Intelligent transportation and storage supervision method

Abstract

The invention discloses an intelligent transportation and storage supervision method, and relates to the technical field of vehicle transportation and storage, the maximum allowable temperature difference and the steady-state heat transfer temperature difference are set, so that the temperature change outside a carriage also serves as a judgment condition for the action of a temperature control module, and the control module can clearly know the thermal resistance coefficient and the thermal insulation performance of a vehicle; the heat preservation action is performed in advance in the subsequent face of temperature fluctuation; through the arrangement of the tunnel mode, when the vehicle transports drugs and other objects sensitive to temperature, temperature shock preparation can be made in advance, different schemes can be executed according to different tunnel types and tunnel lengths, the intelligence and dynamics of the transportation vehicle are improved, and the transportation efficiency is improved. The influence on the goods and the temperature control system after the external temperature changes drastically is greatly improved, the reject ratio of the goods in the compartment due to transportation is reduced, and the service life of the temperature control system is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle transportation and storage, and in particular to an intelligent transportation and storage supervision method. Background Art

[0002] Cold chain transportation refers to a logistics method that controls the temperature of goods that need to be kept at a certain low temperature during transportation. It is mainly used for food (such as meat, seafood, dairy products, fruits and vegetables), medicine (such as vaccines and biological products), and other items that need to be stored within a specific temperature range.

[0003] The temperature control modules of existing cold chain vehicles basically change the power of the HVAC system by monitoring the temperature changes in the compartment. Although high-precision temperature sensors help to achieve compartment temperature control, there are still temperature errors in the actual implementation process. When the temperature changes are monitored and the power of the HVAC system is adjusted, the temperature in the compartment has often changed significantly. The greater the temperature difference between the inside and outside of the compartment, the greater the temperature error. If the power of the HVAC system is adjusted after the temperature in the compartment has changed, not only will the goods be damaged due to temperature fluctuations, but the compressor may also fail under multiple frequencies of instantaneous high-power operation.

[0004] In the existing cold chain transportation system, some vehicles will adjust the temperature of the compartment in advance according to the changes in the outside temperature by the temperature control system. However, due to the differences in the insulation structures of the compartments of different vehicles, the temperature difference between the outside temperature and the inside temperature is also different. The higher the thermal resistance value of the vehicle, the greater the steady-state heat transfer temperature difference and the maximum allowable temperature difference of the compartment. Therefore, different pretreatment schemes should be adopted for different vehicles. When the insulation material of the same vehicle ages, the thermal resistance value of the insulation structure will decrease. The pretreatment scheme should be dynamically adjusted according to the insulation status of the vehicle.

[0005] Similar to the cold chain vehicle mentioned above, when the vehicle is used to transport temperature-sensitive items such as medicines and seafood, the impact of significant fluctuations in the outside temperature of the compartment also needs to be considered. For example, when the vehicle enters a tunnel, the temperature outside the compartment will fluctuate significantly, and the range of temperature fluctuations is related to the type and length of the tunnel.

[0006] Taking a mountain tunnel as an example, when a vehicle enters or exits a tunnel, the surface temperature of the carriage usually undergoes three changes, namely "cooling down - heating up - cooling down". Depending on the length of the tunnel, the time for each temperature change to act on the carriage is also different. When this ambient temperature change affects the interior of the carriage, it will greatly increase the probability of the above-mentioned cargo damage and compressor failure.

[0007] To this end, the present invention proposes an intelligent transportation and storage supervision method to solve the above problems. Summary of the Invention

[0008] The purpose of the present invention is to provide an intelligent transportation, storage and supervision method to solve the technical problems raised in the above background technology.

[0009] To achieve the above objectives, the present invention provides the following technical solution: an intelligent transportation and storage supervision method, comprising the following steps: S1. Use the monitoring module to collect the internal and external temperature data of the vehicle compartment and transmit the data to the control module; S2, the control module determines the maximum allowable temperature difference and the steady-state heat transfer temperature difference based on the difference data of the temperature inside and outside the vehicle compartment; S3. Using the navigation system to determine whether the vehicle is about to enter a tunnel, and changing the operating state of the temperature control module according to the determination result; S4, collecting the temperature data in the vehicle compartment again through the monitoring module and transmitting the data to the control module; S5, the control module determines whether the temperature in the vehicle compartment has returned to a predetermined value based on the collected data, and changes the working state of the temperature control module based on the determination result, while transmitting the data to the deep learning module and entering S1; S6. The deep learning module establishes a feature model based on the operating power of the temperature control module and the temperature change data in the vehicle cabin in S1-S5, predicts the temperature in the vehicle cabin, and transmits the data to the control module. The control module adjusts the operating power or time of the temperature control module to extend the start and stop interval of the temperature control module.

[0010] Preferably, the maximum allowable temperature difference in S2 is: after the vehicle is started, the maximum value of the outside temperature that passes through the insulation structure of the vehicle compartment and affects the temperature inside the vehicle compartment within a unit time, that is, the maximum value of the outside heat radiation that penetrates the vehicle compartment within a unit time; The steady-state heat transfer temperature difference in S2 is the temperature fluctuation value caused by the change in the temperature inside the compartment due to the change in the external temperature after the compartment insulation structure is completely penetrated by heat radiation.

[0011] Preferably, the step S3 includes the following steps: S31, the control module determines whether the vehicle is about to enter a tunnel based on data sent by the navigation system; S32. If the vehicle is not entering a tunnel, adjust the power of the temperature control module according to the temperature change in the vehicle compartment and proceed to S4. If the vehicle is about to enter a tunnel, proceed to S33. S33, the control module determines the season of the vehicle according to the external temperature of the vehicle, and if the vehicle is in spring or summer, the process proceeds to S34; if the vehicle is in autumn or winter, the process proceeds to S35; S34. If the vehicle is about to enter an undersea tunnel, the temperature control module executes plan A. If the vehicle is about to enter a mountain tunnel, the temperature control module executes plan B and proceeds to S36. S35. If the vehicle is about to enter an undersea tunnel, the temperature control module executes plan a. If the vehicle is about to enter a mountain tunnel, the temperature control module executes plan b and proceeds to S36. S36: The control module determines the length of the tunnel, changes the working state of the temperature control module according to the determination result, and enters S4.

[0012] Preferably, the S36 includes the following steps: S361, the control module determines the length of the tunnel according to the data sent by the navigation system; S362. If the tunnel length is a small tunnel, the temperature control module executes the first solution; if the tunnel length is a medium tunnel, the temperature control module executes the second solution; if the tunnel length is a large tunnel, the temperature control module executes the third solution, adjusts the power change of the temperature control module according to the different tunnel lengths, and enters S4.

[0013] Preferably, the solution A in S34 includes: When it is spring or summer outside the tunnel, the temperature inside the submarine tunnel will be significantly lower than the outside temperature. The control module sends a heat preservation signal to the temperature control module, causing the temperature control module to start in advance to appropriately increase the temperature inside the compartment. The solution a in S35 includes: in autumn and winter, the solution a is opposite to solution A, and before the vehicle enters the tunnel, the temperature module is pre-activated to appropriately lower the temperature in the vehicle compartment.

[0014] Preferably, the solution B in S34 includes: When the outside of the tunnel is spring or summer, the vehicle will experience a "cooling down - heating up - cooling down" phase after entering the tunnel. The control module sends a heat preservation signal to the temperature control module, causing the temperature control module to start in advance and adjust the temperature inside the vehicle according to the phase change. The b scheme in S35 includes: in autumn and winter, the temperature inside the tunnel is higher than the outside, and the vehicle will experience a "warming up" stage after entering the tunnel. The control module sends a heat preservation signal to the temperature control module, so that the temperature control module starts in advance and appropriately reduces the temperature inside the car.

[0015] Preferably, the tunnel lengths described in S362 are: Small tunnels <L1; L1<medium-sized tunnel<L2; Large tunnels>L2.

[0016] Preferably, the first solution in S362 is: Since the tunnel is short, the temperature inside the tunnel does not change much compared to the outside temperature. The control module can control the temperature control unit to start within T1 time. The second solution in S362 is: due to the long tunnel length, the control module needs to control the temperature control module to start within T2; The third solution in S363 is: since the tunnel is the longest, the control module needs to control the temperature control module to start within T3 time and adapt to increase its output power, and simultaneously start the auxiliary heat dissipation module of the temperature control module.

[0017] Preferably, the predetermined value in S5 is the optimal storage temperature or the optimal storage temperature range of the transported item.

[0018] Preferably, the monitoring module includes temperature sensors arranged inside and outside the vehicle compartment, the control module adopts a PID algorithm and can control each module to operate, the temperature control module can adjust the temperature inside the vehicle compartment, the deep learning module can learn based on historical data and cooperate with the control module to regulate the remaining modules, and the auxiliary heat dissipation module can improve the heat dissipation efficiency of the temperature control module.

[0019] The beneficial effects of the present invention are: 1. By setting a maximum allowable temperature difference and a steady-state heat transfer temperature difference, the present invention uses temperature changes outside the vehicle compartment as a judgment condition for the operation of the temperature control module. This allows the control module to clearly understand the vehicle's thermal resistance coefficient and thermal insulation performance, and to take early insulation actions when faced with subsequent temperature fluctuations. Moreover, when the outside temperature change is about to exceed the steady-state heat transfer temperature difference, the control module can control the temperature control module to start in advance to alleviate the problem of temperature fluctuations inside the vehicle compartment caused by outside temperature fluctuations. Finally, the control module can also compare the maximum allowable temperature differences measured multiple times and determine whether the insulation structure inside the vehicle compartment has aged and the degree of aging, thereby preventing excessive aging of the vehicle compartment from affecting the energy consumption of the temperature control system and the integrity of the cargo.

[0020] 2. The present invention sets up a deep learning module so that it can obtain the interval time between the first stop and the next start of the temperature control module through historical data, obtain similarity values ​​of historical data according to changes in the outside temperature, and calculate the output power of the temperature control module based on the similarity values ​​of the outside temperature changes, so that the internal temperature of the car and the output power of the temperature control module tend to be constant, thereby extending the start-stop interval of the temperature control module.

[0021] 3. Through the setting of the tunnel mode, the present invention enables vehicles to prepare for temperature shocks in advance when transporting temperature-sensitive items such as medicines. Different plans are implemented according to the type and length of different tunnels, thereby improving the intelligence and dynamism of transportation vehicles, greatly improving the impact of drastic changes in external temperature on goods and temperature control systems, reducing the defective rate of goods in the carriage due to transportation, and increasing the service life of the temperature control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a schematic diagram of the supervision process of Example 1 of the present invention.

[0023] Figure 2 This is a schematic diagram of the supervision process of Example 2 of the present invention.

[0024] Figure 3 Schematic diagram of the supervision process under the tunnel mode of the present invention.

[0025] Figure 4 Schematic diagram of the supervision process under different tunnel lengths of the present invention. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0027] Example 1 To achieve the purpose of intelligent control of the temperature inside the cabin by the temperature control system, avoid the adjustment error caused by the temperature control system adjusting only after detecting the temperature change inside the cabin, and reduce the temperature fluctuation inside the cabin. Figure 1 As shown, in this embodiment, an intelligent transportation and storage supervision method is provided, including the following steps: S1. Use the monitoring module to collect the internal and external temperature data of the vehicle compartment and transmit the data to the control module.

[0028] S2. The control module determines the maximum allowable temperature difference and the steady-state heat transfer temperature difference based on the temperature difference data between the inside and outside of the vehicle compartment.

[0029] S3. Using the navigation system to determine whether the vehicle is about to enter a tunnel, and changing the working state of the temperature control module according to the determination result.

[0030] S4. The monitoring module collects the temperature data in the vehicle compartment again and transmits the data to the control module.

[0031] S5. The control module determines whether the temperature in the vehicle cabin has returned to the preset value based on the collected data, and changes the working state of the temperature control module based on the determination result. At the same time, it transmits the data to the deep learning module and enters S1.

[0032] In this embodiment, S3 includes the following steps: S31. The control module determines whether the vehicle is about to enter a tunnel based on data sent by the navigation system.

[0033] S32: After the control module determines that the vehicle does not enter a tunnel based on the data sent by the navigation system, the process directly proceeds to S4.

[0034] In this embodiment, the maximum allowable temperature difference in S2 is: after the vehicle is started, the maximum temperature of the outside temperature passing through the cabin insulation structure and affecting the temperature inside the cabin per unit time, that is, the maximum value of the outside heat radiation penetrating the cabin per unit time.

[0035] The steady-state heat transfer temperature difference in S2 is the temperature fluctuation value caused by the change in the temperature inside the car due to the change in the external temperature after the car insulation structure is completely penetrated by heat radiation.

[0036] The predetermined value in S5 is the optimal storage temperature or the optimal storage temperature range of the transported item. In this embodiment, the predetermined value is the optimal storage temperature range of the transported item.

[0037] The monitoring module includes temperature sensors installed inside and outside the vehicle compartment. The control module adopts the PID algorithm and can control the operation of each module. The temperature control module can adjust the temperature inside the vehicle compartment. The deep learning module can learn based on historical data and cooperate with the control module to regulate the remaining modules. The auxiliary heat dissipation module can improve the heat dissipation efficiency of the temperature control module.

[0038] For ease of understanding, taking the outside temperature of 30°C and the optimal storage temperature range of items transported in the car as 0°C-5°C as an example, when the vehicle is loaded and the temperature in the car is at the preset value, the monitoring module starts and collects the temperatures inside and outside the car. After the control unit receives the first set of data from the monitoring module, it monitors whether the temperature in the car exceeds the preset value due to the influence of the high outside temperature over a subsequent period of time. Once it exceeds the preset value, it means that during this period of time, the outside heat radiation completely penetrates the insulation structure of the car and directly affects the temperature in the car. The characteristic curve of the maximum allowable temperature difference is obtained by multi-source data sampling. Then, on this basis, the impact of the outside temperature change on the temperature inside the car is monitored, and the steady-state heat transfer temperature difference is obtained again by multi-segment data sampling.

[0039] After obtaining the maximum allowable error and steady-state heat transfer temperature difference as described above, the control module can clearly understand the vehicle's thermal resistance coefficient and insulation performance, and take insulation actions in advance when facing temperature fluctuations in the future.

[0040] In this embodiment, the vehicle does not pass through a tunnel, so the control module only needs to adjust the temperature control system according to the steady-state heat transfer temperature difference. For example, if the steady-state heat transfer temperature difference is ±5°C, that is, when the outside temperature fluctuates by more than 5°C, the temperature inside the vehicle compartment will exceed the predetermined value. Therefore, before the temperature exceeds the predetermined value, the control module will control the power of the temperature control system according to the positive or negative value of the steady-state heat transfer temperature difference, cool down or heat up the vehicle compartment in advance, and realize intelligent adjustment of the vehicle compartment temperature.

[0041] At the same time, the control module can also compare the maximum allowable temperature differences measured multiple times and determine whether the insulation structure in the car is aging. If under the same conditions, the time to reach the maximum allowable temperature difference becomes shorter and shorter, it means that the insulation structure of the car is aging. Once this time is lower than a certain value, the control module needs to send an alarm signal to the driver to remind him that the insulation of the car is too poor.

[0042] However, it should be noted that even if the outside temperature quickly exceeds the value of the steady-state heat transfer temperature difference in a short period of time, when the outside temperature completely affects the entire cabin, the control module can quickly make the temperature control module make adjustments within this period of time.

[0043] By setting the maximum allowable temperature difference and the steady-state heat transfer temperature difference, the temperature change outside the car can also be used as a judgment condition for the action of the temperature control module, so that the control module can clearly know the vehicle's thermal resistance coefficient and thermal insulation performance, and take insulation action in advance when facing temperature fluctuations in the future. Moreover, when the outside temperature change is about to exceed the steady-state heat transfer temperature difference, the control module can control the temperature control module to start in advance to improve the problem of fluctuations in the temperature inside the car caused by outside temperature fluctuations. Finally, the control module can also compare the maximum allowable temperature differences measured multiple times and judge whether the insulation structure in the car is aging and the degree of aging, so as to avoid excessive aging of the car and affect the energy consumption of the temperature control system and the integrity of the goods.

[0044] Example 2 In actual use, it was found that when the vehicle was used to transport temperature-sensitive items such as medicine and seafood, the temperature control module would frequently start and stop. Long-term use would accelerate the aging of electrical components such as relays, and an additional voltage stabilization device would be required, otherwise the circuit protection mechanism might be triggered, causing a shutdown. Further improvements were made based on the above embodiment.

[0045] See also Figure 1 and Figure 2 As shown, in order to overcome the above problems, a deep learning module is added in this embodiment, and S6 is added to the original supervision method; In this embodiment, S6, the deep learning module establishes a feature model based on the operating power of the temperature control module in S1-S5 and the temperature change data in the vehicle cabin, predicts the temperature in the vehicle cabin, and transmits the data to the control module. The control module adjusts the operating power or time of the temperature control module to extend the start and stop interval of the temperature control module.

[0046] In this embodiment, the deep learning module includes a hybrid control unit, an edge computing unit and a storage unit. The hybrid control unit uses a PID algorithm combined with fuzzy logic dynamic adjustment to process data. The edge computing unit is used for rapid processing and response of real-time data, and the storage unit is used to store historical data.

[0047] The deep learning module can obtain the interval between the first stop and the next start of the temperature control module through historical data, and obtain similar values ​​of historical data based on changes in the outside temperature. It can also calculate the output power of the temperature control module based on the similar values ​​of the outside temperature changes, so that the internal temperature of the car and the output power of the temperature control module tend to be constant, extending the start-stop interval of the temperature control module.

[0048] Example 3 When transporting temperature-sensitive items such as medicine and seafood, the impact of significant fluctuations in the vehicle's exterior temperature must also be considered. For example, when a vehicle enters a tunnel, the exterior temperature of the vehicle compartment will experience significant fluctuations, and the range of temperature fluctuations depends on the type and length of the tunnel. When this ambient temperature fluctuation affects the interior of the vehicle compartment, it significantly increases the probability of cargo damage and compressor failure. Further improvements have been made based on the above-mentioned embodiment.

[0049] See also Figures 2 to 4 As shown, different from Example 1, S3 of this embodiment includes the following steps: S31. The control module determines whether the vehicle is about to enter a tunnel based on data sent by the navigation system.

[0050] S32. In this embodiment, the navigation system sends the type and length of the tunnel to the control module according to the road ahead information, and then proceeds to S33.

[0051] S33. The control module determines the season of the vehicle based on the external temperature of the vehicle. If the vehicle is in spring or summer, the process proceeds to S34; if the vehicle is in autumn or winter, the process proceeds to S35.

[0052] S34. If the vehicle is about to enter an undersea tunnel, the temperature control module executes plan A. If the vehicle is about to enter a mountain tunnel, the temperature control module executes plan B and enters S36.

[0053] S35. If the vehicle is about to enter an undersea tunnel, the temperature control module executes plan a; if the vehicle is about to enter a mountain tunnel, the temperature control module executes plan b and enters S36.

[0054] S36: The control module determines the length of the tunnel, changes the working state of the temperature control module according to the determination result, and enters S4.

[0055] In this embodiment, S36 includes the following steps: S361. The control module determines the length of the tunnel based on the data sent by the navigation system.

[0056] S362. If the tunnel length is a small tunnel, the temperature control module executes the first solution; if the tunnel length is a medium tunnel, the temperature control module executes the second solution; if the tunnel length is a large tunnel, the temperature control module executes the third solution, adjusts the power change of the temperature control module according to the different tunnel lengths, and enters S4.

[0057] In this embodiment, plan A in S34 includes: when the temperature outside the tunnel is spring or summer, the temperature inside the submarine tunnel will be significantly lower than the outside temperature. The control module sends a heat preservation signal to the temperature control module, causing the temperature control module to start in advance to appropriately increase the temperature inside the car.

[0058] Plan a in S35 includes: in autumn and winter, plan a is opposite to plan A. Before the vehicle enters the tunnel, the temperature module is pre-activated to appropriately lower the temperature inside the vehicle.

[0059] Solution B in S34 includes: when the outside of the tunnel is spring or summer, the vehicle will experience a "cooling down - heating up - cooling down" phase after entering the tunnel, and the control module sends a heat preservation signal to the temperature control module, causing the temperature control module to start in advance and adjust the temperature inside the vehicle according to the phase change; Plan b in S35 includes: in autumn and winter, the temperature inside the tunnel is higher than the outside temperature. After the vehicle enters the tunnel, it will experience a "warming up" stage. The control module sends a heat preservation signal to the temperature control module, so that the temperature control module starts in advance and appropriately reduces the temperature inside the car.

[0060] In this embodiment, the tunnel lengths in S362 are: small tunnel < L1, L1 < medium tunnel < L2, large tunnel > L2.

[0061] In this embodiment, L1 is 1 km and L2 is 3 km.

[0062] In this embodiment, the first solution in S362 is: since the tunnel is short, the temperature inside the tunnel does not change much compared to the outside temperature, and the control module controls the temperature control unit to start within T1 time.

[0063] The second solution in S362 is: due to the long length of the tunnel, the control module needs to control the temperature control module to start within T2 time.

[0064] The third solution in S363 is: because the tunnel is the longest, the control module needs to control the temperature control module to start within T3 time and adapt to increase its output power, and simultaneously turn on the auxiliary heat dissipation module of the temperature control module.

[0065] In this embodiment, T1 is 5 to 10 minutes, T2 is 10 to 15 minutes, and T3 is 10 to 15 minutes.

[0066] To make it easier to understand, take a 3km long mountain tunnel in summer as an example. Twenty minutes before the vehicle is about to enter the tunnel, the navigation system sends the information about the tunnel to the control module, including the type and length of the tunnel. After the control module receives the information, it controls the temperature control module to start 10 minutes before entering the tunnel. If the preset value of the transported items is 0℃-5℃, then the vehicle needs to be in the middle and high range of the preset value, that is, 4℃-5℃, when it just enters the tunnel. Although the vehicle will experience the "cooling-warming-cooling" stage after entering the tunnel, the overall temperature in the tunnel is generally lower than the outside temperature, so it is necessary to be prepared for the impact of the temperature drop before entering the tunnel.

[0067] An analysis of "cooling-warming-cooling" reveals that "warming" refers to heat accumulation inside the tunnel due to poor natural ventilation or excessive traffic, which causes the temperature in the middle of the tunnel to be significantly higher than the temperature at the tunnel entrance and exit. The longer the tunnel, the longer the "warming" effect lasts and the higher the temperature. Taking a 3km-long tunnel as an example, the two cooling stages are approximately 300m-400m. After passing through the cooling zone and entering the warming zone, the control module controls the temperature control module to reduce the output power and keep the temperature in the car in the low to medium range of the preset value, that is, 2℃-3℃.

[0068] By setting up the tunnel mode, vehicles can prepare for temperature shocks in advance when transporting temperature-sensitive items such as medicines. Different plans can be implemented according to the type and length of different tunnels, thereby improving the intelligence and dynamism of transport vehicles, greatly improving the impact of drastic changes in external temperature on goods and temperature control systems, reducing the defective rate of goods in the carriage due to transportation, and increasing the service life of the temperature control system.

[0069] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. An intelligent transportation and storage supervision method, characterized in that: The following steps are involved: S1. Use the monitoring module to collect the internal and external temperature data of the vehicle compartment and transmit the data to the control module; S2, the control module determines the maximum allowable temperature difference and the steady-state heat transfer temperature difference based on the difference data of the temperature inside and outside the vehicle compartment; S3. Using the navigation system to determine whether the vehicle is about to enter a tunnel, and changing the operating state of the temperature control module according to the determination result; S4, collecting the temperature data in the vehicle compartment again through the monitoring module and transmitting the data to the control module; S5, the control module determines whether the temperature in the vehicle compartment has returned to a predetermined value based on the collected data, and changes the working state of the temperature control module based on the determination result, while transmitting the data to the deep learning module and entering S1; S6. The deep learning module establishes a feature model based on the operating power of the temperature control module and the temperature change data in the vehicle cabin in S1-S5, predicts the temperature in the vehicle cabin, and transmits the data to the control module. The control module adjusts the operating power or time of the temperature control module to extend the start and stop interval of the temperature control module.

2. The intelligent transportation and storage supervision method according to claim 1 is characterized in that: The maximum allowable temperature difference in S2 is: After the vehicle is started, the maximum value of the outside temperature that passes through the insulation structure of the vehicle compartment and affects the temperature inside the vehicle compartment per unit time, that is, the maximum value of the outside heat radiation penetrating the vehicle compartment per unit time; The steady-state heat transfer temperature difference in S2 is the temperature fluctuation value caused by the change in the temperature inside the compartment due to the change in the external temperature after the compartment insulation structure is completely penetrated by heat radiation.

3. The intelligent transportation and storage supervision method according to claim 2 is characterized in that: The S3 includes the following steps: S31, the control module determines whether the vehicle is about to enter a tunnel based on data sent by the navigation system; S32. If the vehicle is not entering a tunnel, adjust the power of the temperature control module according to the temperature change in the vehicle compartment and proceed to S4. If the vehicle is about to enter a tunnel, proceed to S33. S33, the control module determines the season of the vehicle according to the external temperature of the vehicle, and if the vehicle is in spring or summer, the process proceeds to S34; if the vehicle is in autumn or winter, the process proceeds to S35; S34. If the vehicle is about to enter an undersea tunnel, the temperature control module executes plan A. If the vehicle is about to enter a mountain tunnel, the temperature control module executes plan B and proceeds to S36. S35. If the vehicle is about to enter an undersea tunnel, the temperature control module executes plan a. If the vehicle is about to enter a mountain tunnel, the temperature control module executes plan b and proceeds to S36. S36: The control module determines the length of the tunnel, changes the working state of the temperature control module according to the determination result, and enters S4.

4. The intelligent transportation and storage supervision method according to claim 3 is characterized in that: The S36 includes the following steps: S361, the control module determines the length of the tunnel according to the data sent by the navigation system; S362. If the tunnel length is a small tunnel, the temperature control module executes the first solution; if the tunnel length is a medium tunnel, the temperature control module executes the second solution; if the tunnel length is a large tunnel, the temperature control module executes the third solution, adjusts the power change of the temperature control module according to the different tunnel lengths, and enters S4.

5. The intelligent transportation and storage supervision method according to claim 4 is characterized in that: Solution A in S34 includes: When it is spring or summer outside the tunnel, the temperature inside the submarine tunnel will be significantly lower than the outside temperature. The control module sends a heat preservation signal to the temperature control module, causing the temperature control module to start in advance to appropriately increase the temperature inside the compartment. The solution a in S35 includes: in autumn and winter, the solution a is opposite to solution A, and before the vehicle enters the tunnel, the temperature module is pre-activated to appropriately lower the temperature in the vehicle compartment.

6. The intelligent transportation and storage supervision method according to claim 5 is characterized in that: Solution B in S34 includes: When the outside of the tunnel is spring or summer, the vehicle will experience a cooling-heating-cooling phase after entering the tunnel. The control module sends a heat preservation signal to the temperature control module, causing it to start in advance and adjust the temperature inside the vehicle according to the stage changes. The b scheme in S35 includes: in autumn and winter, the temperature inside the tunnel is higher than the outside temperature, and the vehicle will experience a "warming up" stage after entering the tunnel. The control module sends a heat preservation signal to the temperature control module, so that the temperature control module starts in advance and appropriately reduces the temperature inside the vehicle.

7. An intelligent transportation and storage supervision method according to claim 6, characterized in that: The tunnel lengths described in S362 are: Small tunnels <L1; L1<medium-sized tunnel<L2; Large tunnels>L2.

8. An intelligent transportation and storage supervision method according to claim 7, characterized in that: The first solution in S362 is: Since the tunnel is short, the temperature inside the tunnel does not change much compared to the outside temperature. The control module can control the temperature control unit to start within T1 time. The second solution in S362 is: due to the long tunnel length, the control module needs to control the temperature control module to start within T2; The third solution in S363 is: since the tunnel is the longest, the control module needs to control the temperature control module to start within T3 time and adapt to increase its output power, and simultaneously start the auxiliary heat dissipation module of the temperature control module.

9. The intelligent transportation and storage supervision method according to claim 8, characterized in that: The predetermined value in S5 is the optimal storage temperature or the optimal storage temperature range for the transported item.

10. An intelligent transportation and storage supervision method according to claim 9, characterized in that: The monitoring module includes temperature sensors arranged inside and outside the vehicle compartment. The control module adopts a PID algorithm and can control the operation of each module. The temperature control module can adjust the temperature inside the vehicle compartment. The deep learning module can learn based on historical data and cooperate with the control module to regulate the remaining modules. The auxiliary heat dissipation module can improve the heat dissipation efficiency of the temperature control module.

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