Working performance online monitoring method and device for chemical anchor bolt and embedded steel bar

Abstract

The invention relates to the technical field of building monitoring, in particular to a working performance online monitoring method and device for chemical anchor bolts and embedded steel bars. Constructing a chemical glue working condition prediction model according to various acoustic dynamic characteristic data corresponding to the chemical glue within a preset time period before various preset working condition events occur in the working process of the chemical anchor bolt and the embedded steel bar; continuously collecting real-time acoustic characteristic data fed back by the chemical glue in the chemical anchor bolt and the embedded steel bar at a plurality of preset time nodes through a monitoring device; importing a real-time acoustic characteristic dynamic data set into the chemical glue working condition prediction model for prediction; and if the probability value of the preset working condition event of the chemical glue is greater than a preset probability threshold value, generating early warning information. According to the method, the occurrence time of the chemical glue working condition event can be accurately predicted, and the optimal maintenance time period is recommended, so that the accuracy and the maintenance efficiency of working performance monitoring of chemical anchor bolts and embedded steel bars are improved, and the safety and the reliability of a system are remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of building monitoring, in particular to an online monitoring method and device for the working performance of a chemical anchor bolt and a rebar. Background Art

[0002] With the continuous expansion of the scale of construction projects and the increasing complexity of structural forms, chemical anchors and rebars have been widely used as an important connection method in bridges, tunnels, high-rise buildings and other projects. However, the performance of chemical anchors and rebars is affected by many factors, among which the aging of chemical glue is one of the main reasons for the decline in their bearing capacity and failure. The "Technical Standard for Testing the Pullout and Shear Performance of Concrete Post Anchors" DBJ / T15-35-2023 clearly stipulates that the working status of chemical anchors and rebars should be checked regularly, and emphasizes that the first inspection time should not be later than 10 years, which reflects the concern about the long-term performance stability of chemical anchors and rebars. However, traditional detection methods mainly rely on manual regular inspections or destructive tests, which are not only time-consuming and labor-intensive, but also difficult to monitor the performance of chemical glue in real time, unable to timely warn of potential risks, and have certain safety hazards. Therefore, it is urgent to develop a method and device that can monitor the working performance of chemical anchors and rebars online, realize real-time monitoring of the aging state of chemical glue, and timely warn of potential failure risks to ensure structural safety. Summary of the invention

[0003] The invention overcomes the shortcomings of the prior art and provides an online monitoring method and device for the working performance of a chemical anchor bolt and a rebar.

[0004] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is:

[0005] The first aspect of the present invention discloses an online monitoring method for the working performance of chemical anchor bolts and rebars, comprising the following steps:

[0006] Acquire a shared monitoring logbook of chemical anchors and rebars through a big data network, and acquire various acoustic dynamic characteristic data corresponding to chemical glue in a preset time period before each preset working condition event occurs in the chemical anchors and rebars during the working process according to the shared monitoring logbook;

[0007] A chemical glue working condition prediction model is constructed based on various acoustic dynamic characteristic data corresponding to the chemical glue in a preset time period before each preset working condition event occurs during the working process of the chemical anchor bolt and the implanted bar;

[0008] The real-time acoustic characteristic data fed back by the chemical anchor bolts and the chemical glue in the embedded steel bar are continuously collected by the monitoring device at several preset time nodes, and the collected real-time acoustic characteristic data are classified and processed to obtain several real-time acoustic characteristic dynamic data sets;

[0009] The real-time acoustic feature dynamic data set is imported into the chemical glue working condition prediction model for prediction, and the probability value of the chemical glue occurrence of a preset working condition event is obtained; if the probability value of the chemical glue occurrence of a preset working condition event is greater than a preset probability threshold, an early warning message is generated and sent to a preset terminal.

[0010] Preferably, in a preferred embodiment of the present invention, a shared monitoring logbook of chemical anchors and rebars is obtained through a big data network, and various acoustic dynamic characteristic data corresponding to the chemical glue in a preset time period before each preset working condition event occurs in the working process of the chemical anchors and rebars are obtained according to the shared monitoring logbook, specifically:

[0011] Acquire the shared monitoring logbook of chemical anchor bolts and planted steel bars through the big data network, and acquire various working condition events of chemical anchor bolts and planted steel bars during the working process according to the shared monitoring logbook;

[0012] Clustering is performed on various working condition events of chemical anchor bolts and rebar planting during the working process to generate clustering results, and preset working condition events are extracted from the clustering results;

[0013] According to the shared monitoring log, various acoustic dynamic characteristic data corresponding to the chemical glue in a preset time period before each preset working condition event occurs during the working process of the chemical anchor bolt and the implanted bar are obtained;

[0014] Among them, the preset operating conditions include the initial, mid-term and final conditions of chemical glue aging and chemical glue crack conditions; the acoustic dynamic characteristic data include the spectrum, frequency, amplitude, peak value, spectrum entropy and frequency band energy of the sound wave.

[0015] Preferably, in a preferred embodiment of the present invention, a chemical glue working condition prediction model is constructed based on various acoustic dynamic characteristic data corresponding to the chemical glue in a preset time period before each preset working condition event occurs during the working process of the chemical anchor bolt and the implanted bar, specifically:

[0016] Arrange various acoustic dynamic characteristic data corresponding to the chemical glue in a preset time period before the occurrence of a preset working condition event in chronological order to obtain a plurality of time series data sets;

[0017] A conditional random field is introduced, each preset working condition event is defined as a random node of the conditional random field, and each time series data set is used as a conditional node of the conditional random field;

[0018] Connect each conditional node with each random node, and obtain the path length and connection direction between each conditional node and each random node;

[0019] Based on the grey correlation analysis method and combined with the path length and connection direction between each conditional node and each random node, the correlation strength between each conditional node and each random node is analyzed;

[0020] According to the correlation strength between each conditional node and each random node, the conditional correlation probability between the acoustic feature data in each time series data set and each preset working condition event is determined;

[0021] Constructing a conditional association probability table according to the conditional association probability between the acoustic feature data in each time series data set and each preset working condition event;

[0022] The conditional association probability table is imported into the conditional random field for back propagation training; the training parameters of the conditional random field are obtained through training; after the training parameters converge to preset values, the training parameters that converge to the preset values ​​are used as the final learning parameters of the conditional random field, and the chemical glue working condition prediction model is output.

[0023] Preferably, in a preferred embodiment of the present invention, the real-time acoustic characteristic data fed back by the chemical anchor bolts and the chemical glue in the embedded steel bar are continuously collected by the monitoring device at several preset time nodes, and the collected real-time acoustic characteristic data are classified and processed to obtain several real-time acoustic characteristic dynamic data sets, specifically:

[0024] Continuously collect the real-time acoustic characteristic data fed back by the chemical glue in the chemical anchor bolts and the embedded steel bars at several preset time nodes, and store the collected real-time acoustic characteristic data in a database until the collection is completed;

[0025] Constructing a decision tree, obtaining the number of data items of the real-time acoustic feature data to be classified, determining a number of branch points according to the number of data items of the real-time acoustic feature data to be classified, and dividing a number of branch trunks in the decision tree according to the branch points;

[0026] Acquire data feature information of each required classification of real-time acoustic feature data, and assign corresponding feature attributes to each branch trunk according to the data feature information of each required classification of real-time acoustic feature data;

[0027] Acquiring real-time acoustic feature data in the database, and calculating the degree of membership between each real-time acoustic feature data in the database and the characteristic attribute assigned to each branch trunk;

[0028] Allocate each real-time acoustic feature data to the branch trunk with the highest membership, and repeat this step until all the real-time acoustic feature data in the database are allocated;

[0029] After the allocation is completed, the Euclidean distance between the real-time acoustic feature data on each branch trunk and the corresponding branch point is calculated respectively; the Euclidean distance between the real-time acoustic feature data on each branch trunk and the corresponding branch point is compared with a preset distance threshold;

[0030] If the Euclidean distance between a certain real-time acoustic feature data on a branch trunk and the corresponding branch point is greater than a preset distance threshold, the real-time acoustic feature data is marked as noise data, and the noise data is removed on the corresponding branch trunk; repeat this step until the denoising process of the real-time acoustic feature data on all branch trunks is completed;

[0031] Each branch trunk is pruned to obtain several real-time acoustic feature data sets, and each real-time acoustic feature data on each real-time acoustic feature data set is sorted according to the acquisition timestamp to obtain several real-time acoustic feature dynamic data sets.

[0032] Preferably, in a preferred embodiment of the present invention, the real-time acoustic characteristic dynamic data set is imported into the chemical glue working condition prediction model for prediction, and the probability value of the chemical glue occurrence of a preset working condition event is obtained; if the probability value of the chemical glue occurrence of a preset working condition event is greater than a preset probability threshold, an early warning message is generated, specifically:

[0033] Importing the real-time acoustic feature dynamic data set into the chemical glue working condition prediction model to perform prediction and obtain prediction results;

[0034] According to the prediction results, the probability value of the chemical anchor bolt and the embedded steel bar having a preset working condition event of the chemical glue after the preset time is obtained;

[0035] Compare the probability value of the chemical anchor bolt and the rebar undergoing a preset working condition event after the preset time with the preset probability threshold;

[0036] If the probability value of the chemical anchor bolt and the embedded steel bar causing the preset working condition event is greater than the preset probability threshold after the preset time, an early warning message is generated;

[0037] If the probability value of the preset working condition event of the chemical anchor bolt and the embedded steel bar after the preset time is not greater than the preset probability threshold, no warning information will be generated.

[0038] The following steps are also included:

[0039] If the probability value of the preset working condition event occurring in the chemical glue of the chemical anchor bolt and the planted steel bar after the preset time is greater than the preset probability threshold, then the time node of the preset working condition event occurring in the chemical glue of the chemical anchor bolt and the planted steel bar is obtained according to the prediction result;

[0040] Determine several maintenance time periods based on the time nodes of the preset working condition events of the chemical glue, and obtain the predicted meteorological data information of the chemical anchor bolt and the rebar planting area in each maintenance time period based on the meteorological software;

[0041] Calculate the attention score between the predicted meteorological data information and the preset meteorological data in each maintenance time period based on the local sensitive attention mechanism; and sort the attention scores between the predicted meteorological data information and the preset meteorological data in each maintenance time period to obtain the sorting result;

[0042] A maximum attention score is obtained according to the ranking result, and a maintenance time period corresponding to the maximum attention score is obtained, and the maintenance time period corresponding to the maximum attention score is sent to a preset terminal as a recommended maintenance time period.

[0043] The second aspect of the present invention discloses an online monitoring device for the working performance of chemical anchor bolts and rebars, which is applied to any of the online monitoring methods for the working performance of chemical anchor bolts and rebars, comprising:

[0044] The main body of the monitoring device is buried in the concrete during the initial construction of chemical anchor bolts and rebars, and is equipped with an external wire that passes through the concrete to connect with external instruments;

[0045] Acoustic sensor module, used to collect acoustic signals around chemical anchors and rebars;

[0046] The signal acquisition and processing module amplifies, filters, digitizes, and processes the signals collected by the acoustic sensor and converts them into data that can be analyzed;

[0047] The data analysis and processing module analyzes the collected acoustic feature data, extracts characteristic parameters related to chemical glue aging and cracks, and performs data modeling and early warning judgment;

[0048] The data storage and transmission module stores monitoring data and warning information and transmits them to the monitoring center or other equipment.

[0049] The present invention solves the technical defects existing in the background technology, and the present invention has the following beneficial effects: constructing a chemical glue working condition prediction model according to various acoustic dynamic characteristic data corresponding to the chemical glue in a preset time period before each preset working condition event occurs in the working process of the chemical anchor bolt and the implanted bar; continuously collecting the real-time acoustic characteristic data fed back by the chemical glue in the chemical anchor bolt and the implanted bar at several preset time nodes through a monitoring device, and classifying and processing the collected real-time acoustic characteristic data to obtain several real-time acoustic characteristic dynamic data sets; importing the real-time acoustic characteristic dynamic data set into the chemical glue working condition prediction model for prediction, and obtaining the probability value of the chemical glue having a preset working condition event; if the probability value of the chemical glue having a preset working condition event is greater than the preset probability threshold, then generating a warning message, and sending the warning message to a preset terminal. The present invention can accurately predict the occurrence time of the chemical glue working condition event, and recommend the optimal maintenance time period, thereby improving the accuracy and maintenance efficiency of the working performance monitoring of the chemical anchor bolt and the implanted bar, and significantly improving the safety and reliability of the system. 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 This is a flow chart of the first method of the online monitoring method;

[0052] Figure 2 This is a flow chart of the second method of the online monitoring method;

[0053] Figure 3 This is a simplified structural diagram of the online monitoring device. DETAILED DESCRIPTION

[0054] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0055] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0056] The first aspect of the present invention discloses an online monitoring method for the working performance of chemical anchor bolts and rebars. Figure 1 As shown, the following steps are included:

[0057] S102: obtaining a shared monitoring logbook of chemical anchor bolts and planted steel bars through a big data network, and obtaining various acoustic dynamic characteristic data corresponding to chemical glue in a preset time period before each preset working condition event occurs in the chemical anchor bolts and planted steel bars during the working process according to the shared monitoring logbook;

[0058] S104: constructing a chemical glue working condition prediction model according to various acoustic dynamic characteristic data corresponding to the chemical glue in a preset time period before each preset working condition event occurs during the working process of the chemical anchor bolt and the implanted bar;

[0059] S106: continuously collecting real-time acoustic feature data fed back by chemical anchor bolts and chemical glue in the rebar at a number of preset time nodes through the monitoring device, and classifying and processing the collected real-time acoustic feature data to obtain a number of real-time acoustic feature dynamic data sets;

[0060] S108: Import the real-time acoustic feature dynamic data set into the chemical glue working condition prediction model for prediction, and obtain the probability value of the chemical glue occurrence of a preset working condition event; if the probability value of the chemical glue occurrence of a preset working condition event is greater than a preset probability threshold, generate an early warning message, and send the early warning message to a preset terminal.

[0061] It should be noted that, first of all, historical monitoring records of chemical anchors and rebars are collected through the big data network. These records contain the behavior data of chemical glue under different working conditions. Based on historical data, the acoustic characteristic data for a period of time before a specific working condition event (such as the early, middle, and late stages of chemical glue aging, or chemical glue cracks, etc.) is determined, such as the spectrum, frequency, amplitude, peak value, spectrum entropy, and band energy of the sound wave. A mathematical model (i.e., a chemical glue working condition prediction model) is constructed using the above acoustic dynamic characteristic data, which can be used to predict the working state of the chemical glue in the future. The real-time acoustic characteristic data of the chemical glue is continuously collected using a monitoring device, and classified and processed into multiple dynamic data sets. The real-time acoustic characteristic data set is input into the chemical glue working condition prediction model to calculate the probability of a preset working condition event (such as aging or cracks) occurring in the chemical glue. If the probability exceeds the preset threshold, the system will automatically generate a warning message. By predicting the occurrence of potential problems in advance, measures can be taken to avoid safety accidents or structural damage caused by the failure of chemical glue, thereby achieving preventive maintenance; timely warnings can reduce safety hazards caused by aging or cracking of chemical glue, and ensure the safety of buildings or infrastructure; by accurately monitoring the working status of chemical glue, maintenance plans and resources can be arranged more reasonably to avoid unnecessary inspections or excessive maintenance. The prediction results based on a large amount of historical data and real-time data analysis can provide a scientific basis for engineering design, construction and later maintenance, and promote the optimization of the decision-making process. In summary, this online monitoring method not only improves the reliability of the chemical anchor and rebar system, but also enhances the safety and durability of the entire structure, while also contributing to the effective utilization and management of resources.

[0062] Preferably, in a preferred embodiment of the present invention, a shared monitoring log of chemical anchor bolts and planted steel bars is obtained through a big data network, and various acoustic dynamic characteristic data corresponding to the chemical glue in a preset time period before each preset working condition event occurs in the working process of the chemical anchor bolts and planted steel bars are obtained according to the shared monitoring log, such as Figure 2 As shown, specifically:

[0063] S202: obtaining a shared monitoring logbook of chemical anchor bolts and planted steel bars through a big data network, and obtaining various working condition events of the chemical anchor bolts and planted steel bars during the working process according to the shared monitoring logbook;

[0064] S204: performing clustering processing on various working condition events of chemical anchor bolts and rebar planting during the working process, generating clustering results, and extracting preset working condition events from the clustering results;

[0065] Among them, various working condition events can be clustered by using hierarchical clustering algorithm, fuzzy clustering algorithm and other algorithms;

[0066] S206: Acquiring various acoustic dynamic characteristic data corresponding to the chemical glue in a preset time period before each preset working condition event occurs during the working process of the chemical anchor bolt and the implanted bar according to the shared monitoring log book;

[0067] Among them, the preset operating conditions include the initial, mid-term and final conditions of chemical glue aging and chemical glue crack conditions; the acoustic dynamic characteristic data include the spectrum, frequency, amplitude, peak value, spectrum entropy and frequency band energy of the sound wave.

[0068] It should be noted that historical monitoring records of chemical anchors and rebars are collected through the big data network. These records can come from different engineering projects or equipment and contain data under various working conditions. According to the shared monitoring logbook, various working condition events that occur during the working process of chemical anchors and rebars are identified and recorded. These working condition events are clustered to generate clustering results. Clustering can be performed using methods such as hierarchical clustering algorithms or fuzzy clustering algorithms, which can group according to the similarity of working condition events. Preset working condition events are extracted from the clustering results, such as the initial, middle, and final working conditions of chemical glue aging and chemical glue cracking conditions. Acoustic dynamic feature data within a preset time period before the preset working condition events occur during the working process of chemical anchors and rebars are obtained. Through the big data network and clustering algorithm, efficient monitoring and analysis of the working status of chemical anchors and rebars is achieved, the reliability and safety of the system are improved, and the robustness of the monitoring system is effectively improved.

[0069] Preferably, in a preferred embodiment of the present invention, a chemical glue working condition prediction model is constructed based on various acoustic dynamic characteristic data corresponding to the chemical glue in a preset time period before each preset working condition event occurs during the working process of the chemical anchor bolt and the implanted bar, specifically:

[0070] Arrange various acoustic dynamic characteristic data corresponding to the chemical glue in a preset time period before the occurrence of a preset working condition event in chronological order to obtain a plurality of time series data sets;

[0071] A conditional random field is introduced, each preset working condition event is defined as a random node of the conditional random field, and each time series data set is used as a conditional node of the conditional random field;

[0072] Connect each conditional node with each random node, and obtain the path length and connection direction between each conditional node and each random node;

[0073] Based on the grey correlation analysis method and combined with the path length and connection direction between each conditional node and each random node, the correlation strength between each conditional node and each random node is analyzed;

[0074] According to the correlation strength between each conditional node and each random node, the conditional correlation probability between the acoustic feature data in each time series data set and each preset working condition event is determined;

[0075] Constructing a conditional association probability table according to the conditional association probability between the acoustic feature data in each time series data set and each preset working condition event;

[0076] The conditional association probability table is imported into the conditional random field for back propagation training; the training parameters of the conditional random field are obtained through training; after the training parameters converge to preset values, the training parameters that converge to the preset values ​​are used as the final learning parameters of the conditional random field, and the chemical glue working condition prediction model is output.

[0077] It should be noted that the acoustic dynamic characteristic data of chemical glue in a preset time period before the preset working condition event are arranged in chronological order to form several time series data sets. The conditional random field (CRF) model is introduced to define the preset working condition events (such as the initial, middle, final and crack working conditions of chemical glue aging) as random nodes. Each time series data set is defined as a conditional node, that is, the acoustic dynamic characteristic data is used as a conditional node. Each conditional node (time series data set) is connected to each random node (preset working condition event). The path length and connection direction between each conditional node and the random node are obtained. The gray correlation analysis method is used to combine the path length and connection direction to analyze the correlation strength between each conditional node and the random node. The conditional association probability between the acoustic characteristic data and the preset working condition event in each time series data set is determined according to the correlation strength. A conditional association probability table is constructed to record the association probability between each conditional node and the random node. The conditional association probability table is imported into the conditional random field model for back propagation training. Through training, the training parameters of the conditional random field are obtained. The training parameters are gradually adjusted until they converge to the preset values. The converged training parameters are used as the final learning parameters of the conditional random field, and the chemical glue working condition prediction model is output. In summary, through the conditional random field model and the grey correlation analysis method, the high-precision prediction of the chemical glue working condition is achieved, which improves the reliability and safety of the system; and the constructed chemical glue working condition prediction model can monitor the state changes of the chemical glue in real time and issue early warning information when an abnormality occurs.

[0078] Preferably, in a preferred embodiment of the present invention, the real-time acoustic characteristic data fed back by the chemical anchor bolts and the chemical glue in the embedded steel bar are continuously collected by the monitoring device at several preset time nodes, and the collected real-time acoustic characteristic data are classified and processed to obtain several real-time acoustic characteristic dynamic data sets, specifically:

[0079] Continuously collect the real-time acoustic characteristic data fed back by the chemical glue in the chemical anchor bolts and the embedded steel bars at several preset time nodes, and store the collected real-time acoustic characteristic data in a database until the collection is completed;

[0080] Constructing a decision tree, obtaining the number of data items of the real-time acoustic feature data to be classified, determining a number of branch points according to the number of data items of the real-time acoustic feature data to be classified, and dividing a number of branch trunks in the decision tree according to the branch points;

[0081] Acquire data feature information of each required classification of real-time acoustic feature data, and assign corresponding feature attributes to each branch trunk according to the data feature information of each required classification of real-time acoustic feature data;

[0082] Acquiring real-time acoustic feature data in the database, and calculating the degree of membership between each real-time acoustic feature data in the database and the characteristic attribute assigned to each branch trunk;

[0083] Allocate each real-time acoustic feature data to the branch trunk with the highest membership, and repeat this step until all the real-time acoustic feature data in the database are allocated;

[0084] After the allocation is completed, the Euclidean distance between the real-time acoustic feature data on each branch trunk and the corresponding branch point is calculated respectively; the Euclidean distance between the real-time acoustic feature data on each branch trunk and the corresponding branch point is compared with a preset distance threshold;

[0085] If the Euclidean distance between a certain real-time acoustic feature data on a branch trunk and the corresponding branch point is greater than a preset distance threshold, the real-time acoustic feature data is marked as noise data, and the noise data is removed on the corresponding branch trunk; repeat this step until the denoising process of the real-time acoustic feature data on all branch trunks is completed;

[0086] Each branch trunk is pruned to obtain several real-time acoustic feature data sets, and each real-time acoustic feature data on each real-time acoustic feature data set is sorted according to the acquisition timestamp to obtain several real-time acoustic feature dynamic data sets.

[0087] It should be noted that at several preset time nodes, a monitoring device is used to continuously collect real-time acoustic feature data of chemical anchors and chemical glue in embedded bars. The collected real-time acoustic feature data is stored in the database until the collection is completed. A decision tree model is constructed to determine the required number of classification data items. Several branch points are determined according to the required number of classification data items, and several branch trunks are cut out in the decision tree. The data feature information of each required classified real-time acoustic feature data is obtained. Corresponding feature attributes are assigned to each branch trunk according to the data feature information. Real-time acoustic feature data is obtained from the database, and the degree of membership between each real-time acoustic feature data and the feature attributes assigned to each branch trunk is calculated. Each real-time acoustic feature data is respectively assigned to the branch trunk with the highest degree of membership. Repeat this step until all data are assigned.

[0088] Then, calculate the Euclidean distance between the real-time acoustic feature data and the corresponding branch point on each branch trunk. Compare the Euclidean distance between the real-time acoustic feature data and the corresponding branch point on each branch trunk with the preset distance threshold. If the Euclidean distance between a certain real-time acoustic feature data and the corresponding branch point on a branch trunk is greater than the preset distance threshold, the data is marked as noise data and removed from the corresponding branch trunk. Repeat this step until the denoising process is completed on all branch trunks. By calculating the degree of membership and the Euclidean distance, noise data can be effectively identified and eliminated to improve data quality; denoising makes subsequent analysis more reliable and reduces the possibility of misjudgment.

[0089] Each branch trunk is trimmed to obtain several real-time acoustic feature data sets. Each real-time acoustic feature data on each real-time acoustic feature data set is sorted according to the acquisition timestamp to obtain several real-time acoustic feature dynamic data sets. Through trimming and sorting, a real-time acoustic feature dynamic data set is generated to facilitate subsequent time series analysis and trend prediction. The dynamic data set can be used in real-time monitoring and early warning systems to improve the response speed and accuracy of the system. The quality of the real-time acoustic feature data set can provide a reliable basis for the status evaluation of chemical glue.

[0090] In summary, this method realizes efficient monitoring and analysis of the state of chemical glue in chemical anchors and rebars through real-time acquisition, decision tree classification, denoising processing and dynamic data set generation, and improves the reliability and safety of the system.

[0091] Preferably, in a preferred embodiment of the present invention, the real-time acoustic characteristic dynamic data set is imported into the chemical glue working condition prediction model for prediction, and the probability value of the chemical glue occurrence of a preset working condition event is obtained; if the probability value of the chemical glue occurrence of a preset working condition event is greater than a preset probability threshold, an early warning message is generated, specifically:

[0092] Importing the real-time acoustic feature dynamic data set into the chemical glue working condition prediction model to perform prediction and obtain prediction results;

[0093] According to the prediction results, the probability value of the chemical anchor bolt and the embedded steel bar having a preset working condition event of the chemical glue after the preset time is obtained;

[0094] Compare the probability value of the chemical anchor bolt and the rebar undergoing a preset working condition event after the preset time with the preset probability threshold;

[0095] If the probability value of the chemical anchor bolt and the embedded steel bar causing the preset working condition event is greater than the preset probability threshold after the preset time, an early warning message is generated;

[0096] If the probability value of the preset working condition event of the chemical anchor bolt and the embedded steel bar after the preset time is not greater than the preset probability threshold, no warning information will be generated.

[0097] It should be noted that the real-time acoustic feature dynamic data set is imported into the chemical glue working condition prediction model for prediction. The prediction process is based on historical data and current real-time data, and the model calculates the probability value of the preset working condition event of the chemical glue in the future period of time. According to the prediction results, the probability value of the preset working condition event of the chemical anchor bolt and the implanted steel bar after the preset time is obtained. The obtained probability value is compared with the preset probability threshold. The preset probability threshold is a pre-set threshold used to determine whether it is necessary to generate an early warning message. If the probability value of the preset working condition event of the chemical anchor bolt and the implanted steel bar after the preset time is greater than the preset probability threshold, an early warning message is generated. If the probability value is not greater than the preset probability threshold, no early warning message is generated. The prediction model can predict future situations based on current data and provide timely early warning information; the early warning information can notify relevant personnel in advance to take preventive maintenance measures to avoid safety accidents or structural damage caused by the failure of chemical glue; preventive maintenance can extend the service life of chemical anchor bolts and implanted steel bars, improve the safety of buildings or infrastructure, and reduce the possibility of accidents.

[0098] The following steps are also included:

[0099] If the probability value of the preset working condition event occurring in the chemical glue of the chemical anchor bolt and the planted steel bar after the preset time is greater than the preset probability threshold, then the time node of the preset working condition event occurring in the chemical glue of the chemical anchor bolt and the planted steel bar is obtained according to the prediction result;

[0100] Determine several maintenance time periods based on the time nodes of the preset working condition events of the chemical glue, and obtain the predicted meteorological data information of the chemical anchor bolt and the rebar planting area in each maintenance time period based on the meteorological software;

[0101] Calculate the attention score between the predicted meteorological data information and the preset meteorological data in each maintenance time period based on the local sensitive attention mechanism; and sort the attention scores between the predicted meteorological data information and the preset meteorological data in each maintenance time period to obtain the sorting result;

[0102] A maximum attention score is obtained according to the ranking result, and a maintenance time period corresponding to the maximum attention score is obtained, and the maintenance time period corresponding to the maximum attention score is sent to a preset terminal as a recommended maintenance time period.

[0103] It should be noted that if the probability value of the preset working condition event of the chemical anchor bolt and the planted bar after the preset time is greater than the preset probability threshold, the specific time node of the preset working condition event of the chemical glue is obtained according to the prediction result. According to the time node of the preset working condition event of the chemical glue, several maintenance time periods are determined. Based on the meteorological software, the predicted meteorological data information of the area where the chemical anchor bolt and the planted bar are located in each maintenance time period is obtained. The meteorological data information includes temperature, humidity, wind speed, rainfall, etc. These factors will affect the feasibility and safety of the maintenance work. The attention score between the predicted meteorological data information and the preset meteorological data of each maintenance time period is calculated using the local sensitive attention mechanism. The local sensitive attention mechanism can identify which meteorological conditions are most favorable or unfavorable for the maintenance work. The attention scores between the predicted meteorological data information and the preset meteorological data of each maintenance time period are sorted and the sorting results are obtained. According to the sorting results, the maximum attention score is obtained, and the maintenance time period corresponding to the maximum attention score is found. The maintenance time period corresponding to the maximum attention score is used as the recommended maintenance time period. The recommended maintenance time period is sent to the preset terminal for reference and action by relevant personnel. By predicting the time point when the preset working condition events of the chemical glue occur, maintenance work can be planned in advance to avoid safety accidents or structural damage caused by the failure of the chemical glue; comprehensive consideration of meteorological data information can ensure that the maintenance work is carried out under the most suitable meteorological conditions, thereby improving the feasibility and success rate of the maintenance work.

[0104] In addition, the monitoring method may further include the following steps:

[0105] Acquire a real-time acoustic characteristic dynamic data set fed back by the chemical glue in the chemical anchor bolt and the implanted bar, and construct a real-time state model diagram of the chemical glue according to the real-time acoustic characteristic dynamic data set;

[0106] Performing feature extraction processing on the real-time state model graph to obtain defect feature information of the real-time state model graph, and obtaining a volume value of the defect in the real-time state model graph according to the defect feature information; wherein the defect feature information includes length, width, depth, and position information of the crack region and length, width, depth, and position information of the aging region;

[0107] The volume value of the defect in the real-time state model diagram is processed by ratio with the total volume value of the real-time state model diagram to obtain the defect ratio of the chemical glue; the defect ratio is compared with the first preset threshold and the second preset threshold; wherein the first preset threshold is greater than the second preset threshold;

[0108] If the defect ratio is greater than the first preset threshold, a warning message is generated and sent to a preset terminal; if the defect ratio is not greater than the second preset threshold, no warning message is generated;

[0109] If the defect ratio is less than the first preset threshold and greater than the second preset threshold, then continue to obtain the second real-time state model map of the chemical glue after a preset time period; perform registration processing on the real-time state model map and the second real-time state model map based on an iterative closest point algorithm;

[0110] After the registration is completed, the overlapping position area and the non-overlapping position area of ​​the defects at the same position of the real-time state model image and the second real-time state model image are obtained, and the expansion speed of the defects in the chemical glue is calculated according to the overlapping position area and the non-overlapping position area of ​​the defects at the same position;

[0111] If the expansion speed of the defect in the chemical glue is greater than the preset expansion speed, a warning message is generated and sent to a preset terminal; if the expansion speed of the defect in the chemical glue is not greater than the preset expansion speed, no warning message is generated.

[0112] It should be noted that a dynamic data set is formed by collecting the acoustic features (such as sound frequency, intensity, etc.) generated by the chemical glue during actual use. These data reflect the changes in the physical properties of the chemical glue under different conditions. Based on the acoustic feature data collected above, the system can construct a three-dimensional model diagram of the current state of the chemical glue. This model diagram not only shows the overall structure of the chemical glue, but also contains the defect information that may exist inside it. By performing feature extraction processing on the real-time state model diagram, the system can identify defects in the chemical glue, such as cracks, aging areas, etc., and calculate the specific parameters of these defects (such as length, width, depth, and position). Based on the extracted defect feature information, the system further calculates the volume value of each defect, which is crucial for assessing the severity of the defect. The defect ratio is calculated by calculating the ratio of the total volume of all defects in the chemical glue to the total volume of the chemical glue. This indicator is used to quantify the overall health of the chemical glue. When the defect ratio exceeds the first preset threshold, the system will generate an early warning message and notify the relevant personnel. If the defect ratio is lower than the second preset threshold, the chemical glue is considered to be in a safe state and no early warning is required. If the defect ratio is between the first preset threshold and the second preset threshold, the system will re-evaluate the state of the chemical glue after a certain period of time and check whether new defects appear or whether existing defects expand. When the defect ratio is between the two thresholds, the system will re-collect data at intervals and build a new state model diagram. By comparing the defect changes at the same position in the new and old model diagrams, the expansion speed of the defect is calculated. If it is found that the defect expansion speed is too fast, the early warning mechanism is triggered again. By real-time monitoring of the state changes of the chemical glue, measures can be taken at the early stage of the defect to avoid safety accidents caused by the failure of the chemical glue. Based on the monitoring results of the defect expansion speed, it is possible to more accurately determine when maintenance or replacement is required, thereby improving work efficiency and reducing unnecessary downtime. The detailed defect information and expansion speed data provided by the system provide valuable data support for subsequent improvement of material properties and optimization of construction technology. In summary, through advanced acoustic feature data acquisition and analysis technology, comprehensive monitoring of the state of chemical glue in chemical anchors and rebars is achieved, effectively improving the safety and reliability of engineering structures.

[0113] The second aspect of the present invention discloses an online monitoring device for the working performance of chemical anchor bolts and rebars, which is applied to any of the online monitoring methods for the working performance of chemical anchor bolts and rebars, such as Figure 3 As shown, including:

[0114] The monitoring device body 8001 is buried in the concrete during the initial construction of chemical anchor bolts and embedded steel bars, and is provided with an external wire that passes through the concrete to connect with external instruments;

[0115] Acoustic sensor module 8002, used to collect acoustic signals around chemical anchor bolts and rebars;

[0116] The signal acquisition and processing module 8003 amplifies, filters, digitizes, and processes the signals collected by the acoustic sensor, and converts them into data that can be analyzed;

[0117] The data analysis and processing module 8004 analyzes the collected acoustic characteristic data, extracts characteristic parameters related to chemical glue aging and cracks, and performs data modeling and early warning judgment;

[0118] The data storage and transmission module 8005 stores monitoring data and warning information, and transmits them to the monitoring center or other equipment.

[0119] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0120] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0121] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0122] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0123] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0124] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for online monitoring of the working performance of chemical anchor bolts and rebars, characterized in that: The following steps are involved: Acquire a shared monitoring logbook of chemical anchors and rebars through a big data network, and acquire various acoustic dynamic characteristic data corresponding to chemical glue in a preset time period before each preset working condition event occurs in the chemical anchors and rebars during the working process according to the shared monitoring logbook; A chemical glue working condition prediction model is constructed based on various acoustic dynamic characteristic data corresponding to the chemical glue in a preset time period before each preset working condition event occurs during the working process of the chemical anchor bolt and the implanted bar; The real-time acoustic characteristic data fed back by the chemical anchor bolts and the chemical glue in the embedded steel bar are continuously collected by the monitoring device at several preset time nodes, and the collected real-time acoustic characteristic data are classified and processed to obtain several real-time acoustic characteristic dynamic data sets; Importing the real-time acoustic characteristic dynamic data set into the chemical glue working condition prediction model for prediction, and obtaining the probability value of the chemical glue having a preset working condition event; If the probability value of a preset working condition event occurring in the chemical glue is greater than a preset probability threshold, an early warning message is generated and sent to a preset terminal.

2. The method for online monitoring of the working performance of chemical anchor bolts and rebars according to claim 1 is characterized in that: The shared monitoring logbook of chemical anchor bolts and planted steel bars is obtained through the big data network, and the acoustic dynamic characteristic data corresponding to the chemical glue in the preset time period before each preset working condition event occurs in the chemical anchor bolts and planted steel bars during the working process are obtained according to the shared monitoring logbook, specifically: Acquire the shared monitoring logbook of chemical anchor bolts and planted steel bars through the big data network, and acquire various working condition events of chemical anchor bolts and planted steel bars during the working process according to the shared monitoring logbook; Clustering is performed on various working condition events of chemical anchor bolts and rebar planting during the working process to generate clustering results, and preset working condition events are extracted from the clustering results; According to the shared monitoring log, various acoustic dynamic characteristic data corresponding to the chemical glue in a preset time period before each preset working condition event occurs during the working process of the chemical anchor bolt and the implanted bar are obtained; Among them, the preset operating conditions include the initial, mid-term and final conditions of chemical glue aging and chemical glue crack conditions; the acoustic dynamic characteristic data include the spectrum, frequency, amplitude, peak value, spectrum entropy and frequency band energy of the sound wave.

3. The method for online monitoring of the working performance of chemical anchor bolts and rebars according to claim 1 is characterized in that: According to the acoustic dynamic characteristic data corresponding to the chemical glue in the preset time period before the occurrence of each preset working condition event during the working process of the chemical anchor bolt and the implanted bar, a chemical glue working condition prediction model is constructed, specifically: Arrange various acoustic dynamic characteristic data corresponding to the chemical glue in a preset time period before the occurrence of a preset working condition event in chronological order to obtain a plurality of time series data sets; A conditional random field is introduced, each preset working condition event is defined as a random node of the conditional random field, and each time series data set is used as a conditional node of the conditional random field; Connect each conditional node with each random node, and obtain the path length and connection direction between each conditional node and each random node; Based on the grey correlation analysis method and combined with the path length and connection direction between each conditional node and each random node, the correlation strength between each conditional node and each random node is analyzed; According to the correlation strength between each conditional node and each random node, the conditional correlation probability between the acoustic feature data in each time series data set and each preset working condition event is determined; Constructing a conditional association probability table according to the conditional association probability between the acoustic feature data in each time series data set and each preset working condition event; Importing the conditional association probability table into the conditional random field for back propagation training; Through training, the training parameters of the conditional random field are obtained; After the training parameters converge to the preset values, the training parameters that converge to the preset values ​​are used as the final learning parameters of the conditional random field, and the chemical glue working condition prediction model is output.

4. The method for online monitoring of the working performance of chemical anchor bolts and rebars according to claim 1 is characterized in that: The real-time acoustic characteristic data fed back by the chemical anchor bolts and the chemical glue in the rebar are continuously collected by the monitoring device at several preset time nodes, and the collected real-time acoustic characteristic data are classified and processed to obtain several real-time acoustic characteristic dynamic data sets, specifically: Continuously collect the real-time acoustic characteristic data fed back by the chemical anchor bolts and the chemical glue in the embedded reinforcement at several preset time nodes, and store the collected real-time acoustic characteristic data in a database; Until the collection is completed; Constructing a decision tree, obtaining the number of data items of the real-time acoustic feature data to be classified, determining a number of branch points according to the number of data items of the real-time acoustic feature data to be classified, and dividing a number of branch trunks in the decision tree according to the branch points; Acquire data feature information of each required classification of real-time acoustic feature data, and assign corresponding feature attributes to each branch trunk according to the data feature information of each required classification of real-time acoustic feature data; Acquiring real-time acoustic feature data in the database, and calculating the degree of membership between each real-time acoustic feature data in the database and the characteristic attribute assigned to each branch trunk; Allocate each real-time acoustic feature data to the branch trunk with the highest membership, and repeat this step until all the real-time acoustic feature data in the database are allocated; After the allocation is completed, the Euclidean distance between the real-time acoustic feature data on each branch trunk and the corresponding branch point is calculated respectively; the Euclidean distance between the real-time acoustic feature data on each branch trunk and the corresponding branch point is compared with a preset distance threshold; If the Euclidean distance between a certain real-time acoustic feature data on a branch tree trunk and the corresponding branch point is greater than a preset distance threshold, the real-time acoustic feature data is marked as noise data, and the noise data is removed on the corresponding branch tree trunk; Repeat this step until the denoising process of the real-time acoustic feature data on all branches and trunks is completed; Each branch trunk is pruned to obtain several real-time acoustic feature data sets, and each real-time acoustic feature data on each real-time acoustic feature data set is sorted according to the acquisition timestamp to obtain several real-time acoustic feature dynamic data sets.

5. The method for online monitoring of the working performance of chemical anchor bolts and rebar planting according to claim 1 is characterized in that: The real-time acoustic characteristic dynamic data set is imported into the chemical glue working condition prediction model for prediction, and the probability value of the chemical glue having a preset working condition event is obtained; if the probability value of the chemical glue having a preset working condition event is greater than a preset probability threshold, an early warning message is generated, specifically: Importing the real-time acoustic feature dynamic data set into the chemical glue working condition prediction model to perform prediction and obtain prediction results; According to the prediction results, the probability value of the chemical anchor bolt and the embedded steel bar having a preset working condition event of the chemical glue after the preset time is obtained; Compare the probability value of the chemical anchor bolt and the rebar undergoing a preset working condition event after the preset time with the preset probability threshold; If the probability value of the chemical anchor bolt and the embedded steel bar causing the preset working condition event is greater than the preset probability threshold after the preset time, an early warning message is generated; If the probability value of the preset working condition event of the chemical anchor bolt and the embedded steel bar after the preset time is not greater than the preset probability threshold, no warning information will be generated.

6. The method for online monitoring of the working performance of chemical anchor bolts and rebars according to claim 1 is characterized in that: The following steps are also included: If the probability value of the preset working condition event occurring in the chemical glue of the chemical anchor bolt and the planted steel bar after the preset time is greater than the preset probability threshold, then the time node of the preset working condition event occurring in the chemical glue of the chemical anchor bolt and the planted steel bar is obtained according to the prediction result; Determine several maintenance time periods based on the time nodes of the preset working condition events of the chemical glue, and obtain the predicted meteorological data information of the chemical anchor bolt and the rebar planting area in each maintenance time period based on the meteorological software; Calculate the attention score between the predicted meteorological data information and the preset meteorological data in each maintenance time period based on the local sensitive attention mechanism; and sort the attention scores between the predicted meteorological data information and the preset meteorological data in each maintenance time period to obtain the sorting result; A maximum attention score is obtained according to the ranking result, and a maintenance time period corresponding to the maximum attention score is obtained, and the maintenance time period corresponding to the maximum attention score is sent to a preset terminal as a recommended maintenance time period.

7. An online monitoring device for the working performance of chemical anchor bolts and rebars, applied to the online monitoring method for the working performance of chemical anchor bolts and rebars as claimed in any one of claims 1 to 6, characterized in that: include: The main body of the monitoring device is buried in the concrete during the initial construction of chemical anchor bolts and rebars, and is equipped with an external wire that passes through the concrete to connect with external instruments; Acoustic sensor module, used to collect acoustic signals around chemical anchors and rebars; The signal acquisition and processing module amplifies, filters, digitizes, and processes the signals collected by the acoustic sensor and converts them into data that can be analyzed; The data analysis and processing module analyzes the collected acoustic feature data, extracts characteristic parameters related to chemical glue aging and cracks, and performs data modeling and early warning judgment; The data storage and transmission module stores monitoring data and warning information and transmits them to the monitoring center or other equipment.

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