Bridge erecting machine based on machine vision and its control method

Abstract

The present invention discloses a bridge-building machine based on machine vision and a control method thereof, and relates to the technical field of bridge-building machine control. The method discloses the following steps: Step 1: Based on a preset cycle, obtain fluctuation influence data and detection video clips of each cycle; Step 2: Mark the cycle corresponding to the detection video clip as a working condition fluctuation cycle according to the outrigger working condition value; Step 3: Obtain the control stability analysis value of the bridge-building machine, adjust the mode of the bridge-building machine according to the control stability analysis value, perform intelligent analysis on the stability of the bridge-building machine and environmental fluctuations by means of machine vision, give real-time warnings to the operation of the operator, and automatically adjust the mode of the bridge-building machine at the same time, continuously optimize the safety and rationality of the bridge-building machine during operation, set an outrigger detector on the bridge-building machine, and perform working condition detection of the outriggers on the bridge-building machine by means of machine vision, and the operator can adjust his own operation according to the outrigger working condition value.

Description

Technical Field

[0001] The invention relates to the technical field of bridge erection machine control, and more specifically, to a bridge erection machine based on machine vision and a control method thereof. Background Art

[0002] A bridge-erecting machine is a type of lifting equipment, which is a mechanical device that places prefabricated beams on bridge piers. The construction conditions of a bridge-erecting machine are rather special, and the safety requirements are rather stringent. During the operation of the bridge-erecting machine, the outriggers need to be fixed on the bridge piers, and then the main body of the bridge-erecting machine is moved through the walking system. After the outriggers are fixed on the bridge piers, factors such as the vibration of the surrounding ground and the operator's control will cause the outriggers to shake. The current bridge-erecting machines are unable to perform intelligent analysis of the shaking of the outriggers, resulting in the operator being unable to adjust the corresponding operations in a timely manner. Summary of the invention

[0003] In view of the deficiencies in the prior art, the object of the present invention is to provide a bridge erecting machine based on machine vision and a control method thereof.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A machine vision-based bridge erecting machine comprises a bridge erecting machine body, on which a plurality of outrigger detectors are installed;

[0006] The outrigger detector includes an outrigger visual detection module and a bridge erection machine control module;

[0007] After the outrigger is fixed on the bridge pier, the outrigger visual detection module shoots a video of the outrigger, marks the shot video as an outrigger detection video, and based on the T cycle, obtains the fluctuation influence data of the previous cycle at each cycle node, and at the same time intercepts the outrigger detection video of the previous cycle to obtain a detection video segment, obtains the outrigger working condition value of the detection video segment, marks the period corresponding to the detection video segment as a working condition fluctuation period according to the outrigger working condition value, and obtains the vibration influence ratio of the working condition fluctuation period;

[0008] The bridge erection machine control module is used to obtain the operation stability analysis value of the bridge erection machine and adjust the mode of the bridge erection machine according to the operation stability analysis value.

[0009] Furthermore, the fluctuation impact data include wind parameters and vibration parameters. The wind parameters include wind force and wind speed around the bridge-building machine, and the vibration parameters include vibration frequency and vibration amplitude of the platform where the bridge-building machine is located.

[0010] Furthermore, the outrigger working condition value of the detection video clip is obtained by the following method:

[0011] The detected video clip is converted into multiple video image frames and marked as m, where m is the number of the video image frame, m=1, 2, ..., M, and M is a positive integer. The multiple video image frames are sorted in chronological order to obtain the working angle of the outrigger in each video image frame. The working angle is divided into super angle type, low angle type, and flat angle type. The working angle types of two adjacent video image frames after sorting are compared. When the working angle types of two adjacent video image frames are the same, no corresponding processing is performed. When the working angle types of two adjacent video image frames are different, the angle fluctuation times are increased once, and all angle fluctuation times are summed to obtain the total angle fluctuation times, which is marked as BDC. The flat angle number interval PJB is obtained, and the number of video image frames with flat angle type working angles is obtained and marked as SLM. The formula is used Get the outrigger working condition value GCZ of the detected video clip, where a1 is the total angle fluctuation coefficient, a2 is the flat angle number spacing coefficient, and a3 is the flat angle image frame number coefficient.

[0012] Furthermore, the working angles are divided into super angle type, low angle type and flat angle type, specifically:

[0013] Set the upper working angle and the lower working angle. When the working angle is greater than the upper working angle, the type of the working angle is marked as the super angle type. When the working angle is less than the lower working angle, the type of the working angle is marked as the low angle type. When the working angle is between the upper working angle and the lower working angle, the type of the working angle is marked as the flat angle type.

[0014] Furthermore, the flat angle numbering interval is obtained by the following method: all the video image frames of the flat angle type working angles are sorted in sequence according to the numbering order, the numbering difference between the two adjacent video image frames after sorting is calculated to obtain the flat angle numbering interval, all the flat angle numbering differences are summed up and averaged to obtain the flat angle numbering interval, and marked as PJB.

[0015] Furthermore, the period corresponding to the detected video clip is marked as the working condition fluctuation period according to the outrigger working condition value, specifically:

[0016] The outrigger working condition threshold is set. When the outrigger working condition value is greater than or equal to the outrigger working condition threshold, the period corresponding to the detected video clip is marked as the working condition fluctuation period.

[0017] Furthermore, the vibration influence ratio of the operating condition fluctuation period is obtained by the following method:

[0018] Obtain the fluctuation influence data of the working condition fluctuation cycle, use the fluctuation influence data of the working condition fluctuation cycle as the input data of the environmental fluctuation analysis model, obtain the environmental impact value, and mark it as HBT, obtain the leg working condition value GCZ of the detection video clip corresponding to the working condition fluctuation cycle, and use the formula The vibration influence ratio YXB of the working condition fluctuation period is obtained, where b1 is the environmental influence value coefficient and b2 is the outrigger working condition value coefficient.

[0019] Furthermore, the stability analysis value of the bridge erecting machine is obtained by the following method:

[0020] Set the vibration impact ratio threshold. When the vibration impact ratio is greater than or equal to the vibration impact ratio threshold, mark the working condition fluctuation period as the operation impact period. Obtain the number of all operation impact periods after the outrigger is fixed on the pier and mark it as BNY. Sum the vibration impact ratios of all operation impact periods to obtain the total vibration impact ratio and mark it as FCZ. Use the formula The control stability analysis value CWZ of the bridge erecting machine is obtained, where c1 is the coefficient of the number of operation influence cycles and c2 is the total ratio coefficient of vibration influence.

[0021] Furthermore, the mode of the bridge erecting machine is adjusted according to the operating stability analysis value, specifically:

[0022] Set the high value and low value of the operation stability analysis. When the operation stability analysis value is greater than the high value of the operation stability analysis, the bridge-building machine is adjusted to the shutdown mode. When the operation stability analysis value is less than the low value of the operation stability analysis, no corresponding processing is performed. When the operation stability analysis value is between the high value and the low value of the operation stability analysis, the bridge-building machine is adjusted to the warning mode.

[0023] Furthermore, the environmental fluctuation analysis model is obtained in the following manner: multiple groups of fluctuation influence data are obtained, each group of fluctuation influence data includes wind parameters and vibration parameters (wind parameters and vibration parameters are data of the same period), a neural network model is constructed, the fluctuation influence data is used as training data of the neural network model, an environmental impact value is assigned to the training data, and the environmental impact value ranges from (0 to 1), the training data is divided into a training set and a validation set according to a set ratio, the neural network model is iteratively trained with the training set and the validation set, and the environmental fluctuation analysis model is obtained after the training is completed. The larger the value of the environmental impact value, the greater the influence of the fluctuation influence data on the outrigger fluctuation.

[0024] Furthermore, the bridge erection machine control method based on machine vision includes the following steps:

[0025] Step 1: Based on a preset period, obtain the fluctuation impact data and detection video clips of each period;

[0026] Step 2: Mark the period corresponding to the detected video clip as the working condition fluctuation period according to the outrigger working condition value;

[0027] Step 3: Obtain the operational stability analysis value of the bridge-building machine, and adjust the mode of the bridge-building machine according to the operational stability analysis value.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. The method of the present invention uses machine vision to intelligently analyze the stability and environmental fluctuations of the bridge erection machine, and gives real-time warnings to the operator. At the same time, the mode of the bridge erection machine is automatically adjusted to continuously optimize the safety and rationality of the bridge erection machine during operation.

[0030] 2. An outrigger detector is installed on the bridge-building machine, which can detect the working condition of the outriggers on the bridge-building machine through machine vision. The operator can adjust his own operation according to the outrigger working condition value. The outrigger detector combines the surrounding environmental fluctuation data to perform intelligent analysis of the operator's operation and intelligently adjust the working mode of the bridge-building machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flowchart of the bridge erection machine control method based on machine vision;

[0032] Figure 2 A flowchart for adjusting the mode of the bridge erecting machine. DETAILED DESCRIPTION

[0033] Example 1

[0034] Reference Figure 1 , the bridge erection machine control method based on machine vision includes the following steps:

[0035] Step 1: Based on a preset period, obtain the fluctuation impact data and detection video clips of each period;

[0036] Step 2: Mark the period corresponding to the detected video clip as the working condition fluctuation period according to the outrigger working condition value;

[0037] Step 3: Obtain the operational stability analysis value of the bridge-building machine, and adjust the mode of the bridge-building machine according to the operational stability analysis value.

[0038] The method of the present invention uses machine vision to intelligently analyze the stability of the bridge-building machine and environmental fluctuations, issues real-time warnings to operators, and automatically adjusts the mode of the bridge-building machine, thereby continuously optimizing the safety and rationality of the bridge-building machine during operation.

[0039] Example 2

[0040] Reference Figure 2 The machine vision-based bridge-building machine includes a bridge-building machine body on which a plurality of outrigger detectors are installed.

[0041] Because the bridge erector has multiple outriggers, such as front outriggers, middle outriggers, and rear outriggers, including but not limited to the above, multiple outrigger detectors are required, each outrigger corresponds to each outrigger detector, and one outrigger detector is used to detect one outrigger. During the bridge construction process, the outriggers need to be fixed on the bridge piers.

[0042] The outrigger detector includes an outrigger visual detection module and a bridge erection machine control module;

[0043] After the outrigger is fixed on the bridge pier, the outrigger visual inspection module shoots a video of the outrigger and marks the video as an outrigger inspection video. Based on the T cycle, at each cycle node, the fluctuation impact data of the previous cycle is obtained, and at the same time, the outrigger inspection video of the previous cycle is intercepted to obtain the inspection video clip, and the outrigger working condition value of the inspection video clip is obtained.

[0044] The outrigger working condition values ​​of the detection video clips are obtained in the following way:

[0045] The detection video clip is converted into multiple video image frames and marked as m, where m is the number of the video image frame, m=1, 2, ..., M, and M is a positive integer. The multiple video image frames are sorted in chronological order to obtain the working angle of the outrigger in each video image frame. The working angle of the outrigger is obtained in the following way: the video image frame is subjected to edge detection processing by the Canny edge detection algorithm, the outrigger contour and the pier contour in the video image frame are detected by the findContours function of OpenCV, the angle between the outrigger and the pier is calculated by using the best fitting rectangle, and marked as the working angle of the outrigger.

[0046] Set the upper angle and the lower angle of the working condition. When the working condition angle is greater than the upper angle, the type of the working condition angle is marked as the super angle type. When the working condition angle is less than the lower angle, the type of the working condition angle is marked as the low angle type. When the working condition angle is between the upper angle and the lower angle, the type of the working condition angle is marked as the flat angle type. Among them, the upper angle of the working condition is greater than the lower angle of the working condition. The upper angle and the lower angle of the working condition are both system preset values ​​and can be modified according to actual needs. The most standard working angle of the outrigger is 90°, that is, the outrigger and the pier are perpendicular to each other. For example, set the upper angle of the working condition to 91° and the lower angle to 89°.

[0047] The working angle types of two adjacent video image frames after sorting are compared. When the working angle types of two adjacent video image frames are the same, no corresponding processing is performed. When the working angle types of two adjacent video image frames are different, the angle fluctuation times are increased by one, and all angle fluctuation times are summed to obtain the total angle fluctuation times, which is marked as BDC. All video image frames of flat angle type working angles are sorted in sequence according to the numbering sequence, and the numbering difference of the two adjacent video image frames after sorting is calculated to obtain the flat angle numbering interval. All flat angle numbering differences are summed and averaged to obtain the flat angle numbering interval, which is marked as PJB. The number of video image frames of flat angle type working angles is obtained and marked as SLM. The formula is used The outrigger working condition value GCZ of the detected video clip is obtained, where a1 is the total angle fluctuation coefficient, a2 is the flat angle number spacing coefficient, and a3 is the flat angle image frame number coefficient. The value of a1 is 1.35, the value of a2 is 0.97, and the value of a3 is 0.26.

[0048] Set the outrigger working condition threshold. The outrigger working condition threshold is a system preset value and can be modified according to actual needs. When the outrigger working condition value is greater than or equal to the outrigger working condition threshold, the period corresponding to the detected video clip is marked as the working condition fluctuation period, and the vibration impact ratio of the working condition fluctuation period is obtained. When the outrigger working condition value is less than the outrigger working condition threshold, no corresponding processing is performed.

[0049] The outrigger working condition value can be fed back to the control room of the bridge-building machine in real time. The operator can adjust his own operation according to the outrigger working condition value. When the outrigger working condition value is high, the operator can reduce the moving speed of the bridge-building machine's walking system during its operation.

[0050] The vibration influence ratio of the operating condition fluctuation period is obtained by the following method:

[0051] Obtain the fluctuation influence data of the working condition fluctuation cycle, use the fluctuation influence data of the working condition fluctuation cycle as the input data of the environmental fluctuation analysis model, obtain the environmental impact value, and mark it as HBT, obtain the leg working condition value GCZ of the detection video clip corresponding to the working condition fluctuation cycle, and use the formula The vibration influence ratio YXB of the working condition fluctuation period is obtained, where b1 is the environmental influence value coefficient, b2 is the outrigger working condition value coefficient, the value of b1 is 0.87, and the value of b2 is 1.05.

[0052] The outrigger working condition threshold is a system preset value and can be modified according to actual needs;

[0053] The environmental fluctuation analysis model is obtained in the following way: multiple groups of fluctuation influence data are obtained, each group of fluctuation influence data includes wind parameters and vibration parameters (wind parameters and vibration parameters are data of the same period), a neural network model is constructed, and the fluctuation influence data is used as training data of the neural network model. Environmental impact values ​​are assigned to the training data, and the value range of the environmental impact value is (0-1). The neural network model is iteratively trained through training sets and validation sets. After the training is completed, the environmental fluctuation analysis model is obtained. The larger the value of the environmental impact value, the greater the influence of the fluctuation influence data on the outrigger fluctuation. For example, the greater the wind force in the fluctuation influence data, the easier it is to cause the outrigger to fluctuate. When the vibration amplitude of the platform where the bridge erecting machine is located is greater, it is easier to cause the outrigger to fluctuate, and then the larger the value of the environmental impact value.

[0054] The fluctuation impact data include wind parameters (including but not limited to wind force and wind speed around the bridge-building machine) and vibration parameters (vibration frequency and vibration amplitude of the platform where the bridge-building machine is located).

[0055] The bridge erection machine control module is used to obtain the stability analysis value of the bridge erection machine: set the vibration impact ratio threshold, when the vibration impact ratio is greater than or equal to the vibration impact ratio threshold, mark the working condition fluctuation period as the operation impact period, obtain the number of all operation impact periods after the outrigger is fixed on the pier, and mark it as BNY, sum the vibration impact ratios of all operation impact periods, get the total vibration impact ratio, and mark it as FCZ, use the formula The operational stability analysis value CWZ of the bridge erecting machine is obtained, where c1 is the coefficient of the number of operation influence cycles, c2 is the total ratio coefficient of vibration influence, the value of c1 is 0.57, and the value of c2 is 0.66.

[0056] Set the high value and low value of the operation stability analysis, where the high value of the operation stability analysis is greater than the low value of the operation stability analysis. Both the high value and the low value of the operation stability analysis are system preset thresholds and can be modified according to actual needs. When the operation stability analysis value is greater than the high value of the operation stability analysis, the bridge-building machine is adjusted to the shutdown mode; when the operation stability analysis value is less than the low value of the operation stability analysis, no corresponding processing is performed; when the operation stability analysis value is between the high value and the low value of the operation stability analysis, the bridge-building machine is adjusted to the warning mode.

[0057] In the shutdown mode, the bridge-building machine automatically shuts down and the operator can no longer control the bridge-building machine. In the warning mode, a warning is issued in the control room of the bridge-building machine to remind the operator to pay attention to the operation.

[0058] An outrigger detector is installed on the bridge-building machine, which can detect the working condition of the outriggers on the bridge-building machine through machine vision. The operator can adjust his own operation according to the outrigger working condition value. The outrigger detector combines the surrounding environmental fluctuation data to perform intelligent analysis of the operator's operation and intelligently adjust the working mode of the bridge-building machine.

[0059] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0060] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0061] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0062] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0063] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0064] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0065] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0066] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A bridge erecting machine based on machine vision, including a bridge erecting machine body, characterized in that: A plurality of outrigger detectors are installed on the bridge erecting machine body; The outrigger detector includes an outrigger visual detection module and a bridge erection machine control module; After the outrigger is fixed on the bridge pier, the outrigger visual detection module shoots a video of the outrigger, marks the shot video as an outrigger detection video, and based on the T cycle, obtains the fluctuation influence data of the previous cycle at each cycle node, and at the same time intercepts the outrigger detection video of the previous cycle to obtain a detection video segment, obtains the outrigger working condition value of the detection video segment, marks the period corresponding to the detection video segment as a working condition fluctuation period according to the outrigger working condition value, and obtains the vibration influence ratio of the working condition fluctuation period; The bridge erection machine control module is used to obtain the operation stability analysis value of the bridge erection machine and adjust the mode of the bridge erection machine according to the operation stability analysis value; The outrigger working condition values ​​of the detection video clips are obtained in the following way: The detected video clip is converted into multiple video image frames and marked as m, where m is the number of the video image frame, m=1, 2, ..., M, and M is a positive integer. The multiple video image frames are sorted in chronological order to obtain the working angle of the outrigger in each video image frame. The working angle is divided into super angle type, low angle type, and flat angle type. The working angle types of two adjacent video image frames after sorting are compared. When the working angle types of two adjacent video image frames are the same, no corresponding processing is performed. When the working angle types of two adjacent video image frames are different, the angle fluctuation times are increased once, and all angle fluctuation times are summed to obtain the total angle fluctuation times, which is marked as BDC. The flat angle number interval PJB is obtained, and the number of video image frames with flat angle type working angles is obtained and marked as SLM. The formula is used Get the outrigger working condition value GCZ of the detected video clip, where a1 is the total angle fluctuation coefficient, a2 is the flat angle number spacing coefficient, and a3 is the flat angle image frame number coefficient; The vibration influence ratio of the operating condition fluctuation period is obtained by the following method: Obtain the fluctuation influence data of the working condition fluctuation cycle, use the fluctuation influence data of the working condition fluctuation cycle as the input data of the environmental fluctuation analysis model, obtain the environmental impact value, and mark it as HBT, obtain the leg working condition value GCZ of the detection video clip corresponding to the working condition fluctuation cycle, and use the formula The vibration influence ratio YXB of the working condition fluctuation period is obtained, where b1 is the environmental influence value coefficient and b2 is the outrigger working condition value coefficient; The stability analysis value of the bridge erecting machine is obtained by the following method: Set the vibration impact ratio threshold. When the vibration impact ratio is greater than or equal to the vibration impact ratio threshold, mark the working condition fluctuation period as the operation impact period. Obtain the number of all operation impact periods after the outrigger is fixed on the pier and mark it as BNY. Sum the vibration impact ratios of all operation impact periods to obtain the total vibration impact ratio and mark it as FCZ. Use the formula The control stability analysis value CWZ of the bridge erecting machine is obtained, where c1 is the coefficient of the number of operation influence cycles and c2 is the total ratio coefficient of vibration influence.

2. The machine vision-based bridge erecting machine according to claim 1, characterized in that: The fluctuation impact data include wind parameters and vibration parameters. The wind parameters include the wind force and wind speed around the bridge-building machine, and the vibration parameters include the vibration frequency and vibration amplitude of the platform where the bridge-building machine is located.

3. The machine vision-based bridge erecting machine according to claim 1, characterized in that: The working angles are divided into super angle type, low angle type and flat angle type, specifically: Set the upper working angle and the lower working angle. When the working angle is greater than the upper working angle, the type of the working angle is marked as the super angle type. When the working angle is less than the lower working angle, the type of the working angle is marked as the low angle type. When the working angle is between the upper working angle and the lower working angle, the type of the working angle is marked as the flat angle type.

4. The machine vision-based bridge erecting machine according to claim 1, characterized in that: The flat angle numbering interval is obtained by the following method: sort all the video image frames of the flat angle type working angles in sequence according to the numbering order, calculate the numbering difference between the two adjacent video image frames after sorting to obtain the flat angle numbering interval, sum up all the flat angle numbering differences and take the average to obtain the flat angle numbering interval, and mark it as PJB.

5. The machine vision-based bridge erecting machine according to claim 1, characterized in that: According to the outrigger working condition value, the period corresponding to the detected video clip is marked as the working condition fluctuation period, specifically: The outrigger working condition threshold is set. When the outrigger working condition value is greater than or equal to the outrigger working condition threshold, the period corresponding to the detected video clip is marked as the working condition fluctuation period.

6. The machine vision-based bridge erecting machine according to claim 1, characterized in that: The mode of the bridge erecting machine is adjusted according to the operating stability analysis value, specifically: Set the high value and low value of the operation stability analysis. When the operation stability analysis value is greater than the high value of the operation stability analysis, the bridge-building machine is adjusted to the shutdown mode. When the operation stability analysis value is less than the low value of the operation stability analysis, no corresponding processing is performed. When the operation stability analysis value is between the high value and the low value of the operation stability analysis, the bridge-building machine is adjusted to the warning mode.

7. A machine vision-based bridge erection machine control method, applied to the machine vision-based bridge erection machine according to claim 1, characterized in that: The steps include: Step 1: Based on a preset period, obtain the fluctuation impact data and detection video clips of each period; Step 2: Mark the period corresponding to the detected video clip as the working condition fluctuation period according to the outrigger working condition value; Step 3: Obtain the operational stability analysis value of the bridge-building machine, and adjust the mode of the bridge-building machine according to the operational stability analysis value.

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