Multilayer superposition delay correction method and system based on digital twinning

Abstract

The invention provides a multi-layer superposition delay correction method and system based on digital twinning, and relates to the technical field of delay correction. According to the identification result of the delay source, selecting a corresponding measurement method to carry out delay measurement to obtain delay measurement data; establishing a delay mathematical model according to the delay measurement data, and adjusting parameters of the delay mathematical model by comparing the delay mathematical model with simulation data of a preset digital twinborn model; adding a timestamp to each piece of data in the delay mathematical model, and ensuring that the timestamps of the processing modules in the physical system are consistent with the timestamps of the corresponding components in the digital twinborn model; and integrating and comparing the actual data curve and the virtual data curve to obtain a delayed characteristic curve, calculating an error caused by delay according to the delayed characteristic curve, and correcting the delayed data. According to the invention, bad consequences of system stability reduction, control precision reduction and dynamic performance deterioration caused by delay are avoided.

Description

Technical Field

[0001] The present invention relates to the field of delay correction technology, and in particular to a multi-layer superposition delay correction method and system based on digital twins. Background Art

[0002] Delay is an inevitable problem in many engineering systems and control systems. These delays may be caused by sensor response lag, congestion in data transmission, and computing resource limitations. Delays can lead to decreased system stability, reduced control accuracy, and poor dynamic performance. Therefore, developing effective delay correction methods is of great practical significance. As an emerging technology, digital twin technology provides a new perspective and effective means to solve the delay problem. Summary of the invention

[0003] The problem solved by the present invention is the delay problem existing in engineering systems and control systems. Avoiding delay will lead to adverse consequences such as decreased system stability, reduced control accuracy and poor dynamic performance.

[0004] In order to solve the above problems, in a first aspect, the present invention provides a multi-layer superposition delay correction method based on digital twins, the method comprising: According to the identification result of the delay source, a corresponding measurement method is selected to perform delay measurement to obtain delay measurement data; A delay mathematical model is established according to the delay measurement data, and the parameters of the delay mathematical model are adjusted by comparing the delay mathematical model with the simulation data of the preset digital twin model; Add a timestamp to each piece of data in the delayed mathematical model. The timestamp includes the time when the data is generated and the time when the data enters each processing module of the physical system, and ensure that the timestamps of each processing module in the physical system are consistent with those of the corresponding components in the digital twin model. The actual data curve of the delay mathematical model and the virtual data curve of the digital twin system are obtained based on the timestamp. The actual data curve is integrated and compared with the virtual data curve to obtain the characteristic curve of the delay. The error caused by the delay is calculated according to the characteristic curve of the delay, and the delay data is corrected.

[0005] Further, the delay sources include sensor delay, network transmission delay and controller processing delay; Measure the time difference from the change of physical signal to the output of corresponding data by the sensor to obtain sensor delay measurement data; obtain the statistical information of data packet transmission delay in the network in real time to obtain network transmission delay measurement data; embed the timing function in the controller code to record the time consumption from receiving input data to outputting control instructions to obtain controller processing delay measurement data.

[0006] Furthermore, the specific process of adjusting the parameters of the delay mathematical model is as follows: Train the digital twin model based on historical data, determine the influence coefficient of different delay sources on the delay of the physical system, and obtain the trained digital twin model; By comparing the delay mathematical model with the trained digital twin model, the parameters of the delay mathematical model are adjusted to ensure that the influence coefficient of each delay measurement data in the delay mathematical model is consistent with the actual situation.

[0007] Furthermore, the error caused by the delay is calculated according to the characteristic curve of the delay, and the delay data is corrected. The specific process is as follows: Obtaining delay characteristic data in a delay characteristic curve, wherein the delay characteristic data includes a delay data type, a corresponding system delay duration, and a delay data size; Determine the influence coefficient based on the delay data type, and calculate the error caused by the delay by combining the delay data type and the corresponding delay duration; Process the delay data in sequence according to the error size caused by the delay, and divide the delay data into n groups of delay data files to be processed, where n≥1; Correcting n groups of delayed data files to be processed in sequence; During the correction process, the process is monitored in real time, and any anomalies or errors are reported in real time. If anomalies or errors are found, the correction process is stopped and repaired through the system; After the repair is completed, the correction process is restarted to continue the correction process of the delayed data.

[0008] Furthermore, the delay data is processed in sequence according to the error size caused by the delay, and the delay data is divided into n groups of delay data files to be processed. The specific process is as follows: The delay data is divided in order from large to small according to the error caused by the delay, and the file size of each group of delay data to be processed is ensured to be within the preset file size threshold; According to historical data, estimated correction processing time values ​​of delayed data files of different sizes are obtained, and estimated correction processing time values ​​of each group of delayed data files to be processed are estimated by using the estimated correction processing time values ​​of delayed data files of different sizes to obtain n correction processing time thresholds.

[0009] Furthermore, the n groups of delayed data files to be processed are corrected in sequence, and the specific process is as follows: Splitting the delayed data files to be processed among the n groups of delayed data files to be processed according to the initial size, wherein the initial size is set according to the total size of each group of delayed data files to be processed, ensuring that the total size of the delayed data files in each portion is within a preset range; Process each piece of delay data in the delay data file to be processed in turn, and monitor the actual value of the time used to process each preset number of delay data; Calculate the difference between the actual value of the time used for processing each delay data of the initial size and the estimated value of the time used for processing each delay data of the initial size in the corresponding delay data file; Determine whether the difference is within the preset reasonable threshold. If the difference is within the preset reasonable threshold, it is determined that there is no error or abnormality in the correction processing. If the difference is outside the preset reasonable threshold, it is determined that there is an error or abnormality in the correction processing.

[0010] Furthermore, the specific process of repairing the system is as follows: Identify problems, fix identified problems by applying fixes to them, automatically detect if the fix is ​​effective, and ensure the system is back up and running; If automatic repair fails, record detailed failure information and automatic repair attempts, including error logs and repair steps, and verify whether the manual repair solves the problem after manual repair, and test system stability.

[0011] In the second aspect, a multi-layer superposition delay correction system based on digital twin is provided, which includes the following modules: A measurement module, used to select a corresponding measurement method to perform delay measurement according to the identification result of the delay source, and obtain delay measurement data; A model building module is used to build a delay mathematical model according to the delay measurement data, and adjust the parameters of the delay mathematical model by comparing the delay mathematical model with the simulation data of the preset digital twin model; The timestamp design module adds a timestamp to each piece of data in the delayed mathematical model. The timestamp includes the time when the data is generated and the time when the data enters each processing module of the physical system, and ensures that the timestamps of each processing module in the physical system are consistent with those of the corresponding components in the digital twin model. The correction module obtains the actual data curve of the delay mathematical model and the virtual data curve of the digital twin system based on the timestamp, integrates and compares the actual data curve with the virtual data curve to obtain the characteristic curve of the delay, calculates the error caused by the delay according to the characteristic curve of the delay, and corrects the delay data.

[0012] In a third aspect, a computer-readable storage medium storing one or more programs is provided, wherein the one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform the method described.

[0013] In a fourth aspect, a computing device for a multi-layer superposition delay correction method based on digital twin is provided, comprising: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method.

[0014] The present invention provides a multi-layer superposition delay correction method and system based on digital twins. Relying on data twin technology, the delay data is identified and locked by identifying the delay source, delay measurement, delay modeling, and designing information timestamps. At the same time, the delay data is corrected with historical data by data interpolation or prediction. At the same time, by monitoring the delay data correction process, the stability of the delay correction process is ensured, and problems can be discovered in time to repair the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of the multi-layer superposition delay correction method based on digital twins of the present invention; Figure 2 This is a block diagram of the multi-layer superposition delay correction system based on digital twins of the present invention. DETAILED DESCRIPTION

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more clearly understood, the following is a brief description of the present invention in conjunction with the attached drawings. Figure 1-2 The specific embodiments of the present invention are described in detail.

[0017] like Figure 1 The embodiment shown provides a multi-layer superposition delay correction method based on digital twins. Relying on data twin technology, the delay data is identified and locked by identifying the delay source, delay measurement, delay modeling, and designing information timestamps. At the same time, the delay data is corrected with historical data through data interpolation / prediction. In the control system, a feedforward control strategy is designed through a control algorithm to reduce the impact of delay on the system. The specific method flow is as follows: In the system, we must first sort out the various links that may cause delays. The sensor data collection may have a certain delay due to its own hardware characteristics. If the network transmission module has insufficient bandwidth or a complex network topology, it will cause data transmission delays. The limited computing speed of the controller may also cause delays in task execution. Through the digital twin model, each sub-module of the actual physical system can be modeled and analyzed. The virtual model can run in a high-performance computing environment to simulate the flow of data in the system and accurately detect the amount of delay caused by each link.

[0018] According to the identification result of the delay source, the corresponding measurement method is selected to measure the delay and obtain the delay measurement data; for sensor delay, accurate time synchronization equipment can be used to measure the time difference from the change of physical signal to the output of corresponding data by the sensor; in the network transmission part, with the help of network monitoring tools, such as network analyzers, the transmission delay statistics of data packets in the network can be obtained in real time, including minimum delay, maximum delay and average delay, etc.; for the processing delay of the controller, the timing function can be embedded in the controller code to record the time consumption from receiving the input data to outputting the control instruction.

[0019] A delay mathematical model is established based on the delay measurement data, and the parameters of the delay mathematical model are adjusted by comparing the delay mathematical model with the simulation data of the preset digital twin model; the delay is regarded as a random variable or time-varying function. For simple linear delay cases, a first-order delay model can be used to describe the relationship between the delay amount and the input and output; if the delay presents nonlinear characteristics, more complex nonlinear functions such as polynomial functions or neural network modeling tools are required; the digital twin model plays a role of verification and optimization in this process; by comparing the constructed delay model with the simulation data in the digital twin model, the parameters of the model can be continuously adjusted to improve the accuracy of the model.

[0020] A timestamp is added to each piece of data in the delay mathematical model. The timestamp includes the time when the data is generated and the time when the data enters each processing module of the physical system, and ensures that the timestamps of each processing module in the physical system are consistent with the timestamps of the corresponding components in the digital twin model. In the entire system, it is crucial to add a timestamp to each piece of data. The timestamp not only includes the time when the data is generated, but also marks the time when the data enters each processing module. In this way, in the subsequent data processing process, the flow and residence time of the data can be clearly tracked, which helps to determine the specific interval where the delay occurs. The time synchronization mechanism in the digital twin model is used to ensure that the timestamps of each device in the physical system and the corresponding components in the virtual model are consistent, so as to perform accurate data analysis and delay positioning.

[0021] The actual data curve of the delay mathematical model and the virtual data curve of the digital twin system are obtained based on the timestamp. The actual data curve is integrated and compared with the virtual data curve to obtain the characteristic curve of the delay. The error caused by the delay is calculated according to the characteristic curve of the delay, and the delay data is corrected.

[0022] After obtaining the delay source information, measurement data, delay mathematical model and timestamp, the delay data is comprehensively identified and locked; when the timestamp of a certain data shows that its residence time in a certain module exceeds the normal range and matches the established delay model, it is determined that the data is affected by the delay; in the digital twin model, the status of the data can be monitored in real time, and the visual interface can intuitively display which data is affected by the delay and the severity of the delay.

[0023] For the correction of delayed data, data interpolation or prediction methods can be used to correct the delayed data according to the rules of historical data. For smaller delays, if the data changes smoothly, linear interpolation can be used; if the historical data presents periodicity or complex nonlinear rules, consider using time series prediction methods, such as ARIMA (autoregressive moving average model) or long short-term memory network (LSTM) in machine learning algorithms.

[0024] When using historical data for correction, reasonable correction criteria should be formulated. For example, according to the timeliness of the data, set the effective window for historical data to participate in the correction; historical data that is too old may lose its reference value due to changes in the system status, so it is necessary to screen out a suitable subset of historical data for correction operations; at the same time, the reliability and error range of the data should be considered; during the correction process, if it is found that the historical data itself has a large error, or there is a large difference with the current system status, it is necessary to use or discard this part of the historical data with caution to avoid introducing a larger error.

[0025] During the delay correction process, there may be abnormalities or errors in the correction process. Therefore, it is necessary to promptly discover the abnormalities or errors in the delay correction process and repair the system in a timely manner to ensure that the system's delay correction processing returns to normal. The specific process is as follows: Obtaining delay characteristic data in a delay characteristic curve, wherein the delay characteristic data includes a delay data type, a corresponding system delay duration, and a delay data size; Determine the influence coefficient based on the delay data type, and calculate the error caused by the delay by combining the delay data type and the corresponding delay duration; Process the delay data in sequence according to the error size caused by the delay, and divide the delay data into n groups of delay data files to be processed, where n≥1; Correcting n groups of delayed data files to be processed in sequence; During the correction process, the process is monitored in real time, and any anomalies or errors are reported in real time. If anomalies or errors are found, the correction process is stopped and repaired through the system; After the repair is completed, the correction process is restarted to continue the correction process of the delayed data.

[0026] Specifically, the delay data is processed in sequence according to the error size caused by the delay, and the delay data is divided into n groups of delay data files to be processed. The specific process is as follows: Divide the delay data in descending order according to the error caused by the delay, and ensure that the file size of each group of delay data to be processed is within the preset file size threshold; According to historical data, estimated correction processing time values ​​of delayed data files of different sizes are obtained, and estimated correction processing time values ​​of each group of delayed data files to be processed are estimated by using the estimated correction processing time values ​​of delayed data files of different sizes to obtain n correction processing time thresholds.

[0027] Specifically, the n groups of delayed data files to be processed are corrected in turn, and the specific process is as follows: Splitting the delayed data files to be processed among the n groups of delayed data files to be processed according to the initial size, wherein the initial size is set according to the total size of each group of delayed data files to be processed, ensuring that the total size of the delayed data files in each portion is within a preset range; Process each piece of delay data in the delay data file to be processed in turn, and monitor the actual value of the time used to process each preset number of delay data; Calculate the difference between the actual value of the time used for processing each delay data of the initial size and the estimated value of the time used for processing each delay data of the initial size in the corresponding delay data file; Determine whether the difference is within the preset reasonable threshold. If the difference is within the preset reasonable threshold, it is determined that there is no error or abnormality in the correction processing. If the difference is outside the preset reasonable threshold, it is determined that there is an error or abnormality in the correction processing.

[0028] Specifically, the specific process of repairing the system is as follows: Identify problems, fix identified problems by applying fixes to them, automatically detect if the fix is ​​effective, and ensure the system is back up and running; If automatic repair fails, record detailed failure information and automatic repair attempts, including error logs and repair steps, and verify whether the manual repair solves the problem after manual repair, and test system stability.

[0029] like Figure 2 As shown, another embodiment of the present invention provides a multi-layer superposition delay correction system based on digital twins, comprising the following modules: The delay source identification module is used to sort out each link that may cause delays. The sensor data collection may have a certain delay due to its own hardware characteristics. If the network transmission module has insufficient bandwidth or a complex network topology, it will cause data transmission delays. The limited computing speed of the controller may also cause delays in task execution. Through the digital twin model, each sub-module of the actual physical system can be modeled and analyzed. The virtual model can run in a high-performance computing environment to simulate the flow of data in the system and accurately detect the amount of delay generated in each link.

[0030] The measurement module is used to select the corresponding measurement method to measure the delay according to the identification result of the delay source, and obtain the delay measurement data; for sensor delay, accurate time synchronization equipment can be used to measure the time difference from the change of physical signal to the output of corresponding data by the sensor; in the network transmission part, with the help of network monitoring tools, such as network analyzers, the transmission delay statistics of data packets in the network can be obtained in real time, including minimum delay, maximum delay and average delay, etc.; for the processing delay of the controller, a timing function can be embedded in the controller code to record the time consumption from receiving input data to outputting control instructions.

[0031] The model building module is used to establish a delay mathematical model based on the delay measurement data, and adjust the parameters of the delay mathematical model by comparing the delay mathematical model with the simulation data of the preset digital twin model; the delay is regarded as a random variable or time-varying function. For simple linear delay cases, a first-order delay model can be used to describe the relationship between the delay amount and the input and output; if the delay presents nonlinear characteristics, more complex nonlinear functions such as polynomial functions or neural network modeling tools are required; the digital twin model plays a role of verification and optimization in this process; by comparing the constructed delay model with the simulation data in the digital twin model, the parameters of the model can be continuously adjusted to improve the accuracy of the model.

[0032] The timestamp design module is used to add a timestamp to each piece of data in the delay mathematical model. The timestamp includes the time when the data is generated and the time when the data enters each processing module of the physical system, and ensures that the timestamps of each processing module in the physical system are consistent with the timestamps of the corresponding components in the digital twin model. In the entire system, it is crucial to add a timestamp to each piece of data. The timestamp not only includes the time when the data is generated, but also marks the time when the data enters each processing module. In this way, in the subsequent data processing process, the flow and residence time of the data can be clearly tracked, which helps to determine the specific interval where the delay occurs. The time synchronization mechanism in the digital twin model is used to ensure that the timestamps of each device in the physical system and the corresponding components in the virtual model are consistent, so as to perform accurate data analysis and delay positioning.

[0033] The correction module is used to obtain the actual data curve of the delay mathematical model and the virtual data curve of the digital twin system based on the timestamp, integrate and compare the actual data curve with the virtual data curve to obtain the characteristic curve of the delay, calculate the error caused by the delay according to the characteristic curve of the delay, and correct the delay data.

[0034] After obtaining the delay source information, measurement data, delay mathematical model and timestamp, the delay data is comprehensively identified and locked; when the timestamp of a certain data shows that its residence time in a certain module exceeds the normal range and matches the established delay model, it is determined that the data is affected by the delay; in the digital twin model, the status of the data can be monitored in real time, and the visual interface can intuitively display which data is affected by the delay and the severity of the delay.

[0035] For the correction of delayed data, data interpolation or prediction methods can be used to correct the delayed data according to the rules of historical data. For smaller delays, if the data changes smoothly, linear interpolation can be used; if the historical data presents periodicity or complex nonlinear rules, consider using time series prediction methods, such as ARIMA (autoregressive moving average model) or long short-term memory network (LSTM) in machine learning algorithms.

[0036] When using historical data for correction, reasonable correction criteria should be formulated. For example, according to the timeliness of the data, set the effective window for historical data to participate in the correction; historical data that is too old may lose its reference value due to changes in the system status, so it is necessary to screen out a suitable subset of historical data for correction operations; at the same time, the reliability and error range of the data should be considered; during the correction process, if it is found that the historical data itself has a large error, or there is a large difference with the current system status, it is necessary to use or discard this part of the historical data with caution to avoid introducing a larger error.

[0037] The present invention may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0038] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the above. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0039] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0040] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

[0041] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A multi-layer superposition delay correction method based on digital twin, characterized in that: The method comprises: According to the identification result of the delay source, a corresponding measurement method is selected to perform delay measurement to obtain delay measurement data; A delay mathematical model is established according to the delay measurement data, and the parameters of the delay mathematical model are adjusted by comparing the delay mathematical model with the simulation data of the preset digital twin model; Add a timestamp to each piece of data in the delayed mathematical model. The timestamp includes the time when the data is generated and the time when the data enters each processing module of the physical system, and ensure that the timestamps of each processing module in the physical system are consistent with those of the corresponding components in the digital twin model. The actual data curve of the delay mathematical model and the virtual data curve of the digital twin system are obtained based on the timestamp. The actual data curve is integrated and compared with the virtual data curve to obtain the characteristic curve of the delay. The error caused by the delay is calculated according to the characteristic curve of the delay, and the delay data is corrected.

2. The multi-layer superposition delay correction method based on digital twin according to claim 1 is characterized in that: The sources of delay include sensor delay, network transmission delay and controller processing delay; Measure the time difference from the change of physical signal to the output of corresponding data by the sensor to obtain sensor delay measurement data; obtain the statistical information of data packet transmission delay in the network in real time to obtain network transmission delay measurement data; embed the timing function in the controller code to record the time consumption from receiving input data to outputting control instructions to obtain controller processing delay measurement data.

3. The multi-layer superposition delay correction method based on digital twin according to claim 1 is characterized in that: The specific process of adjusting the parameters of the delay mathematical model is as follows: Train the digital twin model based on historical data, determine the influence coefficient of different delay sources on the delay of the physical system, and obtain the trained digital twin model; By comparing the delay mathematical model with the trained digital twin model, the parameters of the delay mathematical model are adjusted to ensure that the influence coefficient of each delay measurement data in the delay mathematical model is consistent with the actual situation.

4. The multi-layer superposition delay correction method based on digital twin according to claim 1 is characterized in that: The error caused by the delay is calculated according to the characteristic curve of the delay, and the delay data is corrected. The specific process is as follows: Obtaining delay characteristic data in a delay characteristic curve, wherein the delay characteristic data includes a delay data type, a corresponding system delay duration, and a delay data size; Determine the influence coefficient based on the delay data type, and calculate the error caused by the delay by combining the delay data type and the corresponding delay duration; Process the delay data in sequence according to the error size caused by the delay, and divide the delay data into n groups of delay data files to be processed, where n≥1; Correcting n groups of delayed data files to be processed in sequence; During the correction process, the process is monitored in real time, and anomalies or errors are reported in real time. If anomalies or errors are found, the correction process is stopped and repaired through the system; After the repair is completed, the correction process is restarted to continue the correction process of the delayed data.

5. The multi-layer superposition delay correction method based on digital twin according to claim 4 is characterized in that: The delay data is processed in sequence according to the error size caused by the delay, and the delay data is divided to obtain n groups of delay data files to be processed. The specific process is as follows: Divide the delay data in descending order according to the error caused by the delay, and ensure that the file size of each group of delay data to be processed is within the preset file size threshold; According to historical data, estimated correction processing time values ​​of delayed data files of different sizes are obtained, and estimated correction processing time values ​​of each group of delayed data files to be processed are estimated by using the estimated correction processing time values ​​of delayed data files of different sizes to obtain n correction processing time thresholds.

6. The multi-layer superposition delay correction method based on digital twin according to claim 5 is characterized in that: The n groups of delayed data files to be processed are corrected in sequence, and the specific process is as follows: Splitting the delayed data files to be processed among the n groups of delayed data files to be processed according to the initial size, wherein the initial size is set according to the total size of each group of delayed data files to be processed, ensuring that the total size of the delayed data files in each portion is within a preset range; Process each piece of delay data in the delay data file to be processed in turn, and monitor the actual value of the time used to process each preset number of delay data; Calculate the difference between the actual value of the time used for processing each delay data of the initial size and the estimated value of the time used for processing each delay data of the initial size in the corresponding delay data file; Determine whether the difference is within the preset reasonable threshold. If the difference is within the preset reasonable threshold, it is determined that there is no error or abnormality in the correction processing. If the difference is outside the preset reasonable threshold, it is determined that there is an error or abnormality in the correction processing.

7. The multi-layer superposition delay correction method based on digital twin according to claim 6 is characterized in that: The specific process of repairing the system is as follows: Identify problems, fix identified problems by applying fixes, automatically detect if the fix is ​​effective, and ensure the system is back up and running; If automatic repair fails, record detailed failure information and automatic repair attempts, including error logs and repair steps, and verify whether the manual repair solves the problem after manual repair, and test system stability.

8. A multi-layer superposition delay correction system based on digital twins, characterized in that: Includes the following modules: A measurement module, used to select a corresponding measurement method to perform delay measurement according to the identification result of the delay source, and obtain delay measurement data; A model building module is used to build a delay mathematical model according to the delay measurement data, and adjust the parameters of the delay mathematical model by comparing the delay mathematical model with the simulation data of the preset digital twin model; The timestamp design module adds a timestamp to each piece of data in the delayed mathematical model. The timestamp includes the time when the data is generated and the time when the data enters each processing module of the physical system, and ensures that the timestamps of each processing module in the physical system are consistent with those of the corresponding components in the digital twin model. The correction module obtains the actual data curve of the delay mathematical model and the virtual data curve of the digital twin system based on the timestamp, integrates and compares the actual data curve with the virtual data curve to obtain the characteristic curve of the delay, calculates the error caused by the delay according to the characteristic curve of the delay, and corrects the delay data.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform the method according to any one of claims 1-7.

10. A computing device for a multi-layer superposition delay correction method based on digital twins, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method according to any one of claims 1-7.

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