Chemical instrument pre-commissioning and on-site commissioning method based on digital twinning

Abstract

This invention discloses a method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins, relating to the fields of chemical instrument commissioning and industrial automation. Addressing the problems of traditional chemical instrument commissioning processes relying on field experience, long commissioning cycles, susceptibility to errors, and lack of pre-verification, this method first creates a digital twin of the physical instruments in the field; second, based on the equipment profile, the pre-commissioning instruction sequence is converted into an executable instruction sequence adapted to the specific attributes of the target physical instrument, and an edge computing gateway distributes the executable instruction sequence to the target physical instrument for execution and collects response data; finally, the collected response data is compared with the expected response data calculated by the digital twin, and if the deviation exceeds a preset threshold, the executable instruction sequence is adjusted until the deviation meets the requirements. This method is mainly used for pre-commissioning and on-site commissioning of chemical instruments, realizing the digitalization, intelligentization, and closed-loop optimization of the commissioning process, significantly improving commissioning efficiency and accuracy.

Description

Technical Field

[0001] This invention belongs to the field of chemical instrument commissioning and industrial automation technology, and relates to a method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins. Background Technology

[0002] In the installation and commissioning of chemical production plants, on-site commissioning of instrumentation systems is a crucial and complex step. Traditional instrumentation commissioning methods mainly rely on engineers' on-site experience, using manual or semi-automatic methods to perform signal testing, functional verification, and integration testing with the control system (DCS / PLC) on one or more instruments. This process has several inherent problems and shortcomings.

[0003] First, traditional commissioning methods lack effective pre-verification means. Commissioning commands and logic are typically designed in the control logic diagram (P&ID) and configuration software, but their execution effect on actual physical equipment can only be tested one by one on-site after the instruments are installed, wired, and powered on. Because it is impossible to predict the dynamic response of instruments under real operating conditions in advance, problems such as command incompatibility, abnormal responses, or timing conflicts frequently occur during commissioning, leading to interruptions in the commissioning process. Engineers then need to analyze the causes on-site and modify the commands or parameters. This "trial and error" model heavily relies on the engineer's personal experience, resulting in long commissioning cycles, low efficiency, and particularly difficult problem localization in complex process loop commissioning.

[0004] Secondly, on-site commissioning faces challenges due to equipment heterogeneity and uncertainties in field conditions. Chemical plants contain a wide variety of instruments; even those with the same function may differ in communication protocols, specific command formats, and response latency characteristics depending on the manufacturer, model, or firmware version. Existing methods typically involve consulting scattered equipment manuals and temporarily writing or adjusting commissioning commands on-site. This process is prone to errors and struggles to guarantee the optimality and safety of the command sequence for specific equipment. Furthermore, the state of the on-site communication network (such as latency, jitter, and packet loss) can affect the reliability of command issuance and the synchronization of data acquisition, and traditional methods lack the ability to pre-assess and adaptively adjust for such risks.

[0005] Furthermore, the debugging process struggles to establish a precise, closed-loop verification and optimization cycle. Instrument response data collected on-site is often used only to arrive at a binary conclusion of whether the instrument function is "normal," lacking a systematic comparison with a high-fidelity expected model. Even when comparison is attempted, it is often ineffective due to the following reasons: first, the collected physical data itself may contain outliers or noise due to communication interference, asynchronous acquisition, etc., leading to misjudgments when used directly for evaluation; second, there is a lack of a continuously learning dynamic instrument model as a benchmark for comparison. The absence or rigidity of this model prevents the debugging process from quantifying the deviation between the actual and ideal responses, and further hinders the automatic and intelligent iterative adjustment of debugging commands based on deviation data until optimal results are achieved. The "endpoint" of debugging largely depends on the engineer's subjective judgment.

[0006] A key reason for these problems is the long-standing "digital divide" between the physical commissioning site and the engineering design and simulation environment. Static data models from the design phase cannot reflect the dynamic behavior of the equipment, while real-time data generated on-site is not effectively fed back into the model to drive its evolution. Previous attempts to address these issues, such as using more complex simulation software for offline testing, have often failed due to the inability to obtain accurate individual equipment characteristic parameters, the difficulty in simulating real-world communication environments, and the lack of an automated data bridge between the simulation and the physical system. This has resulted in pre-commissioning results being disconnected from actual on-site conditions, limiting their practical value.

[0007] Therefore, how to construct an intelligent instrument debugging method that can connect the design, pre-performance and field execution stages, and can perform self-verification and optimization based on real feedback data, thereby overcoming the shortcomings such as insufficient pre-verification, complex field adaptation and difficulty in closed-loop optimization, is a persistent technical challenge in this field. Summary of the Invention

[0008] One object of the present invention is to solve at least the above-mentioned problems and / or defects, and to provide at least the advantages described below.

[0009] Another objective of this invention is to provide a method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins.

[0010] This addresses the problems of existing on-site commissioning methods for chemical instruments, which lack effective pre-verification, rely on manual trial and error during the commissioning process, and cannot form a closed-loop optimization, resulting in long commissioning cycles, low efficiency, and unstable quality.

[0011] This addresses the problem that during the instrument commissioning closed-loop process, noise or anomalies in the field-acquired data and drift of the digital twin model due to environmental or equipment aging can lead to unreliable comparison benchmarks, thereby affecting commissioning accuracy and the long-term effectiveness of the model.

[0012] Therefore, the technical solution provided by this invention is as follows: A method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins includes the following steps: Digital twin modeling and pre-commissioning steps: Create a digital twin for the field physical instrument, the digital twin containing the dynamic response characteristic model of the physical instrument; simulate and verify the physical instrument in the digital twin to generate a pre-commissioning instruction sequence; Field command adaptation and encapsulation steps: The edge computing gateway obtains a pre-established device profile for the target physical instrument; based on the device profile, the pre-debugging command sequence is converted into an executable command sequence adapted to the specific attributes of the target physical instrument; Command issuance and data acquisition steps: The edge computing gateway issues the sequence of commands to be executed to the target physical instrument for execution, and collects the response data generated after execution; Closed-loop verification and iterative adjustment steps: The collected response data is compared with the expected response data calculated by the digital twin; if the comparison deviation exceeds a preset threshold, the sequence of instructions to be executed is adjusted according to the deviation, and the instruction issuance and data acquisition steps and this step are executed iteratively until the comparison deviation meets the requirements.

[0013] Preferably, the aforementioned method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins further includes a model and data collaborative management step after the closed-loop verification and iterative adjustment steps: Data credibility assessment: Perform time-series consistency and correlation checks on the collected response data and identify abnormal data points; Model dynamic calibration: When the triggering conditions are met, the parameters of the dynamic response characteristic model of the digital twin are optimized and updated using verified high-reliability historical data; Data reconstruction: Before comparison, the identified abnormal data points are simulated and reconstructed using the updated digital twin.

[0014] Preferably, in the aforementioned method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins, the device profile includes at least the device model, firmware version, communication protocol details, and calibration response delay parameters of the physical instrument. The conversion operation based on the device profile includes: converting the general instruction code into a specific instruction that the device can recognize according to the communication protocol details, and configuring the time interval between instructions according to the calibration response delay parameter.

[0015] Preferably, in the aforementioned method for pre-commissioning and field debugging of chemical instruments based on digital twins, the edge computing gateway further performs a security arbitration sub-step in the field command adaptation and encapsulation step: Obtain the current process safety constraints and real-time values ​​of associated process parameters; Simulate the impact of executing the sequence of instructions to be executed on associated process parameters in a virtual environment; If the simulation results violate the security constraints, the sequence of instructions to be executed will be modified or replaced with a safe instruction sequence before proceeding to the instruction issuance and data acquisition steps.

[0016] Preferably, the method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins further includes, in the on-site command adaptation and encapsulation step: Communication risk assessment sub-step: The edge computing gateway monitors the performance indicators of the communication link between itself and the target physical instrument in real time; Risk handling sub-step: If the performance index is lower than the preset standard, the digital twin is triggered to perform virtual joint debugging in a simulated degraded communication environment, and a more robust backup instruction sequence is generated as the instruction sequence to be executed.

[0017] Preferably, the aforementioned method for pre-commissioning and field debugging of chemical instruments based on digital twins further includes a collaborative scheduling step before the field command adaptation and encapsulation step when debugging multiple coupled physical instruments in a process loop: Establish a commissioning task dependency graph: Based on the process piping and instrumentation diagram and control logic diagram, define the dependencies between the commissioning tasks corresponding to all instruments to be commissioned, and form a commissioning task dependency graph; Virtual collaborative simulation and conflict detection: The digital twin loads the map and the pre-debugging instruction sequence corresponding to each instrument, simulates the concurrent or sequential execution process, and predicts possible instruction conflicts or process limit exceedance events; Generate an optimized scheduling instruction sequence set: Based on the virtual simulation results, dynamically adjust the relative execution timing and concurrency of the debugging instruction sequences of each instrument to generate a globally optimized scheduling instruction sequence set, and send it to the corresponding edge computing gateways for execution.

[0018] Preferably, the aforementioned method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins further includes a model self-evolution step in the establishment and maintenance of the digital twin: Initial data-driven model building: Collect historical input-output data pairs of the target physical instrument under various known test conditions, and use machine learning algorithms to train and generate an initial dynamic response characteristic model; Online incremental learning and model update: After each closed-loop verification and iterative adjustment, the effective instruction sequence-response data pairs generated in this joint debugging are used as new samples to perform incremental learning and parameter fine-tuning on the dynamic response characteristic model; Model version management and rollback: Save a snapshot of the model after each update. When a decline in model prediction performance is detected, roll back to the best historical model version based on context information.

[0019] Preferably, the method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins further includes an online performance evaluation step of the digital twin in the closed-loop verification and iterative adjustment steps. Evaluation engine initialization: Configure a performance evaluation engine for each digital twin, the engine having multiple preset evaluation dimensions and corresponding quantization algorithms; Online synchronous evaluation: Whenever response data is collected, the performance evaluation engine synchronously obtains the expected response data of the digital twin, calculates the predicted performance indicators of this instance according to preset dimensions, and generates a dynamic comprehensive score by combining historical indicators. Decision guidance based on evaluation results: Apply the comprehensive score to subsequent processes, including dynamically adjusting the preset threshold, prioritizing model updates, or using the score as evidence of the credibility of the debugging report.

[0020] Preferably, the method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins further includes a data stream spatiotemporal alignment step between the instruction issuance and data acquisition steps and the closed-loop verification and iterative adjustment steps. Global logical time base injection: When issuing commands, the edge computing gateway adds a timestamp and command causal identifier; when collecting response data, it adds a collection timestamp and associates it with the corresponding identifier. Data stream buffering and realignment: The edge computing gateway sets up an alignment buffer to cache response packets and receive the expected response data stream from the digital twin; Causal matching and interpolation based on logical time: Match the physical response and expected response data segments according to the instruction causal identifier, align the time axis with the issued timestamp as the origin, and perform interpolation resampling to make the two sets of data correspond on a unified logical time axis; Output aligned data pairs: Output the data pairs after causal matching and time axis alignment to the closed-loop verification and iterative adjustment steps for comparison.

[0021] Preferably, in the aforementioned method for pre-commissioning and field debugging of chemical instruments based on digital twins, the edge computing gateway simultaneously performs state persistence and recovery steps when executing the field command adaptation and encapsulation, command issuance and data acquisition steps: Critical status checkpoint creation: After completing critical operations, the edge computing gateway automatically generates checkpoint files for the critical status of the current debugging task and persists them. Recovery Trigger and State Loading: When the edge computing gateway starts up or recovers from an anomaly, it automatically detects and loads the latest checkpoint file for any incomplete debugging tasks; State consistency verification and continued execution: The edge computing gateway initiates a state synchronization request to the digital twin. The digital twin re-simulates and generates simulation data based on the checkpoint information. The gateway compares and verifies the recovered response data with the simulation data. If the verification is consistent, execution will automatically continue. Otherwise, it requests virtual supplementary data to complete the calculation before continuing execution.

[0022] The present invention has at least the following beneficial effects: This invention pre-commissions the instrument by constructing a digital twin containing a dynamic response model, and then uses an edge computing gateway to adapt and issue field commands based on device profiles. Finally, through closed-loop comparison and iteration between the physical response and the expected response, it transforms instrument commissioning from "on-site trial and error" to "virtual pre-performance and precise execution." This significantly reduces the blind spots in on-site commissioning, improves the first-time success rate through automated iterative optimization, shortens the overall commissioning cycle, and reduces over-reliance on the experience of on-site personnel, making the commissioning process more standardized and intelligent.

[0023] This invention establishes a collaborative management mechanism for data and models by adding steps such as data credibility assessment, dynamic model calibration, and data reconstruction. This effectively eliminates abnormal interference in field-collected data, ensuring the quality of comparison data. Simultaneously, it enables the digital twin model to continuously optimize using high-credibility field data, combating model drift and maintaining its predictive accuracy over the long term, thus guaranteeing the long-term reliability and accuracy of the entire closed-loop debugging system.

[0024] This invention achieves automatic and precise adaptation of debugging commands from a general format to a specific device-specific format by clearly defining a device profile that includes parameters such as the device's specific model, protocol, and delay. Based on this profile, it performs command code conversion and timing configuration. This eliminates manual conversion errors and compatibility issues caused by device heterogeneity, significantly improving the accuracy and efficiency of on-site command issuance and execution, thus laying the foundation for automated debugging.

[0025] This invention introduces a safety arbitration sub-step to perform safety simulation and verification of the process impact of the instruction sequence in a virtual environment, combined with real-time process parameters, before the actual issuance of instructions. This can identify and intercept debugging operations that may violate process safety constraints in advance, curbing potential safety risks at the virtual simulation stage, greatly enhancing the safety of the on-site debugging process, and avoiding production accidents caused by improper debugging.

[0026] This invention assesses the communication link status in real time and triggers virtual commissioning of a digital twin in a simulated degradation environment when communication deteriorates, generating a more robust backup instruction sequence. This enables the commissioning system to adapt to adverse communication conditions. This ensures that commissioning tasks can still be performed reliably and robustly even when network conditions are not ideal, improving the adaptability and robustness of the entire commissioning method to complex field environments.

[0027] This invention solves the coordination and conflict problems during concurrent debugging of multiple instruments by establishing a debugging task dependency graph, performing virtual collaborative pre-simulation, and generating a globally optimized scheduling instruction set. This enables the joint debugging of complex process loops to transform from disordered to ordered, avoiding instruction conflicts and process limit violations, achieving efficient and safe coordination between debugging tasks, and significantly improving the overall efficiency and success rate of joint debugging of large-scale, highly coupled instrument systems.

[0028] This invention solves the problems of difficult model building and updating in digital twins by using data-driven initial model construction and combining online incremental learning and version management to achieve model self-evolution. This enables the model to continuously learn from historical and real-time debugging data, making its dynamic response characteristics increasingly closer to the individualized behavior of real physical devices, significantly improving the fidelity and practicality of digital twins, and providing a solid foundation for accurate pre-debugging and comparison.

[0029] This invention enables quantitative monitoring and dynamic scoring of model prediction performance by configuring a performance evaluation engine for digital twins and conducting online synchronous evaluation. This provides objective data support and decision-making basis for judging model credibility, adaptively adjusting debugging parameters (such as deviation thresholds), and intelligently triggering model updates, making the entire system operate more scientifically, self-reflectively, and optimally.

[0030] This invention establishes a precise causal relationship between instructions and responses and a unified time base by implementing a data flow spatiotemporal alignment step, eliminating data misalignment problems caused by time asynchrony in the issuance, acquisition, and simulation calculation stages. This ensures that the physical response and expected response data can be compared with the correct causal and temporal relationships, making the deviation analysis results true and reliable, and is a key technical guarantee for the effective implementation of closed-loop iterative adjustment.

[0031] This invention, through state persistence and recovery steps, enables edge computing gateways to save critical states of debugging tasks and resume interrupted data transmission. This effectively addresses common network or device outages in the field, preventing debugging tasks from being completely lost due to unexpected interruptions and requiring a restart, saving debugging time and resources, and improving the resilience and continuity of complex debugging tasks.

[0032] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Detailed Implementation

[0033] The present invention will now be described in further detail so that those skilled in the art can implement it based on the description.

[0034] It should be understood that terms such as “having,” “comprising,” and “including” as used herein do not imply the presence or addition of one or more other elements or combinations thereof.

[0035] According to one embodiment of the present invention, a method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins includes the following steps: Digital twin modeling and pre-commissioning steps: Create a digital twin for the field physical instrument, the digital twin containing the dynamic response characteristic model of the physical instrument; simulate and verify the physical instrument in the digital twin to generate a pre-commissioning instruction sequence; Field command adaptation and encapsulation steps: The edge computing gateway obtains a pre-established device profile for the target physical instrument; based on the device profile, the pre-debugging command sequence is converted into an executable command sequence adapted to the specific attributes of the target physical instrument; Command issuance and data acquisition steps: The edge computing gateway issues the sequence of commands to be executed to the target physical instrument for execution, and collects the response data generated after execution; Closed-loop verification and iterative adjustment steps: The collected response data is compared with the expected response data calculated by the digital twin; if the comparison deviation exceeds a preset threshold, the sequence of instructions to be executed is adjusted according to the deviation, and the instruction issuance and data acquisition steps and this step are executed iteratively until the comparison deviation meets the requirements.

[0036] In traditional on-site commissioning of chemical instruments, commissioning engineers typically manually configure or write commissioning programs in the control system based on the control logic diagram and instrument specifications. After the instruments are physically installed and wired, the engineer goes to the site and sends commissioning commands to the target instruments one by one via an engineering workstation or handheld device. The entire commissioning process heavily relies on the engineer's personal experience for on-site command and operation. For example, when commissioning a pressure transmitter, the engineer might manually issue an analog output command and then observe whether the instrument's feedback value in the control system matches expectations. If feedback anomalies occur, such as excessive numerical deviation or response timeout, the engineer needs to investigate the cause on-site, which could be a wiring problem, incorrect instrument parameter settings, or a mismatched command format. Based on this, the engineer manually adjusts the command or instrument parameters and tries again. For complex loops involving multiple interconnected instruments, the commissioning process is even more cumbersome, requiring engineers to repeatedly coordinate the test sequence to avoid signal conflicts. This method lacks a preliminary simulation verification stage; all tests and verifications are performed on actual physical equipment, essentially making it a trial-and-error approach. Its debugging cycle is long and inefficient, and the debugging quality and results are highly dependent on the technical level and experience of the on-site engineers, making it difficult to standardize and optimize.

[0037] The specific implementation method of a chemical instrument pre-commissioning and field commissioning method based on digital twin is as follows. This embodiment takes the single-point commissioning of an intelligent temperature transmitter in a chemical plant as an example.

[0038] The first step is digital twin modeling and pre-commissioning. Before commissioning begins, a corresponding digital twin is created for the specific temperature transmitter within the digital twin platform. This digital twin integrates a dynamic response characteristic model of the instrument model, which can simulate the dynamic process from receiving commands to generating a stable output. Engineers plan commissioning tasks on the digital twin, such as setting a temperature change test from 20 degrees Celsius to 80 degrees Celsius. The digital twin runs its internal model, simulating the transmitter's complete response process under this virtual test, including response delay, rise curve, and stable value, and automatically generates a set of pre-commissioning command sequences to implement the test. These commands are general-format logical instructions.

[0039] The second step is on-site command adaptation and encapsulation. The edge computing gateway located at the factory field layer obtains a pre-built device profile for the target temperature transmitter via the network. This profile includes the instrument's specific model number (XXX), firmware version V2.1, the communication protocol used (MODBUS RTU), and its rated response latency of approximately 500 milliseconds. Based on this information, the edge computing gateway converts the generic pre-debugging command sequence issued by the digital twin. According to the MODBUS RTU protocol rules, it converts the generic command code into specific register read / write commands recognizable by this instrument model, and, based on the 500-millisecond response latency, appropriately configures the transmission time interval between commands, forming a set of executable command sequences adapted to this specific instrument.

[0040] The third step is command issuance and data acquisition. The edge computing gateway reliably sends the packaged sequence of commands to be executed to the actual physical temperature transmitter via the field industrial network. The transmitter receives and executes these commands, causing its output signal to change. The edge computing gateway simultaneously acquires the actual response data generated by the transmitter after executing the commands, including the curve of the output current value changing over time, and uploads the data.

[0041] The fourth step is closed-loop verification and iterative adjustment. The digital twin platform automatically compares the actual response data collected by the edge computing gateway with the expected response data calculated by the digital twin in the first step. The comparison includes key features such as response time and steady-state value. The system presets a deviation threshold, for example, if the steady-state value deviation exceeds one percent of the range. If the initial comparison finds that the actual steady-state value is lower than expected and the deviation exceeds the threshold, the system automatically analyzes the deviation pattern and adjusts the original sequence of instructions to be executed according to a preset algorithm, such as fine-tuning the parameter settings in the instructions. The adjusted new instruction sequence is then adapted, issued, and collected again through the edge computing gateway. This process iterates until the deviation between the latest collected actual response data and the expected data of the digital twin falls within the preset threshold. At this point, the instrument is deemed to have passed the calibration, and the loop terminates.

[0042] By comparing the above embodiments with comparative examples, the function and beneficial effects of this embodiment can be seen. This method, by introducing a digital twin for pre-debugging, verifies the debugging logic in a virtual environment, effectively reducing the number of blind trial-and-error attempts on-site. By using an edge computing gateway to adapt instructions based on accurate device profiles, it solves the instruction compatibility problem caused by the heterogeneity of on-site devices, improving the accuracy and automation level of instruction issuance. Finally, by establishing a real-time closed-loop comparison and iterative adjustment mechanism between physical responses and virtual expected responses, the debugging process can automatically and continuously optimize instructions until the optimal effect is achieved, thereby significantly improving the efficiency, standardization, and final quality of debugging work, and reducing the absolute dependence on personnel's on-site experience.

[0043] According to one embodiment of the present invention, a method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins, preferably, includes a model and data collaborative management step after the closed-loop verification and iterative adjustment steps: Data credibility assessment: Perform time-series consistency and correlation checks on the collected response data and identify abnormal data points; Model dynamic calibration: When the triggering conditions are met, the parameters of the dynamic response characteristic model of the digital twin are optimized and updated using verified high-reliability historical data; Data reconstruction: Before comparison, the identified abnormal data points are simulated and reconstructed using the updated digital twin.

[0044] One feasible implementation involves the system automatically entering the model and data collaborative management step after completing the closed-loop verification and iterative adjustment. Taking the commissioning of a control valve as an example, the system collects full-range response data during the closed-loop verification, showing a step change in valve opening from 20% to 80%. First, a data reliability assessment is performed. The system analyzes the time series of the response data to check its continuity. For example, during normal valve operation, the opening feedback should change monotonically over a certain period. If a bounce point that violates the laws of physical motion appears in the data stream, it is identified as an abnormal data point. Simultaneously, the system correlates and checks the logical relationship between the drive current signal and the valve position feedback signal at the same moment, marking inconsistent data segments as abnormal.

[0045] Subsequently, dynamic model calibration is performed. The system is set to trigger a condition where the average prediction deviation of five consecutive debugging tasks exceeds 0.5%. When this condition is met, the system automatically filters out debugging data from the historical database that has been assessed for reliability and marked as high-confidence, such as ten pairs of valid command response data for the valve at different opening degrees over the past thirty days. Using this data, the parameters of the dynamic response characteristic model of the control valve in the digital twin (such as a second-order system model) are optimized and updated, making the model's output more closely match the valve's recent actual performance.

[0046] Finally, data reconstruction is performed. Before data comparison is required for subsequent closed-loop verification, the system uses updated and more accurate digital twins to simulate the identified abnormal data points in the collected data. For example, for the abnormal bounce point, the system calculates the theoretically reasonable threshold value at that time point through model simulation based on the trend of normal data points before and after it. This reconstructed value replaces the original abnormal collected value and is then compared with the expected response. This ensures that the data used for the final evaluation and reporting is consistent and conforms to physical laws.

[0047] This embodiment establishes a self-optimizing mechanism that continuously maintains high fidelity in the digital twin. By rigorously screening the field data for reliability, it effectively filters out interference noise, ensuring the quality of the original data used for model calibration and decision comparison, and avoiding misjudgments. By periodically or trigger-based optimization of model parameters using high-quality historical data, it enables the digital twin to evolve synchronously with the aging or changes of the physical entity, overcoming model drift problems and extending the effective lifespan of the digital twin system. Through data reconstruction, it can recover reasonable data sequences using a high-reliability model even when some data is damaged, ensuring the integrity and reliability of the debugging process analysis and improving the overall system's adaptability to complex field environments.

[0048] According to one embodiment of the present invention, a method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins is preferred, wherein the device profile includes at least the device model, firmware version, communication protocol details and calibration response delay parameters of the physical instrument; The conversion operation based on the device profile includes: converting the general instruction code into a specific instruction that the device can recognize according to the communication protocol details, and configuring the time interval between instructions according to the calibration response delay parameter.

[0049] One feasible implementation involves the edge computing gateway adapting and encapsulating field commands based on a pre-established, structured device profile for the target physical instrument. For example, if the target instrument to be debugged is identified as a smart level gauge from a certain brand, its device profile includes the following key information: device model is LIT-2000, firmware version is V1.5, communication protocol details are PROFIBUS DP, its Vendor ID and Device ID are clearly defined, the DPV number for reading and writing diagnostic information is 245, and the calibration response delay parameter is 350 milliseconds.

[0050] Based on this device profile, the edge computing gateway's conversion operation is as follows: It receives a general pre-debugging instruction sequence from the digital twin, such as "read the current process variable value" and "modify the setpoint to 50%". According to the communication protocol details in the device profile, the gateway translates the first general instruction, "read the current process variable value," into a specific communication message for the PROFIBUS DP device, i.e., constructs a read request data frame for DPV number 245. It translates the second instruction, "modify the setpoint to 50%," into a write request data frame for the corresponding DPV number and converts the 50% floating-point value into a byte stream conforming to the device's data format.

[0051] Meanwhile, the gateway intelligently schedules the execution of command sequences based on the 350-millisecond response latency parameter specified in the device profile. It doesn't send a write request immediately after sending a read request; instead, it waits at least 350 milliseconds to ensure the device has sufficient time to process the first command and return a response before sending the second write command. If multiple commands need to be sent consecutively, the gateway automatically plans the time intervals between commands, forming a well-paced sequence of commands to be executed that matches the device's response capabilities.

[0052] This embodiment abstracts and solidifies the equipment-specific knowledge that originally relied on manual experience and on-site inspection into structured equipment profile data. Through automated conversion operations based on these profiles, it achieves accurate and error-free translation of debugging commands from general semantics to specific device communication messages, fundamentally avoiding manual configuration errors caused by unfamiliar protocols or incorrect addresses. By integrating and calibrating response delays and automatically configuring command timing accordingly, the rhythm of command issuance matches the actual processing capacity of the equipment, preventing execution failures or data loss due to command congestion and ensuring a smooth and stable debugging process. This significantly improves the automation level and execution efficiency of on-site debugging, reducing the workload and potential risks associated with equipment heterogeneity.

[0053] According to one embodiment of the present invention, a method for pre-commissioning and field commissioning of chemical instruments based on digital twins, preferably, in the field command adaptation and encapsulation step, the edge computing gateway further performs a security arbitration sub-step: Obtain the current process safety constraints and real-time values ​​of associated process parameters; Simulate the impact of executing the sequence of instructions to be executed on associated process parameters in a virtual environment; If the simulation results violate the security constraints, the sequence of instructions to be executed will be modified or replaced with a safe instruction sequence before proceeding to the instruction issuance and data acquisition steps.

[0054] One feasible implementation involves the edge computing gateway initiating a security arbitration sub-step after completing instruction adaptation but before actual instruction delivery. Taking the debugging of a high-temperature switch on a reactor's circulation loop as an example, the sequence of instructions to be executed includes a test instruction to "force trigger an alarm signal." The security arbitration sub-step first retrieves the process safety constraints related to the high-temperature switch from the factory's real-time database or safety instrumented system, such as "reactor temperature T must not exceed 280 degrees Celsius," and reads the associated process parameters in real time, i.e., the current actual temperature of the reactor, assuming it is 265 degrees Celsius.

[0055] Next, the edge computing gateway, either internally or in collaboration with its digital twin, constructs a virtual environment containing the process constraints. It simulates the execution of the command to "forcefully trigger a high-temperature alarm." In the simulation, this command is treated as a real alarm signal and input into the virtual process control logic. The system simulates the subsequent response of this logic; for example, it might trigger an interlock to activate the emergency cooling system or initiate a pre-shutdown sequence.

[0056] The system then analyzes the simulation results. Although the forced alarm itself is a test, the virtual interlocking actions it triggers can cause drastic changes in virtual process parameters. The safety arbitration engine determines whether these simulated process changes violate established safety constraints. For example, the simulation results show that the virtual activation of the emergency cooling system may cause the reactor temperature to drop by more than 50 degrees Celsius within the simulation time. Although this does not exceed the upper limit, it violates another safety constraint that "the rate of temperature drop must not exceed 30 degrees Celsius per minute." Therefore, the system determines that the simulation results violate safety constraints.

[0057] Therefore, the edge computing gateway will not directly issue the original test command. It may modify the command sequence. For example, before issuing the forced alarm signal, it may virtually (or actually) insert an "interlock test mode" signal, causing the system to temporarily block the key interlock actions triggered by the alarm, retaining only the alarm indication. Alternatively, it may replace the original command with a safer self-test command sequence that only tests the contact resistance of the switch without actually triggering an alarm output. Only the new command sequence after such safety modification or replacement will be allowed to proceed to subsequent command issuance and data acquisition steps.

[0058] This embodiment adds a proactive safety defense based on virtual simulation at the final point where debugging commands reach the physical world. By proactively simulating and evaluating the potential chain reactions in the process chain within a virtual environment, combined with real-time process status, before the commands actually take effect, hidden, cross-system safety risks can be identified in advance. Identified risks are mitigated through command modification or safe replacement, thereby isolating debugging operations that could cause process fluctuations or accidents within the virtual space. This significantly enhances the safety of the instrument debugging process, minimizing the potential impact of debugging work on the stable operation of the production system, and realizing a shift from passively complying with safety procedures to proactively designing safe debugging processes.

[0059] According to one embodiment of the present invention, a method for pre-commissioning and field commissioning of chemical instruments based on digital twins, preferably, further includes the following in the field command adaptation and encapsulation step: Communication risk assessment sub-step: The edge computing gateway monitors the performance indicators of the communication link between itself and the target physical instrument in real time; Risk handling sub-step: If the performance index is lower than the preset standard, the digital twin is triggered to perform virtual joint debugging in a simulated degraded communication environment, and a more robust backup instruction sequence is generated as the instruction sequence to be executed.

[0060] One feasible implementation involves the edge computing gateway simultaneously executing a communication risk assessment sub-step during the commissioning of a radar level gauge using wireless communication in a tank farm. This occurs within the field command adaptation and encapsulation process. The gateway continuously monitors real-time performance metrics of the wireless link between itself and the target level gauge, including signal strength, data packet round-trip latency, and packet loss rate. If the system detects a sudden drop in signal strength and a packet loss rate exceeding 10% for several consecutive cycles, it determines that the performance metrics are below preset standards, indicating a risk of communication link degradation.

[0061] At this point, a risk handling sub-step is triggered. Instead of directly issuing the pre-adapted instruction sequence, the edge computing gateway sends a request to the digital twin, instructing it to perform a virtual integration test under a simulated degraded communication environment. Based on the actual link parameters reported by the gateway, such as high latency and high packet loss rate, the digital twin simulates instruction transmission and response under this unreliable channel in its simulation environment. Through virtual integration, the digital twin can test the robustness of the original instruction sequence and explore optimization strategies, such as breaking down a long instruction into multiple shorter, more easily acknowledged atomic instructions; adding redundant acknowledgment query instructions after critical instructions; or adjusting the time interval between instructions to adapt to high latency.

[0062] Based on the results of the virtual integration testing, the digital twin generates a more robust backup instruction sequence. This new sequence may have been specifically optimized in terms of instruction structure, confirmation mechanism, and timing to cope with the current degraded communication environment. The edge computing gateway uses this backup instruction sequence as the final sequence of instructions to be executed and distributes it. In this way, even under poor real-world link conditions, the debugging task can proceed with a higher success rate, and the integrity of the collected data is better guaranteed.

[0063] This embodiment endows the debugging system with the ability to perceive and proactively adapt to changes in the field communication environment. By monitoring link performance in real time, the system can anticipate communication risks in advance, rather than reacting passively after a failure occurs. Through simulation of joint debugging under degraded conditions in a digital twin virtual environment, it can proactively test and generate more robust instruction strategies, thereby significantly improving the success rate and reliability of debugging tasks in real-world unstable networks. This enables the entire debugging method to adapt to more complex and demanding industrial field environments, ensuring the continuity and resilience of the debugging process.

[0064] According to one embodiment of the present invention, a method for pre-commissioning and field commissioning of chemical instruments based on digital twins, preferably, when commissioning multiple physical instruments with coupling relationships in a process loop, a collaborative scheduling step is included before the field command adaptation and encapsulation step: Establish a commissioning task dependency graph: Based on the process piping and instrumentation diagram and control logic diagram, define the dependencies between the commissioning tasks corresponding to all instruments to be commissioned, and form a commissioning task dependency graph; Virtual collaborative simulation and conflict detection: The digital twin loads the map and the pre-debugging instruction sequence corresponding to each instrument, simulates the concurrent or sequential execution process, and predicts possible instruction conflicts or process limit exceedance events; Generate an optimized scheduling instruction sequence set: Based on the virtual simulation results, dynamically adjust the relative execution timing and concurrency of the debugging instruction sequences of each instrument to generate a globally optimized scheduling instruction sequence set, and send it to the corresponding edge computing gateways for execution.

[0065] One feasible implementation involves performing coordinated commissioning on multiple coupled instruments in the feed, temperature, and pressure control loops of a reactor. Before the on-site command adaptation and encapsulation steps, the system first executes a collaborative scheduling step. The first step is to establish a dependency graph for commissioning tasks. The system analyzes the reactor's process piping and instrumentation diagrams and control logic diagram, automatically identifying the relationships between the instruments to be commissioned. For example, commissioning the feed regulating valve will affect the feed flow meter reading; commissioning the temperature regulating valve will affect the reactor temperature, and temperature changes may affect pressure. Based on this, the system defines dependencies for each instrument's corresponding commissioning task (such as valve stroke testing, flow meter zero-point calibration, and pressure transmitter range testing), forming a graph that clearly indicates that flow meter calibration should be performed before or after the associated valve test during a stable period, and pressure testing should avoid periods of drastic temperature fluctuations, etc.

[0066] The second step is virtual collaborative pre-simulation and conflict detection. The digital twin loads this dependency graph, along with pre-commissioned instruction sequences generated individually for each instrument. The digital twin then simulates the entire process of executing these instruction sequences at different timings and concurrency levels. Through simulation, it can predict events such as simultaneous initiation of feed valve stroke testing and flow meter calibration, leading to drastic fluctuations in the flow signal and causing calibration failure; or predicting that pressure transmitter accuracy testing during rapid temperature valve action will result in unstable readings due to temperature-induced pressure changes. These are all flagged as instruction conflicts or process limit violations.

[0067] The third step is to generate an optimized set of scheduling instruction sequences. Based on the prediction results of the virtual simulation, the digital twin dynamically adjusts the execution plan of each task instruction sequence. For example, it might schedule the static testing of the pressure transmitter to be completed independently first, and then, when the system is relatively stable, sequentially execute the feed valve test and flow meter calibration, and schedule the temperature valve test after all tests that might affect the pressure are completed. Finally, it generates a globally optimized set of scheduling instruction sequences that contains the exact execution timing and conditions of each instrument's commissioning instructions, and distributes them to the corresponding edge computing gateways. Each gateway executes collaboratively according to this global plan, avoiding conflicts.

[0068] This embodiment elevates the commissioning tasks of multiple isolated instruments to a unified and optimized scheduling across the entire process loop. By establishing a dependency graph and virtual collaborative pre-simulation, potential instruction conflicts and process interference issues that may arise during multi-task concurrency can be identified and resolved in advance in the digital space, minimizing mutual negative impacts during commissioning. By generating a globally optimized scheduling instruction set, safe, efficient, and orderly parallel execution of multi-instrument commissioning tasks is achieved. This avoids the efficiency bottleneck of sequential commissioning and overcomes the risks of blind concurrency, significantly improving the efficiency, success rate, and safety of overall integrated commissioning of complex automation loops.

[0069] According to one embodiment of the present invention, a method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins, preferably, includes a model self-evolution step in the establishment and maintenance of the digital twin: Initial data-driven model building: Collect historical input-output data pairs of the target physical instrument under various known test conditions, and use machine learning algorithms to train and generate an initial dynamic response characteristic model; Online incremental learning and model update: After each closed-loop verification and iterative adjustment, the effective instruction sequence-response data pairs generated in this joint debugging are used as new samples to perform incremental learning and parameter fine-tuning on the dynamic response characteristic model; Model version management and rollback: Save a snapshot of the model after each update. When a decline in model prediction performance is detected, roll back to the best historical model version based on context information.

[0070] One feasible implementation involves establishing and maintaining the digital twin following a model self-evolution process. The first step is data-driven initial model building. For a newly installed centrifugal pump vibration sensor, the system collects historical data from the sensor under various known operating conditions on a factory test bench, such as the raw voltage signal output by the sensor under excitation from standard vibration sources of different frequencies and amplitudes. Using these extensive input-output data pairs, a machine learning algorithm is employed to train and generate an initial dynamic response characteristic model. This initial model is already able to fit the actual conversion characteristics of the specific sensor better than purely theoretical formulas.

[0071] Secondly, there is online incremental learning and model updating. After each on-site debugging session executing the closed-loop verification and iterative adjustment, the system saves the verified valid command sequences generated during the debugging process and their corresponding real high-confidence response data as a new sample pair. For example, an on-site debugging session verifies the sensor's response to a specific impact vibration. These new samples are continuously added to the model's training set. The system periodically, or after accumulating enough new samples, triggers an incremental learning process to fine-tune the parameters of the existing dynamic response characteristic model, making the model output closer to the sensor's latest, potentially slowly drifting, actual characteristics.

[0072] Finally, there's model version management and rollback. Each time the model's parameters are updated, the system automatically saves a complete snapshot of that version, recording the update time, context, and key performance indicators. The system continuously monitors the model's predictive performance; for example, in subsequent closed-loop validation, it calculates the average deviation between the model's predicted values ​​and the actual values. If it detects that the average deviation has increased significantly multiple times since the most recent model update, it indicates that the update may have introduced adverse changes or caused model degradation due to abnormal data. In this case, the system will automatically select and roll back to the best-performing and most stable model version saved in history, based on the context information, ensuring the predictive reliability of the digital twin.

[0073] This embodiment realizes the transformation of the core model of the digital twin from static to dynamic self-evolution. The initial model is built using a data-driven approach, reducing modeling complexity and improving individual fitting accuracy. Through online incremental learning, the model can continuously optimize itself using real data generated during daily debugging and operation, allowing the digital twin to "grow" and "evolve" alongside the physical entity, maintaining a high degree of synchronization and fidelity. Version management and rollback mechanisms provide fault tolerance and security for model evolution, preventing performance degradation due to erroneous data or improper updates, and ensuring the long-term stability and reliability of the digital twin system.

[0074] According to one embodiment of the present invention, a method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins, preferably, further includes an online performance evaluation step of the digital twin in the closed-loop verification and iterative adjustment steps: Evaluation engine initialization: Configure a performance evaluation engine for each digital twin, the engine having multiple preset evaluation dimensions and corresponding quantization algorithms; Online synchronous evaluation: Whenever response data is collected, the performance evaluation engine synchronously obtains the expected response data of the digital twin, calculates the predicted performance indicators of this instance according to preset dimensions, and generates a dynamic comprehensive score by combining historical indicators. Decision guidance based on evaluation results: Apply the comprehensive score to subsequent processes, including dynamically adjusting the preset threshold, prioritizing model updates, or using the score as evidence of the credibility of the debugging report.

[0075] One feasible implementation involves simultaneously performing an online performance evaluation step for the digital twin during the closed-loop verification and iterative adjustment steps. First, the evaluation engine is initialized. A dedicated performance evaluation engine is configured for the digital twin of the reactor temperature control loop. This engine pre-defines multiple evaluation dimensions, such as prediction steady-state error, response time error, and waveform similarity, and sets corresponding quantization algorithms for each dimension, such as calculating root mean square error, absolute percentage error, or dynamic time warping distance.

[0076] Next, online synchronous evaluation is performed. Whenever the edge computing gateway collects and uploads the actual response data of the temperature transmitter for a step test, the performance evaluation engine immediately and synchronously obtains the complete expected response data calculated by the digital twin for the same test. The engine then starts calculations, according to preset dimensions, to calculate the percentage deviation between the predicted steady-state value and the actual steady-state value, the difference in milliseconds between the predicted rise time and the actual rise time, and the dynamic similarity score of the two response curves in this instance. Then, the engine combines the calculation results with historical indicators from dozens of past evaluations, and through weighted averaging or trend analysis, generates a dynamically updated comprehensive score to characterize the current overall predictive performance of the digital twin.

[0077] Finally, the evaluation results guide decision-making. This comprehensive score is applied in real time to subsequent processes. For example, when a continuously rising score indicates excellent model performance, the system can automatically and dynamically tighten the preset deviation threshold in closed-loop validation to pursue finer debugging accuracy. When the score falls below a certain critical value, the system will prioritize triggering the aforementioned dynamic model calibration process. Furthermore, this score is appended to the final debugging report as important quantitative evidence of the credibility of the debugging conclusions, making the report more scientific and persuasive.

[0078] This embodiment introduces a continuous online "health check" mechanism for the digital twin, transforming its performance from a vague perception to a precise measurement. By synchronously evaluating each debugging instance through multi-dimensional quantification algorithms, the predictive accuracy and trends of the digital twin model can be objectively and in real-time reflected. The generated dynamic comprehensive score provides the system with crucial self-awareness data, enabling subsequent decisions such as threshold adjustments and model update triggers to be based on quantifiable performance evidence rather than empirical guesswork. This significantly improves the intelligence, self-reflection capabilities, and operational transparency of the entire debugging system, ensuring the reliability and authority of the debugging process centered on the digital twin.

[0079] According to one embodiment of the present invention, a method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins, preferably, includes a data stream spatiotemporal alignment step between the instruction issuance and data acquisition step and the closed-loop verification and iterative adjustment step: Global logical time base injection: When issuing commands, the edge computing gateway adds a timestamp and command causal identifier; when collecting response data, it adds a collection timestamp and associates it with the corresponding identifier. Data stream buffering and realignment: The edge computing gateway sets up an alignment buffer to cache response packets and receive the expected response data stream from the digital twin; Causal matching and interpolation based on logical time: Match the physical response and expected response data segments according to the instruction causal identifier, align the time axis with the issued timestamp as the origin, and perform interpolation resampling to make the two sets of data correspond on a unified logical time axis; Output aligned data pairs: Output the data pairs after causal matching and time axis alignment to the closed-loop verification and iterative adjustment steps for comparison.

[0080] One feasible implementation involves performing a data flow spatiotemporal alignment step after the instruction issuance and data acquisition steps, and before the closed-loop verification and iterative adjustment steps. The first step is global logical time base injection. When the edge computing gateway issues an instruction to a pressure transmitter to activate pressure calibration mode, it not only sends the instruction content but also appends a precise timestamp synchronized with the digital twin, such as October 27, 2023, 14:30:05:123, and assigns the instruction a unique causal identifier, such as sequence number 1001. When the gateway acquires the pressure data packet returned by the transmitter after executing the instruction, it appends the acquisition timestamp to the data packet and explicitly associates it with the causal identifier 1001 of the instruction.

[0081] Next comes data stream buffering and realignment. An internal data alignment buffer is set up in the edge computing gateway to temporarily cache the acquired physical response data packets, which contain identifiers and timestamps. Simultaneously, the digital twin begins calculating the expected response based on the instruction content and generates a continuous expected response data stream, which is sent to the gateway's alignment buffer.

[0082] Then comes causal matching and interpolation based on logical time. The alignment buffer processing module performs matching based on the instruction causal identifier. It associates the physical response data segment with identifier 1001 with the expected response data segment generated by the digital twin for instruction 1001. After matching, the time axis of the two data segments is realigned using the issued timestamp 14:30:05:123 as the common time origin. Since the acquisition time of the physical data points and the calculation time of the simulation data points may not completely coincide, the system uses an interpolation algorithm, such as linear interpolation, to resample one of the data streams, thereby generating data point pairs that strictly correspond on a unified logical time axis.

[0083] Finally, the aligned data pairs are output. After the causal matching and timeline alignment processes described above, the one-to-one corresponding physical and expected data pairs are output to the closed-loop verification and iterative adjustment steps. Only at this point are the comparisons truly made to reflect the response differences generated by the same command under the same time reference, ensuring the accuracy and effectiveness of the deviation analysis.

[0084] This embodiment fundamentally eliminates data misalignment caused by physical transmission delays and system clock differences by adding a precise spatiotemporal alignment process before data comparison. By injecting a global logical time base and causal identifiers, a clear traceability link from instruction to response is established. Through buffer realignment and interpolation resampling, point-to-point precise matching of the physical and virtual data sets is ensured on a unified and causally correct timeline. This allows subsequent deviation calculations and performance evaluations to be based on real and comparable data, greatly improving the accuracy and reliability of the closed-loop verification process. This is a key technical guarantee for ensuring that the entire digital twin debugging method yields scientific conclusions. According to one embodiment of the present invention, a method for pre-commissioning and field commissioning of chemical instruments based on digital twins, preferably, involves the edge computing gateway simultaneously performing state persistence and recovery steps when executing the field command adaptation and encapsulation, command issuance and data acquisition steps: Critical status checkpoint creation: After completing critical operations, the edge computing gateway automatically generates checkpoint files for the critical status of the current debugging task and persists them. Recovery Trigger and State Loading: When the edge computing gateway starts up or recovers from an anomaly, it automatically detects and loads the latest checkpoint file for any incomplete debugging tasks; State consistency verification and continued execution: The edge computing gateway initiates a state synchronization request to the digital twin. The digital twin re-simulates and generates simulation data based on the checkpoint information. The gateway compares and verifies the recovered response data with the simulation data. If the verification is consistent, execution will automatically continue. Otherwise, it requests virtual supplementary data to complete the calculation before continuing execution.

[0085] One feasible implementation involves the edge computing gateway simultaneously performing state persistence and recovery steps in the background while executing on-site command adaptation and encapsulation, command issuance, and data acquisition steps. The first step is the creation of critical state checkpoints. After completing critical operation nodes, such as successfully adapting and encapsulating a set of commands to be executed, or successfully acquiring a complete batch of response data, the gateway automatically generates a structured checkpoint file containing the key state information of the current debugging task, including the target instrument identifier, the issued command sequence, the acquired response data fragments, the current command pointer position, and related context parameters. This file is then persistently stored in the gateway's local non-volatile memory or uploaded to the cloud for backup.

[0086] Secondly, there's the recovery trigger and state loading. When the edge computing gateway restarts after planned maintenance or an unexpected failure, or recovers from a network anomaly, its self-recovery module automatically checks the storage medium for the latest checkpoint file of any task not marked as completed. If a checkpoint file for a valve positioner calibration task is found, the system automatically loads that file, restoring the debugging task state to the instant before the interruption, for example, restoring it to the state of "the first three calibration commands have been issued and the corresponding responses have been collected, and the fourth command is about to be issued."

[0087] Finally, there's the state consistency check and execution continuation. After loading its local state, the gateway doesn't immediately and blindly continue execution. It proactively initiates a state synchronization request to the digital twin, sending checkpoint information. Based on the received information, the digital twin re-simulates the complete process from the task's start to the breakpoint in the virtual environment and generates the corresponding simulation data stream. The gateway compares the locally recovered physical response data with the data re-simulated by the digital twin. If the verification matches, proving the recovered state is complete and reliable, the gateway automatically resumes execution from the breakpoint, such as issuing the fourth instruction. If the verification finds inconsistencies, such as missing data, the gateway requests virtual supplementary data from the digital twin to complete the calculation, forming a logically complete continuation point before continuing execution.

[0088] This embodiment provides a "resume interrupted download" capability similar to that of a computer system for on-site debugging tasks, greatly enhancing the resilience and fault tolerance of the debugging process. By automatically creating key status checkpoints, real-time backup of debugging progress is achieved, preventing state loss. Through automatic state loading and consistency verification after anomaly recovery, the system can intelligently and reliably reconstruct the state before the interruption, ensuring the logical correctness of subsequent execution. This effectively addresses various unforeseen interferences and interruptions in industrial settings, avoids repetitive work and resource waste, ensures that long-cycle, complex debugging tasks can be executed continuously and efficiently, and improves the practicality and user satisfaction of the entire debugging system.

[0089] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and embodiments shown and described herein.

Claims

1. A method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins, characterized in that, Includes the following steps: Digital twin modeling and pre-commissioning steps: Create a digital twin for the field physical instrument, the digital twin containing the dynamic response characteristic model of the physical instrument; simulate and verify the physical instrument in the digital twin to generate a pre-commissioning instruction sequence; Field command adaptation and encapsulation steps: The edge computing gateway obtains a pre-established device profile for the target physical instrument; based on the device profile, the pre-debugging command sequence is converted into an executable command sequence adapted to the specific attributes of the target physical instrument; Command issuance and data acquisition steps: The edge computing gateway issues the sequence of commands to be executed to the target physical instrument for execution, and collects the response data generated after execution; Closed-loop verification and iterative adjustment steps: The collected response data is compared with the expected response data calculated by the digital twin; if the comparison deviation exceeds a preset threshold, the sequence of instructions to be executed is adjusted according to the deviation, and the instruction issuance and data acquisition steps and this step are executed iteratively until the comparison deviation meets the requirements.

2. The method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins as described in claim 1, characterized in that, Following the closed-loop verification and iterative adjustment steps, a model and data collaborative management step is also included: Data credibility assessment: Perform time-series consistency and correlation checks on the collected response data and identify abnormal data points; Model dynamic calibration: When the triggering conditions are met, the parameters of the dynamic response characteristic model of the digital twin are optimized and updated using verified high-reliability historical data; Data reconstruction: Before comparison, the identified abnormal data points are simulated and reconstructed using the updated digital twin.

3. The method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins as described in claim 1, characterized in that, The device profile includes at least the device model, firmware version, communication protocol details, and calibration response delay parameters of the physical instrument. The conversion operation based on the device profile includes: converting the general instruction code into a specific instruction that the device can recognize according to the communication protocol details, and configuring the time interval between instructions according to the calibration response delay parameter.

4. The method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins as described in claim 1, characterized in that, In the field command adaptation and encapsulation step, the edge computing gateway also performs a security arbitration sub-step: Obtain the current process safety constraints and real-time values ​​of associated process parameters; Simulate the impact of executing the sequence of instructions to be executed on associated process parameters in a virtual environment; If the simulation results violate the security constraints, the sequence of instructions to be executed will be modified or replaced with a safe instruction sequence before proceeding to the instruction issuance and data acquisition steps.

5. The method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins as described in claim 1, characterized in that, The field command adaptation and encapsulation step also includes: Communication risk assessment sub-step: The edge computing gateway monitors the performance indicators of the communication link between itself and the target physical instrument in real time; Risk handling sub-step: If the performance index is lower than the preset standard, the digital twin is triggered to perform virtual joint debugging in a simulated degraded communication environment, and a more robust backup instruction sequence is generated as the instruction sequence to be executed.

6. The method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins as described in claim 1, characterized in that, When performing joint debugging on multiple physical instruments that are coupled in a process loop, a collaborative scheduling step is included before the field command adaptation and encapsulation step: Establish a commissioning task dependency graph: Based on the process piping and instrumentation diagram and control logic diagram, define the dependencies between the commissioning tasks corresponding to all instruments to be commissioned, and form a commissioning task dependency graph; Virtual collaborative simulation and conflict detection: The digital twin loads the map and the pre-debugging instruction sequence corresponding to each instrument, simulates the concurrent or sequential execution process, and predicts possible instruction conflicts or process limit exceedance events; Generate an optimized scheduling instruction sequence set: Based on the virtual simulation results, dynamically adjust the relative execution timing and concurrency of the debugging instruction sequences of each instrument to generate a globally optimized scheduling instruction sequence set, and send it to the corresponding edge computing gateways for execution.

7. The method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins as described in claim 1, characterized in that, The creation and maintenance of digital twins also includes a model self-evolution step: Initial data-driven model building: Collect historical input-output data pairs of the target physical instrument under various known test conditions, and use machine learning algorithms to train and generate an initial dynamic response characteristic model; Online incremental learning and model update: After each closed-loop verification and iterative adjustment, the effective instruction sequence-response data pairs generated in this joint debugging are used as new samples to perform incremental learning and parameter fine-tuning on the dynamic response characteristic model; Model version management and rollback: Save a snapshot of the model after each update. When a decline in model prediction performance is detected, roll back to the best historical model version based on context information.

8. The method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins as described in claim 1, characterized in that, The closed-loop verification and iterative adjustment steps also include an online performance evaluation step for the digital twin: Evaluation engine initialization: Configure a performance evaluation engine for each digital twin, the engine having multiple preset evaluation dimensions and corresponding quantization algorithms; Online synchronous evaluation: Whenever response data is collected, the performance evaluation engine synchronously obtains the expected response data of the digital twin, calculates the predicted performance indicators of this instance according to preset dimensions, and generates a dynamic comprehensive score by combining historical indicators. Decision guidance based on evaluation results: Apply the comprehensive score to subsequent processes, including dynamically adjusting the preset threshold, prioritizing model updates, or using the score as evidence of the credibility of the debugging report.

9. The method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins as described in claim 1, characterized in that, Between the instruction issuance and data acquisition steps and the closed-loop verification and iterative adjustment steps, a data stream spatiotemporal alignment step is also included: Global logical time base injection: When issuing commands, the edge computing gateway adds a timestamp and command causal identifier; when collecting response data, it adds a collection timestamp and associates it with the corresponding identifier. Data stream buffering and realignment: The edge computing gateway sets up an alignment buffer to cache response packets and receive the expected response data stream from the digital twin; Causal matching and interpolation based on logical time: Match the physical response and expected response data segments according to the instruction causal identifier, align the time axis with the issued timestamp as the origin, and perform interpolation resampling to make the two sets of data correspond on a unified logical time axis; Output aligned data pairs: Output the data pairs after causal matching and time axis alignment to the closed-loop verification and iterative adjustment steps for comparison.

10. The method for pre-commissioning and on-site commissioning of chemical instruments based on digital twins as described in claim 1, characterized in that, When the edge computing gateway performs the field command adaptation and encapsulation, command issuance and data acquisition steps, it simultaneously performs the state persistence and recovery steps: Critical status checkpoint creation: After completing critical operations, the edge computing gateway automatically generates checkpoint files for the critical status of the current debugging task and persists them. Recovery Trigger and State Loading: When the edge computing gateway starts up or recovers from an anomaly, it automatically detects and loads the latest checkpoint file for any incomplete debugging tasks; State consistency verification and continued execution: The edge computing gateway initiates a state synchronization request to the digital twin. The digital twin re-simulates and generates simulation data based on the checkpoint information. The gateway compares and verifies the recovered response data with the simulation data. If the verification is consistent, execution will automatically continue. Otherwise, it requests virtual supplementary data to complete the calculation before continuing execution.