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CN-121981623-A - Intelligent logistics park management system based on digital twinning

CN121981623ACN 121981623 ACN121981623 ACN 121981623ACN-121981623-A

Abstract

The invention discloses a digital twinning-based intelligent logistics park management system, which relates to the technical field of intelligent logistics and digital twinning and comprises a perception acquisition module, a twinning model module, a deduction analysis module, a control acquisition module and a feedback adjustment module. The sensing acquisition module acquires the original data of the park through the Internet of things sensing node. The twinning model module builds a digital twinning body and generates a real-time digital mirror image. The deduction analysis module deducts a dynamic operation reference in the virtual space based on the business rule and the real-time mirror image, and analyzes and generates a control strategy set. And controlling the acquisition module to execute the strategy and acquire actual response data. The feedback adjustment module feeds back the actual data to the model, and adaptively reconstructs a control strategy by calculating the deviation between the mirror image and the reference, and adjusts the physical entity according to the corrected strategy. The system realizes dynamic deduction optimization and closed-loop self-adaptive control on the operation of the logistics park.

Inventors

  • CHEN ZHEN

Assignees

  • 华翊数智(无锡)互联科技有限公司

Dates

Publication Date
20260505
Application Date
20260225

Claims (10)

  1. 1. An intelligent logistics park management system based on digital twinning, which is characterized by comprising: The sensing acquisition module acquires an original sensing data stream describing the physical entity state of the park through an Internet of things sensing node deployed in the logistics park; The twin model module is used for constructing a digital twin model corresponding to the physical entity of the logistics park, synchronizing the original perceived data stream to the digital twin model and driving the digital twin model to generate a real-time digital mirror image of the park operation; The deduction analysis module deducts and generates a dynamic operation reference in a virtual space of the digital twin body model based on a preset business rule and the real-time digital mirror image, and analyzes a control strategy set for adjusting the physical entity operation state of the logistics park according to the dynamic operation reference; The control acquisition module executes the control strategy set in the physical space and synchronously acquires actual response data generated by physical entities of the logistics park; and the feedback adjustment module is used for feeding back the actual response data into the digital twin body model, calculating deviation characteristics between the real-time digital mirror image and the dynamic operation reference, carrying out self-adaptive reconstruction on the control strategy set according to the deviation characteristics, generating a corrected control strategy set, and adjusting and controlling physical entities of the logistics park according to the corrected control strategy set.
  2. 2. The digital twinning-based intelligent logistics park management system of claim 1, wherein the obtaining the raw sensory data stream describing the physical entity status of the park via the internet of things sensory nodes deployed in the logistics park comprises: Configuring a sensing node network of storage units, loading and unloading points, transportation channels and equipment facilities in the covering flow park; The sensing node network continuously captures multi-component heterogeneous sensing signals including cargo displacement, equipment state, environmental indexes and vehicle tracks; and performing time stamp alignment and data format normalization processing on the multi-component heterogeneous sensing signals to form an original sensing data stream with space-time consistency.
  3. 3. The digital twinning-based intelligent logistics park management system of claim 1, wherein said constructing a digital twinning model corresponding to said logistics park physical entity and synchronizing said raw awareness data stream to said digital twinning model comprises: constructing a digital twin model with geometric, physical and rule attributes according to the spatial layout, equipment attributes and business flow logic of the logistics park; establishing a real-time mapping relation between the original perceived data stream and the virtual entity attribute in the digital twin body model; And continuously inputting the original perceived data stream and driving the state update of the corresponding attribute in the digital twin body model through the real-time mapping relation, so as to form a real-time digital mirror image which is synchronously evolved with the physical space.
  4. 4. The digital twinning-based intelligent logistics park management system of claim 1, wherein the deriving and generating dynamic operational benchmarks in the virtual space of the digital twinning body model based on the preset business rules and the real-time digital mirror image comprises: loading a business rule constraint set about efficiency, safety and energy consumption in a digital twin body model; simulating the operation process of the park under the service rule constraint set in a future decision period in a virtual space of the digital twin body model by taking the real-time digital mirror image as an initial state; And extracting expected state sequences related to cargo flow direction, resource occupation and operation time sequence from the simulation operation process, wherein the expected state sequences form the dynamic operation standard.
  5. 5. The digital twinning-based intelligent logistics park management system of claim 4, wherein resolving the set of control strategies for adjusting the physical entity operating state of the logistics park based on the dynamic operating criteria comprises: comparing the current state reflected by the real-time digital mirror image with an expected state sequence in the dynamic operation reference to identify a state difference point; For each state difference point, matching at least one preliminary regulation action according to a predefined strategy mapping library; And arranging and integrating all the matched preliminary regulation actions according to the physical entity objects and the execution time sequences of the actions to form the control strategy set.
  6. 6. The digital twinning-based intelligent logistics park management system of claim 1, wherein said injecting said actual response data feedback into said digital twinning model, calculating a deviation signature between said live digital image and said dynamic operational reference comprises: after the physical space executes the control strategy set, new actual response data are obtained; updating the digital twin body model with the new actual response data as input, thereby refreshing the live digital mirror image; selecting key state indexes from the refreshed real-time digital mirror image, and comparing the key state indexes with expected state indexes at the same moment in the dynamic operation reference item by item; And quantitatively calculating the difference value of each state index contrast, and analyzing the distribution mode and the evolution trend of the difference values of a plurality of indexes, wherein the difference value, the distribution mode and the evolution trend jointly form the deviation feature.
  7. 7. The digital twinning-based intelligent logistics park management system of claim 6, wherein said adaptively reconstructing said set of control strategies based on said bias characteristics, generating a corrected set of control strategies comprises: Establishing a correlation model between deviation characteristics and control strategy efficiency; inputting the calculated deviation characteristics into the correlation model, and evaluating the efficiency achievement degree of each regulation action in the current control strategy set; Performing parameter fine adjustment, timing rearrangement or partial action replacement on the regulation actions in the control strategy set according to the efficiency achievement degree; and re-integrating the regulated, rearranged or replaced regulation actions to form the corrected control strategy set.
  8. 8. The digital twinning-based intelligent logistics park management system of claim 7, wherein said controlling the adjustment of physical entities of a logistics park in accordance with said corrected set of control strategies comprises: converting the corrected control strategy set into a specific control instruction sequence which can be identified by the execution equipment in the logistics park; Issuing the control instruction sequence to execution equipment in a physical entity of a corresponding logistics park; and monitoring the execution feedback of the execution equipment to the control instruction sequence, taking the execution feedback as a part of new actual response data, and inputting the new actual response data into a subsequent processing flow.
  9. 9. The intelligent logistics park management system based on digital twinning of claim 3, wherein the real-time mapping relationship is implemented by configuring a data interface and a communication protocol, so as to ensure that each item of data in the original perceived data stream can accurately trigger the numerical update of the corresponding virtual entity attribute in the digital twinning body model.
  10. 10. The digital twinning-based intelligent logistics park management system of claim 5, wherein the policy mapping library is constructed by analyzing successful regulatory cases in historical management data, wherein correspondence between different state difference patterns and validated regulatory actions is stored.

Description

Intelligent logistics park management system based on digital twinning Technical Field The invention belongs to the technical field of intelligent logistics and digital twinning, and particularly relates to an intelligent logistics park management system based on digital twinning. Background The operation management of the current intelligent logistics park mainly depends on the combination of an internet of things monitoring system and an information management platform. By deploying the sensing equipment in the park, the collection and monitoring of logistics equipment, vehicles, goods and environmental state data can be realized, and management staff can judge and decide according to real-time conditions and alarm information displayed by the platform. This mode constitutes a digital reproduction and passive response to the physical world operating conditions. The essential drawbacks of the prior art solutions are their statics and hysteresis. The system functions are limited to the perception and presentation of events that have occurred, and their control logic relies on fixed rules or thresholds that are set in advance. This model cannot simulate and prospectively deduce the future operating state of multiple loops, multiple targets in digital space, and thus cannot actively generate a cooperative control strategy serving the overall optimization targets. When the system faces complex operation scenes which are dynamically changed and mutually coupled, the static rule is difficult to realize global real-time optimization regulation and control. There is a need for a management system that enables active optimization and self-correction. The system needs to have the capability of carrying out dynamic deduction in a digital space according to the real-time state and the business rule, so as to generate a prospective optimization instruction. The system also needs to continuously compare the deviation between the deduction expectation and the actual state according to the actual execution feedback of the physical world, dynamically adjust the deduction model and the control instruction according to the deviation, form a closed loop with self-adaption capability, and realize the continuous autonomous optimization of the management strategy in response to the uncertainty in the actual operation. Disclosure of Invention The present invention aims to solve at least one of the technical problems existing in the prior art; therefore, the invention provides a digital twinning-based intelligent logistics park management system, which comprises: The sensing acquisition module acquires an original sensing data stream describing the physical entity state of the park through an Internet of things sensing node deployed in the logistics park; The twin model module is used for constructing a digital twin model corresponding to the physical entity of the logistics park, synchronizing the original perceived data stream to the digital twin model and driving the digital twin model to generate a real-time digital mirror image of the park operation; The deduction analysis module deducts and generates a dynamic operation reference in a virtual space of the digital twin body model based on a preset business rule and the real-time digital mirror image, and analyzes a control strategy set for adjusting the physical entity operation state of the logistics park according to the dynamic operation reference; The control acquisition module executes the control strategy set in the physical space and synchronously acquires actual response data generated by physical entities of the logistics park; and the feedback adjustment module is used for feeding back the actual response data into the digital twin body model, calculating deviation characteristics between the real-time digital mirror image and the dynamic operation reference, carrying out self-adaptive reconstruction on the control strategy set according to the deviation characteristics, generating a corrected control strategy set, and adjusting and controlling physical entities of the logistics park according to the corrected control strategy set. Further, the obtaining, by the internet of things sensing node deployed in the logistics park, the original sensing data stream describing the physical entity state of the park includes: Configuring a sensing node network of storage units, loading and unloading points, transportation channels and equipment facilities in the covering flow park; The sensing node network continuously captures multi-component heterogeneous sensing signals including cargo displacement, equipment state, environmental indexes and vehicle tracks; and performing time stamp alignment and data format normalization processing on the multi-component heterogeneous sensing signals to form an original sensing data stream with space-time consistency. Further, the constructing a digital twin model corresponding to the physical entity of the logistics park and synchronizing