Water temperature monitoring method and device for high-temperature heat storage heating system

Abstract

The application relates to the technical field of intelligent sensing systems, in particular to a water temperature monitoring method and equipment for a high-temperature heat storage heating system. The method collects water temperature data and water flow rate data in the high-temperature heat storage heating system, calculates a first index of a monitoring point at a collection time, divides the monitoring point at the collection time into an interference monitoring point and a non-interference monitoring point, calculates a second index of the interference monitoring point and the non-interference monitoring point, combines the first index to calculate a water temperature confidence index, and obtains a water temperature monitoring result of the high-temperature heat storage heating system according to the water temperature confidence index. The application can improve the precision of water temperature monitoring of the high-temperature heat storage heating system.

Description

Technical Field

[0001] This application relates to the field of intelligent sensing system technology, specifically to a water temperature monitoring method and equipment for a high-temperature thermal storage heating system. Background Technology

[0002] Water temperature monitoring in high-temperature thermal storage heating systems is a core and fundamental step in ensuring safe system operation, energy efficiency compliance, and stable heating quality. In some cold regions, the inlet water temperature requirements for high-temperature thermal storage heating systems are relatively high. However, when water is heated to around 60°C, tiny bubbles begin to appear. As the temperature rises, the number of bubbles gradually increases, and their rising speed accelerates. When these bubbles come into contact with or adhere to the temperature sensor, they cause abrupt fluctuations in the monitored water temperature, leading to inaccurate monitoring results.

[0003] On the other hand, chemicals such as ethylene glycol, propylene glycol, and glycerol are added to the water in heating systems to lower the freezing point of the water and prevent it from freezing. The addition of these oil solutions changes the physical properties of the water, such as viscosity and thermal conductivity, which alters the flow state of the fluid in the pipes of the heating system. This results in a difference between the measured local temperature of the fluid and the actual temperature, affecting the accuracy of water temperature monitoring. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a method and device for monitoring water temperature in a high-temperature thermal storage heating system. The specific technical solution adopted is as follows: In a first aspect, one embodiment of this application provides a method for monitoring the water temperature of a high-temperature thermal storage heating system, the method comprising the following steps: Collect water temperature and water flow velocity data at different monitoring points in the high-temperature thermal storage heating system; Based on the difference in water temperature data collected by adjacent monitoring points at adjacent collection times, calculate the first indicator of the monitoring point at the collection time. Based on the water flow velocity data from all monitoring points at the same acquisition time, the monitoring points at that time are divided into interference monitoring points and non-interference monitoring points. This division is used to distinguish whether the monitoring points are affected by the fluid boundary layer at the acquisition time. Based on the distance between the interference monitoring point and the first bend position upstream of the pipeline, a second index is calculated for the interference monitoring point. The second index of the non-interference monitoring point is assigned a value. Based on the first and second indices, the water temperature confidence index at the acquisition time is calculated. The water temperature confidence index is used to evaluate the reliability of the corresponding water temperature data. Based on the water temperature confidence index, the water temperature monitoring results of the high-temperature thermal storage heating system are obtained.

[0005] Furthermore, the first indicator of the monitoring point at the time of collection is positively correlated with the difference in water temperature data collected at adjacent collection times and the difference in water temperature data collected at adjacent collection times of adjacent monitoring points.

[0006] Furthermore, the difference in water temperature data collected at adjacent collection times by the monitoring point is determined by a time jump coefficient. The steps for determining the time jump coefficient are as follows: Any acquisition time is recorded as the target acquisition time, and the first preset number of acquisition times with the smallest time interval between them and the target acquisition time are recorded as the comparison acquisition times. The difference in water temperature data collected at the monitoring point at the target collection time and the comparison collection time is negatively correlated with the time jump coefficient of the monitoring point at the target collection time.

[0007] Furthermore, the difference in water temperature data collected at adjacent monitoring points at adjacent collection times is determined by a spatial jump coefficient. The steps for determining the spatial jump coefficient are as follows: Any monitoring point is designated as the target monitoring point, and the second preset number of monitoring points that are closest to the target monitoring point along the pipeline of the high-temperature thermal storage heating system are designated as comparison monitoring points. The spatial jump coefficient at the target acquisition time is negatively correlated with the difference in water temperature data collected by the target monitoring point and all comparison monitoring points at the target acquisition time and the comparison acquisition time.

[0008] Furthermore, the method for dividing the interference monitoring points into non-interference monitoring points is as follows: The average value of the water flow velocity data from all monitoring points at the same collection time is recorded as the standard water flow velocity data at the same collection time. Based on the numerical relationship between the water flow velocity data at the monitoring point at the time of collection and the standard water flow velocity data, interference monitoring points and non-interference monitoring points are divided.

[0009] Furthermore, the method for obtaining the second indicator of the interference monitoring point is as follows: The first bend in the pipeline of the high-temperature thermal storage heating system upstream of the interference monitoring point is recorded as the distance measurement detection point. The positive correlation result of the distance along the pipeline between the interference monitoring point and the distance measurement detection point is recorded as the second index of the interference monitoring point.

[0010] Furthermore, the assigned value of the second indicator is less than or equal to the minimum value of the second indicator at the interference monitoring point.

[0011] Furthermore, the water temperature confidence index at the monitoring point at the time of data collection is positively correlated with the first and second indicators at the time of data collection.

[0012] Furthermore, the specific method for obtaining the water temperature monitoring results of the high-temperature thermal storage heating system based on the water temperature confidence index includes: The water temperature data corresponding to the collection time of outliers in the water temperature confidence index are removed, and the removed water temperature data is filled in. All the filled water temperature data are used as the water temperature monitoring data of the high-temperature thermal storage heating system.

[0013] Secondly, another embodiment of this application provides a water temperature monitoring device for a high-temperature thermal storage heating system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-mentioned water temperature monitoring method for a high-temperature thermal storage heating system.

[0014] The embodiments of this application have at least the following beneficial effects: Because bubbles from localized overheating and vaporization in the water adhere to the sensor, causing instantaneous temperature jumps, this application first analyzes the impact of bubble interference on temperature measurement. Based on the differences in water temperature data collected from adjacent monitoring points at adjacent sampling times, a first index is calculated for each monitoring point at the sampling time. This first index is determined based on the differences in water temperature data from the same monitoring point at adjacent sampling times and the differences in water temperature data from different monitoring points at adjacent sampling times. Then, considering that the pipes in the high-temperature thermal storage heating system have varying degrees of bends, and that the fluid within the bends experiences differences in centrifugal force and tangential stress on different wall areas, causing localized changes in fluid velocity, this also affects the temperature measurement at the monitoring points. The degree of interference from the fluid boundary layer is evaluated to obtain a second index for the monitoring point. The larger the second index is at the time of data acquisition, the more severe the interference from the fluid boundary layer is, the more obvious the thickening of the fluid boundary layer is, and the less reliable the water temperature data collected at the time of data acquisition. Based on the first and second indices at the time of data acquisition, the water temperature confidence index at the time of data acquisition is calculated. Based on the water temperature confidence index, the water temperature monitoring results of the high-temperature thermal storage heating system are obtained, which solves the problem of insufficient water temperature monitoring accuracy caused by the interference of air bubbles and changes in the flow state of the fluid in the heating system. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1This is a flowchart illustrating the steps of a water temperature monitoring method for a high-temperature thermal storage heating system, as provided in one embodiment of this application. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a water temperature monitoring method and device for a high-temperature thermal storage heating system proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] 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 pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the water temperature monitoring method and equipment for a high-temperature thermal storage heating system provided in this application.

[0020] Please see Figure 1 The diagram illustrates a flowchart of a water temperature monitoring method for a high-temperature thermal storage heating system according to an embodiment of this application. The method includes the following steps: Step S001: Collect water temperature data and water flow velocity data at different monitoring points in the high-temperature thermal storage heating system.

[0021] In a high-temperature thermal storage heating system, multiple monitoring points are set at equal intervals. At each monitoring point, a thermocouple temperature sensor and an impeller flow meter are installed to collect water temperature data and water flow rate data, respectively.

[0022] Preferably, as an embodiment of this application, the sampling frequency of water temperature data and water flow velocity data is 1Hz, and the sampling duration of one cycle is 10 minutes; the distance between adjacent monitoring points should be greater than or equal to 20 cm and less than or equal to 100 cm, and the number of monitoring points should be greater than or equal to 5. In this embodiment, the distance between adjacent monitoring points is set to 50 cm, and the number of monitoring points is set to 30.

[0023] Thus, water temperature and flow rate data at different monitoring points in the high-temperature thermal storage heating system were obtained.

[0024] Step S002: Calculate the first index of the monitoring point at the time of collection based on the difference in water temperature data collected by adjacent monitoring points at adjacent collection times.

[0025] The standard inlet water temperature for high-temperature thermal storage heating systems is typically 70℃–90℃. During operation, bubbles are generated when the water temperature reaches around 60℃, and the number and rising speed of these bubbles increase with rising temperature. When these bubbles adhere to or contact the temperature sensor, the sensor's sensing point becomes covered by gas, leading to a lower measured temperature and causing fluctuations in the water temperature monitoring data, thus affecting the accuracy of the monitoring. Furthermore, to lower the freezing point of water, chemical media such as ethylene glycol are added to the system. These substances alter the viscosity, thermal conductivity, and other physical properties of water, changing the flow state of the fluid within the pipes. Under slower flow conditions, these chemical media can cause the fluid boundary layer to thicken, resulting in the measured temperature of the temperature sensor deviating from the true fluid temperature. Both of these factors combined reduce the accuracy of water temperature monitoring and interfere with the water temperature regulation and control of the high-temperature thermal storage heating system.

[0026] First, we analyze the impact of bubble interference on temperature measurement. Thermocouple sensors are contact-type measuring instruments. When bubbles, vaporized from localized overheating in the water, adhere to the sensor, they cause instantaneous temperature jumps. This means that the water temperature data at a given moment deviates significantly from the data at adjacent moments within a short timeframe. This measurement error is caused by bubble interference. By analyzing the differences in water temperature data between adjacent moments within a short time window, we can determine whether abnormal temperature jumps caused by bubble interference occurred at the corresponding acquisition time of the monitoring point.

[0027] Any acquisition time is designated as the target acquisition time, and the first preset number of acquisition times with the smallest time interval between them and the target acquisition time are designated as comparison acquisition times. Based on the difference between the water temperature data collected by the monitoring point at the target acquisition time and the comparison acquisition time, the time jump coefficient of the monitoring point at the target acquisition time is calculated. The difference between the water temperature data collected by the monitoring point at the target acquisition time and the comparison acquisition time is negatively correlated with the time jump coefficient of the monitoring point at the target acquisition time.

[0028] The value of the first preset quantity should be greater than or equal to 3 and less than or equal to 7. In this embodiment, the value of the first preset quantity is 5. The implementer may also set the value of the first preset quantity according to the implementation scenario. This application does not impose any special restrictions.

[0029] It is understood that the negative correlation in this application refers to the relationship between the independent variable and the dependent variable. The independent variable is the difference in water temperature data collected at the monitoring point at the target collection time and the comparison collection time. The dependent variable is the time jump coefficient at the target collection time. The negative correlation means that the dependent variable decreases (increases) as the independent variable increases (decreases). It can be an inverse relationship, a subtraction relationship, etc.

[0030] Preferably, as an embodiment of this application, for water temperature data collected at the same monitoring point, the mean of the absolute values ​​of the differences between the water temperature data at the target collection time and all comparative collection times is recorded as the adjacent difference of the same monitoring point at the target collection time, and the normalized value of the ratio of the adjacent difference of the monitoring point at the target collection time to the water temperature data is recorded as the time jump coefficient of the monitoring point at the target collection time.

[0031] In this embodiment, the normalized value is calculated using the maximum-minimum normalization method. The maximum-minimum normalization method is a well-known technique and will not be described in detail here. As other implementation methods, implementers can use other methods of the prior art, such as the tanh function, the sigmoid function, etc.

[0032] The larger the time jump coefficient of the monitoring point at the target acquisition time, the more significant the instability of the water temperature data at the monitoring point at the target acquisition time in a short period of time, the greater the possibility that the water temperature data at the monitoring point at the target acquisition time is affected by air bubble interference, and the lower the reliability of the water temperature data at the monitoring point at the target acquisition time.

[0033] The same method can be used to obtain the time jump coefficients of all monitoring points of the high-temperature thermal storage heating system at all collection times.

[0034] However, when the high-temperature thermal storage heating system is operating under high load, the water heating process is very rapid. A rapid rise in water temperature within a short time window can also cause localized abnormal temperature jumps. Therefore, it is necessary to distinguish between normal water heating and localized abnormal temperature jumps caused by air bubbles. Specifically, due to heat loss, the water temperature data at the same monitoring point on the pipes of the high-temperature thermal storage heating system will slowly decrease along the direction away from the heat exchanger outlet. When the above characteristics are significant at the time of data collection, a larger time jump coefficient at the monitoring point is more likely to be a normal phenomenon caused by a rapid rise in water temperature.

[0035] Any monitoring point is designated as the target monitoring point. A second set of predetermined monitoring points, the closest to the target monitoring point along the pipeline of the high-temperature thermal storage heating system, are designated as comparison monitoring points. Based on the difference in water temperature data collected by the target monitoring point and all comparison monitoring points at the target acquisition time and the comparison acquisition time, a spatial jump coefficient for the target monitoring point at the target acquisition time is calculated. This spatial jump coefficient is negatively correlated with the difference in water temperature data collected by the target monitoring point and all comparison monitoring points at the target acquisition time and the comparison acquisition time.

[0036] The value of the second preset quantity should be greater than or equal to 5. In this embodiment, the value of the second preset quantity is 5. The implementer may also set the value of the second preset quantity according to the implementation scenario. This application does not impose any special restrictions.

[0037] Preferably, as an embodiment of this application, the sum of the differences between the water temperature data collected by the target monitoring point and the comparison monitoring point at the target acquisition time and the comparison acquisition time is recorded as the first sum of the target monitoring point and the comparison monitoring point at the target acquisition time, and the normalized value of the mean of the first sum of the target monitoring point and all comparison monitoring points at the target acquisition time is recorded as the spatial jump coefficient of the target monitoring point at the target acquisition time.

[0038] In this embodiment, the absolute value of the difference is selected to evaluate the difference in water temperature data. While achieving the purpose of evaluating the difference in water temperature data, in practical applications, implementers may use other existing methods such as information entropy, approximate entropy, and variance to evaluate the difference in water temperature data. This application does not impose any special restrictions.

[0039] When the spatial jump coefficient of the target monitoring point at the target acquisition time is larger, the similarity of the water temperature data between the target monitoring point and all the comparison monitoring points in the time period adjacent to the target acquisition time is lower. The target monitoring point is more likely to be affected by the interference of bubbles at the target acquisition time, causing the water temperature data to show unstable characteristics in a short period of time. At this time, the reliability of the water temperature data of the monitoring point at the target acquisition time is lower.

[0040] In this embodiment, the normalized value is calculated using the maximum-minimum normalization method. The maximum-minimum normalization method is a well-known technique and will not be described in detail here. As other implementation methods, implementers can use other methods of the prior art, such as the tanh function, the sigmoid function, etc.

[0041] The spatial jump coefficients of all monitoring points in the high-temperature thermal storage heating system at all data collection times can be obtained using the same method.

[0042] The positive correlation between the time jump coefficient and the spatial jump coefficient of the target monitoring point at the target acquisition time is recorded as the first indicator of the target monitoring point at the target acquisition time.

[0043] It is understood that a positive correlation is applied to the time jump coefficient and the spatial jump coefficient, ensuring that both are positively correlated with the first indicator at the target acquisition time. It is also understood that the positive correlation in this application refers to the relationship between the independent and dependent variables, where the independent variables are the time jump coefficient and the spatial jump coefficient, and the dependent variable is the first indicator at the target acquisition time. A positive correlation means that the dependent variable increases (decreases) as the independent variables increase (decreases), and can be an additive or multiplicative relationship.

[0044] Preferably, as an embodiment of this application, the product of the time jump coefficient and the spatial jump coefficient of the target monitoring point at the target acquisition time is recorded as the first index of the target monitoring point at the target acquisition time.

[0045] The higher the first index of the target monitoring point at the target acquisition time, the more significantly the water temperature data of the monitoring point is affected by air bubbles at the target acquisition time, and the more significantly it exhibits unstable characteristics in a short period of time. At this time, the reliability of the water temperature data of the monitoring point at the target acquisition time is lower.

[0046] The same method can be used to obtain the first indicators of all monitoring points of the high-temperature thermal storage heating system at all collection times.

[0047] This provides the first indicator for all monitoring points of the high-temperature thermal storage heating system at all data collection times.

[0048] Step S003: Based on the water flow velocity data of all monitoring points at the same acquisition time, the monitoring points at the acquisition time are divided into interference monitoring points and non-interference monitoring points. Based on the distance between the interference monitoring point and the first bend position upstream of the pipeline, the second index of the interference monitoring point is calculated, and the second index of the non-interference monitoring point is assigned a value. Based on the first and second indices of the monitoring point at the acquisition time, the water temperature confidence index of the monitoring point at the acquisition time is calculated. Based on the water temperature confidence index, the water temperature monitoring result of the high-temperature thermal storage heating system is obtained.

[0049] High-temperature thermal storage heating systems often have pipes with varying degrees of bends. Within these bends, the fluid velocity varies due to differences in centrifugal force and tangential stress across different wall regions. To lower the freezing point of water, chemicals such as propylene glycol are added to create a mixed fluid, which alters the flow pattern. Under slower flow conditions, these chemicals thicken the fluid boundary layer, causing the thermocouple sensor's measured temperature to deviate from the true fluid temperature. Furthermore, turbulence arises at pipe bends due to uneven flow velocities on both sides of the pipe wall. This turbulence disrupts the thickened fluid boundary layer; therefore, areas farther from bends and with slower flow velocities are more prone to temperature readings deviating from the true value due to the thickened fluid boundary layer.

[0050] Therefore, it is necessary to focus on analyzing the water velocity data at monitoring points in the pipelines of high-temperature thermal storage heating systems where the flow velocity is relatively slow, in order to determine whether the water velocity data is affected by the boundary layer.

[0051] The average water flow velocity data of all monitoring points at the same collection time is recorded as the standard water flow velocity data at that same collection time. When the water flow velocity data of a monitoring point at the collection time is greater than the standard water flow velocity data at the collection time, the monitoring point at the collection time is recorded as an interfering monitoring point; when the water flow velocity data of a monitoring point at the collection time is less than or equal to the standard water flow velocity data at the collection time, the monitoring point at the collection time is recorded as a non-interfering monitoring point.

[0052] It is understandable that interference monitoring points indicate that the monitoring points were affected by turbulence generated by the slow-flow side of the pipe wall at the time of data acquisition. The fluid boundary layer was disrupted, and the water velocity data at the monitoring points at the time of data acquisition was abnormal, showing a slower flow rate. The water velocity data would interfere with the water temperature measurement. Non-interference monitoring points indicate that the water velocity data at the monitoring points at the time of data acquisition was normal, and the water velocity data would not interfere with the water temperature measurement.

[0053] The first bend in the pipeline of the high-temperature thermal storage heating system upstream of the interference monitoring point is recorded as the distance measurement detection point. The positive correlation result of the distance along the pipeline between the interference monitoring point and the distance measurement detection point is recorded as the second index of the interference monitoring point.

[0054] Preferably, as an embodiment of this application, the normalized value of the distance along the pipeline between the interference monitoring point and the ranging detection point is recorded as the second index of the interference monitoring point. In this embodiment, the normalized value is calculated using the maximum-minimum value normalization method, which is a well-known technique and will not be described in detail here.

[0055] Understandably, when setting up monitoring points, the distance between adjacent monitoring points along the pipeline can be obtained through measurement.

[0056] It should be noted that when there is no preceding adjacent detection point upstream of the interference monitoring point, the second index of the interference monitoring point will be assigned a preset constant value, which is less than or equal to the minimum value of the second index of the interference monitoring point.

[0057] The second index of the non-interference monitoring point is assigned a preset constant value. In this embodiment, the preset constant value is 0.

[0058] At this point, the second indicator for all monitoring points at each data collection time can be obtained.

[0059] The larger the second index is at the time of data collection, the more severe the interference from the fluid boundary layer at the time of data collection, the more obvious the phenomenon of fluid boundary layer thickening, and the less reliable the water temperature data collected at the time of data collection.

[0060] The positive correlation between the first and second indicators at the time of data collection is recorded as the water temperature confidence index at the time of data collection.

[0061] Specifically, as an embodiment of this application, the first indicator and the second indicator at the time of data collection are weighted and summed, and the weighted summation result is recorded as the water temperature confidence index at the time of data collection. The sum of the weights of the first indicator and the second indicator at the time of data collection is 1. In this embodiment, the weights of the first indicator and the second indicator at the time of data collection are 0.6 and 0.4, respectively.

[0062] The confidence index of water temperature at all monitoring points within the same period was processed using the three-standard-deviation method. Water temperature data at the time of collection corresponding to outliers were removed. The removed water temperature data was then filled in, and all the filled water temperature data were used as the water temperature monitoring data for the high-temperature thermal storage heating system.

[0063] The use of the three-standard-deviation method to screen outliers is a well-known technique and will not be elaborated further. In practical applications, as other implementation methods, in addition to achieving the purpose of outlier identification, implementers may also use other existing methods such as the LOF outlier detection algorithm to achieve outlier identification; this application does not impose any special restrictions. In addition to achieving the purpose of outlier filling, implementers may also use other existing methods such as median filling, mode filling, random sampling filling, K-nearest neighbor filling, regression filling, etc., to fill out outliers; this application does not impose any special restrictions.

[0064] Thus, the water temperature monitoring results of the high-temperature thermal storage heating system were obtained.

[0065] This application also proposes a water temperature monitoring device for a high-temperature thermal storage heating system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the steps described above. Since a water temperature monitoring method for a high-temperature thermal storage heating system has been described in detail above, it will not be repeated here.

[0066] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0067] 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.

[0068] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for monitoring water temperature in a high-temperature thermal storage heating system, characterized in that, The method includes the following steps: Collect water temperature and water flow velocity data at different monitoring points in the high-temperature thermal storage heating system; Based on the difference in water temperature data collected by adjacent monitoring points at adjacent collection times, calculate the first indicator of the monitoring point at the collection time. Based on the water flow velocity data of all monitoring points at the same collection time, the monitoring points at the collection time are divided into interference monitoring points and non-interference monitoring points, including: the average value of the water flow velocity data of all monitoring points at the same collection time is recorded as the standard water flow velocity data at the same collection time; Based on the numerical relationship between the water flow velocity data at the monitoring point at the time of collection and the standard water flow velocity data, interference monitoring points and non-interference monitoring points are divided. The monitoring point division is used to distinguish whether the monitoring point is affected by the fluid boundary layer at the time of data collection. Based on the distance between the interference monitoring point and the first bend position upstream of the pipeline, the second index of the interference monitoring point is calculated, including: the first bend position upstream of the interference monitoring point in the pipeline of the high-temperature thermal storage heating system is recorded as the distance measurement detection point, and the positive correlation processing result of the distance along the pipeline between the interference monitoring point and the distance measurement detection point is recorded as the second index of the interference monitoring point. Assign values ​​to the second indicator for non-interference monitoring points, and the assigned value of the second indicator shall be less than or equal to the minimum value of the second indicator for interference monitoring points; Based on the first and second indicators, the water temperature confidence index at the time of data collection is calculated. The water temperature confidence index is used to evaluate the reliability of the corresponding water temperature data. Based on the water temperature confidence index, the water temperature monitoring results of the high-temperature thermal storage heating system are obtained, including: removing the water temperature data at the time of data collection corresponding to the outliers in the water temperature confidence index, filling in the removed water temperature data, and using all the filled water temperature data as the water temperature monitoring data of the high-temperature thermal storage heating system.

2. The water temperature monitoring method for a high-temperature thermal storage heating system according to claim 1, characterized in that, The first indicator of the monitoring point at the time of collection is positively correlated with the difference in water temperature data collected at adjacent collection times and the difference in water temperature data collected at adjacent collection times of adjacent monitoring points.

3. The water temperature monitoring method for a high-temperature thermal storage heating system according to claim 2, characterized in that, The difference in water temperature data collected at adjacent collection times by the monitoring point is determined by a time jump coefficient. The steps for determining the time jump coefficient are as follows: Any acquisition time is recorded as the target acquisition time, and the first preset number of acquisition times with the smallest time interval between them and the target acquisition time are recorded as the comparison acquisition times. The difference in water temperature data collected at the monitoring point at the target collection time and the comparison collection time is negatively correlated with the time jump coefficient of the monitoring point at the target collection time.

4. The water temperature monitoring method for a high-temperature thermal storage heating system according to claim 3, characterized in that, The difference in water temperature data collected at adjacent monitoring points at adjacent collection times is determined by the spatial jump coefficient. The steps for determining the spatial jump coefficient are as follows: Any monitoring point is designated as the target monitoring point, and the second preset number of monitoring points that are closest to the target monitoring point along the pipeline of the high-temperature thermal storage heating system are designated as comparison monitoring points. The spatial jump coefficient at the target acquisition time is negatively correlated with the difference in water temperature data collected by the target monitoring point and all comparison monitoring points at the target acquisition time and the comparison acquisition time.

5. The water temperature monitoring method for a high-temperature thermal storage heating system according to claim 1, characterized in that, The water temperature confidence index at the monitoring point at the time of data collection is positively correlated with the first and second indicators at the time of data collection.

6. A water temperature monitoring device for a high-temperature thermal storage heating system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the water temperature monitoring method for a high-temperature thermal storage heating system as described in any one of claims 1 to 5.

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