Bridge rotation process detection method and system based on three-dimensional visual data

Abstract

The invention provides a bridge swivel process detection method and system based on three-dimensional visualization data, and relates to the technical field of bridge monitoring, and the method comprises the steps: obtaining real-time sensing data and real-time environment data at a plurality of moments in a detection period; determining a real-time environment safety coefficient according to the real-time environment data; determining a real-time driving safety coefficient and a real-time comprehensive safety coefficient according to the real-time sensing data; processing the real-time environment safety coefficient, the real-time driving safety coefficient and the real-time comprehensive safety coefficient through a trained bridge swivel risk prediction model, and determining a bridge swivel risk prediction coefficient; and generating a bridge swivel process detection report according to the bridge swivel risk prediction coefficient, the real-time environment safety coefficient, the real-time driving safety coefficient and the real-time comprehensive safety coefficient. According to the invention, the accuracy of bridge swivel process detection can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge monitoring, and in particular to a bridge rotation process detection method and system based on three-dimensional visualization data. Background Art

[0002] In related technologies, the detection of bridge rotation process mainly relies on sensor detection combined with manual detection, that is, it mainly relies on human factors. Excessive reliance on human factors may make it difficult to ensure the timeliness and accuracy of data processing, resulting in poor real-time performance of detection results and limited accuracy of detection results.

[0003] The information disclosed in the background technology section of this application is only intended to deepen the understanding of the general background technology of this application, and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the Invention

[0004] The present invention provides a bridge rotation process detection method and system based on three-dimensional visualization data, which can solve the technical problem that related technologies cannot guarantee the real-time and accuracy of detection results.

[0005] According to a first aspect of the present invention, a method for three-dimensional visualization of the entire process of bridge rotation is provided, comprising: acquiring real-time sensor data and real-time environmental data at multiple moments in a detection cycle, wherein the real-time sensor data comprises real-time positioning data, real-time attitude data, real-time stress data and real-time drive system data, and the real-time environmental data comprises real-time wind speed data and real-time temperature data; determining a real-time environmental safety factor based on the real-time environmental data; determining a real-time drive safety factor and a real-time comprehensive safety factor based on the real-time sensor data; processing the real-time environmental safety factor, the real-time drive safety factor and the real-time comprehensive safety factor through a trained bridge rotation risk prediction model to determine a bridge rotation risk prediction factor; generating a bridge rotation process detection report based on the bridge rotation risk prediction factor, the real-time environmental safety factor, the real-time drive safety factor and the real-time comprehensive safety factor.

[0006] According to the present invention, the real-time environmental safety factor is determined based on the real-time environmental data, including: determining the real-time cantilever end temperature difference and the temperature of the key points of the structure based on the real-time temperature data; determining the temperature uniformity safety factor based on the real-time cantilever end temperature difference; determining the temperature range safety factor based on the temperature of the key points of the structure; determining the wind speed environmental safety factor based on the real-time wind speed data; and determining the real-time environmental safety factor based on the temperature uniformity safety factor, the temperature range safety factor and the wind speed environmental safety factor.

[0007] According to the present invention, a real-time driving safety factor and a real-time comprehensive safety factor are determined based on the real-time sensing data, including: determining the hydraulic pump working pressure, the jack stroke and the temperature rise of the motor system based on the real-time driving system data; determining the real-time driving safety factor based on the hydraulic pump working pressure, the jack stroke and the temperature rise of the motor system; and determining the real-time comprehensive safety factor based on the real-time positioning data, the real-time posture data and the real-time stress data.

[0008] According to the present invention, a real-time driving safety factor is determined based on the hydraulic pump working pressure, the jack stroke and the temperature rise of the motor system, including: determining a working pressure fluctuation value based on the hydraulic pump working pressure; determining a hydraulic pump working safety factor based on the working pressure fluctuation value and the hydraulic pump working pressure; determining a stroke difference between adjacent jacks based on the jack stroke; determining a jack synchronization safety factor based on the adjacent jack stroke difference; determining a motor system temperature rise safety factor based on the motor system temperature rise; and determining a real-time driving safety factor based on the hydraulic pump working safety factor, the jack synchronization safety factor and the motor system temperature rise safety factor.

[0009] According to the present invention, a real-time comprehensive safety factor is determined based on the real-time positioning data, the real-time posture data and the real-time stress data, including: determining the key point coordinates of the structural key points in a preset coordinate system based on the real-time positioning data, wherein the preset coordinate system is a coordinate system established with a preset point position in the ground where the rotating bridge is located as the origin; determining the real-time rotation angle and the real-time rotation acceleration based on the real-time posture data; determining the key point stress value based on the real-time stress data; obtaining the standard key point coordinates, standard rotation angle, standard rotation acceleration and standard key point stress value by designing a BIM model; determining the key point priority of each structural key point; and determining the real-time comprehensive safety factor based on the key point priority, the key point coordinates, the real-time rotation angle, the real-time rotation acceleration, the key point stress value, the standard key point coordinates, the standard rotation angle, the standard rotation acceleration and the standard key point stress value.

[0010] According to the present invention, according to the key point priority, the key point coordinates, the real-time rotation angle, the real-time rotation acceleration, the key point stress value, the standard key point coordinates, the standard rotation angle, the standard rotation acceleration and the standard key point stress value, a real-time comprehensive safety factor is determined, including: determining a key point state vector according to the key point coordinates and the key point stress value; determining a rotation state vector according to the real-time rotation angle and the real-time rotation acceleration; determining a standard key point state vector according to the standard key point coordinates and the standard key point stress value; determining a standard rotation state vector according to the standard rotation angle and the standard rotation acceleration; determining a standard rotation state vector according to the formula Determine the real-time comprehensive safety factor at the i-th moment of the detection cycle , where min is the minimum value function, is the key point coordinate of the kth key point at the i-th moment of the detection cycle, is the stress value of the kth key point at the i-th moment of the detection cycle, is the key point state vector of the kth key point at the i-th moment of the detection cycle, for The transposed vector of is the standard key point coordinate of the kth key point at the i-th moment of the detection cycle, is the standard key point stress value of the kth key point at the i-th moment of the detection cycle, is the standard key point state vector of the kth key point at the i-th moment of the detection cycle, is the key point priority of the k-th key point, is the real-time rotation angle at the i-th moment of the detection cycle, is the real-time rotation acceleration at the i-th moment of the detection cycle, is the rotation state vector at the i-th moment of the detection cycle, for The transposed vector of is the standard rotation angle at the i-th moment of the detection cycle, is the standard rotation acceleration at the i-th moment of the detection cycle, is the standard rotation state vector at the i-th moment of the detection period, K is the number of key points, k≤K, and both k and K are positive integers.

[0011] According to the present invention, the training steps of the bridge rotation risk prediction model include: obtaining historical bridge rotation construction records of multiple historical construction periods; determining historical sensor data and historical environmental data based on the historical bridge rotation construction records; determining historical environmental safety factors, historical driving safety factors and historical comprehensive safety factors based on the historical sensor data and the historical environmental data; processing the historical environmental safety factors, the historical driving safety factors and the historical comprehensive safety factors through the bridge rotation risk prediction model to determine the sample bridge rotation risk prediction coefficient; determining historical construction alarm factors based on the historical bridge rotation construction records. information; determine the historical alarm information time difference, historical alarm level and historical construction delay time based on the historical construction alarm information; determine the historical bridge rotation risk coefficient based on the historical alarm information time difference, the historical alarm level and the historical construction delay time; determine the training loss function of the bridge rotation risk prediction model based on the historical bridge rotation risk coefficient, the sample bridge rotation risk prediction coefficient, the historical environmental safety factor, the historical driving safety factor and the historical comprehensive safety factor; train the bridge rotation risk prediction model according to the training loss function to obtain a trained bridge rotation risk prediction model.

[0012] According to the present invention, the training loss function of the bridge rotation risk prediction model is determined based on the historical bridge rotation risk coefficient, the sample bridge rotation risk prediction coefficient, the historical environmental safety factor, the historical driving safety factor and the historical comprehensive safety factor, including: according to the formula Determine the training loss function of the bridge rotation risk prediction model ,in, is the risk prediction coefficient of the sample bridge rotation at the rth moment in the eth historical construction cycle, is the historical bridge rotation risk coefficient for the preset time period after the rth moment of the eth historical construction cycle, is the historical environmental safety factor at the rth moment of the eth historical construction cycle, is the preset environmental safety factor threshold, is the historical driving safety factor at the rth moment of the eth historical construction period, is the preset driving safety factor threshold, is the historical comprehensive safety factor at the rth moment of the eth historical construction cycle, is the preset comprehensive safety factor threshold, E is the number of historical construction cycles, e≤E, R is the number of moments in the historical construction cycle, r≤R, and e, E, r and R are all positive integers.

[0013] According to a second aspect of the present invention, a three-dimensional visualization full-field monitoring system for the entire process of bridge rotation is provided, comprising: a real-time data module for acquiring real-time sensor data and real-time environmental data at multiple moments in a detection cycle, wherein the real-time sensor data comprises real-time positioning data, real-time attitude data, real-time stress data and real-time drive system data, and the real-time environmental data comprises real-time wind speed data and real-time temperature data; an environmental coefficient module for determining a real-time environmental safety factor based on the real-time environmental data; a sensor coefficient module for determining a real-time drive safety factor and a real-time comprehensive safety factor based on the real-time sensor data; a risk prediction module for processing the real-time environmental safety factor, the real-time drive safety factor and the real-time comprehensive safety factor through a trained bridge rotation risk prediction model to determine a bridge rotation risk prediction coefficient; and a detection report module for generating a bridge rotation process detection report based on the bridge rotation risk prediction coefficient, the real-time environmental safety factor, the real-time drive safety factor and the real-time comprehensive safety factor.

[0014] Technical Effect: Real-time sensor data and real-time environmental data can be accurately acquired during the bridge rotation process. Based on these data, the environmental conditions, drive system conditions, and overall bridge conditions can be evaluated to determine the real-time environmental safety factor, real-time drive safety factor, and real-time comprehensive safety factor. Furthermore, these real-time environmental safety factor, real-time drive safety factor, and real-time comprehensive safety factor can be processed using a trained bridge rotation risk prediction model to predict the severity of potential risks, determine the bridge rotation risk prediction factor, and generate a test report, thereby improving the accuracy of bridge rotation process testing. When determining the real-time comprehensive safety factor, the real-time comprehensive safety factor can be determined based on key point priority, key point coordinates, real-time rotation angle, real-time rotation acceleration, key point stress value, standard key point coordinates, standard rotation angle, standard rotation acceleration, and standard key point stress value. During the calculation process, the safety status of the bridge rotation process can be assessed based on both the safety impact of structural key points on the rotation process and the overall bridge posture, improving the comprehensiveness and accuracy of the real-time comprehensive safety factor. When determining the training loss function of the bridge rotation risk prediction model, the training loss function of the bridge rotation risk prediction model can be determined based on the historical bridge rotation risk coefficient, the sample bridge rotation risk prediction coefficient, the historical environmental safety factor, the historical driving safety factor and the historical comprehensive safety factor. During the determination process, the influence of the above data on the error of the sample bridge rotation risk prediction coefficient can be determined based on the possible influence of the historical environmental safety factor, the historical driving safety factor and the historical comprehensive safety factor on the occurrence of risk conditions. Based on this influence and the relative error of the sample bridge rotation risk prediction coefficient, the training loss function is set to reduce the training loss function of the bridge rotation risk prediction model during the training process, and to improve the accuracy of the bridge rotation risk prediction model in a more targeted manner.

[0015] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and not limiting of the present invention. Other features and aspects of the present invention will become more apparent from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can derive other embodiments based on these drawings without inventive efforts. Figure 1 A schematic flow chart of a bridge rotation process detection method based on three-dimensional visualization data according to an embodiment of the present invention is exemplarily shown; Figure 2 A flowchart of determining a real-time environmental safety factor according to an embodiment of the present invention is exemplarily shown; Figure 3 A flowchart for determining a real-time driving safety factor and a real-time comprehensive safety factor according to an embodiment of the present invention is exemplarily shown; Figure 4 A block diagram of a bridge rotation process detection system based on three-dimensional visualization data according to an embodiment of the present invention is exemplarily shown. DETAILED DESCRIPTION

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0018] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0019] Figure 1 A flow chart of a method for three-dimensional visualization of full-field monitoring of the entire process of bridge rotation according to an embodiment of the present invention is exemplarily shown, the method comprising: step S1, acquiring real-time sensor data and real-time environmental data at multiple moments in a detection cycle, wherein the real-time sensor data comprises real-time positioning data, real-time posture data, real-time stress data and real-time drive system data, and the real-time environmental data comprises real-time wind speed data and real-time temperature data; step S2, determining a real-time environmental safety factor based on the real-time environmental data; step S3, determining a real-time drive safety factor and a real-time comprehensive safety factor based on the real-time sensor data; step S4, processing the real-time environmental safety factor, the real-time drive safety factor and the real-time comprehensive safety factor through a trained bridge rotation risk prediction model to determine a bridge rotation risk prediction coefficient; step S5, generating a bridge rotation process detection report based on the bridge rotation risk prediction coefficient, the real-time environmental safety factor, the real-time drive safety factor and the real-time comprehensive safety factor.

[0020] According to the bridge rotation process detection method based on three-dimensional visualization data of an embodiment of the present invention, real-time sensor data and real-time environmental data during the bridge rotation process can be accurately obtained, and the environmental conditions, drive system conditions and overall bridge conditions can be evaluated based on the real-time sensor data and real-time environmental data to determine the real-time environmental safety factor, real-time drive safety factor and real-time comprehensive safety factor. Furthermore, the real-time environmental safety factor, real-time drive safety factor and real-time comprehensive safety factor can be processed through the trained bridge rotation risk prediction model to predict the severity of possible risks, determine the bridge rotation risk prediction coefficient, and generate a detection report, thereby improving the accuracy of bridge rotation process detection.

[0021] According to one embodiment of the present invention, in step S1, real-time sensing data and real-time environmental data are acquired at multiple moments in the detection cycle, wherein the real-time sensing data includes: real-time positioning data, real-time posture data, real-time stress data and real-time drive system data, and the real-time environmental data includes: real-time wind speed data and real-time temperature data.

[0022] For example, GNSS receivers are deployed at key structural points of the bridge (such as the center of the spherical joint and the end of the beam) to obtain real-time positioning data. Fiber optic light shed sensors are installed at key structural points of the bridge to obtain real-time stress data. Dual-axis inclination sensors and accelerometers are installed at the core moving parts of the bridge's rotating structure (such as the spherical joint support points and key sections of the beam body of the bridge rotation project) to obtain real-time posture data of the bridge rotation. Real-time drive system data is obtained through sensors installed in the control system (such as travel encoders and pressure sensors). Real-time environmental data is obtained through anemometers and temperature sensors installed at preset positions (such as the highest operating point of the bridge, the end of the structure, and the key points of the bridge).

[0023] According to an embodiment of the present invention, in step S2, a real-time environmental safety factor is determined based on the real-time environmental data.

[0024] Figure 2 A flow chart for determining a real-time environmental safety factor according to an embodiment of the present invention is exemplarily shown.

[0025] According to one embodiment of the present invention, step S2 includes: step S21, determining the real-time cantilever end temperature difference and the temperature of the key point of the structure based on the real-time temperature data; step S22, determining the temperature uniformity safety factor based on the real-time cantilever end temperature difference; step S23, determining the temperature range safety factor based on the temperature of the key point of the structure; step S24, determining the wind speed environment safety factor based on the real-time wind speed data; step S25, determining the real-time environment safety factor based on the temperature uniformity safety factor, the temperature range safety factor and the wind speed environment safety factor.

[0026] For example, based on real-time temperature data, the temperatures of the cantilever ends on both sides of the bridge body and the temperatures of the key structural points (such as ball joints and support legs) are determined, and the real-time cantilever end temperature difference is determined based on the temperature difference between the cantilever ends on both sides; the temperature uniformity safety factor is determined based on the preset temperature difference threshold (such as 2 degrees Celsius) and the ratio of the difference between the real-time cantilever end temperature difference and the preset temperature difference threshold. The larger the temperature uniformity safety factor, the smaller the temperature difference between the cantilever ends on both sides, the smaller the possibility of the rotation axis deviation due to temperature difference, and the higher the safety; if the temperature of the key structural point belongs to the ideal temperature range (such as 5 degrees Celsius to 25 degrees Celsius), the temperature range safety result corresponding to the key point is 1, otherwise, the corresponding temperature range safety result is 2. The total result is 0, and the sum and average are calculated based on the number of key points to determine the temperature range safety factor. The larger the temperature range safety factor, the more key structural points whose temperatures are within the ideal temperature range, the smaller the impact of thermal expansion and contraction of building materials on rotation construction, and the greater the safety. The wind speed environment safety factor is determined based on the ratio of the difference between the preset wind speed warning threshold (for example, which can be set to 6m / s) and the real-time wind speed data to the preset wind speed warning threshold. The larger the wind speed environment safety factor, the smaller the real-time wind speed, the lower the risk of wind-induced bridge swaying or even overturning, and the higher the safety. The real-time environment safety factor is determined by summing the temperature uniformity safety factor, the temperature range safety factor, and the wind speed environment safety factor.

[0027] According to one embodiment of the present invention, in step S3, a real-time driving safety factor and a real-time comprehensive safety factor are determined based on the real-time sensing data.

[0028] Figure 3 A flowchart for determining a real-time driving safety factor and a real-time comprehensive safety factor according to an embodiment of the present invention is exemplarily shown.

[0029] According to one embodiment of the present invention, step S3 includes: step S31, determining the hydraulic pump working pressure, jack stroke and motor system temperature rise based on the real-time drive system data; step S32, determining the real-time drive safety factor based on the hydraulic pump working pressure, the jack stroke and the motor system temperature rise; step S33, determining the real-time comprehensive safety factor based on the real-time positioning data, the real-time posture data and the real-time stress data.

[0030] For example, the working pressure of the hydraulic pump is obtained by installing a pressure sensor on the hydraulic pump, the stroke of the jack is obtained by using a stroke encoder, and the temperature difference of the motor system relative to the surrounding environment, that is, the temperature rise of the motor system, is obtained by installing a temperature sensor on the motor system. Based on the working pressure of the hydraulic pump, the stroke of the jack and the temperature rise of the motor system, the working condition of the drive system is evaluated to determine the real-time drive safety factor. Based on the real-time positioning data, real-time attitude data and real-time stress data, the attitude, position and stress safety status of the bridge during the bridge rotation process are evaluated to determine the real-time comprehensive safety factor.

[0031] According to one embodiment of the present invention, step S32 includes: step S321, determining the working pressure fluctuation value according to the working pressure of the hydraulic pump; step S323, determining the working safety factor of the hydraulic pump according to the working pressure fluctuation value and the working pressure of the hydraulic pump; step S323, determining the stroke difference of adjacent jacks according to the jack stroke; step S324, determining the jack synchronization safety factor according to the stroke difference of adjacent jacks; step S325, determining the temperature rise safety factor of the motor system according to the temperature rise of the motor system; step S326, determining the real-time drive safety factor according to the working safety factor of the hydraulic pump, the jack synchronization safety factor and the temperature rise safety factor of the motor system.

[0032] For example, the working pressure fluctuation value is determined based on the difference between the hydraulic pump working pressure at the current moment of the detection cycle and the hydraulic pump working pressure at the previous moment; the first ratio is determined based on the ratio of the set pressure fluctuation warning threshold (which can be set to 10% of the rated pressure of the hydraulic pump) and the difference between the working pressure fluctuation value and the set pressure fluctuation warning threshold. The larger the first ratio is, the smaller the working pressure fluctuation value is, the lower the possibility of oil circuit burst is, and the better the working safety condition of the hydraulic pump is. The second ratio is determined based on the ratio of the set working pressure abnormal peak value (which can be set to 130% of the rated pressure of the hydraulic pump) and the difference between the hydraulic pump working pressure and the set working pressure abnormal peak value. The larger the second ratio is, the smaller the hydraulic pump working pressure is, the lower the possibility of hydraulic pump overload is, and the better the working safety condition of the hydraulic pump is. The working safety factor of the hydraulic pump is determined by summing the first ratio and the second ratio. ; According to the jack stroke, determine the adjacent jack stroke difference between adjacent jacks; according to the preset stroke difference threshold (which can be set to 2mm) and the ratio of the difference between the maximum values ​​of the adjacent jack stroke differences of multiple adjacent jacks to the preset stroke difference threshold, determine the jack synchronization safety factor. The larger the jack synchronization safety factor, the smaller the maximum value of the adjacent jack stroke difference of multiple adjacent jacks, the smaller the possibility of structural torque overlimit, and the higher the stroke synchronization of multiple jacks; according to the preset temperature rise threshold (which can be set to 60 degrees Celsius) and the ratio of the difference between the motor system temperature rise and the preset temperature rise threshold, determine the motor system temperature rise safety factor. The larger the motor system temperature rise safety factor, the lower the possibility of burning of the motor system; the real-time drive safety factor is determined by summing the hydraulic pump working safety factor, the jack synchronization safety factor and the motor system temperature rise safety factor.

[0033] According to one embodiment of the present invention, step S33 includes: step S331, determining the key point coordinates of the structural key points in a preset coordinate system based on the real-time positioning data, wherein the preset coordinate system is a coordinate system established with a preset point position in the ground where the rotating bridge is located as the origin; step S332, determining the real-time rotation angle and the real-time rotation acceleration based on the real-time posture data; step S333, determining the key point stress value based on the real-time stress data; step S334, obtaining the standard key point coordinates, standard rotation angle, standard rotation acceleration and standard key point stress value by designing a BIM model; step S335, determining the key point priority of each structural key point; step S336, determining the real-time comprehensive safety factor based on the key point priority, the key point coordinates, the real-time rotation angle, the real-time rotation acceleration, the key point stress value, the standard key point coordinates, the standard rotation angle, the standard rotation acceleration and the standard key point stress value.

[0034] For example, a preset point (such as a rotation support point) in the ground where the rotating bridge is located is used as the origin, the ground is used as the coordinate system xoy plane, and the vertical upward direction is used as the coordinate system z axis to establish a coordinate system, and the key point coordinates of the key points of the structure in the preset coordinate system are determined by the GNSS receiver; the real-time rotation angle and real-time rotation acceleration of the bridge rotation are obtained by the dual-axis inclination sensor and accelerometer; the key point stress values ​​at the key points of the structure are obtained by the fiber optic light shed sensor; a design BIM model is established according to the design parameters of the bridge, and the process of bridge rotation is simulated according to the bridge rotation plan and the design BIM model, and the standard key point coordinates and standard key point stress values ​​of each key point of the structure under standard conditions during the bridge rotation process, as well as the quasi-rotation angle and standard rotation acceleration of the entire bridge are obtained by the design BIM model; according to the influence weight of the structural key points of the bridge on the overall stability, Based on the severity of failure consequences and real-time control requirements, the key point priority of each structural key point is determined. For example, the key point priority of the spherical joint center is 10, the key point priority of the structural key point at the connection point of the pier root and the abutment is 9, the key point priority of the structural key point at the beam end is 8, the key point priority of the structural key point at the critical section of the beam body is 7, the key point priority of the structural key point at the top of the main tower is 6, and the key point priority of the structural key point at the rotation track position is 5. The higher the priority, the greater the importance of the structural key point and the greater the impact on the rotation; based on the key point priority, key point coordinates, real-time rotation angle, real-time rotation acceleration, key point stress value, standard key point coordinates, standard rotation angle, standard rotation acceleration and standard key point stress value, the safety status of the posture, position, speed and angle during the bridge rotation process is evaluated to determine the real-time comprehensive safety factor.

[0035] According to one embodiment of the present invention, step S336 includes: determining a key point state vector according to the key point coordinates and the key point stress value; determining a rotation state vector according to the real-time rotation angle and the real-time rotation acceleration; determining a standard key point state vector according to the standard key point coordinates and the standard key point stress value; determining a standard rotation state vector according to the standard rotation angle and the standard rotation acceleration; determining a real-time comprehensive safety factor at the i-th moment of the detection period according to formula (1) , (1) Among them, min is the minimum value function, is the key point coordinate of the kth key point at the i-th moment of the detection cycle, is the stress value of the kth key point at the i-th moment of the detection cycle, is the key point state vector of the kth key point at the i-th moment of the detection cycle, for The transposed vector of is the standard key point coordinate of the kth key point at the i-th moment of the detection cycle, is the standard key point stress value of the kth key point at the i-th moment of the detection cycle, is the standard key point state vector of the kth key point at the i-th moment of the detection cycle, is the key point priority of the k-th key point, is the real-time rotation angle at the i-th moment of the detection cycle, is the real-time rotation acceleration at the i-th moment of the detection cycle, is the rotation state vector at the i-th moment of the detection cycle, for The transposed vector of is the standard rotation angle at the i-th moment of the detection cycle, is the standard rotation acceleration at the i-th moment of the detection cycle, is the standard rotation state vector at the i-th moment of the detection period, K is the number of key points, k≤K, and both k and K are positive integers.

[0036] According to one embodiment of the present invention, is the cosine similarity between the key point state vector of the kth key point at the i-th moment of the detection cycle and the standard key point state vector. The larger the cosine similarity, the closer the key point coordinates and key point stress values ​​of the kth key point at the i-th moment of the detection cycle are to the preset standard. The minimum value of the cosine similarity between the key point state vector of the K key points at the i-th moment of the detection period and the standard key point state vector is taken. The above minimum value processing can be used to determine the most serious abnormality among the K key points at the i-th moment of the detection period. It is the ratio of the minimum value of the cosine similarity between the key point state vector and the standard key point state vector of the K key points at the i-th moment of the detection period to the corresponding key point priority. It indicates the safety factor of the key point in the rotation process determined according to the abnormal condition and importance of the key point. The greater the priority of the key point, the higher the importance of the key point, which will cause the abnormal condition at the key point to be amplified, resulting in a more serious impact on the rotation process. The lower the value of It is the cosine similarity between the rotation state vector at the i-th moment of the detection period and the standard rotation state vector. The greater the cosine similarity, the closer the real-time rotation angle and real-time rotation acceleration of the bridge at the i-th moment of the detection period are to the standard rotation angle and standard rotation acceleration.

[0037] In this way, the real-time comprehensive safety factor can be determined based on the key point priority, key point coordinates, real-time rotation angle, real-time rotation acceleration, key point stress value, standard key point coordinates, standard rotation angle, standard rotation acceleration and standard key point stress value. During the calculation process, the safety status of the bridge during rotation can be evaluated based on the safety impact of the structural key points on the rotation process and the overall posture of the bridge, thereby improving the comprehensiveness and accuracy of the real-time comprehensive safety factor.

[0038] According to one embodiment of the present invention, in step S4, the real-time environmental safety factor, the real-time driving safety factor and the real-time comprehensive safety factor are processed by a trained bridge rotation risk prediction model to determine a bridge rotation risk prediction coefficient.

[0039] According to one embodiment of the present invention, the training step of the bridge rotation risk prediction model includes: obtaining historical bridge rotation construction records of multiple historical construction periods; determining historical sensor data and historical environmental data based on the historical bridge rotation construction records; determining a historical environmental safety factor, a historical driving safety factor, and a historical comprehensive safety factor based on the historical sensor data and the historical environmental data; processing the historical environmental safety factor, the historical driving safety factor, and the historical comprehensive safety factor through the bridge rotation risk prediction model to determine a sample bridge rotation risk prediction coefficient; determining historical construction alarm information based on the historical bridge rotation construction records; determining a historical alarm information time difference, a historical alarm level, and a historical construction delay time based on the historical construction alarm information; determining a historical bridge rotation risk coefficient based on the historical alarm information time difference, the historical alarm level, and the historical construction delay time; determining a training loss function for the bridge rotation risk prediction model based on the historical bridge rotation risk coefficient, the sample bridge rotation risk prediction coefficient, the historical environmental safety factor, the historical driving safety factor, and the historical comprehensive safety factor; The bridge rotation risk prediction model is trained according to the training loss function to obtain a trained bridge rotation risk prediction model.

[0040] For example, obtain the construction records of historical bridges that have been completed and are similar to the current bridge, that is, the historical bridge rotation construction records; in the historical bridge rotation construction records, determine the historical sensor data and historical environmental data, the historical sensor data include: historical positioning data, historical posture data, historical stress data and historical drive system data, the historical environmental data include: historical wind speed data and historical temperature data; based on the historical sensor data, determine the historical drive safety factor and the historical comprehensive safety factor, the determination method of the historical drive safety factor and the historical comprehensive safety factor is the same as the determination method of the real-time drive safety factor and the real-time comprehensive safety factor, which will not be repeated here, based on the historical environmental data, determine the historical environmental safety factor, the historical The method for determining the environmental safety factor is the same as the method for determining the real-time environmental safety factor, which will not be repeated here; the historical environmental safety factor, the historical driving safety factor and the historical comprehensive safety factor are processed according to the bridge rotation risk prediction model, and the urgency of the dangerous situation that may occur within the preset time period (e.g., thirty minutes) after the time point corresponding to the historical environmental safety factor is predicted to determine the sample bridge rotation risk prediction coefficient; based on the historical bridge rotation construction records, the historical construction alarm information of the preset time period after the time point of obtaining the historical sensor data and the historical environmental data is determined, such as when the historical sensor data and the historical environmental data at the initial moment of the first historical construction cycle are obtained, the historical construction alarm information of the first historical construction cycle is obtained. The alarm information recorded within thirty minutes after the initial moment of the period is the historical construction alarm information; based on the historical construction alarm information, the time difference between the time point when the alarm information is issued and the time point when the historical sensor data and the historical environmental data are obtained is determined, that is, the historical alarm information time difference. Alarms are usually divided into level one, level two and level three, with level one being the most serious. Based on the historical construction alarm information, the historical alarm level is determined. Based on the historical construction alarm information, the time of construction delay caused by the alarm is determined, that is, the historical construction delay time; the third ratio is determined based on the ratio of the preset time difference threshold (which can be set to 1 minute) and the historical alarm information time difference. The smaller the historical alarm information time difference, the more likely it is that a danger will occur in a short time. Condition, the larger the third ratio is, the fourth ratio is determined according to the ratio of the preset alarm level threshold (which can be set to level 3) and the historical alarm level. The smaller the historical alarm level is, the higher the danger level is. The larger the fourth ratio is, the fifth ratio is determined according to the ratio of the historical construction delay time to the preset delay time (which can be set to 1 hour). The longer the historical construction delay time is, the more difficult the danger handling is, and the larger the fifth ratio is. The historical bridge rotation risk coefficient is determined by summing the third ratio, the fourth ratio, and the fifth ratio. The training loss function of the bridge rotation risk prediction model is determined according to the historical bridge rotation risk coefficient, the sample bridge rotation risk prediction coefficient, the historical environmental safety factor, the historical driving safety factor, and the historical comprehensive safety factor.The bridge rotation risk prediction model is trained according to the training loss function to improve the accuracy of the bridge rotation risk prediction model in predicting bridge rotation risks, and a trained bridge rotation risk prediction model is obtained.

[0041] According to one embodiment of the present invention, the training loss function of the bridge rotation risk prediction model is determined based on the historical bridge rotation risk coefficient, the sample bridge rotation risk prediction coefficient, the historical environmental safety factor, the historical driving safety factor, and the historical comprehensive safety factor, including: determining the training loss function of the bridge rotation risk prediction model according to formula (2): , (2) in, is the risk prediction coefficient of the sample bridge rotation at the rth moment in the eth historical construction cycle, is the historical bridge rotation risk coefficient for the preset time period after the rth moment of the eth historical construction cycle, is the historical environmental safety factor at the rth moment of the eth historical construction cycle, is the preset environmental safety factor threshold, is the historical driving safety factor at the rth moment of the eth historical construction period, is the preset driving safety factor threshold, is the historical comprehensive safety factor at the rth moment of the eth historical construction cycle, is the preset comprehensive safety factor threshold, E is the number of historical construction cycles, e≤E, R is the number of moments in the historical construction cycle, r≤R, and e, E, r and R are all positive integers.

[0042] According to one embodiment of the present invention, It is the ratio of the historical environmental safety factor at the rth moment of the eth historical construction cycle to the preset environmental safety factor threshold. The larger the ratio is, the larger the historical environmental safety factor is. The preset environmental safety factor threshold can be set to 1. It is the ratio of the historical driving safety factor at the rth moment of the eth historical construction period to the preset driving safety factor threshold. The larger the ratio is, the larger the historical driving safety factor is. The preset driving safety factor threshold can be set to 1. It is the ratio of the historical comprehensive safety factor at the rth moment of the eth historical construction cycle to the preset comprehensive safety factor threshold. The larger the ratio is, the larger the historical comprehensive safety factor is. The preset comprehensive safety factor threshold can be set to 1. It means that the size of the historical environmental safety factor, the historical driving safety factor and the historical comprehensive safety factor are negatively correlated with the sample bridge rotation risk prediction coefficient. For example, when the historical environmental safety factor is larger, the impact of the temperature and wind force on the construction safety at the construction site is smaller, and the sample bridge rotation risk prediction coefficient is smaller. When the sample bridge rotation risk prediction coefficient is larger, the operating condition of the driving system is more normal, and the sample bridge rotation risk prediction coefficient is smaller. When the historical comprehensive safety factor is larger, the state of the key structural points during the bridge rotation process and the overall rotation acceleration and acceleration of the bridge are more normal, and the sample bridge rotation risk prediction coefficient is smaller. Therefore, the items related to the historical environmental safety factor, the historical driving safety factor and the historical comprehensive safety factor are placed in the denominator, indicating 、 and The larger the value of , the smaller the value of the sample bridge rotation risk prediction coefficient.

[0043] According to one embodiment of the present invention, is the relative error between the sample bridge rotation risk prediction coefficient at the rth moment of the eth historical construction cycle and the historical bridge rotation risk coefficient of the preset time period after the rth moment of the eth historical construction cycle. A training loss function is obtained by taking a weighted average of the relative errors between the sample bridge rotation risk prediction coefficient at the rth moment of the eth historical construction cycle and the historical bridge rotation risk coefficients for a preset period of time after the rth moment of the eth historical construction cycle. During the training process, the training loss function is reduced, thereby reducing the error between the sample bridge rotation risk prediction coefficient and the historical bridge rotation risk coefficient, improving the prediction accuracy of the bridge rotation risk prediction coefficient by the bridge rotation risk prediction model, and thus improving the accuracy of the bridge rotation risk prediction model.

[0044] In this way, the training loss function of the bridge rotation risk prediction model can be determined based on the historical bridge rotation risk coefficient, the sample bridge rotation risk prediction coefficient, the historical environmental safety factor, the historical driving safety factor and the historical comprehensive safety factor. During the determination process, the influence of the above data on the error of the sample bridge rotation risk prediction coefficient can be determined based on the possible influence of the historical environmental safety factor, the historical driving safety factor and the historical comprehensive safety factor on the occurrence of risk conditions. Based on this influence and the relative error of the sample bridge rotation risk prediction coefficient, the training loss function is set to reduce the training loss function of the bridge rotation risk prediction model during the training process, and to improve the accuracy of the bridge rotation risk prediction model in a more targeted manner.

[0045] According to one embodiment of the present invention, in step S5, a bridge rotation process detection report is generated based on the bridge rotation risk prediction coefficient, the real-time environmental safety factor, the real-time driving safety factor and the real-time comprehensive safety factor.

[0046] For example, based on the real-time environmental safety factor, the environmental conditions during the bridge rotation process are determined. The larger the real-time environmental safety factor, the more suitable the environmental conditions are for construction. Based on the real-time drive safety factor, the working conditions of the drive system during the bridge rotation process are determined. The larger the real-time drive safety factor, the better the working conditions of the drive system. Based on the real-time comprehensive safety factor, the force, position and overall posture conditions of the key points during the bridge rotation process are determined. The larger the real-time comprehensive safety factor, the better the overall condition of the bridge. Based on the bridge rotation risk prediction coefficient, the severity of the possible risks is determined. The larger the bridge rotation risk prediction coefficient, the more serious the possible risks.

[0047] According to an embodiment of the present invention, a bridge rotation process detection method based on 3D visualization data can accurately obtain real-time sensor data and real-time environmental data during the bridge rotation process. Based on the real-time sensor data and real-time environmental data, the environmental conditions, the drive system conditions, and the overall bridge conditions are evaluated to determine a real-time environmental safety factor, a real-time drive safety factor, and a real-time comprehensive safety factor. Furthermore, the real-time environmental safety factor, real-time drive safety factor, and real-time comprehensive safety factor can be processed using a trained bridge rotation risk prediction model to predict the severity of potential risks, determine the bridge rotation risk prediction factor, and generate a detection report, thereby improving the accuracy of bridge rotation process detection. When determining the real-time comprehensive safety factor, the real-time comprehensive safety factor can be determined based on key point priorities, key point coordinates, real-time rotation angles, real-time rotation accelerations, key point stress values, standard key point coordinates, standard rotation angles, standard rotation accelerations, and standard key point stress values. During the calculation process, the safety status of the bridge rotation process can be assessed based on both the safety impact of structural key points on the rotation process and the overall bridge posture, thereby improving the comprehensiveness and accuracy of the real-time comprehensive safety factor. When determining the training loss function of the bridge rotation risk prediction model, the training loss function of the bridge rotation risk prediction model can be determined based on the historical bridge rotation risk coefficient, the sample bridge rotation risk prediction coefficient, the historical environmental safety factor, the historical driving safety factor and the historical comprehensive safety factor. During the determination process, the influence of the above data on the error of the sample bridge rotation risk prediction coefficient can be determined based on the possible influence of the historical environmental safety factor, the historical driving safety factor and the historical comprehensive safety factor on the occurrence of risk conditions. Based on this influence and the relative error of the sample bridge rotation risk prediction coefficient, the training loss function is set to reduce the training loss function of the bridge rotation risk prediction model during the training process, and to improve the accuracy of the bridge rotation risk prediction model in a more targeted manner.

[0048] Figure 4A block diagram of a bridge rotation process detection system based on three-dimensional visualization data according to an embodiment of the present invention is exemplarily shown, wherein the system includes: a real-time data module, used to obtain real-time sensor data and real-time environmental data at multiple moments in a detection cycle, wherein the real-time sensor data includes: real-time positioning data, real-time posture data, real-time stress data and real-time drive system data, and the real-time environmental data includes: real-time wind speed data and real-time temperature data; an environmental coefficient module, used to determine a real-time environmental safety factor based on the real-time environmental data; a sensor coefficient module, used to determine a real-time drive safety factor and a real-time comprehensive safety factor based on the real-time sensor data; a risk prediction module, used to process the real-time environmental safety factor, the real-time drive safety factor and the real-time comprehensive safety factor through a trained bridge rotation risk prediction model to determine a bridge rotation risk prediction coefficient; and a detection report module, used to generate a bridge rotation process detection report based on the bridge rotation risk prediction coefficient, the real-time environmental safety factor, the real-time drive safety factor and the real-time comprehensive safety factor.

[0049] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0050] Those skilled in the art will appreciate that the embodiments of the present invention described above and shown in the accompanying drawings are intended to be illustrative only and are not intended to limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and illustrated in the embodiments. Any variations or modifications may be made to the embodiments of the present invention without departing from the principles described.

Claims

1. A bridge rotation process detection method based on three-dimensional visualization data, characterized in that: include: At multiple moments in the detection cycle, real-time sensor data and real-time environmental data are acquired, wherein the real-time sensor data include: real-time positioning data, real-time posture data, real-time stress data and real-time drive system data, and the real-time environmental data include: real-time wind speed data and real-time temperature data; based on the real-time environmental data, a real-time environmental safety factor is determined; based on the real-time sensor data, a real-time drive safety factor and a real-time comprehensive safety factor are determined; the real-time environmental safety factor, the real-time drive safety factor and the real-time comprehensive safety factor are processed by a trained bridge rotation risk prediction model to determine a bridge rotation risk prediction coefficient; based on the bridge rotation risk prediction coefficient, the real-time environmental safety factor, the real-time drive safety factor and the real-time comprehensive safety factor, a bridge rotation process detection report is generated.

2. The bridge rotation process detection method based on three-dimensional visualization data according to claim 1 is characterized in that: The real-time environmental safety factor is determined based on the real-time environmental data, including: determining the real-time cantilever end temperature difference and the temperature of the key points of the structure based on the real-time temperature data; determining the temperature uniformity safety factor based on the real-time cantilever end temperature difference; determining the temperature range safety factor based on the temperature of the key points of the structure; determining the wind speed environmental safety factor based on the real-time wind speed data; and determining the real-time environmental safety factor based on the temperature uniformity safety factor, the temperature range safety factor and the wind speed environmental safety factor.

3. The bridge rotation process detection method based on three-dimensional visualization data according to claim 1 is characterized in that: Based on the real-time sensing data, a real-time driving safety factor and a real-time comprehensive safety factor are determined, including: determining the hydraulic pump working pressure, the jack stroke and the motor system temperature rise based on the real-time driving system data; determining the real-time driving safety factor based on the hydraulic pump working pressure, the jack stroke and the motor system temperature rise; and determining the real-time comprehensive safety factor based on the real-time positioning data, the real-time posture data and the real-time stress data.

4. The bridge rotation process detection method based on three-dimensional visualization data according to claim 3 is characterized in that: The real-time driving safety factor is determined according to the hydraulic pump working pressure, the jack stroke and the temperature rise of the motor system, including: determining the working pressure fluctuation value according to the hydraulic pump working pressure; determining the hydraulic pump working safety factor according to the working pressure fluctuation value and the hydraulic pump working pressure; determining the adjacent jack stroke difference according to the jack stroke; determining the jack synchronization safety factor according to the adjacent jack stroke difference; determining the motor system temperature rise safety factor according to the motor system temperature rise; and determining the real-time driving safety factor according to the hydraulic pump working safety factor, the jack synchronization safety factor and the motor system temperature rise safety factor.

5. The bridge rotation process detection method based on three-dimensional visualization data according to claim 3 is characterized in that: Determine a real-time comprehensive safety factor based on the real-time positioning data, the real-time posture data and the real-time stress data, including: determining the key point coordinates of the structural key points in a preset coordinate system based on the real-time positioning data, wherein the preset coordinate system is a coordinate system established with a preset point position in the ground where the rotating bridge is located as the origin; determine the real-time rotation angle and the real-time rotation acceleration based on the real-time posture data; determine the key point stress value based on the real-time stress data; obtain standard key point coordinates, standard rotation angle, standard rotation acceleration and standard key point stress value by designing a BIM model; determine the key point priority of each structural key point; determine the real-time comprehensive safety factor based on the key point priority, the key point coordinates, the real-time rotation angle, the real-time rotation acceleration, the key point stress value, the standard key point coordinates, the standard rotation angle, the standard rotation acceleration and the standard key point stress value.

6. The bridge rotation process detection method based on three-dimensional visualization data according to claim 5 is characterized in that: According to the key point priority, the key point coordinates, the real-time rotation angle, the real-time rotation acceleration, the key point stress value, the standard key point coordinates, the standard rotation angle, the standard rotation acceleration and the standard key point stress value, a real-time comprehensive safety factor is determined, including: determining a key point state vector according to the key point coordinates and the key point stress value; determining a rotation state vector according to the real-time rotation angle and the real-time rotation acceleration; determining a standard key point state vector according to the standard key point coordinates and the standard key point stress value; determining a standard rotation state vector according to the standard rotation angle and the standard rotation acceleration; determining a standard rotation state vector according to the formula Determine the real-time comprehensive safety factor at the i-th moment of the detection cycle , where min is the minimum value function, is the key point coordinate of the kth key point at the i-th moment of the detection cycle, is the stress value of the kth key point at the i-th moment of the detection cycle, is the key point state vector of the kth key point at the i-th moment of the detection cycle, for The transposed vector of is the standard key point coordinate of the kth key point at the i-th moment of the detection cycle, is the standard key point stress value of the kth key point at the i-th moment of the detection cycle, is the standard key point state vector of the kth key point at the i-th moment of the detection cycle, is the key point priority of the k-th key point, is the real-time rotation angle at the i-th moment of the detection cycle, is the real-time rotation acceleration at the i-th moment of the detection cycle, is the rotation state vector at the i-th moment of the detection cycle, for The transposed vector of is the standard rotation angle at the i-th moment of the detection cycle, is the standard rotation acceleration at the i-th moment of the detection cycle, is the standard rotation state vector at the i-th moment of the detection period, K is the number of key points, k≤K, and both k and K are positive integers.

7. The bridge rotation process detection method based on three-dimensional visualization data according to claim 1 is characterized in that: The training steps of the bridge rotation risk prediction model include: obtaining historical bridge rotation construction records of multiple historical construction cycles; determining historical sensor data and historical environmental data based on the historical bridge rotation construction records; determining a historical environmental safety factor, a historical driving safety factor, and a historical comprehensive safety factor based on the historical sensor data and the historical environmental data; processing the historical environmental safety factor, the historical driving safety factor, and the historical comprehensive safety factor through the bridge rotation risk prediction model to determine a sample bridge rotation risk prediction factor; and determining historical construction alarm information based on the historical bridge rotation construction records. Based on the historical construction alarm information, determine the time difference of historical alarm information, the historical alarm level and the historical construction delay time; based on the time difference of historical alarm information, the historical alarm level and the historical construction delay time, determine the historical bridge rotation risk coefficient; based on the historical bridge rotation risk coefficient, the sample bridge rotation risk prediction coefficient, the historical environmental safety factor, the historical driving safety factor and the historical comprehensive safety factor, determine the training loss function of the bridge rotation risk prediction model; train the bridge rotation risk prediction model according to the training loss function to obtain a trained bridge rotation risk prediction model.

8. The bridge rotation process detection method based on three-dimensional visualization data according to claim 7 is characterized in that: According to the historical bridge rotation risk coefficient, the sample bridge rotation risk prediction coefficient, the historical environmental safety factor, the historical driving safety factor and the historical comprehensive safety factor, the training loss function of the bridge rotation risk prediction model is determined, including: according to the formula Determine the training loss function of the bridge rotation risk prediction model ,in, is the risk prediction coefficient of the sample bridge rotation at the rth moment in the eth historical construction cycle, is the historical bridge rotation risk coefficient for the preset time period after the rth moment of the eth historical construction cycle, is the historical environmental safety factor at the rth moment of the eth historical construction cycle, is the preset environmental safety factor threshold, is the historical driving safety factor at the rth moment of the eth historical construction period, is the preset driving safety factor threshold, is the historical comprehensive safety factor at the rth moment of the eth historical construction cycle, is the preset comprehensive safety factor threshold, E is the number of historical construction cycles, e≤E, R is the number of moments in the historical construction cycle, r≤R, and e, E, r and R are all positive integers.

9. A bridge rotation process detection system based on three-dimensional visualization data, characterized in that: include: A real-time data module is used to obtain real-time sensor data and real-time environmental data at multiple moments in the detection cycle, wherein the real-time sensor data includes: real-time positioning data, real-time posture data, real-time stress data and real-time drive system data, and the real-time environmental data includes: real-time wind speed data and real-time temperature data; an environmental coefficient module is used to determine the real-time environmental safety factor based on the real-time environmental data; a sensor coefficient module is used to determine the real-time drive safety factor and the real-time comprehensive safety factor based on the real-time sensor data; a risk prediction module is used to process the real-time environmental safety factor, the real-time drive safety factor and the real-time comprehensive safety factor through a trained bridge rotation risk prediction model to determine the bridge rotation risk prediction coefficient; a detection report module is used to generate a bridge rotation process detection report based on the bridge rotation risk prediction coefficient, the real-time environmental safety factor, the real-time drive safety factor and the real-time comprehensive safety factor.

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