An intelligent temperature monitoring and processing system for an openable daylighting skylight

Abstract

The present invention relates to the technical field of data processing, and particularly to an intelligent temperature monitoring and processing system for an openable daylighting skylight. The system includes a processor and a memory, and the processor executes the computer program in the memory to implement the following steps: obtaining temperature data of each position to be monitored; obtaining a discrete distribution rate of temperature data according to the distribution characteristics of the temperature data of each position to be monitored; if the discrete distribution rate of temperature data is greater than or equal to a preset threshold of the discrete distribution rate of temperature data, obtaining a dynamic environment influence coefficient of the temperature data of each position to be monitored, obtaining a dynamic weight of the temperature data of each position to be monitored according to the dynamic environment influence coefficient of the temperature data of each position to be monitored, and obtaining the current optimal indoor temperature according to the dynamic weight of the temperature data of each position to be monitored. The present invention can accurately reflect the actual temperature situation and make an accurate judgment on the opening or closing of the skylight.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to an intelligent temperature monitoring and processing system for an openable daylighting skylight. Background Art

[0002] An openable daylighting skylight is an architectural design element that combines natural lighting and ventilation functions. It usually uses transparent glass, which can provide good daylighting effects and is widely used in upper-layer windows of office buildings, shopping malls, and greenhouse buildings. Different from fixed skylights, the openable daylighting skylight automatically adjusts the opening state of the skylight through an intelligent temperature monitoring and processing system, so as to achieve the purpose of maintaining a comfortable indoor temperature and saving energy and reducing consumption, which conforms to the trend of modern energy conservation and environmental protection. The intelligent temperature monitoring and processing system mainly consists of a temperature detection module, an intelligent control module, and a skylight driving module. The temperature detection module collects the indoor environmental temperature in real time. The intelligent control module judges whether to open or close the skylight according to the preset temperature threshold and the real-time temperature data. The skylight driving module executes the control module instruction to open or close the skylight. An accurate and reliable intelligent temperature monitoring and processing system can significantly reduce energy consumption and optimize the energy use and environmental control of buildings.

[0003] At present, most buildings choose to adopt a multi-point temperature acquisition method to comprehensively monitor the overall temperature in the building, and calculate the average value or median value of the data of each sensor as a representative value, output this value to the intelligent control module and judge the size relationship with the threshold to control the opening and closing of the skylight. However, due to the interference to a certain extent caused by unstable sunlight radiation in different periods of some sensors and the influence of air flow fluctuations caused by opening and closing the daylighting skylight, the temperature data collected by all temperature sensors vary greatly at certain times, and the consistency is low. The average value or median value of the data of each sensor can no longer reflect the actual situation of the indoor temperature data, making the intelligent control module unable to accurately judge whether to open or close the skylight.

[0004] Therefore, how to accurately and reliably monitor the overall temperature in the building and then make an accurate judgment on the opening or closing of the skylight has become an urgent problem to be solved. Summary of the Invention

[0005] In view of this, an embodiment of the present invention provides an intelligent temperature monitoring and processing system for an openable daylighting skylight to solve the problem of how to accurately and reliably monitor the overall temperature in the building and then make an accurate judgment on the opening or closing of the skylight.

[0006] An embodiment of the present invention provides an intelligent temperature monitoring and processing system for an openable daylighting skylight, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the following method is implemented:

[0007] Indoors in a building with a daylighting skylight, obtain temperature data of each position to be monitored within a preset time range including the current sampling moment;

[0008] According to the distribution characteristics of the temperature data of each position to be monitored at the current sampling moment, obtain the discrete distribution rate of the temperature data at the current sampling moment;

[0009] If the discrete distribution rate of the temperature data at the current sampling moment is greater than or equal to a preset temperature data discrete distribution rate threshold, record the temperature data of any position to be monitored at the current sampling moment as the target data, and according to the change characteristics of the temperature data in the preset time range that is the same as the position to be monitored where the target data is located, and the difference in the change of the temperature data of different positions to be monitored at the current sampling moment, obtain the dynamic environment impact coefficient of the target data;

[0010] Obtain the dynamic environment impact coefficient of the temperature data of each position to be monitored at the current sampling moment, according to the dynamic environment impact coefficient of the temperature data of each position to be monitored at the current sampling moment, obtain the dynamic weight of the temperature data of each position to be monitored at the current sampling moment, according to the dynamic weight of the temperature data of each position to be monitored at the current sampling moment, obtain the optimal indoor temperature at the current sampling moment, and adjust the daylighting skylight according to the optimal indoor temperature.

[0011] Preferably, after obtaining the discrete distribution rate of the temperature data at the current sampling moment according to the distribution characteristics of the temperature data of each position to be monitored at the current sampling moment, it further includes:

[0012] If the discrete distribution rate of the temperature data at the current sampling moment is less than the preset temperature data discrete distribution rate threshold, then obtain the average value of the temperature data of all positions to be monitored at the current sampling moment as the optimal indoor temperature at the current sampling moment, and adjust the daylighting skylight according to the optimal indoor temperature.

[0013] Preferably, obtaining the discrete distribution rate of the temperature data at the current sampling moment according to the distribution characteristics of the temperature data of each position to be monitored at the current sampling moment includes:

[0014] Obtain the maximum temperature value and the minimum temperature value among the temperature data of all positions to be monitored at the current sampling moment, perform hyperbolic tangent processing on the difference between the maximum temperature value and the minimum temperature value to obtain a first hyperbolic tangent result;

[0015] Obtain the standard deviation of the temperature data at all positions to be monitored at the current sampling moment, substitute the standard deviation into the exponential function with the natural constant as the base to obtain the exponential function result, and perform hyperbolic tangent processing on the difference between the exponential function result and the constant 1 to obtain the second hyperbolic tangent result;

[0016] Obtain the discrete distribution rate of the temperature data at the current sampling moment according to the mean value of the first hyperbolic tangent result and the second hyperbolic tangent result.

[0017] Preferably, the obtaining of the dynamic environment influence coefficient of the target data according to the change characteristics of the temperature data at the same position to be monitored as the target data within the preset time range and the difference in the temperature data changes at different positions to be monitored at the current sampling moment includes:

[0018] Obtain the temperature change rate at each position to be monitored within the preset time range, perform normalization processing on the temperature change rate at each position to be monitored by using the hyperbolic tangent function to obtain the rate factor of each position to be monitored, record the position to be monitored with the rate factor less than the preset rate factor threshold as the normal monitoring position, and obtain the dynamic trend characteristic value of the target data according to the difference in the temperature data between the position to be monitored where the target data is located and each normal monitoring position;

[0019] Obtain the dynamic fluctuation characteristic value of the target data according to the data fluctuation characteristic of the position to be monitored where the target data is located within the preset time range;

[0020] Perform weighted summation on the dynamic trend characteristic value and the dynamic fluctuation characteristic value of the target data to obtain the dynamic environment influence coefficient of the target data.

[0021] Preferably, the obtaining of the dynamic trend characteristic value of the target data according to the difference in the temperature data between the position to be monitored where the target data is located and each normal monitoring position includes:

[0022] Obtain the mean value of the temperature change rates of all normal monitoring positions, and calculate the absolute value of the first difference between the temperature change rate of the position to be monitored where the target data is located and the mean value of the temperature change rates;

[0023] Obtain the mean value of the temperature data of all normal monitoring positions at the current sampling moment, and calculate the absolute value of the second difference between the target data and the mean value of the temperature data;

[0024] Perform hyperbolic tangent processing on the addition result of the absolute value of the first difference and the absolute value of the second difference to obtain the dynamic trend characteristic value of the target data.

[0025] Preferably, obtaining the dynamic fluctuation eigenvalue of the target data according to the data fluctuation characteristics of the to-be-monitored position where the target data is located within the preset time range includes:

[0026] Form a temperature data sequence with the temperature data of the to-be-monitored position where the target data is located within the preset time range, obtain the absolute value of the difference between every two adjacent temperature data in the temperature data sequence, correspondingly obtain the cumulative value of the absolute value of the difference, and perform hyperbolic tangent processing on the cumulative value of the absolute value of the difference to obtain the third hyperbolic tangent result;

[0027] Obtain the maximum value and the minimum value in the temperature data sequence, and perform hyperbolic tangent processing on the difference between the maximum value and the minimum value to obtain the fourth hyperbolic tangent result;

[0028] Obtain the dynamic fluctuation eigenvalue of the target data according to the addition result of the third hyperbolic tangent result and the fourth hyperbolic tangent result.

[0029] Preferably, obtaining the dynamic weight of the temperature data of each to-be-monitored position at the current sampling moment according to the dynamic environment influence coefficient of the temperature data of each to-be-monitored position at the current sampling moment includes:

[0030] Respectively obtain the subtraction results of the constant 1 and the dynamic environment influence coefficients of the temperature data of each to-be-monitored position at the current sampling moment, and obtain the dynamic weight of the temperature data of each to-be-monitored position at the current sampling moment according to the proportion of the subtraction result corresponding to the temperature data of each to-be-monitored position at the current sampling moment in all subtraction results.

[0031] Preferably, obtaining the optimal indoor temperature at the current sampling moment according to the dynamic weight of the temperature data of each to-be-monitored position at the current sampling moment includes:

[0032] Perform weighted summation on the temperature data of all to-be-monitored positions at the current sampling moment according to the dynamic weight of the temperature data of each to-be-monitored position at the current sampling moment to obtain the optimal indoor temperature at the current sampling moment.

[0033] The beneficial effects of the embodiments of the present invention compared with the prior art are:

[0034] In the interior of a building with a daylighting skylight, temperature data of each position to be monitored within a preset time range including the current sampling moment is obtained; according to the distribution characteristics of the temperature data of each position to be monitored at the current sampling moment, the discrete distribution rate of the temperature data at the current sampling moment is obtained; if the discrete distribution rate of the temperature data at the current sampling moment is greater than or equal to a preset temperature data discrete distribution rate threshold, the temperature data of any position to be monitored at the current sampling moment is recorded as target data, and according to the change characteristics of the temperature data in the preset time range that is the same as the position to be monitored where the target data is located, and the difference in the change of the temperature data of different positions to be monitored at the current sampling moment, the dynamic environment influence coefficient of the target data is obtained; the dynamic environment influence coefficient of the temperature data of each position to be monitored at the current sampling moment is obtained, and according to the dynamic environment influence coefficient of the temperature data of each position to be monitored at the current sampling moment, the dynamic weight of the temperature data of each position to be monitored at the current sampling moment is obtained, and according to the dynamic weight of the temperature data of each position to be monitored at the current sampling moment, the optimal indoor temperature at the current sampling moment is obtained, and the daylighting skylight is adjusted according to the optimal indoor temperature. The present invention first obtains the discrete distribution rate of the temperature data at the current sampling moment, determines whether the temperature data of each position to be monitored is affected by the environment, and if the temperature data of a position to be monitored is affected by the environment, the dynamic weight of each position to be monitored at the current sampling moment is obtained, and the optimal indoor temperature is obtained through the dynamic weight of each position to be monitored at the current sampling moment, which more accurately reflects the actual situation of the indoor temperature at the current sampling moment, and then makes an accurate judgment on the opening or closing of the skylight, improving the accuracy of the intelligent temperature detection and processing system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained without creative efforts.

[0036] Figure 1 It is a flowchart of an intelligent temperature monitoring and processing method for an opening daylighting skylight provided in Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The following will describe in detail the embodiments of the present disclosure. The examples of the embodiments are shown in the drawings. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present disclosure and should not be construed as a limitation of the present disclosure.

[0038] It should be noted that the terms "first", "second", etc. in the specification of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0039] In order to illustrate the technical solution of the present invention, specific embodiments will be used for illustration below.

[0040] An embodiment of the present invention provides an intelligent temperature monitoring and processing system for an opening type daylighting skylight, including a processor and a memory. The processor executes the computer program in the memory to implement an intelligent temperature monitoring and processing method for an opening type daylighting skylight, as Figure 1 shown. The method includes the following steps:

[0041] Step S101, in a building room with a daylighting skylight, obtain temperature data of each position to be monitored within a preset time range including the current sampling moment.

[0042] An opening type daylighting skylight is an architectural design element that combines natural lighting and ventilation functions. It usually uses transparent glass, which can provide good lighting effects and is widely used in upper floor windows of office buildings, shopping malls, and greenhouse buildings. The opening type daylighting skylight automatically adjusts the opening state of the skylight through an intelligent temperature monitoring and processing system, so as to achieve the purpose of maintaining a comfortable indoor temperature and saving energy and reducing consumption, which conforms to the trend of modern energy conservation and environmental protection. The intelligent temperature monitoring and processing system mainly consists of a temperature detection module, an intelligent control module, and a skylight drive module. The temperature detection module collects the indoor environmental temperature in real time, the intelligent control module determines whether to open or close the skylight according to the preset temperature threshold and the real-time temperature data, and the skylight drive module executes the control module instruction to open or close the skylight.

[0043] In a building room with a daylighting skylight, the temperature detection module uses an NTC thermistor sensor to obtain the temperature data of each position to be monitored within 11 seconds including the current sampling moment at a sampling frequency of once per second. There is no limit here, and it can be set according to specific implementation scenarios.

[0044] Step S102, according to the distribution characteristics of the temperature data of each position to be monitored at the current sampling moment, obtain the discrete distribution rate of the temperature data at the current sampling moment.

[0045] To comprehensively monitor the overall temperature in a building interior, most buildings adopt a multi-point temperature acquisition method, calculate the average or median of the temperature data at each position to be monitored as a representative value, output this value to the intelligent control module, and judge the size relationship with the threshold to control the opening and closing of the skylight. However, due to the interference to a certain extent caused by unstable sunlight radiation at different times in the temperature data of some positions to be monitored and the influence of air flow fluctuations caused by opening and closing the daylighting skylight, the temperature data collected at all positions to be monitored vary greatly at certain times, with low consistency. The average or median value of the temperature data at each position to be monitored cannot reflect the actual situation of the indoor temperature data, making it impossible for the intelligent control module to accurately judge whether to open or close the skylight.

[0046] Therefore, it is necessary to obtain the discrete distribution rate of the temperature data at the current sampling moment according to the distribution characteristics of the temperature data at each position to be monitored at the current sampling moment, and judge whether the temperature data at the current sampling moment is affected by the environment according to the discrete distribution rate of the temperature data at the current sampling moment. When the temperature data at the current sampling moment is affected by the environment, obtain the optimal indoor temperature that can truly reflect the actual situation of the indoor temperature.

[0047] Among them, the method for obtaining the discrete distribution rate of the temperature data at the current sampling moment according to the distribution characteristics of the temperature data at each position to be monitored at the current sampling moment is as follows:

[0048] Obtain the maximum temperature value and the minimum temperature value in the temperature data of all positions to be monitored at the current sampling moment, perform hyperbolic tangent processing on the difference between the maximum temperature value and the minimum temperature value to obtain the first hyperbolic tangent result;

[0049] Obtain the standard deviation of the temperature data of all positions to be monitored at the current sampling moment, substitute the standard deviation into the exponential function with the natural constant as the base to obtain the exponential function result, and perform hyperbolic tangent processing on the difference between the exponential function result and the constant 1 to obtain the second hyperbolic tangent result;

[0050] According to the mean value of the first hyperbolic tangent result and the second hyperbolic tangent result, obtain the discrete distribution rate of the temperature data at the current sampling moment.

[0051] In an embodiment, obtain the maximum temperature value and the minimum temperature value in the temperature data of all positions to be monitored at the current sampling moment, and the standard deviation of the temperature data of all positions to be monitored at the current sampling moment. According to the maximum temperature value and the minimum temperature value in the temperature data of all positions to be monitored at the current sampling moment, and the standard deviation of the temperature data, calculate the discrete distribution rate of the temperature data at the current sampling moment:

[0052]

[0053] Where Q is the discrete distribution rate of temperature data at the current sampling moment; is the maximum temperature value among the temperature data at all positions to be monitored at the current sampling moment; is the minimum temperature value among the temperature data at all positions to be monitored at the current sampling moment; is the standard deviation of the temperature data at all positions to be monitored at the current sampling moment; e is the natural constant; tanh() is the hyperbolic tangent function.

[0054] It should be noted that represents the temperature data span between different positions to be monitored, the larger it is, the more significant the difference in temperature data between the positions to be monitored, and the larger the discrete distribution rate of temperature data at the current sampling moment; reflects the distribution of temperature data between the positions to be monitored, the larger it is, the more dispersed the distribution of temperature data between the positions to be monitored, and the larger the discrete distribution rate of temperature data at the current sampling moment; the larger the discrete distribution rate of temperature data at the current sampling moment, the more discrete the distribution of temperature data between the current positions to be monitored, the lower the consistency, and the greater the possibility that the temperature data at the current sampling moment is affected by the environment.

[0055] Thus, the discrete distribution rate of temperature data at the current sampling moment is obtained.

[0056] Step S103, if the discrete distribution rate of temperature data at the current sampling moment is greater than or equal to the preset temperature data discrete distribution rate threshold, record the temperature data of any position to be monitored at the current sampling moment as the target data, and obtain the dynamic environment influence coefficient of the target data according to the change characteristics of the temperature data at the same position to be monitored as the target data within the preset time range, and the difference in the change of temperature data at different positions to be monitored at the current sampling moment.

[0057] After obtaining the discrete distribution rate of temperature data at the current sampling moment, it is possible to judge whether the temperature data at the current sampling moment is affected by the environment according to the discrete distribution rate of temperature data at the current sampling moment. Set the temperature data discrete distribution rate threshold to 0.6, which is not limited here and can be set according to the specific implementation scenario. If the discrete distribution rate of temperature data at the current sampling moment is less than the preset temperature data discrete distribution rate threshold (i.e., ), it is determined that the temperature data at the current sampling moment is less affected by the environment. The average value or median value of the temperature data at each position to be monitored can reflect the actual situation of the indoor temperature data. At this time, the average value of the temperature data at all positions to be monitored at the current sampling moment is obtained as the optimal indoor temperature at the current sampling moment, and the daylighting skylight is adjusted according to the optimal indoor temperature.

[0058] If the discrete distribution rate of the temperature data at the current sampling moment is greater than or equal to 0.6 (i.e., ), it is determined that the temperature data at the current sampling moment is greatly affected by the environment, and the average value or median value of the temperature data at each position to be monitored can no longer reflect the actual situation of the indoor temperature data, so that the intelligent control module cannot accurately judge whether to open or close the skylight. Therefore, it is necessary to analyze the temperature data at each position to be monitored, and determine the weight of the temperature data at each position to be monitored according to the degree of environmental interference of the temperature data at each position to be monitored, so as to obtain the optimal indoor temperature that can more truly reflect the actual situation of the indoor temperature.

[0059] Since when each position to be monitored is affected by the environment, the change rate of its temperature data will be significantly accelerated, and the temperature value will also show a state that does not match the temperature data of the remaining positions to be monitored. The fluctuation frequency of the temperature data is greater and more intense. Therefore, the temperature data of any position to be monitored at the current sampling moment can be recorded as the target data. According to the change characteristics of the temperature data in the same position to be monitored as the target data within the preset time range (i.e., within 11 seconds), and the difference in the change of the temperature data at different positions to be monitored at the current sampling moment, the dynamic environmental influence coefficient of the target data is obtained. Furthermore, through the dynamic environmental influence coefficient of the target data, the weight of the temperature data at each position to be monitored is determined, so as to obtain the optimal indoor temperature that can more truly reflect the actual situation of the indoor temperature data.

[0060] Among them, the method for obtaining the dynamic environmental influence coefficient of the target data according to the change characteristics of the temperature data in the same position to be monitored as the target data within the preset time range and the difference in the change of the temperature data at different positions to be monitored at the current sampling moment is as follows:

[0061] (1) Obtain the temperature change rate of each position to be monitored within the preset time range, normalize the temperature change rate of each position to be monitored by using the hyperbolic tangent function to obtain the rate factor of each position to be monitored, and record the position to be monitored with a rate factor less than the preset rate factor threshold as the normal monitoring position. According to the difference between the temperature data of the position to be monitored where the target data is located and the temperature data of each normal monitoring position, the dynamic trend characteristic value of the target data is obtained.

[0062] In one embodiment, taking the i-th position to be monitored as an example, the temperature change rate of the i-th position to be monitored within a preset time range is obtained:

[0063]

[0064] Wherein, is the temperature change rate of the i-th position to be monitored within a preset time range;

[0065] is the temperature data of the i-th position to be monitored at the current sampling moment; is the temperature data of the i-th position to be monitored at the first sampling moment within a preset time range; t0 is the current sampling moment; i is the serial number of the position to be monitored; n is the number of sampling moments within a preset time range; | | is the absolute value symbol; Since the temperature change rate belongs to the prior art, it will not be elaborated here.

[0066] After obtaining the temperature change rate of the i-th position to be monitored within a preset time range, the hyperbolic tangent function is used to normalize the temperature change rate of the i-th position to be monitored, and the rate factor of the i-th position to be monitored is obtained. The hyperbolic tangent function is used to limit the output result to (0, 1), which is convenient for subsequent screening.

[0067] Similarly, the temperature change rate of each position to be monitored within a preset time range and the rate factor of each position to be monitored are obtained. The rate factor threshold is set to 0.6, which is not limited here and can be set according to specific implementation scenarios. The positions to be monitored with a rate factor less than 0.6 are recorded as normal monitoring positions. According to the difference between the temperature data of the position where the target data is located and the temperature data of each normal monitoring position, the dynamic trend characteristic value of the target data is obtained.

[0068] Specifically, the average value of the temperature change rates of all normal monitoring positions is obtained, and the absolute value of the first difference between the temperature change rate of the position where the target data is located and the average value of the temperature change rates is calculated;

[0069] The average value of the temperature data of all normal monitoring positions at the current sampling moment is obtained, and the absolute value of the second difference between the target data and the average value of the temperature data is calculated;

[0070] The hyperbolic tangent processing is performed on the added result of the absolute value of the first difference and the absolute value of the second difference to obtain the dynamic trend characteristic value of the target data.

[0071] In one embodiment, taking the i-th position to be monitored as an example, the temperature data of the i-th position to be monitored at the current sampling moment is the target data. Obtain the average value of the temperature change rates of all normal monitoring positions and the average value of the temperature data of all normal monitoring positions at the current sampling moment, and calculate the dynamic trend characteristic value of the target data:

[0072]

[0073] where P is the dynamic trend characteristic value of the target data; is the temperature change rate of the i-th position to be monitored where the target data is located; i is the serial number of the position to be monitored; is the average value of the temperature change rates of all normal monitoring positions; is the target data (i.e., the temperature data of the i-th position to be monitored at the current sampling moment); is the average value of the temperature data of all normal monitoring positions at the current sampling moment; | | is the absolute value symbol; tanh() is the hyperbolic tangent function; t0 is the current sampling moment.

[0074] It should be noted that represents the difference between the temperature change rate of the i-th position to be monitored where the target data is located and the temperature change rate of the normal position. The larger it is, the greater the environmental interference on the i-th position to be monitored where the target data is located, and the larger the dynamic trend characteristic value of the target data; represents the difference between the target data and the temperature data of the normal position at the current sampling moment. The larger it is, the greater the environmental interference on the target data, and the larger the dynamic trend characteristic value of the target data.

[0075] (2) Obtain the dynamic fluctuation characteristic value of the target data according to the data fluctuation characteristic of the position to be monitored where the target data is located within the preset time range.

[0076] Specifically, form a temperature data sequence with the temperature data of the position to be monitored where the target data is located within the preset time range, obtain the absolute value of the difference between every two adjacent temperature data in the temperature data sequence, and correspondingly obtain the cumulative value of the absolute value of the difference. Perform hyperbolic tangent processing on the cumulative value of the absolute value of the difference to obtain the third hyperbolic tangent result;

[0077] Obtain the maximum value and the minimum value in the temperature data sequence, and perform hyperbolic tangent processing on the difference between the maximum value and the minimum value to obtain the fourth hyperbolic tangent result;

[0078] Obtain the dynamic fluctuation characteristic value of the target data according to the addition result of the third hyperbolic tangent result and the fourth hyperbolic tangent result.

[0079] In one embodiment, taking the i-th position to be monitored as an example, the temperature data of the i-th position to be monitored at the current sampling moment is the target data. The temperature data of the i-th position to be monitored within a preset time range where the target data is located forms a temperature data sequence. The maximum value and the minimum value in the temperature data sequence are obtained, and the dynamic fluctuation characteristic value of the target data is calculated:

[0080]

[0081] where R is the dynamic fluctuation characteristic value of the target data; is the (j + 1)-th temperature data in the temperature data sequence; is the j-th temperature data in the temperature data sequence; is the maximum value in the temperature data sequence; is the minimum value in the temperature data sequence; i is the serial number of the position to be monitored; j is the serial number of the temperature data in the temperature data sequence; n is the number of temperature data in the temperature data sequence (i.e., the number of sampling moments within the preset time range); | | is the absolute value symbol; tanh() is the hyperbolic tangent function.

[0082] It should be noted that represents the absolute value of the difference between two adjacent temperature data in the temperature data sequence, the larger, the greater the environmental interference on the temperature data of the i-th position to be monitored where the target data is located within the preset time range, and the larger the dynamic fluctuation characteristic value of the target data; represents the difference between the maximum value and the minimum value in the temperature data sequence, the larger, the greater the environmental interference on the temperature data of the i-th position to be monitored where the target data is located within the preset time range, and the larger the dynamic fluctuation characteristic value of the target data.

[0083] (3) Perform weighted summation on the dynamic trend characteristic value and the dynamic fluctuation characteristic value of the target data to obtain the dynamic environment influence coefficient of the target data.

[0084] In one embodiment, taking the i-th position to be monitored as an example, the temperature data of the i-th position to be monitored at the current sampling moment is the target data, and the dynamic environment influence coefficient of the target data is calculated:

[0085]

[0086] where is the dynamic environment influence coefficient of the target data; i is the serial number of the position to be monitored; P is the dynamic trend characteristic value of the target data; R is the dynamic fluctuation characteristic value of the target data; is the first weight coefficient, is the second weight coefficient, and the reference value of is taken as 0.5, which is not limited here and can be set according to specific implementation scenarios.

[0087] It should be noted that the larger the dynamic trend eigenvalue of the target data, the greater the environmental interference suffered by the target data, the greater the dynamic environmental impact coefficient of the target data, and the smaller the proportion of the subsequent target data in the optimal indoor temperature; the larger the dynamic fluctuation eigenvalue of the target data, the greater the environmental interference suffered by the target data, the greater the dynamic environmental impact coefficient of the target data, and the smaller the proportion of the subsequent target data in the optimal indoor temperature.

[0088] Thus, the dynamic environmental impact coefficient of the target data is obtained.

[0089] Step S104, obtain the dynamic environmental impact coefficient of the temperature data at each position to be monitored at the current sampling moment, obtain the dynamic weight of the temperature data at each position to be monitored at the current sampling moment according to the dynamic environmental impact coefficient of the temperature data at each position to be monitored at the current sampling moment, obtain the optimal indoor temperature at the current sampling moment according to the dynamic weight of the temperature data at each position to be monitored at the current sampling moment, and adjust the daylighting skylight according to the optimal indoor temperature.

[0090] After obtaining the dynamic environmental impact coefficient of the target data, according to the obtaining method of the dynamic environmental impact coefficient of the target data, obtain the dynamic environmental impact coefficient of the temperature data at each position to be monitored at the current sampling moment, and then, according to the dynamic environmental impact coefficient of the temperature data at each position to be monitored at the current sampling moment, obtain the dynamic weight of the temperature data at each position to be monitored at the current sampling moment, and then, obtain the optimal indoor temperature at the current sampling moment according to the dynamic weight of the temperature data at each position to be monitored at the current sampling moment.

[0091] Among them, the method for obtaining the dynamic weight of the temperature data at each position to be monitored at the current sampling moment according to the dynamic environmental impact coefficient of the temperature data at each position to be monitored at the current sampling moment is as follows:

[0092] Respectively obtain the subtraction results of the constant 1 and the dynamic environmental impact coefficient of the temperature data at each position to be monitored at the current sampling moment, and obtain the dynamic weight of the temperature data at each position to be monitored at the current sampling moment according to the proportion of the subtraction result corresponding to the temperature data at each position to be monitored at the current sampling moment in all subtraction results.

[0093] In an embodiment, taking the i-th position to be monitored as an example, calculate the dynamic weight of the temperature data at the i-th position to be monitored at the current sampling moment:

[0094]

[0095] Among them, is the dynamic weight of the temperature data at the i-th position to be monitored at the current sampling moment; is the dynamic environmental impact coefficient of the temperature data at the i-th position to be monitored at the current sampling moment (i.e., the dynamic environmental impact coefficient of the target data); i is the serial number of the position to be monitored; t0 is the current sampling moment; m is the number of positions to be monitored.

[0096] It should be noted that the greater the dynamic environmental impact coefficient of the temperature data at the i-th position to be monitored at the current sampling moment, the greater the environmental interference on the temperature data at the i-th position to be monitored at the current sampling moment. At this time, the temperature data is difficult to reflect the true indoor temperature situation. The smaller the dynamic weight of the temperature data at the i-th position to be monitored at the current sampling moment, and the smaller the proportion of the temperature data at the i-th position to be monitored at the current sampling moment in the subsequent optimal indoor temperature.

[0097] After obtaining the dynamic weight of the temperature data at the i-th position to be monitored at the current sampling moment, according to the above method for obtaining the dynamic weight of the temperature data at the i-th position to be monitored at the current sampling moment, obtain the dynamic weight of the temperature data at each position to be monitored at the current sampling moment. Then, according to the dynamic weight of the temperature data at each position to be monitored at the current sampling moment, obtain the optimal indoor temperature at the current sampling moment.

[0098] Among them, the method for obtaining the optimal indoor temperature at the current sampling moment according to the dynamic weight of the temperature data at each position to be monitored at the current sampling moment is as follows:

[0099] According to the dynamic weight of the temperature data at each position to be monitored at the current sampling moment, perform weighted summation on the temperature data at all positions to be monitored at the current sampling moment to obtain the optimal indoor temperature at the current sampling moment.

[0100] In an embodiment, the formula for calculating the optimal indoor temperature at the current sampling moment is:

[0101]

[0102] Among them, is the optimal indoor temperature at the current sampling moment; is the dynamic weight of the temperature data at the i-th position to be monitored at the current sampling moment; is the temperature data at the i-th position to be monitored at the current sampling moment; i is the serial number of the position to be monitored; t0 is the current sampling moment; m is the number of positions to be monitored.

[0103] It should be noted that the smaller the dynamic weight of the temperature data at the i-th position to be monitored at the current sampling moment, the smaller the proportion of the temperature data at the i-th position to be monitored at the current sampling moment in the optimal indoor temperature, indicating that the proportion of the temperature data affected by the environment in the optimal indoor temperature is smaller, and the optimal indoor temperature can better reflect the actual indoor temperature situation.

[0104] After obtaining the optimal indoor temperature, the temperature monitoring module feeds back the optimal temperature data to the intelligent control module. The control module judges the relationship with the preset temperature threshold and outputs an instruction to the skylight driving module at the same time. The skylight driving module completes the opening or closing of the daylighting skylight.

[0105] In the indoor of a building with a daylighting skylight according to an embodiment of the present invention, temperature data of each position to be monitored within a preset time range including the current sampling moment is obtained; according to the distribution characteristics of the temperature data of each position to be monitored at the current sampling moment, the discrete distribution rate of the temperature data at the current sampling moment is obtained; if the discrete distribution rate of the temperature data at the current sampling moment is greater than or equal to a preset temperature data discrete distribution rate threshold, the temperature data of any position to be monitored at the current sampling moment is recorded as target data, and according to the change characteristics of the temperature data in the preset time range that is the same as the position to be monitored where the target data is located, and the difference in the change of the temperature data at different positions to be monitored at the current sampling moment, the dynamic environment influence coefficient of the target data is obtained; the dynamic environment influence coefficient of the temperature data of each position to be monitored at the current sampling moment is obtained, and according to the dynamic environment influence coefficient of the temperature data of each position to be monitored at the current sampling moment, the dynamic weight of the temperature data of each position to be monitored at the current sampling moment is obtained, and according to the dynamic weight of the temperature data of each position to be monitored at the current sampling moment, the optimal indoor temperature at the current sampling moment is obtained, and the daylighting skylight is adjusted according to the optimal indoor temperature. In the embodiment of the present invention, first, the discrete distribution rate of the temperature data at the current sampling moment is obtained to judge whether the temperature data of each position to be monitored is affected by the environment. If the temperature data of each position to be monitored is not affected by the environment, the dynamic weight of each position to be monitored at the current sampling moment is obtained, and the optimal indoor temperature is obtained through the dynamic weight of each position to be monitored at the current sampling moment, which can more accurately reflect the actual situation of the indoor temperature at the current sampling moment, and then make an accurate judgment on the opening or closing of the skylight, improving the accuracy of the intelligent temperature detection and processing system.

[0106] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. An intelligent temperature monitoring and processing system for an openable skylight, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the following method is implemented: In a building with skylights, obtaining temperature data of each location to be monitored within a preset time range including the current sampling time; According to the distribution characteristics of the temperature data of each to-be-monitored position at the current sampling moment, obtaining the discrete distribution rate of the temperature data at the current sampling moment; If the discrete distribution rate of the temperature data at the current sampling moment is greater than or equal to a preset discrete distribution rate threshold of the temperature data, the temperature data of any to-be-monitored position at the current sampling moment is recorded as the target data, and the dynamic environmental impact coefficient of the target data is obtained according to the change characteristics of the temperature data at the same to-be-monitored position as the target data within the preset time range, and the difference in the change of the temperature data at different to-be-monitored positions at the current sampling moment; Obtain the dynamic environmental impact coefficient of the temperature data of each position to be monitored at the current sampling moment, obtain the dynamic weight of the temperature data of each position to be monitored at the current sampling moment according to the dynamic environmental impact coefficient of the temperature data of each position to be monitored at the current sampling moment, obtain the optimal indoor temperature at the current sampling moment according to the dynamic weight of the temperature data of each position to be monitored at the current sampling moment, and adjust the skylight according to the optimal indoor temperature.

2. The intelligent temperature monitoring and processing system for an openable skylight according to claim 1, characterized in that: After obtaining the discrete distribution rate of the temperature data at the current sampling moment according to the distribution characteristics of the temperature data of each to-be-monitored position at the current sampling moment, the method further includes: If the discrete distribution rate of the temperature data at the current sampling moment is less than the preset discrete distribution rate threshold of the temperature data, the average value of the temperature data of all the monitored locations at the current sampling moment is obtained as the optimal indoor temperature at the current sampling moment, and the skylight is adjusted according to the optimal indoor temperature.

3. The intelligent temperature monitoring and processing system for an openable skylight according to claim 1 is characterized in that: The step of obtaining the discrete distribution rate of the temperature data at the current sampling moment according to the distribution characteristics of the temperature data of each to-be-monitored position at the current sampling moment comprises: Obtaining the maximum temperature value and the minimum temperature value in the temperature data of all the locations to be monitored at the current sampling moment, performing hyperbolic tangent processing on the difference between the maximum temperature value and the minimum temperature value, and obtaining a first hyperbolic tangent result; Obtaining the standard deviation of the temperature data of all the locations to be monitored at the current sampling moment, substituting the standard deviation into an exponential function with a natural constant as the base to obtain an exponential function result, performing hyperbolic tangent processing on the difference between the exponential function result and the constant 1 to obtain a second hyperbolic tangent result; The discrete distribution rate of the temperature data at the current sampling moment is obtained according to the average of the first hyperbolic tangent result and the second hyperbolic tangent result.

4. The intelligent temperature monitoring and processing system for an openable skylight according to claim 1, characterized in that: The obtaining of the dynamic environmental impact coefficient of the target data according to the change characteristics of the temperature data at the same location to be monitored as the target data within the preset time range and the difference of the temperature data changes at different locations to be monitored at the current sampling time includes: Obtain the temperature change rate of each of the to-be-monitored positions within the preset time range, normalize the temperature change rate of each of the to-be-monitored positions using a hyperbolic tangent function to obtain a rate factor for each of the to-be-monitored positions, record the to-be-monitored positions whose rate factors are less than a preset rate factor threshold as normal monitoring positions, and obtain the dynamic trend characteristic value of the target data based on the difference between the temperature data of the to-be-monitored position where the target data is located and each of the normal monitoring positions; According to the data fluctuation characteristics of the to-be-monitored location where the target data is located within the preset time range, a dynamic fluctuation characteristic value of the target data is obtained; The dynamic trend characteristic value and the dynamic fluctuation characteristic value of the target data are weightedly summed to obtain the dynamic environmental impact coefficient of the target data.

5. The intelligent temperature monitoring and processing system for an openable skylight according to claim 4, characterized in that: The step of obtaining the dynamic trend characteristic value of the target data according to the difference between the temperature data of the to-be-monitored location where the target data is located and each of the normal monitoring locations includes: Obtaining the average of the temperature change rates of all normal monitoring positions, and calculating the absolute value of the first difference between the temperature change rate of the to-be-monitored position where the target data is located and the average of the temperature change rates; Obtain the average value of the temperature data of all normal monitoring positions at the current sampling time, and calculate the second absolute value of the difference between the target data and the average value of the temperature data; A hyperbolic tangent process is performed on the sum of the first difference absolute value and the second difference absolute value to obtain a dynamic trend characteristic value of the target data.

6. The intelligent temperature monitoring and processing system for an openable skylight according to claim 4, characterized in that: The acquiring the dynamic fluctuation characteristic value of the target data according to the data fluctuation characteristic of the location to be monitored where the target data is located within the preset time range includes: The temperature data of the monitored location where the target data is located within the preset time range are combined into a temperature data sequence, the absolute value of the difference between each two adjacent temperature data in the temperature data sequence is obtained, and the accumulated value of the absolute value of the difference is obtained accordingly, and the accumulated value of the absolute value of the difference is subjected to hyperbolic tangent processing to obtain a third hyperbolic tangent result; Obtaining a maximum value and a minimum value in the temperature data sequence, performing a hyperbolic tangent process on a difference between the maximum value and the minimum value, and obtaining a fourth hyperbolic tangent result; The dynamic fluctuation characteristic value of the target data is obtained according to the addition result of the third hyperbolic tangent result and the fourth hyperbolic tangent result.

7. The intelligent temperature monitoring and processing system for an openable skylight according to claim 1, characterized in that: The step of obtaining the dynamic weight of the temperature data of each to-be-monitored location at the current sampling moment according to the dynamic environmental impact coefficient of the temperature data of each to-be-monitored location at the current sampling moment comprises: For any monitored position at the current sampling moment, obtain the subtraction result of the constant 1 and the dynamic environmental impact coefficient of the temperature data of any monitored position; accumulate the subtraction results corresponding to the temperature data of each monitored position at the current sampling moment to obtain an accumulated value of the subtraction results; and record the ratio between the subtraction result corresponding to the temperature data of any monitored position and the accumulated value of the subtraction results as the dynamic weight of the temperature data of any monitored position.

8. The intelligent temperature monitoring and processing system for an openable skylight according to claim 1, characterized in that: The step of obtaining the optimal indoor temperature at the current sampling moment according to the dynamic weight of the temperature data of each location to be monitored at the current sampling moment includes: According to the dynamic weight of the temperature data of each to-be-monitored location at the current sampling moment, the temperature data of all to-be-monitored locations at the current sampling moment are weighted summed to obtain the optimal indoor temperature at the current sampling moment.

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