of the keyword to be searched is interpolated and predicted.

[0079] If λ is close to 1, it indicates that the elements are evenly distributed, and linear interpolation is used directly; if the λ value is less than 0.001, it indicates that the elements are unevenly distributed, and nonlinear interpolation method is used.

[0080] ②The linear interpolation formula is as follows:

[0081] Among them, P guess is the predicted keyword position, P start and P end are the starting and ending positions of the search domain, K target is the keyword to be searched, K start and K end are the starting and ending keywords of the search domain.

[0082] Adjust the search domain: According to the interpolated predicted position P guess , readjust the search domain and update the starting index of the search domain to P guess , and continue to use binary search to accurately locate.

[0083] Step 3: Adaptive search space adjustment and interpolation optimization, such as Figure 4 As shown:

[0084] In step 2, we have made a preliminary estimate of the location of the keyword to be searched through interpolation prediction. This step will further refine the search space and use an adaptive linear interpolation algorithm to optimize the keyword positioning process. The following are the detailed sub-steps and module numbers:

[0085] Compare the predicted position with the keyword to be searched: ① Set the predicted position P guess Compare it with the keyword to be checked to determine whether the predicted position is accurate. ② If the predicted position matches the keyword to be checked, output the result directly; otherwise, proceed to the next step.

[0086] Update the beginning and end of the search space: ① Adaptively adjust the search space based on the comparison between the predicted position and the keyword to be searched. If the predicted position is higher than the keyword to be searched, adjust the end of the search space to the previous element of the predicted position; conversely, if the predicted position is lower than the keyword to be searched, adjust the beginning of the search space to the next element of the predicted position. ② Use the following formula to update the start and end indexes of the search space: If K target >P guess , then P start =P guess +1; otherwise P end =P guess -1

[0087] Linear interpolation calculation of the middle position: In the updated search space, an improved linear interpolation algorithm is used to calculate the possible middle position of the keyword to be searched. The improved linear interpolation algorithm takes into account the local characteristics of element distribution and calculates the middle position P through the following formula mid : Among them, ∈ is a positive number between 0 and 10, which is used to avoid the situation where the denominator is zero, and it can be adjusted according to the actual data distribution.

[0088] Step 4: Accurate location determination and sequential search optimization, such as Figure 5 As shown:

[0089] In step 3, we have made a more accurate estimate of the position of the keyword to be searched through adaptive search space adjustment and interpolation optimization. If the interpolation predicted position matches the keyword to be searched, the interpolation retrieval method is continued to improve the search efficiency; if it does not match, it switches to sequential search mode and performs a more detailed search in the neighborhood of the predicted position. The following are the detailed implementation steps:

[0090] Determine the accuracy of the predicted position: Determine the position P of the interpolated prediction mid Whether it matches the keyword to be searched. If it matches, continue to use the interpolation algorithm to search to speed up the search process.

[0091] Sequential search start condition: If the predicted position is inaccurate, then search around the predicted position P guess Set a search range W between 0 and 10. The range size can be adjusted according to the actual situation. Within the range W, start from the next element of the predicted position and search in order until the keyword to be searched is found or the entire range is traversed.

[0092] Sequential search process: From P quess +1, and compare each element one by one to see if it is the key to be searched, until a matching element is found or the end of the range is reached. If the key to be searched is found in the range, the retrieval process ends; otherwise, the range is expanded and the sequential search continues.

[0093] By combining interpolation retrieval with sequential search, we can maintain high efficiency while ensuring that the keywords to be searched can be quickly located even when the predicted position is inaccurate, effectively improving the flexibility and accuracy of the overall retrieval algorithm.

[0094] Step 5: Combining algorithm optimization with sequential search to improve accuracy, such as Figure 6 As shown:

[0095] In step 4, we set the search range and perform a sequential search to handle the inaccurate prediction position. In order to further improve the performance of the algorithm, especially when the predicted position is close to the actual position of the keyword to be searched, this step proposes an optimization algorithm. The algorithm dynamically adjusts the starting point and range size of the sequential search to reduce the number of unnecessary comparisons, thereby improving the retrieval efficiency.

[0096] Dynamically adjust the order to search for the starting point: When the predicted position P quess When the position of the keyword to be searched is close to the actual position (for example, the error range is within Δp), the starting point of the sequential search is set to P quess Rather than P quess +1. This avoids redundant lookups before known keyword positions.

[0097] Example 2

[0098] Step 1 Implementation process:

[0099] Suppose there is a sorted tile array containing 100 elements, and we want to quickly retrieve the position of the key "tile_56".

[0100] 1. Initialize the search domain’s starting index to 0 and its upper bound to 1.

[0101] 2. According to the multiplication factor α=2, the upper bound of the search domain is multiplied.

[0102] 3. Perform a comparison operation. Assume that the middle element of the first comparison is "tile_50". Since "tile_50" is smaller than "tile_56", adjust the starting index of the search domain to after the middle position.

[0103] 4. Continue to multiply the upper bound of the search domain and repeat the comparison process until the position of the keyword "tile_56" is found or exceeded.

[0104] Step 2 Implementation process:

[0105] Assume that through step 1 we have determined the search domain to be the 20th to 80th elements in the tile array.

[0106] 1. Calculate the distribution uniformity factor λ of the search domain.

[0107] 2. Based on the calculated λ value, interpolate and predict the position of the keyword "tile_56" to be searched.

[0108] 3. Assuming that the position predicted by interpolation is the 50th element, we update the starting index of the search domain to the 50th element.

[0109] 4. Starting from the new search domain starting position, use binary search to perform precise positioning operations.

[0110] Step 3 Implementation process:

[0111] Suppose in step 2, we predicted the position of the keyword "tile_56" to be the 50th element, but the actual position of "tile_56" is the 52nd element.

[0112] 1. Compare the predicted position with the keyword to be checked and find that the predicted position is lower than the keyword to be checked.

[0113] 2. According to the comparison result, the starting index of the search space is adjusted to the next digit of the 50th element, that is, the 51st element.

[0114] 3. Use the improved linear interpolation algorithm to calculate the new intermediate position P mid .

[0115] 4. Assume that the calculated middle position is the 51.5th element. Since the element positions are discrete in practice, take the nearest integer position, which is the 52nd element.

[0116] From the new search space (the 51st element to the 80th element), use the 52nd element as the new predicted position and continue searching.

[0117] Step 4 Implementation process:

[0118] Suppose in step 3, we predicted the position of the keyword "tile_56" to be the 52nd element, but in fact "tile_56" is not on the 52nd element.

[0119] 1. Judge the accuracy of the predicted position and find that the predicted position is inaccurate.

[0120] 2. Set a search range W. For example, if the range size is 5, it starts from the 53rd element and ends at the 57th element.

[0121] 3. Start searching from the 53rd element in order, and compare them one by one until "tile_56" is found or the 57th element is found.

[0122] 4. Assuming that "tile_56" is found on the 55th element, the retrieval process ends.

[0123] 5. If "tile_56" is not found in the entire range, expand the range, such as doubling the range size, and start a new sequential search from the next unchecked element after the 53rd element.

[0124] Step 5 Implementation process:

[0125] 1. Calculate the error ΔP between the predicted position and the actual position, assuming the error is 2.

[0126] 2. According to the error range, determine the adjustment coefficient k (for example, k=1), and use the formula to calculate the size of the new array range w=w0-k×ΔP.

[0127] 3. Adjust the starting point of the sequential search to the predicted position P quess , which is the 54th element.

[0128] 4. Within the new range size, search sequentially starting from the 54th element.

[0129] Assume that after two comparisons, "tile_56" is found in the 56th element, and the search process ends.

[0130] The present invention reduces the number of searches and improves the accuracy of the algorithm when the predicted position is close to the actual position. At the same time, the optimization also maintains the flexibility of the algorithm and can adapt to different prediction error situations.

[0131] Beneficial effects: Improves the map retrieval speed when loading tiles and speeds up the display of tile maps.

[0132] The above specific implementation methods further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A map tile retrieval method, characterized in that: The retrieval method specifically includes: Step S1: Initialize and dynamically expand the search domain and determine the search domain; Step S2: Based on the uniformity of element distribution, an interpolation search algorithm is used to guess the position of the element to be searched; Step S3: using an adaptive linear interpolation algorithm to optimize the keyword positioning process; Step S4: accurate location determination and sequential search optimization; Step S5: Adopt an optimization algorithm to dynamically adjust the starting point and range of the sequential search.

2. A map tile retrieval method according to claim 1, characterized in that: The step S1: initializing and dynamically expanding the search domain, determining the search domain specifically includes: Initialize the search domain, set the starting index of the initial search domain to 0, the first element of the data array; set the upper bound of the search domain to 1, indicating the end position of the current search range; The fast retrieval strategy adopts the idea of ​​binary search and adjusts the upper bound of the search domain; Dynamically adjust the search domain, set a multiplication factor α (α>1), and multiply the upper bound of the search domain by α in each iteration; Use the following formula to update the upper bound of the search domain: new =U old *α Among them, U new represents the new upper bound of the search domain, U old Indicates the current upper bound of the search domain.

3. A map tile retrieval method according to claim 2, characterized in that: The fast search strategy adopts the idea of ​​binary search and continuously adjusts the upper bound of the search domain, specifically including: Compare the size of the keyword to be searched with the middle position element of the current search domain; If the value of the element at the middle position is less than the keyword to be searched, adjust the starting index of the search domain to after the middle position; if the value of the element at the middle position is greater than or equal to the keyword to be searched, adjust the upper limit of the search domain to the middle position; According to the comparison result, the upper limit of the search domain is increased exponentially until it exceeds the maximum position where the keyword to be searched exists.

4. A map tile retrieval method according to claim 1, characterized in that: The step S2: using an interpolation search algorithm to guess the position of the element to be searched according to the uniformity of the element distribution specifically includes: Calculate the distribution uniformity factor, and calculate the distribution uniformity factor λ for the elements in the determined search domain; Among them, N avg Represents the average value of the element interval in the search domain, N max Indicates the maximum interval value; Interpolation algorithm positioning; Adjust the search domain according to the interpolated predicted position p quess , readjust the search domain and update the starting index of the search domain to p quess , and continue to use binary search to accurately locate.

5. A map tile retrieval method according to claim 4, characterized in that: The interpolation algorithm positioning specifically includes: According to the distribution uniformity factor λ, the position of the keyword to be checked is interpolated and predicted; If λ is greater than or equal to 1, it indicates that the elements are evenly distributed and linear interpolation is used directly; if λ is less than 0.001, it indicates that the elements are unevenly distributed and nonlinear interpolation is used; Linear interpolation formula: Among them, P guess is the predicted keyword position, P start and P end are the starting and ending positions of the search domain, K target is the keyword to be searched, K start and K end are the starting and ending keywords of the search domain.

6. A map tile retrieval method according to claim 1, characterized in that: The step S3: using an adaptive linear interpolation algorithm to optimize the keyword positioning process specifically includes: Compare the predicted position with the keyword to be searched, and set the predicted position P guess Compare with the keyword to be checked to determine whether the predicted position is accurate; if the predicted position matches the keyword to be checked, directly output the result; otherwise, proceed to the next step; Update the beginning and end of the search space, and adaptively adjust the search space according to the comparison result between the predicted position and the keyword to be checked; if the predicted position is higher than the keyword to be checked, adjust the end of the search space to the previous element of the predicted position; conversely, if the predicted position is lower than the keyword to be checked, adjust the beginning of the search space to the next element of the predicted position; Use the formula to update the start and end index of the search space: If K target >P guess , then P start =P guess +1; otherwise P end =P guess -1 Linear interpolation calculates the middle position. In the updated search space, an improved linear interpolation algorithm is used to calculate the possible middle position of the keyword to be searched; The improved linear interpolation algorithm takes into account the local characteristics of element distribution and calculates the intermediate position P through the formula mid : Among them, the value range of ∈ is 0 to 10, which is adjusted according to the actual data distribution.

7. A map tile retrieval method according to claim 1, characterized in that: The step S4: accurate location determination and sequential search optimization specifically includes: Determine the accuracy of the predicted position and the position P of the interpolated prediction mid Whether it matches the keyword to be searched; if it matches, use the interpolation algorithm to search; Sequentially search for the start condition. If the predicted position is inaccurate, then search around the predicted position P. mid Set a search range W between 0 and 10, and adjust the range according to the actual situation; within the range W, start from the next element of the predicted position and search in order until the keyword to be searched is found or the entire range is traversed; Sequential search process, starting from P quess +1, and compare each element one by one to see if it is the keyword to be searched, until a matching element is found or the end of the range is reached; if the keyword to be searched is found in the range, the retrieval process ends; otherwise, the range is expanded and sequential search continues.

8. A map tile retrieval system, using a map tile retrieval method according to any one of claims 1 to 7, characterized in that: The retrieval method specifically includes: A search domain determination module, used for search domain initialization and dynamic expansion, and determining the search domain; The interpolation retrieval algorithm module is used to guess the position of the element to be searched by using the interpolation retrieval algorithm according to the uniformity of element distribution; A keyword optimization module is used to optimize the keyword positioning process by using an adaptive linear interpolation algorithm; Search optimization module, used for accurate location determination and sequential search optimization; The dynamic sequence adjustment module is used to dynamically adjust the starting point and range size of the sequential search using an optimization algorithm.

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