Construction progress and equipment cluster collaborative virtual display system based on digital twinning

Abstract

The application discloses a construction progress and equipment cluster cooperative virtual display system based on digital twinning, and relates to the technical field of digital twinning. The application solves the problem of data packet loss and out-of-order caused by blind allocation of computing power during protocol conversion. The application quantitatively calculates the best processing period through feature proportion and repetition number, realizes efficient processing rhythm of "full coverage and less repetition", avoids display lag caused by data backlog, reduces resource waste caused by invalid processing, guarantees real-time synchronization capability of the digital twinning model, clearly defines the collection frequency and time of each monitoring node, ensures the standardization and timeliness of data collection, avoids data source errors caused by fuzzy collection logic, provides high-quality and traceable raw data support for subsequent processing and display, accurately locates protocol difference node segments, and realizes dynamic pre-allocation of computing power by combining historical data, quantitatively converting features and demand computing power.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, specifically to a virtual display system for collaborative construction progress and equipment clusters based on digital twins. Background Technology

[0002] In the construction industry, construction progress control and equipment cluster collaboration have long faced pain points such as data fragmentation, low processing efficiency, and discrepancies between virtual and real mapping.

[0003] In traditional management models, the inconsistent data collection logic of monitoring nodes easily leads to data omissions or duplicate collections; data processing cycles rely on experience-based settings, making it difficult to balance "real-time performance" and "resource utilization," often resulting in digital twin model displays lagging behind the actual scenario; significant differences in data protocols between multiple devices can easily cause packet loss and out-of-order delivery during protocol conversion due to unreasonable allocation of computing power, affecting the reliability of collaboration; at the same time, the lack of an effective virtual-real data verification mechanism makes it difficult to detect deviations between the virtual model and the actual construction status in a timely manner, increasing schedule delays and safety risks.

[0004] On the one hand, the lack of unified data collection logic standards at monitoring nodes leads to chaotic collection frequencies and times, resulting in data omissions or duplicate collections and inconsistent data source quality. On the other hand, data processing cycles rely on manual experience for setting, making it difficult to balance "full coverage" and "efficient processing," often causing digital twin model updates to lag behind actual construction conditions. Furthermore, significant differences in data protocols between multiple devices lead to data loss and out-of-order issues during protocol conversion due to blind allocation of computing power, affecting collaborative reliability. Moreover, the lack of a real-time and effective virtual-real data verification mechanism makes it difficult to detect deviations between virtual scenarios and actual working conditions in a timely manner, increasing the risk of schedule delays and safety management risks.

[0005] Against this backdrop, there is an urgent need to build a digital twin system for end-to-end collaborative optimization to solve the aforementioned management challenges. This system forms a data-driven end-to-end optimization mechanism through the collaborative operation of the monitoring feature confirmation end, processing cycle confirmation end, processing logic confirmation end, and feature verification processing end. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a virtual display system for collaborative construction progress and equipment clusters based on digital twins, which solves the problems of data packet loss and out-of-order processing caused by blind allocation of computing power during protocol conversion.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a digital twin-based virtual display system for collaborative construction progress and equipment clusters, comprising:

[0008] The monitoring feature confirmation terminal confirms the monitoring features associated with different monitoring nodes in the equipment cluster.

[0009] The processing cycle confirmation end confirms the monitoring features associated with different monitoring nodes within a specified cycle, confirms a set of processing times from the confirmed monitoring features, records the monitoring nodes included in the processing time, and selects the optimal processing cycle from the different processing features recorded at different processing times.

[0010] The processing logic confirmation end determines the sequence of acquisition nodes associated with the optimal processing cycle based on the confirmed optimal processing cycle, then confirms the conversion process from the sequence of acquisition nodes, and confirms the calibration time associated with each conversion process based on historical data of the corresponding conversion process in the historical process. Combining the calibration time, the processing logic associated with the sequence of acquisition nodes is determined and output.

[0011] Preferably, the monitoring characteristics include the monitoring frequency and the associated monitoring time.

[0012] Preferably, the processing cycle confirmation terminal confirms the processing time in the following ways:

[0013] Several sets of processing times are generated within a specified period, which is a preset period. The current time is the processing time. Based on the generated processing times, the processing time period associated with the corresponding processing time is determined, and the processing time is ∈ the specified period.

[0014] Based on the different monitoring characteristics associated with different monitoring nodes, the monitoring times associated with the corresponding monitoring nodes are marked within the processing period, and the processing characteristics associated with the processing period are determined: the total number G of different monitoring nodes monitored within the processing period is recorded. i Where i represents different processing time periods, and the total number H of several monitoring nodes in the device cluster is then confirmed synchronously, using: G i ÷H=ZB i Confirm the proportion of features associated with the corresponding processing time period (ZB) i Then, the duplicate monitoring nodes that appear during the processing period are marked synchronously, and the number of repetitions associated with the duplicate monitoring nodes is recorded as CH. i-k Where k represents different duplicate monitoring nodes, and CH represents several sets of duplicate counts associated with k duplicate monitoring nodes. i-k Perform summation to confirm the total number of repetitions ZF. i ZB is used. i ÷ZF i =BD i Confirm the processing characteristics (BD) associated with the corresponding processing time period. i ;

[0015] Different processing characteristics associated with different processing time periods (BD) iIn the process, the maximum value is selected, and the processing period associated with the maximum value is recorded as the optimal processing cycle. The determined optimal processing cycle is then transmitted to the processing logic confirmation terminal.

[0016] Preferably, the specific method by which the processing logic confirmation terminal determines the sequence of acquisition nodes is as follows:

[0017] Identify the processing period associated with the optimal processing cycle, and then, based on the different monitoring characteristics associated with different monitoring nodes, identify the monitoring nodes that have monitoring processes within the processing period and record them as acquisition nodes. Sort several acquisition nodes according to the specific method of sorting them from front to back according to the processing period to confirm the acquisition node sequence.

[0018] The data processing protocols associated with adjacent acquisition nodes within the acquisition node sequence are confirmed. It is determined whether the data processing protocols between adjacent acquisition nodes before and after are the same. If they are the same, no processing is required. If they are different, the adjacent acquisition nodes are recorded as processing logic nodes to be confirmed.

[0019] The nodes to be confirmed associated with several adjacent acquisition nodes in the acquisition node sequence are determined sequentially. The acquisition node at the front end of the node segment to be confirmed is denoted as the front node, and the acquisition node at the back end is denoted as the back node. The data processing protocol associated with the front node is denoted as the front protocol, and the data processing protocol associated with the back node is denoted as the back protocol. The conversion characteristics associated with the front protocol and the back protocol in the historical process are confirmed. The conversion characteristics include conversion computing power and conversion rate. The conversion rate ÷ conversion computing power = conversion characteristics are used to confirm the conversion characteristics associated with the corresponding front protocol and the back protocol in a single historical process. The confirmed conversion characteristics are averaged to lock the conversion characteristics of the front protocol and the back protocol after determination.

[0020] Based on the determined sequence of acquisition nodes, the discontinuity time T between the previous protocol and the subsequent protocol is confirmed. Using the formula: conversion feature × T / 2 = SL, the required computing power SL between the previous protocol and the subsequent protocol is obtained.

[0021] The required computing power (SL) associated with different unconfirmed node segments within the data collection node sequence is confirmed and marked sequentially.

[0022] Preferred options also include:

[0023] After the monitoring data associated with the corresponding monitoring node is processed, the feature verification processing end confirms the linkage features associated with the monitoring nodes and verifies whether the linkage features are consistent from the digital twin model. If they are consistent, no processing is required; if the linkage features are inconsistent, a data transmission anomaly signal is directly generated and displayed.

[0024] This invention provides a virtual display system for collaborative construction progress and equipment clusters based on digital twins. Compared with existing technologies, it has the following advantages:

[0025] The monitoring feature confirmation end clearly defines the collection frequency and time of each monitoring node to ensure the standardization and timeliness of data collection, avoid data source errors caused by ambiguity in collection logic, and provide high-quality and traceable raw data support for subsequent processing and display.

[0026] The processing cycle confirmation end calculates the optimal processing cycle by quantifying the feature ratio and the number of repetitions, achieving a highly efficient processing rhythm of "full coverage and few repetitions". This avoids display delays caused by data backlog and reduces resource waste caused by ineffective processing, ensuring the real-time synchronization capability of the digital twin model.

[0027] The processing logic confirms the precise location of protocol difference nodes, combines historical data to quantify conversion characteristics and required computing power, and realizes dynamic pre-allocation of computing power. This not only eliminates the problems of "waiting" and "overload" in protocol conversion and reduces the risk of packet loss and out-of-order delivery, but also forms a standardized processing flow, improving the system's adaptability to dynamic scenarios such as node additions and subtractions and protocol updates. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the principle framework of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] First Embodiment

[0031] Please see Figure 1 This application provides a construction progress and equipment cluster collaborative virtual display system based on digital twins, including a monitoring feature confirmation end, a processing cycle confirmation end, a processing logic confirmation end, and a feature verification processing end, wherein the monitoring feature confirmation end, the processing cycle confirmation end, the processing logic confirmation end, and the feature verification processing end are electrically connected from the output node to the input node in sequence;

[0032] The monitoring feature confirmation terminal confirms the monitoring features associated with different monitoring nodes in the device cluster. The monitoring features include the monitoring frequency and the associated monitoring time. The monitoring frequency is how often the data is collected. For example, an image acquisition device collects data once per hour. The monitoring time is the time of collection associated with the corresponding monitoring device. If the corresponding image acquisition device collects data at 13:00, the associated monitoring frequency is once per hour. Then the subsequent associated collection times will be 14:00, 15:00, 16:00, and so on.

[0033] The processing cycle confirmation end confirms the monitoring features associated with different monitoring nodes within a specified cycle, identifies a set of processing times from the confirmed monitoring features, records the monitoring nodes included in the processing time, and selects the optimal processing cycle from the different processing features recorded at different processing times. Specifically, the specified cycle is 24 hours, and different processing times are randomly selected within 24 hours, with a processing time ≤ 24 hours. The monitoring features included in the processing time are recorded to confirm the optimal processing cycle, which facilitates the subsequent comprehensive processing of the collected data to ensure that the digital twin model can synchronously display the corresponding collected data and achieve a real-time progress display effect.

[0034] The specific method for selecting the optimal processing cycle is as follows:

[0035] Several sets of processing times are generated within a specified period, which is a preset period, generally 24 hours. The current time is taken as the processing time. Based on the generated processing time, the processing time period associated with the corresponding processing time is determined, and the processing time is ∈ the specified period.

[0036] Based on the different monitoring characteristics associated with different monitoring nodes, the monitoring times associated with the corresponding monitoring nodes are marked within the processing period (based on the monitoring frequency and the actual existing monitoring times, the corresponding monitoring times can be marked within the determined processing period). The processing characteristics associated with the processing period are determined: the total number G of different monitoring nodes monitored within the processing period is recorded. i Where i represents different processing time periods, and the total number H of several monitoring nodes in the device cluster is then confirmed synchronously, using: G i ÷H=ZB i Confirm the proportion of features associated with the corresponding processing time period (ZB) i Then, the duplicate monitoring nodes that appear during the processing period are marked synchronously, and the number of repetitions associated with the duplicate monitoring nodes is recorded as CH. i-k Where k represents different duplicate monitoring nodes, and CH represents several sets of duplicate counts associated with k duplicate monitoring nodes. i-k Perform summation to confirm the total number of repetitions ZF.i ZB is used. i ÷ZF i =BD i Confirm the processing characteristics (BD) associated with the corresponding processing time period. i Specifically, in the corresponding feature confirmation process, the coverage ratio ZB i The larger the value, the fewer the number of repetitions associated with it, and the more associated processing features (BD) are. i The larger the value, the better. When the processing feature associated with the corresponding processing period is at its maximum value among several processing periods, then the corresponding processing period can be directly locked as the determined optimal processing cycle, and the feature it covers is in the optimal state.

[0037] Different processing characteristics associated with different processing time periods (BD) i In the middle, select the maximum value, and record the processing period associated with the maximum value as the optimal processing cycle, and transmit the determined optimal processing cycle to the processing logic confirmation terminal;

[0038] Specifically, within the corresponding optimal processing cycle, the conversion processing logic between different data types can be confirmed according to the corresponding monitoring process. This ensures that when the corresponding monitoring data arrives, a rapid processing process can be implemented, enabling the associated digital twin model to quickly match the data collected in the actual scenario, maintaining the synchronous real-time display effect of the digital twin model, and significantly reducing the associated processing time.

[0039] The processing logic confirmation end determines the sequence of acquisition nodes associated with the optimal processing cycle based on the confirmed optimal processing cycle. It then confirms the conversion process within the acquisition node sequence and, based on historical data about the corresponding conversion process in the historical process, confirms the calibration time associated with each conversion process. Finally, it determines the processing logic associated with the acquisition node sequence based on the calibration time.

[0040] Identify the processing period associated with the optimal processing cycle, and then, based on the different monitoring characteristics associated with different monitoring nodes, identify the monitoring nodes that have monitoring processes within the processing period and record them as acquisition nodes. Sort several acquisition nodes according to the specific method of sorting them from front to back according to the processing period to confirm the acquisition node sequence.

[0041] The data processing protocols associated with adjacent acquisition nodes within the acquisition node sequence are confirmed. It is determined whether the data processing protocols between adjacent acquisition nodes before and after are the same. If they are the same, no processing is required. If they are different, the adjacent acquisition nodes are recorded as processing logic nodes to be confirmed.

[0042] The nodes to be confirmed associated with several adjacent acquisition nodes in the acquisition node sequence are determined sequentially. The acquisition node at the front end of the node segment to be confirmed is denoted as the front node, and the acquisition node at the back end is denoted as the back node. The data processing protocol associated with the front node is denoted as the front protocol, and the data processing protocol associated with the back node is denoted as the back protocol. The conversion characteristics associated with the front protocol and the back protocol in the historical process are confirmed. The conversion characteristics include conversion computing power and conversion rate. The conversion rate ÷ conversion computing power = conversion characteristics are used to confirm the conversion characteristics associated with the corresponding front protocol and the back protocol in a single historical process. The confirmed conversion characteristics are averaged to lock the conversion characteristics of the front protocol and the back protocol after determination.

[0043] Based on the determined sequence of acquisition nodes, the discontinuity time T between the previous protocol and the subsequent protocol is confirmed. Using the formula: conversion feature × T / 2 = SL, the required computing power SL between the previous protocol and the subsequent protocol is obtained.

[0044] The required computing power SL associated with different unconfirmed node segments within the collection node sequence is confirmed and marked in sequence. In the subsequent processing, based on the confirmed required computing power SL, the corresponding computing power is allocated in advance for the conversion of the subsequent protocol after the previous protocol conversion is completed, ensuring that the data associated with the subsequent protocol can be processed in real time, thereby ensuring that the digital twin model and the actual monitored scene are displayed synchronously.

[0045] Specifically, by identifying the differences in data processing protocols between adjacent nodes, the node segment to be converted can be accurately located, avoiding meaningless repetitive processing; the average value of conversion features is calculated based on historical conversion data, and the required computing power SL is derived by combining the intermittent time, so that the computing power allocation for protocol conversion is more in line with the actual scenario, which avoids the waste of computing power and ensures the stability of the conversion process.

[0046] Using historical conversion data as a calibration basis provides data support for determining the processing logic, facilitating traceability and optimization. At the same time, through steps such as node sequence sorting, protocol difference identification, and computing power requirement marking, a standardized processing flow is formed, which can be quickly adjusted according to dynamic scenarios such as the addition or removal of monitoring nodes and protocol updates, thereby improving the system's adaptability to complex working conditions.

[0047] By quantifying the conversion characteristics and required computing power, the processing resource requirements of different node segments are clarified, reducing the "waiting" or "overload" phenomena in protocol conversion; the orderly connection of adjacent nodes and precise computing power configuration can significantly reduce problems such as packet loss and out-of-order transmission during data transmission and conversion, and improve the reliability of overall data collaboration.

[0048] Second Embodiment

[0049] In the specific implementation process, compared with the above embodiments, this embodiment mainly focuses on the correlation verification process of the data monitored by a certain monitoring node, identifies whether there are errors in the data, and performs comprehensive adjustment and verification in this way to avoid large differences between the digital twin model and the actual scenario.

[0050] Among them, the feature verification processing end, after the monitoring data associated with the corresponding monitoring node is processed, confirms the linkage features associated with the monitoring nodes, and confirms whether the linkage features are consistent from the digital twin model. If they are consistent, no processing is required. If they are inconsistent, a data transmission anomaly signal is generated and displayed for external relevant personnel to view.

[0051] In the actual simulation process of the digital twin model, the virtual devices that are linked will generate corresponding linkage data, which are the corresponding linkage features. During the monitoring process, the linkage features between the corresponding monitoring nodes will also be collected synchronously. If the collected linkage features are inconsistent, it means that there is a big difference between the virtual scene of the corresponding digital twin model and the actual scene. It means that there is an anomaly in the transmission of the corresponding data, and the corresponding transmission rate needs to be adjusted to carry out anti-interference processing.

[0052] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0053] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A virtual display system for construction progress and equipment cluster collaboration based on digital twins, characterized in that, include: The monitoring feature confirmation terminal confirms the monitoring features associated with different monitoring nodes in the equipment cluster. The processing cycle confirmation end confirms the monitoring characteristics associated with different monitoring nodes within a specified cycle, identifies a set of processing times from the confirmed monitoring characteristics, records the monitoring nodes included in the processing times, and selects the optimal processing cycle from the different processing characteristics recorded at different processing times. The specific method is as follows: Based on the different monitoring characteristics associated with different monitoring nodes, the monitoring times associated with the corresponding monitoring nodes are marked within the processing period, and the processing characteristics associated with the processing period are determined: the total number G of different monitoring nodes monitored within the processing period is recorded. i Where i represents different processing time periods, and the total number H of several monitoring nodes in the device cluster is then confirmed synchronously, using: G i ÷H=ZB i Confirm the proportion of features associated with the corresponding processing time period (ZB) i Then, the duplicate monitoring nodes that appear during the processing period are marked synchronously, and the number of repetitions associated with the duplicate monitoring nodes is recorded as CH. i-k Where k represents different duplicate monitoring nodes, and CH represents several sets of duplicate counts associated with k duplicate monitoring nodes. i-k Perform summation to confirm the total number of repetitions ZF. i ZB is used. i ÷ZF i =BD i Confirm the processing characteristics (BD) associated with the corresponding processing time period. i ; Different processing characteristics associated with different processing time periods (BD) i In the middle, select the maximum value, and record the processing period associated with the maximum value as the optimal processing cycle, and transmit the determined optimal processing cycle to the processing logic confirmation terminal; The processing logic confirmation end determines the sequence of acquisition nodes associated with the optimal processing cycle based on the confirmed optimal processing cycle, then confirms the conversion process from the sequence of acquisition nodes, and confirms the calibration time associated with each conversion process based on historical data of the corresponding conversion process in the historical process. Combining the calibration time, the processing logic associated with the sequence of acquisition nodes is determined and output.

2. The construction progress and equipment cluster collaborative virtual display system based on digital twins according to claim 1, characterized in that, The monitoring characteristics include the monitoring frequency and the associated monitoring time.

3. The construction progress and equipment cluster collaborative virtual display system based on digital twins according to claim 1, characterized in that, The processing cycle confirmation terminal confirms the processing time in the following ways: Several sets of processing times are generated within a specified period, which is a preset period. The current time is the processing time. Based on the generated processing times, the processing time period associated with the corresponding processing time is determined, and the processing time is ∈ the specified period.

4. The construction progress and equipment cluster collaborative virtual display system based on digital twins according to claim 1, characterized in that, The specific method by which the processing logic confirmation terminal determines the sequence of collected nodes is as follows: Identify the processing period associated with the optimal processing cycle, and then, based on the different monitoring characteristics associated with different monitoring nodes, identify the monitoring nodes that have monitoring processes within the processing period and record them as acquisition nodes. Sort several acquisition nodes according to the specific method of sorting them from front to back according to the processing period to confirm the acquisition node sequence. The data processing protocols associated with adjacent acquisition nodes within the acquisition node sequence are confirmed. It is determined whether the data processing protocols between adjacent acquisition nodes are the same. If they are the same, no processing is required. If they are different, the adjacent acquisition nodes are recorded as nodes to be confirmed in the processing logic.

5. The construction progress and equipment cluster collaborative virtual display system based on digital twins according to claim 4, characterized in that, The specific method by which the processing logic confirmation terminal determines the processing logic associated with the sequence of collected nodes is as follows: The nodes to be confirmed associated with several adjacent acquisition nodes in the acquisition node sequence are determined sequentially. The acquisition node at the front end of the node segment to be confirmed is denoted as the front node, and the acquisition node at the back end is denoted as the back node. The data processing protocol associated with the front node is denoted as the front protocol, and the data processing protocol associated with the back node is denoted as the back protocol. The conversion characteristics associated with the front protocol and the back protocol in the historical process are confirmed. The conversion characteristics include conversion computing power and conversion rate. The conversion rate ÷ conversion computing power = conversion characteristics are used to confirm the conversion characteristics associated with the corresponding front protocol and the back protocol in a single historical process. The confirmed conversion characteristics are averaged to lock the conversion characteristics of the front protocol and the back protocol after determination. Based on the determined sequence of acquisition nodes, the discontinuity time T between the previous protocol and the subsequent protocol is confirmed. Using the formula: conversion feature × T / 2 = SL, the required computing power SL between the previous protocol and the subsequent protocol is obtained. The required computing power (SL) associated with different unconfirmed node segments within the data collection node sequence is confirmed and marked sequentially.

6. The construction progress and equipment cluster collaborative virtual display system based on digital twins according to claim 1, characterized in that, Also includes: The feature verification processing end, after processing the monitoring data associated with the corresponding monitoring node, confirms the linkage features associated with the monitoring nodes and verifies whether the linkage features are consistent from the digital twin model. If they are consistent, no processing is required.

7. The construction progress and equipment cluster collaborative virtual display system based on digital twins according to claim 6, characterized in that, If the linkage characteristics are inconsistent, a data transmission anomaly signal will be generated and displayed directly.

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