High-temperature environment multimodal transport method and system based on composite heat insulation and active temperature control

Abstract

The invention discloses a high-temperature environment multimodal transport method and system based on composite heat insulation and active temperature control. The method comprises the steps that a multimodal transport capacity grid model simulating high-temperature transport cooperation is constructed; determining model condition hypothesis, and defining decision variables, parameters and state variables of the model; high-temperature special items and safety penalty are included in the total cost, a high-temperature associated total cost model is constructed, and a multi-scene cost calculation formula is deduced; with the purpose of minimizing the total cost, a mixed integer programming model including path connection and flow conservation, temperature constraint, refrigeration / heat dissipation energy and power constraint, node refrigeration / sunshade capacity constraint, battery and key component temperature safety constraint, butt joint / node capacity constraint and time window constraint is constructed; solving the model by adopting an improved hybrid immunity-particle swarm dual-optimization algorithm, and outputting an optimal transportation path, transportation mode, transfer node and high-temperature scene exclusive scheduling parameter; and collaborative optimization of high-temperature transportation safety, energy efficiency and economy can be realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of multimodal transport, and more particularly relates to a high-temperature environment multimodal transport method and system based on composite thermal insulation and active temperature control. BACKGROUND

[0002] As a core organization mode of modern logistics, multimodal transport integrates two or more transport modes such as highways, railways, waterways, and aviation to realize "integration, continuity, and high efficiency" of goods transportation from the starting point to the destination, and is a key infrastructure supporting cross-regional resource allocation and cross-border trade. In recent years, with the extension of global resource development to arid / high-temperature regions (such as desert oil fields and tropical mineral areas), cross-border desert routes and tropical coastal trade channels continue to expand, and extreme high-temperature events (the number of days with a daily maximum temperature of ≥40℃ increases by 12%-18% annually) occur frequently due to global warming. The demand for high-temperature environment multimodal transport based on composite thermal insulation and active temperature control is growing explosively, and new requirements are put forward for the "high-temperature tolerance, operation stability, and cargo safety" of the transport system.

[0003] However, the current mainstream multimodal transport technology and supporting devices (such as containers, loading and unloading equipment, and power systems) are designed based on temperate / subtropical mild climate conditions (environmental temperature 5℃-35℃), and the material selection, structure design, and temperature control logic do not consider the extreme conditions of high-temperature scenarios. In a high-temperature environment, the environmental temperature is often stable at 40℃-60℃, and the temperature of closed / exposed surfaces such as the top surface of a container and the metal shell of equipment can exceed 70℃ under direct sunlight. The direct migration of traditional multimodal transport technology to this scenario will trigger a series of systematic technical bottlenecks, severely restricting transport efficiency and safety.

[0004] Firstly, high-temperature reliability failure of devices and key components, including material degradation to structural failure. The high-temperature environment has a triple impact on the "material performance-structure stability-sealing effectiveness" of multimodal transport equipment; high temperature can cause softening, accelerated aging of seals, gaskets, and composite materials, viscosity reduction or oxidative decomposition of lubricants, and increased wear or lubrication failure of sliding parts and transmission chains; bearings, hydraulic components, and plastic / rubber components are prone to deformation, failure, or cracking under long-term high temperature, and high-temperature creep and thermal fatigue of materials reduce the service life of structural components and increase the risk of failure. In addition, ultraviolet light and solar radiation can accelerate the surface degradation of high-molecular-weight materials, leading to sealing leakage, clamp failure, or anticorrosion layer peeling, thereby increasing maintenance frequency and downtime, and weakening the continuous operation capability and reliability of the system.

[0005] Secondly, the degradation of power supply, heat dissipation, and temperature control capabilities makes it difficult to maintain transportation continuity and cargo quality. Under high-temperature conditions, battery life (especially lithium batteries) is shortened, internal resistance increases, and there is a risk of thermal runaway; power electronics and control units have difficulty dissipating heat, are prone to frequency reduction or thermal shutdown, leading to a decline in drive and control performance. For goods requiring constant or low-temperature storage (such as food, pharmaceuticals, and precision electronic components), existing insulation and refrigeration systems experience a significant increase in energy consumption under sustained high-temperature loads, resulting in insufficient refrigeration capacity or soaring energy costs. Inadequate zonal temperature control and real-time temperature and humidity monitoring and response mechanisms make it difficult to guarantee the quality and safety of goods throughout the entire process, increasing the risk of cargo damage and compensation. At the same time, the efficiency of engines and cooling systems decreases and the failure rate increases under high ambient temperatures, affecting the continuity and economy of transportation operations.

[0006] Third, the adaptability of loading and unloading docking, interface coordination, and transportation organization to high temperatures and dust storms is insufficient. The thermal expansion differences and structural deformations caused by solar radiation can lead to dimensional mismatches in vehicles, containers, tracks, and docking mechanisms, resulting in jamming, sealing failures, or difficulties in alignment, reducing loading and unloading efficiency. Road surfaces, tracks, and other infrastructure are prone to softening, rutting, or track expansion (gauge deviation / track undulation) at high temperatures, posing limitations on operational safety and scheduling. Drought / high temperatures are often accompanied by dust storms, wind erosion, and reduced visibility. Dust entering mechanical and electronic equipment accelerates wear and poor contact, limiting on-site maintenance and rapid response capabilities. Furthermore, high temperatures cause heat stress, safety hazards, and reduced labor efficiency for workers. Traditional intermodal transport organization models and timetables lack sufficient flexibility and emergency mechanisms under high-temperature restrictions (e.g., high-temperature speed limits, nighttime operation alternatives), making it difficult to guarantee transport reliability and safe handling capabilities.

[0007] The aforementioned three dimensions of problems overlap, leading to adverse consequences in multimodal transport under high-temperature conditions, such as shortened equipment lifespan, increased fire and thermal runaway risks, increased cargo damage and energy consumption, decreased transport timeliness and availability, and significantly increased operation and maintenance costs. Therefore, it is necessary to propose targeted technical improvement solutions for high-temperature resistant materials and sealing designs, energy efficiency and redundancy assurance of power, heat dissipation and temperature control systems, and high-temperature adaptability of loading and unloading interfaces and organizational scheduling, in order to break through the high-temperature application bottlenecks of existing multimodal transport technologies. Summary of the Invention

[0008] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a high-temperature environment multimodal transport method and system based on composite thermal insulation and active temperature control. This involves constructing a multimodal transport capacity grid model simulating high-temperature transport coordination; determining model assumptions; defining model decision variables, parameters, and state variables; incorporating high-temperature special items and safety penalties into the total cost to construct a high-temperature associated total cost model; and deriving cost calculation formulas for multiple scenarios. With the goal of minimizing total cost, a mixed integer programming model is constructed, including constraints on path connectivity and flow conservation, temperature constraints, cooling / heat dissipation energy and power constraints, node cooling / shading capacity constraints, battery and key component temperature safety constraints, docking / node capacity constraints, and time window constraints. An improved hybrid immune-particle swarm optimization algorithm is used to solve the model, outputting the optimal transport path, transport mode, transfer node, and high-temperature scenario-specific scheduling parameters. This addresses the problems of traditional multimodal transport neglecting cargo temperature control safety, high equipment failure risk, difficulty in controlling energy consumption costs, and poor intermodal coordination in high-temperature environments. Through high-temperature scenario-specific model construction, constraint design, and a dual-optimization algorithm, it achieves coordinated optimization of high-temperature transport safety, energy efficiency, and economy.

[0009] To achieve the above objectives, one aspect of the present invention provides a high-temperature environment multimodal transport method based on composite thermal insulation and active temperature control, comprising:

[0010] S1: Based on the distribution of regional hub nodes, the attributes of connecting road sections, and the parameters of node high-temperature adaptation devices under high-temperature conditions, a multimodal transport capacity grid model simulating high-temperature transport coordination is constructed.

[0011] S2: Determine the conditional assumptions of the model and define the three types of symbol systems for the model's decision variables, parameters, and state variables;

[0012] S3: Incorporate high-temperature special items and safety penalties into the total cost, construct a high-temperature-related total cost model, derive cost calculation formulas for multiple scenarios, and quantify the impact of high temperatures on the total cost;

[0013] S4: With the goal of minimizing total cost, a hybrid integer programming model is constructed, which includes path connectivity and flow conservation, temperature constraints, cooling / heat dissipation energy and power constraints, node cooling / shading capacity constraints, battery and key component temperature safety constraints, docking / node capability constraints, and time window constraints. An improved hybrid immune-particle swarm optimization algorithm is used to solve the model, and the optimal transportation path, transportation mode, transfer node, and high-temperature scenario-specific scheduling parameters are output.

[0014] Furthermore, the multimodal transport capacity grid model for simulating high-temperature transport coordination described in step S1 uses several city / hub nodes as vertices and one or more transport modes among highways, railways, waterways, and air connecting routes as edges; each node is equipped with a high-temperature adaptation device and interface unit, and its availability, cooling and heat dissipation capabilities and dustproof capabilities under high-temperature conditions are marked, so as to simulate the coordinated operation of cargo transport routes and multimodal transport devices under high-temperature scenarios.

[0015] The city / hub nodes include ports, railway marshalling yards, highway transfer yards, and inland transshipment yards;

[0016] The high-temperature adaptation device and interface unit includes a passive heat insulation chamber, an active cooling / cooling docking device, a sunshade / heat dissipation shed, an energy buffer and heat dissipation station, and a sand / dust filtration unit.

[0017] Furthermore, the model condition assumptions in step S2 include: the goods are transported in containers, and the goods within the containers are not split for transport;

[0018] The cryogenic adaptation devices and energy replenishment facilities marked in the area were in a usable state at the time of planning;

[0019] The vehicles operate on the road according to established driving rules, and each transfer node can be loaded and unloaded within a predetermined time window;

[0020] All thermal / energy model parameters were estimated through field tests or historical data and then substituted into the model.

[0021] High-temperature related safety / fire hazard parameters are incorporated into the optimization as penalties or constraints.

[0022] Furthermore, the expression for the high-temperature associated total cost model in step S3 is as follows:

[0023] C total =C trans +C transf +C loss +C energy +C maint +C safety +C time (1)

[0024] Of which, total cost C total Including basic transportation costs C trans Transit cost C transf Loss / damage cost C loss Energy consumption / cooling cost C energy Equipment failure and maintenance costs C maint Safety / fire hazard penalty costs C safety And time window penalties and retention costs C time ;

[0025] In step S3, the loss rate of goods increases due to overheating, temperature fluctuations, and humidity changes under high-temperature conditions. The loss rate is calculated using the Weibull or temperature-corrected failure probability function F(T). travel ;T eff (t) represents the loss / damage cost C based on this distribution. loss The expression is:

[0026] C loss =n c ·P unit ·F(T travel ;T eff (t)) (4)

[0027] Where, n c P represents the number of containers used. unit The value of a unit box of goods, T travel T represents the total transit time of the goods. eff (t) represents the effective temperature curve considering enclosure insulation, active cooling, and the door opening event; if T i (t) exceeds the allowable range Then F increases significantly; T i (t) represents the temperature inside the cargo hold / container.

[0028] Furthermore, in step S3, the instantaneous heat load of the cargo hold Including heat permeability, solar radiation gain, and ventilation / door opening losses; the instantaneous heat load expression for the cargo hold is:

[0029]

[0030] in, Represents the instantaneous heat load of the i-th node at time t; U is the overall heat transfer coefficient; A i T represents the heat transfer area of ​​the i-th node; out (t) represents the ambient temperature at time t; T i (t) represents the internal temperature of the i-th node at time t; α solar Let A be the absorptivity of the cargo hold surface to solar radiation. It is dimensionless and ranges from 0 to 1, where 0 represents complete reflection and 1 represents complete absorption. surf G is the surface area of ​​the cargo hold exposed to solar radiation. solar (t) represents the solar radiation intensity at time t; Let be the heat loss rate of the i-th node due to ventilation or door opening operation;

[0031] To maintain the temperature inside the chamber, the required cooling power P cool,i (t) is:

[0032]

[0033] Among them, P cool (t) represents the cooling power required to maintain the temperature within the i-th node at time t; COP represents the instantaneous heat load of the i-th node at time t; cool (t) is the performance coefficient of the refrigeration system at time t. It is dimensionless and is the ratio of refrigeration capacity to refrigeration power consumption, reflecting the energy efficiency level of the refrigeration system.

[0034] The energy consumption / cooling cost C mentioned in the key items of high temperature scenarios energy The expression is:

[0035] C energy =∑ t (λ elec P elec (t)+λ fuel P fuel (t))Δt (7)

[0036] P elec (t)=P cool,i (t)+P motor-cool (t)+P sand-filter (t)+P control (t)(8)

[0037] Among them, C energy λ represents the total energy cost; elec P represents the unit price of electricity; elec (t) represents the power consumption at time t, including the power of the vehicle's cooling system, electric motor cooling system, dust filtration device, and control system; λ fuel P is the unit price of fuel; fuel (t) represents the fuel consumption power at time t; Δt represents the time step; P cool,i (t) represents the cooling power, determined by the cargo hold heat load and the coefficient of performance of the refrigeration system; P motor-cool (t) represents the power consumption of the motor's cooling system at time t, and its magnitude is related to the motor load and ambient temperature (the motor heats up more at high temperatures, and the power consumption of the cooling system usually increases); P sand-filter (t) represents the power consumption of the dust filtration device (generated when it is activated in a high-temperature dust environment); P control (t) represents the power consumption of the control system (such as temperature and humidity monitoring, dispatching system).

[0038] Furthermore, the safety / fire hazard penalty cost C safety The expression is:

[0039] C safety =W fire·Pr(thermal runaway or fire) (14)

[0040] Among them, C safety For safety / fire hazard penalty costs; W fire The expected economic and reputational losses from a potential accident; Pr (thermal runaway or fire) is the dimensionless probability of battery thermal runaway or fire.

[0041] Furthermore, the expression for the mixed-integer programming model in step S4 is:

[0042] MinC total =C trans +C transf +C loss +C energy +C maint +C safety +C time (16)

[0043] The expression for the cooling / heat dissipation energy and power constraint is as follows:

[0044] ∑ t P elec (t)Δt≤E available ,∑ t P cool (t)Δt≤E cool,avail (19)

[0045] Among them, P elec (t) represents the power consumption at time t, including the power of the vehicle's cooling system, electric motor cooling system, dust filtration device, and control system; Δt represents the time step; E available P represents the total usable electrical energy. cool (t) represents the cooling power at time t; E cool,avail This represents the total energy available to the refrigeration system, i.e., the upper limit of energy that the refrigeration system can put into operation within a cycle;

[0046] The expression for the node cooling / shading capacity constraint is:

[0047]

[0048] Among them, P cool,i (t) represents the cooling power; The maximum cooling power capacity of node i; the enabled state variable of the shading facility at node i. This indicates that the sunshade facilities have been activated. This indicates that it is not enabled; it is a binary variable.

[0049] The temperature safety constraint expressions for batteries and critical components are as follows:

[0050] T batt (t)≤T batt,crit ,Pr(thermal runaway)≤∈ safety (twenty one)

[0051] Among them, T batt (t) represents the temperature of the battery at time t; T batt,crit The critical safe temperature of the battery; Pr (thermal runaway) is the probability of thermal runaway of the battery, dimensionless, reflecting the likelihood of thermal runaway under current operating conditions; ∈ safety This is a dimensionless safety threshold for the probability of thermal runaway, requiring that the probability of battery thermal runaway not exceed this value to ensure safety.

[0052] Furthermore, in step S4, the improved hybrid immune-particle swarm optimization algorithm is used to solve the model, outputting the optimal transportation path, transportation mode, transfer node, and high-temperature scenario-specific scheduling parameters, including:

[0053] S41. Design a hybrid coding scheme to match the type of model decision variables: In view of the dual characteristics of discrete and continuous decision variables in the mixed integer programming (MINLP) model, design a three-segment hybrid coding structure to transform the model variables into antibody / particle forms that the algorithm can process;

[0054] S42. Initialize the algorithm population and incorporate a heuristic strategy specific to high-temperature scenarios to avoid the algorithm getting trapped in local optima and ensure that the initial solution meets the high-temperature safety constraints.

[0055] S42. Based on the goal of minimizing the total cost of the mixed integer programming model, a safety constraint penalty term is introduced to construct the algorithm fitness function, which leads to the search in the direction of low cost and high safety.

[0056] S43. A dual optimization mechanism is adopted, which optimizes discrete variables using immune genetics and continuous variables using particle swarm optimization. The population is updated in stages to achieve synergistic optimization of discrete and continuous decision variables.

[0057] S44. To address the dynamic disturbances of solar radiation fluctuations and sudden changes in ambient temperature in high-temperature environments, the solution process is divided into two layers, with global optimization and real-time adjustments.

[0058] S45. Set dual iteration termination conditions. When the iteration limit is reached or the fitness converges, decode the globally optimal particle / antibody, restore it to the model decision variable value, and output the optimal joint transport scheme and high-temperature specific scheduling parameters.

[0059] Furthermore, the three-segment hybrid encoding structure described in step S41 refers to a string containing three parts: a path-mode binary segment, a relay / module activation segment, and a continuous segment; the specific encoding rules are as follows:

[0060] Path-mode binary segment: Use 0 / 1 binary encoding for each candidate route segment (i,j) and each mode of transport k, or simplify it to compressed encoding of "route node sequence + mode sequence";

[0061] Transit / Module Activation Section: Whether the discrete decision variables specific to high-temperature scenarios are enabled are represented by binary encoding; the discrete decision variables specific to high-temperature scenarios include whether passive shading is enabled at nodes. Enable node / vehicle cooling Should sand and dust filtration be enabled? Should night driving be adopted?

[0062] Continuous Quantity Segment: Real-valued encoding is used for continuous decision variables in the model; the continuous decision variables include cooling power. Road section driving speed Energy saving thresholds and SOC management parameters;

[0063] The initialization of the algorithm population and the integration of a high-temperature scenario-specific heuristic strategy in step S41 include:

[0064] 60%-70% of the initial particles / antibodies are generated through random coding;

[0065] Generate an additional 30%-40% of heuristic initial solutions;

[0066] Initial solutions that violate core constraints are eliminated, and particles / antibodies that meet high-temperature safety requirements are retained to form the initial population;

[0067] The initial heuristic solutions include: "Prioritize nighttime driving + node shading + low cooling power" and "Prioritize coastal low-temperature routes"; The "Prioritize nighttime driving + node shading + low cooling power" solution: For sections with extreme high temperatures during the day, nighttime operation is selected by default, shading facilities are activated at the nodes along the route, and the cooling power is set according to the lower limit of the cargo hold heat load to balance energy consumption and temperature control.

[0068] Solution for “Coastal Low Temperature Route Priority”: For cross-regional transportation, coastal routes are prioritized, and the corresponding coastal route-mode variable in the coding is set to 1.

[0069] The high-temperature safety constraints mentioned in step S41 include that the cargo temperature does not exceed the permissible range and the battery temperature does not exceed the permissible maximum value.

[0070] Further, the fitness function expression in step S42 is:

[0071]

[0072] Among them, C total Let M be the total cost in the mixed-integer programming model; M is the penalty coefficient for boundary violation events. This is an indicator function used to determine whether an out-of-bounds security event exists. When an out-of-bounds security event exists, When there is no boundary crossing security event

[0073] Step S43 includes the discrete variable segment for immune genetic optimization, the continuous segment for particle swarm optimization, and the segment for intervention by the safety repair operator;

[0074] The immunogenetic optimization of discrete variable segments includes: based on fitness function values, using roulette wheel selection or elite retention strategies to screen for particles / antibodies with high fitness to enter the next generation; performing single-point or two-point crossover on the path-mode segment and transit / module activation segment respectively to ensure that path connectivity is still satisfied after crossover; and performing mutations on discrete segments with low probability.

[0075] The continuous segment of particle swarm optimization includes: for each particle in the continuous segment, updating the PSO update rate and position according to the individual optimal solution and the global optimal solution in a short-period PSO; and truncating continuous variables that exceed the physical range after the update.

[0076] The safety repair operator intervention segment includes: if a particle / antibody violates the high-temperature safety constraint after iteration, a safety repair is triggered; the discrete and continuous segment codes are adjusted first by "increasing cooling power, switching to night operation, and enabling node shading"; if the repair is ineffective, the particle / antibody is eliminated.

[0077] Step S44 involves layered solution, including offline / semi-real-time optimization of the upper-layer path-mode selection, transit node determination, and transit / module activation; and real-time optimization of the lower-layer cooling power scheduling.

[0078] The solution algorithm for real-time optimization of the lower-level cooling power scheduling adopts Model Predictive Control (MPC) or Short-Period Particle Swarm Optimization (PSO), and updates the cooling power command every 5-15 minutes to offset real-time heat load fluctuations.

[0079] The termination condition for the double iteration in step S45 is:

[0080] The number of iterations has reached the preset limit;

[0081] If the change in the fitness function value of the global optimal solution is less than the threshold for 10-15 consecutive generations, it is considered convergent.

[0082] The optimal intermodal transport plan includes: the optimal transport route, the transport mode for each segment, and the location of the transfer node;

[0083] The high-temperature-specific scheduling parameters include the activation time and power of node shading / cooling facilities, road sections with sand and dust filtration mode activated, night / low-temperature operation time allocation, and real-time cooling power adjustment sequence.

[0084] A second aspect of the present invention provides a high-temperature environment multimodal transport system based on composite thermal insulation and active temperature control, characterized in that the method for implementing the aforementioned high-temperature environment multimodal transport system based on composite thermal insulation and active temperature control includes:

[0085] The high-temperature scenario data acquisition and preprocessing module is used to collect high-temperature environmental parameters, equipment and cargo status data, and after cleaning, completion and format conversion, output thermal / energy parameters and equipment failure parameters that meet the model requirements.

[0086] The capacity grid model construction and symbol definition module is used to build a capacity grid model with hub nodes as vertices and connected road segments as edges to label the capacity of high-temperature adaptability devices; it clarifies 5 modeling assumptions and defines three types of symbol systems: decision variables, parameters, and state variables, providing a basic framework for mixed integer programming models;

[0087] The high-temperature-related total cost model construction module is used to incorporate high-temperature special items and safety penalties into the total cost, construct a high-temperature-related total cost model, derive cost calculation formulas for multiple scenarios, and quantify the impact of high temperature on the total cost.

[0088] The Mixed Integer Programming (MINLP) model building module is used to construct a MINLP model with the goal of minimizing total cost. This model includes constraints on path connectivity and flow conservation, temperature constraints, cooling / heat dissipation energy and power constraints, node cooling / shading capacity constraints, battery and critical component temperature safety constraints, docking / node capability constraints, and time window constraints.

[0089] An improved hybrid immune-particle swarm optimization module is used to transform variables into an algorithm-processable form through a three-segment hybrid encoding, and incorporates a high-temperature heuristic strategy to initialize the population; a fitness function with safety penalty is constructed, and dual optimization is performed by "immune genetic optimization of discrete variables + particle swarm optimization of continuous variables", combined with hierarchical solution to deal with high-temperature dynamic interference, and finally outputs the optimal solution;

[0090] The optimal solution output and evaluation module is used to decode the optimal solution and output the basic intermodal transport plan and high-temperature-specific scheduling parameters.

[0091] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0092] (1) The high-temperature environment multimodal transport method and system based on composite heat insulation and active temperature control of the present invention accurately depicts the core elements of the transport network under high temperature environment by constructing a transport capacity grid model that combines regional hub nodes, connecting road sections and high-temperature adaptability device parameters, breaking the limitation of traditional multimodal transport models that ignore the characteristics of high-temperature devices; and specifically defining decision variable parameters and state quantities specific to high-temperature scenarios to ensure that the transport scheme conforms to the actual needs of high-temperature environment from the source, avoiding the problem of insufficient adaptability of general schemes in high-temperature scenarios.

[0093] (2) The high-temperature environment multimodal transport method and system based on composite heat insulation and active temperature control of the present invention incorporates high-temperature special items and safety penalties into the total cost model. By deriving multi-scenario cost calculation formulas, the impact of high temperature on transportation costs can be accurately quantified. Compared with the limitation of traditional cost models that only calculate basic transportation and transit costs, the method of the present invention can clearly identify the core cost drivers in high-temperature scenarios, providing accurate data support for subsequent optimization of total cost minimization, and avoiding cost runaway caused by the neglect of hidden high-temperature costs.

[0094] (3) The high-temperature environment multimodal transport method and system based on composite thermal insulation and active temperature control of the present invention constructs a mixed integer programming model to minimize the total cost and sets core safety constraints for high-temperature scenarios: temperature constraints ensure that the goods are always within the allowable temperature range to avoid high-temperature deterioration; cooling / heat dissipation energy and power constraints, battery and key component temperature safety constraints prevent equipment failure due to high-temperature overload or thermal runaway; node cooling / shading capacity constraints ensure that the capacity of high-temperature adaptable devices matches actual needs; these constraints form a full-chain safety guarantee system for goods, equipment and devices, which can solve the problems of lack of safety constraints and high risk of goods and equipment in traditional multimodal transport under high-temperature environments.

[0095] (4) The high-temperature environment multimodal transport method and system based on composite thermal insulation and active temperature control of the present invention adopts an improved hybrid immune-particle swarm optimization algorithm. Through the synergistic mechanism of immune genetic optimization of discrete variables (path, transport mode) and particle swarm optimization of continuous variables (cooling power, running speed), it can not only efficiently handle the complexity of mixed integer programming models, but also take into account the high-temperature safety requirements under the goal of minimizing total cost through a fitness function with safety penalty. The final output of the optimal transport path, mode and high-temperature specific scheduling parameters can achieve a multi-objective balance of safety, cost and efficiency in high-temperature environment, and solve the problem that traditional algorithms are difficult to solve while taking into account multiple constraints and multiple objectives in high temperature. Attached Figure Description

[0096] Figure 1 This is a flowchart of a high-temperature environment multimodal transport method based on composite thermal insulation and active temperature control, according to an embodiment of the present invention.

[0097] Figure 2 This is a schematic diagram of a multimodal transport capacity grid model simulating high-temperature transport coordination according to an embodiment of the present invention;

[0098] Figure 3 This is a schematic diagram of hybrid encoding according to an embodiment of the present invention;

[0099] Figure 4 This is a schematic diagram of the solution process using the improved hybrid immune-particle swarm optimization algorithm in an embodiment of the present invention;

[0100] Figure 5 This is an example diagram of thermal-energy simulation in an embodiment of the present invention;

[0101] Figure 6 This is a schematic diagram of the structure of a high-temperature environment multimodal transport system based on composite thermal insulation and active temperature control, according to an embodiment of the present invention. Detailed Implementation

[0102] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0103] like Figure 1 As shown, one aspect of the present invention provides a high-temperature environment multimodal transport method based on composite thermal insulation and active temperature control, comprising:

[0104] S1: Based on the distribution of regional hub nodes, the attributes of connecting road sections, and the parameters of node high-temperature adaptation devices under high-temperature conditions, a multimodal transport capacity grid model simulating high-temperature transport coordination is constructed.

[0105] S2: Determine the conditional assumptions of the model and define the three types of symbol systems for the model's decision variables, parameters, and state variables;

[0106] S3: Incorporate high-temperature special items and safety penalties into the total cost, construct a high-temperature-related total cost model, derive cost calculation formulas for multiple scenarios, and quantify the impact of high temperatures on the total cost;

[0107] S4: With the goal of minimizing total cost, a hybrid integer programming model is constructed, which includes path connectivity and flow conservation, temperature constraints, cooling / heat dissipation energy and power constraints, node cooling / shading capacity constraints, battery and key component temperature safety constraints, docking / node capability constraints, and time window constraints. An improved hybrid immune-particle swarm optimization algorithm is used to solve the model, and the optimal transportation path, transportation mode, transfer node, and high-temperature scenario-specific scheduling parameters are output.

[0108] Furthermore, such asFigure 2 As shown, the multimodal transport capacity grid model for simulating high-temperature transport coordination described in step S1 uses several city / hub nodes. (Including ports, railway marshalling yards, highway transshipment yards, inland transshipment yards, etc.) as vertices, and several connecting road segments E (each road segment can support one or more modes of transportation, including highways, railways, waterways, and aviation) as edges, the set of transportation modes on the edges is: Each node is equipped with a high-temperature adaptation device and interface unit (passive heat insulation chamber, active cooling / cooling docking device, sunshade / heat dissipation shed, energy buffer and heat dissipation station, sand / dust filter unit, etc.), and its availability under high-temperature conditions, cooling and heat dissipation capabilities and dust prevention capabilities are marked to simulate the collaborative work of cargo transportation routes and multimodal transport devices in high-temperature scenarios.

[0109] Based on the above grid, by selecting nodes, road segments and corresponding transportation modes, the collaborative work of cargo transportation routes and multimodal transport devices / systems in high-temperature environments under real-world conditions is simulated, and the organization mode selection and parameter scheduling are carried out with cost / risk / time as optimization objectives.

[0110] Furthermore, in step S2, to facilitate modeling and solving, five key assumptions are formulated to simplify modeling complexity, taking into account the characteristics of high-temperature scenarios. These assumptions can be relaxed in specific implementations. The model condition assumptions include:

[0111] Goods are transported in containers, and the goods within the containers are not split up for transport (holistic assumption);

[0112] High-temperature adaptation devices and energy replenishment / heat dissipation facilities marked in the area are in an available state during planning (if unavailable, they are set as unreachable or limited capacity in the model);

[0113] The vehicles operate on the road segment according to the established driving rules, and each transfer node can be loaded and unloaded within the predetermined time window (if there are infrastructure restrictions due to high temperature or nighttime construction restrictions, the node accessibility parameter will reflect this).

[0114] All thermal / energy model parameters (such as the heat transfer coefficient of the enclosure, the solar radiation absorption coefficient, the PCM cold capacity / latent heat parameters, the battery high temperature degradation coefficient, and the component failure distribution parameters) can be estimated through field tests or historical data and substituted into the model;

[0115] High-temperature related safety / fire hazard parameters (such as battery thermal runaway probability and critical temperature of flammable materials) can be incorporated into optimization as penalty items or constraints;

[0116] Step S2 also includes defining three types of symbol systems for the model: decision variables, parameters, and state variables;

[0117] The decision variables and symbols are as follows:

[0118] Represents a set of city / hub nodes, indexed ε represents the set of road segments, denoted as (i,j)∈ε; Represents a set of transportation modes, index (1: Highway, 2: Railway, 3: Waterway, 4: Aviation, etc.);

[0119] Define decision variables: This indicates that if transportation mode k is used on road segment (i,j), the value is 1; otherwise, it is 0 (road segment - mode selection). This indicates that if a transit / connection operation occurs at node i, changing from mode p to mode q, then the value is 1 (transit selection); n c ∈Z + This represents the number of containers used (which can be expanded to a vector of container counts for each route); This represents the heating power used for critical components / cargo compartments at time t (which can be a decision-making quantity within the scheduling period); This represents the running speed (a controllable continuous variable) of mode k on road segment (i,j); Indicates whether node i has enabled shading / passive heat dissipation facilities (or temporary heat dissipation sheds, 0 indicates no, 1 indicates yes). Indicates whether the node / vehicle has enabled sand and dust filtration / sealing mode (0 indicates no, 1 indicates yes); This indicates whether to select nighttime or low-temperature periods for driving / loading / unloading (time-slot scheduling decision), with 0 indicating no and 1 indicating yes;

[0120] Define parameters and state variables; d ij This represents the distance (km) of road segment (i, j); This represents the transportation time (in hours) on road segment (i,j) using method k. This represents the basic unit transportation price (currency / container·km) for a unit container and unit distance under mode k; This represents the transit time (in hours) at node i for transferring from mode p to mode q. This represents the unit transit cost (currency / box) for making this transit at node i; T represents the volume or mass of a single container of goods (used for heat load calculation); out (t) represents the external ambient temperature sequence (°C), while also considering the solar direct / radiative gain G. solar (t); T i (t) represents the temperature sequence inside the cargo hold / container; the permissible temperature range for cargo category m is...

[0121] Define energy / equipment parameters: E rated Indicates the battery's rated capacity, α b(T) represents the effective capacity correction factor at temperature T;

[0122] Define equipment failure parameters: The Weibull three parameters (η, β, γ) represent the life / failure distribution of critical components; a high (T) represents the high-temperature acceleration factor;

[0123] Furthermore, the expression for the high-temperature associated total cost model in step S3 is as follows:

[0124] C total =C trans +C transf +C loss +C energy +C maint +C safety +C time (1)

[0125] Of which, total cost C total Including basic transportation costs C trans Transit cost C transf Loss / damage cost C loss Energy consumption / cooling cost C energy Equipment failure and maintenance costs C maint Safety / fire hazard penalty costs C safety And time window penalties and retention costs C time ;

[0126] When mode k is used on road segment (i,j), the basic transportation cost is related to distance and the selected speed (speed affects energy consumption but time); the basic transportation cost C trans The expression is:

[0127]

[0128] Where, n c This refers to the total number of containers transported or the number of containers carried on the corresponding route.

[0129] If a transition from mode p to mode q occurs at node i, the transition cost and time must be factored in, along with a time increment. The transit cost C transf The expression is:

[0130]

[0131] Furthermore, in high-temperature environments, the loss rate of goods increases due to overheating, temperature fluctuations, and humidity changes. The loss rate is calculated using the Weibull or temperature-corrected failure probability function F(T). travel ;T eff (t) represents the loss / damage cost C based on this distribution.loss The expression is:

[0132] C loss =n c ·P unit ·F(T travel ;T eff (t)) (4)

[0133] Where, n c P represents the number of containers used. unit The value of a unit box of goods, T travel Total transit time (including transit); T eff (t) represents the effective temperature curve considering enclosure insulation, active cooling, and the door opening event; if T i (t) exceeds the allowable range Then F increases significantly; T i (t) represents the temperature inside the cargo hold / container.

[0134] Furthermore, the instantaneous heat load of the cargo hold Including heat transmission, solar radiation gain, and ventilation / door opening losses, the expression is:

[0135]

[0136] in, Represents the instantaneous heat load of the i-th node at time t; U is the overall heat transfer coefficient; A i T represents the heat transfer area of ​​the i-th node; out (t) represents the ambient temperature at time t; T i (t) represents the internal temperature of the i-th node at time t; α solar Let A be the absorptivity of the cargo hold surface to solar radiation. It is dimensionless and ranges from 0 to 1, where 0 represents complete reflection and 1 represents complete absorption. surf G is the surface area of ​​the cargo hold exposed to solar radiation. solar (t) represents the solar radiation intensity at time t; Let be the heat loss rate of the i-th node due to ventilation or door opening operation;

[0137] To maintain the internal temperature, the required cooling power is O. cool,i (t) is:

[0138]

[0139] Among them, P cool (t) represents the cooling power required to maintain the temperature within the i-th node at time t; COP represents the instantaneous heat load of the i-th node at time t; cool(t) is the performance coefficient of the refrigeration system at time t. It is dimensionless and is the ratio of refrigeration capacity to refrigeration power consumption, reflecting the energy efficiency level of the refrigeration system.

[0140] The energy consumption / cooling cost C energy The expression for (key items in high-temperature scenarios) is:

[0141] C energy =∑ t (λ elec P elec (t)+λ fuel P fuel (t))Δt (7)

[0142] P elec (t)=P cool,i (t)+P motor-cool (t)+P sand-filter (t)+P control (t) (8)

[0143] Among them, C energy λ represents the total energy cost; elec P represents the unit price of electricity; elec (t) represents the power consumption at time t, including the power of electrical equipment such as vehicle cooling, electric motor heat dissipation system, dust filtration device, and control system; λ fuel P is the unit price of fuel; fuel (t) represents the fuel consumption power at time t; Δt represents the time step; P cool,i (t) represents the cooling power, determined by the cargo hold heat load and the coefficient of performance of the refrigeration system; P motor-cool (t) represents the power consumption of the motor's cooling system at time t, and its magnitude is related to the motor load and ambient temperature (the motor heats up more at high temperatures, and the power consumption of the cooling system usually increases); P sand-filter (t) represents the power consumption of the dust filtration device (generated when it is activated in a high-temperature dust environment); P control (t) represents the power consumption of the control system (such as temperature and humidity monitoring, dispatching system);

[0144] Furthermore, equipment failure and maintenance costs C maint The failure rate of critical components at high temperatures (described using the Weibull model) is multiplied by a high-temperature acceleration factor α. high (T); Equipment failure and maintenance costs C maint This can be expressed by equations (9)-(13):

[0145] C maint =C pre +C rep (9)

[0146]

[0147] C rep =∑ comp Cost comp ·Pr{comp fails during mission} (11)

[0148] Cost comp =Cost rep-comp +Cost delay-comp (12)

[0149] Pr{comp fails during mission}=F comp (t run η comp / a high (T),β comp ,γ comp (13)

[0150] Among them, C maint C represents the total cost of equipment failure and maintenance. pre For preventative maintenance costs; t mission For the entire multimodal transport mission cycle; t pre,comp(T) This refers to the preventative maintenance cycle for a critical component under high-temperature conditions, which is negatively correlated with the ambient temperature T; Cost pre-comp The cost of a single preventative maintenance for a critical component; the cost per maintenance is calculated by multiplying the number of preventative maintenance cycles within the task period by the cost per maintenance cycle, reflecting the impact of high temperatures on maintenance frequency; C rep Cost of troubleshooting; comp Cost represents the replacement or maintenance cost after the failure of a critical component (comp represents different critical components, such as bearings, seals, etc.). It is the direct repair cost after a component fails. rep-comp Cost of intermodal delays due to component failure delay-comp The sum of; Pr{compfails during mission} is the probability that a critical component will fail during the transportation mission. It is dimensionless and reflects the likelihood of the component failing within the mission cycle.

[0151] F comp Let t be the failure distribution function of a critical component. Here, the Weibull distribution function is used to describe how the component's failure probability changes over time. run For the operation time of the transportation task; η comp This refers to the characteristic lifespan parameter of a key component, reflecting the component's lifespan characteristics under normal temperatures; a high(T) is the high-temperature acceleration factor, dimensionless, used to correct the impact of high temperature on component lifespan. T is the ambient temperature. High temperature will cause a high (T) Changes (generally, the higher the temperature, the greater the acceleration factor, and the faster the component fails); β comp β is a dimensionless shape parameter of a critical component, representing the shape parameter of a Weibull distribution. It determines the shape of the curve showing the failure probability changing over time. If β > 1, it indicates that the component failure probability increases with time; if β = 1, it follows an exponential distribution. comp Let γ be the position parameter of a critical component, representing the minimum time at which the component begins to fail. comp =0, then the distribution starts from time zero; at the same time, the costs of material degradation caused by UV and leakage caused by softening of seals are also considered.

[0152] Furthermore, high temperatures bring risks of battery thermal runaway, spontaneous combustion of flammable materials, and fire spread, necessitating the inclusion of potential major accident costs or insurance penalties in the target;

[0153] Safety / fire hazard penalty cost C safety The expression is:

[0154] C safety =W fire ·Pr(thermal runaway or fire) (14)

[0155] Among them, C safety Safety / fire hazard penalty costs are used to quantify the punitive costs that may arise from risks such as battery thermal runaway, spontaneous combustion of flammable materials, or fire propagation under high-temperature environments; W fire The expected economic and reputational losses from a potential accident represent the estimated economic losses (such as damage to goods, equipment, compensation costs, etc.) and the negative impact on the company's reputation that would result in an accident such as battery thermal runaway or fire. Pr (thermal runaway or fire) is the dimensionless probability of battery thermal runaway or fire, reflecting the likelihood of such dangerous events occurring in a high-temperature environment due to the combined effects of factors such as temperature time series (temperature changes at different times), battery state of charge (SOC), battery temperature, and the properties of the goods (whether they are flammable, etc.).

[0156] Furthermore, if a delivery time window exists (e.g., arrival must not be later than T), due Or not earlier than T earliest Then, the cost of detention / penalty is introduced; the time window penalty and the cost of detention C time The expression is:

[0157]

[0158] Where 1{·} is an indicator function, the function value is 1 when the condition in the parentheses is met; T arrive,s T represents the actual time of arrival at station s; due,s P represents the latest allowed arrival time for station s (the upper bound of the time window); exceeding this time is considered late. late,s The costs of late arrival penalties for site s (e.g., late payment penalties per order, service quality deductions, contract breach costs); T earliest,s This is the earliest allowed arrival time for site s (lower bound of the time window). Arrivals earlier than this time are considered too early and require waiting or storage. store,s The costs of early arrival storage or waiting at site s (e.g., costs incurred for refrigerated goods or waiting for vehicles);

[0159] Furthermore, the mixed-integer programming model expression in step S4 is:

[0160] MinC total =C trans +C transf +C loss +C energy +C maint +C safety +C time (16)

[0161] In step S4, the following constraints are established for the mixed-integer programming model, including:

[0162] Path connectivity and flow conservation (ensuring there is a feasible path from the originating node s to the destination node t):

[0163]

[0164] Where u represents an intermediate node.

[0165] Temperature constraints (the temperature of goods must be maintained during each leg of the journey and during transshipment):

[0166]

[0167] Where m represents the category of goods;

[0168] Cooling / heat dissipation energy and power constraints (battery / cooling equipment and node capabilities):

[0169] ∑ t P elec (t)Δt≤E available ,∑ t P cool (t)Δt≤E cool,avail (19)

[0170] Where, p elec (t) represents the power consumption at time t, including the power of electrical equipment such as vehicle cooling, electric motor cooling system, dust filtration device, and control system; Δt represents the time step, used to convert the power integral into energy; E available P represents the total usable electrical energy. cool (t) represents the cooling power at time t; E cool,avail This represents the total energy available to the refrigeration system, i.e., the upper limit of energy that the refrigeration system can put into operation within a cycle;

[0171] Node cooling / shading capacity constraints:

[0172]

[0173] Among them, P cool,i (t) represents the cooling power; The maximum cooling power capacity of node i; the enabled state variable of the shading facility at node i. This indicates that the sunshade facilities have been activated. This indicates that it is not enabled; it is a binary variable.

[0174] Temperature safety constraints for batteries and critical components (to prevent thermal runaway):

[0175] T batt (t)≤T batt,crit ,Pr(thermal runaway)≤∈ safety (twenty one)

[0176] Among them, T batt (t) represents the temperature of the battery at time t; T batt,crit The critical safe temperature of the battery; Pr (thermal runaway) is the probability of thermal runaway of the battery, dimensionless, reflecting the likelihood of thermal runaway under current operating conditions; ∈ safety This is a dimensionless safety threshold for the probability of thermal runaway, requiring that the probability of battery thermal runaway not exceed this value to ensure safety.

[0177] Docking / node capacity constraints (node ​​transfer throughput, maximum power of heating facilities):

[0178]

[0179] in, This indicates whether a transfer from mode p to mode q occurs at node i (a binary variable, 1 indicates occurrence, 0 indicates no occurrence); n c This indicates the number of cargo units transshipped, measured in containers. This represents the maximum transfer capacity of node i, i.e., the number of containers that can be transferred per unit of time. This represents the total power demand of the heating or cooling facilities at node time t; Indicates the maximum heating / cooling power that the node can provide;

[0180] Time window constraint (arrival time meets customer time limit):

[0181] T arrive ≤T due +Δ allow ,T arrive ≥T earliest (twenty three)

[0182] Among them, T arrive Indicates the arrival time of the vehicle or goods; T due Indicates the customer's requested delivery deadline; Δ allow Indicates the allowed delay buffer time; T earliest This indicates the earliest time when goods can be received, preventing premature arrival from causing stockpiling or storage costs.

[0183] Binary and physical variable domain constraints:

[0184]

[0185] in, This represents whether to choose the k-th transportation method from node i to node j; it is a binary variable. This indicates whether a mode of transport change occurs at node i; it is a binary variable. This represents the heating power supplied at time t, and it must be non-negative. This represents the speed of transportation mode k on path (i,j), ranging from... This represents the minimum operating speed of transportation mode k on path (i,j); This represents the maximum speed of transportation mode k on path (i,j);

[0186] Furthermore, such as Figure 4 As shown, step S4 employs an improved hybrid immune-particle swarm optimization algorithm to solve the model, outputting the optimal transportation path, transportation mode, transfer nodes, and high-temperature scenario-specific scheduling parameters, including:

[0187] S41. Design a hybrid coding scheme to match the type of model decision variables: In view of the dual characteristics of discrete and continuous decision variables in the mixed integer programming (MINLP) model, design a three-segment hybrid coding structure to transform the model variables into antibody / particle forms that the algorithm can process; initialize the algorithm population and incorporate a heuristic strategy specific to high temperature scenarios to avoid the algorithm getting trapped in local optima and ensure that the initial solution meets the high temperature safety constraints.

[0188] S42. Based on the goal of minimizing the total cost of the mixed integer programming model, a safety constraint penalty term is introduced to construct the algorithm fitness function, which leads to the search in the direction of low cost and high safety.

[0189] S43. A dual optimization mechanism is adopted, which optimizes discrete variables using immune genetics and continuous variables using particle swarm optimization. The population is updated in stages to achieve synergistic optimization of discrete and continuous decision variables.

[0190] S44. To address the dynamic disturbances of solar radiation fluctuations and sudden changes in ambient temperature in high-temperature environments, the solution process is divided into two layers, with global optimization and real-time adjustments.

[0191] S45. Set dual iteration termination conditions. When the iteration limit is reached or the fitness converges, decode the global optimal particle / antibody, restore it to the model decision variable value, and output the optimal joint transport scheme and high temperature-specific scheduling parameters.

[0192] Furthermore, in step S4, to efficiently solve the complex problem involving discrete path selection and continuous scheduling decisions, a Hybrid PSO-Immune algorithm is proposed. This algorithm borrows from and improves the immune encoding and evolution mechanism in the template. At the same time, particle swarm local optimization is applied to the discrete and continuous decision variables in the mixed integer programming (MINLP) model to achieve a unified encoding expression for discrete decisions and continuous parameters.

[0193] like Figure 3 As shown, the three-segment hybrid encoding structure described in step S41 consists of a string containing three parts: a path-mode binary segment, a relay / module activation segment, and a continuous segment; the specific encoding rules are as follows:

[0194] Path-Mode Binary Segment: Each candidate road segment (i,j) and each mode of transport k are encoded using 0 / 1 binary (0 = not selected, 1 = selected), or simplified to a compressed encoding of "route node sequence + mode sequence"; where the route node sequence uses 0 / 1 to mark the selection status of the candidate road segment (i,j), 1 indicates that the road segment is selected, and 0 indicates that it is not selected, and the path continuity is ensured through node connection relationship; the mode of transport sequence uses 1 / 2 / 3 / 4 to correspond to road / rail / waterway / air transport respectively, realizing the mapping of the discrete variable of "road segment-mode of transport selection";

[0195] Transit / Module Activation Section: Whether the discrete decision variables specific to high-temperature scenarios are enabled is represented by binary encoding (0 = disabled, 1 = enabled); the discrete decision variables specific to high-temperature scenarios include whether passive shading is enabled on nodes. Enable node / vehicle cooling Should sand and dust filtration be enabled? Should night driving be adopted?

[0196] Continuous Quantity Range: Real-valued encoding is used for continuous decision variables in the model, and the value range matches the physical constraints of the variables; the continuous decision variables include cooling power. Road section driving speed Energy-saving thresholds and SOC management parameters; real-valued data are directly used as the optimization object of the particle swarm optimization algorithm, and the parameters are finely adapted through local optimization adjustment;

[0197] The initialization of the algorithm population and the integration of a high-temperature scenario-specific heuristic strategy in step S41 include:

[0198] 60%-70% of the initial particles / antibodies are generated through random coding;

[0199] Generate an additional 30%-40% of heuristic initial solutions;

[0200] Initial solutions that violate core constraints are eliminated, and particles / antibodies that meet high-temperature safety requirements are retained to form the initial population;

[0201] Additional 30%-40% of heuristic initial solutions are generated, including the "prioritize nighttime driving + nodal shading + low cooling power" solution and the "prioritize coastal low-temperature routes" solution.

[0202] "Prioritize nighttime driving + shading at key points + low cooling power" solution: For road sections with extreme daytime temperatures, nighttime operation is selected by default. Shading facilities are activated at key points along the route. Cooling power is set according to the lower limit of cargo hold heat load to balance energy consumption and temperature control.

[0203] Solution for “Coastal Low Temperature Route Priority”: For cross-regional transportation, coastal routes are prioritized. The corresponding coastal route-mode variable in the coding is set to 1 to reduce the impact of high temperatures on equipment and goods.

[0204] High-temperature safety constraints include ensuring that the temperature of the cargo does not exceed the permissible range and that the battery temperature does not exceed the maximum permissible value.

[0205] Further, the fitness function expression in step S42 is:

[0206]

[0207] Among them, C total The total cost in the mixed integer programming (MINLP) model (including the basic transportation cost C for multimodal transport under high-temperature conditions) trans Transit cost C transf Loss / damage cost C loss Energy consumption / cooling cost C energy Equipment failure and maintenance costs C maint Safety / fire hazard penalty costs C safety And time window penalties and retention costs C time M is the penalty coefficient for boundary violation safety events, which is a large positive number used to impose a significant penalty on solutions that have boundary violation safety events, so as to reflect the serious negative impact of boundary violation safety events (such as cargo temperature exceeding the upper limit, battery thermal runaway probability exceeding the threshold, etc.). This is an indicator function used to determine whether an out-of-bounds safety event exists. When an out-of-bounds safety event (such as T) exists, the function will indicate the event. i (t)>T max , among which, T i (t) represents the temperature inside the cargo hold / container at time t. max When the maximum permissible temperature for the goods; or when the probability of battery thermal runaway exceeds a threshold, etc. When there is no boundary crossing security event

[0208] Formula (24) uses a fitness function in reciprocal form to make the trend of fitness value change opposite to that of the denominator; the larger the value of the fitness function F, the better the solution; when the total cost C total The lower the level, and the absence of any boundary breaches (i.e., When the total cost is high or there is a safety violation, the smaller the denominator, the larger the overall fitness function value, and such solutions will be preferred by the algorithm. Conversely, if the total cost is high or there is a safety violation, the denominator increases, the fitness value decreases, and the solution will be considered a poor solution by the algorithm. In this way, the algorithm can optimize cost while ensuring the safety of the solution.

[0209] Furthermore, step S43 includes an immune genetic optimization discrete variable segment, a particle swarm optimization continuous segment, and a safety repair operator intervention segment;

[0210] The discrete variable segment for immune genetic optimization includes: based on fitness function values, using roulette wheel selection or elite retention strategies, screening for particles / antibodies with high fitness to enter the next generation;

[0211] Perform single-point or two-point intersections on the path-mode segment and the transit / module activation segment to ensure that path connectivity is still satisfied after the intersection;

[0212] To prevent premature convergence of the population, the discrete segments are mutated with low probability (e.g., 0.05-0.1) (e.g., switching the transportation mode of a certain road segment or changing the cooling activation status of a certain node).

[0213] The continuous segment of particle swarm optimization includes: for each particle in the continuous segment, based on the individual optimal solution and the global optimal solution, optimizing the PSO update speed and position according to the short-period PSO; and truncating continuous variables that exceed the physical range after the update (taking the upper or lower limit) to ensure that the variables are compliant.

[0214] The safety repair operator intervention segment includes: if a particle / antibody violates the high-temperature safety constraint after iteration, a safety repair is triggered; the discrete and continuous segment codes are adjusted first by means of "increasing cooling power, switching to night operation, and enabling node shading", and if the repair is ineffective, the particle / antibody is eliminated;

[0215] Furthermore, considering the problem scale and real-time requirements, the hierarchical solution in step S44 includes offline / semi-real-time optimization of the upper-level path-mode selection, transit node determination, and transit / module activation; and real-time optimization of the lower-level cooling power scheduling to cope with temperature disturbances and changes in solar radiation.

[0216] The solution algorithm for real-time optimization of the lower-level cooling power scheduling adopts Model Predictive Control (MPC) or Short-Period Particle Swarm Optimization (PSO), and updates the cooling power command every 5-15 minutes to offset real-time heat load fluctuations (such as heat infiltration caused by opening doors for loading and unloading, and sudden increase in solar radiation) to ensure stable cargo temperature.

[0217] The termination condition for the double iteration in step S45 is:

[0218] The number of iterations reaches the preset limit (e.g., 100-200 times, adjusted according to the problem size);

[0219] The fitness function value of the global optimal solution changes less than a threshold (e.g., 10) for 10-15 consecutive generations. -5 The result is considered convergent.

[0220] The optimal intermodal transport plan includes: the optimal transport route, the transport mode for each segment, and the location of the transfer node;

[0221] The high-temperature-specific scheduling parameters include the activation time and power of node shading / cooling facilities, the sections where sand and dust filtration mode is activated, the allocation of night / low-temperature operation time periods, and the real-time adjustment sequence of cooling power (output according to time steps).

[0222] The present invention also provides a specific implementation step, as follows:

[0223] (1) Regional capacity and mode prediction: Based on the high temperature adaptability of each node (whether there is a passive sunshade, node cooling capacity, heat dissipation station, sand and dust filtration and sealing capacity, etc.), the node capacity level is given and the suitable intermodal transport organization mode is initially determined (e.g.: activate node sunshade + night long-distance driving; or adopt the low temperature coastal route during the day and use temporary refrigerated carriages at the transfer point).

[0224] (2) Establish a basic high-temperature transportation capacity grid: Select several nodes in the study area and construct a grid database of road segments, transportation modes and node capabilities (including distance, annual maximum temperature, solar radiation intensity statistics, node cooling / shading capacity, sandstorm frequency, cost parameters, etc.);

[0225] (3) Construct a library of thermal-energy, solar gain and loss parameters: calibrate the heat transfer coefficient of the enclosure, solar absorption coefficient, PCM cold capacity parameters, component Weibull parameters, and battery high-temperature degradation function through experiments or historical data;

[0226] (4) Establish a joint optimization model: Establish the total cost target and constraints, and discretize the time (e.g., using hours as the step size, and dividing the time into night / day segments);

[0227] (5) Set up and run the hybrid PSO-Immune algorithm to solve the problem: set the population size, memory capacity, crossover / mutation probability, PSO parameters and safety repair operator, and run the algorithm to obtain the candidate optimal solution;

[0228] (6) Thermal-Energy Dynamic Simulation Verification and Parameter Fine-tuning: Perform thermal-energy dynamic simulation of the optimal solution in MATLAB / Simulink or Python environment (considering solar radiation timing and door opening events) to verify temperature constraints, energy consumption, and safety margins; such as Figure 5 Adjust the model parameters or penalty weights based on the simulation results and solve again;

[0229] (7) On-site pilot implementation: The algorithm output is sent to the scheduling system, and the node end and vehicle end execute the corresponding sunshade arrangement, cooling start and stop, sand and dust filter switching and night operation scheduling, and temperature and radiation sensors are installed in the vehicle / box for real-time monitoring;

[0230] (8) Online re-optimization and emergency response: During transportation, the system receives real-time environmental (temperature, radiation, dust), equipment status and node availability information, triggering online replanning (e.g., a near real-time version based on MPC+ fast hybrid algorithm) to cope with sudden high temperature / dust events or equipment failures (such as battery overheating or cooling failure).

[0231] (9) Safety strategy and emergency plan: Establish thermal runaway detection and isolation strategies (such as automatic power cut-off, activation of passive fire suppression / shielding, yaw to the nearest cooling node), and incorporate such actions into the optimizer's set of executable actions.

[0232] like Figure 6 As shown, a second aspect of the present invention provides a high-temperature environment multimodal transport system based on composite thermal insulation and active temperature control, used to realize the aforementioned high-temperature environment multimodal transport method based on composite thermal insulation and active temperature control, comprising:

[0233] The high-temperature scenario data acquisition and preprocessing module is used to collect high-temperature environmental parameters (temperature, solar radiation, dust) and equipment and cargo status data (battery temperature, cargo compartment temperature, component status). After cleaning, completion, and format conversion, it outputs thermal / energy parameters, equipment failure parameters, etc. that meet the model requirements, providing accurate data support for subsequent model construction.

[0234] The capacity grid model construction and symbol definition module is used to build a capacity grid model with hub nodes as vertices and connected road segments as edges to label the capacity of high-temperature adaptability devices. It clarifies 5 modeling assumptions and defines three types of symbol systems: decision variables (such as road segment-mode selection, shading activation), parameters (such as road segment distance, transportation unit price), and state variables (such as cargo hold temperature time series), providing a basic framework for the MINLP model.

[0235] The high-temperature-related total cost model construction module is used to incorporate high-temperature special items and safety penalties into the total cost, construct a high-temperature-related total cost model, derive cost calculation formulas for multiple scenarios, and quantify the impact of high temperature on the total cost.

[0236] The Mixed Integer Programming (MINLP) model building module is used to construct a MINLP model with the goal of minimizing total cost. This model includes constraints on path connectivity and flow conservation, temperature constraints, cooling / heat dissipation energy and power constraints, node cooling / shading capacity constraints, battery and critical component temperature safety constraints, docking / node capability constraints, and time window constraints.

[0237] An improved hybrid immune-particle swarm optimization module is used to transform variables into an algorithm-processable form through a three-segment hybrid encoding, and incorporates a high-temperature heuristic strategy to initialize the population; a fitness function with safety penalty is constructed, and dual optimization is performed by "immune genetic optimization of discrete variables + particle swarm optimization of continuous variables", combined with hierarchical solution to deal with high-temperature dynamic interference, and finally outputs the optimal solution;

[0238] The optimal solution output and evaluation module is used to decode the optimal solution, output basic intermodal transport solutions (route, mode, transit nodes) and high-temperature-specific scheduling parameters (shading / cooling plan, nighttime operation period, etc.); analyze cost composition and safety risks (such as the probability of cargo exceeding temperature), and export the solution in a visual form to provide actionable instructions and evaluation basis for operations.

[0239] This invention provides a high-temperature environment multimodal transport method and system based on composite thermal insulation and active temperature control. Addressing the problems of cargo deterioration due to heat, electronic system failure, high energy consumption, and difficulty in maintaining continuous temperature control in traditional transport containers under extreme high-temperature environments, this invention constructs a transport module that combines thermal insulation, active temperature control, and intelligent monitoring functions, fundamentally improving the environmental adaptability and cargo safety during transport. The system incorporates a composite wall structure of multi-layer vacuum insulation panels, a high-reflectivity coating, and a phase change energy storage unit, achieving efficient blocking of external radiant heat and peak-shaving and valley-filling release of internal waste heat. Compared to traditional single-layer insulation designs, this significantly reduces the rate of temperature rise within the container under high-temperature conditions and effectively reduces the load on the active cooling system. A modular micro-refrigeration unit is integrated within the system, forming a collaborative working mechanism with the phase change energy storage unit. Under high-temperature conditions, the refrigeration unit rapidly cools the temperature control zone. Within the temperature fluctuation range, passive regulation is achieved by utilizing the absorption / release of latent heat by the phase change material. The system uses a waste heat recovery device to utilize some of the dissipated heat for energy recycling or equipment preheating, improving overall energy efficiency.

[0240] The present invention provides a high-temperature environment multimodal transport method and system based on composite thermal insulation and active temperature control. For multimodal transport links such as road, rail, water and air, it ensures rapid switching and stable docking under different transport modes through high-temperature resistant sealed interfaces and modular connectors. At the same time, key structural components adopt high heat-resistant lightweight alloys or composite materials to avoid structural deformation and connection failure caused by high temperature environment.

[0241] This invention is also equipped with a distributed temperature / humidity / stress sensor array, combined with an embedded controller to achieve: real-time monitoring of environmental and cargo conditions during transportation; intelligent alarms and emergency response strategies in case of overheating, abnormal energy consumption, or loose interfaces; and online monitoring and predictive maintenance through a remote communication module and scheduling platform, thereby improving the system's security and intelligence.

[0242] This invention also introduces a hybrid energy management strategy, which combines cooling power scheduling, transportation route planning and adaptive control of cargo characteristics to achieve overall energy efficiency optimization in a multimodal transport system. The method of this invention can significantly reduce energy consumption and transportation costs while ensuring temperature control constraints.

[0243] This invention addresses the industry challenges of high energy consumption and high temperature control costs in high-temperature scenarios, achieving cost reduction and efficiency improvement through a dual approach of energy consumption control and cost optimization. Regarding energy efficiency improvement, an active temperature control and waste heat coupling management system enables refrigeration units, phase change energy storage, and waste heat recovery to work collaboratively. This allows for rapid cooling at high temperatures, passive adjustment during fluctuations, and waste heat recycling, significantly reducing the active cooling load. A hybrid energy management strategy, combined with dynamic scheduling of cooling power and transportation route optimization, maximizes energy utilization efficiency while meeting temperature control constraints. In terms of cost control, the aforementioned energy efficiency optimization directly reduces energy consumption costs. Composite insulation structures reduce equipment wear and extend component lifespan, lowering maintenance costs. Furthermore, the mixed integer programming model incorporates implicit costs such as high-temperature cargo damage and safety penalties, ultimately achieving a significant reduction in the overall operating cost of multimodal transport.

[0244] This invention improves transportation efficiency from two dimensions: device adaptation and system collaboration. At the device level, it adapts to various transportation modes such as road, rail, waterway, and air transport through high-temperature resistant sealed interfaces and modular connectors, ensuring rapid and stable docking in high-temperature environments and solving the problem of switching between different transportation modes. At the system level, it can simulate a high-temperature collaborative capacity grid model to accurately characterize the high-temperature adaptation devices and road segment attributes of nodes. It improves the hybrid immune-particle swarm optimization algorithm to output the optimal path, mode, and scheduling scheme based on high-temperature parameters. The hierarchical solution mechanism responds in real time to interferences such as solar radiation fluctuations and temperature changes, achieving efficient collaboration and flexible response across the entire multimodal transport chain in high-temperature scenarios, significantly improving transportation efficiency and system adaptability.

[0245] A third aspect of the present invention also provides a computer-readable storage medium on which a computer program is stored, the computer program being executable by a processor. Figure 1 Any step of the high-temperature environment multimodal transport method based on composite thermal insulation and active temperature control described herein.

[0246] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A high-temperature environment multimodal transport method based on composite thermal insulation and active temperature control, characterized in that, include: S1: Based on the distribution of regional hub nodes, the attributes of connecting road sections, and the parameters of node high-temperature adaptation devices under high-temperature conditions, a multimodal transport capacity grid model simulating high-temperature transport coordination is constructed. S2: Determine the conditional assumptions of the model and define the three types of symbol systems for the model's decision variables, parameters, and state variables; S3: Incorporate high-temperature special items and safety penalties into the total cost, construct a high-temperature-related total cost model, derive cost calculation formulas for multiple scenarios, and quantify the impact of high temperatures on the total cost; S4: With the goal of minimizing total cost, a hybrid integer programming model is constructed, which includes path connectivity and flow conservation, temperature constraints, cooling / heat dissipation energy and power constraints, node cooling / shading capacity constraints, battery and key component temperature safety constraints, docking / node capability constraints, and time window constraints. An improved hybrid immune-particle swarm optimization algorithm is used to solve the model, and the optimal transportation path, transportation mode, transfer node, and high-temperature scenario-specific scheduling parameters are output.

2. The high-temperature environment multimodal transport method based on composite thermal insulation and active temperature control according to claim 1, characterized in that, The multimodal transport capacity grid model for simulating high-temperature transport coordination described in step S1 uses several city / hub nodes as vertices and one or more transport modes among highway, railway, waterway, and air connecting sections as edges; each node is equipped with a high-temperature adaptation device and interface unit, and its availability, cooling and heat dissipation capacity and dustproof capacity under high-temperature conditions are marked to simulate the coordinated work of cargo transport routes and multimodal transport devices in high-temperature scenarios. The city / hub nodes include ports, railway marshalling yards, highway transfer yards, and inland transshipment yards; The high-temperature adaptation device and interface unit includes a passive heat insulation chamber, an active cooling / cooling docking device, a sunshade / heat dissipation shed, an energy buffer and heat dissipation station, and a sand / dust filtration unit.

3. The high-temperature environment multimodal transport method based on composite thermal insulation and active temperature control according to claim 2, characterized in that, The model condition assumptions in step S2 include: the goods are transported in containers, and the goods within the containers are not split for transport. The cryogenic adaptation devices and energy replenishment facilities marked in the area were in a usable state at the time of planning; The vehicles operate on the road according to established driving rules, and each transfer node can be loaded and unloaded within a predetermined time window; All thermal / energy model parameters were estimated through field tests or historical data and then substituted into the model. High-temperature related safety / fire hazard parameters are incorporated into the optimization as penalties or constraints.

4. A high-temperature environment multimodal transport method based on composite thermal insulation and active temperature control according to any one of claims 1-3, characterized in that, The expression for the high-temperature associated total cost model in step S3 is as follows: C total =C trans +C transf +C loss +C energy +C maint +C safety +C time (1) Of which, total cost C total Including basic transportation costs C trans Transit cost C transf Loss / damage cost C loss Energy consumption / cooling cost C energy Equipment failure and maintenance costs C maint Safety / fire hazard penalty costs C safety And time window penalties and retention costs C time ; In high-temperature environments, the loss rate of goods increases due to overheating, temperature fluctuations, and humidity changes. The loss rate is calculated using the Weibull or temperature-corrected failure probability function F(T). travel ;T eff (t) represents the loss / damage cost C based on this distribution. loss The expression is: C loss =n c ·P unit ·F(T travel ;T eff (t)) (4) Where, n c P represents the number of containers used. unit The value of a unit box of goods, T travel T represents the total transit time of the goods. eff (t) represents the effective temperature curve considering enclosure insulation, active cooling, and the door opening event; if T i (t) exceeds the allowable range Then F increases significantly; T i (t) represents the temperature inside the cargo hold / container; Instantaneous heat load in the cargo hold during step S3 Including heat permeability, solar radiation gain, and ventilation / door opening losses; the instantaneous heat load expression for the cargo hold is: in, Represents the instantaneous heat load of the i-th node at time t; U is the overall heat transfer coefficient; A i T represents the heat transfer area of ​​the i-th node; out (t) represents the ambient temperature at time t; T i (t) represents the internal temperature of the i-th node at time t; α solar Let A be the absorptivity of the cargo hold surface to solar radiation. It is dimensionless and ranges from 0 to 1, where 0 represents complete reflection and 1 represents complete absorption. surf G is the surface area of ​​the cargo hold exposed to solar radiation. solar (t) represents the solar radiation intensity at time t; Let be the heat loss rate of the i-th node due to ventilation or door opening operation; To maintain the temperature inside the chamber, the required cooling power P cool，i (t) is: Among them, P cool (t) represents the cooling power required to maintain the temperature within the i-th node at time t; COP represents the instantaneous heat load of the i-th node at time t; cool (t) is the performance coefficient of the refrigeration system at time t. It is dimensionless and is the ratio of refrigeration capacity to refrigeration power consumption, reflecting the energy efficiency level of the refrigeration system. The energy consumption / cooling cost C mentioned in the key items of high temperature scenarios energy The expression is: C energy =Σ t (λ ele cP elec (t)+λ fuel P fuel (t))Δt (7) P elec (t)=P cool，i (t)+P motor-cool (t)+P sand-filter (t)+P control (t) (8) Among them, C energy λ represents the total energy cost; elec P represents the unit price of electricity. elec (t) represents the power consumption at time t, including the power of the vehicle's cooling system, electric motor cooling system, dust filtration device, and control system; λ fuel P is the unit price of fuel; fuel (t) represents the fuel consumption power at time t; Δt represents the time step; P cool，i (t) represents the cooling power, determined by the cargo hold heat load and the coefficient of performance of the refrigeration system; P motor-cool (t) represents the power consumption of the motor's cooling system at time t, and its magnitude is related to the motor load and ambient temperature (the motor heats up more at high temperatures, and the power consumption of the cooling system usually increases); P sand-filter (t) represents the power consumption of the dust filtration device (generated when it is activated in a high-temperature dust environment); P control (t) represents the power consumption of the control system (such as temperature and humidity monitoring, dispatching system).

5. A high-temperature environment multimodal transport method based on composite thermal insulation and active temperature control according to claim 4, characterized in that, The safety / fire hazard penalty cost C safety The expression is: C safety =W fire ·Pr(thermal runaway or fire) (14) Among them, C safety For safety / fire hazard penalty costs; W fire The expected economic and reputational losses from a potential accident; Pr (thermal runaway or fire) is the dimensionless probability of battery thermal runaway or fire.

6. A high-temperature environment multimodal transport method based on composite thermal insulation and active temperature control according to claims 1-3 and 5, characterized in that, The mixed-integer programming model expression in step S4 is: MinC total =C trans +C transf +C loss +C energy +C maint +C safety +C time (16) The expression for the cooling / heat dissipation energy and power constraint is as follows: ∑ t P elec (t)Δt≤E available ,∑ t P cool (t)Δt≤E cool，avail (19) Among them, P elec (t) represents the power consumption at time t, including the power of the vehicle's cooling system, electric motor cooling system, dust filtration device, and control system; Δt represents the time step; E available P represents the total usable electrical energy. cool (t) represents the cooling power at time t; E cool，avail This represents the total energy available to the refrigeration system, i.e., the upper limit of energy that the refrigeration system can put into operation within a cycle; The expression for the node cooling / shading capacity constraint is: Among them, P cool,i (t) represents the cooling power; The maximum cooling power capacity of node i; the enabled state variable of the shading facility at node i. This indicates that the sunshade facilities have been activated. This indicates that it is not enabled; it is a binary variable. The temperature safety constraint expressions for batteries and critical components are as follows: T batt (t)≤T batt,crit ,Pr(thermal runaway)≤∈ safety (21) Among them, T batt (t) represents the temperature of the battery at time t; T batt,crit The critical safe temperature of the battery; Pr (thermal runaway) is the probability of thermal runaway of the battery, dimensionless, reflecting the likelihood of thermal runaway under current operating conditions; ∈ safety This is a dimensionless safety threshold for the probability of thermal runaway, requiring that the probability of battery thermal runaway not exceed this value to ensure safety.

7. A high-temperature environment multimodal transport method based on composite thermal insulation and active temperature control according to claim 6, characterized in that, Step S4 describes using an improved hybrid immune-particle swarm optimization algorithm to solve the model, outputting the optimal transportation path, transportation mode, transfer nodes, and high-temperature scenario-specific scheduling parameters, including: S41. Design a hybrid coding scheme to match the type of model decision variables: In view of the dual characteristics of discrete and continuous decision variables in the mixed integer programming (MINLP) model, design a three-segment hybrid coding structure to transform the model variables into antibody / particle forms that the algorithm can process; initialize the algorithm population and incorporate a heuristic strategy specific to high temperature scenarios to avoid the algorithm getting trapped in local optima and ensure that the initial solution meets the high temperature safety constraints. S42. Based on the goal of minimizing the total cost of the mixed integer programming model, a safety constraint penalty term is introduced to construct the algorithm fitness function, which leads to the search in the direction of low cost and high safety. S43. A dual optimization mechanism is adopted, which optimizes discrete variables using immune genetics and continuous variables using particle swarm optimization. The population is updated in stages to achieve synergistic optimization of discrete and continuous decision variables. S44. To address the dynamic disturbances of solar radiation fluctuations and sudden changes in ambient temperature in high-temperature environments, the solution process is divided into two layers, with global optimization and real-time adjustments. S45. Set dual iteration termination conditions. When the iteration limit is reached or the fitness converges, decode the globally optimal particle / antibody, restore it to the model decision variable value, and output the optimal joint transport scheme and high-temperature specific scheduling parameters.

8. A high-temperature environment multimodal transport method based on composite thermal insulation and active temperature control according to claim 7, characterized in that, The three-segment hybrid encoding structure described in step S41 consists of a string containing three parts: a path-mode binary segment, a relay / module activation segment, and a continuous segment; the specific encoding rules are as follows: Path-mode binary segment: Use 0 / 1 binary encoding for each candidate route segment (i,j) and each mode of transport k, or simplify it to compressed encoding of "route node sequence + mode sequence"; Transit / Module Activation Section: Whether the discrete decision variables specific to high-temperature scenarios are enabled is represented by binary encoding; Discrete decision variables specific to high-temperature scenarios include whether passive shading is enabled at nodes. Enable node / vehicle cooling Should sand and dust filtration be enabled? Should night driving be adopted? Continuous Quantity Segment: Real-valued encoding is used for continuous decision variables in the model; the continuous decision variables include cooling power. Road section driving speed Energy saving thresholds and SOC management parameters; The initialization of the algorithm population and the integration of a high-temperature scenario-specific heuristic strategy in step S41 include: 60%-70% of the initial particles / antibodies are generated through random coding; Generate an additional 30%-40% of heuristic initial solutions; Initial solutions that violate core constraints are eliminated, and particles / antibodies that meet high-temperature safety requirements are retained to form the initial population; The initial heuristic solutions include: "Prioritize nighttime driving + node shading + low cooling power" solution and "Prioritize coastal low temperature routes" solution; "Prioritize nighttime driving + node shading + low cooling power" solution: For sections with extreme high temperatures during the day, nighttime operation is selected by default, shading facilities are activated at the nodes along the route, and the cooling power is set according to the lower limit of the cargo hold heat load to balance energy consumption and temperature control. Solution for "Coastal Low-Temperature Routes Prioritized": For cross-regional transportation, coastal routes are prioritized, and the corresponding coastal route-mode variable in the coding is set to 1. The high-temperature safety constraints mentioned in step S41 include that the cargo temperature does not exceed the permissible range and the battery temperature does not exceed the permissible maximum value.

9. A high-temperature environment multimodal transport method based on composite thermal insulation and active temperature control according to claim 7, characterized in that, The fitness function expression in step S42 is: Among them, C total Let M be the total cost in the mixed-integer programming model; M is the penalty coefficient for boundary violation events. This is an indicator function used to determine whether an out-of-bounds security event exists. When an out-of-bounds security event exists, When there is no boundary crossing security event Step S43 includes the discrete variable segment for immune genetic optimization, the continuous segment for particle swarm optimization, and the segment for intervention by the safety repair operator; The immunogenetic optimization of discrete variable segments includes: based on fitness function values, using roulette wheel selection or elite retention strategies to screen for particles / antibodies with high fitness to enter the next generation; performing single-point or two-point crossover on the path-mode segment and transit / module activation segment respectively to ensure that path connectivity is still satisfied after crossover; and performing mutations on discrete segments with low probability. The continuous segment of particle swarm optimization includes: for each particle in the continuous segment, updating the PSO update rate and position according to the individual optimal solution and the global optimal solution in a short-period PSO; and truncating continuous variables that exceed the physical range after the update. The safety repair operator intervention segment includes: if a particle / antibody violates the high-temperature safety constraint after iteration, a safety repair is triggered; the discrete and continuous segment codes are adjusted first by "increasing cooling power, switching to night operation, and enabling node shading"; if the repair is ineffective, the particle / antibody is eliminated. Step S44 involves layered solution, including offline / semi-real-time optimization of the upper-layer path-mode selection, transit node determination, and transit / module activation; and real-time optimization of the lower-layer cooling power scheduling. The solution algorithm for real-time optimization of the lower-level cooling power scheduling adopts Model Predictive Control (MPC) or Short-Period Particle Swarm Optimization (PSO), and updates the cooling power command every 5-15 minutes to offset real-time heat load fluctuations. The termination condition for the double iteration in step S45 is: The number of iterations has reached the preset limit; If the change in the fitness function value of the global optimal solution is less than the threshold for 10-15 consecutive generations, it is considered convergent. The optimal intermodal transport plan includes: the optimal transport route, the transport mode for each segment, and the location of the transfer node; The high-temperature-specific scheduling parameters include the activation time and power of node shading / cooling facilities, road sections with sand and dust filtration mode activated, night / low-temperature operation time allocation, and real-time cooling power adjustment sequence.

10. A high-temperature environment multimodal transport system based on composite thermal insulation and active temperature control, characterized in that, A method for implementing high-temperature multimodal transport based on composite thermal insulation and active temperature control as described in any one of claims 1-9, comprising: The high-temperature scenario data acquisition and preprocessing module is used to collect high-temperature environmental parameters, equipment and cargo status data, and after cleaning, completion and format conversion, output thermal / energy parameters and equipment failure parameters that meet the model requirements. The capacity grid model construction and symbol definition module is used to build a capacity grid model with hub nodes as vertices and connected road segments as edges to label the capacity of high-temperature adaptability devices; it clarifies 5 modeling assumptions and defines three types of symbol systems: decision variables, parameters, and state variables, providing a basic framework for mixed integer programming models; The high-temperature-related total cost model construction module is used to incorporate high-temperature special items and safety penalties into the total cost, construct a high-temperature-related total cost model, derive cost calculation formulas for multiple scenarios, and quantify the impact of high temperature on the total cost. The mixed-integer programming model building module is used to construct a mixed-integer programming model with the goal of minimizing total cost. This model includes constraints on path connectivity and flow conservation, temperature constraints, cooling / heat dissipation energy and power constraints, node cooling / shading capacity constraints, battery and key component temperature safety constraints, docking / node capability constraints, and time window constraints. An improved hybrid immune-particle swarm optimization module is used to transform variables into an algorithm-processable form through a three-segment hybrid encoding, and incorporates a high-temperature heuristic strategy to initialize the population; a fitness function with safety penalty is constructed, and dual optimization is performed using "immune genetic optimization of discrete variables + particle swarm optimization of continuous variables", combined with hierarchical solution to deal with high-temperature dynamic interference, and finally outputs the optimal solution; The optimal solution output and evaluation module is used to decode the optimal solution and output the basic intermodal transport plan and high-temperature-specific scheduling parameters.

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