A smart cloud warehouse risk evolution evaluation system and method based on digital twinning

Abstract

This invention relates to the field of highway toll station operation and maintenance management technology, specifically to a digital twin-based smart cloud warehouse risk evolution assessment system and method. The digital twin-based smart cloud warehouse risk evolution assessment system includes four functional layers: a perception and execution layer, a twin base layer, an evolution assessment layer, and a strategy feedback layer. This invention, through a toll collection spatiotemporal constraint violation judgment mechanism and a risk ripple evolution model, can proactively identify and warn of "implicit logical break" risks. Furthermore, this invention uses shadow executors to perform prior rehearsals of instructions issued to physical entities within the twin space, eliminating control uncertainties caused by network latency. Combined with a self-healing configuration interface, it dynamically adjusts edge node operating parameters or reconstructs business paths, ensuring accurate execution of control signaling within the physical safety window. This reduces security risks such as vehicle damage from toll gate collisions and toll fee loss in smart cloud warehouses, and improves the physical determinism and security of remote operations in smart cloud warehouses.

Description

Technical Field

[0001] This invention relates to the field of highway toll station operation and maintenance management technology, and in particular to a smart cloud warehouse risk evolution assessment system and method based on digital twins. Background Technology

[0002] In the highway sector, "smart cloud warehouse" (also often referred to as "regional smart cloud warehouse") is a new highway toll collection and operation management model based on cloud computing, artificial intelligence, and big data technologies. It completely changes the traditional decentralized operation mode of "one station, one booth, one person, one post" at toll stations, and realizes remote, intensive, unmanned, and intelligent management of toll collection business.

[0003] Existing "smart cloud warehouses," such as the Chinese patent application CN121415478A (published under CN121415478A), describe a configurable rapid management method and system for toll plazas. This system achieves standardized access to toll station electromechanical equipment and configurable deployment of business logic by constructing digital models and management catalogs for lane terminal equipment. This solution utilizes a virtualized management platform to logically define lane-level intelligent units and station-level intelligent units, enabling heterogeneous hardware to be recognized and controlled by the cloud platform through standard interfaces.

[0004] In a smart cloud warehouse environment, the charging process is a strict time-series chain consisting of nodes such as antenna identification, fee calculation, signaling interaction, and gate control, which has extremely low tolerance for network micro-latency.

[0005] Under the remote and centralized operation architecture of the smart cloud warehouse, the remote control center needs to conduct real-time interactive control of on-site peripherals across the wide area network. However, the transmission process of the control signal sent by the remote control center through the wide area network is highly uncertain, while the vehicles at the toll collection site are in a dynamic driving state, and their physical spatial location is strongly correlated with the timeliness of business processing.

[0006] However, existing "smart cloud warehouse" operation and maintenance monitoring methods mainly focus on physical layer indicators such as equipment online status, network connectivity, and hardware resource load. They fail to establish a deep coupling between physical operating parameters and the spatiotemporal constraints of toll collection services. They cannot map the sub-health jitter of physical links (such as instantaneous packet loss or increased RTT) into the timing deviation risk in the toll collection service chain in real time. This makes it impossible for smart cloud warehouses to predict the break in business logic before physical indicators trigger alarms, and it is difficult to solve problems such as decreased toll collection success rate and insufficient certainty in toll audit caused by timing failures. In addition, existing "smart cloud warehouse" operation and maintenance monitoring methods lack effective pre-simulation of the execution results of control signaling in asymmetric latency environment. Wide area network transmission quality is affected by fluctuations of multiple factors. When the remote control command issued by the remote control center exceeds the business spatiotemporal safety threshold due to network latency, it is easy to cause a misalignment between the execution time of the command and the actual physical spatial location of the vehicle. In extreme cases, this can easily lead to safety accidents such as collision with barriers and vehicle damage or illegal passage, which seriously restricts the safety and consistency of remote operation. Summary of the Invention

[0007] The purpose of this invention is to overcome the problems of disconnect between physical status monitoring and billing business logic, and insufficient security of remote control in existing smart cloud warehouse operation and maintenance supervision technologies, and to provide a smart cloud warehouse risk evolution assessment system and method based on digital twins.

[0008] In a first aspect, this invention provides a smart cloud warehouse risk evolution assessment system based on digital twins, comprising: a perception and execution layer, a twin base layer, an evolution assessment layer, and a strategy feedback layer. The perception and execution layer extracts underlying operational metadata of electromechanical equipment in real time by constructing a distributed edge perception matrix. The twin base layer constructs a digital twin model that synchronously maps the physical toll plaza based on the configured digital model and logical graph of the smart cloud warehouse, mapping the metadata to business state nodes with semantic information. The evolution assessment layer integrates a toll spatiotemporal constraint violation judgment mechanism and, based on a risk analysis algorithm of spatiotemporal causal chains, treats the physical environment jitter mapped in real time by the twin base layer as a disturbance variable, quantifying the degree of intrusion of the physical environment jitter on toll certainty through a nonlinear mapping mechanism. The strategy feedback layer includes: a shadow executor and a self-healing configuration interface. The shadow executor performs a priori rehearsals of the instructions issued to the physical entity within the twin space to verify the security and timeliness of remote operations in the current nondeterministic network environment. If the verification is successful, the self-healing configuration interface dynamically adjusts the operating parameters of the edge nodes or reconstructs the service path via the configuration channel.

[0009] According to a preferred embodiment, the metadata extracted in real time by the perception execution layer includes: device electrical parameters, heartbeat packet latency, operational fingerprint metadata of electromechanical equipment, round-trip latency and jitter frequency of the underlying operational metadata network link of electromechanical equipment, and the perception execution layer injects timestamps into key signaling in the toll service flow.

[0010] According to a preferred embodiment, the twin base layer includes: an input unit for receiving the metadata; a digital twin unit for establishing a digital twin model; and a data synchronization unit for dynamically injecting the collected physical metadata into the real-time attribute parameters of the corresponding twin node in the digital twin model, thereby establishing a two-way binding between the real-time data stream of the perception and execution layer and the attributes of the digital twin model.

[0011] The digital twin unit includes a dynamic activation module, an automatic assembly module, and a state machine injection module. The dynamic activation module accesses the configuration catalog database of the managed platform in the metadata, parses predefined standardized digital models of equipment, and retrieves matching component instances from the metadata database based on the list of field-deployed equipment in the metadata, generating twin node entities in the digital twin model. The automatic assembly module, based on the actual physical layout of the lanes and the business logic connection relationship, uses a topology engine to automatically assemble the virtual plaza, establishing spatial coordinate relationships and logical cascading paths between managed nodes in a three-dimensional virtual space. The state machine injection module injects logical calculation operators corresponding to its function into each twin node, enabling the virtual model to simulate real business behavior.

[0012] According to a preferred embodiment, the state machine injection module includes a state machine modeling submodule and a protocol injection submodule. The state machine modeling submodule defines each managed node within the lane as a protocol entity with logical processing capabilities; it formally describes each twin node using a quintuple. The quintuple includes: a set of business states, a set of input events, a state transition function, an initial business state, and a set of termination states. The protocol injection submodule, by parsing the protocol template issued by the configured management platform, encapsulates the timing constraints, logical priorities, and exception jump branches of the toll collection service into logical code blocks, and dynamically deploys them into the computing kernel of the virtual node.

[0013] According to a preferred embodiment, the twin base layer also utilizes dynamic calibration and simulation technology of response time to establish a response time function for each hardware node that is managed through configuration; and the twin base layer uses the timestamp data continuously reported by the perception execution layer to perform online learning and real-time calibration of the response time function, thereby realizing dynamic calibration of the response delay baseline.

[0014] According to a preferred embodiment, the evolutionary evaluation layer includes: a violation determination unit, a risk quantification unit, and an evolutionary output unit. The violation determination unit, as the executing entity of the evolutionary evaluation layer for determining prior business logic breaks, uses a violation determination mechanism based on toll collection spatiotemporal constraints to deeply couple lane physical geometric parameters with dynamic traffic timing to determine business determinism. The risk quantification unit, when the violation determination unit triggers a violation signal, initiates a risk ripple evolution model to quantify the nonlinear impact of physical faults on business continuity, generating business layer indicators, operational layer indicators, and physical layer indicators. The evolutionary output unit performs spatiotemporal coupling on the business layer indicators, operational layer indicators, and physical layer indicators to generate a full risk map of the smart cloud warehouse.

[0015] According to a preferred embodiment, for a specific vehicle entering the lane, the violation determination unit calculates the time cutoff time of the vehicle speed variable, calculates the cutoff time of each business link in real time based on the kinematic model, and adjusts the time delay tolerance according to different vehicle speeds.

[0016] According to a preferred embodiment, the shadow executor includes: a coupling prediction unit and a security discrimination unit. The coupling prediction unit predicts the expected time when the instruction will arrive at the physical end by retrieving the WAN link status monitored in real time by the perception execution layer. The security discrimination unit determines the validity of instruction execution using a security discrimination criterion based on spatiotemporal conflict.

[0017] According to a preferred embodiment, when the evolutionary evaluation performs an evolutionary evaluation or the shadow actuator performs a shadow pre-simulation, the digital twin model calls the response time function to generate a predicted latency that conforms to the current physical state, thereby realizing function-based business timing simulation.

[0018] According to a preferred embodiment, the policy feedback layer further includes a time delay adaptive compensation unit, which uses feedforward compensation and edge autonomy switching mechanism to ensure the determinism of control command execution and the security of physical entities.

[0019] According to a preferred embodiment, the strategy feedback layer further includes a predictive maintenance unit. The predictive maintenance unit utilizes the digital twin model to couple the degradation of the underlying hardware physical performance with the spatiotemporal constraints of the upper-layer services. By monitoring the microscopic shifts in the hardware's operational fingerprint, it predicts the degree of impact of performance degradation on service determinism.

[0020] According to a preferred embodiment, the self-healing configuration interface can also dynamically reconfigure the lane intelligent unit's configuration logic based on the smart cloud warehouse's full risk map.

[0021] In a first aspect, the present invention provides a method for risk evolution assessment of smart cloud warehouses based on digital twins, which uses the smart cloud warehouse risk evolution assessment system based on digital twins provided by the present invention to assess the risks in the operation of smart cloud warehouses.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0023] This invention provides a digital twin-based smart cloud warehouse risk evolution assessment system. Relying on a digital twin model infused with tolling protocol genes and a risk ripple evolution model, it quantifies the nonlinear impact of physical layer disturbances (such as network packet loss and micro-level latency) on tolling timing, passage success rate, and agent load. This allows for proactive identification and early warning of "hidden logic breakage" risks before physical failures cause business disruptions, achieving a technological leap from monitoring single physical indicators to deterministic assessment of deep business logic. Furthermore, this invention uses a shadow executor to perform prior rehearsals of instructions issued to physical entities within the twin space, effectively eliminating control uncertainties caused by wide area network asymmetric latency. This ensures accurate execution of control signaling within the physical security window, significantly reducing security risks such as vehicle collisions, toll fee losses, and virtual-physical state discrepancies caused by signaling delays during remote management of smart cloud warehouses. This greatly enhances the physical determinism and security of remote smart cloud warehouse operations. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the architecture of a smart cloud warehouse risk evolution assessment system based on digital twins.

[0025] Figure 2 This is a schematic diagram of the dynamic activation and virtual-physical binding process of the configuration model.

[0026] Figure 3 Flowchart of prior verification logic for shadow space instructions. Detailed Implementation

[0027] The present invention will now be described in further detail with reference to specific embodiments. However, this should not be construed as limiting the scope of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0028] Unless otherwise specified, the terms "upper," "lower," "left," "right," "center," "inner," and "outer," etc., used in the description of specific embodiments of the present invention to indicate orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings, or the orientation or positional relationship in which the product / equipment / device is usually placed during use. These terms are merely for the purpose of facilitating the description of the present invention or simplifying the description in specific embodiments, and for enabling those skilled in the art to quickly understand the solution, and do not indicate or imply that a particular device / component / element must have a specific orientation, or be constructed and operated in a specific positional relationship. Therefore, they should not be construed as limitations on the present invention.

[0029] Furthermore, the use of terms such as "horizontal," "vertical," "suspended," "parallel," and "coaxial" does not imply that the corresponding device / component / element must be absolutely horizontal, vertical, suspended, parallel, or coaxial. Slight tilt or deviation is permissible, as long as it does not affect the normal function of the relevant component. For example, "horizontal" simply means that its direction is more horizontal relative to "vertical," not that the structure must be perfectly horizontal; a slight tilt is acceptable. "Coaxial" means that two components are arranged as coaxially as possible, allowing them to move coaxially or approximately coaxially when their relative positions change. Alternatively, it can be simplified to mean that the corresponding device / component / element, when arranged in "horizontal," "vertical," "suspended," "parallel," or "coaxial" directions, can have an error / deviation of ±10% relative to the corresponding direction, more preferably within ±8%, more preferably within ±6%, more preferably within ±5%, and more preferably within ±4%. For example, the deviation in the "coaxial" direction is controlled within 0.2-1mm, preferably within 0.2-0.5mm. As long as the corresponding device / component / element is within the error / deviation range, it can still achieve its function in the solution of the present invention.

[0030] Furthermore, the use of terms such as "first," "second," and "third" in terminology is merely for distinguishing descriptions of identical or similar components and should not be interpreted as emphasizing or implying the relative importance of a particular component.

[0031] Furthermore, in the description of the embodiments of the present invention, "several", "more than", and "a number of" represent at least two. The number can be any number, such as two, three, four, five, six, seven, eight, or nine, and can even exceed nine.

[0032] Furthermore, in the description of the technical solution of this invention, unless otherwise explicitly specified / limited / restricted, the terms "set up," "install," "connect," "link," "provided with," "laid out," and "arranged" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to connection methods commonly used in the art, such as welding, riveting, bolting, and threaded connections. Such connections can be mechanical, electrical, or communication connections; they can be direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components.

[0033] Example 1 This embodiment provides a digital twin-based smart cloud warehouse risk evolution assessment system. The system comprises: a perception and execution layer, a twin base layer, an evolution assessment layer, and a strategy feedback layer. The perception and execution layer extracts underlying operational metadata of electromechanical equipment in real time by constructing a distributed edge perception matrix. The twin base layer constructs a digital twin model that synchronously maps the physical toll plaza based on the smart cloud warehouse's configured digital model and logical graph, mapping metadata to business state nodes with semantic information. The evolution assessment layer integrates a toll collection spatiotemporal constraint violation judgment mechanism and, based on a spatiotemporal causal chain-based risk analysis algorithm, treats the physical environment jitter mapped in real time by the twin base layer as a disturbance variable. Through a nonlinear mapping mechanism, it quantifies and assesses the degree to which physical environment jitter intrudes on toll collection certainty. The strategy feedback layer includes a shadow executor and a self-healing configuration interface. The shadow executor performs a priori rehearsals of instructions issued to physical entities within the twin space to verify the security and timeliness of remote operations in the current nondeterministic network environment. If the verification is successful, the self-healing configuration interface dynamically adjusts the operating parameters of edge nodes or reconstructs business paths via the configuration channel.

[0034] The digital twin-based smart cloud warehouse risk evolution assessment system provided in this embodiment relies on a digital twin model and a risk ripple evolution model with injected tolling protocol genes. By quantifying the nonlinear impact of physical layer disturbances (such as network packet loss and micro-hour latency) on tolling timing, passage success rate, and agent load, it can proactively identify and warn of "hidden logic breakage" risks before physical failures cause business interruptions. This achieves a technological leap from monitoring single physical indicators to deterministic assessment of deep business logic. Furthermore, this embodiment uses a shadow executor to perform prior rehearsals of instructions issued to physical entities in the twin space, effectively eliminating control uncertainties caused by wide area network asymmetric latency. This ensures that control signaling is executed accurately within the physical security window, significantly reducing security risks such as vehicle damage due to signaling delays, toll loss, and loss of synchronization between virtual and physical states caused by signaling delays during remote management of smart cloud warehouses. This greatly improves the physical determinism and security of remote smart cloud warehouse operations.

[0035] Example 2 This embodiment is a further improvement on embodiment 1, and the repeated content will not be described again.

[0036] See Figure 1 Preferably, the perception and execution layer synchronizes metadata such as electromechanical status, network latency, and service timestamps to the twin base layer. The twin base layer generates a digital twin model through configuration model activation and billing protocol logic injection, and maps the digital twin model logic to the evolution evaluation layer. The evolution evaluation layer performs spatiotemporal constraint discrimination and risk ripple evolution evaluation on the digital twin model, and sends the evaluated risk results to the policy feedback layer. The policy feedback layer is configured with a shadow verification mechanism and can also issue self-healing commands. The policy feedback layer can perform virtual-real state calibration on the digital twin model generated by the twin base layer through the shadow verification mechanism; it can also send command interception / supplementary control schemes to the perception and execution layer to ensure the stable operation of the smart cloud warehouse.

[0037] Preferably, the metadata extracted in real time by the perception and execution layer includes: device electrical parameters, heartbeat packet latency, operational fingerprint metadata of electromechanical equipment, round-trip latency and jitter frequency of the underlying operational metadata network link of electromechanical equipment, and the perception and execution layer injects timestamps into key signaling in the charging service flow.

[0038] The perception and execution layer is the cornerstone of comprehensive metadata collection. Relying on configurable lane units and station-level intelligent units, it constructs a distributed edge perception matrix, serving as the data source for the smart cloud warehouse risk evolution assessment system. The core function of the perception and execution layer is to extract low-level operational metadata from heterogeneous electromechanical equipment in real time through standardized interfaces. In addition to conventional equipment electrical parameters and heartbeat packet latency, this layer focuses on capturing the round-trip time (RTT) and jitter frequency of network links, and injects high-precision timestamps into key signaling in the toll collection flow (such as DSRC protocol frames, image recognition trigger signaling, and self-service interaction logs). This metadata, possessing spatiotemporal attributes, provides a realistic reflection of the physical world for upper-layer logical deduction. Besides collecting business logs, the perception and execution layer also simultaneously captures operational fingerprint metadata of key electromechanical equipment, including the motor speed and current waveform of automatic barrier gates, the radio frequency reflection parameters of read / write antennas, and the resource scheduling jitter frequency of industrial main control units, providing high-dimensional physical characteristics for subsequent sub-health assessments.

[0039] The twin base layer is the central hub for physical-logical semantic mapping. By invoking the configurable digital model within the management platform, it constructs a logical mirror highly synchronized with the physical toll plaza. Unlike traditional geometric modeling, this layer achieves co-modeling of physical entity attributes and toll business protocols (such as the national standard DSRC protocol state machine and MTC billing logic). By establishing a digital twin based on a logical graph, fragmented perceptual data can be mapped into business state nodes with semantic information, thereby reproducing the complete temporal process of vehicle passage and toll transactions in virtual space.

[0040] See Figure 2 Preferably, the twin base layer includes: an input unit for receiving metadata; a digital twin unit for establishing a digital twin model; and a data synchronization unit for dynamically injecting the collected physical metadata into the real-time attribute parameters of the corresponding twin node in the digital twin model, thereby establishing a two-way binding between the real-time data stream of the perception and execution layer and the attributes of the digital twin model.

[0041] The digital twin unit includes a dynamic activation module, an automatic assembly module, and a state machine injection module. The dynamic activation module accesses the configuration catalog database of the managed platform in the metadata, parses predefined standardized digital models of equipment, and retrieves matching component instances from the metadata database based on the list of field-deployed equipment in the metadata to generate a twin. The automatic assembly module, based on the actual physical layout of the lanes and the business logic connection relationship, uses a topology engine to automatically assemble the virtual plaza, establishing spatial coordinate relationships and logical cascading paths between managed nodes in a three-dimensional virtual space. The state machine injection module injects logical computation operators corresponding to its function into each twin node, enabling the virtual model to simulate real business behavior.

[0042] Preferably, the state machine injection module includes a state machine modeling submodule and a protocol injection submodule. The state machine modeling submodule defines each managed node within the lane as a protocol entity with logical processing capabilities; it formally describes each twin node using a quintuple. The quintuple includes: a set of business states, a set of input events, a state transition function, an initial business state, and a set of termination states. The protocol injection submodule, by parsing the protocol template issued by the aforementioned configurable management platform, encapsulates the timing constraints, logical priorities, and exception jump branches of the toll collection service into logical code blocks, and dynamically deploys them into the computing kernel of the virtual node.

[0043] The evolutionary assessment layer is the core of business risk projection and decision-making. Integrating a mechanism for judging violations of spatiotemporal constraints on toll collection, and running a risk analysis algorithm based on spatiotemporal causal chains, the evolutionary assessment layer embodies the intelligence of the smart cloud warehouse risk evolution assessment system. Its technical principle lies in using the physical environment jitter (such as network instantaneous latency and packet loss) mapped in real time from the twin base layer as a disturbance variable. Through a nonlinear mapping mechanism, it quantifies and assesses the degree to which these fluctuations intrude on toll collection certainty. This layer innovatively introduces a "business entropy" assessment model to dynamically monitor the evolutionary trend of the smart cloud warehouse from an ordered steady state to a disordered risk state, thereby accurately predicting the transmission rate and diffusion range of risk along the "lane-station-cloud warehouse" chain.

[0044] Preferably, the evolutionary assessment layer includes: a violation determination unit, a risk quantification unit, and an evolutionary output unit. The violation determination unit, based on a toll collection spatiotemporal constraint-based violation determination mechanism, deeply couples lane physical geometric parameters with dynamic traffic timing to determine business determinism. The risk quantification unit uses a risk propagation model to quantify the nonlinear impact of physical failures on business continuity, generating business-layer indicators, operational-layer indicators, and physical-layer indicators. The evolutionary output unit spatiotemporally couples the business-layer indicators, operational-layer indicators, and physical-layer indicators to generate a comprehensive risk map for the smart cloud warehouse.

[0045] The strategy feedback layer is the central control hub for the virtual-physical interaction closed loop. By establishing a reverse control link, the strategy feedback layer proactively hedges risks and enables business self-healing. The core mechanism of the strategy feedback layer consists of a shadow executor and a self-healing configuration interface. Before issuing commands to the physical entity, the shadow executor performs a millisecond-level pre-simulation within the twin space to verify the security and timeliness of remote operations in the current non-deterministic network environment. After successful verification, the self-healing configuration interface dynamically adjusts the operating parameters of edge nodes or reconstructs business paths via a configurable channel. Thus, the smart cloud warehouse risk evolution assessment system completes a full logical closed loop from "situational awareness" to "shadow pre-simulation" and then to "precise execution."

[0046] Preferably, the shadow executor includes: a coupling prediction unit and a security discrimination unit. The coupling prediction unit predicts the expected time when the instruction will arrive at the physical end by retrieving the WAN link status monitored in real time by the perception execution layer. The security discrimination unit determines the validity of instruction execution using a security discrimination criterion based on spatiotemporal conflict.

[0047] Preferably, the strategy feedback layer also includes a time delay adaptive compensation unit, which uses feedforward compensation and edge autonomous switching mechanism to ensure the determinism of control command execution and the security of physical entities.

[0048] Preferably, the strategy feedback layer further includes a predictive maintenance unit. The predictive maintenance unit uses a digital twin model to couple the degradation of the underlying hardware physical performance with the spatiotemporal constraints of the upper-layer business. By monitoring the microscopic shifts in the hardware's operational fingerprint, it predicts the degree of impact of performance degradation on business determinism.

[0049] Preferably, the self-healing configuration interface can also dynamically reconstruct the configuration logic of the lane intelligent unit based on the full risk map of the smart cloud warehouse.

[0050] Example 3 This embodiment is a further explanation of Embodiments 1 and 2, and repeated content will not be repeated.

[0051] Preferably, the smart cloud warehouse risk evolution assessment system can realize the dynamic activation of the configurable management model. By integrating the standard metadata interface of the front-end configurable management platform, it can realize the instantiation evolution from static device attribute definition to dynamic logical computing entity.

[0052] See Figure 2 This embodiment automatically parses the physical topology of the toll collection site and constructs a logical computing base with business consistency in the virtual space, that is, constructs a digital twin model that can perform logical simulation.

[0053] The dynamic activation module is used for model retrieval and metadata instantiation.

[0054] The dynamic activation module in the digital twin base layer accesses the configuration catalog database of the managed platform to parse predefined standardized digital models of equipment. This model not only includes the geometric feature parameters and physical communication interface descriptions of the equipment, but also integrates the performance benchmark curves and instruction set semantics of the electromechanical entities. Based on the equipment list deployed on-site, the dynamic activation module retrieves matching component instances from the metadata database and uses instantiation technology to generate virtual nodes with unique object identifiers (UIDs) in the digital twin model. Through this step, the dynamic activation module completes the identity mapping from "managed assets" to "digital twin nodes." A comparison of its static attributes and dynamic capabilities is shown in Table 1.

[0055] Table 1 Comparison of Features between Configuration-based In-line Management Model and Dynamically Activated Twin

[0056] The automated assembly module adaptively constructs a virtual plaza based on the site topology: Based on the actual physical layout of the lanes and the connection relationship with business logic, the automatic assembly module in the twin base layer uses a topology engine to realize the automatic assembly of the virtual plaza. The automatic assembly module establishes the spatial coordinate relationship and logical cascading path between each managed node in the three-dimensional virtual space by parsing the lane configuration configuration file (such as XML or JSON description file).

[0057] Spatial topology of the automatic assembly module: automatically associate the relative positions of the intelligent lane unit, read / write antenna, capture camera and automatic barrier machine to construct a virtual spatial coordinate system that conforms to the physical site layout.

[0058] Logical topology of the automatic assembly module: Establish business flow relationships between nodes (e.g., antenna identification node → fee display and billing node → railing execution node) to provide path support for subsequent risk transmission simulation.

[0059] State machine injection module, injecting business logic operators and protocol state machines.

[0060] During the model activation process, the state machine injection module synchronously injects logical computation operators that correspond to the functions of each twin node, enabling the digital twin model to simulate real business behavior.

[0061] State machine injection: The charging service protocol (such as the interaction sequence of the national standard DSRC protocol and the MTC billing logic rules) is abstracted into an executable state machine model and deployed inside the virtual node.

[0062] Deterministic verification operator: Each twin node is endowed with the ability to perform independent logical verification on the received sensing data. For example, the antenna twin node can determine whether the interactive signaling reported by the physical layer conforms to the preset timing constraint standard based on the injected protocol state machine.

[0063] The data synchronization unit is used for dynamic binding of real-time data streams in virtual-physical mapping.

[0064] The final stage of dynamic activation is establishing a two-way binding between the real-time data stream of the perception and execution layer and the attributes of the digital twin model. The data synchronization unit utilizes a distributed message bus to dynamically inject the collected physical metadata (such as signal strength, network latency, and execution status) into the real-time attribute parameters of the corresponding twin node. By defining a high-frequency data refresh strategy and a timestamp alignment mechanism, the data synchronization unit achieves millisecond-level synchronization between the virtual logical state and the physical operating state of the digital twin model, thereby completing the transformation of the managed model from an "offline static asset" to an "online dynamic computing entity."

[0065] Preferably, the state machine injection module is used to inject a "fee agreement gene" into each twin node during the construction of the digital twin.

[0066] Through the state machine injection module, this invention achieves a breakthrough from "geometric mirroring" to "protocol mirroring".

[0067] Traditional 3D visualization (i.e., "geometric mirroring") primarily addresses the spatial positional offset and appearance reproduction of physical devices, but it cannot effectively simulate the internal business logic and temporal coordination relationships of physical entities. This invention proposes a construction technology that evolves from "geometric mirroring" to "protocol mirroring." By abstracting toll collection processes such as ETC transactions, license plate recognition triggering, and barrier gate control into deterministic state machines (DSMs), and injecting them as "protocol genes" into digital twin nodes, semantic simulation of the logical essence of the toll collection process is achieved.

[0068] The twin base layer models a state machine based on the determinism of the toll collection process. Preferably, the twin base layer defines each managed node within a lane as a protocol entity with logical processing capabilities, rather than a simple data storage object, through a state machine modeling submodule. Each twin node M can be formally described by a quintuple.

[0069]

[0070] In the formula, S represents the set of possible business states of a node (e.g., idle, pre-wake-up, data exchange in progress, transaction completed, abnormal suspension, etc.). Represents the set of input events (e.g., receiving a DSRC request frame, receiving a vehicle sensing signal, instruction arrival delay, etc.); δ: S×Σ→S is the state transition function, which defines how a node transitions from the current business state to the next state under a specific input; ∈S represents the initial business state; F S is the set of termination states.

[0071] Through this abstraction, the digital twin model can not only reproduce the physical state of whether a device is "present" or not, but also deduce the logical state of whether a business is "connected" or not in real time.

[0072] The twin base layer injects protocol genes and performs semantic reconstruction on the digital twin model.

[0073] Unlike traditional 3D displays that only contain coordinates (X, Y, Z) and visual textures, this invention performs a "gene injection" operation during the construction of the digital twin model. This operation parses the protocol template issued by the aforementioned configurable management platform, encapsulates the timing constraints, logical priorities, and exception jump branches of the charging business into logical code blocks, and dynamically deploys them to the computing kernel of the virtual node. As shown in Table 2, the protocol image is significantly superior to the traditional geometric image in terms of logical expression capabilities.

[0074] Table 2 Comparison of Technical Features of Geometric Mirroring and Protocol Mirroring

[0075] Once the "protocol gene" is injected, the nodes in the digital twin model are no longer isolated individuals, but form an organic whole based on a toll-based time-series causal chain, enabling protocol linkage simulation based on the causal chain.

[0076] The digital twin's foundation layer uses timing logic constraint operators to define the collaborative relationships between nodes. For example, the state transition result (transaction success / failure) of the ETC antenna node will directly serve as the input trigger event for the subsequent state transition of the toll display node and the gate machine node. Through this protocol-level linkage simulation, the digital twin model can accurately predict, within the virtual space, protocol handshake failures (timeouts) or logical timing misalignments caused by network latency in the physical world.

[0077] In actual operation, the digital twin model continuously receives real-time business timestamps reported by the perception and execution layer. The evolutionary evaluation layer compares the deviations between the theoretical transition times predicted by the "standard state machine" and the "physical feedback values." It determines the health of business logic and enables dynamic evolution and verification of protocol images.

[0078] like:

[0079] The evolution assessment layer then determines that the protocol image deviates from the physical entity and immediately triggers the risk ripple assessment procedure to analyze whether this timing deviation will cause the subsequent barrier control logic to fail.

[0080] Through the aforementioned leap from the geometric level to the protocol level, the digital twin constructed by this invention possesses the ability to self-explain and self-predict complex charging business processes, laying a logical foundation for subsequent risk evolution assessment and shadow execution in a wide area network environment.

[0081] To achieve high-fidelity simulation of the behavior of physical entities in the digital twin model, the twin base layer establishes a response time function for each hardware node managed through configurable configuration, using dynamic calibration and simulation techniques. This function quantitatively describes the latency distribution characteristics of the hardware node when processing specific business instructions, and is the core calculation basis for determining the determinism of business logic and predicting risk evolution.

[0082] The twin base layer adjusts the response time for different types of network-connected devices (such as R-SUI read / write antennas, automatic barrier gates, industrial cameras, etc.). Defined as a multidimensional response time function that incorporates inherent hardware processing biases, network transmission disturbances, and the impact of service load. The mathematical definition of the multidimensional response time function is as follows:

[0083] In the formula, The static baseline processing time for the equipment to execute business instructions is determined by the equipment calibration parameters in the configuration model library; Network link disturbance factor reflects the round-trip time (RTT) and jitter of wide area network or local area network in real time. The business load correction factor is dynamically adjusted based on the current traffic flow concurrency and the resource utilization rate of the smart cloud warehouse risk evolution assessment system; The equipment health degradation factor is a hardware fatigue correction value derived from historical operating data and current and voltage waveform characteristics. It compares the deviation between the execution fingerprint of the physical entity and the standard twin performance baseline in real time, calculates the hardware performance degradation trajectory, and thus dynamically corrects the predicted service execution time in the twin space.

[0084] The twin base layer utilizes the high-precision timestamp data continuously reported by the perception and execution layer to perform online learning and real-time calibration of the response time function, thereby achieving dynamic calibration of the response delay baseline.

[0085] Initial calibration: The perception execution layer calls the configuration metadata in the management platform to obtain the theoretical response threshold of the device model as the initial baseline.

[0086] Dynamic sliding window correction: The perception execution layer employs a weighted moving average algorithm to statistically analyze the actual response times of the past N transaction flows and dynamically correct the function parameters. As shown in Table 3, through continuous calibration, the digital twin can reflect the true performance of a specific physical node in the current environment.

[0087] Table 3. Example Table of Calibration Parameters for Response Time Function of Pipeline Equipment

[0088] When performing evolutionary assessments or shadow simulations, the digital twin model no longer uses a fixed constant delay. Instead, it generates a predicted delay that conforms to the current physical state by calling the response time function, thereby achieving function-based business timing simulation.

[0089] For example, when an increase in packet loss rate is detected in the wide area network, the perception execution layer automatically increases... The parameters cause the virtual antenna nodes in the twin space to generate larger random latency deviations during simulated transactions. This function-based simulation method enables the smart cloud warehouse risk evolution assessment system to predict the risk of business timing conflicts caused by response time drift before a failure actually occurs.

[0090] The evolutionary evaluation layer compares the actual response time of physical entities in real time. With the function prediction value in the digital twin model residual This enables the determination of abnormal response and sub-health status.

[0091]

[0092] If residuals If the variance consistently exceeds the preset range or shows a trend of increase, the evolution assessment layer determines that the physical node has entered a "sub-healthy" state. Even if the node can still complete business processing, the evolution assessment layer will mark it as a potential risk source through the risk ripple model and send a maintenance warning or downgrade operation instruction to the strategy feedback layer.

[0093] By establishing and continuously calibrating the response time function, a technological leap from "equipment status monitoring" to "performance trend prediction" has been achieved, ensuring that the digital twin model is not only synchronized with the physical scene in terms of spatial topology, but also highly consistent with the physical entity in terms of temporal characteristics.

[0094] This invention integrates a violation determination mechanism based on toll-based spatiotemporal constraints into the evolutionary evaluation layer. The twin base layer provides the "benchmark" (standard state machine, safe space coordinates). The perception and execution layer provides the "measured values" (real-time location, real-time timestamp). The role of the evolutionary evaluation layer is "comparison and deduction".

[0095] The determination of "violation" by the evolutionary assessment layer is essentially an assessment process: only by comparing the "measured value" with the "benchmark value" and finding a mismatch (i.e., a violation) can the subsequent "risk evolution (ripple effect)" calculation be initiated; without violation determination, evolution has no starting point.

[0096] In a smart cloud warehouse architecture, the successful execution of paid services depends not only on the correctness of the logic but also on the strict matching of physical location and time sequence. The evolutionary evaluation layer establishes a dynamic spatiotemporal safety window. The lane physical geometry parameters are deeply coupled with the dynamic traffic timing, serving as the core operator for determining the determinism of the business.

[0097] The evolution evaluation layer is based on the spatial layout parameters in the digital twin model, using the initial boundary of the ETC antenna radiation area within the lane as the origin of the spatial coordinate system. Establish a one-dimensional displacement axis along the vehicle's direction of travel. A lane position coordinate system is established. Based on the physical distribution of the toll business logic, the evolution evaluation layer divides the lanes into four functional constraint zones (see Table 4 below) to complete the spatial partitioning modeling.

[0098] Table 4. Definition of Lane Service Function Zones and Their Spatial Constraint Parameters

[0099] It can be seen that the recognition area ( This refers to the physical area where the vehicle completes antenna handshake and OBU information reading; the calculation interaction area ( The physical area for completing fee calculation, blacklist verification, and fee deduction; the alert and warning area ( The physical area is the zone for displaying toll information, providing voice announcements, and executing anomaly alerts; the physical interception zone ( () is the final section where the automatic barrier machine performs the opening action or physical blocking.

[0100] This invention overcomes the limitations of traditional fixed-time monitoring, and collects vehicle speed data in real time. A decision operator is introduced. For a specific vehicle entering the lane, the violation decision unit in the evolutionary evaluation layer introduces the calculation of the time cutoff moment for the vehicle speed variable, and calculates the cutoff moment of each business step in real time based on the kinematic model. .

[0101] Let the time when the vehicle enters the origin be Then the first The formula for the dynamic cutoff time of each business node is defined as follows:

[0102] In the formula, These are the endpoint displacement coordinates for the corresponding business partition; Real-time vehicle speed (obtained by speed radar or ground-sensor time difference fitting); The inherent mechanical action delay of the actuator (such as the time it takes for a barrier gate to raise the barrier); This is to reserve a redundancy safety margin.

[0103] The violation judgment unit can automatically adjust the time delay tolerance according to different vehicle speeds. When the vehicle speed is high, the safety window automatically shrinks and the deadline is moved forward; when the vehicle speed is low, the window is appropriately widened.

[0104] Dynamic Spatiotemporal Safety Window Constrained by spatial displacement With logical time constraints Common definition. For any charging service node. It must satisfy the following spatiotemporal consistency conditions:

[0105] If the violation determination unit detects the business completion time reported by the perception and execution layer... It deviates from the window area, that is, it satisfies If so, it is determined that a "spatiotemporal constraint violation" has occurred.

[0106] Unlike traditional monitoring, the violation judgment unit's judgment logic is that even if the physical link shows "device online", if the vehicle has already crossed the corresponding spatial constraint range when the business instruction arrives at the physical execution end (for example, when the instruction to successfully deduct payment arrives, the vehicle has already physically crossed the payment display position), the violation judgment unit will immediately determine that the business logic has failed.

[0107] This spatiotemporal constraint-based judgment mechanism enables the smart cloud warehouse to assess the fundamental impact of wide area network jitter on physical access safety in real time, providing precise trigger thresholds for subsequent shadow simulation and policy interception.

[0108] This invention utilizes a violation determination unit within the evolutionary evaluation layer to execute prior judgment logic for business logic breaks. In the remote management scenario of a smart cloud warehouse for highways, the effectiveness of business execution depends not only on the connectivity of network links but also on the degree of coordination between communication latency and physical traffic timing. Prior judgment logic based on "logical breaks" can identify and handle latent faults where the physical network links remain connected (e.g., a normal Ping command response), but the business signaling has lost its spatiotemporal execution significance.

[0109] Traditional monitoring methods typically use network layer connectivity as the sole criterion for service execution, assuming that as long as there is no "connectivity failure," services can operate normally. The Smart Cloud Warehouse Risk Evolution Assessment System introduces a service-level execution effectiveness assessment through a violation judgment unit, defining "logical breaks" as those caused by signaling interaction delays. The accumulation of time leads to the time when business instructions arrive at the executor. It is already behind the physical security deadline corresponding to this business node. This results in a spatiotemporal misalignment between the expected action and the physical entity's state.

[0110] The violation determination unit in the evolutionary evaluation layer utilizes network link jitter parameters and vehicle dynamics states acquired in real time by the perception and execution layer to synchronously simulate the service execution process in virtual space. Let... For the business launch moment, The current vehicle displacement coordinates, If the physical location coordinates of the automatic barrier gate or toll display device are given, the determination logic follows the process below.

[0111] The first step is to predict the remaining physical time. This is based on the vehicle's real-time speed. Calculate the remaining time for the vehicle to reach the execution boundary. .

[0112]

[0113] The second step is to estimate the service loop latency. This involves considering the current WAN round-trip latency. Business logic processing latency and the mechanical response delay of the actuator Calculate the expected total delay .

[0114]

[0115] Step 3, Judgment Criteria. The violation judgment unit continuously verifies the following inequalities. If satisfied:

[0116] This is considered a logical disconnection. Even if the network layer status shows "Connected" at this time, the system will still trigger the service interruption protection logic; where, A safety redundancy threshold reserved for the system.

[0117] Once a logical break occurs in the violation determination unit (as shown in Table 5), the policy feedback layer will no longer blindly wait for the final execution result of the remote signaling, but will immediately initiate emergency intervention through the policy feedback layer.

[0118] If the shadow system simulation shows that the vehicle has already crossed the safety control zone when the command arrives, the strategy feedback layer will automatically intercept the invalid command to prevent the risk of "vehicle over the pole" or "collision with the pole".

[0119] The strategy feedback layer automatically records a snapshot of the business at the moment of the break and switches the lane intelligent unit to edge autonomous mode to ensure the integrity of the transaction flow.

[0120] Table 5 Comparison of Detection Characteristics for Connectivity Failures and Logical Breaks

[0121] To reduce the false positive rate, the violation determination unit introduces a dynamic compensation weight coefficient. During peak traffic periods or in inclement weather conditions, the violation determination unit adjusts... Value to correct The computational expectation makes the judgment of logical break more forward-looking, and realizes the judgment optimization based on dynamic weight compensation. In this way, the technology has been transformed from "passive operation and maintenance based on physical connectivity" to "active protection based on business determinism", ensuring the security of cross-wide area network control under the cloud warehouse architecture.

[0122] The evolution assessment layer utilizes risk quantification units to achieve cross-level "risk ripple" evolution assessment.

[0123] In the smart cloud warehouse charging architecture, the risks of smart cloud warehouses do not exist in isolation. Abnormal disturbances in local nodes will be transmitted across levels along the business logic chain to station-level units, regional cloud warehouses and central management platforms, forming a significant "ripple effect". The risk quantification unit is configured with a risk ripple evolution model that introduces a "charging traffic damping factor" to accurately assess the system evolution trend by quantifying the nonlinear impact of physical failures on business continuity.

[0124] Unlike traditional fault analysis methods based on a single threshold or linear superposition, this model introduces the concept of thermodynamic "entropy" to describe the transformation of the toll collection system from an ordered steady state to a disordered risk state. The business entropy risk evolution formula defined by the risk ripple evolution model is as follows:

[0125] In the formula, The end-to-end business risk entropy value is used to characterize the degree of instability of the system after it has been disturbed. Perceive the real-time signaling interaction latency increment reported by the execution layer; Real-time packet loss rate variation of WAN links; Sensitivity weighting coefficients are preset based on different lane configuration attributes (such as dedicated ETC and self-service hybrid).

[0126] Nonlinear damping factor of charge flow Definition: This operator characterizes the amplification effect of traffic flow on risk transmission. The system displays the real-time traffic flow density of lanes. With lane design capacity Perform coupling and define the damping factor. as follows

[0127] In the formula, This is the scene adjustment coefficient. The nonlinear damping factor for chargeable flow rate. The introduction of this feature simulates the nonlinear characteristics of real highway tolling scenarios, when the traffic flow... At lower levels, When the value is close to 1, minor fluctuations at the physical layer (such as a 0.1s delay) are absorbed by system redundancy, resulting in a weak ripple effect; however, when the flow rate... Approaching capacity threshold hour, It grows exponentially.

[0128] The risk ripple evolution model incorporates a nonlinear amplification mechanism for risk ripples.

[0129] As shown in Table 6, the disruptive power of the same physical disturbance to business continuity varies fundamentally under different traffic flow conditions. By introducing a damping factor, the risk quantification unit can identify hidden risks that are masked during off-peak hours but are sufficient to cause station-wide paralysis during peak hours through the risk ripple evolution model.

[0130] Table 6 Comparison of risk evolution of the same physical disturbance under different flow damping conditions

[0131] In the risk ripple evolution model, the propagation of risk ripples follows the principle of "physical perturbation". Spacetime Conflict Business entropy increase The logical path of "system failure". Physical layer oscillation, resulting in minor fluctuations in the WAN link. This forms the ripple center; the logic layer is coupled, and the spatiotemporal constraint operator determines the impact of this delay on the current... Vehicles at high speeds pose a logical threat; this is amplified at the business layer during peak traffic. Under the damping effect, the delay quickly translates into a decrease in single-vehicle processing efficiency, resulting in a backlog of subsequent vehicles in the physical space; the cloud warehouse layer erupts, the backlog of vehicles triggers a large number of work order requests, the risk ripples spread to the remote cloud warehouse, causing the agent processing queue to overflow, and finally completing the evolution from a local failure to a global risk in the smart cloud warehouse.

[0132] By using the risk ripple evolution model, the stability of the smart cloud warehouse system is dynamically quantified, enabling the management platform to detect potential risks in a "critical state" in advance and to perform preventive resource scheduling in advance in conjunction with traffic prediction.

[0133] Building upon the risk ripple evolution model, the evolutionary assessment layer further defines a multi-dimensional quantitative indicator system tailored to the characteristics of smart cloud warehouse operations. This system incorporates abstract risk entropy values. This translates into intuitive business operation metrics that can guide production scheduling, covering three dimensions: business success rate prediction, agent load assessment, and traffic physical flow evolution.

[0134] (1) Business-level indicators - cycle Predicted success rate of internal charges

[0135] The risk quantification unit, based on the protocol state machine in the digital twin foundation, predicts future time periods. The probability that the internal fee collection process satisfies the spatiotemporal constraints. Its prediction model considers the degree to which the current risk entropy intrudes on the determinism of the transaction closed loop, and the calculation formula is defined as follows:

[0136] In the formula, The baseline charging success rate (usually close to 100%) is the system's steady-state charging success rate. The failure rate function is affected by risk entropy and information delay jitter.

[0137] This indicator, through simulation and deduction, can predict in advance the risk of logical breaks such as transaction timeouts and billing failures caused by network jitter or timing misalignment, providing data support for the assessment of fee losses.

[0138] (2) Operational level indicators—Remote control center handles load increments

[0139] This metric is used to quantitatively assess the operational pressure generated after risk ripples spread to the cloud warehouse management platform. The risk quantification unit calculates the additional work order load caused by the increased need for manual intervention based on the special situation conversion rate resulting from lane-end logic breakdowns.

[0140]

[0141] In the formula, For the first Real-time traffic flow for each lane; This is the special incident work order conversion coefficient, representing the probability that a single business logic interruption will trigger an agent call.

[0142] As shown in Table 7, this indicator can identify how the increase in risk entropy is transformed into overload pressure at the agent end, and help the system achieve elastic balance and resource pre-allocation of agent resources across regions.

[0143] Table 7. Correlation Mapping Table Between Risk Assessment Indicators and Cloud Warehouse Operation Status

[0144] (3) Physical layer index—Evolution trend of average queue length of lanes

[0145] For the traffic flow status of the toll plaza, the risk quantification unit combines queuing theory and risk evolution assessment results to deduce the congestion trend of the lane physical space. Its evolution model follows the following difference equation.

[0146]

[0147] In the formula, For vehicle arrival speed; This is the average vehicle processing time after risk entropy correction (including response delay jitter).

[0148] When risk ripples lead to As the number increases, the evolutionary evaluation layer automatically simulates and predicts the future. The queue length growth curve within a period. If the queue length is predicted... If the lane's physical buffer zone limit is exceeded, the evolution assessment layer will immediately trigger an orange risk warning, and it is recommended to enable the emergency release configuration logic.

[0149] The evolution assessment layer uses the evolution output unit to spatiotemporally couple business layer indicators, operational layer indicators, and physical layer indicators to generate and output a full risk map of the smart cloud warehouse.

[0150] The evolutionary output unit of the evolutionary assessment layer spatiotemporally couples the quantitative indicators of the above three dimensions to generate a "Smart Cloud Warehouse Risk Map". This map, using a digital twin model, demonstrates through a heatmap how risks evolve from underlying hardware fluctuations to decreased business success rates, ultimately leading to physical traffic congestion and remote agent overload. This multi-dimensional quantitative approach achieves a precise "portrait" of the entire cloud warehouse's status, ensuring that management decisions are based on quantitative risk evolution data rather than empirical data, significantly improving the resilience and continuity of billing operations in complex environments.

[0151] This invention achieves remote operation consistency pre-verification based on "shadow execution" through a shadow executor.

[0152] In the wide area network remote control architecture of a smart cloud warehouse, there is a significant spatial span and uncertain network latency between the physical actuators (such as barrier gates) and the cloud control center. The shadow actuator in the strategy feedback layer provides a shadow space command prior technology, which establishes a "shadow execution environment" with real-time simulation capabilities in a virtual digital space to predict the effects of control commands on the physical end and intercept risks in advance.

[0153] The architectural logic of the shadow executor: The shadow actuator acts as a "logic buffer" during the remote control command issuance process. When the remote control center triggers a control action targeting the field peripherals... When (e.g., "remote lever raising") occurs, the shadow actuator in the policy feedback layer does not immediately send the control message to the physical communication link, but instead injects it into the shadow actuator first. The shadow actuator, combining the current physical state snapshot with environmental disturbance parameters, completes a "prior exercise" in virtual space. Its core process logic is as follows: Figure 3 As shown.

[0154] The shadow executor retrieves the WAN link status monitored in real time by the perception execution layer. Predict the expected time when the command arrives at the physical end. .

[0155]

[0156] In the formula, The timestamp of the control command initiated by the cloud-based agent; Transmission delay fluctuations predicted based on current link RTT and jitter characteristics; The logical processing of received messages by edge nodes takes a fixed amount of time.

[0157] Simultaneously, the shadow actuator utilizes real-time dynamic data of the lane vehicle (current displacement) Real-time speed acceleration ), predicted in Physical coordinates of the vehicle at any time .

[0158]

[0159] The safety discrimination unit of the shadow executor in the policy feedback layer calls the prior judgment result of the violation judgment unit in the evolution evaluation layer, and combines it with the physical safety constraint window defined in the digital twin model. Compare predicted locations The validity of remote control command execution is determined according to the following logic: Command approval. If... Within the safe control zone Within, and satisfying the remaining braking distance. The system determines that the instruction can be executed safely.

[0160] Command interception (REJECT). If the prediction shows that the vehicle has deviated from the safe range by the time the command arrives, or if the execution time is seriously misaligned with the physical spatial position due to excessive time delay (e.g., the front of the vehicle has touched the pole), the risk evolution assessment system will immediately intercept the command and send a "execution environment mismatch" warning to the operator.

[0161] Table 8. Comparison of technical features between direct execution mode and shadow pre-verification mode

[0162] The computation process of the shadow executor is a millisecond-level iterative loop. For example... Figure 3 As shown, its execution steps include...

[0163] The first step is instruction interception and logic caching. Remote control instructions sent to the smart cloud warehouse remote control center are intercepted. The engine captures the control signals, and the instructions are sent to the shadow system (virtual space), suspending physical delivery.

[0164] The second step is motion state synchronization. Quickly read the physical twin snapshot of the current lane (read the current vehicle speed, vehicle position, network edge, displacement, and link RTT).

[0165] The third step is shadow space simulation and pre-playing. For example, the physical position of the vehicle at the time of the predicted command arrival is simulated in the shadow space, and the "virtual lever raising" action is run in the shadow space in a forward simulation mode.

[0166] Step 4: Safety window matching verification. Calculate the space safety margin at the instant the command takes effect. To determine whether the predicted location is within a safe control zone.

[0167] Step 5: Action Execution. If the predicted location is within the safety control zone, the instruction access is granted, a physical message is sent, the peripheral device is activated, and control is successful. If the predicted location is not within the safety control zone, risk interception is performed, invalid messages are intercepted, the device status is locked, and risk hedging and alarms are issued.

[0168] By using shadow space command prior technology, the risk evolution assessment system eliminates the "uncertainty" caused by physical distance and network fluctuations in remote control at the logical level, so that every control command that crosses the wide area network has a prior security boundary, thereby ensuring the control consistency of the smart cloud warehouse in complex network environments.

[0169] In a wide area network (WAN) environment, control commands issued by cloud-based agents often fail to arrive on time within the expected physical security window due to network asymmetric latency and dynamic jitter. The policy feedback layer configures a latency adaptive compensation technique for control signaling through a latency adaptive compensation unit. By introducing feedforward compensation and edge autonomy switching mechanisms on top of shadow verification, it ensures the determinism of control command execution and the security of physical entities.

[0170] The latency adaptive compensation unit uses a digital twin model to predict network latency in real time. This is used as a feedforward compensation parameter. When the cloud-based operator triggers a control action, the strategy feedback layer does not adopt an immediate pass-through strategy. Instead, the time-delay adaptive compensation unit calculates the "control lead" based on the vehicle's current operating speed and the predicted arrival time.

[0171] If the latency adaptive compensation unit detects a trend of latency in the current network environment, and the latency is within the compensable threshold range, the latency adaptive compensation unit will automatically adjust the signaling transmission priority or utilize the pre-execution mechanism of the edge node to initiate the pre-processing process of the actuator (such as the pre-excitation of the barrier gate motor) at the physical layer in advance to offset the latency loss caused by network transmission.

[0172] set up The optimal time for the physical end to execute the action. The advance compensation amount calculated by the delay adaptive compensation unit is then the actual signaling trigger time. The correction logic is as follows:

[0173] Through this mechanism, the time delay adaptive compensation unit can eliminate the negative impact of wide area network jitter on control accuracy within a certain range.

[0174] When the security judgment unit of the shadow actuator assesses that the current signaling latency compensation is insufficient to support physical security (i.e., the assessment result is "insecure"), the smart cloud warehouse risk evolution assessment system will activate the highest priority security protection strategy, instantly switching from "cloud control" to "edge autonomy".

[0175] ① Command Interception Strategy. The Smart Cloud Warehouse Risk Evolution Assessment System immediately intercepts expired cloud control messages to prevent "late" commands from triggering physical actions at the wrong time and place.

[0176] ② Edge takeover logic. The smart cloud warehouse risk evolution assessment system issues a "takeover command" to the lane intelligent unit through the policy feedback layer. At this time, the lane end uses locally cached configured business logic and real-time perception data to autonomously complete billing verification and barrier lifting control without cloud commands, ensuring the continuity and security of toll collection services under extreme network fluctuations.

[0177] The policy feedback layer (or the delay adaptive compensation unit in the policy feedback layer) calculates the real-time risk entropy output by the evolution evaluation layer. The decision thresholds for compensation and interception are dynamically adjusted. As shown in Table 9, the strategy feedback layer exhibits differentiated control determinism characteristics for different risk levels.

[0178] Table 9 Adaptive Control Decision Matrix Based on Risk Evolution

[0179] Preferably, to further eliminate state inconsistencies caused by asymmetric latency, a clock synchronization compensation algorithm is introduced in the policy feedback layer. The smart cloud warehouse risk evolution assessment system encapsulates a service sequence number and an execution deadline timestamp for each control signaling instruction. After receiving the instruction, the lane execution terminal first checks the current local time. .

[0180] If the following conditions are met:

[0181] Then execute the action, and according to Make fine adjustments to the action rate (such as adjusting the lever angle curve). If the conditions are not met, the instruction will be invalidated, and abnormal log entries and local redundancy processing will be automatically triggered.

[0182] By deeply integrating signaling advance compensation and edge autonomous switching, the problem of "control uncertainty" caused by the large physical and temporal span in the smart cloud warehouse model is solved, and the fluctuation risk of wide area network is closed at the control front end, which significantly improves the robustness and security of the highway remote control system.

[0183] The strategy feedback layer also includes a predictive maintenance unit, which is used to implement predictive maintenance strategies based on business impact.

[0184] Based on the digital twin model, the predictive maintenance unit establishes a predictive maintenance (PdM) system that deeply couples the degradation of the underlying hardware physical performance with the spatiotemporal constraints of the upper-layer business. This enables a predictive maintenance strategy based on business impact. The core of this strategy lies in predicting the degree of impact of performance degradation on business determinism by monitoring the micro-shifts in the hardware's operational fingerprint, thereby achieving a shift from "post-event maintenance" to "precise maintenance based on business risks".

[0185] The perception and execution layer continuously collects operational fingerprint data from key equipment (such as the motor of an automatic barrier gate and the radio frequency unit of the read / write antenna). The prediction and maintenance unit in the policy feedback layer uses a Long Short-Term Memory (LSTM) network or other prediction algorithms with temporal feature extraction capabilities, combined with the digital twin model and historical operational data, to construct a hardware health degradation model.

[0186] The predictive maintenance unit extracts feature vectors that include dimensions such as motor start-up and shutdown current waveforms, antenna signal-to-noise ratio drift, and control command processing jitter. .

[0187] The predictive maintenance unit calculates the remaining lifetime (RUL) of the hardware by analyzing historical operating data and fitting a non-linear degradation trajectory of hardware performance.

[0188] Predictive maintenance units primarily calculate the performance degradation as a function of response time. The impact of declining hardware health will affect the predicted response time. Over time The growth curve of evolution.

[0189]

[0190] In the formula, As a current indicator for evaluating hardware health, This is the degradation evolution operator.

[0191] Unlike traditional maintenance logic that only focuses on whether hardware fails, the smart cloud warehouse risk evolution assessment system sets the maintenance trigger criterion as the "probability of business failure." The predictive maintenance unit compares the predicted response time in real time. With dynamic spatiotemporal safety window .

[0192] set up The probability of spatiotemporal constraint violations occurring within future periods is calculated by convolution between the predicted response delay distribution and the vehicle passage dead-line:

[0193] When it is predicted that hardware performance degradation will cause the probability of spatiotemporal constraint violations to exceed a preset threshold (such as 30%), the predictive maintenance unit will still determine that it has entered the "business non-compliance risk period" even if the physical hardware is still in an operational state.

[0194] Table 10 Predictive Maintenance Tier Based on Business Impact

[0195] Once the proactive maintenance threshold is triggered, the smart cloud warehouse risk evolution assessment system will perform the following closed-loop operation: The first step is automatic work order generation. The strategy feedback layer automatically generates early warning maintenance work orders through the management platform, clearly indicating the root cause of performance degradation (such as the rod lifting time drifting from 0.9s to 1.2s due to wear of a certain bearing).

[0196] The second step is adaptive protection. Before manual maintenance is completed, the strategy feedback layer reconfigures the lane intelligence unit's configuration logic through a self-healing configuration interface, automatically adjusting the lane's traffic guidance strategy (such as reducing the recommended driving speed). Alternatively, increase vehicle spacing constraints to compensate for time delays with spatial redundancy and reduce the risk of violations.

[0197] The third step is performance verification. After maintenance, the policy feedback layer recalibrates the response time function using the newly reported physical fingerprints, thereby resetting the health baseline and achieving a self-healing closed loop.

[0198] By transforming hardware physical indicators into business deterministic indicators, predictive maintenance based on business impact is achieved, effectively avoiding operational accidents such as missed charges and pole collisions caused by equipment "operating with defects", and significantly improving the reliability and economic benefits of the smart cloud warehouse system throughout its entire life cycle.

[0199] The smart cloud warehouse risk evolution assessment system can establish a business closed-loop feedback mechanism based on the risk evolution assessment results. When the evolution assessment layer identifies that the risk entropy value of the entire system link or the degree of spatiotemporal constraint violation of a specific node exceeds the preset threshold, the strategy feedback layer dynamically reconstructs the configuration logic of the lane intelligent unit through the self-healing configuration interface via the management platform, realizing a smooth switch from centralized control to edge-side autonomous defense.

[0200] The strategy feedback layer will output quantitative indicators (such as business entropy) from the risk evolution assessment. Prediction success rate As the trigger for logical restructuring, a strategy mapping matrix is ​​established to match the optimal configuration operation mode for different risk levels. Restructuring decision function. The definition is as follows:

[0201] In the formula, This represents the reconstructed lane operation mode state. Based on the function's output value, the management platform retrieves the corresponding logic description file from the pre-configured configuration logic library and issues the execution command.

[0202] The self-healing configuration interface, based on a configurable management architecture, enables online hot loading of business logic.

[0203] When the assessment results show that the WAN latency is sufficient to cause a "logical break", the self-healing configuration interface sends a reconstruction command to the lane intelligent unit to temporarily decouple logical operators that rely on cloud interaction (such as remote toll calculation requests and cloud barrier lifting confirmation processes).

[0204] A configuration description file with edge autonomy capabilities is injected synchronously. This file contains locally backed-up billing logic, an offline blacklist database, and locally-aware discrimination rules.

[0205] Through this dynamic reconfiguration, the lane intelligent unit can instantly switch its operating logic without interrupting business operations.

[0206] To ensure the continuity of charging services, the self-healing configuration interface defines a multi-level logical degradation strategy. As shown in Table 11, the system dynamically adjusts the operational characteristics of the virtual toll plaza based on the depth of risk evolution. Table 11 Risk-Driven Configuration Logic Reconfiguration Hierarchical Strategy Table

[0207] Configuration logic reconfiguration is not a one-way degradation process, but a closed-loop link with self-healing capabilities.

[0208] After the lane intelligent unit performs the logic switch, it sends a "configuration reconfiguration successful" signal through the management platform, and the twin base layer synchronously updates the corresponding "protocol image" digital twin model.

[0209] The strategy feedback layer continuously monitors the recovery status of environmental factors through the evolutionary evaluation layer. If the WAN link RTT recovers to the steady-state threshold, and the risk entropy... If the decline continues, the strategy feedback layer will activate the shadow system to perform "logic recovery verification".

[0210] After successful verification, the management platform reissues the centralized configuration logic, cancels the edge takeover strategy, and enables the system to smoothly switch back from a self-healing state to a fully functional normal state.

[0211] Through risk-driven configuration logic reconfiguration, rigid hardware systems can be transformed into flexible business networks with "stress perception" and "logic reorganization" capabilities. Based on the dynamic scheduling mechanism of the managed platform, the survivability of the smart cloud warehouse in complex network environments and extreme conditions is enhanced, ensuring that the deterministic boundaries of the billing business logic remain within a controllable range.

[0212] In the clustered operation mode of smart cloud warehouses, the seat resources of cloud warehouses in each region are dynamically distributed. Based on the risk ripple prediction data output by the evolution assessment layer, a cross-regional resource elastic scheduling mechanism can be established. This mechanism realizes the transformation of work order flow from "load-triggered" to "risk-pre-triggered" mode by quantifying the conversion rate of risk into operational load, ensuring the service continuity of the entire system under extreme special circumstances.

[0213] The intelligent cloud warehouse risk evolution assessment system utilizes the business entropy increment and lane capacity reduction prediction values ​​obtained from the risk ripple evolution assessment to establish a load prediction model for remote agents. Let...

[0214] For the first In the future, regional cloud warehouses The calculation logic for the predicted arrival volume of work orders is as follows:

[0215] In the formula, Real-time traffic density of the affected lanes; The probability function of risk transforming into a special situation work order; Special situation complexity correction coefficient (such as weight gain for heavy fog, equipment cluster failures, etc.); Routine business background load.

[0216] When the predictive model shows the resource utilization rate of a specific cloud warehouse region When the risk of exceeding the preset threshold (such as 85%) is expected to be exceeded in the near future, the smart cloud warehouse risk evolution assessment system will immediately initiate a cross-regional linkage procedure.

[0217] Unlike traditional passive traffic diversion based on current queue length, this system implements "proactive traffic guidance" technology. The smart cloud warehouse risk evolution assessment system modifies the routing weights of the business distribution engine in advance, based on the speed of risk ripple spread, before work orders are actually generated.

[0218] Dynamic adjustment of routing weights. The smart cloud warehouse risk evolution assessment system issues weight adjustment instructions to the intelligent distribution gateway through the management platform. For lanes on the risk evolution path, the priority of their pre-generated work orders is marked as "high," and the routing weights pointing to the agents in that area are adjusted accordingly. Reduce the routing weight of adjacent idle cloud warehouses and simultaneously increase it.

[0219] The operator terminals in the target receiving area automatically retrieve digital twin snapshots and historical transaction data of the risk source lanes to preload the business environment and reduce cognitive latency during cross-regional takeover.

[0220] The smart cloud warehouse risk evolution assessment system dynamically adjusts the allocation of virtual resource pools based on the full risk map covering the entire region / road segment. As shown in Table 12, the smart cloud warehouse risk evolution assessment system demonstrates the evolution of scheduling logic from local autonomy to global collaboration.

[0221] Table 12: Description of Resource Scheduling Levels Based on Risk Ripple Prediction

[0222] After the cross-regional scheduling instruction is issued, the policy feedback layer performs the following closed-loop verification.

[0223] The strategy feedback layer uses the shadow execution mechanism to retrieve the link delay parameters in the digital twin model to verify whether the signaling transmission delay between cross-regional nodes meets the toll time and space constraints for this special situation handling.

[0224] The strategy feedback layer uses the secure authorization interface of the management platform to dynamically push temporary control permissions for external devices in the faulty lane to the takeover agent, and ensures that the permissions are immediately cancelled when the work order is completed.

[0225] The evolution assessment layer continuously monitors the risk entropy evolution trend after diversion. If the load growth rate in the target area is lower than the predicted value and the charging success rate rebounds, the evolution assessment layer determines that the scheduling is effective; otherwise, the ripple analysis scope is further expanded, and the evolution assessment layer seeks resource support in a wider area.

[0226] By implementing cross-regional resource elastic scheduling based on risk prediction, the risk of "local emergencies causing global paralysis" under the smart cloud warehouse architecture has been resolved. This has enabled precise alignment of operational resources with physical risks, greatly improving the business elasticity and management precision of large-scale road networks under extreme load conditions.

[0227] Example 4 This embodiment provides a method for risk evolution assessment of smart cloud warehouses based on digital twins, which uses the risk evolution assessment system for smart cloud warehouses based on digital twins involved in Embodiment 1 or Embodiment 2 to assess the risks during the operation of smart cloud warehouses.

[0228] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart cloud warehouse risk evolution assessment system based on digital twins, characterized in that, include: The perception and execution layer extracts the underlying operational metadata of electromechanical equipment in real time by constructing a distributed edge perception matrix. The twin base layer constructs a digital twin model that synchronously maps the physical toll plaza based on the configured digital model and logical graph of the smart cloud warehouse, and maps the metadata into business status nodes with semantic information. The evolution evaluation layer integrates a toll collection spatiotemporal constraint violation judgment mechanism and a risk analysis algorithm based on spatiotemporal causal chain. It takes the physical environment jitter mapped in real time by the twin base layer as a disturbance variable and uses a nonlinear mapping mechanism to quantitatively evaluate the degree of intrusion of the physical environment jitter on toll collection certainty. The strategy feedback layer includes a shadow executor and a self-healing configuration interface. The shadow executor performs a priori rehearsals of the instructions issued to the physical entity in the twin space to verify the security and timeliness of remote operations in the current nondeterministic network environment. If the verification is successful, the self-healing configuration interface dynamically adjusts the operating parameters of the edge node or reconstructs the service path through the configuration channel.

2. The smart cloud warehouse risk evolution assessment system based on digital twins according to claim 1, characterized in that, The metadata extracted in real time by the perception and execution layer includes: device electrical parameters, heartbeat packet latency, operational fingerprint metadata of electromechanical equipment, round-trip latency and jitter frequency of the underlying operational metadata network link of electromechanical equipment, and the perception and execution layer injects timestamps into key signaling in the toll service flow.

3. The smart cloud warehouse risk evolution assessment system based on digital twins according to claim 2, characterized in that, The twin base layer includes: Input unit, used to receive the metadata; Digital twin units are used to build digital twin models; The data synchronization unit dynamically injects the collected physical metadata into the real-time attribute parameters of the corresponding twin node in the digital twin model, establishing a two-way binding between the real-time data stream of the perception and execution layer and the attributes of the digital twin model. The digital twin unit includes: The dynamic activation module accesses the configuration catalog database of the management platform in the metadata, parses the predefined standardized digital model of the equipment, and retrieves matching component instances from the metadata database according to the list of field-deployed equipment in the metadata to generate twin node entities in the digital twin model. The automatic assembly module, based on the actual physical layout of the lanes and the business logic connection relationship, uses the topology engine to realize the automatic assembly of the virtual square, and establishes the spatial coordinate relationship and logical cascade path between each managed node in the three-dimensional virtual space. The state machine injection module injects logical computation operators corresponding to the functions of each twin node, enabling the virtual model to simulate real business behavior.

4. The smart cloud warehouse risk evolution assessment system based on digital twins according to claim 3, characterized in that, The state machine injection module includes: The state machine modeling submodule defines each managed node within the lane as a protocol entity with logical processing capabilities; it formally describes each twin node using a quintuple; the quintuple includes: a set of business states, a set of input events, a state transition function, an initial business state, and a set of termination states; The protocol injection submodule parses the protocol template issued by the configurable management platform, encapsulates the timing constraints, logical priorities, and exception jump branches of the charging business into logical code blocks, and dynamically deploys them to the computing kernel of the virtual node.

5. The smart cloud warehouse risk evolution assessment system based on digital twins according to claim 4, characterized in that, The twin base layer also utilizes dynamic calibration and simulation technology to establish a response time function for each hardware node that is managed through configurable configuration; and the twin base layer uses the timestamp data continuously reported by the perception and execution layer to perform online learning and real-time calibration of the response time function, thereby realizing dynamic calibration of the response delay baseline.

6. The smart cloud warehouse risk evolution assessment system based on digital twins according to claim 5, characterized in that, The evolutionary evaluation layer includes: The violation judgment unit, as the execution subject of the business logic break prior judgment in the evolution evaluation layer, uses the violation judgment mechanism based on the toll time and space constraints to deeply couple the lane physical geometric parameters with the dynamic passage time sequence to determine the business determinism. The risk quantification unit is used to activate the risk ripple evolution model when the violation judgment unit triggers a violation signal, quantify the nonlinear impact of physical failures on business continuity, and generate business layer indicators, operation layer indicators, and physical layer indicators. The evolution output unit performs spatiotemporal coupling on the business layer indicators, operation layer indicators, and physical layer indicators to generate a full risk map of the smart cloud warehouse.

7. The smart cloud warehouse risk evolution assessment system based on digital twins according to claim 6, characterized in that, For a specific vehicle entering the lane, the violation determination unit calculates the time cutoff time of the vehicle speed variable, calculates the cutoff time of each business link in real time based on the kinematic model, and adjusts the time delay tolerance according to different vehicle speeds.

8. The smart cloud warehouse risk evolution assessment system based on digital twins according to claim 7, characterized in that, The shadow executor includes: The coupling prediction unit predicts the expected time when the command arrives at the physical end by retrieving the WAN link status monitored in real time by the perception execution layer. The security discrimination unit uses a security discrimination criterion based on spatiotemporal conflict to determine the validity of instruction execution.

9. A smart cloud warehouse risk evolution assessment system based on digital twins according to claim 8, characterized in that, When the evolutionary assessment is performed or the shadow executor performs a shadow rehearsal, the digital twin model calls the response time function to generate a predicted latency that conforms to the current physical state, thereby realizing function-based business timing simulation.

10. A smart cloud warehouse risk evolution assessment system based on digital twins according to claim 9, characterized in that, The policy feedback layer also includes a time delay adaptive compensation unit, which uses feedforward compensation and edge autonomous switching mechanism to ensure the determinism of control command execution and the security of physical entities.

11. A smart cloud warehouse risk evolution assessment system based on digital twins according to claim 10, characterized in that, The strategy feedback layer also includes a prediction maintenance unit; The predictive maintenance unit uses the digital twin model to couple the degradation of the underlying hardware physical performance with the spatiotemporal constraints of the upper-layer business. By monitoring the microscopic shifts in the hardware operation fingerprint, it predicts the degree of impact of performance degradation on business determinism.

12. The smart cloud warehouse risk evolution assessment system based on digital twins according to claim 11, characterized in that, The strategy feedback layer, through its self-healing configuration interface, can also dynamically reconstruct the configuration logic of the lane intelligent unit based on the full risk map of the smart cloud warehouse.

13. A method for assessing the risk evolution of a smart cloud warehouse based on digital twins, characterized in that, The risks during the operation of a smart cloud warehouse are assessed using the digital twin-based smart cloud warehouse risk evolution assessment system as described in any one of claims 1 to 10.

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