Intelligent bridge inspection and evaluation system and inspection and evaluation method

Abstract

The invention relates to a bridge intelligent inspection and evaluation system and inspection and evaluation method, and the system comprises a cluster bridge multi-source information database building module which is used for building a cluster bridge multi-source information database according to the collected bridge design, construction, maintenance, disease and service environment information; the intelligent inspection equipment is used for identifying and positioning a to-be-evaluated bridge, shooting a disease picture, identifying the type and position of the disease, and uploading the identified disease information to the cluster bridge multi-source information database; and the cloud assessment module performs risk dynamic assessment based on the Bayesian network on the to-be-assessed bridge by using the cluster bridge multi-source information stored in the cluster bridge multi-source information database. Visual interference factors in photos can be greatly reduced through manual photographing, the imaging quality is improved, the intelligent inspection equipment can automatically recognize the types and positions of diseases, inspection personnel do not need to manually input disease information, the data input standardization can be improved, and the recognition accuracy is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge detection and evaluation, and in particular to an intelligent bridge inspection and evaluation system and an inspection and evaluation method. Background Art

[0002] With the development of society and economy, the demand for transportation is increasing, the number and weight of vehicles passing through are growing rapidly, and bridge defects are increasing. As bridges gradually transition from the new construction period to the maintenance period, the bridge inspection and maintenance work is increasing. Data show that when the inspection tasks increase, the number of reported bridge defect information will decrease relatively, which is extremely unfavorable for bridge operation and maintenance.

[0003] In the related technologies, in terms of intelligent inspection, some teams advocate the development of drones, inspection robots, etc. for fully automated inspection and identification. However, at present, under the premise of requiring high recognition precision and accuracy, machine learning algorithms are still not suitable for automatic identification in large fields of view and complex environments, and drones and inspection robots are still inconvenient to operate and cannot meet all environmental detection tasks. The commonly used information inspection method is to develop mobile inspection clients, but it still requires manual photography, input of bridge information, disease information, etc. Due to the different habits and work attitudes of inspection personnel, the background data description is not standardized and unified, and there are even many errors.

[0004] Therefore, it is necessary to design a new bridge intelligent inspection and evaluation system and inspection and evaluation method to overcome the above problems. Summary of the invention

[0005] The embodiments of the present invention provide an intelligent bridge inspection and evaluation system and inspection and evaluation method to solve the problems that drones and inspection robots in related technologies still have inconveniences in operation and cannot meet all environmental detection tasks. In addition, the use of mobile inspection clients will result in non-standard and inconsistent background data descriptions and even many errors due to different habits and work attitudes of inspection personnel.

[0006] In a first aspect, a bridge intelligent inspection and evaluation system is provided, which includes: a cluster bridge multi-source information database building module, the cluster bridge multi-source information database building module is used to build a cluster bridge multi-source information database based on collected bridge design, construction, maintenance, disease and service environment information; intelligent inspection equipment, the intelligent inspection equipment is used to identify and locate the bridge to be evaluated, take photos of the disease, and identify the type and location of the disease, and upload the identified disease information to the cluster bridge multi-source information database; an evaluation module, the evaluation module includes a cloud-based evaluation module, the cloud-based evaluation module uses the cluster bridge multi-source information stored in the cluster bridge multi-source information database to perform a Bayesian network-based dynamic risk evaluation of the bridge to be evaluated.

[0007] In some embodiments, the intelligent inspection device assists in identifying the current inspection bridge based on the geographic positioning system; or, the intelligent inspection device automatically scans the QR code of the bridge based on the camera to realize the identification of the current inspection bridge; or, realizes the automatic identification of the current inspection bridge based on Bluetooth and NFC chips; after completing the identification of the current bridge, the basic information, structural form, historical disease information, and historical maintenance records of the inspection bridge are automatically read. In some embodiments, the intelligent inspection device is provided with a shooting button. When the shooting button is clicked, the intelligent inspection device takes multiple disease photos within a preset time and records the data of the accelerometer and gyroscope when each disease photo is taken; the intelligent inspection device is also provided with a bridge disease intelligent visual recognition software module. The intelligent inspection device intelligently identifies the location of the disease in the picture and the type of the disease based on the bridge disease intelligent visual recognition software module and the disease photos taken; the intelligent inspection device is also used to calculate the size information of the disease based on the data of the accelerometer and gyroscope using the ARKit algorithm.

[0008] In some embodiments, the intelligent inspection device is also used to display the type, location and size information of the disease on the screen of the intelligent inspection device, and prompt manual confirmation whether the disease identification is accurate; if accurate, the disease information is uploaded to the cluster bridge multi-source information database; otherwise, the disease information is uploaded to the cloud for manual review and correction before being uploaded to the cluster bridge multi-source information database.

[0009] In some embodiments, the intelligent bridge inspection and evaluation system also includes a defect analysis module, which is used to analyze and determine whether the defect is a historical defect when the defect identification is manually confirmed to be accurate; if so, the current defect information is recorded in the matched historical defect information and uploaded to the cluster bridge multi-source information database; otherwise, a new defect is created and uploaded to the cluster bridge multi-source information database.

[0010] In some embodiments, the bridge intelligent inspection and evaluation system also includes an intelligent recognition training module, which is used to receive the damage information after manual review and annotation in the cloud, incorporate it into the machine learning model for training, and then dynamically update the bridge damage intelligent visual recognition software module.

[0011] In some embodiments, the evaluation module is used to calculate the bridge information integrity of the bridge to be evaluated based on the cluster bridge multi-source information stored in the cluster bridge multi-source information database, and determine whether the bridge information integrity meets the preset requirements; if so, enter the Bayesian network to evaluate the bridge status and give maintenance measures; otherwise, correct the current bridge evaluation result by using similar cluster bridges; The formula for bridge information completeness is as follows: , in, : No. Bridge information completeness score, : No. The bridge information weight, : No. Bridge No. Item information update time, : No. Bridge No. The actual situation of the information, : No. Bridge No. Full score standard for item information.

[0012] In a second aspect, a bridge intelligent inspection and evaluation method is provided, which comprises the following steps: Handheld intelligent inspection equipment can identify and locate the bridges to be assessed and take photos of the defects; Based on the disease photos, the bridge disease intelligent visual recognition software module is used to intelligently identify the type and location of the disease, and the identified disease information is uploaded to the cluster bridge multi-source information database; The status of the bridge to be evaluated is evaluated based on the Bayesian network and the bridge information stored in the cluster bridge multi-source information database.

[0013] In some embodiments, the evaluating the state of the bridge to be evaluated based on the Bayesian network and the bridge information stored in the cluster bridge multi-source information database includes: Calculating the bridge information integrity of the bridge to be evaluated based on the bridge information stored in the cluster bridge multi-source information database, and judging whether the bridge information integrity meets the preset requirements; If yes, then use Bayesian network to evaluate the bridge status and give maintenance measures; otherwise, evaluate the most similar cluster bridges and correct the bridge information completeness; The most similar cluster bridge assessment revisions include: Determine whether there is a bridge in the cluster bridge multi-source information database that has the highest similarity to the bridge to be evaluated; If so, the evaluation result of the bridge to be evaluated is modified according to the bridge with the highest similarity in the multi-source information database of the cluster bridges; Otherwise, a Bayesian network status assessment is performed on the bridge to be assessed, and it is prompted that the bridge information is incomplete and the assessment is potentially inaccurate.

[0014] In some embodiments, the cluster bridge similarity calculation formula is as follows: , in, : The similarity score between the bridge and the most similar bridge (span) in the cluster bridges, : Similarity score of two bridges, : Take the maximum value among the multiple values ​​in brackets. : No. The bridge information weight, Bridge Cluster Bridge No. Item information, : The bridge cluster Bridge No. Time when the information is entered. : The bridge cluster Age of bridges; When corrected: , Where: : Final score of bridge assessment; : Bridge information completeness score; : Similarity score between the bridge and the most similar bridge (span) in the cluster bridges; :and The bridge with the highest degree of similarity Service risk assessment scores (based on Bayesian networks); : Bridge Service risk assessment scores (based on Bayesian networks); When not corrected: , Where: : Final score of bridge assessment; : Bridge information completeness score.

[0015] The beneficial effects brought about by the technical solution provided by the present invention include: The embodiment of the present invention provides a bridge intelligent inspection and evaluation system and inspection and evaluation method. Since the inspectors can take pictures of the defects with the intelligent inspection equipment, manual photography can better eliminate visual interference factors and ensure the shooting quality. In addition, the intelligent inspection equipment can automatically identify the type and location of the defects, and the inspectors do not need to manually enter the defect information, which can avoid data irregularities caused by different habits and work attitudes of the inspectors. In addition, the complexity of the work of the inspectors is reduced, the work enthusiasm is improved, and the false alarm rate and missed alarm rate of the inspection defects are effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0017] Figure 1 A schematic diagram of the structure of an intelligent bridge inspection and evaluation system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not 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.

[0019] With the advancement of informatization, bridge information is becoming more and more abundant. However, in bridge assessment work, multi-source information has not been fully utilized. For example, the data of the bridge health monitoring system generally sets a single threshold as the warning line, which has the problem of high false alarm rate. At present, bridge assessment often relies on regular inspection reports, and a large number of bridges (spans) with similar structural forms and service environments have not been subjected to correlation analysis, resulting in high inspection costs.

[0020] How to accurately and efficiently complete bridge inspection work, and from the perspective of cluster bridges, efficiently associate and integrate multi-source heterogeneous big data of bridges has become an important research direction.

[0021] The embodiments of the present invention provide an intelligent bridge inspection and evaluation system and inspection and evaluation method, which can solve the problems that drones and inspection robots in related technologies still have inconveniences in operation and cannot meet all environmental detection tasks. In addition, the use of mobile phone inspection clients will result in non-standard and inconsistent background data descriptions, or even errors, due to different habits and work attitudes of inspection personnel. Due to the cumbersome work process, it is also prone to a high rate of underreporting.

[0022] See also Figure 1As shown, a bridge intelligent inspection and evaluation system provided by an embodiment of the present invention may include: a cluster bridge multi-source information database building module, the cluster bridge multi-source information database building module is used to build or improve the cluster bridge multi-source information database according to the collected bridge design, construction, maintenance, disease and service environment information, so that the system can be applicable with or without the cluster bridge multi-source information database; intelligent inspection equipment, the intelligent inspection equipment is used to identify and locate the bridge to be evaluated, take photos of the disease, and identify the type and location of the disease, and upload the identified disease information to the cluster bridge multi-source information database; and an evaluation module, the evaluation module includes a cloud-based evaluation module, the cloud-based evaluation module is used to perform a Bayesian network-based risk dynamic evaluation of the bridge to be evaluated based on the cluster bridge multi-source information stored in the cluster bridge multi-source information database.

[0023] In this embodiment, the intelligent inspection device can be a handheld device. Since the inspection personnel can use the intelligent inspection device to take pictures of the defects, manual photography can ensure good lighting conditions and eliminate visual interference factors. The intelligent inspection device can narrow the matching range according to the historical defect information of the current inspected bridge (where the inspected bridge is also the bridge to be evaluated), thereby improving the recognition accuracy. The intelligent inspection device can automatically identify the type and location of the defect, and there is no need for the inspection personnel to manually enter the defect information, thereby reducing the inspection workload. It can largely avoid data omissions, misreporting, non-standardization and other problems caused by different habits and work attitudes of the inspection personnel, further improving the accuracy of inspection data reporting.

[0024] Furthermore, in the absence of a multi-source information database for cluster bridges, during the early inspection process, the inspection personnel and cloud (back-end) staff can jointly improve the bridge information and build a multi-source information database for cluster bridges. Specifically, the cloud (back-end) staff can first collect bridge information and establish bridge archives (structural form, bridge age, span, BCI level, etc.), and then improve the bridge database during the inspection process. The inspection personnel match the bridge name during the inspection, create new defects and take photos (indicate the size and specific location of the defect), and upload the inspected bridge name and defect information to the cloud; then the back-end will manually process it, mainly including manual annotation of defects (classifying defects, marking defect areas in pictures for machine learning training), annotating bridge information (including bridge design, construction data, regular bridge inspections, etc.), and associating bridge monitoring systems (if any). During the database construction period, the main work is completed by cloud staff, and the workload of front-line inspection and maintenance personnel remains basically unchanged. Finally, with the progress of the daily inspection and maintenance work of urban cluster bridges, the construction and improvement of the multi-source information database for cluster bridges will be gradually completed.

[0025] Preferably, the intelligent inspection device can be based on a mature smartphone platform and used in conjunction with a smart inspection APP. The intelligent inspection device can have a camera with more than 24 million pixels, a high-precision accelerometer and gyroscope, support 5G communication, and can also expand fill light equipment and laser ranging equipment through a data interface. The expansion device can be attached closely to the device for easy use by inspection personnel.

[0026] In some embodiments, when identifying and locating the bridge to be evaluated, an intelligent bridge positioning method can be used. The main methods can be: (1) The intelligent inspection device can assist in identifying the current inspection bridge based on the geographic positioning system and automatically read the current inspection bridge information; for example, through the mobile phone GNSS positioning system, determine the current inspection bridge, and automatically read the bridge information, narrow the scope of bridge defects, and improve the accuracy of AI visual recognition. Or, (2) The intelligent inspection device can automatically scan the QR code of the current inspection bridge based on the camera for identification and positioning; that is, the bridge guardrail, beam bottom, bridge pier, etc. will be posted with a QR code. Before clicking on the defect to shoot, the camera is in the on state, and can automatically and real-time identify the QR code posted nearby, accurately determine the current inspection bridge (span) and the precise inspection location. Or, (3) Based on Bluetooth and NFC chips, the automatic identification of the current inspection bridge is realized to determine the inspection location. After completing the identification of the current bridge, the basic information, structural form, historical disease information, historical maintenance records and other multi-source information of the inspection bridge are automatically read. Of course, in other embodiments, manual input may be used. For areas where the GNSS system may have positioning deviations or no GPS signals, manual selection may be used to determine the current inspection bridge.

[0027] In some optional embodiments, the intelligent inspection device may be provided with a shooting button. When the inspection personnel click the shooting button, the intelligent inspection device may take multiple disease photos within a preset time, and record the data of the accelerometer and gyroscope when each disease photo is taken. For example, multiple photos can be automatically and continuously taken within 1-2 seconds; the APP of the intelligent inspection device may also be provided with a bridge disease intelligent visual recognition software module, wherein the model may be a bridge disease intelligent visual recognition software module based on machine vision, so that the intelligent inspection device may intelligently identify the location of the disease in the picture and the type of the disease based on the bridge disease intelligent visual recognition software module and the taken disease photos; the APP of the intelligent inspection device may also calculate the size information of the disease based on the data of the accelerometer and gyroscope and the ARKit algorithm.

[0028] In this embodiment, the ARKit algorithm is implemented based on the principle of visual odometry as follows: ARKit is a visual inertial odometry (VIO) system with simple 2D plane detection capabilities. The VIO system tracks the distance the device moves in 6D space, where 6D represents xyz movement (conversion) in the 3D world, plus 3D movement (rotation) of pitch / yaw / roll. VIO technology can track the position of the device in space in real time through software. The user's posture can be tracked through the visual (camera) system, which requires matching a point in the real world with a pixel on each frame captured by the camera sensor. In addition, the user's posture needs to be tracked through an inertial system (accelerometer and gyroscope). The output results of these systems are then merged through the Kalman Filter to determine which system can provide the best estimate of the user's "actual" position, and the position update is published through the ARKit SDK. The coordinates given by the ARKit algorithm can be used to solve the distance between the two points in the image.

[0029] In some embodiments, the intelligent inspection device is also used to display identification information such as the type, location, and size of the disease on the screen of the intelligent inspection device, and prompt manual confirmation whether the disease identification is accurate; if accurate, the disease information is uploaded to the multi-source information database of the cluster bridges; otherwise, the disease information is uploaded to the cloud for manual review and correction before being uploaded to the multi-source information database of the cluster bridges. In this embodiment, when confirming the identification of the disease, the intelligent inspection device can directly display the disease identification information on the screen for verification by the inspection personnel, who only need to click correct or incorrect. When the identification result is inaccurate, the disease picture, accelerometer information, gyroscope data and visual solution results, and the disease area marking results are uploaded to the cloud server, and the background staff will classify and mark the disease area.

[0030] In some optional embodiments, the bridge intelligent inspection and evaluation system may further include a disease analysis module, which is used to analyze and determine whether the disease is a historical disease when the disease identification is manually confirmed to be accurate; if so, the current disease information is recorded in the matched historical disease information and uploaded to the cluster bridge multi-source information database; otherwise, a new disease is created and uploaded to the cluster bridge multi-source information database. In this embodiment, when the disease identification result is accurate (direct identification is accurate, or the accurate identification result after background correction), the disease is matched with the historical disease of the bridge (span). If it is a new disease, a new disease record is created, including the disease location, inspection time, disease type, disease photo, disease area, disease size, etc. If the bridge (span) has a matching historical disease, the disease information of the day is added to the record, and the disease development record is generated; the newly added or updated disease record is synchronized to the cluster bridge multi-source information database.

[0031] In this embodiment, the system can automatically identify the disease and compare it with the historical disease database of the bridge (span) to be evaluated, which can effectively avoid the problem of repeated records of a disease on the traditional management and maintenance platform. After successfully matching with the historical disease, the disease information will be integrated to form a disease time series evolution report for reference by maintenance personnel. In addition, records of disease records, health monitoring, regular inspections, etc. can be transmitted back to the multi-source information database of the cluster bridge in real time.

[0032] Preferably, the intelligent bridge inspection and evaluation system may also include an intelligent recognition training module. The intelligent recognition training module may be set in the cloud to perform defect recognition training in the cloud. The intelligent recognition training module is used to receive the defect information that has been manually reviewed and labeled in the cloud, and incorporate it into the machine learning model for training, and then dynamically update the bridge defect intelligent visual recognition software module. That is, after completing the manual correction, the correct result is incorporated into the machine learning model for training, and the bridge defect intelligent visual recognition software module is dynamically updated until the recognition result is accurate. In this embodiment, by setting up an intelligent recognition training module for machine learning training, the bridge defect image recognition model can be dynamically updated, and the corrected recognition result can be fed back to improve the recognition accuracy.

[0033] In some embodiments, the evaluation module can be used to calculate the bridge information completeness of the bridge to be evaluated based on the cluster bridge multi-source information stored in the cluster bridge multi-source information database, and determine whether the bridge information completeness meets the preset requirements; if so, enter the Bayesian network to evaluate the bridge status and give maintenance measures; otherwise, the most similar bridge in the cluster bridges is used to correct the current bridge evaluation result. Among them, when the last inspection information of a certain inspection bridge is uploaded for a period of time, for example, 15 minutes after the last information of a certain bridge is uploaded, the bridge is evaluated. The evaluation module can be divided into five parts: bridge information completeness calculation, bridge status Bayesian network evaluation, cluster bridge similarity matching, cluster bridge evaluation correction, maintenance measures recommendation and dynamic update.

[0034] (1) Regarding the calculation of bridge information completeness: The evaluation module may include an evaluation system for evaluating the bridge information completeness.

[0035] Users can set the completeness of bridge information and various weights according to the situation, which can generally include: bridge regular inspection reports, bridge load test data, bridge inspection results, bridge design information, bridge construction information, bridge traffic flow information, bridge heavy-load vehicle traffic statistics, etc. The formula for bridge information completeness is as follows: , in, : No. Bridge information completeness score, : No. The bridge information weight, : No. Bridge No. Item information update time, : No. Bridge No. The actual situation of the information, : No. Bridge No. Full score standard for item information.

[0036] (2) Bayesian network evaluation of bridge status.

[0037] In this embodiment, for example, when the calculated bridge information completeness is ≥90 points, the Bayesian network can be directly evaluated. When the calculated bridge information completeness is <90 points, the bridge information in the cluster bridge library can be retrieved to perform cluster bridge similarity matching.

[0038] (3) About cluster bridge similarity matching.

[0039] The most similar bridge (span) can be queried in real time from the multi-source information database of cluster bridges. The similarity calculation formula of cluster bridges is as follows: , in, : The similarity score between the bridge and the most similar bridge (span) in the cluster bridges, : Similarity score of two bridges, : Take the maximum value among the multiple values ​​in brackets. : No. The bridge information weight, Bridge Cluster Bridge No. Item information, : The bridge cluster Bridge No. Time when the information is entered. : The bridge cluster The age of the bridge.

[0040] : Define operator 1, Operation: Compare the similarity of the same information of two bridges. The rules can be set according to the actual situation of each structural form of bridges in the cluster. Four commonly used scoring methods are: ① Segment scoring: The span of bridge A is a, and the span of bridge B is b (a≥b>0).

[0041] but The operation scores are shown in Table 1 below: Table 1: Span Calculation score table

[0042] ②Formula scoring: For example, the traffic flow of bridges is: A bridge has a vehicles per lane per hour, and the traffic flow of bridge B is b vehicles per lane per hour, then The operation score is: .

[0043] ③01 Scoring: For example, bridge structure forms: hollow slab bridge, T-beam bridge, concrete box girder bridge, steel box girder bridge, composite beam bridge, variable-section box girder bridge, T-structure bridge, cable-stayed bridge, suspension bridge, arch bridge, etc.

[0044] When the structure is the same The calculation score is: 100 points, different at the same time The calculation score is: 0 points.

[0045] ④Combined scoring: Based on the segmented scoring, 01 scoring or formula scoring method can be used.

[0046] (3) Cluster bridge assessment and correction. The correction scheme can be combined based on the calculated bridge information completeness and the cluster bridge similarity values. For bridges with incomplete information (i.e., those that do not meet the preset completeness requirements), the evaluation results are given as shown in Table 2 below: Table 2: Cluster bridge correction condition judgment table

[0047] The score calculation formula is as follows: When corrected: , Where: : Final score of bridge assessment; : Bridge information completeness score; : Similarity score between the bridge and the most similar bridge (span) in the cluster bridges; :and The bridge with the highest degree of similarity Service risk assessment scores (based on Bayesian networks); : Bridge Service risk assessment scores (based on Bayesian network).

[0048] When not corrected: , Where: : Final score of bridge assessment; : Bridge information completeness score.

[0049] (4) Recommendations and dynamic updates on maintenance measures.

[0050] The conclusion based on the condition assessment is given, and the reference maintenance method is shown in Table 3. The specific content is given by the system in combination with the "Technical Standard for Urban Bridge Maintenance" (CJJ99-2017).

[0051] Table 3 Maintenance measures recommended

[0052] After the score is corrected, it is fed back to the multi-source information database of the cluster bridges in real time to update the score of the bridge assessment information. The system makes recommendations and warnings based on the final score of the Bayesian network assessment of the cluster bridges - the maintenance and alarm measures table.

[0053] The corrected content will be transmitted to the cloud server in real time. The server will periodically (usually once a day) conduct training based on the feedback data and dynamically update the training results to the smart terminal to dynamically improve the recognition accuracy.

[0054] In this embodiment, the edge computing terminal retains the basic data of all bridges from the previous day. When a bridge is updated, the Bayesian network evaluation and cluster correction are performed according to the current data of the bridge (span) and the database of other bridges from the previous day, and a preliminary evaluation score is given on the spot; when the bridge is updated, it is uploaded to the cloud server, and the cloud server will perform a dynamic evaluation of the cluster bridges according to the real-time data, and give evaluation scores, risk items and rectification suggestions, among which the rectification suggestions can be given in combination with the current national standard "Technical Standard for Urban Bridge Maintenance" (CJJ99-2017). A large number of methods and examples are given, which can be selected by different cluster bridges according to the project and engineering, and have good applicability.

[0055] The embodiment of the present invention further provides a bridge intelligent inspection and evaluation method. The bridge intelligent inspection and evaluation method in the embodiment of the present invention can be implemented by using the bridge intelligent inspection and evaluation system provided in any of the above embodiments, wherein the bridge intelligent inspection and evaluation method may include the following steps: Step 1: Use handheld intelligent inspection equipment to identify and locate the bridge to be evaluated and take photos of the damage.

[0056] Step 2: Use the bridge disease intelligent visual recognition software module to intelligently identify the type and location of the disease based on the disease photos, and upload the identified disease information to the cluster bridge multi-source information database.

[0057] Step 3: Evaluate the status of the bridge to be evaluated based on the Bayesian network and the bridge information stored in the cluster bridge multi-source information database.

[0058] Furthermore, the status of the bridge to be evaluated based on the Bayesian network and the bridge information stored in the cluster bridge multi-source information database may include: calculating the bridge information completeness of the bridge to be evaluated based on the bridge information stored in the cluster bridge multi-source information database, and judging whether the bridge information completeness meets the preset requirements; if so, entering the Bayesian network to evaluate the bridge status and give maintenance measures; otherwise, revising the evaluation results of the bridge to be evaluated by similar cluster bridges.

[0059] In some embodiments, the correction of the evaluation results of the bridge to be evaluated by similar cluster bridges may include: determining whether there is a bridge in the multi-source information database of the cluster bridges that matches the similarity of the bridge to be evaluated; if so, correcting the evaluation score of the bridge to be evaluated based on the evaluation results of the bridges with similarity matching in the multi-source information database of the cluster bridges; otherwise, performing a Bayesian network status evaluation on the bridge to be evaluated, and prompting that the bridge information is incomplete and the evaluation is potentially inaccurate.

[0060] In the description of the present invention, it should be noted that the terms "upper", "lower", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0061] It should be noted that, in the present invention, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0062] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A bridge intelligent inspection and evaluation system, characterized in that: It includes: A cluster bridge multi-source information database building module, wherein the cluster bridge multi-source information database building module is used to build a cluster bridge multi-source information database based on the collected bridge design, construction, maintenance, disease and service environment information; Intelligent inspection equipment, which is used to identify and locate the bridge to be evaluated, take photos of the defects, identify the type and location of the defects, and upload the identified defect information to the cluster bridge multi-source information database; An evaluation module, the evaluation module comprising a cloud evaluation module, the cloud evaluation module using the cluster bridge multi-source information stored in the cluster bridge multi-source information database to perform a Bayesian network-based dynamic risk evaluation on the bridge to be evaluated; The evaluation module is used to calculate the bridge information integrity of the bridge to be evaluated based on the cluster bridge multi-source information stored in the cluster bridge multi-source information database, and determine whether the bridge information integrity meets the preset requirements; if so, enter the Bayesian network to evaluate the bridge status and give maintenance measures; otherwise, correct the current bridge evaluation result by using similar cluster bridges; The formula for bridge information completeness is as follows: ， in, : No. Bridge information completeness score, : No. The bridge information weight, : No. Bridge No. Item information update time, : No. Bridge No. The actual situation of the information, : No. Bridge No. Full score standard for item information.

2. The intelligent bridge inspection and evaluation system according to claim 1, characterized in that: The intelligent inspection device assists in identifying the current inspection bridge based on the geographic positioning system; or, the intelligent inspection device automatically scans the QR code of the bridge based on the camera to realize the identification of the current inspection bridge; or, realizes automatic identification of the current inspection bridge based on Bluetooth or NFC chip; after completing the identification of the current bridge, automatically reads the basic information, structural form, historical disease information, historical maintenance records and other multi-source information of the inspection bridge.

3. The intelligent bridge inspection and evaluation system according to claim 1, characterized in that: The intelligent inspection device is provided with a shooting button. When the shooting button is clicked, the intelligent inspection device takes a plurality of disease photos within a preset time, and records the data of the accelerometer and the gyroscope when each disease photo is taken; The intelligent inspection equipment is also provided with a bridge disease intelligent visual recognition software module, and the intelligent inspection equipment intelligently recognizes the location of the disease in the picture and the type of the disease based on the bridge disease intelligent visual recognition software module and the taken disease photos; The intelligent inspection device is also used to calculate the size information of the disease based on the data of the accelerometer and the gyroscope using the ARKit algorithm.

4. The intelligent bridge inspection and evaluation system according to claim 3, characterized in that: The intelligent inspection device is also used to display the type, location and size information of the disease on the screen of the intelligent inspection device, and prompt manual confirmation whether the disease identification is accurate; If accurate, the disease information is uploaded to the cluster bridge multi-source information database; Otherwise, the disease information is uploaded to the cloud for manual review and correction before being uploaded to the cluster bridge multi-source information database.

5. The intelligent bridge inspection and evaluation system according to claim 4, characterized in that: The intelligent bridge inspection and evaluation system also includes a defect analysis module, which is used to analyze and determine whether the defect is a historical defect when the defect identification is manually confirmed to be accurate; if so, the current defect information is recorded in the matched historical defect information and uploaded to the cluster bridge multi-source information database; otherwise, a new defect is created and uploaded to the cluster bridge multi-source information database.

6. The intelligent bridge inspection and evaluation system according to claim 4, characterized in that: The bridge intelligent inspection and evaluation system also includes an intelligent recognition training module, which is used to receive the disease information that has been manually reviewed and labeled in the cloud, incorporate it into the machine learning model for training, and then dynamically update the bridge disease intelligent visual recognition software module.

7. A bridge intelligent inspection and evaluation method, characterized in that: It includes the following steps: Handheld intelligent inspection equipment can identify and locate the bridges to be assessed and take photos of the defects; Based on the disease photos, the bridge disease intelligent visual recognition software module is used to intelligently identify the type and location of the disease, and the identified disease information is uploaded to the cluster bridge multi-source information database; Evaluate the status of the bridge to be evaluated based on the Bayesian network and the bridge information stored in the cluster bridge multi-source information database; The evaluating the state of the bridge to be evaluated based on the Bayesian network and the bridge information stored in the cluster bridge multi-source information database includes: Calculating the bridge information integrity of the bridge to be evaluated based on the bridge information stored in the cluster bridge multi-source information database, and judging whether the bridge information integrity meets the preset requirements; If yes, then use Bayesian network to evaluate the bridge status and give maintenance measures; otherwise, evaluate the most similar cluster bridges and correct the bridge information completeness; The formula for bridge information completeness is as follows: ， in, : No. Bridge information completeness score, : No. The bridge information weight, : No. Bridge No. Item information update time, : No. Bridge No. The actual situation of the information, : No. Bridge No. Full score standard for item information.

8. The intelligent bridge inspection and evaluation method according to claim 7, characterized in that: The most similar cluster bridge assessment revisions include: Determine whether there is a bridge in the cluster bridge multi-source information database that has the highest similarity to the bridge to be evaluated; If so, the evaluation result of the bridge to be evaluated is modified according to the bridge with the highest similarity in the multi-source information database of the cluster bridges; Otherwise, a Bayesian network status assessment is performed on the bridge to be assessed, and it is prompted that the bridge information is incomplete and the assessment is potentially inaccurate.

9. The intelligent bridge inspection and evaluation method according to claim 8, characterized in that: The cluster bridge similarity calculation formula is as follows: ， in, : The similarity score between the bridge and the most similar bridge in the cluster, : Similarity score of two bridges, : Take the maximum value among the multiple values ​​in brackets. : No. The bridge information weight, Bridge Cluster Bridge No. Item information, : The bridge cluster Bridge No. Time when the information is entered. : The bridge cluster Age of bridges; When corrected: ， Where: : Final score of bridge assessment; : Bridge information completeness score; : Similarity score between the bridge and the most similar bridge in the cluster; :and The bridge with the highest degree of similarity Service risk assessment score; : Bridge Service risk assessment score; When not corrected: ， Where: : Final score of bridge assessment; : Bridge information completeness score.

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