Intelligent temperature monitoring and processing system of openable daylighting skylight

Abstract

The invention relates to the technical field of data processing, in particular to an intelligent temperature monitoring and processing system of an openable daylighting skylight, the system comprises a processor and a memory, and the processor executes a computer program of the memory to realize the following steps: acquiring temperature data of each to-be-monitored position; according to the distribution characteristics of the temperature data of each to-be-monitored position, obtaining a temperature data discrete distribution rate; if the temperature data discrete distribution rate is greater than or equal to a preset temperature data discrete distribution rate threshold value, obtaining a dynamic environment influence coefficient of the temperature data of each to-be-monitored position, and obtaining a dynamic weight of the temperature data of each to-be-monitored position according to the dynamic environment influence coefficient of the temperature data of each to-be-monitored position; and obtaining the current optimal indoor temperature according to the dynamic weight of the temperature data of each to-be-monitored position. According to the invention, the actual temperature condition can be accurately reflected, and the opening or closing of the skylight can be accurately judged.

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 skylight. Background Art

[0002] The openable skylight is an architectural design element that combines natural lighting and ventilation functions. It usually uses transparent glass to provide good lighting effects and is widely used in the upper windows of office buildings, shopping malls and greenhouse buildings. Unlike fixed skylights, the openable skylight automatically adjusts the opening state of the skylight through an intelligent temperature monitoring and processing system, thereby achieving the purpose of maintaining a comfortable indoor temperature and saving energy and reducing consumption, which is in line with the modern trend of energy conservation and environmental protection. The intelligent temperature monitoring and processing system is mainly composed of a temperature detection module, an intelligent control module and a skylight drive module. The temperature detection module collects the indoor ambient temperature in real time. The intelligent control module determines whether the skylight needs to be opened or closed based on the preset temperature threshold and real-time temperature data. The skylight drive module executes the control module's instructions to open or close the skylight. The accurate and reliable intelligent temperature monitoring and processing system can significantly reduce energy consumption and optimize the building's energy use and environmental control.

[0003] At present, most buildings choose to adopt a multi-point temperature collection method to comprehensively monitor the overall temperature conditions in the building, and calculate the average or median of each sensor data as a representative value, output this value to the intelligent control module and judge the relationship with the threshold to control the opening and closing of the skylight. However, due to the fact that some sensors are affected by a certain degree of interference caused by unstable solar radiation at different times and the air flow fluctuations caused by opening and closing the skylight, the temperature data collected by all temperature sensors at certain times are quite different and have low consistency. The average or median value of each sensor data can no longer reflect the actual situation of the indoor temperature data, making it impossible for the intelligent control module to accurately determine whether the skylight needs to be opened or closed.

[0004] Therefore, how to accurately and reliably monitor the overall temperature in the building and then make accurate judgments 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 skylight to solve the problem of how to accurately and reliably monitor the overall temperature in a 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 skylight, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the following method when executing the computer program: 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.

[0007] 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 to-be-monitored position at the current sampling moment, the method further comprises: If the discrete distribution rate of the temperature data at the current sampling moment is less than a preset discrete distribution rate threshold of the temperature data, the average value of the temperature data of all 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.

[0008] 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 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.

[0009] Preferably, 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 in the change of the temperature data 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.

[0010] Preferably, 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.

[0011] Preferably, obtaining 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.

[0012] Preferably, obtaining the dynamic weight of the temperature data of each location to be monitored at the current sampling moment according to the dynamic environmental impact coefficient of the temperature data of each location to be monitored at the current sampling moment comprises: Obtain the subtraction results of the constant 1 and the dynamic environmental impact coefficient of the temperature data of each to-be-monitored location at the current sampling moment respectively, and obtain the dynamic weight of the temperature data of each to-be-monitored location at the current sampling moment according to the proportion of the subtraction results corresponding to the temperature data of each to-be-monitored location at the current sampling moment in all subtraction results.

[0013] Preferably, 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.

[0014] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention obtains temperature data of each position to be monitored within a preset time range including the current sampling moment in a building indoor with a skylight; obtains 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; if the discrete distribution rate of the temperature data at the current sampling moment is greater than or equal to the preset discrete distribution rate threshold of the temperature data, the temperature data of any position to be monitored 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 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; the dynamic environmental impact coefficient of the temperature data of each position to be monitored at the current sampling moment is obtained, the dynamic weight of the temperature data of each position to be monitored at the current sampling moment is obtained according to the dynamic environmental impact coefficient 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 according to the dynamic weight of the temperature data of each position to be monitored at the current sampling moment, and the skylight is adjusted according to the optimal indoor temperature. The present invention first obtains the discrete distribution rate of temperature data at the current sampling moment, and determines whether the temperature data of each to-be-monitored position is affected by the environment. If the temperature data of the to-be-monitored position is affected by the environment, the dynamic weight of each to-be-monitored position at the current sampling moment is obtained, and the optimal indoor temperature is obtained through the dynamic weight of each to-be-monitored position 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, thereby improving the accuracy of the intelligent temperature detection and processing system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0016] Figure 1 It is a flow chart of an intelligent temperature monitoring and processing method for an openable skylight provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, but should not be understood as limiting the present disclosure.

[0018] 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 are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, 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. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0019] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0020] An embodiment of the present invention provides an intelligent temperature monitoring and processing system for an openable skylight, comprising a processor and a memory, wherein the processor executes a computer program in the memory to implement an intelligent temperature monitoring and processing method for an openable skylight, such as Figure 1 As shown, the method comprises the following steps: Step S101, in a building with skylights, obtaining temperature data of each location to be monitored within a preset time range including a current sampling time.

[0021] The openable skylight is an architectural design element that combines natural lighting and ventilation functions. It usually uses transparent glass to provide good lighting effects and is widely used in the upper windows of office buildings, shopping malls and greenhouse buildings. The openable skylight automatically adjusts the opening state of the skylight through the intelligent temperature monitoring and processing system, so as to maintain a comfortable indoor temperature and save energy and reduce consumption, which is in line with the modern trend of energy conservation and environmental protection. The intelligent temperature monitoring and processing system is mainly composed of a temperature detection module, an intelligent control module and a skylight drive module. The temperature detection module collects the indoor ambient temperature in real time. The intelligent control module determines whether the skylight needs to be opened or closed according to the preset temperature threshold and real-time temperature data. The skylight drive module executes the control module's instructions to open or close the skylight.

[0022] In the indoor building with skylights, the temperature detection module uses an NTC thermistor to obtain temperature data for each monitored location within 11 seconds including the current sampling moment at a sampling frequency of once per second. There is no restriction here and it can be set according to the specific implementation scenario.

[0023] Step S102, obtaining a 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.

[0024] In order to comprehensively monitor the overall indoor temperature of the building, most buildings use a multi-point temperature collection method to calculate the average or median of the temperature data of each monitored location as a representative value, output the value to the intelligent control module, and determine the relationship with the threshold to control the opening and closing of the skylight. However, due to the fact that the temperature data of some monitored locations are disturbed to a certain extent by unstable solar radiation at different times and the airflow fluctuations caused by the opening and closing of the skylight, the temperature data collected at certain times by all monitored locations are quite different and have low consistency. The average or median value of the temperature data of each monitored location cannot reflect the actual situation of the indoor temperature data, making it impossible for the intelligent control module to accurately determine whether the skylight needs to be opened or closed.

[0025] 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 of each monitored location at the current sampling moment, and judge whether the temperature data at the current sampling moment is affected by environmental interference 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 environmental interference, the optimal indoor temperature that can truly reflect the actual indoor temperature situation is obtained.

[0026] Among them, according to the distribution characteristics of the temperature data of each to-be-monitored position at the current sampling moment, the method for obtaining the discrete distribution rate of the temperature data at the current sampling moment is as follows: 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.

[0027] In one embodiment, the maximum temperature value and the minimum temperature value in the temperature data of all the positions to be monitored at the current sampling time, and the standard deviation of the temperature data of all the positions to be monitored at the current sampling time are obtained, and the discrete distribution rate of the temperature data at the current sampling time is calculated according to the maximum temperature value and the minimum temperature value in the temperature data of all the positions to be monitored at the current sampling time, and the standard deviation of the temperature data:

[0028] Among them, Q is the discrete distribution rate of temperature data at the current sampling moment; The maximum temperature value among the temperature data of all locations to be monitored at the current sampling time; The minimum temperature value among the temperature data of all locations to be monitored at the current sampling time; is the standard deviation of the temperature data of all monitored locations at the current sampling moment; e is a natural constant; tanh() is the hyperbolic tangent function.

[0029] It should be noted that It represents the temperature data span between different monitored locations. The larger the value is, the more significant the difference in temperature data between the monitored locations is, and the greater the discrete distribution rate of temperature data at the current sampling time is. It reflects the distribution of temperature data between the locations to be monitored. The larger it is, the more dispersed the distribution of temperature data between the monitored positions is, and the greater the discrete distribution rate of temperature data at the current sampling moment is; the larger the discrete distribution rate of temperature data at the current sampling moment is, the more discrete the distribution of temperature data between the monitored positions is, the lower the consistency is, and the greater the possibility that the temperature data at the current sampling moment is affected by environmental interference is.

[0030] At this point, the discrete distribution rate of the temperature data at the current sampling moment is obtained.

[0031] Step S103, if the discrete distribution rate of the temperature data at the current sampling moment is greater than or equal to the preset discrete distribution rate threshold of the temperature data, the temperature data of any 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 monitored position as the target data within the preset time range, and the difference in the change of temperature data at different monitored positions at the current sampling moment.

[0032] After obtaining the discrete distribution rate of the temperature data at the current sampling moment, it is possible to determine whether the temperature data at the current sampling moment is interfered with by the environment based on the discrete distribution rate of the temperature data at the current sampling moment. The discrete distribution rate threshold of the temperature data is set to 0.6. There is no restriction here. It can be set according to the specific implementation scenario. 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 (i.e. ), it is determined that the temperature data at the current sampling moment is less affected by the environmental interference, and the average value or median value of the temperature data of each monitored position can reflect the actual situation of the indoor temperature data. At this time, the average value of the temperature data of all monitored positions 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.

[0033] If the discrete distribution rate of the temperature data at the current sampling time is greater than or equal to 0.6 (i.e. ), it is determined that the temperature data at the current sampling time is greatly disturbed by the environment, and the average or median value of the temperature data of each monitored location can no longer reflect the actual situation of the indoor temperature data, making it impossible for the intelligent control module to accurately determine whether the skylight needs to be opened or closed. Therefore, it is necessary to analyze the temperature data of each monitored location, and determine the weight of the temperature data of each monitored location according to the degree of environmental interference of the temperature data of each monitored location, so as to obtain the optimal indoor temperature that can more truly reflect the actual situation of the indoor temperature.

[0034] When each monitored position is disturbed by the environment, the rate of change of its temperature data will be significantly accelerated, and the temperature value will also show a state that is inconsistent with the temperature data of other monitored positions. The temperature data fluctuates more frequently and violently. Therefore, the temperature data of any monitored position at the current sampling time can be recorded as target data. The dynamic environmental impact coefficient of the target data is obtained based on the change characteristics of the temperature data at the same monitored position as the target data within a preset time range (i.e., within 11 seconds) and the difference in the change of temperature data at different monitored positions at the current sampling time. Then, the weight of the temperature data of each monitored position is determined through the dynamic environmental impact coefficient of the target data to obtain the optimal indoor temperature that can more truly reflect the actual situation of the indoor temperature data.

[0035] Among them, 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 in the change of temperature data at different locations to be monitored at the current sampling time, the method for obtaining the dynamic environmental impact coefficient of the target data is as follows: (1) 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 in temperature data between the to-be-monitored position where the target data is located and each of the normal monitoring positions.

[0036] In one embodiment, taking the ith position to be monitored as an example, the temperature change rate of the ith position to be monitored within a preset time range is obtained:

[0037] in, is the temperature change rate of the i-th position to be monitored within a preset time range; is the temperature data of the i-th location to be monitored at the current sampling time; is the temperature data of the ith monitored position at the first sampling moment within the preset time range; t0 is the current sampling moment; i is the serial number of the monitored position; n is the number of sampling moments within the preset time range; | | is the absolute value symbol; since the temperature change rate belongs to the prior art, it will not be repeated here.

[0038] After obtaining the temperature change rate of the ith monitored position within the preset time range, the temperature change rate of the ith monitored position is normalized using the hyperbolic tangent function to obtain the rate factor of the ith monitored position. The hyperbolic tangent function is used to limit the output result to (0, 1) to facilitate subsequent screening.

[0039] Similarly, obtain the temperature change rate of each monitored location within the preset time range, as well as the rate factor of each monitored location, and set the rate factor threshold to 0.6. There is no restriction here. It can be set according to the specific implementation scenario. The monitored location with a rate factor less than 0.6 is recorded as a normal monitoring location. According to the difference in temperature data between the monitored location where the target data is located and each normal monitoring location, the dynamic trend characteristic value of the target data is obtained.

[0040] Specifically, the average temperature change rate of all normal monitoring positions is obtained, and 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 temperature change rate is calculated; 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.

[0041] 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 time is the target data, the average value of the temperature change rate of all normal monitoring positions and the average value of the temperature data of all normal monitoring positions at the current sampling time are obtained, and the dynamic trend characteristic value of the target data is calculated:

[0042] Among them, 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 temperature change rate of all normal monitoring locations; is the target data (i.e., the temperature data of the i-th location to be monitored at the current sampling time); is the mean temperature data of all normal monitoring positions at the current sampling time; | | is the absolute value symbol; tanh() is the hyperbolic tangent function; t0 is the current sampling time.

[0043] It should be noted that It represents the difference between the temperature change rate of the i-th monitored location where the target data is located and the temperature change rate of the normal location. The larger it is, the greater the environmental interference to the i-th monitored location where the target data is located is, and the larger the dynamic trend characteristic value of the target data is; Indicates the difference between the target data and the temperature data at the normal position at the current sampling time. The larger it is, the greater the environmental interference to the target data is, and the larger the dynamic trend characteristic value of the target data is.

[0044] (2) Acquire a dynamic fluctuation characteristic value of the target data according to the data fluctuation characteristic of the to-be-monitored location where the target data is located within the preset time range.

[0045] Specifically, the temperature data of the to-be-monitored position 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 values ​​of the differences is obtained accordingly, and the accumulated value of the absolute values ​​of the differences 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.

[0046] 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 time is the target data, the temperature data of the i-th position to be monitored where the target data is located within a preset time range is formed into 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:

[0047] Among them, R is the dynamic fluctuation characteristic value of the target data; is the j+1th temperature data in the temperature data sequence; is the jth temperature data in the temperature data sequence; is the maximum value in the temperature data series; 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 (that is, the number of sampling moments within the preset time range); | | is the absolute value symbol; tanh() is the hyperbolic tangent function.

[0048] It should be noted that Indicates the absolute value of the difference between two adjacent temperature data in the temperature data sequence. The larger it is, the greater the environmental interference to the temperature data of the i-th monitored location where the target data is located within the preset time range, and the greater the dynamic fluctuation characteristic value of the target data; Represents the difference between the maximum and minimum values ​​in the temperature data series, The larger it is, the greater the environmental interference to the temperature data of the i-th monitored location where the target data is located within the preset time range, and the greater the dynamic fluctuation characteristic value of the target data.

[0049] (3) Performing weighted summation on the dynamic trend characteristic value and the dynamic fluctuation characteristic value of the target data to obtain the dynamic environmental impact coefficient of the target data.

[0050] In one implementation, taking the ith position to be monitored as an example, the temperature data of the ith position to be monitored at the current sampling time is the target data, and the dynamic environmental impact coefficient of the target data is calculated: in, is the dynamic environmental impact coefficient of the target data; i is the serial number of the location 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 is 0.5, which is not limited here and can be set according to the specific implementation scenario.

[0051] It should be noted that the larger the dynamic trend characteristic value of the target data, the greater the environmental interference to the target data, the larger the dynamic environmental impact coefficient of the target data, and the smaller the proportion of subsequent target data in the optimal indoor temperature; the larger the dynamic fluctuation characteristic value of the target data, the greater the environmental interference to the target data, the larger the dynamic environmental impact coefficient of the target data, and the smaller the proportion of subsequent target data in the optimal indoor temperature.

[0052] At this point, the dynamic environmental impact coefficient of the target data is obtained.

[0053] Step S104, obtaining the dynamic environmental impact coefficient of the temperature data of each to-be-monitored position at the current sampling moment, obtaining the dynamic weight of the temperature data of each to-be-monitored position at the current sampling moment according to the dynamic environmental impact coefficient of the temperature data of each to-be-monitored position at the current sampling moment, 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, and adjusting the skylight according to the optimal indoor temperature.

[0054] After obtaining the dynamic environmental impact coefficient of the target data, the dynamic environmental impact coefficient of the temperature data of each to-be-monitored location at the current sampling moment is obtained according to the method for obtaining the dynamic environmental impact coefficient of the target data, and then the dynamic weight of the temperature data of each to-be-monitored location at the current sampling moment is obtained according to the dynamic environmental impact coefficient of the temperature data of each to-be-monitored location at the current sampling moment, and then, according to the dynamic weight of the temperature data of each to-be-monitored location at the current sampling moment, the optimal indoor temperature at the current sampling moment is obtained.

[0055] Among them, according to the dynamic environmental impact coefficient of the temperature data of each to-be-monitored location at the current sampling time, the method for obtaining the dynamic weight of the temperature data of each to-be-monitored location at the current sampling time is as follows: Obtain the subtraction results of the constant 1 and the dynamic environmental impact coefficient of the temperature data of each to-be-monitored location at the current sampling moment respectively, and obtain the dynamic weight of the temperature data of each to-be-monitored location at the current sampling moment according to the proportion of the subtraction results corresponding to the temperature data of each to-be-monitored location at the current sampling moment in all subtraction results.

[0056] In one implementation, taking the ith position to be monitored as an example, the dynamic weight of the temperature data of the ith position to be monitored at the current sampling time is calculated: in, is the dynamic weight of the temperature data of the ith location to be monitored at the current sampling time; is the dynamic environmental impact coefficient of the temperature data of the i-th position to be monitored at the current sampling time (that is, 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 time; and m is the number of the positions to be monitored.

[0057] It should be noted that, the larger the dynamic environmental impact coefficient of the temperature data of the i-th monitored position at the current sampling moment is, the greater the environmental interference to the temperature data of the i-th monitored position at the current sampling moment is, and the temperature data at this time is difficult to reflect the actual indoor temperature conditions. The smaller the dynamic weight of the temperature data of the i-th monitored position at the current sampling moment is, the smaller the proportion of the temperature data of the i-th monitored position at the current sampling moment in the subsequent optimal indoor temperature is.

[0058] After obtaining the dynamic weight of the temperature data of the i-th 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 according to the method for obtaining the dynamic weight of the temperature data of the i-th position to be monitored at the current sampling moment. Then, 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.

[0059] Among them, according to the dynamic weight of the temperature data of each location to be monitored at the current sampling time, the method for obtaining the optimal indoor temperature at the current sampling time is as follows: 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.

[0060] In one embodiment, the formula for calculating the optimal indoor temperature at the current sampling time is: in, is the optimal indoor temperature at the current sampling time; is the dynamic weight of the temperature data of the ith location to be monitored at the current sampling time; is the temperature data of the i-th position to be monitored at the current sampling time; i is the serial number of the position to be monitored; t0 is the current sampling time; m is the number of the positions to be monitored.

[0061] It should be noted that the smaller the dynamic weight of the temperature data of the i-th monitored position at the current sampling moment, the smaller the proportion of the temperature data of the i-th monitored position at the current sampling moment in the optimal indoor temperature, which means that the smaller the proportion of the temperature data affected by environmental interference in the optimal indoor temperature, the more the optimal indoor temperature can reflect the actual indoor temperature situation.

[0062] After obtaining the optimal indoor temperature, the temperature monitoring module feeds back the optimal temperature data to the intelligent control module, which determines the relationship with the preset temperature threshold and outputs instructions to the skylight drive module, which completes the opening or closing of the skylight.

[0063] According to an embodiment of the present invention, in a building with a skylight, temperature data of each position to be monitored within a preset time range including a current sampling moment is obtained; a discrete distribution rate of the temperature data at 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; 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 position to be monitored at the current sampling moment is recorded as the target data, and a dynamic environmental impact coefficient of the target data is obtained according to the change characteristics of the temperature data of the same position to be monitored as the target data within the preset time range, and the difference in the change of temperature data of different positions to be monitored at the current sampling moment; the dynamic environmental impact coefficient of the temperature data of each position to be monitored at the current sampling moment is obtained, and the dynamic weight of the temperature data of each position to be monitored at the current sampling moment is obtained according to the dynamic environmental impact coefficient of the temperature data of each position to be monitored at the current sampling moment, and the optimal indoor temperature at the current sampling moment is obtained according to the dynamic weight of the temperature data of each position to be monitored at the current sampling moment, and the skylight is adjusted according to the optimal indoor temperature. The embodiment of the present invention first obtains the discrete distribution rate of the temperature data at the current sampling moment, and determines whether the temperature data of each to-be-monitored position is affected by the environment. If the temperature data of each to-be-monitored position is not affected by the environment, the dynamic weight of each to-be-monitored position at the current sampling moment is obtained. The optimal indoor temperature is obtained through the dynamic weight of each to-be-monitored position 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, thereby improving the accuracy of the intelligent temperature detection and processing system.

[0064] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in 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 is 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, 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: Obtain the subtraction results of the constant 1 and the dynamic environmental impact coefficient of the temperature data of each to-be-monitored location at the current sampling moment respectively, and obtain the dynamic weight of the temperature data of each to-be-monitored location at the current sampling moment according to the proportion of the subtraction results corresponding to the temperature data of each to-be-monitored location at the current sampling moment in all subtraction results.

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.

Images