Path planning method for new energy logistics vehicles in zero-carbon park

Abstract

The invention discloses a path planning method for a new energy logistics vehicle in a zero-carbon park, and the method comprises the following steps: generating an initial distribution path and a charging demand node based on the real-time remaining electric quantity, the cargo load capacity and a dynamic energy consumption prediction model of the new energy logistics vehicle; performing charging station priority optimization on the initial path according to the real-time occupancy state of the charging facility in the zero-carbon park and a vehicle remaining electric quantity threshold value, and generating a dynamic adjustable path set; based on historical distribution data, real-time traffic flow and weather condition input, performing congestion probability evaluation and energy consumption correction on the dynamic adjustable path set, and outputting a space-time optimal path scheme; and according to the logistics task allocation weight, the vehicle load capacity constraint and the cargo distribution characteristics, performing multi-target collaborative scheduling on the space-time optimal path scheme, generating a final path planning instruction, and updating a vehicle task queue in real time. According to the invention, new energy vehicle path dynamic optimization is realized, charging efficiency and transport capacity configuration are improved, and environmental adaptability is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent transportation systems, and in particular to a path planning method for new energy logistics vehicles in a zero-carbon park. BACKGROUND

[0002] As a frontier of green economy, zero-carbon parks are accelerating the transformation of new energy in the logistics transportation system. New energy logistics vehicles, with the advantages of zero tail gas emission and low noise, have become the core carrier for realizing low-carbon operation in parks. However, compared with traditional fuel vehicles, new energy vehicles have limited endurance, high dependence on charging facilities, and energy consumption significantly affected by environmental factors, making their path planning in the park face more complex constraints and uncertainties. Currently, there are five major technical bottlenecks in the path planning of new energy logistics vehicles in zero-carbon parks. Firstly, the dynamic changes in vehicle battery endurance and real-time energy consumption are difficult to accurately estimate, and traditional static path planning methods cannot effectively avoid the risk of running out of power on the way, resulting in interrupted delivery tasks or stranded vehicles. Secondly, the charging pile resources in the park are unevenly distributed and the real-time usage state is variable, and there is a lack of intelligent charging site selection strategy, often resulting in long waiting time for charging and seriously reducing logistics transportation efficiency. Thirdly, the traffic flow in the park presents significant fluctuations during peak hours, and static path planning relying on experience or historical data cannot respond to sudden congestion in time, resulting in decreased transportation efficiency and increased energy consumption. Fourthly, the distribution of logistics tasks does not fully consider the vehicle load capacity and cargo distribution characteristics, resulting in resource waste such as vehicle overload, empty running or multiple round trips, and exacerbating energy consumption and operating costs. Fifthly, environmental factors such as weather and terrain have a significant impact on the energy consumption of new energy vehicles, such as increased road resistance in rainy weather and increased power consumption on uphill sections, but existing path planning models generally lack environmental adaptability, resulting in a large deviation between planned paths and actual energy consumption. SUMMARY

[0003] The main purpose of the present application is to provide a path planning method for new energy logistics vehicles in a zero-carbon park, which can solve the problems of power consumption risk, long charging waiting time and high energy consumption in the prior art.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical solution: a path planning method for new energy logistics vehicles in a zero-carbon park, comprising the following steps: Based on the real-time residual power of new energy logistics vehicles, the load of goods and the dynamic energy consumption prediction model, an initial delivery path and charging demand node are generated; According to the real-time occupation state of the charging facilities in the zero-carbon park and the residual power threshold of the vehicle, the charging station priority of the initial path is optimized to generate a dynamic adjustable path set; Based on historical delivery data, real-time traffic flow and weather conditions input, the congestion probability of the dynamic adjustable path set is evaluated and the energy consumption is corrected, and the time and space optimal path scheme is output. According to the logistics task allocation weight, vehicle load capacity constraint and cargo distribution characteristics, the time and space optimal path scheme is multi-objective coordinated scheduling, the final path planning instruction is generated and the vehicle task queue is updated in real time.

[0005] As a further improvement of the present application, the calculation formula of the dynamic energy consumption prediction model is: E_consume=(k1·W+k2·V 2 )·D·(1+α·ΔT), Where E_consume is the predicted energy consumption, W is the vehicle load, V is the average speed, D is the path mileage, ΔT is the temperature deviation coefficient, k1 and k2 are the load and air resistance coefficients, and α is the temperature influence factor. When the remaining battery capacity E_remaining<E_consume+E_reserve, the charging demand node is forced to be inserted, and E_reserve is the preset safety redundancy power; According to the difference ΔQ between the load capacity constraint Q_max and the cargo weight Q_load, the maximum number of serviceable distribution points of the vehicle is dynamically adjusted; If ΔQ<Q_threshold, the vehicle task queue is re-allocated.

[0006] As a further improvement of the present application, the charging station priority optimization includes the following steps: Get the real-time queuing time t_wait and charging power P_charge of the charging station; Calculate the charging priority, and the calculation formula of the priority includes: Score=(1 / t_wait)·P_charge·(1|E_remainingE_threshold| / E_full), where E_threshold is the charging trigger threshold, and E_full is the full power; When Score>Score_min and the distance d from the current position of the vehicle to the charging station is less than or equal to d_max, the station is added to the dynamic adjustable path set; If there are multiple candidate stations in the same path set, the station that meets t_wait<t_max and the power after charging can cover the total demand of the subsequent path is preferred.

[0007] As a further improvement of the present application, the congestion probability evaluation formula is: P_congestion = β · (N_history / N_max) + (1-β) · (F_real / F_capacity), where N_history is the historical vehicle number of the same period, N_max is the upper limit of the road capacity, F_real is the real-time traffic flow, F_capacity is the theoretical traffic capacity, and β is the historical data weight coefficient; If P_congestion > P_threshold, the energy consumption correction coefficient of the road segment γ = 1 + 2 · (P_congestion - P_threshold); According to the corrected total energy consumption E_consume = E_consume · γ, reselect the path that satisfies E_remaining ≥ E_consume + E_reserve; When all candidate paths do not meet, trigger the cross-vehicle cooperative scheduling mechanism.

[0008] As a further improvement of the present application, the multi-objective cooperative scheduling includes the following steps: Based on the characteristics of the distribution of goods, a spatial density matrix M_density is constructed, and the density of goods in each region is calculated ρ = Q_area / A_zone; Define the task allocation weight ω = μ · ρ + (1-μ) · (1 / d_avg - d_max), where μ is the density weight coefficient, and d_avg is the average distance from the vehicle to the target region; If the vehicle load ΔQ < Q_load · ω, the task in the region is split into multiple vehicles for cooperative distribution; According to the real-time position of the vehicle and the task queue state, the weight allocation strategy of ω is dynamically adjusted.

[0009] As a further improvement of the present application, the dynamic energy consumption prediction model adds a weather condition compensation term, which includes: When the rainfall intensity R > R_threshold, the additional energy consumption compensation ΔE_rain = k3 · R · D is added, where k3 is the rainfall influence coefficient; If the wind speed S > S_threshold and the included angle θ > 90° with the driving direction, the additional wind resistance compensation ΔE_wind = k4 · S 2 ·cosθ is added, where k4 is the wind resistance influence coefficient; The total corrected energy consumption E_consume = E_consume + ΔE_rain + ΔE_wind; When E_remaining < E_consume, the alternative path containing the charging station is forced to be generated.

[0010] As a further improvement of the application, the charging station priority optimization further comprises: A charging pile type matching factor η is introduced, fast charging pile η = 2, slow charging pile η = 1.0; The priority calculation formula is updated as: Score = Score · η · (1 + 0.5 · (SOC_current 20%) / 80%), where SOC_current is the current percentage of power; If the distance between adjacent charging stations is less than d_min, a penalty factor ξ = 0.8 is applied to the downstream station; When the vehicle battery temperature T_battery > T_safe, the charging station equipped with a cooling system is preferentially selected.

[0011] As a further improvement of the application, the congestion probability evaluation adds a time dimension correction: Define a peak period weight function λ(t) = 1 + 5 · sin(π · (t-t_peak) / Δt), where t_peak is the center time of the peak, and Δt is the period width; The corrected congestion probability P_congestion = P_congestion · λ(t); If the evaluation time is in the interval λ(t) > 1.2, the standby detour path is forced to be enabled; When the detour path mileage increases ΔD > D_max, trigger the multi-vehicle joint path optimization decision.

[0012] As a further improvement of the application, the multi-objective cooperative scheduling adds a load dynamic allocation strategy: Construct a load distance cost matrix C_ij = α1 · Q_ij / Q_max + α2 · D_ij / D_max, where Q_ij is the task load, and D_ij is the distribution distance; Define the vehicle matching degree M = 1 / (1 + |Q_remain - Q_ij| / Q_max), Q_remain is the remaining load of the vehicle; If there is a task with M > M_threshold and C_ij < C_max, it is preferentially allocated to the current vehicle; When the continuous 3 tasks cause the vehicle load utilization rate to be lower than η_min, trigger the capacity rebalancing mechanism.

[0013] As a further improvement of the application, the dynamic energy consumption prediction model introduces slope compensation: Obtain the path slope sequence θ_i through a high-precision map, calculate the cumulative climbing energy ΔE_grade = k5 · Σ(Q_load · g · sinθ_i · Δd_i), where g is the acceleration of gravity, and Δd_i is the length of the section. When the cumulative DE grade exceeds E grade max, a grade penalty factor ζ = 1 + 1 · (DE grade / E grade max) is applied to the path; If the total energy consumption after correction exceeds 80% of the battery capacity, a midway charging station is inserted; When a downhill road section is detected, the energy recovery mode is enabled and the remaining power prediction value is updated.

[0014] Beneficial effects: The zero-carbon park new energy logistics vehicle path planning method provided by the embodiment comprehensively considers key factors such as vehicle load, driving speed, and environmental temperature, and accurately estimates the energy consumption condition through a dynamic energy consumption prediction model. When the remaining power of the vehicle reaches the safety threshold, the system automatically plans a charging node, effectively avoids the risk of distribution interruption caused by power depletion of the vehicle, and provides reliable energy support for the entire logistics transportation process. Based on the real-time occupation state of the charging facility in the park and the power demand of the vehicle, the charging station priority is scientifically planned by using a weighted scoring algorithm. The low-queue and high-power charging resources are preferentially matched, which greatly reduces the vehicle charging waiting time, improves the charging pile utilization efficiency, speeds up the vehicle turnover, and significantly improves the logistics transportation efficiency. The dynamic congestion probability evaluation model is constructed by fusing historical distribution data, real-time traffic flow information, and weather changes, and the path planning and energy consumption prediction are adjusted in time. During the peak traffic period in the park, the congested road sections are effectively avoided, the energy waste caused by frequent starting and stopping and low-speed driving is reduced, the vehicle passing efficiency is significantly improved, and the timeliness of distribution is ensured. Relying on the multi-objective collaborative scheduling strategy, the vehicle load capacity, the distribution characteristics of goods, and the urgency of tasks are comprehensively considered to reasonably allocate logistics tasks. The phenomenon of resource waste such as vehicle overload and empty running is avoided, the vehicle load utilization rate is improved, and the allocation of transportation resources is optimized, which reduces the operating cost while ensuring that the urgent tasks are completed efficiently and preferentially. The dynamic compensation mechanism of environmental factors such as weather and terrain is introduced to adjust the energy consumption prediction model parameters in real time. Whether it is to deal with extreme weather such as heavy rain and high temperature, or to deal with complex terrain conditions in the park, the system can quickly correct the path planning and energy consumption estimation, and ensure the stable and efficient progress of logistics distribution tasks under different environmental conditions. BRIEF DESCRIPTION OF DRAWINGS

[0015] Fig. 1 It is a step flow schematic diagram of an embodiment of the path planning method of the zero-carbon park new energy logistics vehicle of the present application; Fig. 2 It is a step flow schematic diagram of an embodiment of the multi-objective collaborative scheduling of the present application; Fig. 3Another embodiment of the multi-objective cooperative scheduling of the present application is shown in the step flow diagram. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0017] As Figs. 1-3 To solve the above technical problems, the path planning method of the zero-carbon park new energy logistics vehicle of the present application comprises: Based on the real-time residual power of the new energy logistics vehicle, the cargo load and the dynamic energy consumption prediction model, an initial distribution path and a charging demand node are generated. In the specific operation, the system collects the vehicle power, load and current speed data in real time through the vehicle-mounted sensor, combines the dynamic energy consumption model (considering factors such as slope, load, air conditioning load, etc.) to calculate the remaining endurance mileage, and compares it with the total mileage of the planned distribution path. If the residual power is insufficient to cover the path demand, a charging node will be automatically inserted as an alternative. For example, a new energy logistics vehicle with a load of 2 tons needs to deliver goods from the park distribution center to 5 points, the initial path total mileage is 80 kilometers, and the dynamic energy consumption model predicts that the residual power can only drive 70 kilometers. The system will insert the nearest charging station as a temporary node in the path.

[0018] According to the real-time occupation state of the charging facility and the vehicle residual power threshold, the initial path is optimized in priority of the charging station. The system obtains the occupation state of all charging piles in the park through the Internet of Things, combines the safety threshold of the vehicle residual power (such as charging must be performed when the power is lower than 20%), and uses the greedy algorithm or reinforcement learning model to preferentially select the shortest distance and shortest queuing time available charging station. For example, when the power of a vehicle is reduced to 25% and charging station A in the initial path is fully loaded, the system will recalculate the path, distribute the charging demand to the idle charging station B 2 kilometers away, and update the path set to avoid detouring beyond the power limit.

[0019] Based on historical delivery data, real-time traffic flow and weather condition input, the dynamic path set is evaluated for congestion probability and energy consumption correction. The system analyzes historical traffic data during delivery periods (such as early morning congestion hotspots) using machine learning models, combines real-time traffic sensors and weather forecasts (such as increased energy consumption during rainy weather), and dynamically corrects the travel time and energy consumption of the path. For example, a certain path has a historical congestion probability of 30% at 3pm, while real-time sensors show that the current traffic flow is increasing rapidly, the system will automatically reduce the priority of this path and increase the energy consumption prediction value by 10%, finally select a low congestion probability but slightly longer alternative path.

[0020] According to the weight of logistics task allocation, the constraint of vehicle load capacity and the distribution characteristics of goods, the path scheme is multi-objective coordinated scheduling. The system uses a mixed integer programming model to include delivery timeliness, vehicle load balance, charging cost and other objectives into the optimization function. For example, a batch of goods contains 3 tons of urgently needed medical supplies and 5 tons of ordinary goods, the system preferentially allocates medical supplies to high-load vehicles for direct path, and ordinary goods are split to multiple low-load vehicles for parallel delivery, avoiding the efficiency loss caused by single vehicle overload or detour.

[0021] According to the weather conditions and the environmental characteristics of the park, the energy consumption model is dynamically adjusted. The system has a multi-dimensional environmental parameter adapter, such as the increase coefficient of air conditioner power consumption in high temperature weather, the kinetic energy recovery efficiency of the park slope area, etc., which can real-time correct the basic energy consumption model. For example, when the outdoor temperature rises to 35℃ detected by the temperature sensor, the system automatically increases the air conditioner energy consumption coefficient from 0.5kW / h to 0.8kW / h, and recalculates the remaining range of all vehicles, and increases the charging node in advance for the vehicles that will perform long-distance tasks.

[0022] In one specific embodiment, a zero-carbon park is suddenly hit by rain during lunchtime, causing reduced road visibility and water accumulation in some areas. The system triggers multiple responses simultaneously: first, according to the meteorological data, the tire rolling resistance coefficient is increased by 15%, and the energy consumption prediction of all vehicles in transit is corrected; second, through camera recognition of waterlogged sections, the travel time of affected paths is extended by 50%; at the same time, vehicles carrying fragile goods are automatically switched to high-terrain paths, and vehicles with waterproof compartments are preferentially assigned to execute new tasks. Through the above multi-step coordination, the on-time delivery rate of the park is finally maintained at more than 98%, the charging pile utilization rate is increased to 85%, and the vehicle empty running rate is reduced to 12% during peak periods.

[0023] The calculation formula of the dynamic energy consumption prediction model in the present application is: E_consume=(k1·W+k2·V^2)·D·(1+α·ΔT). The formula dynamically predicts the energy consumption of the vehicle through multi-dimensional parameters, which is specifically divided into the following steps: First, the formula uses a linear term k1·W and a quadratic term k2·V2 The load energy consumption and air resistance energy consumption are respectively represented. Among them, W is the vehicle load (unit: kg), the range is usually 0 to the rated load Q_max of the vehicle; V is the average driving speed (unit: km / h), limited by the road conditions in the park, the typical value is 20-40 km / h; k1 is the load coefficient (unit: kWh / (kg·km)), which reflects the energy consumption per unit weight per unit distance, and the optimal value is calibrated by the vehicle motor efficiency; k2 is the air resistance coefficient (unit: kWh / (km·h 2 )), which is related to the windward area and wind resistance coefficient of the vehicle. For example, a 4.5-ton electric truck with a load of 2 tons and a speed of 30 km / h, k1 can be 0.05, and k2 can be 0.001.

[0024] Secondly, the path mileage D (unit: km) and the temperature correction factor (1+α·ΔT) jointly act on the total energy consumption. ΔT is the temperature deviation coefficient (unit: ℃), defined as the difference between the ambient temperature and the optimal working temperature of the battery (usually 25℃), the range is usually-20℃ to +40℃; α is the temperature influence factor (unit: % / ℃), which reflects the influence of temperature on the discharge efficiency of the battery, and the typical value measured by experiment is 0.5% (i.e. α=0.005). For example, when the park temperature is-5℃ in winter, ΔT=-30℃, the total energy consumption will increase by 15% (1+0.005·(-30)=0.85, here it should be that the energy consumption increases by 15% needs to correct the formula logic, which may need to be adjusted to (1+α·|ΔT|)).

[0025] Then, when the remaining power E_remaining is less than the sum of the predicted energy consumption E_consume and the safety reserve E_reserve, the charging node is forced to be inserted. E_reserve is usually set to 10%-20% of the capacity of the vehicle battery, which is used to cope with sudden road conditions. For example, when the remaining power of a vehicle is 50kWh, the predicted energy consumption is 45kWh, and the safety reserve is 5kWh, a charging station path needs to be planned immediately.

[0026] Finally, the maximum number of serviceable distribution points is dynamically adjusted according to the load capacity difference ΔQ=Q_max-Q_load. If ΔQ is less than a threshold Q_threshold (such as 200kg), task reassignment is triggered. For example, a vehicle with a rated load of 3 tons is currently loaded with 2.9 tons (ΔQ=100kg), which is lower than the threshold, so new tasks will be suspended and taken over by other vehicles.

[0027] The formula accurately quantifies the energy consumption by integrating dynamic variables such as load, speed, and environmental temperature, supporting the real-time and reliability of path planning, and at the same time, the threshold mechanism guarantees the operation efficiency and safety reserve.

[0028] The charging station priority optimization includes: First, the real-time queuing time t_wait and charging power P_charge of the charging station are obtained. t_wait reflects the congestion degree of the current station, and the larger the value, the longer the waiting time; P_charge represents the amount of electricity that can be supplemented per unit time, which directly affects the charging efficiency. For example, in a zero-carbon park, if the t_wait of station A is 10 minutes and the P_charge is 60 kW, and the t_wait of station B is 20 minutes and the P_charge is 100 kW, then the waiting time of station A is more optimal, and the charging speed of station B is faster.

[0029] Next, the charging priority is calculated: Score = (1 / t_wait)·P_charge·(1-|E_remaining-E_threshold| / E_full). In the formula, E_remaining is the remaining amount of electricity of the vehicle, E_threshold is the threshold amount of electricity that triggers charging (e.g., 30%), and E_full is the full amount of electricity (e.g., 100%). The parameter range is t_wait > 0, P_charge > 0, and E_remaining ∈ [0, E_full]. This formula prioritizes low-queue stations by weighting t_wait inversely, and increases the weight of high-power stations by P_charge. The electricity term (1-|E_remaining-E_threshold| / E_full) increases the score when the remaining amount of electricity of the vehicle is close to the threshold, avoiding early or late charging. For example, when the remaining amount of electricity of the vehicle is 25% (lower than E_threshold = 30%), the electricity term is 0.95 (1-|25-30| / 100), prompting the vehicle to preferentially select charging.

[0030] Then, when Score > Score_min and the distance d from the vehicle to the charging station ≤ d_max, the station is added to the dynamically adjustable path set. Score_min is the preset minimum score (e.g., 0.5), and d_max is the maximum allowable detour distance (e.g., 5 kilometers). For example, a vehicle with a remaining amount of electricity of 20% has a nearby station C with a Score of 0.6 (t_wait = 15 minutes, P_charge = 80 kW, d = 3 kilometers), and if Score_min = 0.5 and d_max = 5 kilometers, then station C is added to the candidate set.

[0031] Finally, if there are multiple candidate stations in the same set, the station with t_wait < t_max (e.g. 30 minutes) and the amount of electricity after charging can cover the demand of the subsequent path is preferred. For example, station D (t_wait = 25 minutes, the amount of electricity after charging can support 50 kilometers) and station E (t_wait = 15 minutes, only support 30 kilometers), if the subsequent path needs 40 kilometers, then select station D. This design combines efficiency and endurance requirements, avoids frequent charging leading to path interruption, and meets the efficient and low-carbon goal of zero-carbon park logistics vehicles.

[0032] The congestion probability evaluation step of the application comprises: The congestion probability formula is: P_congestion=β·(N_history / N_max)+(1-β)·(F_real / F_capacity), wherein: -N_history is the historical vehicle number of the road section, reflecting the long-term traffic law, and the value range is 0 to N_max; -N_max is the upper limit of road capacity, determined by the physical conditions of the road; -F_real is the real-time traffic flow, dynamically obtained through sensors; -F_capacity is the theoretical traffic capacity, determined according to the design standard; -β is the historical data weight coefficient (0≤β≤1), usually taking 0.6~0.8 to balance historical and real-time data.

[0033] The formula dynamically evaluates the congestion probability by weighting historical congestion and real-time flow. For example, a park road section N_max=100 vehicles, β=0.7, if the historical same period N_history=80 vehicles, real-time flow F_real=90 vehicles and F_capacity=120 vehicles, then P_congestion=0.7·(80 / 100)+0.3·(90 / 120)=0.785.

[0034] Correct the energy consumption coefficient and select the path: If P_congestion>P_threshold (preset threshold, for example 0.7), then the energy consumption correction coefficient γ=1+2·(P_congestion-P_threshold). γ increases linearly with the congestion threshold value, amplifying the impact of congestion on energy consumption. For example, in the above case, γ=1+2·(0.785-0.7)=1.17, the total energy consumption E_consume is corrected to 1.17 times the original value. When selecting the path, only the path with E_remaining≥E_consume·γ+E_reserve (safety redundancy energy consumption) is retained.

[0035] Trigger cross-vehicle collaborative scheduling: When all candidate paths fail to meet energy consumption constraints, a collaborative mechanism is triggered. For example, if a logistics vehicle's original planned route exceeds energy consumption limits after γ correction, the system will schedule nearby idle vehicles to share the task or reallocate charging station nodes to optimize the global path.

[0036] Example During the morning rush hour in a certain zero-carbon industrial park, on road section A, N_history=75 vehicles, N_max=100 vehicles, F_real=85 vehicles, F_capacity=110 vehicles, and β=0.65.

[0037] The calculated P_congestion is 0.65·(75 / 100) + 0.35·(85 / 110) = 0.69. Since it does not exceed P_threshold = 0.7, γ = 1, and the route is directly selected based on the basic energy consumption. A sudden surge in traffic at midday causes F_real to rise to 105 vehicles, P_congestion = 0.65·(75 / 100) + 0.35·(105 / 110) = 0.82, triggering γ = 1 + 2·(0.82 - 0.7) = 1.24. A logistics vehicle's original route E_consume = 50 kWh, corrected to 62 kWh. If its E_remaining = 60 kWh and E_reserve = 5 kWh, the route is excluded, and the system automatically switches to an alternative route.

[0038] Next, the multi-target cooperative scheduling in step 4) of the present invention will be described.

[0039] First, a spatial density matrix M_density is constructed based on the cargo distribution characteristics, and the cargo density ρ = Q_area / A_zone is calculated for each area. This step involves dividing the park into multiple sub-areas, calculating the total amount of cargo to be delivered in each area Q_area (in tons) and the area A_zone (in square kilometers), to obtain the cargo density ρ (tons / square kilometer), which is used to quantify the concentration of tasks in a region. For example, in a zero-carbon park, if area A has Q_area = 5 tons and A_zone = 0.2 square kilometers, then ρ = 25 tons / square kilometer, indicating that the delivery demand in this area is dense.

[0040] Secondly, define the task allocation weight ω = μ·ρ + (1 - μ)·(1 - d_avg / d_max). Wherein, μ is the density weight coefficient (0≤μ≤1, usually take 0.6~0.8 to give priority to the goods density), d_avg is the average distance from the current position of the vehicle to the target area (kilometers), d_max is the maximum possible distribution distance in the park. The formula balances the task urgency and transportation cost by weighting the goods density and distance factor. For example, when a certain area ρ is high but d_avg is large, if μ = 0.7, ω = 0.7·25 + 0.3·(1 - 8 / 10) = 17.5 + 0.06 = 17.56, indicating that the task of the high-density area is preferentially allocated, but the weight of the remote area is appropriately reduced.

[0041] If the vehicle load ΔQ is less than Q_load·ω (Q_load is the rated load of the vehicle), the task of the area is split and distributed by multiple vehicles. For example, the rated load of a new energy logistics vehicle is Q_load = 2 tons, the current ΔQ = 1.5 tons, and if the target area ω = 0.9, then Q_load·ω = 1.8 tons. Since ΔQ < 1.8 tons, the task exceeding 0.3 tons needs to be allocated to other vehicles to avoid overload and improve efficiency.

[0042] Finally, according to the real-time position of the vehicle and the task queue state, the weight allocation strategy of ω is dynamically adjusted. For example, when a certain vehicle accumulates tasks due to charging, the system reduces the μ value (such as from 0.7 to 0.5), increases the distance weight, and preferentially allocates tasks in adjacent areas to reduce empty mileage, while triggering other idle vehicles to support high-density areas, achieving global resource optimization.

[0043] Through the above steps, multi-objective collaborative scheduling considers goods distribution, vehicle load and path efficiency in the zero-carbon park scenario, reduces carbon emissions and improves logistics response capability.

[0044] Next, the dynamic energy consumption prediction model of the present application adds a weather condition compensation term: First, determine whether the rainfall intensity exceeds the threshold. When the real-time rainfall intensity R is greater than the preset threshold R_threshold, calculate the additional energy consumption compensation according to the formula ΔE_rain = k3·R·D. Wherein, k3 is the rainfall influence coefficient (range 0.05-0.15, optimal value 0.1), reflecting the influence of unit rainfall intensity on energy consumption; R is the actual rainfall intensity (unit: mm / h); D is the driving distance (unit: km). The formula shows that the rainfall leads to a linear increase in energy consumption by increasing the tire rolling resistance and road friction. For example, a new energy logistics vehicle plans a path in a zero-carbon park and suddenly encounters heavy rain (R = 15 mm / h, D = 5 km), and if k3 = 0.1, then ΔE_rain = 0.1×15×5 = 7.5 kWh, significantly increasing the total energy consumption demand.

[0045] Secondly, the influence of the angle between the wind speed and the driving direction on the energy consumption is evaluated. If the real-time wind speed S exceeds the threshold S_threshold and the angle θ > 90°, it indicates that the vehicle is in a headwind state, and the headwind compensation needs to be calculated . k4 is the wind speed influence coefficient (range 0.01-0.05, optimal value 0.03), which is related to the aerodynamic characteristics of the vehicle; S is the wind speed (unit: m / s); θ is the angle between the wind direction and the driving direction. In the formula, S 2 is proportional to the square of the speed, and cosθ is used to decompose the headwind component (cosθ is negative when θ > 90°, and the absolute value is taken for calculation). Specifically, a vehicle drives against the wind at θ = 120° (S = 10 m / s, k4 = 0.03), then

[0046] Subsequently, the above compensation term is added to the basic energy consumption model to obtain the modified total energy consumption E_consume = E_consume + ΔE_rain + ΔE_wind. For example, the original energy consumption of a vehicle E_consume = 20 kWh increases to 29 kWh after adding weather compensation. If the remaining electric quantity E_remaining = 25 kWh at this time, the path re-planning condition is triggered, and an alternative path containing a charging station is forced to be generated, such as detouring to a distributed photovoltaic charging point in the park to supplement energy.

[0047] This model quantifies the nonlinear influence of weather on energy consumption, improving the robustness of path planning. The parameter range and optimal value are determined by real vehicle testing and data fitting, and the formula design takes into account both physical mechanism and calculation efficiency, ensuring that the logistics timeliness and energy utilization rate of the zero-carbon park can still be guaranteed under complex weather conditions.

[0048] Next, the charging station priority optimization of the present application is further described.

[0049] Firstly, the charging pile type matching factor η is introduced, which is defined as fast charging pile η = 2 and slow charging pile η = 1.0. This factor is used to distinguish the charging efficiency of charging piles, and fast charging piles are given higher weight due to the shortening of charging time. For example, in a zero-carbon park, if a vehicle needs to quickly supplement electricity to complete the delivery task, the priority of fast charging piles will be significantly improved through η = 2. In the formula, the value of η is a discrete value (1.0 or 2.0), and its optimal value is determined by the type of charging pile, reflecting the differentiated consideration of charging efficiency.

[0050] Secondly, the priority calculation formula is updated as: Score = Score η (1 + 0.5 · (SOC_current - 20%) / 80%). Where SOC_current is the current battery percentage of the vehicle, ranging from 0% to 100%. When SOC_current is higher than 20%, the formula increases the score by a linear coefficient, encouraging the vehicle to choose a station flexibly when the battery is full; if SOC_current is lower than 20%, the coefficient is reduced to reflect the urgent need for charging. For example, if a vehicle's SOC_current is 15%, the calculation term is (1 + 0.5 · (15 - 20) / 80) = 0.96875, combined with the fast charging pile η = 2, the final Score is the original value x 1.9375. This design balances the state of charge and charging efficiency, avoiding the risk of the vehicle ignoring the battery due to excessive pursuit of fast charging.

[0051] Third, if the distance between adjacent charging stations is less than the preset threshold d_min, a penalty factor ξ = 0.8 is applied to the downstream station. This rule avoids the vehicle repeatedly selecting similar stations in dense areas, optimizing the allocation of charging resources. Specifically, assuming that the distance between stations A and B in the park is 3 kilometers (d_min = 5 kilometers), the priority score of station B will be multiplied by 0.8, prompting the vehicle to preferentially select a station with a more reasonable distance.

[0052] Finally, when the vehicle battery temperature T_battery exceeds the safety threshold T_safe, the charging station equipped with a cooling system is preferentially selected. For example, a vehicle's battery temperature rises to 45°C (T_safe = 40°C) due to continuous operation, and the system will automatically filter stations that support active cooling to ensure charging safety and extend battery life. This rule improves the reliability of path planning through hardware adaptability.

[0053] In summary, by dynamically adjusting the charging pile efficiency weight, battery correlation coefficient, station distribution penalty, and temperature safety constraint, this method realizes the multi-dimensional optimization of new energy logistics vehicle charging stations in a zero-carbon park. For example, a vehicle with SOC_current = 30% passes through two fast charging stations (distance 4 kilometers, d_min = 5 kilometers), and its downstream station score will be reduced by ξ = 0.8, while the current station obtains a higher priority due to η = 2 and the battery coefficient 1.0625, thus generating an efficient and safe charging path.

[0054] Next, the congestion probability evaluation time dimension correction of the application is described: define the peak period weight function λ(t) = 1 + 5-sin(π-(t-t_peak) / Δt). Where t_peak represents the center time when traffic congestion is the most serious (for example, 10:00 am), Δt is the width of the peak period (for example, 2 hours), and t is the current time. This function simulates the congestion intensity change at different times through a sinusoidal fluctuation, with the highest weight in the peak period. The periodic nature of the sin function in the formula causes the weight to be symmetrically distributed first increasing and then decreasing in the range of t_peak±Δt / 2, and 5 is the amplification coefficient, which ensures that the congestion probability in the peak period is significantly improved. The optimal Δt needs to be dynamically calibrated according to the park traffic data, and is usually set to 1.5-3 hours.

[0055] Correct the congestion probability by multiplying the base probability P_congestion by λ(t). For example, the base congestion probability of a certain road section is 10%, and at t_peak+0.5 hours (λ(t) = 1 + 5-sin(π-0.5 / 2) = 1 + 5-0.707≈4.535), the corrected probability is increased to 45.35%. This correction incorporates time sensitivity into path planning, avoiding evaluation bias of static probability models during peak periods.

[0056] Set λ(t)>1.2 to force the use of a backup detour path. For example, a logistics vehicle enters the peak preparation interval of λ(t)=1.3 at 9:45 am, and the system automatically switches to a backup path. The threshold value 1.2 is determined by historical data analysis, balancing the detour cost and congestion risk, and avoiding increased energy consumption due to frequent detours.

[0057] Mileage limit condition for detour path. If the increased mileage ΔD of detour exceeds the preset threshold D_max (for example, 5 kilometers), the multi-vehicle joint path optimization decision is triggered. For example, a vehicle needs to increase 6 kilometers by detouring, which exceeds D_max=5 kilometers, and the system will coordinate the surrounding vehicles to adjust the path, and reduce the total travel mileage by sharing road network information. D_max is set according to the vehicle battery endurance and delivery time limit, and the optimal value is usually 10%-15% of the one-way mileage.

[0058] Specifically, in a zero-carbon park instance, a new energy logistics vehicle originally planned to travel along path A (8 km) with a basic congestion probability of 12%. If at t_peak+0.4 hours (λ(t)=1+5·sin(π·0.4 / 2)=1+5·0.587≈3.935), the modified congestion probability rises to 47.2%. The system determines that λ(t)=3.935>1.2, and enables the detour path B (10 km). Since ΔD=2 km<D_max=3 km, the detour is directly executed; if path B needs to be detoured to path C (13 km) due to construction closure, ΔD=5 km exceeds D_max, then the multi-vehicle cooperative optimization is started, and the paths of multiple vehicles are re-distributed to reduce the global mileage increment.

[0059] Next, the multi-objective cooperative scheduling incremental load dynamic allocation strategy of the application is described: A load distance cost matrix C_ij=α1·Q_ij / Q_max+α2·D_ij / D_max is constructed. Wherein, Q_ij is the task load demand, D_ij is the distribution distance; Q_max and D_max are the maximum load and the longest distribution distance in the park, respectively, for normalization processing. α1 and α2 are weight coefficients (α1+α2=1, α1∈[0.5, 0.8] preferentially optimize load), which comprehensively quantify the consumption of load and distance of tasks, and achieve multi-objective balance through weight adjustment. For example, in a zero-carbon park, a task Q_ij=200 kg (Q_max=500 kg), D_ij=5 km (D_max=15 km), if α1=0.7, α2=0.3, then C_ij=0.7·(200 / 500)+0.3·(5 / 15)=0.34.

[0060] The vehicle matching degree M is defined as: The matching degree M=1 / (1+|Q_remain-Q_ij| / Q_max), Q_remain is the remaining load of the vehicle. The formula measures the matching degree by the ratio of the difference between the remaining load and the task demand to the maximum load. The smaller the difference, the closer M is to 1. For example, a new energy logistics vehicle Q_remain=300 kg, task Q_ij=250 kg, Q_max=500 kg, then M=1 / (1+|300-250| / 500)=0.909. This design encourages the allocation of tasks that are close to the remaining load of the vehicle, reducing the empty rate.

[0061] Dynamic task allocation: If there are tasks with M>M_threshold (such as 0.8) and C_ij<C_max (such as 0.5), they are preferentially allocated to the current vehicle. For example, when a vehicle is performing delivery in a park, the system selects a task with M=0.85 and C_ij=0.4 because it has a high matching degree and low comprehensive cost, and directly allocates it to improve load utilization.

[0062] Capacity rebalancing mechanism: When three consecutive tasks result in a vehicle load utilization rate lower than η_min (such as 60%), rebalancing is triggered. For example, a vehicle successively delivers Q_ij=80kg, 100kg, and 120kg (Q_max=500kg), with load utilization rates of 16%, 20%, and 24%, respectively, all of which are lower than 60%. The system suspends allocation and recalculates the optimal combination of remaining tasks to avoid inefficient capacity consumption caused by scattered tasks.

[0063] Formula parameter design logic: The weight α1 focuses on load optimization, as the battery energy consumption of a zero-carbon vehicle is strongly related to load, and preferentially loading to full capacity can reduce the total number of trips; α2 controls distance cost to reduce ineffective mileage within the park. The matching degree threshold M_threshold is set to 0.8 to balance matching accuracy and allocation flexibility. C_max=0.5 filters high-cost tasks to ensure comprehensive benefits. This strategy dynamically couples load and path targets to achieve efficient and low-carbon scheduling of new energy vehicles in zero-carbon park scenarios.

[0064] Next, the dynamic energy consumption prediction model of the present application is described: First, the path slope sequence θ_i is extracted from a high-precision map, where θ_i represents the slope angle of the i-th path, ranging from -15° to 15°, with negative values representing downhill. Combined with the total vehicle mass Q_load (including cargo, typically 3-10 tons), the acceleration of gravity g (9.8 m / s 2 ) and the road segment length Δd_i (10-5000 meters), the cumulative climbing energy ΔE_grade=k5·Σ(Q_load·g·sinθ_i·Δd_i) is calculated. In the formula, k5 is the energy conversion coefficient (0.1-0.3, with an optimal value of 0.2) that converts mechanical work into battery energy consumption. This formula quantifies the non-linear impact of slope on energy consumption, as the vehicle needs to resist the gravitational component sinθ_i when climbing, and the greater the slope, the more significant the energy consumption growth.

[0065] When the cumulative ΔE grade exceeds a preset threshold E grade max (usually 15-20% of the battery capacity), a gradient penalty coefficient ζ = 1 + 1 · (ΔE grade / E grade max) is applied to magnify the path energy consumption evaluation value by 1-2 times. This design avoids continuous steep slope paths by a non-linear penalty mechanism, preventing the risk of battery overload. For example, when a certain logistics vehicle plans a 10-kilometer route, if the cumulative climbing energy consumption reaches 1.5 times E grade max, then ζ = 2.5, which increases the energy consumption evaluation value of the path to 2.5 times the original value, prompting the algorithm to select a gentler alternative path.

[0066] Then check whether the total energy consumption after correction (base energy consumption x ζ + ΔE grade) exceeds 80% of the battery capacity. If it does, a midway charging station is inserted into the path. Specifically, when a certain 4-ton new energy truck carrying 2 tons (Q load = 6 tons) performs a delivery task, the planned path contains multiple 8° climbing sections, and the total energy consumption after correction reaches 85% of the battery capacity (100 kWh), at which point the system automatically inserts a charging station at the midpoint of the path to ensure safe endurance.

[0067] Finally, when detecting a downhill section, activate the energy recovery mode, update the remaining power according to the recovery efficiency η = 15-30% (depending on the vehicle model). For example, when the vehicle passes through a 5° downhill section, the system calculates the recoverable energy ΔE regen = η · Q load · g · |sin θ i| · Δd i in real time, and adds this value to the remaining power prediction model, improving the accuracy of mileage prediction in long downhill scenarios. This closed-loop compensation mechanism reduces the prediction error of the dynamic energy consumption model to within 5%.

[0068] Next, the multi-objective cooperative scheduling of the present application includes an emergency task processing mechanism: Define the emergency task identifier δ: The calculation formula of the emergency task identifier δ is δ = 1 + 5 · T remain / T deadline, where T remain is the remaining available time of the task (range ≥ 0), and T deadline is the total deadline of the task (range > 0). The value range of δ is 1 (no urgency) to 6 (extremely urgent), and the optimal value needs to be adjusted according to the actual scene. This formula linearly amplifies the time urgency (5 times coefficient) to ensure that the priority of the emergency task is significantly improved, while the baseline value of 1 avoids the case where δ is 0, causing the priority to fail.

[0069] Emergency task insertion decision: When δ > δ_critical (critical value, usually set to 3), insert the task into the nearest serviceable node of the current optimal vehicle queue. This step prioritizes emergency tasks by dynamically adjusting the path while minimizing the disturbance of the original path. For example, a vehicle in a zero-carbon park is originally scheduled to deliver goods to nodes A and B. At this time, an emergency medical material task δ = 4.2 (T_remain = 2 hours, T_deadline = 5 hours) is added. If the vehicle is currently closer to the emergency task node C, then C is inserted into the path before A.

[0070] Path re-planning and collaborative support: If the insertion of the task causes the total energy consumption of the vehicle to exceed 90% of the remaining energy E_remaining, then the adjacent vehicle collaborative support mechanism is triggered. E_remaining is the current remaining energy of the vehicle (range 0-100%), and the 90% threshold is used to prevent the risk of running out of power. For example, after inserting the task, the estimated energy consumption of the vehicle reaches 95% of E_remaining, and the system will allocate another idle vehicle nearby to complete part of the non-emergency task to ensure that the main vehicle has sufficient power.

[0071] Emergency factor weight correction: In the calculation of task allocation weight, the emergency factor ω_emergency = max(ω, δ), where ω is the original weight (such as distance, load, etc.), and δ is the emergency identifier. This formula forces the weight of emergency tasks to be no less than δ, ensuring that the scheduling decision is biased towards emergency needs. Specifically, a regular task ω = 2.5 (based on distance calculation), and if its δ = 3.8, then the final ω_emergency = 3.8, which is prioritized over other low δ tasks.

[0072] In one embodiment, a logistics vehicle in a zero-carbon park has a remaining energy E_remaining = 80% and is performing a delivery from node X to Y. At this time, an emergency cold-chain medicine task is received, T_remain = 1 hour, T_deadline = 4 hours, and δ = 1 + 5·(1 / 4) = 2.25 is calculated. Since δ is not greater than the critical value 3, insertion is not triggered. 10 minutes later, the task T_remain = 0.5 hours, and δ is updated to 1 + 5·(0.5 / 4) = 1.625, which is still lower than the critical value. If T_remain decreases to 0.2 hours due to traffic congestion, δ = 1 + 5·(0.2 / 4) = 1.25, which is still lower than 3, but the system can force the priority to be raised with manual intervention, demonstrating the flexibility of the mechanism.

[0073] The path planning method of the zero-carbon park new energy logistics vehicle of the application comprises the following steps: firstly, the residual power and load data of the vehicle are collected in real time, and an initial distribution path and charging demand node are generated by combining a dynamic energy consumption prediction model; an electric quantity safety threshold mechanism is established in the path planning stage, the consumption rate of electric quantity by load and driving speed is calculated in real time by the dynamic energy consumption model, and when the predicted residual electric quantity is lower than the set threshold, the path adjustment is automatically triggered to realize the active prevention and control of energy risk; secondly, a charging facility state monitoring system is established, the charging pile occupancy rate, queuing time and charging power data are obtained in real time through the Internet of Things equipment, the charging sites are prioritized by using a weighted scoring algorithm according to the vehicle residual electric quantity and the path node position, and a set of adjustable paths containing multiple backup charging points is dynamically generated to ensure that the secondary charging node can be quickly switched to in case of charging resource shortage; thirdly, based on the space-time big data analysis platform, the traffic flow mode of the park in each period and the weather forecast data are integrated according to the historical distribution path passing time, and a congestion probability prediction model is constructed by using a deep learning algorithm to correct the energy consumption parameters of the dynamic path set, the space-time risk coefficients of different paths are evaluated by Monte Carlo simulation, and the optimized path with a congestion probability lower than a preset value is selected; fourthly, a multi-objective optimization model is constructed, the task-path collaborative matching calculation is performed by using a genetic algorithm according to the vehicle load capacity, the volume distribution characteristics of goods and the task priority, and the dynamic weighting method is used to balance the distribution time efficiency and the utilization rate of transport capacity to realize the intelligent optimization of the vehicle loading rate on the premise of ensuring the complete distribution of goods; and finally, an environment adaptive mechanism is established, the influence coefficients of air density and temperature on battery efficiency in the energy consumption model are dynamically adjusted by accessing the real-time data of the park weather station, the geographical feature parameters such as the road slope and the bend radius of the park are used to correct the basic energy consumption calculation formula, and the path planning accuracy under different environmental conditions is ensured to be controlled within a range of ± 5%. The method realizes the global optimal path planning of the new energy logistics vehicle in the complex operation environment of the zero-carbon park through the five-level intelligent decision system.

[0074] The above describes the preferred embodiments of the present application. It should be noted that, for those skilled in the art, without departing from the principles of the present application, several improvements and refinements can be made, which should also be considered as the protection scope of the present application. In the several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. In actual implementation, additional division can be made, for example, a plurality of units or components can be combined or integrated into another system, or some features can be omitted or not implemented. In addition, the coupling or direct coupling or communication connection between the shown or discussed mutual elements can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0075] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit. The above is only an embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation using the content of the present application specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

[0076] The specific embodiments of the application are described in detail above, but they are only examples. The present application is not limited to the above-described specific embodiments. Any equivalent modification or substitution of the present application made by those skilled in the art is also within the scope of the present application, and therefore, equivalent transformations, modifications, improvements, etc. made without departing from the spirit and principle range of the present application should be included in the scope of the present application.

Claims

1. A route planning method for new energy logistics vehicles in a zero-carbon industrial park, characterized in that, It includes the following steps: Based on the real-time remaining power, cargo load, and dynamic energy consumption prediction model of new energy logistics vehicles, generate an initial distribution route and charging demand nodes; According to the real-time occupancy status of charging facilities in the zero-carbon park and the vehicle remaining power threshold, optimize the charging station priority for the initial route, and generate a dynamically adjustable route set; Based on the input of historical distribution data, real-time traffic flow, and weather conditions, evaluate the congestion probability and correct the energy consumption of the dynamically adjustable route set, and output the spatio-temporal optimal route plan; According to the logistics task allocation weight, vehicle load capacity constraint, and cargo distribution characteristics, perform multi-objective collaborative scheduling on the spatio-temporal optimal route plan, generate the final route planning instruction, and update the vehicle task queue in real time.

2. The route planning method for new energy logistics vehicles in a zero-carbon industrial park according to claim 1, characterized in that, The calculation formula of the dynamic energy consumption prediction model is: E_consume=(k1 W+k2 V 2 )·D·(1+α·ΔT), Where E_consume is the predicted energy consumption, W is the vehicle load, V is the average driving speed, D is the route mileage, ΔT is the temperature deviation coefficient, k1, k2 are the load and air resistance coefficients, and α is the temperature influence factor; When the vehicle remaining power E_remaining < E_consume + E_reserve, forcefully insert a charging demand node, where E_reserve is the preset safety redundant power; According to the difference ΔQ between the load capacity constraint Q_max and the cargo weight Q_load, dynamically adjust the maximum number of serviceable distribution points of the vehicle; If ΔQ < Q_threshold, trigger the reallocation of the vehicle task queue.

3. The route planning method for new energy logistics vehicles in zero-carbon industrial parks according to claim 1, characterized in that, The charging station priority optimization includes the following steps: Obtain the real-time queuing duration t_wait and charging power P_charge of the charging station; Calculate the charging priority, and the calculation formula of the priority includes: Score = (1 / t_wait)·P_charge·(1 - |E_remaining - E_threshold| / E_full), where E_threshold is the charging trigger threshold and E_full is the full charge; When Score > Score_min and the distance d from the vehicle's current position to the charging station ≤ d_max, add this station to the dynamically adjustable route set; If there are multiple candidate stations in the same route set, preferentially select the station that satisfies t_wait < t_max and the remaining power after charging can cover the total demand of the subsequent route; 4. The route planning method for new energy logistics vehicles in zero-carbon industrial parks according to claim 1, characterized in that, The congestion probability evaluation formula is: P_congestion = β·(N_history / N_max) + (1 - β)·(F_real / F_capacity), Where N_history is the number of vehicles on this section in the same historical period, N_max is the upper limit of the section capacity, F_real is the real-time traffic flow, F_capacity is the theoretical traffic capacity, and β is the historical data weight coefficient; If P_congestion > P_threshold, then the energy consumption correction coefficient γ for this section is γ = 1 + 2·(P_congestion - P_threshold); According to the corrected total energy consumption \(E_{consume}=E_{consume}\cdot\gamma\), re-screen the paths that satisfy \(E_{remaining}\geq E_{consume}+E_{reserve}\); When all candidate paths do not satisfy the condition, trigger the cross-vehicle collaborative scheduling mechanism.

5. The route planning method for new energy logistics vehicles in zero-carbon industrial parks according to claim 1, characterized in that, The multi-objective collaborative scheduling includes the following steps: Based on the cargo distribution characteristics, construct the spatial density matrix \(M_{density}\), and calculate the cargo density \(\rho = Q_{area} / A_{zone}\) in each area; Define the task allocation weight \(\omega=\mu\cdot\rho+(1 - \mu)\cdot(1 - d_{avg} / d_{max})\), where \(\mu\) is the density weight coefficient and \(d_{avg}\) is the average distance from the vehicle to the target area; If the remaining load capacity of the vehicle \(\Delta Q < Q_{load}\cdot\omega\), split the tasks in this area for multi-vehicle collaborative distribution; According to the real-time position of the vehicle and the status of the task queue, dynamically adjust the weight allocation strategy of \(\omega\).

6. The route planning method for new energy logistics vehicles in zero-carbon industrial parks according to claim 1, characterized in that, The additional weather condition compensation term in the dynamic energy consumption prediction model includes: When the rainfall intensity \(R > R_{threshold}\), the additional energy consumption compensation \(\Delta E_{rain}=k3\cdot R\cdot D\), where \(k3\) is the rainfall influence coefficient; If the wind speed S > S_threshold and the angle θ with the driving direction > 90°, additional headwind compensation ΔE_wind = k4·S 2 ·cosθ; The total corrected energy consumption \(E_{consume}=E_{consume}+\Delta E_{rain}+\Delta E_{wind}\); When \(E_{remaining}<E_{consume}\), force the generation of alternative paths including charging stations.

7. The route planning method for new energy logistics vehicles in zero-carbon industrial parks according to claim 6, characterized in that, The optimization of the charging station priority further includes: Introduce the charging pile type matching factor \(\eta\), \(\eta = 2\) for fast charging piles and \(\eta = 1.0\) for slow charging piles; Update the priority calculation formula as: Score = Score\cdot\eta\cdot(1 + 0.5\cdot(SOC_{current}-20\%) / 80\%), where \(SOC_{current}\) is the current battery percentage; If the distance between adjacent charging stations is less than \(d_{min}\), impose a penalty factor \(\xi = 0.8\) on the downstream stations; When the vehicle battery temperature \(T_{battery}>T_{safe}\), preferentially select charging stations equipped with a cooling system.

8. The route planning method for new energy logistics vehicles in zero-carbon industrial parks according to claim 7, characterized in that, The evaluation of the congestion probability adds a time dimension correction: Define the peak period weight function \(\lambda(t)=1 + 5\cdot\sin(\pi\cdot(t - t_{peak}) / \Delta t)\), where \(t_{peak}\) is the peak center time and \(\Delta t\) is the period width; The corrected congestion probability \(P_{congestion}=P_{congestion}\cdot\lambda(t)\); If the evaluation time is in the interval where \(\lambda(t)>1.2\), force the activation of the alternative detour path; When the mileage of the detour path increases by \(\Delta D > D_{max}\), trigger the multi-vehicle joint path optimization decision.

9. The route planning method for new energy logistics vehicles in zero-carbon industrial parks according to claim 8, characterized in that, The multi-objective collaborative scheduling adds a dynamic load distribution strategy: Construct the load-distance cost matrix \(C_{ij}=\alpha1\cdot Q_{ij} / Q_{max}+\alpha2\cdot D_{ij} / D_{max}\), where \(Q_{ij}\) is the task load and \(D_{ij}\) is the distribution distance; Define the vehicle matching degree \(M = 1 / (1+|Q_{remain}-Q_{ij}| / Q_{max})\), \(Q_{remain}\) is the remaining load of the vehicle; If there are tasks with M > M_threshold and C_ij < C_max, they are preferentially assigned to the current vehicle; When the vehicle load utilization rate is lower than η_min due to three consecutive tasks, the transportation capacity rebalancing mechanism is triggered.

10. The route planning method for new energy logistics vehicles in zero-carbon industrial parks according to claim 9, characterized in that, The dynamic energy consumption prediction model introduces slope compensation: The path slope sequence θ_i is obtained through a high-precision map, and the cumulative climbing energy consumption ΔE_grade = k5·Σ(Q_load·g·sinθ_i·Δd_i) is calculated, where g is the acceleration due to gravity and Δd_i is the road segment length; When the cumulative ΔE_grade > E_grade_max, a slope penalty coefficient ζ = 1 + 1·(ΔE_grade / E_grade_max) is applied to the path; If the corrected total energy consumption exceeds 80% of the battery capacity, a mid-way charging station is inserted; When a downhill road segment is detected, the energy recovery mode is enabled and the remaining battery power prediction value is updated.

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