Method for searching minimum closed loop of leveling network based on station grouping and degree priority

Abstract

The application provides a leveling network minimum closed loop fast search method based on station grouping and degree priority, belongs to the technical field of surveying and mapping engineering, and carries out pretreatment on measurement original data, rejects abnormal values and redundant information, ensures control network connectivity, constructs an adjacent list type topology graph, takes stations as nodes, takes survey lines as edges, records node degrees and initializes traversal parameters, in the traversal stage, a degree priority strategy is adopted, nodes with more connection edges are preferentially accessed, so that the discovery of short loops is accelerated, an initial closed loop set is generated through deep priority recursive traversal, independence and minimumness checking is carried out, redundant loops and non-minimum loops are rejected, finally, the required minimum independent closed loop is reserved, the closed difference of each loop is calculated, and comparison with the limit difference is carried out, a result report containing loop paths, closed differences and qualified states is generated, and is used for subsequent adjustment or visual processing. Through optimization of the traversal strategy and the loop checking mechanism, the search efficiency and the result reliability are improved.

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:

[0004] 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.

[0005] 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.

[0006] 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.

[0007] 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.

[0008] 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.

[0009] 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

[0010] This invention proposes a fast search method for the minimum closed loop in a leveling network based on station grouping and degree priority, applicable to the automatic analysis of leveling closure error and independent loop extraction in two-dimensional surveying and control networks. Its core algorithm combines graph theory with an improved depth-first search strategy, and introduces optimization mechanisms such as node degree sorting, recursion depth constraints, and path backtracking pruning.

[0011] To achieve the above objectives, this application provides the following technical solution:

[0012] According to a first aspect of the present invention, the present invention claims protection for a fast search method for the minimum closed loop of a leveling network based on station grouping and degree priority, comprising:

[0013] S1, acquire the raw measurement data of the control network to be searched, and preprocess the raw measurement data to obtain preprocessed measurement data;

[0014] S2, construct an adjacency phenotype topology graph based on the measurement preprocessing data, and initialize parameters and auxiliary labels for the adjacency phenotype topology graph;

[0015] S3, obtain the node degree of the adjacency table topology graph, and select the traversal strategy of the nodes of the adjacency table topology graph based on the node degree;

[0016] S4, based on the traversal strategy, recursively traverse the nodes of the adjacency topology graph to obtain the first closed loop set of the adjacency topology graph;

[0017] S5, perform closure loop verification on the first set of closed loops, and store the closure loops that pass the verification to obtain the second set of closed loops;

[0018] S6, calculate the closure difference for each closed loop in the second closed loop set, and generate an output result report based on the closure difference.

[0019] Furthermore, S1 also includes:

[0020] Obtain the raw measurement data of the control network to be searched, and read the survey lines and stations of the control network to be searched;

[0021] The original measurement data is verified and cleaned, and duplicate measurement lines, isolated nodes, and missing or outlier values ​​are removed to obtain measurement preprocessing data, ensuring the connectivity of the control network to be searched.

[0022] Furthermore, S2 also includes:

[0023] Based on the measurement preprocessing data, an adjacency table topology graph is constructed, with the measurement station as the node and the measurement line as the edge. The list of adjacent edges of each node is recorded, and the adjacency table or adjacency matrix representation of the control network to be searched is built in memory.

[0024] Calculate and record the connectivity degree of each station to provide a basis for traversal sorting;

[0025] Count the number of points P and edges E of the control network to be searched, and calculate the theoretical minimum number of independent closed loops:

[0026] ;

[0027] The adjacency table topology graph is initialized with parameters and auxiliary tags, and a path stack is configured to store the node sequence on the current recursive path;

[0028] Configure an access flag table to determine whether a node is already in the current path;

[0029] Configure edges to use tags and disable observation edges that have been assigned to closed loops.

[0030] Furthermore, S3 also includes:

[0031] The number of edges connected to the station is obtained as the node degree of the adjacency topology graph. The traversal priority is configured based on the node degree, and the traversal priority of the node with higher degree is higher than that of the node with lower degree.

[0032] The degree-first traversal strategy is enabled by default to accelerate the discovery of short cycles. You can switch to the default sequential traversal strategy as needed to verify the impact of different traversal orders on the results.

[0033] The default sequential traversal strategy visits nodes sequentially according to the input order or node number.

[0034] The degree-first traversal strategy involves sorting the stations in descending or ascending order of their degrees before accessing them.

[0035] Furthermore, S4 also includes:

[0036] Starting from each station or edge of the adjacency topology graph, a depth-first recursive traversal is initiated based on the traversal strategy.

[0037] When accessing a new node, the station and the introduced survey line are pushed onto the path stack and marked in the access mark table;

[0038] When the recursion reaches the starting node, a closed loop is formed, and the process is switched to S5 for verification; otherwise, the process continues to delve deeper along the unvisited adjacent edges.

[0039] Limit the recursion depth; if the current path length exceeds the preset maximum number of edges, backtrack immediately to avoid stack overflow caused by invalid deep search.

[0040] If a dead end is encountered with no unvisited adjacent edges, or if a non-starting node already in the path is encountered, immediately perform path backtracking and skip the branch in question.

[0041] Furthermore, S5 also includes:

[0042] Perform independence and minimumity checks on the first set of closed loops;

[0043] Store the edge set and path of the closed loop that passes the independence check and minimumity check, disable the corresponding observation edge to avoid subsequent repeated searches, and obtain the second closed loop set;

[0044] The independence check ensures that the new loop is not a linear combination of existing loops;

[0045] The minimumity check verifies that the number of loop edges is the minimum possible value. If multiple loops with the same number of edges coexist, the one with the shortest total path length is selected.

[0046] Furthermore, S6 also includes:

[0047] The elevation differences of each closed loop in the second set of closed loops are accumulated, and the algebraic sum of the elevation differences between the start and end points of each closed loop is calculated as the closure error ΔH.

[0048] Calculate the limit ΔH_lim for each closed loop based on the loop length or measurement level of each closed loop, and determine whether the limit is exceeded.

[0049] A result report is generated based on the loop number, loop path, ΔH, ΔH_lim, and pass / fail indicators for each closed loop.

[0050] Provides a results export interface for processing by adjustment software or visualization tools.

[0051] This invention proposes a rapid search method for the minimum closed loop in a leveling network based on station grouping and degree priority. Belonging to the field of surveying and mapping engineering technology, the method preprocesses the raw measurement data, removing outliers and redundant information to ensure control network connectivity. An adjacency phenotypic topology graph is constructed, with stations as nodes and survey lines as edges. Node degrees are recorded and traversal parameters are initialized. During the traversal phase, a degree-priority strategy is adopted, prioritizing nodes with more connected edges to accelerate the discovery of short loops. An initial set of closed loops is generated through depth-first recursive traversal, and independence and minimumity checks are performed to remove redundant and non-minimum loops. Finally, the minimum independent closed loops that meet the requirements are retained. The closure error of each loop is calculated and compared with the limit error, generating a result report containing the loop path, closure error, and qualification status for subsequent adjustment or visualization processing. This invention improves search efficiency and result reliability by optimizing the traversal strategy and loop verification mechanism. Attached Figure Description

[0052] Figure 1 A flowchart illustrating the fast search method for the minimum closed loop of a leveling network based on station grouping and degree priority, as claimed in the embodiments of this application.

[0053] Figure 2 A schematic diagram of the leveling network closure path search for the leveling network closure route search method based on station grouping and degree priority, which is claimed in the embodiments of this application.

[0054] Figure 3 A schematic diagram of a leveling network for the fast search method of minimum closed loop of leveling network based on station grouping and degree priority, as claimed in the embodiments of this application. Detailed Implementation

[0055] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0056] The terms "first," "second," and "third" in this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0057] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0058] According to a first embodiment of the present invention, the present invention claims protection for a fast search method for the minimum closed loop of a leveling network based on station grouping and degree priority, referring to... Figure 1 ,include:

[0059] S1, acquire the raw measurement data of the control network to be searched, and preprocess the raw measurement data to obtain preprocessed measurement data;

[0060] S2, construct an adjacency phenotype topology graph based on the measurement preprocessing data, and initialize parameters and auxiliary labels for the adjacency phenotype topology graph;

[0061] S3, obtain the node degree of the adjacency table topology graph, and select the traversal strategy of the nodes of the adjacency table topology graph based on the node degree;

[0062] S4, based on the traversal strategy, recursively traverse the nodes of the adjacency topology graph to obtain the first closed loop set of the adjacency topology graph;

[0063] S5, perform closure loop verification on the first set of closed loops, and store the closure loops that pass the verification to obtain the second set of closed loops;

[0064] S6, calculate the closure difference for each closed loop in the second closed loop set, and generate an output result report based on the closure difference.

[0065] Furthermore, S1 also includes:

[0066] Obtain the raw measurement data of the control network to be searched, and read the survey lines and stations of the control network to be searched;

[0067] The original measurement data is verified and cleaned, and duplicate measurement lines, isolated nodes, and missing or outlier values ​​are removed to obtain measurement preprocessing data, ensuring the connectivity of the control network to be searched.

[0068] Furthermore, S2 also includes:

[0069] Based on the measurement preprocessing data, an adjacency table topology graph is constructed, with the measurement station as the node and the measurement line as the edge. The list of adjacent edges of each node is recorded, and the adjacency table or adjacency matrix representation of the control network to be searched is built in memory.

[0070] Calculate and record the connectivity degree of each station to provide a basis for traversal sorting;

[0071] Count the number of points P and edges E of the control network to be searched, and calculate the theoretical minimum number of independent closed loops:

[0072] ;

[0073] The adjacency table topology graph is initialized with parameters and auxiliary tags, and a path stack is configured to store the node sequence on the current recursive path;

[0074] Configure an access flag table to determine whether a node is already in the current path;

[0075] Configure edges to use tags and disable observation edges that have been assigned to closed loops.

[0076] Furthermore, S3 also includes:

[0077] The number of edges connected to the station is obtained as the node degree of the adjacency topology graph. The traversal priority is configured based on the node degree, and the traversal priority of the node with higher degree is higher than that of the node with lower degree.

[0078] The degree-first traversal strategy is enabled by default to accelerate the discovery of short cycles. You can switch to the default sequential traversal strategy as needed to verify the impact of different traversal orders on the results.

[0079] The default sequential traversal strategy visits nodes sequentially according to the input order or node number.

[0080] The degree-first traversal strategy involves sorting the stations in descending or ascending order of their degrees before accessing them.

[0081] In this embodiment, the traversal method is selected as either parallel or optional default sequential traversal, accessing the adjacency list sequentially according to the input file or station number. This is simple to program and suitable for small / regular networks.

[0082] The drawbacks are that it is easy to miss local small loops, and the efficiency is not sensitive to sparse networks.

[0083] Degree-first traversal counts the connectivity of all nodes and sorts the adjacency list in descending (or ascending) degree order, prioritizing recursion of high (or low) degree nodes. This approach converges to short cycles and critical cycles more quickly, significantly reducing unnecessary deep searches. Degree-first traversal is recommended when the default traversal misses small cycles or is inefficient.

[0084] Parallel comparison and switching are performed, with two strategies that can be switched as parameters, facilitating verification of actual engineering results and performance comparison. Automatic mode is supported: if the default traversal is inefficient, it automatically switches to degree-first traversal.

[0085] Furthermore, S4 also includes:

[0086] Starting from each station or edge of the adjacency topology graph, a depth-first recursive traversal is initiated based on the traversal strategy.

[0087] When accessing a new node, the station and the introduced survey line are pushed onto the path stack and marked in the access mark table;

[0088] When the recursion reaches the starting node, a closed loop is formed, and the process is switched to S5 for verification; otherwise, the process continues to delve deeper along the unvisited adjacent edges.

[0089] Limit the recursion depth; if the current path length exceeds the preset maximum number of edges, backtrack immediately to avoid stack overflow caused by invalid deep search.

[0090] If a dead end is encountered with no unvisited adjacent edges, or if a non-starting node already in the path is encountered, immediately perform path backtracking and skip the branch in question.

[0091] In this embodiment, implementing a recursion depth limit during recursive search for closed loops and optimization is a key optimization.

[0092] A dynamic depth threshold is used, and the recursion depth during the search must not exceed "theoretical minimum number of loop edges + M", where M is an elastic parameter (usually 2-4) that 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.

[0093] If the path length exceeds the limit, backtracking is immediately performed to prevent stack overflow and invalid search of long paths, thereby improving the stability of the algorithm.

[0094] For complex large-scale networks or long-loop scenarios, the M value can be flexibly adjusted.

[0095] During optimization, the depth threshold is dynamically optimized by statistically analyzing the actual loop length distribution to improve the balance between integrity and performance.

[0096] For the key optimization of path backtracking pruning, when encountering dead ends and duplicate nodes, if the recursion encounters a non-starting node and the node is already in the current path, backtrack directly and do not continue recursion, thus pruning the dead branch.

[0097] For path length pruning, if continuing down the current path would result in a cycle edge count exceeding the shortest cycle already discovered, the path is terminated immediately, saving a significant amount of unnecessary traversal.

[0098] For pruning repeated loop combinations, check if the current path is a duplicate of a saved closed loop combination. If a duplicate is found, backtrack to ensure the uniqueness and efficiency of the result.

[0099] For network segmentation and parallel processing, subnetting is performed. For ultra-large or high-density networks, the entire network is divided into multiple subnets according to the survey area, geographical location, and topological attributes.

[0100] Parallel recursion is performed on subnets, with each subnet independently executing closed-loop recursion, significantly reducing the complexity of a single search.

[0101] It supports multi-threaded / distributed processing, reducing the overall computation time.

[0102] For handling cross-region / boundary loops, additional checks are performed on loops across subnets during the merging process to ensure the global integrity of the closed loop set.

[0103] Furthermore, S5 also includes:

[0104] Perform independence and minimumity checks on the first set of closed loops;

[0105] Store the edge set and path of the closed loop that passes the independence check and minimumity check, disable the corresponding observation edge to avoid subsequent repeated searches, and obtain the second closed loop set;

[0106] The independence check ensures that the new loop is not a linear combination of existing loops;

[0107] The minimumity check verifies that the number of loop edges is the minimum possible value. If multiple loops with the same number of edges coexist, the one with the shortest total path length is selected.

[0108] In this embodiment, during the verification, recording, and optimization of closed loops, the independence and minimumity checks must ensure independence, that is, whether the new loop can be obtained by linear combination of the discovered loops (e.g., matrix rank, hashing, etc. can be used to determine duplicates) and minimumity, that is, the loop with the fewest edges is retained first, and the shortest distance is selected when the number of edges is the same. If multiple shortest loops exist, they are all saved for subsequent adjustment evaluation.

[0109] During optimization, each ring can be uniquely marked with an identifier (such as the node starting point + ring path hash) to ensure accurate and efficient deduplication.

[0110] Furthermore, S6 also includes:

[0111] The elevation differences of each closed loop in the second set of closed loops are accumulated, and the algebraic sum of the elevation differences between the start and end points of each closed loop is calculated as the closure error ΔH.

[0112] Calculate the limit ΔH_lim for each closed loop based on the loop length or measurement level of each closed loop, and determine whether the limit is exceeded.

[0113] A result report is generated based on the loop number, loop path, ΔH, ΔH_lim, and pass / fail indicators for each closed loop.

[0114] Provides a results export interface for processing by adjustment software or visualization tools.

[0115] In this embodiment, when determining the automatic limit of error and exporting reports, it supports automatic comparison with the national standard limit of error based on the loop length and measurement level. If the closure error exceeds the limit, it will be automatically marked and an alarm will be triggered.

[0116] Loop lists, loop paths, closure errors, etc., can be exported as tables, reports, or input formats for adjustment software with one click;

[0117] Supports visualization of ring difference distribution, assisting technicians in identifying suspected outlier distribution areas;

[0118] It supports automatic recording of algorithm execution logs for fault tracing and process quality review.

[0119] Reference Figure 2 This is a schematic diagram of the leveling network closure route search in an embodiment of the present invention.

[0120] The verification process of this invention using examples is as follows:

[0121] This example leveling network includes 68 observation sides and 41 station points, as shown in Tables 1 and 2.

[0122] Table 1 List of observation sides of the leveling network

[0123]

[0124] Table 2 List of Leveling Network Measurement Points

[0125]

[0126] By analyzing and sorting the data from the observation sides of the leveling network, observation data from stations with the same data are grouped together.

[0127] The stations are sorted according to the degree of the observed values ​​at each station, with the station having the highest degree of observed values ​​listed first.

[0128] For each observation edge in the leveling network, an index is created for the next observation edge connected to the current observation value.

[0129] Establish a loop for the list of leveling network measurement points, and find the smallest loop connected to each point.

[0130] Establish a loop again at the current measuring point according to the observed edges, and search for each edge one by one.

[0131] Set the search depth during the search process.

[0132] A mechanism is set up during the search process to prevent the search path from reverting to its original path.

[0133] Compare whether the search start point and end point are the same. If they 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 cycle has been found.

[0134] Check if this ring already exists.

[0135] By accumulating the elevation differences along the path of this loop, the closure error of this loop is calculated, and the closure error is checked against the specification requirements to see if it exceeds the limit.

[0136] The theoretical closed loop is calculated based on the number of measuring points and the number of measuring sides. 68 - 41 + 1 = 28, the theoretical number of closed loops in this leveling network is 28. Table 3 shows the smallest closed loops found by this invention.

[0137] Table 3 Minimum Closed Loop Table

[0138]

[0139] The search yielded no missing or duplicate minimum closed loops, and all of them were minimum loops; the search speed was fast, and in tests on a regular computer, this example only required 0.03 seconds to complete the calculation.

[0140] The schematic diagram of the leveling network in this example can be found here. Figure 3 .

[0141] Compared with existing literature and patent solutions, this invention has significant advantages in the rapid search and verification of leveling network closed loops, mainly in the following aspects:

[0142] (1) Improved search efficiency: By adopting a station grouping and degree-first search strategy, blind searches and redundant explorations are greatly reduced. The early search of high-priority stations allows the main loops to be discovered as early as possible, while the remaining stations are processed efficiently under pruning optimization, resulting in a significant improvement in overall search speed compared to the traditional depth-first search algorithm. In tests on large and complex control networks, this algorithm only needs to establish a station adjacency index once to complete the search of all closed loops and traverse routes, with a significantly shorter running time than methods that rely on matrix operations or traditional software.

[0143] (2) Avoiding repeated traversal: This invention eliminates repeated searches and counts of the same loop through a sophisticated path recording and pruning mechanism. For example, the sibling edge index is used to avoid finding the same closed loop multiple times from different starting stations; the visited vertex record is used to avoid getting trapped in local loops and iterating repeatedly. Compared with some methods based on spanning trees or matrices that may require manual avoidance of loop repetition, this algorithm automatically achieves a non-repeating and non-omissionful search of all independent closed loops, ensuring the minimum independence and completeness of the result set.

[0144] (3) Integrated Search and Verification: Unlike the separate process of searching for loops first and then calculating the closure error separately, this invention organically combines closed loop search with closure error calculation. While searching for each closed loop, the algorithm simultaneously calculates the cumulative closure error and determines the limit for that loop, integrating loop identification and error verification. This avoids repeated data transfer between different modules, improving processing efficiency; simultaneously, if a loop's closure error exceeds the limit, the algorithm can promptly mark it, facilitating adjustments or prompts during the search phase. This integrated process improves the system's automation level, enabling simultaneous completion of search, calculation, and verification.

[0145] (4) Enhanced real-time feedback capability: Due to the high efficiency of the algorithm and the richness of the output content, surveyors can immediately run the software of this invention to obtain the closed loop and closure error results after completing the observation in the field, thereby realizing real-time quality control. Compared with the previous situation where it was necessary to return to the room for calculation or to use complex software to obtain the closure error, this invention significantly shortens the result output time, enabling the problem loop to be found on-site and timely supplementary measurement or correction, thus improving the real-time feedback capability and efficiency of the measurement work.

[0146] (5) Reliable results and fast convergence: Experiments show that the number of closed loops searched by this algorithm is consistent with the theoretical expectation, that is, it satisfies the relationship that the number of independent closed loops = number of segments - number of stations + 1. This indicates that this method does not miss any closed loops and has no redundant loops. Compared with some algorithms that miss closed loops or have search distortions when the network topology is complex, the results of this invention are more accurate and reliable. In addition, the algorithm can converge quickly during the iterative deepening process—as the main loops are found, the remaining search space is reduced sharply, and the search can complete the identification of all loops in a short time. In summary, this invention achieves high efficiency, stability and comprehensiveness in closed loop search and closure error verification while ensuring accuracy.

[0147] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0148] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0149] The specific embodiments of the invention have been described in detail above, but they are only examples, and this application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this application. Therefore, all equivalent changes, modifications, and improvements made without departing from the spirit and principles of this application should be covered within the scope of this application.

Claims

1. A fast search method for the minimum closed loop of a leveling network based on station grouping and degree priority, characterized in that, include: S1, acquire the raw measurement data of the control network to be searched, and preprocess the raw measurement data to obtain preprocessed measurement data; S2, construct an adjacency phenotype topology graph based on the measurement preprocessing data, and initialize parameters and auxiliary labels for the adjacency phenotype topology graph; S3, obtain the node degree of the adjacency table topology graph, and select the traversal strategy of the nodes of the adjacency table topology graph based on the node degree; S4, based on the traversal strategy, recursively traverse the nodes of the adjacency topology graph to obtain the first closed loop set of the adjacency topology graph; S5, perform closure loop verification on the first set of closed loops, and store the closure loops that pass the verification to obtain the second set of closed loops; S6, calculate the closure difference for each closed loop in the second closed loop set, and generate an output result report based on the closure difference; The S3 further includes: The number of edges connected to the station is obtained as the node degree of the adjacency topology graph. The traversal priority is configured based on the node degree, and the traversal priority of the node with higher degree is higher than that of the node with lower degree. The degree-first traversal strategy is enabled by default to accelerate the discovery of short cycles. You can switch to the default sequential traversal strategy as needed to verify the impact of different traversal orders on the results. The default sequential traversal strategy visits nodes sequentially according to the input order or node number. The degree-first traversal strategy involves sorting the stations in descending or ascending order of their degrees before accessing them. The S4 further includes: Starting from each station or edge of the adjacency topology graph, a depth-first recursive traversal is initiated based on the traversal strategy. When visiting a new node, the station and the introduced survey line are pushed onto the path stack and marked in the visit mark table; When the recursion reaches the starting node, a closed loop is formed, and the process is switched to S5 for verification; otherwise, the process continues to delve deeper along the unvisited adjacent edges. Limit the recursion depth; 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 with no unvisited adjacent edges, or if a non-starting node already in the path is encountered, immediately perform path backtracking and skip the branch in question. The S6 further includes: The elevation differences of each closed loop in the second set of closed loops are accumulated, and the algebraic sum of the elevation differences between the start and end points of each closed loop is calculated as the closure error ΔH. Calculate the limit ΔH_lim for each closed loop based on the loop length or measurement level of each closed loop, and determine whether the limit is exceeded. A result report is generated based on the loop number, loop path, ΔH, ΔH_lim, and identifier of each closed loop; The marking indicates whether the item is qualified or exceeds the limit. Provides a results export interface for processing by adjustment software or visualization tools.

2. The method for fast search of minimum closed loop in a leveling network based on station grouping and degree priority, as described in claim 1, is characterized in that... The S1 further includes: Obtain the raw measurement data of the control network to be searched, and read the survey lines and stations of the control network to be searched; The original measurement data is verified and cleaned, and duplicate measurement lines, isolated nodes, and missing or outlier values ​​are removed to obtain measurement preprocessing data, ensuring the connectivity of the control network to be searched.

3. The method for fast search of minimum closed loop in a leveling network based on station grouping and degree priority, as described in claim 2, is characterized in that... The S2 further includes: Based on the measurement preprocessing data, an adjacency table topology graph is constructed, with the measurement station as the node and the measurement line as the edge. The list of adjacent edges of each node is recorded, and the adjacency table or adjacency matrix representation of the control network to be searched is built in memory. Calculate and record the connectivity degree of each station to provide a basis for traversal sorting; Count the number of points P and edges E of the control network to be searched, and calculate the theoretical minimum number of independent closed loops: ； The adjacency table topology graph is initialized with parameters and auxiliary tags, and a path stack is configured to store the node sequence on the current recursive path; Configure an access flag table to determine whether a node is already in the current path; Configure edges to use tags and disable observation edges that have been assigned to closed loops.

4. The method for fast search of minimum closed loop in a leveling network based on station grouping and degree priority, as described in claim 3, is characterized in that... The S5 also includes: Perform independence and minimumity checks on the first set of closed loops; Store the edge set and path of the closed loop that passes the independence check and minimumity check, disable the corresponding observation edge to avoid subsequent repeated searches, and obtain the second closed loop set; The independence check ensures that the new loop is not a linear combination of existing loops; The minimumity check prioritizes retaining the cycle with the fewest sides. When the number of sides is the same, the cycle with the shortest distance is selected. If multiple shortest cycles exist, they are all saved for subsequent adjustment evaluation.

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