A digital twin system for the whole process of building a prefabricated component of an assembled bridge

Abstract

The application relates to the technical field of bridge construction management, and discloses a digital twin system for the whole-process construction of a prefabricated component of an assembled bridge, which comprises a logic configuration unit, a state conversion module, a logic verification unit, a risk assessment module and a resource scheduling module; a logic mirror image and a discrete management state sequence are configured; a completion timestamp in a process update event is analyzed, and a migration instruction is generated; a time difference between the timestamp and a completion timestamp of a previous management node is calculated, a logic deviation residual representing the degree of physical progress advance is generated; when the residual is greater than a threshold value, a locking instruction is generated, and the priority of the mirror image in a resource queue is lowered accordingly; the application internalizes the supervision regulations into a state machine constraint mechanism, implements compliance arbitration on a physical execution sequence through a logic deviation residual, eliminates the resource deadlock risk caused by the administrative acceptance of the physical progress advance, and realizes the dynamic balance of management resources in the time and space dimensions.

Description

Technical Field

[0001] This invention belongs to the field of bridge construction management technology, and in particular relates to a digital twin system for the entire process of prefabricated bridge component construction. Background Technology

[0002] Current methods utilize radio frequency identification (RFID) units or machine vision units to collect component locations, map the collected data onto a 3D model, and record production progress and process flow status. Physical spatial mapping improves construction visibility. However, component construction is driven by administrative approval and quality supervision logic. Simple physical displacement data is insufficient to reflect compliance. Physical construction events and administrative supervision status are decoupled. Management logic relies on post-event data entry, leading to non-compliant transitions in the digital mirror state. For example, the physical level may enter the pouring process, but the regulatory logic may not have completed acceptance. The asynchrony between logic and behavior increases the difficulty of compliance supervision.

[0003] Even at the software control level, existing digital methods mostly focus on the static presentation of physical states. For example, Chinese invention patent CN117575991A discloses a prefabricated bridge quality defect location system based on digital twin technology. By constructing a digital twin model and combining it with neural network algorithms, it can accurately identify and locate surface defects and shape changes of components. However, it is essentially still in the category of passive monitoring or spatial mapping. Its technical focus is on the synchronization of shape and defect features between the physical entity and the twin model. It fails to internalize administrative regulatory procedures into a hard constraint mechanism within the system. This logical disconnect leads to a long-term decoupling between physical construction events and administrative regulatory status. The management logic still relies heavily on post-event recording, making it easy for physical progress to exceed compliance acceptance. This not only increases regulatory blind spots but also fails to cope with systemic congestion caused by the overload of management resources.

[0004] Therefore, the technical problem to be solved by this invention is how to build a synchronization mechanism between administrative supervision logic and physical event flow at the data processing level, use the logic layer to perform legality arbitration on the physical execution sequence, and thereby realize management resource scheduling prediction. Summary of the Invention

[0005] This invention provides a digital twin system for the entire construction process of prefabricated bridge components, the system comprising:

[0006] The logical configuration unit is used to configure globally unique logical identifiers for physical components, establish logical images corresponding to the logical identifiers, and preset discrete management state sequences covering production, circulation, and on-site assembly processes.

[0007] The state transition module is used to receive process update events from the production site, parse the process identifier and physical completion timestamp in the process update event, and input the physical completion timestamp into the logical mirror to generate state transition instructions.

[0008] The logic verification unit is used to extract the completion timestamp of the management node of the corresponding preceding process in the logic mirror, generate a timing deviation vector by calculating the time difference between the physical completion timestamp and the management node completion timestamp, and calculate the timing deviation vector by weighting it based on the preset process risk weight, and generate a logic deviation residual that represents the physical progress of the physical component being ahead of the management progress.

[0009] The risk assessment module is used to generate a lock command when the logical deviation residual exceeds a preset deviation threshold.

[0010] The resource scheduling module is used to establish a mapping relationship model between managed resources and discrete management state sequences. When it receives a request to migrate the logical image to the restricted resource state, it responds by executing a locking command, lowering the allocation priority of physical components in the restricted resource queue, until the corresponding management node completes the timestamp update.

[0011] Preferably, the logic verification unit obtains the dwell time of the logic image in the current discrete management state, with the dwell time in hours; the difference between the physical completion timestamp and the management node completion timestamp is defined as the single-step time delay residual, with the single-step time delay residual in seconds; the single-step time delay residual corresponding to the completed process is accumulated using the process risk weight to generate the logic deviation residual; wherein, the value of the logic deviation residual is negatively correlated with the allocation priority.

[0012] Preferably, the state transition module includes a logic compensation subunit for pre-storing a topological logic model containing unidirectional causal constraints of the process. When the state transition module detects a jump process trigger signal and the adjacent preceding process signal is missing, it calls the topological logic model to perform logical compensation for the missing state, drives the logic image to continuously migrate to the target discrete management state corresponding to the jump process trigger signal, and marks the discrete management state generated by the compensation as a logical compensation attribute.

[0013] Preferably, the logic verification unit includes a delay perception subunit, which is used to calculate the instruction execution time difference from the generation of the process update event to the completion of the corresponding discrete management state conversion; the risk assessment module quantifies the approval lag parameter of the corresponding construction node according to the probability distribution characteristics of the instruction execution time difference, and corrects the risk prediction increment in the risk assessment module according to the approval lag parameter.

[0014] Preferred approval lag parameters Calculated using the following formula: ,in, For parameters that are delayed in approval, For instruction execution time difference, The preset historical average execution time difference, The preset process weighting coefficients; the risk assessment module uses approval lag parameters. When the deviation deviates from the preset information interval, an execution efficiency warning instruction is output for the corresponding construction section.

[0015] Preferably, the resource scheduling module establishes a correlation model between management resources, including quality signature permissions, pedestal space, and hoisting time periods, and states in each discrete management state sequence; when multiple logical images request the same management resource at the same time, priority arbitration is performed based on the logical deviation residuals corresponding to each logical image, and the state transition instructions are sorted according to the allocation priority.

[0016] Preferably, the risk assessment module records the physical execution completion time and the management state closing time of the physical component under a specific discrete management state; calculates the offset duration between the two and identifies the evolution trend of the offset duration; and outputs a management delay prediction signal for subsequent construction procedures based on the evolution trend.

[0017] Preferably, the logic configuration unit configures a globally unique logical identifier for the physical component and binds the logical identifier to the logical image; it presets access logic rules covering production, curing, tensioning, circulation and on-site assembly, and associates the access logic rules with the discrete management state sequence.

[0018] Preferably, the state transition module parses the process identifier and physical completion timestamp in the process update event; retrieves the logical jump path corresponding to the process identifier; and when it detects that the preset admission conditions are met, it migrates the logical image from the current discrete management state to the target discrete management state.

[0019] Preferably, the risk assessment module extracts the dynamic deviation characteristics of the logical deviation residual under different production task loads; when the dynamic deviation characteristics show that the queuing length of the managed resources exceeds the preset load length threshold, a logical congestion signal is sent to the monitoring terminal.

[0020] Furthermore, compared to existing technologies, the digital twin system for the entire construction process of prefabricated bridge components of this invention has the following advantages:

[0021] 1. In the prefabrication of prefabricated bridges, the system configures a globally unique logical management image for each physical component, and presets mutually exclusive atomic management states covering all stages of production, circulation and on-site assembly. This transforms the physical event flow of the entire construction process into logical state machine transition requests, and at the data processing level, it ensures that the physical construction progress is consistent with the administrative supervision logic. This avoids regulatory blind spots caused by the decoupling of physical construction behavior and compliance acceptance status in the data flow, and transforms construction records into a proactive compliance logic interception mechanism, eliminating compliance risks caused by physical execution exceeding logical acceptance.

[0022] 2. By utilizing a pre-stored topological logic model containing unidirectional causal constraints of processes, when signal interference or data chain breakage occurs on the production site, resulting in the loss of signals for key processes, the state transition engine identifies jump-triggered signals, calls the topological logic model to automatically fill in the missing intermediate states, and drives the logic management mirror to continuously jump to the target state. This ensures the logical resilience of the construction management flow in discrete manufacturing environments, eliminates the phenomenon of administrative approval deadlock caused by data silos or hardware sensor failures, and ensures the integrity of the entire life cycle supervision data chain.

[0023] 3. The resource scheduling unit analyzes the rate of change of the residence time of the management mirror in the current atomic management state, calculates the logical momentum of the physical component transition to the subsequent state, drives the decision optimization engine to perform administrative resource pre-occupancy verification in the virtual resource pool for subsequent processes, and outputs administrative resource load fluctuation scheduling instructions to smooth out the physical resource conflict before the mirror transition weight is adjusted. This solves the problem of uneven resource allocation in the construction scenario of multi-line concurrent construction in large prefabrication yards, which leads to systemic idle work, and realizes the transformation from single component state tracking to whole-site resource collaborative optimization. Attached Figure Description

[0024] Figure 1 This is the logical architecture and closed-loop control flowchart of the digital twin system of this invention;

[0025] Figure 2 This is a quantitative relationship diagram between the logic deviation residual and resource allocation priority of this invention;

[0026] Figure 3 This is a diagram of the hardware deployment architecture and signal interaction topology of the system of this invention. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0028] It should be noted that all directional and positional terms used in this invention, such as: up, down, left, right, front, back, vertical, horizontal, inner, outer, top, low, lateral, longitudinal, center, etc., are only used to explain the relative positional relationship and connection between components in a specific state (as shown in the accompanying drawings). They are only for the convenience of describing this invention and do not require that this invention be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the descriptions of "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated.

[0029] In the description of this invention, unless otherwise explicitly specified and limited, the terms installation, connection, and linking should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0030] In the description of this specification, references to the terms "an embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0031] The present invention will be further described in detail below with reference to the accompanying drawings. This part is intended to explain the embodiments of the present invention, rather than to limit the scope of protection of the present invention.

[0032] This invention discloses a digital twin system for the entire construction process of prefabricated bridge components, comprising a logic configuration unit, a state transition module, a logic verification unit, a risk assessment module, and a resource scheduling module. The logic configuration unit is used to configure globally unique logical identifiers for physical components, establish logical mirrors corresponding to the logical identifiers, and preset discrete management state sequences covering production, circulation, and on-site assembly stages. These discrete management state sequences are composed of mutually exclusive atomic management states. The state transition module is used to receive process update events from the production site and parse the process identifiers and physical completion timestamps from the process update events. The logical image generates state transition instructions; the logical verification unit is used to extract the completion timestamp of the management node corresponding to the preceding process in the logical image. The logic verification unit calculates the physical completion timestamp. Completed with management node timestamp The time difference between them generates a time deviation vector. The logic verification unit is based on preset process risk weights. For timing deviation vector Perform weighted calculations to generate logical deviation residuals that characterize the physical progress of the physical component being ahead of the management progress. Logical deviation residual Calculate using the following formula: ,in, For logical deviation residuals; For the first Quality risk weighting coefficient for each process step; For the first Timing deviation vector and logic deviation residual of each process step The value is negatively correlated with the allocation priority.

[0033] The risk assessment module is used for logical deviation residuals When the deviation exceeds a preset threshold, a locking instruction is generated. The resource scheduling module establishes a mapping model between managed resources and discrete management state sequences. Upon receiving a request to migrate a logical image to a restricted resource state, the resource scheduling module responds by executing the locking instruction, lowering the allocation priority of physical components in the restricted resource queue until the corresponding management node completes the timestamp. Updated, managed resources include quality approval permissions, pedestal space, and hoisting time slots. When multiple logical images simultaneously request the same managed resource, the resource scheduling module uses the logical deviation residuals corresponding to each logical image. Execute priority arbitration, input component logic deviation residual Applying a nonlinear decay model Mapping allocation priority percentage attenuation coefficient Based on experimental measurements of the administrative resource turnover rate in prefabrication sites, and targeting concurrent requests for the same quality signature permission or pedestal space components, a resource scheduling matrix is ​​constructed by aggregating the priority scalars of each logical image, and execution is based on... Value descending arbitration generates an admission time sequence chain, when Below the preset compliance security threshold This triggers the execution of a locking command and blocks the image state transition. (Parameters) The state transition module is determined based on the coverage density of quality inspectors and the steady-state distribution of concurrent load on the production line. The state transition module includes a logic compensation subunit, which is used to pre-store a topological logic model containing unidirectional causal constraints of the process. When the state transition module detects a jump process trigger signal and the adjacent preceding process signal is missing, it calls the topological logic model to perform logical supplementation of the missing state, drives the logic image to continuously migrate to the target discrete management state corresponding to the jump process trigger signal, and marks the discrete management state generated by the supplementation as a logical supplementation attribute. The topological logic model defines the irreversible causal path between atomic management states. Taking the production node update as an example, if the system receives a jump subsequent process signal and the preceding process signal is missing, the logic compensation subunit infers and fills in the missing intermediate atomic management state according to the topological logic model.

[0034] The logic verification unit includes a delay-aware subunit, used to calculate the instruction execution time difference from the generation of a process update event to the completion of the corresponding discrete management state transition. The risk assessment module is based on the instruction execution time difference. The probability distribution characteristics quantify the approval lag parameters corresponding to the construction nodes. Approval delay parameters Calculated using the following formula: ,in, This is a parameter indicating a delay in approval. This refers to the instruction execution time difference. The preset historical average execution time difference is determined based on the statistical average of the historical execution data of this construction node. The risk assessment module uses preset process weighting coefficients and approval lag parameters. When the deviation from the preset information interval occurs, an execution efficiency warning instruction is output for the corresponding construction section. The risk assessment module is also used to record the physical execution completion time and management status closure time of physical components under specific discrete management states, calculate the offset duration between the two and identify the evolution trend of the offset duration, and output a management delay prediction signal for subsequent construction processes based on this, and aggregate the historical quality defect frequency of construction nodes. Structural safety sensitivity coefficient Through formula The risk weight coefficients for the calibration process are based on statistical data of engineering bearing capacity failure modes. Simultaneously, the risk assessment module adaptively optimizes the deviation threshold and collects the first batch of measured data from components. Establish a probability density distribution model for the sequence and extract the mean. and three standard deviations Establish dynamic boundaries for compliant arbitration To isolate false deviations caused by random fluctuations in administrative operations, quantified parameters and boundary rules are written into the access rule base of the logical configuration unit, driving the automated decision-making of the transition path of the mirror state machine. The system configures unique logical identifiers and discrete management state sequences for physical components through the logical configuration unit, and the state transition module uses the physical completion timestamps collected on-site. Driven by state transition, the logic verification unit quantifies management deviations by comparing the time difference between physical execution and administrative approval. The resource scheduling module adjusts resource allocation priorities based on management deviations. This data processing method based on state machine constraints makes physical production progress subject to hard constraints of administrative supervision logic, thereby eliminating the risk of resource occupation conflicts caused by physical execution exceeding logical acceptance.

[0035] Example 1: This example demonstrates a digital twin system for the entire construction process of prefabricated bridge components, specifically applied in a scenario where administrative oversight resources are scarce and production tasks are occurring frequently and concurrently. In precast beam yards with multiple production lines and where on-site quality supervision personnel cannot cover the entire process of acceptance in real time, the objective condition of insufficient quality inspection personnel causing administrative approval processes to lag behind the physical construction progress has become a core obstacle restricting construction compliance. When the number is... The physical components were completed and the steel cage was inserted into the formwork, and the on-site sensors transmitted back the physical completion timestamp. At that time, the system retrieves the logical image of the component based on the logical identifier and identifies the completion timestamp of the management node corresponding to the acceptance stage of its preceding concealed works. Currently at a standstill, to address the regulatory blind spot caused by the decoupling of physical execution and administrative confirmation, the logic verification unit calculates... and The discrete deviation between them generates a time-series deviation vector. Combined with the process risk weight of the corresponding process The logic deviation residual, which characterizes the degree of physical progress advancement of the component, was calculated. The risk assessment module is based on The numerical changes identified a non-compliant transition tendency in the component and generated an execution lock instruction. The system used this instruction to lock the component. Before entering the next restricted management state, namely the pouring state, the access boundary is anchored at the current node.

[0036] In this application scenario, because administrative resources, namely specific quality approval authority and health care platform space, are limited by personnel quotas, the resource scheduling module utilizes logical deviation residuals. The negative correlation between the component and the resource allocation weight should be adjusted to lower the component's weight. Prioritize allocation within the constrained resource queue to ensure that other administrative approval procedures are completed and Beam segments with indicators within a safe range are given priority to enter the curing or pouring stage. This step uses deviations in administrative regulatory data to drive adaptive adjustments in the production sequence, resolving compliance risks and resource scheduling conflicts arising from the pursuit of production speed when regulatory resources are scarce. As quality inspectors complete the signing procedures in subsequent periods, the management nodes complete the timestamps. Update: The logic verification unit detects the logic deviation residual. When the value falls back to within the preset deviation threshold, the system responds by updating the status and reversing the status update for the component. The execution of the locking instruction restores the component's normal weight in the scheduling queue, enabling the construction pipeline to achieve self-balancing of progress under the hard constraints of administrative supervision logic, and transforming the originally disconnected external approval process into a resource coordination variable within the system.

[0037] Example 2: This example uses a prefabrication yard digital management scenario built on a discrete event simulation platform to verify the effectiveness of the synchronization mechanism between physical construction progress and administrative supervision logic. The experimental platform consists of physical measurement sensors and a logic processing server, with a data acquisition resolution of [resolution missing]. s, data sampling rate Hz, the test process actively injects a signal-to-noise ratio of Hz into the original data stream. dB of Gaussian white noise is used to simulate electromagnetic interference and signal jitter in a real working environment, with a sampling period of [number missing]. The settings follow the computing resource load balancing rules: when the process update event density is within a unit of time... Greater than sampling period (times per minute) Values s, otherwise the value is s, selected in this verification The operation records of the concurrent production lines were used as the experimental group, while a traditional system with only physical location mapping function was selected as the control group; the experiment involved changing the physical completion timestamp. Completed with management node timestamp The timing deviation between them is used to construct management runaway pressure gradients of different intensities, extract the logical mirrors of the corresponding components, and calculate the logical deviation residuals according to the formula. See Table 1.

[0038] Table 1: System Response Data under Different Lead Time Conditions

[0039]

[0040] in, This is the time deviation vector, representing the time difference between the physical completion time and the administrative approval closure time; This is the process risk weighting coefficient, preset according to the process level; The logical deviation residual reflects the degree of physical progress advance; according to the data in Table 1, obvious nonlinear response characteristics were observed, when the physical advance time... In h to When the h interval is used, the allocation priority follows The increase is followed by a gradual decrease; during this phase, the system allows physical execution units to continue migrating to an unrestricted state. Exceed h after, The value exceeds the preset deviation threshold, i.e. The system generates and executes a locking instruction and forcibly compresses the allocation priority to [a lower level]. The following section describes how lowering the allocation priority restricts the use of restricted resources, namely steam curing platforms, by violating regulations. In contrast, the control group had a physical lead time of [missing information]. At time h, its digital mirror still displays as a normal physical migration state, and the risk of administrative resource deadlock cannot be detected; experimental data confirms the logical deviation residual. The causal relationship between resource scheduling instructions exists. Under dB noise interference conditions, the system's response to The recognition accuracy is maintained at This mechanism quantifies the degree of deviation in physical construction through temporal residual analysis of administrative data flow, shifting management decisions from post-event supplementation to logical interception, thereby eliminating systemic congestion caused by premature administrative acceptance of physical execution in a discrete manufacturing environment.

[0041] Example 3: This example combines Figures 1 to 3 This document describes a digital twin system for the entire construction process of prefabricated bridge components, such as... Figure 1 As shown, the system configures logical images and discrete management state sequences by a logical configuration unit, and provides logical images to the state transition module and management node completion timestamps to the logical verification unit. At the same time, the production site sends process update event data streams to the state transition module. The state transition module parses the process identifier and timestamp, generates state transition instructions, and inputs the physical completion timestamp to the logical verification unit. The logical verification unit generates logical deviation residuals by calculating the timing deviation vector and outputs them to the risk assessment module. When the residual is greater than a threshold, it generates an execution lock instruction and triggers the resource scheduling module. The resource scheduling module responds to the lock instruction and lowers the priority of the resource queue. After arbitration, it outputs the dynamic self-balancing management resource scheduling execution result.

[0042] like Figure 2As shown, the horizontal axis displays component numbers from B101 to B402, the left vertical axis represents the Logical Deviation Residual (MLR), and the right vertical axis represents the percentage of allocation priority. The chart uses horizontal bar charts to represent the MLR and vertical bar charts to represent allocation priority. The data shows a negative correlation between the two; components numbered B401 and B402 show higher MLR values, corresponding to extremely low allocation priority, while components with lower MLR values ​​maintain higher allocation priority. Figure 3 As shown, the physical sensing node includes a radio frequency identification (RFID) unit, a machine vision unit, and a timestamp generator, used to collect data and send process update events to the core computing center. The core computing center is deployed on a cloud or local server and integrates a logic configuration unit, a state transition module, a logic verification unit, a risk assessment module, a resource scheduling module, as well as a logic mirror database and a topology rule base. The core computing center sends logic congestion signals or warnings to the management workstation containing the handheld monitoring terminal according to the rule configuration, and at the same time sends execution lock commands or priority adjustment signals to the field execution control node. The field execution control node specifically includes the controlled tensioning equipment and the steam curing platform as a confined resource.

[0043] Example 4: When the work surface is at the edge of a communication blind spot and the wireless transmission link is in the physical components In a prefabrication yard for assembled bridges where the prestressing tensioning process is periodically interrupted, the on-site logic server remains in the curing state because it has not received the corresponding tensioning completion node update event. Upon receiving a grouting access request signal from the downstream grouting equipment, the state transition module recognizes that the signal is a jump-type process trigger signal and calls the pre-stored topological logic model containing unidirectional causal constraints of the process. The logic compensation subunit performs a reverse retrieval based on the preceding associated nodes of the grouting node in the directed causal graph, automatically fills in the missing tension management status and marks it as a logical compensation attribute.

[0044] To quantify the risk of administrative regulatory lag caused by physical data disconnection, the delay sensing subunit extracts the timestamp of the grouting access request and the theoretical completion time of the tensioning process in the topology model, and obtains the instruction execution time difference by numerical subtraction between the two. Value s, where process weight coefficient The calibration follows the risk matrix mapping rule, that is, it retrieves the preset process risk level table in the database and determines the risk weight of the first-level process in the tensioning stage. Historical average execution time difference The calibration uses a sliding window averaging algorithm, which calculates the preceding sequence of the constructed node. The average of the execution records of the same type of components is used to derive the statistical benchmark value. The risk assessment module substitutes the variables, which have been calibrated by engineering statistics, into the calculation formula. The approval lag parameter was calculated. Value The system initiates an adaptive correction process, specifically by adjusting the approval lag parameter. As input for judgment, the degree of deviation in the execution of the current management flow is checked. Deviation from to When the pre-set confidence interval is formed, the decision optimization engine responds to the deviation by executing a priority suppression action for subsequent beam storage processes, reducing the logical momentum of the component in the storage resource scheduling queue. .

[0045] Example 5: When the system is deployed at a newly built precast beam production base, the logic configuration unit executes the initial commissioning procedure. It reads pre-stored production records of similar precast components from the memory, extracts the frequency of occurrence of processes such as concealed works acceptance, prestressing tensioning, and grouting operations, and constructs a risk weight matrix for each process. The system then assigns values ​​based on the process's role in structural load-bearing capacity. to Risk weighting coefficients between , before Physical completion timestamp of beam segment under controlled conditions Completed with management node timestamp The measured data were normalized for deviation values, and the logic deviation residuals were determined accordingly. The zero-point offset is used to eliminate calculation system errors caused by differences in specific project management processes.

[0046] During the calibration period before entering the formal operation phase, the delay sensing subunit's entry time is... The baseline data collection status of the day is used to calibrate the approval lag parameters. The dynamic monitoring range; the system continuously records the execution time difference of instructions when quality inspectors handle node update events during this period. The original sequence is used to perform probability density estimation on the sequence by calling the statistical operation unit, and the lag parameters are calculated. Statistical distribution mean with standard deviation And set the monitoring range to be in to The numerical range between; this adaptive parameter adjustment process based on the actual on-site execution efficiency benchmark corrects the model boundary according to the execution intensity of different working environments, so that the lagging risk prediction signal output by the risk assessment module can eliminate false interference caused by differences in management habits, and lock the judgment logic within the deviation range caused by the substantial deterioration of administrative efficiency.

[0047] Example 6: In the pre-deployment calibration procedure for a special large-span precast box girder production line, the logic configuration unit executes a standardized parameter matrix construction process to determine the process risk weight coefficients. The initial distribution; by retrieving past data recorded in the database. Record the quality defects of similar components within a month, and extract the frequency of defect occurrence for each process. Using logarithmic mapping logic The statistical facts of defects are transformed into discrete weighted scalars, where This is the process risk weighting coefficient. Based on the frequency of defects in the corresponding process, the risk weight coefficient for the grouting process is calculated using this procedure. for This value is entered as a static baseline value into the admission rule base of the logical configuration unit.

[0048] In the priority calculation procedure of the resource scheduling module, the system uses a nonlinear decay model to establish allocation priorities. The quantitative judgment logic; when the system detects the number as The beam segment at the physical execution completion timestamp After it is generated, due to the completion timestamp of the administrative approval node management node. Extracting the time-series deviation vector without updating for At time h, the system combines the risk weight coefficients obtained from the aforementioned calibration. for Calculate the generated logical deviation residual Value This leads to the application of the exponential decay formula. Get the allocation priority of the current component. for ,in To assign priorities, For logical deviation residuals, the system calculates the allocation priority. Compared with the preset resource usage threshold Perform numerical comparisons, in When the value falls below this threshold, a logical blocking instruction for the storage location resource will be automatically generated.

[0049] Example 7: In a concurrent operation scenario where multiple precast beams simultaneously request entry into a restricted steam curing kiln, the system executes a dynamic self-balancing procedure to address resource overload. The resource scheduling module, by retrieving the preset mapping relationship model in the logical configuration unit, identifies the current physical capacity limit of the steam curing kiln. The real-time request queue contains [the following]. The system extracts the logical deviation residuals corresponding to each logical mirror of the component. The numerical values ​​are then processed and a numerical sorting procedure is executed; the ranking result is displayed, with the number being... and The components are based on the completion timestamp of their corresponding concealed works acceptance management nodes. Not yet updated, resulting in logical deviation residuals. The values ​​reached respectively and Based on allocation priority Logical deviation residual The negative correlation in the mapping pattern allows for the calculation of the allocation priority of the two components. All below The decision optimization engine outputs targeted solutions based on this. and The logical blocking instruction reduces its priority in the constrained resource scheduling queue to the lowest level and locks it in the pre-migration state, thereby ensuring complete procedures and logical deviation residuals. The rest within the safety threshold Components have priority in obtaining the right to occupy health-preserving resources.

[0050] To address the challenge of identifying administrative efficiency degradation in large-scale construction environments, the friction sensing unit executes a statistical audit procedure for residuals of node update events; the system collects the timestamps of node update events. When the logical mirror state flips Execution residuals between And use it as the original input data to characterize the intensity of managed friction; through continuous analysis over a specific time period Execution residuals at each sampling point By performing a sliding window variance calculation, the system quantifies the management friction index of the current construction node. ,in To manage friction indicators, their values ​​reflect the intensity of execution interference in the administrative approval process; when management friction indicators are monitored in the quality inspection stage... continuous The performance exceeded the preset benchmark value for one cycle. At that time, the risk assessment module determines that there is a hidden management blockage at the node and sends a logical congestion signal to the regulatory terminal, so that the management can identify the degradation of execution efficiency caused by human negligence or unstable data links before physical shutdown occurs, and complete the closed-loop conversion from raw time-series data to administrative efficiency early warning instructions.

[0051] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A digital twin system for the entire construction process of prefabricated bridge components, characterized in that, The system includes: The logical configuration unit is used to configure globally unique logical identifiers for physical components, establish logical images corresponding to the logical identifiers, and preset discrete management state sequences covering production, circulation, and on-site assembly processes. The state transition module is used to receive process update events from the production site, parse the process identifier and physical completion timestamp in the process update event, and input the physical completion timestamp into the logical mirror to generate state transition instructions. The logic verification unit is used to extract the completion timestamps of the management nodes of the preceding processes in the logic mirror. It generates a timing deviation vector by calculating the time difference between the physical completion timestamp and the management node completion timestamp. The timing deviation vector is in seconds. It performs an accumulation calculation on the timing deviation vectors corresponding to the completed processes using the quality risk weights of the completed processes to generate a logic deviation residual that represents the physical progress of the physical component being ahead of the management progress. The value of the logic deviation residual is negatively correlated with the allocation priority. The risk assessment module is used to generate a lock command when the logical deviation residual exceeds a preset deviation threshold. The resource scheduling module is used to establish a mapping relationship model between managed resources and discrete management state sequences. When it receives a request to migrate the logical image to the restricted resource state, it responds by executing a locking command, lowering the allocation priority of physical components in the restricted resource queue, until the corresponding management node completes the timestamp update.

2. The digital twin system for the entire construction process of prefabricated bridge components according to claim 1, characterized in that, The logic verification unit obtains the dwell time of the logic image under the current discrete management state, and the dwell time is in hours.

3. The digital twin system for the whole process of building a prefabricated component of a fabricated bridge according to claim 1, characterized in that, The state transition module includes a logic compensation subunit, which is used to pre-store a topological logic model containing unidirectional causal constraints of the process. When the state transition module detects a jump process trigger signal and the adjacent preceding process signal is missing, it calls the topological logic model to perform logical compensation for the missing state, drives the logic image to continuously migrate to the target discrete management state corresponding to the jump process trigger signal, and marks the discrete management state generated by the compensation as a logical compensation attribute.

4. The digital twin system for the whole process of building a fabricated bridge prefabricated component according to claim 1, characterized in that, The logic verification unit includes a delay perception subunit, which is used to calculate the instruction execution time difference from the generation of the process update event to the completion of the corresponding discrete management state transition; the risk assessment module quantifies the approval lag parameter of the corresponding construction node according to the probability distribution characteristics of the instruction execution time difference, and corrects the risk prediction increment in the risk assessment module according to the approval lag parameter.

5. The digital twin system for the whole process of building a prefabricated component of an assembled bridge according to claim 4, characterized in that, Approval delay parameters Calculated using the following formula: ,in, For parameters that are delayed in approval, For instruction execution time difference, The preset historical average execution time difference, The preset process weighting coefficients; the risk assessment module uses approval lag parameters. When the deviation deviates from the preset information interval, an execution efficiency warning instruction is output for the corresponding construction section.

6. The digital twin system for the whole process of building a fabricated bridge prefabricated component according to claim 1, characterized in that, The resource scheduling module establishes a model relating management resources, including quality approval permissions, pedestal space, and hoisting time periods, to the states in each discrete management state sequence. When multiple logical images request the same management resource at the same time, priority arbitration is performed based on the logical deviation residuals corresponding to each logical image, and the state transition instructions are sorted according to the allocation priority.

7. The digital twin system for the whole process of building a fabricated bridge prefabricated component according to claim 1, characterized in that, The risk assessment module records the physical execution completion time and management state closure time of physical components under specific discrete management states; calculates the offset duration between the two and identifies the evolution trend of the offset duration; based on the evolution trend, it outputs a management delay prediction signal for subsequent construction procedures.

8. The digital twin system for the whole process of building a fabricated bridge prefabricated component according to claim 1, characterized in that, The logic configuration unit configures a globally unique logical identifier for the physical component and binds the logical identifier to the logical image; it presets access logic rules covering production, curing, tensioning, circulation and on-site assembly, and associates the access logic rules with the discrete management state sequence.

9. The digital twin system for the whole process of building a fabricated bridge prefabricated component according to claim 1, characterized in that, The state transition module parses the process identifier and physical completion timestamp in the process update event; retrieves the logical jump path corresponding to the process identifier; and when it detects that the preset admission conditions are met, it migrates the logical image from the current discrete management state to the target discrete management state.

10. The digital twin system for the whole process of building a fabricated bridge prefabricated component according to claim 1, wherein, The risk assessment module extracts the dynamic deviation characteristics of the logical deviation residual under different production task loads; when the dynamic deviation characteristics show that the queue length of the managed resources exceeds the preset load length threshold, a logical congestion signal is sent to the monitoring terminal.

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