An intelligent temperature and humidity control system for a clean air conditioning unit

Abstract

The present invention discloses an intelligent temperature and humidity control system for a clean air-conditioning unit. The present invention relates to the technical field of air-conditioning unit control, and solves the problem that numerical regulation is not carried out based on the relevant change process of temperature and humidity data, and the control accuracy still needs to be improved. By analyzing the associated test data and based on the specific analysis process, the present invention can determine the associated changes at different air volumes and different temperatures. Based on the specific numerical changes, the relevant confirmation in the corresponding interval is carried out, so as to lock the corresponding change interval, which is convenient for subsequent precise regulation of temperature and humidity. Based on the specific temperature and humidity regulation data, the humidity is regulated in real time to achieve a better control effect. For the relevant humidity data after adjustment, based on the relevant results of real-time monitoring, the humidity data is abnormally evaluated to identify whether there is a large deviation in the humidity data, and the humidity data is readjusted to achieve a better temperature and humidity control effect.

Description

Technical Field

[0001] The present invention relates to the technical field of air-conditioning unit control, and specifically to an intelligent control system for the temperature and humidity of a clean air-conditioning unit. Background Art

[0002] For a pharmaceutical clean room, the temperature and humidity inside need to be precisely controlled to ensure the effective storage of items inside. The control of the air-conditioning unit mainly involves the precise adjustment of parameters such as temperature, humidity, and air flow to ensure the comfort of the indoor environment and air quality. Modern air-conditioning units are usually equipped with intelligent control systems, which can achieve remote monitoring and control through network connections; users can use devices such as mobile phones and tablets to adjust the air-conditioning parameters at any time and place, improving the convenience of use.

[0003] The application with the patent publication number CN112503732B relates to an air-conditioning unit temperature and humidity control method, device, system, and an air-conditioning unit. The air-conditioning unit temperature and humidity control method includes: obtaining the actual temperature value inside the room and the actual humidity values inside and outside the room; respectively comparing the actual temperature value with the target temperature value, and the actual humidity values inside and outside the room with the target humidity values; determining the operating mode of the air-conditioning unit according to the comparison results; controlling the operation of the air-conditioning unit according to the operating mode to adjust the temperature and humidity inside the room; the control method of the present invention determines the corresponding operating mode by collecting the actual temperature value inside the room and the actual humidity values inside and outside the room, and analyzing and judging the actual temperature value inside the room and the actual humidity values inside and outside the room, and switches the air-conditioning unit to operate under the operating mode, so as to achieve precise control of the indoor temperature and humidity to meet the usage requirements of users.

[0004] During the temperature control process of its clean air-conditioning unit, the humidity will change during temperature regulation. Therefore, when the air-conditioning unit adjusts the temperature and humidity, due to the influence of temperature on humidity, there will be relevant errors when adjusting the humidity subsequently, resulting in a large deviation between the temperature and humidity in the actual control process. Numerical regulation is not carried out based on the relevant change process of temperature and humidity data, and its control accuracy needs to be improved. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides an intelligent control system for the temperature and humidity of a clean air-conditioning unit, which solves the problem that numerical regulation is not carried out based on the relevant change process of temperature and humidity data, and its control accuracy needs to be improved.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent control system for the temperature and humidity of a clean air-conditioning unit includes:

[0007] The test data acquisition terminal acquires the test data generated by the air-conditioning unit during the test process, and transmits the acquired test data to the test data analysis terminal;

[0008] The test data analysis end receives and analyzes the acquired test data, confirms the indoor temperature change data and indoor humidity change data associated with the same air volume from the test data, and constructs two sets of data change curves. Based on the change trends of the two sets of data change curves, the change ranges of the corresponding air volume and the corresponding output temperature are locked. The specific method is as follows:

[0009] Analyze the acquired single group of test data, and generate a temperature change curve belonging to the temperature change data and a humidity change curve belonging to the humidity change data based on the time sequence. Different test data correspond to different air volumes and different output temperatures. The time lines associated with the humidity change curve and the temperature change curve are consistent, and the horizontal coordinate axis associated with the two is the time line, and the vertical coordinate axis is the humidity data or the temperature data.

[0010] Identify the temperature difference CZ at adjacent moments from the temperature change curve i , and CZ i = Temperature data of the next moment - temperature data of the previous moment, where i represents different adjacent moments, and then the humidity difference ZZ of adjacent moments is identified from the humidity change curve i , and ZZ i = humidity data at the next moment - humidity data at the previous moment;

[0011] Adopt G i =CZ i ÷ZZ i Lock the associated variable G i , according to the time sequence, the confirmed several associated variable values ​​G i Sort and determine the variable value sequence;

[0012] For the associated variable G contained in the variable sequence i Perform clustering: From the first group of associated variables G i Start by selecting G i Perform variance processing and determine the variance parameter F. If F < Y1, continue to select, where Y1 is the preset value. If F ≥ Y1, the G selected at this moment is i As the initial G of the second stage i , and the several G associated with the first stage i As the first data column, and perform the same variance processing again, from the initial G i Start to follow up with G i Select and confirm the variance, and confirm the subsequent different data columns in turn;

[0013] For the different associated variable values G included in several confirmed data columns i Perform mean processing to determine the associated mean value J belonging to the corresponding data column k , where k represents different data columns, and then select the minimum value from several associated mean values J k Select the data column associated with the minimum value as the standard data column, and based on the minimum and maximum values of the associated variable values G in the standard data column i Determine the change interval belonging to this test data, and transmit the determined change interval to the interval data storage end for storage;

[0014] The temperature control end, based on the externally input temperature control data, controls the temperature of the clean air conditioning unit, and based on the specific temperature control process and the determined change interval, performs associated humidity control. The specific method is as follows:

[0015] Based on the temperature control data, determine the air volume to be adjusted and the temperature to be adjusted of this clean air conditioning unit, and directly control this clean air conditioning unit based on the air volume to be adjusted and the temperature to be adjusted;

[0016] Based on the air volume to be adjusted and the temperature to be adjusted, search for the change interval associated with the air volume to be adjusted and the temperature to be adjusted from the interval data storage end, and use the middle value of this change interval as the selection value Xz;

[0017] Based on the control process of the clean air conditioning unit, identify the temperature change value Sz per unit time, and use Sz÷Xz = Sd to determine the humidity value Sd to be adjusted. Based on Sd, make the clean air conditioning unit perform reverse humidity adjustment, and its adjustment value is (-Sd);

[0018] The associated control end monitors the humidity data of the indoor real-time control, and based on the monitoring results, evaluates whether the humidity data of the indoor control meets the standard. If it does not meet the standard, perform relevant adjustments to make the stable data meet the standard. If it meets the standard, continue to monitor. The specific method is as follows:

[0019] Monitor the indoor humidity after regulation in real time, generate an indoor humidity change curve in real time, and confirm the median line of this humidity change curve in real time. The median line is the middle value between the maximum and minimum values of the humidity change curve;

[0020] Based on the position of the median line, move the median line up and down to determine the standard range. The moving range value is Y2, where Y2 is a preset value. The median line changes up and down following the real-time generation process of the indoor humidity change curve, and the determined standard range also changes accordingly. When the indoor humidity change curve does not belong to the standard range, perform re-regulation:

[0021] If the indoor humidity change curve is in an upward state, it means that the adjusted humidity value is too large, and the humidity value needs to be decreased until the indoor humidity change curve falls within the standard range;

[0022] If the indoor humidity change curve is in a downward state, it means that the adjusted humidity value is too small, and the humidity value needs to be increased until the indoor humidity change curve falls within the standard range.

[0023] Preferably, the test data includes indoor temperature change data at different air volumes and different output temperatures and the associated indoor humidity change data.

[0024] Preferably, the interval data storage end stores the change intervals associated with different air volumes and different output temperatures and provides them for extraction by the temperature control end.

[0025] Preferably, the median line is a set of straight lines.

[0026] The present invention provides an intelligent temperature and humidity control system for a clean air-conditioning unit. Compared with the prior art, it has the following beneficial effects:

[0027] By analyzing the associated test data, based on the specific analysis process, the change situations associated with different air volumes and different temperatures can be determined. Based on the specific numerical change situations, the relevant intervals are confirmed, so as to lock the corresponding change intervals, which is convenient for subsequent precise control of temperature and humidity. Based on the specific temperature and humidity control data, the humidity is adjusted in real time to achieve a better control effect;

[0028] For the adjusted relevant humidity data, based on the results of real-time monitoring, the humidity data is abnormally evaluated to identify whether there is a large deviation in the humidity data. Based on the identification results, the humidity data is readjusted to achieve a better temperature and humidity control effect and ensure the precise control of humidity in the case of temperature changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a schematic diagram of the principle framework of the present invention;

[0030] Figure 2 is a schematic diagram of the change of the humidity change curve of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the protection scope of the present invention.

[0032] First Embodiment

[0033] Please refer to Figure 1 , this application provides an intelligent temperature and humidity control system for a clean air-conditioning unit, including a test data acquisition end, a test data analysis end, an interval data storage end, a temperature control end, and an associated control end. Among them, the test data acquisition end is electrically connected to the input node of the test data analysis end, and the test data analysis end, the interval data storage end, the temperature control end, and the associated control end are all electrically connected from the output node to the input node;

[0034] Among them, the test data acquisition end acquires the test data generated during the test process of the air-conditioning unit and transmits the acquired test data to the test data analysis end. The test data includes indoor temperature change data with different air volumes and different output temperatures and the associated indoor humidity change data. The test data is obtained by relevant operators through prior tests or data input based on test results. The reason for performing relevant analysis on the test data is to lock the corresponding analysis results to determine the corresponding data intervals, so as to facilitate subsequent control and adjustment of temperature and humidity data, thereby ensuring a more accurate control effect;

[0035] Among them, the test data analysis end receives and analyzes the acquired test data, confirms the indoor temperature change data and indoor humidity change data associated with the same air volume from the test data, constructs two sets of data change curves, and based on the change trends of the two sets of data change curves, locks the change intervals belonging to the corresponding air volume and corresponding output temperature (where the output temperature is the relevant temperature output by the corresponding air-conditioning unit after receiving relevant instructions), and transmits the determined change intervals to the interval data storage end. The specific method for determining the change intervals is as follows:

[0036] Analyze the acquired single set of test data, and generate a temperature change curve belonging to the temperature change data and a humidity change curve belonging to the humidity change data based on the time sequence. Different test data corresponds to different air volumes and different output temperatures. The time lines associated with the humidity change curve and the temperature change curve are the same, and the horizontal coordinate axes associated with both are the time line, and the vertical coordinate axes are humidity data or temperature data;

[0037] Identify the temperature difference CZ i at adjacent moments from the temperature change curve i , and CZ i = temperature data at the latter moment - temperature data at the previous moment, where i represents different adjacent moments. Then identify the humidity difference ZZ i at adjacent moments from the humidity change curve, and ZZ

[0038] Adopt G i = CZ i ÷ZZ i Lock the associated variable value G i , according to the chronological relationship, sort the confirmed several associated variable values G i to determine the variable value sequence;

[0039] Perform clustering processing on the associated variable values G i contained in the variable value sequence: start from the first group of associated variable values G i and select G i in turn backward for variance processing, and determine the variance parameter F. If F < Y1, continue to select, where Y1 is a preset value, and its specific value is determined by the operator according to experience. If F ≥ Y1, then the selected G i at this moment is used as the initial G i in the second stage, and the several G i associated in the first stage are used as the first data column, and the same variance processing method is executed again. Starting from the initial G i , select and confirm the variance of the subsequent G i , and confirm the subsequent different data columns in turn. Example: Suppose the variable value sequence is {11, 12, 13, 14, 17, 18, 19, 21, 23, 25}. According to this processing method, assume that the value of Y1 is 2. The variance value of 11, 12, 13, 14 is 1.25, which meets the evaluation conditions. When the variance of 11, 12, 13, 14, 17 is calculated, the determined variance value is 4.24, exceeding the Y1 value. Therefore, the determined 11, 12, 13, 14 are the first data column, and then use the value 17 as the initial value of the second value and perform the relevant confirmation of the subsequent data column. The subsequent data columns are 17, 18, 19 and 21, 23, 25;

[0040] Perform mean processing on the different associated variable values G i contained in the confirmed several data columns to determine the associated mean J k , where k represents different data columns, and then select the minimum value from the several associated means J k . The data column associated with the minimum value is used as the standard data column, and based on the minimum value and the maximum value of the associated variable value G i in the standard data column, determine the change interval belonging to this test data, and transmit the determined change interval to the interval data storage end for storage.

[0041] Among them, the interval data storage end stores the change intervals associated with different air volumes and different output temperatures, and provides them for extraction by the temperature control end. Since the air volumes and output temperatures corresponding to the test data are different, the corresponding change intervals can be confirmed for each group of test data. Therefore, for different air volumes and different output temperatures, different change intervals need to be determined, and subsequent relevant temperature control processes are carried out based on the determined different change intervals to achieve a better control effect.

[0042] Second Embodiment

[0043] In the specific implementation process of this embodiment, compared with the above embodiment, this embodiment mainly focuses on the specific temperature control process of the unit, and its specific execution end includes a temperature control end and an associated control end;

[0044] Among them, the temperature control end performs temperature control on the clean air conditioning unit based on the externally input temperature control data, and performs associated control on the humidity based on the specific temperature control process and the determined change interval. The specific method of performing associated control is as follows:

[0045] Based on the temperature control data, determine the air volume to be adjusted and the temperature to be adjusted of this clean air conditioning unit, and directly control this clean air conditioning unit based on this air volume to be adjusted and the temperature to be adjusted;

[0046] Based on this air volume to be adjusted and the temperature to be adjusted, search for the change interval associated with the air volume to be adjusted and the temperature to be adjusted in the interval data storage end, and use the median value of this change interval as the selection value Xz;

[0047] Based on the control process of the clean air conditioning unit, identify the temperature change value Sz within a unit time, use Sz÷Xz = Sd to determine the humidity value Sd to be adjusted, and based on Sd, make the clean air conditioning unit perform reverse adjustment on the humidity, and its adjustment value is (-Sd). Specifically, because when the temperature data changes, the corresponding humidity data will change, and thus the indoor humidity will change accordingly. In order to keep the corresponding indoor humidity in the original unchanged state, it is necessary to perform reverse adjustment according to the changed relevant values to ensure that the indoor humidity data is relatively up to standard and the indoor humidity will not be affected by the specific temperature control process;

[0048] This adjustment control process belongs to the relevant process of real-time adjustment control. When the temperature data within a unit time changes, the corresponding humidity data will also change, so real-time adjustment is required to ensure the spatial humidity of the indoor environment;

[0049] Example: The determined selected value Xz is -2, where the temperature data rising per unit time is 4. Then the humidity value to be adjusted determined after processing is -2. Therefore, humidity control is required to increase the indoor humidity by 2 per unit time to complete the synchronous control process.

[0050] Among them, the associated regulation terminal monitors the humidity data of real-time regulation in the room, and based on the monitoring results, evaluates whether the humidity data of indoor regulation meets the standard. If it does not meet the standard, relevant adjustments are made to make the stable data meet the standard. If it meets the standard, continuous monitoring is carried out. Specifically, when the humidity data is regulated, with the relevant changes in the temperature data, the humidity data will also change accordingly. Therefore, the adjustment process will cause deviation in the humidity data.

[0051] The specific method for evaluating whether the humidity data of indoor regulation meets the standard is as follows:

[0052] Real-time monitor the indoor humidity after regulation, and generate an indoor humidity change curve in real time, and confirm the median line of this humidity change curve in real time. The median line is the intermediate value between the maximum value and the minimum value of the humidity change curve, and the median line is a group of straight lines.

[0053] Based on the position of the median line, move the median line up and down to determine the standard range, and the range value of the movement is Y2, where Y2 is a preset value, and its specific value is determined by the operator according to experience. The median line changes up and down with the real-time generation process of the indoor humidity change curve, and the determined standard range also changes accordingly. When the indoor humidity change curve does not belong to the standard range, re-regulation is carried out:

[0054] If the indoor humidity change curve is in an upward state, it means that the adjusted humidity value is too large, and the humidity value needs to be reduced until the indoor humidity change curve belongs to the standard range.

[0055] If the indoor humidity change curve is in a downward state, it means that the adjusted humidity value is too small, and the humidity value needs to be increased until the indoor humidity change curve belongs to the standard range.

[0056] Specifically, when the humidity data in the room changes greatly, it is necessary to make an associated change to the associated humidity value, either increase it or decrease it, in order to ensure the associated effect of humidity regulation. Combined with Figure 2, the median line of the humidity change curve is confirmed in real time, and the median line moves up and down along with the temperature change curve generated in real time. When moving, the corresponding standard range also moves accordingly. During the movement, when the corresponding humidity change curve changes, it will exceed the corresponding standard range, which means that the corresponding humidity of the real-time regulation is in a state where the change does not meet the standard. It is necessary to make relevant adjustments to the state where the humidity does not meet the standard, so as to complete the specific humidity adjustment process, thereby ensuring that the humidity can be effectively controlled during the temperature change process and achieving a better data control effect.

[0057] The Third Embodiment

[0058] In the specific implementation process of this embodiment, it includes all the implementation processes of the above two groups of embodiments.

[0059] Some of the data in the above formula are numerically calculated by removing their dimensions, and the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0060] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent temperature and humidity control system for a clean air conditioning unit, characterized in that, Including: A test data acquisition end, which acquires the test data generated during the test process of the air-conditioning unit and transmits the acquired test data into the test data analysis end; A test data analysis end, which receives and analyzes the acquired test data, confirms the indoor temperature change data and indoor humidity change data associated with the same air volume from the test data, constructs two sets of data change curves, and locks the change intervals belonging to the corresponding air volume and corresponding output temperature based on the change trends of the two sets of data change curves. The specific method is as follows: Analyze the acquired single set of test data, generate a temperature change curve belonging to the temperature change data and a humidity change curve belonging to the humidity change data based on the time sequence. Different test data correspond to different air volumes and different output temperatures. The time lines associated with their humidity change curves and temperature change curves are the same, and the horizontal axes associated with both are the time line, and the vertical axes are humidity data or temperature data; Identify the temperature difference CZ between adjacent moments from the temperature change curve i , and CZ i = temperature data of the latter moment - temperature data of the previous moment, where i represents different adjacent moments, and then identify the humidity difference ZZ between adjacent moments from the humidity change curve i , and ZZ i = humidity data of the latter moment - humidity data of the previous moment; Adopt G i = CZ i ÷ ZZ i Lock the associated variable value G i , according to the chronological relationship, sort the confirmed several associated variable values G i to determine the variable value sequence; For the associated variable value G included in the variable value sequence i Perform clustering processing: starting from the first group of associated variable values G i Select G sequentially from the beginning i Perform variance processing and determine the variance parameter F. If F < Y1, continue to select, where Y1 is a preset value. If F ≥ Y1, then the G selected at this moment i Is used as the initial G in the second stage i , and several Gs associated in the first stage i Are used as the first data column, and the same variance processing method is executed again. Starting from the initial G i Select and confirm the variance of the subsequent G i For sequential confirmation of different subsequent data columns; For the different associated variable values G included in several confirmed data columns i perform mean processing to determine the associated mean value J belonging to the corresponding data column k , where k represents different data columns, and then select the minimum value from several associated mean values J k , take the data column associated with the minimum value as the standard data column, and based on the minimum and maximum values of the associated variable value G in the standard data column i , determine the change interval belonging to this test data, and transmit the determined change interval to the interval data storage end for storage; A temperature control end, which controls the temperature of the clean air-conditioning unit based on the externally input temperature control data, and performs associated control on the humidity based on the specific temperature control process and the determined change interval; An associated control end, which monitors the humidity data of the indoor real-time control, and based on the monitoring result, evaluates whether the humidity data of the indoor control meets the standard. If it does not meet the standard, relevant adjustments are made to make the stable data meet the standard. If it meets the standard, continuous monitoring is carried out.

2. The intelligent temperature and humidity control system for a clean air conditioning unit according to claim 1, characterized in that, The test data includes indoor temperature change data with different air volumes and different output temperatures and the associated indoor humidity change data.

3. The intelligent temperature and humidity control system for a clean air conditioning unit according to claim 1, characterized in that The interval data storage end stores the change intervals associated with different air volumes and different output temperatures and provides them for the temperature control end to extract.

4. The intelligent temperature and humidity control system for a clean air-conditioning unit according to claim 1, characterized in that, The specific method for the temperature control end to perform associated control on the humidity is as follows: Based on the temperature control data, determine the air volume to be adjusted and the temperature to be adjusted of this clean air-conditioning unit, and directly control this clean air-conditioning unit based on this air volume to be adjusted and the temperature to be adjusted; And based on this air volume to be adjusted and the temperature to be adjusted, find the change interval associated with the air volume to be adjusted and the temperature to be adjusted from the interval data storage end, and use the middle value of this change interval as the selection value Xz; Based on the control process of the clean air-conditioning unit, identify the temperature change value Sz per unit time, use Sz÷Xz = Sd to determine the humidity value Sd to be adjusted, and based on Sd, make the clean air-conditioning unit perform reverse adjustment on the humidity, and its adjustment value is (-Sd).

5. The intelligent temperature and humidity control system for a clean air-conditioning unit according to claim 1, characterized in that, The specific method for the associated control end to evaluate whether the humidity data of the indoor control meets the standard is as follows: Real-time monitor the indoor humidity after regulation, generate an indoor humidity change curve in real time, and confirm the median line of this humidity change curve in real time. The median line is the middle value between the maximum value and the minimum value of the humidity change curve; Based on the position of the median line, move the median line up and down to determine the standard range, and the range value of the movement is Y2, where Y2 is a preset value. The median line changes up and down following the real-time generation process of the indoor humidity change curve, and the determined standard range also changes accordingly. When the indoor humidity change curve does not belong to the standard range, re-regulation is carried out: If the indoor humidity change curve is in an upward state, it means that the adjusted humidity value is too large, and the humidity value needs to be decreased until the indoor humidity change curve falls within the standard range.

6. The intelligent temperature and humidity control system for a clean air-conditioning unit according to claim 5, wherein, If the indoor humidity change curve is in a downward state, it means that the adjusted humidity value is too small, and the humidity value needs to be increased until the indoor humidity change curve falls within the standard range.

7. An intelligent temperature and humidity control system for a clean air-conditioning unit according to claim 5, characterized in that, The median line is a set of straight lines.

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