Bridge acceleration acquisition method and acquisition system based on improved Dijkstra algorithm

Abstract

The invention discloses a bridge acceleration acquisition method and acquisition system based on an improved Dijkstra algorithm, and the method comprises the steps: S1, enabling a bridge acceleration measurement device, network transmission equipment and a data processing center to serve as nodes, enabling communication links between the equipment to serve as edges, enabling w to represent the weight of each link, and enabling w to serve as the weight of each link; abstracting a data transmission network from the bridge acceleration measurement device to the data processing center into a network topology structure diagram; s2, taking the data processing center as a source node, and calculating a shortest path from the bridge acceleration measurement device to the data processing center based on an improved Dijkstra algorithm; s3, transmitting data measured by the bridge acceleration measuring device to a data processing center based on the shortest path obtained in S2; the invention discloses a bridge acceleration acquisition method and acquisition system based on an improved Dijkstra algorithm, the link weight is adjusted according to the change of the network state, a Dijkstra algorithm optimization module can dynamically adjust the data transmission path, and the real-time performance and reliability of data transmission are ensured.

Description

Technical Field

[0001] The present invention relates to the field of bridge detection. More specifically, the present invention relates to a method and a system for collecting bridge acceleration based on an improved Dijkstra algorithm. Background Art

[0002] As one of the key devices for bridge health monitoring, the real-time performance and accuracy of the measurement data of the bridge acceleration measurement device directly affect the efficiency and accuracy of bridge safety assessment. However, the existing bridge acceleration measurement devices often face problems such as unreasonable path planning and data transmission delay during data collection and transmission, resulting in poor real-time performance.

[0003] As a classic graph theory algorithm, the Dijkstra algorithm is widely used in fields such as network routing and path planning, and can efficiently solve the single-source shortest path problem in a graph. However, the traditional Dijkstra algorithm cannot handle graphs containing negative-weight edges (such as network delays, sensor failures, etc.), because the greedy strategy may lead to incorrect results. Therefore, there is no instance in the prior art of widely applying the traditional Dijkstra algorithm to path planning (that is, it is not applicable to the scenario of processing negative-weight paths). Summary of the Invention

[0004] An object of the present invention is to solve at least the above problems and / or defects, and provide at least the advantages described later.

[0005] To achieve these objects and other advantages of the present invention, a method for collecting bridge acceleration based on an improved Dijkstra algorithm is provided, including:

[0006] S1. Regarding the bridge acceleration measurement device, the network transmission device, and the data processing center as nodes, regarding the communication links between the devices as edges, using w to represent the weights of each link, and abstracting the data transmission network from the bridge acceleration measurement device to the data processing center as a network topology structure diagram;

[0007] S2. Using the data processing center as the source node, calculating the shortest path from the bridge acceleration measurement device to the data processing center based on the improved Dijkstra algorithm;

[0008] S3. Transmitting the data measured by the bridge acceleration measurement device to the data processing center based on the shortest path obtained in S2;

[0009] Wherein, the improved Dijkstra algorithm obtains the globally optimal shortest path set by continuously selecting the unvisited node closest to the source node currently and updating the shortest paths of its neighbor nodes.

[0010] Preferably, the processing flow for improving the Dijkstra algorithm includes:

[0011] S20. Based on the existing historical data dataset X0 and the control group X C calculate the cosine similarity data R = {r i} to complete path correction;

[0012] S21. Divide the node set in the network topology structure diagram in S1 into two groups: the first group is the node set S for which the shortest path has been found, and the second group is the remaining node set U for which the shortest path has not been determined;

[0013] In the initial state, S only includes the source node A and the distance is 0, U contains other nodes except the source node A. If the source node A is adjacent to the nodes in U, then w takes the normal weight value, otherwise it takes ∞;

[0014] S22. Traverse the set U and select a node B with the smallest distance from the source node from U and add it to S;

[0015] S23. With node B as the center point, update the distances of each node in U, and determine whether to modify the distance value of node C through the following formula:

[0016] W AB +W BC <W AC

[0017] In the above formula, W AB is the link weight from node A to node B, W BC is the link weight from node B to node C, W AC is the link weight from node A to node C;

[0018] S24. Repeat S22 - S23 until all nodes are included in S.

[0019] Preferably, in S20, the weight coefficient r i in the cosine similarity data R = {r i} is obtained through the following formula:

[0020]

[0021] In the above formula, x 0j is the index of the historical data dataset, m is the number of indices, x ij is the index of the control group X C dataset, i = 1, 2, 3,..., n, and n is the number of groups of control data;

[0022] The path correction refers to taking the data group with the largest r i as the control group of X0 to pair with the data X of the i-th timei Perform path correction on the path weight.

[0023] A collection system that applies a bridge acceleration collection method based on an improved Dijkstra algorithm, including:

[0024] A bridge acceleration measurement device constructed by multiple acceleration sensors;

[0025] A data processing center with an optimized module for the improved Dijkstra algorithm;

[0026] A data transmission network that constructs a transmission link between the bridge acceleration measurement device and the data processing center through routers and / or switches.

[0027] The present invention has at least the following beneficial effects:

[0028] First, it has a data correction effect. The present invention combines existing legal data and corrects the path weight coefficient based on the cosine similarity r, solving the shortcoming that the traditional Dijkstra algorithm is not applicable to negative weight paths.

[0029] Second, it can effectively reduce transmission delay. The present invention can adjust the link weight according to changes in the network state, and the Dijkstra algorithm optimization module can dynamically adjust the data transmission path to ensure the real-time and reliable data transmission.

[0030] Third, it can improve bandwidth utilization. The present invention selects links with small transmission delay and high bandwidth for data transmission, improves bandwidth utilization, and further shortens the data transmission time.

[0031] Other advantages, objectives, and features of the present invention will be partially reflected by the following description and partially understood by those skilled in the art through the research and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic flowchart of data collection using the bridge acceleration collection method based on the improved Dijkstra algorithm;

[0033] Figure 2 It is a network topology structure diagram after abstracting the collection system;

[0034] Figure 3 It is a connection diagram of each node in the example of the present invention;

[0035] Figure 4 It is a corrected path connection diagram in the embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0036] The present invention will be further described in detail below in conjunction with the accompanying drawings, so that those skilled in the art can implement it with reference to the text of the specification.

[0037] The present invention introduces a wireless transmission system with an improved Dijkstra algorithm between an acceleration sensor (a plurality of acceleration sensors form a bridge acceleration measurement device) and a data acquisition and transmission subsystem (i.e., between data processing centers) to significantly improve the real-time performance of bridge acceleration measurement data. The introduction of the improved Dijkstra algorithm in the wireless transmission system optimizes the path planning by considering the weights of side paths to improve the real-time performance of data acquisition by the bridge acceleration measurement device.

[0038] The acquisition system of the present invention includes:

[0039] A bridge acceleration measurement device constructed by a plurality of acceleration sensors;

[0040] A data processing center with an improved Dijkstra algorithm optimization module;

[0041] A data transmission network that constructs a transmission link between the bridge acceleration measurement device and the data processing center through routers and / or switches. In this solution, the bridge acceleration measurement device is responsible for collecting the acceleration data of the bridge and sending it to the data processing center through the data transmission network. The improved Dijkstra algorithm optimization module is integrated into the data processing center and is used to optimize the data transmission path in real time.

[0042] The present invention provides a bridge acceleration acquisition method based on an improved Dijkstra algorithm. By combining cosine similarity, possible negative edge weight paths are converted into equivalent positive edge weight paths, and an improved Dijkstra algorithm is established to optimize the data transmission path of the bridge acceleration measurement device, which can effectively improve the real-time performance of data transmission.

[0043] As Figure 1 shown, the specific implementation process of the bridge acceleration acquisition method based on the improved Dijkstra algorithm includes:

[0044] S1. Graph model construction: Abstract the data transmission network into a graph model. As Figure 2 shown, the bridge acceleration measurement device, network transmission equipment, and data processing center are all regarded as nodes. That is, Figure 2 the nodes (A - F) in AB(which is the weight of the AB link), abstract the data transmission network from the bridge acceleration measurement device to the data processing center as a network topology structure diagram.

[0045] S2. Shortest path planning: Use the improved Dijkstra algorithm, with the data processing center as the source node, such as Figure 2 the F node in it, and calculate the shortest path from the bridge acceleration measurement device node A to the data processing center node F in real time. The algorithm continuously selects the unvisited node that is currently closest to the source node and updates the shortest paths of its neighbor nodes, and finally obtains the globally optimal set of shortest paths.

[0046] The specific process is as follows:

[0047] Taking the above as an example, let G=(A→F) be a weighted directed graph, and calculate the optimal path from A→F.

[0048] (1) Path correction: Let a certain data set be X0={x 01 ,x 02 ,…,x 0m }, which is the historical data set (m represents the number of indicators);

[0049]

[0050] X C forms a data set with X0:

[0051]

[0052] Calculate the cosine similarity data R={r i }, i = 1, 2, 3, …, n.

[0053]

[0054] Take the data group with the largest r i as the control group of X0, and correct the path with the path weight of the i-th data X i and perform path operations with the corrected path weight (that is, by calculating the similarity r of each group of data, seeing which group has the largest r, and using it as the control group for path weight correction).

[0055] (2) Divide the vertex set in the graph into two groups: The first group is the set S of vertices for which the shortest path has been found, and the second group is the set U of the remaining vertices for which the shortest path has not been determined.

[0056] (3) Initially, S only includes the source point, that is, S={A}, and the distance of A is 0. U contains other vertices except A, that is, U={B, C, D, E, F}. If A is adjacent to the nodes in U, then w takes the normal weight value, otherwise it takes ∞. The specific value-taking method is shown in the following table;

[0057] Node B C D E F A <![CDATA[w AB > <![CDATA[w AC > ∞ ∞ ∞

[0058] (3) Traverse the set U, select a vertex with the minimum distance from A in U, and add it to S. Suppose it is node B.

[0059] (4) Taking node B as the center point, update the distances of each vertex in U; if the distance from the source point A to node C (passing through vertex B) is shorter than the original distance (not passing through vertex B), then modify the distance value of vertex C. The modified distance value is the distance of vertex B plus the weight on the BC side.

[0060] (5) Repeat steps (3) and (4) until all vertices are included in S.

[0061] S3. Transmit the data measured by the bridge acceleration measurement device to the data processing center based on the shortest path obtained in S2.

[0062] Example: Suppose the connection diagram of each node in a certain test is as Figure 3 shown, and the weights on the path are empirical weights. P1 is the source point, and find the shortest path from P1 to other nodes. Use the set S to store the shortest paths that have been found. It is obvious that the shortest path from P1 to itself is the initial shortest path.

[0063] Comparative example:

[0064] Use the traditional method to find the shortest path from P1 to other nodes. Its working steps are shown in Table 1:

[0065]

[0066]

[0067] Table 1

[0068] Based on the traditional method, it can be seen from Table 1 that the shortest paths from P1 to each node are P1→P6→P2, P1→P6→P4→P3, P1→P6→P4, P1→P6→P4→P5, P1→P6.

[0069] Example:

[0070] Suppose the initial data set in the case is W0 = (9, -5, 7, -5, 3, 4, 7, 2, -6, 2), and the arrangement rule is as follows: The nodes are sorted in ascending order of serial number, and the path weights starting from each sorted node.

[0071] The path weights corresponding to the data collected twice are W1 = (8, -3, 7, -4, 2, 3, 7, 2, -2, 1) and W2 = (9, -1, 6, -3, 4, 5, 8, 9, -1, 1). After calculation according to the collected data and the existing reference data by the following formula, it is found that the r of the second group is larger, so the path weight of the second group is used to correct the existing weight.

[0072]

[0073] Among them, w 0i and w 2i are the i-th elements in W0 and W2 respectively, where i = 1, 2,..., 10.

[0074] It is obtained that The corrected path diagram is as shown in Figure 4 As shown, the corrected path is used to find the shortest path from P1 to other nodes, and its working steps are shown in Table 2:

[0075]

[0076]

[0077] Table 2

[0078] Based on Table 2, it can be seen that through the path correction method of this example, the shortest paths from P1 to each node are: P1→P6→P2, P1→P6→P4→P3, P1→P6→P4, P1→P6→P5, P1→P6.

[0079] By comparing the two methods of the comparative example and the embodiment, it can be seen that in the traditional method from node 1 to node 5, it is necessary to pass through nodes 4 and 6, while the corrected algorithm only needs to pass through node 6, optimizing the data transmission nodes and avoiding the risk of loss during data transmission.

[0080] The above scheme is only an illustration of an example, but is not limited thereto. When implementing the present invention, appropriate substitutions and / or modifications can be made according to the needs of users.

[0081] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and embodiments. It can be fully applied to various fields suitable for the present invention. For those familiar with the field, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the equivalent scope, the present invention is not limited to the specific details and the illustrations shown and described here.

Claims

1. A bridge acceleration acquisition method based on an improved Dijkstra algorithm, characterized in that: include: S1. The bridge acceleration measurement device, network transmission equipment, and data processing center are all taken as nodes, the communication links between the devices are taken as edges, w represents the weight of each link, and the data transmission network from the bridge acceleration measurement device to the data processing center is abstracted into a network topology diagram; S2, taking the data processing center as the source node, calculating the shortest path from the bridge acceleration measurement device to the data processing center based on the improved Dijkstra algorithm; S3, transmitting the data measured by the bridge acceleration measurement device to the data processing center based on the shortest path obtained in S2; The improved Dijkstra algorithm continuously selects the unvisited node that is currently closest to the source node and updates the shortest paths of its neighboring nodes, and finally obtains the global optimal shortest path set.

2. The bridge acceleration acquisition method based on the improved Dijkstra algorithm as claimed in claim 1, characterized in that: The process of improving Dijkstra algorithm includes: S20, based on the existing historical data set X0 and the control group X C Calculate the cosine similarity data R = {r i }, to complete the path correction; S21, dividing the node set in the network topology structure diagram in S1 into two groups: the first group is the node set S for which the shortest path has been determined, and the second group is the node set U for which the shortest path has not been determined; In the initial state, S only includes the source node A and the distance is 0, U contains other nodes except the source node A, if the source node A is adjacent to the node in U, then w takes the normal weight, otherwise it takes ∞; S22, traverse the set U, select a node B from U that is the shortest distance from the source node and add it to S; S23, taking node B as the center point, update the distances of each node in U, and determine whether to modify the distance value of node C by the following formula: IN AB +W BC <W AC In the above formula, W AB is the link weight from node A to node B, W BC is the link weight from node B to node C, W AC is the link weight from node A to node C; S24. Repeat S22-S23 until all nodes are included in S.

3. The bridge acceleration acquisition method based on the improved Dijkstra algorithm as claimed in claim 1, characterized in that: In S20, the cosine similarity data R = {r i The weight coefficient r in} i Obtained by the following formula: In the above formula, x 0j is the index of the historical data set, m is the number of indicators, x ij For the control group X C The index of the data set, i = 1, 2, 3, ... n, n is the number of control data groups; The path correction refers to taking r i The largest data group is used as the control group of X0 to compare the i-th data X i The path weight is used to correct the path.

4. A collection system, which uses the bridge acceleration collection method based on the improved Dijkstra algorithm as described in any one of claims 1 to 3, characterized in that: include: A bridge acceleration measuring device comprising a plurality of acceleration sensors; Data processing center with improved Dijkstra algorithm optimization module; A data transmission network is constructed by means of routers and / or switches to form a transmission link between the bridge acceleration measurement device and the data processing center.

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