CN112785458A - Intelligent management and maintenance system for bridge health big data - Google Patents
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Abstract
The invention discloses an intelligent management and maintenance system for bridge health big data, which relates to the technical field of bridge health management and maintenance and comprises sensors, a data acquisition module, a data processing module and a data processing module, wherein the sensors are arranged at each node of a bridge and used for monitoring data in real time to diagnose structural abnormality and damage; and the data acquisition unit is used for receiving the data of each bridge node, the daily manual inspection data and the daily service data which are monitored by the sensors. The invention relates to an innovative project for intelligently managing regional urban bridge groups by an informationized big data technical means, which is based on data such as sensing detection data of each node of a bridge, daily manual inspection data and the like, realizes storage, query and backup of mass data through a big data processing mechanism and a data quality analysis model, constructs a set sensor index library, a bridge disease library, a management and maintenance plan library, a disposal case library, a bridge basic information library, a model algorithm library and the like, and has intelligent and unified management specifications and processes.
Description
Technical Field
The invention relates to the technical field of bridge health management and maintenance, in particular to a bridge health big data intelligent management and maintenance system.
Background
A bridge (bridge) refers to a building constructed for a road to cross a natural or artificial obstacle, and is erected on rivers, lakes and seas to enable vehicles, pedestrians and the like to smoothly pass through. The bridge generally consists of an upper structure, a lower structure and an auxiliary structure, wherein the upper structure mainly refers to a bridge span structure and a support system; the substructure includes a bridge abutment, a pier and a foundation.
With the rapid development of domestic bridge construction, more and more bridges are managed and maintained by bridge management departments at all levels, and the information quantity of bridge data is increased day by day. The traditional working modes of bridge maintenance and management, such as surface observation, handwriting and pen reading, field query, manual processing and decision by experience, are far from being adapted to the requirements of modern bridge management.
Aiming at the design of a bridge health monitoring system, how to utilize an information technology means of the information society and the knowledge economy era to better perform the supervision and maintenance work of a bridge, accurately know the safe use condition of a bridge structure in real time, timely master and evaluate the operation safety state of the monitored bridge and potential threats, accurately predict the change trend of the safe operation state of the bridge, improve the health detection and evaluation capability of the operation state of the bridge, and implement effective monitoring and knowledge management of services such as bridge operation management and maintenance, which is not only very important, but also is an urgent demand for effective analysis of the bridge health safety supervision and maintenance in the information era. Therefore, it is necessary to invent an intelligent management and maintenance system for big data of bridge health to solve the above problems.
Disclosure of Invention
The invention aims to provide an intelligent management and maintenance system for big data of bridge health, which aims to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: the utility model provides a bridge health big data intelligence management and maintenance system, includes:
the sensors are arranged at each node of the bridge and used for monitoring data in real time to diagnose structural abnormality and damage;
the data acquisition unit is used for receiving the data of each bridge node, the daily manual inspection data and the daily service data which are monitored by the sensors;
the data analysis unit is arranged at the output end of the data acquisition unit and can be used for receiving the big data acquired by the acquisition unit and realizing the preprocessing, storage, query and backup of the big bridge health data through a big data management mechanism and a data quality analysis model;
the data display unit is arranged at the output end of the data analysis unit and can express the bridge structure processed by the data analysis unit in a three-dimensional form and display the bridge structure in a client/server framework in an interface form by combining with the basic bridge information,
in the concrete, the content of the compound,
the data acquisition unit comprises a bridge health monitoring subsystem for receiving sensor monitoring data, a bridge inspection management subsystem for receiving inspection management data and a cooperative office subsystem for receiving daily service data;
the data analysis unit comprises a bridge big data subsystem;
the data display unit comprises a three-dimensional panoramic display subsystem, a bridge comprehensive information platform and a mobile terminal system.
Preferably, the bridge health monitoring subsystem comprises a monitoring subsystem, a threshold value alarming subsystem, a dynamic weighing analysis subsystem, instant information, a safety evaluation subsystem, a trend analysis subsystem, a report reporting subsystem, a user management subsystem and other functional modules;
the bridge health monitoring subsystem realizes the real-time display of various data to users according to requirements and receives the control and input of the users to the system.
Preferably, the bridge inspection management subsystem comprises detection management, intelligent inspection, maintenance management, historical data query, attendance management, statistical analysis, evaluation decision, report and knowledge intelligent experts.
Preferably, the collaborative office subsystem includes office portal, materials management, training management, personnel management, equipment management, cost management, and office OA process functions.
Preferably, the three-dimensional panoramic display subsystem comprises panoramic roaming, monitoring data, video information and inspection information.
Preferably, the bridge comprehensive information platform comprises public information, bridge basic information, real-time monitoring data, inspection maintenance records, common analysis statistics, a GIS map and a cockpit interface for monitoring large-screen display,
the public information may be set to current date, system time, geographic location, weather conditions, new message alerts, and notification announcements.
Preferably, the mobile terminal system is designed into a client/server architecture;
wherein the client is set as an Android tablet personal computer or a smart phone and is used for being responsible for foreground interface display and information acquisition, the server is responsible for data receiving and storing, in addition,
the client mainly comprises three parts, specifically: displaying health monitoring data and monitoring video, inputting and displaying inspection disease information and issuing WeChat information;
still including patrolling and examining maintenance system to it patrols and examines module, patrols and examines module and frequent inspection module by the day to patrol and examine maintenance system and constitute night.
Preferably, the bridge big data subsystem comprises an intelligent management expert and information interaction portal subsystem, a big data management subsystem, a big data mining subsystem, an expert system service set subsystem and a bridge health state monitoring and evaluating subsystem, and the specific steps are as follows:
the intelligent management expert and information interaction portal subsystem is used for being responsible for information interaction with external bridge users, matters such as user information management and the like, importing/exporting bridge data related to the users, interface visual display and the like, and comprises the work of bridge archive data, bridge operation environment and meteorological data, load data, flood earthquake data and the like of the users;
the big data management subsystem is used for integrating, extracting, processing, analyzing and the like the static data, and various tasks of big data management are realized by the dispatching of the big data management engine;
the big data mining subsystem mainly combines a data management system and machine learning technology in artificial intelligence, finds the inferred modes/rules of abnormal analysis, disease identification, performance evaluation, management and maintenance schemes and the like of the bridge health state monitoring from the collected bridge data, and manages and stores the mined modes/rules into a rule database by a big data mining engine;
the expert system service set subsystem is mainly used for standardizing the existing model and knowledge of the bridge user, is added into a knowledge base corresponding to a platform of the system and is used for being responsible for knowledge management and maintenance work related to the platform, and an expert system service set engine is used for scheduling and completing various tasks;
the bridge health state monitoring and evaluating subsystem is used for monitoring and analyzing sensing detection data and daily manual inspection data from each node of the bridge, tracking and predicting the safe operation state and the change trend of the bridge in real time, providing comprehensive evaluation, abnormity early warning and residual service life prediction of the bridge in real time, providing maintenance measures/strategies/schemes and the like for the bridge, and providing decision support services for safe operation, tracking maintenance, knowledge management and the like of the bridge structure.
The invention also provides a data flow for the operation of the bridge health big data support platform, which comprises the following specific steps:
A. on one hand, the data in the cloud static data warehouse are acquired through the cloud data interface, and the acquired data and the information data are integrated to form a uniform data set for data quality analysis and data mining; on the other hand, data of a dynamic data warehouse which is acquired by a cloud sensor and is recorded into the cloud is manually inspected, data preprocessing is applied, useless or invalid data are removed, and effective bridge detection data are generated and sent into a cloud database for storage;
B. respectively preprocessing the data sets integrated and integrated by the data and the information data;
C. the quality analysis is to analyze the preprocessed data to generate a high-quality data set convenient for data mining, and to analyze the quality of the data to extract useful knowledge (such as an index library, a standard model database, a field expert library, a case library, a plan library and the like);
D. generating various rule bases for detecting the health state of the bridge by applying a data mining algorithm/technology through an internal interface;
E. in the operation process of the system, on one hand, data of sensor data and manual inspection data are read in a centralized and real-time manner from a dynamic data warehouse after cloud preprocessing through an external interface, an index library and a standard model library are firstly applied to abnormality detection, on the other hand, abnormal data are detected, and on the basis of the model library, a knowledge base, a rule library, a case library, a plan library and the like through an internal interface, a blackboard knowledge reasoning module is applied to give out auxiliary decision information such as abnormality early warning, trend prediction analysis, disease management and maintenance schemes of bridge health states, performance evaluation (including safety grade, residual life and the like) of bridge structures in real time and report output for decision reference of bridge management and maintenance personnel.
The invention also provides a design of a sensor data anomaly detection algorithm, which comprises a bridge health anomaly detection algorithm design, a bridge health anomaly index detection algorithm design and a trend prediction model design, and specifically comprises the following steps:
the design flow of the bridge health anomaly detection algorithm is as follows:
a. importing data monitored by a sensor, and performing equal-length segmentation according to a node where the sensor is installed, for example, calculating the sensor installed in the length range according to the length of 10 m;
b. then, calculating the data counted in the step a according to autoregressive parameters to obtain a feature space;
c. then judging whether the feature space is normalized, if so, outputting the abnormal probability of the state, and if not, inputting again to the judgment normalization;
designing a bridge health abnormal degree index detection algorithm:
firstly, acquiring bridge health abnormal degree data from a bridge big data subsystem, acquiring the first three data packets of the data, and then performing abnormal degree calculation to further obtain the abnormal degree value of the data packets;
designing a trend prediction model:
the method comprises the steps of firstly, obtaining bridge health abnormal degree data from a bridge big data subsystem, obtaining the first data packets of the data, carrying out model algorithm on the obtained data packets, and further obtaining the model trend value of the data packets.
The invention has the technical effects and advantages that: the invention relates to a bridge health big data intelligent management and maintenance system, which is an innovative project for intelligently managing regional urban bridge groups by adopting an informatization big data technical means, on one hand, on the basis of data such as sensing detection data of each node of a bridge, daily manual inspection data and the like, the storage, the query and the backup of mass data are realized through a big data processing mechanism and a data quality analysis model, a sensor-integrated index library, a bridge disease library, a management and maintenance plan library, a disposal case library, a bridge basic information library, a model algorithm library and the like are constructed, and the system has an intelligent and unified management standard and a plurality of expert knowledge library integrated management systems of processes; on the other hand, the method applies the data stored in the knowledge base, intelligently analyzes the main data/information characteristics, and combines a self-researched data mining algorithm to establish a comprehensive application platform with the functions of bridge structure health state trend prediction, abnormity early warning, bridge disease and management decision scheme and the like of expert knowledge reasoning, thereby providing decision support services for safe operation, tracking maintenance, knowledge management and the like of the bridge structure.
Drawings
FIG. 1 is a schematic flow chart of an intelligent management and maintenance system for bridge health big data according to the invention.
FIG. 2 is a schematic structural diagram of a bridge health monitoring subsystem according to the present invention.
Fig. 3 is a schematic structural diagram of the bridge inspection pipe nutrient subsystem of the invention.
Fig. 4 is a schematic structural diagram of a cooperative office subsystem of the present invention.
FIG. 5 is a schematic structural diagram of a three-dimensional panoramic display subsystem according to the present invention.
FIG. 6 is a schematic structural diagram of a bridge integrated information platform according to the present invention.
FIG. 7 is a schematic structural diagram of a bridge big data subsystem according to the present invention.
FIG. 8 is a schematic data flow diagram illustrating the operation of the bridge health big data support platform according to the present invention.
FIG. 9 is a flow chart of the anomaly detection algorithm design of the present invention.
FIG. 10 is a design diagram of an abnormality degree calculation model according to the present invention.
Fig. 11 is an abnormality degree line graph according to the present invention.
FIG. 12 is a design diagram of an abnormal trend prediction model according to the present invention.
FIG. 13 is a graph illustrating an abnormal data trend analysis according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a bridge health big data intelligent management and maintenance system as shown in fig. 1 to 13, which comprises: the sensors are arranged at each node of the bridge and used for monitoring data in real time to diagnose structural abnormality and damage; the data acquisition unit is used for receiving the data of each bridge node, the daily manual inspection data and the daily service data which are monitored by the sensors; the data analysis unit is arranged at the output end of the data acquisition unit and can be used for receiving the big data acquired by the acquisition unit and realizing the preprocessing, storage, query and backup of the big bridge health data through a big data management mechanism and a data quality analysis model; the data display unit is arranged at the output end of the data analysis unit, can express the bridge structure processed by the data analysis unit in a three-dimensional form, and displays the bridge structure in an interface form by combining with the basic information of the bridge in a framework of a client/server,
the data acquisition unit comprises a bridge health monitoring subsystem for receiving sensor monitoring data, a bridge inspection management subsystem for receiving inspection management data and a cooperative office subsystem for receiving daily service data; the data analysis unit comprises a bridge big data subsystem; the data display unit comprises a three-dimensional panoramic display subsystem, a bridge comprehensive information platform and a mobile terminal system; by means of the arrangement, an innovative project for intelligently managing regional urban bridge groups is achieved by means of an informationized big data technology. On one hand, on the basis of data such as sensing detection data of each node of the bridge, daily manual inspection data and the like, the mass data are stored, inquired and backed up through a big data processing mechanism and a data quality analysis model, and a multiple expert knowledge base integrated management system with intelligent and unified management specifications and processes, such as a sensor set index base, a bridge disease base, a management plan base, a treatment case base, a bridge basic information base, a model algorithm base and the like, is constructed; on the other hand, the method applies data stored in a knowledge base, intelligently analyzes main data/information characteristics, combines a self-developed data mining algorithm, establishes a comprehensive application platform with functions of bridge structure health state trend prediction, abnormity early warning, bridge disease and management decision-making schemes and the like of expert knowledge on the basis of following national and industrial standard reasoning such as CJJ99-2017 urban bridge maintenance technical standard, JT/T1037-2016 highway bridge structure safety monitoring system technical regulation, GB50982-2014 building and bridge structure monitoring technical specification, and the like, and provides decision-making support services for safe operation, tracking maintenance, knowledge management and the like of the bridge structure.
Preferably, the bridge health monitoring subsystem comprises a monitoring subsystem, a threshold value alarming subsystem (the specific threshold value is detailed in the sensor threshold value limit below), a dynamic weighing analysis subsystem, instant information, a safety evaluation subsystem, a trend analysis subsystem, a report reporting subsystem, a user management subsystem and other functional modules; the bridge health monitoring subsystem realizes the real-time display of various data to users according to requirements and receives the control and input of the users to the system; more specifically, the system is established on a monitoring center Server, and based on a series of visual software components of a B/S (Browser/Server) architecture, friendly human-computer interaction interfaces are provided for field operators of the monitoring center and authorized remote client users, so that convenient system control, three-dimensional query of monitoring data and online analysis are realized.
The bridge inspection management subsystem comprises detection management, intelligent inspection, maintenance management, historical data query, attendance management, statistical analysis, evaluation decision, report and knowledge intelligent experts, and is mainly based on risk analysis through the arrangement of the system engineering, the system implementation aims at establishing a risk management process for the bridge, implementing active, preventive and specialized maintenance management, and providing an electronic data interface, a management decision and plan guidance for inspection management and maintenance work in the long-term operation process of the bridge in the future.
The cooperative office subsystem comprises office portals, material management, training management, personnel management, equipment management, cost management, office OA processes and other functions, and is mainly a novel office mode combining modern office and computer network functions and mainly responsible for circulation management of bridge digital management and maintenance information in daily office. By realizing the cooperative office automation, optimizing the existing management organization structure and adjusting the management system, the cooperative office capacity is increased on the basis of improving the efficiency, the decision consistency is strengthened, and finally the purpose of improving the decision efficiency is realized.
The three-dimensional panoramic display subsystem comprises panoramic roaming, monitoring data, video information and inspection information; through foretell setting, express the bridge construction with three-dimensional form, be favorable to strengthening the intuitiveness and the maneuverability of system, convenience of customers establishes the whole impression to whole bridge fast, with bridge information with two-dimensional plane description to three-dimensional expression conversion, promote bridge safety monitoring information's readability.
The bridge comprehensive information platform comprises public information, bridge basic information, real-time monitoring data, inspection maintenance records, common analysis statistics, a GIS map and a cockpit interface for monitoring large-screen display, wherein the public information can be set to be current date, system time, geographic position, weather condition, new message prompt and notice bulletin.
The mobile terminal system is designed as a client/server architecture (not shown); wherein the client sets to Android panel computer or smart mobile phone for be responsible for proscenium interface display and information acquisition, the server is responsible for data reception and storage, in addition, the client mainly includes three parts, specifically is: displaying health monitoring data and monitoring video, inputting and displaying inspection disease information and issuing WeChat information; the system also comprises an inspection maintenance system, and the inspection maintenance system consists of a daily inspection module, a night inspection module and a frequent inspection module; the daily inspection module, the night inspection module and the frequent inspection module are divided according to the contents of bridge maintenance standards; specifically, the daily inspection module mainly comprises three main categories of contents, namely bridge structure inspection, bridge floor cleaning, traffic and special events, and a user can carry out inspection entry work according to sub items below each main category; the night patrol module mainly comprises items needing patrol at night; the frequent inspection module comprises three types of contents, namely a bridge deck system, a bridge internal structure and bridge auxiliary facilities, wherein the parts of the components on the bridge are described in more detail when the diseases are recorded, the positions of the diseases can be accurately positioned, and simultaneously, the recording including photographing, recording and recording multimedia information is performed in an inspection mode, and after a user records the information, the information is stored in a local database and stored in a local folder, and then the client can upload the information to a server.
The bridge big data subsystem comprises an intelligent management expert and information interaction portal subsystem, a big data management subsystem, a big data mining subsystem, an expert system service set subsystem and a bridge health state monitoring and evaluating subsystem, the received big data is preprocessed, stored, inquired and backed up through a big data management mechanism and a data quality analysis model, the big data analysis model with expert knowledge reasoning and decision is established through a data mining algorithm and an existing knowledge base, and a bridge structure health knowledge base, a bridge health evaluation and bridge health early warning model are generated. Meanwhile, an expert system design idea in the field of artificial intelligence application is adopted, on one hand, a service knowledge management system with intelligent and unified management specifications and processes is constructed by analyzing data messages and applying main data/information characteristics of the data messages on the basis of dynamic data such as data detected by each sensing node from a bridge and daily artificial inspection data; on the other hand, the safe operation state and the change trend of the bridge are tracked and predicted through monitoring and analyzing the detection data and the daily manual inspection data from each node sensor of the bridge, comprehensive evaluation of the health state of the bridge, abnormal early warning and maintenance measures/strategies for the bridge are provided, and decision support services are provided for safe operation, tracking maintenance, knowledge management and the like of the bridge structure.
The method specifically comprises the following steps:
the intelligent management expert and information interaction portal subsystem is used for being responsible for information interaction with external bridge users, matters such as user information management and the like, importing/exporting bridge data related to the users, interface visual display and the like, and comprises the work of bridge archive data, bridge operation environment and meteorological data, load data, flood earthquake data and the like of the users; the specific related modules are (not shown): the system comprises a bridge data user template, a user demand and service configuration template, a bridge user data configuration template, a service integration scheduling engine, an evaluation decision information intelligent distribution engine, bridge archive data, historical detection, routing inspection data and the like, importing, managing and maintaining, and real-time input data of detection, routing inspection, environment, weather, load, flood earthquake and the like in bridge operation, wherein the bridge user data configuration template comprises a business management process and the like.
The big data management subsystem is used for integrating, extracting, processing, analyzing and the like the static data, and various tasks of big data management are realized by the dispatching of the big data management engine; the modules specifically related to the method are as follows: the system comprises a big data management engine, a big data preprocessing system, a data quality analysis system and a source database management system, wherein the big data preprocessing system comprises data cleaning, data integration, data reduction, data transformation and the like, and the source database management system comprises a data warehouse establishing tool, a data backup and recovery tool, a data safety management tool, a data service performance monitoring tool and the like.
The big data mining subsystem mainly combines a data management system and machine learning technology in artificial intelligence, finds the inferred modes/rules of abnormal analysis, disease identification, performance evaluation, management and maintenance schemes and the like of the bridge health state monitoring from the collected bridge data, and manages and stores the mined modes/rules into a rule database by a big data mining engine; according to data and information collected by a daily bridge management system, the data format mainly comprises the following components: numerical and textual. For numerical data, such as data detected by a sensor, a structured storage mode is generally adopted; for text-based data (such as routing inspection reports, maintenance and repair reports), it is common to store the data in a semi-structured or unstructured manner. Therefore, the data mining algorithms chosen for them are also different. Here, the big data mining engine completes the scheduling work; the specific related modules are (not shown): the method comprises the steps of big data mining engine, numerical data mining, non-numerical data mining, establishment, management and maintenance of a rule database and the like.
The expert system service set subsystem is mainly used for standardizing the existing model and knowledge of the bridge user, is added into a knowledge base corresponding to a platform of the system and is used for being responsible for knowledge management and maintenance work related to the platform, and an expert system service set engine is used for scheduling and completing various tasks; the modules specifically related to the method are as follows: the system comprises a bridge index database, a bridge disease database, a bridge field expert knowledge database, a law and regulation database, a mining rule database, a bridge management case database, a management plan database, a model database and the like.
The bridge health state monitoring and evaluating subsystem is used for monitoring and analyzing sensing detection data and daily manual inspection data from each node of the bridge, tracking and predicting the safe operation state and the change trend of the bridge in real time, providing comprehensive evaluation, abnormal early warning and residual service life prediction of the bridge in real time, providing maintenance measures/strategies/schemes and the like for the bridge, and providing decision support services for safe operation, tracking maintenance, knowledge management and the like of the bridge structure; the modules specifically related to the method are as follows: the system comprises a bridge health management engine, a blackboard knowledge reasoning module, a bridge health state abnormity detection, a bridge health state trend prediction and abnormity early warning, bridge disease safety grade division, bridge structure performance evaluation, bridge remaining life prediction, bridge management and maintenance scheme generation, bridge management and maintenance decision scheme generation, report generation and the like.
Referring to fig. 8, the invention further provides a data flow for the operation of the bridge health big data support platform, which comprises the following specific steps:
A. on one hand, the data in the cloud static data warehouse are acquired through the cloud data interface, and the acquired data and the information data are integrated to form a uniform data set for data quality analysis and data mining; on the other hand, data of a dynamic data warehouse which is acquired by a cloud sensor and is recorded into the cloud is manually inspected, data preprocessing is applied, useless or invalid data are removed, and effective bridge detection data are generated and sent into a cloud database for storage;
B. respectively preprocessing the data sets integrated and integrated by the data and the information data;
C. the quality analysis is to analyze the preprocessed data to generate a high-quality data set convenient for data mining, and to analyze the quality of the data to extract useful knowledge (such as an index library, a standard model database, a field expert library, a case library, a plan library and the like);
D. generating various rule bases for detecting the health state of the bridge by applying a data mining algorithm/technology through an internal interface;
E. in the operation process of the system, on one hand, data of sensor data and manual inspection data are read in a centralized and real-time manner from a dynamic data warehouse after cloud preprocessing through an external interface, an index library and a standard model library are firstly applied to abnormality detection, on the other hand, abnormal data are detected, and on the basis of the model library, a knowledge base, a rule library, a case library, a plan library and the like through an internal interface, a blackboard knowledge reasoning module is applied to give out auxiliary decision information such as abnormality early warning, trend prediction analysis, disease management and maintenance schemes of bridge health states, performance evaluation (including safety grade, residual life and the like) of bridge structures in real time and report output for decision reference of bridge management and maintenance personnel.
The invention also provides a design of a sensor data anomaly detection algorithm, which comprises a bridge health anomaly detection algorithm design, a bridge health anomaly index detection algorithm design and a trend prediction model design, and specifically comprises the following steps:
the design flow of the bridge health anomaly detection algorithm is as follows (refer to fig. 9):
a. importing data monitored by a sensor, and performing equal-length segmentation according to a node where the sensor is installed, for example, calculating the sensor installed in the length range according to the length of 10 m;
b. then, calculating the data counted in the step a according to autoregressive parameters to obtain a feature space;
c. then judging whether the feature space is normalized, if so, outputting the abnormal probability of the state, and if not, inputting again to the judgment normalization;
designing a bridge health abnormality index detection algorithm (refer to fig. 10):
firstly, acquiring bridge health abnormal degree data from a bridge big data subsystem, acquiring the first three data packets of the data, and then performing abnormal degree calculation to further obtain the abnormal degree value of the data packets;
trend prediction model design (see fig. 12):
the method comprises the steps of firstly, obtaining bridge health abnormal degree data from a bridge big data subsystem, obtaining the first data packets of the data, carrying out model algorithm on the obtained data packets, and further obtaining the model trend value of the data packets.
Referring to fig. 11, more specifically, the present invention further discloses a module for classifying the abnormal security level of the sensor, and the specific classification method is as follows: dividing according to the size of the calculated abnormal degree value, establishing a database by the division, and modifying according to user or expert knowledge, wherein the specific format is as follows:
further, when the threshold value obtained by the data is not in the range of the sensor threshold value (detection range) and the range of the bridge structure state design threshold value, the grade is red early warning, and when the data is normal, green is displayed; the specific sensor threshold limits are:
when the data abnormal degree threshold value is 0.8<, the abnormal degree is less than 1, and the grade is red early warning;
when the data abnormality degree threshold value is 0.5<, the abnormality degree is less than 0.8, yellow early warning is performed;
and when the threshold value of the data abnormality degree is less than 0.5, the blue early warning is carried out.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.
Claims (10)
1. The utility model provides a healthy big data intelligence management and maintenance system of bridge which characterized in that includes:
the sensors are arranged at each node of the bridge and used for monitoring data in real time to diagnose structural abnormality and damage;
the data acquisition unit is used for receiving the data of each bridge node, the daily manual inspection data and the daily service data which are monitored by the sensors;
the data analysis unit is arranged at the output end of the data acquisition unit and can be used for receiving the big data acquired by the acquisition unit and realizing the preprocessing, storage, query and backup of the big bridge health data through a big data management mechanism and a data quality analysis model;
the data display unit is arranged at the output end of the data analysis unit and can express the bridge structure processed by the data analysis unit in a three-dimensional form and display the bridge structure in a client/server framework in an interface form by combining with the basic bridge information,
in the concrete, the content of the compound,
the data acquisition unit comprises a bridge health monitoring subsystem for receiving sensor monitoring data, a bridge inspection management subsystem for receiving inspection management data and a cooperative office subsystem for receiving daily service data;
the data analysis unit comprises a bridge big data subsystem;
the data display unit comprises a three-dimensional panoramic display subsystem, a bridge comprehensive information platform and a mobile terminal system.
2. The intelligent management and maintenance system for bridge health big data according to claim 1, characterized in that: the bridge health monitoring subsystem comprises a monitoring subsystem, a threshold value alarming subsystem, a dynamic weighing analysis subsystem, instant information, a safety evaluation subsystem, a trend analysis subsystem, a report subsystem, a user management subsystem, other functional modules and the like;
the bridge health monitoring subsystem realizes the real-time display of various data to users according to requirements and receives the control and input of the users to the system.
3. The intelligent management and maintenance system for bridge health big data according to claim 1, characterized in that: the bridge inspection management subsystem comprises detection management, intelligent inspection, maintenance management, historical data query, attendance management, statistical analysis, evaluation decision, report and knowledge intelligent experts.
4. The intelligent management and maintenance system for bridge health big data according to claim 1, characterized in that: the cooperative office subsystem comprises the functions of office portals, material management, training management, personnel management, equipment management, cost management, office OA processes and the like.
5. The intelligent management and maintenance system for bridge health big data according to claim 1, characterized in that: the three-dimensional panoramic display subsystem comprises panoramic roaming, monitoring data, video information and inspection information.
6. The intelligent management and maintenance system for bridge health big data according to claim 1, characterized in that: the bridge comprehensive information platform comprises public information, bridge basic information, real-time monitoring data, inspection maintenance records, common analysis statistics, a GIS map and a cockpit interface for monitoring large-screen display,
the public information may be set to current date, system time, geographic location, weather conditions, new message alerts, and notification announcements.
7. The intelligent management and maintenance system for bridge health big data according to claim 1, characterized in that: the mobile terminal system is designed into a client/server architecture;
wherein the client is set as an Android tablet personal computer or a smart phone and is used for being responsible for foreground interface display and information acquisition, the server is responsible for data receiving and storing, in addition,
the client mainly comprises three parts, specifically: displaying health monitoring data and monitoring video, inputting and displaying inspection disease information and issuing WeChat information;
still including patrolling and examining maintenance system to it patrols and examines module, patrols and examines module and frequent inspection module by the day to patrol and examine maintenance system and constitute night.
8. The intelligent management and maintenance system for bridge health big data according to claim 1, characterized in that: the bridge big data subsystem comprises an intelligent management expert and information interaction portal subsystem, a big data management subsystem, a big data mining subsystem, an expert system service set subsystem and a bridge health state monitoring and evaluating subsystem, and specifically comprises the following steps:
the intelligent management expert and information interaction portal subsystem is used for being responsible for information interaction with external bridge users, matters such as user information management and the like, importing/exporting bridge data related to the users, interface visual display and the like, and comprises the work of bridge archive data, bridge operation environment and meteorological data, load data, flood earthquake data and the like of the users;
the big data management subsystem is used for integrating, extracting, processing, analyzing and the like the static data, and various tasks of big data management are realized by the dispatching of the big data management engine;
the big data mining subsystem mainly combines a data management system and machine learning technology in artificial intelligence, finds the inferred modes/rules of abnormal analysis, disease identification, performance evaluation, management and maintenance schemes and the like of the bridge health state monitoring from the collected bridge data, and manages and stores the mined modes/rules into a rule database by a big data mining engine;
the expert system service set subsystem is mainly used for standardizing the existing model and knowledge of the bridge user, is added into a knowledge base corresponding to a platform of the system and is used for being responsible for knowledge management and maintenance work related to the platform, and an expert system service set engine is used for scheduling and completing various tasks;
the bridge health state monitoring and evaluating subsystem is used for monitoring and analyzing sensing detection data and daily manual inspection data from each node of the bridge, tracking and predicting the safe operation state and the change trend of the bridge in real time, providing comprehensive evaluation, abnormity early warning and residual service life prediction of the bridge in real time, providing maintenance measures/strategies/schemes and the like for the bridge, and providing decision support services for safe operation, tracking maintenance, knowledge management and the like of the bridge structure.
9. The invention also provides a data flow for the operation of the bridge health big data support platform, which is characterized by comprising the following specific steps:
A. on one hand, the data in the cloud static data warehouse are acquired through the cloud data interface, and the acquired data and the information data are integrated to form a uniform data set for data quality analysis and data mining; on the other hand, data of a dynamic data warehouse which is acquired by a cloud sensor and is recorded into the cloud is manually inspected, data preprocessing is applied, useless or invalid data are removed, and effective bridge detection data are generated and sent into a cloud database for storage;
B. respectively preprocessing the data sets integrated and integrated by the data and the information data;
C. the quality analysis is to analyze the preprocessed data to generate a high-quality data set convenient for data mining, and to analyze the quality of the data to extract useful knowledge (such as an index library, a standard model database, a field expert library, a case library, a plan library and the like);
D. generating various rule bases for detecting the health state of the bridge by applying a data mining algorithm/technology through an internal interface;
E. in the operation process of the system, on one hand, data of sensor data and manual inspection data are read in a centralized and real-time manner from a dynamic data warehouse after cloud preprocessing through an external interface, an index library and a standard model library are firstly applied to abnormality detection, on the other hand, abnormal data are detected, and on the basis of the model library, a knowledge base, a rule library, a case library, a plan library and the like through an internal interface, a blackboard knowledge reasoning module is applied to give out auxiliary decision information such as abnormality early warning, trend prediction analysis, disease management and maintenance schemes of bridge health states, performance evaluation (including safety grade, residual life and the like) of bridge structures in real time and report output for decision reference of bridge management and maintenance personnel.
10. The invention also provides a design of a sensor data anomaly detection algorithm, which is characterized by comprising a bridge health anomaly detection algorithm design, a bridge health anomaly index detection algorithm design and a trend prediction model design, and the method specifically comprises the following steps:
the design flow of the bridge health anomaly detection algorithm is as follows:
a. importing data monitored by a sensor, and performing equal-length segmentation according to a node where the sensor is installed, for example, calculating the sensor installed in the length range according to the length of 10 m;
b. then, calculating the data counted in the step a according to autoregressive parameters to obtain a feature space;
c. then judging whether the feature space is normalized, if so, outputting the abnormal probability of the state, and if not, inputting again to the judgment normalization;
designing a bridge health abnormal degree index detection algorithm:
firstly, acquiring bridge health abnormal degree data from a bridge big data subsystem, acquiring the first three data packets of the data, and then performing abnormal degree calculation to further obtain the abnormal degree value of the data packets;
designing a trend prediction model:
the method comprises the steps of firstly, obtaining bridge health abnormal degree data from a bridge big data subsystem, obtaining the first data packets of the data, carrying out model algorithm on the obtained data packets, and further obtaining the model trend value of the data packets.
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| CN114657874A (en) * | 2022-04-08 | 2022-06-24 | 哈尔滨工业大学 | Intelligent inspection robot for bridge structure diseases |
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