Intelligent bridge fabrication machine construction safety monitoring system

Abstract

The invention relates to the technical field of safety control, and discloses an intelligent bridge fabrication machine construction safety monitoring system. The virtual construction module is used for constructing a virtual model; the parameter adjusting module is used for calculating a superposition amplitude and carrying out superposition adjustment on the safety monitoring parameters; the model simulation module is used for simulating the simulation state of the digital twin model; the intelligent control module is used for identifying risk parameters of the intelligent bridge fabrication machine and sending out safety early warning information; according to the method, virtual-real combination can be carried out on the entity parameters and the virtual model, an accurate theoretical basis is provided for subsequent intelligent control over the entity structure and the control logic in the intelligent bridge fabrication machine, and limitation and inaccuracy existing when the intelligent bridge fabrication machine breaks away from the actual construction environment to carry out intelligent control can be avoided; meanwhile, accurate guarantee is provided for simulation operation of a subsequent digital twinborn model, and the timeliness and accuracy of intelligent control operation of the intelligent bridge fabrication machine are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of safety control, more particularly, the present application relates to a wisdom bridge construction machine construction safety monitoring system. BACKGROUND

[0002] The wisdom bridge construction machine integrates the front technologies of mechanical engineering, automation control, Internet of Things, big data and 5G communication, and has shown significant advantages in the field of bridge construction today. Since the control system and the entity structure involved in the wisdom bridge construction machine are more, the probability of uncontrollable dangerous phenomena occurring during the construction of the wisdom bridge construction machine is higher. In order to ensure that the wisdom bridge construction machine can be kept in a safe construction state, it is necessary to monitor the safety of the wisdom bridge construction machine during construction and intelligently control the construction state.

[0003] The patent application with publication number CN103926903A discloses a safety monitoring system for a bridge erecting machine, which comprises a detection module, a data acquisition module, a data encryption compression module, a control module, a wireless communication module and a monitoring host. The detection module detects the lifting weight of the winch, the data acquisition module transmits the weight signal of the weight sensor to the control module, the data encryption compression module encrypts and compresses the weight signal, the control module sends the encrypted and compressed weight signal to the monitoring host through the wireless communication module, the monitoring host comprises a decompression module and a data analysis module, the decompression module decompresses the encrypted and compressed weight signal, and the data analysis module judges whether the weight signal exceeds the set range. The existing construction safety monitoring system uses a simulation model to simulate the simulation mode, which can only simulate the entity structure components of the wisdom bridge construction machine, cannot realize the accurate mapping of the entity structure and the control logic in the three-dimensional space, and can only evaluate from the equipment itself dimension when monitoring the construction safety state of the wisdom bridge construction machine. The coupling effect of the dynamic changing construction environment on the construction safety state cannot be fully considered, thereby causing the results of the subsequent digital twin model simulation operation to have limitations and inaccuracy, resulting in that the control operation of the wisdom bridge construction machine is not timely and accurate enough.

[0004] In view of this, the present application provides a wisdom bridge construction machine construction safety monitoring system to solve the above problems. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, in order to achieve the above-mentioned purpose, the present application provides the following technical scheme: a wisdom bridge construction machine construction safety monitoring system, comprising: A virtual construction module is used to acquire inherent entity parameters of the wisdom bridge construction machine, the inherent entity parameters including geometric parameters, physical parameters and logical parameters, and a virtual model with a display station is constructed based on the inherent entity parameters; The parameter adjustment module is configured to collect safety monitoring parameters and environmental parameters of the intelligent bridge building machine at a current time, perform overrun analysis on the environmental parameters, calculate a superposition amplitude of the safety monitoring parameters, and perform superposition adjustment on the safety monitoring parameters according to the superposition amplitude. The model simulation module is configured to perform model alignment on the superposition-adjusted safety monitoring parameters and environmental parameters and a virtual model, convert the virtual model into a digital twin model, and perform simulation operation under simulation logic rules, query a simulation state of the digital twin model through a display station, and determine whether to execute a deep analysis mode. The simulation logic rule is that a simulation permission is less than or equal to an actual permission, and a simulation logic is consistent with an actual logic. The intelligent control module is configured to perform risk assessment on the safety monitoring parameters in the deep analysis mode, identify risk parameters of the intelligent bridge building machine, and issue corresponding safety warning information.

[0006] Further, the construction method of the virtual model is as follows: The parameter design drawing is taken as a reference to construct a basic model in a three-dimensional space, and A basic structures in the basic model are marked. The size and shape of the A basic structures are adjusted to be consistent with corresponding geometric parameters, so as to convert the basic structures into entity structures. Attribute units are respectively established on the A entity structures, and physical parameters corresponding to the entity structures are added in the attribute units, so as to convert the entity structures into physical structures. Physical structures involved in a same logical parameter are composed into a logical unit, and corresponding logical parameters are assigned to the logical unit according to a logical control sequence of the intelligent bridge building machine, so as to convert the physical structures into logical structures. Independent conventional stations and real-time stations are simulated, the display state of the conventional stations is initialized as an overt state, the display state of the real-time stations is initialized as a covert state, and the basic model is converted into the virtual model.

[0007] Further, the safety monitoring parameters include a regional stress reference value, a working vibration frequency, a jacking oil pressure temperature difference, and a walking speed difference value; and the environmental parameters include a humidity value and a wind load.

[0008] Further, the collection method of the regional stress reference value is as follows: All model points on the virtual model are marked one by one, a model point inside the virtual model is randomly selected as a center of a hollow sphere, a unit length is taken as a radius to draw a hollow sphere bounding box, the position of the center of the hollow sphere bounding box and the size of the radius are continuously adjusted until the hollow sphere bounding box first wraps all the model points, and the center of the adjusted hollow sphere bounding box is recorded as a geometric center. The distance from each model point corresponding to the pressure sensor on the virtual model to the geometric center is measured one by one, and the initial distance value is recorded, and the average of the maximum and minimum of the initial distance value is calculated as the distance average value; The stress area is scanned in a ring shape with the geometric center as the area center point and the distance average value as the scanning radius, the pressure sensors located in the stress area are recorded as target sensors, the pressure values of all target sensors are read one by one, and the average of the sum of all pressure values is calculated as the area stress index.

[0009] Further, the collection method of the jacking oil temperature difference is: The hydraulic oil pressure and the hydraulic oil temperature of the E hydraulic oil cylinders of the jacking mechanism are queried one by one, and the hydraulic oil pressure and the hydraulic oil temperature are de-dimensioned to obtain the pressure value and the temperature value, respectively; The pressure value and the temperature value of the E hydraulic oil cylinders are compared respectively to calculate the E oil temperature ratio values, the average of the sum of the E oil temperature ratio values is calculated, and the difference between the average and the standard temperature ratio value is calculated as the jacking oil temperature difference.

[0010] Further, the determination method of the superposition amplitude is: When the humidity value is greater than the calibrated humidity threshold value, the difference between the humidity value and the calibrated humidity threshold value is obtained as the humidity difference value, and the humidity difference value is compared with the calibrated humidity threshold value to calculate the humidity superposition ratio; When the wind load is greater than the calibrated load threshold value, the difference between the wind load and the calibrated load threshold value is obtained as the load difference value, and the load difference value is compared with the calibrated load threshold value to calculate the load superposition ratio; The humidity superposition ratio and the load superposition ratio are respectively assigned different proportion coefficients and added to calculate the superposition amplitude.

[0011] Further, when the digital twin model is converted, the virtual model is imported into the three-dimensional engine, and four inherent parameter positions and two external parameter positions are established on the virtual model; The superposition-adjusted area stress index, the working vibration frequency, the jacking oil temperature difference, and the speed difference value are respectively input into the four inherent parameter positions, and the humidity value and the wind load are respectively input into the two external parameter positions, so as to convert the virtual model into a digital twin model.

[0012] Further, the simulation state includes a safe construction state and a dangerous construction state; The determination method of whether to execute the deep analysis mode is: After the digital twin model simulation runs, the display state of the conventional station is switched from the explicit state to the implicit state, the display state of the real-time station is switched from the implicit state to the explicit state, and the text content in the real-time station is read; When the literal content is "11", the simulation state is a safe construction state, and it is determined not to execute the deep analysis mode; when the literal content is "00", the simulation state is a dangerous construction state, and it is determined to execute the deep analysis mode.

[0013] Further, the risk parameter identification method is: The adjusted regional stress index value is subtracted from the regional stress index value at the current time to obtain a stress adjustment difference value; when the stress adjustment difference value is greater than or equal to the calibrated stress adjustment threshold value, the adjusted regional stress index value is recorded as a risk parameter; The adjusted working vibration frequency is subtracted from the working vibration frequency at the current time to obtain a vibration adjustment difference value; when the vibration adjustment difference value is greater than or equal to the calibrated vibration adjustment threshold value, the adjusted vibration adjustment difference value is recorded as a risk parameter; The adjusted jacking oil pressure temperature ratio difference is subtracted from the jacking oil pressure temperature ratio difference at the current time to obtain a pressure temperature ratio adjustment difference value; when the pressure temperature ratio adjustment difference value is greater than or equal to the calibrated pressure temperature ratio adjustment threshold value, the adjusted jacking oil pressure temperature ratio difference is recorded as a risk parameter; The adjusted walking speed difference value is subtracted from the walking speed difference value at the current time to obtain a speed adjustment difference value; when the speed adjustment difference value is greater than or equal to the calibrated speed adjustment threshold value, the adjusted walking speed difference value is recorded as a risk parameter.

[0014] Further, the safety warning information includes safety maintenance prompt information and suspension operation prompt information; The number of risk parameters at the current time is counted, when the number of risk parameters is 1, the safety maintenance prompt information is sent; when the number of risk parameters is 2, 3 or 4, the suspension operation prompt information is sent.

[0015] The technical effect of the intelligent bridge construction machine construction safety monitoring system is: (1) The virtual model is constructed by the inherent entity parameters of the intelligent bridge construction machine, which can effectively map the entity structure and control logic of the intelligent bridge construction machine in three-dimensional space, and through the operation of fusing and converting the superimposed adjusted safety monitoring parameters and the virtual model, the entity parameters and the virtual model of the intelligent bridge construction machine can be combined virtually under the assistance of digital twinning technology, which provides an accurate theoretical basis for the subsequent intelligent control of the entity structure and control logic of the intelligent bridge construction machine, and guarantees the safe construction state of the intelligent bridge construction machine.

[0016] (2): The present application can combine and superimpose the negative influence of the construction environment on the actual construction safety state of the intelligent bridge building machine, so as to realize real-time and dynamic fusion interaction of the influence of the construction environment on the actual operation state of the intelligent bridge building machine, avoid the limitations and inaccuracy of intelligent control of the intelligent bridge building machine when it is separated from the actual construction environment, provide accurate guarantee for the simulation operation of the subsequent digital twin model, and improve the timeliness and accuracy of the intelligent control operation of the intelligent bridge building machine in dangerous construction conditions. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A logic architecture schematic diagram of the intelligent bridge building machine construction safety monitoring system provided by the embodiment one of the present application is provided. Figure 2 A flowchart schematic diagram of the intelligent bridge building machine construction safety monitoring method provided by the embodiment two of the present application is provided. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0019] Embodiment one: please refer to Figure 1 The intelligent bridge building machine construction safety monitoring system described in the embodiment includes: The virtual construction module collects inherent entity parameters of the intelligent bridge building machine, the inherent entity parameters include geometric parameters, physical parameters and logical parameters, and a virtual model of the intelligent bridge building machine is constructed based on the inherent entity parameters. When the intelligent bridge building machine performs bridge building construction on a bridge segment, multiple functional components such as a suspension mechanism, a pouring mechanism, an anchoring mechanism and a displacement mechanism need to move together. In order to ensure that the intelligent bridge building machine maintains a safe operation state during construction, it is necessary to ensure that the suspension mechanism, the pouring mechanism, the anchoring mechanism and the displacement mechanism maintain a normal state.

[0020] In order to achieve the above-mentioned safe operation effect of the intelligent bridge building machine, a virtual model consistent with the entity structure of the intelligent bridge building machine needs to be constructed. Before constructing the virtual model, the inherent entity parameters of the intelligent bridge building machine need to be collected, so that the inherent entity parameters can represent the specific conditions of the intelligent bridge building machine in terms of form size, physical structure and control logic. Specifically, the inherent entity parameters include geometric parameters, physical parameters, and logical parameters.

[0021] The geometric parameters are parameters used to represent the shape, size, and assembly relationship of each component structure in the intelligent bridge building machine. Specifically, the geometric parameters include, but are not limited to, the length of the cantilever, the height of the support leg, the hinge movement direction, etc. The geometric parameters are obtained through parameter design drawing query and three-dimensional laser scanning equipment scanning.

[0022] The physical parameters are parameters used to represent the specific material characteristics, constraint relationships, and load weights of each component structure in the intelligent bridge building machine. Specifically, the physical parameters include, but are not limited to, the elastic modulus, density, yield strength, and maximum wind load of steel materials. The physical parameters are obtained through parameter design drawing query.

[0023] The logical parameters are parameters used to represent the dynamic behavior relationship and mapping rules of each component structure in the intelligent bridge building machine. Specifically, the logical parameters include, but are not limited to, the cantilever displacement oil pressure relationship, the horizontal displacement oil cylinder relationship, etc. The logical parameters are obtained through logic controller query.

[0024] After collecting the inherent entity parameters of the intelligent bridge building machine, a virtual model that maintains consistency with the shape size, physical structure, and control logic of the intelligent bridge building machine can be constructed based on the inherent entity parameters and three-dimensional modeling technology. The construction method of the virtual model is as follows: Based on the parameter design drawings of the intelligent bridge building machine, a base model is constructed in three-dimensional space through three-dimensional modeling software, and A base structures in the base model are marked. The size and shape of the A base structures are adjusted to be consistent with the corresponding geometric parameters, prompting the A base structures to be converted into A entity structures. Property units are established on the A entity structures, and the physical parameters corresponding to the entity structures are added to the property units, prompting the A entity structures to be converted into A physical structures. The physical structures involved in the same logical parameter are combined into a logical unit, obtaining B logical units. According to the sequence of the logical control of the intelligent bridge building machine, the logical parameters are sequentially assigned to the B logical units, prompting the A physical structures to be converted into A logical structures. Through the gradual conversion of entity structures-physical structures-logical structures, the model conversion of the structure size, entity attributes, and control logic of the intelligent bridge building machine can be performed in sequence, ensuring the orderliness and rationality of the virtual model construction steps, and avoiding the mixing and crossing of different dimension data during virtual conversion. Simulate two independent display stations, respectively, as a conventional station and a real-time station, and initialize the display state of the conventional station as an explicit state and the display state of the real-time station as an implicit state, so as to convert the basic model into a virtual model.

[0025] It should be noted that the display state is used to indicate whether the specific content and result in the real station are displayed outwardly, and specifically, the display state includes an explicit state and an implicit state; the explicit state and the implicit state respectively refer to the outward display and non-outward display of the specific content and result.

[0026] The parameter adjustment module collects the safety monitoring parameters and environmental parameters of the intelligent bridge building machine at the current time, determines the superposition amplitude of the safety monitoring parameters according to the environmental parameters, and adjusts the safety monitoring parameters according to the superposition amplitude as the standard; The safety monitoring parameters refer to various specific parameters that can affect the normal operation state of the intelligent bridge building machine, and can also be used as data objects for subsequent intelligent control of the intelligent bridge building machine in dangerous situations; Specifically, the safety monitoring parameters include regional stress reference value, working vibration frequency, jacking oil pressure temperature difference, and walking speed difference.

[0027] The regional stress reference value refers to the average value of the stress of different stress monitoring points of the intelligent bridge building machine in a specific stress influence area, which can represent the stress change of the intelligent bridge building machine during operation; the larger the regional stress reference value, the less safe the operation state of the intelligent bridge building machine; Specifically, the collection method of the regional stress reference value is: Mark all model points on the virtual model one by one, randomly select a model point as the center of the hollow sphere bounding box in the virtual model, and draw a hollow sphere bounding box with a unit length as the radius; the unit length refers to a smaller length value set in advance, so that the bounding box drawn with the unit length cannot wrap all the model points; by drawing the bounding box, a local and closed area can be established in the virtual model, ensuring that the calculation of the regional stress reference value will not be disturbed by the stress data in other areas, improving the calculation accuracy of the regional stress reference value; Continuously adjust the position of the center of the bounding box and the size of the radius until the bounding box first wraps all the model points, and record the center of the adjusted bounding box as the geometric center; Measure the distance from all pressure sensor corresponding model points on the virtual model to the geometric center one by one, record the initial distance value, and calculate the distance average by adding the maximum value of the initial distance value and the minimum value of the initial distance value and then averaging; The calculation formula of the distance average is: ; In the formula, The mean distance The maximum value of the initial distance. The minimum value of the initial distance; Using the geometric center as the center point of the region and a distance average as the scanning radius, the stress region is scanned in a ring, and the pressure sensor located in the stress region is recorded as the target sensor. The pressure values ​​of all target sensors are read one by one, and the average of all pressure values ​​is calculated to obtain the regional stress value.

[0028] The working vibration frequency refers to the frequency at which the intelligent bridge-building machine vibrates under the action of external forces during bridge construction. It can be used to represent the vibration rate during the operation of the intelligent bridge-building machine. The higher the working vibration frequency, the less safe the operation of the intelligent bridge-building machine is. In this embodiment, the operating vibration frequency is obtained by real-time detection using a vibration sensor.

[0029] The hydraulic pressure-temperature ratio difference of the lifting oil refers to the difference between the ratio of hydraulic oil pressure to hydraulic oil temperature of the lifting cylinder corresponding to the bottom formwork being poured by the intelligent bridge-building machine at the current moment and the standard ratio. It can be used to represent the changes in hydraulic oil during the lifting process of the intelligent bridge-building machine. The method for collecting the pressure-temperature ratio difference of the lifting oil is as follows: The hydraulic oil pressure and temperature of each of the E hydraulic cylinders on the lifting mechanism are retrieved one by one. The hydraulic oil pressure and temperature are then dimensionless to obtain the pressure and temperature values ​​respectively. Compare the pressure and temperature values ​​of each of the E hydraulic cylinders to calculate the E oil pressure-temperature ratio values; The formula for calculating the oil pressure-temperature ratio is: ; In the formula, This is the oil pressure-temperature ratio. This is the pressure value. This is the temperature value; The average of the E hydraulic pressure-temperature ratios is calculated and then compared with the standard temperature ratio to obtain the lifting hydraulic pressure-temperature ratio difference. The standard temperature ratio refers to the hydraulic pressure-temperature ratio of the intelligent bridge-building machine under normal and safe construction conditions, which can be used as the numerical basis for subsequent calculation of the lifting hydraulic pressure-temperature ratio difference. The formula for calculating the pressure-temperature ratio difference of the lifting oil is: ; In the formula, To increase the oil pressure-temperature ratio difference, For the first Oil pressure-temperature ratio This is the standard temperature ratio.

[0030] The walking speed difference refers to the difference between the real-time speeds of the two walking mechanisms of the intelligent bridge construction machine at the current time, that is, the consistency of the walking mechanism of the intelligent bridge construction machine can be represented; the greater the walking speed difference, the more unsafe the running state of the intelligent bridge construction machine is; In this embodiment, when the walking speed difference is collected, the speed sensors on the two walking mechanisms detect the motion speed at the current time, and the walking speed difference can be obtained by taking the absolute value of the difference between the two motion speeds.

[0031] The environmental parameter refers to a specific parameter of the construction environment that can have an additional impact on the running state of the intelligent bridge construction machine, and serves as a basis for subsequent changes in the safety monitoring parameter; Specifically, the environmental parameter includes humidity value and wind load.

[0032] The humidity value refers to the environmental humidity value of the construction environment of the intelligent bridge construction machine at the current time, and different environmental humidity will have different degrees of influence on the running state of the intelligent bridge construction machine. The greater the humidity value, the greater the probability of moisture failure of electronic components in the intelligent bridge construction machine and the displacement of various component structures, and the more unsafe the running state of the intelligent bridge construction machine is. The humidity value is obtained by real-time detection by a temperature sensor.

[0033] The wind load refers to the load pressure brought by the wind force of the construction environment of the intelligent bridge construction machine at the current time. The greater the wind load, the greater the probability of irregular shaking of various component structures in the intelligent bridge construction machine, and the more unsafe the running state of the intelligent bridge construction machine is. The wind load is obtained by real-time detection by a wind direction sensor.

[0034] After obtaining the safety monitoring parameter and the environmental parameter, the specific influence range of the humidity value and the wind load on each safety monitoring parameter is determined based on the environmental parameter, and the influence range brought by the temperature value and the wind load is superimposed to adjust the safety monitoring parameter; The superimposed range refers to the influence degree of the humidity value and the wind load on the regional stress value, the working vibration frequency, the jacking oil pressure temperature difference and the walking speed difference when exceeding the calibrated value, and serves as a direct basis for superimposing and adjusting the regional stress value, the working vibration frequency, the jacking oil pressure temperature difference and the walking speed difference; Specifically, the determination method of the superimposed range is: The humidity value at the current time is compared with the calibrated humidity threshold value; the calibrated humidity threshold value refers to the maximum value of the humidity value of the intelligent bridge construction machine under normal and safe construction state, and serves as a basis for determining whether the humidity value has a superimposed range; When the humidity value is less than or equal to the calibrated humidity threshold value, at this time, the construction environment humidity will not cause negative effects on the safety monitoring parameters, at this time, the humidity value does not exist the superposition amplitude; When the humidity value is greater than the calibrated humidity threshold value, at this time, the construction environment humidity will cause negative effects on the safety monitoring parameters, then the humidity value is subtracted from the calibrated humidity threshold value to obtain the humidity difference value, and after comparing the humidity difference value with the calibrated humidity threshold value, the humidity superposition ratio is calculated; The calculation formula of the humidity superposition ratio is: ; In the formula, the humidity superposition ratio, the humidity value, the calibrated humidity threshold value; The current wind load is compared with the calibrated load threshold value; the calibrated load threshold value refers to the maximum value of the wind load of the intelligent bridge building machine in the normal and safe construction state, and is used as the judgment basis for determining whether the wind load exists the superposition amplitude; When the wind load is less than or equal to the calibrated load threshold value, at this time, the construction environment wind will not cause negative effects on the safety monitoring parameters, at this time, the wind load does not exist the superposition amplitude; When the wind load is greater than the calibrated load threshold value, at this time, the construction environment wind will cause negative effects on the safety monitoring parameters, then the wind load is subtracted from the calibrated load threshold value to obtain the load difference value, and after comparing the load difference value with the calibrated load threshold value, the load superposition ratio is calculated; The calculation formula of the load superposition ratio is: ; In the formula, the load superposition ratio, the wind load, the calibrated load threshold value; After the humidity superposition ratio and the load superposition ratio are respectively given different proportionality coefficients and added, the superposition amplitude is calculated; The calculation formula of the superposition amplitude is: ; In the formula, the superposition amplitude, , the proportionality coefficients of the humidity superposition ratio and the load superposition ratio respectively, and .

[0035] After the superimposed amplitude is calculated, the regional stress index, working vibration frequency, jacking oil pressure temperature ratio difference and walking speed difference value can be superimposed and adjusted at this time to make the safety monitoring parameters superimposed with the influence caused by the environmental parameters, and then convert the safety monitoring parameters into a state that can be directly matched with the virtual model; In this embodiment, when the safety monitoring parameters are superimposed and adjusted, the regional stress index, working vibration frequency, jacking oil pressure temperature ratio difference and walking speed difference value are respectively expanded by one superimposed amplitude to obtain the superimposed and adjusted regional stress index, working vibration frequency, jacking oil pressure temperature ratio difference and walking speed difference value, which are then used as direct data for subsequent fusion with the virtual model.

[0036] The model simulation module aligns the superimposed and adjusted safety monitoring parameters and environmental parameters with the virtual model, constructs a digital twin model, and simulates the simulation state of the digital twin model based on simulation logic rules to determine whether to execute a deep analysis mode. After the safety monitoring parameters are superimposed and adjusted, the superimposed and adjusted safety monitoring parameters and environmental parameters need to be fused and interacted with the virtual model, so that the virtual model can be aligned with specific parameters, and the virtual model can be converted into a digital twin model. In this embodiment, the digital twin model is obtained by fusing the inherent parameters of the intelligent bridge construction machine and the external detected parameters with the virtual model, and can directly reflect the visual model of the safety situation of the intelligent bridge construction machine construction operation state.

[0037] Specifically, when constructing the digital twin model, first, the virtual model is imported into the three-dimensional engine, and four inherent parameter positions and two external parameter positions are established on the virtual model. Then, the superimposed and adjusted regional stress index, working vibration frequency, jacking oil pressure temperature ratio difference and walking speed difference value are respectively input into the four inherent parameter positions, and the humidity value and wind load are respectively input into the two external parameter positions, so as to convert the virtual model into a digital twin model.

[0038] After the digital twin model is constructed, in order to ensure that the constructed digital twin model is consistent with the actual running logic of the intelligent bridge construction machine, simulation logic rules need to be configured on the digital twin model to ensure that the digital twin model can be simulated and run in order and correctly under the limitation of the simulation logic rules. Specifically, the simulation logic rule is: the simulation authority is less than or equal to the actual authority, and the simulation logic is consistent with the actual logic. This can ensure that the digital twin model does not appear to run with super authority and logic mismatch when running in simulation, and improves the simulation accuracy of the digital twin model.

[0039] The actual authority and actual logic mentioned above are inherent parameters of the intelligent bridge construction machine, which are obtained by querying the logic controller.

[0040] After the digital twin model is simulated to run under the simulation logic rules, the simulation state of the digital twin model needs to be observed and identified, so that the simulation state can be used as a direct basis for judging whether the operation state of the intelligent bridge building machine at the current time is safe; Specifically, the simulation state includes a safe construction state and a dangerous construction state.

[0041] After obtaining the specific simulation state, it is necessary to determine whether to execute the deep analysis mode according to the different simulation states, so that the deep analysis mode can be used as a mode for further analyzing the construction safety of the intelligent bridge building machine; Specifically, the determination method of whether to execute the deep analysis mode is: After the digital twin model simulation is completed, the display state of the conventional station is switched from the explicit state to the implicit state, and the display state of the real-time station is switched from the implicit state to the explicit state, and the text content in the real-time station is read; When the text content is "11", it indicates that the simulation state of the digital twin model at the current time is a safe construction state, and the intelligent bridge building machine will not appear dangerous construction phenomenon at the current time, and it is determined that the deep analysis mode is not executed; When the text content is "00", it indicates that the simulation state of the digital twin model at the current time is a dangerous construction state, and the intelligent bridge building machine will appear dangerous construction phenomenon at the current time, and it is determined that the deep analysis mode is executed.

[0042] The intelligent control module, in the deep analysis mode, performs risk assessment on the safety monitoring parameters, identifies the risk parameters of the intelligent bridge building machine, and issues corresponding safety warning information; In the deep analysis mode, the specific numerical value of the safety monitoring parameter of the intelligent bridge building machine needs to be analyzed and compared, so as to determine the number of risk parameters that appear risk hidden dangers, and provide data basis for subsequent safety monitoring results of the intelligent bridge building machine; The risk parameter refers to the safety monitoring parameter that will cause the intelligent bridge building machine to appear dangerous construction phenomenon at the current time, and is used as a component of the subsequent safety monitoring result.

[0043] Specifically, the identification method of the risk parameter is: The adjusted regional stress index value is subtracted from the regional stress index value at the current time to obtain a stress adjustment difference value, and the stress adjustment difference value is compared with a calibrated stress adjustment threshold value; the calibrated stress adjustment threshold value refers to the minimum value when the stress adjustment difference value is recorded as a risk parameter; When the stress adjustment difference is greater than or equal to the calibrated stress adjustment threshold value, the adjusted regional stress index value will have a negative impact on the construction state of the intelligent bridge machine at this time, and the adjusted regional stress index value is recorded as a risk parameter; otherwise, the opposite is true. The adjusted working vibration frequency is subtracted from the working vibration frequency at the current time to obtain a vibration adjustment difference, and the vibration adjustment difference is compared with a calibrated vibration adjustment threshold value; the calibrated vibration adjustment threshold value is the minimum value when the vibration adjustment difference is recorded as a risk parameter. When the vibration adjustment difference is greater than or equal to the calibrated vibration adjustment threshold value, the adjusted working vibration frequency will have a negative impact on the construction state of the intelligent bridge machine at this time, and the adjusted vibration adjustment difference is recorded as a risk parameter; otherwise, the opposite is true. The adjusted jacking oil pressure temperature ratio difference is subtracted from the jacking oil pressure temperature ratio difference at the current time to obtain a pressure temperature ratio adjustment difference, and the pressure temperature ratio adjustment difference is compared with a calibrated pressure temperature ratio adjustment threshold value; the calibrated pressure temperature ratio adjustment threshold value is the minimum value when the pressure temperature ratio adjustment difference is recorded as a risk parameter. When the pressure temperature ratio adjustment difference is greater than or equal to the calibrated pressure temperature ratio adjustment threshold value, the adjusted jacking oil pressure temperature ratio difference will have a negative impact on the construction state of the intelligent bridge machine at this time, and the adjusted jacking oil pressure temperature ratio difference is recorded as a risk parameter; otherwise, the opposite is true. The adjusted walking speed difference is subtracted from the walking speed difference at the current time to obtain a speed adjustment difference, and the speed adjustment difference is compared with a calibrated speed adjustment threshold value; the calibrated speed adjustment threshold value is the minimum value when the speed adjustment difference is recorded as a risk parameter. When the speed adjustment difference is greater than or equal to the calibrated speed adjustment threshold value, the adjusted walking speed difference will have a negative impact on the construction state of the intelligent bridge machine at this time, and the adjusted walking speed difference is recorded as a risk parameter; otherwise, the opposite is true.

[0044] After obtaining the risk parameters, the final safety warning information to be output externally needs to be formulated according to the risk parameters, so as to serve as the final result of the construction safety monitoring operation of the intelligent bridge machine at the current time, and to provide reliable and real-time guidance opinions and data for the construction personnel, thereby ensuring the safe construction operation of the intelligent bridge machine; The safety warning information includes safety maintenance prompt information and suspension operation prompt information. Specifically, when the safety warning information is issued, the number of risk parameters at the current time needs to be counted first, when the number of risk parameters is 1, the risk of dangerous construction operation of the intelligent bridge machine is low at this time, and the safety maintenance prompt information is issued. When the number of risk parameters is 2, 3 or 4, the risk of dangerous construction operation of the intelligent bridge machine is high at this time, and the suspension operation prompt information is issued.

[0045] In this embodiment, when the safety maintenance prompt information is issued, the intelligent bridge building machine can perform maintenance operation in the risk parameter corresponding aspect without stopping to avoid the safety hidden danger of the intelligent bridge building machine at the current time; when the work suspension prompt information is issued, the intelligent bridge building machine must be stopped to perform maintenance operation in the risk parameter corresponding aspect to ensure that the intelligent bridge building machine can normally build a bridge under the condition of safety and no risk, thereby realizing the real-time, reliable and accurate safety monitoring effect of the intelligent bridge building machine, and effectively avoiding the potential risk hidden danger of the intelligent bridge building machine construction operation.

[0046] Embodiment two: please refer to Figure 2 As shown in the drawings, the part not described in detail in this embodiment is described in embodiment one, and a construction safety monitoring method of an intelligent bridge building machine is provided, which is realized based on a construction safety monitoring system of an intelligent bridge building machine, and comprises the following steps: S01: collecting inherent entity parameters of the intelligent bridge building machine, and constructing a virtual model with a display station based on the inherent entity parameters; S02: collecting safety monitoring parameters and environmental parameters of the intelligent bridge building machine at the current time, performing overrun analysis on the environmental parameters, calculating the superposition amplitude of the safety monitoring parameters, and adjusting the safety monitoring parameters based on the superposition amplitude as a standard; S03: aligning the adjusted safety monitoring parameters and environmental parameters with the virtual model, converting the virtual model into a digital twin model, and performing simulation operation under simulation logic rules, querying the simulation state of the digital twin model through the display station, and determining whether to execute a deep analysis mode; if the deep analysis mode is executed, step S04 is executed; if the deep analysis mode is not executed, the process is ended; S04: in the deep analysis mode, performing risk assessment on the safety monitoring parameters, identifying the risk parameters of the intelligent bridge building machine, and issuing corresponding safety warning information.

[0047] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any skilled person in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A smart bridge-building machine construction safety monitoring system, characterized in that, include: The virtual construction module is used to collect the inherent entity parameters of the intelligent bridge building machine. The inherent entity parameters include geometric parameters, physical parameters and logical parameters. Based on the inherent entity parameters, a virtual model with display workstations is constructed. The parameter adjustment module is used to collect the safety monitoring parameters and environmental parameters of the intelligent bridge building machine at the current moment, perform over-limit analysis on the environmental parameters, calculate the superposition range of the safety monitoring parameters, and adjust the safety monitoring parameters based on the superposition range. The model simulation module is used to align the superimposed and adjusted safety monitoring parameters and environmental parameters with the virtual model, convert the virtual model into a digital twin model, and run the simulation under the simulation logic rules. The simulation status of the digital twin model can be queried through the display workstation to determine whether to execute the deep analysis mode. The simulation logic rules are: simulated permissions are less than or equal to actual permissions, and the simulation logic is consistent with the actual logic; The intelligent control module is used to perform risk assessment on safety monitoring parameters in deep analysis mode, identify the risk parameters of the intelligent bridge building machine, and issue corresponding safety warning information.

2. The intelligent bridge-building machine construction safety monitoring system according to claim 1, characterized in that, The method for constructing a virtual model is as follows: Based on the parametric design drawings, a basic model is constructed in three-dimensional space, and the A basic structures in the basic model are marked. Adjust the size and shape of the basic structure A to match the corresponding geometric parameters, thereby converting the basic structure into a solid structure. Attribute units are created on each of the A entity structures, and the physical parameters corresponding to the entity structures are added to the attribute units to cause the entity structures to be converted into physical structures. The physical structures involved in the same logical parameter are grouped into a logical unit, and the corresponding logical parameters are assigned to the logical unit according to the order of the intelligent bridge-building machine's logical control, so as to cause the physical structure to be converted into a logical structure. Simulate independent conventional workstations and real-time workstations, initialize the display state of conventional workstations to an explicit state, and initialize the display state of real-time workstations to an implicit state, thereby converting the basic model into a virtual model.

3. The intelligent bridge-building machine construction safety monitoring system according to claim 2, characterized in that, Safety monitoring parameters include regional stress values, operating vibration frequency, jacking oil pressure-temperature ratio difference, and travel speed difference; environmental parameters include humidity and wind load.

4. The intelligent bridge-building machine construction safety monitoring system according to claim 3, characterized in that, The method for collecting regional stress values ​​is as follows: Mark all the model points on the virtual model one by one. Randomly select a model point inside the virtual model as the center and draw a hollow spherical bounding box with a unit length as the radius. Continuously adjust the position of the center and the size of the radius in the bounding box until the bounding box first encloses all the model points. Record the center of the adjusted bounding box as the geometric center. Measure the distance from each pressure sensor point on the virtual model to the geometric center, record it as the initial distance value, and then add the maximum and minimum initial distance values ​​and average them to calculate the average distance. Using the geometric center as the center point of the region and a distance average as the scanning radius, the stress region is scanned in a ring. The pressure sensors located in the stress region are recorded as target sensors. The pressure values ​​of all target sensors are read one by one, and the average of all pressure values ​​is calculated to obtain the stress value of the region.

5. The intelligent bridge-building machine construction safety monitoring system according to claim 4, characterized in that, The method for collecting the pressure-temperature ratio difference of the lifting oil is as follows: The hydraulic oil pressure and temperature of each of the E hydraulic cylinders on the lifting mechanism are retrieved one by one. The hydraulic oil pressure and temperature are then dimensionless to obtain the pressure and temperature values ​​respectively. The pressure and temperature values ​​of each of the E hydraulic cylinders are compared to calculate the E hydraulic pressure-temperature ratio. The average of the E hydraulic pressure-temperature ratios is then calculated and the difference is taken from the standard temperature ratio to calculate the lifting hydraulic pressure-temperature ratio difference.

6. The intelligent bridge-building machine construction safety monitoring system according to claim 5, characterized in that, The method for determining the superposition amplitude is as follows: When the humidity value is greater than the calibrated humidity threshold, the difference between the humidity value and the calibrated humidity threshold is obtained, and the humidity difference is compared with the calibrated humidity threshold to calculate the humidity superposition ratio. When the wind load is greater than the calibrated load threshold, the difference between the wind load and the calibrated load threshold is calculated to obtain the load difference value. The load difference value is then compared with the calibrated load threshold to calculate the load superposition ratio. The humidity superposition ratio and the load superposition ratio are assigned different scaling factors and then added together to calculate the superposition amplitude.

7. The intelligent bridge-building machine construction safety monitoring system according to claim 6, characterized in that, During the conversion of the digital twin model, the virtual model is imported into the 3D engine, and four intrinsic parameter bits and two extrinsic parameter bits are established on the virtual model; The superimposed and adjusted regional stress values, working vibration frequency, jacking oil pressure-temperature ratio difference, and travel speed difference are input into four inherent parameter positions, and the humidity value and wind load are input into two external parameter positions, thereby converting the virtual model into a digital twin model.

8. The intelligent bridge-building machine construction safety monitoring system according to claim 7, characterized in that, The simulated conditions include safe construction conditions and hazardous construction conditions; The method for determining whether to execute deep parsing mode is as follows: After the digital twin model simulation is completed, the display status of the regular workstations switches from the visible state to the invisible state, and the display status of the real-time workstations switches from the invisible state to the visible state, and the text content in the real-time workstations is read. When the text content is "11", the simulation state is a safe construction state, and the deep analysis mode is not executed; when the text content is "00", the simulation state is a dangerous construction state, and the deep analysis mode is executed.

9. The intelligent bridge-building machine construction safety monitoring system according to claim 8, characterized in that, The method for identifying risk parameters is as follows: The stress adjustment difference is obtained by subtracting the adjusted regional stress value from the current regional stress value. When the stress adjustment difference is greater than or equal to the calibrated stress adjustment threshold, the adjusted regional stress calibrated value is recorded as a risk parameter. The vibration adjustment difference is obtained by subtracting the adjusted operating vibration frequency from the current operating vibration frequency; when the vibration adjustment difference is greater than or equal to the calibrated vibration adjustment threshold, the adjusted vibration adjustment difference is recorded as a risk parameter. The difference between the adjusted lifting oil pressure-temperature ratio difference and the current lifting oil pressure-temperature ratio difference is used to obtain the pressure-temperature ratio adjustment difference value; when the pressure-temperature ratio adjustment difference value is greater than or equal to the calibrated pressure-temperature ratio adjustment threshold, the adjusted lifting oil pressure-temperature ratio difference is recorded as a risk parameter. The speed adjustment difference is obtained by subtracting the adjusted travel speed difference from the current travel speed difference. When the speed adjustment difference is greater than or equal to the calibrated speed adjustment threshold, the adjusted travel speed difference is recorded as a risk parameter.

10. The intelligent bridge-building machine construction safety monitoring system according to claim 9, characterized in that, Safety warning information includes safety maintenance reminders and work suspension reminders; The system counts the number of risk parameters at the current moment. When the number of risk parameters is 1, a safety maintenance prompt is issued; when the number of risk parameters is 2, 3, or 4, a work stoppage prompt is issued.

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