Remote data communication method for heating temperature compensation system using 4G communication

Abstract

The present application relates to the technical field of digital information transmission, in particular to a remote data communication method of a heat supply temperature compensation system using 4G communication, comprising: obtaining temperature time series data of indoor and outdoor based on monitoring points, and measuring the distance between any two monitoring points; analyzing the temperature time series data based on each monitoring point to obtain the temperature anomaly factor of any monitoring point at any collection time; determining the target monitoring point, analyzing the persistence of temperature change in time to obtain the possibility of indoor ventilation; determining the target time, calculating the influence degree of any target monitoring point in any target time affected by ventilation to obtain the authenticity of the temperature time series data of any target monitoring point in any target time; obtaining comprehensive temperature data and constructing a sequence, transmitting the comprehensive temperature data sequence to the heat supply temperature compensation system using 4G communication, setting a judgment threshold to realize dynamic regulation and control of temperature; not only the data transmission efficiency is high, but also the temperature control is accurately realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital information transmission, and particularly relates to a remote data communication method of a heat temperature compensation system using 4G communication. BACKGROUND

[0002] The heat temperature compensation system is an advanced intelligent system, which aims to optimize the heating process and ensure the stability of indoor temperature and meet the specific needs of users. The system obtains real-time user indoor temperature data, including inlet temperature, outlet temperature and heating rod temperature, through temperature sensors, and then transmits the data to the intelligent control mainboard, which is responsible for accurate adjustment and control of these temperature parameters. Through advanced data compression and encryption technology, the system ensures the safety and efficiency of data transmission. In addition, the system uses 4G communication Internet of Things card for data communication, and sends the collected temperature data to the remote monitoring platform in real time. At the same time, the 4G communication Internet of Things card can also receive instructions from the remote platform to realize remote start-stop control of the heat temperature compensation device, so as to achieve the purpose of accurately adjusting indoor temperature.

[0003] In the heat temperature compensation system, the temperature sensor plays a crucial role in monitoring and feeding back user indoor temperature data. However, in actual application, the temperature sensor may fail, usually due to the gradual aging of internal electronic components such as thermistors, thermocouples, etc. Once a failure occurs, the temperature data may be abnormal, which will directly affect the performance of the heat temperature compensation system. To solve this problem, the existing technology usually obtains the mean value of temperature data of all monitoring points in the user's room at the same time to predict the indoor temperature change in the future period of time. However, this method has strong limitations and is often based on fixed rules to make up for data, which cannot make dynamic adjustments to temperature according to actual conditions, especially when the temperature sensor fails, which will cause abnormal temperature data and affect the accuracy of prediction. In addition, in the data transmission link, due to the limited bandwidth resources of 4G communication, the transmission of a large amount of raw temperature data will occupy too much bandwidth, resulting in low transmission efficiency, and even may affect the transmission of other key data. Therefore, if the historical temperature data collected is directly used for prediction, it may cause over-heating or insufficient heating of the heating system, which not only affects the user's comfort experience, but also causes unreasonable use and waste of energy. SUMMARY

[0004] To address the technical problems that existing methods cannot dynamically resolve temperature anomalies and that the low transmission efficiency of 4G communication leads to overheating or underheating in heating systems when using raw temperature data for prediction, this invention aims to provide a remote data communication method for a heating temperature compensation system utilizing 4G communication. The specific technical solution adopted is as follows:

[0005] Indoor temperature time series data is collected by setting up monitoring points indoors, measuring the distance between any two monitoring points, and obtaining outdoor temperature time series data.

[0006] Based on the analysis of temperature time series data at each monitoring point, the temperature anomaly factor at any monitoring point at any acquisition time is obtained.

[0007] Target monitoring points are determined by using temperature anomaly factors. Based on the temperature time series data of the target monitoring points, the persistence of temperature changes over time is analyzed to determine the possibility of indoor ventilation.

[0008] The target time is determined by the possibility of indoor ventilation. The degree of influence of ventilation on any target monitoring point at any target time is calculated based on outdoor temperature time series data. The authenticity of the temperature time series data of any target monitoring point at any target time is obtained by combining the time interval between the target time and the current analysis time.

[0009] Based on the authenticity of the temperature time series data, comprehensive temperature data is obtained, and a comprehensive temperature data sequence is constructed. The comprehensive temperature data sequence is transmitted to the heating temperature compensation system using 4G communication, and a judgment threshold is set to realize dynamic temperature control.

[0010] Preferably, based on the analysis of temperature time-series data at each monitoring point, the temperature anomaly factor at any monitoring point at any acquisition time is obtained, including:

[0011] Based on the temperature time-series data of each monitoring point, the first-order difference value and the second-order difference value are obtained sequentially. The temperature anomaly factor of any monitoring point at any acquisition time is calculated, and the corresponding calculation formula is as follows:

[0012]

[0013] in, Indicates the first The monitoring point at the 1st Temperature anomaly factors at each data acquisition time; Indicates the first The monitoring point at the 1st First-order difference values ​​at each acquisition time; Indicates the first The monitoring point at the 1st The second-order difference value at each acquisition time; 0.1 represents a preset constant used to prevent the denominator from being 0; This represents the normalization function.

[0014] Preferably, target monitoring points are determined using temperature anomaly factors, and the persistence of temperature changes over time is analyzed based on the time-series temperature data of the target monitoring points to determine the possibility of indoor ventilation, including:

[0015] Set a screening threshold one, and determine the monitoring points whose temperature anomaly factor at each collection time is greater than or equal to the screening threshold one as target monitoring points. Based on the current analysis time, extract some continuous historical collection times to generate an analysis period, and use the analysis period to determine the average temperature anomaly factor.

[0016] Based on the analysis period of any acquisition time, the intersection of all target monitoring points corresponding to all acquisition times is obtained, and the duration of the target monitoring points in time at any acquisition time is calculated in conjunction with the average temperature anomaly factor.

[0017] By calculating the dynamic time warping between any two target monitoring points through intersection calculation, the possibility of indoor ventilation can be obtained.

[0018] Preferably, the persistence of the target monitoring point in time at any given acquisition time within the corresponding analysis period is calculated using the following formula:

[0019]

[0020] in, Indicates the first The temporal continuity of the target monitoring point during the analysis period corresponding to each collection time; Indicates the first The average temperature anomaly factor of all target monitoring points in the intersection of all target monitoring points corresponding to all acquisition times in the analysis period corresponding to each acquisition time; Indicates the first The number of target monitoring points in the intersection of all target monitoring points corresponding to each collection time in the analysis period corresponding to each collection time; Indicates the first The maximum number of target monitoring points corresponding to all acquisition times within the analysis period corresponding to a given acquisition time.

[0021] Preferably, the possibility of indoor ventilation is determined by the following calculation formula:

[0022]

[0023] in, Indicates the first There is a possibility that the room was ventilated at the time of the data collection. Indicates the first The temporal continuity of the target monitoring point within the analysis period corresponding to each collection time; Indicates the first The mean of the distances between any two target monitoring points in the intersection of all target monitoring points corresponding to all collection times in the analysis period corresponding to each collection time; Indicates the first Dynamic time regularization of any two target monitoring points in the intersection of all target monitoring points corresponding to each collection time in the analysis period corresponding to each collection time; This represents the normalization function.

[0024] Preferably, the target time is determined by utilizing the possibility of indoor ventilation, and the degree of influence of ventilation on any target monitoring point at any target time is calculated based on outdoor temperature time-series data. The authenticity of the temperature time-series data of any target monitoring point at any target time is obtained by combining the time interval between the target time and the current analysis time, including:

[0025] Set a second screening threshold, and determine the collection time corresponding to the possibility that there is ventilation indoors that is greater than or equal to the second screening threshold as the target time. Select consecutive adjacent target times to construct the target time period.

[0026] Calculate the degree to which any target monitoring point is affected by ventilation at any target time;

[0027] The time interval is determined based on the target time and the current analysis time to calculate the authenticity of the temperature time series data of any target monitoring point at any target time.

[0028] Preferably, calculating the degree to which any target monitoring point is affected by ventilation at any target time includes:

[0029] Define the first The target time is defined as the number of acquisition times, and the corresponding calculation formula is:

[0030]

[0031] in, Indicates the first The target monitoring points at the first The degree to which ventilation affects each target at any given time; Indicates the first There is a possibility of indoor ventilation at any given time. Indicates the first The total duration of the target time period corresponding to each target moment; Indicates the first The target monitoring points at the first Temperature time series data at each target time point; represents the temperature time series data of the outdoor at the target moment.

[0032] Preferably, the authenticity of the temperature time series data of any target monitoring point at any target moment is calculated, and the corresponding calculation formula is:

[0033]

[0034] wherein, represents the temperature time series data of the target monitoring point at the target moment. represents a normalization function.

[0035] Preferably, the comprehensive temperature data is obtained according to the authenticity of the temperature time series data, and a comprehensive temperature data sequence is constructed, the comprehensive temperature data sequence is transmitted to the heating temperature compensation system by 4G communication, a judgment threshold is set, and dynamic regulation and control of temperature is realized, including:

[0036] The authenticity of the temperature time series data of each target monitoring point at each non-target moment is marked as 0.1, and the authenticity of the temperature time series data of each non-target monitoring point at each non-target moment and / or each target moment is marked as 1.

[0037] The comprehensive temperature data of any collection moment is calculated, and a comprehensive temperature data sequence is constructed.

[0038] Based on the comprehensive temperature data sequence, predicted temperature data is obtained, if the predicted temperature data is less than the judgment threshold, an instruction is sent to the heating temperature compensation system by 4G communication to heat in advance, and the heating power is dynamically adjusted; if the predicted temperature data is close to the judgment threshold, the heating power is reduced; and if the predicted temperature data is greater than the judgment threshold, the heating power is increased.

[0039] Preferably, the comprehensive temperature data of any collection moment is calculated, and the corresponding calculation formula is:

[0040]

[0041] wherein, represents the comprehensive temperature data of the current collection moment; represents the comprehensive temperature data of the current collection moment;​​​​​​​​​​​ temperature time series data of the monitoring point; represent the first temperature time series data of the monitoring point at the current collection time the authenticity of the temperature time series data of the monitoring point at the current collection time.

[0042] The present application has the following beneficial effects:

[0043] The present application obtains the temperature time series data of the indoor and the distance between any two monitoring points based on the laid monitoring points, obtains the outdoor temperature time series data through the meteorological bureau, analyzes the possibility of the user indoor ventilation according to the abnormal degree of each monitoring point, judges the authenticity of the temperature time series data of each monitoring point at each collection time, that is, judges the credibility of the temperature time series data of each monitoring point through the consistency and the time continuity of the fluctuation of the temperature time series data of each monitoring point caused by the ventilation, so as to realize the dynamic regulation and control of the temperature according to the actual situation; at the same time, through the data compression technology, the data transmission efficiency is improved, so as to optimize the operation of the whole heating temperature compensation system, improve the accuracy of the prediction data, dynamically adjust the heating power, and realize the precise temperature control. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art and the advantages thereof, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0045] Figure 1 A step flow chart of a remote data communication method of a heating temperature compensation system using 4G communication provided by an embodiment of the present application;

[0046] Figure 2 The intersection diagram of the target monitoring point of the remote data communication method of the heating temperature compensation system using 4G communication provided by an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following will combine the drawings and the preferred embodiments to specifically describe the remote data communication method of the heating temperature compensation system using 4G communication according to the present application, the specific implementation, structure, features and effects thereof, as follows. 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.

[0048] Unless otherwise defined, 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 application belongs.

[0049] The application provides a remote data communication method of a heating temperature compensation system using 4G communication.

[0050] Please refer to Figure 1 , which shows a step flow chart of a remote data communication method of a heating temperature compensation system using 4G communication according to an embodiment of the application, the method comprising:

[0051] Step S1: Collecting temperature time series data in the room by arranging monitoring points in the room, measuring the distance between any two monitoring points, and obtaining outdoor temperature time series data;

[0052] Step S2: Analyzing the temperature time series data based on each monitoring point to obtain the temperature anomaly factor of any monitoring point at any collection time;

[0053] Step S3: Determining the target monitoring point using the temperature anomaly factor, analyzing the time persistence of temperature change based on the temperature time series data of the target monitoring point, and obtaining the possibility of indoor ventilation;

[0054] Step S4: Determining the target time using the possibility of indoor ventilation, calculating the influence degree of ventilation on any target monitoring point at any target time based on the outdoor temperature time series data, and obtaining the authenticity of the temperature time series data of any target monitoring point at any target time combined with the time interval between the target time and the current analysis time;

[0055] Step S5: Obtaining comprehensive temperature data according to the authenticity of the temperature time series data, constructing a comprehensive temperature data sequence, transmitting the comprehensive temperature data sequence to the heating temperature compensation system using 4G communication, setting a judgment threshold, and realizing dynamic regulation and control of temperature.

[0056] For better illustration, the heating temperature compensation system generally includes key components such as temperature sensors, controllers, and actuators, which work cooperatively through various components, calculate the temperature compensation value required by abnormal temperature through accurate algorithm, and monitor and adjust the running state of the heating equipment in real time, which can automatically adjust the output temperature of the heating system according to the change of external environment temperature to ensure the constancy and comfort of indoor temperature.

[0057] 4G communication refers to the fourth generation of mobile communication technology, which is an advanced wireless communication technology that can provide faster data transmission speed and higher quality communication services than 3G. It relies on key technologies such as orthogonal frequency division multiple access and multiple-input multiple-output to effectively improve spectrum utilization and network capacity, and supports intelligent antenna technology to improve signal quality and coverage by precisely controlling signal transmission and reception. It greatly promotes the development of mobile Internet, enabling users to enjoy high-speed network connections anytime and anywhere, such as video calls, online games, and high-definition video viewing.

[0058] The introduction of 4G communication technology makes remote control of the heating temperature compensation system more efficient and real-time. The operation process can be free from geographical restrictions, allowing for monitoring and adjustment of the system at any time and place, ensuring stable operation of the heating system. The optimization of the heating temperature compensation system's temperature acquisition algorithm provides more accurate comprehensive temperature data. In addition, through the 4G network, the system can transmit temperature data and operating status in real time, making fault diagnosis and maintenance more rapid and accurate. This reduces the frequency and cost of manual inspections, improves the economic efficiency of the entire heating system, and enhances the system's safety, allowing for timely response to various emergencies and avoiding potential losses.

[0059] As an optional implementation, in this embodiment, the monitoring point refers to a room temperature collector, which includes 1 five-hole socket panel, 1 temperature sensor, 1 intelligent control mainboard, 1 4G communication Internet of Things card, and 1 temperature display screen. The five-hole socket panel can provide power connection points for various devices. The temperature sensor is used to monitor the ambient temperature in real time. The intelligent control mainboard is the processing core of the entire room temperature collector. The 4G communication Internet of Things card ensures that the device can perform remote data transmission through the mobile network. The temperature display screen allows users to intuitively view the current temperature information. The room temperature collector intelligently analyzes temperature data and automatically adjusts indoor temperature to achieve energy-saving and optimized operation.

[0060] Specifically, in step S1, monitoring points are arranged in the room, i.e., several room temperature collectors are installed on the indoor wall to collect real-time temperature time series data of the user's room and measure the distance between any two room temperature collectors. Then, the user's outdoor temperature time series data is obtained from the meteorological bureau to enable the user to understand and master the temperature changes of the outdoor environment in real time.

[0061] Optionally, in this embodiment, the change pattern of the user's room temperature is analyzed for the past two hours, i.e., the indoor temperature change is analyzed based on the historical two-hour temperature time series data, and the collection frequency is 2 minutes.

[0062] It can be understood that by analyzing historical temperature data, user behavior patterns and weather information, the temperature trend in the room in the future period of time is predicted, that is, the comprehensive temperature data is recorded as the predicted temperature, when the predicted temperature is lower than the judgment threshold, the heating device is started in advance to avoid lag compensation, and the heating power is dynamically adjusted according to the prediction result to realize accurate temperature control; when monitoring the temperature in the user's room, a temperature time series data sequence is constructed, when the indoor temperature changes, the temperature change rule is slow, and the difference between the temperature time series data of the monitoring point and the surrounding other temperature time series data in the temperature time series data sequence is greater, the temperature changes faster, indicating that the temperature of the monitoring point is more abnormal.

[0063] Further, in step S2, comprising:

[0064] Based on the temperature time series data of each monitoring point, a first-order difference value and a second-order difference value are obtained in turn, and a temperature anomaly factor of any monitoring point at any collection time is calculated, and the corresponding calculation formula is:

[0065]

[0066] Among them, represents the temperature anomaly factor of the i-th monitoring point at the j-th collection time; 0.1 represents a preset constant, used to prevent the denominator from being 0; represents a normalization function, which can be specifically, for example, a maximum and minimum value normalization.

[0067] For better illustration, the first-order difference value of the temperature time series data refers to the difference value of the temperature change between adjacent two time points through the sequence data of the temperature time series data changing with time, so as to reflect the rate of temperature change with time; the second-order difference value of the temperature time series data refers to the difference value obtained by calculating the first-order difference of the temperature time series data twice in succession, which is helpful to analyze and understand the trend and pattern of temperature data changing with time.

[0068] It can be explained that, represents the first-order difference value of the i-th monitoring point at the j-th collection time, when the absolute value of the first-order difference value is greater, it means that the temperature change at the corresponding collection time is faster, that is, the temperature time series data at the j-th collection time is more abnormal; ​​​​​​​​​​Indicates the first The monitoring point at the 1st The second-order difference value at each acquisition time, when the second-order difference value The larger the absolute value of , the stronger its corresponding . The greater the difference in the rate of temperature change before and after the first data collection moment, the more significant the difference in the rate of temperature change. The temperature time series data of the monitoring point shows a trend change, that is, the temperature of the first monitoring point... If the temperature time series data of all monitoring points show the same change, the smaller the degree of temperature anomaly, the temperature time series data of that monitoring point at that acquisition time can be retained. Conversely, if the absolute value of the first-order difference of a monitoring point at a certain acquisition time is smaller and the absolute value of the second-order difference is larger, or the absolute value of the first-order difference is larger and the absolute value of the second-order difference is smaller, that is, the temperature time series data of the corresponding monitoring point shows different changes and the degree of temperature anomaly is greater, the temperature time series data of that monitoring point at that acquisition time needs to be anomaly processed.

[0069] The explanation is that by calculating the temperature anomaly factor of each monitoring point at each acquisition time, acquisition times and monitoring points with abnormal temperature changes are screened out, so that the corresponding temperature time series data can be retained or anomaly processed respectively. This helps to retain key information and avoid the loss of important temperature time series data during subsequent data compression.

[0070] Understandably, during indoor ventilation, the significant temperature difference between indoors and outdoors causes air convection, which affects the temperature time-series data readings at various monitoring points, leading to changes in the data. Ventilation-induced temperature time-series data anomalies typically exhibit persistence over time and are relatively concentrated in spatial distribution among monitoring points. However, when a temperature sensor malfunctions, the anomalies in the collected temperature time-series data may become random and scattered in spatial distribution. Therefore, both ventilation and temperature sensor malfunctions can result in the acquired temperature time-series data failing to accurately reflect the actual heating effect and temperature change trend indoors. If this abnormal temperature time-series data is received by a 4G communication platform (i.e., a remote platform), it may cause the remote platform to make incorrect assessments of the heating temperature compensation system's operating status, leading to incorrect adjustment commands and erroneous temperature control of the indoor environment.

[0071] Furthermore, step S3 includes:

[0072] Step S311: setting a screening threshold one, determining the monitoring point as a target monitoring point when the temperature anomaly factor of each collection time is greater than and equal to the screening threshold one corresponding to the monitoring point, generating an analysis period based on the current analysis time and extracting part of the continuous historical collection time, and determining the average temperature anomaly factor by using the analysis period.

[0073] It can be explained that the temperature change is a relatively slow and continuous process, so the abnormal monitoring of the target monitoring point depends not only on the running state of a single collection time, but also on the temperature change mode in the whole historical collection time; therefore, only relying on the temperature time series data of a single collection time may miss some potential problems caused by trend changes or short-term temperature fluctuations, so in order to comprehensively understand the temperature behavior of the target monitoring point, it is necessary to analyze the distribution of the target monitoring point in the analysis period.

[0074] As an optional implementation, in the embodiment, the screening threshold one is 0.6; that is, when the temperature anomaly factor of any collection time is greater than and equal to 0.6, it is recorded as a target monitoring point; and the analysis period is composed of 30 continuous collection times before the current analysis of the 30th collection time.

[0075] It is explained that the average temperature anomaly factor is determined by using the analysis period, that is, the temperature anomaly factors of all collection times in the analysis period are calculated, the mean value is obtained to obtain the average temperature anomaly factor, so as to reflect the average temperature anomaly level in the analysis period.

[0076] Step S312: obtaining the intersection of the target monitoring points corresponding to all collection times based on the analysis period of any collection time, and calculating the time persistence of the target monitoring point in the corresponding analysis period of any collection time in combination with the average temperature anomaly factor.

[0077] Please refer to Figure 2 , which shows a target monitoring point intersection diagram of a heat supply temperature compensation system remote data communication method using 4G communication according to an embodiment of the present application.

[0078] It is explained that the analysis period obtained at the 30th collection time is explained in the embodiment, wherein the target monitoring points corresponding to the 30th collection time include {monitoring point 2, monitoring point 3, monitoring 4}, the target monitoring points corresponding to the 29th collection time include {monitoring point 2, monitoring point 3, monitoring 4, monitoring 5}, the target monitoring points corresponding to the 28th collection time include {monitoring 1, monitoring point 2, monitoring point 3}, the target monitoring points corresponding to the 27th collection time include {monitoring point 2, monitoring point 3}, and the target monitoring points corresponding to the 26th collection time include {monitoring point 2, monitoring point 3}, so the target monitoring points corresponding to the 25th collection time include {monitoring point 2, monitoring point 3}. ​​​​​​The intersection of the target monitoring points corresponding to all collection time points in the analysis period obtained at the collection time point is denoted as .

[0079] It can be explained that the more the number of target monitoring points contained in the intersection , the more the monitoring points of the same position in the corresponding analysis period continuously appear temperature abnormalities, which indicates that the temperature abnormality of the monitoring point is not a transient phenomenon caused by accidental factors, i.e., the temperature abnormality of the monitoring point is persistent rather than an isolated event that occurs occasionally, and has stability and persistence; on the contrary, the fewer the number of target monitoring points contained in the intersection , the more different monitoring points appear temperature abnormalities at different collection time points in the corresponding analysis period, which indicates that the indoor temperature abnormality is dynamically changing and is not fixed in certain positions but changes with time and space; through the analysis of the intersection, the dynamic characteristics of the indoor temperature distribution can be better mastered, and the possible reasons and patterns of temperature abnormalities can be analyzed.

[0080] Further, in step S312, the time persistence of the target monitoring point in the corresponding analysis period at any collection time point is calculated, and the corresponding calculation formula is:

[0081]

[0082] Wherein, represents the time persistence of the target monitoring point in the corresponding analysis period at the i-th collection time point; represents the average temperature abnormality factor of all target monitoring points in the intersection of the target monitoring points corresponding to all collection time points in the corresponding analysis period at the i-th collection time point; represents the number of target monitoring points in the intersection of the target monitoring points corresponding to all collection time points in the corresponding analysis period at the i-th collection time point; represents the number of target monitoring points in the intersection of the target monitoring points corresponding to all collection time points in the corresponding analysis period at the i-th collection time point; represents the maximum value of the number of target monitoring points corresponding to all collection time points in the corresponding analysis period at the i-th collection time point. It is explained that obtaining the time persistence of the target monitoring point can reflect whether the temperature change is continuous, and further reflect the authenticity of the temperature change; i.e., by calculating the continuity of the temperature time series data in time, the consistency of the temperature change is verified, and if similar temperature fluctuations occur continuously, it indicates that the observed temperature change is a universal phenomenon rather than an abnormality of individual target monitoring points.

[0083]

[0084] ​​​It can be understood that, under the condition of relatively low outdoor temperature, if the user ventilates in the room, due to the significant temperature difference between indoor and outdoor, the indoor and outdoor air will form a convection phenomenon when ventilating, which will directly affect the temperature readings of the user's indoor monitoring points, and then cause the temperature fluctuations of these monitoring points. Therefore, if the temperature time series data monitored by the monitoring points fluctuate similarly, that is, the temperature time series data of all monitoring points rise or fall almost simultaneously, it indicates that this consistency may be caused by ventilation.

[0085] Step S313: Calculate the dynamic time warping between any two target monitoring points in the intersection to obtain the possibility of ventilation in the room.

[0086] For better illustration, dynamic time warping, i.e. DTW (Dynamic Time Warping), is to stretch or compress the temperature time series data sequence corresponding to the target monitoring point elastically, so that the two sequences are aligned on the time axis, find the best matching path, and minimize the total distance between the two sequences. It can measure the similarity of two sequences.

[0087] It is explained that, in the analysis period constructed based on the first acquisition time, the dynamic time warping between any two target monitoring points in the intersection is calculated, denoted as , The smaller the value is, the higher the matching degree of the temperature change between the corresponding two target monitoring points in the time sequence is, and the more consistent the temperature fluctuation trend of each target monitoring point in the analysis period is. At this time, the temperature fluctuation is likely to be caused by ventilation.

[0088] Further, in step S313, the possibility of ventilation in the room is obtained, and the corresponding calculation formula is:

[0089]

[0090] Among them, represents the possibility of ventilation in the room at the first acquisition time; represents the time persistence of the target monitoring point in the analysis period corresponding to the first acquisition time; represents the average distance of all arbitrary two target monitoring points in the intersection of all target monitoring points corresponding to all acquisition times in the analysis period corresponding to the first acquisition time; represents the dynamic time warping of any two target monitoring points in the intersection of all target monitoring points corresponding to all acquisition times in the analysis period corresponding to the first acquisition time; represents a normalized function.

[0091] It is illustrated that, represents the first acquisition time corresponding to the analysis period, the larger the value, the greater the possibility that the temperature anomaly of the target monitoring point is caused by ventilation; represents the first acquisition time corresponding to the analysis period, the smaller the value, the more concentrated the spatial distribution of the temperature time series data of the target monitoring point, that is, there is a universal temperature anomaly in a relatively small area, indicating that it is not a problem of individual target monitoring point, but has a certain spatial concentration; By analyzing these several values, it can be seen that the monitoring point is more likely to be affected by ventilation, and the possibility of temperature sensor failure is smaller.

[0092] It can be illustrated that, represents the first acquisition time, the larger the value, the greater the possibility that the temperature change of the monitoring point is related to the ventilation, if the value is larger, the influence of ventilation on temperature change is significant, the temperature fluctuation trend of each monitoring point is consistent and may be caused by ventilation, so when the temperature time series data is compressed based on 4G communication, it is considered that this part of the temperature time series data has certain overall correlation and regularity, and can be compressed as a whole or follow a certain specific rule, rather than processing each monitoring point's temperature time series data in isolation, which helps to improve the compression efficiency and better utilize the internal relationship and regularity of the temperature time series data.

[0093] It can be understood that the heating temperature compensation system has certain thermal inertia and adjustment ability, which means that for short-time and occasional ventilation, it can restore the indoor temperature to the appropriate range relatively quickly after the ventilation ends through its own heat storage and adjustment mechanism, without the need to increase the heating temperature significantly; but when the ventilation time is longer, the indoor heat will be lost in large quantities and continuously, and the heating temperature compensation system alone cannot maintain the indoor temperature stable, that is, when the indoor and outdoor temperature difference is large, the heating temperature compensation system needs to increase the heating temperature and increase the heating capacity to compensate for the loss of indoor heat and ensure the stability of indoor temperature; therefore, when the ventilation time is longer, the authenticity of the temperature time series data of each acquisition time is stronger, so the comprehensive temperature data obtained should be given a larger weight; when the ventilation time is shorter, the authenticity of the temperature time series data of each acquisition time is weaker, and the comprehensive temperature data should be given a smaller weight.

[0094] Further, in step S4, it includes:

[0095] Step S411: Set the second screening threshold, determine the collection time corresponding to the possibility of indoor ventilation being greater than or equal to the second screening threshold as the target time, and select consecutive adjacent target times to construct the target time period.

[0096] As an optional implementation, in this embodiment, the second screening threshold is 0.7, that is, the collection time when the probability of indoor ventilation is greater than or equal to 0.7 is recorded as the target time.

[0097] Provide an explanation Indicates the first The smaller the value, the more likely there is ventilation indoors at the time of the data collection. The more likely an anomaly is at the target monitoring point corresponding to a collection time, the more likely it is caused by a malfunction or other abnormal factors. In this case, the collected temperature time series data is less reliable and less likely to reflect the true indoor temperature of the user. Therefore, it needs to be assigned a smaller weight. Conversely, the larger the value, the less likely the indoor ventilation is at that collection time. This indicates that the anomaly at the target monitoring point is caused by the temperature sensor. Therefore, it needs to be assigned a larger weight to ensure the accuracy of the comprehensive temperature data obtained later.

[0098] Step S412: Calculate the degree of influence of ventilation on any target monitoring point at any target time.

[0099] Further, in step S412, the degree of influence of ventilation on any target monitoring point at any target time is calculated, including:

[0100] Define the first The target time is defined as the number of acquisition times, and the corresponding calculation formula is:

[0101]

[0102] in, Indicates the first The target monitoring points at the first The degree to which ventilation affects each target at any given time; Indicates the first There is a possibility of indoor ventilation at any given time. Indicates the first The total duration of the target time period corresponding to each target moment; Indicates the first The target monitoring points at the first Temperature time series data at each target time point; Indicates the first Outdoor temperature time series data corresponding to each target time.

[0103] Provide an explanation Indicates the first The longer the target time point, the longer the target time period. The more sufficient the heat exchange between indoors and outdoors at a given time, the greater the probability of indoor ventilation. Indicates the first The target monitoring points at the first The difference between indoor and outdoor temperature time series data at a target time point; the larger this value, the greater the influence of outdoor temperature time series data on indoor temperature time series data.

[0104] Step S413: Determine the time interval based on the target time and the current analysis time, and calculate the authenticity of the temperature time series data of any target monitoring point at any target time.

[0105] It can be explained that the time interval is determined based on the target time and the current analysis time. In other words, the further away the temperature time series data of the target time is from the current analysis time, the lower the reference value of the temperature time series data of the target time for the temperature prediction of the current analysis time, and the lower the accuracy of the comprehensive temperature data obtained.

[0106] Further, in step S413, the authenticity of the temperature time series data of any target monitoring point at any target time is calculated, and the corresponding calculation formula is:

[0107]

[0108] in, Indicates the first The target monitoring points at the first The authenticity of the temperature time series data at each target time point; Indicates the first The target monitoring points at the first The degree to which ventilation affects each target at any given time; Indicates the first The time interval between the target time and the current analysis time; This represents the normalization function.

[0109] The authenticity, or reliability, of temperature time series data refers to the accuracy and reliability of the data, ensuring that it truly reflects the actual temperature changes and is unaffected by external interference and errors.

[0110] Further, step S5 includes:

[0111] Step S511: Mark the authenticity of the temperature time series data of each target monitoring point at each non-target time as 0.1, and mark the authenticity of the temperature time series data of each non-target monitoring point at each non-target time and / or each target time as 1.

[0112] It is explained that based on the authenticity of the temperature time series data of each target monitoring point at each target time obtained in the foregoing, a mark value of the authenticity of the temperature time series data of each target monitoring point at each non-target time, the temperature time series data of each non-target monitoring point at each non-target time, and the temperature time series data of each non-target monitoring point at each target time is respectively preset; and then the authenticity of the temperature time series data of each monitoring point at each collection time is obtained.

[0113] Step S512: calculating the comprehensive temperature data at any collection time to construct a comprehensive temperature data sequence.

[0114] Further, in step S512, the comprehensive temperature data at any collection time is calculated, and the corresponding calculation formula is:

[0115]

[0116] wherein, represents the comprehensive temperature data at the current collection time; represents the number of indoor monitoring points; represents the temperature time series data of the i th monitoring point at the current collection time; represents the authenticity of the temperature time series data of the i th monitoring point at the current collection time.

[0117] It is explained that based on the calculation formula, the comprehensive temperature data at each collection time in the analyzed historical period is obtained, and then the comprehensive temperature data sequence is constructed.

[0118] Step S513: obtaining predicted temperature data based on the comprehensive temperature data sequence, if the predicted temperature data is less than a judgment threshold, sending an instruction to the heat temperature compensation system through 4G communication to perform heating in advance, and dynamically adjusting the heating power; if the predicted temperature data is close to the judgment threshold, reducing the heating power; and if the predicted temperature data is greater than the judgment threshold, increasing the heating power.

[0119] Optionally, the judgment threshold is a set temperature range, and in the embodiment, the set predicted temperature range is 18-22℃. .

[0120] ​​​​​For better illustration, in the field of communication technology today, 4G communication technology is usually used for data transmission, but the bandwidth resources provided by 4G communication technology are limited, which limits the efficiency and speed of data transmission to some extent; especially in the heating temperature compensation system, a large amount of integrated temperature data sequence needs to be transmitted in real time, so the data amount is reduced by compressing and storing the integrated temperature data; in order to reduce the bandwidth resources occupied in the data transmission process, so that the heating temperature compensation system can make more efficient use of limited 4G bandwidth resources; by reducing the data transmission amount, the heating temperature compensation system can also support more concurrent data transmission of users, or transmit more other necessary data under the same bandwidth condition, so as to improve the transmission efficiency and response speed of the entire 4G communication system, and ensure that the heating temperature compensation system can operate more stably and efficiently.

[0121] Preferably, in the embodiment, the integrated temperature data sequence is compressed and stored using Huffman coding, which improves compression efficiency and ensures data accuracy; wherein, the Huffman coding assigns an unequal length binary code word to each symbol in the integrated temperature data sequence, based on the statistical characteristics of the integrated temperature data, short codes are assigned to characters with high frequency, and long codes are assigned to characters with low frequency, so as to reduce the storage space or transmission bandwidth of the overall integrated temperature data, realize efficient coding of the integrated temperature data, and achieve the purpose of compressing data; then the compressed integrated temperature data is transmitted to the management platform, and the autoregressive moving average model is used on the management platform to obtain the predicted temperature data corresponding to the collection time based on the integrated temperature data sequence; wherein, the autoregressive moving average model, ARMA (Autoregressive Moving Average Model), can effectively capture the trend and seasonal components in the integrated temperature data sequence, and combines the characteristics of autoregressive model and moving average model to obtain the predicted temperature data.

[0122] Specifically, when the predicted temperature data obtained based on the integrated temperature data sequence is lower than the judgment threshold, i.e. the predicted temperature data is lower than When the predicted temperature data obtained based on the integrated temperature data sequence is lower than the judgment threshold, i.e. the predicted temperature data is lower than

[0123] Understandably, this application obtains indoor temperature time-series data and the distance between any two monitoring points based on the deployed monitoring points, obtains outdoor temperature time-series data through the meteorological bureau, and analyzes the possibility of ventilation in the user's indoor space based on the degree of anomaly at each monitoring point. This allows for the verification of the authenticity of the temperature time-series data at each monitoring point at each acquisition time. Specifically, the reliability of the temperature time-series data at each monitoring point is judged by the consistency and temporal continuity of the temperature time-series data fluctuations caused by ventilation, so as to achieve dynamic temperature control based on the actual situation. At the same time, data compression technology is used to improve data transmission efficiency, thereby optimizing the operation of the entire heating temperature compensation system, improving the accuracy of predicted data, dynamically adjusting heating power, and achieving precise temperature control.

[0124] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0125] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A remote data communication method for a heating temperature compensation system utilizing 4G communication, characterized in that, The method includes: Indoor temperature time series data is collected by setting up monitoring points indoors, measuring the distance between any two monitoring points, and obtaining outdoor temperature time series data. Based on the analysis of temperature time series data at each monitoring point, the temperature anomaly factor at any monitoring point at any acquisition time is obtained. Target monitoring points are determined by using temperature anomaly factors. Based on the temperature time series data of the target monitoring points, the persistence of temperature changes over time is analyzed to determine the possibility of indoor ventilation. The target time is determined by the possibility of indoor ventilation. The degree of influence of ventilation on any target monitoring point at any target time is calculated based on outdoor temperature time series data. The authenticity of the temperature time series data of any target monitoring point at any target time is obtained by combining the time interval between the target time and the current analysis time. Based on the authenticity of the temperature time series data, comprehensive temperature data is obtained, and a comprehensive temperature data sequence is constructed. The comprehensive temperature data sequence is transmitted to the heating temperature compensation system using 4G communication, and a judgment threshold is set to realize dynamic temperature control. Target monitoring points are identified using temperature anomaly factors. Based on the time-series temperature data from these monitoring points, the persistence of temperature changes over time is analyzed to determine the likelihood of indoor ventilation, including: Set a screening threshold one, and determine the monitoring points whose temperature anomaly factor at each collection time is greater than or equal to the screening threshold one as target monitoring points. Based on the current analysis time, extract some continuous historical collection times to generate an analysis period, and use the analysis period to determine the average temperature anomaly factor. Based on the analysis period of any acquisition time, the intersection of all target monitoring points corresponding to all acquisition times is obtained, and the duration of the target monitoring points in time at any acquisition time is calculated in conjunction with the average temperature anomaly factor. By calculating the dynamic time warping between any two target monitoring points through intersection calculation, the possibility of indoor ventilation can be obtained. The formula for calculating the comprehensive temperature data at any given time point is as follows: in, Indicates the current data collection time Comprehensive temperature data; Indicates the number of indoor monitoring points; Indicates the first Each monitoring point at the current data collection time Temperature time series data; Indicates the first Each monitoring point at the current data collection time The authenticity of temperature time series data.

2. The remote data communication method for a heating temperature compensation system utilizing 4G communication as described in claim 1, characterized in that, Based on the analysis of temperature time-series data at each monitoring point, the temperature anomaly factor at any monitoring point at any acquisition time is obtained, including: Based on the temperature time-series data of each monitoring point, the first-order difference value and the second-order difference value are obtained sequentially. The temperature anomaly factor of any monitoring point at any acquisition time is calculated, and the corresponding calculation formula is as follows: in, Indicates the first The monitoring point at the 1st Temperature anomaly factors at each data acquisition time; Indicates the first The monitoring point at the 1st First-order difference values ​​at each acquisition time; Indicates the first The monitoring point at the 1st The second-order difference value at each acquisition time; 0.1 represents a preset constant used to prevent the denominator from being 0; This represents the normalization function.

3. The remote data communication method for a heating temperature compensation system utilizing 4G communication as described in claim 1, characterized in that, The formula for calculating the temporal persistence of a target monitoring point at any given acquisition time within the corresponding analysis period is as follows: in, Indicates the first The temporal continuity of the target monitoring point within the analysis period corresponding to each collection time; Indicates the first The average temperature anomaly factor of all target monitoring points in the intersection of all target monitoring points corresponding to all acquisition times in the analysis period corresponding to each acquisition time; Indicates the first The number of target monitoring points in the intersection of all target monitoring points corresponding to each collection time in the analysis period corresponding to each collection time; Indicates the first The maximum number of target monitoring points corresponding to all acquisition times within the analysis period corresponding to a given acquisition time.

4. The remote data communication method for a heating temperature compensation system utilizing 4G communication as described in claim 1, characterized in that, The formula for calculating the possibility of indoor ventilation is as follows: in, Indicates the first There is a possibility that the room was ventilated at the time of the data collection. Indicates the first The temporal continuity of the target monitoring point within the analysis period corresponding to each collection time; Indicates the first The mean of the distances between any two target monitoring points in the intersection of all target monitoring points corresponding to all collection times in the analysis period corresponding to each collection time; Indicates the first Dynamic time regularization of any two target monitoring points in the intersection of all target monitoring points corresponding to each collection time in the analysis period corresponding to each collection time; This represents the normalization function.

5. A remote data communication method for a heating temperature compensation system utilizing 4G communication as described in claim 1, characterized in that, The target time is determined by utilizing the possibility of indoor ventilation. The degree of influence of ventilation on any target monitoring point at any target time is calculated based on outdoor temperature time-series data. The accuracy of the temperature time-series data for any target monitoring point at any target time is obtained by combining the time interval between the target time and the current analysis time, including: Set a second screening threshold, and determine the collection time corresponding to the possibility that there is ventilation indoors that is greater than or equal to the second screening threshold as the target time. Select consecutive adjacent target times to construct the target time period. Calculate the degree to which any target monitoring point is affected by ventilation at any target time; The time interval is determined based on the target time and the current analysis time to calculate the authenticity of the temperature time series data of any target monitoring point at any target time.

6. A remote data communication method for a heating temperature compensation system utilizing 4G communication as described in claim 5, characterized in that, Calculate the degree to which any target monitoring point is affected by ventilation at any target time, including: Define the first The target time is defined as the number of acquisition times, and the corresponding calculation formula is: in, Indicates the first The target monitoring points at the first The degree to which ventilation affects each target at any given time; Indicates the first There is a possibility of indoor ventilation at any given time. Indicates the first The total duration of the target time period corresponding to each target moment; Indicates the first The target monitoring points at the first Temperature time series data at each target time point; Indicates the first Outdoor temperature time series data corresponding to each target time.

7. A remote data communication method for a heating temperature compensation system utilizing 4G communication as described in claim 5, characterized in that, The formula for verifying the authenticity of temperature time-series data at any target monitoring point at any target time is as follows: in, Indicates the first The target monitoring points at the first The authenticity of the temperature time series data at each target time point; Indicates the first The target monitoring points at the first The degree to which ventilation affects each target at any given time; Indicates the first The time interval between the target time and the current analysis time; This represents the normalization function.

8. A remote data communication method for a heating temperature compensation system utilizing 4G communication as described in claim 1, characterized in that, Based on the accuracy of the temperature time-series data, comprehensive temperature data is obtained, and a comprehensive temperature data sequence is constructed. This sequence is then transmitted to the heating temperature compensation system using 4G communication. A judgment threshold is set to achieve dynamic temperature control, including: The authenticity of the temperature time series data of each target monitoring point at each non-target time is marked as 0.1, and the authenticity of the temperature time series data of each non-target monitoring point at each non-target time and / or each target time is marked as 1. Calculate the comprehensive temperature data at any given time of data collection and construct a comprehensive temperature data sequence; Based on the comprehensive temperature data sequence, the predicted temperature data is obtained. If the predicted temperature data is less than the judgment threshold, an instruction is sent to the heating temperature compensation system via 4G communication to start heating in advance and dynamically adjust the heating power. If the predicted temperature data is close to the judgment threshold, the heating power is reduced. If the predicted temperature data is greater than the judgment threshold, the heating power is increased.

Images