Digital Twin-Based Model Management and Collaborative Analysis Method and System

Abstract

The present invention provides a method and system for model management and collaborative analysis based on digital twin, including: Step S1: Construct a digital twin 3D model of a target object; Step S2: Collect the point position data of the target object at a fixed frequency and store it in the IHD real-time database; Step S3: Construct different business models, including: a data model and a rule model; Step S4: Select a business model according to requirements and calculate corresponding business indicators based on the point position data in the IHD real-time database; Step S5: Push the calculated business indicators to the digital twin 3D model; Step S6: The digital twin 3D model restores the inherent attributes of the target object based on the collected point position data of the target object and the received business indicators; The present invention provides a unified digital twin model management and collaborative analysis framework for complex industrial systems, aiming to solve the problems of heterogeneous complexity of tasks, dynamic changes in the environment, and real-time interaction existing in digital twin currently.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent manufacturing, and specifically, to a method and system for model management and collaborative analysis based on digital twin. Background Art

[0002] In modern industry, the normal operation and efficient maintenance of equipment are key factors for enterprises to improve production efficiency and reduce operating costs. However, traditional equipment maintenance methods are often based on fixed schedules or carried out after equipment failures, which may lead to over-maintenance or production losses due to unexpected downtimes. Digital twin enables enterprises to monitor equipment status in real time and analyze operation data by simulating the entire production process, identifying bottlenecks and optimizing resource allocation, so as to achieve precise predictive maintenance.

[0003] Although digital twin systems can achieve the monitoring, analysis, and optimization of physical entities, problems such as task heterogeneous complexity, environmental dynamic changes, and interaction real-time performance in practical applications need to be solved urgently. Therefore, it is crucial to provide a unified integrated framework for model management and collaborative scheduling for complex industrial environments, which can integrate models and data from different fields, break information silos, and achieve cross-domain collaborative analysis.

[0004] The present invention proposes a method for model management and collaborative analysis based on digital twin, which uniformly encapsulates heterogeneous tasks according to business requirements and automatically distributes them, and conducts collaborative analysis using rule models and data models. This ability not only reduces the need for manual intervention but also greatly improves the efficiency and accuracy of the system in processing heterogeneous tasks, featuring automation, high efficiency, and flexibility. Summary of the Invention

[0005] Aiming at the deficiencies in the prior art, the purpose of the present invention is to provide a method and system for model management and collaborative analysis based on digital twin.

[0006] According to a method for model management and collaborative analysis based on digital twin provided by the present invention, it includes:

[0007] Step S1: Construct a digital twin 3D model of the target object;

[0008] Step S2: Collect the point data of the target object at a fixed frequency and save it in the IHD real-time database;

[0009] Step S3: Construct different business models, including: data model and rule model;

[0010] Step S4: Select a business model according to requirements and calculate corresponding business indicators based on the point data in the IHD real-time database;

[0011] Step S5: Push the calculated business metrics to the digital twin 3D model;

[0012] Step S6: The digital twin 3D model restores the internal attributes of the target object based on the point data of the target object collected and the received business metrics;

[0013] The data model is a mathematical or statistical model constructed for business objectives based on point data (i.e., variable data collected during the operation of equipment or systems), enabling the organic organization, analysis, prediction, and anomaly detection of data. The construction of the data model is based on multi-dimensional data in industrial scenarios, and through data cleaning, integration, modeling, and analysis, it provides support for business decision-making and operation optimization;

[0014] The rule model is based on Boolean functions and obtains the alarm status based on rules according to the point data of the target object;

[0015] Preferably, step S2 includes: collecting the point data of the target object from the PLC or WinCC system at a fixed frequency and storing it in the data table of the IHD real-time database; based on the IHD real-time database, obtaining the real-time data and historical data of the points through the http interface; the structure of the data table includes: sensor ID, timestamp, measured value, status code; where the status code represents the quality of data collection;

[0016] Among them, the point data of the target object includes: physical attributes driving the target object, including: speed, temperature, and pressure.

[0017] Preferably, step S3 includes:

[0018] Step S3.1: Construct a data model management system, including: data model classification management, data model path configuration, data model dynamic loading, and loading status management;

[0019] Among them, the data model classification management classifies data models using a dictionary structure and establishes a mapping relationship between configuration parameters and detection functions for each type of data model; among them, different data models construct different parameter configurations;

[0020] The data model path configuration obtains the weight file paths of different types of data models based on the configuration parameters of each type of data model and stores the obtained paths in the corresponding dictionary structure;

[0021] The data model dynamic loading loads the specified data model detection function by checking the model paths defined in the configuration file and loads the trained weight file based on the detection function;

[0022] The loading status management records the loading and unloading status of the data model through a status dictionary;

[0023] Step S3.2: Build a rule model management system, including: rule definition, trigger configuration, rule parsing, and logical operations;

[0024] Among them, the trigger configuration includes: time-driven mechanism, event-driven mechanism, and a driving mechanism combining events and time;

[0025] The rule parsing and the logical operations are to parse the rules and trigger configurations in the rule model and execute corresponding tasks based on the rule judgment function.

[0026] Preferably, the step S4 includes:

[0027] Step S4.1: Build a system status management system, including: initialization management when the server starts, including: current status check, log record, exception handling, and global status update;

[0028] Step S4.2: Build a multi-task scheduling management system, including: task definition, task status management, task parameter persistence, and batch management of tasks;

[0029] Step S4.3: Heterogeneous task distribution, according to different task requirements, call the corresponding data model, rule model, or data model and rule model, and calculate the corresponding business indicators based on the point data in the IHD real-time database.

[0030] Preferably, the step S5 includes: storing the calculated business indicators in the db2 database, and then using the kafka message queue to push the latest business indicators to the digital twin 3D model.

[0031] According to a model management and collaborative analysis system based on digital twin provided by the present invention, including:

[0032] Module M1: Build a digital twin 3D model of the target object;

[0033] Module M2: Collect the point data of the target object at a fixed frequency and store it in the IHD real-time database;

[0034] Module M3: Build different business models, including: data model and rule model;

[0035] Module M4: Select a business model according to requirements and calculate the corresponding business indicators based on the point data in the IHD real-time database;

[0036] Module M5: Push the calculated business indicators to the digital twin 3D model;

[0037] Module M6: The digital twin 3D model restores the inherent attributes of the target object based on the point position data of the target object collected and the received business indicators;

[0038] The data model is a mathematical or statistical model constructed for business objectives based on point position data (i.e., variable data collected during the operation of equipment or systems), enabling the organic organization, analysis, prediction, and anomaly detection of data. The construction of the data model is based on multi-dimensional data in industrial scenarios, and through data cleaning, integration, modeling, and analysis, it provides support for business decision-making and operation optimization;

[0039] The rule model is based on Boolean functions and obtains the alarm status based on rules according to the point position data of the target object;

[0040] Preferably, the module M2 includes: collecting the point position data of the target object from the PLC or WinCC system at a fixed frequency and storing it in a data table of the IHD real-time database; obtaining the real-time data and historical data of the point position through the http interface based on the IHD real-time database; the structure of the data table includes: sensor ID, timestamp, measured value, status code; where the status code represents the quality of data collection;

[0041] Among them, the point position data of the target object includes: physical attributes driving the target object, including: speed, temperature, and pressure.

[0042] Preferably, the module M3 includes:

[0043] Module M3.1: Construct a data model management system, including: data model classification management, data model path configuration, data model dynamic loading, and loading status management;

[0044] Among them, the data model classification management classifies data models using a dictionary structure and establishes a mapping relationship between configuration parameters and detection functions for each type of data model; among them, different data models construct different parameter configurations;

[0045] The data model path configuration obtains the weight file paths of different types of data models based on the configuration parameters of each type of data model and stores the obtained paths in the corresponding dictionary structure;

[0046] The data model dynamic loading loads the specified data model detection function by checking the model paths defined in the configuration file and loads the trained weight file based on the detection function;

[0047] The loading status management records the loading and unloading status of the data model through a status dictionary;

[0048] Module M3.2: Build a rule model management system, including: rule definition, trigger configuration, rule parsing, and logical operations;

[0049] Among them, the trigger configuration includes: time-driven mechanism, event-driven mechanism, and a driving mechanism that combines events and time;

[0050] The rule parsing and the logical operations are to parse the rules and trigger configurations in the rule model and execute corresponding tasks based on the rule judgment function.

[0051] Preferably, the module M4 includes:

[0052] Module M4.1: Build a system status management system, including: performing initialization management when the server starts, including: current status check, log recording, exception handling, and global status update;

[0053] Module M4.2: Build a multi-task scheduling management system, including: task definition, task status management, task parameter persistence, and batch management of tasks;

[0054] Module M4.3: Heterogeneous task distribution. According to different task requirements, call the corresponding data model, rule model, or data model and rule model, and calculate the corresponding business indicators based on the point data in the IHD real-time database.

[0055] Preferably, the module M5 includes: storing the calculated business indicators in the db2 database, and then using the kafka message queue to push the latest business indicators to the digital twin 3D model.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. The present invention constructs a unified digital twin model management and collaborative analysis framework to realize data processing and collaborative analysis in a complex industrial environment;

[0058] 2. Combine the task scheduling module with the digital twin model to dynamically adjust the task execution order and resource allocation, so that the industrial system can respond to changes in the external environment in real time;

[0059] 3. Integrate multiple business models and link them with the digital twin model to support cross-domain and multi-dimensional collaborative analysis, and form an all-round evaluation of the product operation status. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, purposes, and advantages of the present invention will become more obvious:

[0061] Figure 1Flowchart of the model management and collaborative analysis method based on digital twin in the present invention

[0062] Figure 2 Data model parameter configuration diagram in the present invention;

[0063] Figure 3 Rule model parameter configuration diagram in the present invention;

[0064] Figure 4 Multi-task scheduling management screen in the present invention;

[0065] Figure 5 Digital twin 3D simulation sample diagram in the present invention. Detailed implementation manners

[0066] The present invention will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those of ordinary skill in the art can make several changes and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0067] Embodiment 1

[0068] The present invention provides a model management and collaborative analysis method and system based on digital twin, which combines digital twin technology with task scheduling and business models to realize data processing and collaborative analysis in complex industrial environments.

[0069] As Figure 1 shown, a model management and collaborative analysis method based on digital twin includes the following steps:

[0070] Step 1: Create a digital twin 3D model of the product, where the product can be a factory, a production line, or a device;

[0071] Step 2: Collect product point data from the PLC or WinCC system at a fixed frequency and save it in the IHD real-time database. Provide an http interface to obtain the real-time data and historical data of the points. The data table structure includes sensor ID, timestamp, measurement value, and status code, where the status code represents the quality of data collection.

[0072] Step 3: Configure business model parameters and data points. The business model includes a data-driven model and a rule-driven model.

[0073] Step 3.1: Build a data model management system, including model classification management, model path configuration, model dynamic loading, and loading status management;

[0074] The model is classified using a dictionary structure, and a mapping relationship between configuration parameters and detection functions is established for each type of model. Each type of model has corresponding detection functions and status management logic.

[0075] Read the paths of different types of model weights from the given configuration parameters and store the paths in the corresponding dictionary. Different model types have different parameter configuration logics. In this embodiment, the data models are divided into prediction models and anomaly detection models. The prediction models include single-signal prediction, multi-signal prediction, and online incremental learning models. The configuration parameters of the prediction model include model name, algorithm type, input length, output length, detection frequency, and data points. The configuration parameters of the anomaly detection model include: model name, algorithm type, and data points.

[0076] As Figure 2 shown, after the model configuration parameters, a dynamic loading mechanism is adopted to load the specified model detection function by checking the model path defined in the configuration file, rather than directly loading all models. This makes the system lighter at startup and only loads specific models when needed.

[0077] Initialize a status dictionary for each model to record the loading and unloading status of the model. The status of 0 indicates that the model is not loaded, and the status of 1 or n indicates that the model has been loaded 1 time or n times. The unloading status is decremented by 1 each time. Control the loading process of the model through the status variable to avoid repeated loading of the model and save computing resources.

[0078] Step 3.2: Build a rule model management system, including rule definition, trigger configuration, rule parsing, and logical operations.

[0079] Each rule can be regarded as a Boolean function. The input is the point data of the product, and the output is the alarm status (true / false) based on the rule. In the embodiment, the configured rule types are as follows:

[0080]

[0081] With different trigger configurations, the rule execution methods are different. In the embodiment, the trigger configuration types include time-driven mechanism, event-driven mechanism, and event-time combined driven mechanism, and the corresponding status codes are 0, 1, 2 respectively, as follows:

[0082]

[0083] As Figure 3As shown, parse the incoming rules and trigger configurations, and execute the corresponding tasks. For some rule types, there can be nested sub-rule configurations. The function recursively calls itself to parse these nested structures. The start event and end event in the trigger configuration are also rules in essence and can be recursively parsed. Among them, the trigger configuration parameters, including the monitoring start time, monitoring end time, delay detection time, detection frequency, detection points, start event, end event, detection duration, etc., all need to be converted to a unified time unit (such as seconds) for processing.

[0084] Rules can be combined into complex rules through logical operations such as "AND", "OR", and "NOT". For complex rules, it is necessary to traverse the rule configuration and execute the corresponding rule judgment function. Judge exceptions according to the rule type and summarize the judgment results according to the relationship (AND / OR) of the rules.

[0085] For the AND relationship: All rules must trigger an exception for the overall exception to occur. Formula:

[0086]

[0087] For the OR relationship: As long as one rule triggers an exception, it is an overall exception. Formula:

[0088]

[0089] Step 4: Build a multi-task scheduling and management system to realize the distribution of heterogeneous tasks and the collaborative analysis of business models.

[0090] Step 4.1: Build a system status management system:

[0091] The initialization process when the server starts involves the inspection of the current system status, log recording, exception handling, and global status update.

[0092] Define the set of system status inspection results: All_Status = {S1, S2, S3, S4, S5}, where S1 is the real-time database connection status, S2 is the rule task scheduling connection status, S3 is the algorithm library connection status, S4 is the exception detection model task scheduling connection status, and S5 is the prediction model task scheduling connection status. When all statuses are normally connected, the system initialization process is completed; otherwise, it exits abnormally and logs are recorded.

[0093] Step 4.2 Build a multi-task scheduling and management system, including task definition, task status management, task parameter persistence, and task batch management.

[0094] The scheduling task can be represented as a function S(w, r, t, p), where w represents the warning ID, r represents the business model configuration, t represents the trigger configuration, and p represents the priority setting of the task. High-priority tasks preempt resources during scheduling, and their execution order is ensured through a priority queue. The priority p of the task is dynamically adjusted according to the scheduling system. Critical tasks are inserted into the high-priority queue in real time to ensure their quick response and execution. The system monitors the load status of Workers (such as CPU usage, memory occupancy, task queue length, etc.) in real time through status monitoring. When the node load is too high, non-critical tasks in the task S(w, r, t, p) are preferentially transferred to other idle nodes to avoid task backlog caused by resource bottlenecks.

[0095] The task scheduling is successful, and the task ID is returned; otherwise, the failure status is returned. In the embodiment, the scheduling tasks are divided into three categories according to the business model: monitoring tasks based on expert rules, anomaly monitoring tasks based on data models, and monitoring tasks for trend prediction and rule collaboration.

[0096] During the task scheduling process, the system decides whether to terminate the task according to the current state of the task and updates the task status information in the database. The corresponding task status codes are 0 to 4. In the embodiment, 5 task states are set, and the corresponding task status codes are as follows:

[0097]

[0098] When the scheduling task processes the business model, it involves conditions such as anomaly duration and anomaly times. These status information need to be persisted through Redis. In the embodiment, for the management of anomaly duration and anomaly times, Redis key-value pairs are used for persistence: where k is the key name, usually a combination of the warning ID and the task type, and v is the previous status value, such as the previous anomaly occurrence time and anomaly times.

[0099] As Figure 4 shown, after creating the task, the task can be managed in batches. Let the set of all tasks to be managed in the scheduling engine be T = {t1, t2,..., tn}, where tn represents the nth task. Select the operation o ∈ O to be executed, such as o = start, o = stop, or o = delete. Apply the operation o to each task tj ∈ S in the selected subset S to form a new task set T' = A(S, o); when the operation o = start, the task status in S is updated to "started"; when the operation o = stop, the task status in S is updated to "stopped"; when the operation o = delete, the tasks in S are removed from T.

[0100] Step 4.3: Heterogeneous task distribution and business model collaborative analysis.

[0101] After the heterogeneous tasks are dispatched, the data model and the rule model can be executed independently. When the data model and the rules are analyzed collaboratively, it is necessary to verify their applicability. Check whether the tags in the rule configuration exist in the model configuration parameters. The matching process can be expressed as: M = m(C, T), where C represents the model parameter dictionary, T represents the rule configuration dictionary, and M represents the matching result (True or False). When the matching result is True, the task is dispatched for execution; when the matching result is False, the task is not dispatched, and a matching failure status message is returned.

[0102] Step 5: The execution result of the business model is saved in the db2 database, and the latest result data is pushed to the digital twin 3D model using the kafka message queue.

[0103] Step 6: As Figure 5 shown, the digital twin constructed in this embodiment, the digital twin 3D model receives and verifies the dynamic data transmitted from the background, and restores the inherent attributes of the product. The dynamic data includes two parts: one part is the point data collected in real time, which is used to drive the physical attributes of the product, such as speed, temperature, pressure, etc.; the other part is the execution result of the business model, which is used to predict the potential state of the product, such as whether there are potential anomalies, future change trends, fault diagnosis results, etc.

[0104] The present invention also provides a model management and collaborative analysis system based on digital twin. The model management and collaborative analysis system based on digital twin can be implemented by executing the process steps of the model management and collaborative analysis method based on digital twin. That is, those skilled in the art can understand the model management and collaborative analysis method based on digital twin as the preferred implementation manner of the model management and collaborative analysis system based on digital twin.

[0105] Those skilled in the art know that in addition to implementing the system and its various devices, modules, and units provided by the present invention in the form of pure computer-readable program code, the method steps can be logically programmed to make the system and its various devices, modules, and units provided by the present invention be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers to achieve the same function. Therefore, the system and its various devices, modules, and units provided by the present invention can be regarded as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be regarded as the structure within the hardware component; the devices, modules, and units for implementing various functions can also be regarded as both software modules for implementing the method and the structure within the hardware component.

[0106] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. Without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A model management and collaborative analysis method based on digital twins, characterized in that: include: Step S1: construct a digital twin 3D model of the target object; Step S2: collecting target object point data at a fixed frequency and saving it in the IHD real-time database; Step S3: construct different business models, including: data model and rule model; Step S4: Select a business model according to the task requirements, and calculate the corresponding business indicators based on the point data in the IHD real-time database; Step S5: Push the calculated business indicators to the digital twin 3D model; Step S6: The digital twin 3D model predicts whether the product has abnormalities, future change trends, and fault diagnosis results based on the collected point data of the target object and the received business indicators; The data model is a mathematical or statistical model built based on point data for business objectives, which realizes the organic organization, analysis, prediction and anomaly detection of data; The rule model is based on a Boolean function, and obtains an alarm state based on the rule according to the point data of the target object.

2. The model management and collaborative analysis method based on digital twins according to claim 1 is characterized in that: The step S2 includes: collecting target object point data from the PLC or WinCC system at a fixed frequency and saving it in a data table of the IHD real-time database; based on the IHD real-time database, obtaining real-time data and historical data of the point through the http interface; the structure of the data table includes: sensor ID, timestamp, measurement value, status code; wherein the status code indicates the quality of data collection; The target object point data includes: the physical properties of the driven target object, including: speed, temperature and pressure.

3. The model management and collaborative analysis method based on digital twins according to claim 1 is characterized in that: The step S3 comprises: Step S3.1: Construct a data model management system, including: data model classification management, data model path configuration, data model dynamic loading and loading status management; The data model classification management is to classify the data models using a dictionary structure, and establish a mapping relationship between configuration parameters and detection functions for each type of data model; different data models construct different parameter configurations; The data model path configuration is to obtain the weight file paths of different types of data models based on the configuration parameters of each type of data model, and store the obtained paths in the corresponding dictionary structure; The data model is dynamically loaded by checking the model path defined in the configuration file, loading the formulated data model detection function, and loading the trained weight file based on the detection function; The loading state management is to record the loading and unloading state of the data model through the state dictionary; Step S3.2: Build a rule model management system, including: rule definition, trigger configuration, rule parsing and logical operations; The trigger configuration includes: a time-driven mechanism, an event-driven mechanism, and a driving mechanism combining events and time; The rule analysis and the logic operation are to analyze the rules and trigger configurations in the rule model, and execute corresponding tasks based on the rule judgment function.

4. The model management and collaborative analysis method based on digital twins according to claim 1, characterized in that: The step S4 comprises: Step S4.1: Build a system status management system, including: initialization management when the server starts, including: current status check, log recording, exception handling and global status update; Step S4.2: Construct a multi-task scheduling management system, including: task definition, task status management, task parameter persistence and task batch management; Step S4.3: Heterogeneous tasks are issued. According to different task requirements, the corresponding data model, rule model, or data model and rule model are called to calculate the corresponding business indicators based on the point data in the IHD real-time database.

5. The model management and collaborative analysis method based on digital twins according to claim 1 is characterized in that: The step S5 includes: storing the calculated business indicators in the db2 database, and then using the kafka message queue to push the latest business indicators to the digital twin 3D model.

6. A model management and collaborative analysis system based on digital twins, characterized in that: include: Module M1: Build a digital twin 3D model of the target object; Module M2: collects target object point data at a fixed frequency and saves it in the IHD real-time database; Module M3: Build different business models, including data model and rule model; Module M4: Select the business model according to the task requirements and calculate the corresponding business indicators based on the point data in the IHD real-time database; Module M5: Push the calculated business indicators to the digital twin 3D model; Module M6: The digital twin 3D model predicts whether the product has abnormalities, future change trends, and fault diagnosis results based on the collected point data of the target object and the received business indicators; The data model is a mathematical or statistical model built based on point data for business objectives, which realizes the organic organization, analysis, prediction and anomaly detection of data; The rule model is based on a Boolean function, and obtains an alarm state based on the rule according to the point data of the target object.

7. The digital twin-based model management and collaborative analysis system according to claim 6, characterized in that: The module M2 includes: collecting target object point data from the PLC or WinCC system at a fixed frequency and saving it in the data table of the IHD real-time database; based on the IHD real-time database, obtaining the real-time data and historical data of the point through the http interface; the structure of the data table includes: sensor ID, timestamp, measurement value, status code; wherein the status code indicates the quality of data collection; The target object point data includes: the physical properties of the driven target object, including: speed, temperature and pressure.

8. The digital twin-based model management and collaborative analysis system according to claim 6, characterized in that: The module M3 comprises: Module M3.1: Build a data model management system, including: data model classification management, data model path configuration, data model dynamic loading and loading status management; The data model classification management is to classify the data models using a dictionary structure, and establish a mapping relationship between configuration parameters and detection functions for each type of data model; different data models construct different parameter configurations; The data model path configuration is to obtain the weight file paths of different types of data models based on the configuration parameters of each type of data model, and store the obtained paths in the corresponding dictionary structure; The data model is dynamically loaded by checking the model path defined in the configuration file, loading the formulated data model detection function, and loading the trained weight file based on the detection function; The loading state management is to record the loading and unloading state of the data model through the state dictionary; Module M3.2: Build a rule model management system, including: rule definition, trigger configuration, rule parsing and logical operations; The trigger configuration includes: a time-driven mechanism, an event-driven mechanism, and a driving mechanism combining events and time; The rule analysis and the logic operation are to analyze the rules and trigger configurations in the rule model, and execute corresponding tasks based on the rule judgment function.

9. The digital twin-based model management and collaborative analysis system according to claim 6, characterized in that: The module M4 comprises: Module M4.1: Build a system status management system, including: initialization management when the server starts, including: current status check, logging, exception handling and global status update; Module M4.2: Build a multi-task scheduling management system, including: task definition, task status management, task parameter persistence and task batch management; Module M4.3: Heterogeneous tasks are issued. According to different task requirements, the corresponding data model, rule model, or data model and rule model are called to calculate the corresponding business indicators based on the point data in the IHD real-time database.

10. The digital twin-based model management and collaborative analysis system according to claim 6, characterized in that: The module M5 includes: storing the calculated business indicators in the db2 database, and then using the kafka message queue to push the latest business indicators to the digital twin 3D model.

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