Leveling network minimum closed loop rapid search method based on survey station grouping and degree priority

Abstract

The invention provides a level network minimum closed loop rapid search method based on survey station grouping and degree priority, which belongs to the technical field of surveying and mapping engineering, and comprises the following steps of: preprocessing measurement original data, eliminating abnormal values and redundant information, ensuring connectivity of a control network, constructing an adjacency phenotype topological graph, and constructing a minimum closed loop by taking a survey station as a node and a survey line as an edge; the method comprises the following steps: recording node degrees and initializing traversal parameters, preferentially accessing nodes with a large number of connection edges in a traversal stage by adopting a degree priority strategy to accelerate discovery of short rings, generating an initial closed ring set through depth-first recursive traversal, performing independence and minimum verification, eliminating redundant rings and non-minimum rings, and finally obtaining an initial closed ring set through depth-first recursive traversal. Finally, the minimum independent closed ring meeting the requirement is reserved, the closing error of each ring is calculated and compared with the tolerance, and an achievement report containing the ring path, the closing error and the qualified state is generated and used for subsequent adjustment or visualization processing. According to the method, the search efficiency and the result reliability are improved by optimizing a traversal strategy and a ring check mechanism.

Description

Technical Field

[0001] This application relates to the field of surveying and mapping engineering technology, and in particular to a fast search method for the minimum closed loop of a leveling network based on station grouping and degree priority. Background Technology

[0002] In leveling network (elevation control network) surveying, to ensure the inherent consistency and measurement accuracy of the observation data, it is necessary to calculate the closure error and check the accuracy of the closed loop formed by each observation route. A closed loop (closed route) refers to a loop formed by traversing multiple elevation difference observation routes from a certain station back to the origin; the cumulative value of the elevation difference error is the loop closure error. If round-trip observations are used, the round trip along the same route is also considered a closed loop. The closure error is one of the core indicators for measuring measurement accuracy; the measurement can only be considered qualified if the "elevation difference closure error ≤ specified limit error".

[0003] Although existing technologies have achieved certain results in engineering applications, they still have the following shortcomings: Spanning tree / matrix methods consume significant amounts of computation and memory for ultra-large-scale networks. BFS / DF, without heuristic sorting, is also prone to generating numerous invalid traversals, impacting real-time performance. Traditional DFS methods take over 5 minutes to search in networks with more than 100 stations. The memory complexity of spanning tree methods is too high, making them unsuitable for ultra-large networks.

[0004] The classic DFS method may "miss cycles" in complex network structures due to edge labeling strategies, requiring additional mechanisms for compensation; the spanning tree method needs to be combined with the shortest path algorithm, which is complex.

[0005] Many methods require separate traverse line search after loop closure, lacking integrated processing; while error checking can often only be evaluated after loop search is completed, making it impossible to achieve simultaneous rapid loop closure search and closure error limit determination.

[0006] Existing methods mostly start from the perspective of graph topology or adjustment matrix, and rarely prioritize "number of station observations" or "node degree" to accelerate the search for the minimum closed loop.

[0007] Support for mixed observation networks of different levels is limited, often requiring tiered and step-by-step processing; the lack of real-time progress feedback and termination mechanisms is not conducive to large-scale network field applications.

[0008] Based on the aforementioned technological status and practical engineering needs, a novel closed-loop rapid search method is required that balances efficiency, completeness, real-time feedback, and integrated error checking to meet the rapid quality control requirements of modern large-scale leveling network surveys. Summary of the Invention

[0009] The application provides a leveling network minimum closed loop fast search method based on station grouping and degree priority, which is suitable for automatic analysis and independent loop extraction of leveling closed error in a two-dimensional measurement control network.

[0010] To achieve the above object, the application provides the following technical scheme. According to the first aspect of the application, the application claims to protect a leveling network minimum closed loop fast search method based on station grouping and degree priority, comprising: S1, obtaining measurement original data of a control network to be searched, and obtaining measurement pretreatment data by pretreating the measurement original data; S2, constructing an adjacency list type topology graph based on the measurement pretreatment data, and initializing parameters and auxiliary markers of the adjacency list type topology graph; S3, obtaining node degrees of the adjacency list type topology graph, and selecting a traversal strategy of nodes of the adjacency list type topology graph based on the node degrees; S4, recursively traversing the nodes of the adjacency list type topology graph based on the traversal strategy, and obtaining a first closed loop set of the adjacency list type topology graph; S5, performing closed loop verification on the first closed loop set, and obtaining a second closed loop set by storing closed loops passing the verification; S6, calculating closed errors of each closed loop of the second closed loop set, and obtaining an output result report based on the closed errors.

[0011] Further, the S1 further comprises: obtaining measurement original data of a control network to be searched, and reading measurement lines and stations of the control network to be searched; verifying and cleaning the measurement original data, eliminating repeated measurement lines, isolated nodes and missing or abnormal values, obtaining measurement pretreatment data, and ensuring connectivity of the control network to be searched.

[0012] Further, the S2 further comprises: constructing an adjacency list type topology graph based on the measurement pretreatment data, taking the stations as nodes, taking the measurement lines as edges, recording an adjacency edge list of each node, and building an adjacency list or an adjacency matrix representation of the control network to be searched in the memory; calculating and recording connection degrees of each station, and providing a basis for traversal sorting; counting a point number P and an edge number E of the control network to be searched, and calculating a theoretical minimum independent closed loop number: ; Initialize parameters and auxiliary markers for the adjacency list topology graph, configure path stack to store node sequence on current recursive path; Configure access marker table to determine whether a node has been in the current path; Configure edge usage markers to disable observed edges that have been incorporated into closed loops.

[0013] Further, the S3 further comprises: Obtain the number of measured edges connected to the station as the node degree of the adjacency list topology graph, and configure traversal priorities based on the node degrees, with the traversal priority of a node with a high degree being higher than that of a node with a low degree; Default degree priority traversal strategy is enabled as the traversal strategy to accelerate short loop discovery, and the default sequential traversal strategy is switched according to requirements to verify the influence of different traversal sequences on the effect; The default sequential traversal strategy accesses in input sequence or node number; The degree priority traversal strategy accesses after sorting in descending or ascending order of station degree.

[0014] Further, the S4 further comprises: Take each station or each edge of the adjacency list topology graph as a starting point, and start deep-first recursive traversal based on the traversal strategy; When a new node is accessed, the station and the introduced measured line are pushed into the path stack, and the station is marked in the access marker table; When the recursion reaches the starting point node, a closed loop is formed, and S5 is entered for verification, otherwise the recursion continues to deepen along the unvisited adjacent edges; Limit the recursion depth, and if the current path length exceeds the preset maximum number of edges, backtrack immediately to avoid stack overflow caused by invalid deep search; If a dead end is encountered without unvisited adjacent edges or a non-starting point node in the path is encountered, path backtracking is performed immediately and the branch is skipped.

[0015] Further, the S5 further comprises: Independence check and minimality check are performed on the first closed loop set; The edge set and path of the closed loop passing the independence check and the minimality check are stored, the corresponding observed edge is disabled, subsequent repeated search is avoided, and a second closed loop set is obtained; The independence check ensures that the new loop is not a linear combination of existing loops; The minimality check verifies that the number of loop edges is the minimum possible value, and if multiple loops with the same number of edges coexist, the one with the shortest total path length is selected.

[0016] Further, the S6 further comprises: Accumulate the height difference of each closed loop of the second closed loop set, and calculate the algebraic sum of the start and end point elevation difference of each closed loop as the closure difference AH; Calculate the limit difference AH_lim of each closed loop according to the loop length or the measurement level of each closed loop, and determine whether it is out of limit; Generate a result report based on the loop number, loop path, AH, AH_lim and qualified or out-of-limit identification of each closed loop, Provide a result export interface for adjustment software or visualization tools.

[0017] The present application proposes a leveling network minimum closed loop fast search method based on station grouping and degree priority, which belongs to the technical field of surveying and mapping engineering. The original measurement data is preprocessed to eliminate outliers and redundant information, ensure the connectivity of the control network, construct an adjacency list type topology graph, take the station as the node and the survey line as the edge, record the node degree and initialize the traversal parameters. In the traversal stage, the degree priority strategy is adopted to preferentially access the nodes with more connection edges to accelerate the discovery of short loops. The initial closed loop set is generated through depth-first recursive traversal, and the independence and minimality check is performed to eliminate redundant loops and non-minimal loops. Finally, the minimum independent closed loop meeting the requirements is retained, the closure difference of each loop is calculated, and compared with the limit difference to generate a result report containing the loop path, closure difference and qualified state for subsequent adjustment or visualization processing. The present application improves the search efficiency and result reliability by optimizing the traversal strategy and loop checking mechanism. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The workflow diagram of the leveling network minimum closed loop fast search method based on station grouping and degree priority claimed by the embodiments of the present application; Figure 2 The leveling network closed route search schematic diagram of the leveling network minimum closed loop fast search method based on station grouping and degree priority claimed by the embodiments of the present application; Figure 3 The leveling network schematic diagram of the leveling network minimum closed loop fast search method based on station grouping and degree priority claimed by the embodiments of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0020] The terms "first", "second", "third", etc. in the present application are only for descriptive purpose and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second", "third" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality of" is at least two, such as two, three, etc., unless otherwise explicitly and specifically limited. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present application are only used to explain the relative position relationship, movement condition, etc. between components in a certain posture (as shown in the drawings), and if the certain posture changes, the directional indications also change accordingly. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device.

[0021] Reference herein to "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment can be included in at least one embodiment of the application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily independent or alternative embodiments to other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] According to the first embodiment of the present application, the present application claims a leveling network minimum closed loop fast search method based on station grouping and degree priority, referring to Figure 1 , comprising: S1, obtaining measurement raw data of a control network to be searched, and preprocessing the measurement raw data to obtain measurement preprocessed data; S2, constructing an adjacency list type topology graph based on the measurement preprocessed data, and initializing parameters and auxiliary markers for the adjacency list type topology graph; S3, obtaining node degrees of the adjacency list type topology graph, and selecting a traversal strategy for nodes of the adjacency list type topology graph based on the node degrees; S4, recursively traversing nodes of the adjacency list type topology graph based on the traversal strategy, and obtaining a first closed loop set of the adjacency list type topology graph; S5, performing closed loop verification on the first closed loop set, and storing closed loops passing the verification to obtain a second closed loop set; S6, performing calculation on each closed loop of the second closed loop set to obtain a closed difference, and obtaining an output result report based on the closed difference.

[0023] Further, the S1 further comprises: Obtaining the measurement raw data of the control network to be searched, and reading the survey lines and survey stations of the control network to be searched; Verifying and cleaning the measurement raw data, eliminating repeated survey lines, isolated nodes, and missing or abnormal values, to obtain measurement pretreatment data, and ensuring the connectivity of the control network to be searched.

[0024] Further, the S2 further comprises: Based on the measurement pretreatment data, constructing an adjacency list type topology graph, taking the survey stations as nodes and the survey lines as edges, recording the adjacency edge list of each node, and building an adjacency list or adjacency matrix representation of the control network to be searched in the memory; Calculating and recording the connection degree of each survey station, providing a basis for traversal ordering; Counting the number of points P and the number of edges E of the control network to be searched, and calculating the theoretical minimum number of independent closed loops: ; Initializing parameters and auxiliary markers for the adjacency list type topology graph, and configuring a path stack to store the node sequence on the current recursive path; Configuring an access marker table to determine whether a node has been in the current path; Configuring an edge usage marker to disable the observation edges that have been included in the closed loop.

[0025] Further, the S3 further comprises: Obtaining the number of survey edges connected to the survey stations as the node degree of the adjacency list type topology graph, and configuring traversal priorities based on the node degree, with the traversal priority of nodes with high degree being higher than that of nodes with low degree; Defaulting to enable the degree priority traversal strategy as the traversal strategy to accelerate short loop discovery, and switching to the default sequential traversal strategy according to requirements to verify the influence of different traversal sequences on the effect; The default sequential traversal strategy accesses in turn according to the input sequence or node number; The degree priority traversal strategy accesses after sorting in descending or ascending order according to the degree of the survey stations.

[0026] In this embodiment, the default sequential traversal is selected as an alternative for the traversal mode, the adjacency list is accessed in turn according to the input file or survey station number, and the programming implementation is simple, suitable for small / regular networks.

[0027] The disadvantage is that local small loops are easily missed, and the efficiency is not sensitive to sparse networks.

[0028] Degree priority traversal counts all node connection degrees, arranges the adjacency list in descending (or ascending) order of degree, and recursively traverses high (or low) degree nodes first. It can converge faster to short and key loops, and significantly reduce invalid deep search. When the default traversal misses small loops or is inefficient, degree priority traversal is recommended.

[0029] Parallel comparison and switching are performed, and the two strategies can be used as parameter switching to facilitate engineering actual result verification and performance comparison. Automatic mode is supported: if the default traversal is inefficient, it is automatically switched to degree priority traversal.

[0030] Further, the S4 further comprises: Taking each station or each edge of the adjacency list type topology graph as a starting point, a depth-first recursive traversal is started based on the traversal strategy; When a new node is accessed, the station and the introduced survey line are pushed into the path stack, and are marked in the access mark table; When the recursion reaches the starting point node, a closed loop is formed, and S5 is entered for checking, otherwise the unvisited adjacent edge is further accessed; The recursion depth is limited, and if the current path length exceeds the preset maximum number of edges, backtracking is immediately performed to avoid stack overflow caused by invalid deep search; If a dead end is encountered without unvisited adjacent edges or a non-starting point node in the path is encountered, path backtracking is immediately performed and the branch is skipped.

[0031] In this embodiment, the recursion depth limit is performed as a key optimization when the recursive search closed loop is searched and optimized; A dynamic depth threshold is used, and the recursion depth during the search cannot exceed "theoretical minimum loop edge number + M", and M is a flexible parameter (usually 2-4) which can be set according to the network size. Small network (<30 stations): M=2; medium network (30-100 stations): M=3; large network (>100 stations): M=4.

[0032] Once the path length exceeds the limit, backtracking is immediately performed to prevent stack overflow and invalid search of long paths, and to improve the stability of the algorithm.

[0033] For complex large networks or long loop scenarios, the M value can be flexibly adjusted.

[0034] During tuning, the depth threshold is dynamically optimized through actual loop length distribution statistics to improve the integrity and performance balance.

[0035] For the key optimization of path backtracking pruning, when a dead end and a repeated node are pruned, the recursion encounters a non-starting point node, if the node is in the current path, backtracking is directly performed, and the recursion is not continued, and the dead branch is pruned.

[0036] For path length pruning, if the number of edges of the current path will exceed the shortest loop found so far, terminate immediately, saving a lot of useless traversal.

[0037] For repeated loop combination pruning, check if the current path is repeated with the saved closed loop combination. If repeated, backtrack to ensure the uniqueness and efficiency of the results.

[0038] For network partitioning and parallel processing, subnet division is performed. For large or high-density networks, the entire network is divided into multiple subnets according to the survey area, geography, and topology.

[0039] Perform subnet parallel recursion. Each subnet independently performs closed loop recursion, significantly reducing the complexity of a single search.

[0040] Support multi-threading / distributed processing to shorten the overall computation time.

[0041] For cross-zone / border loop processing, perform supplementary checks on cross-subnet loops when merging results to ensure the global integrity of the closed loop set.

[0042] Further, the S5 further comprises: Independence checking and minimality checking are performed on the first closed loop set; The edge set and path of the closed loop that passes the independence checking and minimality checking are stored, and the corresponding observation edge is disabled to avoid subsequent repeated search, obtaining a second closed loop set; The independence checking ensures that the new loop is not a linear combination of existing loops; The minimality checking verifies that the number of loop edges is the minimum possible value. If multiple loops with the same number of edges exist, the one with the shortest total path length is selected.

[0043] In this embodiment, the closed loop verification, recording, and optimization require independence checking to ensure independence, i.e., to determine whether the new loop can be obtained by linear combination of discovered loops (e.g., using matrix rank, hash, and minimality judgment methods). The minimality means that the loop with the fewest edges is preferred. For loops with the same number of edges, the one with the shortest distance is selected. If multiple shortest loops exist, they are all saved for subsequent adjustment and evaluation.

[0044] During optimization, an identification code (e.g., node start point + loop path hash) can be used to uniquely mark each loop, ensuring accurate and efficient duplicate detection.

[0045] Further, the S6 further comprises: The height difference of each closed loop in the second closed loop set is accumulated, and the algebraic sum of the start and end point elevations of each closed loop is calculated as the closure difference ΔH. The limit difference ΔH_lim of each closed loop is calculated according to the loop length or measurement level, and whether it is out of limit is determined. Based on the ring number, ring path, delta H, delta H_lim and qualified or over-limit identification of each closed ring, a result report is generated, A result export interface is provided for adjustment software or visualization tools to process.

[0046] In this embodiment, the automatic limit difference determination and report export support automatic comparison with national specification limit difference according to ring length and measurement level, and the closed difference over-limit is automatically marked and alarmed; The ring list, ring path, and closed difference can be exported as a table, report, or adjustment software input format with one key; Support for ring difference distribution visualization to assist technicians in judging suspected gross error distribution areas; Support for automatic recording of algorithm execution logs for fault tracing and process quality review.

[0047] Reference Figure 2 A schematic diagram of the closed route search of the leveling network according to an embodiment of the present application.

[0048] The use of the example verification process is as follows: This example leveling network includes 68 observed edges and 41 survey points, as shown in Tables 1 and 2. Table 1: List of observed edges of the leveling network

[0049] Table 2: List of survey points of the leveling network

[0050] Through data analysis and sorting of the observed edges of the leveling network, the same observed data of the survey points are placed together.

[0051] According to the degree of the observed value at the survey point, the survey point with the highest observed value degree is placed in the front.

[0052] An index of the next observed edge connected to the observed value of the observed edge of the leveling network is established.

[0053] A loop of the survey point list of the leveling network is established to find the smallest ring connected to the current point.

[0054] A loop is established again according to the observed edge at the current survey point to search each edge.

[0055] A search depth is set during the search process.

[0056] A mechanism is set during the search process to prevent the search route from returning to the original route.

[0057] If the start point and the end point are the same, and the depth is greater than or equal to 3, and the search depth is less than or equal to the set value, it is determined that a loop is found.

[0058] It is checked whether the loop already exists.

[0059] The height difference is accumulated according to the path of the loop, the closure error of the loop is calculated, and it is checked whether the closure error exceeds the specification requirement.

[0060] The theoretical closed loop is calculated according to the number of measuring points and the number of measuring edges, 68 - 41 + 1 = 28, the number of theoretical closed loops of the present leveling network is 28, and Table 3 is the smallest closed loop searched by the present application.

[0061] Table 3 smallest closed loop table

[0062] The smallest closed loop searched is not missed or repeated, and is the smallest loop; the search speed is fast, and in the test on an ordinary computer, the present example can be calculated in 0.03 seconds.

[0063] The leveling network diagram of the present example is shown in Figure 3 .

[0064] Compared with the existing literature and patent schemes, the present application has significant advantages in the fast search and checking of the closed loop of the leveling network, mainly in the following aspects: (1) Search efficiency is improved: by adopting the search strategy of station grouping and degree priority, blind search and repeated exploration are greatly reduced. The search of high-priority stations first makes the main loop be found as soon as possible, and the remaining stations are efficiently processed under the pruning optimization, so that the overall search speed is greatly improved compared with the traditional depth-first algorithm. In the test of a large and complex control network, the present algorithm can complete the search of all closed loops and attached routes only by establishing a station adjacency index once, and the running time is significantly shorter than the method relying on matrix operation or traditional software.

[0065] (2) Avoiding repeated traversal: the present application avoids repeated search and repeated counting of the same loop through a clever path recording and pruning mechanism. For example, the use of sibling edge index avoids the situation of finding the same closed loop from different start stations multiple times; the use of visited vertex record avoids falling into local loop iteration. Compared with some methods based on spanning tree or matrix, which may need to avoid loop repetition manually, the present algorithm automatically searches all independent closed loops without repetition and omission, ensuring the minimality and completeness of the result set.

[0066] (3) Search and check integration: Unlike the separated process of searching the loop first and then calculating the closure error separately, the present application combines the closed loop search and the closure error calculation organically. The algorithm completes the closure error accumulation calculation and the limit error determination of the loop at the same time when searching each closed loop, and integrates the loop identification and error checking. The advantage of this is to avoid repeated data transmission between different modules, improve the processing efficiency; at the same time, once the loop closure error is found to be out of limit, the algorithm can be marked in time, which helps to make adjustments or prompts in the search stage. This integrated process improves the degree of automation of the system and realizes the synchronous completion of search, calculation and checking.

[0067] (4) Enhanced real-time feedback capability: Due to the high efficiency of the algorithm and the rich output content, the measurement personnel can run the software of the present application to obtain the closed loop and the closure error result immediately after completing the observation in the field, realizing real-time quality control. Compared with the past situation that the closure error can only be obtained by returning to the indoor calculation or with the help of complex software, the present application greatly shortens the result output time, so that the problem loop can be found in the field and timely supplemented or corrected, improving the real-time feedback capability and efficiency of the measurement work.

[0068] (5) Reliable and fast convergence results: Experiments show that the number of closed loops searched by the algorithm is consistent with the theoretical expected value, that is, it meets the relationship of independent closed loop number = number of measurement sections - number of measurement stations + 1. This shows that the present method does not miss any closed loop and has no redundant loop. Compared with the situation that some algorithms miss the closed loop or search distorted when the network topology is complex, the results of the present application are more accurate and reliable. In addition, the algorithm can quickly converge in the iteration deepening process - with the finding of the main loop, the remaining search space is sharply reduced, and the search can complete the identification of all loops in a short time. In summary, the present application realizes the efficient, stable and comprehensive search of the closed loop and the checking of the closure error under the premise of ensuring the accuracy.

[0069] In the several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of units is only a logical function division. In actual implementation, additional division can be made, or a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0070] In addition, the various functional units in the embodiments of the present application can be integrated in one processing unit, or each can exist as an independent physical unit, or two or more than two of them can be integrated in one physical unit. The above-mentioned integrated unit can be implemented in the form of hardware, or in the form of a software functional unit. The above is only an embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation using the content of the specification and drawings of the present application, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

[0071] The specific embodiments of the application are described above, but it is only as an example, and the present application is not limited to the specific embodiments described above. Any equivalent modification or replacement of the present application for those skilled in the art is also within the scope of the present application, therefore, any equivalent transformation and modification, improvement, etc. made without departing from the spirit and principle range of the present application should be covered in the scope of the present application.

Claims

1. A method for fast searching the minimum closed loop of a leveling network based on station grouping and degree priority, characterized in that, The method comprises the following steps: S1, obtaining the measurement raw data of the control network to be searched, and preprocessing the measurement raw data to obtain measurement pretreatment data; S2, constructing an adjacency list topology graph based on the measurement pretreatment data, and initializing parameters and auxiliary markers of the adjacency list topology graph; S3, obtaining the node degree of the adjacency list topology graph, and selecting a traversal strategy for the nodes of the adjacency list topology graph based on the node degree; S4, recursively traversing the nodes of the adjacency list topology graph based on the traversal strategy to obtain a first closed loop set of the adjacency list topology graph; S5, performing closed loop verification on the first closed loop set, and storing the closed loops that pass the verification to obtain a second closed loop set; S6, calculating each closed loop of the second closed loop set to obtain a closure difference, and obtaining an output result report based on the closure difference.

2. The method according to claim 1, wherein, The S1 further comprises the following steps: Obtaining the measurement raw data of the control network to be searched, and reading the survey lines and survey stations of the control network to be searched; Verifying and cleaning the measurement raw data, eliminating repeated survey lines, isolated nodes, and missing or abnormal values to obtain measurement pretreatment data, and ensuring the connectivity of the control network to be searched.

3. The method of fast searching for the minimum closed loop of the leveling network based on the station group and degree priority according to claim 2, characterized in that, The S2 further comprises the following steps: Based on the measurement pretreatment data, an adjacency list topology graph is constructed, with the survey stations as nodes and the survey lines as edges, the adjacency edge list of each node is recorded, and the adjacency list or adjacency matrix representation of the control network to be searched is built in the memory; The connection degree of each survey station is calculated and recorded to provide a basis for traversal sorting; The number of points P and the number of edges E of the control network to be searched are counted, and the theoretical minimum number of independent closed loops is calculated: ； Initialization parameters and auxiliary markers are performed on the adjacency list topology graph, and a path stack is configured to store the node sequence on the current recursive path; An access marker table is configured to determine whether a node has been in the current path; An edge usage marker is configured to disable the observation edges that have been included in the closed loop.

4. The method of claim 2, wherein, The S3 further comprises the following steps: The number of survey edges connected to the survey station is obtained as the node degree of the adjacency list topology graph, and the traversal priority is configured based on the node degree, that is, the traversal priority of the node with high degree is higher than that of the node with low degree; The degree priority traversal strategy is enabled by default as the traversal strategy to accelerate short loop discovery, and the default sequential traversal strategy is switched according to the requirement to verify the influence of different traversal sequences on the effect; The default sequential traversal strategy accesses in the input sequence or node number; The degree priority traversal strategy accesses after sorting in descending or ascending order according to the survey station degree.

5. The method of claim 3, wherein, The S4 further comprises the following steps: Each survey station or each edge of the adjacency list topology graph is taken as a starting point, and a depth-first recursive traversal is started based on the traversal strategy; When a new node is accessed, the survey station and the introduced survey line are pushed into the path stack, and the access marker table is marked; When the recursion reaches the starting node, a closed loop is formed, and the verification is performed in S5, otherwise the recursion continues along the unvisited adjacent edge; The recursion depth is limited, and if the current path length exceeds the preset maximum number of edges, the recursion is immediately backtracked to avoid stack overflow caused by invalid deep search; If a dead end is encountered with no unvisited adjacent edges or a non-starting node in the path is encountered, immediately perform path backtracking and skip the branch.

6. The method of fast search for the minimum closed loop of leveling network based on station group and degree priority according to claim 3, characterized in that, The S5 further comprises: independence checking and minimality checking on the first closed loop set; storing the edge set and path of the closed loop passing the independence checking and minimality checking, disabling the corresponding observed edge, avoiding subsequent repeated search, and obtaining a second closed loop set; the independence checking ensures that the new loop is not a linear combination of existing loops; the minimality checking verifies that the number of loop edges is the minimum possible value, and if multiple loops with the same number of edges coexist, the loop with the shortest total path length is selected.

7. The method of claim 3, wherein, The S6 further comprises: accumulating the height difference of each closed loop in the second closed loop set to calculate the algebraic sum of the starting and ending point elevation differences of each closed loop as the closure difference ΔH; calculating the limit difference ΔH_lim of each closed loop according to the loop length or the measurement level of each closed loop, and determining whether it is out of limit; generating a result report based on the loop number, loop path, ΔH, ΔH_lim and qualified or out-of-limit identifier of each closed loop, providing a result export interface for adjustment software or visualization tools to process.

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