A Data Storage and Display Method for an Intelligent Temperature Compensation System

Abstract

The present invention relates to the technical field of data processing, and specifically relates to a method for data storage and display of an intelligent temperature compensation system, including: clustering all historical cycles of each user according to the temperature difference distance to obtain several clustering clusters of each user; obtaining the temperature concentration coefficient of each user according to the size and distribution of the clustering clusters; obtaining the temperature regular evaluation of each user in each historical cycle according to the temperature concentration coefficient and the temperature difference distance; obtaining the adjacent influence weight of each user in each historical cycle according to the adjacent temperature difference coefficient and the temperature regular evaluation; obtaining the fuzzy evaluation of each user in each historical cycle according to the adjacent influence weight and the temperature regular evaluation; storing and displaying the indoor and outdoor temperature data of the user in each historical cycle based on the fuzzy evaluation. The present invention improves the data storage efficiency of the intelligent temperature compensation system.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method for data storage and display of an intelligent temperature compensation system. Background Art

[0002] The intelligent temperature compensation system is an intelligent control system commonly used in household heating water supply. Household heating water supply refers to a heating method that supplies hot water to residential households through a centralized heating system. This heating method is particularly common in cold northern regions, aiming to achieve efficient and energy-saving heating and hot water supply through centralized heat sources. In recent years, due to urban development, the construction of new urban areas, and changes in population distribution, the heating suspension rate in some areas has reached as high as 40%. The occupancy rates of existing building communities and some newly built communities are relatively low, and the number of users who suspend heating and those who steal heat has increased, making the living rooms of hot users who pay for heating normally in an "island" environment, increasing the cold wall effect of heat loss and resulting in a low room temperature.

[0003] At this time, the intelligent temperature compensation system can timely identify the problem of insufficient heating and heat up the users with insufficient heating to ensure stable heat supply for the users; the storage and display of various data during the temperature compensation process ensure that the system can effectively track, analyze, and feedback the heating status, facilitating users to view the heating status or for administrators to maintain and debug the system; however, the amount of data generated by real-time monitoring of indoor heating data of multiple users is very large. Therefore, in order to improve the storage efficiency, reduce the system cost, and accelerate the data transmission speed, data compression processing is often required; when compressing the data of the intelligent temperature compensation system, since the temperature data is sequential data that changes continuously, the differential coding method is usually used to compress the temperature data. However, due to different user habits, the temperature change situations are also different. Therefore, when the temperature change is large, the compression effect of the differential coding method on the temperature data will become poor, resulting in a reduction in the data storage efficiency of the intelligent temperature compensation system. Summary of the Invention

[0004] In order to solve the above problems, the present invention provides a method for data storage and display of an intelligent temperature compensation system, and the method includes:

[0005] Obtain the indoor and outdoor temperature data of each user at each moment in a number of historical cycles;

[0006] Obtain the temperature change coefficient of each user in each historical cycle according to the change range of the indoor and outdoor temperature data of each user in each historical cycle;

[0007] Based on the differences in indoor temperature data at the same moment between any two historical periods of a user, as well as the differences in temperature change coefficients, obtain the temperature difference distance of each user between any two historical periods; cluster all historical periods of each user according to the temperature difference distance to obtain several clustering clusters; according to the size and distribution of the clustering clusters, obtain the temperature concentration coefficient of each user; according to the temperature concentration coefficient and the temperature difference distance, obtain the temperature routine evaluation of each user in each historical period.

[0008] Based on the temperature routine evaluation and the differences in indoor temperature data at the same moment between each user and surrounding users in each historical period, obtain the adjacent influence weight of each user in each historical period; according to the adjacent influence weight and the temperature routine evaluation, obtain the fuzzy evaluation of each user in each historical period; store and display the indoor and outdoor temperature data of the user in each historical period based on the fuzzy evaluation.

[0009] Preferably, the method for obtaining the temperature change coefficient of each user in each historical period according to the change range of indoor and outdoor temperature data in each historical period of the user includes the following specific steps:

[0010] Take the ratio of the range of indoor temperature data at all moments in the th historical period of the th user to the minimum value of the ranges of indoor temperature data at all moments in the th historical period of all users, and denote it as the indoor temperature change amplitude of the th user in the th historical period; take the normalized value of the product of the indoor temperature change amplitude of the th user in the th historical period and the range of outdoor temperature data at all moments in the th historical period of the th user, and use it as the temperature change coefficient of the th user in the th historical period.

[0011] Preferably, the method for obtaining the temperature difference distance of each user between any two historical periods according to the differences in indoor temperature data at the same moment between any two historical periods of the user and the differences in temperature change coefficients includes the following specific steps:

[0012] Based on the differences in indoor temperature data at the same moment between any two historical periods of the user, obtain the temperature difference coefficient of each user between any two historical periods;

[0013] Take the th user in the th historical period of the temperature change coefficient and the th user in the The absolute value of the difference between the periodic temperature change coefficients is denoted as the temperature change difference factor between the nth user in the mth cycle and the kth cycle; the normalized value of the product of the temperature difference coefficient of the nth user in the mth cycle and the kth cycle and the temperature change difference factor between the pth user in the qth cycle and the rth cycle is used as the temperature difference distance of the nth user in the mth cycle and the kth cycle.

[0014] Preferably, the method for obtaining the temperature difference coefficient of each user between any two historical cycles according to the difference in indoor temperature data of the user at the same moment between any two historical cycles specifically includes:

[0015] The absolute value of the difference between the indoor temperature data of the nth user at the ith moment in the mth cycle and the indoor temperature data of the nth user at the jth moment in the kth cycle is denoted as the indoor temperature difference of the nth user between the mth cycle and the kth cycle at the ith moment; if the indoor temperature difference of the nth user between the mth cycle and the kth cycle at the ith moment is greater than 0, the indoor temperature difference of the nth user between the mth cycle and the kth cycle at the ith moment is denoted as the temperature change difference factor of the nth user between the mth cycle and the kth cycle at the ith moment; if the indoor temperature difference of the nth user between the mth cycle and the kth cycle at the ith moment is less than or equal to 0, 0 is denoted as the nth user in the Period and the period between the indoor temperature difference factor at the th moment; the average value of the indoor temperature difference factors of the th user at all moments between the th period and the th period in history is used as the th user's temperature difference coefficient between the

[0016] Preferably, the method for obtaining the temperature concentration coefficient of each user according to the size and distribution of the clustering clusters includes the following specific steps:

[0017] Among all the clustering clusters of the th user, the maximum value of the number of periods within the clustering cluster is denoted as the first quantity; the clustering cluster corresponding to the first quantity is denoted as the target clustering cluster; the ratio between the first quantity and the number of all periods in history is denoted as the th user's period number concentration factor; the Euclidean distance between the clustering center of the th user's th clustering cluster and the clustering center of the target clustering cluster is denoted as the th user's th clustering cluster's first distance from the target clustering cluster; the reciprocal of the average value of the first distances between all the clustering clusters of the th user and the target clustering cluster is denoted as the first reciprocal; the normalized value of the product of the first reciprocal and the th user's period number concentration factor is used as the

[0018] Preferably, the method for obtaining the temperature regular evaluation of each user in each historical period according to the temperature concentration coefficient and the temperature difference distance includes the following specific steps:

[0019] The product of the number of periods within the clustering cluster where the th user's th historical period is located and the th user's temperature concentration coefficient is denoted as the th user's regular evaluation factor in the th user's th historical period; in the clustering cluster where the th historical period of the th user is located, the average value of the distances between the The normalized value of the ratio between the conventional evaluation factor of the cycle and the first mean value is used as the temperature conventional evaluation of the th user in the

[0020] historical

[0021] cycle. Preferably, the method for obtaining the proximity influence weight of each user in each historical cycle according to the temperature conventional evaluation and the difference in indoor temperature data between the user and surrounding users at the same time in each historical cycle includes the following specific steps:

[0022] Obtain the neighboring users of each user;

[0023] According to the difference in indoor temperature data between the user and surrounding users at the same time in each historical cycle, obtain the proximity temperature difference coefficient between the user and surrounding users in each historical cycle; For the th neighboring user of the th user, denote the proximity temperature difference coefficient between the th user and the th neighboring user in the historical cycle as the first coefficient; denote the product of the temperature conventional evaluation of the th neighboring user in the historical cycle and the first coefficient as the reference validity of the th neighboring user; denote the sum of the reference validities of all neighboring users of the th user as the first cumulative sum; take the ratio between the first cumulative sum and the cumulative sum of the temperature conventional evaluations of all neighboring users of the th user in the

[0024] historical

[0025] cycle as the proximity influence weight of the

[0026] th user in the

[0027] Preferably, the method for obtaining the neighboring users of each user includes the following specific steps: For any user, in the same unit building, denote the adjacent users of the any user as the neighboring users of the any user. Preferably, the method for obtaining the proximity temperature difference coefficient between the user and surrounding users in each historical cycle according to the difference in indoor temperature data between the user and surrounding users at the same time in each historical cycle includes the following specific steps: Denote any neighboring user of the The absolute value of the difference between the indoor temperature data at a certain moment of a user and the indoor temperature data of neighboring users at the th moment within the th cycle of history is denoted as the indoor temperature difference between the th user and neighboring users at the th moment within the th cycle of history; if the indoor temperature difference between the th user and neighboring users at the th moment within the th cycle of history is greater than 0, then the indoor temperature difference between the th user and neighboring users at the th moment within the th cycle of history is denoted as the indoor temperature difference factor between the th user and neighboring users at the th moment within the th cycle of history; if the indoor temperature difference between the th user and neighboring users at the th moment within the th cycle of history is less than or equal to 0, then 0 is denoted as the indoor temperature difference factor between the th user and neighboring users at the th moment within the th cycle of history; the average value of the indoor temperature difference factors of all moments between the th user and neighboring users at the th cycle of history is used as the adjacent temperature difference coefficient between the th user and neighboring users at the th cycle of history.

[0028] Preferably, the specific method for obtaining the fuzzy evaluation of each user in each historical cycle according to the adjacent influence weight and temperature conventional evaluation includes:

[0029] The difference between 1 and the adjacent influence weight of the th user in the th cycle of history is denoted as the weight difference; the normalized value of the product of the weight difference and the temperature conventional evaluation of the th user in the th cycle of history is used as the fuzzy evaluation of the th user in the th cycle of history.

[0030] The beneficial effects of the technical solution of the present invention are as follows: According to the temperature difference coefficient and the difference in the temperature change coefficient between any two historical periods of the user, the temperature difference distance of each user between any two historical periods is obtained; clustering is performed on all historical periods of each user according to the temperature difference distance to obtain several clustering clusters of each user; according to the size and distribution of the clustering clusters, the temperature concentration coefficient of each user is obtained; according to the temperature concentration coefficient and the temperature difference distance, the temperature regular evaluation of each user in each historical period is obtained; according to the adjacent temperature difference coefficient and the temperature regular evaluation, the adjacent influence weight of each user in each historical period is obtained; according to the adjacent influence weight and the temperature regular evaluation, the fuzzy evaluation of each user in each historical period is obtained; based on the fuzzy evaluation, the indoor and outdoor temperature data of the user in each historical period are stored and displayed; thereby improving the compression effect of the temperature data and further improving the data storage efficiency of the intelligent temperature compensation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0032] Figure 1 It is a flowchart of the steps of a method for data storage and display of an intelligent temperature compensation system according to the present invention;

[0033] Figure 2 It is a flowchart of the characteristic relationship of a method for data storage and display of an intelligent temperature compensation system according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, describe in detail the specific implementation manner, structure, characteristics and effects of a method for data storage and display of an intelligent temperature compensation system according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0036] The following will specifically describe the specific solution of a method for data storage and display of an intelligent temperature compensation system provided by the present invention in conjunction with the accompanying drawings.

[0037] Please refer to Figure 1 , which shows the step flow chart of a data storage and display method of an intelligent temperature compensation system provided by an embodiment of the present invention. The method includes the following steps:

[0038] Step S001: Obtain the indoor and outdoor temperature data of each user at each moment in several historical cycles.

[0039] It should be noted that the intelligent temperature compensation system uploads the real-time collected and monitored indoor temperature of users to the software platform. When it detects that the indoor heating of a user is insufficient (for example, continuously lower than the set temperature for a period of time), the platform issues an instruction to control the heating device inside the intelligent temperature compensation system to heat the room temperature of the user with insufficient heating until the room temperature of the user is normally heated (for example, back above the set temperature for a period of time). Therefore, in this embodiment, the habits of users are reflected through the law of change of the indoor temperature of users, and then the compression rule of temperature data is dynamically adjusted.

[0040] Specifically, first, it is necessary to collect the indoor and outdoor temperature data of each user in several historical cycles at each moment in the same unit building. The specific process is as follows:

[0041] This embodiment is described by taking one cycle as an example;

[0042] For any user living in the same unit building, the indoor temperature data and outdoor temperature data of the user at each moment in the historical 30 cycles are regularly collected through the temperature sensor in the intelligent temperature compensation system; among them, a collection moment is taken every 5 minutes for data collection.

[0043] So far, the indoor and outdoor temperature data of each user at each moment in several historical cycles are obtained through the above method.

[0044] Step S002: Obtain the temperature change coefficient of each user in each historical cycle according to the change range of the indoor and outdoor temperature data of the user in each historical cycle.

[0045] It should be noted that the modes of the temperature compensation system include automatic compensation and timed compensation; due to different living habits of users, it will lead to different setting habits of users when using temperature compensation. For example, some users are at home for a long time and usually use automatic compensation, and immediately perform temperature compensation when the temperature is low to meet the needs; while some users may go out for a long time every day, turn off the temperature compensation when there is no one at home, and turn on the temperature compensation only after returning home to save energy when temperature compensation is not required. In this case, the temperature will change greatly, resulting in poor compression effect when directly using differential coding for compression. Therefore, it is first necessary to analyze the temperature change situation of each user in each cycle.

[0046] Preferably, in some implementation manners of the embodiments of the present invention, since the temperature change per cycle may fluctuate slightly due to reasons such as periodic gas, etc., the ratio between the temperature range of each user per cycle and the minimum value of the temperature range of the current cycle can be used to reflect the relative amplitude of the temperature change of the user in the current cycle; then, according to the change ranges of the indoor temperature data and the outdoor temperature data per cycle in the history of each user, the specific method for obtaining the temperature change coefficient of each user in each historical cycle is as follows:

[0047] Denote the difference between the maximum value and the minimum value of the indoor temperature data at all times in the th user in the th historical cycle as the range of the indoor temperature data of the th user in the th historical cycle; denote the difference between the maximum value and the minimum value of the outdoor temperature data at all times in the th user in the th historical cycle as the range of the outdoor temperature data of the th user in the th historical cycle;

[0048] Denote the ratio between the range of the indoor temperature data of the th user in the th historical cycle and the minimum value of the ranges of the indoor temperature data of all users in the th historical cycle as the indoor temperature change amplitude of the th user in the th historical cycle; denote the normalized value of the product of the indoor temperature change amplitude of the th user in the th historical cycle and the range of the outdoor temperature data of the th user in the th historical cycle as the temperature change coefficient of the th user in the th historical cycle;

[0049] The specific formula is:

[0050]

[0051] In the formula, represents the temperature change coefficient of the th user in the th historical cycle; represents the range of the indoor temperature data of the th user in the th historical cycle; represents the minimum value of the ranges of the indoor temperature data of all users in the The minimum value of the range of indoor temperature data within a period; denote the th user's range of outdoor temperature data within the th period in history; denote the linear normalization function.

[0052] It should be noted that when the temperature change coefficient is large and the number of periods with a large temperature change coefficient is large, it indicates that the heating temperature of the user is often adjusted. Then, when directly compressing the user's temperature data using differential coding, the compression effect is poor.

[0053] So far, the temperature change coefficient of each user in each period of history has been obtained through the above method.

[0054] Step S003: According to the difference in indoor temperature data at the same moment between any two periods in history of the user, and the difference in temperature change coefficient, obtain the temperature difference distance of each user between any two periods in history; cluster all periods in history of each user according to the temperature difference distance, and obtain several clustering clusters; according to the size and distribution of the clustering clusters, obtain the temperature concentration coefficient of each user; according to the temperature concentration coefficient and the temperature difference distance, obtain the temperature regular evaluation of each user in each period of history.

[0055] It should be noted that a large temperature change coefficient may be caused by different heating habits of users. For example, some users may turn off the temperature compensation when going out to save energy and then turn it on again when they get home. At this time, the indoor temperature is lower than the set temperature, and the temperature compensation system will start the heating device to raise the temperature of the user's room; in this case, for some users with regular going-out time (such as users with fixed working hours), their temperature adjustment usually shows regularity. Although the temperature changes greatly at this time, it is not caused by an abnormal situation of the heating system. For the storage accuracy requirements of these temperature data are relatively low. Therefore, although the temperature data changes greatly, it can be blurred to improve the compression effect of differential coding.

[0056] Preferably, in some implementation manners of the embodiments of the present invention, when the habit of the user adjusting the temperature setting is relatively regular, the temperature change characteristics between different periods are relatively similar, that is, the indoor temperature data at the same moment in different periods are close or the same; therefore, by comparing the difference in indoor temperature data at the corresponding moments between any two periods in history of the user, obtain the difference situation of the indoor temperature data of the user between different periods in history, so as to analyze the regularity of the user adjusting the temperature setting; then, according to the difference in indoor temperature data at the same moment between any two periods in history of each user, the specific method for obtaining the temperature difference coefficient of each user between any two periods in history is as follows:

[0057] The The indoor temperature data of the th period at the th moment of a user, and the absolute value of the difference from the indoor temperature data of the th user at the th period at the th moment is denoted as the indoor temperature difference of the th user between the th period and the th period at the th moment; if the indoor temperature difference of the th user between the th period and the th period at the th moment is greater than 0, then the indoor temperature difference of the th user between the th period and the th period at the th moment is denoted as the indoor temperature difference factor of the th user between the th period and the th period at the th moment; if the indoor temperature difference of the th user between the th period and the th period at the th moment is less than or equal to 0, then 0 is denoted as the indoor temperature difference factor of the th user between the th period and the th period at the th moment; the mean value of the indoor temperature difference factors of all moments between the th user between the th period and the th period is taken as the temperature difference coefficient of the th user between the th period and the th period;

[0058] The specific formula is:

[0059]

[0060] In the formula, represents the temperature difference coefficient of the th user between the th period and the th period; represents the number of all moments in each historical period; represents the th user in the historical The indoor temperature data at the -th moment within a cycle; Indicates the -th user's indoor temperature data at the -th moment within the -th cycle in history; Indicates taking the absolute value; Indicates the tolerance parameter, set here as , which can be adjusted according to specific circumstances. Since there may be regular user habits, but there may also be a difference of one or several moments in the moments of adjusting the temperature between different cycles, a certain tolerance parameter is set to avoid the subtle differences between different cycles from affecting the calculation results; Indicates the ReLU function. Here, the ReLU function is selected to obtain the indoor temperature difference factor between the -th user at the -th cycle and the -th cycle in history. The expression of the ReLU function is: ). )

[0061] Preferably, in some implementation manners of the embodiments of the present invention, the specific method for obtaining the temperature difference distance of each user between any two cycles in history according to the temperature difference coefficient and the difference in the temperature change coefficient of the user between any two cycles in history:

[0062] Take the absolute value of the difference between the temperature change coefficient of the -th user at the -th cycle in history and the temperature change coefficient of the -th user at the -th cycle in history, and denote it as the temperature change difference factor of the -th user between the -th cycle and the -th cycle in history; Take the normalized value of the product of the temperature difference coefficient of the -th user between the -th cycle and the -th cycle in history and the temperature change difference factor of the -th user between the -th cycle and the -th cycle in history, as the temperature difference distance of the -th user between the -th cycle and the -th cycle in history;

[0063] The specific formula is:

[0064]

[0065] Wherein, represents the temperature difference distance of the th user in the th cycle and the th cycle in history; represents the temperature change coefficient of the th user in the th cycle in history; represents the temperature change coefficient of the th user in the th cycle in history; represents the temperature difference coefficient of the th user in the th cycle and the th cycle in history; represents the linear normalization function.

[0066] It should be noted that the temperature difference distance reflects the difference in the change of indoor temperature data between different two cycles in the history of the user. The greater the temperature difference distance, the more inconsistent the change. For users with relatively regular temperature setting adjustments, the temperature difference distance shows the characteristic that the temperature difference distances between more cycles are relatively small. Therefore, the temperature difference distance can be used as a distance metric to cluster all the cycles of each user, obtain clustering clusters with similar temperature changes, and then judge the regularity of the temperature change of each cycle in the history of each user according to the distribution characteristics of the clustering clusters.

[0067] Preferably, in some implementation manners of the embodiments of the present invention, the specific method for clustering all the cycles in the history of each user according to the temperature difference distance to obtain several clustering clusters of each user is as follows:

[0068] For any user, using the AP (affinity propagation) clustering algorithm, with the temperature difference distance as the distance between any two cycles in the history of the any user, cluster all the cycles in the history of the any user to obtain several clustering clusters of each user.

[0069] Among them, each clustering cluster contains several cycles with similar temperature change situations of the any user; the AP clustering algorithm is a prior art, and no more details are described here in this embodiment.

[0070] Preferably, in some implementation manners of the embodiments of the present invention, the specific method for obtaining the temperature concentration coefficient of each user according to the size and distribution of the clustering clusters is as follows:

[0071] In the Among all the clustering clusters of a user, the maximum number of cycles within a clustering cluster is denoted as the first quantity; the clustering cluster corresponding to the first quantity is denoted as the target clustering cluster; the ratio between the first quantity and the number of all historical cycles is denoted as the cycle concentration factor of the user; the Euclidean distance between the clustering center of the th clustering cluster of the user and the clustering center of the target clustering cluster is denoted as the first distance between the th clustering cluster of the user and the target clustering cluster; the reciprocal of the mean of the first distances between all the clustering clusters of the th user and the target clustering cluster is denoted as the first reciprocal; the normalized value of the product of the first reciprocal and the cycle concentration factor of the th user is used as the temperature concentration coefficient of the

[0072] Specific formula is:

[0073]

[0074] In the formula, represents the temperature concentration coefficient of the th user; represents the maximum number of cycles within a clustering cluster among all the clustering clusters of the th user; represents the number of all historical cycles; represents the mean of the first distances between all the clustering clusters of the th user and the target clustering cluster;

[0075] Preferably, in some implementation manners of the embodiments of the present invention, the specific method for obtaining the temperature regular evaluation of each user in each historical cycle according to the temperature concentration coefficient and the temperature difference distance is:

[0076] The product of the number of cycles within the clustering cluster where the th user's historical cycle is located and the temperature concentration coefficient of the th user is denoted as the regular evaluation factor of the th user in the historical th cycle; in the clustering cluster where the th user's historical th cycle is located, the mean of the distances between the historical th user's historical The normalized value of the ratio between the conventional evaluation factor of the cycle and the first mean value is used as the temperature conventional evaluation of the th user in the th historical cycle;

[0077] The specific formula is:

[0078]

[0079] In the formula, represents the temperature conventional evaluation of the th user in the th historical cycle; represents the temperature concentration coefficient of the th user; represents the number of cycles in the cluster where the th user's th historical cycle is located; represents the mean value of the distances between the th user's th historical cycle and all other historical cycles in the cluster where the th historical cycle is located; represents the linear normalization function.

[0080] It should be noted that the temperature concentration coefficient reflects whether the temperature change of each user shows certain habits as a whole; by combining the distance between each historical cycle and other historical cycles in the same cluster and the size of the cluster where it is located, the temperature conventional evaluation of the user in each historical cycle is obtained; the larger the temperature conventional evaluation, the more the indoor temperature data change of the user in the corresponding cycle is the conventional change with certain rules and habits of the user. For such temperature change situations, fuzzy processing can be performed on them to improve the compression effect.

[0081] Thus, through the above method, the temperature conventional evaluation of the user in each historical cycle is obtained.

[0082] Step S004: According to the temperature conventional evaluation and the difference in the indoor temperature data at the same moment between the user and the surrounding users in each historical cycle, obtain the proximity influence weight of each user in each historical cycle; according to the proximity influence weight and the temperature conventional evaluation, obtain the fuzzy evaluation of each user in each historical cycle; based on the fuzzy evaluation, store and display the indoor and outdoor temperature data of the user in each historical cycle.

[0083] It should be noted that in household heating, the indoor temperature of each user is usually also affected by the heat transfer of the indoor temperature of its surrounding users; the surrounding users are defined as the users on the same floor as each user and the users on the adjacent upper and lower floors; in the intelligent temperature compensation system, the influence of the indoor temperature of the surrounding users is usually related to the power set during temperature compensation. For example, when the indoor temperature of the surrounding users is lower than that of the current user, higher power is required to compensate the current user to increase the temperature. Therefore, the temperature of the surrounding users will affect the temperature compensation efficiency of the current user; if the indoor temperature of the surrounding users has a relatively large temperature influence on the current user regularly, such influence has certain reference value for the subsequent setting of the temperature compensation power. Therefore, the temperature data generated under the regular influence needs to be stored with high precision for the subsequent system to adjust the appropriate power for temperature compensation, rather than being stored in a lossy manner after fuzzy processing of these data.

[0084] Preferably, in some implementation manners of the embodiments of the present invention, the specific method for obtaining the adjacent temperature difference coefficient of each user and its surrounding users according to the difference in the indoor temperature data of the user and its surrounding users at the same moment between each historical cycle is as follows:

[0085] For any user in the same unit building, the adjacent users living next to the any user are all recorded as the adjacent users of the any user; wherein, the adjacent users refer to those sharing the same wall with the any user.

[0086] The any adjacent user of the user is recorded as the neighborhood user; the absolute value of the difference between the indoor temperature data of the user at the th moment in the th historical cycle and the indoor temperature data of the neighborhood user at the th moment in the th historical cycle is recorded as the indoor temperature difference between the user and the neighborhood user at the th moment in the th historical cycle; if the indoor temperature difference between the user and the neighborhood user at the th moment in the th historical cycle is greater than 0, the indoor temperature difference between the user and the neighborhood user at the th moment in the th historical cycle is recorded as the indoor temperature difference factor between the user and the neighborhood user at the th moment in the If the indoor temperature difference between a user and neighboring users at the th moment in the th cycle is less than or equal to 0, record 0 as the indoor temperature difference factor between the th user and neighboring users at the th moment in the th cycle; take the mean value of the indoor temperature difference factors of the th user and neighboring users at all moments in the th cycle as the neighboring temperature difference coefficient of the th user and neighboring users in the th cycle;

[0087] It should be noted that since the influence of surrounding users on the current user may be accidental or a regular fixed influence, for accidental influence, its reference value for subsequent data analysis is not high. However, if there is a fixed influence, it has a certain reference value for the power adjustment of subsequent temperature compensation. Therefore, the data affected by the fixed influence is stored losslessly for subsequent use.

[0088] Preferably, in some implementation manners of the embodiments of the present invention, the specific method for obtaining the neighboring influence weight of each user in each historical cycle according to the neighboring temperature difference coefficient and the temperature conventional evaluation is as follows:

[0089] For the th neighboring user of the th user, record the neighboring temperature difference coefficient between the th user and the th neighboring user in the th cycle as the first coefficient; record the product of the temperature conventional evaluation of the th neighboring user in the th cycle and the first coefficient as the reference validity of the th neighboring user; record the sum of the reference validities of all neighboring users of the th user as the first cumulative sum; take the ratio between the first cumulative sum and the cumulative sum of the temperature conventional evaluations of all neighboring users of the th user in the th cycle as the neighboring influence weight of the th user in the th cycle;

[0090] The specific formula is:

[0091]

[0092] In the formula, represents the th user in the historical The proximity influence weight is affected by the approaching period; Denote the number of all neighboring users of the th user; Denote the th neighboring user of the th user in the th period of the historical temperature regular evaluation; Denote the th user and the th neighboring user in the th period of the historical proximity temperature difference coefficient.

[0093] It should be noted that the storage temperature compensation system usually uses differential coding to compress temperature data. Differential coding has a good compression effect when the data change is small, while when the data change is larger, the compression effect of differential coding is worse. To improve the compression effect, fuzzy processing of some data can be considered. When evaluating whether the temperature data of each user in each period can be fuzzy processed to improve the compression effect, the regular temperature changes of the user due to the heating supply habit are analyzed. Since the temperature change is usually the subjective adjustment of the temperature compensation switch by the user, rather than a system abnormality, and it shows regularity in multiple periods, its data can usually be fuzzy processed. However, for users who often adjust the temperature compensation switch, since the user may be affected by the temperature heat transfer of surrounding users during the process of turning on the temperature compensation each time, when the heat transfer situation of surrounding users is different, the influence on the power and temperature increase rate of the temperature compensation is different. When the user is fixed by a certain temperature influence, a relatively high-precision storage of this influence is also required to help the temperature compensation system set an appropriate power to increase the indoor temperature for the user.

[0094] Preferably, in some implementation manners of the embodiments of the present invention, the specific method for obtaining the fuzzy evaluation of each user in each historical period according to the proximity influence weight and the temperature regular evaluation is as follows:

[0095] Denote the difference between 1 and the proximity influence weight of the th user in the th period of the history as the weight difference; Denote the normalized value of the product of the weight difference and the temperature regular evaluation of the th user in the th period of the history as the fuzzy evaluation of the th user in the th period of the history;

[0096] Specific formula:

[0097]

[0098] In the formula, Indicates the th user's fuzzy evaluation in the historical Indicates the th user's proximity influence weight in the historical Indicates the th user's regular temperature evaluation in the historical Indicates the linear normalization function.

[0099] Preferably, in some implementation manners of the embodiments of the present invention, the specific method for storing and displaying the indoor and outdoor temperature data of users in each historical period based on the fuzzy evaluation is as follows:

[0100] Preset a threshold parameter , where in this embodiment, is taken as an example for description, and this embodiment does not make specific limitations, where is determined according to the specific implementation situation;

[0101] If the fuzzy evaluation of the th user in the historical period is greater than or equal to the threshold parameter , then use the fuzzy algorithm to perform fuzzy processing on all the indoor temperature data of the th user at all times in the historical period, and then use the differential coding algorithm to compress it to obtain the compressed temperature data of the

[0102] th user in the

[0103] historical Figure 2 period; compress the indoor temperature data of users in each historical period through the above method to obtain the compressed temperature data of each user in each historical period; for any user, take the compressed temperature data of the user in each historical period as a node every 30 minutes, and display it in the intelligent temperature compensation system in the form of a line chart of time-indoor temperature data on the software platform.

[0104] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for data storage and display of an intelligent temperature compensation system, characterized in that The method includes the following steps: Obtain the indoor and outdoor temperature data of each user at each moment in a number of historical cycles; According to the change range of the indoor and outdoor temperature data of each user in each historical cycle, obtain the temperature change coefficient of each user in each historical cycle; According to the difference in indoor temperature data of each user at the same moment between any two historical cycles, and the difference in temperature change coefficients, obtain the temperature difference distance of each user between any two historical cycles; cluster all historical cycles of each user according to the temperature difference distance, and obtain several clustering clusters; according to the size and distribution of the clustering clusters, obtain the temperature concentration coefficient of each user; according to the temperature concentration coefficient and the temperature difference distance, obtain the temperature regular evaluation of each user in each historical cycle; According to the temperature regular evaluation, and the difference in indoor temperature data of each user and surrounding users at the same moment in each historical cycle, obtain the proximity influence weight of each user in each historical cycle; according to the proximity influence weight and the temperature regular evaluation, obtain the fuzzy evaluation of each user in each historical cycle; store and display the indoor and outdoor temperature data of each user in each historical cycle based on the fuzzy evaluation; The specific method for obtaining the temperature regular evaluation of each user in each historical cycle according to the temperature concentration coefficient and the temperature difference distance includes: Multiply the number of cycles within the cluster where the th user's historical th cycle is located by the temperature concentration coefficient of the th user, and denote it as the conventional evaluation factor of the th user in the historical th cycle; In the cluster where the th user's historical th cycle is located, denote the average value of the distances between the historical th cycle and all other historical cycles as the first average value; Take the normalized value of the ratio between the conventional evaluation factor of the th user in the historical th cycle and the first average value as the temperature conventional evaluation of the th user in the historical th cycle; The specific method for obtaining the fuzzy evaluation of each user in each historical cycle according to the proximity influence weight and the temperature regular evaluation includes: Denote the difference between 1 and the influence weight near the nd user in the th historical period as the weight difference; Take the normalized value of the product of the weight difference and the temperature regular evaluation of the th user in the th historical period as the fuzzy evaluation of the th user in the th historical period; The specific method for storing and displaying the indoor and outdoor temperature data of each user in each historical cycle based on the fuzzy evaluation includes: If the fuzzy evaluation of the th user in the th historical period is greater than or equal to the preset threshold parameter , then after performing fuzzy processing on the indoor temperature data of the th user at all times during the th historical period using the fuzzy algorithm, and then compressing it using the differential coding algorithm, the compressed temperature data of the th user in the th historical period is obtained; the indoor temperature data of each user in each historical period is compressed to obtain the compressed temperature data of each user in each historical period; for any user, the compressed temperature data of the user in each historical period is presented in the intelligent temperature compensation system in the form of a line graph of time - indoor temperature data on a software platform with a 30 - minute interval as each node.

2. The data storage and display method of an intelligent temperature compensation system according to claim 1, characterized in that The specific method for obtaining the temperature change coefficient of each user in each historical cycle according to the change range of the indoor and outdoor temperature data of each user in each historical cycle includes: The ratio between the range of the indoor temperature data of the th user at all times during the th historical period and the minimum value of the ranges of the indoor temperature data of all users at all times during the th historical period is denoted as the indoor temperature change amplitude of the th user during the th historical period; The normalized value of the product of the indoor temperature change amplitude of the th user during the th historical period and the range of the outdoor temperature data of the th user at all times during the th historical period is taken as the temperature change coefficient of the th user during the th historical period.

3. The data storage and display method of an intelligent temperature compensation system according to claim 1, characterized in that The specific method for obtaining the temperature difference distance of each user between any two historical cycles according to the difference in indoor temperature data of each user at the same moment between any two historical cycles, and the difference in temperature change coefficients includes: According to the difference in indoor temperature data of each user at the same moment between any two historical cycles, obtain the temperature difference coefficient of each user between any two historical cycles; Take the absolute value of the difference between the temperature change coefficient of the th user in the th historical period and the temperature change coefficient of the th user in the th historical period, and denote it as the temperature change difference factor between the th user in the th historical period and the th historical period; Take the normalized value of the product of the temperature difference coefficient of the th user in the th historical period and the th historical period and the temperature change difference factor between the th user in the th historical period and the th historical period as the temperature difference distance of the th user in the th historical period and the th historical period.

4. The data storage and display method of an intelligent temperature compensation system according to claim 3, characterized in that The specific method for obtaining the temperature difference coefficient of each user between any two historical cycles according to the difference in indoor temperature data of each user at the same moment between any two historical cycles includes: Take the absolute value of the difference between the indoor temperature data of the nd user at the th moment in the th historical cycle and the indoor temperature data of the th user at the th moment in the th historical cycle, and denote it as the indoor temperature difference of the th user at the th historical cycle and the th historical cycle at the th moment; if the indoor temperature difference of the th user at the th historical cycle and the th historical cycle at the th moment is greater than 0, then take the indoor temperature difference of the th user at the th historical cycle and the th historical cycle at the th moment, and denote it as the indoor temperature difference factor of the th user at the th historical cycle and the th historical cycle at the th moment; if the indoor temperature difference of the th user at the th historical cycle and the th historical cycle at the th moment is less than or equal to 0, then denote 0 as the indoor temperature difference factor of the th user at the th historical cycle and the th historical cycle at the th moment; take the mean value of the indoor temperature difference factors of all moments between the th user at the th historical cycle and the th historical cycle as the temperature difference coefficient of the th user at the th historical cycle and the th historical cycle.

5. The data storage and display method of an intelligent temperature compensation system according to claim 1, characterized in that, The specific method for obtaining the temperature concentration coefficient of each user according to the size and distribution of the clustering clusters includes: Among all the clusters of the th user, the maximum value of the number of cycles within a cluster is denoted as the first quantity; the cluster corresponding to the first quantity is denoted as the target cluster; the ratio between the first quantity and the number of all historical cycles is denoted as the th user's cycle concentration factor; the Euclidean distance between the cluster center of the th user's th cluster and the cluster center of the target cluster is denoted as the th user's th cluster's first distance from the target cluster; the reciprocal of the mean of the first distances between all the clusters of the th user and the target cluster is denoted as the first reciprocal; the normalized value of the product of the first reciprocal and the th user's cycle concentration factor is taken as the th user's temperature concentration coefficient.

6. The data storage and display method of an intelligent temperature compensation system according to claim 1, characterized in that, The specific method for obtaining the proximity influence weight of each user in each historical cycle according to the temperature regular evaluation, and the difference in indoor temperature data of each user and surrounding users at the same moment in each historical cycle includes: Obtain the neighboring users of each user; According to the difference in indoor temperature data of each user and surrounding users at the same moment in each historical cycle, obtain the proximity temperature difference coefficient of each user and surrounding users in each historical cycle; For the th user's th adjacent user, record the adjacent temperature difference coefficient between the th user and the th adjacent user in the historical th cycle as the first coefficient; record the product of the temperature regular evaluation of the th adjacent user in the historical th cycle and the first coefficient as the reference validity of the th adjacent user; Sum up the reference validities of all adjacent users of the th user, and denote it as the first sum; Take the ratio between the first sum and the cumulative sum of the temperature regular evaluations of all adjacent users of the th user in the th period in history as the adjacent influence weight of the th user in the th period in history.

7. The data storage and display method of an intelligent temperature compensation system according to claim 6, characterized in that, The specific method for obtaining the neighboring users of each user includes: For any user, in the same unit building, record the adjacent users living next to the any user as the neighboring users of the any user.

8. The data storage and display method of an intelligent temperature compensation system according to claim 6, characterized in that, Obtaining the adjacent temperature difference coefficient of each user and surrounding users in each historical period according to the difference in indoor temperature data of the user and surrounding users at the same time between historical periods, the specific method included is as follows: Take any adjacent user of the th user as the neighborhood user; take the absolute value of the difference between the indoor temperature data of the th user at the th moment in the th historical period and the indoor temperature data of the neighborhood user at the th moment in the th historical period, and denote it as the indoor temperature difference between the th user and the neighborhood user at the th moment in the th historical period; if the indoor temperature difference between the th user and the neighborhood user at the th moment in the th historical period is greater than 0, denote the indoor temperature difference between the th user and the neighborhood user at the th moment in the th historical period as the indoor temperature difference factor between the th user and the neighborhood user at the th moment in the th historical period; if the indoor temperature difference between the th user and the neighborhood user at the th moment in the th historical period is less than or equal to 0, denote 0 as the indoor temperature difference factor between the th user and the neighborhood user at the th moment in the th historical period; take the mean of the indoor temperature difference factors of all moments between the th user and the neighborhood user in the th historical period as the adjacent temperature difference coefficient between the th user and the neighborhood user in the th historical period.

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