Direct expansion constant temperature and humidity machine air conditioning control method and system based on cloud computing

Abstract

This application provides a cloud computing-based direct expansion constant temperature and humidity machine air conditioning control method and system, which relates to the field of air conditioning control technology, including: collecting temperature and humidity data, density data, and external environment data of the target area based on the cloud computing platform to generate updated data; constructing an air conditioning model based on the updated records and air conditioning records; performing multiple regional transfer learning on the air conditioning model to obtain a transfer model; performing air conditioning prediction on the transfer model through the updated data, performing air dynamic control based on the prediction results, and generating a control strategy. This application can solve the technical problem in the prior art that the independent training of air conditioning models in each region and the lack of an effective experience sharing mechanism lead to excessively long model training time, which further affects the overall operating efficiency. It can also achieve the technical goal of cross-regional model sharing and rapid adaptation to regional environments, thereby achieving the technical effect of improving air conditioning efficiency and reducing energy consumption.

Description

Technical Field

[0001] The present application relates to the field of air conditioning control technology, and in particular to a direct expansion constant temperature and humidity machine air conditioning control method and system based on cloud computing. Background Art

[0002] With the acceleration of urbanization and rising demands for a comfortable living environment, air conditioning systems are becoming increasingly common in various buildings. Direct expansion thermo-hygrometers, due to their high efficiency and excellent temperature and humidity control performance, are widely used in public places and production environments such as shopping malls, office buildings, subways, and cleanrooms. However, existing air conditioning control methods still have technical shortcomings, limiting their further development in terms of intelligence and efficiency.

[0003] Currently, traditional air conditioning methods are mostly based on customized control strategies. However, existing direct expansion constant temperature and humidity control air conditioning technology relies primarily on independent control systems for each area when dealing with complex and dynamic environmental conditions, especially in cleanrooms and other environments with extremely high temperature and humidity control requirements. This requires training the air conditioning model for each area from scratch, which is not only time-consuming but also consumes a lot of manpower and computing resources. In addition, the environmental characteristics and customer flow patterns of each area are often similar, but the existing system cannot effectively share the experience and model parameters derived from these similarities, limiting the flexibility and efficiency of the technology. For example, parameters such as temperature, humidity, and density in different cleanrooms may show similar trends during peak and off-peak production periods. If each workshop conducts independent model training, it may take days or even weeks to adapt to the new environmental conditions, which not only affects the effectiveness of real-time control but also increases the overall energy consumption of the system.

[0004] In summary, the existing technology has technical problems such as the independent training of air conditioning models in each area and the lack of an effective experience sharing mechanism, which leads to excessive model training time and excessive resource consumption, further affecting the system's ability to respond to dynamic environmental changes and overall operating efficiency. Summary of the Invention

[0005] The purpose of this application is to provide a cloud computing-based direct expansion constant temperature and humidity machine air conditioning control method and system to solve the technical problems in the existing technology that the air conditioning models of each area are trained independently and there is a lack of an effective experience sharing mechanism, resulting in long model training time and excessive resource consumption, which further affects the system's response ability to dynamic environmental changes and overall operating efficiency.

[0006] In view of the above problems, the present application provides a direct expansion constant temperature and humidity machine air conditioning control method and system based on cloud computing.

[0007] In the first aspect, the present application provides a direct expansion constant temperature and humidity machine air conditioning control method based on cloud computing, which is implemented through a direct expansion constant temperature and humidity machine air conditioning control system based on cloud computing, including: collecting temperature and humidity data, density data and external environment data of the target area based on the cloud computing platform to generate update data; constructing an air conditioning model based on update records and air conditioning records; performing multiple region transfer learning on the air conditioning model to obtain a transfer model; performing air conditioning prediction on the transfer model through the updated data, performing air dynamic control according to the prediction results, and generating a control strategy.

[0008] In the second aspect, the present application also provides a cloud computing-based direct expansion constant temperature and humidity machine air conditioning control system, which is used to execute the cloud computing-based direct expansion constant temperature and humidity machine air conditioning control method as described in the first aspect, including: an update data generation module, the update data generation module is used to collect temperature and humidity data, density data and external environment data of the target area based on the cloud computing platform to generate update data; an air conditioning model construction module, the air conditioning model construction module is used to construct an air conditioning model according to update records and air conditioning records; a migration model acquisition module, the migration model acquisition module is used to perform multiple area migration learning on the air conditioning model to obtain a migration model; a control strategy generation module, the control strategy generation module is used to perform air conditioning prediction on the migration model through the update data, perform air dynamic control according to the prediction results, and generate a control strategy.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] By collecting temperature and humidity data, density data and external environment data of the target area based on the cloud computing platform, update data is generated; an air conditioning model is constructed based on the update records and air conditioning records; the air conditioning model is subjected to multi-region transfer learning to obtain a migration model; air conditioning prediction is performed on the migration model using the updated data, and air dynamic control is performed based on the prediction results to generate a control strategy. In other words, by achieving the technical goals of cross-regional model sharing and rapid adaptation to the regional environment, the technical effects of improving air conditioning efficiency and reducing energy consumption are achieved.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the description, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0013] Figure 1 This is a flow chart of a direct expansion constant temperature and humidity machine air conditioning control method based on cloud computing in this application;

[0014] Figure 2 This is a schematic diagram of the structure of the direct expansion constant temperature and humidity machine air conditioning control system based on cloud computing in this application.

[0015] Description of reference numerals:

[0016] Update data generation module 11, air conditioning model construction module 12, migration model acquisition module 13, control strategy generation module 14. DETAILED DESCRIPTION

[0017] This application provides a cloud-based direct expansion constant temperature and humidity air conditioning control method and system. This solves the existing technical issues of independent training of regional air conditioning models and the lack of an effective experience sharing mechanism, which results in lengthy model training times and excessive resource consumption, further impacting the system's ability to respond to dynamic environmental changes and overall operational efficiency. This method achieves the technical goals of cross-regional model sharing and rapid adaptation to regional environments, thereby improving air conditioning efficiency and reducing energy consumption.

[0018] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0019] For example, see the attached Figure 1 The present application provides a cloud computing-based direct expansion constant temperature and humidity machine air conditioning control method, which is applied to a cloud computing-based direct expansion constant temperature and humidity machine air conditioning control system, and specifically includes the following steps:

[0020] Step 1: Collect temperature and humidity data, density data, and external environment data of the target area based on the cloud computing platform to generate updated data.

[0021] Specifically, real-time temperature and humidity data are acquired from the target area. Temperature data indicates the degree of air temperature, while humidity data indicates the moisture content in the air. Furthermore, density data indicates the volume of people or objects within a unit area. External environmental data, including parameters such as outdoor temperature, humidity, air pressure, and wind speed, helps calculate differences between indoor and outdoor environments. This data is continuously transmitted to a control center via a cloud computing platform for centralized processing and analysis, generating real-time updated data that informs subsequent temperature and humidity control strategies.

[0022] Step 2: Construct an air conditioning model based on the update records and air conditioning records.

[0023] Specifically, an air conditioning model is constructed based on data from update records and air conditioning records. Update records are data records of changes in various environmental parameters over time. Air conditioning records represent records of specific adjustment operations performed in response to environmental changes. By integrating update records with corresponding air conditioning records, the control requirements under different environmental conditions are analyzed, resulting in an air conditioning model. This air conditioning model enables predictive control strategies to consistently maintain temperature and humidity within a comfortable range, adapting to varying densities and environmental changes.

[0024] Step 3: Perform multiple region transfer learning on the air conditioning model to obtain a transfer model.

[0025] Specifically, by performing transfer learning on the constructed air conditioning model across multiple regions, control strategies can be applied and optimized in different areas. Transfer learning refers to the process of applying existing model knowledge from one region to another. For example, if a model performs well in areas with high temperature and high humidity, transfer learning can be used to apply this model to a new area with similar temperature and humidity. This migration process allows for the rapid creation of a model adapted to the new environment, avoiding the time-consuming process of retraining from scratch.

[0026] Step 4: Perform air conditioning prediction on the migration model using the updated data, perform air dynamic control according to the prediction results, and generate a control strategy.

[0027] Specifically, the migration model is used to make air conditioning predictions through updated data, and the air is dynamically controlled based on the prediction results. That is, the settings of the direct expansion constant temperature and humidity machine are adjusted according to the real-time temperature and humidity requirements to adapt to environmental changes and keep the temperature and humidity within the appropriate range, thereby ensuring air quality.

[0028] The cloud computing-based direct expansion constant temperature and humidity machine air conditioning control method is applied to the cloud computing-based direct expansion constant temperature and humidity machine air conditioning control system, which can achieve the technical goals of cross-regional model sharing and rapid adaptation to regional environments, and achieve the technical effects of improving air conditioning efficiency and reducing energy consumption.

[0029] Furthermore, this application also includes:

[0030] The temperature and humidity data, density data and external environment data are used as update features; a rolling window is configured with a fixed time length, and the rolling window is updated according to the fixed time length for the update features to generate the update data.

[0031] Specifically, temperature and humidity data, density data, and external environment data are used as update features. For example, temperature can reflect the heating and cooling needs of a space, humidity shows the moisture content of the air, density indicates the heat load caused by factors such as human traffic, and the external environment provides data such as outdoor temperature and humidity. These update features constitute the basic data source for a comprehensive assessment of the current environment.

[0032] By configuring a fixed duration for the rolling window, you divide the data collection period into a set duration, allowing you to collect data for updated features within this timeframe. For example, setting a rolling window of 24 hours means that data for the selected updated features will be collected and analyzed every 24 hours, ensuring that you focus on the latest environmental data. This fixed duration configuration improves model adaptability because it only retains the most recent environmental data, preventing outdated data from influencing decision-making.

[0033] Update features are updated in a rolling window at a fixed interval, periodically updating features such as temperature, humidity, density, and external environmental data based on a set time period. The resulting updated data is a summary of the latest environmental conditions within that fixed time period. For example, in a 24-hour rolling window, information such as the average temperature, humidity range, and maximum density over the past 24 hours is calculated, and the old data is replaced in the next time period to ensure the timeliness and reliability of the analysis results.

[0034] By using temperature, humidity, density, and external environmental data as update features and setting a fixed duration for a rolling window, these features are regularly updated to generate the latest update data, ensuring that the model adapts to current environmental dynamics in real time and improving the accuracy of prediction and adjustment.

[0035] Furthermore, this application also includes:

[0036] Based on the cloud computing platform, the temperature and humidity data characteristics, density data characteristics and external environment data characteristics of the target area are collected to obtain target characteristics; similar area groups are set for multiple areas according to the target characteristics to obtain similar area groups; the air conditioning model is shared among the similar area groups to obtain the migration model.

[0037] Specifically, the cloud computing platform collects multiple characteristics of the target area, monitoring temperature and humidity, pedestrian and object density, and external environmental data in real time. For example, temperature and humidity data might include a subway station temperature of 27 degrees Celsius and humidity of 60%; density data might include a passenger density of 3 people per square meter; and external environmental data might include an outside temperature of 30 degrees Celsius and humidity of 55%. These characteristic data are centrally managed on the cloud computing platform, enabling continuous updates of target area characteristics and providing a reliable data foundation for subsequent analysis.

[0038] Multiple areas are grouped according to target characteristics, categorizing areas with similar environmental conditions into the same group. Similar groups are determined based on similarities in parameters such as temperature, humidity, density, and external environmental parameters. For example, multiple subway stations with temperatures close to 25-27 degrees Celsius, humidity around 60%, and similar peak passenger flow density can be grouped together. After classification, similar areas can share a common air conditioning model.

[0039] Then, the air conditioning model is shared among the similar area groups, that is, the optimized air conditioning model is applied to multiple similar areas in the group to obtain a migration model.

[0040] Sharing models saves retraining time and allows new areas to adapt to environmental requirements more quickly, while ensuring that temperature and humidity are within ideal ranges.

[0041] Furthermore, this application also includes:

[0042] Taking the target feature as the standard, the feature similarity of the multiple regions is measured to obtain similar features; updating is performed according to the rolling windows of the multiple regions, and the updated results are fed back to the similar features to obtain abnormal feedback results; based on the abnormal feedback results, the similar features are updated with group conditions, and groups are set according to the updated group conditions to obtain the similar region group.

[0043] Specifically, the target feature is used as the standard to measure the feature similarity of multiple regions and obtain similar features, which are used to subsequently identify multiple regions with similar environmental conditions.

[0044] Then, similar features are updated using rolling windows across multiple regions, using data from a fixed time period. This means that data is collected within a fixed time period using a rolling window. A rolling window focuses only on data collected within the time period within the rolling window, for example, collecting environmental data changes from the past 48 hours to the past 24 hours, and then collecting environmental data changes from the past 24 hours. This allows for continuous acquisition of real-time updated data and feedback to similar features, promptly capturing subtle changes in environmental conditions and more accurately reflecting the dynamic state of each region.

[0045] Based on the abnormal feedback results, the group conditions of similar features are updated, and by analyzing the feedback, it can be decided whether to adjust the group of the area. For example, if a certain area no longer meets the temperature and humidity standards of the current group due to abnormal feedback, it will be removed from the current group or added to a new group by resetting the group conditions. When an abnormal value different from the normal value appears in the dynamic state, it indicates that there is a special case or environmental change, and the corresponding similar features are eliminated. If the features of the area still belong to similar features later, the group division is performed again. For example, if the temperature drops sharply in the environmental data of an area, the area may be deactivated, and the abnormal feedback result is recorded.

[0046] Dynamic updates of the groups ensure that each group's area is always maintained under similar environmental conditions. The resulting similar area groups have the ability to dynamically adapt to environmental changes, enabling more efficient and accurate air conditioning management of multiple areas.

[0047] Furthermore, this application also includes:

[0048] The density data is evaluated for switching strength to generate a switching strength; a switching strategy is generated based on the switching strength and the prediction result; the operating mode is switched based on the switching strategy to generate a switching result; the migration model is short-term adjusted in response to the switching result and fed back to the migration model to generate a control strategy.

[0049] Specifically, density data is used to evaluate switching intensity, assessing the frequency of air conditioning based on changes in the density of people or objects within an area. Density data represents the flow of people or the density of objects per unit area. For example, an increase in density from 3 to 5 people per square meter may affect the air conditioning load. The switching intensity is generated based on changes in density data to determine the intensity or magnitude of adjustments. A larger increase in density results in a higher switching intensity.

[0050] Switching force data is combined with forecasts of future environmental conditions to determine the appropriate operating mode to switch to in different scenarios. The forecast includes air conditioning strategies based on real-time environmental factors such as temperature and humidity. Based on these factors, the switching strategy can determine the most appropriate control method, such as increasing fan speed or reducing fresh air intake, to adapt to the impending environmental changes.

[0051] The switching strategy is used to switch operating modes. The DX constant temperature and humidity unit's operating mode is adjusted accordingly, such as switching from partial fresh air to full fresh air mode, or from mechanical ventilation to mixed mode. The switching results are provided as feedback after implementation. For example, a 2°C decrease in temperature and 5% decrease in humidity in full fresh air mode indicates the switching strategy has been effective. This mode switching ensures greater flexibility in responding to dynamic changes in density and environmental conditions.

[0052] The current migration model is adjusted based on actual feedback from the switching results. For example, if temperature and humidity control do not achieve the expected results after switching to the new mode, the migration model for the corresponding area will be adjusted, such as temporarily increasing cooling power or reducing valve opening. This feedback is then fed back to the migration model, helping it gradually adapt to the specific environmental conditions of the area. Ultimately, a control strategy is generated to automatically respond to similar situations in the future.

[0053] By evaluating the switching intensity of density data and then combining the prediction results to generate a switching strategy, the operating mode is switched through the execution of the strategy and the switching results are obtained. Based on the feedback of the results, the migration model is adjusted in a short time to optimize the control. The final control strategy enables the system to have a higher response speed and accuracy, ensuring a stable and comfortable air environment under density changes and environmental fluctuations.

[0054] Furthermore, this application also includes:

[0055] An internal environment enthalpy value is evaluated using the temperature and humidity data and the density data as internal environment evaluation features to generate an internal environment enthalpy value; an external environment enthalpy value is evaluated using the external environment data as external environment evaluation features to generate an external environment enthalpy value; an enthalpy value difference between the internal environment enthalpy value and the external environment enthalpy value is obtained, and a preliminary switching strategy is generated based on the enthalpy value difference; the preliminary switching strategy is used as a switching guide and the switching strategy is used as a switching execution, a matching rate between the switching guide and the switching execution is evaluated, a switching result accuracy rate is generated, and the migration model is adjusted according to the switching result accuracy rate.

[0056] Specifically, the system uses indoor temperature, humidity, and crowd density or object density as evaluation parameters to calculate the enthalpy of the air, or the total heat content of the air. For example, if the temperature is 28 degrees Celsius, the humidity is 65%, and the density is 3 people per square meter, the internal environment enthalpy is calculated, which represents the heat content of the indoor air.

[0057] The system uses outdoor environmental data as input and calculates the external environment's enthalpy using parameters such as temperature, humidity, and wind speed. For example, if the outdoor temperature is 32 degrees Celsius, the humidity is 50%, and the wind speed is 4 meters per second, the external environment's enthalpy is estimated. The external environment's enthalpy reflects the heat content of the outside air and provides a basis for further adjusting indoor and outdoor air exchange.

[0058] Compare the difference in enthalpy between the indoor and outdoor environments. For example, if the indoor enthalpy is 85 kJ / kg air and the outdoor enthalpy is 70 kJ / kg air, the enthalpy difference is 15 kJ / kg air. Based on this enthalpy difference, the demand for indoor and outdoor air conditioning can be determined, leading to a preliminary switching strategy, such as whether to introduce fresh air or increase cooling under these conditions.

[0059] The preliminary switching strategy serves as a guideline for switching, meaning it serves as a reference for actual switching operations. Simultaneously, the switching strategy predicted by the air conditioning model is used as the actual switching execution, and the results are monitored. The matching rate between the switching guidance and the switching execution is then evaluated, specifically examining the degree of consistency between the assumed switching strategy and the actual switching operation. A high degree of consistency indicates that the switching strategy is more suitable for the current air conditioning scenario, while a low degree of consistency indicates that the switching strategy is unsuitable for the scenario.

[0060] By evaluating the matching rate between switching guidance and switching execution, the applicability of the strategy is evaluated, and the control strategy is further optimized.

[0061] Furthermore, this application also includes:

[0062] Adaptively optimize the migration model whose switching result accuracy does not meet the error threshold to generate an optimized migration model; evaluate the adaptability of the optimized migration model according to the prediction accuracy to generate an adaptability coefficient; and update the model structure of the optimized migration model whose adaptability coefficient is lower than the preset adaptability coefficient.

[0063] Specifically, adaptive optimization is performed on migration models whose switching accuracy meets the error threshold. This means that models whose switching results do not meet expectations and whose errors are outside the acceptable range are further adjusted and optimized, initiating the adaptive optimization process for the migration model. This adaptive optimization process involves adjusting model parameters to improve the model's adaptability to current environmental conditions or using a different model architecture to generate another air conditioning model as the optimized migration model. This ensures that the model can better respond to environmental changes in subsequent data processing and prediction.

[0064] The adaptability of the optimized migration model is evaluated based on its prediction accuracy. After optimization, the model's predictions are comprehensively evaluated to determine its performance under different environmental conditions. The adaptability coefficient, a measure of the optimized migration model's adaptability, is generated by comparing the model's prediction accuracy in actual applications with its expected accuracy.

[0065] The model structure of the optimized migration model whose adaptability coefficient is lower than the preset adaptability coefficient is updated. This means that if the adaptability coefficient of the optimized migration model is lower than a predetermined standard, the structure of the optimized migration model needs to be updated, including introducing new data features, redesigning the model architecture, or increasing the complexity of the model, in order to improve the overall performance and adaptability of the model.

[0066] By adaptively optimizing the migration model whose switching result accuracy does not meet the standard, an optimized migration model is generated. The adaptability of the optimized model is evaluated based on the prediction accuracy, and the adaptability coefficient is obtained. If the coefficient is lower than the preset standard, the model structure is further updated to ensure that the model maintains efficient prediction capabilities in the face of environmental changes, and ultimately achieves a more accurate air conditioning effect.

[0067] In summary, the cloud computing-based direct expansion constant temperature and humidity machine air conditioning control method provided in this application has the following technical effects:

[0068] By collecting temperature and humidity data, density data and external environment data of the target area based on the cloud computing platform, update data is generated; an air conditioning model is constructed based on the update records and air conditioning records; the air conditioning model is subjected to multi-region transfer learning to obtain a migration model; air conditioning prediction is performed on the migration model using the updated data, and air dynamic control is performed based on the prediction results to generate a control strategy. In other words, by achieving the technical goals of cross-regional model sharing and rapid adaptation to the regional environment, the technical effects of improving air conditioning efficiency and reducing energy consumption are achieved.

[0069] In the second embodiment, based on the cloud computing-based direct expansion constant temperature and humidity machine air conditioning control method and the same invention concept, this application also provides a cloud computing-based direct expansion constant temperature and humidity machine air conditioning control system, please refer to the attached Figure 2 ,include:

[0070] An update data generation module 11 is used to collect temperature and humidity data, density data and external environment data of the target area based on the cloud computing platform to generate update data; an air conditioning model construction module 12 is used to construct an air conditioning model based on update records and air conditioning records; a migration model acquisition module 13 is used to perform multi-region migration learning on the air conditioning model to obtain a migration model; a control strategy generation module 14 is used to perform air conditioning prediction on the migration model through the update data, perform air dynamic control according to the prediction results, and generate a control strategy.

[0071] Furthermore, the update data generation module 11 in the cloud computing-based direct expansion constant temperature and humidity machine air conditioning control system is further used to:

[0072] The temperature and humidity data, density data and external environment data are used as update features; a rolling window is configured with a fixed time length, and the rolling window is updated according to the fixed time length for the update features to generate the update data.

[0073] Furthermore, the migration model obtaining module 13 in the cloud computing-based direct expansion constant temperature and humidity machine air conditioning control system is also used to:

[0074] Based on the cloud computing platform, the temperature and humidity data characteristics, density data characteristics and external environment data characteristics of the target area are collected to obtain target characteristics; similar area groups are set for multiple areas according to the target characteristics to obtain similar area groups; the air conditioning model is shared among the similar area groups to obtain the migration model.

[0075] Furthermore, the migration model obtaining module 13 in the cloud computing-based direct expansion constant temperature and humidity machine air conditioning control system is also used to:

[0076] Taking the target feature as the standard, the feature similarity of the multiple regions is measured to obtain similar features; updating is performed according to the rolling windows of the multiple regions, and the updated results are fed back to the similar features to obtain abnormal feedback results; based on the abnormal feedback results, the similar features are updated with group conditions, and groups are set according to the updated group conditions to obtain the similar region group.

[0077] Furthermore, the control strategy generation module 14 in the cloud computing-based direct expansion constant temperature and humidity machine air conditioning control system is also used to:

[0078] The density data is evaluated for switching strength to generate a switching strength; a switching strategy is generated based on the switching strength and the prediction result; the operating mode is switched based on the switching strategy to generate a switching result; the migration model is short-term adjusted in response to the switching result and fed back to the migration model to generate a control strategy.

[0079] Furthermore, the control strategy generation module 14 in the cloud computing-based direct expansion constant temperature and humidity machine air conditioning control system is also used to:

[0080] An internal environment enthalpy value is evaluated using the temperature and humidity data and the density data as internal environment evaluation features to generate an internal environment enthalpy value; an external environment enthalpy value is evaluated using the external environment data as external environment evaluation features to generate an external environment enthalpy value; an enthalpy value difference between the internal environment enthalpy value and the external environment enthalpy value is obtained, and a preliminary switching strategy is generated based on the enthalpy value difference; the preliminary switching strategy is used as a switching guide and the switching strategy is used as a switching execution, a matching rate between the switching guide and the switching execution is evaluated, a switching result accuracy rate is generated, and the migration model is adjusted according to the switching result accuracy rate.

[0081] Furthermore, the control strategy generation module 14 in the cloud computing-based direct expansion constant temperature and humidity machine air conditioning control system is also used to:

[0082] Adaptively optimize the migration model whose switching result accuracy does not meet the error threshold to generate an optimized migration model; evaluate the adaptability of the optimized migration model according to the prediction accuracy to generate an adaptability coefficient; and update the model structure of the optimized migration model whose adaptability coefficient is lower than the preset adaptability coefficient.

[0083] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The cloud computing-based direct expansion constant temperature and humidity machine air conditioning control method and specific examples in the aforementioned embodiment 1 are also applicable to the cloud computing-based direct expansion constant temperature and humidity machine air conditioning control system in this embodiment. Through the aforementioned detailed description of the cloud computing-based direct expansion constant temperature and humidity machine air conditioning control method, those skilled in the art can clearly understand the cloud computing-based direct expansion constant temperature and humidity machine air conditioning control system in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0084] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0085] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A cloud computing-based direct expansion constant temperature and humidity machine air conditioning control method, characterized in that: include: Based on the cloud computing platform, temperature and humidity data, density data and external environment data of the target area are collected to generate updated data, wherein the density data represents the flow of people per unit area; constructing an air conditioning model based on the update records and air conditioning records; Performing multiple region transfer learning on the air conditioning model to obtain a transfer model; Performing air conditioning prediction on the migration model using the updated data, performing air dynamic control according to the prediction results, and generating a control strategy; The method of performing air dynamic control based on the prediction results and generating a control strategy includes: performing a switching strength evaluation on the density data to generate a switching strength; generating a switching strategy according to the switching intensity and the prediction result; Switching the operating mode based on the switching strategy and generating a switching result; In response to the switching result, the migration model is temporarily adjusted, and the result is fed back to the migration model to generate a control strategy; The short-term adjustment of the migration model in response to the switching result includes: Performing an internal environment enthalpy evaluation using the temperature and humidity data and the density data as internal environment evaluation features to generate an internal environment enthalpy value; Performing an external environment enthalpy value evaluation using the external environment data as an external environment evaluation feature to generate an external environment enthalpy value; Acquire an enthalpy difference between the internal environment enthalpy value and the external environment enthalpy value, and generate a preliminary switching strategy based on the enthalpy difference; The preliminary switching strategy is used as a switching guide, the switching strategy is used as a switching execution, a matching rate between the switching guide and the switching execution is evaluated, a switching result accuracy rate is generated, and the migration model is adjusted according to the switching result accuracy rate.

2. The cloud computing-based direct expansion constant temperature and humidity machine air conditioning control method according to claim 1, characterized in that: The collecting of temperature and humidity data, density data, and external environment data of the target area to generate updated data includes: The temperature and humidity data, density data and external environment data are used as update features; A fixed duration is configured for the rolling window, and the rolling window is updated according to the fixed duration for the update feature to generate the update data.

3. The cloud computing-based direct expansion constant temperature and humidity machine air conditioning control method according to claim 1, characterized in that: The performing multiple region transfer learning on the air conditioning model to obtain a transfer model includes: Based on the cloud computing platform, the temperature and humidity data characteristics, density data characteristics and external environment data characteristics of the target area are collected to obtain target characteristics; Setting similar region groups for the plurality of regions according to the target features to obtain similar region groups; The air conditioning model is shared among the similar area groups to obtain the migration model.

4. The cloud computing-based direct expansion constant temperature and humidity machine air conditioning control method according to claim 3, characterized in that: The step of grouping the multiple regions into similar regions according to the target features to obtain similar region groups includes: Taking the target feature as a standard, performing feature similarity measurement on the multiple regions to obtain similar features; updating according to the rolling windows of the multiple regions, and feeding back the updated result to the similar feature to obtain an abnormal feedback result; The group condition of the similar features is updated based on the abnormal feedback result, and the group is set according to the updated group condition to obtain the similar area group.

5. The cloud computing-based direct expansion constant temperature and humidity machine air conditioning control method according to claim 1, characterized in that: Also includes: Adaptively optimizing the migration model whose handover result accuracy does not meet the error threshold to generate an optimized migration model; Evaluating the adaptability of the optimized migration model according to the prediction accuracy to generate an adaptability coefficient; The model structure of the optimized migration model whose adaptability coefficient is lower than the preset adaptability coefficient is updated.

6. The cloud computing-based direct expansion constant temperature and humidity air conditioning control system is characterized by: The steps for implementing the cloud computing-based direct expansion constant temperature and humidity machine air conditioning control method according to any one of claims 1 to 5 include: An update data generation module, the update data generation module is used to collect temperature and humidity data, density data and external environment data of the target area based on the cloud computing platform to generate update data, wherein the density data represents the flow of people per unit area; An air conditioning model building module, the air conditioning model building module being used to build an air conditioning model according to the update record and the air conditioning record; a migration model obtaining module, the migration model obtaining module being used to perform multiple-region migration learning on the air conditioning model to obtain a migration model; A control strategy generation module is used to perform air conditioning prediction on the migration model using the updated data, perform air dynamic control according to the prediction result, and generate a control strategy.

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