Microclimate detection method, system, device, and medium

Abstract

The application discloses the household electrical appliances field and provides a microclimate detection method, a system, equipment and a medium.The method comprises the following steps: acquiring outdoor environment data detected by each outdoor machine node of a topology network within a target time; wherein, the topology network is constructed based on a to-be-controlled outdoor machine node and other outdoor machine nodes in a target area of the to-be-controlled outdoor machine node; inputting the outdoor environment data detected by each outdoor machine node of the topology network within the target time into a microclimate prediction model to obtain a target area climate prediction result output by the microclimate prediction model.The application solves the defect that a control strategy is not accurate enough due to excessive dependence on traditional weather forecast data, effectively predicts a microclimate by sensing an external environment through multiple outdoor machine nodes in a target area, improves the accuracy of the prediction, and further provides support for real-time decision-making of air conditioning control.

Description

Technical Field

[0001] This invention relates to the field of household appliance technology, and in particular to a microclimate detection method, system, device and medium. Background Technology

[0002] Since its invention in the early 20th century, air conditioning has evolved from simple cooling to dual-purpose cooling and heating, and then to intelligent and energy-saving systems. With technological advancements, modern air conditioning systems have begun to incorporate weather forecast data for intelligent control.

[0003] Currently, intelligent control based on weather forecast data primarily involves obtaining weather forecast data via the internet, reading data from indoor and outdoor temperature sensors, and adjusting the air conditioner's operating mode according to the current indoor temperature, set temperature, and weather forecast data. For example, when weather changes are predicted (such as impending rain or a sudden drop in temperature), the cooling / heating capacity can be adjusted in advance.

[0004] However, the aforementioned intelligent control methods rely too heavily on traditional weather forecast data and cannot reflect local microclimates, resulting in inaccurate control strategies that affect user comfort and energy efficiency. Furthermore, professional meteorological equipment is expensive, and its low deployment density results in insufficient spatial resolution of data, making it impossible to capture microclimate differences in complex urban environments. Summary of the Invention

[0005] This invention provides a microclimate detection method, system, device, and medium to address the shortcomings of existing technologies that rely excessively on traditional weather forecast data, resulting in inaccurate control strategies. By sensing the external environment through multiple outdoor unit nodes within the target area, effective microclimate prediction can be performed, improving the accuracy of prediction and thus providing support for real-time decision-making in air conditioning control.

[0006] This invention provides a microclimate detection method, comprising: acquiring outdoor environmental data detected by each outdoor node of a topology network within a target time period; wherein, the topology network is constructed based on the outdoor node to be controlled and other outdoor nodes within the target area of ​​the outdoor node to be controlled; inputting the outdoor environmental data detected by each outdoor node of the topology network within the target time period into a microclimate prediction model to obtain the climate prediction result of the target area output by the microclimate prediction model; wherein, the microclimate prediction model is trained based on historical outdoor environmental data collected by corresponding nodes of the historical topology network and their corresponding microclimate labels, and the microclimate prediction model is used to extract features from all input outdoor environmental data and perform microclimate prediction based on the extracted features to obtain the climate prediction result of the target area.

[0007] It should be noted that the topology network is used to achieve comprehensive monitoring of multiple outdoor unit nodes within the target area, ensuring the breadth and representativeness of the data. By acquiring outdoor environmental data of each outdoor unit node within the target time, the external environment can be fully perceived. Furthermore, the trained microclimate prediction model is used to identify key environmental factors within the target area, thereby enabling effective microclimate prediction, improving the accuracy of the prediction, and ultimately providing support for real-time decision-making in air conditioning control.

[0008] According to the present invention, a microclimate detection method and a microclimate prediction model are provided, comprising: a feature extraction layer, which extracts features from the outdoor environmental data detected by each outdoor unit node within a target time to obtain the temporal meteorological features of the corresponding outdoor unit node, and extracts features from the outdoor environmental data detected by each outdoor unit node in the topology network at the same time within the target time to obtain the spatial meteorological features at the corresponding time; a feature fusion layer, which fuses the temporal meteorological features of each outdoor unit node with the spatial meteorological features at each time within the target time to obtain fused features; and a microclimate prediction layer, which performs microclimate prediction based on the fused features to obtain the climate prediction results for the target area.

[0009] It should be noted that by identifying the temporal characteristics of meteorological data at the same external node within the target time period and the spatial distribution characteristics of meteorological data at various external nodes at the same moment, it is possible to understand the meteorological patterns and trends behind the data. By fusing these characteristics, a more comprehensive understanding of meteorological changes can be achieved, facilitating climate prediction from multiple dimensions and improving the accuracy of climate prediction. This results in more accurate weather change warnings. Furthermore, feature fusion can adjust the weights and combinations of features according to different application scenarios and needs, allowing for customization based on the characteristics of specific regions and improving the relevance and practicality of predictions.

[0010] According to a microclimate detection method provided by the present invention, before inputting the outdoor environmental data detected by each outdoor node of the topology network within a target time into the microclimate prediction model, the method includes: acquiring historical outdoor environmental data collected by corresponding nodes of the historical topology network and their corresponding microclimate tags; wherein, the historical topology nodes are constructed based on historical outdoor nodes to be tested and other outdoor nodes within the target area; using the historical outdoor environmental data collected by corresponding nodes of the historical topology network as input data for training, and using the microclimate tags as labels for training, the microclimate prediction model to be trained is trained offline to obtain a microclimate prediction model for microclimate prediction.

[0011] It should be noted that by acquiring historical outdoor environmental data and their corresponding microclimate labels collected from the corresponding nodes of the historical topology network, the microclimate prediction model is trained. This enables the microclimate prediction model to learn the long-term patterns and trends of microclimate change, thereby improving the accuracy of prediction. Furthermore, through the historical topology network, the model learns the spatial relationships between nodes of different topology networks, which is beneficial for the model to understand the spatial distribution characteristics of microclimate.

[0012] According to a microclimate detection method provided by the present invention, after obtaining the climate prediction result of the target area output by the microclimate prediction model, the method includes: acquiring the outdoor environmental data detected by each outdoor node of the topology network at the time corresponding to the climate prediction result of the target area, and obtaining the actual climate data; comparing the climate prediction result of the target area with the actual climate data; if the deviation between the climate prediction result of the target area and the actual climate data is greater than a preset climate deviation threshold, then using the outdoor environmental data detected by each outdoor node of the topology network at the corresponding time and the actual climate data, the microclimate prediction model is updated online.

[0013] It should be noted that by comparing the predicted results with the actual data, deviations in the model predictions can be detected in a timely manner, thereby updating the microclimate prediction model online and correcting prediction errors promptly. This reduces prediction deviations caused by outdated or inaccurate models, improves the reliability of model prediction results, and enables the model to dynamically adjust to quickly adapt to environmental changes, thus improving the model's prediction accuracy.

[0014] According to a microclimate detection method provided by the present invention, after obtaining the climate prediction result of the target area output by the microclimate prediction model, the method includes: sending the climate prediction result of the target area to the user terminal; receiving the control signal returned by the user based on the climate prediction result of the target area; controlling the operation of the air conditioner according to the control signal; or, obtaining the air conditioner operation prediction strategy using a strategy prediction model based on the climate prediction result of the target area and the previously acquired indoor temperature and user setting preferences; wherein the strategy prediction model is trained based on historical microclimate data, historical indoor temperature and user setting preferences corresponding to the historical microclimate data, and operation strategy labels corresponding to the historical microclimate data; and controlling the operation of the air conditioner according to the air conditioner operation prediction strategy.

[0015] It's important to note that by sending climate predictions for the target area to users, they can gain advance knowledge of future weather conditions, enabling them to make more informed preparations and decisions. This strengthens user trust and reliance on the smart home system, increases user satisfaction, and provides a user feedback mechanism. Based on user feedback signals, the system can control the air conditioning to operate according to user needs, achieving a personalized comfort experience. Alternatively, by considering indoor temperature, user preferences, and external microclimate predictions, a strategy prediction model can be used to develop more precise air conditioning operation strategies. This provides personalized air conditioning solutions based on different user preferences, enhancing the user experience. Controlling the air conditioning based on the predicted operation strategy achieves proactive temperature control, ensuring the indoor environment always meets user comfort requirements and improving the quality of living and working environments. Furthermore, training the model with historical data allows it to learn from past operating strategies, continuously optimizing prediction strategies to adapt to changing environments and user needs, improving the model's accuracy and generalization ability.

[0016] According to a microclimate detection method provided by the present invention, after controlling the operation of the air conditioner according to the air conditioner operation prediction strategy, the method includes: obtaining the actual operation strategy of the air conditioner; comparing whether the air conditioner operation prediction strategy and the actual operation strategy of the air conditioner are consistent; if they are inconsistent, obtaining the outdoor environmental data detected by each outdoor unit node of the topology network at the time corresponding to the actual operation strategy of the air conditioner to obtain the actual microclimate data; and using the actual microclimate data, the actual operation strategy of the air conditioner, and the indoor temperature and user setting preferences at the time corresponding to the actual operation strategy of the air conditioner to optimize the strategy prediction model online.

[0017] It should be noted that by acquiring the actual operating strategy of the air conditioner, a direct means of verifying the accuracy of the strategy prediction model is provided. By comparing the predicted air conditioner operating strategy with the actual air conditioner operating strategy, the prediction accuracy of the model can be evaluated. Furthermore, in the event of inconsistency, the strategy prediction model can be optimized online in a timely manner using actual microclimate data, the actual air conditioner operating strategy, and the indoor temperature and user settings preferences at the corresponding time of the actual air conditioner operating strategy. This allows the model to adapt to changes in the environment and user behavior, maintain the long-term effectiveness of the model, and enable the strategy prediction model to respond quickly to environmental changes, thereby improving the dynamic adaptability of the system.

[0018] According to a microclimate detection method provided by the present invention, the outdoor environmental data includes at least one of temperature, humidity, air pressure, wind speed, wind direction, light intensity, rainfall detection results, and air quality; the outdoor environmental data is obtained by analyzing the data collected using corresponding sensors, preprocessing the analyzed data, and integrating the preprocessed data.

[0019] It should be noted that by fully exploring the data collection potential of air conditioner outdoor units as widely distributed environmental contact points, no additional space or significant increase in hardware costs are required. Furthermore, it can comprehensively perceive the external environment and, through the analysis, preprocessing, and integration of the perceived data, facilitate subsequent microclimate prediction based on the perceived external environment.

[0020] This invention also provides a microclimate detection system, comprising: a data acquisition module for acquiring outdoor environmental data detected by each outdoor node of a topology network within a target time period; wherein the topology network is constructed based on the outdoor node to be controlled and other outdoor nodes within the target area of ​​the outdoor node to be controlled; and a microclimate prediction module for inputting the outdoor environmental data detected by each outdoor node of the topology network within the target time period into a microclimate prediction model to obtain the climate prediction result of the target area output by the microclimate prediction model; wherein the microclimate prediction model is trained based on historical outdoor environmental data collected by corresponding nodes of the historical topology network and their corresponding microclimate labels, and the microclimate prediction model is used to extract features from all input outdoor environmental data and perform microclimate prediction based on the extracted features to obtain the climate prediction result of the target area.

[0021] It should be noted that the data acquisition module utilizes a topology network to achieve comprehensive monitoring of multiple outdoor unit nodes within the target area, ensuring the breadth and representativeness of the data. By acquiring outdoor environmental data of each outdoor unit node within the target time period, the external environment can be fully perceived. Furthermore, the microclimate prediction module uses a trained microclimate prediction model to identify key environmental factors within the target area, thereby enabling effective microclimate prediction, improving the accuracy of the prediction, and ultimately providing support for real-time decision-making in air conditioning control.

[0022] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described microclimate detection methods.

[0023] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described microclimate detection methods. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1This is one of the flowcharts of the microclimate detection method provided by the present invention; Figure 2 This is the second flowchart of the microclimate detection method provided by the present invention; Figure 3 This is a schematic diagram of the microclimate detection device provided by the present invention; Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0027] Figure 1 A flowchart illustrating a microclimate detection method according to the present invention is provided. The method includes: S11, acquire outdoor environmental data detected by each outdoor node of the topology network within the target time period; wherein, the topology network is constructed based on the outdoor node to be controlled and other outdoor nodes within the target area of ​​the outdoor node to be controlled; S12, input the outdoor environmental data detected by each outdoor node of the topology network within the target time into the microclimate prediction model to obtain the climate prediction result of the target area output by the microclimate prediction model; wherein, the microclimate prediction model is trained based on the historical outdoor environmental data collected by the corresponding nodes of the historical topology network and their corresponding microclimate labels. The microclimate prediction model is used to extract features from all input outdoor environmental data and perform microclimate prediction based on the extracted features to obtain the climate prediction result of the target area.

[0028] It should be noted that the step number "S1N" in this manual does not represent the order of the microclimate detection methods. The following details will explain further. Figure 2 The microclimate detection method of the present invention is described.

[0029] Step S11: Obtain outdoor environmental data detected by each outdoor node of the topology network within the target time period; wherein, the topology network is constructed based on the outdoor node to be controlled and other outdoor nodes within the target area of ​​the outdoor node to be controlled.

[0030] It should be added that outdoor environmental data includes at least one of temperature, humidity, air pressure, wind speed, wind direction, light intensity, rainfall detection results, and air quality; outdoor environmental data is obtained by analyzing data collected using corresponding sensors, preprocessing the analyzed data, and integrating the preprocessed data.

[0031] It should be noted that by fully exploring the data collection potential of air conditioner outdoor units as widely distributed environmental contact points, no additional space or significant increase in hardware costs are required. Furthermore, it can comprehensively perceive the external environment, and by analyzing, preprocessing, and integrating the perceived data, it can facilitate subsequent microclimate prediction based on the perceived external environment.

[0032] In other words, before acquiring the outdoor environment data detected by each outdoor node of the topology network within the target time period, the process includes: using the outdoor environment data detected by each outdoor node of the topology network within the target time period; parsing the outdoor environment data detected by each outdoor node to obtain parsed data; preprocessing the parsed data to obtain preprocessed data; and integrating the preprocessed data corresponding to the outdoor nodes to package and upload all the data collected by the outdoor nodes to the cloud.

[0033] It should be noted that if the microclimate prediction is a prediction of the current outdoor environment, the target time can be the previous historical time of the current moment. If the microclimate prediction is a prediction of the outdoor environment of the future moment, the target time can be a time period composed of the previous historical time and the current moment.

[0034] It should be added that, for the outdoor environmental data to be collected, the outdoor unit node needs to be configured with corresponding sensors. Sensor types include at least one of the following: temperature sensor, humidity sensor, barometric pressure sensor, wind speed sensor, wind direction sensor, light intensity sensor, rainfall detection sensor, and PM2.5 sensor. The specific configuration can be based on the outdoor environmental data to be collected; no further limitations are made here. Additionally, preprocessing methods can include removing erroneous data, removing outliers, noise reduction, and filtering, etc., which can be configured according to actual needs; no further limitations are made here.

[0035] In addition, when uploading all the data collected by the external node to the cloud, the corresponding data can be uploaded to the cloud using an appropriate communication method based on the preset priority of the data. The preset priority can be set based on the timeliness and / or importance of the data, and no further restrictions are made here.

[0036] In one optional embodiment, before uploading all the data collected by the external node to the cloud, the process includes: detecting network latency, detecting whether the communication link is disconnected, and if the network latency is greater than a preset threshold, or the number of disconnections of the communication link within a target time is greater than a preset threshold, then determining whether the communication is not smooth; based on the communication not smooth, caching the data locally and uploading the data after detecting smooth communication, or uploading the data in combination with a preset delay transmission strategy; wherein, the preset delay transmission strategy is used to limit the delay transmission duration corresponding to different network performance.

[0037] In one optional embodiment, before detecting outdoor environmental data using each outdoor node in the topology network within the target time period, the process includes: monitoring the outdoor node to be controlled and other outdoor nodes within the target area; and constructing the topology network based on the monitored outdoor node locations and the communication quality of each outdoor node. It should be noted that by forming a distributed network based on the outdoor nodes within the target area, the spatial resolution and timeliness of climate data are improved, information isolation between outdoor nodes is avoided, a synergistic effect is achieved, and the overall level of intelligence is enhanced.

[0038] Furthermore, a topology network is constructed based on the monitored locations of the external nodes and the communication quality of each external node, including: laying out the network according to the locations of the external nodes and the previously selected topology type; and selecting the connection paths between nodes in the laid-out network according to the communication quality of each external node, and constructing the topology network.

[0039] In one alternative embodiment, after constructing the topology network, the method further includes: continuously monitoring the network status and dynamically adjusting the network structure in a timely manner.

[0040] Step S12: Input the outdoor environmental data detected by each outdoor node of the topology network within the target time into the microclimate prediction model to obtain the climate prediction result of the target area output by the microclimate prediction model; wherein, the microclimate prediction model is trained based on the historical outdoor environmental data collected by the corresponding nodes of the historical topology network and their corresponding microclimate labels. The microclimate prediction model is used to extract features from all the input outdoor environmental data and perform microclimate prediction based on the extracted features to obtain the climate prediction result of the target area.

[0041] In this embodiment, the microclimate prediction model includes: a feature extraction layer, which extracts features from the outdoor environmental data detected by each outdoor node within a target time to obtain the temporal meteorological features of the corresponding outdoor node, and extracts features from the outdoor environmental data detected by each outdoor node in the topology network at the same moment within the target time to obtain the spatial meteorological features at the corresponding moment; a feature fusion layer, which fuses the temporal meteorological features of each outdoor node with the spatial meteorological features at each moment within the target time to obtain fused features; and a microclimate prediction layer, which performs microclimate prediction based on the fused features to obtain the climate prediction results for the target area.

[0042] It should be noted that by identifying the temporal characteristics of meteorological data at the same external node within the target time period and the spatial distribution characteristics of meteorological data at various external nodes at the same moment, it is possible to understand the meteorological patterns and trends behind the data. By fusing these characteristics, a more comprehensive understanding of meteorological changes can be achieved, facilitating climate prediction from multiple dimensions and improving the accuracy of climate prediction. This results in more accurate weather change warnings. Furthermore, feature fusion can adjust the weights and combinations of features according to different application scenarios and needs, allowing for customization based on the characteristics of specific regions and improving the relevance and practicality of predictions.

[0043] In addition, feature fusion can be achieved by splicing or cross-referencing time-series meteorological features and spatial meteorological features, or by using a multi-input branch neural network, where one branch processes time-series meteorological features and another branch processes spatial meteorological features, and then these features are fused together at a certain layer of the network. The specific configuration can be configured according to actual design requirements, and no further limitations are made here.

[0044] Accordingly, the outdoor environmental data detected by each external node of the topology network within the target time period is input into the microclimate prediction model to obtain the climate prediction results of the target area output by the microclimate prediction model. This includes: inputting the outdoor environmental data detected by each external node of the topology network within the target time period into the feature extraction layer to extract features from the outdoor environmental data detected by each external node within the target time period to obtain the temporal meteorological features of the corresponding external node; and extracting features from the outdoor environmental data detected by each external node of the topology network at the same time within the target time period to obtain the spatial meteorological features at the corresponding time. The temporal meteorological features of each external node and the spatial meteorological features at each time within the target time period are input into the feature fusion layer for feature fusion to obtain the fused features. The fused features are input into the microclimate prediction layer for microclimate prediction to obtain the climate prediction results of the target area.

[0045] In one optional embodiment, before inputting the outdoor environmental data detected by each outdoor node of the topology network within the target time into the microclimate prediction model, the method includes: acquiring historical outdoor environmental data collected by the corresponding nodes of the historical topology network and their corresponding microclimate labels; wherein, the historical topology nodes are constructed based on the historical outdoor nodes to be tested and other outdoor nodes within the target area; using the historical outdoor environmental data collected by the corresponding nodes of the historical topology network as input data for training, and using the microclimate labels as labels for training, the microclimate prediction model to be trained is trained offline to obtain a microclimate prediction model for microclimate prediction.

[0046] It should be noted that by acquiring historical outdoor environmental data and their corresponding microclimate labels collected from the corresponding nodes of the historical topology network, the microclimate prediction model is trained. This enables the microclimate prediction model to learn the long-term patterns and trends of microclimate change, thereby improving the accuracy of prediction. Furthermore, through the historical topology network, the model learns the spatial relationships between nodes of different topology networks, which is beneficial for the model to understand the spatial distribution characteristics of microclimate.

[0047] In addition, the microclimate prediction model to be trained can be an existing network built into the training device. This existing network usually includes a network structure, or it can be other networks specified by the user. The specific configuration can be configured according to actual design requirements, and no further restrictions are made here.

[0048] Specifically, the microclimate prediction model to be trained is trained offline, including: inputting historical outdoor environmental data collected from the corresponding nodes of the historical topology network into the microclimate prediction model to be trained, and obtaining the microclimate prediction results output by the microclimate prediction model to be trained; constructing a loss function based on the microclimate prediction results and the corresponding microclimate labels, and ending the training based on the convergence of the loss function.

[0049] In one optional embodiment, after obtaining the climate prediction results of the target area output by the microclimate prediction model, the process includes: acquiring the outdoor environmental data detected by each external node of the topology network at the time corresponding to the climate prediction results of the target area, and obtaining the actual climate data; comparing the climate prediction results of the target area with the actual climate data, and if the deviation between the climate prediction results of the target area and the actual climate data is greater than a preset climate deviation threshold, then using the outdoor environmental data detected by each external node of the topology network at the corresponding time and the actual climate data to update the microclimate prediction model online.

[0050] It should be noted that by comparing the predicted results with the actual data, deviations in the model predictions can be detected in a timely manner, thereby updating the microclimate prediction model online and correcting prediction errors promptly. This reduces prediction deviations caused by outdated or inaccurate models, improves the reliability of model prediction results, and enables the model to dynamically adjust to quickly adapt to environmental changes, thus improving the model's prediction accuracy.

[0051] Specifically, the outdoor environmental data detected by each outdoor node of the topology network at the corresponding time of the climate prediction results for the target area can be obtained by referring to the method for obtaining outdoor environmental data described above, and will not be repeated here. In addition, the actual climate data obtained includes: calculating the average value of the data detected by each outdoor node based on the temperature, humidity, and air pressure, as the reference temperature, reference humidity, and reference air pressure for the target area; determining the reference wind direction and reference wind speed for the target area based on the wind direction and wind speed detected by each outdoor node; determining the air quality status of the target area using spatial interpolation based on the air quality detected by each outdoor node; determining the rainfall intensity distribution and light intensity distribution for the corresponding target area based on the rainfall detection results and light intensity distribution results detected by each outdoor node; and obtaining the actual climate data for the target area based on the reference temperature, reference humidity, reference air pressure, reference wind direction, reference wind speed, air quality status, rainfall intensity distribution, and light intensity distribution.

[0052] Furthermore, based on the wind direction and speed detected by each outdoor unit node, the reference wind direction and speed within the target area are determined. This includes: performing vector transformation on the wind direction and speed detected by each outdoor unit node to obtain the corresponding vector for that node; summing the vectors of all outdoor unit nodes to obtain a composite vector; using a preset function to transform the composite vector into a wind direction angle to obtain the reference wind direction; and obtaining the reference wind speed based on the magnitude of the composite vector and the number of outdoor unit nodes. It should be noted that the preset function can be selected according to actual design requirements. For example, the `math.atan2` function in the high-level programming language Python can be chosen. The `math.atan2` function is used to calculate the arctangent value based on given x and y coordinates, i.e., it returns the angle traversed by rotating counterclockwise from the positive x-axis to the point (x, y). No further limitations are imposed here.

[0053] Furthermore, when determining the rainfall intensity distribution of a target area based on the rainfall detection results from each outdoor unit node, spatial interpolation can also be used to obtain the corresponding rainfall intensity distribution. Similarly, when determining the light intensity distribution of a target area based on the light intensity detected by each outdoor unit node, spatial interpolation can also be used to obtain the corresponding light intensity distribution.

[0054] Furthermore, when updating the microclimate prediction model online using outdoor environmental data and actual climate data detected by each outdoor node of the topology network at the corresponding time, the corresponding algorithm can be selected for online updating based on the actual online update requirements and design goals, such as the online gradient descent algorithm, to avoid retraining with all the data.

[0055] In one possible implementation, after obtaining the climate prediction results for the target area output by the microclimate prediction model, the process includes: sending the climate prediction results for the target area to the user terminal; receiving a control signal returned by the user based on the climate prediction results for the target area; and controlling the operation of the air conditioner according to the control signal.

[0056] It should be noted that by sending the climate forecast results of the target area to the user's terminal, users can understand the future climate conditions in advance, thereby making more appropriate preparations and decisions, enhancing users' trust and reliance on the smart home system, improving user satisfaction, and providing a user feedback mechanism to control the operation of the air conditioner based on the control signals provided by the user, so that the air conditioner operates according to the user's needs and achieves a personalized comfort experience.

[0057] In another possible implementation, after obtaining the target area climate prediction results output by the microclimate prediction model, the method further includes: based on the target area climate prediction results and the previously acquired indoor temperature and user setting preferences, using a strategy prediction model to obtain an air conditioning operation prediction strategy; wherein, the strategy prediction model is trained based on historical microclimate data, the historical indoor temperature and user setting preferences corresponding to the historical microclimate data, and the operation strategy labels corresponding to the historical microclimate data; and the air conditioning operation is controlled according to the air conditioning operation prediction strategy.

[0058] It should be noted that by considering indoor temperature, user preferences, and external microclimate predictions, a more precise air conditioning operation strategy is formulated using a strategy prediction model. This allows for personalized air conditioning operation solutions based on different user preferences, enhancing the user experience. Furthermore, the air conditioning operation is controlled according to the predicted strategy to achieve proactive temperature control, ensuring that the indoor environment consistently meets user comfort requirements and improving the quality of living and working environments. Additionally, historical data is used to train the model, enabling it to learn from past operation strategies and continuously optimize its prediction strategies. This allows it to adapt to constantly changing environments and user needs, improving the model's accuracy and generalization ability.

[0059] Furthermore, after controlling the air conditioner operation according to the air conditioner operation prediction strategy, the process includes: obtaining the actual air conditioner operation strategy; comparing whether the air conditioner operation prediction strategy and the actual air conditioner operation strategy are consistent; if they are inconsistent, obtaining the outdoor environment data detected by each outdoor unit node of the topology network at the time corresponding to the actual air conditioner operation strategy to obtain the actual microclimate data; and using the actual microclimate data, the actual air conditioner operation strategy, and the indoor temperature and user setting preferences at the time corresponding to the actual air conditioner operation strategy to optimize the strategy prediction model online.

[0060] It should be noted that by acquiring the actual operating strategy of the air conditioner, a direct means of verifying the accuracy of the strategy prediction model is provided. By comparing the predicted air conditioner operating strategy with the actual air conditioner operating strategy, the prediction accuracy of the model can be evaluated. Furthermore, in the event of inconsistency, the strategy prediction model can be optimized online in a timely manner using actual microclimate data, the actual air conditioner operating strategy, and the indoor temperature and user settings preferences at the corresponding time of the actual air conditioner operating strategy. This allows the model to adapt to changes in the environment and user behavior, maintain the long-term effectiveness of the model, and enable the strategy prediction model to respond quickly to environmental changes, thereby improving the dynamic adaptability of the system.

[0061] Furthermore, the online optimization of the strategy prediction model can refer to the online update method of the microclimate prediction model mentioned above, and will not be repeated here. In addition, during the online optimization of the strategy prediction model, higher weights can be assigned to recently updated user settings based on the timeliness of user settings. The specific weights can be configured based on prior experience or experiments, and will not be further limited here.

[0062] In one alternative embodiment, reference Figure 2 After sending the climate prediction results for the target area to the user terminal, the process includes: determining whether to receive the control signal returned by the user based on the climate prediction results for the target area within a preset time; if received, controlling the air conditioner operation according to the control signal; otherwise, obtaining the air conditioner operation prediction strategy based on the climate prediction results for the target area, the previously acquired indoor temperature, and the user's setting preferences using a strategy prediction model, in order to control the air conditioner operation.

[0063] In an optional embodiment, after sending the climate prediction results for the target area to the user terminal, the method further includes: sending a control permission confirmation request to the user terminal and receiving a confirmation result returned by the user terminal; if the confirmation result is user control, then controlling the air conditioner operation according to the user's control signal; if the confirmation result is abandoning user control, then using a strategy prediction model based on the climate prediction results for the target area and the previously acquired indoor temperature and user setting preferences, obtaining an air conditioner operation prediction strategy to control the air conditioner operation.

[0064] In summary, this embodiment of the invention uses a topology network to achieve comprehensive monitoring of multiple outdoor unit nodes within a target area, ensuring the breadth and representativeness of the data. By acquiring outdoor environmental data of each outdoor unit node within a target time period, it comprehensively perceives the external environment and uses a trained microclimate prediction model to identify key environmental factors within the target area, thereby enabling effective microclimate prediction, improving the accuracy of the prediction, and ultimately providing support for real-time decision-making in air conditioning control.

[0065] The microclimate detection system provided by the present invention is described below. The microclimate detection system described below can be referred to in correspondence with the microclimate detection method described above.

[0066] Figure 3 A schematic diagram of a microclimate detection system is shown. The system includes: The data acquisition module 31 acquires outdoor environmental data detected by each outdoor node of the topology network within a target time period; wherein, the topology network is constructed based on the outdoor node to be controlled and other outdoor nodes within the target area of ​​the outdoor node to be controlled; The microclimate prediction module 32 inputs the outdoor environmental data detected by each outdoor node of the topology network within the target time into the microclimate prediction model to obtain the climate prediction results of the target area output by the microclimate prediction model. The microclimate prediction model is trained based on the historical outdoor environmental data collected by the corresponding nodes of the historical topology network and their corresponding microclimate labels. The microclimate prediction model is used to extract features from all the input outdoor environmental data and perform microclimate prediction based on the extracted features to obtain the climate prediction results of the target area.

[0067] It should be added that outdoor environmental data includes at least one of temperature, humidity, air pressure, wind speed, wind direction, light intensity, rainfall detection results, and air quality; outdoor environmental data is obtained by analyzing data collected using corresponding sensors, preprocessing the analyzed data, and integrating the preprocessed data.

[0068] In other words, the system also includes: a data acquisition module, which acquires outdoor environmental data detected by each outdoor node of the topology network within the target time period before acquiring such data; and a data processing module, which parses the outdoor environmental data detected by each outdoor node to obtain parsed data, preprocesses the parsed data to obtain preprocessed data, and integrates the preprocessed data corresponding to the outdoor nodes to package and upload all the data collected by the outdoor nodes to the cloud.

[0069] In addition, the data processing module is also used to: when uploading all the data collected by the external node to the cloud in a package, upload the corresponding data to the cloud using an appropriate communication method based on the preset priority of the data. The preset priority can be set based on the timeliness and / or importance of the data, and no further limitations are made here.

[0070] In an optional embodiment, the system further includes: a detection module, which detects network latency and whether the communication link is disconnected before packaging and uploading all data collected by the external node to the cloud; if the network latency is greater than a preset threshold, or the number of disconnections of the communication link within a target time is greater than a preset threshold, then it is determined whether the communication is not smooth; based on the communication not smooth, the data processing module caches the data locally and uploads the data after detecting smooth communication, or uploads the data in combination with a preset delay transmission strategy; wherein, the preset delay transmission strategy is used to limit the delay transmission duration corresponding to different network performance.

[0071] In an optional embodiment, the system further includes: a monitoring module, which, before detecting outdoor environmental data using each outdoor node in the topology network within a target time period, monitors the outdoor node to be controlled and other outdoor nodes within its target area based on the outdoor node to be controlled; and a network construction module, which constructs a topology network based on the monitored outdoor node locations and the communication quality of each outdoor node.

[0072] Furthermore, the network construction module includes: a layout unit, which lays out the network according to the location of the external nodes and the previously selected topology type; and a connection unit, which selects the connection paths between nodes in the laid-out network according to the communication quality of each external node, and constructs the topology network.

[0073] In an optional embodiment, the monitoring module is further configured to: continuously monitor the network status after the topology network is constructed, and dynamically adjust the network structure in a timely manner.

[0074] In this embodiment, the microclimate prediction model includes: a feature extraction layer, which extracts features from the outdoor environmental data detected by each outdoor node within a target time to obtain the temporal meteorological features of the corresponding outdoor node, and extracts features from the outdoor environmental data detected by each outdoor node in the topology network at the same moment within the target time to obtain the spatial meteorological features at the corresponding moment; a feature fusion layer, which fuses the temporal meteorological features of each outdoor node with the spatial meteorological features at each moment within the target time to obtain fused features; and a microclimate prediction layer, which performs microclimate prediction based on the fused features to obtain the climate prediction results for the target area.

[0075] Accordingly, the microclimate prediction module 32 includes: a feature extraction unit, which inputs the outdoor environmental data detected by each outdoor node of the topology network within the target time into the feature extraction layer, so as to extract features from the outdoor environmental data detected by each outdoor node within the target time to obtain the temporal meteorological features of the corresponding outdoor node, and extracts features from the outdoor environmental data detected by each outdoor node of the topology network at the same time within the target time to obtain the spatial meteorological features at the corresponding time; a feature fusion unit, which inputs the temporal meteorological features of each outdoor node and the spatial meteorological features at each time within the target time into the feature fusion layer for feature fusion to obtain fused features; and a microclimate prediction unit, which inputs the fused features into the microclimate prediction layer for microclimate prediction to obtain the climate prediction results for the target area.

[0076] In an optional embodiment, the system further includes: a training data acquisition module, which acquires historical outdoor environmental data collected by corresponding nodes of the historical topology network and their corresponding microclimate labels before inputting the outdoor environmental data detected by each outdoor node of the topology network within the target time into the microclimate prediction model; wherein, the historical topology nodes are constructed based on the historical outdoor nodes to be tested and other outdoor nodes within the target area; and a training module, which uses the historical outdoor environmental data collected by corresponding nodes of the historical topology network as input data for training and the microclimate labels as labels for training, and performs offline training on the microclimate prediction model to be trained to obtain a microclimate prediction model for microclimate prediction.

[0077] Specifically, the training module is used to: input historical outdoor environmental data collected from the corresponding nodes of the historical topology network into the microclimate prediction model to be trained, and obtain the microclimate prediction results output by the microclimate prediction model to be trained; construct a loss function based on the microclimate prediction results and the corresponding microclimate labels, and end the training based on the convergence of the loss function.

[0078] In an optional embodiment, the system further includes: a climate acquisition module, which, after obtaining the climate prediction result of the target area output by the microclimate prediction model, acquires the outdoor environmental data detected by each external node of the topology network at the corresponding time of the climate prediction result of the target area to obtain the actual climate data; and a climate comparison module, which compares the climate prediction result of the target area with the actual climate data, and if the deviation between the climate prediction result of the target area and the actual climate data is greater than a preset climate deviation threshold, then an update module updates the microclimate prediction model online using the outdoor environmental data detected by each external node of the topology network at the corresponding time and the actual climate data.

[0079] Specifically, the climate acquisition module is used to: calculate the average value of the data from each outdoor unit node based on the temperature, humidity, and air pressure detected by each outdoor unit node, and use this average value as the reference temperature, reference humidity, and reference air pressure for the target area; determine the reference wind direction and reference wind speed for the target area based on the wind direction and wind speed detected by each outdoor unit node; determine the air quality status of the target area using spatial interpolation based on the air quality detected by each outdoor unit node; determine the rainfall intensity distribution and light intensity distribution for the target area based on the rainfall detection results and light intensity distribution results detected by each outdoor unit node; and obtain the actual climate data for the target area based on the reference temperature, reference humidity, reference air pressure, reference wind direction, reference wind speed, air quality status, rainfall intensity distribution, and light intensity distribution.

[0080] Furthermore, the climate acquisition module is also used to: perform vector transformation on the wind direction and wind speed detected by each outdoor unit node to obtain the vector of the corresponding outdoor unit node; add the vectors of each outdoor unit node to obtain a comprehensive vector; perform wind direction angle transformation on the comprehensive vector using a preset function to obtain a reference wind direction; and obtain a reference wind speed based on the magnitude of the comprehensive vector and the number of outdoor unit nodes.

[0081] In addition, the climate acquisition module is also used to: when determining the rainfall intensity distribution of a corresponding target area based on the rainfall detection results from each outdoor unit node, it can also combine spatial interpolation to obtain the corresponding rainfall intensity distribution result. Similarly, the climate acquisition module is also used to: when determining the light intensity distribution of a corresponding target area based on the light intensity detected by each outdoor unit node, it can also combine spatial interpolation to obtain the corresponding light intensity distribution result.

[0082] In one possible implementation, the system further includes: a result feedback module, which sends the target area climate prediction results to the user terminal after obtaining the target area climate prediction results output by the microclimate prediction model; a signal receiving module, which receives the control signals returned by the user based on the target area climate prediction results; and a control module, which controls the operation of the air conditioner according to the control signals.

[0083] In another possible implementation, the system further includes: a strategy prediction module, which, after obtaining the climate prediction results for the target area output by the microclimate prediction model, uses the strategy prediction model to obtain an air conditioning operation prediction strategy based on the climate prediction results for the target area and the previously acquired indoor temperature and user setting preferences; wherein, the strategy prediction model is trained based on historical microclimate data, historical indoor temperature and user setting preferences corresponding to the historical microclimate data, and operation strategy labels corresponding to the historical microclimate data; and a control module, which controls the air conditioning operation according to the air conditioning operation prediction strategy.

[0084] Furthermore, the system also includes: an actual strategy acquisition module, which acquires the actual air conditioning operation strategy after controlling the air conditioning operation according to the air conditioning operation prediction strategy; a strategy comparison module, which compares whether the air conditioning operation prediction strategy and the actual air conditioning operation strategy are consistent. If they are inconsistent, the strategy optimization module acquires the outdoor environmental data detected by each outdoor unit node of the topology network at the time corresponding to the actual air conditioning operation strategy, obtains the actual microclimate data, and uses the actual microclimate data, the actual air conditioning operation strategy, and the indoor temperature and user setting preferences at the time corresponding to the actual air conditioning operation strategy to optimize the strategy prediction model online.

[0085] In an optional embodiment, the system further includes: a control judgment module, which, after sending the climate prediction results of the target area to the user terminal, determines whether to receive the control signal returned by the user based on the climate prediction results of the target area within a preset time. If received, the system controls the air conditioner to operate according to the control signal; otherwise, the system uses a strategy prediction model to obtain an air conditioner operation prediction strategy based on the climate prediction results of the target area, the previously acquired indoor temperature, and the user's setting preferences, so as to control the operation of the air conditioner.

[0086] In an optional embodiment, the control judgment module is further configured to: after sending the climate prediction results of the target area to the user terminal, send a control permission confirmation request to the user terminal and receive the confirmation result returned by the user terminal; if the confirmation result is user control, control the air conditioner operation according to the user's control signal; if the confirmation result is abandoning user control, obtain the air conditioner operation prediction strategy based on the climate prediction results of the target area, the previously acquired indoor temperature, and the user's setting preferences, using a strategy prediction model, so as to control the air conditioner operation.

[0087] In summary, this embodiment of the invention utilizes a topological network through a data acquisition module to achieve comprehensive monitoring of multiple outdoor unit nodes within a target area, ensuring the breadth and representativeness of the data. By acquiring outdoor environmental data of each outdoor unit node within a target time period, it comprehensively perceives the external environment. Furthermore, through a microclimate prediction module, it uses a trained microclimate prediction model to identify key environmental factors within the target area, thereby enabling effective microclimate prediction, improving prediction accuracy, and ultimately providing support for real-time decision-making in air conditioning control.

[0088] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4As shown, the electronic device may include a processor 410, a communication interface 420, a memory 430, and a communication bus 440. The processor 410, communication interface 420, and memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a microclimate detection method. This method includes: acquiring outdoor environmental data detected by each outdoor node of the topology network within a target time period; wherein the topology network is constructed based on the outdoor node to be controlled and other outdoor nodes within the target area of ​​the outdoor node to be controlled; inputting the outdoor environmental data detected by each outdoor node of the topology network within the target time period into a microclimate prediction model to obtain the target area climate prediction result output by the microclimate prediction model; wherein the microclimate prediction model is trained based on historical outdoor environmental data collected by corresponding nodes of the historical topology network and their corresponding microclimate labels. The microclimate prediction model is used to extract features from all input outdoor environmental data and perform microclimate prediction based on the extracted features to obtain the target area climate prediction result.

[0089] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0090] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute the microclimate detection method provided by the above methods, the method comprising: acquiring outdoor environmental data detected by each external node of the topology network within a target time; wherein the topology network is constructed based on the external node to be controlled and other external nodes within the target area of ​​the external node to be controlled; inputting the outdoor environmental data detected by each external node of the topology network within the target time into a microclimate prediction model, and obtaining the target area climate prediction result output by the microclimate prediction model; wherein the microclimate prediction model is trained based on historical outdoor environmental data collected by corresponding nodes of the historical topology network and their corresponding microclimate labels, the microclimate prediction model is used to extract features from all input outdoor environmental data, and perform microclimate prediction based on the extracted features, and obtain the target area climate prediction result.

[0091] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, is implemented to perform the aforementioned microclimate detection methods. The method includes: acquiring outdoor environmental data detected by each external node of a topology network within a target time period; wherein the topology network is constructed prior to the external node to be controlled and other external nodes within the target area of ​​the external node to be controlled; inputting the outdoor environmental data detected by each external node of the topology network within the target time period into a microclimate prediction model to obtain a target area climate prediction result output by the microclimate prediction model; wherein the microclimate prediction model is trained based on historical outdoor environmental data collected from corresponding nodes of the historical topology network and their corresponding microclimate labels, and the microclimate prediction model is used to extract features from all input outdoor environmental data and perform microclimate prediction based on the extracted features to obtain a target area climate prediction result.

[0092] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0093] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A microclimate detection method, characterized in that, include: Acquire outdoor environmental data detected by each outdoor node in the topology network within a target time period; wherein, the topology network is constructed based on the outdoor node to be controlled and other outdoor nodes within the target area of ​​the outdoor node to be controlled; The outdoor environmental data detected by each external node of the topology network within the target time period is input into the microclimate prediction model to obtain the climate prediction result of the target area output by the microclimate prediction model. The microclimate prediction model is trained based on the historical outdoor environmental data collected by the corresponding nodes of the historical topology network and their corresponding microclimate labels. The microclimate prediction model is used to extract features from all input outdoor environmental data and perform microclimate prediction based on the extracted features to obtain the climate prediction result of the target area.

2. The microclimate detection method according to claim 1, characterized in that, The microclimate prediction model includes: The feature extraction layer extracts features from the outdoor environmental data detected by each outdoor node within a target time period to obtain the temporal meteorological features of the corresponding outdoor node. It also extracts features from the outdoor environmental data detected by each outdoor node in the topology network at the same time within the target time period to obtain the spatial meteorological features at the corresponding time. The feature fusion layer fuses the temporal meteorological features of each of the external nodes with the spatial meteorological features at each time point within the target time period to obtain fused features; The microclimate prediction layer performs microclimate prediction based on the fusion characteristics to obtain the climate prediction results for the target area.

3. The microclimate detection method according to claim 1, characterized in that, Before inputting the outdoor environmental data detected by each outdoor node of the topology network within the target time period into the microclimate prediction model, the following steps are included: Acquire historical outdoor environmental data and their corresponding microclimate labels collected by nodes in the historical topology network; wherein, the historical topology nodes are constructed based on historical outdoor unit nodes to be tested and other outdoor unit nodes within the target area; The historical outdoor environmental data collected by the corresponding nodes of the historical topology network is used as the input data for training, and the microclimate labels are used as the labels for training. The microclimate prediction model to be trained is trained offline to obtain a microclimate prediction model for microclimate prediction.

4. The microclimate detection method according to claim 1, characterized in that, After obtaining the climate prediction results for the target area output by the microclimate prediction model, the following steps are included: Obtain the outdoor environmental data detected by each outdoor node of the topology network at the time corresponding to the climate prediction results of the target area, and obtain the actual climate data; If the deviation between the climate prediction results and the actual climate data of the target area is greater than a preset climate deviation threshold, the microclimate prediction model is updated online using the outdoor environmental data detected by each outdoor node of the topology network at the corresponding time and the actual climate data.

5. The microclimate detection method according to claim 1, characterized in that, After obtaining the climate prediction results for the target area output by the microclimate prediction model, the following steps are included: Send the climate prediction results for the target area to the user terminal; Receive the control signal returned by the user based on the climate prediction results for the target area; Control the air conditioner to operate according to the control signal; or, Based on the climate prediction results of the target area and the previously acquired indoor temperature and user setting preferences, an air conditioning operation prediction strategy is obtained using a strategy prediction model; wherein, the strategy prediction model is trained based on historical microclimate data, the historical indoor temperature and user setting preferences corresponding to the historical microclimate data, and the operation strategy labels corresponding to the historical microclimate data. The air conditioning operation is controlled according to the air conditioning operation prediction strategy.

6. The microclimate detection method according to claim 5, characterized in that, After controlling the air conditioner operation according to the air conditioner operation prediction strategy, the following steps are included: Obtain the actual operating strategy of the air conditioner; The air conditioning operation prediction strategy and the actual air conditioning operation strategy are compared to see if they are consistent. If they are inconsistent, the outdoor environment data detected by each outdoor unit node of the topology network at the time corresponding to the actual air conditioning operation strategy are obtained to obtain the actual microclimate data. The actual microclimate data, the actual air conditioning operation strategy, and the indoor temperature and user settings preference at the time corresponding to the actual air conditioning operation strategy are used to optimize the strategy prediction model online.

7. The microclimate detection method according to any one of claims 1-6, characterized in that, The outdoor environmental data includes at least one of temperature, humidity, air pressure, wind speed, wind direction, light intensity, rainfall detection results, and air quality; The outdoor environmental data is obtained by parsing the data collected by the corresponding sensors, preprocessing the parsed data, and integrating the preprocessed data.

8. A microclimate detection system, characterized in that, include: The data acquisition module acquires outdoor environmental data detected by each outdoor node in the topology network within a target time period; wherein, the topology network is constructed based on the outdoor node to be controlled and other outdoor nodes within the target area of ​​the outdoor node to be controlled; The microclimate prediction module inputs the outdoor environmental data detected by each outdoor node of the topology network within the target time period into the microclimate prediction model to obtain the climate prediction result of the target area output by the microclimate prediction model. The microclimate prediction model is trained based on historical outdoor environmental data collected by corresponding nodes of the historical topology network and their corresponding microclimate labels. The microclimate prediction model is used to extract features from all input outdoor environmental data and perform microclimate prediction based on the extracted features to obtain the climate prediction result of the target area.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the microclimate detection method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the microclimate detection method as described in any one of claims 1 to 7.

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