Safety monitoring system and method for cross-high-speed bridge construction based on Internet of Things

Abstract

The invention provides a safety monitoring system and method for construction across a high-speed bridge based on the Internet of Things, and the method comprises the steps: building a current construction state of the bridge according to real-time construction parameters of the bridge, and fusing the current construction state in a time sequence, and obtaining a real-time construction process and an accumulated construction result of the bridge, carrying out risk identification on a real-time construction process by utilizing the fusion risk identification matrix, searching corresponding risk factors in accumulated construction results, constructing a risk decomposition tree of the bridge, transmitting historical storage data and field data of the bridge to a Bayesian network for risk tracing, constructing a field risk grading rule of the bridge, and carrying out risk classification on the bridge. And carrying out grading evaluation on the risk decomposition tree by utilizing a field risk grading rule to obtain a plurality of risk positions of the bridge, and generating and displaying a visual risk report so as to solve the problems of limited range, poor real-time performance and low data processing efficiency of a traditional monitoring mode and unstable transmission and low early warning precision of an existing monitoring system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of construction safety monitoring, in particular to a cross-high-speed-bridge construction safety monitoring system and method based on Internet of Things. BACKGROUND

[0002] During the construction of a cross-high-speed-bridge, due to the influence of high-speed traffic flow, complex geological conditions, construction load changes and adverse environmental factors, the bridge structure is prone to safety hazards such as stress concentration, displacement overrun and vibration anomaly. If these hazards cannot be discovered and handled in time, it may cause accidents such as bridge collapse and construction equipment falling, which not only causes huge economic losses, but also threatens the life safety of construction personnel and the normal operation of high-speed traffic. The traditional cross-high-speed-bridge construction safety monitoring method mainly relies on manual inspection and portable monitoring equipment, which has the following shortcomings: first, the monitoring range is limited, and manual inspection cannot cover all key parts of the bridge, especially the bottom of the bridge girder and the root of the pier column which are difficult to reach; second, the monitoring frequency is low, manual inspection is usually performed once or twice a day, which cannot realize real-time monitoring and is difficult to capture instantaneous safety hazards during construction; third, the data processing efficiency is low, the manually collected monitoring data needs to be analyzed through offline methods, which cannot generate risk assessment results in time, and the early warning is lagging; fourth, it relies on human experience, and the risk judgment is easily affected by subjective factors, so the accuracy cannot be guaranteed. With the development of Internet of Things technology and artificial intelligence technology, some bridge construction safety monitoring systems have begun to use sensors and wireless transmission technology, but the existing systems still have problems such as poor data transmission stability, simple data analysis model, and low early warning accuracy, which cannot meet the high reliability, high real-time and high accuracy requirements of cross-high-speed-bridge construction safety monitoring.

[0003] Therefore, the present application provides a cross-high-speed-bridge construction safety monitoring system and method based on Internet of Things. SUMMARY

[0004] The cross-high-speed-bridge construction safety monitoring system and method based on Internet of Things of the present application are provided to solve the problems of limited range, poor real-time performance, low data processing efficiency of traditional monitoring methods, and unstable transmission and low early warning accuracy of existing monitoring systems.

[0005] The present application provides a cross-high-speed-bridge construction safety monitoring system based on Internet of Things, which comprises: A construction identification module is configured to construct a current construction state of the bridge according to real-time construction parameters of the bridge, and fuse the current construction state in time sequence to obtain real-time construction procedures and cumulative construction achievements of the bridge. A risk identification module is configured to identify risks of the real-time construction process by using a fusion risk identification matrix, and find corresponding risk factors in the accumulated construction results, and construct a risk breakdown tree of the bridge; A deep analysis module is configured to transmit historical storage data and field data of the bridge to a Bayesian network for risk tracing, and construct field risk grading rules of the bridge; A safety detection module is configured to grade and evaluate the risk breakdown tree by using the field risk grading rules, obtain a plurality of risk positions of the bridge, generate a visual risk report, and display the visual risk report.

[0006] In an implementable manner, Further comprising: A networking execution module is configured to connect a plurality of data acquisition devices of the bridge to a network; Real-time acquisition data of each data acquisition device is transmitted to the construction identification module respectively.

[0007] In an implementable manner, The construction identification module comprises: A position determination unit is configured to deduce a plurality of completed projects of the bridge according to real-time construction parameters of the bridge, draw a bridge structure corresponding to each completed project respectively, determine a plurality of current construction positions of the bridge by matching each construction position with the real-time construction parameters, and determine a plurality of current construction positions of the bridge. A state construction unit is configured to simulate construction at each current construction position according to the real-time construction parameters, obtain a real-time construction model of the bridge, obtain real-time construction characteristics corresponding to each current construction position by running the real-time construction model, and construct a current construction state of the bridge. A state fusion unit is configured to determine a coarse state logic between different current construction states according to a construction time corresponding to each current construction state, fuse the current construction states corresponding to different construction times according to the coarse state logic, and identify a plurality of fusion conflict points. A state generation unit is configured to optimize each fusion conflict point according to the real-time construction parameters, obtain a current fusion state of the bridge, find a plurality of current construction characteristics of the bridge in the current fusion state, generate real-time construction processes, accumulate real-time construction processes corresponding to different construction times, and obtain accumulated construction results of the bridge.

[0008] In an implementable manner, Further comprising: a logic optimization unit, configured to acquire an optimization process corresponding to each fused contradiction point, and perform corresponding optimization on the coarse state logic according to the optimization process, to obtain effective state logic between different current construction states; perform rearrangement on the current construction state of the bridge by using the effective state logic, to obtain a current fused state of the bridge.

[0009] In an implementable manner, The risk identification module comprises: a matrix analysis unit, configured to determine a plurality of risk identification dimensions of the bridge by using a plurality of risk identification dimensions of the fused risk identification matrix and a plurality of basic information of the bridge, determine an identification condition corresponding to each risk identification dimension, and input the real-time construction process into the fused risk identification matrix for identification respectively, to obtain a risk identification result corresponding to each risk identification dimension; a feature generation unit, configured to determine a plurality of single risk features of each real-time construction process according to a plurality of risk identification results corresponding to each real-time construction process, fuse a plurality of risk identification results corresponding to each real-time construction process, to obtain a comprehensive risk feature corresponding to each real-time construction process; a factor matching unit, configured to perform process iteration on process operations between different real-time construction processes by using each single risk feature and the comprehensive risk feature respectively, to obtain a plurality of single risk factors corresponding to each single risk feature and a comprehensive risk factor corresponding to each comprehensive risk feature; a dynamic analysis unit, configured to determine a plurality of risk items of the bridge according to the single risk factors and the comprehensive risk factors, identify a risk dynamic feature corresponding to each risk item, and perform dynamic coupling on the risk dynamic feature to obtain a risk resolution tree of the bridge.

[0010] In an implementable manner, The deep analysis module comprises: a data fusion unit, configured to construct field data of the bridge according to the real-time construction parameters, acquire historical storage data of the bridge, fuse the field data and the historical storage data, and obtain comprehensive field data of the bridge; a risk traceability unit, configured to identify a plurality of risk sub-data contained in the comprehensive field data according to the risk resolution tree, input each risk sub-data into the Bayesian network for risk traceability respectively, and obtain risk information corresponding to each risk sub-data in the bridge; The rule building unit is configured to perform semantic recognition on each risk information respectively to obtain several risk key features of the bridge, search for corresponding risk consequences of each risk key feature in big data respectively, and build the field risk grading rule of the bridge according to the risk consequences in descending order and in combination with the risk information.

[0011] In an implementable manner, The rule building unit is configured to perform semantic recognition on each risk information respectively to obtain several risk key features of the bridge, search for corresponding risk consequences of each risk key feature in big data respectively, and build the field risk grading rule of the bridge according to the risk consequences in descending order and in combination with the risk information. The rule building unit is configured to perform semantic recognition on each risk information respectively to obtain several risk key features of the bridge, search for corresponding risk consequences of each risk key feature in big data respectively, and build the field risk grading rule of the bridge according to the risk consequences in descending order and in combination with the risk information. The rule building unit is configured to perform semantic recognition on each risk information respectively to obtain several risk key features of the bridge, search for corresponding risk consequences of each risk key feature in big data respectively, and build the field risk grading rule of the bridge according to the risk consequences in descending order and in combination with the risk information.

[0012] In an implementable manner, The safety detection module comprises: The grading evaluation unit is configured to identify the bridge risk features corresponding to each tree branch in the risk resolution tree respectively, divide the bridge risk features into several grades by using the field risk rule, and perform safety evaluation on each grade respectively to determine the safety probability corresponding to each grade. The visual positioning unit is configured to identify several risk positions corresponding to each grade in the bridge respectively, set corresponding risk marks for each risk position according to the grade, build a visual report of the bridge according to the bridge structure and the risk mark situation of the bridge, and display the visual report.

[0013] The application provides a cross-high-speed-bridge construction safety monitoring method based on Internet of Things, which comprises the following steps: Step 1: constructing a current construction state of the bridge according to real-time construction parameters of the bridge, and fusing the current construction state in time sequence to obtain real-time construction procedures and cumulative construction achievements of the bridge; Step 2: performing risk identification on the real-time construction procedures by using a fusion risk identification matrix, and searching for corresponding risk factors in the cumulative construction achievements to construct a risk resolution tree of the bridge; Step 3: transmitting historical storage data and field data of the bridge to a Bayesian network to perform risk tracing and construct a field risk grading rule of the bridge; Step 4: performing grading evaluation on the risk resolution tree by using the field risk grading rule to obtain several risk positions of the bridge, generating a visual risk report and displaying the visual risk report.

[0014] In an implementable manner, The step 1 comprises: Step 11: according to the real-time construction parameters of the bridge, a plurality of completed projects of the bridge are derived, and a bridge structure corresponding to each of the completed projects is drawn respectively, a plurality of construction positions corresponding to each of the bridge structures are determined, each of the construction positions is matched with the real-time construction parameters respectively, and a plurality of current construction positions of the bridge are determined; Step 12: according to the real-time construction parameters, construction simulation is performed at each of the current construction positions, a real-time construction model of the bridge is obtained, real-time construction characteristics corresponding to each of the current construction positions are obtained by running the real-time construction model, and a current construction state of the bridge is constructed; Step 13: according to the construction time corresponding to each of the current construction states, coarse state logic between different current construction states is determined, the current construction states corresponding to different construction times are fused according to the coarse state logic, and a plurality of fusion conflict points are identified; Step 14: according to the real-time construction parameters, each of the fusion conflict points is optimized respectively, a current fusion state of the bridge is obtained, a plurality of current construction characteristics of the bridge are searched in the current fusion state, real-time construction procedures are generated, real-time construction procedures corresponding to different construction times are accumulated, and accumulated construction achievements of the bridge are obtained.

[0015] The above technical scheme has the following beneficial effects: in order to improve the safety monitoring quality and efficiency of the bridge construction site, firstly, the current construction state is constructed and time-series fused through the real-time construction parameters, the real-time construction procedures and the accumulated construction achievements are formed, the construction state dynamic perception is realized to eliminate the management blind area, and accurate data support is provided for subsequent links, then the high-risk procedures are quickly identified by means of the fusion risk identification matrix, the risk decomposition tree is constructed, the risk identification efficiency and the traceability visualization degree are effectively improved, the misjudgment and disposal delay are avoided, the historical and on-site data are fused through the Bayesian network, the scientificity of risk traceability is enhanced, the unified on-site risk grading rules are constructed, the problem of non-unified traditional grading standards is solved, finally, the risk position is accurately positioned and the visual report is generated based on the grading rules, the differentiated risk control is realized to reduce resource waste, the management decision efficiency and the cross-department collaboration ability are improved, a whole-process monitoring system is formed, and finally the monitoring precision is greatly improved, the response speed is accelerated, the management cost is reduced, and the cross-highway bridge construction safety is effectively ensured.

[0016] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be achieved and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0017] The technical solutions of the present application are described in further detail below with reference to the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate the present application and are used to explain the present application, and do not constitute a limitation on the present application. In the drawings: Figure 1 A schematic diagram of the composition of the cross-highway bridge construction safety monitoring system based on the Internet of Things in the embodiment of the present application; Figure 2 A schematic diagram of the working process of the cross-highway bridge construction safety monitoring method based on the Internet of Things in the embodiment of the present application. DETAILED DESCRIPTION

[0019] The preferred embodiments of the present application are described below with reference to the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application.

[0020] Example 1: The present embodiment provides a cross-highway bridge construction safety monitoring system based on the Internet of Things, as shown in Figure 1 , comprising: a construction identification module for constructing a current construction state of the bridge according to real-time construction parameters of the bridge, and fusing the current construction state in time sequence to obtain real-time construction procedures and cumulative construction achievements of the bridge; a risk identification module for identifying risks of the real-time construction procedures by using a fusion risk identification matrix, and searching for corresponding risk factors in the cumulative construction achievements to construct a risk breakdown tree of the bridge; a deep analysis module for transmitting historical storage data and field data of the bridge to a Bayesian network for risk tracing to construct field risk grading rules of the bridge; a safety detection module for grading and evaluating the risk breakdown tree by using the field risk grading rules to obtain a plurality of risk positions of the bridge, generating and displaying a visual risk report.

[0021] In this example, the fusion risk identification matrix is a tool for quantitatively evaluating and graphically presenting the probability and impact of risks; In this example, the real-time construction parameters represent real-time parameters generated at the construction site; In this example, the current construction state represents the state of the bridge at the current time; In this example, the real-time construction procedure represents the procedure being performed by the bridge at the current time; In this example, the cumulative construction achievement represents the achievement presented by the bridge after the bridge is constructed; In this example, the risk breakdown tree represents a binary tree used to present the relationship between different risks; In this example, the risk factor represents a potential risk in the cumulative construction achievement; In this example, the risk position represents the position of the risk in the bridge.

[0022] The working principle and beneficial effects of the above technical solution are as follows: In order to improve the safety monitoring quality and efficiency of the bridge construction site, first, the real-time construction parameters are used to construct and time-series fuse the current construction state, forming real-time construction procedures and cumulative construction achievements, which not only realizes dynamic perception of the construction state to eliminate the management blind area, but also provides accurate data support for subsequent links. Then, with the help of the risk identification matrix, high-risk procedures are quickly identified, and a risk breakdown tree is constructed, effectively improving the risk identification efficiency and traceability visualization degree, avoiding misjudgment and disposal delay. Further, by fusing historical and on-site data through a Bayesian network, the scientificity of risk tracing is enhanced, and a unified on-site risk grading rule is constructed to solve the problem of non-uniformity of traditional grading standards. Finally, based on the grading rule, the risk position is accurately located and a visual report is generated, realizing differentiated risk management to reduce resource waste, and improving management decision-making efficiency and cross-department collaboration ability to form a whole-process monitoring system. Ultimately, the monitoring accuracy is greatly improved, the response speed is accelerated, and the management cost is reduced, effectively ensuring the safety of cross-highway bridge construction.

[0023] Embodiment 2: Based on the embodiment 1, the cross-highway bridge construction safety monitoring system based on the Internet of Things further comprises: A networking execution module is configured to connect the data acquisition devices of the bridge to the network. The real-time acquisition data of each data acquisition device is transmitted to the construction identification module respectively.

[0024] The working principle and beneficial effects of the above technical solution are as follows: The data acquisition devices of the construction site are connected to the network, which facilitates real-time data uploading.

[0025] Embodiment 3: Based on the embodiment 1, the cross-highway bridge construction safety monitoring system based on the Internet of Things, the construction identification module comprises: A position determination unit is configured to determine a plurality of completed projects of the bridge according to the real-time construction parameters of the bridge, draw a bridge structure corresponding to each completed project respectively, determine a plurality of current construction positions of the bridge by matching each construction position with the real-time construction parameters. a state construction unit configured to simulate construction at each current construction position according to the real-time construction parameters to obtain a real-time construction model of the bridge, run the real-time construction model to obtain real-time construction characteristics corresponding to each current construction position, and construct a current construction state of the bridge; a state fusion unit configured to determine coarse state logic between different current construction states according to construction time corresponding to each current construction state, fuse the current construction states corresponding to different construction time according to the coarse state logic, and identify a plurality of fusion conflict points; a state generation unit configured to optimize each fusion conflict point according to the real-time construction parameters respectively to obtain a current fusion state of the bridge, find a plurality of current construction characteristics of the bridge in the current fusion state to generate real-time construction procedures, accumulate the real-time construction procedures corresponding to different construction time to obtain accumulated construction achievements of the bridge.

[0026] In this example, the completed project represents a project that has completed construction; In this example, the constructible position represents a position on the completed project that can be constructed; In this example, the current construction position represents a position in the bridge that is currently under construction; In this example, the real-time construction model represents a virtual model used to present the construction process of the bridge; In this example, the coarse state logic represents the approximate logic between different current construction states derived according to time sequence; In this example, the fusion conflict point represents a point that cannot be integrated after fusing the current construction state.

[0027] The working principle and beneficial effects of the above technical solution are as follows: first, the current construction position is accurately locked by the real-time construction parameters, avoiding position misjudgment and omission of traditional manual inspection, laying a spatial foundation for subsequent monitoring; then the construction simulation is used by the construction unit to generate a real-time construction model and extract features, solving the problem that traditional text records cannot intuitively reflect construction details, facilitating timely detection of deviations; then the state logic is sorted according to the construction time and the fusion conflict points are identified, avoiding the confusion of traditional fused data, ensuring data continuity; finally, the real-time construction procedures and accumulated construction achievements are generated after optimizing the conflict points, solving the traditional data integration problem, providing reliable data for risk identification, and providing a basis for management personnel to adjust plans, providing precise, reliable, and coherent data support for cross-highway bridge construction safety monitoring, and comprehensively improving monitoring accuracy and management efficiency, reducing safety control loopholes and quality risks.

[0028] Embodiment 4: On the basis of embodiment 3, the Internet of Things-based cross-high-speed bridge construction safety monitoring system further comprises: A logic optimization unit is configured to obtain an optimization process corresponding to each fused contradiction point, perform corresponding optimization on the coarse state logic according to the optimization process, and obtain effective state logic between different current construction states. The effective state logic is used to rearrange the current construction state of the bridge, and the current fusion state of the bridge is obtained.

[0029] The working principle and beneficial effects of the above technical solution are as follows: the logic between different current construction states is identified, which facilitates subsequent state arrangement.

[0030] Embodiment 5: On the basis of embodiment 1, the Internet of Things-based cross-high-speed bridge construction safety monitoring system, the risk identification module comprises: A matrix analysis unit is configured to determine a plurality of risk identification dimensions of the bridge by using a plurality of risk identification dimensions of the fused risk identification matrix and a plurality of basic information of the bridge, determine an identification condition corresponding to each risk identification dimension, input the real-time construction process into the fused risk identification matrix for identification respectively, and obtain a risk identification result corresponding to each risk identification dimension. A feature generation unit is configured to determine a plurality of single risk features of each real-time construction process according to a plurality of risk identification results corresponding to each real-time construction process, fuse a plurality of risk identification results corresponding to each real-time construction process, and obtain a comprehensive risk feature corresponding to each real-time construction process. A factor matching unit is configured to perform process iteration on process operations between different real-time construction processes by using each single risk feature and the comprehensive risk feature respectively, and obtain a plurality of single risk factors corresponding to each single risk feature and a comprehensive risk factor corresponding to each comprehensive risk feature. A dynamic analysis unit is configured to determine a plurality of risk items of the bridge according to the single risk factors and the comprehensive risk factors, identify a risk dynamic feature corresponding to each risk item, and perform dynamic coupling on the risk dynamic feature to obtain a risk resolution tree of the bridge.

[0031] In this example, the fused risk identification matrix represents a fused risk identification matrix, which combines a plurality of risk assessment dimensions together to form a more comprehensive risk assessment tool. In this example, the basic information represents information used to describe the bridge; In this example, the risk identification dimensions include: danger possibility, danger influence, danger controllability, danger exposure, and danger timeliness. In this example, the single risk feature represents the feature of the construction process under one risk identification dimension; In this example, the comprehensive risk feature represents the feature of one construction process under the joint influence of several risk identification results; In this example, the risk dynamic feature represents the dynamics contained in one risk item.

[0032] The working principle and beneficial effects of the above technical solution are as follows: firstly, the adaptive risk identification dimensions and conditions are determined by fusing the risk identification matrix and the basic information of the bridge, and the risk of each dimension is efficiently identified by fusing the risk identification matrix, avoiding the problems of traditional fixed dimensions and low efficiency; then the single and comprehensive risk features are extracted for risk determination, which not only captures independent risk signals but also understands the risk superposition effect, making up for the limitations of traditional single dimension focus, further positioning risk factors by traversing the process through risk features, solving the problems of low efficiency and easy omission of manual inspection, providing clear direction for disposal, finally generating a risk decomposition tree by dynamically coupling risk features, reflecting the risk evolution in real time, avoiding the problem of fixed decomposition tree, greatly improving the accuracy, comprehensiveness and dynamic control capability of cross-highway bridge construction risk identification, shortening the response time of disposal, and reducing the probability of safety accidents.

[0033] Embodiment 6: Based on embodiment 1, the cross-highway bridge construction safety monitoring system based on Internet of Things, the deep analysis module comprises: A data fusion unit is configured to construct the field data of the bridge according to the real-time construction parameters, acquire the historical storage data of the bridge, fuse the field data and the historical storage data, and obtain the comprehensive field data of the bridge; A risk tracing unit is configured to identify several risk sub-data contained in the comprehensive field data according to the risk decomposition tree, input each risk sub-data into the Bayesian network for risk tracing, and obtain the corresponding risk information of each risk sub-data in the bridge; A rule construction unit is configured to perform semantic recognition on each risk information to obtain several risk key features of the bridge, find the corresponding risk consequences of each risk key feature in big data, construct the field risk grading rules of the bridge according to the risk consequences from high to low in combination with the risk information.

[0034] In this example, the risk information represents the information of the risk sub-data in the bridge; In this example, the risk key feature represents the feature of the bridge related to the risk.

[0035] The working principle and beneficial effects of the above technical solution are: by constructing field data and fusing historical stored data to obtain comprehensive field data, the limitations of traditional monitoring relying on single data and being prone to analysis deviation due to one-sided data are broken, the data is more complete and has reference value, a comprehensive data foundation is laid for subsequent risk tracing, then risk sub-data is extracted from the comprehensive field data by using risk decomposition tree, risk information is obtained by using Bayesian network for accurate tracing, the problems of traditional risk tracing relying on artificial experience, low efficiency and poor accuracy are solved, the risk root cause can be quickly located, and a precise direction is provided for risk disposal, further, risk key features are extracted through semantic recognition, field risk grading rules are constructed in combination with risk consequences found by big data, the defects of subjective traditional grading rules and lack of data support are avoided, the risk grading is more scientific and reasonable, a unified standard is provided for subsequent grading evaluation of the safety detection module, and the comprehensiveness, accuracy and standardization of the cross-high-speed-bridge construction risk analysis are significantly improved, thereby providing strong technical support for safety control.

[0036] Embodiment 7: Based on the embodiment 6, the cross-high-speed-bridge construction safety monitoring system based on the Internet of Things constructs field risk grading rules of the bridge in combination with the risk information according to the order from high to low of the risk consequences, and the field risk grading rules comprise: a plurality of grading standards of the bridge are constructed according to the order from high to low of the risk consequences, and each risk information is matched with a corresponding grading standard; each grading standard is described according to the risk information corresponding to the grading standard, and the field grading rules of the bridge are obtained.

[0037] The working principle and beneficial effects of the above technical solution are: the field grading rules are set to lay a foundation for subsequent safety monitoring.

[0038] Embodiment 8: Based on the embodiment 1, the cross-high-speed-bridge construction safety monitoring system based on the Internet of Things, the safety detection module comprises: a grading evaluation unit, configured to identify bridge risk features corresponding to each tree branch in the risk decomposition tree respectively, divide the bridge risk features into a plurality of grades by using the field risk rules, and perform safety evaluation on each grade to determine a safety probability corresponding to each grade; a visual positioning unit, configured to identify a plurality of risk positions corresponding to each grade in the bridge respectively, set corresponding risk marks for each risk position according to the grade, construct a visual report of the bridge according to the bridge structure and the risk mark situation of the bridge, and display the visual report.

[0039] The working principle and beneficial effects of the above technical solution are as follows: by identifying the bridge risk characteristics of each branch of the risk breakdown tree, combining the field risk rules to complete risk classification and determine the safety probability of each classification, the limitations of traditional risk assessment, which is only qualitative and standard subjective, are broken, the risk level is clear, the safety degree is quantified, over-treatment or under-treatment is avoided, the management focus of different classifications can be accurately positioned to improve the pertinence of management, then the risk position corresponding to each classification is accurately identified and a special risk mark is set, a visual report is constructed in combination with the bridge structure, the problem that the traditional risk notification relies on text and the position is difficult to locate is solved, the management personnel can intuitively master the risk distribution and the association with the key components without being on site, clear basis is provided for preferential treatment of high-risk positions, abstract monitoring data is converted into intuitive action guidelines, the scientific nature of risk assessment and the response efficiency of treatment are significantly improved, and the safety of personnel, equipment and road traffic in the construction of the cross-highway bridge is effectively ensured.

[0040] Embodiment 9: The embodiment provides a bridge construction safety monitoring method based on Internet of Things, as shown in the following formula: Figure 2 The embodiment provides a bridge construction safety monitoring method based on Internet of Things, as shown in the following formula: Step 1: constructing a current construction state of the bridge according to real-time construction parameters of the bridge, and fusing the current construction state in time sequence to obtain a real-time construction process and cumulative construction achievement of the bridge; Step 2: identifying risks of the real-time construction process by using a fusion risk identification matrix, and searching for corresponding risk factors in the cumulative construction achievement to construct a risk breakdown tree of the bridge; Step 3: transmitting historical storage data and field data of the bridge to a Bayesian network for risk tracing to construct field risk classification rules of the bridge; Step 4: classifying and evaluating the risk breakdown tree by using the field risk classification rules to obtain a plurality of risk positions of the bridge, generating a visual risk report and displaying the visual risk report.

[0041] In this example, the real-time construction parameter represents a real-time parameter generated in the construction site; In this example, the current construction state represents a state of the bridge at the current time; In this example, the real-time construction process represents a process being performed by the bridge at the current time; In this example, the cumulative construction achievement represents an achievement of the bridge after the bridge is constructed; In this example, the risk breakdown tree represents a binary tree used to present the relationship between different risks; In this example, the risk factor represents a potential risk in the cumulative construction achievement; In this example, the risk position represents a position of the bridge where the risk is presented.

[0042] The working principle and beneficial effects of the above technical solution are as follows: in order to improve the safety monitoring quality and efficiency of the bridge construction site, firstly, the real-time construction parameters are used to construct and time-series fuse the current construction state, forming real-time construction procedures and cumulative construction achievements, which realizes dynamic perception of the construction state to eliminate the management blind area, and provides accurate data support for the subsequent link, then the risk identification matrix is used to quickly identify high-risk procedures and construct a risk decomposition tree, which effectively improves the risk identification efficiency and traceability visualization degree, avoids misjudgment and disposal delay, further enhances the scientificity of risk traceability by fusing historical and site data through the Bayesian network, and solves the problem of non-uniformity of traditional grading standards by constructing a unified site risk grading rule, finally, the risk position is accurately located and a visual report is generated based on the grading rule, realizing differentiated risk control to reduce resource waste, and improving the management decision efficiency and cross-department collaboration ability to form a whole-process monitoring system, which greatly improves the monitoring accuracy, speeds up the response speed, and reduces the management cost, and effectively safeguards the construction safety of the cross-highway bridge.

[0043] Embodiment 10: Based on the embodiment 9, the cross-highway bridge construction safety monitoring method based on the Internet of Things, the step 1 comprises: Step 11: according to the real-time construction parameters of the bridge, a plurality of completed projects of the bridge are derived, and a bridge structure corresponding to each completed project is drawn, a plurality of construction positions corresponding to each bridge structure are determined, each construction position is matched with the real-time construction parameters, and a plurality of current construction positions of the bridge are determined; Step 12: according to the real-time construction parameters, construction simulation is performed at each current construction position to obtain a real-time construction model of the bridge, the real-time construction model is run to obtain real-time construction characteristics corresponding to each current construction position, and a current construction state of the bridge is constructed; Step 13: the coarse state logic between different current construction states is determined according to the construction time corresponding to each current construction state, the current construction states corresponding to different construction times are fused according to the coarse state logic, and a plurality of fusion conflict points are identified; Step 14: each fusion conflict point is optimized according to the real-time construction parameters to obtain a current fusion state of the bridge, a plurality of current construction characteristics of the bridge are searched in the current fusion state to generate real-time construction procedures, and the real-time construction procedures corresponding to different construction times are accumulated to obtain cumulative construction achievements of the bridge.

[0044] In this example, the completed project represents a project that has been completed; In this example, the constructible position indicates a position on the completed project that can be constructed; In this example, the current construction position indicates a position in the bridge that is currently under construction; In this example, the real-time construction model indicates a virtual model used to present the construction process of the bridge; In this example, the coarse state logic indicates the approximate logic between different current construction states derived according to the time sequence; In this example, the fusion conflict point indicates a point that cannot be integrated after fusing the current construction states.

[0045] The working principle and beneficial effects of the above technical solutions are as follows: first, the current construction position is accurately locked by the real-time construction parameter, avoiding the position misjudgment and omission of traditional manual inspection, laying a spatial foundation for subsequent monitoring; then the unit constructs the real-time construction model by means of construction simulation and extracts features, solving the problem that traditional text records cannot intuitively reflect construction details, facilitating timely discovery of deviations; then the state logic is sorted according to the construction time and the fusion conflict point is identified, avoiding the traditional fusion data confusion, ensuring data continuity; finally, the real-time construction process and cumulative construction achievements are generated after optimizing the conflict point, solving the traditional data integration problem, providing reliable data for risk identification, and providing a basis for management personnel to adjust plans, providing precise, reliable, and coherent data support for cross-highway bridge construction safety monitoring, and comprehensively improving monitoring accuracy and management efficiency, reducing safety control loopholes and quality risks.

[0046] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application also intends to include these modifications and variations.

Claims

1. The Internet of Things-based cross-high-speed bridge construction safety monitoring system is characterized in that, The application comprises: a construction identification module, which is used to construct a current construction state of a bridge according to real-time construction parameters of the bridge, and fuse the current construction state in time sequence to obtain a real-time construction procedure and cumulative construction achievement of the bridge; a risk identification module, which is used to identify risks of the real-time construction procedure by using a fused risk identification matrix, and find corresponding risk factors in the cumulative construction achievement to construct a risk breakdown tree of the bridge; a deep analysis module, which is used to transmit historical storage data and field data of the bridge to a Bayesian network for risk tracing to construct field risk grading rules of the bridge; a safety detection module, which is used to grade and evaluate the risk breakdown tree by using the field risk grading rules to obtain a plurality of risk positions of the bridge, generate a visual risk report and display the report.

2. The IoT-based cross-highway bridge construction safety monitoring system according to claim 1, wherein, The application further comprises: a networking execution module, which is used to connect a plurality of data acquisition devices of the bridge to a network; real-time acquisition data of each data acquisition device is transmitted to the construction identification module.

3. The IoT-based cross-highway bridge construction safety monitoring system according to claim 1, wherein, The construction identification module comprises: a position determination unit, which is used to deduce a plurality of completed projects of the bridge according to real-time construction parameters of the bridge, draw a bridge structure corresponding to each completed project, determine a plurality of current construction positions of the bridge by matching each construction position with the real-time construction parameters, and determine a plurality of current construction positions of the bridge; a state construction unit, which is used to simulate construction at each current construction position according to the real-time construction parameters to obtain a real-time construction model of the bridge, obtain real-time construction characteristics corresponding to each current construction position by running the real-time construction model, and construct a current construction state of the bridge; a state fusion unit, which is used to determine a coarse state logic between different current construction states according to a construction time corresponding to each current construction state, fuse the current construction states corresponding to different construction times according to the coarse state logic, and identify a plurality of fusion conflict points; a state generation unit, which is used to optimize each fusion conflict point according to the real-time construction parameters to obtain a current fusion state of the bridge, find a plurality of current construction characteristics of the bridge in the current fusion state to generate a real-time construction procedure, accumulate real-time construction procedures corresponding to different construction times to obtain a cumulative construction achievement of the bridge.

4. The IoT-based cross-highway bridge construction safety monitoring system of claim 3, wherein, The application further comprises: a logic optimization unit, which is used to obtain an optimization process corresponding to each fusion conflict point, optimize the coarse state logic according to the optimization process to obtain an effective state logic between different current construction states, and rearrange the current construction state of the bridge according to the effective state logic to obtain a current fusion state of the bridge. The risk identification module comprises:

5. The IoT based cross high speed bridge construction safety monitoring system as claimed in claim 1, wherein, ​ A matrix analysis unit is configured to determine a plurality of risk identification dimensions of the bridge by using the plurality of risk identification dimensions of the fusion risk identification matrix and the plurality of basic information of the bridge, determine an identification condition corresponding to each of the risk identification dimensions, and input the real-time construction process into the fusion risk identification matrix for identification to obtain a risk identification result corresponding to each of the risk identification dimensions. A feature generation unit is configured to determine a plurality of single risk features of each of the real-time construction processes according to the plurality of risk identification results corresponding to each of the real-time construction processes, fuse the plurality of risk identification results corresponding to each of the real-time construction processes to obtain a comprehensive risk feature corresponding to each of the real-time construction processes. A factor matching unit is configured to perform process iteration on process operations between different real-time construction processes by using each of the single risk features and the comprehensive risk feature to obtain a plurality of single risk factors corresponding to each of the single risk features and a comprehensive risk factor corresponding to each of the comprehensive risk features. A dynamic analysis unit is configured to determine a plurality of risk items of the bridge according to the single risk factors and the comprehensive risk factors, identify a risk dynamic feature corresponding to each of the risk items, and dynamically couple the risk dynamic features to obtain a risk resolution tree of the bridge.

6. The IoT based cross high speed bridge construction safety monitoring system as claimed in claim 1, wherein, The deep analysis module comprises: A data fusion unit is configured to construct field data of the bridge according to the real-time construction parameters, acquire historical storage data of the bridge, fuse the field data and the historical storage data to obtain comprehensive field data of the bridge. A risk traceability unit is configured to identify a plurality of risk sub-data contained in the comprehensive field data according to the risk resolution tree, input each of the risk sub-data into the Bayesian network for risk traceability to obtain risk information corresponding to each of the risk sub-data in the bridge. A rule construction unit is configured to perform semantic identification on each of the risk information to obtain a plurality of risk key features of the bridge, find a risk consequence corresponding to each of the risk key features in big data, and construct a field risk classification rule of the bridge by combining the risk information in a descending order of the risk consequence.

7. The IoT-based cross-highway bridge construction safety monitoring system of claim 6, wherein, The deep analysis module comprises: The deep analysis module comprises: The deep analysis module comprises:

8. The IoT based cross high speed bridge construction safety monitoring system as claimed in claim 1, wherein, The safety detection module comprises: A classification evaluation unit is configured to identify a bridge risk feature corresponding to each of tree branches in the risk resolution tree, divide the bridge risk feature into a plurality of classifications by using the field risk rule, perform safety evaluation on each of the classifications, and determine a safety probability corresponding to each of the classifications. A visual positioning unit is configured to identify a plurality of risk positions corresponding to each of the hierarchical levels in the bridge, set corresponding risk marks for each of the risk positions according to the hierarchical levels, construct a visual report of the bridge according to the bridge structure and the risk marks of the bridge, and display the visual report.

9. The method for monitoring the safety of construction across a high-speed bridge based on the Internet of Things, characterized in that, The method comprises the following steps: Step 1: constructing a current construction state of the bridge according to real-time construction parameters of the bridge, fusing the current construction state in time sequence to obtain real-time construction procedures and cumulative construction achievements of the bridge; Step 2: identifying risks in the real-time construction procedures by using a fused risk identification matrix, and searching for corresponding risk factors in the cumulative construction achievements to construct a risk decomposition tree of the bridge; Step 3: transmitting historical storage data and field data of the bridge to a Bayesian network to trace risks and construct field risk grading rules of the bridge; Step 4: grading and evaluating the risk decomposition tree by using the field risk grading rules to obtain a plurality of risk positions of the bridge, generating a visual risk report and displaying the visual risk report. 10.The Internet of Things based cross-high-speed-bridge construction safety monitoring method according to claim 9, wherein, The step 1 comprises the following steps: Step 11: deriving a plurality of completed projects of the bridge according to real-time construction parameters of the bridge, drawing a bridge structure corresponding to each of the completed projects, determining a plurality of constructible positions corresponding to each of the bridge structures, matching each of the constructible positions with the real-time construction parameters, and determining a plurality of current construction positions of the bridge; Step 12: performing construction simulation at each of the current construction positions according to the real-time construction parameters to obtain a real-time construction model of the bridge, running the real-time construction model to obtain real-time construction characteristics corresponding to each of the current construction positions, and constructing a current construction state of the bridge; Step 13: determining coarse state logic between different current construction states according to construction time points corresponding to each of the current construction states, fusing the current construction states corresponding to different construction time points according to the coarse state logic, and identifying a plurality of fusion conflict points; Step 14: optimizing each of the fusion conflict points according to the real-time construction parameters to obtain a current fusion state of the bridge, searching for a plurality of current construction characteristics of the bridge in the current fusion state to generate real-time construction procedures, accumulating the real-time construction procedures corresponding to different construction time points to obtain cumulative construction achievements of the bridge.

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