Intelligent control method and system for air conditioning purification based on big data

Abstract

The present application relates to the field of intelligent air conditioning control, and in particular to an intelligent air conditioning purification control method and system based on big data, the method comprising: calculating the effective humidity in a space; when the effective humidity is greater than a preset humidity threshold, controlling the humidity in the air conditioning adjustment space; wherein the humidity adjustment method is: calculating the environmental sensitivity coefficient of each space, matching the environmental sensitivity coefficient with the air speed of the air conditioner to obtain the optimal wind speed, and adjusting the air speed of the air conditioner to the optimal wind speed. The present application matches the optimal wind speed of the air conditioner to each space to meet the requirements of each space for the humidity of the space.

Description

Technical Field

[0001] The present application relates to the field of intelligent air conditioning control, and in particular to an intelligent air conditioning purification control method and system based on big data. Background Art

[0002] In places with strict requirements on environmental conditions such as hospitals, laboratories, operating rooms, pharmacies, and the production of precision instruments and electronic equipment, a constant temperature and humidity environment is crucial. These places usually need to maintain a certain temperature and humidity level to ensure the safety of medical operations, the accuracy of experiments, and the stability of product quality. The humidity in the space can be adjusted by adjusting the air outlet speed of the air conditioner. For example, the patent document with authorization announcement number CN110781969B, which is based on the air conditioning air volume control method, device and medium of deep reinforcement learning, combines the air conditioning to control and regulate the actual temperature and humidity within the process limit range.

[0003] As the use time increases, the sealing performance of the building may gradually decline, which may be caused by material aging, construction defects or wear and tear during daily use. The decline in sealing performance will make it easier for outdoor moisture to penetrate into the room, thereby affecting the stability of indoor humidity. Secondly, the limitations of the original design conditions may also become a problem. In the early stage of design, various situations that may occur in future use may not be fully considered, or the predictions during design may deviate from the actual use. These design limitations may cause the central air-conditioning system to be unable to fully meet the needs of indoor humidity control. In addition, as time goes by, more and more items may accumulate indoors, which may hinder air circulation and affect the working efficiency of the air-conditioning system. The presence of debris may form local humidity hotspots indoors, making the humidity in some areas inconsistent with other areas. And because the central air-conditioning system is usually designed for the entire building or a specific area, rather than for a single room, in some rooms with particularly high requirements for humidity, inconsistent humidity may occur, and this inconsistency in humidity may have an adverse effect on the accuracy of experimental results, the safety of medical operations, and the stability of product quality. Therefore, due to the above-mentioned factors, the prior art cannot meet the requirements of each space for the humidity of the space when adjusting the indoor humidity through air conditioning. Summary of the invention

[0004] In order to solve the problem that the humidity requirements of each space cannot be met, the present application provides an intelligent control method and system for air conditioning purification based on big data.

[0005] In the first aspect, the present application provides an intelligent control method for air conditioning purification based on big data, which adopts the following technical solutions:

[0006] The intelligent control method for air conditioning purification based on big data includes the steps of: calculating the effective humidity in the space; when the effective humidity is greater than a preset humidity threshold, controlling the humidity in the air conditioning adjustment space; wherein the humidity adjustment method is: calculating the environmental sensitivity coefficient of each space, matching the environmental sensitivity coefficient with the wind speed of the air conditioner to obtain the optimal wind speed, and adjusting the air speed of the air conditioner to the optimal wind speed; the calculation formula of the environmental sensitivity coefficient is: ,in, represents the environmental sensitivity coefficient, A duration collection representing the ambient humidity. represents the minimum function, represents the maximum value function, represents the hyperbolic tangent curve, Represents variance.

[0007] The beneficial effects are: according to the humidity characteristics in the space, the humidity of each space is adaptively controlled, and the environmental sensitivity coefficient is calculated. The environmental sensitivity coefficient reflects the sensitivity of the humidity change in the environment. The wind speed of each space is matched according to the environmental sensitivity of each space, so as to achieve the purpose of adaptively controlling the humidity of each space, thereby meeting the humidity requirements of each space.

[0008] Optionally, the environmental sensitivity coefficient is matched with the wind speed of the air conditioner to obtain the optimal wind speed, and the air outlet speed of the air conditioner is adjusted to the optimal wind speed, including: presetting the wind speed level, matching the optimal wind speed level according to the environmental sensitivity coefficient, and controlling the wind speed level of the air conditioner in each space to be adjusted to the optimal level. At this time, the air outlet speed of the air conditioner is the optimal wind speed.

[0009] The beneficial effect is: when the wind speed level is preset, the optimal level is matched according to the corresponding environmental sensitivity coefficient of each space, the wind speed level of the air conditioner in each space is controlled to be adjusted to the optimal level, and the humidity of the space is adjusted according to the situation of the space itself.

[0010] Optionally, matching the optimal gear of wind speed according to the environmental sensitivity coefficient includes the steps of: in any space, sorting the collected environmental sensitivity coefficients in order from large to small to obtain an environmental sensitivity coefficient sequence of the space; clustering the environmental sensitivity coefficients in the environmental sensitivity coefficient sequence to obtain multiple clusters, and the environmental sensitivity coefficients in each cluster correspond to the same optimal gear.

[0011] The beneficial effect is: when the air conditioner wind speed gear is preset, by clustering the environmental sensitivity coefficient, the number of clusters is made the same as the number of gears, one cluster corresponds to one gear, and the wind speed gear of the air conditioner is adjusted to the gear corresponding to the cluster where the environmental sensitivity coefficient is located.

[0012] Optionally, the environmental sensitivity coefficient is matched with the wind speed of the air conditioner to obtain the optimal wind speed, and the air speed of the air conditioner is adjusted to the optimal wind speed, including the steps of: in any space, sorting the collected environmental sensitivity coefficients in order from large to small to obtain an environmental sensitivity coefficient sequence of the space; clustering the environmental sensitivity coefficients in the environmental sensitivity coefficient sequence to obtain multiple clusters, dividing the power levels according to the number of clusters, the environmental sensitivity coefficient in each cluster corresponds to a power level, and adjusting the operating power of the fan in the air conditioner to the corresponding power level.

[0013] The beneficial effect is: in the absence of preset air-conditioning wind speed levels, the environmental sensitivity coefficients are clustered to obtain multiple clusters, the operating power levels of the fans in the air conditioners are divided according to the number of clusters, and the operating power of the fans in the air conditioners is adjusted to the power level corresponding to the cluster where the environmental sensitivity coefficient is located.

[0014] Optionally, the effective duration of the humidity in the space is calculated, and the set of effective durations is used as the set of durations of the ambient humidity.

[0015] Optionally, in calculating the environmental sensitivity coefficient of each space, multiple collection points are set in the space, the environmental sensitivity coefficient of each collection point in the space is calculated, and the minimum value or average value of the environmental sensitivity coefficients in the collection points is used as the environmental sensitivity coefficient of the space.

[0016] The beneficial effect is that when the space is first adjusted to the required humidity, the minimum value of the environmental sensitivity coefficient in the collection point can be used as the environmental sensitivity coefficient of the space. The minimum value of the environmental sensitivity coefficient usually reflects the humidity change in the corner of the space. At this time, the humidity in the corner is used as the basis for the humidity change in the space. After a period of adjustment, in the process of maintaining constant humidity, the average value of the environmental sensitivity coefficient can be used as the environmental sensitivity coefficient of the space.

[0017] Optionally, the effective humidity in the space is calculated, including the steps of: calculating the extreme difference of the humidity value of each collection point at any time, constructing a range sequence; calculating the extreme difference mutation coefficient, when the extreme difference mutation coefficient is greater than a preset coefficient threshold, taking the time period consisting of the moments corresponding to the extreme value as the effective duration, and taking the average of the humidity values ​​at the collection points within the effective duration as the effective humidity in the space; the calculation formula of the extreme difference mutation coefficient is: ,in, for The extreme mutation coefficient at the moment, express The extreme difference in humidity values ​​at all times, express The bad time, The number of moments included in the valid duration.

[0018] The beneficial effects are: The numerator represents the range at any moment, and the denominator represents the mean of the range during the effective duration. It can reflect the degree of mutation of the range at any moment relative to other ranges. The closer the range mutation coefficient is to 0, the smaller the degree of mutation of the range at that moment is, and vice versa.

[0019] In the second aspect, the present application provides an air conditioning purification intelligent control system based on big data using the following technical solutions:

[0020] An intelligent control system for air conditioning purification based on big data includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the intelligent control method for air conditioning purification based on big data is implemented.

[0021] The beneficial effect is: the above-mentioned air conditioning purification intelligent control method based on big data is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a system based on the memory and the processor for easy use.

[0022] This application has the following technical effects:

[0023] 1. According to the humidity characteristics in the space, the humidity of each space is adaptively controlled, and the environmental sensitivity coefficient is calculated. The environmental sensitivity coefficient reflects the sensitivity of the humidity change in the environment. The wind speed of each space is matched according to the environmental sensitivity of each space to achieve the purpose of adaptively controlling the humidity of each space, thereby meeting the humidity requirements of each space.

[0024] 2. When there are preset air conditioning wind speed levels, the environmental sensitivity coefficients are clustered so that the number of clusters is the same as the number of speed levels, one cluster corresponds to one speed level, and the air conditioning wind speed level is adjusted to the level corresponding to the cluster where the environmental sensitivity coefficient is located. This achieves the purpose of adaptively controlling the humidity of each space.

[0025] 3. In the absence of a preset air-conditioning wind speed level, the environmental sensitivity coefficients are clustered to obtain multiple clusters. The operating power levels of the fans in the air conditioners are divided according to the number of clusters. The operating power of the fans in the air conditioners is adjusted to the power level corresponding to the cluster where the environmental sensitivity coefficient is located, so as to achieve the purpose of adaptively controlling the humidity in each space. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present application will become easily understood. In the accompanying drawings, several embodiments of the present application are shown in an exemplary and non-restrictive manner, and the same or corresponding numbers represent the same or corresponding parts.

[0027] Figure 1 It is a flowchart of the steps for calculating effective humidity in the intelligent control method for air conditioning purification based on big data in an embodiment of the present application.

[0028] Figure 2 It is a flowchart of the steps of adjusting the wind speed in each space to the optimal level in the intelligent control method of air conditioning purification based on big data in the embodiment of the present application.

[0029] Figure 3 It is a flow chart of the steps of adjusting the optimal wind speed according to power in the intelligent control method of air conditioning purification based on big data in the embodiment of the present application. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0031] It should be understood that when the terms "first", "second", etc. are used in the claims, specification and drawings of the present application, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the specification and claims of the present application indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections.

[0032] The embodiment of the present application discloses an intelligent control method for air conditioning purification based on big data, including: calculating the effective humidity in the space; when the effective humidity is greater than a preset humidity threshold, controlling the air conditioner to adjust the humidity in the space. Otherwise, maintaining the current air conditioner operation state.

[0033] For specific calculation method of effective humidity in space, refer to Figure 1 , including step S10-step S11, which are specifically as follows:

[0034] S10: Calculate the range of humidity values ​​at each collection point at any time and construct a range sequence.

[0035] In this application, the range refers to the difference between the maximum value and the minimum value. After a period of time, for example, after 10 moments, the range corresponding to each moment is calculated. For example, at the 11th moment, the difference between the maximum value and the minimum value from moment 1 to moment 11 is calculated as the extreme value of the 11th moment, and so on, to obtain the humidity value range at each moment. After sorting the humidity value ranges according to the order of the moments, a range sequence is obtained.

[0036] S11: Calculate the extreme difference mutation coefficient. When the extreme difference mutation coefficient is greater than the preset coefficient threshold, the time period consisting of the moments corresponding to the extreme values ​​is taken as the effective duration, and the average of the humidity values ​​collected at each collection point within the effective duration is taken as the effective humidity in the space.

[0037] The calculation formula of the extreme difference mutation coefficient is: ,in, for The extreme mutation coefficient at the moment, express The extreme difference in humidity values ​​at all times, express The bad time, The number of moments included in the valid duration.

[0038] The molecule represents The denominator represents the mean of the range within the effective duration. It can reflect the degree of mutation of the range at any moment relative to other ranges. The closer it is to 0, the smaller the degree of extreme mutation at that moment, and vice versa, the greater the degree of mutation.

[0039] Exemplarily, the coefficient threshold may be 0.1. When the extreme mutation coefficient is greater than 0.1 for the first time, the corresponding moment is recorded as the starting point of the effective duration. When the extreme mutation coefficient is greater than 0.1 for the first time, the corresponding moment is recorded as the end point of the effective duration. The time period composed of the moments between the starting point and the end point, and the starting point and the end point together constitute an effective duration. By analogy, multiple effective durations are obtained, wherein the next moment of the end point of the previous effective time is used as the starting point of the next effective time. The set of effective durations is used as the duration set of ambient humidity.

[0040] A room is divided into a space, and multiple collection points are set inside the room. A humidity sensor is set at each collection point to monitor the ambient humidity at the collection point. The longer the effective duration is, the lower the gas exchange rate at that location is. The average of the humidity values ​​at each collection point during the effective duration is taken as the effective humidity in the space. When the effective humidity is greater than the preset humidity threshold, the air conditioner is controlled to adjust the humidity in the space. Otherwise, the current air conditioner operation state is maintained.

[0041] The humidity adjustment method is: calculate the environmental sensitivity coefficient of each space, match the environmental sensitivity coefficient with the air outlet speed of the air conditioner to obtain the optimal wind speed, and adjust the air outlet speed of the air conditioner to the optimal wind speed.

[0042] Specifically, the calculation formula of the environmental sensitivity coefficient is: ,in, represents the environmental sensitivity coefficient, A duration collection representing the ambient humidity. represents the minimum function, represents the maximum value function, represents the hyperbolic tangent curve, Represents variance.

[0043] The closer the ratio is to 1, the less change there is in the humidity in the environment. On the contrary, the more change there is in the humidity in the environment. In order to Normalization, the role of the hyperbolic tangent function is to limit the impact of the variance to a reasonable range Even if the variance is large, it will not affect the final environmental sensitivity coefficient Have too great an impact.

[0044] In one embodiment, at least one humidity sensor is arranged at each corner of the geometric center of the floor in the room. The ventilation effect of the corner of the room is poorer than that of the geometric center of the floor in the room, so the humidity change in the corner is smaller and the humid air flows slower.

[0045] Set up multiple collection points in the space, calculate the environmental sensitivity coefficient of each collection point in the space, and use the minimum or average value of the environmental sensitivity coefficients in the collection points as the environmental sensitivity coefficient of the space. When the space is first adjusted to the required humidity, the minimum value of the environmental sensitivity coefficients in the collection points can be used as the environmental sensitivity coefficient of the space. The minimum value of the environmental sensitivity coefficient usually reflects the humidity change in the corner of the space. At this time, the humidity in the corner is used as the basis for the humidity change in the space. After a period of adjustment, when the humidity is maintained at a constant level, the average value of the environmental sensitivity coefficient can be used as the environmental sensitivity coefficient of the space.

[0046] The environmental sensitivity coefficient is matched with the wind speed of the air conditioner to obtain the optimal wind speed, and the method steps of adjusting the air speed of the air conditioner to the optimal wind speed are provided in the present application. Two embodiments are provided, one embodiment is applicable to the application scenario where the air conditioner has a preset wind speed gear, and the other embodiment is applicable to the application scenario where the air conditioner does not have a preset wind speed gear, which are as follows:

[0047] In one embodiment, the wind speed level is preset, and the optimal wind speed level is matched according to the environmental sensitivity coefficient, and the wind speed level of the air conditioner in each space is controlled to be adjusted to the optimal level. At this time, the air outlet speed of the air conditioner is the optimal wind speed. Figure 3 , including step S20-step S21:

[0048] S20: In any space, the collected environmental sensitivity coefficients are sorted in descending order to obtain an environmental sensitivity coefficient sequence of the space.

[0049] S21: clustering the environmental sensitivity coefficients in the environmental sensitivity coefficient sequence to obtain a plurality of clusters, wherein the environmental sensitivity coefficient in each cluster corresponds to the same optimal gear position.

[0050] In the case of preset wind speed levels, the environmental sensitivity coefficients are clustered so that the number of clusters is the same as the number of speed levels, one cluster corresponds to one speed level, and the wind speed level of the air conditioner is adjusted to the speed level corresponding to the cluster where the environmental sensitivity coefficient is located. For example, the environmental sensitivity coefficient in a cluster is a value between 0.7 and 0.9, such as environmental sensitivity coefficients of 0.7 and 0.8 both correspond to wind speed level 3.

[0051] The clustering algorithm may adopt the K-means clustering algorithm or other clustering algorithms suitable for this application scenario. The K-means clustering algorithm is an existing technology and will not be described in detail here.

[0052] In one embodiment, the environmental sensitivity coefficient is matched with the wind speed of the air conditioner to obtain the optimal wind speed, and the air speed of the air conditioner is adjusted to the optimal wind speed. Figure 3 , including step S30-step S31:

[0053] S30: In any space, the collected environmental sensitivity coefficients are sorted in descending order to obtain an environmental sensitivity coefficient sequence of the space.

[0054] S31: clustering the environmental sensitivity coefficients in the environmental sensitivity coefficient sequence to obtain a plurality of clusters, dividing the power levels according to the number of clusters, the environmental sensitivity coefficients in each cluster corresponding to a power level, and adjusting the operating power of the fan in the air conditioner to the corresponding power level.

[0055] The environmental sensitivity coefficients are clustered to obtain a plurality of clusters, the operating power levels of the fans in the air conditioner are divided according to the number of clusters, and the operating power of the fans in the air conditioner is adjusted to the power level corresponding to the cluster where the environmental sensitivity coefficient is located.

[0056] Exemplarily, clustering obtains five clusters, and the size of the wind speed level fan operating power is divided into five levels. If the environmental sensitivity coefficient in a space is in the cluster corresponding to level 5, the operating power of the fan in the air conditioner is adjusted to the corresponding power level.

[0057] The clustering algorithm may adopt the K-means clustering algorithm or other clustering algorithms suitable for this application scenario. The K-means clustering algorithm is an existing technology and will not be described in detail here.

[0058] An embodiment of the present application also discloses an intelligent control system for air conditioning purification based on big data, including a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, the intelligent control method for air conditioning purification based on big data according to the present application is implemented.

[0059] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0060] In the present application, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high bandwidth memory HBM (High Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device.

[0061] Although this specification has shown and described a plurality of embodiments of the present application, it is obvious to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will think of many changes, modifications and alternatives without departing from the thought and spirit of the present application. It should be understood that in the process of practicing the present application, various alternatives to the embodiments of the present application described herein may be adopted.

[0062] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. An intelligent control method for air conditioning purification based on big data, characterized in that: Includes steps: Calculate the effective humidity in the space; when the effective humidity is greater than a preset humidity threshold, control the air conditioner to adjust the humidity in the space; The humidity adjustment method is as follows: calculate the environmental sensitivity coefficient of each space, match the environmental sensitivity coefficient with the air outlet speed of the air conditioner to obtain the optimal wind speed, and adjust the air outlet speed of the air conditioner to the optimal wind speed; the calculation formula of the environmental sensitivity coefficient is: ,in, represents the environmental sensitivity coefficient, A duration collection representing the ambient humidity. represents the minimum function, represents the maximum value function, represents the hyperbolic tangent curve, Represents variance.

2. The air conditioning purification intelligent control method based on big data according to claim 1 is characterized in that: Matching the environmental sensitivity coefficient with the wind speed of the air conditioner to obtain the optimal wind speed, and adjusting the air speed of the air conditioner to the optimal wind speed, including: presetting the wind speed level, matching the optimal wind speed level according to the environmental sensitivity coefficient, and controlling the wind speed level of the air conditioner in each space to be adjusted to the optimal level. At this time, the air speed of the air conditioner is the optimal wind speed.

3. The intelligent control method for air conditioning purification based on big data according to claim 2 is characterized in that: Matching the optimal gear of wind speed according to the environmental sensitivity coefficient includes the following steps: In any space, the collected environmental sensitivity coefficients are sorted from large to small to obtain the environmental sensitivity coefficient sequence of the space; The environmental sensitivity coefficients in the environmental sensitivity coefficient sequence are clustered to obtain a plurality of clusters, and the environmental sensitivity coefficient in each cluster corresponds to the same optimal gear position.

4. The air conditioning purification intelligent control method based on big data according to claim 1 is characterized in that: Matching the environmental sensitivity coefficient with the wind speed of the air conditioner to obtain the optimal wind speed, and adjusting the air speed of the air conditioner to the optimal wind speed, including the steps of: In any space, the collected environmental sensitivity coefficients are sorted from large to small to obtain the environmental sensitivity coefficient sequence of the space; The environmental sensitivity coefficients in the environmental sensitivity coefficient sequence are clustered to obtain multiple clusters, and the power levels are divided according to the number of clusters. The environmental sensitivity coefficient in each cluster corresponds to a power level, and the operating power of the fan in the air conditioner is adjusted to the corresponding power level.

5. The air conditioning purification intelligent control method based on big data according to any one of claims 1 to 4, characterized in that: The method also includes the steps of calculating the effective duration of humidity in the space, and using the set of effective durations as the set of durations of ambient humidity.

6. The intelligent control method for air conditioning purification based on big data according to claim 5 is characterized in that: In calculating the environmental sensitivity coefficient of each space, multiple collection points are set in the space, and the environmental sensitivity coefficient of each collection point in the space is calculated. The minimum value or average value of the environmental sensitivity coefficients in the collection points is used as the environmental sensitivity coefficient of the space.

7. The intelligent control method for air conditioning purification based on big data according to claim 6 is characterized in that: Calculate the effective humidity in the space, including the following steps: Calculate the range of humidity values ​​at each collection point at any time and construct a range sequence; Calculate the extreme difference mutation coefficient. When the extreme difference mutation coefficient is greater than the preset coefficient threshold, take the period of time corresponding to the extreme value as the effective duration, and take the average of the humidity values ​​at each collection point within the effective duration as the effective humidity in the space. The calculation formula of the extreme difference mutation coefficient is: ,in, for The extreme mutation coefficient at the moment, express The extreme difference in humidity values ​​at all times, express The bad time, The number of moments included in the valid duration.

8. The intelligent control system for air conditioning purification based on big data is characterized by: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the air conditioning purification intelligent control method based on big data according to any one of claims 1 to 7 is implemented.

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