A system integration rehearsal verification method and platform

Abstract

This invention relates to a system integration exercise and verification method and platform. The platform includes a platform end and a client end. The platform end includes: a core simulation carrier subsystem for constructing a reconfigurable physical model and its digital twin simulation model; a disaster simulation driving subsystem for integrated driving of the physical model to generate effects and synchronously driving the digital model simulation; a high-precision metrological benchmark subsystem for providing benchmark measurement data to verify the accuracy of the client's sensing system; and a central processing and evaluation subsystem for automated performance assessment. Through the coordinated operation of the above platforms, this method achieves quantitative verification and graded evaluation of the end-to-end performance of the client's system under verification in a simulated disaster environment, encompassing perception, decision-making, and cross-domain collaboration. This invention solves the problem of lacking practical and standardized verification benchmarks after complex system integration in existing technologies, providing a key tool for reliability assessment, technical capability grading, and healthy industrial development of various IoT sensing and decision-making systems in smart cities, resilient cities, and other fields.

Description

Technical Field

[0001] This invention relates to the fields of system simulation, testing and verification, and performance evaluation. Specifically, it relates to an integrated exercise and verification method and platform that supports "physical-digital" hybrid simulation and cross-system interaction. It is particularly suitable for quantitative verification and graded evaluation of the cross-domain collaborative performance of various sensing systems, decision-making systems, and other complex systems in smart cities, resilient cities, and other complex systems. Background Technology

[0002] In the wave of smart city and resilient city construction, IoT monitoring systems have become the cornerstone of ensuring the safety of urban infrastructure. However, the huge investment has not been fully transformed into reliable security capabilities. The core bottleneck lies in the lack of authoritative means to verify the true effectiveness of the system, leading to a dilemma of "emphasizing construction but neglecting practical results" in technological development.

[0003] The current technological system in the field of smart city security monitoring faces a profound lack of validation in three dimensions: First, there is a significant gap between the "laboratory accuracy" and the "field accuracy" of sensors. The calibration certificates provided by sensor manufacturers are typically valid only in ideal laboratory environments. Once deployed in real, complex, and constantly changing infrastructure environments (such as bridges or tunnels subjected to vibration, temperature differences, humidity, and electromagnetic interference), the accuracy, stability, and reliability of their long-term monitoring data become a "black box." Existing acceptance and maintenance processes cannot continuously and traceably evaluate the effective accuracy of sensors in actual service environments, leading to doubts about the reliability of monitoring data. Early warnings and decisions based on this data are like "building a tower on sand."

[0004] Second, there is a lack of verifiable benchmarks for structural safety evolution and damage early warning. The transition of infrastructure from a healthy state to damage is a complex process influenced by multiple coupled factors. While current technologies can monitor parameters such as strain and displacement, they cannot accurately answer the two core safety questions: "What is the remaining lifespan of the structure?" or "When will critical failure occur?" This is not entirely due to outdated sensing technology, but rather the lack of a standardized verification method that can correlate specific structural failure modes with the response characteristics of monitoring systems. Whether existing systems can issue warnings early and accurately enough before damage occurs, and the effectiveness of their warning logic (algorithm), cannot be repeatedly and comparablely verified on real structures.

[0005] Third, the lack of a comprehensive technical capability evaluation system hinders healthy competition and technological iteration within the industry. The current market lacks a clear and universally accepted standard for classifying technical capabilities, similar to the "Level 1-Level 5 autonomous driving" system. A monitoring system that can only visualize data is often treated vaguely in procurement and evaluation compared to an intelligent system that can provide accurate early warnings, automatic analysis, and trigger cross-departmental collaboration. This "indiscriminate" approach makes it difficult for industry competition to focus on substantial improvements in technical capabilities. It also prevents developers from matching products with appropriate technical levels based on actual risk levels, leading to misallocation of resources or wasted efficiency.

[0006] The aforementioned lack of verification is particularly prominent and urgent in the field of security monitoring for smart cities. However, the core contradiction it exposes—the lack of practical and standardized verification benchmarks for system effectiveness—is also widespread in many complex scenarios requiring deep multi-system collaboration and decision optimization, such as urban planning simulations, infrastructure operation and maintenance, public safety emergency drills, and even ecological environment governance (e.g., blue-green space resilience simulation). These scenarios collectively face the challenge of objectively and quantitatively assessing the reliability of heterogeneous system integration, the effectiveness of strategic logic, and the smoothness of cross-domain collaboration beforehand.

[0007] Therefore, to solve this series of interconnected systemic problems, we cannot rely on isolated improvements to any single point in the sensor, algorithm, or process. Instead, we must build a unified, high-fidelity integrated verification benchmark platform. This platform must be able to simultaneously create three indispensable verification conditions: (1) reproduce disturbances in complex field environments that affect sensor accuracy (such as structural vibration and local temperature changes); (2) safely, repeatably, and quantifiably induce the entire progressive process from microscopic damage to macroscopic destruction, thereby providing real physical input and judgment scales for the effectiveness of early warning and decision-making algorithms; and (3) provide traceable physical metrological benchmarks for data comparison in this dynamic environment. However, there is currently no integrated technical solution that can deeply integrate the above elements. Existing methods either focus on pure digital simulation and deviate from physical reality, or are limited to single-point physical testing and cannot evaluate system synergy, or cannot reflect the impact of complex field environments on test results. None of these can meet the urgent need for "real-world" comprehensive performance calibration of complex systems.

[0008] This invention is proposed to build such a universal, high-fidelity integrated verification benchmark platform, aiming to fundamentally solve the common system performance verification problem across the above-mentioned fields, and to make a breakthrough and provide practical demonstration with smart city security monitoring as a key application scenario. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a system integration exercise and verification method and platform to solve the common technical problem in smart cities, critical infrastructure, and other fields where there is a lack of end-to-end, quantifiable, and repeatable verification benchmarks for the cross-domain collaborative effectiveness of IoT sensing systems, business decision-making systems, and other systems. Specifically, it aims to solve: The problem of the inability to objectively verify the "effective accuracy in the field" of sensors: to bridge the gap between laboratory calibration and complex service environments, and to provide a quantitative evaluation method for synchronizing and tracing the comparison of sensors under simulated real-world environmental disturbances.

[0010] The lack of a validation "benchmark" for system risk warning and damage assessment capabilities: Provide a method that can safely and repeatedly induce the progressive physical process from damage initiation to structural failure, providing an objective physical verification benchmark for the accuracy and timeliness of warning algorithms and the effectiveness of damage assessment models.

[0011] The problem of the inability to quantify, grade, and compare the effectiveness of technology: Establish a standardized system effectiveness grading and evaluation system based on multi-dimensional verification data to provide objective and transparent evaluation criteria for technology selection, industrial competition, and continuous iteration, and guide the industry to shift from "relationship competition" to "technology effectiveness competition".

[0012] To achieve the above objectives, the present invention adopts the following technical solution: a system integration exercise and verification method, based on a modular exercise and verification platform, used to verify the perception and decision-making effectiveness of at least one target operating system; the exercise and verification platform includes a platform terminal and an accessible client terminal; the method includes the following steps: S1: In response to the verification command for the target operating system, construct a reconfigurable physical model corresponding to the target operating system on the platform and simultaneously generate its digital twin simulation model. S2: Connect the client's system to be verified to the platform. The system to be verified includes at least a customized perception system and a customized business decision system that responds based on perception data. S3: Through the disaster simulation driving subsystem on the platform, the reconfigurable physical model is driven to generate physical effects in an integrated manner according to the preset disaster scenario script related to the target operating system, and the digital twin simulation model is driven to perform simulation calculations simultaneously. S4: Through the high-precision measurement benchmark subsystem on the platform, the benchmark physical quantity data of the reconfigurable physical model under simulated disaster is collected synchronously and compared with the sensing data of the customized sensing system; S5: The customized business decision-making system generates decision instructions based on the perception data of the customized perception system; S6: Through the central processing and evaluation subsystem of the platform, at least the comparison results of step S4 and the decision instructions and response time of step S5 are combined to generate an automated evaluation result for the system to be verified.

[0013] Furthermore, the disaster simulation drive subsystem simulates physical effects through a scalable composite actuation mechanism; the composite actuation mechanism includes one, two, or all three of the following components: Servo actuators are used to apply mechanical loads to structural components to simulate tension, compression, bending moment, or displacement (including static and dynamic forces, respectively). The fluid control module is used to simulate changes in the state of the fluid network; Distributed actuators are used to control the start and stop of scene equipment, object detachment, partial structural loss, or the opening and closing of environmental elements. The disaster simulation driving subsystem prioritizes driving the reconfigurable physical model to reproduce disaster effects that can be simulated physically; for disaster effects that cannot or are difficult to reproduce physically, the digital twin simulation model performs simulation calculations to assess the applicability of the system to be verified in a wider range of disaster scenarios.

[0014] Furthermore, the high-precision metrology benchmark subsystem ensures metrology benchmark through the following steps: selecting traceable benchmark sensors; establishing an internal metrology calibration chain within the platform; and aligning the data collected by the benchmark sensors and the sensors of the customized sensing system through a time synchronization mechanism during drills, and calculating the sensing error index based on a comparison algorithm.

[0015] Furthermore, the customized sensing system and the high-precision metrology reference subsystem together constitute a heterogeneous fusion sensing network. The heterogeneous fusion sensing network is connected to the platform through a unified data and service access interface that supports multi-protocol conversion, and performs time synchronization, spatial registration and feature-level fusion processing in the data fusion server on the platform.

[0016] Furthermore, the digital twin simulation model is a computable model that is dynamically mapped to the reconfigurable physical model. It receives real-time data from the heterogeneous fusion sensing network and updates its own state, while feeding back the simulation calculation results to the disaster simulation driving subsystem to dynamically adjust the physical simulation parameters and form a verification closed loop.

[0017] The present invention also provides a system integration exercise and verification platform for implementing the above method, including a platform terminal, wherein the platform terminal includes: The core simulation carrier subsystem is used to carry the reconfigurable physical model and its corresponding digital twin simulation model, which are constructed through modular components. A disaster simulation driving subsystem is used to drive the reconfigurable physical model and the digital twin simulation model in an integrated manner. A high-precision metrology reference subsystem is used to provide reference measurement data during disaster simulations; The central processing and evaluation subsystem is used to control the exercise process, aggregate data, and conduct performance evaluations. The client is used to integrate the customized perception system and the customized business decision system to be verified; A unified data and service access interface is used to connect the client to the platform.

[0018] Furthermore, the core simulation carrier subsystem includes a modular reconfigurable substrate system, which can quickly construct reconfigurable physical models corresponding to different types of operating systems by replacing or combining multiple different dedicated functional modules. The dedicated functional modules in the modular reconfigurable substrate system are constructed with pre-set weakening or deformable materials that differ from the prototype structure at the predetermined damage locations. Driven by the disaster simulation driving subsystem, observable physical damage effects can be generated at these locations. These physical damage effects can be recovered after the driving is deactivated or quickly reset by module replacement, thereby enabling repeated verification of the damage monitoring sensitivity of the customized sensing system.

[0019] Furthermore, the disaster simulation drive subsystem is also equipped with a standardized actuation interface library for integrating dedicated simulation devices that match different types of operating systems.

[0020] Furthermore, the platform also includes an external system simulation subsystem for simulating related systems outside the logical boundary of the target operating system; the external system simulation subsystem injects simulated cross-domain interactive data streams into the customized business decision-making system according to the disaster scenario script; the decision instructions of the customized business decision-making system are further generated based on the cross-domain interactive data streams; the evaluation of the central processing and evaluation subsystem further includes a cross-domain collaborative effectiveness evaluation of the decisions made by the customized business decision-making system based on the cross-domain interactive data streams.

[0021] Furthermore, the central processing and evaluation subsystem evaluates the performance of the system to be verified by classifying it based on multiple preset evaluation dimensions and thresholds, and outputs level information reflecting its intelligence level. The central processing and evaluation subsystem includes an evaluation knowledge base, which is pre-set with evaluation indicators and grading rules corresponding to different types of operating systems. The different categories of operating systems include infrastructure and public space systems such as urban communities, large commercial complexes, underground spaces, bridges, tunnels, roads, underground pipe networks, transportation hubs, and river, lake, and reservoir systems.

[0022] Compared with the prior art, the present invention has the following beneficial effects: 1. Achieved reliable measurement of "on-site accuracy": By using the high-precision measurement benchmark subsystem (130) to synchronously compare with the customer's perception system under simulated real environmental disturbances, the sensor's "laboratory accuracy" verification was upgraded to "on-site effective accuracy" evaluation, which fundamentally solved the core bottleneck of the reliability of monitoring data and provided a reliable data foundation for decision-making.

[0023] 2. Provides a physical verification benchmark for progressive damage: Through the modular reconfigurable substrate system (113) and its two dedicated functional modules, it can safely and economically test the basic early warning capability of the system with the "preset weakened material module" and verify the accuracy of the system's judgment on actual damage evolution and remaining bearing capacity with the "real material module" with high fidelity, providing an irreplaceable physical verification environment for early warning and evaluation algorithms.

[0024] 3. A clearly oriented capability grading system has been established: Through the tiered, capability-oriented verification process and grading assessment implemented by the central processing and evaluation subsystem (150), a clear "capability map" of the tested system can be drawn, not only providing a grading conclusion but also accurately locating technical shortcomings. This provides a scientific quantitative basis for product performance benchmarking, technology iteration direction, and industry procurement standards, and powerfully drives industrial technological progress.

[0025] 4. It has high versatility and scalability: The platform adopts a decoupled and modular architecture design. By replacing different dedicated functional modules (113a), disaster simulation devices and assessment knowledge base rules, it can quickly adapt to the verification needs of a wide range of objects, from bridges, tunnels, and pipelines to resilient communities and river and lake ecosystems. It achieves the economy and efficiency of "one platform, multiple verifications" and has broad industry application prospects. Attached Figure Description

[0026] Figure 1 This is an overall block diagram of the system integration drill and verification platform provided in the embodiments of the present invention.

[0027] Figure 2 This is a schematic diagram of the hierarchical verification of system integration drills in this embodiment of the invention.

[0028] Figure 3 This is a schematic diagram of the core architecture and data interaction of the platform in this embodiment of the invention.

[0029] Figure 4 This is a flowchart illustrating the system integration drill and verification method provided in this embodiment of the invention.

[0030] Figure 5This is a schematic diagram of the modular reconfigurable substrate system and two key modules in an embodiment of the present invention.

[0031] Figure 6 This is a schematic diagram of a specific application scenario in an embodiment of the present invention, taking bridge grade verification as an example. Detailed Implementation

[0032] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0033] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0034] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0035] This invention provides a system integration exercise and verification method and platform, the core of which lies in constructing a general verification environment that is a hybrid "physical-digital" system, a closed-loop data synchronization system, and a quantifiable and graded effectiveness system.

[0036] The system integration exercise and verification method is executed based on a modular exercise and verification platform 10, and is used to verify the perception and decision-making effectiveness of at least one target operating system. The exercise and verification platform 10 includes a platform terminal 100 and an accessible client terminal 200. The specific implementation steps of the method are as follows.

[0037] S1: In response to the verification command for the target operating system, a reconfigurable physical model 111 corresponding to the target operating system is constructed on the platform 100, and its digital twin simulation model 112 is generated simultaneously.

[0038] S2: Connect the system to be verified of the client 200 to the platform 100. The system to be verified includes at least a customized perception system 210 and a customized business decision system 220 that responds based on perception data.

[0039] S3: Through the disaster simulation driving subsystem 120 of the platform terminal 100, the reconfigurable physical model 111 is driven to generate physical effects in an integrated manner according to the preset disaster scenario script related to the target operating system, and the digital twin simulation model 112 is driven to perform simulation calculations simultaneously.

[0040] The disaster simulation drive subsystem 120 simulates physical effects through an expandable composite actuation mechanism 121. The composite actuation mechanism 121 can be one, any combination of two, or all three of the following components: a servo actuator 121a for applying mechanical loads to structural components to simulate tension, pressure, bending moment, or displacement (including static and dynamic forces, respectively); a fluid control module 121b for simulating changes in the state of a fluid network; and a distributed actuator 121c for controlling the start and stop of scene equipment, object detachment, local structural defects, or the opening and closing of environmental elements.

[0041] The disaster simulation driving subsystem 120 prioritizes driving the reconfigurable physical model 111 to reproduce disaster effects that can be simulated by physical means; for disaster effects that cannot or are difficult to reproduce by physical means, the digital twin simulation model 112 performs simulation calculations to form a "physical-digital" hybrid verification environment, thereby realizing the applicability assessment of the system to be verified under a wider range of disaster scenarios.

[0042] S4: Through the high-precision measurement benchmark subsystem 130 of the platform terminal 100, the benchmark physical quantity data of the reconfigurable physical model 111 under simulated disaster is collected synchronously and compared with the sensing data of the customized sensing system 210.

[0043] The high-precision metrology reference subsystem 130 ensures the metrology reference through the following steps: selecting a traceable reference sensor 131; establishing an internal metrology calibration chain within the platform; and aligning the data collected by the reference sensor 131 and the sensors of the customized sensing system 210 through a time synchronization mechanism during the exercise, and calculating the sensing error index based on the comparison algorithm.

[0044] S5: The customized business decision system 220 generates decision instructions based on the perception data of the customized perception system 210.

[0045] The customized sensing system 210 and the high-precision metrology reference subsystem 130 together constitute the heterogeneous fusion sensing network 300. The heterogeneous fusion sensing network 300 is connected to the platform 100 through the unified data and service access interface 400 that supports multi-protocol conversion, and time synchronization, spatial registration and feature-level fusion processing are performed in the data fusion server 151 of the platform 100.

[0046] The digital twin simulation model 112 is a computable model that is dynamically mapped to the reconfigurable physical model 111. It receives real-time data from the heterogeneous fusion sensing network 300 and updates its own state. At the same time, it feeds back the simulation calculation results to the disaster simulation driving subsystem 120 to dynamically adjust the physical simulation parameters and form a verification closed loop.

[0047] S6: Through the central processing and evaluation subsystem 150 of the platform 100, at least the comparison results of step S4 and the decision instructions and response time of step S5 are combined to generate an automated evaluation result for the system to be verified.

[0048] like Figure 1 As shown, the system integration and verification platform includes a platform terminal 100, a client terminal 200, and a unified data and service access interface 400. The platform terminal 100 includes a core simulation carrier subsystem 110, a disaster simulation driving subsystem 120, a high-precision metrological benchmark subsystem 130, and a central processing and evaluation subsystem 150. The client terminal 200 is used to integrate the customized perception system 210 and the customized business decision-making system 220 to be verified. The unified data and service access interface 400 is used to connect the client terminal 200 to the platform terminal 100.

[0049] The core simulation carrier subsystem 110 is used to carry the reconfigurable physical model 111 constructed through modular components and its corresponding digital twin simulation model 112.

[0050] The core simulation carrier subsystem 110 includes a modular reconfigurable substrate system 113, which can quickly construct reconfigurable physical models 111 corresponding to different types of operating systems by replacing or combining multiple different dedicated functional modules 113a. The dedicated functional modules 113a in the modular reconfigurable substrate system 113 are constructed using pre-set weakening or deformable materials different from the prototype structure at predetermined damage locations. Driven by the disaster simulation drive subsystem 120, observable physical damage effects can be generated at these locations, and these physical damage effects can be recovered after the drive is released or quickly reset by module replacement, thereby enabling repeated verification of the damage monitoring sensitivity of the customized sensing system 210.

[0051] The dedicated functional module includes at least two module types: a) a module containing preset weakening materials, used to efficiently and repeatably verify the system's basic perception and alarm sensitivity to damage signals (corresponding to L1-L3 level capability verification); b) a miniature module made of real materials consistent with the prototype, used to realistically simulate the nonlinear process of material damage to verify the system's damage assessment and residual bearing capacity judgment capabilities (corresponding to L4-L5 level capability verification).

[0052] The disaster simulation drive subsystem 120 is used to drive the reconfigurable physical model 111 and the digital twin simulation model 112 in an integrated manner. The disaster simulation drive subsystem 120 also has a standardized actuation interface library for integrating dedicated simulation devices that are compatible with different types of operating systems.

[0053] The high-precision metrology reference subsystem 130 is used to provide reference measurement data during simulated disasters.

[0054] The central processing and evaluation subsystem 150 is used to control the exercise process, aggregate data, and conduct performance evaluations.

[0055] The central processing and evaluation subsystem 150 evaluates the performance of the system to be verified by classifying it based on multiple preset evaluation dimensions and thresholds, and outputs level information reflecting its intelligence level.

[0056] The central processing and evaluation subsystem 150 has a built-in evaluation knowledge base 152 with evaluation indicators and grading rules corresponding to different types of operating systems. It can automatically grade and evaluate the effectiveness of the verification system according to the preset tiered capability standards (e.g., L1-L5 levels) that are directly linked to engineering safety, and output a comprehensive report including capability deficiency analysis.

[0057] Platform 100 may also optionally include an external system simulation subsystem 140, which is used to simulate related systems outside the logical boundary of the target operating system. The external system simulation subsystem 140 injects simulated cross-domain interactive data streams into the customized business decision-making system 220 according to the disaster scenario script; the decision instructions of the customized business decision-making system 220 are further generated based on the cross-domain interactive data streams; the evaluation of the central processing and evaluation subsystem 150 further includes a cross-domain collaborative effectiveness evaluation of the decisions made by the customized business decision-making system 220 based on the cross-domain interactive data streams.

[0058] The different types of operating systems include, but are not limited to: infrastructure and public space systems such as urban communities, large commercial complexes, underground spaces, bridges, tunnels, roads, underground pipe networks, transportation hubs, and river, lake, and reservoir systems.

[0059] The following is combined Figure 1-6 The specific implementation of the technical solution of the present invention will be further described in detail.

[0060] 1. Platform Overall Architecture and Interaction Implementation Methods See Figure 1 This demonstrates the overall system composition of the exercise and verification platform (10) provided by the present invention. The platform is logically and physically divided into two core parts: the platform end (100) and the client end (200).

[0061] The platform (100) is a fixed, standardized verification benchmark facility that integrates all the core subsystems used to generate a controlled verification environment, perform precise measurements, and conduct comprehensive evaluations. For example... Figure 1As shown, it mainly includes a core simulation carrier subsystem (110), a disaster simulation driving subsystem (120), a high-precision measurement benchmark subsystem (130), an external system simulation subsystem (140), and a central processing and evaluation subsystem (150). These subsystems are interconnected through an internal high-speed data network and are uniformly scheduled and controlled by the central processing and evaluation subsystem (150).

[0062] The client (200) represents the target system to be verified, which is usually provided by the user. It includes at least a customized sensing system (210) (such as various sensor networks deployed on infrastructure) and a customized business decision system (220) (such as a monitoring and early warning platform, emergency command software, etc.). The client (200) can be in the form of a physical hardware device or a pure software system.

[0063] The unified data and service access interface (400) is key to achieving "plug and play". This interface defines standardized physical connectors, communication protocols (such as OPC UA, MQTT, HTTP / API, etc.) and data formats. The client (200) accesses the platform (100) through this interface, and its perceived data and decision commands are seamlessly integrated into the platform's verification process. At the same time, it receives simulation commands and feedback from the platform, forming a complete verification loop.

[0064] 2. Specific implementation methods of core subsystems (e.g.) Figure 2 , Figure 3 , Figure 4 (As shown) 2.1 Implementation of the core simulation carrier subsystem (110) This subsystem is responsible for carrying the physical entity of the verification object and its digital image. Its innovative implementation is reflected in "modular reconfigurability" and "physical-digital consistency".

[0065] Implementation of the reconfigurable physics model (111): See Figure 5 Its construction relies on a modular, reconfigurable baseboard system (113). This system is a rigid platform with standardized gridded mounting holes, unified power supply, and data bus. Users can quickly assemble the target operating system (such as a community, bridge, commercial area, subway tunnel, river, lake, or reservoir) by selecting the corresponding modules (113a-n, e.g., pier module 113a-1, bridge module 113a-2, tunnel segment module 113a-3, frame building 113a-4, and old brick-concrete building 113a-5) from a dedicated functional module library. These modules are designed on a scale (including 1:1 scale) according to similarity theory, retaining the geometric features and key structural details of the prototype.

[0066] Key Implementation of Recoverable Damage Verification: To support repeated, quantitative testing of the sensitivity of the monitoring system's damage warning system, some dedicated functional modules (such as 113a-2) integrate a "pre-defined weakened material zone" in the designed vulnerable areas (potential damage areas of the simulated prototype). This zone is constructed using materials with mechanical properties drastically different from those of the prototype material; for example, a brittle resin with a specific formulation simulates the sudden cracking of concrete, or highly elastic rubber simulates the large deformation after yielding of steel components. When subjected to loads from the disaster simulation driving subsystem (120), this zone produces controllable, visually or instrumentally observable physical damage effects (such as cracking and buckling). Once the load is removed, this damage effect can be "reset" through material elastic recovery or rapid replacement of the module, thereby achieving low-cost, high-efficiency repeated verification. This design directly addresses the problem in the background technology of "lack of a verifiable benchmark for damage warning."

[0067] Implementation of the digital twin simulation model (112): Based on the accurate 3D scanning data and material parameters of the physical model, a high-fidelity computable model is constructed in a simulation engine (such as ANSYS, UnitySim, etc.). This model maintains consistency with the physical model (111) in terms of geometry, physical properties, and state mapping. It receives data from the physical model's sensors and simulation scripts in real time to perform simulation calculations in dynamics, fluid mechanics, thermodynamics, etc., to reproduce disaster effects that are difficult or impossible to simulate physically (such as large-scale seismic wave propagation and flood inundation processes), thereby expanding the coverage of the verification scenarios.

[0068] 2.2 Implementation of the Disaster Simulation Driven Subsystem (120) This subsystem is the "power source" that stimulates physical effects and drives digital simulation. Its key lies in "precise controllability" and "synchronous driving".

[0069] Implementation of the composite actuation mechanism (121): The system integrates multiple high-precision actuation units and manages them uniformly through a standardized actuation interface library (122).

[0070] Servo actuator (121a): Employs an electric or electro-hydraulic servo system, which can apply precisely programmed force or displacement loads to structural modules to simulate static loading, fatigue cycles, vehicle and ship impact force time histories, seismic acceleration waves, etc.

[0071] Fluid control module (121b): Composed of a precision variable frequency pump, regulating valve, flow meter and pipeline, it is used to accurately reproduce hydraulic and water quality processes such as pressure fluctuations, leakage flow and pollutant concentration changes in the pipeline network model.

[0072] Distributed actuators (121c): including linear motors, electromagnets, igniters, sprinkler heads, etc., are used to simulate discrete or environmental events such as objects falling from heights, sudden equipment failures, local fire ignition, and simulated rainfall.

[0073] Physical-digital synchronization drive implementation: See Figures 2-4 The central processing and assessment subsystem (150) distributes a unified "disaster scenario script" to the disaster simulation driving subsystem (120). This subsystem controls the composite actuation mechanism (121) to apply physical loads to the physical model (111); on the other hand, it synchronously sends identical load parameters and boundary conditions to the digital twin simulation model (112) through a real-time interface, driving it to perform parallel simulation calculations. This "integrated driving" ensures the consistency of spatiotemporal evolution between the physical world and the digital world during the verification process, laying the foundation for core data comparison and fusion analysis.

[0074] 2.3 Implementation of the High-Precision Metrological Reference Subsystem (130) This subsystem provides a reliable "metric" for the entire verification process, directly addressing the "black box" problem of sensor field accuracy in the background technology.

[0075] Deployment and traceability of reference sensors (131): At key measurement points of the physical model (111), rigorously calibrated reference sensors (131) are installed alongside the sensors of the customized sensing system (210). These reference sensors (such as laser displacement gauges, high-precision strain gauges, and standard pressure transmitters) are sent to national legal metrology institutions or laboratories with CNAS accreditation for calibration before being put into use and periodically to ensure that their measurement uncertainty is known and that their overall performance indicators (such as accuracy, linearity, and long-term stability) are significantly better than those of the customer sensors to be validated.

[0076] Synchronization Comparison and Error Analysis Implementation: During the exercise, the platform synchronized all data acquisition devices (including the reference sensor 131 and the customer sensor 210) at the hardware level using a high-precision hardware clock. The data acquisition unit of the high-precision metrology reference subsystem (130) synchronously recorded the reference data and the customer's original data. After the exercise, a dedicated data alignment and comparison algorithm was used to calculate quantitative indicators such as the measurement error, signal-to-noise ratio, response delay, and nonlinearity of the customer sensor relative to the reference sensor on a time-stamp basis, generating an objective "Sensor Field Accuracy Verification Report".

[0077] 2.4 Implementation of the Central Processing and Evaluation Subsystem (150) and External System Simulation (140) These two subsystems work together to achieve cross-domain collaborative verification and intelligent hierarchical evaluation.

[0078] Cross-domain interaction simulation implementation: The external system simulation subsystem (140) is a software-defined virtual system generator. Users can configure the communication protocols, data formats, and interaction logic of the "external related systems" (such as the superior emergency platform and parallel operation systems) to be simulated according to the verification scenario. In the exercise, the subsystem dynamically generates and injects simulated data streams (such as simulated traffic control instructions, simulated power outage alarms, and simulated risk information in adjacent areas) that conform to the scenario into the customized business decision system (220) to test its decision-making and coordination capabilities under the impact of multi-source information.

[0079] The core implementation of the hierarchical assessment: The central processing and assessment subsystem (150) is the "brain" of performance evaluation. Its built-in assessment knowledge base (152) pre-sets assessment indicator systems and hierarchical rule bases for different application areas (such as bridges, tunnels, and pipelines). Figure 4 As shown in the process flow, this subsystem aggregates end-to-end, multi-dimensional, and time-series-labeled verification data from metrological comparisons, physical simulations, digital simulations, and cross-domain interactions. Subsequently, it invokes rule models from the knowledge base to automatically calculate scores for each indicator and, based on preset capability level thresholds (e.g., drawing on the L1-L5 concept, defining five levels from "no environmental impact perception" to "fully autonomous safety decision-making"), ultimately generates a structured "System Comprehensive Performance Level Evaluation Report." This report not only provides the overall level but also details the scores and shortcomings of each sub-indicator, offering direct and quantitative basis for technological improvements and procurement selection.

[0080] 3. Specific Application Example 1 (Cross-system integrated exercise and verification platform and method for intelligent bridge maintenance and emergency response) To more systematically and meticulously illustrate how this invention achieves full-chain, step-by-step quantitative verification of the bridge monitoring system's capabilities "from basic perception to safety prediction," this embodiment uses a progressive disaster scenario—"the entire process of a bridge under vehicle load, from normal operation, overloading, damage, to eventual destruction"—as an example, and employs a redefined five-level (L1-L5) capability evaluation system for illustration.

[0081] 3.1 Preparation and Model Configuration Phase: On the modular reconfigurable baseboard system (113) of the platform (100), the operator selects and assembles the corresponding physical model from the dedicated functional module library according to the bridge type of the target verification bridge. In this embodiment, a physical model (111) of a beam bridge is constructed using the pier module (113a-1) and the main beam module (113a-2).

[0082] Key model configuration strategy: The verification platform of this invention can flexibly configure the model according to the verification target, which is an important manifestation of its versatility. Validation of Levels L1-L3 (Sensing and Load Recognition): To test the system's sensitivity in monitoring the "failure process," a dedicated sub-module made of a pre-defined weakened material (such as a brittle composite material with a specific ratio) can be integrated into the bottom mid-span region of the main beam module (113a-2). This material exhibits well-defined mechanical behavior but has significantly lower strength than the prototype. It is used to generate clear and repeatable physical failure signals under controlled loads to verify whether the monitoring system can "see" and "clearly perceive" the occurrence and development of failure.

[0083] For validating Levels L4-L5 (damage assessment and safety prediction): When a realistic assessment of the system's ability to judge damage, stiffness degradation, and remaining load-bearing capacity of actual bridge materials is required, the aforementioned preset weakening module needs to be replaced with a module made of miniature materials proportioned according to the similarity law of actual bridge materials (such as concrete and steel). This module can realistically simulate the entire nonlinear process of elasticity, plasticity, and even failure of the prototype material, providing a physical basis for high-fidelity damage state assessment.

[0084] Simultaneously, a digital twin simulation model (112) of the bridge is generated in the simulation platform, whose material constitutive relationship accurately covers the entire process from elasticity and plasticity to failure. The client "Smart Bridge Management System" to be verified is connected to the platform as a client (200), and its sensor network (210) and core algorithm software (220) are connected to a unified interface (400).

[0085] 3.2 Exercise Execution and Tiered Verification Phase: The engineer loads and initiates the graded verification script for "Progressive Response to Heavy Traffic Guidance" on the central processing and evaluation subsystem (150). The entire process is automatically controlled by the platform, which synchronously collects data and performs online comparative analysis.

[0086] Phase 1: Load effect perception verification (assessment of L1 and L2 levels) Verification objective: To assess the system's accuracy in perceiving the bridge's response (effect) under known standard loads.

[0087] Implementation process: The disaster simulation drive subsystem (120) controls the servo actuator (121a) to apply a series of precisely known force / displacement loads to the bridge model according to the standard vehicle load spectrum. The sensors (131) of the high-precision measurement reference subsystem (130) and the customer sensors (210) synchronously collect strain, deflection and other effect data of key sections.

[0088] Grading determination: Level L1 (Basic Environmental Awareness): The platform automatically calculates the error between the load effect (such as maximum strain) measured by the customer's sensors and the benchmark value. If the error is less than or equal to a preset threshold, it is determined that Level L1 has been achieved.

[0089] Level 2 (Complex Environment Awareness): Based on Level 1, considering the impact of complex environments on test accuracy, if the error is ≤ a certain preset threshold, it is determined to have reached Level 2.

[0090] Phase Two: Load Back Calculation and Identification Verification (Level L3 Assessment) Verification objective: To assess the system's ability to reverse-calculate the load on the bridge under unknown loads and accurately determine whether it is overloaded.

[0091] Implementation process: The script simulates the passage of a heavy vehicle of unknown weight. The platform records the actual load effect measured by the reference sensor (131). The customer system needs to use an algorithm to calculate the vehicle load based on the data from its own sensor (210).

[0092] Level determination: The platform compares the load value calculated by the customer's system with the actual load value calibrated by the actuator control parameters and reference data. If the error of the calculated load is less than or equal to a certain preset threshold and can correctly trigger the overload warning, it is determined to reach Level L3 (load identification).

[0093] Phase 3: Damage status monitoring and residual bearing capacity assessment verification (Level L4 assessment) Verification objective: To assess the system's ability to not only monitor damage after real material damage occurs in the structure, but also to quantitatively evaluate the structure's remaining safety reserve (remaining load-bearing capacity). This stage must be verified using a model module consistent with the materials of the actual bridge.

[0094] Implementation Process: After replacing the model module, the script switches to a loading program simulating long-term overload or fatigue. As the load increases, the beam module (113a-2) made of real materials will enter a nonlinear stage, producing real damage such as microcracks and stiffness degradation. The client's system needs to identify the damage based on changes in monitoring data and calculate the estimated remaining load-bearing capacity.

[0095] Grading determination: The platform compares the remaining bearing capacity assessed by the customer system with the theoretical remaining bearing capacity calculated by the digital twin model (112) based on the constitutive model of real materials. If the errors of load effect monitoring, load identification and remaining bearing capacity assessment are all ≤ a certain preset threshold, then it is determined to reach level L4 (damage assessment).

[0096] Phase 4: Comprehensive safety assessment and verification based on load-bearing capacity comparison (Level L5 rating) Verification objective: To assess the system's ability to make ultimate safety judgments, namely, to compare the "current and predicted loads" with the "current remaining structural bearing capacity" in real time and to accurately warn whether the structure has entered a dangerous state.

[0097] Implementation process: Continue loading onto the realistic material model. The client system needs to continuously execute L3 (real-time load back-calculation) and L4 (real-time bearing capacity assessment) tasks, integrating various possible failure modes and performing dynamic comparisons. When the system determines that "load > bearing capacity," it should issue a final warning of impending failure.

[0098] Level Assessment: The platform accurately records the moment the client's system issues the final warning, along with the estimated load and bearing capacity at that time. When the model ultimately fails, the actual failure load is obtained. By comparing the accuracy of the warning with the prediction error of the failure load, if the combined error is ≤ a certain preset threshold, it is determined to reach Level L5 (safety prediction).

[0099] 3.3 Comprehensive Assessment and Capability Map Generation Stage: The central processing and evaluation subsystem (150) summarizes all the quantitative results of the above four stages and generates a "System Capability Classification Verification Report" based on the pre-set bridge level 5 capability standards in the evaluation knowledge base (152).

[0100] Example of report conclusions: "Based on the full-process, step-by-step verification of 'progressive response to heavy traffic guidance,' the capabilities of the bridge's intelligent management and maintenance system are assessed as follows:" L1 / L2 Level (Load Effect Sensing): Meets L2 level standards. Strain measurement error under standard load is 3.2% (≤5%).

[0101] Level L3 (Load Recognition): Meets the standard. The load calculation error for overloaded vehicles is 7.1%, and the overload alarm is accurate.

[0102] Level L4 (Damage and Load Capacity Assessment): Not met. In actual material model testing, the identification delay for stiffness degradation was high, and the remaining load capacity assessment error was 18.5% (>10%). This indicates a deviation between the damage model and actual conditions.

[0103] Level 5 (Comprehensive Security Prediction): Not covered. Due to insufficient Level 4 capabilities, effective Level 5 verification could not be conducted.

[0104] Comprehensive Capability Profile and Improvement Guidelines: The system demonstrates reliability in data acquisition and conventional load identification (L1-L3), and has practical value. However, its core weakness lies in the lack of an accurate damage mechanics model, which prevents the quantitative assessment and early warning of the structure's true safety status (L4). It is recommended to focus on developing structural nonlinear analysis algorithms and damage index fusion technologies.

[0105] The current overall system capability is positioned as Level 3 (a monitoring system with load identification capabilities). This embodiment fully demonstrates the core value of the platform of this invention: it is not only a testing tool, but also a capability diagnosis and evolution guidance system. Through modular model configuration and a tiered verification process, it can clearly define the "capability boundaries" of a technology and clearly point out the specific technical bottlenecks that need to be overcome to cross the next capability level, thereby guiding industry competition and technological progress onto the right track of solving practical engineering safety problems.

[0106] 4. Specific Application Example 2: Cross-system integrated exercise and verification platform and method for resilient communities with multi-hazard scenarios To demonstrate the versatility and scalability of the platform of this invention, this embodiment uses a typical urban community as the verification object to simulate the system performance evaluation under a multi-hazard coupling scenario. This community unit includes high-rise frame buildings, multi-story brick-concrete buildings, micro-road networks, and lifeline infrastructure such as water supply, drainage, and gas pipelines.

[0107] 4.1 Model Construction and Configuration On the modular reconfigurable baseboard system (113) at the platform end (100), the operator selects and assembles the following modules from the community functional module library (113b) according to the physical characteristics of the target community: Building structure modules: These include a first-class structural module (113b-1) simulating a high-rise frame building and a second-class structural module (113b-2) simulating a multi-story brick-concrete building. To verify the sensitivity of the monitoring system to damage, a pre-set weakening material strip made of brittle resin is embedded in the weak areas of the wall of the second-class module. This area can generate controllable cracks under load, and can be quickly replaced by the module after unloading to achieve reset.

[0108] Lifeline Pipeline Module (113b-3): Made of transparent pipes, filled with colored liquid, and integrated with miniature valves and flow meters, it is used to simulate normal operation and leakage scenarios of water supply, drainage and gas pipeline networks.

[0109] Road base module (113b-4): integrates a micro road grid and parking space model, and is equipped with a movable cover plate that can simulate local collapse.

[0110] Meanwhile, a digital twin simulation model (112) of the community is generated in the simulation environment. Its geometric and physical properties are accurately mapped to the scaled model, and it can simulate disaster effects that are difficult to physically reproduce, such as flooding and toxic gas diffusion.

[0111] 4.2 Multi-hazard scenario script design The central processing and assessment subsystem (150) loads a pre-defined "community multi-hazard coupling scenario script", which includes the following sequential stages: Phase 1: Earthquake Trigger: The disaster simulation drive subsystem (120) applies a dynamic load of simulated seismic waves to the building module through a servo actuator (121a), which simultaneously induces vibration and local damage to the building structure.

[0112] Phase Two: Secondary Fire: An earthquake causes a short circuit in the wiring inside a high-rise building. The distributed actuator (121c) triggers a controllable igniter, simulating an indoor fire and releasing non-toxic smoke.

[0113] Phase 3: Pipeline Leakage: An earthquake causes a rupture in a destructible section of the gas pipeline module. The fluid control module (121b) adjusts the pressure to simulate a gas leak.

[0114] Phase 4: Flooding and Road Disruption: Heavy rain causes excessive load on the drainage network. The movable cover of the base module opens to simulate road collapse, while the digital twin model calculates the depth and spread of water accumulation.

[0115] 4.3 Verification Execution and Data Acquisition The “Community Smart Safety Management System” to be verified is used as a client (200) to access the platform. Its customized perception system (210) includes various sensors (such as strain gauges, displacement gauges, smoke detectors, pressure transmitters, and water level gauges) deployed in buildings, pipelines, and roads, and the customized business decision system (220) is community emergency command software.

[0116] After the exercise was launched, the disaster simulation driving subsystem (120) automatically drove the physical model to generate the aforementioned disaster effects according to the script sequence, and simultaneously drove the digital twin model to perform parallel simulation. The reference sensor (131) of the high-precision measurement reference subsystem (130) synchronously collected key physical quantities (such as structural strain, displacement, temperature, pressure, and water level) with the customer's sensors. The external system simulation subsystem (140) simultaneously injected simulated cross-domain data streams into the customer's decision-making system, including surrounding road control information released by the urban traffic command platform and material dispatchability information from the regional emergency material warehouse, to test the system's collaborative capabilities in a real emergency environment.

[0117] 4.4 Data Comparison and Grading Assessment The central processing and evaluation subsystem (150) aggregates all data and performs the following analyses: Sensing accuracy verification: Compare customer sensor data with benchmark data time-stamped, calculate the measurement error under environmental disturbances (vibration, temperature rise), and generate a sensor field accuracy report.

[0118] Decision-making effectiveness evaluation: Record the timeliness and accuracy of the response to warnings, contingency plans, and requests for external support issued by the customer's decision-making system.

[0119] Cross-domain collaboration compliance: This examines whether the instructions output by the decision-making system follow the preset emergency collaboration protocol (such as the interface format with the urban transportation platform) and the delay in information exchange.

[0120] Based on a pre-built community resilience assessment knowledge base (152), the system automatically generates tiered assessment results, using a step-by-step capability level representation: Level L1 (Basic Environmental Sensing): Under basic environmental disturbances, the sensing error of the physical quantity of a single disaster (such as an earthquake) is ≤ a certain preset threshold, thus enabling local alarm. Level 2 (Complex Environment Perception): Under complex environmental disturbances, the perception error is ≤ a certain preset threshold, and the disaster type can be distinguished; Level L3 (Disaster Source Identification): In coupled disasters, it can accurately identify the source of the disaster (such as determining the location of a leaking gas pipeline) and its scale, with an error ≤ a certain preset threshold; Level 4 (Impact Preparation and Response): Can predict the extent of a disaster’s impact on critical community functions (such as evacuation numbers and areas without water). Level 5 (Dynamic Chain Evolution Prediction): It can dynamically predict the evolution of disaster chains (such as fire spread paths) based on real-time data and optimize resource scheduling strategies.

[0121] 4.5 Significance of the Implementation Examples This embodiment fully verifies the versatility and effectiveness of the platform of the present invention at the community level and in multi-hazard coupled scenarios. Through modular model rapid reconstruction, automatic disaster simulation, accuracy comparison under environmental disturbances, cross-domain data injection, and quantitative hierarchical evaluation, the platform provides a standardized system performance testing tool for the construction of smart communities and resilient cities.

[0122] 5. Specific Application Example 3: Cross-system integration exercise and verification platform and method for integrated monitoring and sensing of rivers, lakes and reservoirs To fully demonstrate the versatility and scalability of the platform in the water resources and water conservancy field, this embodiment uses a typical river, lake, and reservoir system as the verification object to simulate the performance evaluation of the monitoring system under a multi-hazard coupled scenario, including flood evolution, water pollution, and dam leakage. This verification scenario covers rivers, lakes, reservoirs, and their ancillary facilities (dikes, gates, water intakes, etc.), aiming to test the sensing accuracy, early warning timeliness, and cross-departmental collaborative capabilities of the "Integrated River, Lake, and Reservoir Monitoring and Sensing System" in complex hydrological environments.

[0123] 5.1 Model Construction and Configuration In the core simulation carrier subsystem (110) of the platform (100), a reconfigurable physical model (111) corresponding to the target river, lake, and reservoir is constructed using a modular reconfigurable substrate system (113) and a dedicated functional module library (113c). Specific modules include: River and lake / reservoir topography module: Made with high-precision topography sculpting technology, it simulates the meandering shape of typical rivers, the depth variation of lakes, and the reservoir capacity characteristics. The module surface is covered with simulated riverbed substrate material and can be embedded with micro water level logging wells.

[0124] The dam and gate module includes scaled-down structures simulating earth-rock dams and concrete dams, as well as miniature electric gate and spillway models. To verify the sensitivity of seepage monitoring, a seepage simulation zone composed of permeable materials or microporous structures is preset in the dam module. This zone can produce observable seepage effects under controlled water pressure and can be reset by replacing the module after unloading.

[0125] Hydrological and water quality monitoring station module: An integrated mounting base for miniature water level gauges, flow meters, and multi-parameter water quality sensors (pH, dissolved oxygen, turbidity, conductivity). The sensors use standard interfaces for easy and quick replacement and calibration.

[0126] Pipeline connection module: simulates the connection between rivers, lakes, and reservoirs and upstream and downstream water supply and drainage networks, and is connected to the fluid control module through micro valves.

[0127] Simultaneously, a digital twin simulation model of the river, lake and reservoir is generated in the simulation environment (112). Its geometric and hydrodynamic parameters are accurately mapped to the scaled model, and it can calculate complex physical phenomena such as flood inundation range and pollutant transport and diffusion process based on hydrological and hydrodynamic models (such as MIKE 21, SWMM, etc.).

[0128] 5.2 Multi-hazard scenario script design The central processing and assessment subsystem (150) loads a pre-defined "multi-hazard coupling scenario script for rivers, lakes and reservoirs", which may contain the following single or combined stages: Phase 1: Upstream flood evolution: The disaster simulation drive subsystem (120) adjusts the upstream water flow through the fluid control module (121b) to simulate the evolution of floods of different frequencies (such as once in 20 years and once in 100 years), and at the same time applies hydrostatic pressure loads generated by water level changes to the dam module through the servo actuator (121a).

[0129] Phase Two: Sudden Water Pollution: Tracers (such as food coloring or harmless chemicals) are injected at preset locations via distributed actuators (121c) to simulate water pollution caused by industrial wastewater leaks or hazardous materials transportation accidents. At the same time, the digital twin model calculates the pollutant diffusion concentration field and arrival time in parallel.

[0130] Phase 3: Dam Leakage and Piping: By adjusting the permeability coefficient of the pre-set seepage zone in the dam module, the development process of seepage or piping in the dam foundation is simulated, and data such as seepage flow and soil deformation are collected simultaneously.

[0131] Phase 4: Multi-hazard coupling: such as the superimposed diffusion of pollutants during flood processes, or the increased leakage of dikes caused by high water levels, which tests the monitoring system's ability to identify complex scenarios.

[0132] 5.3 Verification Execution and Data Acquisition The "Integrated Monitoring and Sensing System for Rivers, Lakes and Reservoirs" to be verified is connected to the platform as a client (200). Its customized sensing system (210) includes water level gauges, flow meters, water quality sensors, pressure gauges, deformation sensors, etc. deployed on key sections of the model; the customized business decision system (220) is a flood control and drought relief command system or a river and lake management platform, which is responsible for data aggregation, early warning release and dispatch instruction generation.

[0133] After the exercise was launched, the disaster simulation driving subsystem (120) automatically drove the physical model to generate the above-mentioned hydrological, water quality, and structural effects according to the script sequence, and simultaneously drove the digital twin model to perform parallel simulation. The reference sensor (131) of the high-precision measurement reference subsystem (130) was installed alongside the customer's sensor to synchronously collect reference physical quantities such as water level, flow rate, water quality concentration, seepage pressure, and displacement. The external system simulation subsystem (140) simultaneously injected simulated cross-domain data streams into the customer's decision-making system, including rainfall forecasts issued by the meteorological department, dispatch instructions from the superior flood control command center, and water intake requirements of downstream water plants, in order to test the system's collaborative decision-making capabilities in a real emergency environment.

[0134] 5.4 Data Comparison and Grading Assessment The central processing and evaluation subsystem (150) aggregates all data and performs the following analyses: Sensing accuracy verification: Compare customer sensor data with benchmark data time-stamped, calculate measurement error under dynamic hydrological environment (such as water level fluctuation, flow velocity change), and generate sensor on-site accuracy report.

[0135] Event identification timeliness: Record the time delay and accuracy from the occurrence of a disaster to the customer system issuing an early warning (such as flood exceeding the warning level, pollutant exceeding the standard, leakage alarm).

[0136] Decision-making effectiveness assessment: Evaluate the rationality and timeliness of scheduling instructions (such as gate opening adjustment and downstream early warning issuance) generated by the customer system.

[0137] Cross-domain collaboration compliance: Verify whether the data exchange between the client system and the simulated external system complies with standard protocols (such as the Hydrological Monitoring Data Communication Protocol SL651-2014 and the Flood Control and Drought Relief Command System Interface Specification), and the consistency of collaborative responses.

[0138] Based on a pre-built knowledge base for river, lake, and reservoir performance evaluation (152), the system automatically generates hierarchical evaluation results, using a tiered capability level representation: Level L1 (Basic Environmental Sensing): Under basic environmental conditions, the sensing error of a single physical quantity (such as water level or flow rate) is ≤ a certain preset threshold, enabling local monitoring data reporting; Level 2 (Complex Environment Perception): In complex environments, the perception error is ≤ a certain preset threshold, and it can distinguish different hydrological events (such as distinguishing between rainfall runoff and upstream water). Level L3 (Event Identification and Early Warning): It can accurately identify the occurrence of flood processes, pollution events or leaks, and the early warning lead time meets the preset threshold (such as issuing a pollution arrival warning 30 minutes in advance), and the false alarm rate is ≤ a certain preset threshold. Level L4 (Impact Prediction and Scheduling): Can predict the scope of disaster impact (such as flooded areas, pollution spread trajectory) based on real-time monitoring data, with prediction error ≤ a certain preset threshold; Level L5 (Adaptive Optimization and Coordination): It can dynamically predict the evolution of disaster chains (such as water quality deterioration and escalation of dike risks caused by floods) under multi-hazard coupling scenarios, and adaptively coordinate upstream and downstream and cross-departmental emergency resources to achieve optimal regional water security resilience.

[0139] 5.5 Significance of the Implementation Examples This embodiment fully verifies the versatility and effectiveness of the platform of this invention in the field of integrated monitoring of rivers, lakes, and reservoirs. Through rapid reconstruction of modular terrain models, coupled simulation of multiple hydrological and water quality processes, accuracy comparison under environmental disturbances, cross-departmental data injection, and quantitative hierarchical evaluation, the platform provides a system-level performance verification tool for the construction of smart water conservancy and digital twin watersheds. This further proves that the invention can be widely applied to the integrated verification of various complex systems, including but not limited to urban water affairs, watershed flood control, and water environment management scenarios.

[0140] 6. Extended Explanation The exercise and verification platform provided by this invention is not limited to structural safety monitoring. By replacing different dedicated functional modules (113a), configuring corresponding disaster simulation devices (122a), and configuring the assessment knowledge base (152), this platform can be seamlessly applied to a wide range of fields, such as emergency verification of leaks and bursts in urban underground integrated pipe corridors, multi-hazard coupled scenario simulation of resilient communities, resilience assessment of urban transportation networks under emergencies, and testing of water quality early warning systems for dams in river, lake, and reservoir systems. This versatility design ensures the broad scope of protection and long-term value of this invention as a fundamental patent.

[0141] This embodiment also provides a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the above-described method.

[0142] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0143] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0144] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0145] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0146] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A system integration drill and verification method, characterized in that, The method is executed based on the exercise verification platform (10) and is used to verify the perception and decision-making effectiveness of at least one target operating system; the exercise verification platform (10) includes a platform terminal (100) and an accessible client terminal (200); the method includes the following steps: S1: In response to the verification command for the target operating system, a reconfigurable physical model (111) corresponding to the target operating system is constructed on the platform (100), and its digital twin simulation model (112) is generated simultaneously. S2: Connect the system to be verified of the client (200) to the platform (100), wherein the system to be verified includes at least a customized perception system (210) and a customized business decision system (220) that responds based on perception data. S3: Through the disaster simulation driving subsystem (120) of the platform (100), the reconfigurable physical model (111) is driven to generate physical effects in an integrated manner according to the preset disaster scenario script related to the target operating system, and the digital twin simulation model (112) is driven to perform simulation calculations in a synchronous manner. S4: Through the high-precision measurement benchmark subsystem (130) of the platform (100), the benchmark physical quantity data of the reconfigurable physical model (111) under simulated disaster is collected synchronously and compared with the sensing data of the customized sensing system (210); S5: The customized business decision system (220) generates decision instructions based on the perception data of the customized perception system (210); S6: Through the central processing and evaluation subsystem (150) of the platform (100), at least the comparison results of step S4 and the decision instructions and response time of step S5 are combined to generate an automated evaluation result of the system to be verified.

2. The system integration drill and verification method according to claim 1, characterized in that, The disaster simulation drive subsystem (120) simulates physical effects through a scalable composite actuation mechanism (121); the composite actuation mechanism (121) includes one, two, or all three of the following components: Servo actuator (121a) is used to apply mechanical loads to structural components to simulate tension, compression, bending moment or displacement; The fluid control module (121b) is used to simulate changes in the state of the fluid network; Distributed actuators (121c) are used to control the start and stop of scene devices, object detachment, partial structural loss, or the opening and closing of environmental elements. The disaster simulation driving subsystem (120) prioritizes driving the reconfigurable physical model (111) to reproduce disaster effects that can be simulated by physical means; for disaster effects that cannot or are difficult to reproduce by physical means, the digital twin simulation model (112) performs simulation calculations to achieve an assessment of the applicability of the system to be verified in a wider range of disaster scenarios.

3. The system integration drill and verification method according to claim 1, characterized in that, The high-precision metrology reference subsystem (130) ensures the metrology reference through the following steps: selecting a traceable reference sensor (131); establishing an internal metrology calibration chain within the platform; and aligning the data collected by the reference sensor (131) and the sensor of the customized sensing system (210) through a time synchronization mechanism during the exercise, and calculating the sensing error index based on the comparison algorithm.

4. The system integration drill and verification method according to claim 1, characterized in that, The customized sensing system (210) and the high-precision metrology reference subsystem (130) together constitute a heterogeneous fusion sensing network (300). The heterogeneous fusion sensing network (300) is connected to the platform (100) through a unified data and service access interface (400) that supports multi-protocol conversion, and performs time synchronization, spatial registration and feature-level fusion processing in the data fusion server (151) of the platform (100).

5. The system integration drill and verification method according to claim 4, characterized in that, The digital twin simulation model (112) is a computable model that is dynamically mapped to the reconfigurable physical model (111). It receives real-time data from the heterogeneous fusion sensing network (300) and updates its own state. At the same time, it feeds back the simulation calculation results to the disaster simulation driving subsystem (120) to dynamically adjust the physical simulation parameters and form a verification closed loop.

6. A system integration training and verification platform for implementing the method as described in any one of claims 1-5, characterized in that, Includes a platform (100), which includes: The core simulation carrier subsystem (110) is used to carry the reconfigurable physical model (111) constructed through modular components and its corresponding digital twin simulation model (112). The disaster simulation driving subsystem (120) is used to drive the reconfigurable physical model (111) and the digital twin simulation model (112) in an integrated manner. A high-precision metrological reference subsystem (130) is used to provide reference measurement data during simulated disasters; The central processing and evaluation subsystem (150) is used to control the exercise process, aggregate data and conduct performance evaluation; Client (200) is used to integrate the customer-aware system (210) to be verified and the customer-determined business decision system (220). A unified data and service access interface (400) is used to connect the client (200) to the platform (100).

7. The system integration drill and verification platform according to claim 6, characterized in that, The core simulation carrier subsystem (110) includes a modular reconfigurable substrate system (113), which can quickly construct reconfigurable physical models (111) corresponding to different types of operating systems by replacing or combining multiple different dedicated functional modules (113a). The dedicated functional module (113a) in the modular reconfigurable substrate system (113) is made of a pre-set weakening material or deformable material that is different from the prototype structure at the predetermined damage location; through the drive of the disaster simulation drive subsystem (120), an observable physical damage effect can be generated at the location, and the physical damage effect can be recovered after the drive is released or quickly reset by module replacement, so as to realize repeated verification of the damage monitoring sensitivity of the customized sensing system (210).

8. The system integration drill and verification platform according to claim 6, characterized in that, The disaster simulation drive subsystem (120) is also equipped with a standardized actuation interface library for integrating dedicated simulation devices that match different types of operating systems.

9. The system integration drill and verification platform according to claim 6, characterized in that, The platform (100) also includes an external system simulation subsystem (140) for simulating related systems outside the logical boundary of the target operating system; the external system simulation subsystem (140) injects simulated cross-domain interactive data streams into the customized business decision system (220) according to the disaster scenario script; the decision instructions of the customized business decision system (220) are further generated based on the cross-domain interactive data streams; the evaluation of the central processing and evaluation subsystem (150) further includes cross-domain collaborative effectiveness evaluation of the decisions made by the customized business decision system (220) based on the cross-domain interactive data streams.

10. The system integration drill and verification platform according to claim 6, characterized in that, The central processing and evaluation subsystem (150) evaluates the performance of the system to be verified by classifying it based on multiple preset evaluation dimensions and thresholds, and outputs level information reflecting its intelligence level. The central processing and evaluation subsystem (150) includes an evaluation knowledge base (152), which is pre-set with evaluation indicators and grading rules corresponding to different types of operating systems. The different categories of operating systems include infrastructure and public space systems such as urban communities, large commercial complexes, underground spaces, bridges, tunnels, roads, underground pipe networks, transportation hubs, and river, lake, and reservoir systems.

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