Temperature control method, device and equipment based on refrigerator car
By acquiring and analyzing environmental and cargo data from refrigerated trucks using intelligent agent technology, temperature control parameters are generated, solving the problem of inaccurate refrigerated truck temperatures and achieving efficient and precise refrigeration temperature control.
Patent Information
- Application Number
- CN202511526121.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2025-10-17
- Filing Date
- 2025-10-23
- Publication Date
- 2026-01-09
AI Technical Summary
Existing refrigerated trucks rely on manual temperature settings, resulting in inaccurate temperatures that cannot adapt to the actual refrigeration temperature requirements of different goods, leading to high energy consumption and low control precision.
By employing intelligent agent technology, the system acquires data on the refrigerated compartment environment, cargo storage temperature range, and vehicle energy parameters. It then uses deep reinforcement learning algorithms to train the intelligent agent, generating temperature control parameters to automatically adjust the temperature of the refrigerated compartment.
It improves the accuracy and precision of refrigeration temperature control, reduces energy consumption, and enables intelligent management of the temperature inside refrigerated truck compartments.
Smart Images
Figure CN121291036A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of refrigerated transportation technology, specifically providing a temperature control method, device, and equipment based on refrigerated trucks. Background Technology
[0002] With the rapid development of fresh food e-commerce and cold chain logistics, the requirements for refrigerated transportation technology are increasing. Refrigerated transportation refers to the rapid transport of perishable goods using methods such as refrigeration, insulation, and ventilation. In regions with significant climate differences, where there are many types of perishable goods and long transport distances, refrigerated trucks are the primary means of refrigerated transportation.
[0003] In existing technology, traditional refrigerated trucks rely on manual temperature settings to maintain a constant temperature inside the refrigerated compartment. However, different types of goods require different refrigeration temperatures, and manually set temperatures may be lower or higher than the actual refrigeration temperature required for the goods, leading to inaccurate refrigeration temperatures.
[0004] Accordingly, there is a need in the field for a new temperature control method based on refrigerated trucks to solve the above problems. Summary of the Invention
[0005] In order to overcome the above-mentioned defects, this application is made to provide a solution or at least a partial solution to the technical problem in the prior art where the refrigeration temperature set manually may be lower or higher than the actual refrigeration temperature of the goods, thus causing inaccurate refrigeration temperature.
[0006] Firstly, this application provides a temperature control method based on a refrigerated truck, including:
[0007] Acquire data on the vehicle's interior environment, cargo storage temperature range, and vehicle energy parameters;
[0008] The temperature control parameters are obtained by inputting the cabin environment data, the cargo storage temperature range and the vehicle energy parameters into a preset intelligent agent.
[0009] The temperature control parameters are sent to the corresponding execution unit so that the execution unit can perform cooling treatment on the carriage.
[0010] In one technical solution of the above-mentioned temperature control method based on refrigerated trucks, obtaining the preset intelligent agent includes:
[0011] Acquire historical car compartment environmental data, historical cargo storage temperature range, historical vehicle energy parameters, historical temperature control parameters, and reward functions;
[0012] Based on the historical carriage environment data, the historical cargo storage temperature range, the historical vehicle energy parameters, the historical temperature control parameters, and the reward function, the initial temperature control agent, the initial energy management agent, and the initial anomaly handling agent are trained to obtain the temperature control agent, the energy management agent, and the anomaly handling agent.
[0013] The preset agent is obtained based on the temperature control agent, the energy management agent, and the anomaly handling agent.
[0014] In one technical solution of the above-mentioned temperature control method based on refrigerated trucks, obtaining the reward function includes:
[0015] Acquire temperature difference, refrigeration power consumption, freshness of goods, and abnormal alarms;
[0016] The reward function is obtained based on temperature difference, refrigeration power consumption, freshness of goods, abnormal alarms, and preset weights.
[0017] In one technical solution of the above-mentioned temperature control method based on refrigerated trucks, obtaining the cargo storage temperature range includes:
[0018] Acquire cargo images and cargo spectra;
[0019] The cargo image and cargo spectrum are input into a preset cargo recognition model to obtain the cargo category and target region;
[0020] The storage temperature range for the goods is determined based on the category of goods.
[0021] In one technical solution of the above-mentioned temperature control method based on refrigerated trucks, obtaining the freshness of the goods includes:
[0022] Based on the target region, the cargo image is cropped to obtain a cargo segmentation image;
[0023] The segmented cargo image is input into a preset cargo status classification model to obtain the cargo quality status.
[0024] The freshness of the goods is obtained based on their quality status.
[0025] In one technical solution of the above-mentioned temperature control method based on refrigerated trucks, the method further includes:
[0026] The temperature control parameters are input into a preset digital twin model to obtain the current compartment temperature and current vehicle parameters;
[0027] The current carriage temperature and the current equipment parameters are input into a preset prediction model to obtain the predicted carriage temperature and predicted equipment parameters.
[0028] The temperature of the carriage is adjusted based on the predicted carriage temperature and the parameters of the prediction device.
[0029] In one technical solution of the above-mentioned temperature control method based on refrigerated trucks, the truck compartment environment data includes external environment data and internal temperature. Therefore, the method further includes:
[0030] Based on the cargo category and the external environmental data of the carriage, a dynamic temperature threshold is obtained;
[0031] If it is determined that the temperature inside the carriage is greater than the dynamic temperature threshold, then the temperature inside the carriage is determined to be abnormal, and the duration of the abnormality is obtained.
[0032] The alarm level is determined based on the duration of the anomaly.
[0033] The alarm level is sent to the management personnel so that they can adjust the temperature of the carriage.
[0034] In one technical solution of the above-mentioned temperature control method based on refrigerated trucks, after inputting the cargo image and the cargo spectrum into a preset cargo recognition model to obtain the cargo category and target area, the method further includes:
[0035] Obtain the true category of the goods;
[0036] If it is determined that the cargo category is different from the actual cargo category, then the cargo image and the actual cargo category are input into the preset cargo recognition model;
[0037] The preset cargo recognition model is trained to obtain an optimized cargo recognition model.
[0038] Secondly, this application provides a temperature control device based on a refrigerated truck, comprising:
[0039] The acquisition module is used to acquire data on the vehicle's interior environment, cargo storage temperature range, and vehicle energy parameters.
[0040] The analysis module is used to input the cabin environment data, the cargo storage temperature range and the vehicle energy parameters into a preset intelligent agent to obtain temperature control parameters;
[0041] The refrigeration module is used to send the temperature control parameters to the corresponding execution unit so that the execution unit can perform refrigeration on the carriage.
[0042] Thirdly, this application provides a temperature control device based on a refrigerated truck, including at least one processor and a memory; wherein,
[0043] The memory stores computer-executed instructions;
[0044] The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the method described in any one of the first aspects.
[0045] This application provides a temperature control method, device, and equipment based on a refrigerated truck. The method specifically includes: acquiring the truck compartment environment data, cargo storage temperature range, and vehicle energy parameters; inputting the truck compartment environment data, cargo storage temperature range, and vehicle energy parameters into a preset intelligent agent to obtain temperature control parameters; and sending the temperature control parameters to the corresponding execution unit so that the execution unit can perform refrigeration treatment on the truck compartment, thereby improving the accuracy of the refrigerated temperature of the truck compartment. Attached Figure Description
[0046] The disclosure of this application will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application. Furthermore, similar numbers in the drawings are used to denote similar components, wherein:
[0047] Figure 1 This is a schematic flowchart of a temperature control method based on a refrigerated truck according to an embodiment of this application. Figure 2 This is a flowchart illustrating a second embodiment of a temperature control method based on a refrigerated truck provided in this application. Figure 3 This is a flowchart illustrating a third embodiment of a temperature control method based on a refrigerated truck provided in this application. Figure 4 This is a flowchart illustrating a fourth embodiment of a temperature control method based on a refrigerated truck provided in this application. Figure 5 This is a flowchart illustrating a fifth embodiment of a temperature control method based on a refrigerated truck provided in this application. Figure 6 This is a schematic flowchart of a sixth embodiment of a temperature control method based on a refrigerated truck provided in this application. Figure 7 This is a flowchart illustrating Embodiment Seven of a temperature control method based on a refrigerated truck provided in this application. Figure 8 This is a flowchart illustrating an eighth embodiment of a temperature control method based on a refrigerated truck provided in this application. Figure 9 A schematic diagram of a temperature control device based on a refrigerated truck provided in this application embodiment; Figure 10 This is a schematic diagram of a temperature control device based on a refrigerated truck, provided in an embodiment of this application. Reference numerals: 11: Acquisition module; 12: Analysis module; 13: Refrigeration module; 21: Processor; 22: Memory. Detailed Implementation
[0048] Some embodiments of this application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of this application and are not intended to limit the scope of protection of this application.
[0049] In the description of this application, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and can also include software components, such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Non-transitory computer-readable storage media includes any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular terms "a" or "this" can also include plural forms.
[0050] In existing technologies, traditional refrigerated trucks rely on manual setting of the refrigeration temperature inside the compartment. This temperature is typically low, causing the refrigeration unit to operate at high power continuously, resulting in high energy consumption. Furthermore, the feedback control mechanism based on a fixed threshold is slow to respond and has low control accuracy due to the constantly changing state of the items inside the refrigerated compartment and the external environment. All of these factors contribute to the inability of existing refrigerated trucks to accurately control the refrigeration temperature.
[0051] Based on this, in order to solve the above-mentioned technical problems, the technical concept of this application is to provide a new temperature control method based on refrigerated trucks to improve the accuracy of refrigeration temperature.
[0052] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.
[0053] Figure 1 This is a schematic flowchart of an embodiment of a temperature control method based on a refrigerated truck provided in this application. Figure 1 As shown, specifically, the method includes:
[0054] Step S101: Obtain the vehicle compartment environment data, cargo storage temperature range, and vehicle energy parameters.
[0055] In this embodiment, the vehicle compartment environment data may include the temperature and humidity inside and outside the compartment, the weight of the cargo inside the compartment, and the ethylene gas concentration. Secondly, different types of cargo correspond to different cargo storage temperature ranges. Vehicle energy parameters include battery charge, generator parameters, and solar panel output power.
[0056] In this embodiment, various sensors are installed in each independent compartment of the refrigerated truck compartment. Specifically, a high-resolution camera and a near-infrared spectral sensor are installed on the top of each compartment, an infrared temperature sensor is installed on the side wall, and temperature and humidity sensors, ethylene gas sensors and weight sensors are evenly distributed inside the compartment. All sensors are connected to the data collection device via a wireless network.
[0057] Step S102: Input the compartment environment data, cargo storage temperature range and vehicle energy parameters into the preset intelligent agent to obtain temperature control parameters.
[0058] In this embodiment, a deep reinforcement learning algorithm is used to train the dataset to obtain a preset intelligent agent.
[0059] In this embodiment, the temperature control parameters include temperature, compressor frequency, and fan speed.
[0060] Step S103: Send the temperature control parameters to the corresponding execution unit so that the execution unit can perform refrigeration on the carriage.
[0061] In this embodiment, the compressor frequency is sent to the DC inverter compressor, the fan speed is sent to the fan, and the temperature is sent to the controller. After receiving the corresponding temperature control parameters, the execution unit performs refrigeration on the carriage according to the temperature control parameters.
[0062] In this embodiment, the vehicle compartment environment data, cargo storage temperature range, and vehicle energy parameters are acquired. These data are then input into a preset intelligent agent to obtain temperature control parameters. These parameters are then sent to the corresponding execution unit for refrigeration. Compared to existing refrigerated trucks where the refrigeration temperature is manually set, leading to inaccuracies, this application inputs these data into a preset intelligent agent to obtain temperature control parameters, which are then sent to the corresponding execution unit for refrigeration. The intelligent agent analyzes multimodal data from the refrigerated truck and utilizes its intelligence to obtain temperature control parameters for controlling the compartment temperature, thereby improving the accuracy of the refrigerated compartment temperature.
[0063] Figure 2 This is a flowchart illustrating a second embodiment of a temperature control method based on a refrigerated truck provided in this application. Based on the above embodiments, as follows... Figure 2 As shown, specifically, obtaining the preset agent in step S102 includes:
[0064] Step S201: Obtain historical car compartment environmental data, historical cargo storage temperature range, historical vehicle energy parameters, historical temperature control parameters, and reward function.
[0065] In this embodiment, a training dataset includes historical carriage environment data, historical cargo storage temperature range, and historical vehicle energy parameters corresponding to historical temperature control parameters.
[0066] Among them, historical carriage environment data, historical cargo storage temperature range, and historical vehicle energy parameters are used as status data, while historical temperature control parameters are used as action data.
[0067] In this embodiment, a reward function is obtained based on the power consumption of the refrigeration system, the freshness of the goods, abnormal alarm data, and the difference between the current temperature of the compartment and the set temperature.
[0068] In this embodiment, after adding time and location codes to the temperature, humidity, and gas concentration data, the data is cleaned using a sliding window and median filtering algorithm, and outlier correction and interpolation compensation are performed based on the Laida criterion. Then, the data is transmitted through encryption and compression.
[0069] Step S202: Based on historical car environment data, historical cargo storage temperature range, historical vehicle energy parameters, historical temperature control parameters, and reward function, train the initial temperature control agent, the initial energy management agent, and the initial anomaly handling agent to obtain the temperature control agent, the energy management agent, and the anomaly handling agent.
[0070] In this embodiment, the initial temperature control agent, the initial energy management agent, and the initial anomaly handling agent share state data and are trained to obtain the temperature control agent, the energy management agent, and the anomaly handling agent.
[0071] Step S203: Obtain the preset agent based on the temperature control agent, energy management agent, and anomaly handling agent.
[0072] In this embodiment, the temperature control agent, the energy management agent, and the anomaly handling agent cooperate and compete to obtain a preset agent that can maximize the reward.
[0073] In this embodiment, historical carriage environment data, historical cargo storage temperature range, historical vehicle energy parameters, historical temperature control parameters, and reward function are acquired. Based on the historical carriage environment data, historical cargo storage temperature range, historical vehicle energy parameters, historical temperature control parameters, and reward function, an initial temperature control agent, an initial energy management agent, and an initial anomaly handling agent are trained to obtain the temperature control agent, energy management agent, and anomaly handling agent. Based on the temperature control agent, energy management agent, and anomaly handling agent, a preset agent is obtained, thereby obtaining an agent with high robustness and stability.
[0074] Figure 3 This is a flowchart illustrating a third embodiment of a temperature control method based on a refrigerated truck provided in this application. Based on the above embodiments, as... Figure 3 As shown, specifically, obtaining the reward function in step S201 includes:
[0075] Step S301: Obtain temperature difference, refrigeration power consumption, freshness of goods, and abnormal alarms.
[0076] In this embodiment, temperature difference is the difference between the current temperature of the compartment and the set cooling temperature; cargo freshness refers to the difference between the current degree of spoilage of the cargo and the degree of spoilage when it was first placed in the compartment; and abnormal alarm refers to the number and level of alarms in the cargo compartment.
[0077] Step S302: Obtain the reward function based on temperature difference, refrigeration power consumption, freshness of goods, abnormal alarms and preset weights.
[0078] In this embodiment, according to formula 1:
[0079] R=w1*(﹣ΔT)+w2*(﹣P)+w3*F+w4*(﹣A) (1)
[0080] The reward function R is obtained. Here, w1, w2, w3, and w4 are preset weights, ΔT is the temperature difference, P is the refrigeration power consumption, F is the freshness of the goods, and A is the abnormal alarm.
[0081] In this embodiment, temperature difference, refrigeration power consumption, freshness of goods, and abnormal alarms are obtained; based on temperature difference, refrigeration power consumption, freshness of goods, abnormal alarms, and preset weights, a reward function is obtained, which in turn allows the agent to achieve the optimal reward function, thereby obtaining accurate temperature control parameters.
[0082] Figure 4 This is a flowchart illustrating a fourth embodiment of a temperature control method based on a refrigerated truck provided in this application. Based on the above embodiments, as... Figure 4 As shown, specifically, obtaining the cargo storage temperature range in step S101 includes:
[0083] Step S401: Acquire cargo images and cargo spectra.
[0084] In this embodiment, a high-resolution camera and a near-infrared spectral sensor are installed on the inner wall of the carriage to obtain cargo images and cargo spectra.
[0085] Step S402: Input the cargo image and cargo spectrum into the preset cargo recognition model to obtain the cargo category and target area.
[0086] In this embodiment, the preset cargo recognition model can be a neural network model, for example, a lightweight improved YOLOv7 model.
[0087] In this embodiment, the cargo image and cargo spectrum are input into a preset cargo recognition model to obtain the cargo category with the highest probability value, as well as the rectangular box surrounding the cargo in the image, i.e., the target area.
[0088] Step S403: Obtain the storage temperature range of the goods according to the goods category.
[0089] In this embodiment, there is a pre-defined correspondence between goods categories and goods storage temperature ranges. From this correspondence, the goods storage temperature range corresponding to the goods category can be obtained.
[0090] In this embodiment, for example, the storage temperature range for broccoli is 0~4℃, the storage temperature range for strawberries is 0~2℃, the storage temperature range for chilled pork is -2~2℃, the storage temperature range for frozen tuna is -50~-60℃, and the storage temperature range for a certain type of vaccine is 2~8℃.
[0091] Figure 5 This is a flowchart illustrating Embodiment 5 of a temperature control method based on a refrigerated truck provided in this application. Based on the above embodiments, as follows... Figure 5 As shown, specifically, obtaining the freshness of goods in step S301 includes:
[0092] Step S501: Based on the target region, crop the cargo image to obtain a cargo segmentation image.
[0093] In this embodiment, the acquired image is cropped according to the target area, i.e., the rectangular frame in the cargo image, to obtain a cargo segmentation image, thereby removing other parts of the cargo image.
[0094] Step S502: Input the cargo segmentation image into the preset cargo status classification model to obtain the cargo quality status.
[0095] In this embodiment, the segmented image of the goods is input into a neural network model to obtain the quality status of the goods. For example, the neural network model can be a ResNet-50 network.
[0096] Step S503: Obtain the freshness of the goods based on their quality status.
[0097] In this embodiment, by using steps S501 and S502, the current quality status of the goods and the initial quality status of the goods are obtained, and the change between the current quality status and the initial quality status of the goods is the freshness of the goods.
[0098] In this embodiment, the quality status of the goods includes fresh, slightly spoiled, and severely spoiled. If the current quality status of the goods is slightly spoiled and the initial quality status of the goods was fresh, then the freshness of the goods is medium.
[0099] In this embodiment, the cargo image is cropped according to the target area to obtain a cargo segmentation image; the cargo segmentation image is input into a preset cargo status classification model to obtain the cargo quality status; based on the cargo quality status, the cargo freshness is obtained to achieve real-time monitoring of the cargo in the cargo compartment.
[0100] Figure 6 This is a flowchart illustrating a sixth embodiment of a temperature control method based on a refrigerated truck provided in this application. Based on the above embodiments, as... Figure 6 As shown, specifically, after step S103, the method further includes:
[0101] Step S601: Input the temperature control parameters into the preset digital twin model to obtain the current compartment temperature and current vehicle parameters.
[0102] In this embodiment, the preset digital twin model is a virtual mapping of the refrigerated truck. After the temperature control parameters are input into the preset digital twin model, the digital twin model will control the refrigeration system of the virtual refrigerated truck according to the temperature control parameters, thereby obtaining the current compartment temperature and current vehicle parameters.
[0103] Step S602: Input the current carriage temperature and current equipment parameters into the preset prediction model to obtain the predicted carriage temperature and predicted equipment parameters.
[0104] In this embodiment, the preset prediction model can be a model that has the ability to predict linear data.
[0105] In this embodiment, for example, the current carriage temperature and current equipment parameters are input into the LSTM model to obtain the predicted carriage temperature and predicted equipment parameters.
[0106] Step S603: Adjust the car temperature according to the predicted car temperature and the predicted equipment parameters.
[0107] In this embodiment, temperature control parameters are input into a preset digital twin model to obtain the current compartment temperature and current vehicle parameters; the current compartment temperature and current equipment parameters are input into a preset prediction model to obtain the predicted compartment temperature and predicted equipment parameters; based on the predicted compartment temperature and predicted equipment parameters, the compartment temperature is adjusted, thereby predicting the compartment temperature and equipment parameters for a future period of time, and obtaining the compartment temperature changes and equipment health status based on the compartment temperature and equipment parameters for a future period of time, so as to adjust the compartment temperature in advance and ensure that the goods in the compartment are at the optimal refrigeration temperature.
[0108] Figure 7 This is a flowchart illustrating Embodiment Seven of a temperature control method based on a refrigerated truck provided in this application. Based on the above embodiments, as... Figure 7 As shown, specifically, after step S503, the method further includes:
[0109] Step S701: Obtain the dynamic temperature threshold based on the cargo category and external environmental data of the carriage.
[0110] In this embodiment, the optimal storage temperature of the goods is obtained according to the category of goods, and a dynamic temperature threshold is obtained based on the optimal storage temperature and the external environmental data of the vehicle compartment.
[0111] Step S702: If it is determined that the temperature inside the carriage is greater than the dynamic temperature threshold, then the temperature inside the carriage is determined to be abnormal, and the duration of the abnormality is obtained.
[0112] In this embodiment, when the temperature inside the carriage is determined to be greater than the dynamic temperature threshold, the temperature inside the carriage is determined to be abnormal. At this time, the abnormal duration of the temperature inside the carriage being greater than the dynamic temperature threshold is monitored.
[0113] Step S703: Determine the alarm level based on the duration of the anomaly.
[0114] In this embodiment, a pre-defined correspondence between the abnormal duration range and the alarm level is established. The abnormal duration range is determined based on the abnormal duration, and then the alarm level corresponding to the abnormal duration range is obtained based on the correspondence.
[0115] In this embodiment, for example, 30 to 60 seconds is a warning, 60 to 300 seconds is an alarm, and more than 300 seconds is an emergency alarm.
[0116] Step S704: Send the alarm level to the management personnel so that they can adjust the temperature of the carriage.
[0117] In this embodiment, a dynamic temperature threshold is obtained based on the cargo category and external environmental data of the compartment. If it is determined that the internal temperature of the compartment is greater than the dynamic temperature threshold, an abnormal internal temperature is identified, and the duration of the abnormality is obtained. Based on the duration of the abnormality, an alarm level is determined. The alarm level is sent to the management personnel so that they can adjust the compartment temperature, thereby improving the accuracy of the refrigerated temperature abnormality alarm.
[0118] Figure 8 This is a flowchart illustrating an eighth embodiment of a temperature control method based on a refrigerated truck provided in this application. Based on the above embodiments, as... Figure 8 As shown, specifically, after step S402, the method further includes:
[0119] Step S801: Obtain the true category of goods.
[0120] In this embodiment, the true category can be obtained by manually judging the cargo image to obtain the true category of the cargo.
[0121] Step S802: If the cargo category is determined to be different from the actual cargo category, then input the cargo image and the actual cargo category into the preset cargo recognition model.
[0122] In this embodiment, if the cargo category is determined to be different from the actual cargo category, then the preset cargo recognition model is determined to be unable to recognize the cargo in the cargo compartment.
[0123] Step S803: Train the preset cargo recognition model to obtain an optimized cargo recognition model.
[0124] In this embodiment, the true category of goods is obtained; if it is determined that the goods category is different from the true category, the goods image and the true category are input into a preset goods recognition model; the preset goods recognition model is trained to obtain an optimized goods recognition model, which can recognize new goods, thereby improving the recognition capability of the goods recognition model.
[0125] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effect of this application, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these variations are all within the scope of protection of this application.
[0126] Furthermore, this application also provides a temperature control device based on a refrigerated truck.
[0127] Figure 9 This is a schematic diagram of a temperature control device based on a refrigerated truck, provided as an embodiment of this application. Figure 9As shown, the temperature control device based on a refrigerated truck in this embodiment mainly includes an acquisition module 11, an analysis module 12, and a refrigeration module 13. In some embodiments, one or more of the acquisition module 11, analysis module 12, and refrigeration module 13 can be combined into a single module. In some embodiments, the acquisition module 11 can be configured to acquire vehicle compartment environment data, cargo storage temperature range, and vehicle energy parameters. The analysis module 12 can be configured to input the vehicle compartment environment data, cargo storage temperature range, and vehicle energy parameters into a preset intelligent agent to obtain temperature control parameters. The refrigeration module 13 can be configured to send the temperature control parameters to the corresponding execution unit for refrigeration of the vehicle compartment.
[0128] The aforementioned temperature control device based on refrigerated trucks is used to perform... Figures 1 to 8 The temperature control method embodiments based on refrigerated trucks shown are similar in technical principle, the technical problems solved, and the technical effects produced. Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the temperature control device based on refrigerated trucks can be found in the embodiments of the temperature control method based on refrigerated trucks, and will not be repeated here.
[0129] Those skilled in the art will understand that all or part of the processes in the method of the above-described embodiment can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.
[0130] Furthermore, this application also provides a temperature control device based on a refrigerated truck.
[0131] Figure 10 This is a schematic diagram of a temperature control device based on a refrigerated truck, provided as an embodiment of this application. Figure 10As shown, the temperature control device based on a refrigerated truck includes a processor 21 and a memory 22. The memory 22 can be configured to store a program that executes the temperature control method based on a refrigerated truck according to the above method embodiments. The processor 21 can be configured to execute the program stored in the memory, which includes, but is not limited to, a program that executes the temperature control method based on a refrigerated truck according to the above method embodiments. For ease of explanation, only the parts related to the embodiments of this application are shown. For specific technical details not disclosed, please refer to the method section of the embodiments of this application. The temperature control device based on a refrigerated truck can be a control device device comprising various electronic devices.
[0132] Furthermore, it should be understood that since the various modules are only provided to illustrate the functional units of the device described in this application, the physical devices corresponding to these modules may be the processor itself, or a part of the processor's software, hardware, or a combination of both. Therefore, the number of modules shown in the figures is merely illustrative.
[0133] Those skilled in the art will understand that the various modules in the device can be adaptively split or combined. Such splitting or combining of specific modules will not cause the technical solution to deviate from the principles of this application; therefore, the technical solutions after splitting or combining will fall within the protection scope of this application.
[0134] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.
Claims
1. A temperature control method based on a refrigerated truck, characterized in that, include: Acquire data on the vehicle's interior environment, cargo storage temperature range, and vehicle energy parameters; The temperature control parameters are obtained by inputting the cabin environment data, the cargo storage temperature range and the vehicle energy parameters into a preset intelligent agent. The temperature control parameters are sent to the corresponding execution unit so that the execution unit can perform cooling treatment on the carriage.
2. The temperature control method based on a refrigerated truck according to claim 1, characterized in that, Obtaining the preset intelligent agent includes: Acquire historical car compartment environmental data, historical cargo storage temperature range, historical vehicle energy parameters, historical temperature control parameters, and reward functions; Based on the historical carriage environment data, the historical cargo storage temperature range, the historical vehicle energy parameters, the historical temperature control parameters, and the reward function, the initial temperature control agent, the initial energy management agent, and the initial anomaly handling agent are trained to obtain the temperature control agent, the energy management agent, and the anomaly handling agent. The preset agent is obtained based on the temperature control agent, the energy management agent, and the anomaly handling agent.
3. The temperature control method based on a refrigerated truck according to claim 2, characterized in that, Obtaining the reward function includes: Acquire temperature difference, refrigeration power consumption, freshness of goods, and abnormal alarms; The reward function is obtained based on temperature difference, refrigeration power consumption, freshness of goods, abnormal alarms, and preset weights.
4. The temperature control method based on a refrigerated truck according to claim 3, characterized in that, Obtaining the storage temperature range of the goods includes: Acquire cargo images and cargo spectra; The cargo image and cargo spectrum are input into a preset cargo recognition model to obtain the cargo category and target region; The storage temperature range for the goods is determined based on the category of goods.
5. The temperature control method based on a refrigerated truck according to claim 4, characterized in that, Obtaining the freshness of the goods includes: Based on the target region, the cargo image is cropped to obtain a cargo segmentation image; The segmented cargo image is input into a preset cargo status classification model to obtain the cargo quality status. The freshness of the goods is obtained based on their quality status.
6. The temperature control method based on a refrigerated truck according to claim 1, characterized in that, The method further includes: The temperature control parameters are input into a preset digital twin model to obtain the current compartment temperature and current vehicle parameters; The current carriage temperature and the current equipment parameters are input into a preset prediction model to obtain the predicted carriage temperature and predicted equipment parameters. The temperature of the carriage is adjusted based on the predicted carriage temperature and the parameters of the prediction device.
7. The temperature control method based on a refrigerated truck according to claim 5, characterized in that, The carriage environment data includes external carriage environment data and internal carriage temperature, and the method further includes: Based on the cargo category and the external environmental data of the carriage, a dynamic temperature threshold is obtained; If it is determined that the temperature inside the carriage is greater than the dynamic temperature threshold, then the temperature inside the carriage is determined to be abnormal, and the duration of the abnormality is obtained. The alarm level is determined based on the duration of the anomaly. The alarm level is sent to the management personnel so that they can adjust the temperature of the carriage.
8. The temperature control method based on a refrigerated truck according to claim 4, characterized in that, After inputting the cargo image and the cargo spectrum into a preset cargo recognition model to obtain the cargo category and target region, the method further includes: Obtain the true category of the goods; If it is determined that the cargo category is different from the actual cargo category, then the cargo image and the actual cargo category are input into the preset cargo recognition model; The preset cargo recognition model is trained to obtain an optimized cargo recognition model.
9. A temperature control device based on a refrigerated truck, characterized in that, include: The acquisition module is used to acquire data on the vehicle's interior environment, cargo storage temperature range, and vehicle energy parameters. The analysis module is used to input the cabin environment data, the cargo storage temperature range and the vehicle energy parameters into a preset intelligent agent to obtain temperature control parameters; The refrigeration module is used to send the temperature control parameters to the corresponding execution unit so that the execution unit can perform refrigeration on the carriage.
10. A temperature control device based on a refrigerated truck, comprising at least one processor and a memory; wherein, The memory stores computer-executed instructions; The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the method of any one of claims 1 to 8.