An internet-of-things management method and system applied to high-speed service area construction

Abstract

The application relates to the field of high-speed service areas, in particular to a management method and system for high-speed service area construction. The method comprises the following steps: acquiring a high-speed vehicle dynamic information set, analyzing the charging urgency index of each required charging vehicle according to the high-speed vehicle dynamic information set, and constructing a virtual priority charging queue; acquiring a construction site multi-dimensional heat source information set, analyzing the distribution of dynamic point heat sources and dynamic area heat sources based on the construction site multi-dimensional heat source information set, combining a real-time sunshine shadow map, and constructing a dynamic heat field optimization map; based on the virtual priority charging queue and the dynamic heat field optimization map, carrying out accurate pile position allocation, providing construction dynamic regulation and control suggestions, and generating a high-speed service area construction period charging scheduling report. In the high-speed service area construction process, the application ensures that the service area can still maintain efficient, stable and safe operation under complex construction environment.

Description

Technical Field

[0001] This application relates to the field of highway service areas, and in particular to an IoT management method and system for use in the construction of highway service areas. Background Technology

[0002] In the field of green construction and intelligent transportation integration in highway service areas, service area charging piles are key infrastructure to ensure the long-distance range of new energy vehicles. Their stable and efficient operation during construction is directly related to road traffic efficiency, user satisfaction and construction safety. It is a core link to promote the seamless upgrading of transportation infrastructure and the optimization of user experience.

[0003] However, existing service area charging scheduling methods lack a mechanism for accurately sensing and dynamically offsetting the micro-environmental heat load under construction disturbances when faced with multiple physical field coupling interferences from construction activities. This not only fails to allocate suitable thermal safety charging pile positions to vehicles that urgently need charging, but may also induce a chain of problems such as equipment overheating and queuing chaos in complex scenarios where construction and charging are carried out simultaneously, thus constituting a dual bottleneck in operational efficiency and safety. Summary of the Invention

[0004] This application provides an IoT management method and system for construction in highway service areas to solve the above-mentioned technical problems.

[0005] Firstly, this application provides an IoT management method for construction in highway service areas. The method includes: acquiring a dynamic information set of highway vehicles; analyzing the charging urgency index of each vehicle requiring charging based on the dynamic information set, and constructing a virtual priority charging queue; acquiring a multi-dimensional heat source information set of the construction site; analyzing the distribution of dynamic point heat sources and dynamic area heat sources based on the multi-dimensional heat source information set, and constructing a dynamic thermal field optimization map by combining it with a real-time solar shading map; accurately allocating charging pile positions based on the virtual priority charging queue and the dynamic thermal field optimization map, providing suggestions for dynamic construction control, and generating a charging scheduling report for the highway service area construction period.

[0006] Through the above technical solutions, intelligent priority allocation of charging resources is achieved through virtual queues, effectively reducing vehicle queuing and waiting, ensuring power supply for emergency vehicles, and improving user satisfaction; dynamic thermal field management can prevent equipment overheating failures and construction safety risks, enhancing construction reliability; the final generated dispatch report provides comprehensive decision support for managers, ensuring that the service area can still maintain efficient, stable and safe operation in complex construction environments.

[0007] Optionally, constructing the virtual priority charging queue includes: the high-speed vehicle dynamic information set includes the vehicle's real-time remaining battery power, the vehicle model's basic power consumption weight, and its real-time location and driving trajectory; based on the high-speed vehicle dynamic information set, calculating the vehicle's baseline energy consumption urgency and predicting the vehicle's remaining time to reach the service area; dynamically analyzing the coupling relationship between the baseline energy consumption urgency and the remaining time, and generating the charging urgency index using the urgency index calculation formula; and sorting all vehicles waiting to be charged according to the charging urgency index to construct the virtual priority charging queue.

[0008] Optionally, the construction of the dynamic thermal field optimization map includes: the multi-dimensional heat source information set at the construction site includes thermal radiation data of construction machinery, thermal field distribution data of clustered vehicles, and ambient background temperature data; based on the thermal radiation data of construction machinery, high-power construction equipment is identified and located, and the equipment is modeled as the dynamic point heat source with radiation intensity and influence radius; based on the thermal field distribution data of clustered vehicles, through thermal imaging clustering analysis, the vehicle clusters under atypical congestion patterns caused by construction are modeled as the dynamic area heat source with temperature gradient and diffusion effect; the ambient background temperature data is called, and the real-time solar shadow map rendered in real time based on the astronomical algorithm and the fusion of the temporary construction facility model is superimposed as the solar correction coefficient; the dynamic point heat source, the dynamic area heat source, and the solar correction coefficient are dynamically coupled and spatially superimposed to generate the dynamic thermal field optimization map.

[0009] Optionally, the process of modeling the dynamic planar heat source with temperature gradient and diffusion effect includes: deconstructing the atypical congestion pattern induced by construction, analyzing the vehicle mixing ratio and thermal state within the cluster, modeling high-heat-generating vehicles as high-heat cores, and calculating the thermal bridging strength between heat cores based on real-time vehicle spacing; constructing a non-uniform temperature field within the cluster with heat cores as peak values ​​and vehicle spacing as diffusion resistance based on the heat core distribution and the thermal bridging strength; coupling the forced convection wind field generated by the periodic operation of construction machinery, analyzing the stripping and transport effects of the wind field on the non-uniform temperature field, and predicting the coverage path and intensity of the transported heat plume on downstream charging piles; and generating the dynamic planar heat source by combining the non-uniform temperature field and the heat plume diffusion model corrected by the forced convection wind field.

[0010] Optionally, the process of using the solar radiation correction coefficient includes: constructing a dynamic digital twin scene specific to the service area construction period, integrating the outlines of permanent buildings, charging piles, and temporary construction facilities dynamically loaded based on the construction progress; calculating the real-time solar vector based on astronomical algorithms to drive scene lighting and shadow rendering, focusing on calculating the composite dynamic shadow boundary of the temporary construction facility outlines and permanent buildings in the pile location area to obtain the real-time shading efficiency of each pile location under the current construction state; to isolate and quantify the net impact of the construction activities themselves on the thermal environment of the pile location, constructing a dynamic construction-induced shading efficiency coefficient by comparing the actual shading area under current construction with the shading area without construction reference; weightedly fusing the ambient background temperature data with the construction-induced shading efficiency coefficient to generate a pile location-specific micro-thermal environment correction value for each charging pile location, integrating the micro-thermal environment correction values ​​of all pile locations to construct the solar radiation correction coefficient strongly correlated with construction.

[0011] Optionally, the process of dynamic coupling and spatial overlay includes: assigning construction perception weights to the dynamic point heat source, the dynamic area heat source, and the solar radiation correction coefficient based on the construction stage and activity type: increasing the weight of mechanical point heat sources during the pile foundation construction stage and increasing the weight of vehicle area heat sources during the road paving stage; constructing a spatiotemporal calculation grid for the heat load of pile locations in the service area, overlaying the weighted heat source data on the grid under construction disturbance, and simultaneously integrating the unsteady heat source trajectories formed by the movement paths of construction machinery and the diversion vehicles; applying a construction isolation zone thermal buffer correction to the overlaid comprehensive heat field, identifying the local thermal barrier effect formed by construction enclosures and temporary greening, and downgrading the heat load of the corresponding pile locations; outputting the comprehensive heat load index of each pile location during the construction period, and completing the construction of the dynamic heat field optimization map.

[0012] Optionally, generating the charging scheduling report during the construction period of the highway service area includes: executing a dynamic charging pile allocation strategy, which is based on the matching mapping between charging urgency and charging pile heat load, dynamically activating charging piles with high urgency for vehicles and optimizing previously unavailable high heat load charging piles through a pre-cooling strategy, and triggering dynamic replanning of charging pile allocation in real time; generating construction dynamic control suggestions in reverse based on the charging pile allocation results and the execution requirements of the pre-cooling strategy, integrating the dynamic charging pile allocation results with the closed-loop control instruction set, and generating the charging scheduling report during the construction period of the highway service area.

[0013] Optionally, the implementation of the dynamic charging pile allocation strategy includes: prioritizing the allocation of high-quality charging piles with low heat load levels in the dynamic thermal field optimization map for vehicles ranked higher in the virtual priority charging queue; for vehicles with high charging urgency but only high heat load charging piles currently available, initiating a pre-cooling strategy for construction coordination: before the vehicle arrives, based on the construction dynamic control suggestions, temporarily adjusting the operating position of construction machinery or activating temporary sprinkler facilities to proactively reduce the local heat load of the target charging pile, and performing reallocation after the heat load level of the charging pile is reduced to an available threshold; and tracking construction progress and thermal field changes in real time, dynamically replanning the charging pile allocation for vehicles that have not yet arrived at the service area to respond to sudden changes in the thermal field caused by construction activities.

[0014] Optionally, the generation of construction dynamic control suggestions includes: generating construction plan fine-tuning suggestions based on the clustering of high-heat-load piles in the pile location allocation results, the construction plan fine-tuning suggestions including proposing to adjust high-heat construction activities to non-peak charging periods, or temporarily changing the dense operation area of ​​heavy machinery; generating traffic diversion optimization suggestions for the construction area based on the pre-cooling strategy implemented to alleviate the heat field, dynamically planning vehicle travel paths to bypass the area where pre-cooling operations are being performed, and avoiding flow line conflicts between charging vehicles and cooling operations; and integrating all suggestions into an executable construction linkage instruction set to constitute the construction dynamic control suggestions.

[0015] Secondly, this application provides an IoT management system for construction in highway service areas. The system includes: a charging queue sorting module, used to acquire a dynamic information set of highway vehicles, analyze the charging urgency index of each vehicle requiring charging based on the dynamic information set, and construct a virtual priority charging queue; a dynamic thermal field generation module, used to acquire a multi-dimensional heat source information set of the construction site, analyze the distribution of dynamic point heat sources and dynamic area heat sources based on the multi-dimensional heat source information set, and construct a dynamic thermal field optimization map in conjunction with a real-time solar shading map; and a construction report generation module, used to accurately allocate charging pile positions based on the virtual priority charging queue and the dynamic thermal field optimization map, provide suggestions for dynamic construction control, and generate a charging scheduling report for the highway service area construction period. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram illustrating an application scenario provided in one embodiment of this application;

[0018] Figure 2 A flowchart illustrating an IoT management method applied to highway service area construction, as provided in one embodiment of this application;

[0019] Figure 3 This is a schematic diagram of the structure of an IoT management system applied to the construction of a highway service area, provided as an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0021] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0022] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0023] Existing service area charging scheduling methods lack a mechanism for accurately sensing and dynamically offsetting the micro-environmental heat load under construction disturbances when faced with multiple physical field coupling interferences from construction activities. This not only fails to allocate suitable thermal safety charging pile positions to vehicles that urgently need charging, but may also induce a chain of problems such as equipment overheating and queuing chaos in complex scenarios where construction and charging are carried out simultaneously, thus creating a dual bottleneck for operational efficiency and safety.

[0024] Based on this, this application provides an IoT management method and system for highway service area construction. First, real-time vehicle dynamic information is acquired through road network monitoring and vehicle-to-everything (V2X) networks. Parameters such as remaining battery power and distance are analyzed to calculate the charging urgency index for each vehicle. Based on this, a dynamically updated virtual priority charging queue is constructed to ensure that vehicles with high urgency receive priority service. Simultaneously, sensor networks deployed in the construction area collect data on machinery, personnel, and environmental heat sources. Combined with real-time solar shading maps generated by meteorological and geographic information systems, the distribution of dynamic point and area heat sources is analyzed to construct a visualized dynamic thermal field optimization map, accurately identifying hotspots and risk areas in the construction area. Finally, the system integrates the priority queue and thermal field map to intelligently and precisely allocate charging stations (e.g., guiding vehicles to cooler, safer stations) and generate dynamic construction control suggestions (e.g., adjusting operating times in high-temperature areas). This is automated into a charging scheduling report containing all key decisions and data, which is then output to construction management personnel, providing an integrated solution for construction period management. By using virtual queues to achieve intelligent priority allocation of charging resources, vehicle queuing is effectively reduced, power supply for emergency vehicles is guaranteed, and user satisfaction is improved; dynamic thermal field management can prevent equipment overheating failures and construction safety risks, and enhance construction reliability; the final generated dispatch report provides management personnel with comprehensive decision support, ensuring that the service area can maintain efficient, stable and safe operation even in complex construction environments.

[0025] Figure 1 This is a schematic diagram illustrating an application scenario provided by this application. During the construction of highway service areas, the method provided in this application is used to ensure the efficient, stable, and safe operation of the service area even in complex construction environments.

[0026] Specifically, the method of this application is applied to any server that communicates with the highway network monitoring system and the high-speed IoT monitoring equipment. The server acquires dynamic vehicle information from the highway network monitoring system and multi-dimensional heat source information from the construction site provided by the high-speed IoT monitoring equipment. First, it obtains real-time vehicle dynamic information through network monitoring and vehicle-to-everything (V2X) communication, analyzes parameters such as remaining battery power and distance, calculates the charging urgency index for each vehicle, and constructs a dynamically updated virtual priority charging queue to ensure that vehicles with high urgency receive priority service. Simultaneously, it utilizes a sensor network deployed in the construction area to collect data on mechanical, personnel, and environmental heat sources. Combined with a real-time solar shading map generated by a meteorological and geographic information system, it analyzes the distribution of dynamic point and area heat sources, constructs a visualized dynamic thermal field optimization map, and accurately identifies hotspots and risk areas in the construction area. Finally, the system integrates the priority queue and the thermal field map, intelligently executes precise pile allocation (e.g., guiding vehicles to cool, safe piles), generates dynamic construction control suggestions (e.g., adjusting operating times in high-temperature areas), and automatically generates a charging scheduling report containing all key decisions and data, which is then output to construction management personnel, providing an integrated solution for construction period management.

[0027] For specific implementation details, please refer to the following examples.

[0028] Figure 2 This is a flowchart illustrating an IoT management method for construction in a highway service area, provided as an embodiment of this application. The method of this embodiment can be applied to servers in the above scenario. Figure 2 As shown, the method includes:

[0029] S201. Obtain the dynamic information set of high-speed vehicles. Based on the dynamic information set of high-speed vehicles, analyze the charging urgency index of each vehicle that needs to be charged, and construct a virtual priority charging queue.

[0030] The high-speed vehicle dynamic information set can be a real-time data collection of vehicles traveling near highway service areas, including real-time remaining battery power, vehicle model's basic power consumption weight, and real-time location and driving trajectory. The data originates from the highway network monitoring system. The charging urgency index is an indicator used to quantify the urgency of a vehicle's charging needs, calculated based on factors such as remaining battery power, distance to the service area, and battery health. The virtual priority charging queue is a dynamically sorted virtual queue based on the charging urgency index, used to determine the priority allocation order of vehicles at service area charging stations. This queue is not a physical queue but a priority list simulated by software algorithms, and can be updated based on real-time data.

[0031] Specifically, during highway service area construction, certain areas will inevitably be occupied to ensure construction safety and progress. These areas often include the original charging station locations, significantly reducing the number of available charging stations (for example, 10 charging stations may be reduced to 4-5 due to construction). Simultaneously, construction may cause temporary power outages or prevent normal access for some charging stations (e.g., due to line modifications or construction machinery obstruction), further exacerbating the scarcity of charging station locations. Furthermore, the charging needs of highway vehicles are sudden and unpredictable. Without a scientific prioritization mechanism, a chaotic situation may arise where "vehicles with sufficient battery power occupy charging stations for extended periods, while vehicles with extremely low battery power or about to break down cannot charge in time." This could even lead to charging delays affecting the work of emergency vehicles. Traditional charging scheduling allocates charging stations based solely on a "first-come, first-served" or simple reservation order, completely disregarding the sharp reduction in charging stations during construction and the urgency of vehicle charging needs, thus failing to guarantee core charging requirements. This step involves acquiring real-time vehicle location, remaining battery power, and other dynamic information about high-speed vehicles from multiple sources. It analyzes the charging urgency index for each vehicle and sorts them according to the index, estimated arrival time at the service area, and interface compatibility. A virtual priority charging queue is then constructed and updated in real-time. By building this virtual priority charging queue, intelligent allocation of charging resources can be achieved during service area construction, prioritizing the needs of vehicles with high urgency, reducing vehicle waiting time and congestion risks, and improving user satisfaction. Simultaneously, this queue helps optimize charging pile utilization, avoids energy waste, and supports smooth and safe traffic flow during construction.

[0032] S202. Obtain a multi-dimensional heat source information set at the construction site. Based on the multi-dimensional heat source information set at the construction site, analyze the distribution of dynamic point heat sources and dynamic area heat sources, and combine it with a real-time solar shadow map to construct a dynamic thermal field optimization map.

[0033] The multi-dimensional heat source information set at the construction site can be a data collection of various heat sources within the construction area of ​​the highway service area, including thermal radiation data of construction machinery, thermal field distribution data of clustered vehicles, and ambient background temperature data. The data originates from highway IoT monitoring equipment. Dynamic point heat sources refer to localized heat sources that are mobile or intermittent within the construction area, such as welding equipment or temporary generators. The location and intensity of these heat sources change with the construction progress. Dynamic area heat sources refer to heat sources covering a large area within the construction area, such as exhaust vents of mobile vehicles. These heat sources typically cover a wide area and are affected by human factors. The real-time solar radiation and shadow map refers to a solar radiation and shadow distribution map generated based on real-time weather data, solar altitude angle, and geographical information of the construction area. The dynamic thermal field optimization map is a comprehensive visualization map of heat source distribution and solar radiation and shadow, used to identify hotspots, temperature gradients, and optimization suggestions within the construction area. This map is generated through data fusion technology to support thermal environment management decisions.

[0034] Specifically, the construction process at highway service areas generates numerous dynamic heat sources, a unique and critical issue during construction. On one hand, construction machinery (such as excavators, pavers, and cranes) continuously dissipates heat, creating dynamic point-like heat sources with temperatures potentially reaching 60-80°C. If a vehicle charging station is located near such a heat source, the high temperature directly impacts the battery's heat dissipation efficiency, potentially leading to overheating, bulging, or even fires. On the other hand, moving vehicles create dynamic area-like heat sources. These heat sources cover a wide area and change rapidly. Combined with direct sunlight in summer, this can cause significant temperature increases in localized areas, affecting not only the driving experience of charging vehicles but also potentially accelerating the aging of electronic components within the charging station and reducing its lifespan. Furthermore, the movement of shadows caused by sunlight leads to dynamic changes in the area affected by heat sources (e.g., the heat source impact is weaker in the morning, but significantly increases in heat intensity after direct sunlight in the afternoon), further complicating the thermal environment. Traditional charging scheduling completely disregards heat source factors, allocating charging stations solely based on availability. This can easily guide vehicles to dangerous areas with excessive heat loads, posing serious safety hazards. This step obtains multi-dimensional heat source information, such as heat dissipation from construction machinery, identifies dynamic point / area heat sources, draws real-time solar shading maps, and calculates heat intensity after fusion to delineate safe / cautious / prohibited charging zones, constructing a dynamic thermal field optimization map. By constructing this dynamic thermal field optimization map, the thermal environment of the construction area can be monitored in real time, preventing equipment failures and safety accidents caused by overheating, and improving construction reliability and efficiency. Simultaneously, this map helps optimize energy use, such as reducing unnecessary cooling energy consumption, and supports the rational allocation of charging piles, enhancing the overall intelligence level of construction management.

[0035] S203. Based on the virtual priority charging queue and dynamic thermal field optimization map, accurately allocate charging pile positions, provide suggestions for dynamic construction control, and generate a charging scheduling report for the construction period of highway service areas.

[0036] Precise charging pile allocation refers to the process of intelligently allocating available charging piles to vehicles based on virtual priority charging queues and dynamic thermal field optimization maps, while considering charging urgency and thermal environment factors to ensure that the allocation plan meets vehicle needs while avoiding heat-related risks. Dynamic construction control suggestions refer to construction adjustment recommendations based on thermal field analysis and charging demand, such as adjusting construction equipment operating times, changing material stacking locations, or optimizing personnel scheduling to reduce thermal impact and improve charging efficiency. The highway service area construction period charging scheduling report can be a comprehensive report including charging pile allocation results, control suggestions, a thermal field status summary, and key performance indicators. This report is generated using data visualization technology for construction management personnel to make decisions and record information.

[0037] Specifically, during construction at highway service areas, conflicts inevitably arise between construction areas, charging areas, and vehicle routes. For example, the movement of construction machinery may block the path of some charging piles, preventing vehicles from reaching their assigned charging positions. An unreasonable construction schedule (such as large-scale road work during peak charging hours) may further reduce charging space and exacerbate traffic congestion. Traditional charging scheduling completely ignores construction coordination, focusing only on matching vehicles with charging piles, leading to mutual interference between construction and charging—construction may be hampered by charging vehicles occupying lanes, and charging vehicles may be unable to charge normally due to construction obstructions, creating a two-way dilemma of "difficult construction, difficult charging." Furthermore, temporary facilities during construction (such as construction barriers and material storage areas) are subject to change at any time, causing dynamic changes in the accessibility of charging areas, which traditional fixed charging pile allocation methods cannot adapt to. Therefore, precise allocation of charging piles based on virtual priority charging queues and dynamic thermal field optimization maps, while providing dynamic control suggestions to construction teams (such as adjusting construction areas, optimizing machinery scheduling, and avoiding high-impact operations during peak charging periods), is a core solution for coordinating construction and charging conflicts and ensuring their orderly progress. This is also a unique management requirement during the construction phase. This step obtains the real-time status of charging piles, precisely allocates piles using virtual queues and thermal field maps, provides dynamic control suggestions such as adjusting construction routes, and summarizes the information to generate and update a charging scheduling report for the highway service area during the construction period. Through precise pile allocation and dynamic construction control suggestions, efficient coordination between charging resources and construction activities can be achieved, improving charging safety and efficiency, and reducing construction delays and energy waste. The generated charging scheduling report provides comprehensive decision support, helping managers optimize construction plans and improve the overall operational level of the service area during the construction period.

[0038] The method provided in this embodiment first acquires real-time vehicle dynamic information through road network monitoring and vehicle-to-everything (V2X) networks, analyzes parameters such as remaining battery power and distance, calculates the charging urgency index for each vehicle, and constructs a dynamically updated virtual priority charging queue to ensure that vehicles with high urgency receive priority service. Simultaneously, a sensor network deployed in the construction area collects data on machinery, personnel, and environmental heat sources, and combines this with a real-time solar shading map generated by a meteorological and geographic information system to analyze the distribution of dynamic point and area heat sources, constructing a visualized dynamic thermal field optimization map to accurately identify hotspots and risk areas in the construction area. Finally, the system integrates the priority queue and the thermal field map, intelligently executes precise charging station allocation (such as guiding vehicles to cool, safe charging stations), and generates dynamic construction control suggestions (such as adjusting operating times in high-temperature areas), automatically generating a charging scheduling report containing all key decisions and data, and outputting it to construction management personnel, providing an integrated solution for construction period management. By using virtual queues to achieve intelligent priority allocation of charging resources, vehicle queuing is effectively reduced, power supply for emergency vehicles is guaranteed, and user satisfaction is improved; dynamic thermal field management can prevent equipment overheating failures and construction safety risks, and enhance construction reliability; the final generated dispatch report provides management personnel with comprehensive decision support, ensuring that the service area can maintain efficient, stable and safe operation even in complex construction environments.

[0039] In some embodiments, the high-speed vehicle dynamic information set includes the vehicle's real-time remaining battery power, the vehicle model's basic power consumption weight, and its real-time location and driving trajectory; based on the high-speed vehicle dynamic information set, the vehicle's baseline energy consumption urgency is calculated, and the remaining time for the vehicle to reach the service area is predicted; the coupling relationship between the baseline energy consumption urgency and the remaining time is dynamically analyzed, and a charging urgency index is generated through the urgency index calculation formula; all vehicles waiting to be charged are sorted according to the charging urgency index to construct a virtual priority charging queue.

[0040] The vehicle's real-time remaining battery power can be the percentage or absolute value of the battery's remaining power at the current moment, representing the vehicle's remaining usable electrical energy. The vehicle model's basic power consumption weight can be an energy consumption efficiency index determined based on parameters such as vehicle model, weight, and motor efficiency, reflecting the typical energy consumption level of different vehicle models per unit distance or unit time. Real-time location and driving trajectory can be the vehicle's current latitude and longitude coordinates, driving speed, direction, historical movement path, and expected driving route. The baseline energy consumption urgency can be a quantitative indicator characterizing the risk level of a vehicle reaching its destination without external intervention based on its current remaining battery power and energy consumption level. The charging urgency index can be a quantitative indicator used to accurately prioritize vehicle charging, derived from the charging urgency index calculation formula. A higher value indicates a more urgent charging need, and the vehicle should be given priority in obtaining charging resources; it is the core basis for constructing a virtual priority charging queue. The urgency index calculation formula can be a mathematical logic formula that integrates the baseline energy consumption urgency and remaining time to quantitatively calculate the final charging urgency of the vehicle, including weight allocation and collaborative calculation rules for the two core factors, ensuring that the index can scientifically reflect the actual charging demand priority of the vehicle.

[0041] Specifically, during construction at highway service areas, traditional charging scheduling methods typically employ a first-come, first-served approach or rely solely on remaining battery power, which has significant drawbacks. It ignores the combined effects of vehicle energy efficiency, real-time driving status, and time pressure. This can lead to high-demand vehicles (such as large trucks and small cars both having 20% ​​battery remaining, but trucks have a higher base power consumption weight (e.g., trucks have a weight of 3, cars 1), resulting in an actual range of only 50 kilometers, while cars have a range of 150 kilometers. This can easily lead to scarce charging stations being occupied by low-demand cars, while high-demand trucks face the risk of breakdowns) arriving late or experiencing unfair scheduling, resulting in vehicle congestion, energy depletion, and even traffic jams, severely impacting construction safety and operational efficiency. To address the above issues, this step first acquires dynamic information about high-speed vehicles using multi-source technology: Real-time remaining battery power (e.g., 28%) is obtained from the vehicle's vehicle-to-everything (V2X) module; a pre-defined vehicle model database is retrieved to determine the basic power consumption weight (e.g., 2.5 for large freight vehicles); real-time location (e.g., 15 km from a service area) and driving trajectory are obtained using roadside millimeter-wave radar and navigation software (e.g., Gaode Maps); next, the baseline energy consumption urgency is calculated, for example, by multiplying the remaining battery power (28%) by the battery capacity (100 kWh) and the weight (2.5), resulting in a remaining range of approximately 112 km. Compared to the 15 km distance to the service area, the baseline urgency is initially determined to be relatively low; then, combined with real-time data from construction sections (e.g., construction causing a 50 km / h speed limit), a path planning algorithm is used to predict the remaining time (15 km ÷ 50 km / h). h = 0.3 hours, or 18 minutes); then the coupling relationship is analyzed. If another small SUV has 22% battery remaining (enough to support 88 kilometers) and is 10 kilometers away from the service area (construction is expected to take 12 minutes to reach, weight 1.2), its baseline urgency is lower but the time is closer, so its priority needs to be increased after coupling; finally, the formula is substituted (e.g., urgency index = baseline score × 0.6 + time correction score × 0.4, truck baseline score 55, time score 70, index 61; SUV baseline score 45, time score 85, index 61, and then fine-tuned according to interface compatibility, SUV is prioritized because it is compatible with fast charging piles), a virtual priority charging queue is generated, and the queue order is dynamically adjusted based on the real-time updated vehicle data of the roadside equipment (e.g., the time for trucks to encounter sudden congestion increases to 25 minutes).

[0042] The method provided in this embodiment analyzes the dynamic information set of high-speed vehicles, calculates the baseline energy consumption urgency and predicts the remaining time, dynamically analyzes the coupling relationship between the two to generate a charging urgency index, and constructs a virtual priority charging queue accordingly. This makes the allocation of charging piles more scientific and reasonable, can prioritize the charging needs of vehicles with high urgency, reduce vehicle waiting time and the risk of energy depletion, and improve the utilization rate of charging piles and the operational efficiency of highway service areas.

[0043] In some embodiments, the multidimensional heat source information set at the construction site includes thermal radiation data of construction machinery, thermal field distribution data of clustered vehicle groups, and ambient background temperature data. Based on the thermal radiation data of construction machinery, high-power construction equipment is identified and located, and the equipment is modeled as a dynamic point heat source with radiation intensity and influence radius. Based on the thermal field distribution data of clustered vehicle groups, through thermal imaging clustering analysis, the vehicle clusters under atypical congestion patterns caused by construction are modeled as dynamic area heat sources with temperature gradients and diffusion effects. Ambient background temperature data is called, and a real-time solar shadow map rendered in real time based on an astronomical algorithm and a fusion of temporary construction facility models is overlaid as a solar correction coefficient. The dynamic point heat sources, dynamic area heat sources, and solar correction coefficient are dynamically coupled and spatially superimposed to generate a dynamic thermal field optimization map.

[0044] Construction machinery thermal radiation data can be data on the intensity, range, peak temperature, and movement trajectory of thermal radiation generated by high-power construction equipment (such as excavators, pavers, cranes, and welding machines) during construction at highway service areas. This data directly reflects the core characteristics of dynamic point-like heat sources. Data on the thermal field distribution of clustered vehicles can be data on the thermal field generated by atypical vehicle congestion clusters caused by traffic control measures (such as lane reduction or entrance closure) or scarcity of parking spaces due to highway service area construction. This includes the cluster's temperature gradient, thermal diffusion range, core heat-generating area area, and duration, reflecting the distribution and intensity changes of dynamic areal heat sources. Background environmental temperature data can be basic environmental temperature data for the construction area and surrounding areas of the highway service area, unaffected by direct heat sources. This data serves as the baseline for correcting the thermal radiation of construction machinery and the thermal field of vehicle clusters. The astronomical algorithm can be based on the latitude and longitude of the highway service area (e.g., 35°N, 118°E) and the real-time date and time (e.g., 10:30 AM on October 13, 2024) to calculate the real-time solar operating parameters. The temporary construction facility model can be a three-dimensional digital model constructed based on the highway service area construction plan, containing various temporary facilities, including construction enclosures, material storage areas, temporary work sheds, and temporary power distribution boxes. The solar radiation correction coefficient can be a quantitative parameter used to adjust the solar radiation effect in the thermal field model, calculated based on environmental background temperature data and real-time solar shading maps.

[0045] Specifically, traditional thermal field analysis techniques suffer from core flaws, including being static and ignoring the characteristics of the construction scenario: they only focus on fixed heat sources (such as buildings) and cannot capture dynamic point heat sources generated by the movement of construction machinery (such as pavers and excavators); they fail to identify dynamic area heat sources formed by atypical vehicle congestion clusters caused by construction; and they do not take into account the shading effect of temporary facilities such as construction barriers and material piles on sunlight, leading to a disconnect between thermal field analysis and actual construction. During the construction period of highway service areas, the heat sources are highly dynamic (machine movement) and the thermal field is complex (vehicle clusters + machine radiation). If the traditional method is used to allocate charging pile positions, it is easy to guide vehicles to high-temperature areas (such as 60°C areas next to machinery), causing the risk of battery overheating, and misjudgment of the thermal field may also lead to conflicts between construction and charging space. To address the above issues, this step first involves collecting multi-dimensional heat source information from the construction site using multiple devices: high-precision infrared temperature sensors are installed on the engines of equipment such as excavators and pavers (power ≥ 50kW) to collect thermal radiation data (e.g., 1.2kW / m² during paver operation) and real-time location; a high-definition infrared thermal imager (e.g., FLIR T1040) is used to photograph the vehicle clusters caused by construction, obtaining the thermal field distribution (core area 45℃, transition area 38℃); an environmental monitoring station (e.g., DS-TH10) is set up in the non-construction area to collect the 28℃ ambient background temperature and correct it with meteorological data. Next, heat sources are modeled: machinery with power ≥ 50kW is modeled as dynamic point heat sources (influence radius 6 meters), and the vehicle clusters are modeled as dynamic area heat sources (temperature gradient 45℃-38℃-32℃) using a thermal imaging clustering algorithm. Next, a service area model including construction barriers (2 meters high) is constructed using a 3D GIS (such as ArcGIS Pro). A real-time solar eclipse map is generated using a ray tracing algorithm, combined with the solar azimuth angle (e.g., 30° at noon), and a solar eclipse correction factor is applied (1.2 for direct sunlight areas and 0.5 for barrier-shaded areas). Finally, the heat source is coupled with the correction factor, and a dynamic thermal field optimization map is generated using color grading (red ≥40℃, yellow 30-40℃, green <30℃), iterating in real-time as the machinery moves (updating its position every 5 minutes).

[0046] The method provided in this embodiment analyzes the multi-dimensional heat source information set at the construction site, identifies dynamic point heat sources and dynamic area heat sources, calculates the solar radiation correction coefficient by combining it with a real-time solar radiation shadow map, and dynamically couples to generate a dynamic thermal field optimization map. This enables the allocation of charging piles and construction control to be based on accurate thermal field data, avoids heat-related risks, improves the thermal management efficiency of the construction area, and enhances the reliability and safety of the charging process.

[0047] In some embodiments, atypical congestion patterns induced by construction are deconstructed, the vehicle mixing ratio and thermal state within the cluster are analyzed, high-heat vehicles are modeled as high-heat cores, and the thermal bridging strength between heat cores is calculated based on real-time vehicle spacing. Based on the heat core distribution and thermal bridging strength, a non-uniform temperature field within the cluster is constructed with heat cores as peak values ​​and vehicle spacing as diffusion resistance. The forced convection wind field generated by the periodic operation of construction machinery is coupled to analyze the stripping and transport effects of the wind field on the non-uniform temperature field, and predict the coverage path and intensity of the transported heat plume on downstream charging piles. By integrating the non-uniform temperature field and the heat plume diffusion model modified by the forced convection wind field, a dynamic planar heat source is generated.

[0048] Atypical congestion patterns can be caused by traffic control measures implemented during highway service area construction (such as lane reduction, entrance closure, or road occupancy due to construction), resulting in unique vehicle aggregation patterns distinct from regular road network congestion. Vehicle mixing ratio refers to the proportion of different types of vehicles within a congested cluster, such as the mix of gasoline-powered vehicles, hybrid vehicles, and pure electric vehicles, reflecting the overall heat output characteristics of the cluster. Thermal bridging strength is the degree of mutual influence between high-temperature cores through thermal radiation or conduction, calculated based on real-time vehicle spacing and vehicle thermal characteristics. Non-uniform temperature field can be a cluster internal temperature distribution model constructed based on the distribution of heat cores and thermal bridging strength, showing the spatial variation where peaks are at the heat cores and attenuation is affected by vehicle spacing resistance. Forced convection wind field can be a directional airflow field generated by the periodic operation of construction machinery (such as excavators or fan equipment), used to describe the dynamic influence of external wind on the thermal field. Thermal plume can be a hot air mass transported by a forced convection wind field, possessing specific temperature intensity and spatial movement characteristics.

[0049] Specifically, traditional dynamic planar heat source modeling technology has three major shortcomings in adapting to construction scenarios: First, it treats vehicle clusters as uniform heat fields, ignoring the differences in vehicle mixing ratios under atypical congestion during construction (e.g., trucks make up 35%, and their 2.8kW idle heat output is three times that of cars at 0.9kW), easily underestimating the temperature of high-heat areas. Second, it fails to consider the thermal bridging effect when vehicle distance is ≤2 meters (e.g., the superposition of heat radiation from adjacent trucks causes a local temperature increase of 6°C), leading to deviations in heat field intensity calculations. Third, it overlooks the forced convection wind fields generated by the periodic operation of construction machinery (e.g., pavers generate 2.5m / s airflow every 30 minutes), which can blow hot plumes towards charging piles without being detected. These shortcomings cause the thermal field analysis to become disconnected from actual construction, easily leading vehicles to high-temperature risk areas and causing battery accidents. To address the above issues, this step first uses high-definition monitoring (such as Hikvision DS-2CD7A26FWD-A) to identify atypical congestion vehicle mix ratios (e.g., 60% cars, 35% trucks, 5% SUVs), matches them with a vehicle thermal energy database (trucks 2.8kW, cars 0.9kW), and filters out high-heat cores with thermal energy ≥2.5kW (e.g., 5 idling trucks). Then, real-time vehicle distances are measured (e.g., 2.2 meters), and the thermal bridging strength (0.8) is calculated based on thermal radiation parameters. Next, using the high-heat core as the peak value (45℃), a cluster non-uniform temperature field is constructed based on vehicle distances (3 meters for diffusion resistance) (core 45℃, transition 38℃, edge 32℃). An infrared thermal imager (FLIR) is then used to analyze this field. The actual thermal field (core 43℃) captured by the T1040 was calibrated; then the forced convection wind field (2.3m / s, towards the northeast charging pile) of the paver (operation cycle 30 minutes) was collected by the micro wind speed sensor (SEN0234), and the stripping and transport effects of the wind field on the thermal field were coupled and analyzed to predict the thermal plume diffusion path (towards the charging pile 15 meters to the northeast); finally, the thermal plume model was corrected by combining the construction fence (2 meters high) (so that the thermal plume avoids the charging pile on the west side) to generate a dynamic planar heat source, which is updated every 10 minutes according to the increase or decrease of machinery operation and vehicles.

[0050] The method provided in this embodiment can accurately model the dynamic thermal field of vehicle clusters, taking into account differences in vehicle type and the influence of wind field, and generate high-fidelity dynamic planar heat sources. This makes the thermal field optimization map more consistent with actual construction conditions, thereby optimizing the layout of charging piles, avoiding the impact of high-heat areas on equipment performance, improving thermal management efficiency and overall safety during construction, and enhancing the adaptability and reliability of energy dispatch.

[0051] In some embodiments, a dynamic digital twin scene specific to the service area construction period is constructed, integrating permanent buildings, charging pile positions, and a library of outlines of temporary construction facilities dynamically loaded based on the construction progress; real-time solar vectors are calculated based on astronomical algorithms to drive scene lighting and shadow rendering, focusing on calculating the composite dynamic shadow boundary of the temporary construction facility outline library and permanent buildings in the pile position area to obtain the real-time shading efficiency of each pile position under the current construction state; to isolate and quantify the net impact of construction activities themselves on the thermal environment of the pile position, a dynamic construction-induced shading efficiency coefficient is constructed by comparing the actual shading area under current construction with the shading area without construction reference; the ambient background temperature data and the construction-induced shading efficiency coefficient are weighted and fused to generate a pile position-specific micro-thermal environment correction value for each charging pile position, and the micro-thermal environment correction values ​​of all pile positions are integrated to construct a solar radiation correction coefficient strongly correlated with construction.

[0052] A dynamic digital twin scenario can be a virtual physical environment constructed using 3D modeling technology during the construction of a highway service area, used to map the spatial structure and dynamic changes of the actual construction area in real time. A temporary construction facility outline library can be a collection of 3D models of temporary structures used during construction (such as scaffolding, fences, and temporary sheds), used to describe the spatial outline and shading characteristics of these facilities. A composite dynamic shadow boundary can be a composite shading boundary formed by the shadows cast by permanent buildings and temporary construction facilities in the charging pile area, reflecting the shadow effect of multiple structures superimposed. Real-time shading efficiency can be the proportion of the area covered by shadow at a charging pile location, used to quantify the shading effect of that location. A construction-induced shading efficiency coefficient can be a coefficient used to quantify the degree to which construction activities change the shading efficiency of a charging pile location, calculated by comparing the difference in shading area before and after construction. A micro-thermal environment correction value can be a thermal environment adjustment value calculated based on shading efficiency and ambient temperature for a single charging pile location, used to reflect the local thermal state of that location.

[0053] Specifically, traditional solar shading correction techniques suffer from three major shortcomings in adapting to different construction scenarios: First, they rely solely on static modeling of permanent buildings, completely ignoring the dynamic changes of temporary construction facilities (such as 2.5-meter-high barriers or 20㎡ material piles) as construction progresses, failing to capture their significant impact on pile shading. Second, they fail to separate the net impact of construction activities, conflating natural sunlight, permanent buildings, and construction shading, making it difficult to distinguish whether changes in pile shading are caused by construction or natural factors. Third, they use a uniform regional correction coefficient, ignoring the differentiated effects of temporary construction facilities on different pile locations (e.g., shading efficiency of 80% for piles near barriers and 30% for piles further away). These shortcomings cause the correction coefficients to become disconnected from actual construction conditions, easily leading to misjudgments of the pile location's thermal environment in thermal field maps, potentially driving vehicles into high-temperature risk areas. To address the above issues, this step first uses the service area BIM model (including the main building outline: 50 meters long, 20 meters wide, and 8 meters high) as a foundation to construct a dynamic digital twin scene. This integrates charging pile locations (coordinates 118.5°E, 32.8°N, each pile occupying 10 square meters) and loads a library of temporary construction facility outlines (such as a 2.5-meter-high, 30-meter-long construction fence, and a 20-square-meter, 3-meter-high material pile), linking it to the construction schedule (e.g., loading the western fence for days 1-10). Then, an astronomical algorithm is used to calculate the real-time solar vector (e.g., azimuth at 9 AM 55°, altitude...). (Angle 30°) Drive scene lighting and shadow rendering, solve the composite dynamic shadow boundary of permanent buildings and fences in the pile location area (e.g., the shadow of a certain pile location covers 8㎡), and obtain a real-time shading efficiency of 80%; then unload the temporary facilities, calculate the shading area of ​​the pile location without construction benchmark of 3㎡, and obtain the construction-induced shading efficiency coefficient of 2.67 by "8÷3"; finally, combined with the background temperature of 30℃ on site, calculate the micro-thermal environment correction value of the pile location according to "30℃×2.67", integrate all pile location correction values ​​to form the solar radiation correction coefficient, and update it every 15 minutes according to the solar vector.

[0054] The method provided in this embodiment can dynamically and accurately quantify the impact of sunlight on the thermal field during construction. Through digital twin and real-time shading analysis, a high-precision sunlight correction coefficient is generated, making the thermal field optimization map more consistent with the actual construction, thereby optimizing the allocation of charging pile positions, avoiding local overheating or energy efficiency reduction caused by changes in sunlight, and improving the accuracy of thermal management and the reliability of overall scheduling during construction.

[0055] In some embodiments, based on the construction stage and activity type, construction perception weights are assigned to dynamic point heat sources, dynamic area heat sources, and solar radiation correction coefficients respectively: the weight of mechanical point heat sources is increased during the pile foundation construction stage, and the weight of vehicle area heat sources is increased during the road paving stage; a spatiotemporal calculation grid for the heat load of pile locations in the service area is constructed, and the weighted heat source data is superimposed on the grid under construction disturbance, while simultaneously integrating the unsteady heat source trajectories formed by the movement paths of construction machinery and the diversion vehicles; a construction isolation zone thermal buffer correction is applied to the superimposed comprehensive heat field, the local thermal barrier effect formed by construction enclosures and temporary greening is identified, and the heat load of the corresponding pile locations is downgraded; the comprehensive heat load index of each pile location during the construction period is output, and the construction of the dynamic heat field optimization map is completed.

[0056] Construction perception weights can be dynamic weighting coefficients assigned to various heat sources based on different construction stages and activity types, reflecting the degree of influence of various heat sources on the thermal field under different construction conditions. The spatiotemporal calculation grid for heat load can be a three-dimensional spatiotemporal grid structure covering the charging pile area of ​​the service area, used to calculate the distribution and changes of heat load in time and space. The unsteady heat source trajectory can be the time-varying movement path of heat sources formed during the movement of construction machinery and vehicle diversion, reflecting the dynamic changes of heat sources in space. Thermal buffer correction can be a downgrade adjustment of the thermal field based on the thermal barrier effect (such as fencing and green belts) within the construction isolation zone, used to quantify the blocking effect of these barriers on heat propagation. Pile location heat load downgrading can be the process of reasonably reducing the original calculated heat load value of affected pile locations around the isolation zone during the thermal buffer correction process, due to the local thermal barrier effect blocking heat source diffusion; the reduction magnitude is positively correlated with the intensity of the thermal barrier effect. The comprehensive heat load index during the construction period can be a quantitative indicator characterizing the thermal environment state of the pile location after comprehensively considering the influence of various heat sources, used to assess the heat load level of the charging pile location.

[0057] Specifically, traditional thermal field coupling and superposition technology has four major defects in adapting to construction scenarios: it uses fixed weighted superposition of heat sources, ignoring the differences in construction stages (e.g., 60% of mechanical point heat sources in the pile foundation stage and 55% of vehicle surface heat sources in the road stage), leading to misjudgment of core heat sources; it does not include the non-steady-state heat source trajectories formed by the movement of construction machinery and diversion vehicles, resulting in incomplete thermal field calculations; it ignores the thermal barrier effect of construction enclosures (e.g., 2.5-meter-high enclosures) and temporary greening, leading to misjudgment of the heat load of pile positions around the isolation zone; and it superimposes in a coarse spatiotemporal dimension, failing to adapt to the dynamic changes in the construction thermal field. To address the above issues, this step first assigns construction perception weights based on the construction stage (e.g., pile foundation construction) and activity type (mechanical excavation): dynamic point heat sources 0.7, dynamic area heat sources 0.2, and solar radiation correction factor 0.1. If switching to the road paving stage, the weights are adjusted to area heat sources 0.6 and point heat sources 0.3. Then, using a GIS system, a spatiotemporal calculation grid for pile location heat load is constructed with a spatial accuracy of 1m×1m and a time interval of 5 minutes. Next, the weighted heat source data (e.g., excavator thermal radiation) is input into the grid, overlaid with construction disturbances, and simultaneously integrated with the mechanical GPS track. The non-steady-state heat source trajectory is formed by the movement of the excavator within a 3-meter range of the pile position and the trajectory of the diversion vehicle; then the parameters of the 2.5-meter high and 50-meter long construction fence are collected, and its thermal barrier effect is measured by temperature sensors (inner heat intensity 40kW / m², outer heat intensity 28kW / m²), and the heat load of the pile positions 2-5 meters around the fence is reduced by 30%; finally, the thermal field data is standardized into a comprehensive heat load index of 0-100 (0-30 is safe), and a dynamic thermal field optimization map is generated by combining the pile position coordinates with color grading (green-yellow-red), which is updated every 5 minutes according to the construction dynamics.

[0058] The method provided in this embodiment enables precise dynamic coupling and spatial superposition of the thermal field during construction. The dynamic allocation of construction perception weights ensures accurate characterization of the impact of heat sources at different construction stages. The spatiotemporal calculation grid integration fully captures the dynamic changes of unsteady heat source trajectories. The thermal buffer correction scientifically quantifies the thermal barrier effect of the construction isolation zone, enabling the constructed dynamic thermal field optimization map to truly reflect the thermal environment state under construction conditions. This provides a reliable basis for precise pile location allocation and dynamic construction control, effectively improving the accuracy of thermal management and energy dispatch efficiency during construction.

[0059] In some embodiments, a dynamic charging pile allocation strategy is implemented, which is based on the matching mapping between charging urgency and charging pile heat load. This strategy dynamically enables high-urgency vehicles and optimizes previously unavailable high-heat-load charging piles through a pre-cooling strategy, and triggers dynamic replanning of charging pile allocation in real time. Based on the charging pile allocation results and the execution requirements of the pre-cooling strategy, construction dynamic control suggestions are generated in reverse. The dynamic charging pile allocation results and closed-loop control instruction set are integrated to generate a charging scheduling report for the construction period of the highway service area.

[0060] Dynamic charging station allocation strategy can be a decision-making method that dynamically allocates charging stations based on real-time charging demand and thermal environment data, achieving optimal matching of charging station resources and vehicle demand through algorithms. Charging urgency can be a quantitative index characterizing the urgency of vehicle charging demand, reflecting the combined pressure of vehicle remaining battery power, driving status, and arrival time. Charging station thermal load can be a thermal environment load index of the location of the charging station, used to quantify the cumulative impact of heat sources on the operating environment of the charging station. Pre-cooling strategy can be a pre-cooling measure taken for high heat load charging stations, restoring previously unusable charging stations to a usable state through physical cooling and environmental adjustments. High heat load charging stations are those whose heat load index exceeds the safety threshold, and whose surface temperature is too high due to the accumulation of construction heat sources, making them unusable directly. Construction dynamic control suggestions can be construction adjustment schemes generated based on the charging station allocation results, proposing thermal environment optimization measures through reverse derivation. Closed-loop control instruction set can be a complete set of control instructions, achieving coordinated control of the construction environment and charging system through a feedback mechanism. Highway service area construction period charging scheduling report can be a comprehensive output document integrating all scheduling decisions and control suggestions, used to guide on-site operation and construction management.

[0061] Specifically, during the charging scheduling process in highway service area construction, traditional technologies suffer from three fundamental defects: the static pile allocation mechanism (such as a fixed schedule) cannot respond to sudden changes in pile heat load caused by construction heat sources, forcing previously available piles to be shut down due to instantaneous heat load exceeding the standard (such as a sudden increase from 0.5 to 0.8); the rigid resource matching mode completely ignores the optimizability of high heat load piles, resulting in more than 30% of pile resources being idle due to temporary thermal environment effects; and the lack of minute-level dynamic replanning capability means that when the movement of construction machinery causes sudden changes in the thermal field, the original allocation scheme becomes invalid and manual intervention is required for adjustment, with an average response delay of more than 15 minutes. To address the above issues, this step first obtains the charging urgency of the vehicles to be charged (e.g., 85 for emergency rescue vehicles, 40 for ordinary cars) and the heat load of the charging piles (pile A 65 is a high heat load, pile B 35 is a safe pile). According to the matching rules, vehicles with a urgency ≥ 80 are prioritized for safe piles. If three high-urgency vehicles only have two safe piles, pre-cooling is initiated for pile A (activating the cooling fan at 5°C / 10 minutes + deploying a mobile cooling device), monitored every 5 minutes. When the heat load ≤ 50, the vehicle is allocated to a high-urgency vehicle. Construction dynamics (e.g., excavation) are continuously monitored. If an excavator is moved to block the airflow next to pile A, or if an emergency vehicle with an urgency level of 90 is added, dynamic replanning is immediately triggered (the ordinary trolley on pile A is moved to pile C, and after pre-cooling pile C, it is assigned to the new vehicle); based on the allocation results and pre-cooling requirements, construction suggestions are made (the excavator is moved 5 meters away from pile A, and the original 10 o'clock high-heat asphalt operation is changed to 15 o'clock); finally, the pile allocation results, pre-cooling data (such as the cooling time of pile A being 20 minutes), construction suggestions, etc., are integrated to generate a scheduling report, which is updated every 30 minutes according to changes in the thermal field and construction.

[0062] The method provided in this embodiment enables the intelligent generation of charging scheduling reports during the construction period of highway service areas. It ensures that vehicles with high urgency receive charging resources first through a dynamic charging pile allocation strategy, effectively activates the utilization potential of high heat load charging piles through a pre-cooling strategy, optimizes the allocation scheme in real time through a dynamic replanning mechanism to cope with environmental changes, and achieves collaborative management of the charging system and construction activities by generating construction dynamic control suggestions in reverse.

[0063] In some embodiments, vehicles ranked high in the virtual priority charging queue are given priority allocation to high-quality charging piles marked with low heat load levels in the dynamic thermal field optimization map; for vehicles with high charging urgency but only high heat load charging piles are currently available, a pre-cooling strategy for construction collaboration is initiated: before the vehicle arrives, based on construction dynamic control recommendations, the operating position of construction machinery is temporarily adjusted or temporary sprinkler facilities are activated to proactively reduce the local heat load of the target charging pile, and reallocation is performed after the heat load level of the charging pile is reduced to the available threshold; construction progress and thermal field changes are tracked in real time, and charging pile allocation for vehicles that have not yet arrived at the service area is dynamically replanned to respond to thermal field changes caused by construction activities.

[0064] High-quality charging stations are those with low heat load levels, indicating a favorable thermal environment suitable for fast and safe vehicle charging. These stations are identified through dynamic thermal field optimization mapping. High-heat-load charging stations, on the other hand, have high heat load levels, indicating a poor thermal environment that may affect charging safety or efficiency. Temporary sprinkler systems refer to mobile cooling equipment (such as small spray cooling machines or handheld sprinkler devices) temporarily deployed to quickly reduce the local heat load at high-heat-load charging stations. They rapidly cool the station through the principle of water mist evaporation and heat absorption, serving as an important supplementary measure for pre-cooling during construction.

[0065] Specifically, static pile location allocation mechanisms (such as fixed schedules) cannot respond to dynamic changes in the construction thermal environment, resulting in high-urgency vehicles (such as emergency vehicles with less than 15% remaining battery power) being forcibly allocated to high-heat-load pile locations (such as heat load index exceeding 0.8), causing an average charging efficiency drop of more than 40%. The complete lack of active cooling measures results in more than 35% of high-heat-load pile locations being idle for extended periods due to excessive thermal environment, leading to serious resource waste. Once the pile location allocation scheme is established, it remains fixed. When the movement of construction machinery or the gathering of vehicles causes drastic changes in the thermal field (such as the heat load index rising from 0.5 to 0.8 within 5 minutes), the original allocation scheme becomes invalid and cannot be adjusted in time, with an average response delay of more than 20 minutes. To address the above issues, this step first obtains a virtual priority charging queue (e.g., the top 30% of 5 high-urgency vehicles) and a dynamic thermal field optimization map. It then selects 3 high-quality charging piles with a heat load ≤30. Based on the queue order, it assigns a charging pile with a heat load of 22 to emergency rescue vehicle #1, charging piles with heat loads of 18 and 25 to long-distance trucks #2 and #3, and temporarily assigning medium-heat-load charging piles with heat loads of 40-50 to trucks #4 and #5. For emergency support vehicles (arriving in 30 minutes) with an urgency level ≥85 and a heat load of 68, the charging piles are moved 3 meters away according to the construction dynamic control recommendations. Two temporary sprinkler systems are deployed 5 meters away from the paver (cooling down by 3℃ / 5 minutes). Temperature is measured every 5 minutes using a temperature sensor. The heat load of the pile position drops to 48 (≤50) 10 minutes before the vehicle arrives, and the allocation is executed. The construction is tracked in real time. If the heat load of a high-quality pile increases from 28 to 58 due to mechanical movement, the high-quality pile with a heat load of 26 is redistributed to long-distance buses arriving 40 minutes later. The low-urgency vehicle originally occupying that pile is moved to the medium-heat load pile with a heat load of 45. The new pile position and the route that avoids construction road occupation are pushed through the navigation.

[0066] The method provided in this embodiment enables dynamic optimization and efficient resource utilization of the charging scheduling process during the construction period of highway service areas. It ensures fast and safe charging for vehicles with high urgency by prioritizing the allocation of high-quality charging piles, activates the utilization potential of high-heat-load charging piles and expands the available resource pool through pre-cooling strategies, and adapts to changes in the construction heat field in real time through dynamic replanning and improves the robustness of the allocation scheme.

[0067] In some embodiments, based on the clustering of high-heat-load piles in the pile location allocation results, reverse-engineering suggestions for fine-tuning the construction plan are generated. These suggestions include proposing to adjust high-heat construction activities to off-peak charging periods or temporarily changing the densely populated work areas of heavy machinery. Based on the pre-cooling strategy implemented to alleviate the heat field, traffic flow optimization suggestions for the construction area are generated, and vehicle travel paths that bypass the pre-cooling operation area are dynamically planned to avoid flow line conflicts between charging vehicles and cooling operations. All suggestions are integrated into an executable set of construction linkage instructions to form a dynamic construction control suggestion.

[0068] Construction plan fine-tuning suggestions can be construction adjustment schemes generated based on pile location allocation and thermal field optimization results, proposing thermal environment optimization and traffic diversion measures through reverse derivation. Traffic diversion optimization suggestions for the construction area can be optimized schemes for vehicle travel paths within the construction area, aiming to avoid conflicts between charging vehicles and cooling operations. The construction linkage instruction set can be a set of executable instructions integrating all construction adjustment schemes, used to coordinate construction activities and charging scheduling.

[0069] Specifically, during the charging scheduling process in highway service area construction periods, traditional technologies suffer from three fundamental defects: First, the construction plan is completely disconnected from charging demand, making it impossible to dynamically adjust construction activities based on the heat load accumulation at charging pile locations. This results in high-heat operations (such as asphalt paving) continuing during peak charging periods, causing the heat load index of surrounding charging pile locations to surge (e.g., from 0.6 to 0.9), rendering them unusable. Second, there is a lack of coordination between traffic diversion and cooling operations. When pre-cooling strategies are implemented, vehicle travel paths overlap with the work area, causing flow conflicts (e.g., charging vehicles mistakenly entering the spray cooling area, resulting in an average traffic delay of over 15 minutes). Third, construction adjustment suggestions are scattered and non-standardized, with a lack of integration of instructions from different departments, resulting in low execution efficiency and response delays exceeding 30 minutes. To address the above issues, this step first obtains the pile location allocation results, extracts the coordinates of piles with high heat loads (≥60), and through spatial cluster analysis, identifies a cluster of three pile locations on the west side of the service area. The original construction plan scheduled asphalt paving (high-heat construction) and intensive operation of two pavers during the peak charging period of 11:00-14:00 (daily average charging volume accounting for 40%). Therefore, a minor adjustment to the construction plan is proposed: shift the asphalt paving to the off-peak charging period of 6:00-8:00, and move one paver to the idle area on the east side; then, for the pre-cooling operations of piles 1 and 3 (with a 5-meter radius around the perimeter designated as the work area),... Temporary sprinkler equipment was deployed, and GIS path planning algorithms were used to optimize traffic flow, guiding charging vehicles from the north entrance to bypass the western auxiliary road and head towards the pile locations, avoiding the work area. New routes were pushed through the service area navigation system, and "detour signs" were set up on-site. Finally, the suggestions were converted into standardized instructions (such as "Instruction 1: 6:00-8:00 asphalt paving on the west side, person in charge Zhang XX; Instruction 2: 9:00 move the paver to the east side, person in charge Li XX"), integrated into a set of linkage instructions, and supplemented with the requirement to provide feedback on the heat load data of the pile locations every 2 hours, forming a dynamic control suggestion for construction that was pushed to the construction party and the service area management.

[0070] The method provided in this embodiment enables efficient coordination between charging scheduling and construction activities during the construction period of highway service areas. By generating construction plan fine-tuning suggestions in reverse, it effectively reduces the accumulation of heat load at charging pile locations and improves the availability of charging resources. By optimizing traffic flow suggestions, it avoids flow line conflicts between vehicles and cooling operations and improves traffic efficiency. By integrating the construction linkage instruction set, it ensures the rapid implementation and consistency of adjustment measures. Ultimately, it significantly enhances the overall coordination and adaptability of the construction and charging systems, optimizes the operational efficiency of service areas, and ensures charging safety and user experience.

[0071] Figure 3 A schematic diagram of the structure of an IoT management system applied to the construction of a highway service area, as provided in one embodiment of this application, is shown below. Figure 3 As shown, an IoT management system 300 applied to highway service area construction in this embodiment includes: a charging queue sorting module 301, a dynamic thermal field generation module 302, and a construction report generation module 303;

[0072] The charging queue sorting module 301 is used to acquire a dynamic information set of high-speed vehicles, analyze the charging urgency index of each vehicle requiring charging based on the dynamic information set, and construct a virtual priority charging queue. The dynamic thermal field generation module 302 is used to acquire a multi-dimensional heat source information set of the construction site, analyze the distribution of dynamic point heat sources and dynamic area heat sources based on the multi-dimensional heat source information set of the construction site, and construct a dynamic thermal field optimization map in combination with a real-time solar shading map. The construction report generation module 303 is used to accurately allocate charging pile positions based on the virtual priority charging queue and the dynamic thermal field optimization map, provide suggestions for dynamic construction control, and generate a charging scheduling report for the construction period of the highway service area.

[0073] Optionally, the charging queue sorting module 301 is specifically used for: the high-speed vehicle dynamic information set including the vehicle's real-time remaining battery power, the vehicle model's basic power consumption weight, and real-time location and driving trajectory; based on the high-speed vehicle dynamic information set, calculating the vehicle's baseline energy consumption urgency and predicting the vehicle's remaining time to reach the service area; dynamically analyzing the coupling relationship between the baseline energy consumption urgency and the remaining time, and generating the charging urgency index through the urgency index calculation formula; and sorting all vehicles to be charged according to the charging urgency index to construct the virtual priority charging queue.

[0074] Optionally, when constructing the dynamic thermal field optimization map, the dynamic thermal field generation module 302 is specifically used for: the multi-dimensional heat source information set at the construction site includes thermal radiation data of construction machinery, thermal field distribution data of clustered vehicles, and ambient background temperature data; based on the thermal radiation data of construction machinery, identifying and locating high-power construction equipment, and modeling the equipment as the dynamic point heat source with radiation intensity and influence radius; based on the thermal field distribution data of clustered vehicles, through thermal imaging clustering analysis, modeling the vehicle cluster under the atypical congestion mode caused by construction as the dynamic area heat source with temperature gradient and diffusion effect; calling the ambient background temperature data, and overlaying the real-time solar shadow map rendered in real time based on the astronomical algorithm and the fusion of the temporary construction facility model as a solar correction coefficient; dynamically coupling and spatially superimposing the dynamic point heat source, the dynamic area heat source, and the solar correction coefficient to generate the dynamic thermal field optimization map.

[0075] Optionally, the dynamic thermal field generation module 302, when modeling the dynamic planar heat source as having a temperature gradient and diffusion effect, is specifically used for: deconstructing the atypical congestion pattern induced by construction, analyzing the vehicle mixing ratio and thermal state within the cluster, modeling high-heat-generating vehicles as high-heat cores, and calculating the thermal bridging strength between heat cores based on real-time vehicle spacing; constructing a non-uniform temperature field within the cluster with heat cores as peak values ​​and vehicle spacing as diffusion resistance based on the heat core distribution and the thermal bridging strength; coupling the forced convection wind field generated by the periodic operation of construction machinery, analyzing the stripping and transport effects of the wind field on the non-uniform temperature field, and predicting the coverage path and intensity of the transported heat plume on downstream charging piles; and generating the dynamic planar heat source by combining the non-uniform temperature field and the heat plume diffusion model corrected by the forced convection wind field.

[0076] Optionally, the dynamic thermal field generation module 302, when based on the process of using the solar radiation correction coefficient, is specifically used for: constructing a dynamic digital twin scene specific to the service area construction period, integrating permanent buildings, charging pile positions, and a library of outlines of temporary construction facilities dynamically loaded based on the construction progress; calculating the real-time solar vector based on astronomical algorithms, driving scene lighting and shadow rendering, focusing on calculating the composite dynamic shadow boundary of the temporary construction facility outline library and permanent buildings in the pile position area, to obtain the real-time shading efficiency of each pile position under the current construction state; in order to isolate and quantify the net impact of the construction activity itself on the thermal environment of the pile position, constructing a dynamic construction-induced shading efficiency coefficient by comparing the actual shading area of ​​the current construction with the shading area without construction reference; weightedly fusing the ambient background temperature data with the construction-induced shading efficiency coefficient to generate a pile position-specific micro-thermal environment correction value for each charging pile position, integrating the micro-thermal environment correction values ​​of all pile positions, and constructing the solar radiation correction coefficient strongly correlated with construction.

[0077] Optionally, the dynamic thermal field generation module 302, during the dynamic coupling and spatial superposition process, specifically performs the following: based on the construction stage and activity type, assigns construction perception weights to the dynamic point heat source, the dynamic area heat source, and the solar radiation correction coefficient: increasing the weight of mechanical point heat sources during the pile foundation construction stage and increasing the weight of vehicle area heat sources during the road paving stage; constructing a spatiotemporal calculation grid for the heat load of service area pile locations, superimposing the weighted heat source data on the grid under construction disturbance, and simultaneously integrating the unsteady heat source trajectories formed by the movement paths of construction machinery and the diversion vehicles; applying thermal buffer correction to the superimposed comprehensive thermal field, identifying the local thermal barrier effect formed by construction enclosures and temporary greening, and downgrading the heat load of the corresponding pile locations; outputting the comprehensive heat load index of each pile location during the construction period, and completing the construction of the dynamic thermal field optimization map.

[0078] Optionally, when generating the charging scheduling report for the highway service area construction period, the construction report generation module 303 is specifically used to: execute a dynamic pile location allocation strategy, which is based on the matching mapping between charging urgency and pile location heat load, dynamically activates high-urgency vehicles and optimizes previously unavailable high-heat-load pile locations through a pre-cooling strategy, and triggers dynamic replanning of pile location allocation in real time; based on the pile location allocation results and the execution requirements of the pre-cooling strategy, reverse-generates construction dynamic control suggestions, integrates the dynamic pile location allocation results with the closed-loop control instruction set, and generates the charging scheduling report for the highway service area construction period.

[0079] Optionally, when the construction report generation module 303 is based on the dynamic pile location allocation strategy, it is specifically used to: prioritize the allocation of high-quality pile locations marked with low heat load levels in the dynamic thermal field optimization map for vehicles ranked high in the virtual priority charging queue; for vehicles with high charging urgency but only high heat load pile locations are currently available, initiate a pre-cooling strategy for construction coordination: before the vehicle arrives, based on the construction dynamic control suggestions, temporarily adjust the operating position of construction machinery or activate temporary sprinkler facilities to actively reduce the local heat load of the target pile location, and perform reallocation after the heat load level of the pile location is reduced to the available threshold; track construction progress and thermal field changes in real time, and dynamically replan the pile location allocation for vehicles that have not yet arrived at the service area to respond to sudden changes in the thermal field caused by construction activities.

[0080] Optionally, when generating the construction report generation module 303 based on the construction dynamic control suggestions, it is specifically used to: generate construction plan fine-tuning suggestions based on the clustering of high heat load piles in the pile location allocation results, the construction plan fine-tuning suggestions including proposing to adjust high heat construction activities to non-peak charging periods, or temporarily change the dense operation area of ​​heavy machinery; generate traffic diversion optimization suggestions for the construction area based on the pre-cooling strategy implemented to alleviate the heat field, dynamically plan the vehicle travel path to bypass the area where pre-cooling operations are being performed, and avoid flow line conflicts between charging vehicles and cooling operations; and integrate all suggestions into an executable construction linkage instruction set to constitute the construction dynamic control suggestions.

[0081] The system in this embodiment can be used to execute the methods of any of the above embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

Claims

1. An IoT management method applied to the construction of highway service areas, characterized in that, include: Acquire a set of dynamic information about high-speed vehicles, and based on the set of dynamic information about high-speed vehicles, analyze the charging urgency index of each vehicle that needs to be charged, and construct a virtual priority charging queue. A multi-dimensional heat source information set is obtained at the construction site. Based on the multi-dimensional heat source information set at the construction site, the distribution of dynamic point heat sources and dynamic area heat sources is analyzed. Combined with the real-time solar shadow map, a dynamic thermal field optimization map is constructed. Based on the virtual priority charging queue and the dynamic thermal field optimization map, the charging pile positions are accurately allocated, and suggestions for dynamic construction control are provided to generate a charging scheduling report for the construction period of the highway service area. The construction of the dynamic thermal field optimization map includes: The multi-dimensional heat source information set at the construction site includes thermal radiation data of construction machinery, thermal field distribution data of clustered vehicles, and ambient background temperature data. Based on the thermal radiation data of the construction machinery, high-power construction equipment is identified and located, and the equipment is modeled as a dynamic point heat source with radiation intensity and influence radius. Based on the thermal field distribution data of the clustered vehicles, the vehicle clusters under the atypical congestion mode caused by construction are modeled as dynamic planar heat sources with temperature gradients and diffusion effects through thermal imaging cluster analysis. The ambient background temperature data is called and superimposed on the real-time solar shadow map rendered in real time based on the 3D building model that integrates astronomical algorithms and the temporary construction facility model, as a solar correction coefficient; The dynamic point heat source, the dynamic area heat source and the solar radiation correction coefficient are dynamically coupled and spatially superimposed to generate the dynamic thermal field optimization map. The generation of the highway service area construction period charging dispatch report includes: The dynamic charging station allocation strategy is implemented, which is based on the matching mapping between charging urgency and charging station thermal load. It dynamically enables high-urgency vehicles and optimizes previously unavailable high-heat-load charging stations through a pre-cooling strategy, and triggers dynamic replanning of charging station allocation in real time. Based on the pile location allocation results and the execution requirements of the pre-cooling strategy, reverse dynamic control suggestions for construction are generated, and the dynamic pile location allocation results and closed-loop control instruction set are integrated to generate the charging scheduling report for the construction period of the expressway service area. The execution of the dynamic pile location allocation strategy includes: For vehicles that rank higher in the virtual priority charging queue, priority will be given to allocating high-quality charging pile positions marked with low heat load levels in the dynamic thermal field optimization map. For vehicles with high charging urgency but only high heat load charging piles available at present, a pre-cooling strategy for construction coordination is initiated: before the vehicle arrives, based on the aforementioned construction dynamic control recommendations, the operating position of construction machinery is temporarily adjusted or temporary spray facilities are activated to proactively reduce the local heat load of the target charging pile. Once the heat load level of the charging pile is reduced to the available threshold, redistribution is performed. Real-time tracking of construction progress and thermal field changes allows for dynamic replanning of pile allocation for vehicles that have not yet reached the service area, in response to sudden changes in the thermal field caused by construction activities.

2. The method according to claim 1, characterized in that, The construction of the virtual priority charging queue includes: The high-speed vehicle dynamic information set includes the vehicle's real-time remaining battery power, the vehicle model's basic power consumption weight, and its real-time location and driving trajectory. Based on the aforementioned high-speed vehicle dynamic information set, the vehicle's baseline energy consumption urgency is calculated, and the remaining time for the vehicle to reach the service area is predicted. The coupling relationship between the baseline energy consumption urgency and the remaining time is dynamically analyzed, and the charging urgency index is generated through the urgency index calculation formula. All vehicles awaiting charging are sorted according to the charging urgency index to construct the virtual priority charging queue.

3. The method according to claim 2, characterized in that, The process of modeling the dynamic planar heat source with temperature gradient and diffusion effect includes: The atypical congestion patterns induced by construction are deconstructed, the vehicle mixing ratio and thermal state within the cluster are analyzed, high-heat vehicles are modeled as high-heat cores, and the thermal bridging strength between heat cores is calculated based on real-time vehicle distance. Based on the distribution of thermonuclear cells and the thermal bridging strength, a non-uniform temperature field inside the cluster is constructed with thermonuclear cells as the peak value and vehicle spacing as the diffusion resistance. Coupled with the forced convection wind field generated by the periodic operation of construction machinery, the stripping and transport effects of the wind field on the non-uniform temperature field are analyzed, and the coverage path and intensity of the transported heat plume on the downstream charging pile site are predicted. The dynamic planar heat source is generated by combining the non-uniform temperature field with the thermal plume diffusion model modified by the forced convection wind field.

4. The method according to claim 1, characterized in that, The process for using the solar radiation correction factor includes: Construct a dynamic digital twin scene specifically for the construction period of the service area, integrating the outline library of permanent buildings, charging piles, and temporary construction facilities dynamically loaded based on the construction progress; Based on astronomical algorithms, real-time solar vectors are calculated to drive scene lighting and shadow rendering. The focus is on calculating the composite dynamic shadow boundary of the temporary construction facility outline library and permanent buildings in the pile location area to obtain the real-time shading efficiency of each pile location under the current construction state. To isolate and quantify the net impact of construction activities on the thermal environment of the pile site, a dynamic construction-induced shading efficiency coefficient is constructed by comparing the actual shading area during current construction with the shading area without a construction benchmark. The ambient background temperature data and the construction-induced shading efficiency coefficient are weighted and fused to generate a micro-thermal environment correction value for each charging pile. The micro-thermal environment correction values ​​of all charging piles are integrated to construct the solar radiation correction coefficient that is strongly correlated with construction.

5. The method according to claim 1, characterized in that, The process of dynamic coupling and spatial superposition includes: Based on the construction stage and activity type, construction perception weights are assigned to the dynamic point heat source, the dynamic area heat source and the solar radiation correction coefficient respectively: the weight of mechanical point heat source is increased in the pile foundation construction stage, and the weight of vehicle area heat source is increased in the road paving stage. A spatiotemporal calculation grid for the heat load of pile locations in the service area is constructed. The weighted heat source data is superimposed on the grid under the heat field under construction disturbance, and the unsteady heat source trajectory formed by the movement path of construction machinery and the diversion vehicle is integrated simultaneously. For the superimposed comprehensive thermal field, a thermal buffer correction for the construction isolation zone is applied, the local thermal barrier effect formed by construction enclosures and temporary greening is identified, and the thermal load of the corresponding pile locations is downgraded; Output the comprehensive heat load index during the construction period for each pile location to complete the construction of the dynamic thermal field optimization map.

6. The method according to claim 5, characterized in that, The generated construction dynamic control suggestions include: Based on the clustering of high-heat-load piles in the pile location allocation results, reverse-engineering the construction plan fine-tuning suggestions, which include proposing to adjust high-heat construction activities to non-peak charging periods, or temporarily change the dense operation area of ​​heavy machinery. Based on the pre-cooling strategy implemented to alleviate the heat field, traffic diversion optimization suggestions are generated for the construction area, and vehicle travel paths that bypass the area where pre-cooling operations are being carried out are dynamically planned to avoid flow line conflicts between charging vehicles and cooling operations. All suggestions are integrated into an executable set of construction linkage instructions, which constitute the aforementioned construction dynamic control suggestions.

7. An IoT management system applied to the construction of highway service areas, characterized in that, The method applied to any one of claims 1-6 includes: The charging queue sorting module is used to acquire a set of dynamic information about high-speed vehicles, analyze the charging urgency index of each vehicle that needs to be charged based on the set of dynamic information about high-speed vehicles, and construct a virtual priority charging queue. The dynamic thermal field generation module is used to acquire a multi-dimensional heat source information set at the construction site. Based on the multi-dimensional heat source information set at the construction site, it analyzes the distribution of dynamic point heat sources and dynamic area heat sources, and combines the real-time solar shadow map to construct a dynamic thermal field optimization map. The construction report generation module is used to accurately allocate charging pile positions based on the virtual priority charging queue and the dynamic thermal field optimization map, provide suggestions for dynamic construction control, and generate a charging scheduling report for the construction period of the highway service area.

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