An intelligent temperature-controlled linear bathroom heater cooling system

Abstract

The present invention relates to the field of data processing technology, and specifically to an intelligent temperature-controlled linear bathroom heater heat dissipation system, which obtains a temperature data sequence of the bathroom heater and divides it into multiple adaptive temperature data sequences. Then, a temperature fluctuation index is calculated for each adaptive sequence to evaluate the degree of temperature change. The target adaptive temperature data sequence is screened out according to the fluctuation index. Then, the temperature index difference and temperature trend index of the target adaptive temperature data sequence are calculated to obtain the difference and trend of temperature change. The heat dissipation enhancement factor is calculated using these indicators to adjust the heat dissipation performance of the bathroom heater. Finally, by comparing the heat dissipation enhancement factors of all target sequences, the final modified heat dissipation enhancement factor is obtained to complete the adjustment of the heat dissipation of the bathroom heater. The present invention obtains the instructions for adjusting the temperature of the bathroom heater by analyzing the historical temperature data of the bathroom heater, thereby improving the heat dissipation efficiency of the bathroom heater.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an intelligent temperature-controlled linear bathroom heater heat dissipation system. Background Art

[0002] A bathroom heater is a device commonly found in bathrooms, usually mounted on the ceiling. It combines lighting and heating functions to provide a comfortable bathroom environment. The main components of a bathroom heater include lamps and heaters, and some models also include fans. Lamps are used to provide lighting, while heaters provide warmth by heating the air or radiating it. However, a lot of heat is generated during use. Excessive heat can cause damage to the electronic components or LED lights inside the bathroom heater, and may also cause dangers such as fire.

[0003] Existing problem: When the traditional cooling system cools the smart temperature-controlled bathroom heater, a temperature threshold needs to be set. When the temperature threshold is not set appropriately, it will affect the heat dissipation efficiency of the bathroom heater. That is, when the temperature threshold is set too large, the heat cannot be dissipated in time, resulting in excessively high bathroom temperature. When the temperature threshold is set too small, the heat dissipation times will be too many and the frequency will be high, which reduces the heat dissipation efficiency of the bathroom heater. Summary of the invention

[0004] The present invention provides an intelligent temperature-controlled linear bathroom heater heat dissipation system to solve the existing problems.

[0005] The intelligent temperature-controlled linear bathroom heater heat dissipation system of the present invention adopts the following technical solutions:

[0006] An embodiment of the present invention provides an intelligent temperature-controlled linear bathroom heater heat dissipation system, which includes the following modules:

[0007] Data acquisition module: used to obtain the temperature data sequence of the bathroom heater;

[0008] Data segmentation module: used to obtain several adaptive temperature data sequences segmented from the temperature data sequence;

[0009] Target sequence screening module: used to obtain the temperature fluctuation index of each adaptive temperature data sequence according to the difference between the temperature data in each adaptive temperature data sequence; and screen out several target adaptive temperature data sequences from all adaptive temperature data sequences according to the temperature fluctuation index of each adaptive temperature data sequence;

[0010] Temperature trend index analysis module: used to obtain the temperature index difference of each target adaptive temperature data sequence according to the difference of temperature data of each target adaptive temperature data sequence; obtain the temperature trend index of each target adaptive temperature data sequence according to the difference of temperature data of each target adaptive temperature data sequence and the temperature index difference;

[0011] Heat dissipation enhancement factor analysis module: used to obtain the heat dissipation enhancement factor of each target adaptive temperature data sequence according to the temperature fluctuation index, temperature trend index and temperature index difference of each target adaptive temperature data sequence;

[0012] Modified heat dissipation enhancement factor analysis module: used to obtain the modified heat dissipation enhancement factor of the last target adaptive temperature data sequence according to the difference between the heat dissipation enhancement factors of all target adaptive temperature data sequences;

[0013] Heat dissipation adjustment and analysis module: used to adjust the heat dissipation of the bathroom heater according to the modified heat dissipation enhancement factor of the last target adaptive temperature data sequence.

[0014] Furthermore, the specific calculation formula corresponding to the temperature fluctuation index of each adaptive temperature data sequence is obtained according to the difference between the temperature data in each adaptive temperature data sequence:

[0015]

[0016] Among them, Z v represents the temperature fluctuation index of the vth adaptive temperature data sequence; N v represents the number of temperature data in the vth adaptive temperature data sequence; T v,i represents the i-th temperature data of the v-th adaptive temperature data sequence; represents the mean value of all temperature data of the vth adaptive temperature data sequence; represents the maximum value among the means of all temperature data in all adaptive temperature data sequences; || represents the absolute value.

[0017] Furthermore, the step of selecting a plurality of target adaptive temperature data sequences from all adaptive temperature data sequences according to the temperature fluctuation index of each adaptive temperature data sequence includes:

[0018] When Z v When it is greater than the preset empirical threshold, the vth adaptive temperature data sequence is recorded as the target adaptive temperature data sequence; where Z v It is the temperature fluctuation index of the vth adaptive temperature data sequence.

[0019] Furthermore, obtaining the temperature index difference of each target adaptive temperature data sequence according to the difference of the temperature data of each target adaptive temperature data sequence includes:

[0020] The average of the first f temperature data in the c-th target adaptive temperature data sequence is recorded as the initial temperature index of the c-th target adaptive temperature data sequence;

[0021] The average of the last f temperature data in the c-th target adaptive temperature data sequence is recorded as the final temperature index of the c-th target adaptive temperature data sequence; wherein f is the preset number of data;

[0022] The difference between the final temperature index of the cth target adaptive temperature data sequence and the initial temperature index of the cth target adaptive temperature data sequence is recorded as the temperature index difference of the cth target adaptive temperature data sequence.

[0023] Furthermore, the specific calculation formula corresponding to the temperature trend index of each target adaptive temperature data sequence is obtained according to the difference of the temperature data of each target adaptive temperature data sequence and the temperature index difference:

[0024]

[0025] Among them, S c Represents the temperature trend index of the cth target adaptive temperature data sequence; T' c,m+1 represents the m+1th temperature data of the cth target adaptive temperature data sequence; T' c,m represents the mth temperature data of the cth target adaptive temperature data sequence; D c Represents the temperature index difference of the cth target adaptive temperature data sequence; N' c represents the number of temperature data in the cth target adaptive temperature data sequence; || represents the absolute value.

[0026] Further, the heat dissipation enhancement factor of each target adaptive temperature data sequence is obtained according to the temperature fluctuation index, the temperature trend index and the temperature index difference of each target adaptive temperature data sequence, including:

[0027] According to the temperature fluctuation index of each target adaptive temperature data sequence and the size of the temperature data, the relative fluctuation size of each target adaptive temperature data sequence is obtained;

[0028] According to the relative fluctuation size, temperature trend index and temperature index difference of each target adaptive temperature data sequence, the heat dissipation enhancement factor of each target adaptive temperature data sequence is obtained.

[0029] Furthermore, the specific calculation formula corresponding to the heat dissipation enhancement factor of each target adaptive temperature data sequence is obtained according to the relative fluctuation size, temperature trend index and temperature index difference of each target adaptive temperature data sequence:

[0030]

[0031] Among them, W crepresents the heat dissipation enhancement factor of the cth target adaptive temperature data sequence; S c represents the temperature trend index of the cth target adaptive temperature data sequence; D c Represents the temperature index difference of the cth target adaptive temperature data sequence; Z' c Represents the temperature fluctuation index of the cth target adaptive temperature data sequence; T' c,max Indicates the maximum temperature data in the cth target adaptive temperature data sequence; T' c,min Indicates the minimum temperature data in the cth target adaptive temperature data sequence; N' c represents the number of temperature data in the cth target adaptive temperature data sequence; f represents the preset number of data; exp() represents an exponential function with a natural constant as the base; softmax[] represents a normalized exponential function; is the relative fluctuation size of the cth target adaptive temperature data sequence.

[0032] Further, obtaining the corrected heat dissipation enhancement factor of the last target adaptive temperature data sequence according to the difference between the heat dissipation enhancement factors of all target adaptive temperature data sequences includes:

[0033] According to the temperature fluctuation index and the temperature trend index of each target adaptive temperature data sequence, a plurality of reference adaptive temperature data sequences are selected from all target adaptive temperature data sequences;

[0034] According to the difference between the heat dissipation enhancement factor of the last target adaptive temperature data sequence and each reference adaptive temperature data sequence, a corrected heat dissipation enhancement factor of the last target adaptive temperature data sequence is obtained.

[0035] Further, the step of selecting a plurality of reference adaptive temperature data sequences from all target adaptive temperature data sequences according to the temperature fluctuation index and the temperature trend index of each target adaptive temperature data sequence includes:

[0036] The product of the temperature fluctuation index and the temperature trend index of each target adaptive temperature data sequence is recorded as the similar temperature index of each target adaptive temperature data sequence;

[0037] Recording the other target adaptive temperature data sequences except the last target adaptive temperature data sequence as sub-target adaptive temperature data sequences;

[0038] Calculate the absolute value of the difference between the last target adaptive temperature data sequence and the similar temperature index of each sub-target adaptive temperature data sequence, and record the sub-target adaptive temperature data sequences corresponding to the first b minimum values ​​of the absolute values ​​of the difference between the last target adaptive temperature data sequence and the similar temperature index of all sub-target adaptive temperature data sequences as reference adaptive temperature data sequences; where b is the preset number of sequences.

[0039] Furthermore, the specific calculation formula corresponding to the modified heat dissipation enhancement factor of the last target adaptive temperature data sequence is obtained according to the difference between the heat dissipation enhancement factor of the last target adaptive temperature data sequence and each reference adaptive temperature data sequence:

[0040]

[0041] W' now represents the modified heat dissipation enhancement factor of the last target adaptive temperature data sequence; W now represents the heat dissipation enhancement factor of the last target adaptive temperature data sequence; G represents the number of reference adaptive temperature data sequences; W' k represents the heat dissipation enhancement factor of the kth reference adaptive temperature data sequence; Q k Represents the absolute value of the difference between the similarity temperature index of the last target adaptive temperature data sequence and the kth reference adaptive temperature data sequence.

[0042] The beneficial effects of the technical solution of the present invention are:

[0043] The temperature data sequence of the bathroom heater is obtained; several adaptive temperature data sequences are obtained from the temperature data sequence, so as to ensure that the same working mode of the bathroom heater is maintained in each adaptive temperature data sequence. According to the difference between the temperature data in each adaptive temperature data sequence, the temperature fluctuation index of each adaptive temperature data sequence is obtained; according to the temperature fluctuation index of each adaptive temperature data sequence, several target adaptive temperature data sequences are screened out from all adaptive temperature data sequences, so as to ensure that the bathroom heater is in the on mode in each target adaptive temperature data sequence. According to the difference in the temperature data of each target adaptive temperature data sequence, the temperature index difference of each target adaptive temperature data sequence is obtained, so as to ensure that the bathroom heater is in the state of heating in the sequence. According to the difference in the temperature data of each target adaptive temperature data sequence and the temperature index difference, the temperature trend index of each target adaptive temperature data sequence is obtained, so as to further ensure that the bathroom heater is in the state of heating in the sequence. According to the temperature fluctuation index, the temperature trend index and the temperature index difference of each target adaptive temperature data sequence, the heat dissipation enhancement factor of each target adaptive temperature data sequence is obtained, so as to improve the heat dissipation efficiency of the bathroom heater. According to the difference between the heat dissipation enhancement factors of all target adaptive temperature data sequences, the corrected heat dissipation enhancement factor of the last target adaptive temperature data sequence is obtained, thereby further improving the heat dissipation efficiency of the bathroom heater. According to the corrected heat dissipation enhancement factor of the last target adaptive temperature data sequence, the heat dissipation of the bathroom heater is adjusted. The present invention obtains instructions for adjusting the temperature of the bathroom heater by analyzing the historical temperature data of the bathroom heater, thereby improving the heat dissipation efficiency of the bathroom heater. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, 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 work.

[0045] Figure 1 This is a module flow chart of an intelligent temperature-controlled linear bathroom heater cooling system of the present invention;

[0046] Figure 2 This is a flow chart for adjusting the heat dissipation of the bathroom heater in this embodiment. DETAILED DESCRIPTION

[0047] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation method, structure, characteristics and effects of an intelligent temperature-controlled linear bathroom heater cooling system proposed by the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0048] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0049] The specific scheme of the intelligent temperature-controlled linear bathroom heater heat dissipation system provided by the present invention is described in detail below with reference to the accompanying drawings.

[0050] See also Figure 1 , which shows a module flow chart of an intelligent temperature-controlled linear bathroom heater cooling system provided by an embodiment of the present invention, the system includes the following modules:

[0051] Module 101: Data acquisition module.

[0052] This module is used to obtain the temperature data series of the bathroom heater.

[0053] What needs to be explained is that: there are temperature sensors and heat dissipation devices on the bathroom heater, among which the temperature sensor can obtain the current real-time temperature of the bathroom heater, and the heat dissipation device can dissipate the heat of the bathroom heater to ensure that the components and equipment inside the bathroom heater will not be damaged due to excessive heat. Regardless of whether the bathroom heater is turned on or not, the temperature sensor will collect the temperature. The frequency of collecting the temperature data sequence of the bathroom heater is t minutes each time. When the bathroom heater is not turned on, the temperature sensor will collect the temperature of the bathroom heater, and the temperature is generally related to the surrounding environment. The present invention determines that the temperature of the surrounding environment remains basically constant and will not interfere with the temperature change after the bathroom heater is turned on. Among them, t is the preset frequency. The preset frequency t in this embodiment is 1 minute each time, and this example is used for description.

[0054] Module 102: Data segmentation module.

[0055] This module is used to obtain several adaptive temperature data sequences divided from the temperature data sequence.

[0056] The temperature data sequence is operated using an adaptive piecewise constant approximation algorithm to divide the temperature data sequence into several adaptive temperature data sequences.

[0057] It should be noted that: since the collected temperature is affected by the turning on of the bathroom heater and the cooling system, there are relatively large differences in the temperature changes in different time periods collected. Among them, when the bathroom heater is not turned on, the temperature obtained by the temperature sensor is relatively low and relatively constant; when the bathroom heater is turned on, the temperature will gradually rise; when the cooling system is turned on, the temperature may drop. In order to better understand and analyze the current working status of the bathroom heater, an adaptive piecewise constant approximation algorithm can be used to distinguish and divide the collected temperature data sequence. The role of the adaptive piecewise constant approximation algorithm is to perform adaptive piecewise approximation on a given temperature data sequence, and to determine as much as possible that the same working mode of the bathroom heater is maintained in each divided data sequence. Among them, the adaptive piecewise constant approximation algorithm is a well-known technology, and the specific method will not be introduced here.

[0058] Module 103: Target sequence screening module.

[0059] This module is used to obtain the temperature fluctuation index of each adaptive temperature data sequence according to the difference between the temperature data in each adaptive temperature data sequence; according to the temperature fluctuation index of each adaptive temperature data sequence, a number of target adaptive temperature data sequences are screened out from all adaptive temperature data sequences.

[0060] What needs to be explained is: when the bathroom heater is not turned on, the temperature obtained by the temperature sensor is approximately constant and relatively low; after the bathroom heater is turned on, the temperature obtained by the temperature sensor begins to increase. Although the temperature will be lowered after the subsequent cooling system is turned on, the temperature is still higher than the period when the bathroom heater is not turned on. Therefore, the fluctuation of the temperature data in each adaptive temperature data sequence is used to determine whether the bathroom heater is in the turned-on state in each adaptive temperature data sequence.

[0061] Taking the vth adaptive temperature data sequence as an example, the specific calculation formula corresponding to the temperature fluctuation index of the vth adaptive temperature data sequence is:

[0062]

[0063] Among them, Z v represents the temperature fluctuation index of the vth adaptive temperature data sequence; N v represents the number of temperature data in the vth adaptive temperature data sequence; T v,i represents the i-th temperature data of the v-th adaptive temperature data sequence; represents the mean value of all temperature data of the vth adaptive temperature data sequence; represents the maximum value among the means of all temperature data in all adaptive temperature data sequences; || represents the absolute value.

[0064] What needs to be explained is: The larger the value of , the greater the fluctuation of the temperature data of the vth adaptive temperature data sequence, and the possibility that the bathroom heater is not turned on in the vth adaptive temperature data sequence is relatively small; The larger the value of , the higher the overall temperature data of the vth adaptive temperature data sequence is, and the possibility that the bathroom heater is not turned on in the vth adaptive temperature data sequence is relatively small.

[0065] When Z v When it is greater than the preset empirical threshold, the vth adaptive temperature data sequence is recorded as the target adaptive temperature data sequence. In this embodiment, the preset empirical threshold is 10, which is used as an example for description.

[0066] According to the above method, it is determined whether all adaptive temperature data sequences are target adaptive temperature data sequences.

[0067] Module 104: Temperature trend indicator analysis module.

[0068] This module is used to obtain the temperature index difference of each target adaptive temperature data sequence according to the difference of temperature data of each target adaptive temperature data sequence; and to obtain the temperature trend index of each target adaptive temperature data sequence according to the difference of temperature data of each target adaptive temperature data sequence and the temperature index difference.

[0069] It should be noted that: since the collected temperature data are arranged in chronological order, the degree of change of the current temperature can be determined through the first temperature data of the target adaptive temperature data sequence; at the same time, if the current temperature is rising or falling, the change of the current temperature can also be determined through the difference between the temperature data of adjacent intervals in the collection interval; therefore, the temperature trend index of the sequence can be determined through the temperature data that changes over time in the target adaptive temperature data sequence.

[0070] Taking the cth target adaptive temperature data sequence as an example, the average of the first f temperature data in the cth target adaptive temperature data sequence is recorded as the initial temperature index of the cth target adaptive temperature data sequence.

[0071] The average of the last f temperature data in the cth target adaptive temperature data sequence is recorded as the final temperature index of the cth target adaptive temperature data sequence. Wherein, f is the preset number of data, and the preset number of data f in this embodiment is 3, which is used as an example for description.

[0072] It should be noted that: when the temperature data in the target adaptive temperature data sequence is less than or equal to 2f, the calculation process of the temperature trend index of the target adaptive temperature data sequence is not performed.

[0073] The difference between the final temperature index of the cth target adaptive temperature data sequence and the initial temperature index of the cth target adaptive temperature data sequence is recorded as the temperature index difference of the cth target adaptive temperature data sequence.

[0074] Then the specific calculation formula corresponding to the temperature trend index of the cth target adaptive temperature data sequence is:

[0075]

[0076] Among them, S c Represents the temperature trend index of the cth target adaptive temperature data sequence; T' c,m+1 represents the m+1th temperature data of the cth target adaptive temperature data sequence; T' c,m represents the mth temperature data of the cth target adaptive temperature data sequence; D c Represents the temperature index difference of the cth target adaptive temperature data sequence; N' c represents the number of temperature data in the cth target adaptive temperature data sequence; || represents the absolute value.

[0077] What needs to be explained is: D c The larger the value of , the more likely it is that the bathroom heater is heating up during the time period corresponding to the cth target adaptive temperature data sequence; T' c,m+1 -T' c,m It shows the temperature change trend over time. Indicates the overall temperature change trend within the time period of the cth target adaptive temperature data sequence. The larger the value, the greater the temperature change trend.

[0078] It should be further explained that: since the temperature difference and the temperature rise trend have the same positive and negative sign when the temperature rises or falls, the temperature difference is given an absolute value. If the temperature trend index of the target adaptive temperature data sequence is larger, it means that the possibility of the bathroom heater temperature rising in the time period corresponding to the target adaptive temperature data sequence is greater; if the temperature trend index of the target adaptive temperature data sequence is smaller, it means that the possibility of the bathroom heater temperature rising in the time period corresponding to the target adaptive temperature data sequence is smaller.

[0079] Through the above process, the temperature trend index of the cth target adaptive temperature data sequence is obtained.

[0080] All target adaptive temperature data sequences are operated according to the above process to obtain a temperature trend index for each target adaptive temperature data sequence.

[0081] Module 105: Heat dissipation enhancement factor analysis module.

[0082] This module is used to obtain the heat dissipation enhancement factor of each target adaptive temperature data sequence according to the temperature fluctuation index, temperature trend index and temperature index difference of each target adaptive temperature data sequence.

[0083] What needs to be explained is that when the bathroom heater is just turned on, the temperature rises first, and then the cooling system is turned on to dissipate the heat of the bathroom heater. If the temperature rises faster when the bathroom heater is turned on, the higher the temperature value, that is, the larger the temperature trend index of the target adaptive temperature data sequence, the greater the degree of heat dissipation of the bathroom heater needs to be. Similarly, if the temperature data of the current target adaptive temperature data sequence is relatively high compared to the overall temperature data sequence, and the faster the temperature data of the target adaptive temperature data sequence changes, it means that the degree of heat dissipation of the target adaptive temperature data sequence also needs to be increased.

[0084] Still taking the cth target adaptive temperature data sequence as an example, the specific calculation formula corresponding to the heat dissipation enhancement factor of the cth target adaptive temperature data sequence is:

[0085]

[0086] Among them, W c represents the heat dissipation enhancement factor of the cth target adaptive temperature data sequence; S c represents the temperature trend index of the cth target adaptive temperature data sequence; D c Represents the temperature index difference of the cth target adaptive temperature data sequence; Z' c Represents the temperature fluctuation index of the cth target adaptive temperature data sequence; T' c,max Indicates the maximum temperature data in the cth target adaptive temperature data sequence; T' c,min Indicates the minimum temperature data in the cth target adaptive temperature data sequence; N' c represents the number of temperature data in the cth target adaptive temperature data sequence; f represents the preset number of data; exp() represents an exponential function with a natural constant as the base; softmax[] represents a normalized exponential function.

[0087] What needs to be explained is: is the relative fluctuation size of the cth target adaptive temperature data sequence. The larger its value is, the greater the fluctuation of the temperature data in the cth target adaptive temperature data sequence is. When the temperature of the cth target adaptive temperature data sequence is higher in the overall temperature value, the temperature change is also relatively large, indicating that the increase in the heat dissipation degree of the cth target adaptive temperature data sequence needs to be relatively large. c The larger the value, the greater the current heat dissipation level. The larger the value, the faster the current temperature increases, which means that the need for increased heat dissipation is greater.

[0088] Through the above process, the heat dissipation enhancement factor of the cth target adaptive temperature data sequence is obtained.

[0089] All target adaptive temperature data sequences are calculated according to the above process to obtain the heat dissipation enhancement factor of each target adaptive temperature data sequence.

[0090] Module 106: Modify the heat dissipation enhancement factor analysis module.

[0091] This module is used to obtain the corrected heat dissipation enhancement factor of the last target adaptive temperature data sequence according to the difference between the heat dissipation enhancement factors of all target adaptive temperature data sequences.

[0092] It should be noted that: since the temperature data fluctuations of the last target adaptive temperature data sequence are similar in the historical temperature data, in order to reduce errors, the heat dissipation enhancement factor of the last target adaptive temperature data sequence can be corrected using the similar target adaptive temperature data sequence of the historical temperature data to increase the credibility of the heat dissipation enhancement factor of the last target adaptive temperature data sequence.

[0093] It is further necessary to explain that the similarity of the temperature data of the target adaptive temperature data sequence can be expressed as the similarity between the temperature trends of the data, or the similarity in the fluctuation degree of the temperature data, or the similarity in the relative size of the target adaptive temperature data sequence in the overall temperature data sequence. Therefore, the present invention uses the product of the temperature trend index and the temperature fluctuation index of the target adaptive temperature data sequence as a benchmark.

[0094] The product of the temperature fluctuation index and the temperature trend index of each target adaptive temperature data sequence is recorded as the similar temperature index of each target adaptive temperature data sequence.

[0095] The other target adaptive temperature data sequences except the last target adaptive temperature data sequence are recorded as sub-target adaptive temperature data sequences.

[0096] Calculate the absolute value of the difference between the last target adaptive temperature data sequence and the similar temperature index of each sub-target adaptive temperature data sequence, and record the sub-target adaptive temperature data sequence corresponding to the first b minimum values ​​of the absolute value of the difference between the last target adaptive temperature data sequence and the similar temperature index of all sub-target adaptive temperature data sequences as the reference adaptive temperature data sequence.

[0097] It should be noted that: when the number of reference adaptive temperature data sequences is less than b, the heat dissipation enhancement factor of the last target adaptive temperature data sequence is not corrected, and the heat dissipation enhancement factor of the last target adaptive temperature data sequence is directly used as the initial value of Q-learning in module 107. Wherein, b is the preset number of sequences, and the preset number of sequences b in this embodiment is 4, which is used as an example for description.

[0098] Then the specific calculation formula corresponding to the modified heat dissipation enhancement factor of the last target adaptive temperature data sequence is:

[0099]

[0100] W' now represents the modified heat dissipation enhancement factor of the last target adaptive temperature data sequence; W now represents the heat dissipation enhancement factor of the last target adaptive temperature data sequence; G represents the number of reference adaptive temperature data sequences; W' k represents the heat dissipation enhancement factor of the kth reference adaptive temperature data sequence; Q k Represents the absolute value of the difference between the similarity temperature index of the last target adaptive temperature data sequence and the kth reference adaptive temperature data sequence.

[0101] What needs to be explained is: Q k The larger the value, the smaller the similarity between the temperature data in the kth reference adaptive temperature data sequence and the target adaptive temperature data sequence; The smaller the similarity between the temperature data in the kth reference adaptive temperature data sequence and the last target adaptive temperature data sequence, the smaller the correction weight. now -W' k It represents the difference between the heat dissipation enhancement factor of the last target adaptive temperature data sequence and the kth reference adaptive temperature data sequence. The larger the difference is, the greater the degree of correction required is.

[0102] Through the above process, the corrected heat dissipation enhancement factor of the last target adaptive temperature data sequence is obtained.

[0103] Module 107: heat dissipation regulation analysis module.

[0104] This module is used to adjust the heat dissipation of the bathroom heater according to the modified heat dissipation enhancement factor of the last target adaptive temperature data sequence.

[0105] After determining the corrected heat dissipation enhancement factor of the last target adaptive temperature data sequence, the corrected heat dissipation enhancement factor of the last target adaptive temperature data sequence is used as the initial value, and the Q-learning algorithm is used to continuously adaptively determine the subsequent heat dissipation enhancement factor. According to the subsequent heat dissipation enhancement factor, the future bathroom heater temperature adjustment instruction is obtained, so as to adjust the heat dissipation of the bathroom heater. Among them, the flow chart for adjusting the heat dissipation of the bathroom heater is as follows: Figure 2 shown.

[0106] It should be noted that: Q-learning is a reinforcement learning algorithm based on value iteration. It guides the intelligent agent to make decisions in the environment by learning a value function. Q-learning is a well-known technology and the specific method will not be introduced here. The action-state instructions in the Q-table are {(temperature increase, -1), (temperature unchanged, 0), (temperature decrease, +1)}, using the ε-greedy strategy, in which the probability of determining ε is 0.1, the Q-value update equation is determined to be the Bellman equation, and the number of iterations is determined to be 1000 times. This is described as an example. After the Q-learning learning algorithm reaches convergence, the learned decision algorithm can be used to adaptively adjust the heat dissipation of the current bathroom heater cooling system according to the changes in the current bathroom heater temperature.

[0107] So far, the present invention is completed.

[0108] In summary, in an embodiment of the present invention, a temperature data sequence of a bathroom heater is obtained and divided into a plurality of adaptive temperature data sequences. Next, a temperature fluctuation index is calculated for each adaptive sequence to evaluate the degree of temperature change. The target adaptive temperature data sequence is screened out according to the fluctuation index. Then, the temperature index difference and the temperature trend index of the target adaptive temperature data sequence are calculated to obtain the difference and trend of the temperature change. These indicators are used to calculate the heat dissipation enhancement factor, which is used to adjust the heat dissipation performance of the bathroom heater. Finally, by comparing the heat dissipation enhancement factors of all target sequences, the final corrected heat dissipation enhancement factor is obtained to complete the adjustment of the heat dissipation of the bathroom heater. The present invention obtains the instructions for adjusting the temperature of the bathroom heater by analyzing the historical temperature data of the bathroom heater, thereby improving the heat dissipation efficiency of the bathroom heater.

[0109] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent temperature-controlled linear bathroom heater cooling system, characterized in that: The system includes the following modules: Data acquisition module: used to obtain the temperature data sequence of the bathroom heater; Data segmentation module: used to obtain several adaptive temperature data sequences segmented from the temperature data sequence; Target sequence screening module: used to obtain the temperature fluctuation index of each adaptive temperature data sequence according to the difference between the temperature data in each adaptive temperature data sequence; and screen out several target adaptive temperature data sequences from all adaptive temperature data sequences according to the temperature fluctuation index of each adaptive temperature data sequence; Temperature trend index analysis module: used to obtain the temperature index difference of each target adaptive temperature data sequence according to the difference of temperature data of each target adaptive temperature data sequence; obtain the temperature trend index of each target adaptive temperature data sequence according to the difference of temperature data of each target adaptive temperature data sequence and the temperature index difference; Heat dissipation enhancement factor analysis module: used to obtain the heat dissipation enhancement factor of each target adaptive temperature data sequence according to the temperature fluctuation index, temperature trend index and temperature index difference of each target adaptive temperature data sequence; Modified heat dissipation enhancement factor analysis module: used to obtain the modified heat dissipation enhancement factor of the last target adaptive temperature data sequence according to the difference between the heat dissipation enhancement factors of all target adaptive temperature data sequences; Heat dissipation adjustment analysis module: used to adjust the heat dissipation of the bathroom heater according to the modified heat dissipation enhancement factor of the last target adaptive temperature data sequence; The specific calculation formula corresponding to the temperature fluctuation index of each adaptive temperature data sequence is obtained according to the difference between the temperature data in each adaptive temperature data sequence: in, Indicates A temperature fluctuation indicator for an adaptive temperature data series; Indicates The number of temperature data in an adaptive temperature data sequence; Indicates The first adaptive temperature data sequence Temperature data; Indicates The mean of all temperature data in an adaptive temperature data sequence; It represents the maximum value among the means of all temperature data in all adaptive temperature data sequences; It indicates the absolute value; The step of selecting a plurality of target adaptive temperature data sequences from all adaptive temperature data sequences according to the temperature fluctuation index of each adaptive temperature data sequence includes: when When it is greater than the preset experience threshold, An adaptive temperature data sequence is recorded as a target adaptive temperature data sequence; The step of obtaining the temperature index difference of each target adaptive temperature data sequence according to the difference of the temperature data of each target adaptive temperature data sequence comprises: The first The first target adaptive temperature data sequence The mean of the temperature data is recorded as The initial temperature index of a target adaptive temperature data sequence; The first The next target adaptive temperature data sequence The mean of the temperature data is recorded as The final temperature index of the target adaptive temperature data sequence; among them, is the preset number of data; The first The final temperature index of the target adaptive temperature data series and the The difference between the initial temperature index of the target adaptive temperature data sequence is recorded as The temperature index difference of the target adaptive temperature data sequence; The specific calculation formula corresponding to the temperature trend index of each target adaptive temperature data sequence is obtained according to the difference of the temperature data of each target adaptive temperature data sequence and the temperature index difference: in, Indicates Temperature trend indicator of target adaptive temperature data series; Indicates The first target adaptive temperature data sequence Temperature data; Indicates The first target adaptive temperature data sequence Temperature data; Indicates The temperature index difference of the target adaptive temperature data sequence; Indicates The number of temperature data in a target adaptive temperature data sequence; The heat dissipation enhancement factor of each target adaptive temperature data sequence is obtained according to the temperature fluctuation index, the temperature trend index and the temperature index difference of each target adaptive temperature data sequence, including: According to the temperature fluctuation index of each target adaptive temperature data sequence and the size of the temperature data, the relative fluctuation size of each target adaptive temperature data sequence is obtained; According to the relative fluctuation size, temperature trend index and temperature index difference of each target adaptive temperature data sequence, the heat dissipation enhancement factor of each target adaptive temperature data sequence is obtained; The specific calculation formula corresponding to the heat dissipation enhancement factor of each target adaptive temperature data sequence is obtained according to the relative fluctuation size, temperature trend index and temperature index difference of each target adaptive temperature data sequence: in, Indicates The heat dissipation enhancement factor of the target adaptive temperature data series; Indicates Temperature fluctuation index of target adaptive temperature data series; Indicates The maximum temperature data in the target adaptive temperature data sequence; Indicates The minimum temperature data in the target adaptive temperature data sequence; represents an exponential function with a natural constant as base; represents the normalized exponential function; For the The relative fluctuation size of the target adaptive temperature data series; The method of obtaining the corrected heat dissipation enhancement factor of the last target adaptive temperature data sequence according to the difference between the heat dissipation enhancement factors of all target adaptive temperature data sequences comprises: According to the temperature fluctuation index and the temperature trend index of each target adaptive temperature data sequence, a plurality of reference adaptive temperature data sequences are selected from all target adaptive temperature data sequences; Obtaining a modified heat dissipation enhancement factor of the last target adaptive temperature data sequence according to a difference between the heat dissipation enhancement factor of the last target adaptive temperature data sequence and each reference adaptive temperature data sequence; The method of selecting a plurality of reference adaptive temperature data sequences from all target adaptive temperature data sequences according to the temperature fluctuation index and the temperature trend index of each target adaptive temperature data sequence includes: The product of the temperature fluctuation index and the temperature trend index of each target adaptive temperature data sequence is recorded as the similar temperature index of each target adaptive temperature data sequence; Recording the other target adaptive temperature data sequences except the last target adaptive temperature data sequence as sub-target adaptive temperature data sequences; Calculate the absolute value of the difference between the last target adaptive temperature data sequence and the similar temperature index of each sub-target adaptive temperature data sequence, and record the sub-target adaptive temperature data sequence corresponding to the first b minimum values ​​of the absolute value of the difference between the last target adaptive temperature data sequence and the similar temperature index of all sub-target adaptive temperature data sequences as the reference adaptive temperature data sequence; wherein b is the preset number of sequences; The specific calculation formula corresponding to the modified heat dissipation enhancement factor of the last target adaptive temperature data sequence is obtained according to the difference between the heat dissipation enhancement factor of the last target adaptive temperature data sequence and each reference adaptive temperature data sequence: A modified heat dissipation enhancement factor representing the last target adaptive temperature data sequence; represents the heat dissipation enhancement factor of the last target adaptive temperature data sequence; Indicates the number of reference adaptive temperature data sequences; Indicates A heat dissipation enhancement factor based on a reference adaptive temperature data sequence; Represents the last target adaptive temperature data sequence and the The absolute value of the difference between similar temperature indicators of the reference adaptive temperature data series.

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