A reinforced concrete in-situ load test device and control method thereof

Abstract

The present invention relates to the field of concrete detection technology, and in particular to a reinforced concrete in-situ load test device and a control method thereof. It comprises a base, an L-shaped bracket is integrally formed on the base, a pressure cylinder is fixedly installed on the L-shaped bracket, the output end of the pressure cylinder is matched and connected with one end of a telescopic rod, and the other end of the telescopic rod is matched and connected with an end joint bearing; an axle frame is hinged on the end joint bearing, and an L-shaped slider is fixedly connected to the bottom of the axle frame, two linear sleeves are installed on the L-shaped slider, a sliding rod is sleeved in the linear sleeve, the bottom end of the sliding rod is fixedly connected to a mounting plate, and a test head is fixedly installed on the mounting plate; and a pressure spring is sleeved on the sliding rod between the L-shaped slider and the mounting plate, which can more accurately evaluate the bearing capacity and performance of reinforced concrete structures and provide strong technical support for engineering design and construction.

Description

Technical Field

[0001] The invention relates to the technical field of concrete detection, in particular to a reinforced concrete in-situ load test device and a control method thereof. Background Art

[0002] Reinforced concrete structures are widely used in the construction field because of their high strength, durability and good seismic performance. However, in actual engineering applications, the performance and safety of reinforced concrete structures need to be verified through load tests. In-situ load testing is a method of load testing actual engineering structures, which can effectively evaluate the bearing capacity and performance of the structure.

[0003] The existing reinforced concrete in-situ load test device and its control method have certain limitations, which are mainly manifested in the following aspects: 1. The degree of automation of the test process is not high, and more manual operation and monitoring are required, which can easily lead to test errors and safety hazards. 2. The load application and unloading speeds are difficult to control accurately, which may affect the accuracy of the test results. 3. The versatility and scalability of the test device are poor, and it is difficult to meet the testing needs of reinforced concrete structures of different types and scales. 4. The data collection and processing during the test are not perfect, which may lead to reduced reliability of the test results. In view of this, the present application proposes a new reinforced concrete in-situ load test device and its control method to solve the above-mentioned technical problems. Summary of the invention

[0004] The invention overcomes the shortcomings of the prior art and provides a reinforced concrete in-situ load test device and a control method thereof.

[0005] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is:

[0006] The first aspect of the present invention discloses an in-situ load test device for reinforced concrete, comprising a base, an L-shaped bracket integrally formed on the base, a pressure cylinder fixedly mounted on the L-shaped bracket, an output end of the pressure cylinder being cooperatively connected to one end of a telescopic rod, and an end joint bearing being cooperatively connected to the other end of the telescopic rod;

[0007] The end joint bearing is hinged with an axle frame, the bottom of the axle frame is fixedly connected with an L-shaped slider, two linear sleeves are installed on the L-shaped slider, a slide bar is sleeved in the linear sleeve, the bottom end of the slide bar is fixedly connected with a mounting plate, a test head is fixedly installed on the mounting plate; and a pressure spring is sleeved on the slide bar between the L-shaped slider and the mounting plate; the test head is provided with a pressure sensor, a displacement sensor and a vibration sensor;

[0008] The L-shaped bracket is provided with a mounting frame, a guide rail is fixedly mounted on the mounting frame, a guide block is fixedly mounted on the L-shaped slider, and the guide block is embedded in the guide rail, so that the L-shaped slider can slide along the guide rail.

[0009] Furthermore, in a preferred embodiment of the present invention, the test device also includes a control system, a data acquisition system and a data processing system. The control system is used to control the size and rate of the load applied by the loading system to ensure the accuracy and stability of the load application during the test; the data acquisition system is used to collect stress, deformation, and vibration data fed back by pressure sensors, displacement sensors, and vibration sensors; the data processing system is used to process the stress, deformation, and vibration data of the concrete structure collected by the sensors, thereby generating analysis results.

[0010] A second aspect of the present invention discloses a control method for a reinforced concrete in-situ load test device, which is applied to any one of the reinforced concrete in-situ load test devices described above, and comprises the following steps:

[0011] The test device is installed on the concrete structure of the target test point. After the installation is completed, the actual coordinate information of the test device is obtained, and the preset test parameters are obtained by pairing the actual coordinate information in the knowledge graph;

[0012] The preset test parameters obtained by pairing are sent to the control system, and the test device is controlled by the control system to perform the test according to the preset test parameters;

[0013] Acquire actual test parameters of the test device at a preset time node, and compare the actual test parameters with preset test parameters at the corresponding preset time node to correct the actual test parameters of the test device;

[0014] When the loading conditions are met, the data acquisition system is started to obtain the characteristic data of the concrete detected by each sensor within the preset loading time, and the acquired characteristic data is transmitted to the data processing system; wherein the characteristic data includes stress, deformation, and vibration data;

[0015] The data processing system performs dimensionality reduction and clustering processing on the collected characteristic data to obtain various types of characteristic data in the target test point; the various types of characteristic data in the target test point are compared and analyzed with the corresponding preset characteristic data to generate a concrete analysis report corresponding to the current target test point.

[0016] Furthermore, in a preferred embodiment of the present invention, the actual coordinate information of the test device is obtained, and the preset test parameters are obtained by pairing the actual coordinate information in the knowledge graph, specifically:

[0017] Obtaining coordinate information of each preset test point in the target area, and formulating preset test parameters for testing each preset test point in advance; wherein the preset test parameters include a preset loading rate, a preset loading pressure, a preset loading time, and a preset data acquisition frequency;

[0018] Bundle the preset test parameters and coordinate information of each preset test point to obtain several test data packets; construct a knowledge graph, and import each test data packet into the knowledge graph;

[0019] Acquire actual coordinate information of the test device, import the actual coordinate information of the test device into the knowledge graph, and pair the actual coordinate information of the test device with the coordinate information of each preset test point to determine whether the actual coordinate information of the test device coincides with the coordinate information of a preset test point;

[0020] If the actual coordinate information of the test device coincides with the coordinate information of a preset test point, a test data packet of the coincident preset test point is retrieved from the knowledge graph, and preset test parameters are obtained according to the retrieved test data packet;

[0021] If the actual coordinate information of the test device does not coincide with the coordinate information of all preset test points, an alarm message is generated to remind that the installation position of the test device is incorrect.

[0022] Furthermore, in a preferred embodiment of the present invention, the actual test parameters of the test device are obtained at a preset time node, and the actual test parameters are compared with the preset test parameters of the corresponding preset time node to correct the actual test parameters of the test device, specifically:

[0023] Acquiring actual test parameters of the test device at a preset time node; and acquiring preset test parameters of the test device at a corresponding preset time node;

[0024] Calculating the difference between the actual test parameter and the preset test parameter to obtain a test parameter difference; comparing the test parameter difference with a preset threshold;

[0025] If the test parameter difference is not greater than the preset threshold, there is no need to correct the corresponding actual test parameter;

[0026] If the test parameter difference is greater than a preset threshold, a control instruction is generated based on the test parameter difference, and the control instruction is sent to the control system, so that the control system corrects the corresponding actual test parameter according to the control instruction.

[0027] Furthermore, in a preferred embodiment of the present invention, the collected characteristic data is subjected to dimensionality reduction and clustering processing by a data processing system to obtain various types of characteristic data in the target test point, specifically:

[0028] Calculate the Euclidean distance between all feature data, and construct a distance matrix based on the Euclidean distance between all feature data;

[0029] For each feature data, find the row corresponding to the feature data in the distance matrix, then find k elements whose Euclidean distance is less than a preset threshold, and mark the feature data corresponding to the k elements whose Euclidean distance is less than the preset threshold as the nearest k neighbors of the feature data;

[0030] Create an empty graph, traverse each feature data, and add edges to each feature data according to the nearest k neighbors found in the previous step, connecting them to the corresponding neighbor vertices; at the same time, assign a weight to each edge, and the weight is the corresponding distance value in the distance matrix until a neighbor graph is generated;

[0031] According to the neighbor graph, the shortest path of each characteristic data is obtained based on the Dijkstra algorithm, and the high-dimensional characteristic data is mapped to a low-dimensional space according to the shortest path of each characteristic data to obtain the reduced-dimensional characteristic data.

[0032] Furthermore, in a preferred embodiment of the present invention, the following steps are also included:

[0033] Treat each dimension-reduced characteristic data as a cluster to obtain n clusters, where n is the number of dimension-reduced characteristic data; obtain the position coordinates of each cluster in the low-dimensional space, and calculate the Mahalanobis distance between each cluster according to the position coordinates;

[0034] Compare the Mahalanobis distances between clusters to find a pair of clusters with the shortest Mahalanobis distance between clusters, and merge the pair of clusters with the shortest Mahalanobis distance into a new cluster; add the merged cluster to the cluster list, and delete the merged cluster from the list. At this time, the number of clusters is reduced to n-1;

[0035] Continue to calculate the distance between clusters, continue to merge the clusters with the shortest Mahalanobis distance, and then update the cluster list; repeat this process until the preset number of clusters is reached and several clustering clusters are obtained;

[0036] Calculate the adjusted Rand index of each cluster to determine whether the adjusted Rand index of each cluster is less than the preset index value; if the adjusted Rand index of a cluster is not less than the preset index value, it means that the clustering effect of the cluster meets the standard, and the cluster is directly output as the final cluster;

[0037] If the adjusted Rand index of a cluster is less than the preset index value, it means that the clustering effect of the cluster is not up to standard, and the cluster needs to be corrected. After the correction is completed, the corrected cluster is output as the final cluster.

[0038] Furthermore, in a preferred embodiment of the present invention, if the adjusted Rand index of a cluster is less than the preset index value, it means that the clustering effect of the cluster is not up to standard, and the cluster needs to be corrected, specifically:

[0039] If the adjusted Rand index of a cluster is less than the preset index value, the Mahalanobis distance between each characteristic data in the cluster and the cluster center is calculated;

[0040] Constructing a sorting table, importing the Mahalanobis distance between each characteristic data and the cluster center into the sorting table for sorting, and after the sorting is completed, extracting the maximum Mahalanobis distance from the sorting table, and filtering out the characteristic data corresponding to the maximum Mahalanobis distance from the cluster;

[0041] After the screening is completed, the adjusted Rand index of the cluster is recalculated. If the adjusted Rand index of the cluster is still less than the preset index value, the second largest Mahalanobis distance is extracted from the sorting table, and the characteristic data corresponding to the second largest Mahalanobis distance is screened out from the cluster; and so on, this process is repeated until the adjusted Rand index of the cluster is not less than the preset index value, and the correction process of the cluster is completed.

[0042] Furthermore, in a preferred embodiment of the present invention, various types of characteristic data in the target test point are compared and analyzed with corresponding preset characteristic data to generate a concrete analysis report corresponding to the current target test point, specifically:

[0043] Calculate hash values ​​between various types of characteristic data in the target test point and corresponding preset characteristic data by using a perceptual hash algorithm;

[0044] Determine the similarity between various types of characteristic data in the target test point and corresponding preset characteristic data according to the hash value;

[0045] comparing the similarity between various types of characteristic data in the target test point and the corresponding preset characteristic data with a preset similarity;

[0046] If the similarity between a certain type of characteristic data in the target test point and the corresponding preset characteristic data is not greater than the preset similarity, the characteristic data of this type is marked as abnormal characteristic data;

[0047] If the similarity between a certain type of characteristic data in the target test point and the corresponding preset characteristic data is greater than the preset similarity, the characteristic data of this type is marked as normal characteristic data;

[0048] The abnormal characteristic data and the normal characteristic data in the target test point are obtained, and a concrete analysis report of the current target test point is prepared according to the abnormal characteristic data and the normal characteristic data in the target test point.

[0049] The present invention solves the technical defects existing in the background technology, and has the following beneficial effects: the device adopts an automatic control system to realize the automatic operation of the test process, reduce manual intervention, and improve the test efficiency; and the device can accurately control the speed of load application and unloading to ensure the accuracy and reliability of the test process. By combining the control method to carry out in-situ load testing, the bearing capacity and performance of reinforced concrete structures can be more accurately evaluated, providing strong technical support for engineering design and construction. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0051] Figure 1 is a first three-dimensional structural schematic diagram of the device;

[0052] Figure 2 is a second three-dimensional structural schematic diagram of the device;

[0053] Figure 3 is a third three-dimensional structural schematic diagram of the device;

[0054] Figure 4 is a fourth three-dimensional structural schematic diagram of the device;

[0055] Figure 5 The overall method flow chart of this control method is as follows;

[0056] The reference numerals are as follows: 101, base; 102, L-shaped bracket; 103, pressure cylinder; 104, telescopic rod; 105, end joint bearing; 106, shaft frame; 107, L-shaped slider; 108, linear sleeve; 109, slide rod; 201, mounting plate; 202, test head; 203, pressure spring; 204, mounting frame; 205, guide rail; 206, guide block. DETAILED DESCRIPTION

[0057] In order to more clearly understand the above-mentioned purposes, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. These drawings are all simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the structures related to the present invention. It should be noted that the embodiments in this application and the features in the embodiments can be combined with each other without conflict.

[0058] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the scope of protection of the present application. In addition, the terms "first", "second", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, features defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the invention, unless otherwise specified, "multiple" means two or more.

[0059] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0060] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. The preferred embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thoroughly understood.

[0061] like Figure 1-4 As shown, the first aspect of the present invention discloses a reinforced concrete in-situ load test device, comprising a base 101, an L-shaped bracket 102 is integrally formed on the base 101, a pressure cylinder 103 is fixedly installed on the L-shaped bracket 102, an output end of the pressure cylinder 103 is matched and connected with one end of a telescopic rod 104, and the other end of the telescopic rod 104 is matched and connected with an end joint bearing 105;

[0062] The end joint bearing 105 is hinged with an axis frame 106, the bottom of the axis frame 106 is fixedly connected with an L-shaped slider 107, two linear sleeves 108 are installed on the L-shaped slider 107, a slide bar 109 is sleeved in the linear sleeve 108, the bottom end of the slide bar 109 is fixedly connected with a mounting plate 201, and a test head 202 is fixedly installed on the mounting plate 201; and a pressure spring 203 is sleeved on the slide bar 109 between the L-shaped slider 107 and the mounting plate 201; the test head 202 is provided with a pressure sensor, a displacement sensor and a vibration sensor;

[0063] The L-shaped bracket 102 is provided with a mounting frame 204 , a guide rail 205 is fixedly mounted on the mounting frame 204 , a guide block 206 is fixedly mounted on the L-shaped slider 107 , and the guide block 206 is embedded in the guide rail 205 , so that the L-shaped slider 107 can slide along the guide rail 205 .

[0064] Furthermore, in a preferred embodiment of the present invention, the test device also includes a control system, a data acquisition system and a data processing system. The control system is used to control the size and rate of the load applied by the loading system to ensure the accuracy and stability of the load application during the test; the data acquisition system is used to collect stress, deformation, and vibration data fed back by pressure sensors, displacement sensors, and vibration sensors; the data processing system is used to process the stress, deformation, and vibration data of the concrete structure collected by the sensors, thereby generating analysis results.

[0065] It should be noted that, first, the base 101 is fixed to the concrete structure of the target test point by fixing screws, fixing bolts and other fixing devices, and then the pressure cylinder 103 is controlled by the control system to start, so as to drive the telescopic rod 104 downward through the pressure cylinder 103, so as to drive the test head 202 downward, so as to apply a preset load force to the concrete through the test head 202, and then the stress, deformation and vibration data fed back by the target test point are collected by the pressure sensor, displacement sensor and vibration sensor, and then the collected data are processed and analyzed, so as to obtain an analysis report of the concrete structure in the target test point. In addition, the test head 202 is limited by the guide rail 205 and the guide block 206 to avoid the position deviation of the test head 202 during the downward and loading process, which can further improve the reliability of the device. The device adopts an automatic control system, which can realize the automatic operation of the test process, reduce manual intervention, and improve the test efficiency; and the device can accurately control the load application and unloading speed to ensure the accuracy and reliability of the test process.

[0066] like Figure 5As shown, the second aspect of the present invention discloses a control method for a reinforced concrete in-situ load test device, which is applied to any one of the reinforced concrete in-situ load test devices described above, and comprises the following steps:

[0067] S102: Install the test device on the concrete structure of the target test point. After the installation is completed, obtain the actual coordinate information of the test device, and obtain the preset test parameters by pairing in the knowledge graph according to the actual coordinate information;

[0068] S104: Sending the preset test parameters obtained by pairing to the control system, and controlling the test device through the control system to perform the test according to the preset test parameters;

[0069] S106: acquiring actual test parameters of the test device at a preset time node, and comparing the actual test parameters with preset test parameters at the corresponding preset time node to correct the actual test parameters of the test device;

[0070] S108: When the loading conditions are met, the data acquisition system is started to obtain the characteristic data of the concrete detected by each sensor within the preset loading time, and the acquired characteristic data is transmitted to the data processing system; wherein the characteristic data includes stress, deformation, and vibration data;

[0071] S110: Performing dimension reduction and clustering processing on the collected characteristic data through a data processing system to obtain various types of characteristic data in the target test point; comparing and analyzing the various types of characteristic data in the target test point with the corresponding preset characteristic data to generate a concrete analysis report corresponding to the current target test point.

[0072] Furthermore, in a preferred embodiment of the present invention, the actual coordinate information of the test device is obtained, and the preset test parameters are obtained by pairing the actual coordinate information in the knowledge graph, specifically:

[0073] Obtaining coordinate information of each preset test point in the target area, and formulating preset test parameters for testing each preset test point in advance; wherein the preset test parameters include a preset loading rate, a preset loading pressure, a preset loading time, and a preset data acquisition frequency;

[0074] Bundle the preset test parameters and coordinate information of each preset test point to obtain a number of test data packets; construct a knowledge graph, and import each test data packet into the knowledge graph;

[0075] Acquire actual coordinate information of the test device, import the actual coordinate information of the test device into the knowledge graph, and pair the actual coordinate information of the test device with the coordinate information of each preset test point to determine whether the actual coordinate information of the test device coincides with the coordinate information of a preset test point;

[0076] If the actual coordinate information of the test device coincides with the coordinate information of a preset test point, a test data packet of the coincident preset test point is retrieved from the knowledge graph, and preset test parameters are obtained according to the retrieved test data packet;

[0077] If the actual coordinate information of the test device does not coincide with the coordinate information of all preset test points, an alarm message is generated to remind that the installation position of the test device is incorrect.

[0078] It should be noted that engineering and technical personnel delineate each preset test point in the target area and obtain the coordinate information of each preset test point, and engineering and technical personnel formulate preset test parameters for testing each preset test point in advance, thereby generating a test data packet for each preset test point, and importing each test data packet into the knowledge graph.

[0079] When the tester installs the device on the concrete structure, the GPS positioning module in the device will automatically obtain the actual coordinate information of the device, and match the actual coordinate information of the device with the coordinate information of each preset test point in the knowledge graph to determine whether the actual coordinate information of the test device coincides with the coordinate information of a preset test point; if the actual coordinate information of the test device coincides with the coordinate information of a preset test point, the test data packet of the preset test point that coincides with the test device is retrieved from the knowledge graph, and the preset test parameters are obtained according to the retrieved test data packet, thereby obtaining the preset test parameters for testing the test point, realizing the automation of the test process, reducing manual intervention, and improving the reliability of the test results and the test efficiency. If the actual coordinate information of the test device does not coincide with the coordinate information of all preset test points, it means that the device is not accurately installed at the test point, and an alarm message is generated to remind the tester that the installation position of the test device is incorrect, which can effectively avoid the test point error caused by human error, ensure that the test device applies the load at the correct position, and avoid the influence of position deviation on the test results, thereby optimizing the accuracy and reliability of the test results.

[0080] Furthermore, in a preferred embodiment of the present invention, the actual test parameters of the test device are obtained at a preset time node, and the actual test parameters are compared with the preset test parameters of the corresponding preset time node to correct the actual test parameters of the test device, specifically:

[0081] Acquiring actual test parameters of the test device at a preset time node; and acquiring preset test parameters of the test device at a corresponding preset time node;

[0082] Calculating the difference between the actual test parameter and the preset test parameter to obtain a test parameter difference; comparing the test parameter difference with a preset threshold;

[0083] If the test parameter difference is not greater than the preset threshold, there is no need to correct the corresponding actual test parameter;

[0084] If the test parameter difference is greater than a preset threshold, a control instruction is generated based on the test parameter difference, and the control instruction is sent to the control system, so that the control system corrects the corresponding actual test parameter according to the control instruction.

[0085] It should be noted that when the test device is performing a load test on the test point, the test parameters of the device are corrected by the above method, and intelligent regulation is achieved, which can ensure that the device tests the test point according to the preset test parameters, and can more accurately evaluate the bearing capacity and performance of the reinforced concrete structure, providing strong technical support for engineering design and construction.

[0086] Furthermore, in a preferred embodiment of the present invention, the collected characteristic data is subjected to dimensionality reduction and clustering processing by a data processing system to obtain various types of characteristic data in the target test point, specifically:

[0087] Calculate the Euclidean distance between all feature data, and construct a distance matrix based on the Euclidean distance between all feature data;

[0088] For each feature data, find the row corresponding to the feature data in the distance matrix, then find k elements whose Euclidean distance is less than a preset threshold, and mark the feature data corresponding to the k elements whose Euclidean distance is less than the preset threshold as the nearest k neighbors of the feature data;

[0089] Create an empty graph, traverse each feature data, and add edges to each feature data according to the nearest k neighbors found in the previous step, connecting them to the corresponding neighbor vertices; at the same time, assign a weight to each edge, and the weight is the corresponding distance value in the distance matrix until a neighbor graph is generated;

[0090] According to the neighbor graph, the shortest path of each characteristic data is obtained based on the Dijkstra algorithm, and the high-dimensional characteristic data is mapped to a low-dimensional space according to the shortest path of each characteristic data to obtain the reduced-dimensional characteristic data.

[0091] It should be noted that, since the characteristic data collected is high-temperature data, the high-dimensional characteristic data is mapped to the low-dimensional space through the isometric mapping algorithm to obtain the characteristic data after dimensionality reduction. Specifically, the Euclidean distance metric is first used to calculate the distance between all data points in the high-dimensional space, and the k nearest neighbors are found for each data point according to the distance matrix (or the number of neighbors is determined by other methods), and then a neighbor graph is constructed. In this graph, each data point is represented as a vertex, and the edge represents the connection between the vertices (i.e., the neighbor relationship); in the neighbor graph, the shortest path between all vertices is calculated, and the Dijkstra algorithm can be used to solve the shortest path. The original data points are mapped to a low-dimensional space (such as two-dimensional or three-dimensional) so that the shortest path distance in the high-dimensional space is retained in the low-dimensional space. Through this step, high-dimensional data can be compressed into a low-dimensional space, reducing data storage and computing costs, effectively reducing the dimension of the data, improving the efficiency and speed of data processing, and helping to extract important features in the data and reduce noise and redundant information in the data.

[0092] Furthermore, in a preferred embodiment of the present invention, the following steps are also included:

[0093] Treat each dimension-reduced characteristic data as a cluster to obtain n clusters, where n is the number of dimension-reduced characteristic data; obtain the position coordinates of each cluster in the low-dimensional space, and calculate the Mahalanobis distance between each cluster according to the position coordinates;

[0094] Compare the Mahalanobis distances between clusters to find a pair of clusters with the shortest Mahalanobis distance between clusters, and merge the pair of clusters with the shortest Mahalanobis distance into a new cluster; add the merged cluster to the cluster list, and delete the merged cluster from the list. At this time, the number of clusters is reduced to n-1;

[0095] Continue to calculate the distance between clusters, continue to merge the clusters with the shortest Mahalanobis distance, and then update the cluster list; repeat this process until the preset number of clusters is reached and several clustering clusters are obtained;

[0096] Calculate the adjusted Rand index of each cluster to determine whether the adjusted Rand index of each cluster is less than the preset index value; if the adjusted Rand index of a cluster is not less than the preset index value, it means that the clustering effect of the cluster meets the standard, and the cluster is directly output as the final cluster;

[0097] If the adjusted Rand index of a cluster is less than the preset index value, it means that the clustering effect of the cluster is not up to standard, and the cluster needs to be corrected. After the correction is completed, the corrected cluster is output as the final cluster.

[0098] It should be noted that a separate cluster is created for each point in the reduced-dimensional data. At this point, there are n clusters in total, where n is the number of data points after dimensionality reduction. The distances between all clusters are calculated, and the closest pair of clusters is found between all clusters, and they are merged into a new cluster. The merged cluster is added to the cluster list, and the merged cluster is deleted from the list. At this point, the number of clusters is reduced to n-1. Continue to calculate the distances between clusters, merge the nearest clusters, and then update the cluster list. Repeat this process until all points are merged into one cluster or the preset number of clusters is reached. Calculate the adjusted Rand index of each cluster cluster to determine whether the adjusted Rand index of each cluster cluster is less than the preset index value; if the adjusted Rand index of a cluster cluster is not less than the preset index value, it means that the cluster cluster has met the standard, and the cluster cluster is directly output as the final cluster.

[0099] Furthermore, in a preferred embodiment of the present invention, if the adjusted Rand index of a cluster is less than the preset index value, it means that the clustering effect of the cluster is not up to standard, and the cluster needs to be corrected, specifically:

[0100] If the adjusted Rand index of a cluster is less than the preset index value, the Mahalanobis distance between each characteristic data in the cluster and the cluster center is calculated;

[0101] Constructing a sorting table, importing the Mahalanobis distance between each characteristic data and the cluster center into the sorting table for sorting, and after the sorting is completed, extracting the maximum Mahalanobis distance from the sorting table, and filtering out the characteristic data corresponding to the maximum Mahalanobis distance from the cluster;

[0102] After the screening is completed, the adjusted Rand index of the cluster is recalculated. If the adjusted Rand index of the cluster is still less than the preset index value, the second largest Mahalanobis distance is extracted from the sorting table, and the characteristic data corresponding to the second largest Mahalanobis distance is screened out from the cluster; and so on, this process is repeated until the adjusted Rand index of the cluster is not less than the preset index value, and the correction process of the cluster is completed.

[0103] It should be noted that if the adjusted Rand index of a cluster is less than the preset index value, it means that the clustering effect of the cluster is not up to standard. For example, there may be some noise data in the cluster. At this time, first, the characteristic data corresponding to the maximum Mahalanobis distance is screened out in the cluster, and then the adjusted Rand index of the cluster is recalculated. If the adjusted Rand index of the cluster is still less than the preset index value, the second largest Mahalanobis distance is extracted from the sorting table, and the characteristic data corresponding to the second largest Mahalanobis distance is screened out in the cluster; and so on, repeat this process until the adjusted Rand index of the cluster is not less than the preset index value, and the correction process of the cluster is completed. By continuously optimizing and correcting the cluster in this step, the accuracy and stability of clustering can be improved, thereby better realizing data classification and analysis.

[0104] In summary, hierarchical clustering can build a hierarchical structure based on the similarity between data points, so as to better capture the intrinsic structure of the data. By clustering the feature data after dimensionality reduction, the relationship between the data can be more accurately described, and the accuracy of clustering can be improved. By adjusting the Rand index, the quality of clustering can be evaluated, clusters with poor clustering effects can be found in time, and corrected. Combining hierarchical clustering and adjusting the Rand index can effectively improve the clustering effect, making the clustering results more accurate and stable, and can bring more accurate, stable and explainable clustering results.

[0105] Furthermore, in a preferred embodiment of the present invention, various types of characteristic data in the target test point are compared and analyzed with corresponding preset characteristic data to generate a concrete analysis report corresponding to the current target test point, specifically:

[0106] Calculate hash values ​​between various types of characteristic data in the target test point and corresponding preset characteristic data by using a perceptual hash algorithm;

[0107] Determine the similarity between various types of characteristic data in the target test point and corresponding preset characteristic data according to the hash value;

[0108] comparing the similarity between various types of characteristic data in the target test point and the corresponding preset characteristic data with a preset similarity;

[0109] If the similarity between a certain type of characteristic data in the target test point and the corresponding preset characteristic data is not greater than the preset similarity, the characteristic data of this type is marked as abnormal characteristic data;

[0110] If the similarity between a certain type of characteristic data in the target test point and the corresponding preset characteristic data is greater than the preset similarity, the characteristic data of this type is marked as normal characteristic data;

[0111] The abnormal characteristic data and the normal characteristic data in the target test point are obtained, and a concrete analysis report of the current target test point is prepared according to the abnormal characteristic data and the normal characteristic data in the target test point.

[0112] It should be noted that the hash value between various types of characteristic data in the target test point and the corresponding preset characteristic data is calculated by the perceptual hash algorithm, and the similarity between various types of characteristic data in the target test point and the corresponding preset characteristic data is determined according to the hash value, such as calculating the hash value between the actual vibration data of the concrete structure during the test and the preset vibration data, thereby determining the similarity between the two. If the similarity between a certain type of characteristic data in the target test point and the corresponding preset characteristic data is not greater than the preset similarity, the characteristic data of this type is marked as abnormal characteristic data; if the similarity between a certain type of characteristic data in the target test point and the corresponding preset characteristic data is greater than the preset similarity, the characteristic data of this type is marked as normal characteristic data; then the situation of abnormal characteristic data and normal characteristic data in the target test point is obtained, and the concrete analysis report of the current target test point is prepared according to the situation of abnormal characteristic data and normal characteristic data in the target test point.

[0113] In addition, the control method further comprises the following steps:

[0114] Determine the possible fault types in the test device, including sensor faults, control system faults, and recording system faults, and define the fault types as variables in the network;

[0115] Analyze the dependency relationship between various fault variables, and construct a directed acyclic graph according to the dependency relationship, wherein nodes represent fault variables and directed edges represent the dependency relationship between variables;

[0116] Assign a conditional probability table to each node, indicating the probability of failure of the current node given the parent node, and the probability is estimated based on expert experience, historical data or information in the manual provided by the equipment manufacturer;

[0117] Constructing a Bayesian network model, and training the Bayesian network model according to the conditional probability table of each node to obtain a trained Bayesian network model;

[0118] Acquire real-time working parameters of the test device, which include real-time load parameters, real-time displacement parameters, and real-time electrical parameters; import the real-time working parameters into a trained Bayesian network model for fault prediction to obtain the fault probability of the test device;

[0119] If the failure probability is greater than the preset failure probability, failure warning information is generated.

[0120] It should be noted that the Bayesian network model can quickly calculate the posterior probability of each fault variable, helping diagnostic personnel to quickly identify key components or systems that may cause problems. Combining the Bayesian network model to perform fault diagnosis on the reinforced concrete in-situ load test device can improve diagnostic efficiency and accuracy, optimize maintenance strategies, and support better decision making.

[0121] The above is based on the ideal embodiment of the present invention, and its description is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the attached claims.

Claims

1. A control method for a reinforced concrete in-situ load test device, characterized in that: The test device comprises a base, an L-shaped bracket is integrally formed on the base, a pressure cylinder is fixedly mounted on the L-shaped bracket, an output end of the pressure cylinder is cooperatively connected with one end of a telescopic rod, and the other end of the telescopic rod is cooperatively connected with an end joint bearing; The end joint bearing is hinged with an axle frame, the bottom of the axle frame is fixedly connected with an L-shaped slider, two linear sleeves are installed on the L-shaped slider, a slide bar is sleeved in the linear sleeve, the bottom end of the slide bar is fixedly connected with a mounting plate, a test head is fixedly installed on the mounting plate; and a pressure spring is sleeved on the slide bar between the L-shaped slider and the mounting plate; the test head is provided with a pressure sensor, a displacement sensor and a vibration sensor; The L-shaped bracket is provided with a mounting frame, a guide rail is fixedly mounted on the mounting frame, a guide block is fixedly mounted on the L-shaped slider, and the guide block is embedded in the guide rail, so that the L-shaped slider can slide along the guide rail; The test device also includes a control system, a data acquisition system and a data processing system. The control system is used to control the size and rate of the load applied by the loading system to ensure the accuracy and stability of the load application during the test; the data acquisition system is used to collect stress, deformation and vibration data fed back by the pressure sensor, displacement sensor and vibration sensor; The data processing system is used to process the stress, deformation and vibration data of the concrete structure collected by the sensor to generate analysis results; The control method comprises the following steps: The test device is installed on the concrete structure of the target test point. After the installation is completed, the actual coordinate information of the test device is obtained, and the preset test parameters are obtained by pairing the actual coordinate information in the knowledge graph; The preset test parameters obtained by pairing are sent to the control system, and the test device is controlled by the control system to perform the test according to the preset test parameters; Acquire actual test parameters of the test device at a preset time node, and compare the actual test parameters with preset test parameters at the corresponding preset time node to correct the actual test parameters of the test device; When the loading conditions are met, the data acquisition system is started to obtain the characteristic data of the concrete detected by each sensor within the preset loading time, and the acquired characteristic data is transmitted to the data processing system; wherein the characteristic data includes stress, deformation, and vibration data; The data processing system performs dimension reduction and clustering processing on the collected characteristic data to obtain various types of characteristic data in the target test point; compares and analyzes various types of characteristic data in the target test point with corresponding preset characteristic data to generate a concrete analysis report corresponding to the current target test point; Among them, the actual coordinate information of the test device is obtained, and the preset test parameters are obtained by pairing the actual coordinate information in the knowledge graph, specifically: Obtaining coordinate information of each preset test point in the target area, and formulating preset test parameters for testing each preset test point in advance; wherein the preset test parameters include a preset loading rate, a preset loading pressure, a preset loading time, and a preset data acquisition frequency; Bundle the preset test parameters and coordinate information of each preset test point to obtain a number of test data packets; construct a knowledge graph, and import each test data packet into the knowledge graph; Acquire actual coordinate information of the test device, import the actual coordinate information of the test device into the knowledge graph, and pair the actual coordinate information of the test device with the coordinate information of each preset test point to determine whether the actual coordinate information of the test device coincides with the coordinate information of a preset test point; If the actual coordinate information of the test device coincides with the coordinate information of a preset test point, a test data packet of the coincident preset test point is retrieved from the knowledge graph, and preset test parameters are obtained according to the retrieved test data packet; If the actual coordinate information of the test device does not coincide with the coordinate information of all preset test points, an alarm message is generated to remind that the installation position of the test device is incorrect; Among them, the collected characteristic data is subjected to dimensionality reduction and clustering processing by the data processing system to obtain various types of characteristic data in the target test point, specifically: Calculate the Euclidean distance between all feature data, and construct a distance matrix based on the Euclidean distance between all feature data; For each feature data, find the row corresponding to the feature data in the distance matrix, then find k elements whose Euclidean distance is less than a preset threshold, and mark the feature data corresponding to the k elements whose Euclidean distance is less than the preset threshold as the nearest k neighbors of the feature data; Create an empty graph, traverse each feature data, and add edges to each feature data according to the nearest k neighbors found in the previous step, connecting them to the corresponding neighbor vertices; at the same time, assign a weight to each edge, and the weight is the corresponding distance value in the distance matrix until a neighbor graph is generated; According to the neighbor graph, the shortest path of each characteristic data is obtained based on the Dijkstra algorithm, and the high-dimensional characteristic data is mapped to a low-dimensional space according to the shortest path of each characteristic data to obtain the characteristic data after dimensionality reduction; The following steps are also included: Treat each dimension-reduced characteristic data as a cluster to obtain n clusters, where n is the number of dimension-reduced characteristic data; obtain the position coordinates of each cluster in the low-dimensional space, and calculate the Mahalanobis distance between each cluster according to the position coordinates; Compare the Mahalanobis distances between clusters to find a pair of clusters with the shortest Mahalanobis distance between clusters, and merge the pair of clusters with the shortest Mahalanobis distance into a new cluster; add the merged cluster to the cluster list, and delete the merged cluster from the list. At this time, the number of clusters is reduced to n-1; Continue to calculate the distance between clusters, continue to merge the clusters with the shortest Mahalanobis distance, and then update the cluster list; repeat this process until the preset number of clusters is reached and several clustering clusters are obtained; Calculate the adjusted Rand index of each cluster to determine whether the adjusted Rand index of each cluster is less than the preset index value; if the adjusted Rand index of a cluster is not less than the preset index value, it means that the clustering effect of the cluster meets the standard, and the cluster is directly output as the final cluster; If the adjusted Rand index of a cluster is less than the preset index value, it means that the clustering effect of the cluster is not up to standard, and the cluster needs to be corrected. After the correction is completed, the corrected cluster is output as the final cluster.

2. The control method of a reinforced concrete in-situ load test device according to claim 1, characterized in that: Acquire actual test parameters of the test device at a preset time node, and compare the actual test parameters with preset test parameters at the corresponding preset time node to correct the actual test parameters of the test device, specifically: Acquiring actual test parameters of the test device at a preset time node; and acquiring preset test parameters of the test device at a corresponding preset time node; Calculating the difference between the actual test parameter and the preset test parameter to obtain the test parameter difference; Comparing the test parameter difference with a preset threshold; If the test parameter difference is not greater than the preset threshold, there is no need to correct the corresponding actual test parameter; If the test parameter difference is greater than a preset threshold, a control instruction is generated based on the test parameter difference, and the control instruction is sent to the control system, so that the control system corrects the corresponding actual test parameter according to the control instruction.

3. The control method of a reinforced concrete in-situ load test device according to claim 1, characterized in that: If the adjusted Rand index of a cluster is less than the preset index value, it means that the clustering effect of the cluster is not up to standard, and the cluster needs to be corrected, specifically: If the adjusted Rand index of a cluster is less than the preset index value, the Mahalanobis distance between each characteristic data in the cluster and the cluster center is calculated; Constructing a sorting table, importing the Mahalanobis distance between each characteristic data and the cluster center into the sorting table for sorting, and after the sorting is completed, extracting the maximum Mahalanobis distance from the sorting table, and filtering out the characteristic data corresponding to the maximum Mahalanobis distance from the cluster; After the screening is completed, the adjusted Rand index of the cluster is recalculated. If the adjusted Rand index of the cluster is still less than the preset index value, the second largest Mahalanobis distance is extracted from the sorting table, and the characteristic data corresponding to the second largest Mahalanobis distance is screened out from the cluster; By analogy, this process is repeated until the adjusted Rand index of the cluster is not less than the preset index value, and the correction process for the cluster is completed.

4. The control method of a reinforced concrete in-situ load test device according to claim 1, characterized in that: Compare and analyze various types of characteristic data in the target test point with the corresponding preset characteristic data to generate a concrete analysis report corresponding to the current target test point, specifically: Calculate hash values ​​between various types of characteristic data in the target test point and corresponding preset characteristic data by using a perceptual hash algorithm; Determine the similarity between various types of characteristic data in the target test point and corresponding preset characteristic data according to the hash value; comparing the similarity between various types of characteristic data in the target test point and the corresponding preset characteristic data with a preset similarity; If the similarity between a certain type of characteristic data in the target test point and the corresponding preset characteristic data is not greater than the preset similarity, the characteristic data of this type is marked as abnormal characteristic data; If the similarity between a certain type of characteristic data in the target test point and the corresponding preset characteristic data is greater than the preset similarity, the characteristic data of this type is marked as normal characteristic data; The abnormal characteristic data and the normal characteristic data in the target test point are obtained, and a concrete analysis report of the current target test point is generated according to the abnormal characteristic data and the normal characteristic data in the target test point.

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