Inspection robot for tunnel maintenance based on RFID technology

Abstract

The present invention belongs to the field of tunnel maintenance, and relates to data analysis technology, which is used to solve the problem that the existing tunnel maintenance inspection robot cannot perform processing decision analysis when the maintenance status inside the tunnel is abnormal. Specifically, it is an inspection robot for tunnel maintenance based on radio frequency identification technology, including a processor and a server connected to the processor in communication, the processor is connected to a data acquisition module, an instant analysis module and a storage module, and the server is connected to an identification layout module and a database; the instant analysis module is used to process and analyze the images collected by the data acquisition module; the identification layout module is used to perform identification terminal layout processing in the tunnel; the present invention can perform identification terminal layout processing in the tunnel, read and upload data through the set electronic tags, and collect images of the inner wall of the tunnel in combination with the data acquisition module. Provide data support for the analysis of the combination of instant data and overall data.

Description

Technical Field

[0001] The present invention belongs to the field of tunnel maintenance and relates to data analysis technology, in particular to an inspection robot for tunnel maintenance based on radio frequency identification technology. Background Art

[0002] Tunnel maintenance robots can realize fully automatic, high-speed and high-precision tunnel inspections. Through the sensors and visual cameras equipped, they can detect and monitor the internal structures, facilities, equipment, pipelines, etc. of the tunnel, and promptly detect various abnormal conditions such as leaks, cracks, damage, etc., so as to take timely measures for maintenance and repair.

[0003] Existing tunnel maintenance inspection robots are unable to combine instant analysis results with historical inspection data, resulting in one-sided output inspection results that cannot fully reflect the maintenance status inside the tunnel, nor can they perform processing decision analysis when the maintenance status inside the tunnel is abnormal. The treatment measures do not correspond to the problems inside the tunnel, resulting in poor treatment effects.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide a tunnel maintenance inspection robot based on radio frequency identification technology, which is used to solve the problem that the existing tunnel maintenance inspection robot cannot perform processing decision analysis when the maintenance status inside the tunnel is abnormal;

[0006] The technical problem to be solved by the present invention is: how to provide a tunnel maintenance inspection robot based on radio frequency identification technology that can perform processing decision analysis when the maintenance status inside the tunnel is abnormal.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An inspection robot for tunnel maintenance based on radio frequency identification technology, comprising a processor and a server in communication with the processor, wherein the processor is in communication with a data acquisition module, an instant analysis module and a storage module, and the server is in communication with an identification layout module and a database;

[0009] The instant analysis module is used to process and analyze the images collected by the data collection module: mark the received images as processing objects, obtain the instant coefficient JS of the processing objects, and mark the processing objects as normal objects, risk objects or dangerous objects according to the instant coefficient JS;

[0010] The maintenance coefficient YH is obtained by numerically calculating the number of dangerous objects, risk objects and treatment objects that have been marked in the inspection area; the maintenance coefficient YH is used to determine whether the instant analysis results of the inspection area meet the requirements;

[0011] The processor is also communicatively connected to a maintenance decision module, and the maintenance decision module is used to perform maintenance decision analysis when the inspection robot reaches the end point of the inspection area.

[0012] As a preferred embodiment of the present invention, the identification layout module is used to perform identification terminal layout processing in the tunnel: the tunnel is divided into several inspection areas, and electronic tags are set at the end points of the inspection areas, and the electronic tags are used to upload and read inspection data; the database is used to store the inspection data uploaded by the electronic tags.

[0013] As a preferred embodiment of the present invention, the data acquisition module is used to collect images of the inner wall of the tunnel: after the inspection robot enters the inspection area, images of the inner wall of the tunnel are captured every L1 seconds and the captured images are sent to the instant analysis module.

[0014] As a preferred embodiment of the present invention, the process of obtaining the instantaneous coefficient JS of the processing object includes: enlarging the processing object into a pixel grid image and performing grayscale transformation, extracting the number of cracks, the number of water seepage areas and the number of block loss areas in the processing object through image recognition technology and marking them as crack values ​​LW, water seepage values ​​SS and block loss values ​​DK respectively; using the formula The instantaneous coefficient JS of the processing object is obtained, wherein p1, p2 and p3 are all proportional coefficients, and p1>p2>p3>1.

[0015] As a preferred embodiment of the present invention, the specific process of marking the processing object as a normal object, a risk object or a dangerous object includes: obtaining the instantaneous thresholds JSmin and JSmax through the storage module, and comparing the instantaneous coefficient JS of the processing object with the instantaneous thresholds JSmin and JSmax: if JS≤JSmin, the processing object is marked as a normal object; if JSmin<JS<JSmax, the processing object is marked as a risk object; if JS≥JSmax, the processing object is marked as a dangerous object.

[0016] As a preferred embodiment of the present invention, the specific process of determining whether the instant analysis results of the inspection area meet the requirements includes: obtaining the maintenance threshold YHmax through the storage module, and comparing the maintenance coefficient YH with the maintenance threshold YHmax: if the maintenance coefficient YH is less than the maintenance threshold YHmax, then it is determined that the instant analysis results of the inspection area meet the requirements; if the maintenance coefficient YH is greater than or equal to the maintenance threshold YHmax, then it is determined that the instant analysis results of the inspection area do not meet the requirements, generate an instant maintenance signal and send the instant maintenance signal to the mobile phone terminal of the management personnel.

[0017] As a preferred embodiment of the present invention, the specific process of the maintenance decision analysis performed by the maintenance decision module includes: when the inspection robot reaches the end point of the inspection area, the instantaneous coefficients JS of all processing objects in the inspection area are summed to obtain the overall coefficient of the inspection area, and then the overall coefficient of the inspection area at the time of the last inspection is read from the database of the server by scanning the electronic tag, and the difference between the current overall coefficient and the overall coefficient at the time of the last inspection is marked as the growth coefficient of the inspection area, and whether the tunnel maintenance status of the inspection area meets the requirements is judged by the overall coefficient and the growth coefficient; then the current overall coefficient is uploaded to the database of the server through the electronic tag for storage.

[0018] As a preferred embodiment of the present invention, the specific process of determining whether the tunnel maintenance status of the inspection area meets the requirements includes: obtaining the overall threshold and the growth threshold through the storage module, and comparing the current overall coefficient and the growth coefficient with the overall threshold and the growth threshold respectively: if the overall coefficient is less than the overall threshold and the growth coefficient is less than the overall threshold, then it is determined that the tunnel maintenance status of the inspection area meets the requirements; if the overall coefficient is greater than or equal to the overall threshold and the growth coefficient is less than the growth threshold, then it is determined that the tunnel maintenance status of the inspection area does not meet the requirements, a maintenance training signal is generated and the maintenance training signal is sent to the mobile phone terminal of the manager; otherwise, it is determined that the tunnel maintenance status of the inspection area does not meet the requirements, a maintenance processing signal is generated and the maintenance processing signal is sent to the mobile phone terminal of the manager.

[0019] The present invention has the following beneficial effects:

[0020] The identification and layout module can be used to arrange identification terminals in the tunnel, read and upload data through the set electronic tags, and collect images of the inner wall of the tunnel in combination with the data acquisition module. This provides data support for the analysis of real-time data combined with overall data;

[0021] The instant analysis module can process and analyze images, and combine image recognition technology to extract and analyze multiple parameters in the processing object to obtain instant coefficients, so as to differentiate the processing object according to the instant coefficients, and then perform numerical calculations on the differentiated marking results to obtain the maintenance coefficients, and evaluate the necessity of instant maintenance of the tunnel through the maintenance coefficients;

[0022] The maintenance decision module can be used to perform maintenance decision analysis when the inspection robot reaches the end point of the inspection area. The overall coefficient and growth coefficient are obtained by combining the real-time coefficients of all processing objects in the inspection area with the data of the previous inspection. The overall coefficient and growth coefficient are used to provide feedback on the abnormal degree of the tunnel inner wall maintenance status in various dimensions, thereby improving the accuracy of the tunnel maintenance inspection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 is a system block diagram of Embodiment 1 of the present invention;

[0025] Figure 2 is a system block diagram of Embodiment 2 of the present invention;

[0026] Figure 3 This is a flow chart of the method of embodiment 3 of the present invention. DETAILED DESCRIPTION

[0027] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Embodiment 1

[0028] like Figure 1 As shown, the inspection robot for tunnel maintenance based on radio frequency identification technology includes a processor and a server connected to the processor in communication, the processor is connected to a data acquisition module, an instant analysis module and a storage module in communication, and the server is connected to an identification layout module and a database in communication.

[0029] The identification layout module is used to perform identification terminal layout processing in the tunnel: the tunnel is divided into several inspection areas, and electronic tags are set at the end points of the inspection areas. The electronic tags are used to upload and read inspection data; the database is used to store the inspection data uploaded by the electronic tags.

[0030] The data acquisition module is used to collect images of the inner wall of the tunnel: after the inspection robot enters the inspection area, it takes images of the inner wall of the tunnel every L1 seconds and sends the images to the real-time analysis module; the identification terminal is arranged in the tunnel, and the data is read and uploaded through the set electronic tags, and the images of the inner wall of the tunnel are collected in combination with the data acquisition module. This provides data support for the analysis of the combination of real-time data and overall data.

[0031] The real-time analysis module is used to process and analyze the image: mark the received image as the processing object, enlarge the processing object into a pixel grid image and perform grayscale transformation, extract the number of cracks, water seepage areas and block loss areas in the processing object through image recognition technology and mark them as crack value LW, water seepage value SS and block loss value DK respectively; use the formula The instant coefficient JS of the processing object is obtained, where p1, p2 and p3 are all proportional coefficients, and p1>p2>p3>1; the instant thresholds JSmin and JSmax are obtained through the storage module, and the instant coefficient JS of the processing object is compared with the instant thresholds JSmin and JSmax: if JS≤JSmin, the processing object is marked as a normal object; if JSmin<JS<JSmax, the processing object is marked as a risk object; if JS≥JSmax, the processing object is marked as a dangerous object; the maintenance coefficient YH of the inspection area is obtained through the formula YH=(k1×WX+k2×FX) / CL, where k1 and k2 are all proportional coefficients, and k1>k2>1, WX, FX and CL are the dangerous objects and risk objects that have been marked in the inspection area, respectively. The number of dangerous objects and processing objects; the maintenance threshold value YHmax is obtained through the storage module, and the maintenance coefficient YH is compared with the maintenance threshold value YHmax: if the maintenance coefficient YH is less than the maintenance threshold value YHmax, it is determined that the instant analysis result of the inspection area meets the requirements; if the maintenance coefficient YH is greater than or equal to the maintenance threshold value YHmax, it is determined that the instant analysis result of the inspection area does not meet the requirements, and an instant maintenance signal is generated and sent to the mobile phone terminal of the manager; the image is processed and analyzed, and multiple parameters in the processing object are extracted and analyzed in combination with image recognition technology to obtain the instant coefficient, so that the processing object is differentially marked according to the instant coefficient, and then the differential marking result is numerically calculated to obtain the maintenance coefficient, and the necessity of instant maintenance of the tunnel is evaluated through the maintenance coefficient. Embodiment 2

[0032] like Figure 2As shown, the processor is also communicatively connected to a maintenance decision module, and the maintenance decision module is used to perform maintenance decision analysis when the inspection robot reaches the end point of the inspection area: when the inspection robot reaches the end point of the inspection area, the instantaneous coefficients JS of all processing objects in the inspection area are summed to obtain the overall coefficient of the inspection area, and then the overall coefficient of the inspection area at the time of the last inspection is read from the database of the server by scanning the electronic tag, and the difference between the current overall coefficient and the overall coefficient at the time of the last inspection is marked as the growth coefficient of the inspection area, and the overall threshold and the growth threshold are obtained through the storage module, and the current overall coefficient and the growth coefficient are compared with the overall threshold and the growth threshold respectively: if the overall coefficient is less than the overall threshold and the growth coefficient is less than the overall threshold, it is determined that the tunnel maintenance status of the inspection area meets the requirements; if the overall If the coefficient is greater than or equal to the overall threshold and the growth coefficient is less than the growth threshold, it is determined that the tunnel maintenance status in the inspection area does not meet the requirements, a maintenance training signal is generated and sent to the mobile terminal of the manager; otherwise, it is determined that the tunnel maintenance status in the inspection area does not meet the requirements, a maintenance processing signal is generated and sent to the mobile terminal of the manager; then the current overall coefficient is uploaded to the database of the server through the electronic tag for storage; when the inspection robot reaches the end of the inspection area, a maintenance decision analysis is performed, and the overall coefficient and growth coefficient are obtained by combining the instant coefficients of all processing objects in the inspection area with the last inspection data. The overall coefficient and growth coefficient are used to feedback the abnormality of the tunnel inner wall maintenance status in various dimensions, thereby improving the accuracy of the tunnel maintenance inspection results. Embodiment 3

[0033] like Figure 3 As shown, the working method of the inspection robot for tunnel maintenance based on radio frequency identification technology includes the following steps:

[0034] Step 1: Arrange identification terminals in the tunnel: divide the tunnel into several inspection areas and set electronic tags at the end points of the inspection areas;

[0035] Step 2: Collect images of the inner wall of the tunnel: After the inspection robot enters the inspection area, it takes images of the inner wall of the tunnel every L1 seconds;

[0036] Step 3: Process and analyze the image: mark the received image as a processing object, obtain the instant coefficient JS of the processing object, and mark the processing object as a normal object, a risk object or a dangerous object through the instant coefficient JS;

[0037] Step 4: Numerical calculation is performed on the number of dangerous objects, risk objects and treatment objects that have been marked in the inspection area to obtain the maintenance coefficient YH, and the maintenance coefficient YH is used to determine whether the instant analysis results of the inspection area meet the requirements;

[0038] Step 5: When the inspection robot reaches the end of the inspection area, a maintenance decision analysis is performed to obtain the overall coefficient and the growth coefficient. The overall coefficient and the growth coefficient are used to determine whether the tunnel maintenance status of the inspection area meets the requirements.

[0039] The inspection robot for tunnel maintenance based on radio frequency identification technology divides the tunnel into several inspection areas when working, and sets electronic tags at the end points of the inspection areas; after the inspection robot enters the inspection area, it takes images of the inner wall of the tunnel every L1 seconds; marks the received image as a processing object, obtains the instant coefficient JS of the processing object, and marks the processing object as a normal object, a risk object or a dangerous object through the instant coefficient JS; numerically calculates the number of marked dangerous objects, risk objects and processing objects in the inspection area to obtain the maintenance coefficient YH, and determines whether the instant analysis results of the inspection area meet the requirements through the maintenance coefficient YH; when the inspection robot reaches the end point of the inspection area, it performs maintenance decision analysis and obtains the overall coefficient and growth coefficient, and determines whether the tunnel maintenance status of the inspection area meets the requirements through the overall coefficient and growth coefficient.

[0040] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0041] The above formulas are obtained by collecting a large amount of data and performing software simulation to select a formula close to the actual value. The coefficients in the formula are set by technicians in this field according to actual conditions; for example: Formula ; A technician in this field collects multiple groups of sample data and sets corresponding instantaneous coefficients for each group of sample data; Substitute the set instantaneous coefficients and the collected sample data into the formula, any three formulas constitute a three-variable linear equation system, screen the calculated coefficients and take the average, and obtain the values ​​of p1, p2 and p3, which are 4.42, 2.85 and 2.63 respectively;

[0042] The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the initial setting of the corresponding instantaneous coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantified value, such as the instantaneous coefficient is proportional to the crack value.

[0043] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0044] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A patrol robot for tunnel maintenance based on radio frequency identification technology, characterized in that: It includes a processor and a server in communication with the processor, wherein the processor is in communication with a data acquisition module, an instant analysis module and a storage module, and the server is in communication with an identification and arrangement module and a database; The instant analysis module is used to process and analyze the images collected by the data collection module: mark the received images as processing objects, obtain the instant coefficient JS of the processing objects, and mark the processing objects as normal objects, risk objects or dangerous objects according to the instant coefficient JS; The maintenance coefficient YH is obtained by numerically calculating the number of dangerous objects, risk objects and treatment objects that have been marked in the inspection area; the maintenance coefficient YH is used to determine whether the instant analysis results of the inspection area meet the requirements; The processor is also communicatively connected to a maintenance decision module, and the maintenance decision module is used to perform maintenance decision analysis when the inspection robot reaches the end point of the inspection area; The process of obtaining the instantaneous coefficient JS of the processing object includes: enlarging the processing object into a pixel grid image and performing grayscale transformation, extracting the number of cracks, water seepage areas and block loss areas in the processing object through image recognition technology and marking them as crack value LW, water seepage value SS and block loss value DK respectively; Get the instantaneous coefficient JS of the processing object, where p1, p2 and p3 are all proportional coefficients, and p1>p2>p3>1; The specific process of marking the processing object as a normal object, a risk object or a dangerous object includes: obtaining the instantaneous thresholds JSmin and JSmax through the storage module, and comparing the instantaneous coefficient JS of the processing object with the instantaneous thresholds JSmin and JSmax: if JS≤JSmin, the processing object is marked as a normal object; if JSmin<JS<JSmax, the processing object is marked as a risk object; if JS≥JSmax, the processing object is marked as a dangerous object; The identification arrangement module is used to perform identification terminal arrangement processing in the tunnel: the tunnel is divided into several inspection areas, and electronic tags are set at the end points of the inspection areas. The electronic tags are used to upload and read inspection data; the database is used to store the inspection data uploaded by the electronic tags.

2. The inspection robot for tunnel maintenance based on radio frequency identification technology according to claim 1 is characterized in that: The data acquisition module is used to collect images of the inner wall of the tunnel: after the inspection robot enters the inspection area, it takes images of the inner wall of the tunnel every L1 seconds and sends the captured images to the instant analysis module.

3. The inspection robot for tunnel maintenance based on radio frequency identification technology according to claim 2 is characterized in that: The specific process of determining whether the instant analysis results of the inspection area meet the requirements includes: obtaining the maintenance threshold YHmax through the storage module, and comparing the maintenance coefficient YH with the maintenance threshold YHmax: if the maintenance coefficient YH is less than the maintenance threshold YHmax, it is determined that the instant analysis results of the inspection area meet the requirements; if the maintenance coefficient YH is greater than or equal to the maintenance threshold YHmax, it is determined that the instant analysis results of the inspection area do not meet the requirements, and an instant maintenance signal is generated and sent to the mobile phone terminal of the management personnel.

4. The inspection robot for tunnel maintenance based on radio frequency identification technology according to claim 3 is characterized in that: The specific process of the maintenance decision analysis performed by the maintenance decision module includes: when the inspection robot reaches the end point of the inspection area, the instantaneous coefficients JS of all processing objects in the inspection area are summed to obtain the overall coefficient of the inspection area, and then the overall coefficient of the inspection area at the time of the last inspection is read from the database of the server by scanning the electronic tag, and the difference between the current overall coefficient and the overall coefficient at the time of the last inspection is marked as the growth coefficient of the inspection area. The overall coefficient and the growth coefficient are used to determine whether the tunnel maintenance status of the inspection area meets the requirements; then the current overall coefficient is uploaded to the database of the server through the electronic tag for storage.

5. The inspection robot for tunnel maintenance based on radio frequency identification technology according to claim 4 is characterized in that: The specific process of determining whether the tunnel maintenance status of the inspection area meets the requirements includes: obtaining the overall threshold and the growth threshold through the storage module, and comparing the current overall coefficient and the growth coefficient with the overall threshold and the growth threshold respectively: if the overall coefficient is less than the overall threshold and the growth coefficient is less than the overall threshold, then it is determined that the tunnel maintenance status of the inspection area meets the requirements; if the overall coefficient is greater than or equal to the overall threshold and the growth coefficient is less than the growth threshold, then it is determined that the tunnel maintenance status of the inspection area does not meet the requirements, a maintenance training signal is generated and the maintenance training signal is sent to the mobile phone terminal of the manager; otherwise, it is determined that the tunnel maintenance status of the inspection area does not meet the requirements, a maintenance processing signal is generated and the maintenance processing signal is sent to the mobile phone terminal of the manager.

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