Intelligent temperature control method and device

Abstract

The application discloses an intelligent temperature control method and device, and relates to the technical field of big data.The specific embodiment of the method comprises the following steps: obtaining article data and real-time temperature data of a space where the article is located; determining a temperature control adjustment strategy and generating a temperature control adjustment instruction based on the article data and the real-time temperature data; and executing the temperature control adjustment strategy to control the storage temperature of the article according to the temperature control adjustment instruction.The embodiment can automatically determine the temperature control adjustment strategy and control the storage temperature of the article according to the article information of the article and the real-time temperature change of the external environment, improve the preservation effect of the article in the transportation process, and reduce the loss of the article caused by improper temperature.

Description

Technical Field

[0001] This invention relates to the field of big data technology, and in particular to an intelligent temperature control method and device. Background Technology

[0002] With the rapid development of e-commerce and logistics, especially the increasing popularity of online sales of fresh food and pharmaceutical products, the requirements for temperature control during transportation are becoming increasingly stringent. Traditional temperature control methods typically rely on manual temperature settings, which cannot flexibly adapt to temperature changes, leading to temperature fluctuations during transportation and consequently causing spoilage of goods and losses for users. Summary of the Invention

[0003] In view of this, the intelligent temperature control method and device provided in this embodiment of the invention can automatically determine the temperature control adjustment strategy and control the storage temperature of the items based on the item information and the real-time temperature changes of the external environment, thereby improving the preservation effect of the items during transportation and reducing the loss of items due to improper temperature.

[0004] To achieve the above objectives, according to one aspect of the present invention, an intelligent temperature control method is provided, comprising:

[0005] Acquire item data and real-time temperature data of the space where the item is located;

[0006] Determine the temperature control adjustment strategy based on item data and real-time temperature data, and generate temperature control adjustment instructions;

[0007] According to the temperature control adjustment instructions, the temperature control adjustment strategy is executed to control the storage temperature of the items.

[0008] Optionally, the item information includes item type, set preservation temperature, and transportation time; based on item data and real-time temperature data, a temperature control adjustment strategy is determined and a temperature control adjustment command is generated, including:

[0009] Temperature control adjustment strategies are determined based on item type using classification algorithms;

[0010] Based on the type of goods, the set preservation temperature, the transportation time, and real-time temperature data, a regression algorithm is used to predict the target preservation temperature of the goods.

[0011] Temperature control adjustment commands are generated based on the temperature control adjustment strategy and the target preservation temperature.

[0012] Optionally, a temperature control adjustment strategy is determined based on the item type using a classification algorithm, including:

[0013] The item types are classified using a pre-trained classification model to obtain the classification results;

[0014] Based on the classification results, a pre-configured temperature control strategy library is matched, and the temperature control adjustment strategy is determined according to the matching results.

[0015] Optionally, a pre-trained classification model is used to classify the item types to obtain classification results, including:

[0016] The item types are classified using multiple classification models, resulting in multiple classification results;

[0017] Multiple classification results are processed by voting integration strategy, and the classification result is determined based on the voting results.

[0018] Optionally, after executing the temperature control adjustment strategy to control the storage temperature of the items according to the temperature control adjustment instruction, the method further includes:

[0019] Monitor changes in storage temperature and determine measured temperature data and time-series temperature data based on the monitoring results;

[0020] Compare the measured temperature data with the target preservation temperature to generate comparison results;

[0021] Predict future environmental temperature trends using pre-trained deep learning models based on temperature time-series data;

[0022] Based on the comparison results and future ambient temperature trends, a pre-trained reinforcement learning model is used to optimize the temperature control adjustment strategy and generate new temperature control adjustment commands.

[0023] Optionally, a regression algorithm is used to predict the target preservation temperature of the item, including:

[0024] Multiple initial preservation temperatures are obtained by using multiple regression algorithms to predict the initial preservation temperature of the items.

[0025] The target preservation temperature is determined based on the weighted average of multiple initial preservation temperatures.

[0026] According to a second aspect of the present invention, an intelligent temperature control device is provided, comprising:

[0027] The acquisition module is used to acquire item data and real-time temperature data of the space where the item is located;

[0028] The determination module is used to determine the temperature control adjustment strategy based on the item data and real-time temperature data and generate temperature control adjustment instructions.

[0029] The execution module is used to execute temperature control adjustment strategies to control the storage temperature of items based on temperature control adjustment instructions.

[0030] According to a third aspect of the present invention, an electronic device is provided, comprising:

[0031] One or more processors;

[0032] Memory, used to store one or more programs.

[0033] When one or more programs are executed by one or more processors, the one or more processors implement the methods of any of the above embodiments.

[0034] According to a fourth aspect of the present invention, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method of any of the above embodiments.

[0035] According to a fifth aspect of the present invention, a computer program product is provided, including a computer program that, when executed by a processor, implements the method of any of the above embodiments.

[0036] One embodiment of the above invention has the following advantages or beneficial effects: by acquiring item data and real-time temperature data; determining a temperature control adjustment strategy and generating a temperature control adjustment command based on the item data and the real-time temperature data of the space where the item is located; executing the temperature control adjustment strategy to control the storage temperature of the item according to the temperature control adjustment command; thereby, the temperature control adjustment strategy can be automatically determined and the storage temperature of the item can be controlled according to the item information and the real-time temperature changes of the external environment, improving the preservation effect of the item during transportation and reducing the loss of the item due to improper temperature.

[0037] The further effects of the aforementioned unconventional alternative methods will be explained below in conjunction with specific implementation methods. Attached Figure Description

[0038] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein:

[0039] Figure 1 This is a schematic diagram of the main process of the intelligent temperature control method according to an embodiment of the present invention;

[0040] Figure 2 This is a schematic diagram of the main flow of an intelligent temperature control method according to a preferred embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of the main process of implementing a temperature control adjustment strategy based on an intelligent insulated box according to a preferred embodiment of the present invention;

[0042] Figure 4 This is a schematic diagram of the main modules of the intelligent temperature control device according to an embodiment of the present invention;

[0043] Figure 5 This is an exemplary system architecture diagram in which embodiments of the present invention can be applied;

[0044] Figure 6 This is a schematic diagram of the structure of a computer system suitable for implementing terminal devices or servers of the present invention. Detailed Implementation

[0045] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0046] It should be noted that the acquisition, storage, and application of personal information involved in the embodiments of the present invention comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0047] With the rapid development of e-commerce and logistics, especially the increasing popularity of online sales of fresh food and pharmaceutical products, the requirements for temperature control during transportation are becoming increasingly stringent. Traditional temperature control methods typically rely on manual temperature settings, which cannot flexibly adapt to temperature changes, leading to temperature fluctuations during transportation and consequently causing spoilage of goods and losses for users.

[0048] In view of this, according to one aspect of the present invention, an intelligent temperature control method is provided.

[0049] Figure 1 This is a schematic diagram of the main flow of the intelligent temperature control method according to an embodiment of the present invention. Figure 1 As shown, the intelligent temperature control method according to an embodiment of the present invention includes the following steps S101 to S103.

[0050] Step S101: Obtain item data and real-time temperature data of the space where the item is located.

[0051] Item data refers to attribute information related to the stored items, either input by the user or automatically identified by the system. This includes the item's name, type, material, shelf life, recommended storage temperature range, sensitivity level, etc. Item data can be used to characterize the response characteristics of different items to temperature changes. Real-time temperature data is the current temperature information of the space where the item is located, collected by sensor devices or obtained through a network. This real-time temperature data reflects the instantaneous temperature changes in the storage environment. In this embodiment, item data can be obtained in various ways. Specifically, the user can actively input item information in the system interface, for example, by filling in relevant fields on a control terminal or mobile application. Alternatively, the system can automatically identify items using barcodes, QR codes, or RFID tags and extract the corresponding item data from a preset item database. Real-time temperature data can also be obtained in various ways. Specifically, temperature sensors deployed in the storage area can be used to collect the ambient temperature in real time and upload the data to the control system. Real-time temperature information can also be obtained directly from a networked smart storage device through a temperature data interface. By combining the two types of data acquisition methods mentioned above, we can fully understand the storage requirements of items and the current storage environment, thereby providing basic data support for the generation and execution of subsequent temperature control strategies.

[0052] Step S102: Determine the temperature control adjustment strategy based on the item data and real-time temperature data, and generate a temperature control adjustment command.

[0053] A temperature control adjustment strategy is a specific plan for adjusting the storage temperature of goods. This strategy includes setting the storage temperature, adjusting the temperature speed, adjusting the time cycle, and whether time-segmented temperature control is required. By implementing the temperature control adjustment strategy, the storage temperature of goods is kept within a suitable range to ensure product quality, extend shelf life, or meet specific storage requirements. The temperature control adjustment command is a corresponding control instruction generated based on the temperature control adjustment strategy. It drives temperature control equipment (such as refrigeration equipment, heating modules, fan assemblies, etc.) to perform corresponding operations, achieving real-time control of the storage temperature. This temperature control adjustment command can be output in the form of machine-recognizable control signals, ensuring that the temperature control adjustment strategy can be accurately executed by the temperature control equipment.

[0054] In this embodiment, a temperature control adjustment strategy can be determined based on a rule-matching mechanism. This involves pre-setting multiple sets of typical items and corresponding temperature control rules within the system. When the current item type matches a specific temperature control rule, real-time temperature data is used to determine if the current ambient temperature deviates from the target range. Based on this, a temperature control adjustment strategy is formulated and then converted into an executable temperature control adjustment command, which is sent to the temperature control device. Alternatively, a machine learning model can be used to determine the temperature control adjustment strategy. By training a classification or regression model, the optimal temperature setpoint and adjustment method for the current scenario can be identified. The machine learning model takes current item data and real-time temperature data as input and outputs the parameters corresponding to the temperature control adjustment strategy. The machine learning model then automatically generates temperature control adjustment commands, achieving intelligent decision-making. Through these methods, efficient and accurate formulation of temperature control adjustment strategies can be achieved, ensuring the reliability and real-time performance of command execution.

[0055] Step S103: According to the temperature control adjustment instruction, execute the temperature control adjustment strategy to control the storage temperature of the items.

[0056] In this embodiment, a direct control mechanism can be used to execute the temperature control adjustment strategy. Upon receiving a temperature control adjustment command, the temperature control device is directly driven to operate. For example, when the temperature control adjustment command includes a target temperature reduction, the refrigeration module is activated, and the ambient temperature is fed back in real time via a temperature sensor. When the temperature reaches the target set value, the refrigeration device is automatically shut down, achieving precise temperature control. During this process, the temperature control device maintains high-speed communication with the system to ensure the accuracy and safety of the execution process. Alternatively, a closed-loop feedback control mechanism can be used. After executing the initial temperature control adjustment command, the trend of ambient temperature changes is continuously monitored, and the execution strategy is dynamically adjusted based on the feedback data. For example, if the ambient temperature changes slowly during execution, the device's operating time can be appropriately extended or its power increased; if the temperature rapidly approaches the target value, the adjustment intensity is reduced in advance to avoid over-adjustment. Through this dynamic closed-loop control method, not only can the temperature control adjustment strategy be executed in response to the temperature control adjustment command, but it can also be finely adjusted according to environmental changes during execution, improving the stability and energy efficiency of temperature control, thereby achieving precise control of the storage temperature of items.

[0057] This invention acquires item data and real-time temperature data of the space where the item is located; determines a temperature control adjustment strategy and generates a temperature control adjustment command based on the item data and real-time temperature data; and executes the temperature control adjustment strategy to control the storage temperature of the item according to the temperature control adjustment command. Thus, it can automatically determine the temperature control adjustment strategy and control the storage temperature of the item based on the item information and the real-time temperature changes of the external environment, thereby improving the preservation effect of the item during transportation and reducing the loss of the item due to improper temperature.

[0058] Optionally, the item information includes item type, set preservation temperature, and transportation time; determining a temperature control adjustment strategy and generating a temperature control adjustment command based on item data and real-time temperature data includes: determining a temperature control adjustment strategy based on item type using a classification algorithm; predicting the target preservation temperature of the item using a regression algorithm based on item type, set preservation temperature, transportation time, and real-time temperature data; and generating a temperature control adjustment command based on the temperature control adjustment strategy and the target preservation temperature.

[0059] In this embodiment, item information is first obtained through user input or preset data. This item information includes key attributes such as item type, set preservation temperature, and transportation time. The set preservation temperature can be the user-input desired preservation temperature. The obtained item data and the real-time temperature data of the current environment are used as input parameters for intelligent analysis. In the specific implementation, based on the item type, a trained classification algorithm model can be used to determine the temperature control category to which the item belongs, thereby determining a temperature control adjustment strategy suitable for that item type. Simultaneously, the set preservation temperature, transportation time, and current real-time temperature data can be further combined to apply a regression algorithm to predict the optimal preservation temperature for the item in its current state, i.e., the target preservation temperature. Based on this, a temperature control adjustment command is generated by combining the temperature control adjustment strategy and the predicted target preservation temperature. This command includes the temperature control operation mode, execution time, and adjustment range, and is sent to the temperature control device for execution.

[0060] In addition to using a joint modeling approach combining classification and regression algorithms, an expert rule base-based method can be employed to determine temperature control adjustment strategies. For example, by matching the current item and temperature status with preset temperature control rules, an adjustment plan can be quickly derived. Furthermore, reinforcement learning methods can be applied to continuously optimize the temperature control adjustment strategy based on historical control performance, thereby enhancing the intelligence level of temperature control.

[0061] Through the above implementation methods, it is possible to automatically sense the status of goods and the environment, make intelligent analysis and decisions, and generate targeted temperature control adjustment instructions, thereby improving the preservation efficiency and safety of goods, reducing human intervention, improving the intelligence level and temperature control accuracy of the entire storage and transportation process, and enhancing user experience and the ability to ensure the quality of goods.

[0062] Optionally, the temperature control adjustment strategy is determined based on the item type using a classification algorithm, including: classifying the item type using a pre-trained classification model to obtain the classification result; matching the classification result with a pre-configured temperature control strategy library, and determining the temperature control adjustment strategy based on the matching result.

[0063] In this embodiment, a classification algorithm is used to intelligently determine the corresponding temperature control adjustment strategy by analyzing the item type. Specifically, a classification model is pre-built and trained based on a large number of known item types and their temperature control requirements. During actual operation, after receiving item type data, it is input into the pre-trained classification model for classification processing to obtain the classification result of the item. This classification result is matched with a pre-configured temperature control strategy library, which stores different item types and their corresponding temperature control adjustment strategies. Based on the matching result, a temperature control adjustment strategy suitable for the current item type is automatically determined, thus providing a basis for subsequent temperature control command generation and temperature control device execution. In addition, strategy identification can also be performed based on the item attribute association method of knowledge graph, that is, a relationship graph is established based on multiple attributes of the item, and the temperature control adjustment strategy is determined through rule reasoning. Alternatively, more complex models such as graph neural networks can be used to model the structural information between item attributes, thereby improving the accuracy and generalization ability of strategy matching.

[0064] The embodiments of this invention can effectively improve the ability to identify different types of goods and the matching efficiency of corresponding temperature control adjustment strategies, enabling rapid and automated formulation of temperature control adjustment strategies. Furthermore, it reduces the workload of manually configuring temperature control adjustment strategies, enhances the intelligence of temperature control adjustment responses, and improves the preservation effect and quality safety of different types of goods during storage and transportation.

[0065] Optionally, the item types are classified using a pre-trained classification model to obtain classification results, including: classifying item types based on multiple classification models to obtain multiple classification results; and voting on the multiple classification results according to a voting ensemble strategy to determine the classification result based on the voting results.

[0066] In this embodiment, instead of relying solely on a single classification model, multiple pre-trained classification models are introduced to construct a classification model cluster, thereby enhancing the accuracy and robustness of the classification results. Specifically, item types are input into multiple pre-trained classification models, such as decision tree models, support vector machine models, and neural network models. Each classification model outputs an independent classification result based on its own training parameters. A voting ensemble strategy is used to integrate these multiple classification results, that is, by statistically analyzing the frequency of occurrence of item categories predicted by each classification model, the item category with the highest occurrence frequency is selected as the final classification result. This approach fully leverages the advantages of different models through model ensemble, avoiding classification errors caused by the prediction bias of a single model, thereby improving the overall stability and accuracy of the classification process. In addition to result ensemble based on the voting strategy, a weighted voting strategy can also be used, that is, assigning different weights to each classification model according to its validation performance during training, so that the classification results of high-precision models have a greater weight in the final classification. Through the above methods, not only is the accuracy of item type recognition improved, but the system's fault tolerance to data disturbances or abnormal inputs is also enhanced. The final classification results are more reliable, improving the overall intelligence level and practical application effect of the temperature control process.

[0067] Optionally, after executing the temperature control adjustment strategy to control the storage temperature of the items according to the temperature control adjustment instruction, the method further includes: monitoring changes in the storage temperature, determining measured temperature data and temperature time-series data based on the monitoring results; comparing the measured temperature data with the target preservation temperature to generate a comparison result; predicting future ambient temperature trends using a pre-trained deep learning model based on the temperature time-series data; and optimizing the temperature control adjustment strategy using a pre-trained reinforcement learning model based on the comparison result and the future ambient temperature trend, and generating new temperature control adjustment instructions.

[0068] After executing the corresponding temperature control strategy according to the temperature control adjustment command to control the storage temperature of the goods, the temperature control adjustment process can be intelligently monitored and dynamically optimized to improve the accuracy and responsiveness of the temperature control adjustment strategy. Specifically, sensor components monitor the temperature changes in the current storage environment in real time, obtaining the measured temperature data and continuous records of this data over a period of time, forming temperature time-series data. The measured temperature data is compared with the target preservation temperature predicted by the regression model to determine whether the current temperature control state meets the preservation requirements of the goods, thus generating a comparison result. Based on this, a pre-trained deep learning model, such as a recurrent neural network or a long short-term memory network, is further used to predict the environmental temperature trend over a future period based on the temperature time-series data. This prediction result can reflect possible temperature fluctuations or abnormal changes in the environment, providing a basis for adjusting the strategy in advance. Combining the current comparison result with the future environmental temperature trend, a pre-trained reinforcement learning model is invoked to optimize the current temperature control adjustment strategy. This reinforcement learning model dynamically learns and updates the optimal temperature control adjustment strategy by simulating feedback performance under different temperature control adjustment strategies, and generates new temperature control adjustment commands to drive the temperature control process towards more precise and stable operation. Besides dynamic adjustment based on deep learning and reinforcement learning models, rule-based methods based on expert systems can also be used to formulate emergency adjustment strategies for specific item types and temperature fluctuation ranges, or fuzzy control methods can be combined to cope with uncertainties. The solution in this embodiment not only achieves rapid response to temperature anomalies but also autonomously adapts to environmental changes, realizing dynamic self-optimization of the temperature control adjustment strategy, thereby improving the intelligence level, energy efficiency, and stability and reliability of item preservation in the temperature control process.

[0069] Optionally, the target preservation temperature of the item is predicted using a regression algorithm, including: predicting the initial preservation temperature of the item using multiple regression algorithms to obtain multiple initial preservation temperatures; calculating a weighted average of the multiple initial preservation temperatures; and determining the target preservation temperature based on the calculation results.

[0070] In this embodiment, the prediction accuracy and robustness of the target preservation temperature are improved by integrating the outputs of multiple prediction models. Specifically, the item data and real-time temperature data are input into multiple pre-trained regression models, such as linear regression models, support vector regression models, gradient boosting regression models, or deep neural network regression models. Each regression model outputs a corresponding initial preservation temperature prediction value based on its internal modeling mechanism, thus forming multiple initial preservation temperatures. To improve the accuracy and stability of the final target preservation temperature, these initial preservation temperatures are weighted and averaged. The weights can be determined based on the performance of the regression models in historical predictions, for example, by using the error rate obtained from cross-validation as the weights, or by dynamically adjusting the influence of each regression model based on the confidence level of the model fit on the training set. The result obtained by weighted averaging is the target preservation temperature, which will be used for the formulation of subsequent temperature control adjustment strategies and the generation of temperature control adjustment instructions. In addition to the above-mentioned weighted integration method based on regression models, a stacked ensemble learning approach can also be used to introduce a meta-regression model to further learn and fuse multiple regression outputs. Through this embodiment, the system, by integrating the prediction capabilities of multiple models, avoids the prediction bias caused by overfitting or weak generalization ability of a single model, thereby more accurately determining the target preservation temperature suitable for the current environment and the characteristics of the items, and improving the intelligent decision-making level and environmental adaptability of temperature control.

[0071] Figure 2 This is a schematic diagram of the main flow of an intelligent temperature control method according to a preferred embodiment of the present invention. Figure 2As shown, this embodiment provides a method for generating and optimizing temperature control strategies based on multi-model fusion and intelligent regulation. It combines classification models, regression models, time-series prediction models, and reinforcement learning models to achieve dynamic and intelligent temperature adjustment during the storage and transportation of goods. Specifically, the system receives user-input data such as item type, preservation requirements, and transportation time, and performs preprocessing operations such as cleaning and normalization on the input data. The system calls the classification model and regression model in parallel. The classification model determines whether the current item type belongs to a modeled temperature control category. If so, it calls the corresponding basic temperature control strategy from the temperature control strategy library, such as suggesting a temperature control range of 0 to 4 degrees Celsius and temperature fluctuations controlled within ±1 degree Celsius; otherwise, it uses the default strategy as the control range. Simultaneously, the regression model predicts the target preservation temperature required for the item, such as 3.5 degrees Celsius, based on features such as item code, external temperature, and transportation time. The system fuses the temperature control range obtained from the classification model with the target preservation temperature predicted by the regression model to generate an initial temperature control adjustment command and controls the refrigeration or heating system to begin execution. During the temperature control strategy execution phase, the system monitors real-time temperature changes in the storage environment using sensors, comparing them with the set target preservation temperature to identify potential deviations. Furthermore, the system incorporates a deep learning module based on a long short-term memory neural network model, using 60 minutes of temperature time-series data for trend prediction, such as a potential 2-degree Celsius temperature increase. To address dynamic temperature fluctuations and unpredictable external disturbances, a reinforcement learning mechanism is further introduced. Using current temperature, external temperature, and energy consumption as input, the system learns how to adjust the power of the refrigeration or heating system. A reward mechanism evaluates the effectiveness of each adjustment action, including temperature stability, the reasonableness of fluctuation range, and energy consumption, thereby continuously optimizing the temperature control adjustment strategy. Finally, the system determines whether to adjust the current temperature control strategy based on the combined results of trend prediction and reinforcement learning. If the prediction indicates that the current temperature control strategy is insufficient to meet the temperature control target, a new temperature control adjustment command is generated; otherwise, the original strategy is maintained. This embodiment integrates multiple artificial intelligence algorithms and real-time data sensing mechanisms to achieve precise temperature control of goods during storage and transportation. This effectively improves the preservation effect, reduces temperature control energy consumption, and enhances the system's adaptability to sudden environmental changes. For example, it is suitable for cold chain transportation scenarios such as fresh produce and pharmaceuticals that require high temperature control accuracy.

[0072] Figure 3 This is a schematic diagram illustrating the main process of implementing a temperature control adjustment strategy based on an intelligent insulated box according to a preferred embodiment of the present invention. Figure 3As shown, the overall process realizes the entire closed-loop temperature control adjustment. First, the user inputs product-related information through the smart insulated box's human-machine interface, including product type (e.g., fresh food or pharmaceuticals), desired preservation temperature, and estimated transportation time. The AI ​​control unit uses this information as the basis for formulating the temperature control adjustment strategy. The temperature sensor inside the smart insulated box begins working, collecting real-time data on the internal temperature and the external ambient temperature, and continuously transmitting the temperature data to the AI ​​control unit. In the data analysis phase, the AI ​​control unit integrates the user input information with the temperature sensor monitoring data, calculates the difference between the current internal temperature and the user-set desired preservation temperature, assesses the potential impact of the external temperature on the internal temperature of the smart insulated box, and uses a machine learning model to predict future temperature change trends. This step enables the system to proactively determine whether temperature adjustment is needed, enhancing its adaptability to sudden environmental changes. If the analysis results show that the current temperature does not meet the preservation conditions, the decision-making phase begins. The AI ​​control unit determines whether to adjust the temperature based on the degree of deviation and the trend of change. For example, if the internal temperature is too high, the cooling mechanism is triggered; if the temperature is too low, the heating mechanism is activated. Meanwhile, the impact of drastic changes in external temperature on internal temperature control is also considered to ensure the rationality and foresight of the temperature adjustment strategy. During the temperature adjustment phase, the intelligent insulated box automatically responds to the temperature adjustment commands issued by the artificial intelligence control unit. By activating the internal refrigeration or heating system, it precisely adjusts the internal temperature of the box to quickly return it to the target preservation temperature range. This process requires no user intervention and is entirely controlled automatically by the system based on actual temperature fluctuations, demonstrating a high degree of intelligence and automation. Finally, the system continuously monitors and provides feedback on the temperature adjustment results. Temperature sensors dynamically collect the latest temperature data inside the box and feed the data back to the artificial intelligence control unit. If the target preservation temperature is not reached after temperature adjustment, the system will automatically enter the next round of adjustment decision-making and execution, thereby achieving closed-loop temperature control and ensuring that the internal temperature of the intelligent insulated box remains at its optimal state.

[0073] This invention, by introducing artificial intelligence technology and a real-time temperature sensing mechanism, constructs a complete closed-loop temperature control process. This not only significantly improves the preservation effect of products during transportation and reduces product losses caused by temperature control errors, but also simplifies the user operation process. Users do not need professional temperature control knowledge; they can enjoy intelligent and precise temperature control services simply by inputting simple information.

[0074] According to a second aspect of the present invention, an intelligent temperature control device is provided.

[0075] Figure 4 This is a schematic diagram of the main modules of an intelligent temperature control device according to an embodiment of the present invention. Figure 4As shown, an intelligent temperature control device 400 includes:

[0076] The acquisition module 401 is used to acquire item data and real-time temperature data of the space where the item is located;

[0077] The determination module 402 is used to determine the temperature control adjustment strategy based on the item data and real-time temperature data and generate the temperature control adjustment command;

[0078] The execution module 403 is used to execute a temperature control adjustment strategy to control the storage temperature of the items according to the temperature control adjustment command.

[0079] Optionally, the item information includes item type, set preservation temperature, and transportation time; the determination module 402 is also used for:

[0080] Temperature control adjustment strategies are determined based on item type using classification algorithms;

[0081] Based on the type of goods, the set preservation temperature, the transportation time, and real-time temperature data, a regression algorithm is used to predict the target preservation temperature of the goods.

[0082] Temperature control adjustment commands are generated based on the temperature control adjustment strategy and the target preservation temperature.

[0083] Optionally, the determining module 402 is also used for:

[0084] The item types are classified using a pre-trained classification model to obtain the classification results;

[0085] Based on the classification results, a pre-configured temperature control strategy library is matched, and the temperature control adjustment strategy is determined according to the matching results.

[0086] Optionally, the determining module 402 is also used for:

[0087] The item types are classified using multiple classification models, resulting in multiple classification results;

[0088] Multiple classification results are processed by voting integration strategy, and the classification result is determined based on the voting results.

[0089] Optionally, the intelligent temperature control device 400 also includes an optimization module, which is used for:

[0090] Monitor changes in storage temperature and determine measured temperature data and time-series temperature data based on the monitoring results;

[0091] Compare the measured temperature data with the target preservation temperature to generate comparison results;

[0092] Predict future environmental temperature trends using pre-trained deep learning models based on temperature time-series data;

[0093] Based on the comparison results and future ambient temperature trends, a pre-trained reinforcement learning model is used to optimize the temperature control adjustment strategy and generate new temperature control adjustment commands.

[0094] Optionally, the determining module 402 is also used for:

[0095] Multiple initial preservation temperatures are obtained by using multiple regression algorithms to predict the initial preservation temperature of the items.

[0096] The target preservation temperature is determined based on the weighted average of multiple initial preservation temperatures.

[0097] It should be noted that the specific implementation details of the intelligent temperature control device of the present invention have been described in detail in the above data export method, so the content will not be repeated here.

[0098] According to a third aspect of the present invention, an electronic device is provided, comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method provided in the first aspect of the present invention.

[0099] According to a fourth aspect of the present invention, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method provided in the first aspect of the present invention.

[0100] According to a fifth aspect of the present invention, a computer program product is provided, including a computer program that, when executed by a processor, implements the method provided in the first aspect of the present invention.

[0101] Figure 5 An exemplary system architecture 500 is shown that can be applied to the intelligent temperature control method or intelligent temperature control device of the present application embodiments.

[0102] like Figure 5 As shown, system architecture 500 may include terminal devices 501, 502, and 503, a network 504, and a server 505. Network 504 serves as the medium for providing communication links between terminal devices 501, 502, and 503 and server 505. Network 504 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0103] Users can use terminal devices 501, 502, and 503 to interact with server 505 via network 504 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 501, 502, and 503, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0104] Terminal devices 501, 502, and 503 can be various electronic devices with displays that support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0105] Server 505 can be a server that provides various services, such as a backend management server that supports shopping websites browsed by users using terminal devices 501, 502, and 503 (for example only). The backend management server can analyze and process data such as received smart temperature control requests, and feed back the processing results (such as smart temperature control results - for example only) to the terminal devices.

[0106] It should be noted that the intelligent temperature control method provided in this application embodiment is generally executed by server 505, and correspondingly, the intelligent temperature control device is generally set in server 505.

[0107] It should be understood that Figure 5 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0108] The following is for reference. Figure 6 It shows a schematic diagram of the structure of a computer system 600 suitable for implementing a terminal device according to the embodiments of this application. Figure 6 The terminal device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0109] like Figure 6 As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 602 or programs loaded from storage section 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of the computer system 600. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0110] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0111] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs the functions defined above in the system of this application.

[0112] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. Computer-readable storage media can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0113] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0114] The units described in the embodiments of this application can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor can be described as including an acquisition module, a determination module, and an execution module. The names of these modules do not necessarily limit the module itself; for example, the acquisition module can be described as "a module for acquiring item data and real-time temperature data of the space where the item is located."

[0115] In another aspect, this application also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs that, when executed by the device, cause the device to: acquire item data and real-time temperature data of the space where the item is located; determine a temperature control adjustment strategy based on the item data and the real-time temperature data and generate a temperature control adjustment instruction; and execute the temperature control adjustment strategy according to the temperature control adjustment instruction to control the storage temperature of the item.

[0116] The computer program product provided in this embodiment of the invention includes a computer program that, when executed by a processor, implements the intelligent temperature control method in this embodiment of the invention.

[0117] According to the technical solution of the present invention, the following advantages or beneficial effects are achieved: by acquiring item data and real-time temperature data; determining a temperature control adjustment strategy and generating a temperature control adjustment command based on the item data and the real-time temperature data of the space where the item is located; and executing the temperature control adjustment strategy to control the storage temperature of the item according to the temperature control adjustment command; thereby, the temperature control adjustment strategy can be automatically determined and the storage temperature of the item can be controlled according to the item information and the real-time temperature changes of the external environment, improving the preservation effect of the item during transportation and reducing the loss of the item due to improper temperature.

[0118] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

[0119] It should be noted that the acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

Claims

1. An intelligent temperature control method, characterized in that, include: Acquire item data and real-time temperature data of the space where the item is located; Based on the item data and the real-time temperature data, a temperature control adjustment strategy is determined and a temperature control adjustment command is generated. According to the temperature control adjustment command, the temperature control adjustment strategy is executed to control the storage temperature of the items.

2. The method according to claim 1, characterized in that, The item information includes item type, set preservation temperature, and transportation time; Based on the item data and the real-time temperature data, a temperature control adjustment strategy is determined and a temperature control adjustment command is generated, including: Based on the type of item, a classification algorithm is used to determine the temperature control adjustment strategy; Based on the type of item, the set preservation temperature, the transportation time, and the real-time temperature data, a regression algorithm is used to predict the target preservation temperature of the item. A temperature control adjustment command is generated based on the temperature control adjustment strategy and the target preservation temperature.

3. The method according to claim 2, characterized in that, Based on the item type, a classification algorithm is used to determine the temperature control adjustment strategy, including: The item types are classified using a pre-trained classification model to obtain classification results; Based on the classification results, a pre-configured temperature control strategy library is matched, and the temperature control adjustment strategy is determined according to the matching results.

4. The method according to claim 3, characterized in that, The item types are classified using a pre-trained classification model to obtain classification results, including: The item types are classified using multiple classification models to obtain multiple classification results; The multiple classification results are voted on according to the voting integration strategy, and the classification result is determined based on the voting results.

5. The method according to claim 2, characterized in that, After executing the temperature control adjustment strategy to control the storage temperature of the item according to the temperature control adjustment instruction, the method further includes: Monitor the changes in the storage temperature, and determine the measured temperature data and temperature time-series data based on the monitoring results; Compare the measured temperature data with the target preservation temperature to generate a comparison result; Based on the aforementioned temperature time-series data, a pre-trained deep learning model is used to predict future environmental temperature trends. Based on the comparison results and the future ambient temperature trend, the temperature control adjustment strategy is optimized using a pre-trained reinforcement learning model, and new temperature control adjustment instructions are generated.

6. The method according to claim 2, characterized in that, Predicting the target preservation temperature of items using regression algorithms includes: Multiple initial preservation temperatures are obtained by using multiple regression algorithms to predict the initial preservation temperature of the items. The target preservation temperature is determined based on the weighted average of the multiple initial preservation temperatures.

7. An intelligent temperature control device, characterized in that, include: The acquisition module is used to acquire item data and real-time temperature data of the space where the item is located; The determination module is used to determine the temperature control adjustment strategy and generate a temperature control adjustment command based on the item data and the real-time temperature data; An execution module is used to execute the temperature control adjustment strategy to control the storage temperature of the item according to the temperature control adjustment command.

8. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

9. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

Images