Remote data communication method for heat supply temperature compensation system by utilizing 4G communication

Abstract

The invention relates to the technical field of digital information transmission, in particular to a heat supply temperature compensation system remote data communication method using 4G communication, which comprises the following steps: acquiring indoor and outdoor temperature time sequence data based on monitoring points, and measuring the distance between any two monitoring points; analyzing the temperature time sequence data based on each monitoring point to obtain a temperature anomaly factor of any monitoring point at any acquisition moment; a target monitoring point is determined, the continuity of temperature change in time is analyzed, and the possibility of indoor ventilation is obtained; target moments are determined, the influence degree of ventilation on any target monitoring point at any target moment is calculated, and the authenticity of the temperature time sequence data of any target monitoring point at any target moment is obtained; comprehensive temperature data are obtained, a sequence is constructed, the comprehensive temperature data sequence is transmitted to a heat supply temperature compensation system through 4G communication, a judgment threshold value is set, and dynamic regulation and control of the temperature are achieved; the data transmission efficiency is high, and the temperature control is accurately realized.

Description

Technical Field

[0001] The present invention relates to the technical field of digital information transmission, and in particular to a remote data communication method for a heating temperature compensation system using 4G communication. Background Art

[0002] The heating temperature compensation system is an advanced intelligent system designed to optimize the heating process, ensure the stability of indoor temperature and meet the specific needs of users. The system obtains the user's indoor temperature data in real time through temperature sensors, including key parameters such as water inlet temperature, water outlet temperature and heating rod temperature, and then transmits the data to the intelligent control mainboard, which is responsible for accurately adjusting and controlling these temperature parameters; through advanced data compression and encryption technology, the system ensures the security and efficiency of data transmission; in addition, the system uses 4G communication Internet of Things cards 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 and stop control of the heating temperature compensation device, so as to achieve the purpose of accurately adjusting the indoor temperature.

[0003] In the heating temperature compensation system, the temperature sensor plays a vital role. It is responsible for monitoring and feeding back the user's indoor temperature data. However, in actual applications, the temperature sensor may fail. This is usually due to the gradual aging of the electronic components inside the sensor, such as thermistors, thermocouples, etc., after long-term use. Once a failure occurs, the temperature data may be abnormal, which will directly affect the performance of the heating temperature compensation system. To solve this problem, the existing technology usually obtains the average temperature data of all monitoring points in the user's room at the same time, so as to predict the indoor temperature changes in the future. However, this method has strong limitations and is often based on fixed rules to fill in data gaps. It is impossible to make dynamic adjustments to the temperature according to actual conditions. Especially when the temperature sensor fails, it will cause abnormal temperature data and affect the accuracy of the prediction. In addition, in the data transmission link, due to the limited bandwidth resources of 4G communication, the transmission of a large amount of original temperature data will occupy too much bandwidth, resulting in low transmission efficiency, and may even affect the transmission of other key data. Therefore, if the prediction is made directly based on the collected historical temperature data, it may cause the heating system to be overheated or underheated, which will not only affect the user's comfort experience, but also cause unreasonable use and waste of energy. Summary of the invention

[0004] In order to solve the technical problem that the existing method cannot dynamically solve the temperature anomaly problem, and the transmission efficiency based on 4G communication is low, if the original temperature data is used for prediction, it will cause the heating system to overheat or underheat. The purpose of the present invention is to provide a remote data communication method for a heating temperature compensation system using 4G communication. The technical solution adopted is as follows: Monitoring points are set up indoors to collect indoor temperature time series data, measure the distance between any two monitoring points, and obtain outdoor temperature time series data; Analyze the temperature time series data based on each monitoring point to obtain the temperature anomaly factor of any monitoring point at any collection time; The target monitoring point is determined by using the temperature anomaly factor, and the continuity of temperature change over time is analyzed based on the temperature time series data of the target monitoring point to obtain 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 the 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. According to the authenticity of the temperature time series data, the comprehensive temperature data is obtained, and the comprehensive temperature data sequence is constructed. The comprehensive temperature data sequence is transmitted to the heating temperature compensation system using 4G communication, and the judgment threshold is set to realize dynamic temperature control.

[0005] Preferably, the temperature time series data is analyzed based on each monitoring point to obtain the temperature anomaly factor of any monitoring point at any collection time, 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 in turn, and the temperature anomaly factor of any monitoring point at any collection time is calculated. The corresponding calculation formula is: in, Indicates The monitoring point is Temperature anomaly factor at each collection moment; Indicates The monitoring point is The first-order difference value at each acquisition moment; Indicates The monitoring point is The second-order difference value at each acquisition moment; 0.1 represents a preset constant, which is used to prevent the denominator from being 0; Represents the normalization function.

[0006] Preferably, the target monitoring point is determined by using the temperature anomaly factor, and the continuity of the temperature change over time is analyzed based on the temperature time series data of the target monitoring point to obtain the possibility of indoor ventilation, including: Set a screening threshold of one, determine the monitoring point corresponding to the temperature anomaly factor greater than or equal to the screening threshold of one at each collection moment as the target monitoring point, extract some continuous historical collection moments based on the current analysis moment to generate an analysis period, and use the analysis period to determine the average temperature anomaly factor; Based on the analysis period of any collection time, the intersection of the target monitoring points corresponding to all collection times is obtained, and the temporal continuity of the target monitoring points in the corresponding analysis period at any collection time is calculated in combination with the average temperature anomaly factor; The dynamic time warping between any two target monitoring points is calculated by intersection, and the possibility of indoor ventilation is obtained.

[0007] Preferably, the temporal continuity of the target monitoring point in the corresponding analysis period at any acquisition moment is calculated, and the corresponding calculation formula is: in, Indicates The temporal continuity of the target monitoring point in the analysis period corresponding to each collection moment; Indicates The average temperature anomaly factor of all target monitoring points in the intersection of all target monitoring points corresponding to the acquisition time in the analysis period corresponding to the acquisition time; Indicates The number of target monitoring points in the intersection of all target monitoring points corresponding to the collection time in the analysis period corresponding to the collection time; Indicates The maximum value of the number of target monitoring points corresponding to all collection moments in the analysis period corresponding to a collection moment.

[0008] Preferably, the possibility of indoor ventilation is obtained, and the corresponding calculation formula is: in, Indicates The possibility of indoor ventilation at the time of collection; Indicates The temporal continuity of the target monitoring point in the analysis period corresponding to each collection moment; Indicates The mean value of the distances between any two target monitoring points in the intersection of all target monitoring points corresponding to the acquisition time in the analysis period corresponding to the acquisition time; Indicates Dynamic time warping of any two target monitoring points in the intersection of all target monitoring points corresponding to the collection time in the analysis period corresponding to the collection time; Represents the normalization function.

[0009] 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 the outdoor temperature time series data, and 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: Set screening threshold 2, determine the collection time corresponding to the possibility of indoor ventilation being greater than and equal to screening threshold 2 as the target time, and screen consecutive adjacent target times to construct the target period; Calculate the degree of influence of ventilation on any target monitoring point at any target time; The time interval is determined based on the target moment and the current analysis moment, and the authenticity of the temperature time series data of any target monitoring point at any target moment is calculated.

[0010] Preferably, calculating the degree of influence of ventilation on any target monitoring point at any target time comprises: Define The collection time is the target time, and the corresponding calculation formula is: in, Indicates The target monitoring point is The degree of influence of ventilation at each target moment; Indicates There is a possibility of indoor ventilation at the target time; Indicates The total duration of the target period corresponding to the target moment; Indicates The target monitoring point is Temperature time series data at each target moment; Indicates The outdoor temperature time series data corresponding to the target time.

[0011] Preferably, 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: in, Indicates The target monitoring point is The authenticity of the temperature time series data at each target moment; Indicates The target monitoring point is The degree of influence of ventilation at each target moment; Indicates The time interval between the target moment and the current analysis moment; Represents the normalization function.

[0012] Preferably, according to the authenticity of the temperature time series data, the comprehensive temperature data is obtained, and the comprehensive temperature data sequence is constructed, and the comprehensive temperature data sequence is transmitted to the heating temperature compensation system by using 4G communication, and the judgment threshold is set to realize the dynamic regulation of temperature, including: 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; Calculate the comprehensive temperature data at any collection moment and construct a comprehensive temperature data sequence; The predicted temperature data is obtained based on the comprehensive temperature data sequence. If the predicted temperature data is less than the judgment threshold, an instruction is sent to the heating temperature compensation system through 4G communication to heat 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.

[0013] Preferably, the comprehensive temperature data at any collection time is calculated, and the corresponding calculation formula is: in, Indicates the current collection time Comprehensive temperature data of Indicates the number of indoor monitoring points; Indicates Monitoring points at the current collection time Temperature time series data; Indicates Monitoring points at the current collection time The authenticity of the temperature time series data.

[0014] The present invention has the following beneficial effects: 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 room according to the degree of abnormality of each monitoring point, so as to obtain the authenticity of the temperature time series data of each monitoring point at each collection moment, that is, the credibility of the temperature time series data of each monitoring point is judged through the consistency of fluctuations caused by ventilation and the continuity in time, so as to realize dynamic temperature regulation according to actual conditions; at the same time, through data compression technology, the data transmission efficiency is improved to optimize the operation of the entire heating temperature compensation system, improve the accuracy of predicted data, dynamically adjust the heating power, and realize precise temperature control. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0016] Figure 1 A flowchart of a remote data communication method for a heating temperature compensation system using 4G communication provided by an embodiment of the present invention; Figure 2 A schematic diagram of the intersection of target monitoring points of a remote data communication method for a heating temperature compensation system using 4G communication provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a remote data communication method of a heating temperature compensation system using 4G communication proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The following is a specific scheme of a remote data communication method for a heating temperature compensation system using 4G communication provided by the present invention, which is described in detail with reference to the accompanying drawings.

[0020] See also Figure 1, which shows a flowchart of a method for remote data communication of a heating temperature compensation system using 4G communication provided by an embodiment of the present invention, the method comprising: Step S1: Setting up monitoring points indoors to collect indoor temperature time series data, measuring the distance between any two monitoring points, and obtaining outdoor temperature time series data; Step S2: Analyze the temperature time series data based on each monitoring point to obtain the temperature anomaly factor of any monitoring point at any collection time; Step S3: Determine the target monitoring point using the temperature anomaly factor, analyze the continuity of temperature change over time based on the temperature time series data of the target monitoring point, and obtain the possibility of indoor ventilation; Step S4: Determine the target time by using the possibility of indoor ventilation, calculate the degree of influence of ventilation on any target monitoring point at any target time based on the outdoor temperature time series data, and obtain the authenticity of the temperature time series data of any target monitoring point at any target time by combining the time interval between the target time and the current analysis time; Step S5: According to the authenticity of the temperature time series data, the 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.

[0021] To better explain, the heating temperature compensation system usually includes key components such as temperature sensors, controllers and actuators. Through the coordinated work of various components, the temperature compensation value required for abnormal temperature is calculated through precise algorithms to monitor and adjust the operating status of the heating equipment in real time. It can automatically adjust the output temperature of the heating system according to changes in the external ambient temperature to ensure constant and comfortable indoor temperature.

[0022] 4G communication refers to the fourth generation of mobile communication technology, which is an advanced wireless communication technology that can provide faster data transmission speeds and higher quality communication services than 3G. It relies on key technologies such as orthogonal frequency division multiple access and multiple-input multiple-output, effectively improving spectrum utilization and network capacity, and supporting smart antenna technology. By precisely controlling the sending and receiving of signals, it improves signal quality and coverage. It has greatly promoted the development of mobile Internet, allowing users to enjoy high-speed network connections anytime and anywhere, and conduct data-intensive applications such as video calls, online games, and high-definition video viewing.

[0023] The introduction of 4G communication technology has made the remote control of the heating temperature compensation system more efficient and real-time. The operation process is not restricted by geographical location, and the system can be monitored and adjusted anytime and anywhere to ensure the stable operation of the heating system. The temperature acquisition algorithm of the heating temperature compensation system is optimized to obtain 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 faster and more accurate. It reduces the frequency and cost of manual inspections, improves the economic benefits of the entire heating system, and enhances the safety of the system, so that it can respond to various emergencies in a timely manner and avoid possible losses.

[0024] As an optional implementation, in this embodiment, the monitoring point refers to a room temperature collector, including a five-hole socket panel, a temperature sensor, an intelligent control mainboard, a 4G communication Internet of Things card, and a temperature display screen, wherein the five-hole socket panel can provide a power connection point for a variety of 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 can ensure 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 performs intelligent analysis of the temperature data and automatically adjusts the indoor temperature to achieve energy saving and optimized operation.

[0025] Specifically, in step S1, monitoring points are arranged indoors, that is, several room temperature collectors are installed on the indoor walls to collect real-time temperature time series data of the user's indoor room, and the distance between any two room temperature collectors is measured. Then, the user's outdoor temperature time series data is obtained through the Meteorological Bureau, so that the user can understand and grasp the temperature changes of the outdoor environment in real time.

[0026] Optionally, in this embodiment, the change pattern of the user's room temperature is analyzed with the time period two hours before the current analysis time, that is, the indoor temperature change is analyzed with the historical two hours of temperature time series data, and the collection frequency is once every 2 minutes.

[0027] It can be understood that by analyzing historical temperature data, through user behavior patterns and weather information, the indoor temperature change trend 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. At the same time, the heating power is dynamically adjusted according to the prediction results to achieve precise temperature control; and when the user's indoor temperature is monitored, a temperature time series data sequence is constructed. When the indoor temperature changes, the temperature changes regularly and slowly. In the temperature time series data sequence, the greater the difference between the temperature time series data of the monitoring point and other surrounding temperature time series data, the faster the temperature changes, indicating that the temperature of the monitoring point is more abnormal.

[0028] Furthermore, step S2 includes: Based on the temperature time series data of each monitoring point, the first-order difference value and the second-order difference value are obtained in turn, and the temperature anomaly factor of any monitoring point at any collection time is calculated. The corresponding calculation formula is: in, Indicates The monitoring point is Temperature anomaly factor at each collection moment; Indicates The monitoring point is The first-order difference value at each acquisition moment; Indicates The monitoring point is The second-order difference value at each acquisition moment; 0.1 represents a preset constant, which is used to prevent the denominator from being 0; represents a normalization function, which may specifically be, for example, maximum and minimum value normalization.

[0029] For better explanation, the first-order difference value of temperature time series data refers to the difference in temperature change between two adjacent time points of the temperature time series data that changes over time, which reflects the rate of temperature change over time; the second-order difference value of temperature time series data refers to the difference value obtained by performing two consecutive first-order difference calculations on the temperature time series data, which helps to analyze and understand the trend and pattern of temperature data changing over time.

[0030] It can be explained that Indicates The monitoring point is The first-order difference value at the acquisition time. The larger the absolute value of is, the faster the temperature changes at the corresponding acquisition time. The more abnormal the temperature time series data at each collection moment is; Indicates The monitoring point is The second-order difference value at the acquisition time. The larger the absolute value of The greater the difference in temperature change rate before and after the first acquisition moment, the The temperature time series data of each monitoring point shows a trend change, that is, If the temperature time series data of the monitoring points change in the same way and the temperature abnormality is smaller, the temperature time series data of the monitoring point at the collection time can be retained; conversely, if the absolute value of the first-order difference value of a monitoring point at a certain collection time is smaller, the absolute value of the second-order difference value is larger, or the absolute value of the first-order difference value is larger, the absolute value of the second-order difference value is smaller, that is, the temperature time series data of the corresponding monitoring points change differently, the temperature abnormality is greater, and the temperature time series data of the monitoring point at the collection time needs to be handled as an exception.

[0031] It is explained that by calculating the temperature anomaly factor of each monitoring point at each collection moment, the collection moments and monitoring points with abnormal temperature changes are screened out, so that the corresponding temperature time series data can be retained or processed abnormally, respectively. This helps to retain key information in the subsequent data compression process and avoid the loss of important temperature time series data.

[0032] Understandably, during the ventilation process of the user's room, due to the significant temperature difference between indoors and outdoors, air convection is triggered during ventilation, and this convection phenomenon will affect the reading of the temperature time series data of each monitoring point in the room, causing the temperature time series data to change; among them, the temperature time series data anomaly caused by ventilation usually shows a certain continuity in the time series, and in terms of spatial distribution, the monitoring points with temperature anomalies are relatively concentrated; and when the temperature sensor fails, the temporal anomaly of the collected temperature time series data may appear random, and will appear scattered in spatial distribution; therefore, whether it is ventilation or temperature sensor failure, the acquired temperature time series data will not accurately reflect the actual indoor heating effect and temperature change trend. If this abnormal temperature time series data is received by 4G communication, that is, the remote platform, it may cause the remote platform to make an incorrect assessment of the operating status of the heating temperature compensation system, and then send an incorrect adjustment instruction to the heating temperature compensation system, resulting in incorrect regulation of the indoor temperature.

[0033] Furthermore, step S3 includes: Step S311: Set a screening threshold of one, determine the monitoring point corresponding to each collection moment whose temperature anomaly factor is greater than and equal to the screening threshold of one as the target monitoring point, extract some continuous historical collection moments based on the current analysis moment to generate an analysis period, and use the analysis period to determine the average temperature anomaly factor.

[0034] It can be explained that temperature change is a relatively slow and coherent process, so the abnormal monitoring of the target monitoring point depends not only on the operating status at a single collection moment, but also on the temperature change pattern within the entire historical collection moment; therefore, relying only on the temperature time series data at a single collection moment may miss some potential problems caused by trend changes or short-term temperature fluctuations. Therefore, in order to fully 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.

[0035] As an optional implementation, in this embodiment, the first screening threshold is 0.6; that is, when the temperature anomaly factor at any acquisition time is greater than or equal to 0.6, it is recorded as the target monitoring point; the analysis period is the first time period of the current analysis. It consists of 30 consecutive collection moments before the collection moment.

[0036] It is explained that the average temperature anomaly factor is determined using the analysis period, that is, the temperature anomaly factors of all sampling moments in the analysis period are calculated, and the average temperature anomaly factor is obtained by taking the average value to reflect the average temperature anomaly level in the analysis period.

[0037] Step S312: based on the analysis period of any collection moment, the intersection of the target monitoring points corresponding to all collection moments is obtained, and the temporal continuity of the target monitoring points at any collection moment in the corresponding analysis period is calculated in combination with the average temperature anomaly factor.

[0038] See also Figure 2 , which shows a schematic diagram of the intersection of target monitoring points of a remote data communication method for a heating temperature compensation system using 4G communication provided by an embodiment of the present invention.

[0039] To illustrate, in this embodiment, the The analysis period obtained at each collection moment is described, where The target monitoring points corresponding to the collection time include {monitoring point 2, monitoring point 3, monitoring point 4}, The target monitoring points corresponding to the collection time include {monitoring point 2, monitoring point 3, monitoring point 4, monitoring point 5}, The target monitoring points corresponding to the collection time include {monitoring point 1, monitoring point 2, monitoring point 3}, The target monitoring points corresponding to the collection time include {monitoring point 2, monitoring point 3}, so in the The intersection of the target monitoring points corresponding to all the collection moments in the analysis period obtained by the collection moments is recorded as .

[0040] It can be explained that the intersection The more target monitoring points are included in , the more abnormal temperatures will appear at the monitoring points at the same location in the corresponding analysis period. This means that the abnormal temperature at the monitoring point is not a transient phenomenon caused by accidental factors, that is, the abnormal temperature at the monitoring point is continuous, rather than an isolated event that occurs occasionally, and is stable and continuous. On the contrary, if the intersection The fewer the number of target monitoring points included in , it means that in the corresponding analysis period, different monitoring points have temperature anomalies at different collection times, which means that the indoor temperature anomaly is dynamically changing and is not fixed at certain specific locations, but changes with time and space. The intersection analysis can better grasp the dynamic characteristics of indoor temperature distribution, and then analyze the possible causes and patterns of temperature anomalies.

[0041] Furthermore, in step S312, the temporal continuity of the target monitoring point in the corresponding analysis period at any collection time is calculated, and the corresponding calculation formula is: in, Indicates The temporal continuity of the target monitoring point in the analysis period corresponding to each collection moment; Indicates The average temperature anomaly factor of all target monitoring points in the intersection of all target monitoring points corresponding to the acquisition time in the analysis period corresponding to the acquisition time; Indicates The number of target monitoring points in the intersection of all target monitoring points corresponding to the collection time in the analysis period corresponding to the collection time; Indicates The maximum value of the number of target monitoring points corresponding to all collection moments in the analysis period corresponding to a collection moment.

[0042] It is explained that obtaining the temporal continuity of the target monitoring point can reflect whether the temperature change is consistent, and thus reflect the authenticity of the temperature change; that is, by calculating the temporal continuity of the temperature time series data, the consistency of the temperature change is verified. If similar temperature fluctuations occur continuously, it means that the observed temperature change is a universal phenomenon, rather than an abnormality of an individual target monitoring point.

[0043] It is understandable that when the outdoor temperature is relatively low, if the user performs ventilation indoors, due to the significant temperature difference between indoors and outdoors, the air indoors and outdoors will form convection when ventilation is performed. This phenomenon will directly affect the temperature readings of various monitoring points in the user's room, thereby causing temperature fluctuations at these monitoring points. Therefore, if the fluctuations in the temperature time series data monitored by the monitoring points are similar, that is, the temperature time series data of all monitoring points rise or fall almost at the same time, it indicates that this consistency may be caused by ventilation.

[0044] Step S313: The possibility of indoor ventilation is obtained by calculating the dynamic time regularization between any two target monitoring points through intersection.

[0045] To better illustrate, dynamic time warping, or DTW (Dynamic Time Warping), elastically stretches or compresses the temperature time series data sequence corresponding to the target monitoring point so that the two sequences are aligned on the time axis, finds the best matching path to minimize the total distance between the two sequences, and can measure the similarity of the two sequences.

[0046] To explain, in the above based on In the analysis period constructed by the collection moments, the intersection is calculated The dynamic time warping between any two target monitoring points in is denoted as , The smaller it is, the higher the degree of matching between the temperature changes of the corresponding two target monitoring points in the time series, which means that the temperature fluctuation trends of each target monitoring point in the analysis period are more consistent, and the temperature fluctuation at this time is likely to be caused by ventilation.

[0047] Furthermore, in step S313, the possibility of indoor ventilation is obtained, and the corresponding calculation formula is: in, Indicates The possibility of indoor ventilation at the time of collection; Indicates The temporal continuity of the target monitoring point in the analysis period corresponding to each collection moment; Indicates The mean value of the distances between any two target monitoring points in the intersection of all target monitoring points corresponding to the acquisition time in the analysis period corresponding to the acquisition time; Indicates Dynamic time warping of any two target monitoring points in the intersection of all target monitoring points corresponding to the collection time in the analysis period corresponding to the collection time; Represents the normalization function.

[0048] To explain, Indicates The persistence of the target monitoring point in time during the analysis period corresponding to each collection moment. The larger the value, the greater the possibility that the temperature anomaly at the target monitoring point is caused by ventilation. Indicates The average of the distances between any two target monitoring points in the intersection of all target monitoring points corresponding to all acquisition moments in the analysis period corresponding to the acquisition moment. The smaller the value, the more concentrated the temperature time series data of the target monitoring points are in space, that is, there are widespread temperature anomalies in a relatively small area, indicating that it is not a problem of scattered individual target monitoring points, but has a certain spatial concentration; by analyzing these values, it means that the more likely the monitoring point is to be affected by ventilation, the less likely the temperature sensor is to fail.

[0049] It can be explained that Indicates There is a possibility of ventilation in the room at each collection moment, which reflects the degree of correlation between the temperature change of the monitoring point and ventilation. If the value is large, it means that the influence of ventilation on temperature change is significant, and the temperature fluctuation trend of each monitoring point is consistent and may be caused by ventilation. Therefore, when the temperature time series data is compressed based on 4G communication in the future, it is considered that this part of the temperature time series data has a certain overall correlation and regularity, and can be compressed as a whole or following a specific rule, rather than isolating the temperature time series data of each monitoring point, which helps to improve the compression efficiency and can better utilize the inherent connection and regularity between the temperature time series data.

[0050] It can be understood that the heating temperature compensation system has a certain thermal inertia and adjustment ability, which means that for short-term 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 significantly increase the heating temperature. However, when the ventilation time is long, the user's indoor heat will be lost in large quantities and continuously, and the heating temperature compensation system will find it difficult to maintain the indoor temperature stable only by relying on its own adjustment ability. That is, when the temperature difference between indoor and outdoor is large, the heating temperature compensation system needs to increase the heating temperature and increase the amount of heat to compensate for the loss of indoor heat and ensure the stability of the indoor temperature. Therefore, when the ventilation time is long, the authenticity of the temperature time series data at each collection moment is stronger, so the comprehensive temperature data obtained should be given a larger weight. When the ventilation time is short, the authenticity of the temperature time series data at each collection moment is weaker, and the comprehensive temperature data should be given a smaller weight.

[0051] Furthermore, step S4 includes: Step S411: Set a second screening threshold, determine the collection time corresponding to the possibility of indoor ventilation greater than and equal to the second screening threshold as the target time, and screen consecutive adjacent target times to construct a target period.

[0052] As an optional implementation, in this embodiment, the second screening threshold is 0.7, that is, the collection time when the possibility of indoor ventilation is greater than and equal to 0.7 is recorded as the target time.

[0053] To explain, Indicates The possibility of indoor ventilation at the time of collection is small, indicating that the The more likely the abnormality of the target monitoring point corresponding to a collection moment is caused by a fault or other abnormal factors, the weaker the authenticity of the collected temperature time series data, the smaller the credibility, and the less likely it is to reflect the actual temperature of the user's room, so a smaller weight needs to be given; on the contrary, the larger the value, the smaller the possibility of indoor ventilation at the collection moment, and the more it indicates that the abnormality of the target monitoring point is caused by the temperature sensor, and a larger weight needs to be given to ensure the accuracy of the comprehensive temperature data obtained subsequently.

[0054] Step S412: Calculate the degree of influence of ventilation on any target monitoring point at any target time.

[0055] Furthermore, in step S412, the degree of influence of ventilation on any target monitoring point at any target time is calculated, including: Define The collection time is the target time, and the corresponding calculation formula is: in, Indicates The target monitoring point is The degree of influence of ventilation at each target moment; Indicates There is a possibility of indoor ventilation at the target time; Indicates The total duration of the target period corresponding to the target moment; Indicates The target monitoring point is Temperature time series data at each target moment; Indicates The outdoor temperature time series data corresponding to the target time.

[0056] To explain, Indicates The total duration of the target period corresponding to the target moment. The longer the duration, the higher the The more sufficient the indoor and outdoor heat exchange is at the target moment, the greater the probability of indoor ventilation; Indicates The target monitoring point is The difference between the indoor and outdoor temperature time series data at the target time. The larger the value, the greater the impact of the outdoor temperature time series data on the indoor temperature time series data.

[0057] 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.

[0058] It can be explained that the time interval is determined based on the target moment and the current analysis moment, that is, the farther the temperature time series data at the target moment is from the current analysis moment, the lower the reference value of the temperature time series data at the target moment for the temperature prediction at the current analysis moment, and the authenticity of the obtained comprehensive temperature data may be lower.

[0059] Furthermore, 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: in, Indicates The target monitoring point is The authenticity of the temperature time series data at each target moment; Indicates The target monitoring point is The degree of influence of ventilation at each target moment; Indicates The time interval between the target moment and the current analysis moment; Represents the normalization function.

[0060] It should be noted that the authenticity, or credibility, of temperature time series data refers to the accuracy and reliability of temperature time series data, so that it can truly reflect the actual situation of temperature changes without being affected by external interference and errors.

[0061] Furthermore, step S5 includes: Step S511: mark the authenticity of the temperature time series data of each target monitoring point at each non-target moment as 0.1, and mark the authenticity of the temperature time series data of each non-target monitoring point at each non-target moment and / or each target moment as 1.

[0062] To explain, based on the authenticity of the temperature time series data of each target monitoring point at each target moment obtained above, the mark values ​​of the authenticity of the temperature time series data of each target monitoring point at each non-target moment, each non-target monitoring point at each non-target moment, and each non-target monitoring point at each target moment are preset respectively; and then the authenticity of the temperature time series data of each monitoring point at each collection moment is obtained.

[0063] Step S512: Calculate the comprehensive temperature data at any collection time and construct a comprehensive temperature data sequence.

[0064] Furthermore, in step S512, the comprehensive temperature data at any collection time is calculated, and the corresponding calculation formula is: in, Indicates the current collection time Comprehensive temperature data; Indicates the number of indoor monitoring points; Indicates Monitoring points at the current collection time Temperature time series data; Indicates Monitoring points at the current collection time The authenticity of the temperature time series data.

[0065] It is explained that based on this calculation formula, the comprehensive temperature data of each collection moment in the analyzed historical period is obtained, and then the comprehensive temperature data sequence is constructed.

[0066] Step S513: Obtain predicted temperature data based on the comprehensive temperature data sequence. If the predicted temperature data is less than the judgment threshold, send instructions to the heating temperature compensation system through 4G communication to perform heating in advance and dynamically adjust the heating power; if the predicted temperature data is close to the judgment threshold, reduce the heating power; if the predicted temperature data is greater than the judgment threshold, increase the heating power.

[0067] Optionally, the judgment threshold is a set temperature range. In this embodiment, the set predicted temperature range is .

[0068] To better illustrate, in today's communication technology field, 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 a certain extent; especially in the heating temperature compensation system, a large amount of comprehensive temperature data sequence needs to be transmitted in real time, so the amount of data is reduced by compressing and storing the comprehensive temperature data; so as to reduce the bandwidth resources occupied in the data transmission process, so that the heating temperature compensation system can more efficiently utilize the limited 4G bandwidth resources; by reducing the amount of data transmission, the heating temperature compensation system can also support more users' concurrent data transmission, or transmit more other necessary data under the same bandwidth conditions, 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.

[0069] Preferably, in this embodiment, Huffman coding is used to compress and store the comprehensive temperature data sequence to improve compression efficiency and ensure data accuracy; wherein, Huffman coding assigns a binary codeword of unequal length to each symbol in the comprehensive temperature data sequence, and assigns short codes to characters with high frequency of occurrence and long codes to characters with low frequency of occurrence based on the statistical characteristics of the comprehensive temperature data, so as to reduce the storage space or transmission bandwidth of the overall comprehensive temperature data, realize efficient encoding of the comprehensive temperature data, and achieve the purpose of compressing data; then, the compressed comprehensive 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 at the corresponding collection time based on the comprehensive temperature data sequence; wherein, the autoregressive moving average model, namely ARMA (Autoregressive Moving Average Model), can effectively capture the trend and seasonal components in the comprehensive temperature data sequence, and combines the characteristics of the autoregressive model and the moving average model to obtain the predicted temperature data.

[0070] Specifically, when the predicted temperature data obtained based on the comprehensive temperature data sequence is lower than the judgment threshold, that is, the predicted temperature data is lower than When the indoor temperature drops, the heating device is started in advance by sending instructions to the heating temperature compensation system through 4G communication to avoid the lag in starting to compensate for the heating only after the indoor temperature drops. At the same time, the heating power is dynamically adjusted according to the temperature change rate of the predicted temperature data and the temperature data that may be finally reached. If the predicted temperature data is close to the set comfortable temperature range, the heating power is reduced to reduce energy consumption and maintain the stability of the indoor temperature. On the contrary, if it is still far from the comfortable temperature, the heating power is increased to quickly increase the indoor temperature and achieve precise temperature control, which not only ensures the comfort of the indoor temperature but also improves the energy utilization efficiency.

[0071] It can be understood that the present 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 room according to the degree of abnormality of each monitoring point, so as to obtain the authenticity of the temperature time series data of each monitoring point at each collection moment, that is, the credibility of the temperature time series data of each monitoring point is judged through the consistency of the fluctuation of the temperature time series data of each monitoring point caused by ventilation and the continuity relationship in time, so as to realize dynamic temperature regulation according to actual conditions; at the same time, through data compression technology, the data transmission efficiency is improved to optimize the operation of the entire heating temperature compensation system, improve the accuracy of the predicted data, dynamically adjust the heating power, and realize precise temperature control.

[0072] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0073] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A remote data communication method for a heating temperature compensation system using 4G communication, characterized in that: The method comprises: Monitoring points are set up indoors to collect indoor temperature time series data, measure the distance between any two monitoring points, and obtain outdoor temperature time series data; Analyze the temperature time series data based on each monitoring point to obtain the temperature anomaly factor of any monitoring point at any collection time; The target monitoring point is determined by using the temperature anomaly factor, and the continuity of temperature change over time is analyzed based on the temperature time series data of the target monitoring point to obtain 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 the 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. According to the authenticity of the temperature time series data, the 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 the judgment threshold is set to realize dynamic temperature control.

2. A remote data communication method for a heating temperature compensation system using 4G communication as claimed in claim 1, characterized in that: Based on the analysis of the temperature time series data at each monitoring point, the temperature anomaly factor of any monitoring point at any collection 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 in turn, and the temperature anomaly factor of any monitoring point at any collection time is calculated. The corresponding calculation formula is: in, Indicates The monitoring point is Temperature anomaly factor at each collection moment; Indicates The monitoring point is The first-order difference value at each acquisition moment; Indicates The monitoring point is The second-order difference value at each acquisition moment; 0.1 represents a preset constant, which is used to prevent the denominator from being 0; Represents the normalization function.

3. A remote data communication method for a heating temperature compensation system using 4G communication as claimed in claim 1, characterized in that: The target monitoring point is determined by using the temperature anomaly factor. The continuity of temperature change over time is analyzed based on the temperature time series data of the target monitoring point to obtain the possibility of indoor ventilation, including: Set a screening threshold of one, determine the monitoring point corresponding to the temperature anomaly factor greater than or equal to the screening threshold of one at each collection moment as the target monitoring point, extract some continuous historical collection moments based on the current analysis moment to generate an analysis period, and use the analysis period to determine the average temperature anomaly factor; Based on the analysis period of any collection time, the intersection of the target monitoring points corresponding to all collection times is obtained, and the temporal continuity of the target monitoring points in the corresponding analysis period at any collection time is calculated in combination with the average temperature anomaly factor; The dynamic time warping between any two target monitoring points is calculated by intersection, and the possibility of indoor ventilation is obtained.

4. A remote data communication method for a heating temperature compensation system using 4G communication as claimed in claim 3, characterized in that: Calculate the temporal continuity of the target monitoring point in the corresponding analysis period at any collection time. The corresponding calculation formula is: in, Indicates The temporal continuity of the target monitoring point in the analysis period corresponding to each collection moment; Indicates The average temperature anomaly factor of all target monitoring points in the intersection of all target monitoring points corresponding to the acquisition time in the analysis period corresponding to the acquisition time; Indicates The number of target monitoring points in the intersection of all target monitoring points corresponding to the collection time in the analysis period corresponding to the collection time; Indicates The maximum value of the number of target monitoring points corresponding to all collection moments in the analysis period corresponding to a collection moment.

5. A remote data communication method for a heating temperature compensation system using 4G communication as claimed in claim 3, characterized in that: The possibility of indoor ventilation is obtained, and the corresponding calculation formula is: in, Indicates The possibility of indoor ventilation at the time of collection; Indicates The temporal continuity of the target monitoring point in the analysis period corresponding to each collection moment; Indicates The mean value of the distances between any two target monitoring points in the intersection of all target monitoring points corresponding to the acquisition time in the analysis period corresponding to the acquisition time; Indicates Dynamic time warping of any two target monitoring points in the intersection of all target monitoring points corresponding to the collection time in the analysis period corresponding to the collection time; Represents the normalization function.

6. A remote data communication method for a heating temperature compensation system using 4G communication as claimed in claim 1, characterized in that: 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 the 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: Set screening threshold 2, determine the collection time corresponding to the possibility of indoor ventilation being greater than and equal to screening threshold 2 as the target time, and screen consecutive adjacent target times to construct the target period; Calculate the degree of influence of ventilation on any target monitoring point at any target time; The time interval is determined based on the target moment and the current analysis moment, and the authenticity of the temperature time series data of any target monitoring point at any target moment is calculated.

7. A remote data communication method for a heating temperature compensation system using 4G communication as claimed in claim 6, characterized in that: Calculate the degree of influence of ventilation on any target monitoring point at any target time, including: Define The collection time is the target time, and the corresponding calculation formula is: in, Indicates The target monitoring point is The degree of influence of ventilation at each target moment; Indicates There is a possibility of indoor ventilation at the target time; Indicates The total duration of the target period corresponding to the target moment; Indicates The target monitoring point is Temperature time series data at each target moment; Indicates The outdoor temperature time series data corresponding to the target time.

8. A remote data communication method for a heating temperature compensation system using 4G communication as claimed in claim 6, characterized in that: Calculate the authenticity of the temperature time series data of any target monitoring point at any target time. The corresponding calculation formula is: in, Indicates The target monitoring point is The authenticity of the temperature time series data at each target moment; Indicates The target monitoring point is The degree of influence of ventilation at each target moment; Indicates The time interval between the target moment and the current analysis moment; Represents the normalization function.

9. A remote data communication method for a heating temperature compensation system using 4G communication as claimed in claim 1, characterized in that: According to the authenticity of the temperature time series data, the comprehensive temperature data is obtained, and the comprehensive temperature data sequence is constructed. The comprehensive temperature data sequence is transmitted to the heating temperature compensation system using 4G communication, and the judgment threshold is set to realize the dynamic regulation of temperature, including: 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; Calculate the comprehensive temperature data at any collection moment and construct a comprehensive temperature data sequence; The predicted temperature data is obtained based on the comprehensive temperature data sequence. If the predicted temperature data is less than the judgment threshold, an instruction is sent to the heating temperature compensation system through 4G communication to heat 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.

10. A remote data communication method for a heating temperature compensation system using 4G communication as claimed in claim 9, characterized in that: Calculate the comprehensive temperature data at any collection time, the corresponding calculation formula is: in, Indicates the current collection time Comprehensive temperature data of Indicates the number of indoor monitoring points; Indicates Monitoring points at the current collection time Temperature time series data; Indicates Monitoring points at the current collection time The authenticity of the temperature time series data.

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