Intelligent early warning and dynamic evaluation method for logistics park

Abstract

The invention relates to the technical field of logistics park management, and particularly discloses a logistics park intelligent early warning and dynamic evaluation method, and the method comprises the steps: carrying out the time-space alignment of vehicle trajectory data, cargo pressure distribution data and environment data collected by a heterogeneous sensor network, and inputting the data into a dynamic threshold adjustment module to generate a self-adaptive warning threshold; the dynamic threshold adjustment module establishes a dynamic calculation model containing the equipment utilization rate and the vehicle density based on the matching relationship between the historical operation mode and the real-time operation state; based on a multi-dimensional evaluation system constructed based on a self-adaptive warning threshold, generating a comprehensive evaluation index through collaborative analysis of three groups of indexes including transportation efficiency, safety risk and resource utilization; and when the comprehensive evaluation index deviates from the preset range, triggering a grading early warning mechanism associated with the deviation degree. According to the invention, dynamic evaluation and intelligent early warning are realized, and real-time monitoring and intelligent evaluation regulation and control of the operation state of the logistics park are realized through space-time correlation analysis and an adaptive decision-making mechanism of multi-source heterogeneous data.

Description

Technical Field

[0001] This invention relates to the field of logistics park management technology, and more specifically to a smart early warning and dynamic evaluation method for logistics parks. Background Technology

[0002] A logistics park is a place in which various logistics facilities and different types of logistics companies are spatially concentrated in an area where logistics operations are concentrated and where several modes of transportation connect. It is also a gathering point for logistics companies of a certain scale and with multiple service functions.

[0003] The intelligent development of logistics parks is a transformation process that improves operational efficiency and service quality through digitalization. Its core lies in using intelligent devices to monitor the park's operational status in real time. For example, sensors collect data on the storage environment of goods, combined with automated equipment to achieve unmanned warehouse management. Modern smart logistics parks commonly use IoT sensing devices to achieve full-scenario data collection. This includes RFID electronic tags for cargo tracking, temperature and humidity sensors for monitoring the storage environment, and intelligent gate systems for tracking vehicle entry and exit.

[0004] As logistics parks expand in scale and increase in business complexity, traditional monitoring and early warning systems have revealed significant shortcomings in terms of dynamic adaptability, multi-dimensional collaboration, and resource allocation linkage.

[0005] Chinese Patent Publication No. CN116090947A discloses a digital logistics park management platform, including a warehouse management module, a vehicle management module, a security management module, and an environmental management module. The warehouse management module includes a storage location management unit, a task management unit, and an operation management unit. The security management module includes a video surveillance unit, a fire safety visualization monitoring management unit, and an alarm unit. The environmental management module includes a temperature monitoring unit, a humidity monitoring unit, and a water level monitoring unit. The vehicle management module manages the location of parking spaces, loading and unloading spaces, and vehicle reservations within the logistics park, locates warehouse forklifts within the logistics park, and displays the forklift trajectories in real time. Compared to existing technologies, this platform optimizes and innovates upon traditional logistics park management methods, achieving integrated warehouse management, vehicle management, security management, and environmental management, enabling information sharing and data linkage, and improving the overall control efficiency of the park.

[0006] Existing logistics park management systems suffer from significant shortcomings in dynamic adaptability, multi-dimensional collaborative evaluation, early warning-resource allocation linkage, and visual interaction, leading to low early warning accuracy, delayed response, and resource waste. Currently, there is an urgent need for a smart management solution that can integrate multi-source data, dynamically adjust thresholds, support multi-dimensional comprehensive evaluation, and link with real-time resource allocation.

[0007] Therefore, how to provide a smart early warning and dynamic evaluation method for logistics parks is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0008] In view of this, the present invention provides a smart early warning and dynamic evaluation method for logistics parks. Based on data fusion, it realizes dynamic evaluation and intelligent early warning. Through spatiotemporal correlation analysis of multi-source heterogeneous data and adaptive decision-making mechanism, it realizes real-time monitoring and intelligent evaluation and control of the operation status of logistics parks.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] A method for intelligent early warning and dynamic evaluation of logistics parks includes the following steps:

[0011] Vehicle trajectory data, cargo pressure distribution data, and environmental data collected through a heterogeneous sensor network are processed by spatiotemporal alignment and then input into a dynamic threshold adjustment module to generate an adaptive warning threshold.

[0012] The dynamic threshold adjustment module establishes a dynamic calculation model that includes equipment utilization and vehicle density based on the matching relationship between historical operating modes and real-time operation status.

[0013] The multi-dimensional evaluation system built on the adaptive warning threshold generates a comprehensive evaluation index through the collaborative analysis of three sets of indicators: transportation efficiency, safety risk, and resource utilization.

[0014] When the comprehensive evaluation index deviates from the preset range, a graded early warning mechanism associated with the degree of deviation is triggered, and a resource allocation instruction corresponding to the early warning level is pushed to the park control system.

[0015] This invention achieves real-time performance and adaptability of the early warning mechanism through the synergistic effect of heterogeneous data acquisition and dynamic threshold adjustment, reducing the false alarm rate compared to fixed threshold systems. Simultaneously, the adoption of a multi-dimensional evaluation system improves security management efficiency and enhances the accuracy of resource allocation commands compared to traditional methods.

[0016] Preferably, in the above-mentioned intelligent early warning and dynamic evaluation method for logistics parks, the heterogeneous sensor network includes a UWB positioning module, a pressure sensor array, and a temperature and humidity sensor group.

[0017] The UWB positioning module works in conjunction with the vehicle navigation system to collect three-dimensional spatial coordinates. The pressure sensor array captures the stress transmission path of stacked goods through a honeycomb distribution structure. The temperature and humidity sensor group generates environmental monitoring data by combining the operating parameters of the ventilation system in the storage area. The pressure sensor array and the temperature and humidity sensor group perform data preprocessing through edge computing nodes. The data from the UWB positioning module and the pressure sensor array establish a spatial mapping relationship through a clock synchronization protocol.

[0018] Preferably, in the above-mentioned intelligent early warning and dynamic evaluation method for logistics parks, the dynamic threshold adjustment module is constructed in the following manner:

[0019] A work intensity benchmark is established by using equipment operation curves from a historical operation mode library for multiple consecutive cycles. A congestion index is calculated based on the spatiotemporal overlap of vehicle movement trajectories using a real-time status analysis unit. The equipment operation curves and vehicle congestion index are cross-validated to generate a safe distance threshold adjustment coefficient. The maximum stacking height threshold of the storage area is dynamically corrected by combining real-time readings from temperature and humidity sensors.

[0020] Preferably, in the above-mentioned intelligent early warning and dynamic evaluation method for logistics parks, the multi-dimensional evaluation system is constructed in the following manner:

[0021] The transportation efficiency dimension calculates scheduling optimization parameters by the ratio of vehicle turnover rate to route planning deviation rate; the safety risk dimension generates regional risk levels based on the weighted value of cargo tilt angle and fire lane occupancy rate; the resource utilization dimension determines energy-saving control strategies based on the correlation curve between equipment idle rate and energy consumption rate; the transportation efficiency dimension, safety risk dimension indicators, and resource utilization dimension generate comprehensive evaluation indicators through a priority weight allocation module.

[0022] Preferably, in the above-mentioned intelligent early warning and dynamic evaluation method for logistics parks, the regional risk level is calculated in the following way:

[0023] Risk correction is applied to personnel intrusion events by superimposing time decay factors, with differentiated decay coefficients for working and non-working periods. The risk correction results and cargo pressure distribution data are used to generate a risk heat map through a spatial overlay algorithm.

[0024] Preferably, in the above-mentioned intelligent early warning and dynamic evaluation method for logistics parks, the triggering of the hierarchical early warning mechanism includes:

[0025] The Level 1 warning triggers a platform allocation optimization instruction in the storage area, which reduces the path planning deviation rate through real-time replanning of vehicle dispatch routes;

[0026] The Level 2 warning activates the emergency lane guidance indicator control module, which generates an avoidance guidance path based on vehicle location data;

[0027] The Level 3 warning system sends an emergency stop command to the loading and unloading equipment and simultaneously activates the linkage control protocol of the fire protection system.

[0028] Preferably, in the above-mentioned intelligent early warning and dynamic evaluation method for logistics parks, the park control system is equipped with a three-dimensional visualization terminal. The three-dimensional visualization terminal uses a digital twin engine to spatially fuse and render vehicle trajectory data, cargo pressure data and evaluation indicators. Among them, the transportation efficiency parameter is mapped to the color gradient effect of the trajectory line, the safety risk parameter is converted into the transparency parameter of the three-dimensional model, and the resource utilization parameter generates a dynamically updated statistical panel.

[0029] Preferably, in the above-mentioned intelligent early warning and dynamic evaluation method for logistics parks, the generation of the resource allocation instruction includes:

[0030] The platform utilization optimization scheme is generated based on vehicle density distribution by the transportation resource reallocation unit, and the equipment scheduling optimization unit adjusts the equipment sharing strategy according to the deviation direction of the evaluation index. The platform utilization optimization scheme and the equipment sharing strategy generate joint control commands through resource matching algorithm.

[0031] Preferably, in the above-mentioned intelligent early warning and dynamic evaluation method for logistics parks, the park control system includes an early warning execution terminal, which integrates a device control interface and a visualization display module.

[0032] Preferably, in the above-mentioned intelligent early warning and dynamic evaluation method for logistics parks, the visualization display module is equipped with a HUD display device, which generates dynamic guidance signs based on the spatial relationship between the real-time coordinates of the vehicle and the avoidance path data.

[0033] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a smart early warning and dynamic evaluation method for logistics parks. The present invention achieves a comprehensive improvement in the operational efficiency and safety management level of logistics parks by constructing an early warning and dynamic evaluation system with dynamic perception, intelligent analysis, hierarchical response and resource linkage.

[0034] By leveraging the collaborative sensing and spatiotemporal alignment of heterogeneous sensor networks, precise acquisition and correlation analysis of vehicle trajectories, cargo stacking mechanical characteristics, and environmental parameters are achieved. Multi-source data fusion technology effectively eliminates the problem of relying on a single data source. Based on cross-validation with historical pattern libraries and real-time operational status, a warning threshold is generated that dynamically adjusts with vehicle density and equipment load. This mechanism improves the accuracy of safety distance threshold adjustment compared to fixed threshold systems.

[0035] A collaborative analysis model integrating three-dimensional indicators—transportation efficiency, safety risk, and resource utilization—breaks through the limitations of traditional single-dimensional evaluation. The correlation calculation between route planning deviation rate and vehicle turnover rate improves scheduling optimization efficiency. A real-time linkage mechanism between three-level early warning and resource allocation instructions realizes a complete early warning system encompassing "monitoring-assessment-response." A digital twin engine transforms vehicle trajectories, cargo pressure, and evaluation parameters into a three-dimensional interactive scene, improving the efficiency of monitoring data parsing.

[0036] This invention achieves comprehensive benefits such as reduced security risks, increased resource utilization, and improved operational efficiency in logistics parks through a data-driven dynamic perception and intelligent decision-making mechanism. It effectively solves problems such as delayed early warning, biased evaluation, and disconnected response in traditional systems, and provides closed-loop, adaptive, and intelligent development for smart logistics park management. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0038] Figure 1 The attached figure is a schematic diagram of the process of this invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] This invention discloses a smart early warning and dynamic evaluation method for logistics parks, comprising the following steps:

[0041] Vehicle trajectory data, cargo pressure distribution data, and environmental data collected through a heterogeneous sensor network are processed by spatiotemporal alignment and then input into a dynamic threshold adjustment module to generate an adaptive warning threshold.

[0042] The dynamic threshold adjustment module establishes a dynamic calculation model that includes equipment utilization and vehicle density based on the matching relationship between historical operating modes and real-time operation status.

[0043] A multi-dimensional evaluation system based on adaptive warning thresholds generates comprehensive evaluation indicators through the collaborative analysis of three sets of indicators: transportation efficiency, safety risk, and resource utilization.

[0044] When the comprehensive evaluation index deviates from the preset range, a graded early warning mechanism associated with the degree of deviation is triggered, and a resource allocation instruction corresponding to the early warning level is pushed to the park control system.

[0045] This invention achieves real-time performance and adaptability of the early warning mechanism through the synergistic effect of heterogeneous data acquisition and dynamic threshold adjustment, reducing the false alarm rate compared to fixed threshold systems. Simultaneously, the adoption of a multi-dimensional evaluation system improves security management efficiency and enhances the accuracy of resource allocation commands compared to traditional methods.

[0046] To further optimize the above technical solution, the heterogeneous sensor network includes a UWB positioning module, a pressure sensing array, and a temperature and humidity sensor group.

[0047] The UWB positioning module works in conjunction with the vehicle navigation system to collect three-dimensional spatial coordinates. The pressure sensor array captures the stress transmission path of stacked goods through a honeycomb distribution structure. The temperature and humidity sensor group generates environmental monitoring data by combining the operating parameters of the ventilation system in the storage area. The pressure sensor array and the temperature and humidity sensor group perform data preprocessing through edge computing nodes. The data from the UWB positioning module and the pressure sensor array establish a spatial mapping relationship through a clock synchronization protocol.

[0048] The spatial mapping between the UWB positioning module and the pressure sensor array enables vehicle positioning accuracy to reach ±15cm, significantly reducing the error rate of cargo stacking stress detection. The honeycomb distribution structure effectively captures the mechanical transmission characteristics of pallets, shortening the response time for shelf tilt warnings.

[0049] To further optimize the above technical solution, the dynamic threshold adjustment module is constructed in the following manner:

[0050] A work intensity benchmark is established by using equipment operation curves from a historical operation mode library for multiple consecutive cycles. A congestion index is calculated based on the spatiotemporal overlap of vehicle movement trajectories using a real-time status analysis unit. The equipment operation curves and vehicle congestion index are cross-validated to generate a safe distance threshold adjustment coefficient. The maximum stacking height threshold of the storage area is dynamically corrected by combining real-time readings from temperature and humidity sensors.

[0051] The operational intensity benchmark established by the historical operation mode database reduces the deviation rate of equipment utilization assessment. The spatiotemporal overlap algorithm of the vehicle congestion index improves road traffic efficiency and increases the accuracy of dynamic correction of warehouse stacking height threshold.

[0052] To further optimize the above technical solutions, a multi-dimensional evaluation system is constructed using the following method:

[0053] The transportation efficiency dimension calculates scheduling optimization parameters by the ratio of vehicle turnover rate to route planning deviation rate; the safety risk dimension generates regional risk levels based on the weighted value of cargo tilt angle and fire lane occupancy rate; the resource utilization dimension determines energy-saving control strategies based on the correlation curve between equipment idle rate and energy consumption rate; the transportation efficiency dimension, safety risk dimension indicators, and resource utilization dimension generate comprehensive evaluation indicators through a priority weight allocation module.

[0054] The introduction of route planning deviation rate in the transportation efficiency dimension improves the calculation efficiency of vehicle scheduling optimization parameters. The weighted algorithm for the safety risk dimension enhances the accuracy of hazardous area identification, and the resource utilization correlation curve model effectively reduces energy waste.

[0055] To further optimize the above technical solutions, the regional risk level is calculated using the following method:

[0056] Risk correction is applied to personnel intrusion events using a time decay factor, with differentiated decay coefficients for operational and non-operational periods. The risk correction results are then overlaid with cargo pressure distribution data to generate a risk heatmap using a spatial overlay algorithm. The time decay factor design enhances the sensitivity of risk assessment for intrusion events during non-operational periods. The spatial overlay algorithm for the risk heatmap accelerates the location of key monitoring areas and reduces the cost of manual patrols.

[0057] To further optimize the above technical solution, the triggering of the tiered early warning mechanism includes:

[0058] The Level 1 warning triggers a platform allocation optimization instruction in the storage area, which reduces the path planning deviation rate through real-time replanning of vehicle dispatch routes;

[0059] The Level 2 warning activates the emergency lane guidance indicator control module, which generates an avoidance guidance path based on vehicle location data;

[0060] The Level 3 early warning system sends an emergency stop command to the loading and unloading equipment and simultaneously activates the fire protection system's linkage control protocol. This progressive response mechanism of the Level 3 early warning system reduces the incidence of major accidents. The route replanning algorithm improves vehicle evacuation efficiency after emergency lanes are opened.

[0061] To further optimize the above technical solutions, the park control system is equipped with a 3D visualization terminal. The 3D visualization terminal uses a digital twin engine to spatially fuse and render vehicle trajectory data, cargo pressure data and evaluation indicators. Among them, transportation efficiency parameters are mapped to the color gradient effect of trajectory lines, safety risk parameters are converted into the transparency parameters of the 3D model, and resource utilization parameters generate dynamically updated statistical panels.

[0062] The parameter mapping technology of the 3D visualization terminal improves the efficiency of monitoring data parsing. The transparency parameter conversion method enhances the visibility of safety hazard identification, and the dynamic update frequency of the statistical panel can reach the millisecond level.

[0063] To further optimize the above technical solution, the generation of resource configuration instructions includes:

[0064] The platform utilization optimization scheme is generated based on vehicle density distribution by the transportation resource reallocation unit, and the equipment scheduling optimization unit adjusts the equipment sharing strategy according to the deviation direction of evaluation indicators. The platform utilization optimization scheme and the equipment sharing strategy generate joint control commands through a resource matching algorithm. The joint control commands of the resource matching algorithm improve the platform utilization rate. The dynamic adjustment function of the equipment sharing strategy improves the response speed of cross-regional cooperation and reduces the equipment idle rate.

[0065] To further optimize the above technical solution, the park control system includes an early warning execution terminal, which integrates a device control interface and a visualization display module.

[0066] To further optimize the above technical solution, the visualization display module is equipped with a HUD display device. The HUD display device generates dynamic guidance signs based on the spatial relationship between the vehicle's real-time coordinates and the avoidance path data. The dynamic guidance signs of the HUD display device greatly improve the accuracy of vehicle avoidance operations.

[0067] Technical principle:

[0068] The core principle of this invention lies in achieving real-time monitoring and intelligent control of the operational status of logistics parks through spatiotemporal correlation analysis of multi-source heterogeneous data and an adaptive decision-making mechanism. Specific technical principles include:

[0069] Multimodal data collaborative perception: Through spatiotemporal synchronous acquisition of heterogeneous sensor networks, multi-dimensional physical quantities such as vehicle 3D coordinates, cargo stacking stress distribution, and environmental parameters are obtained. Each sensor group performs clock alignment and data preprocessing through edge computing nodes to establish a spatial mapping model between vehicle trajectory and cargo pressure, eliminating the data silo problem of traditional independent sensor systems.

[0070] Dynamic threshold generation mechanism: Based on the historical operation mode library, the equipment utilization benchmark curve is extracted, and a dynamic congestion index is calculated by combining real-time vehicle density and path overlap. A cross-validation algorithm is used to generate safe distance thresholds and stacking height thresholds that adaptively adjust with work intensity. This mechanism overcomes the rigidity of fixed threshold systems, improving early warning sensitivity and dynamically matching it with the actual operating conditions of the park.

[0071] A multi-dimensional collaborative evaluation model is constructed, integrating three evaluation dimensions: transportation efficiency (vehicle turnover rate, route deviation), safety risks (cargo tilting, intrusion incidents), and resource utilization (energy consumption, equipment idle rate). Priority weight allocation and time decay factor correction algorithms are used to transform discrete indicators into comprehensive evaluation parameters. A 3D visualization engine maps the evaluation results into an interactive digital twin scene, enabling a holistic understanding of the park's operational status.

[0072] Tiered response and resource allocation linkage: A three-tiered early warning trigger mechanism is designed (yellow optimization instruction, orange emergency control, and red emergency stop protocol), establishing a real-time mapping relationship between early warning levels and resource allocation instructions. When evaluation parameters are detected to deviate from the threshold, control instructions such as platform scheduling optimization, emergency channel opening, and equipment sharing strategy adjustment are automatically triggered, forming a closed-loop management chain of "monitoring-assessment-response".

[0073] At the hardware level, it integrates terminal devices such as HUD guidance and audible and visual alarms to ensure the accurate physical execution of warning commands.

[0074] This invention solves the problems of slow response, one-sided evaluation, and low intelligence level in traditional logistics park management systems by deeply coordinating data-driven decision-making with physical systems, and achieves synchronous optimization of safety control and multi-dimensional evaluation.

[0075] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0076] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for intelligent early warning and dynamic evaluation of logistics parks, characterized in that, Includes the following steps: Vehicle trajectory data, cargo pressure distribution data, and environmental data collected through a heterogeneous sensor network are processed by spatiotemporal alignment and then input into a dynamic threshold adjustment module to generate an adaptive warning threshold. The dynamic threshold adjustment module establishes a dynamic calculation model that includes equipment utilization and vehicle density based on the matching relationship between historical operating modes and real-time operation status. A multi-dimensional evaluation system based on adaptive warning thresholds generates comprehensive evaluation indicators through the collaborative analysis of three sets of indicators: transportation efficiency, safety risk, and resource utilization. When the comprehensive evaluation index deviates from the preset range, a graded early warning mechanism associated with the degree of deviation is triggered, and a resource allocation instruction corresponding to the early warning level is pushed to the park control system.

2. The intelligent early warning and dynamic evaluation method for logistics parks according to claim 1, characterized in that, The heterogeneous sensor network includes a UWB positioning module, a pressure sensing array, and a temperature and humidity sensor group. The UWB positioning module works in conjunction with the vehicle navigation system to collect three-dimensional spatial coordinates. The pressure sensor array captures the stress transmission path of stacked goods through a honeycomb distribution structure. The temperature and humidity sensor group generates environmental monitoring data by combining the operating parameters of the ventilation system in the storage area. The pressure sensor array and the temperature and humidity sensor group perform data preprocessing through edge computing nodes. The data from the UWB positioning module and the pressure sensor array establish a spatial mapping relationship through a clock synchronization protocol.

3. The intelligent early warning and dynamic evaluation method for logistics parks according to claim 1, characterized in that, The dynamic threshold adjustment module is constructed in the following manner: A work intensity benchmark is established by using equipment operation curves from a historical operation mode library for multiple consecutive cycles. A congestion index is calculated based on the spatiotemporal overlap of vehicle movement trajectories using a real-time status analysis unit. The equipment operation curves and vehicle congestion index are cross-validated to generate a safe distance threshold adjustment coefficient. The maximum stacking height threshold of the storage area is dynamically corrected by combining real-time readings from temperature and humidity sensors.

4. The intelligent early warning and dynamic evaluation method for logistics parks according to claim 1, characterized in that, The multi-dimensional evaluation system is constructed in the following way: The transportation efficiency dimension calculates scheduling optimization parameters by the ratio of vehicle turnover rate to route planning deviation rate; the safety risk dimension generates regional risk levels based on the weighted value of cargo tilt angle and fire lane occupancy rate. The resource utilization rate dimension determines energy-saving control strategies based on the correlation curve between equipment idle rate and energy consumption rate; the transportation efficiency dimension, safety risk dimension indicators, and resource utilization rate dimension generate comprehensive evaluation indicators through the priority weight allocation module.

5. The intelligent early warning and dynamic evaluation method for logistics parks according to claim 4, characterized in that, The risk level of the area is calculated in the following way: Risk correction is applied to personnel intrusion events by superimposing time decay factors, with differentiated decay coefficients for working and non-working periods. The risk correction results and cargo pressure distribution data are used to generate a risk heat map through a spatial overlay algorithm.

6. The intelligent early warning and dynamic evaluation method for logistics parks according to claim 1, characterized in that, The triggering of the tiered early warning mechanism includes: The Level 1 warning triggers a platform allocation optimization instruction in the storage area, which reduces the path planning deviation rate through real-time replanning of vehicle dispatch routes; The Level 2 warning activates the emergency lane guidance indicator control module, which generates an avoidance guidance path based on vehicle location data; The Level 3 warning system sends an emergency stop command to the loading and unloading equipment and simultaneously activates the linkage control protocol of the fire protection system.

7. The intelligent early warning and dynamic evaluation method for logistics parks according to claim 1, characterized in that, The park's control system is equipped with a 3D visualization terminal. The 3D visualization terminal uses a digital twin engine to spatially fuse and render vehicle trajectory data, cargo pressure data, and evaluation indicators. Among them, transportation efficiency parameters are mapped to the color gradient effect of trajectory lines, safety risk parameters are transformed into transparency parameters of the 3D model, and resource utilization parameters generate dynamically updated statistical panels.

8. The intelligent early warning and dynamic evaluation method for logistics parks according to claim 1, characterized in that, The generation of the resource configuration instructions includes: The platform utilization optimization scheme is generated based on vehicle density distribution by the transportation resource reallocation unit, and the equipment scheduling optimization unit adjusts the equipment sharing strategy according to the deviation direction of the evaluation index. The platform utilization optimization scheme and the equipment sharing strategy generate joint control commands through resource matching algorithm.

9. The intelligent early warning and dynamic evaluation method for logistics parks according to claim 1, characterized in that, The park control system includes an early warning execution terminal, which integrates a device control interface and a visualization display module.

10. The intelligent early warning and dynamic evaluation method for logistics parks according to claim 9, characterized in that, The visualization module is equipped with a HUD display device, which generates dynamic guidance signs based on the spatial relationship between the vehicle's real-time coordinates and the avoidance path data.

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